diff --git a/.github/CHANGELOG.md b/.github/CHANGELOG.md index b9005a6d33..123a7d7147 100644 --- a/.github/CHANGELOG.md +++ b/.github/CHANGELOG.md @@ -1,30 +1,511 @@ -# Release 0.38.0-dev +# Release 0.40.0-dev ### New features since last release +* Add Exact Tensor Network C++ backend to `lightning.tensor`. + [(#977)](https://github.com/PennyLaneAI/pennylane-lightning/pull/977) + +* Add native N-controlled generators and adjoint support to `lightning.gpu`'s single-GPU backend. + [(#970)](https://github.com/PennyLaneAI/pennylane-lightning/pull/970) + +* Add a Catalyst-specific wrapping class for Lightning-Qubit. + [(#960)](https://github.com/PennyLaneAI/pennylane-lightning/pull/960) + [(#999)](https://github.com/PennyLaneAI/pennylane-lightning/pull/999) + +* Add native N-controlled gate/matrix operations and adjoint support to `lightning.kokkos`. + [(#950)](https://github.com/PennyLaneAI/pennylane-lightning/pull/950) + +* Add native N-controlled gates support to `lightning.gpu`'s single-GPU backend. + [(#938)](https://github.com/PennyLaneAI/pennylane-lightning/pull/938) + ### Breaking changes +* Handling for the legacy operator arithmetic (the `Hamiltonian` and `Tensor` classes in PennyLane) is removed. + [(#994)](https://github.com/PennyLaneAI/pennylane-lightning/pull/994) + [(#997)](https://github.com/PennyLaneAI/pennylane-lightning/pull/997) + +* Removed all instances of `qml.QubitStateVector`. + [(#985)](https://github.com/PennyLaneAI/pennylane-lightning/pull/985) + +### Improvements + +* Optimize lightning.tensor by adding direct MPS sites data set with `qml.MPSPrep`. + [(#983)](https://github.com/PennyLaneAI/pennylane-lightning/pull/983) + +* Replace the `dummy_tensor_update` method with the `cutensornetStateCaptureMPS`API to ensure that further gates apply is allowed after the `cutensornetStateCompute` call. + [(#1028)](https://github.com/PennyLaneAI/pennylane-lightning/pull/1028/) + +* Add unit test for measurement with shots for Lightning Tensor with `tn` method. + [(#1027)](https://github.com/PennyLaneAI/pennylane-lightning/pull/1027) + +* Update the python layer UI of Lightning Tensor. + [(#1022)](https://github.com/PennyLaneAI/pennylane-lightning/pull/1022/) + +* Catalyst device interfaces support dynamic shots, and no longer parses the device init op's attribute dictionary for a static shots literal. + [(#1017)](https://github.com/PennyLaneAI/pennylane-lightning/pull/1017) + +* Update the logic for enabling `grad_on_execution` during device execution. + [(#1016)](https://github.com/PennyLaneAI/pennylane-lightning/pull/1016) + +* Reduce flaky test and increase test shots count. + [(#1015)](https://github.com/PennyLaneAI/pennylane-lightning/pull/1015) + +* Add Exact Tensor Network cpp binding. + [(#1014)](https://github.com/PennyLaneAI/pennylane-lightning/pull/1014/) + +* Reverse Lightning Qubit generators vector insertion order. + [(#1009)](https://github.com/PennyLaneAI/pennylane-lightning/pull/1009) + +* Update Kokkos version support to 4.5. + [(#1007)](https://github.com/PennyLaneAI/pennylane-lightning/pull/1007) + +* Enable N-controlled gate and matrix support to `lightning.gpu` simulator for Catalyst. + [(#1005)](https://github.com/PennyLaneAI/pennylane-lightning/pull/1005) + +* Generalize seeding mechanism for all measurements. + [(#1003)](https://github.com/PennyLaneAI/pennylane-lightning/pull/1003) + +* `lightning.qubit`, `lightning.gpu`, and `lightning.kokkos` now define + the `eval_jaxpr` method for integration with the experimental + capture project. + [(#1002)](https://github.com/PennyLaneAI/pennylane-lightning/pull/1002) + +* Update Kokkos version support to 4.4.1 and enable Lightning-Kokkos[CUDA] C++ tests on CI. + [(#1000)](https://github.com/PennyLaneAI/pennylane-lightning/pull/1000) + +* Add C++ unit tests for Exact Tensor Network backends. + [(#998)](https://github.com/PennyLaneAI/pennylane-lightning/pull/998) + +* Add native BLAS support to the C++ layer via dynamic `scipy-openblas32` loading. + [(#995)](https://github.com/PennyLaneAI/pennylane-lightning/pull/995) + +* Update installation instruction for Lightning-GPU-MPI to explicitly ask users to add `path/to/libmpi.so` to the `LD_LIBRARY_PATH`. Update the runtime error message to ensure users know how to fix. + [(#993)](https://github.com/PennyLaneAI/pennylane-lightning/pull/993) + +* The TOML files for the devices are updated to use the new schema for declaring device capabilities. + [(#988)](https://github.com/PennyLaneAI/pennylane-lightning/pull/988) + +* Unify excitation gates memory layout to row-major for both LGPU and LT. + [(#959)](https://github.com/PennyLaneAI/pennylane-lightning/pull/959) + +* Update the `lightning.kokkos` CUDA backend for compatibility with Catalyst. + [(#942)](https://github.com/PennyLaneAI/pennylane-lightning/pull/942) + +* `SX` and `C(SX)` gates are natively supported with `lightning.qubit`. + [(#731)](https://github.com/PennyLaneAI/pennylane-lightning/pull/731) + +### Documentation + +* Add the exact tensor network to the Lightning-Tensor docs. + [(#1021)](https://github.com/PennyLaneAI/pennylane-lightning/pull/1021) + +* Update Lightning-Tensor installation docs and usage suggestions. + [(#979)](https://github.com/PennyLaneAI/pennylane-lightning/pull/979) + +### Bug fixes + +* Add `RTLD_NODELETE` flag to `dlopen` in order to mitigate the segfault issues for arm64-macos Catalyst support. + [(#1030)](https://github.com/PennyLaneAI/pennylane-lightning/pull/1030) + +* Set rpath with `@loader_path` instead of `$ORIGIN` for macOS. + [(#1029)](https://github.com/PennyLaneAI/pennylane-lightning/pull/1029) + +* Update CUDA version to 12.4 for GPU tests on CI. + [(#1023)](https://github.com/PennyLaneAI/pennylane-lightning/pull/1023) + +* Fix Lightning Kokkos `[[maybe_unused]]` and `exp2` errors with hipcc. + [(#1018)](https://github.com/PennyLaneAI/pennylane-lightning/pull/1018) + +* Pin `jax[cpu]==0.4.28` for compatibility with PennyLane and Catalyst. + [(#1019)](https://github.com/PennyLaneAI/pennylane-lightning/pull/1019) + +* Fix Lightning Kokkos editable mode path. + [(#1010)](https://github.com/PennyLaneAI/pennylane-lightning/pull/1010) + +* Fix the missing `ninja` in Linux AARCH64 wheels recipe. + [(#1007)](https://github.com/PennyLaneAI/pennylane-lightning/pull/1007) + +* Fix version switch condition the GPU workflow tests for LGPU and LKokkos. + [(#1006)](https://github.com/PennyLaneAI/pennylane-lightning/pull/1006) + +* Fix issue with `lightning.gpu` Rot operation with adjoint. + [(#1004)](https://github.com/PennyLaneAI/pennylane-lightning/pull/1004) + +* Fix the Lightning-Kokkos[CUDA] C++ API `cudaFree` segfaults in `applyMatrix`. + [(#1000)](https://github.com/PennyLaneAI/pennylane-lightning/pull/1000) + +* Fix issue with adjoint-jacobian of adjoint ops. + [(#996)](https://github.com/PennyLaneAI/pennylane-lightning/pull/996) + +* Fix the `upload-pypi` token issues for Linux and MacOS (x86_64 & AARCH64) wheels. + [(#989)](https://github.com/PennyLaneAI/pennylane-lightning/pull/989) + +* Fix Pennylane dependency branch (`v0.39_rc0` to `master`). + [(#984)](https://github.com/PennyLaneAI/pennylane-lightning/pull/984) + +* Fix PTM stable latest. Removing FIXME patch for v0.39. + [(#982)](https://github.com/PennyLaneAI/pennylane-lightning/pull/982) + +### Contributors + +This release contains contributions from (in alphabetical order): + +Ali Asadi, +Astral Cai, +Amintor Dusko, +Christina Lee, +Joseph Lee, +Anton Naim Ibrahim, +Luis Alfredo Nuñez Meneses, +Andrija Paurevic, +Shuli Shu, +Raul Torres, +Haochen Paul Wang + +--- + +# Release 0.39.0 + +### New features since last release + +* Add support for out-of-order `qml.probs` in `lightning.gpu`. + [(#941)](https://github.com/PennyLaneAI/pennylane-lightning/pull/941) + +* Add mid-circuit measurements support to `lightning.gpu`'s single-GPU backend. + [(#931)](https://github.com/PennyLaneAI/pennylane-lightning/pull/931) + +* Integrate Lightning-GPU with Catalyst so that hybrid programs can be seamlessly QJIT-compiled and executed on this device following `pip install pennylane-lightning-gpu`. + [(#928)](https://github.com/PennyLaneAI/pennylane-lightning/pull/928) + +* Add `qml.Projector` observable support via diagonalization to Lightning-GPU. + [(#894)](https://github.com/PennyLaneAI/pennylane-lightning/pull/894) + +* Add 1-target wire controlled gate support to `lightning.tensor`. Note that `cutensornet` only supports 1-target wire controlled gate as of `v24.08`. A controlled gate with more than 1 target wire should be converted to dense matrix. + [(#880)](https://github.com/PennyLaneAI/pennylane-lightning/pull/880) + +* Build and upload Lightning-Tensor wheels (x86_64, AARCH64) to PyPI. + [(#862)](https://github.com/PennyLaneAI/pennylane-lightning/pull/862) + [(#905)](https://github.com/PennyLaneAI/pennylane-lightning/pull/905) + +* Add Matrix Product Operator (MPO) for all gates support to `lightning.tensor`. Note current C++ implementation only works for MPO sites data provided by users. + [(#859)](https://github.com/PennyLaneAI/pennylane-lightning/pull/859) + +* Add shots measurement support to `lightning.tensor`. + [(#852)](https://github.com/PennyLaneAI/pennylane-lightning/pull/852) + +* Lightning-GPU and Lightning-Kokkos migrated to the new device API. + [(#853)](https://github.com/PennyLaneAI/pennylane-lightning/pull/853) + [(#810)](https://github.com/PennyLaneAI/pennylane-lightning/pull/810) + +### Breaking changes + +* Update MacOS wheel build to 13.0 for X86_64 and ARM due to the deprecation of MacOS-12 CI runners. + [(#969)](https://github.com/PennyLaneAI/pennylane-lightning/pull/969) + +* Deprecate `initSV()` and add `resetStateVector()` from the C++ API Lightning-GPU. This is to remove the `reset_state` additional call in the Python layer. + [(#933)](https://github.com/PennyLaneAI/pennylane-lightning/pull/933) + +* Deprecate PI gates implementation in Lightning-Qubit. The PI gates were the first implementation of gate kernels in `lightning.qubit` using pre-computed indices, prior to the development of LM (less memory) and AVX kernels. This deprecation is in favour of reducing compilation time and ensuring that Lightning-Qubit only relies on LM kernels in the dynamic dispatcher across all platforms. + [(#925)](https://github.com/PennyLaneAI/pennylane-lightning/pull/925) + +* Remove PowerPC wheel build recipe for Lightning-Qubit. + [(#902)](https://github.com/PennyLaneAI/pennylane-lightning/pull/902) + +* Update MacOS wheel builds to require Monterey (12.0) or greater for x86_64 and ARM. This was required to update Pybind11 to the latest release (2.13.5) for enabling Numpy 2.0 support in Lightning. + [(#901)](https://github.com/PennyLaneAI/pennylane-lightning/pull/901) + +* Remove support for Python 3.9 for all Lightning simulators. + [(#891)](https://github.com/PennyLaneAI/pennylane-lightning/pull/891) + +### Improvements + +* Update the `lightning.tensor` Python layer unit tests, as `lightning.tensor` cannot be cleaned up like other state-vector devices because the data is attached to the graph. It is recommended to use one device per circuit for `lightning.tensor`. + [(#971)](https://github.com/PennyLaneAI/pennylane-lightning/pull/971) + +* Add joint check for the N-controlled wires support in `lightning.qubit`. + [(#949)](https://github.com/PennyLaneAI/pennylane-lightning/pull/949) + +* Optimize `GlobalPhase` and `C(GlobalPhase)` gate implementation in `lightning.gpu`. + [(#946)](https://github.com/PennyLaneAI/pennylane-lightning/pull/946) + +* Add missing `liblightning_kokkos_catalyst.so` when building Lightning-Kokkos in editable installation. + [(#945)](https://github.com/PennyLaneAI/pennylane-lightning/pull/945) + +* Optimize the cartesian product to reduce the amount of memory necessary to set the `StatePrep` in Lightning-Tensor. + [(#943)](https://github.com/PennyLaneAI/pennylane-lightning/pull/943) + +* Update the `qml.probs` data-return in Lightning-GPU C++ API to align with other state-vector devices. + [(#941)](https://github.com/PennyLaneAI/pennylane-lightning/pull/941) + +* Add zero-state initialization to both `StateVectorCudaManaged` and `StateVectorCudaMPI` constructors to remove the `reset_state` in the Python layer ctor and refactor `setBasisState(state, wires)` in the C++ API. + [(#933)](https://github.com/PennyLaneAI/pennylane-lightning/pull/933) + +* Add `setStateVector(state, wire)` support to the Lightning-GPU C++ API. + [(#930)](https://github.com/PennyLaneAI/pennylane-lightning/pull/930) + +* The `generate_samples` methods of `lightning.qubit` and `lightning.kokkos` can now take in a seed number to make the generated samples deterministic. This can be useful when, among other things, fixing flaky tests in CI. + [(#927)](https://github.com/PennyLaneAI/pennylane-lightning/pull/927) + +* Remove dynamic decomposition rules for all Lightning devices. + [(#926)](https://github.com/PennyLaneAI/pennylane-lightning/pull/926) + +* Always decompose `qml.QFT` in all Lightning devices. + [(#924)](https://github.com/PennyLaneAI/pennylane-lightning/pull/924) + +* Uniform Python format to adhere PennyLane style. + [(#924)](https://github.com/PennyLaneAI/pennylane-lightning/pull/924) + +* Add the `ci:use-gpu-runner` GitHub label to `lightning.kokkos` GPU Testing CIs. + [(#916)](https://github.com/PennyLaneAI/pennylane-lightning/pull/916) + +* Update the test suite to remove deprecated code. + [(#912)](https://github.com/PennyLaneAI/pennylane-lightning/pull/912) + +* Merge `lightning.gpu` and `lightning.tensor` GPU tests in single Python and C++ CIs controlled by the `ci:use-gpu-runner` label. + [(#911)](https://github.com/PennyLaneAI/pennylane-lightning/pull/911) + +* Skip the compilation of Lightning simulators and development requirements to boost the build of public docs up to 5x. + [(#904)](https://github.com/PennyLaneAI/pennylane-lightning/pull/904) + +* Build Lightning wheels in `Release` mode to reduce the binary sizes. + [(#903)](https://github.com/PennyLaneAI/pennylane-lightning/pull/903) + +* Update Pybind11 to 2.13.5. + [(#901)](https://github.com/PennyLaneAI/pennylane-lightning/pull/901) + +* Migrate wheels artifacts to v4. + [(#893)](https://github.com/PennyLaneAI/pennylane-lightning/pull/893) + +* Update GitHub actions in response to a high-severity vulnerability. + [(#887)](https://github.com/PennyLaneAI/pennylane-lightning/pull/887) + +* Optimize and simplify controlled kernels in Lightning-Qubit. + [(#882)](https://github.com/PennyLaneAI/pennylane-lightning/pull/882) + +* Optimize gate cache recording for Lightning-Tensor C++ API. + [(#879)](https://github.com/PennyLaneAI/pennylane-lightning/pull/879) + +* Unify Lightning-Kokkos and Lightning-Qubit devices under a Lightning-Base abstracted class. + [(#876)](https://github.com/PennyLaneAI/pennylane-lightning/pull/876) + +* Smarter defaults for the `split_obs` argument in the serializer. The serializer splits linear combinations into chunks instead of all their terms. + [(#873)](https://github.com/PennyLaneAI/pennylane-lightning/pull/873/) + +* Prefer `tomlkit` over `toml` for building Lightning wheels, and choose `tomli` and `tomllib` over `toml` when installing the package. + [(#857)](https://github.com/PennyLaneAI/pennylane-lightning/pull/857) + +* Lightning-Kokkos gains native support for the `PauliRot` gate. + [(#855)](https://github.com/PennyLaneAI/pennylane-lightning/pull/855) + +### Documentation + +* Update Lightning-Tensor installation docs and usage suggestions. + [(#971)](https://github.com/PennyLaneAI/pennylane-lightning/pull/971) + [(#972)](https://github.com/PennyLaneAI/pennylane-lightning/pull/971) + +* Update `README.rst` installation instructions for `lightning.gpu` and `lightning.tensor`. + [(#957)](https://github.com/PennyLaneAI/pennylane-lightning/pull/957) + +* Update `lightning.tensor` documentation to include all the new features added since pull request #756. The new features are: 1. Finite-shot measurements; 2. Expval-base quantities; 3. Support for `qml.state()` and `qml.stateprep()`; 4. Support for all gates support via Matrix Product Operator (MPO). + [(#909)](https://github.com/PennyLaneAI/pennylane-lightning/pull/909) + +### Bug fixes + +* Fix Lightning Kokkos test_device for `kokkos_args` fail for MacOS due to `np.complex256` + [(#974)](https://github.com/PennyLaneAI/pennylane-lightning/pull/974) + +* Fix PTM stable-latest related to `default.qubit.legacy` and the `latest` flag usage. + [(#961)](https://github.com/PennyLaneAI/pennylane-lightning/pull/961) + [(#966)](https://github.com/PennyLaneAI/pennylane-lightning/pull/966) + +* Fix build failure for Lightning-Kokkos editable installation on MacOS due to `liblightning_kokkos_catalyst.so` copy and `liblightning_kokkos_catalyst.so` not copied to correct build path for editable installation. + [(#947)](https://github.com/PennyLaneAI/pennylane-lightning/pull/947) + [(#968)](https://github.com/PennyLaneAI/pennylane-lightning/pull/968) + +* Add concept restriction to ensure `ConstMult` inline function only hit with arithmetic-values times complex values. Fixes build failures with the test suite when enabling OpenMP, and disabling BLAS and Python under clang. + [(#936)](https://github.com/PennyLaneAI/pennylane-lightning/pull/936) + +* Bug fix for `applyMatrix` in Lightning-Tensor. Matrix operator data is not stored in the `cuGateCache` object to support `TensorProd` obs with multiple `Hermitian` obs. + [(#932)](https://github.com/PennyLaneAI/pennylane-lightning/pull/932) + +* Bug fix for `_pauli_word` of `QuantumScriptSerializer`. `_pauli_word` can process `PauliWord` object: `I`. + [(#919)](https://github.com/PennyLaneAI/pennylane-lightning/pull/919) + +* Bug fix for analytic `qml.probs` in the Lightning-Tensor C++ API. + [(#906)](https://github.com/PennyLaneAI/pennylane-lightning/pull/906) + +### Contributors + +This release contains contributions from (in alphabetical order): + +Ali Asadi, Amintor Dusko, Diego Guala, Joseph Lee, Luis Alfredo Nuñez Meneses, Vincent Michaud-Rioux, Lee J. O'Riordan, Mudit Pandey, Shuli Shu, Haochen Paul Wang + +--- + +# Release 0.38.0 + +### New features since last release + +* Add `qml.StatePrep()` and `qml.QubitStateVector()` support to `lightning.tensor`. + [(#849)](https://github.com/PennyLaneAI/pennylane-lightning/pull/849) + +* Add analytic `qml.probs()` measurement support to `lightning.tensor`. + [(#830)](https://github.com/PennyLaneAI/pennylane-lightning/pull/830) + +* Add `qml.state()` measurement support to `lightning.tensor`. + [(#827)](https://github.com/PennyLaneAI/pennylane-lightning/pull/827) + +* Add Lightning-GPU Linux (AArch64 + GraceHopper) wheels to PyPI. + [(#815)](https://github.com/PennyLaneAI/pennylane-lightning/pull/815) + +* Add `var` support to `lightning.tensor`. Note that `var` support is added via `obs**2` and this implementation scales as `O(num_obs**2)`. + [(#804)](https://github.com/PennyLaneAI/pennylane-lightning/pull/804) + +### Breaking changes + +* Update python packaging to follow PEP 517/518/621/660 standards. + [(#832)](https://github.com/PennyLaneAI/pennylane-lightning/pull/832) + +* Add `getData()` in the `lightning.tensor` C++ backend. Users are responsible for ensuring sufficient host memory is allocated for the full state vector. + [(#827)](https://github.com/PennyLaneAI/pennylane-lightning/pull/827) + +* Remove `NDpermuter.hpp` which is no longer required. + [(#795)](https://github.com/PennyLaneAI/pennylane-lightning/pull/795) + * Remove temporary steps from the CI, such as downgrading Scipy to <1.14 and installing Kokkos v4.2 for `lightning-version == 'stable'`. [(#792)](https://github.com/PennyLaneAI/pennylane-lightning/pull/792) * Do not run GPU tests and Docker workflows on release. [(#788)](https://github.com/PennyLaneAI/pennylane-lightning/pull/788) +* Update python packaging to follow PEP 517/518/621/660 standards. + [(#832)](https://github.com/PennyLaneAI/pennylane-lightning/pull/832) + ### Improvements +* Updated calls of ``size_t`` to ``std::size_t`` everywhere. + [(#816)](https://github.com/PennyLaneAI/pennylane-lightning/pull/816) + +* Update Lightning tests to support the generalization of basis state preparation. + [(#864)](https://github.com/PennyLaneAI/pennylane-lightning/pull/864) + +* Add `SetState` and `SetBasisState` to `Lightning-KokkosSimulator`. + [(#861)](https://github.com/PennyLaneAI/pennylane-lightning/pull/861) + +* Remove use of the deprecated `Operator.expand` in favour of `Operator.decomposition`. + [(#846)](https://github.com/PennyLaneAI/pennylane-lightning/pull/846) + +* The `setBasisState` and `setStateVector` methods of `StateVectorLQubit` and `StateVectorKokkos` are overloaded to support PennyLane-like parameters. + [(#843)](https://github.com/PennyLaneAI/pennylane-lightning/pull/843) + +* Move `setBasisState`, `setStateVector` and `resetStateVector` from `StateVectorLQubitManaged` to `StateVectorLQubit`. + [(#841)](https://github.com/PennyLaneAI/pennylane-lightning/pull/841) + +* Update `generate_samples` in Lightning-Kokkos and Lightning-GPU to support `qml.measurements.Shots` type instances. + [(#839)](https://github.com/PennyLaneAI/pennylane-lightning/pull/839) + +* Add a Catalyst-specific wrapping class for Lightning Kokkos. + [(#837)](https://github.com/PennyLaneAI/pennylane-lightning/pull/837) + [(#770)](https://github.com/PennyLaneAI/pennylane-lightning/pull/770) + +* Lightning-Qubit natively supports the `PauliRot` gate. + [(#834)](https://github.com/PennyLaneAI/pennylane-lightning/pull/834) + +* Multiple calls to the `append_mps_final_state()` API is allowed in `lightning.tensor`. + [(#830)](https://github.com/PennyLaneAI/pennylane-lightning/pull/830) + +* Add `initial_state_prep` option to Catalyst TOML file. + [(#826)](https://github.com/PennyLaneAI/pennylane-lightning/pull/826) + +* `ENABLE_LAPACK` is `OFF` by default for all Lightning backends. + [(#825)](https://github.com/PennyLaneAI/pennylane-lightning/pull/825) + +* Update `ctrl_decomp_zyz` tests with `len(control_wires) > 1`. + [(#821)](https://github.com/PennyLaneAI/pennylane-lightning/pull/821) + +* Update the Catalyst-specific wrapping class for Lightning Kokkos to track Catalyst's new support for MCM seeding. + [(#819)](https://github.com/PennyLaneAI/pennylane-lightning/pull/819) + +* Replace ``size_t`` by ``std::size_t`` everywhere. + [(#816)](https://github.com/PennyLaneAI/pennylane-lightning/pull/816/) + +* Shot batching is made more efficient by executing all the shots in one go on Lightning-Qubit. + [(#814)](https://github.com/PennyLaneAI/pennylane-lightning/pull/814) + +* Lightning-Qubit calls `generate_samples(wires)` on a minimal subset of wires when executing in finite-shot mode. + [(#813)](https://github.com/PennyLaneAI/pennylane-lightning/pull/813) + +* Update `LightingQubit.preprocess` to work with changes to preprocessing for mid-circuit measurements. + [(#812)](https://github.com/PennyLaneAI/pennylane-lightning/pull/812) + +* Avoid unnecessary memory reset in Lightning-Qubit's state vector class constructor. + [(#811)](https://github.com/PennyLaneAI/pennylane-lightning/pull/811) + +* Add `generate_samples(wires)` support in Lightning-Qubit, which samples faster for a subset of wires. + [(#809)](https://github.com/PennyLaneAI/pennylane-lightning/pull/809) + +* Optimize the OpenMP parallelization of Lightning-Qubit's `probs` for all number of targets. + [(#807)](https://github.com/PennyLaneAI/pennylane-lightning/pull/807) + +* Optimize `probs(wires)` of Lightning-Kokkos using various kernels. Which kernel is to be used depends on the device, number of qubits and number of target wires. + [(#802)](https://github.com/PennyLaneAI/pennylane-lightning/pull/802) + +* Add GPU device compute capability check for Lightning-Tensor. + [(#803)](https://github.com/PennyLaneAI/pennylane-lightning/pull/803) + +* Refactor CUDA utils Python bindings to a separate module. + [(#801)](https://github.com/PennyLaneAI/pennylane-lightning/pull/801) + +* Parallelize Lightning-Qubit `probs` with OpenMP when using the `-DLQ_ENABLE_KERNEL_OMP=1` CMake argument. + [(#800)](https://github.com/PennyLaneAI/pennylane-lightning/pull/800) + +* Implement `probs(wires)` using a bit-shift implementation akin to the gate kernels in Lightning-Qubit. + [(#795)](https://github.com/PennyLaneAI/pennylane-lightning/pull/795) + * Enable setting the PennyLane version when invoking, for example, `make docker-build version=master pl_version=master`. [(#791)](https://github.com/PennyLaneAI/pennylane-lightning/pull/791) * Add a Catalyst-specific wrapping class for Lightning Kokkos. [(#770)](https://github.com/PennyLaneAI/pennylane-lightning/pull/770) - -* `SX` and `C(SX)` gates are natively supported with `lightning.qubit`. - [(#731)](https://github.com/PennyLaneAI/pennylane-lightning/pull/731) ### Documentation +* The installation instructions for all lightning plugins have been improved. + [(#858)](https://github.com/PennyLaneAI/pennylane-lightning/pull/858) + [(#851)](https://github.com/PennyLaneAI/pennylane-lightning/pull/851) + +* Updated the README and added citation format for Lightning arXiv preprint. + [(#818)](https://github.com/PennyLaneAI/pennylane-lightning/pull/818) + ### Bug fixes +* Point to the right Lightning root folder independently from the invocation location, when configuring the project. + [(#874)](https://github.com/PennyLaneAI/pennylane-lightning/pull/874) + +* Update dependencies and `build` command options following changes in the build system. + [(#863)](https://github.com/PennyLaneAI/pennylane-lightning/pull/863) + +* Replace structured bindings by variables in `GateImplementationsLM.hpp`. + [(#856)](https://github.com/PennyLaneAI/pennylane-lightning/pull/856) + +* Remove wrong `-m` when calling `setup.py`. + [(#854)](https://github.com/PennyLaneAI/pennylane-lightning/pull/854) + +* Fix plugin-test-matrix CI/CD workflows. + [(#850)](https://github.com/PennyLaneAI/pennylane-lightning/pull/850) + +* Set the `immutable` parameter value as `false` for the `cutensornetStateApplyTensorOperator` to allow the following `cutensornetStateUpdateTensorOperator` call. + [(#845)](https://github.com/PennyLaneAI/pennylane-lightning/pull/845) + +* Fix cuQuantum SDK path pass-though in CMake. + [(#831)](https://github.com/PennyLaneAI/pennylane-lightning/pull/831) + +* Fix CUDA sync issues on AArch64 + GraceHopper. + [(#823)](https://github.com/PennyLaneAI/pennylane-lightning/pull/823) + +* Check for the number of wires for Hermitian observables in Lightning-Tensor. Only 1-wire Hermitian observables are supported as of `cuTensorNet-v24.03.0`. + [(#806)](https://github.com/PennyLaneAI/pennylane-lightning/pull/806) + * Set `PL_BACKEND` for the entire `build-wheel-lightning-gpu` Docker-build stage to properly build the Lightning-GPU wheel. [(#791)](https://github.com/PennyLaneAI/pennylane-lightning/pull/791) @@ -37,11 +518,14 @@ * Fix checkout command in test workflows for rc branches. [(#777)](https://github.com/PennyLaneAI/pennylane-lightning/pull/777) +* Point to the right Lightning root folder independently from the invocation location, when configuring the project. + [(#874)](https://github.com/PennyLaneAI/pennylane-lightning/pull/874) + ### Contributors This release contains contributions from (in alphabetical order): -Amintor Dusko, Luis Alfredo Nuñez Meneses, Vincent Michaud-Rioux +Ali Asadi, Astral Cai, Ahmed Darwish, Amintor Dusko, Vincent Michaud-Rioux, Luis Alfredo Nuñez Meneses, Erick Ochoa Lopez, Lee J. O'Riordan, Mudit Pandey, Shuli Shu, Raul Torres, Paul Haochen Wang --- @@ -86,7 +570,7 @@ Amintor Dusko, Luis Alfredo Nuñez Meneses, Vincent Michaud-Rioux * Release candidate branches automatically use the new large GitHub runner pool. [(#769)](https://github.com/PennyLaneAI/pennylane-lightning/pull/769) -* Lightning-Kokkos dev wheels for MacOS (x86_64, ARM64) and Linux (aarch64) are uploaded to TestPyPI upon merging a pull request. +* Lightning-Kokkos dev wheels for MacOS (x86_64, ARM64) and Linux (AArch64) are uploaded to TestPyPI upon merging a pull request. [(#765)](https://github.com/PennyLaneAI/pennylane-lightning/pull/765) * Lightning-Kokkos Linux (x86_64) dev wheels are pushed to [Test PyPI](https://test.pypi.org/project/PennyLane-Lightning-Kokkos/) upon merging a pull request. @@ -141,7 +625,7 @@ Amintor Dusko, Luis Alfredo Nuñez Meneses, Vincent Michaud-Rioux * Remove the daily schedule from the "Compat Check w/PL - release/release" GitHub action. [(#746)](https://github.com/PennyLaneAI/pennylane-lightning/pull/746) -* Remove the required `scipy` config file for LightningQubit. The support is now maintained by passing `SCIPY_LIBS_PATH` to the compiler. +* Remove the required `scipy` config file for Lightning-Qubit. The support is now maintained by passing `SCIPY_LIBS_PATH` to the compiler. [(#775)](https://github.com/PennyLaneAI/pennylane-lightning/pull/775) ### Documentation @@ -181,7 +665,7 @@ Amintor Dusko, Luis Alfredo Nuñez Meneses, Vincent Michaud-Rioux * The `tests_lkcpu_python.yml` workflow properly checks out the release or stable version of Lightning-Qubit during the test job. [(#723)](https://github.com/PennyLaneAI/pennylane-lightning/pull/723) -* Fix PennyLane Lightning-Kokkos and LightningQubit tests for stable/stable configuration. +* Fix PennyLane Lightning-Kokkos and Lightning-Qubit tests for stable/stable configuration. [(#734)](https://github.com/PennyLaneAI/pennylane-lightning/pull/734) * Remove the Autoray dependency from requirement files. @@ -325,19 +809,19 @@ Ali Asadi, Amintor Dusko, Lillian Frederiksen, Pietropaolo Frisoni, David Ittah, ### Bug fixes -* Fix wire order permutations when using `qml.probs` with out-of-order wires. +* Fix wire order permutations when using `qml.probs` with out-of-order wires in Lightning-Qubit. [(#707)](https://github.com/PennyLaneAI/pennylane-lightning/pull/707) -* Lightning Qubit once again respects the wire order specified on device instantiation. +* Lightning-Qubit once again respects the wire order specified on device instantiation. [(#705)](https://github.com/PennyLaneAI/pennylane-lightning/pull/705) -* `dynamic_one_shot` was refactored to use `SampleMP` measurements as a way to return the mid-circuit measurement samples. `LightningQubit`'s `simulate` is modified accordingly. +* `dynamic_one_shot` was refactored to use `SampleMP` measurements as a way to return the mid-circuit measurement samples. `LightningQubit's `simulate` is modified accordingly. [(#694)](https://github.com/PennyLaneAI/pennylane-lightning/pull/694) -* `LightningQubit` correctly decomposes state prep operations when used in the middle of a circuit. +* Lightning-Qubit correctly decomposes state prep operations when used in the middle of a circuit. [(#687)](https://github.com/PennyLaneAI/pennylane-lightning/pull/687) -* `LightningQubit` correctly decomposes `qml.QFT` and `qml.GroverOperator` if `len(wires)` is greater than 9 and 12 respectively. +* Lightning-Qubit correctly decomposes `qml.QFT` and `qml.GroverOperator` if `len(wires)` is greater than 9 and 12 respectively. [(#687)](https://github.com/PennyLaneAI/pennylane-lightning/pull/687) * Specify `isort` `--py` (Python version) and `-l` (max line length) to stabilize `isort` across Python versions and environments. @@ -352,7 +836,7 @@ Ali Asadi, Amintor Dusko, Lillian Frederiksen, Pietropaolo Frisoni, David Ittah, * Fix the failed observable serialization unit tests. [(#683)](https://github.com/PennyLaneAI/pennylane-lightning/pull/683) -* Update the `LightningQubit` new device API to work with Catalyst. +* Update the Lightning-Qubit new device API to work with Catalyst. [(#665)](https://github.com/PennyLaneAI/pennylane-lightning/pull/665) * Update the version of `codecov-action` to v4 and fix the CodeCov issue with the PL-Lightning check-compatibility actions. @@ -446,7 +930,7 @@ Vincent Michaud-Rioux * The `BlockEncode` operation from PennyLane is now supported on all Lightning devices. [(#599)](https://github.com/PennyLaneAI/pennylane-lightning/pull/599) -* OpenMP acceleration can now be enabled at compile time for all `lightning.qubit` gate kernels using the "-DLQ_ENABLE_KERNEL_OMP=1" CMake argument. +* OpenMP acceleration can now be enabled at compile time for all `lightning.qubit` gate kernels using the `-DLQ_ENABLE_KERNEL_OMP=1` CMake argument. [(#510)](https://github.com/PennyLaneAI/pennylane-lightning/pull/510) * Enable building Docker images for any branch or tag. Set the Docker build cron job to build images for the latest release and `master`. @@ -765,7 +1249,7 @@ Ali Asadi, Amintor Dusko, Vincent Michaud-Rioux, Lee J. O'Riordan, Shuli Shu ### Breaking changes -* Rename `QubitStateVector` to `StatePrep` in the `LightningQubit` and `LightningKokkos` classes. +* Rename `QubitStateVector` to `StatePrep` in the Lightning-Qubit and `Lightning-Kokkos` classes. [(#486)](https://github.com/PennyLaneAI/pennylane-lightning/pull/486) * Modify `adjointJacobian` methods to accept a (maybe unused) reference `StateVectorT`, allowing device-backed simulators to directly access state vector data for adjoint differentiation instead of copying it back-and-forth into `JacobianData` (host memory). @@ -1783,7 +2267,7 @@ Thomas Bromley, Theodor Isacsson, Christina Lee, Thomas Loke, Antal Száva. ### Bug fixes -* Fixes a bug where the `QNode` would swap `LightningQubit` to +* Fixes a bug where the `QNode` would swap Lightning-Qubit to `DefaultQubitAutograd` on device execution due to the inherited `passthru_devices` entry of the `capabilities` dictionary. [(#61)](https://github.com/PennyLaneAI/pennylane-lightning/pull/61) diff --git a/.github/ISSUE_TEMPLATE.md b/.github/ISSUE_TEMPLATE.md index ed6f8ed2f8..dd1ce14cb5 100644 --- a/.github/ISSUE_TEMPLATE.md +++ b/.github/ISSUE_TEMPLATE.md @@ -10,7 +10,7 @@ If making a feature request, delete the following template and describe, in deta the feature and why it is needed. For general technical details check out our documentation: -https://pennylane-lightning.readthedocs.io/ +https://docs.pennylane.ai/projects/lightning ------------------------------------------------------------------------------------------------------------- diff --git a/.github/workflows/build_and_cache_Kokkos_linux.yml b/.github/workflows/build_and_cache_Kokkos_linux.yml index 90d7b562a9..62f367dec2 100644 --- a/.github/workflows/build_and_cache_Kokkos_linux.yml +++ b/.github/workflows/build_and_cache_Kokkos_linux.yml @@ -20,7 +20,7 @@ on: kokkos_version: required: false type: string - default: 4.3.01 + default: 4.5.00 outputs: exec_model: description: "The execution model for Kokkos." diff --git a/.github/workflows/compat-check-latest-latest.yml b/.github/workflows/compat-check-latest-latest.yml index a840aa92f9..f6eb0e6732 100644 --- a/.github/workflows/compat-check-latest-latest.yml +++ b/.github/workflows/compat-check-latest-latest.yml @@ -6,9 +6,9 @@ on: workflow_dispatch: jobs: - tests_lgpu_python: - name: Lightning Compatibility test (tests_lgpu_python) - latest/latest - uses: ./.github/workflows/tests_lgpu_python.yml + tests_gpu_python: + name: Lightning Compatibility test (tests_gpu_python) - latest/latest + uses: ./.github/workflows/tests_gpu_python.yml with: lightning-version: latest pennylane-version: latest diff --git a/.github/workflows/compat-check-latest-stable.yml b/.github/workflows/compat-check-latest-stable.yml index ea7c42e804..085305f9a2 100644 --- a/.github/workflows/compat-check-latest-stable.yml +++ b/.github/workflows/compat-check-latest-stable.yml @@ -6,9 +6,9 @@ on: workflow_dispatch: jobs: - tests_lgpu_python: - name: Lightning Compatibility test (tests_lgpu_python) - latest/stable - uses: ./.github/workflows/tests_lgpu_python.yml + tests_gpu_python: + name: Lightning Compatibility test (tests_gpu_python) - latest/stable + uses: ./.github/workflows/tests_gpu_python.yml with: lightning-version: latest pennylane-version: stable @@ -48,4 +48,3 @@ jobs: lightning-version: latest pennylane-version: stable secrets: inherit - diff --git a/.github/workflows/compat-check-release-release.yml b/.github/workflows/compat-check-release-release.yml index 0e8d1d9611..17ca99a6b0 100644 --- a/.github/workflows/compat-check-release-release.yml +++ b/.github/workflows/compat-check-release-release.yml @@ -2,11 +2,13 @@ name: Compat Check w/PL - release/release on: workflow_dispatch: + schedule: + - cron: "0 5 * * 0-6" # Run daily at 5am UTC everyday jobs: - tests_lgpu_python: - name: Lightning Compatibility test (tests_lgpu_python) - release/release - uses: ./.github/workflows/tests_lgpu_python.yml + tests_gpu_python: + name: Lightning Compatibility test (tests_gpu_python) - release/release + uses: ./.github/workflows/tests_gpu_python.yml with: lightning-version: release pennylane-version: release @@ -46,4 +48,4 @@ jobs: lightning-version: release pennylane-version: release secrets: inherit - \ No newline at end of file + diff --git a/.github/workflows/compat-check-stable-latest.yml b/.github/workflows/compat-check-stable-latest.yml index 8f32091357..3de4a9c27a 100644 --- a/.github/workflows/compat-check-stable-latest.yml +++ b/.github/workflows/compat-check-stable-latest.yml @@ -6,9 +6,9 @@ on: workflow_dispatch: jobs: - tests_lgpu_python: - name: Lightning Compatibility test (tests_lgpu_python) - stable/latest - uses: ./.github/workflows/tests_lgpu_python.yml + tests_gpu_python: + name: Lightning Compatibility test (tests_gpu_python) - stable/latest + uses: ./.github/workflows/tests_gpu_python.yml with: lightning-version: stable pennylane-version: latest @@ -48,4 +48,3 @@ jobs: lightning-version: stable pennylane-version: latest secrets: inherit - diff --git a/.github/workflows/compat-check-stable-stable.yml b/.github/workflows/compat-check-stable-stable.yml index 62420af3bd..30cb5799f3 100644 --- a/.github/workflows/compat-check-stable-stable.yml +++ b/.github/workflows/compat-check-stable-stable.yml @@ -6,9 +6,9 @@ on: workflow_dispatch: jobs: - tests_lgpu_python: - name: Lightning Compatibility test (tests_lgpu_python) - stable/stable - uses: ./.github/workflows/tests_lgpu_python.yml + tests_gpu_python: + name: Lightning Compatibility test (tests_gpu_python) - stable/stable + uses: ./.github/workflows/tests_gpu_python.yml with: lightning-version: stable pennylane-version: stable @@ -48,4 +48,4 @@ jobs: lightning-version: stable pennylane-version: stable secrets: inherit - \ No newline at end of file + diff --git a/.github/workflows/compat-docker-release.yml b/.github/workflows/compat-docker-release.yml index d6da5b015f..0fc8146ff3 100644 --- a/.github/workflows/compat-docker-release.yml +++ b/.github/workflows/compat-docker-release.yml @@ -2,6 +2,8 @@ name: Compat Check Docker - Lightning@release on: workflow_dispatch: + schedule: + - cron: "0 5 * * 0-6" # Run daily at 5am UTC everyday concurrency: group: docker_linux_x86_64-release @@ -12,7 +14,7 @@ jobs: name: Docker release - Linux::x86_64 uses: ./.github/workflows/docker_linux_x86_64.yml with: - lightning-version: v0.37.0 - pennylane-version: v0.37.0-rc0 + lightning-version: v0.39.0_rc + pennylane-version: v0.39.0-rc0 push-to-dockerhub: false secrets: inherit # pass all secrets diff --git a/.github/workflows/determine-workflow-runner.yml b/.github/workflows/determine-workflow-runner.yml deleted file mode 100644 index 981c45f7d9..0000000000 --- a/.github/workflows/determine-workflow-runner.yml +++ /dev/null @@ -1,49 +0,0 @@ -name: Determine Workflow Runner group - -on: - workflow_call: - inputs: - default_runner: - description: The runner type that is used by the calling workflow by default - required: true - type: string - outputs: - runner_group: - description: The runner all subsequent jobs within the calling workflow should run on - value: ${{ jobs.determine_workflow_runner.outputs.runner_group || inputs.default_runner }} - -env: - LARGE_RUNNER_GROUP_NAME: pl-4-core-large-runner - -jobs: - determine_workflow_runner: - runs-on: >- - ${{ - ( - github.event_name == 'pull_request' - && contains(github.event.pull_request.labels.*.name, 'urgent') - ) && 'pl-4-core-large-runner' || 'ubuntu-latest' - }} - - outputs: - runner_group: ${{ steps.runner_group.outputs.runner_group }} - - steps: - - name: Output Runner Group name - if: >- - ${{ - github.event_name == 'pull_request' - && startsWith(inputs.default_runner, 'ubuntu') - }} - id: runner_group - env: - # We are not able to use \d to check numeric values as bash does not allow them (not POSIX compliant) - RC_BRANCH_FORMAT_REGEX: v[0-9]+\.[0-9]+\.[0-9]+-rc[0-9]? - REPO_FULL_NAME: PennyLaneAI/pennylane-lightning - run: | - if [[ '${{ contains(github.event.pull_request.labels.*.name, 'urgent') }}' == 'true' || ('${{ github.event.pull_request.head.repo.full_name }}' == "$REPO_FULL_NAME" && '${{ github.event.pull_request.head.ref }}' =~ $RC_BRANCH_FORMAT_REGEX) ]]; then - echo "This job requires usage of the large runner group '$LARGE_RUNNER_GROUP_NAME'"; - echo "runner_group=$LARGE_RUNNER_GROUP_NAME" >> $GITHUB_OUTPUT - else - echo "This job does not require usage of large runners ..."; - fi diff --git a/.github/workflows/docker_linux_x86_64.yml b/.github/workflows/docker_linux_x86_64.yml index 0ce9fdb95f..4a08d2d5c0 100644 --- a/.github/workflows/docker_linux_x86_64.yml +++ b/.github/workflows/docker_linux_x86_64.yml @@ -47,7 +47,7 @@ jobs: timeout-minutes: 180 name: docker::${{ matrix.os }}::${{ matrix.pl_backend }}::${{ inputs.lightning-version }} runs-on: - group: 'Lightning Additional Runners' + group: 'PL Additional Runners' steps: - name: Checkout @@ -63,7 +63,7 @@ jobs: password: ${{ secrets.DOCKERHUB_TOKEN }} - name: Build and export to Docker - uses: docker/build-push-action@v5 + uses: docker/build-push-action@v6 with: load: true push: false @@ -80,7 +80,7 @@ jobs: docker run --rm test:${{ inputs.lightning-version }}-${{ matrix.pl_backend }} - name: Build and push version - uses: docker/build-push-action@v5 + uses: docker/build-push-action@v6 if: ${{ inputs.push-to-dockerhub }} with: push: ${{ inputs.push-to-dockerhub }} diff --git a/.github/workflows/format.yml b/.github/workflows/format.yml index bc29a7dfb3..25c1797a78 100644 --- a/.github/workflows/format.yml +++ b/.github/workflows/format.yml @@ -17,14 +17,14 @@ jobs: - name: Set up Python uses: actions/setup-python@v5 with: - python-version: '3.11' + python-version: '3.12' - name: Checkout PennyLane-Lightning uses: actions/checkout@v4 - name: Install dependencies run: - python -m pip install -r requirements-dev.txt + python -m pip install -r requirements.txt - name: Run isort & black --check run: make format-python check=1 verbose=1 @@ -38,7 +38,7 @@ jobs: uses: actions/checkout@v4 - name: Install dependencies - run: sudo apt update && sudo apt -y install python3 python3-pip && python -m pip install pip~=22.0 && python3 -m pip install -r requirements-dev.txt + run: sudo apt update && sudo apt -y install python3 python3-pip python-is-python3 && python -m pip install -r requirements.txt - name: Run formatter run: PATH=$PATH:/home/ubuntu/.local/bin/:$(dirname $(which python3))/ ./bin/format --check ./pennylane_lightning/core/src diff --git a/.github/workflows/post_release_version_bump.yml b/.github/workflows/post_release_version_bump.yml index 1c60fec33e..b859a28bfe 100644 --- a/.github/workflows/post_release_version_bump.yml +++ b/.github/workflows/post_release_version_bump.yml @@ -15,7 +15,7 @@ jobs: - name: Install Python uses: actions/setup-python@v5 with: - python-version: '3.9' + python-version: '3.10' - name: Install dependencies run: | @@ -29,7 +29,7 @@ jobs: --changelog_path "./.github/CHANGELOG.md" --post_release - name: Create Pull Request - uses: peter-evans/create-pull-request@v3 + uses: peter-evans/create-pull-request@v7 with: commit-message: post release version bump title: Version Bump diff --git a/.github/workflows/pre_release_version_bump.yml b/.github/workflows/pre_release_version_bump.yml index 2bebc9fbc4..6038d2642c 100644 --- a/.github/workflows/pre_release_version_bump.yml +++ b/.github/workflows/pre_release_version_bump.yml @@ -14,7 +14,7 @@ jobs: - name: Install Python uses: actions/setup-python@v5 with: - python-version: '3.9' + python-version: '3.10' - name: Install dependencies run: | @@ -28,7 +28,7 @@ jobs: --changelog_path "./.github/CHANGELOG.md" --pre_release - name: Create Pull Request - uses: peter-evans/create-pull-request@v3 + uses: peter-evans/create-pull-request@v7 with: commit-message: pre release version bump title: Version Bump diff --git a/.github/workflows/python_lightning_qubit_test_durations.json b/.github/workflows/python_lightning_qubit_test_durations.json index 20574cb751..b7dcd8ece7 100644 --- a/.github/workflows/python_lightning_qubit_test_durations.json +++ b/.github/workflows/python_lightning_qubit_test_durations.json @@ -1,8403 +1,9381 @@ { - 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"tests/test_vjp.py::TestVectorJacobianProduct::test_provide_starting_state[complex128]": 0.0002413729962427169, + "tests/test_vjp.py::TestVectorJacobianProduct::test_provide_starting_state[complex64]": 0.0001568020088598132, + "tests/test_vjp.py::TestVectorJacobianProduct::test_single_expectation_value[complex128]": 0.0027611829864326864, + "tests/test_vjp.py::TestVectorJacobianProduct::test_single_expectation_value[complex64]": 0.002852984005585313, + "tests/test_vjp.py::TestVectorJacobianProduct::test_unsupported_op[complex128]": 0.0015668240084778517, + "tests/test_vjp.py::TestVectorJacobianProduct::test_unsupported_op[complex64]": 0.002341456973226741, + "tests/test_vjp.py::TestVectorJacobianProduct::test_use_device_state[complex128]": 0.00016355200204998255, + "tests/test_vjp.py::TestVectorJacobianProduct::test_use_device_state[complex64]": 0.00018333698972128332, + "tests/test_vjp.py::TestVectorJacobianProduct::test_wrong_dy_expval[complex128]": 0.0012960570165887475, + "tests/test_vjp.py::TestVectorJacobianProduct::test_wrong_dy_expval[complex64]": 0.00138136200257577, + "tests/test_vjp.py::TestVectorJacobianProduct::test_zero_dy[complex128]": 0.0017653219983913004, + "tests/test_vjp.py::TestVectorJacobianProduct::test_zero_dy[complex64]": 0.001791216986021027 +} diff --git a/.github/workflows/set_wheel_build_matrix.yml b/.github/workflows/set_wheel_build_matrix.yml index 0f3c73fe17..60e299cfc7 100644 --- a/.github/workflows/set_wheel_build_matrix.yml +++ b/.github/workflows/set_wheel_build_matrix.yml @@ -1,7 +1,7 @@ name: Set wheel build matrix env: - PYTHON3_MIN_VERSION: "9" + PYTHON3_MIN_VERSION: "10" PYTHON3_MAX_VERSION: "12" on: @@ -52,7 +52,7 @@ jobs: - name: Kokkos version id: kokkos_version - run: echo "kokkos_version=[\"4.3.01\"]" >> $GITHUB_OUTPUT + run: echo "kokkos_version=[\"4.5.00\"]" >> $GITHUB_OUTPUT outputs: python_version: ${{ steps.pyver.outputs.python_version }} diff --git a/.github/workflows/tests_lgpu_cpp.yml b/.github/workflows/tests_gpu_cpp.yml similarity index 74% rename from .github/workflows/tests_lgpu_cpp.yml rename to .github/workflows/tests_gpu_cpp.yml index 1c8a6cebde..d66f7722b4 100644 --- a/.github/workflows/tests_lgpu_cpp.yml +++ b/.github/workflows/tests_gpu_cpp.yml @@ -1,4 +1,4 @@ -name: Testing::x86_64::LGPU::C++ +name: Testing::x86_64::GPU::C++ on: workflow_call: inputs: @@ -11,12 +11,11 @@ on: required: true description: The version of PennyLane to use. Valid values are either 'release' (most recent release candidate), 'stable' (most recent git-tag) or 'latest' (most recent commit from master) pull_request: - paths: - - .github/workflows/tests_lgpu_cpp.yml - - pennylane_lightning/core/src/** - - '!pennylane_lightning/core/src/simulators/lightning_kokkos/**' - - '!pennylane_lightning/core/src/simulators/lightning_qubit/**' - - '!pennylane_lightning/core/src/simulators/lightning_tensor/**' + types: + - opened + - reopened + - synchronize + - ready_for_review push: branches: - master @@ -25,15 +24,14 @@ env: CI_CUDA_ARCH: 86 COVERAGE_FLAGS: "--cov=pennylane_lightning --cov-report=term-missing --cov-report=xml:./coverage.xml --no-flaky-report -p no:warnings --tb=native" GCC_VERSION: 11 - TF_VERSION: 2.10.0 - TORCH_VERSION: 1.11.0+cpu concurrency: - group: tests_lgpu_cpp-${{ github.ref }}-${{ github.event_name }}-${{ inputs.lightning-version }}-${{ inputs.pennylane-version }} + group: tests_gpu_cpp-${{ github.ref }}-${{ github.event_name }}-${{ inputs.lightning-version }}-${{ inputs.pennylane-version }} cancel-in-progress: true jobs: builddeps: + if: ${{ github.event_name != 'pull_request' || (github.event.pull_request.draft == false && contains(github.event.pull_request.labels.*.name, 'ci:use-gpu-runner')) }} runs-on: - self-hosted - ubuntu-22.04 @@ -42,25 +40,26 @@ jobs: strategy: max-parallel: 1 matrix: - cuda_version: ["12"] + cuda_version_maj: ["12"] + cuda_version_min: ["4"] steps: - name: Validate GPU version and installed compiler run: | source /etc/profile.d/modules.sh module use /opt/modules - module load cuda/${{ matrix.cuda_version }} + module load cuda/${{ matrix.cuda_version_maj }}.${{ matrix.cuda_version_min }} echo "${PATH}" >> $GITHUB_PATH echo "LD_LIBRARY_PATH=${LD_LIBRARY_PATH}" >> $GITHUB_ENV nvcc --version nvidia-smi - cpptestswithLGPU: + cpptests: if: ${{ !contains(fromJSON('["schedule", "workflow_dispatch"]'), github.event_name) }} needs: [builddeps] strategy: matrix: - pl_backend: ["lightning_gpu"] + pl_backend: ["lightning_gpu", "lightning_tensor"] cuda_version: ["12"] name: C++ Tests (${{ matrix.pl_backend }}, cuda-${{ matrix.cuda_version }}) @@ -80,20 +79,20 @@ jobs: nvcc --version nvidia-smi - - name: Checkout PennyLane-Lightning-GPU + - name: Checkout PennyLane-Lightning uses: actions/checkout@v4 with: path: main - uses: actions/setup-python@v5 - name: Install Python + name: Setup Python with: - python-version: '3.9' + python-version: '3.10' - name: Setup Python virtual environment id: setup_venv env: - VENV_NAME: ${{ github.workspace }}/venv_${{ steps.setup_python.outputs.python-version }}_${{ github.sha }} + VENV_NAME: ${{ github.workspace }}/venv_${{ steps.setup_python.outputs.python-version }}_${{ github.sha }}_${{ matrix.pl_backend }} run: | # Clear any pre-existing venvs rm -rf venv_* @@ -109,8 +108,8 @@ jobs: echo "venv_name=${{ env.VENV_NAME }}" >> $GITHUB_OUTPUT echo "Python_ROOT_DIR=${{ env.VENV_NAME }}" >> $GITHUB_ENV echo "Python3_ROOT_DIR=${{ env.VENV_NAME }}" >> $GITHUB_ENV - - # Adding venv site-packages to output for subsequent step to referecen if needed + + # Adding venv site-packages to output for subsequent step to referece if needed echo "site_packages_dir=$(${{ env.VENV_NAME }}/bin/python -c 'import sysconfig; print(sysconfig.get_path("platlib"))')" >> $GITHUB_OUTPUT - name: Display Python-Path @@ -119,15 +118,24 @@ jobs: py_path=$(which python) echo "Python Interpreter Path => $py_path" echo "python=$py_path" >> $GITHUB_OUTPUT - + pip_path=$(which pip) echo "PIP Path => $pip_path" echo "pip=$pip_path" >> $GITHUB_OUTPUT - - name: Install required packages + - name: Install required basic packages + run: | + python -m pip install ninja cmake scipy-openblas32 + + - name: Install required lightning_gpu only packages + if: matrix.pl_backend == 'lightning_gpu' + run: | + python -m pip install custatevec-cu${{ matrix.cuda_version }} + + - name: Install required lightning_tensor only packages + if: matrix.pl_backend == 'lightning_tensor' run: | - python -m pip install ninja cmake custatevec-cu${{ matrix.cuda_version }} scipy - sudo apt-get -y -q install liblapack-dev black + python -m pip install cutensornet-cu${{ matrix.cuda_version }} - name: Build and run unit tests run: | @@ -137,6 +145,7 @@ jobs: -DBUILD_TESTS=ON \ -DENABLE_PYTHON=OFF \ -DPL_BACKEND=${{ matrix.pl_backend }} \ + -DSCIPY_OPENBLAS=$(python -c "import scipy_openblas32; print(scipy_openblas32.get_lib_dir())") \ -DCMAKE_CXX_COMPILER=$(which g++-$GCC_VERSION) \ -DENABLE_COVERAGE=ON \ -G Ninja @@ -147,23 +156,28 @@ jobs: lcov --directory . -b ../pennylane_lightning/core/src --capture --output-file coverage.info lcov --remove coverage.info '/usr/*' --output-file coverage.info mv coverage.info coverage-${{ github.job }}-${{ matrix.pl_backend }}.info + - name: Upload test results - uses: actions/upload-artifact@v3 + uses: actions/upload-artifact@v4 if: always() with: name: ubuntu-tests-reports-${{ github.job }}_${{ matrix.pl_backend }} path: ./main/Build/tests/results_${{ github.job }}_${{ matrix.pl_backend }} + retention-days: 1 if-no-files-found: error + include-hidden-files: true - name: Upload code coverage results - uses: actions/upload-artifact@v3 + uses: actions/upload-artifact@v4 with: - name: ubuntu-codecov-results-cpp + name: ubuntu-codecov-results-cpp-${{ matrix.pl_backend }} path: ./main/Build/coverage-${{ github.job }}-${{ matrix.pl_backend }}.info + retention-days: 1 if-no-files-found: error + include-hidden-files: true upload-to-codecov-linux-cpp: - needs: [cpptestswithLGPU] + needs: [cpptests] name: Upload coverage data to codecov runs-on: ubuntu-latest steps: @@ -171,9 +185,10 @@ jobs: uses: actions/checkout@v4 - name: Download coverage reports - uses: actions/download-artifact@v3 + uses: actions/download-artifact@v4 with: - name: ubuntu-codecov-results-cpp + pattern: ubuntu-codecov-* + merge-multiple: true - name: Upload to Codecov uses: codecov/codecov-action@v4 @@ -181,7 +196,7 @@ jobs: fail_ci_if_error: true verbose: true token: ${{ secrets.CODECOV_TOKEN }} - + - name: Cleanup if: always() run: | diff --git a/.github/workflows/tests_lgpu_python.yml b/.github/workflows/tests_gpu_python.yml similarity index 62% rename from .github/workflows/tests_lgpu_python.yml rename to .github/workflows/tests_gpu_python.yml index 51f02859ba..4044e72e69 100644 --- a/.github/workflows/tests_lgpu_python.yml +++ b/.github/workflows/tests_gpu_python.yml @@ -1,4 +1,4 @@ -name: Testing::x86_64::LGPU::Python +name: Testing::x86_64::GPU::Python on: workflow_call: inputs: @@ -11,16 +11,11 @@ on: required: true description: The version of PennyLane to use. Valid values are either 'release' (most recent release candidate), 'stable' (most recent git-tag) or 'latest' (most recent commit from master) pull_request: - paths-ignore: - - .github/** - - '!.github/workflows/tests_lgpu_python.yml' - - pennylane_lightning/core/_version.py - - pennylane_lightning/core/src/simulators/lightning_kokkos/** - - pennylane_lightning/core/src/simulators/lightning_qubit/** - - pennylane_lightning/core/src/simulators/lightning_tensor/** - - pennylane_lightning/lightning_kokkos/** - - pennylane_lightning/lightning_qubit/** - - pennylane_lightning/lightning_tensor/** + types: + - opened + - reopened + - synchronize + - ready_for_review push: branches: - master @@ -29,15 +24,14 @@ env: CI_CUDA_ARCH: 86 COVERAGE_FLAGS: "--cov=pennylane_lightning --cov-report=term-missing --cov-report=xml:./coverage.xml --no-flaky-report -p no:warnings --tb=native" GCC_VERSION: 11 - TF_VERSION: 2.10.0 - TORCH_VERSION: 1.11.0+cpu concurrency: - group: tests_lgpu_python-${{ github.ref }}-${{ github.event_name }}-${{ inputs.lightning-version }}-${{ inputs.pennylane-version }} + group: tests_gpu_python-${{ github.ref }}-${{ github.event_name }}-${{ inputs.lightning-version }}-${{ inputs.pennylane-version }} cancel-in-progress: true jobs: builddeps: + if: ${{ github.event_name != 'pull_request' || (github.event.pull_request.draft == false && contains(github.event.pull_request.labels.*.name, 'ci:use-gpu-runner')) }} runs-on: - self-hosted - ubuntu-22.04 @@ -46,25 +40,25 @@ jobs: strategy: max-parallel: 1 matrix: - cuda_version: ["12"] + cuda_version_maj: ["12"] + cuda_version_min: ["4"] steps: - name: Validate GPU version and installed compiler run: | source /etc/profile.d/modules.sh module use /opt/modules - module load cuda/${{ matrix.cuda_version }} + module load cuda/${{ matrix.cuda_version_maj }}.${{ matrix.cuda_version_min }} echo "${PATH}" >> $GITHUB_PATH echo "LD_LIBRARY_PATH=${LD_LIBRARY_PATH}" >> $GITHUB_ENV nvcc --version nvidia-smi - pythontestswithLGPU: + pythontestswithGPU: needs: [builddeps] strategy: matrix: - pl_backend: ["lightning_gpu"] - default_backend: ["lightning_qubit"] + pl_backend: ["lightning_gpu", "lightning_tensor"] cuda_version: ["12"] name: Python Tests (${{ matrix.pl_backend }}, cuda-${{ matrix.cuda_version }}) @@ -88,31 +82,28 @@ jobs: uses: actions/checkout@v4 with: fetch-tags: true - path: main - name: Switch to release build of Lightning if: inputs.lightning-version == 'release' run: | - cd main git fetch --all git checkout $(git branch -a --list "origin/v0.*rc*" | sort | tail -1) - name: Switch to stable build of Lightning if: inputs.lightning-version == 'stable' run: | - cd main git fetch --tags --force git checkout latest_release - uses: actions/setup-python@v5 name: Install Python with: - python-version: '3.9' + python-version: '3.10' - name: Setup Python virtual environment id: setup_venv env: - VENV_NAME: ${{ github.workspace }}/venv_${{ steps.setup_python.outputs.python-version }}_${{ github.sha }} + VENV_NAME: ${{ github.workspace }}/venv_${{ steps.setup_python.outputs.python-version }}_${{ github.sha }}_${{ matrix.pl_backend }} run: | # Clear any pre-existing venvs rm -rf venv_* @@ -129,9 +120,8 @@ jobs: echo "venv_name=${{ env.VENV_NAME }}" >> $GITHUB_OUTPUT echo "Python_ROOT_DIR=${{ env.VENV_NAME }}" >> $GITHUB_ENV echo "Python3_ROOT_DIR=${{ env.VENV_NAME }}" >> $GITHUB_ENV - - - # Adding venv site-packages to output for subsequent step to referecen if needed + + # Adding venv site-packages to output for subsequent step to reference if needed echo "site_packages_dir=$(${{ env.VENV_NAME }}/bin/python -c 'import sysconfig; print(sysconfig.get_path("platlib"))')" >> $GITHUB_OUTPUT @@ -148,17 +138,33 @@ jobs: - name: Install required packages run: | - cd main python -m pip install -r requirements-dev.txt - python -m pip install cmake custatevec-cu${{ matrix.cuda_version }} openfermionpyscf + python -m pip install cmake openfermionpyscf - - name: Checkout PennyLane for release build - if: inputs.pennylane-version == 'release' + - name: Install required lightning_gpu only packages + if: matrix.pl_backend == 'lightning_gpu' + run: | + python -m pip install custatevec-cu${{ matrix.cuda_version }} + + - name: Install required lightning_tensor only packages + if: matrix.pl_backend == 'lightning_tensor' + run: | + python -m pip install cutensornet-cu${{ matrix.cuda_version }} + + - name: Checkout PennyLane for release or latest build + if: inputs.pennylane-version == 'release' || inputs.pennylane-version == 'latest' uses: actions/checkout@v4 with: path: pennylane repository: PennyLaneAI/pennylane + - name: Switch to latest build of PennyLane + if: inputs.pennylane-version == 'latest' + run: | + cd pennylane + git checkout master + python -m pip uninstall -y pennylane && python -m pip install . -vv --no-deps + - name: Switch to release build of PennyLane if: inputs.pennylane-version == 'release' run: | @@ -170,74 +176,66 @@ jobs: - name: Install Stable PennyLane if: inputs.pennylane-version == 'stable' run: | - cd main python -m pip uninstall -y pennylane && python -m pip install -U pennylane - name: Build and install package env: CUQUANTUM_SDK: $(python -c "import site; print( f'{site.getsitepackages()[0]}/cuquantum')") run: | - cd main - rm -rf build - CMAKE_ARGS="-DPL_BACKEND=${{ matrix.default_backend }} -DENABLE_PYTHON=ON -DCMAKE_CXX_COMPILER=$(which g++-$GCC_VERSION)" \ - python -m pip install . -vv rm -rf build + PL_BACKEND=lightning_qubit python scripts/configure_pyproject_toml.py || true + PL_BACKEND=lightning_qubit python -m pip install . -vv rm -rf build - CMAKE_ARGS="-DPL_BACKEND=${{ matrix.pl_backend }} -DENABLE_PYTHON=ON -DCMAKE_CXX_COMPILER=$(which g++-$GCC_VERSION) -DPython_EXECUTABLE=${{ steps.python_path.outputs.python }}" \ - python -m pip install . -vv + PL_BACKEND=${{ matrix.pl_backend }} python scripts/configure_pyproject_toml.py || true + PL_BACKEND=${{ matrix.pl_backend }} python -m pip install . -vv - - name: Run PennyLane-Lightning-GPU unit tests - if: ${{ matrix.pl_backend != 'all'}} + - name: Run PennyLane unit tests env: OMP_NUM_THREADS: 1 OMP_PROC_BIND: false + if: matrix.pl_backend == 'lightning_gpu' run: | - cd main/ DEVICENAME=`echo ${{ matrix.pl_backend }} | sed "s/_/./g"` pl-device-test --device ${DEVICENAME} --skip-ops --shots=20000 $COVERAGE_FLAGS --cov-append pl-device-test --device ${DEVICENAME} --shots=None --skip-ops $COVERAGE_FLAGS --cov-append - PL_DEVICE=${DEVICENAME} python -m pytest tests/ $COVERAGE_FLAGS - mv coverage.xml coverage-${{ github.job }}-${{ matrix.pl_backend }}.xml - - name: Install all backend devices - if: ${{ matrix.pl_backend == 'all' }} + - name: Run PennyLane Lightning unit tests env: - CUQUANTUM_SDK: $(python -c "import site; print( f'{site.getsitepackages()[0]}/cuquantum')") + OMP_NUM_THREADS: 1 + OMP_PROC_BIND: false run: | - cd main - rm -rf build - CMAKE_ARGS="-DPL_BACKEND=${{matrix.default_backend}} -DENABLE_PYTHON=ON -DCMAKE_CXX_COMPILER=$(which g++-$GCC_VERSION)" \ - python -m pip install . -vv - rm -rf build + DEVICENAME=`echo ${{ matrix.pl_backend }} | sed "s/_/./g"` + PL_DEVICE=${DEVICENAME} python -m pytest tests/ $COVERAGE_FLAGS - CMAKE_ARGS="-DPL_BACKEND=${{ matrix.pl_backend }} -DENABLE_PYTHON=ON -DCMAKE_CXX_COMPILER=$(which g++-$GCC_VERSION)" \ - python -m pip install . -vv + - name: Test wheels for Lightning-GPU + if : matrix.pl_backend == 'lightning_gpu' + run: | + python -m pip install -r requirements-dev.txt + PL_BACKEND=lightning_qubit python scripts/configure_pyproject_toml.py + SKIP_COMPILATION=True python -m pip install . -vv - - name: Run PennyLane-Lightning unit tests for lightning.qubit with all devices installed - if: ${{ matrix.pl_backend == 'all' }} - env: - OMP_NUM_THREADS: 1 - OMP_PROC_BIND: false + DEVICENAME=`echo ${{ matrix.pl_backend }} | sed "s/_/./g"` + PL_BACKEND=${DEVICENAME} python scripts/configure_pyproject_toml.py + python -m build + python -m pip install dist/*.whl --force-reinstall --no-deps + PL_DEVICE=${DEVICENAME} python -m pytest tests/test_device.py $COVERAGE_FLAGS + + - name: Move coverage file run: | - cd main/ - PL_DEVICE=lightning.qubit python -m pytest tests/ -k "not test_native_mcm" $COVERAGE_FLAGS - pl-device-test --device lightning.qubit --skip-ops --shots=20000 $COVERAGE_FLAGS --cov-append - pl-device-test --device lightning.qubit --shots=None --skip-ops $COVERAGE_FLAGS --cov-append - PL_DEVICE=lightning.gpu python -m pytest tests/ $COVERAGE_FLAGS - pl-device-test --device lightning.gpu --skip-ops --shots=20000 $COVERAGE_FLAGS --cov-append - pl-device-test --device lightning.gpu --shots=None --skip-ops $COVERAGE_FLAGS --cov-append mv coverage.xml coverage-${{ github.job }}-${{ matrix.pl_backend }}.xml - name: Upload code coverage results - uses: actions/upload-artifact@v3 + uses: actions/upload-artifact@v4 with: - name: ubuntu-codecov-results-python - path: ./main/coverage-${{ github.job }}-${{ matrix.pl_backend }}.xml + name: ubuntu-codecov-results-python-${{ matrix.pl_backend }} + path: coverage-${{ github.job }}-${{ matrix.pl_backend }}.xml + retention-days: 1 if-no-files-found: error + include-hidden-files: true upload-to-codecov-linux-python: - needs: [pythontestswithLGPU] + needs: [pythontestswithGPU] name: Upload coverage data to codecov runs-on: ubuntu-latest steps: @@ -245,9 +243,10 @@ jobs: uses: actions/checkout@v4 - name: Download coverage reports - uses: actions/download-artifact@v3 + uses: actions/download-artifact@v4 with: - name: ubuntu-codecov-results-python + pattern: ubuntu-codecov-* + merge-multiple: true - name: Upload to Codecov uses: codecov/codecov-action@v4 diff --git a/.github/workflows/tests_lgpumpi_cpp.yml b/.github/workflows/tests_lgpumpi_cpp.yml index 58c86ce22c..fdd17b0fbe 100644 --- a/.github/workflows/tests_lgpumpi_cpp.yml +++ b/.github/workflows/tests_lgpumpi_cpp.yml @@ -14,6 +14,11 @@ on: branches: - main pull_request: + types: + - opened + - reopened + - synchronize + - ready_for_review paths: - .github/workflows/tests_lgpumpi_cpp.yml - pennylane_lightning/core/src/** @@ -33,7 +38,7 @@ concurrency: jobs: cpp_tests: - if: ${{ contains(github.event.pull_request.labels.*.name, 'ci:use-multi-gpu-runner') && !contains(fromJSON('["schedule", "workflow_dispatch"]'), github.event_name) }} + if: ${{ github.event.pull_request.draft == false && (contains(github.event.pull_request.labels.*.name, 'ci:use-multi-gpu-runner') || contains(fromJSON('["schedule", "workflow_dispatch"]'), github.event_name)) }} runs-on: - self-hosted - linux @@ -63,7 +68,7 @@ jobs: id: setup_python name: Install Python with: - python-version: '3.9' + python-version: '3.10' # Since the self-hosted runner can be re-used. It is best to set up all package # installations in a virtual environment that gets cleaned at the end of each workflow run @@ -123,7 +128,8 @@ jobs: - name: Build and run unit tests run: | source /etc/profile.d/modules.sh && module use /opt/modules/ && module load ${{ matrix.mpilib }}/cuda-${{ matrix.cuda_version_maj }}.${{ matrix.cuda_version_min }} - export CUQUANTUM_SDK=$(python -c "import site; print( f'{site.getsitepackages()[0]}/cuquantum/lib')") + export CUQUANTUM_SDK=$(python -c "import site; print( f'{site.getsitepackages()[0]}/cuquantum')") + export SCIPY_OPENBLAS=$(python -c "import scipy_openblas32; print(scipy_openblas32.get_lib_dir())") cmake . -BBuild \ -DPL_BACKEND=lightning_gpu \ -DENABLE_PYTHON=OFF \ @@ -134,6 +140,8 @@ jobs: -DCMAKE_CXX_COMPILER=mpicxx \ -DCMAKE_CUDA_COMPILER=$(which nvcc) \ -DCMAKE_CUDA_ARCHITECTURES="86" \ + -DCUQUANTUM_SDK=${CUQUANTUM_SDK} \ + -DSCIPY_OPENBLAS=${SCIPY_OPENBLAS} \ -DPython_EXECUTABLE:FILE="${{ steps.python_path.outputs.python }}" \ -G Ninja cmake --build ./Build @@ -146,19 +154,23 @@ jobs: mv coverage.info coverage-${{ github.job }}-lightning_gpu_${{ matrix.mpilib }}.info - name: Upload test results - uses: actions/upload-artifact@v3 + uses: actions/upload-artifact@v4 if: always() with: - name: ubuntu-tests-reports + name: ubuntu-tests-reports-${{ matrix.mpilib }} path: ./Build/tests/results/ + retention-days: 1 if-no-files-found: error + include-hidden-files: true - name: Upload code coverage results - uses: actions/upload-artifact@v3 + uses: actions/upload-artifact@v4 with: - name: ubuntu-codecov-results-cpp + name: ubuntu-codecov-results-cpp-${{ matrix.mpilib }} path: ./Build/coverage-${{ github.job }}-lightning_gpu_${{ matrix.mpilib }}.info + retention-days: 1 if-no-files-found: error + include-hidden-files: true - name: Cleanup if: always() @@ -176,9 +188,10 @@ jobs: uses: actions/checkout@v4 - name: Download coverage reports - uses: actions/download-artifact@v3 + uses: actions/download-artifact@v4 with: - name: ubuntu-codecov-results-cpp + pattern: ubuntu-codecov-* + merge-multiple: true - name: Upload to Codecov uses: codecov/codecov-action@v4 diff --git a/.github/workflows/tests_lgpumpi_python.yml b/.github/workflows/tests_lgpumpi_python.yml index 705ae79540..fa195d397e 100644 --- a/.github/workflows/tests_lgpumpi_python.yml +++ b/.github/workflows/tests_lgpumpi_python.yml @@ -14,6 +14,11 @@ on: branches: - main pull_request: + types: + - opened + - reopened + - synchronize + - ready_for_review paths-ignore: - .github/** - '!.github/workflows/tests_lgpumpi_python.yml' @@ -35,7 +40,7 @@ concurrency: jobs: python_tests: - if: ${{ contains(github.event.pull_request.labels.*.name, 'ci:use-multi-gpu-runner') || contains(fromJSON('["schedule", "workflow_dispatch"]'), github.event_name) }} + if: ${{ github.event.pull_request.draft == false && (contains(github.event.pull_request.labels.*.name, 'ci:use-multi-gpu-runner') || contains(fromJSON('["schedule", "workflow_dispatch"]'), github.event_name)) }} runs-on: - self-hosted - linux @@ -105,20 +110,30 @@ jobs: echo "pip=$pip_path" >> $GITHUB_OUTPUT - name: Install required packages + env: + PL_BACKEND: lightning_qubit run: | source /etc/profile.d/modules.sh && module use /opt/modules/ && module load ${{ matrix.mpilib }}/cuda-${{ matrix.cuda_version_maj }}.${{ matrix.cuda_version_min }} python -m pip install -r requirements-dev.txt python -m pip install custatevec-cu${{ matrix.cuda_version_maj }} mpi4py openfermionpyscf - PL_BACKEND=lightning_qubit python -m pip install . -vv + python scripts/configure_pyproject_toml.py || true + python -m pip install . -vv - - name: Checkout PennyLane for release build - if: inputs.pennylane-version == 'release' + - name: Checkout PennyLane for release or latest build + if: inputs.pennylane-version == 'release' || inputs.pennylane-version == 'latest' uses: actions/checkout@v4 with: path: pennylane repository: PennyLaneAI/pennylane + - name: Switch to latest build of PennyLane + if: inputs.pennylane-version == 'latest' + run: | + cd pennylane + git checkout master + python -m pip uninstall -y pennylane && python -m pip install . -vv --no-deps + - name: Switch to release build of PennyLane if: inputs.pennylane-version == 'release' run: | @@ -129,11 +144,13 @@ jobs: - name: Build and install package env: - CUQUANTUM_SDK: $(python -c "import site; print( f'{site.getsitepackages()[0]}/cuquantum/lib')") + CUQUANTUM_SDK: $(python -c "import site; print( f'{site.getsitepackages()[0]}/cuquantum')") + PL_BACKEND: lightning_gpu run: | source /etc/profile.d/modules.sh && module use /opt/modules/ && module load ${{ matrix.mpilib }}/cuda-${{ matrix.cuda_version_maj }}.${{ matrix.cuda_version_min }} + python scripts/configure_pyproject_toml.py || true CMAKE_ARGS="-DCMAKE_C_COMPILER=mpicc -DCMAKE_CXX_COMPILER=mpicxx -DENABLE_MPI=ON -DCMAKE_CUDA_COMPILER=$(which nvcc) -DCMAKE_CUDA_ARCHITECTURES=${{ env.CI_CUDA_ARCH }} -DPython_EXECUTABLE=${{ steps.python_path.outputs.python }}" \ - PL_BACKEND=lightning_gpu python -m pip install . --verbose + python -m pip install . -vv # There are issues running py-cov with MPI. A solution is to use coverage as reported # [here](https://github.com/pytest-dev/pytest-cov/issues/237#issuecomment-544824228) @@ -146,11 +163,13 @@ jobs: coverage xml -o coverage-${{ github.job }}-lightning_gpu_${{ matrix.mpilib }}_cu${{ matrix.cuda_version_maj }}-main.xml - name: Upload code coverage results - uses: actions/upload-artifact@v3 + uses: actions/upload-artifact@v4 with: - name: ubuntu-codecov-results-python + name: ubuntu-codecov-results-python-${{ matrix.mpilib }} path: coverage-${{ github.job }}-lightning_gpu_${{ matrix.mpilib }}_cu${{ matrix.cuda_version_maj }}-*.xml + retention-days: 1 if-no-files-found: error + include-hidden-files: true - name: Cleanup if: always() @@ -168,9 +187,10 @@ jobs: uses: actions/checkout@v4 - name: Download coverage reports - uses: actions/download-artifact@v3 + uses: actions/download-artifact@v4 with: - name: ubuntu-codecov-results-python + pattern: ubuntu-codecov-* + merge-multiple: true - name: Upload to Codecov uses: codecov/codecov-action@v4 diff --git a/.github/workflows/tests_linux_cpp.yml b/.github/workflows/tests_linux_cpp.yml index 34d1184d10..88dbc7c935 100644 --- a/.github/workflows/tests_linux_cpp.yml +++ b/.github/workflows/tests_linux_cpp.yml @@ -11,6 +11,11 @@ on: required: true description: The version of PennyLane to use. Valid values are either 'release' (most recent release candidate), 'stable' (most recent git-tag) or 'latest' (most recent commit from master) pull_request: + types: + - opened + - reopened + - synchronize + - ready_for_review paths: - .github/workflows/tests_linux_cpp.yml - pennylane_lightning/core/src/** @@ -34,15 +39,8 @@ concurrency: cancel-in-progress: true jobs: - determine_runner: - name: Determine runner type to use - uses: ./.github/workflows/determine-workflow-runner.yml - with: - default_runner: ubuntu-22.04 - cpptests: if: ${{ !contains(fromJSON('["schedule", "workflow_dispatch"]'), github.event_name) }} - needs: [determine_runner] strategy: matrix: pl_backend: ["lightning_qubit"] @@ -53,13 +51,13 @@ jobs: enable_kernel_avx_streaming: ON timeout-minutes: 60 name: C++ Tests (${{ matrix.pl_backend }}, ENABLE_KERNEL_OMP=${{ matrix.enable_kernel_omp }}, ENABLE_KERNEL_AVX_STREAMING=${{ matrix.enable_kernel_avx_streaming }}) - runs-on: ${{ needs.determine_runner.outputs.runner_group }} + runs-on: pl-4-core-large-runner steps: - uses: actions/setup-python@v5 name: Install Python with: - python-version: '3.9' + python-version: '3.10' - name: Checkout PennyLane-Lightning uses: actions/checkout@v4 @@ -67,7 +65,7 @@ jobs: - name: Install dependencies run: | echo ${{ github.event_name }} && sudo apt-get update && sudo apt-get -y -q install cmake gcc-$GCC_VERSION g++-$GCC_VERSION ninja-build gcovr lcov - python -m pip install scipy + python -m pip install scipy-openblas32 - name: Build and run unit tests run: | @@ -75,6 +73,7 @@ jobs: -DCMAKE_BUILD_TYPE=Debug \ -DBUILD_TESTS=ON \ -DENABLE_PYTHON=OFF \ + -DSCIPY_OPENBLAS=$(python -c "import scipy_openblas32; print(scipy_openblas32.get_lib_dir())") \ -DPL_BACKEND=${{ matrix.pl_backend }} \ -DCMAKE_CXX_COMPILER=$(which g++-$GCC_VERSION) \ -DENABLE_COVERAGE=ON \ @@ -91,17 +90,19 @@ jobs: mv coverage.info coverage-${{ github.job }}-${{ matrix.pl_backend }}-${{ matrix.enable_kernel_avx_streaming }}-${{ matrix.enable_kernel_omp }}.info - name: Upload test results - uses: actions/upload-artifact@v3 + uses: actions/upload-artifact@v4 if: always() with: name: ubuntu-tests-reports-${{ github.job }}-${{ matrix.pl_backend }}-${{ matrix.enable_kernel_avx_streaming }}-${{ matrix.enable_kernel_omp }} + retention-days: 1 + include-hidden-files: true path: | ./Build/tests/results/ if-no-files-found: error - name: Upload code coverage results - uses: actions/upload-artifact@v3 + uses: actions/upload-artifact@v4 with: name: ubuntu-codecov-results-${{ matrix.pl_backend }}-${{ matrix.enable_kernel_avx_streaming }}-${{ matrix.enable_kernel_omp }} path: ./Build/coverage-${{ github.job }}-${{ matrix.pl_backend }}-${{ matrix.enable_kernel_avx_streaming }}-${{ matrix.enable_kernel_omp }}.info @@ -109,19 +110,18 @@ jobs: cpptestswithOpenBLAS: if: ${{ !contains(fromJSON('["schedule", "workflow_dispatch"]'), github.event_name) }} - needs: [determine_runner] strategy: matrix: pl_backend: ["lightning_qubit"] timeout-minutes: 60 name: C++ Tests (${{ matrix.pl_backend }}, blas-ON) - runs-on: ${{ needs.determine_runner.outputs.runner_group }} + runs-on: pl-4-core-large-runner steps: - uses: actions/setup-python@v5 name: Install Python with: - python-version: '3.9' + python-version: '3.10' - name: Checkout PennyLane-Lightning uses: actions/checkout@v4 @@ -129,7 +129,7 @@ jobs: - name: Install dependencies run: | sudo apt-get update && sudo apt-get -y -q install cmake gcc-$GCC_VERSION g++-$GCC_VERSION libopenblas-base libopenblas-dev ninja-build gcovr lcov - python -m pip install scipy + python -m pip install scipy-openblas32 - name: Build and run unit tests run: | @@ -138,6 +138,7 @@ jobs: -DENABLE_PYTHON=OFF \ -DENABLE_BLAS=ON \ -DPL_BACKEND=${{ matrix.pl_backend }} \ + -DSCIPY_OPENBLAS=$(python -c "import scipy_openblas32; print(scipy_openblas32.get_lib_dir())") \ -DBUILD_TESTS=ON \ -DCMAKE_CXX_COMPILER=$(which g++-$GCC_VERSION) \ -DENABLE_COVERAGE=ON @@ -150,31 +151,34 @@ jobs: mv coverage.info coverage-${{ github.job }}-${{ matrix.pl_backend }}.info - name: Upload test results - uses: actions/upload-artifact@v3 + uses: actions/upload-artifact@v4 if: always() with: name: ubuntu-tests-reports-blas-${{ github.job }}-${{ matrix.pl_backend }} path: ./Build/tests/results/ + retention-days: 1 if-no-files-found: error + include-hidden-files: true - name: Upload code coverage results - uses: actions/upload-artifact@v3 + uses: actions/upload-artifact@v4 with: name: ubuntu-codecov-results-blas-${{ matrix.pl_backend }} path: ./Build/coverage-${{ github.job }}-${{ matrix.pl_backend }}.info + retention-days: 1 if-no-files-found: error + include-hidden-files: true build_and_cache_Kokkos: name: "Build and cache Kokkos" - needs: [determine_runner] uses: ./.github/workflows/build_and_cache_Kokkos_linux.yml with: - runs_on: ${{ needs.determine_runner.outputs.runner_group }} + runs_on: pl-4-core-large-runner os: ubuntu-22.04 cpptestswithKokkos: if: ${{ !contains(fromJSON('["schedule", "workflow_dispatch"]'), github.event_name) }} - needs: [build_and_cache_Kokkos, determine_runner] + needs: [build_and_cache_Kokkos] strategy: matrix: os: [ubuntu-22.04] @@ -183,13 +187,13 @@ jobs: kokkos_version: ${{ fromJson(needs.build_and_cache_Kokkos.outputs.kokkos_version) }} timeout-minutes: 60 name: C++ Tests (${{ matrix.pl_backend }}, kokkos-${{ matrix.kokkos_version }}, model-${{ matrix.exec_model }}) - runs-on: ${{ needs.determine_runner.outputs.runner_group }} + runs-on: pl-4-core-large-runner steps: - uses: actions/setup-python@v5 name: Install Python with: - python-version: '3.9' + python-version: '3.10' - name: Checkout PennyLane-Lightning uses: actions/checkout@v4 @@ -211,7 +215,7 @@ jobs: - name: Install dependencies run: | sudo apt-get update && sudo apt-get -y -q install cmake gcc-$GCC_VERSION g++-$GCC_VERSION ninja-build gcovr lcov - python -m pip install scipy + python -m pip install scipy-openblas32 - name: Build and run unit tests run: | @@ -222,6 +226,7 @@ jobs: -DCMAKE_PREFIX_PATH=${{ github.workspace }}/Kokkos \ -DPL_BACKEND=${{ matrix.pl_backend }} \ -DCMAKE_CXX_COMPILER=$(which g++-$GCC_VERSION) \ + -DSCIPY_OPENBLAS=$(python -c "import scipy_openblas32; print(scipy_openblas32.get_lib_dir())") \ -DENABLE_COVERAGE=ON cmake --build ./Build cd ./Build @@ -232,19 +237,23 @@ jobs: mv coverage.info coverage-${{ github.job }}-${{ matrix.pl_backend }}.info - name: Upload test results - uses: actions/upload-artifact@v3 + uses: actions/upload-artifact@v4 if: always() with: name: ubuntu-tests-reports-${{ github.job }}-${{ matrix.pl_backend }} path: ./Build/tests/results-${{ github.job }}-${{ matrix.pl_backend }} + retention-days: 1 if-no-files-found: error + include-hidden-files: true - name: Upload code coverage results - uses: actions/upload-artifact@v3 + uses: actions/upload-artifact@v4 with: - name: ubuntu-codecov-results-cpp + name: ubuntu-codecov-results-cpp-${{ github.job }}-${{ matrix.pl_backend }} path: ./Build/coverage-${{ github.job }}-${{ matrix.pl_backend }}.info + retention-days: 1 if-no-files-found: error + include-hidden-files: true upload-to-codecov-linux-cpp: needs: [cpptests, cpptestswithOpenBLAS, cpptestswithKokkos] @@ -255,9 +264,10 @@ jobs: uses: actions/checkout@v4 - name: Download coverage reports - uses: actions/download-artifact@v3 + uses: actions/download-artifact@v4 with: - name: ubuntu-codecov-results-cpp + pattern: ubuntu-codecov-* + merge-multiple: true - name: Upload to Codecov uses: codecov/codecov-action@v4 @@ -269,7 +279,7 @@ jobs: cpptestsWithMultipleBackends: # Device-specific tests are performed for both. Device-agnostic tests default to LightningQubit. if: ${{ !contains(fromJSON('["schedule", "workflow_dispatch"]'), github.event_name) }} - needs: [build_and_cache_Kokkos, determine_runner] + needs: [build_and_cache_Kokkos] strategy: matrix: os: [ubuntu-22.04] @@ -277,13 +287,13 @@ jobs: kokkos_version: ${{ fromJson(needs.build_and_cache_Kokkos.outputs.kokkos_version) }} timeout-minutes: 60 name: C++ Tests (multiple-backends, kokkos-${{ matrix.kokkos_version }}, model-${{ matrix.exec_model }}) - runs-on: ${{ needs.determine_runner.outputs.runner_group }} + runs-on: pl-4-core-large-runner steps: - uses: actions/setup-python@v5 name: Install Python with: - python-version: '3.9' + python-version: '3.10' - name: Checkout PennyLane-Lightning uses: actions/checkout@v4 @@ -305,7 +315,7 @@ jobs: - name: Install dependencies run: | sudo apt-get update && sudo apt-get -y -q install cmake gcc-$GCC_VERSION g++-$GCC_VERSION ninja-build gcovr lcov - python -m pip install scipy + python -m pip install scipy-openblas32 - name: Build and run unit tests run: | @@ -315,6 +325,7 @@ jobs: -DENABLE_PYTHON=OFF \ -DCMAKE_PREFIX_PATH=${{ github.workspace }}/Kokkos \ -DPL_BACKEND="lightning_qubit;lightning_kokkos" \ + -DSCIPY_OPENBLAS=$(python -c "import scipy_openblas32; print(scipy_openblas32.get_lib_dir())") \ -DCMAKE_CXX_COMPILER=$(which g++-$GCC_VERSION) cmake --build ./Build cd ./Build @@ -322,9 +333,11 @@ jobs: for file in *runner ; do ./$file --order lex --reporter junit --out ./tests/results_multiple_backends/report_$file.xml; done; - name: Upload test results - uses: actions/upload-artifact@v3 + uses: actions/upload-artifact@v4 if: always() with: - name: ubuntu-tests-reports-${{ github.job }}-multiple-backends + name: ubuntu-tests-reports-${{ github.job }}-multiple-backends-kokkos${{ matrix.kokkos_version }}-${{ matrix.exec_model }} path: ./Build/tests/results_multiple_backends/ + retention-days: 1 if-no-files-found: error + include-hidden-files: true diff --git a/.github/workflows/tests_lkcpu_python.yml b/.github/workflows/tests_lkcpu_python.yml index 0e211f2e91..dc28f88edb 100644 --- a/.github/workflows/tests_lkcpu_python.yml +++ b/.github/workflows/tests_lkcpu_python.yml @@ -11,6 +11,11 @@ on: required: true description: The version of PennyLane to use. Valid values are either 'release' (most recent release candidate), 'stable' (most recent git-tag) or 'latest' (most recent commit from master) pull_request: + types: + - opened + - reopened + - synchronize + - ready_for_review paths-ignore: - .github/** - '!.github/workflows/tests_lkcpu_python.yml' @@ -38,22 +43,16 @@ concurrency: cancel-in-progress: true jobs: - - determine_runner: - name: Determine runner type to use - uses: ./.github/workflows/determine-workflow-runner.yml - with: - default_runner: ubuntu-22.04 - build_and_cache_Kokkos: + if: github.event.pull_request.draft == false name: "Build and cache Kokkos" uses: ./.github/workflows/build_and_cache_Kokkos_linux.yml with: os: ubuntu-22.04 - kokkos_version: "4.3.01" + kokkos_version: "4.5.00" build_lightning_kokkos_wheels: - needs: [determine_runner, build_and_cache_Kokkos] + needs: [build_and_cache_Kokkos] strategy: matrix: os: [ubuntu-22.04] @@ -62,26 +61,23 @@ jobs: kokkos_version: ${{ fromJson(needs.build_and_cache_Kokkos.outputs.kokkos_version) }} timeout-minutes: 60 name: Build (${{ matrix.pl_backend }}, kokkos-${{ matrix.kokkos_version }}, model-${{ matrix.exec_model }}) - runs-on: ${{ needs.determine_runner.outputs.runner_group }} + runs-on: pl-4-core-large-runner steps: - name: Checkout PennyLane-Lightning uses: actions/checkout@v4 with: fetch-tags: true - path: main - name: Switch to release build of Lightning if: inputs.lightning-version == 'release' run: | - cd main git fetch --all git checkout $(git branch -a --list "origin/v0.*rc*" | sort | tail -1) - name: Switch to stable build of Lightning if: inputs.lightning-version == 'stable' run: | - cd main git fetch --tags --force git checkout latest_release git log -1 --format='%H' @@ -90,7 +86,7 @@ jobs: - uses: actions/setup-python@v5 name: Install Python with: - python-version: '3.9' + python-version: '3.10' - name: Restoring cached dependencies id: kokkos-cache @@ -110,20 +106,16 @@ jobs: - name: Install dependencies run: | sudo apt-get update && sudo apt-get -y -q install cmake gcc-$GCC_VERSION g++-$GCC_VERSION - python -m pip install scipy wheel + python -m pip install scipy-openblas32 wheel - name: Get required Python packages run: | - cd main python -m pip install -r requirements-dev.txt - - name: Create device wheel - env: - PL_BACKEND: ${{ matrix.pl_backend }} + - name: Create device wheel ${{ inputs.lightning-version }} run: | - cd main - python setup.py build_ext -i --define="PL_BACKEND=${{ matrix.pl_backend }};CMAKE_PREFIX_PATH=${{ github.workspace }}/Kokkos;ENABLE_PYTHON=ON;CMAKE_CXX_COMPILER=$(which g++-$GCC_VERSION)" - python setup.py bdist_wheel + PL_BACKEND=${{ matrix.pl_backend }} python scripts/configure_pyproject_toml.py + CMAKE_ARGS="-DCMAKE_PREFIX_PATH=${{ github.workspace }}/Kokkos" python -m build cd dist WHEEL_NAME=$(ls *.whl) cp $WHEEL_NAME ${{ github.workspace }}/wheel_${{ matrix.pl_backend }}-${{ matrix.exec_model }}.whl @@ -135,6 +127,7 @@ jobs: name: ${{ matrix.pl_backend }}-${{ matrix.exec_model }}_name.txt retention-days: 1 if-no-files-found: error + include-hidden-files: true path: ${{ github.workspace }}/${{ matrix.pl_backend }}-${{ matrix.exec_model }}_name.txt - name: Cache wheel directories @@ -143,11 +136,12 @@ jobs: name: wheel_${{ matrix.pl_backend }}-${{ matrix.exec_model }}.whl retention-days: 1 if-no-files-found: error + include-hidden-files: true path: | ${{ github.workspace }}/wheel_${{ matrix.pl_backend }}-${{ matrix.exec_model }}.whl test_lightning_kokkos_wheels: - needs: [determine_runner, build_and_cache_Kokkos, build_lightning_kokkos_wheels] + needs: [build_and_cache_Kokkos, build_lightning_kokkos_wheels] strategy: matrix: os: [ubuntu-22.04] @@ -157,24 +151,22 @@ jobs: kokkos_version: ${{ fromJson(needs.build_and_cache_Kokkos.outputs.kokkos_version) }} timeout-minutes: 60 name: Python Tests (${{ matrix.pl_backend }}, kokkos-${{ matrix.kokkos_version }}, model-${{ matrix.exec_model }}, test-group-${{ matrix.group }}) - runs-on: ${{ needs.determine_runner.outputs.runner_group }} + runs-on: pl-4-core-large-runner steps: - uses: actions/setup-python@v5 name: Install Python with: - python-version: '3.9' + python-version: '3.10' - name: Checkout PennyLane-Lightning uses: actions/checkout@v4 with: fetch-tags: true - path: main - name: Switch to release tag of Lightning if: inputs.lightning-version == 'release' run: | - cd main git fetch --all git checkout $(git branch -a --list "origin/v0.*rc*" | sort | tail -1) @@ -186,13 +178,11 @@ jobs: - name: Switch to stable tag of Lightning if: inputs.lightning-version == 'stable' run: | - cd main git fetch --tags --force git checkout latest_release git log -1 --format='%H' git status - - uses: actions/download-artifact@v4 with: name: ${{ matrix.pl_backend }}-${{ matrix.exec_model }}_name.txt @@ -207,21 +197,28 @@ jobs: run: | WHEEL_NAME=$(cat ${{ github.workspace }}/${{ matrix.pl_backend }}-${{ matrix.exec_model }}_name.txt) mv ${{ github.workspace }}/wheel_${{ matrix.pl_backend }}-${{ matrix.exec_model }}.whl ${{ github.workspace }}/$WHEEL_NAME - cd main python -m pip install -r requirements-dev.txt python -m pip install openfermionpyscf if [ '${{ inputs.lightning-version }}' != 'stable' ]; then + python scripts/configure_pyproject_toml.py SKIP_COMPILATION=True python -m pip install . -vv fi python -m pip install ${{ github.workspace }}/$WHEEL_NAME --no-deps - - name: Checkout PennyLane for release build - if: inputs.pennylane-version == 'release' + - name: Checkout PennyLane for release or latest build + if: inputs.pennylane-version == 'release' || inputs.pennylane-version == 'latest' uses: actions/checkout@v4 with: path: pennylane repository: PennyLaneAI/pennylane + - name: Switch to latest build of PennyLane + if: inputs.pennylane-version == 'latest' + run: | + cd pennylane + git checkout master + python -m pip uninstall -y pennylane && python -m pip install . -vv --no-deps + - name: Switch to release build of PennyLane if: inputs.pennylane-version == 'release' run: | @@ -233,25 +230,33 @@ jobs: - name: Install Stable PennyLane if: inputs.pennylane-version == 'stable' run: | - cd main python -m pip uninstall -y pennylane && python -m pip install -U pennylane - name: Install ML libraries for interfaces run: | python -m pip install --upgrade torch==$TORCH_VERSION -f https://download.pytorch.org/whl/cpu/torch_stable.html - python -m pip install --upgrade "jax[cpu]" # This also installs jaxlib python -m pip install --upgrade tensorflow~=$TF_VERSION keras~=$TF_VERSION - name: Run PennyLane-Lightning unit tests run: | - cd main/ DEVICENAME=`echo ${{ matrix.pl_backend }} | sed "s/_/./g"` - PL_DEVICE=${DEVICENAME} python -m pytest tests/ $COVERAGE_FLAGS --splits 7 --group ${{ matrix.group }} \ - --store-durations --durations-path='.github/workflows/python_lightning_kokkos_test_durations.json' --splitting-algorithm=least_duration - mv .github/workflows/python_lightning_kokkos_test_durations.json ${{ github.workspace }}/.test_durations-${{ matrix.exec_model }}-${{ matrix.group }} + # Remove `python -m` to avoid running tests with relative modules + PL_DEVICE=${DEVICENAME} pytest tests/ $COVERAGE_FLAGS --splits 7 --group ${{ matrix.group }} \ + --store-durations --durations-path='.github/workflows/python_lightning_kokkos_test_durations.json' --splitting-algorithm=least_duration + mv .github/workflows/python_lightning_kokkos_test_durations.json ${{ github.workspace }}/.test_durations-${{ matrix.exec_model }}-${{ matrix.group }} pl-device-test --device ${DEVICENAME} --skip-ops --shots=20000 $COVERAGE_FLAGS --cov-append pl-device-test --device ${DEVICENAME} --shots=None --skip-ops $COVERAGE_FLAGS --cov-append - mv .coverage .coverage-${{ github.job }}-${{ matrix.pl_backend }}-${{ matrix.group }} + mv .coverage ${{ github.workspace }}/.coverage-${{ github.job }}-${{ matrix.pl_backend }}-${{ matrix.group }} + + - name: Test editable install + run: | + pip uninstall pennylane-lightning pennylane-lightning-kokkos -y + python scripts/configure_pyproject_toml.py + SKIP_COMPILATION=True python -m pip install -e . --config-settings editable_mode=compat + DEVICENAME=`echo ${{ matrix.pl_backend }} | sed "s/_/./g"` + PL_BACKEND=${DEVICENAME} python scripts/configure_pyproject_toml.py + python -m pip install -e . --config-settings editable_mode=compat -vv + PL_DEVICE=${DEVICENAME} python -m pytest tests/test_device.py $COVERAGE_FLAGS - name: Upload test durations uses: actions/upload-artifact@v4 @@ -259,35 +264,39 @@ jobs: name: .test_durations-${{ matrix.exec_model }}-${{ matrix.group }} retention-days: 1 if-no-files-found: error + include-hidden-files: true path: ${{ github.workspace }}/.test_durations-${{ matrix.exec_model }}-${{ matrix.group }} - name: Upload code coverage results - uses: actions/upload-artifact@v3 + uses: actions/upload-artifact@v4 with: - name: ubuntu-codecov-results-python - path: ./main/.coverage-${{ github.job }}-${{ matrix.pl_backend }}-${{ matrix.group }} + name: .coverage-${{ github.job }}-${{ matrix.pl_backend }}-${{ matrix.group }} + path: ${{ github.workspace }}/.coverage-${{ github.job }}-${{ matrix.pl_backend }}-${{ matrix.group }} + retention-days: 1 if-no-files-found: error + include-hidden-files: true upload-to-codecov-linux-python: - needs: [determine_runner, test_lightning_kokkos_wheels] + if: github.event_name == 'pull_request' + needs: [test_lightning_kokkos_wheels] name: Upload python coverage data to codecov - runs-on: ${{ needs.determine_runner.outputs.runner_group }} + runs-on: ubuntu-latest steps: - name: Checkout uses: actions/checkout@v4 with: fetch-tags: true - path: main - name: Download coverage reports - uses: actions/download-artifact@v3 + uses: actions/download-artifact@v4 with: - name: ubuntu-codecov-results-python + pattern: .coverage-* + merge-multiple: true - name: Combine coverage files run: | python -m pip install coverage - python -m coverage combine .coverage* + python -m coverage combine .coverage-* # Added cov xml -i to ignore "No source for code" random errors # https://stackoverflow.com/questions/2386975/no-source-for-code-message-in-coverage-py python -m coverage xml -i -o coverage-${{ github.job }}.xml @@ -297,4 +306,4 @@ jobs: with: fail_ci_if_error: true verbose: true - token: ${{ secrets.CODECOV_TOKEN }} \ No newline at end of file + token: ${{ secrets.CODECOV_TOKEN }} diff --git a/.github/workflows/tests_lkcuda_cpp.yml b/.github/workflows/tests_lkcuda_cpp.yml index eb95afa76e..cba4e7c867 100644 --- a/.github/workflows/tests_lkcuda_cpp.yml +++ b/.github/workflows/tests_lkcuda_cpp.yml @@ -11,12 +11,16 @@ on: required: true description: The version of PennyLane to use. Valid values are either 'release' (most recent release candidate), 'stable' (most recent git-tag) or 'latest' (most recent commit from master) pull_request: + types: + - opened + - reopened + - synchronize + - ready_for_review paths: - pennylane_lightning/core/src/** - '!pennylane_lightning/core/src/simulators/lightning_gpu/**' - '!pennylane_lightning/core/src/simulators/lightning_qubit/**' - '!pennylane_lightning/core/src/simulators/lightning_tensor/**' - push: branches: - master @@ -34,6 +38,7 @@ concurrency: jobs: builddeps: + if: ${{ github.event_name != 'pull_request' || (github.event.pull_request.draft == false && contains(github.event.pull_request.labels.*.name, 'ci:use-gpu-runner')) }} runs-on: - self-hosted - ubuntu-22.04 @@ -43,15 +48,17 @@ jobs: max-parallel: 1 matrix: os: [ubuntu-22.04] + kokkos_version: ["4.5.00"] exec_model: ["CUDA"] - kokkos_version: ["4.3.01"] + cuda_version_maj: ["12"] + cuda_version_min: ["4"] steps: - name: Validate GPU version and installed compiler run: | source /etc/profile.d/modules.sh module use /opt/modules - module load cuda/12.2 + module load cuda/${{ matrix.cuda_version_maj }}.${{ matrix.cuda_version_min }} echo "${PATH}" >> $GITHUB_PATH echo "LD_LIBRARY_PATH=${LD_LIBRARY_PATH}" >> $GITHUB_ENV nvcc --version @@ -85,32 +92,30 @@ jobs: env: CMAKE_PREFIX_PATH: ${{ github.workspace }}/Kokkos_install/${{ matrix.exec_model }} run: | - set -x sudo apt-get update && sudo apt-get -y -q install gfortran-$GCC_VERSION cd kokkos_${{ matrix.exec_model }}/example/build_cmake_installed which -a gfortran || which -a gfortran-$GCC_VERSION cmake -B build -DCMAKE_Fortran_COMPILER=gfortran-$GCC_VERSION cmake --build build cmake --build build --target test - set +x - name: Cache installation directories id: kokkos-cache - uses: actions/cache/save@v3 + uses: actions/cache/save@v4 with: - key: ${{ matrix.os }}-kokkos${{ matrix.kokkos_version }}-${{ matrix.exec_model }}-${{ github.ref }}-${{ github.sha }} + key: ${{ matrix.os }}-kokkos${{ matrix.kokkos_version }}-${{ matrix.exec_model }} path: ${{ github.workspace }}/Kokkos_install/${{ matrix.exec_model }} cpptestswithKokkos: - # TODO: The issue related to Kokkos with CUDA-12 running out of memory needs to be fixed. - if: ${{ github.sha == 'xxxxxxxx' }} needs: [builddeps] strategy: matrix: os: [ubuntu-22.04] pl_backend: ["lightning_kokkos"] + kokkos_version: ["4.5.00"] exec_model: ["CUDA"] - kokkos_version: ["4.3.01"] + cuda_version_maj: ["12"] + cuda_version_min: ["4"] name: C++ Tests (${{ matrix.pl_backend }}, kokkos-${{ matrix.kokkos_version }}, model-${{ matrix.exec_model }}) runs-on: @@ -123,7 +128,7 @@ jobs: run: | source /etc/profile.d/modules.sh module use /opt/modules - module load cuda/12.2 + module load cuda/${{ matrix.cuda_version_maj }}.${{ matrix.cuda_version_min }} echo "${PATH}" >> $GITHUB_PATH echo "LD_LIBRARY_PATH=${LD_LIBRARY_PATH}" >> $GITHUB_ENV nvcc --version @@ -131,11 +136,11 @@ jobs: - name: Restoring cached dependencies id: kokkos-cache - uses: actions/cache/restore@v3 + uses: actions/cache/restore@v4 with: path: ${{ github.workspace }}/Kokkos_install/${{ matrix.exec_model }} - key: ${{ matrix.os }}-kokkos${{ matrix.kokkos_version }}-${{ matrix.exec_model }}-${{ github.ref }}-${{ github.sha }} - restore-keys: ${{ matrix.os }}-kokkos${{ matrix.kokkos_version }}-${{ matrix.exec_model }}-${{ github.ref }}- + key: ${{ matrix.os }}-kokkos${{ matrix.kokkos_version }}-${{ matrix.exec_model }} + restore-keys: ${{ matrix.os }}-kokkos${{ matrix.kokkos_version }}-${{ matrix.exec_model }} - name: Copy cached libraries run: | @@ -151,7 +156,7 @@ jobs: - uses: actions/setup-python@v5 name: Install Python with: - python-version: '3.9' + python-version: '3.10' # Since the self-hosted runner can be re-used. It is best to set up all package # installations in a virtual environment that gets cleaned at the end of each workflow run @@ -181,20 +186,23 @@ jobs: - name: Install dependencies - run: sudo apt-get update && sudo apt-get -y -q install cmake gcc-$GCC_VERSION g++-$GCC_VERSION ninja-build gcovr lcov + run: | + sudo apt-get update && sudo apt-get -y -q install cmake gcc-$GCC_VERSION g++-$GCC_VERSION ninja-build gcovr lcov + python -m pip install scipy-openblas32 - name: Build and run unit tests run: | cd main cmake . -BBuild -G Ninja \ - -DCMAKE_BUILD_TYPE=Debug \ + -DCMAKE_BUILD_TYPE=Release \ -DBUILD_TESTS=ON \ -DENABLE_PYTHON=OFF \ + -DSCIPY_OPENBLAS=$(python -c "import scipy_openblas32; print(scipy_openblas32.get_lib_dir())") \ -DCMAKE_PREFIX_PATH=${{ github.workspace }}/Kokkos \ -DPL_BACKEND=${{ matrix.pl_backend }} \ -DCMAKE_CXX_COMPILER=$(which g++-$GCC_VERSION) \ -DENABLE_COVERAGE=ON - cmake --build ./Build + cmake --build ./Build --verbose cd ./Build mkdir -p ./tests/results_${{ github.job }}_${{ matrix.pl_backend }} for file in *runner ; do ./$file --order lex --reporter junit --out ./tests/results_${{ github.job }}_${{ matrix.pl_backend }}/report_$file.xml; done; @@ -203,16 +211,27 @@ jobs: mv coverage.info coverage-${{ github.job }}-${{ matrix.pl_backend }}.info - name: Upload test results - uses: actions/upload-artifact@v3 + uses: actions/upload-artifact@v4 if: always() with: name: ubuntu-tests-reports-${{ github.job }}_${{ matrix.pl_backend }} path: ./main/Build/tests/results_${{ github.job }}_${{ matrix.pl_backend }} + retention-days: 1 if-no-files-found: error + include-hidden-files: true - name: Upload code coverage results - uses: actions/upload-artifact@v3 + uses: actions/upload-artifact@v4 with: name: ubuntu-codecov-results-cpp path: ./main/Build/coverage-${{ github.job }}-${{ matrix.pl_backend }}.info + retention-days: 1 if-no-files-found: error + include-hidden-files: true + + - name: Cleanup + if: always() + run: | + rm -rf ${{ steps.setup_venv.outputs.venv_name }} + rm -rf * .git .gitignore .github + pip cache purge diff --git a/.github/workflows/tests_lkcuda_python.yml b/.github/workflows/tests_lkcuda_python.yml index dd0a5c00d8..c4b8497102 100644 --- a/.github/workflows/tests_lkcuda_python.yml +++ b/.github/workflows/tests_lkcuda_python.yml @@ -11,6 +11,11 @@ on: required: true description: The version of PennyLane to use. Valid values are either 'release' (most recent release candidate), 'stable' (most recent git-tag) or 'latest' (most recent commit from master) pull_request: + types: + - opened + - reopened + - synchronize + - ready_for_review paths-ignore: - .github/** - '!.github/workflows/tests_lkcuda_python.yml' @@ -38,6 +43,7 @@ concurrency: jobs: builddeps: + if: ${{ github.event_name != 'pull_request' || (github.event.pull_request.draft == false && contains(github.event.pull_request.labels.*.name, 'ci:use-gpu-runner')) }} runs-on: - self-hosted - ubuntu-22.04 @@ -47,15 +53,17 @@ jobs: max-parallel: 1 matrix: os: [ubuntu-22.04] + kokkos_version: ["4.5.00"] exec_model: ["CUDA"] - kokkos_version: ["4.3.01"] + cuda_version_maj: ["12"] + cuda_version_min: ["4"] steps: - name: Validate GPU version and installed compiler run: | source /etc/profile.d/modules.sh module use /opt/modules - module load cuda/12.2 + module load cuda/${{ matrix.cuda_version_maj }}.${{ matrix.cuda_version_min }} echo "${PATH}" >> $GITHUB_PATH echo "LD_LIBRARY_PATH=${LD_LIBRARY_PATH}" >> $GITHUB_ENV nvcc --version @@ -89,22 +97,19 @@ jobs: env: CMAKE_PREFIX_PATH: ${{ github.workspace }}/Kokkos_install/${{ matrix.exec_model }} run: | - set -x sudo apt-get update && sudo apt-get -y -q install gfortran-$GCC_VERSION cd kokkos_${{ matrix.exec_model }}/example/build_cmake_installed which -a gfortran || which -a gfortran-$GCC_VERSION cmake -B build -DCMAKE_Fortran_COMPILER=gfortran-$GCC_VERSION cmake --build build cmake --build build --target test - set +x - name: Cache installation directories id: kokkos-cache - uses: actions/cache/save@v3 + uses: actions/cache/save@v4 with: - key: ${{ matrix.os }}-kokkos${{ matrix.kokkos_version }}-${{ matrix.exec_model }}-${{ github.ref }}-${{ github.sha }} + key: ${{ matrix.os }}-kokkos${{ matrix.kokkos_version }}-${{ matrix.exec_model }} path: ${{ github.workspace }}/Kokkos_install/${{ matrix.exec_model }} - retention-days: 1 pythontestswithKokkos: needs: [builddeps] @@ -112,8 +117,10 @@ jobs: matrix: os: [ubuntu-22.04] pl_backend: ["lightning_kokkos", "all"] + kokkos_version: ["4.5.00"] exec_model: ["CUDA"] - kokkos_version: ["4.3.01"] + cuda_version_maj: ["12"] + cuda_version_min: ["4"] name: Python Tests (${{ matrix.pl_backend }}, kokkos-${{ matrix.kokkos_version }}, model-${{ matrix.exec_model }}) runs-on: @@ -126,7 +133,7 @@ jobs: run: | source /etc/profile.d/modules.sh module use /opt/modules - module load cuda/12.2 + module load cuda/${{ matrix.cuda_version_maj }}.${{ matrix.cuda_version_min }} echo "${PATH}" >> $GITHUB_PATH echo "LD_LIBRARY_PATH=${LD_LIBRARY_PATH}" >> $GITHUB_ENV nvcc --version @@ -134,11 +141,11 @@ jobs: - name: Restoring cached dependencies id: kokkos-cache - uses: actions/cache/restore@v3 + uses: actions/cache/restore@v4 with: path: ${{ github.workspace }}/Kokkos_install/${{ matrix.exec_model }} - key: ${{ matrix.os }}-kokkos${{ matrix.kokkos_version }}-${{ matrix.exec_model }}-${{ github.ref }}-${{ github.sha }} - restore-keys: ${{ matrix.os }}-kokkos${{ matrix.kokkos_version }}-${{ matrix.exec_model }}-${{ github.ref }}- + key: ${{ matrix.os }}-kokkos${{ matrix.kokkos_version }}-${{ matrix.exec_model }} + restore-keys: ${{ matrix.os }}-kokkos${{ matrix.kokkos_version }}-${{ matrix.exec_model }} - name: Copy cached libraries run: | @@ -171,7 +178,7 @@ jobs: - uses: actions/setup-python@v5 name: Install Python with: - python-version: '3.9' + python-version: '3.10' # Since the self-hosted runner can be re-used. It is best to set up all package # installations in a virtual environment that gets cleaned at the end of each workflow run @@ -209,13 +216,20 @@ jobs: python -m pip install -r requirements-dev.txt python -m pip install openfermionpyscf - - name: Checkout PennyLane for release build - if: inputs.pennylane-version == 'release' + - name: Checkout PennyLane for release or latest build + if: inputs.pennylane-version == 'release' || inputs.pennylane-version == 'latest' uses: actions/checkout@v4 with: path: pennylane repository: PennyLaneAI/pennylane + - name: Switch to latest build of PennyLane + if: inputs.pennylane-version == 'latest' + run: | + cd pennylane + git checkout master + python -m pip uninstall -y pennylane && python -m pip install . -vv --no-deps + - name: Switch to release build of PennyLane if: inputs.pennylane-version == 'release' run: | @@ -233,7 +247,6 @@ jobs: - name: Install ML libraries for interfaces run: | python -m pip install --upgrade torch==$TORCH_VERSION -f https://download.pytorch.org/whl/cpu/torch_stable.html - python -m pip install --upgrade "jax[cpu]" # This also installs jaxlib python -m pip install --upgrade tensorflow~=$TF_VERSION keras~=$TF_VERSION - name: Install backend device @@ -241,8 +254,12 @@ jobs: run: | ls -l ${{ github.workspace }}/Kokkos cd main - SKIP_COMPILATION=True PL_BACKEND="lightning_qubit" python -m pip install . -vv - CMAKE_ARGS="-DPL_BACKEND=${{ matrix.pl_backend }} -DCMAKE_PREFIX_PATH=${{ github.workspace }}/Kokkos -DENABLE_PYTHON=ON -DCMAKE_CXX_COMPILER=$(which g++-$GCC_VERSION)" \ + + PL_BACKEND=lightning_qubit python scripts/configure_pyproject_toml.py || true + PL_BACKEND=lightning_qubit SKIP_COMPILATION=True python -m pip install . -vv + + PL_BACKEND=${{ matrix.pl_backend }} python scripts/configure_pyproject_toml.py || true + PL_BACKEND=${{ matrix.pl_backend }} CMAKE_ARGS="-DCMAKE_PREFIX_PATH=${{ github.workspace }}/Kokkos" \ python -m pip install . -vv - name: Run PennyLane-Lightning unit tests @@ -262,11 +279,12 @@ jobs: if: ${{ matrix.pl_backend == 'all' }} run: | cd main - CMAKE_ARGS="-DPL_BACKEND="lightning_qubit" -DCMAKE_PREFIX_PATH=${{ github.workspace }}/Kokkos -DENABLE_PYTHON=ON -DCMAKE_CXX_COMPILER=$(which g++-$GCC_VERSION)" \ - python -m pip install . -vv - rm -rf build - CMAKE_ARGS="-DPL_BACKEND="lightning_kokkos" -DCMAKE_PREFIX_PATH=${{ github.workspace }}/Kokkos -DENABLE_PYTHON=ON -DCMAKE_CXX_COMPILER=$(which g++-$GCC_VERSION)" \ - python -m pip install . -vv + + PL_BACKEND=lightning_qubit python scripts/configure_pyproject_toml.py || true + PL_BACKEND=lightning_qubit python -m pip install . -vv + + PL_BACKEND="lightning_kokkos" python scripts/configure_pyproject_toml.py || true + PL_BACKEND="lightning_kokkos" CMAKE_ARGS="-DCMAKE_PREFIX_PATH=${{ github.workspace }}/Kokkos" python -m pip install . -vv - name: Run PennyLane-Lightning unit tests for lightning.qubit with all devices installed if: ${{ matrix.pl_backend == 'all' }} @@ -282,8 +300,10 @@ jobs: mv coverage.xml coverage-${{ github.job }}-${{ matrix.pl_backend }}.xml - name: Upload code coverage results - uses: actions/upload-artifact@v3 + uses: actions/upload-artifact@v4 with: - name: ubuntu-codecov-results-python + name: ubuntu-codecov-results-python-${{ matrix.pl_backend }} path: ./main/coverage-${{ github.job }}-${{ matrix.pl_backend }}.xml - if-no-files-found: error \ No newline at end of file + retention-days: 1 + if-no-files-found: error + include-hidden-files: true diff --git a/.github/workflows/tests_lmps_tncuda_cpp.yml b/.github/workflows/tests_lmps_tncuda_cpp.yml deleted file mode 100644 index 35e1339b99..0000000000 --- a/.github/workflows/tests_lmps_tncuda_cpp.yml +++ /dev/null @@ -1,202 +0,0 @@ -name: Testing::Linux::x86_64::MPSTNCuda::C++ -# TODO remove MPS from the workflow name and the workflow filename once exact is in. -# TODO remove the `PL_TENSOR_METHOD` option once exact TN is added. -# TODO remove the `pl_tensor_method` once exact TN is added. -on: - workflow_call: - inputs: - lightning-version: - type: string - required: true - description: The version of Lightning to use. Valid values are either 'release' (most recent release candidate), 'stable' (most recent git-tag) or 'latest' (most recent commit from master) - pennylane-version: - type: string - required: true - description: The version of PennyLane to use. Valid values are either 'release' (most recent release candidate), 'stable' (most recent git-tag) or 'latest' (most recent commit from master) - pull_request: - paths: - - .github/workflows/tests_lmps_tncuda_cpp.yml - - pennylane_lightning/core/src/** - - '!pennylane_lightning/core/src/simulators/lightning_kokkos/**' - - '!pennylane_lightning/core/src/simulators/lightning_qubit/**' - - '!pennylane_lightning/core/src/simulators/lightning_gpu/**' - push: - branches: - - master - -env: - CI_CUDA_ARCH: 86 - COVERAGE_FLAGS: "--cov=pennylane_lightning --cov-report=term-missing --cov-report=xml:./coverage.xml --no-flaky-report -p no:warnings --tb=native" - GCC_VERSION: 11 - -concurrency: - # TODO: Revert back to using event_name - group: tests_lmps_tncuda_cpp-${{ github.ref }}-${{ github.event_name }}-${{ inputs.lightning-version }}-${{ inputs.pennylane-version }} - cancel-in-progress: true - -jobs: - builddeps: - runs-on: - - self-hosted - - ubuntu-22.04 - - gpu - - strategy: - max-parallel: 1 - matrix: - os: [ubuntu-22.04] - pl_backend: ["lightning_tensor"] - cuda_version: ["12"] - - steps: - - name: Validate GPU version and installed compiler - run: | - source /etc/profile.d/modules.sh - module use /opt/modules - module load cuda/${{ matrix.cuda_version }} - echo "${PATH}" >> $GITHUB_PATH - echo "LD_LIBRARY_PATH=${LD_LIBRARY_PATH}" >> $GITHUB_ENV - nvcc --version - nvidia-smi - - cpptestswithMPSTNCuda: - if: ${{ !contains(fromJSON('["schedule", "workflow_dispatch"]'), github.event_name) }} - needs: [builddeps] - strategy: - matrix: - os: [ubuntu-22.04] - pl_backend: ["lightning_tensor"] - pl_tensor_method: ["mps"] - pl_tensor_backend: ["cutensornet"] - cuda_version: ["12"] - - name: C++ Tests (${{ matrix.pl_backend }}, method-${{ matrix.pl_tensor_method }}, backend-${{ matrix.pl_tensor_backend }}, cuda-${{ matrix.cuda_version }}) - runs-on: - - ${{ matrix.os }} - - self-hosted - - gpu - - steps: - - name: Validate GPU version and installed compiler - run: | - source /etc/profile.d/modules.sh - module use /opt/modules - module load cuda/${{ matrix.cuda_version }} - echo "${PATH}" >> $GITHUB_PATH - echo "LD_LIBRARY_PATH=${LD_LIBRARY_PATH}" >> $GITHUB_ENV - nvcc --version - nvidia-smi - - name: Checkout PennyLane-Lightning-Tensor-MPS-TNCuda - uses: actions/checkout@v4 - with: - path: main - - - uses: actions/setup-python@v5 - name: Install Python - with: - python-version: '3.9' - - # Since the self-hosted runner can be re-used. It is best to set up all package - # installations in a virtual environment that gets cleaned at the end of each workflow run - - name: Setup Python virtual environment - id: setup_venv - env: - VENV_NAME: ${{ github.workspace }}/venv_${{ steps.setup_python.outputs.python-version }}_${{ github.sha }} - run: | - # Clear any pre-existing venvs - rm -rf venv_* - - # Create new venv for this workflow_run - python --version - python -m venv ${{ env.VENV_NAME }} - - # Add the venv to PATH for subsequent steps - echo ${{ env.VENV_NAME }}/bin >> $GITHUB_PATH - - # Adding venv name as an output for subsequent steps to reference if needed - echo "venv_name=${{ env.VENV_NAME }}" >> $GITHUB_OUTPUT - echo "Python_ROOT_DIR=${{ env.VENV_NAME }}" >> $GITHUB_ENV - echo "Python3_ROOT_DIR=${{ env.VENV_NAME }}" >> $GITHUB_ENV - - - # Adding venv site-packages to output for subsequent step to referecen if needed - echo "site_packages_dir=$(${{ env.VENV_NAME }}/bin/python -c 'import sysconfig; print(sysconfig.get_path("platlib"))')" >> $GITHUB_OUTPUT - - - - name: Display Python-Path - id: python_path - run: | - py_path=$(which python) - echo "Python Interpreter Path => $py_path" - echo "python=$py_path" >> $GITHUB_OUTPUT - - pip_path=$(which python) - echo "PIP Path => $pip_path" - echo "pip=$pip_path" >> $GITHUB_OUTPUT - - name: Install required packages - run: | - python -m pip install ninja cmake scipy custatevec-cu${{ matrix.cuda_version }} cutensornet-cu${{ matrix.cuda_version }} - - name: Build and run unit tests - run: | - set -x - cd main - cmake . -BBuild \ - -DCUQUANTUM_SDK=$(python -c "import site; print( f'{site.getsitepackages()[0]}/cuquantum')")\ - -DBUILD_TESTS=ON \ - -DENABLE_PYTHON=OFF \ - -DPL_BACKEND=${{ matrix.pl_backend }} \ - -DPL_TENSOR_METHOD=${{ matrix.pl_tensor_method }} \ - -DPL_TENSOR_BACKEND=${{ matrix.pl_tensor_backend }} \ - -DCMAKE_CXX_COMPILER=$(which g++-$GCC_VERSION) \ - -DENABLE_COVERAGE=ON \ - -G Ninja - cmake --build ./Build - cd ./Build - mkdir -p ./tests/results_${{ github.job }}_${{ matrix.pl_backend }} - for file in *runner ; do ./$file --order lex --reporter junit --out ./tests/results_${{ github.job }}_${{ matrix.pl_backend }}/report_$file.xml; done; - lcov --directory . -b ../pennylane_lightning/core/src --capture --output-file coverage.info - lcov --remove coverage.info '/usr/*' --output-file coverage.info - mv coverage.info coverage-${{ github.job }}-${{ matrix.pl_backend }}.info - set +x - - name: Upload test results - uses: actions/upload-artifact@v3 - if: always() - with: - name: ubuntu-tests-reports-${{ github.job }}_${{ matrix.pl_backend }} - path: ./main/Build/tests/results_${{ github.job }}_${{ matrix.pl_backend }} - if-no-files-found: error - - - name: Upload code coverage results - uses: actions/upload-artifact@v3 - with: - name: ubuntu-codecov-results-cpp - path: ./main/Build/coverage-${{ github.job }}-${{ matrix.pl_backend }}.info - if-no-files-found: error - - - upload-to-codecov-linux-cpp: - needs: [cpptestswithMPSTNCuda] - name: Upload coverage data to codecov - runs-on: ubuntu-latest - steps: - - name: Checkout - uses: actions/checkout@v4 - - - name: Download coverage reports - uses: actions/download-artifact@v3 - with: - name: ubuntu-codecov-results-cpp - - - name: Upload to Codecov - uses: codecov/codecov-action@v4 - with: - fail_ci_if_error: true - verbose: true - token: ${{ secrets.CODECOV_TOKEN }} - - - name: Cleanup - if: always() - run: | - rm -rf ${{ steps.setup_venv.outputs.venv_name }} - rm -rf * .git .gitignore .github - pip cache purge diff --git a/.github/workflows/tests_lmps_tncuda_python.yml b/.github/workflows/tests_lmps_tncuda_python.yml deleted file mode 100644 index 1bfec32e0b..0000000000 --- a/.github/workflows/tests_lmps_tncuda_python.yml +++ /dev/null @@ -1,213 +0,0 @@ -name: Testing::Linux::x86_64::MPSTNCuda::Python -# TODO remove MPS from the workflow name and the workflow filename once exact is in. -# TODO remove the `pl_tensor_method` once exact TN is added. -on: - workflow_call: - inputs: - lightning-version: - type: string - required: true - description: The version of Lightning to use. Valid values are either 'release' (most recent release candidate), 'stable' (most recent git-tag) or 'latest' (most recent commit from master) - pennylane-version: - type: string - required: true - description: The version of PennyLane to use. Valid values are either 'release' (most recent release candidate), 'stable' (most recent git-tag) or 'latest' (most recent commit from master) - pull_request: - paths-ignore: - - .github/** - - '!.github/workflows/tests_lmps_tncuda_python.yml' - - pennylane_lightning/core/src/simulators/lightning_kokkos/** - - pennylane_lightning/core/src/simulators/lightning_qubit/** - - pennylane_lightning/core/src/simulators/lightning_gpu/** - - pennylane_lightning/core/_version.py - - pennylane_lightning/lightning_gpu/** - - pennylane_lightning/lightning_qubit/** - - pennylane_lightning/lightning_kokkos/** - push: - branches: - - master - -env: - CI_CUDA_ARCH: 86 - COVERAGE_FLAGS: "--cov=pennylane_lightning --cov-report=term-missing --cov-report=xml:./coverage.xml --no-flaky-report -p no:warnings --tb=native" - GCC_VERSION: 11 - -concurrency: - group: tests_lmps_tncuda_python-${{ github.ref }}-${{ github.event_name }}-${{ inputs.lightning-version }}-${{ inputs.pennylane-version }} - cancel-in-progress: true - -jobs: - builddeps: - runs-on: - - self-hosted - - ubuntu-22.04 - - gpu - - strategy: - max-parallel: 1 - matrix: - os: [ubuntu-22.04] - pl_backend: ["lightning_tensor"] - cuda_version: ["12"] - - steps: - - name: Validate GPU version and installed compiler - run: | - source /etc/profile.d/modules.sh - module use /opt/modules - module load cuda/${{ matrix.cuda_version }} - echo "${PATH}" >> $GITHUB_PATH - echo "LD_LIBRARY_PATH=${LD_LIBRARY_PATH}" >> $GITHUB_ENV - nvcc --version - nvidia-smi - - pythontestswithMPSTNCuda: - if: ${{ !contains(fromJSON('["schedule", "workflow_dispatch"]'), github.event_name) }} - needs: [builddeps] - strategy: - matrix: - os: [ubuntu-22.04] - pl_backend: ["lightning_tensor"] - default_backend: ["lightning_qubit"] - pl_tensor_method: ["mps"] - pl_tensor_backend: ["cutensornet"] - cuda_version: ["12"] - - name: Python Tests (${{ matrix.pl_backend }}, method-${{ matrix.pl_tensor_method }}, backend-${{ matrix.pl_tensor_backend }}, cuda-${{ matrix.cuda_version }}) - runs-on: - - ${{ matrix.os }} - - self-hosted - - gpu - - steps: - - name: Validate GPU version and installed compiler - run: | - source /etc/profile.d/modules.sh - module use /opt/modules - module load cuda/${{ matrix.cuda_version }} - echo "${PATH}" >> $GITHUB_PATH - echo "LD_LIBRARY_PATH=${LD_LIBRARY_PATH}" >> $GITHUB_ENV - nvcc --version - nvidia-smi - - - name: Checkout PennyLane-Lightning-Tensor-MPS-TNCuda - uses: actions/checkout@v4 - with: - path: main - - - uses: actions/setup-python@v5 - name: Install Python - with: - python-version: '3.9' - - # Since the self-hosted runner can be re-used. It is best to set up all package - # installations in a virtual environment that gets cleaned at the end of each workflow run - - name: Setup Python virtual environment - id: setup_venv - env: - VENV_NAME: ${{ github.workspace }}/venv_${{ steps.setup_python.outputs.python-version }}_${{ github.sha }} - run: | - # Clear any pre-existing venvs - rm -rf venv_* - - # Create new venv for this workflow_run - python --version - python -m venv ${{ env.VENV_NAME }} - - # Add the venv to PATH for subsequent steps - echo ${{ env.VENV_NAME }}/bin >> $GITHUB_PATH - - # Adding venv name as an output for subsequent steps to reference if needed - source ${{ env.VENV_NAME }}/bin/activate - echo "venv_name=${{ env.VENV_NAME }}" >> $GITHUB_OUTPUT - echo "Python_ROOT_DIR=${{ env.VENV_NAME }}" >> $GITHUB_ENV - echo "Python3_ROOT_DIR=${{ env.VENV_NAME }}" >> $GITHUB_ENV - - # Adding venv site-packages to output for subsequent step to referecen if needed - echo "site_packages_dir=$(${{ env.VENV_NAME }}/bin/python -c 'import sysconfig; print(sysconfig.get_path("platlib"))')" >> $GITHUB_OUTPUT - - - - name: Display Python-Path - id: python_path - run: | - py_path=$(which python) - echo "Python Interpreter Path => $py_path" - echo "python=$py_path" >> $GITHUB_OUTPUT - - echo "PIP Path => $py_path" - echo "pip=$py_path" >> $GITHUB_OUTPUT - - - name: Install required packages - run: | - cd main - python -m pip install -r requirements-dev.txt - python -m pip install ninja cmake scipy custatevec-cu${{ matrix.cuda_version }} cutensornet-cu${{ matrix.cuda_version }} openfermionpyscf - - - name: Checkout PennyLane for release build - if: inputs.pennylane-version == 'release' - uses: actions/checkout@v4 - with: - path: pennylane - repository: PennyLaneAI/pennylane - - - name: Switch to release build of PennyLane - if: inputs.pennylane-version == 'release' - run: | - cd pennylane - git fetch --all - git checkout $(git branch -a --list "origin/v0.*rc*" | sort | tail -1) - python -m pip uninstall -y pennylane && python -m pip install . -vv --no-deps - - - name: Install Stable PennyLane - if: inputs.pennylane-version == 'stable' - run: | - cd main - python -m pip uninstall -y pennylane && python -m pip install -U pennylane - - - name: Build and install package - run: | - cd main - rm -rf build - CMAKE_ARGS="-DPL_BACKEND=${{ matrix.default_backend }} -DCMAKE_CXX_COMPILER=$(which g++-$GCC_VERSION)" \ - python -m pip install . -vv - rm -rf build - - rm -rf build - CMAKE_ARGS="-DPL_BACKEND=${{ matrix.pl_backend }} -DCMAKE_CXX_COMPILER=$(which g++-$GCC_VERSION)" \ - python -m pip install . -vv - - - name: Run PennyLane-Lightning-Tensor unit tests - if: ${{ matrix.pl_backend != 'all'}} - run: | - cd main/ - DEVICENAME=`echo ${{ matrix.pl_backend }} | sed "s/_/./g"` - PL_DEVICE=${DEVICENAME} python -m pytest tests $COVERAGE_FLAGS - mv coverage.xml coverage-${{ github.job }}-${{ matrix.pl_backend }}.xml - - - name: Upload code coverage results - uses: actions/upload-artifact@v3 - with: - name: ubuntu-codecov-results-python - path: ./main/coverage-${{ github.job }}-${{ matrix.pl_backend }}.xml - if-no-files-found: error - - - upload-to-codecov-linux-python: - needs: [pythontestswithMPSTNCuda] - name: Upload coverage data to codecov - runs-on: ubuntu-latest - steps: - - name: Checkout - uses: actions/checkout@v4 - - - name: Download coverage reports - uses: actions/download-artifact@v3 - with: - name: ubuntu-codecov-results-python - - - name: Upload to Codecov - uses: codecov/codecov-action@v4 - with: - fail_ci_if_error: true - verbose: true - token: ${{ secrets.CODECOV_TOKEN }} diff --git a/.github/workflows/tests_lqcpu_python.yml b/.github/workflows/tests_lqcpu_python.yml index d4cdc35f08..f64d5a17cb 100644 --- a/.github/workflows/tests_lqcpu_python.yml +++ b/.github/workflows/tests_lqcpu_python.yml @@ -11,6 +11,11 @@ on: required: true description: The version of PennyLane to use. Valid values are either 'release' (most recent release candidate), 'stable' (most recent git-tag) or 'latest' (most recent commit from master) pull_request: + types: + - opened + - reopened + - synchronize + - ready_for_review push: branches: - master @@ -28,45 +33,43 @@ concurrency: cancel-in-progress: true jobs: - determine_runner: - name: Determine runner type to use - uses: ./.github/workflows/determine-workflow-runner.yml - with: - default_runner: ubuntu-22.04 - build_lightning_qubit_wheels: - needs: [determine_runner] strategy: matrix: pl_backend: ["lightning_qubit"] blas: ["OFF", "ON"] timeout-minutes: 60 name: Build (${{ matrix.pl_backend }}, blas-${{ matrix.blas }}) - runs-on: ${{ needs.determine_runner.outputs.runner_group }} + runs-on: pl-4-core-large-runner steps: + - name: Make disk space + run: | + sudo apt-get autoremove -y && sudo apt-get autoclean -y && sudo apt-get clean -y + for DIR in /usr/share/dotnet /usr/local/share/powershell /usr/share/swift; do + sudo du -sh $DIR || echo $DIR not found + sudo rm -rf $DIR + done + - uses: actions/setup-python@v5 name: Install Python with: - python-version: '3.9' + python-version: '3.10' - name: Checkout PennyLane-Lightning uses: actions/checkout@v4 with: fetch-tags: true - path: main - name: Switch to release build of Lightning if: inputs.lightning-version == 'release' run: | - cd main git fetch --all git checkout $(git branch -a --list "origin/v0.*rc*" | sort | tail -1) - name: Switch to stable build of Lightning if: inputs.lightning-version == 'stable' run: | - cd main git fetch --tags --force git checkout latest_release git log -1 --format='%H' @@ -75,19 +78,16 @@ jobs: - name: Install dependencies run: | sudo apt-get update && sudo apt-get -y -q install cmake gcc-$GCC_VERSION g++-$GCC_VERSION libopenblas-base libopenblas-dev - python -m pip install scipy wheel + python -m pip install scipy-openblas32 wheel - name: Get required Python packages run: | - cd main python -m pip install -r requirements-dev.txt - - name: Create device wheel - id: create_lq_wheel + - name: Create device wheel ${{ inputs.lightning-version }} run: | - cd main - python setup.py build_ext -i --define="PL_BACKEND=${{ matrix.pl_backend }};ENABLE_BLAS=${{ matrix.blas }};LQ_ENABLE_KERNEL_OMP=ON;ENABLE_PYTHON=ON;CMAKE_CXX_COMPILER=$(which g++-$GCC_VERSION)" - PL_BACKEND=${{ matrix.pl_backend }} python setup.py bdist_wheel + PL_BACKEND=${{ matrix.pl_backend }} python scripts/configure_pyproject_toml.py + CMAKE_ARGS="-DENABLE_BLAS=${{ matrix.blas }} -DLQ_ENABLE_KERNEL_OMP=ON -DENABLE_PYTHON=ON" python -m build cd dist WHEEL_NAME=$(ls *.whl) cp $WHEEL_NAME ${{ github.workspace }}/wheel_${{ matrix.pl_backend }}-${{ matrix.blas }}.whl @@ -99,6 +99,7 @@ jobs: name: ${{ matrix.pl_backend }}-${{ matrix.blas }}_name.txt retention-days: 1 if-no-files-found: error + include-hidden-files: true path: ${{ github.workspace }}/${{ matrix.pl_backend }}-${{ matrix.blas }}_name.txt - name: Cache wheel directories @@ -107,11 +108,12 @@ jobs: name: wheel_${{ matrix.pl_backend }}-${{ matrix.blas }}.whl retention-days: 1 if-no-files-found: error + include-hidden-files: true path: | ${{ github.workspace }}/wheel_${{ matrix.pl_backend }}-${{ matrix.blas }}.whl test_lightning_qubit_wheels: - needs: [determine_runner, build_lightning_qubit_wheels] + needs: [build_lightning_qubit_wheels] strategy: matrix: pl_backend: ["lightning_qubit"] @@ -119,19 +121,18 @@ jobs: group: [1, 2, 3, 4] timeout-minutes: 60 name: Python Tests (${{ matrix.pl_backend }}, blas-${{ matrix.blas }}, test-group-${{ matrix.group }}) - runs-on: ${{ needs.determine_runner.outputs.runner_group }} + runs-on: pl-4-core-large-runner steps: - uses: actions/setup-python@v5 name: Install Python with: - python-version: '3.9' + python-version: '3.10' - name: Checkout PennyLane-Lightning uses: actions/checkout@v4 with: fetch-tags: true - path: main - uses: actions/download-artifact@v4 with: @@ -146,19 +147,26 @@ jobs: - name: Get required Python packages run: | WHEEL_NAME=$(cat ${{ github.workspace }}/${{ matrix.pl_backend }}-${{ matrix.blas }}_name.txt) - cd main mv ${{ github.workspace }}/wheel_${{ matrix.pl_backend }}-${{ matrix.blas }}.whl ${{ github.workspace }}/$WHEEL_NAME python -m pip install -r requirements-dev.txt python -m pip install openfermionpyscf + python -m pip install semantic-version python -m pip install ${{ github.workspace }}/$WHEEL_NAME --no-deps --force-reinstall - - name: Checkout PennyLane for release build - if: inputs.pennylane-version == 'release' + - name: Checkout PennyLane for release or latest build + if: inputs.pennylane-version == 'release' || inputs.pennylane-version == 'latest' uses: actions/checkout@v4 with: path: pennylane repository: PennyLaneAI/pennylane + - name: Switch to latest build of PennyLane + if: inputs.pennylane-version == 'latest' + run: | + cd pennylane + git checkout master + python -m pip uninstall -y pennylane && python -m pip install . -vv --no-deps + - name: Switch to release build of PennyLane if: inputs.pennylane-version == 'release' run: | @@ -170,19 +178,16 @@ jobs: - name: Install Stable PennyLane if: inputs.pennylane-version == 'stable' run: | - cd main python -m pip uninstall -y pennylane && python -m pip install -U pennylane --no-deps - name: Install ML libraries for interfaces run: | python -m pip install --upgrade torch==$TORCH_VERSION -f https://download.pytorch.org/whl/cpu/torch_stable.html - python -m pip install --upgrade "jax[cpu]" # This also installs jaxlib python -m pip install --upgrade tensorflow~=$TF_VERSION keras~=$TF_VERSION - name: Switch to stable tag of Lightning if: inputs.lightning-version == 'stable' run: | - cd main git fetch --tags --force git checkout latest_release git log -1 --format='%H' @@ -190,44 +195,54 @@ jobs: - name: Run PennyLane-Lightning unit tests run: | - cd main/ DEVICENAME=`echo ${{ matrix.pl_backend }} | sed "s/_/./g"` - OMP_NUM_THREADS=1 PL_DEVICE=${DEVICENAME} python -m pytest -n auto tests/ -k "not unitary_correct" \ - $COVERAGE_FLAGS --splits 4 --group ${{ matrix.group }} \ + # Remove `python -m` to avoid running tests with relative modules + OMP_NUM_THREADS=1 PL_DEVICE=${DEVICENAME} pytest -n auto tests/ -k "not unitary_correct" \ + $COVERAGE_FLAGS --store-durations --splits 4 --group ${{ matrix.group }} \ --durations-path='.github/workflows/python_lightning_qubit_test_durations.json' --splitting-algorithm=least_duration - PL_DEVICE=${DEVICENAME} python -m pytest tests/ -k "unitary_correct" $COVERAGE_FLAGS --cov-append + PL_DEVICE=${DEVICENAME} pytest tests/ -k "unitary_correct" $COVERAGE_FLAGS --cov-append pl-device-test --device ${DEVICENAME} --skip-ops --shots=20000 $COVERAGE_FLAGS --cov-append pl-device-test --device ${DEVICENAME} --shots=None --skip-ops $COVERAGE_FLAGS --cov-append - mv .coverage .coverage-${{ github.job }}-${{ matrix.pl_backend }}-${{ matrix.group }} + mv .coverage ${{ github.workspace }}/.coverage-${{ github.job }}-${{ matrix.pl_backend }}-${{ matrix.blas }}-${{ matrix.group }} + + - name: Upload test durations + uses: actions/upload-artifact@v4 + with: + name: .test_durations-${{ matrix.exec_model }}-${{ matrix.group }} + retention-days: 1 + path: ${{ github.workspace }}/.test_durations-${{ matrix.exec_model }}-${{ matrix.group }} - name: Upload code coverage results - uses: actions/upload-artifact@v3 + uses: actions/upload-artifact@v4 with: - name: ubuntu-codecov-results-python + name: .coverage-${{ github.job }}-${{ matrix.pl_backend }}-${{ matrix.blas }}-${{ matrix.group }} path: | - ./main/.coverage-${{ github.job }}-${{ matrix.pl_backend }}-${{ matrix.group }} + ${{ github.workspace }}/.coverage-${{ github.job }}-${{ matrix.pl_backend }}-${{ matrix.blas }}-${{ matrix.group }} + retention-days: 1 if-no-files-found: error + include-hidden-files: true upload-to-codecov-linux-python: - needs: [determine_runner, test_lightning_qubit_wheels] + if: github.event_name == 'pull_request' + needs: [test_lightning_qubit_wheels] name: Upload python coverage data to codecov - runs-on: ${{ needs.determine_runner.outputs.runner_group }} + runs-on: ubuntu-latest steps: - name: Checkout uses: actions/checkout@v4 with: fetch-tags: true - path: main - name: Download coverage reports - uses: actions/download-artifact@v3 + uses: actions/download-artifact@v4 with: - name: ubuntu-codecov-results-python + pattern: .coverage-* + merge-multiple: true - name: Combine coverage files run: | python -m pip install coverage - python -m coverage combine .coverage* + python -m coverage combine .coverage-* # Added cov xml -i to ignore "No source for code" random errors # https://stackoverflow.com/questions/2386975/no-source-for-code-message-in-coverage-py python -m coverage xml -i -o coverage-${{ github.job }}.xml diff --git a/.github/workflows/tests_windows_cpp.yml b/.github/workflows/tests_windows_cpp.yml index 2faccc0831..4c0df2df80 100644 --- a/.github/workflows/tests_windows_cpp.yml +++ b/.github/workflows/tests_windows_cpp.yml @@ -14,6 +14,11 @@ on: branches: - master pull_request: + types: + - opened + - reopened + - synchronize + - ready_for_review paths: - .github/workflows/tests_windows_cpp.yml - pennylane_lightning/core/src/** @@ -25,6 +30,7 @@ concurrency: jobs: win-set-matrix-x86: + if: github.event.pull_request.draft == false name: Set builder matrix runs-on: ubuntu-latest @@ -38,7 +44,7 @@ jobs: - name: Kokkos version id: kokkos_version - run: echo "kokkos_version=[\"4.3.01\"]" >> $GITHUB_OUTPUT + run: echo "kokkos_version=[\"4.5.00\"]" >> $GITHUB_OUTPUT outputs: exec_model: ${{ steps.exec_model.outputs.exec_model }} @@ -98,7 +104,7 @@ jobs: cmake --install ./Build --config Debug --verbose cpptests: - if: ${{ !contains(fromJSON('["schedule", "workflow_dispatch"]'), github.event_name) }} + if: ${{ !contains(fromJSON('["schedule", "workflow_dispatch"]'), github.event_name) && github.event.pull_request.draft == false}} timeout-minutes: 30 runs-on: ${{ matrix.os }} strategy: @@ -111,27 +117,32 @@ jobs: - uses: actions/setup-python@v4 name: Install Python with: - python-version: '3.9' + python-version: '3.10' - name: Checkout PennyLane-Lightning - uses: actions/checkout@v3 + uses: actions/checkout@v4 - name: Install dependencies run: | - python -m pip install cmake build ninja scipy + python -m pip install cmake build ninja scipy-openblas32 - name: Setup OpenCppCoverage and add to PATH run: | choco install OpenCppCoverage -y echo "C:\Program Files\OpenCppCoverage" >> $env:GITHUB_PATH + - name: Add path to scipy-openblas32 to environment variable + run: | + $scipy_openblas32_path = python -c "import scipy_openblas32; print(scipy_openblas32.get_lib_dir())" + echo "SCIPY_OPENBLAS32=${scipy_openblas32_path}" | Out-File -Append -FilePath $env:GITHUB_ENV + Get-Content $env:GITHUB_ENV + - name: Build and run unit tests for code coverage run: | cmake -BBuild ` -DBUILD_TESTS=ON ` -DENABLE_OPENMP=OFF ` -DENABLE_PYTHON=OFF ` - -DENABLE_LAPACK=OFF ` -DENABLE_GATE_DISPATCHER=OFF ` -DPL_BACKEND=${{ matrix.pl_backend }} ` -DENABLE_WARNINGS=OFF @@ -150,19 +161,23 @@ jobs: Move-Item -Path .\coverage.xml -Destination .\coverage-${{ github.job }}-${{ matrix.pl_backend }}.xml - name: Upload test results - uses: actions/upload-artifact@v3 + uses: actions/upload-artifact@v4 if: always() with: name: windows-test-report-${{ github.job }}-${{ matrix.pl_backend }} path: .\Build\tests\results\ + retention-days: 1 if-no-files-found: error + include-hidden-files: true - name: Upload coverage results - uses: actions/upload-artifact@v3 + uses: actions/upload-artifact@v4 with: name: windows-coverage-report path: .\coverage-${{ github.job }}-${{ matrix.pl_backend }}.xml + retention-days: 1 if-no-files-found: error + include-hidden-files: true cpptestswithkokkos: if: ${{ !contains(fromJSON('["schedule", "workflow_dispatch"]'), github.event_name) }} @@ -182,7 +197,7 @@ jobs: - uses: actions/setup-python@v4 name: Install Python with: - python-version: '3.9' + python-version: '3.10' - name: Restoring cached Kokkos id: kokkos-cache @@ -193,7 +208,7 @@ jobs: - name: Install dependencies run: | - python -m pip install cmake build ninja + python -m pip install cmake build ninja scipy-openblas32 - name: Checkout PennyLane-Lightning uses: actions/checkout@v4 @@ -212,6 +227,12 @@ jobs: run: | choco install OpenCppCoverage -y echo "C:\Program Files\OpenCppCoverage" >> $env:GITHUB_PATH + + - name: Add path to scipy-openblas32 to environment variable + run: | + $scipy_openblas32_path = python -c "import scipy_openblas32; print(scipy_openblas32.get_lib_dir())" + echo "SCIPY_OPENBLAS32=${scipy_openblas32_path}" | Out-File -Append -FilePath $env:GITHUB_ENV + Get-Content $env:GITHUB_ENV - name: Build and run unit tests for code coverage run: | @@ -240,19 +261,23 @@ jobs: Move-Item -Path .\coverage.xml -Destination .\coverage-${{ github.job }}-${{ matrix.pl_backend }}.xml - name: Upload test results - uses: actions/upload-artifact@v3 + uses: actions/upload-artifact@v4 if: always() with: name: windows-test-report-${{ github.job }}-${{ matrix.pl_backend }} path: .\Build\tests\results\ + retention-days: 1 if-no-files-found: error + include-hidden-files: true - name: Upload coverage results - uses: actions/upload-artifact@v3 + uses: actions/upload-artifact@v4 with: - name: windows-coverage-report + name: windows-coverage-report-${{ matrix.pl_backend }} path: .\coverage-${{ github.job }}-${{ matrix.pl_backend }}.xml + retention-days: 1 if-no-files-found: error + include-hidden-files: true upload-to-codecov-windows: needs: [cpptests, cpptestswithkokkos] @@ -263,9 +288,10 @@ jobs: uses: actions/checkout@v4 - name: Download coverage reports - uses: actions/download-artifact@v3 + uses: actions/download-artifact@v4 with: - name: windows-coverage-report + pattern: windows-coverage* + merge-multiple: true - name: Upload to Codecov uses: codecov/codecov-action@v4 diff --git a/.github/workflows/tests_without_binary.yml b/.github/workflows/tests_without_binary.yml index eb2745e6ce..624b8927fc 100644 --- a/.github/workflows/tests_without_binary.yml +++ b/.github/workflows/tests_without_binary.yml @@ -11,6 +11,11 @@ on: required: true description: The version of PennyLane to use. Valid values are either 'release' (most recent release candidate), 'stable' (most recent git-tag) or 'latest' (most recent commit from master) pull_request: + types: + - opened + - reopened + - synchronize + - ready_for_review paths-ignore: - pennylane_lightning/core/src/** push: @@ -25,16 +30,9 @@ concurrency: cancel-in-progress: true jobs: - determine_runner: - name: Determine runner type to use - uses: ./.github/workflows/determine-workflow-runner.yml - with: - default_runner: ubuntu-22.04 - pythontests: - needs: [determine_runner] timeout-minutes: 30 - runs-on: ${{ needs.determine_runner.outputs.runner_group }} + runs-on: pl-4-core-large-runner strategy: matrix: pl_backend: ["lightning_qubit", "lightning_kokkos", "lightning_gpu", "lightning_tensor"] @@ -42,23 +40,27 @@ jobs: name: Python Tests without Binary (${{ matrix.pl_backend }}) steps: + - name: Make disk space + run: | + for DIR in /usr/share/dotnet /usr/local/share/powershell /usr/share/swift; do + sudo du -sh $DIR || echo $DIR not found + sudo rm -rf $DIR + done + - name: Checkout PennyLane-Lightning uses: actions/checkout@v4 with: fetch-tags: true - path: main - name: Switch to release build of Lightning if: inputs.lightning-version == 'release' run: | - cd main git fetch --all git checkout $(git branch -a --list "origin/v0.*rc*" | sort | tail -1) - name: Switch to stable build of Lightning if: inputs.lightning-version == 'stable' run: | - cd main git fetch --tags --force git checkout latest_release git log -1 --format='%H' @@ -67,21 +69,27 @@ jobs: - uses: actions/setup-python@v5 name: Install Python with: - python-version: '3.9' + python-version: '3.10' - name: Get required Python packages run: | rm -fr $(python -m pip cache dir)/selfcheck/ - cd main python -m pip install -r requirements-dev.txt - - name: Checkout PennyLane for release build - if: inputs.pennylane-version == 'release' + - name: Checkout PennyLane for release or latest build + if: inputs.pennylane-version == 'release' || inputs.pennylane-version == 'latest' uses: actions/checkout@v4 with: path: pennylane repository: PennyLaneAI/pennylane + - name: Switch to latest build of PennyLane + if: inputs.pennylane-version == 'latest' + run: | + cd pennylane + git checkout master + python -m pip uninstall -y pennylane && python -m pip install . -vv --no-deps + - name: Switch to release build of PennyLane if: inputs.pennylane-version == 'release' run: | @@ -93,39 +101,32 @@ jobs: - name: Install Stable PennyLane if: inputs.pennylane-version == 'stable' run: | - cd main python -m pip uninstall -y pennylane && python -m pip install -U pennylane - - name: Install the pennylane_lightning package - if: ${{ contains(fromJson('["lightning_kokkos", "lightning_gpu", "lightning_tensor"]'), matrix.pl_backend) }} + - name: Install the pennylane_lightning core package (lightning_qubit) + env: + SKIP_COMPILATION: True run: | - cd main - SKIP_COMPILATION=True PL_BACKEND="lightning_qubit" python -m pip install . -vv + PL_BACKEND="lightning_qubit" python scripts/configure_pyproject_toml.py || true + PL_BACKEND="lightning_qubit" python -m pip install . -vv - - name: Install backend device - # FIXME: Remove this condition with v0.37.0 release - if: matrix.pl_backend != 'lightning_tensor' || inputs.lightning-version != 'stable' + - name: Install extra backend device + if: matrix.pl_backend != 'lightning_qubit' env: SKIP_COMPILATION: True - PL_BACKEND: ${{ matrix.pl_backend }} run: | - cd main - python -m pip install . -vv + PL_BACKEND=${{ matrix.pl_backend }} python scripts/configure_pyproject_toml.py || true + PL_BACKEND=${{ matrix.pl_backend }} python -m pip install . -vv - name: Run PennyLane-Lightning unit tests for all backends - # FIXME: Remove this condition with v0.37.0 release - if: matrix.pl_backend != 'lightning_tensor' || inputs.lightning-version != 'stable' run: | - cd main/ DEVICENAME=`echo ${{ matrix.pl_backend }} | sed "s/_/./g"` PL_DEVICE=${DEVICENAME} python -m pytest tests/ $COVERAGE_FLAGS - name: Upload coverage to Codecov - # FIXME: Remove this condition with v0.37.0 release - if: matrix.pl_backend != 'lightning_tensor' || inputs.lightning-version != 'stable' uses: codecov/codecov-action@v4 with: - files: ./main/coverage.xml + files: coverage.xml fail_ci_if_error: true verbose: true token: ${{ secrets.CODECOV_TOKEN }} diff --git a/.github/workflows/tidy.yml b/.github/workflows/tidy.yml index 68e9dd1a9c..4720843922 100644 --- a/.github/workflows/tidy.yml +++ b/.github/workflows/tidy.yml @@ -1,6 +1,11 @@ name: Clang-tidy check on: pull_request: + types: + - opened + - reopened + - synchronize + - ready_for_review paths: - .github/workflows/tidy.yml - pennylane_lightning/core/src/** @@ -17,6 +22,7 @@ concurrency: jobs: tidy-cpp: + if: github.event.pull_request.draft == false strategy: matrix: pl_backend: ["lightning_qubit"] @@ -45,6 +51,7 @@ jobs: -DCLANG_TIDY_BINARY=$(dirname $(which python))/clang-tidy \ -DBUILD_TESTS=ON \ -DENABLE_WARNINGS=ON \ + -DSCIPY_OPENBLAS=$(python -c "import scipy_openblas32; print(scipy_openblas32.get_lib_dir())") \ -DPL_BACKEND=${{ matrix.pl_backend }} \ -DCMAKE_CXX_COMPILER="$(which g++-11)" \ -DCMAKE_C_COMPILER="$(which gcc-11)" diff --git a/.github/workflows/update_dev_version.yml b/.github/workflows/update_dev_version.yml index eba14db6eb..75a290df39 100644 --- a/.github/workflows/update_dev_version.yml +++ b/.github/workflows/update_dev_version.yml @@ -35,7 +35,7 @@ jobs: - uses: actions/setup-python@v5 name: Install Python with: - python-version: '3.9' + python-version: '3.10' - name: Install Python Dependencies run: pip install semver diff --git a/.github/workflows/wheel_linux_aarch64.yml b/.github/workflows/wheel_linux_aarch64.yml index ec090d9f7c..979d67471a 100644 --- a/.github/workflows/wheel_linux_aarch64.yml +++ b/.github/workflows/wheel_linux_aarch64.yml @@ -1,12 +1,17 @@ name: Wheel::Linux::ARM # **What it does**: Builds python wheels for Linux (ubuntu-latest) architecture ARM 64 and store it as artifacts. -# Python versions: 3.9, 3.10, 3.11, 3.12. +# Python versions: 3.10, 3.11, 3.12. # **Why we have it**: To build wheels for pennylane-lightning installation. # **Who does it impact**: Wheels to be uploaded to PyPI. on: pull_request: + types: + - opened + - reopened + - synchronize + - ready_for_review push: branches: - master @@ -18,12 +23,14 @@ concurrency: group: wheel_linux_aarch64-${{ github.ref }} cancel-in-progress: true +env: + GCC_VERSION: 13 + jobs: set_wheel_build_matrix: if: | github.event_name != 'pull_request' || - contains(github.event.pull_request.labels.*.name, 'ci:build_wheels') || - github.event_name == 'workflow_dispatch' + (github.event.pull_request.draft == false && contains(github.event.pull_request.labels.*.name, 'ci:build_wheels')) name: "Set wheel build matrix" uses: ./.github/workflows/set_wheel_build_matrix.yml with: @@ -33,7 +40,7 @@ jobs: needs: [set_wheel_build_matrix] strategy: matrix: - os: [ubuntu-latest] + os: [pl-4-core-large-runner] arch: [aarch64] exec_model: ${{ fromJson(needs.set_wheel_build_matrix.outputs.exec_model) }} kokkos_version: ${{ fromJson(needs.set_wheel_build_matrix.outputs.kokkos_version) }} @@ -56,7 +63,7 @@ jobs: git clone --branch ${{ matrix.kokkos_version }} https://github.com/kokkos/kokkos.git pushd . &> /dev/null - - uses: docker/setup-qemu-action@v2 + - uses: docker/setup-qemu-action@v3 name: Set up QEMU - name: Build Kokkos core library @@ -71,8 +78,8 @@ jobs: -i ${{ matrix.container_img }} \ bash -c "git config --global --add safe.directory /io && \ cd /io && \ - python3.9 -m pip install ninja && \ - ln -s /opt/python/cp39-cp39/bin/ninja /usr/bin/ninja && \ + python3.10 -m pip install ninja && \ + ln -s /opt/python/cp310-cp310/bin/ninja /usr/bin/ninja && \ cmake -BBuild . -DCMAKE_INSTALL_PREFIX=/install \ -DKokkos_ENABLE_COMPLEX_ALIGN=OFF \ -DKokkos_ENABLE_SERIAL=ON \ @@ -90,7 +97,7 @@ jobs: strategy: fail-fast: false matrix: - os: [ubuntu-latest] + os: [pl-4-core-large-runner] arch: [aarch64] pl_backend: ["lightning_kokkos", "lightning_qubit"] cibw_build: ${{ fromJson(needs.set_wheel_build_matrix.outputs.python_version) }} @@ -98,7 +105,7 @@ jobs: kokkos_version: ${{ fromJson(needs.set_wheel_build_matrix.outputs.kokkos_version) }} container_img: ["quay.io/pypa/manylinux_2_28_aarch64"] timeout-minutes: 45 - name: ${{ matrix.os }}::${{ matrix.arch }} - ${{ matrix.pl_backend }} (Python ${{ fromJson('{ "cp39-*":"3.9","cp310-*":"3.10","cp311-*":"3.11", "cp312-*":"3.12" }')[matrix.cibw_build] }}) + name: ${{ matrix.os }}::${{ matrix.arch }} - ${{ matrix.pl_backend }} (Python ${{ fromJson('{ "cp310-*":"3.10","cp311-*":"3.11", "cp312-*":"3.12" }')[matrix.cibw_build] }}) runs-on: ${{ matrix.os }} steps: @@ -119,10 +126,18 @@ jobs: mkdir Kokkos cp -rf ${{ github.workspace }}/Kokkos_install/${{ matrix.exec_model }}/* Kokkos/ - - name: Install cibuildwheel - run: python -m pip install cibuildwheel~=2.16.0 + - name: Install Python 3.10 + uses: actions/setup-python@v5 + with: + python-version: '3.10' + + - name: Install dependencies + run: python3.10 -m pip install cibuildwheel~=2.20.0 tomlkit - - uses: docker/setup-qemu-action@v2 + - name: Configure pyproject.toml file + run: PL_BACKEND="${{ matrix.pl_backend }}" python3.10 scripts/configure_pyproject_toml.py + + - uses: docker/setup-qemu-action@v3 name: Set up QEMU - name: Build wheels @@ -136,9 +151,14 @@ jobs: # Python build settings CIBW_BEFORE_BUILD: | cat /etc/yum.conf | sed "s/\[main\]/\[main\]\ntimeout=5/g" > /etc/yum.conf - python -m pip install ninja cmake~=3.24.0 + python -m pip install ninja cmake~=3.27.0 + dnf clean all -y + dnf install gcc-toolset-13 -y + source /opt/rh/gcc-toolset-13/enable -y + PATH="/opt/rh/gcc-toolset-13/root/usr/bin:$PATH" - CIBW_ENVIRONMENT: PL_BACKEND="${{ matrix.pl_backend }}" CMAKE_ARGS="-DENABLE_LAPACK=OFF" + CIBW_ENVIRONMENT: | + PATH="/opt/rh/gcc-toolset-13/root/usr/bin:$PATH" CMAKE_ARGS="-DCMAKE_BUILD_TYPE=Release" CIBW_MANYLINUX_AARCH64_IMAGE: manylinux_2_28 @@ -146,18 +166,28 @@ jobs: CIBW_BEFORE_TEST: | python -m pip install -r requirements-tests.txt - if ${{ matrix.pl_backend == 'lightning_kokkos'}}; then SKIP_COMPILATION=True PL_BACKEND="lightning_qubit" python -m pip install . -vv; fi + # FIXME(v0.40.0): install PennyLane/latest to fix pennylane.devices.capabilities requirements + python -m pip install git+https://github.com/PennyLaneAI/pennylane.git@master + if ${{ matrix.pl_backend == 'lightning_kokkos'}} + then + PL_BACKEND="lightning_qubit" python scripts/configure_pyproject_toml.py + SKIP_COMPILATION=True python -m pip install . -vv + fi CIBW_TEST_COMMAND: | DEVICENAME=`echo ${{ matrix.pl_backend }} | sed "s/_/./g"` + python -c "import pennylane as qml; print(qml.about())" + python -c "import pennylane as qml; dev = qml.device(\"lightning.qubit\", wires=3); print(dev); dev2 = qml.device(\"lightning.kokkos\", wires=5); print(dev2)" + which pl-device-test + ls pl-device-test --device=${DEVICENAME} --skip-ops -x --tb=short --no-flaky-report - run: python3 -m cibuildwheel --output-dir wheelhouse + run: python3.10 -m cibuildwheel --output-dir wheelhouse - name: Validate wheels run: | - python3 -m pip install twine - python3 -m twine check ./wheelhouse/*.whl + python3.10 -m pip install twine + python3.10 -m twine check ./wheelhouse/*.whl - uses: actions-ecosystem/action-regex-match@main id: rc_build @@ -165,35 +195,39 @@ jobs: text: ${{ github.event.pull_request.head.ref }} regex: '.*[0-9]+.[0-9]+.[0-9]+[-_]?rc[0-9]+' - - uses: actions/upload-artifact@v3 + - uses: actions/upload-artifact@v4 if: | github.event_name == 'release' || github.event_name == 'workflow_dispatch' || github.ref == 'refs/heads/master' with: - name: ${{ runner.os }}-wheels-${{ matrix.pl_backend }}-${{ matrix.arch }}.zip + name: ${{ runner.os }}-wheels-${{ matrix.pl_backend }}-${{ fromJson('{ "cp310-*":"py310","cp311-*":"py311","cp312-*":"py312" }')[matrix.cibw_build] }}-${{ matrix.arch }}.zip path: ./wheelhouse/*.whl + retention-days: 1 + include-hidden-files: true upload-pypi: - needs: linux-wheels-aarch64 + needs: [set_wheel_build_matrix, linux-wheels-aarch64] strategy: matrix: arch: [aarch64] pl_backend: ["lightning_qubit", "lightning_kokkos"] + cibw_build: ${{ fromJson(needs.set_wheel_build_matrix.outputs.python_version) }} runs-on: ubuntu-latest + permissions: + id-token: write if: | github.event_name == 'release' || github.ref == 'refs/heads/master' steps: - - uses: actions/download-artifact@v3 + - uses: actions/download-artifact@v4 with: - name: Linux-wheels-${{ matrix.pl_backend }}-${{ matrix.arch }}.zip + name: ${{ runner.os }}-wheels-${{ matrix.pl_backend }}-${{ fromJson('{ "cp310-*":"py310","cp311-*":"py311","cp312-*":"py312" }')[matrix.cibw_build] }}-${{ matrix.arch }}.zip path: dist - - name: Upload wheels to PyPI + - name: Upload wheels to TestPyPI uses: pypa/gh-action-pypi-publish@release/v1 with: - user: __token__ - password: ${{ ( matrix.pl_backend == 'lightning_qubit' ) && secrets.TEST_PYPI_API_TOKEN || secrets.TEST_PYPI_LKOKKOS_TOKEN }} + verbose: true repository-url: https://test.pypi.org/legacy/ diff --git a/.github/workflows/wheel_linux_aarch64_cuda.yml b/.github/workflows/wheel_linux_aarch64_cuda.yml new file mode 100644 index 0000000000..6a7c4a9e22 --- /dev/null +++ b/.github/workflows/wheel_linux_aarch64_cuda.yml @@ -0,0 +1,144 @@ +name: Wheel::Linux::ARM::CUDA + +# **What it does**: Builds python wheels for Linux (ubuntu-latest) architecture ARM 64 and store it as artifacts. +# Python versions: 3.10, 3.11, 3.12. +# **Why we have it**: To build wheels for pennylane-lightning installation. +# **Who does it impact**: Wheels to be uploaded to PyPI. + +on: + pull_request: + push: + branches: + - master + release: + types: [published] + workflow_dispatch: + +concurrency: + group: wheel_linux_aarch64_cu12-${{ github.ref }} + cancel-in-progress: true + +jobs: + set_wheel_build_matrix: + if: | + github.event_name != 'pull_request' || + contains(github.event.pull_request.labels.*.name, 'ci:build_wheels') || + github.event_name == 'workflow_dispatch' + name: "Set wheel build matrix" + uses: ./.github/workflows/set_wheel_build_matrix.yml + with: + event_name: ${{ github.event_name }} + + linux-wheels-aarch64: + needs: [set_wheel_build_matrix] + strategy: + fail-fast: false + matrix: + os: [pl-4-core-large-runner] + arch: [aarch64] + pl_backend: ["lightning_gpu", "lightning_tensor"] + cuda_version: ["12"] + cibw_build: ${{ fromJson(needs.set_wheel_build_matrix.outputs.python_version) }} + container_img: ["quay.io/pypa/manylinux_2_28_aarch64"] + timeout-minutes: 45 + name: ${{ matrix.os }}::${{ matrix.arch }} - ${{ matrix.pl_backend }} (Python ${{ fromJson('{ "cp310-*":"3.10","cp311-*":"3.11", "cp312-*":"3.12" }')[matrix.cibw_build] }}) + runs-on: ${{ matrix.os }} + + steps: + - name: Checkout PennyLane-Lightning + uses: actions/checkout@v4 + + - name: Install Python 3.10 + uses: actions/setup-python@v5 + with: + python-version: '3.10' + + - name: Install cibuildwheel + run: python3.10 -m pip install cibuildwheel~=2.20.0 tomlkit + + - name: Configure pyproject.toml file + run: PL_BACKEND="${{ matrix.pl_backend }}" python scripts/configure_pyproject_toml.py + + - uses: docker/setup-qemu-action@v3 + name: Set up QEMU + + - name: Build wheels + env: + CIBW_ARCHS_LINUX: ${{matrix.arch}} + + CIBW_BUILD: ${{ matrix.cibw_build }} + + CIBW_SKIP: "*-musllinux*" + + # Python build settings + CIBW_BEFORE_BUILD: | + python -m pip install ninja cmake~=3.27.0 auditwheel~=5.0 custatevec-cu${{ matrix.cuda_version }} cutensornet-cu${{ matrix.cuda_version }} + dnf clean all -y + dnf install gcc-toolset-12 dnf-utils -y + source /opt/rh/gcc-toolset-12/enable -y + yum-config-manager --add-repo http://developer.download.nvidia.com/compute/cuda/repos/rhel8/sbsa/cuda-rhel8.repo -y + dnf -y install cuda-toolkit-${{ matrix.cuda_version }}-0 git + + # ensure nvcc is available + CIBW_ENVIRONMENT: | + PATH=/opt/rh/gcc-toolset-12/root/usr/bin:$PATH:/usr/local/cuda-${{ matrix.cuda_version }}/bin \ + LD_LIBRARY_PATH=/opt/rh/gcc-toolset-12/root/usr/lib64:/opt/rh/gcc-toolset-12/root/usr/lib:/opt/rh/gcc-toolset-12/root/usr/lib64/dyninst:/opt/rh/gcc-toolset-12/root/usr/lib/dyninst:$LD_LIBRARY_PATH:/usr/local/cuda-${{ matrix.cuda_version }}/lib64 \ + PKG_CONFIG_PATH=/opt/rh/gcc-toolset-12/root/usr/lib64/pkgconfig:$PKG_CONFIG_PATH \ + CMAKE_ARGS="-DCMAKE_BUILD_TYPE=Release" + + CIBW_REPAIR_WHEEL_COMMAND_LINUX: "./bin/auditwheel repair -w {dest_dir} {wheel}" + + CIBW_MANYLINUX_AARCH64_IMAGE: manylinux_2_28 + + CIBW_BUILD_VERBOSITY: 3 + + run: python3 -m cibuildwheel --output-dir wheelhouse + + - name: Validate wheels + run: | + python3 -m pip install twine + python3 -m twine check ./wheelhouse/*.whl + + - uses: actions-ecosystem/action-regex-match@main + id: rc_build + with: + text: ${{ github.event.pull_request.head.ref }} + regex: '.*[0-9]+.[0-9]+.[0-9]+[-_]?rc[0-9]+' + + - uses: actions/upload-artifact@v4 + if: | + github.event_name == 'release' || + github.event_name == 'workflow_dispatch' || + github.ref == 'refs/heads/master' + with: + name: ${{ runner.os }}-wheels-${{ matrix.pl_backend }}-${{ fromJson('{ "cp310-*":"py310","cp311-*":"py311","cp312-*":"py312" }')[matrix.cibw_build] }}-${{ matrix.arch }}-cu${{ matrix.cuda_version }}.zip + path: ./wheelhouse/*.whl + retention-days: 1 + include-hidden-files: true + + upload-pypi: + needs: [set_wheel_build_matrix, linux-wheels-aarch64] + strategy: + matrix: + arch: [aarch64] + pl_backend: ["lightning_gpu", "lightning_tensor"] + cuda_version: ["12"] + cibw_build: ${{ fromJson(needs.set_wheel_build_matrix.outputs.python_version) }} + runs-on: ubuntu-latest + permissions: + id-token: write + if: | + github.event_name == 'release' || + github.ref == 'refs/heads/master' + + steps: + - uses: actions/download-artifact@v4 + with: + name: ${{ runner.os }}-wheels-${{ matrix.pl_backend }}-${{ fromJson('{ "cp310-*":"py310","cp311-*":"py311","cp312-*":"py312" }')[matrix.cibw_build] }}-${{ matrix.arch }}-cu${{ matrix.cuda_version }}.zip + path: dist + + - name: Upload wheels to TestPyPI + uses: pypa/gh-action-pypi-publish@release/v1 + with: + verbose: true + repository-url: https://test.pypi.org/legacy/ diff --git a/.github/workflows/wheel_linux_ppc64le.yml b/.github/workflows/wheel_linux_ppc64le.yml deleted file mode 100644 index d305e53342..0000000000 --- a/.github/workflows/wheel_linux_ppc64le.yml +++ /dev/null @@ -1,191 +0,0 @@ -name: Wheel::Linux::PowerPC - -# **What it does**: Builds python wheels for Linux (ubuntu-latest) architecture PowerPC 64 and store it as artifacts. -# Python versions: 3.9, 3.10, 3.11, 3.12. -# **Why we have it**: To build wheels for pennylane-lightning installation. -# **Who does it impact**: Wheels to be uploaded to PyPI. - -on: - pull_request: - push: - branches: - - master - release: - types: [published] - workflow_dispatch: - -concurrency: - group: wheel_linux_ppc64le-${{ github.ref }} - cancel-in-progress: true - -jobs: - set_wheel_build_matrix: - if: | - github.event_name != 'pull_request' || - contains(github.event.pull_request.labels.*.name, 'ci:build_wheels') || - github.event_name == 'workflow_dispatch' - name: "Set wheel build matrix" - uses: ./.github/workflows/set_wheel_build_matrix.yml - with: - event_name: ${{ github.event_name }} - - build_dependencies: - needs: [set_wheel_build_matrix] - strategy: - matrix: - os: [ubuntu-latest] - arch: [ppc64le] - exec_model: ${{ fromJson(needs.set_wheel_build_matrix.outputs.exec_model) }} - kokkos_version: ${{ fromJson(needs.set_wheel_build_matrix.outputs.kokkos_version) }} - container_img: ["quay.io/pypa/manylinux_2_28_ppc64le"] - timeout-minutes: 45 - name: Kokkos core (${{ matrix.exec_model }}::${{ matrix.arch }}) - runs-on: ${{ matrix.os }} - - steps: - - name: Cache installation directories - id: kokkos-cache - uses: actions/cache@v4 - with: - path: ${{ github.workspace }}/Kokkos_install/${{ matrix.exec_model }} - key: ${{ matrix.container_img }}-kokkos${{ matrix.kokkos_version }}-${{ matrix.exec_model }} - - - name: Clone Kokkos libs - if: steps.kokkos-cache.outputs.cache-hit != 'true' - run: | - git clone --branch ${{ matrix.kokkos_version }} https://github.com/kokkos/kokkos.git - pushd . &> /dev/null - - - uses: docker/setup-qemu-action@v2 - name: Set up QEMU - - - name: Build Kokkos core library - if: steps.kokkos-cache.outputs.cache-hit != 'true' - run: | - mkdir -p ${{ github.workspace }}/Kokkos_install/${{ matrix.exec_model }} - cd kokkos - docker run --platform linux/ppc64le \ - -v /var/run/docker.sock:/var/run/docker.sock \ - -v `pwd`:/io \ - -v ${{ github.workspace }}/Kokkos_install/${{ matrix.exec_model }}:/install \ - -i ${{ matrix.container_img }} \ - bash -c "git config --global --add safe.directory /io && \ - cd /io && \ - python3.9 -m pip install ninja && \ - ln -s /opt/python/cp39-cp39/bin/ninja /usr/bin/ninja && \ - cmake -BBuild . -DCMAKE_INSTALL_PREFIX=/install \ - -DKokkos_ENABLE_COMPLEX_ALIGN=OFF \ - -DKokkos_ENABLE_SERIAL=ON \ - -DKokkos_ENABLE_${{ matrix.exec_model }}=ON \ - -DKokkos_ENABLE_DEPRECATION_WARNINGS=OFF \ - -DCMAKE_CXX_STANDARD=20 \ - -DCMAKE_POSITION_INDEPENDENT_CODE=ON \ - -G Ninja && \ - cmake --build ./Build --verbose && \ - cmake --install ./Build; " - cd - - - linux-wheels-ppc64le: - needs: [set_wheel_build_matrix, build_dependencies] - strategy: - fail-fast: false - matrix: - os: [ubuntu-latest] - arch: [ppc64le] - pl_backend: ["lightning_kokkos", "lightning_qubit"] - cibw_build: ${{fromJson(needs.set_wheel_build_matrix.outputs.python_version)}} - exec_model: ${{ fromJson(needs.set_wheel_build_matrix.outputs.exec_model) }} - kokkos_version: ${{ fromJson(needs.set_wheel_build_matrix.outputs.kokkos_version) }} - container_img: ["quay.io/pypa/manylinux_2_28_ppc64le"] - timeout-minutes: 45 - name: ${{ matrix.os }}::${{ matrix.arch }} - ${{ matrix.pl_backend }} (Python ${{ fromJson('{"cp39-*":"3.9","cp310-*":"3.10","cp311-*":"3.11", "cp312-*":"3.12"}')[matrix.cibw_build] }}) - runs-on: ${{ matrix.os }} - - steps: - - name: Checkout PennyLane-Lightning - uses: actions/checkout@v4 - - - name: Restoring cached dependencies - id: kokkos-cache - uses: actions/cache@v4 - with: - path: ${{ github.workspace }}/Kokkos_install/${{ matrix.exec_model }} - key: ${{ matrix.container_img }}-kokkos${{ matrix.kokkos_version }}-${{ matrix.exec_model }} - - - name: Copy cached libraries - if: steps.kokkos-cache.outputs.cache-hit == 'true' - run: | - mkdir Kokkos - cp -rf ${{ github.workspace }}/Kokkos_install/${{ matrix.exec_model }}/* Kokkos/ - - - name: Install cibuildwheel - run: python -m pip install cibuildwheel~=2.16.0 - - - uses: docker/setup-qemu-action@v2 - name: Set up QEMU - - - name: Build wheels - env: - CIBW_ARCHS_LINUX: ${{matrix.arch}} - - CIBW_BUILD: ${{ matrix.cibw_build }} - - CIBW_SKIP: "*-musllinux*" - - # Python build settings - CIBW_BEFORE_BUILD: | - cat /etc/yum.conf | sed "s/\[main\]/\[main\]\ntimeout=5/g" > /etc/yum.conf - python -m pip install ninja cmake~=3.24.0 - - CIBW_ENVIRONMENT: PL_BACKEND="${{ matrix.pl_backend }}" CMAKE_ARGS="-DENABLE_LAPACK=OFF" - - CIBW_MANYLINUX_PPC64LE_IMAGE: manylinux_2_28 - - CIBW_BUILD_VERBOSITY: 3 - - run: python3 -m cibuildwheel --output-dir wheelhouse - - - name: Validate wheels - run: | - python3 -m pip install twine - python3 -m twine check ./wheelhouse/*.whl - - - uses: actions-ecosystem/action-regex-match@main - id: rc_build - with: - text: ${{ github.event.pull_request.head.ref }} - regex: '.*[0-9]+.[0-9]+.[0-9]+[-_]?rc[0-9]+' - - - uses: actions/upload-artifact@v3 - if: | - github.event_name == 'release' || - github.event_name == 'workflow_dispatch' || - github.ref == 'refs/heads/master' || - steps.rc_build.outputs.match != '' - with: - name: ${{ runner.os }}-wheels-${{ matrix.pl_backend }}-${{ matrix.arch }}.zip - path: ./wheelhouse/*.whl - - upload-pypi: - needs: linux-wheels-ppc64le - strategy: - matrix: - arch: [ppc64le] - pl_backend: ["lightning_qubit"] - runs-on: ubuntu-latest - if: | - github.event_name == 'release' || - github.ref == 'refs/heads/master' - - steps: - - uses: actions/download-artifact@v3 - with: - name: Linux-wheels-${{ matrix.pl_backend }}-${{ matrix.arch }}.zip - path: dist - - - name: Upload wheels to PyPI - uses: pypa/gh-action-pypi-publish@release/v1 - with: - user: __token__ - password: ${{ secrets.TEST_PYPI_API_TOKEN }} - repository-url: https://test.pypi.org/legacy/ \ No newline at end of file diff --git a/.github/workflows/wheel_linux_x86_64.yml b/.github/workflows/wheel_linux_x86_64.yml index f91ff34e88..d87dcb5226 100644 --- a/.github/workflows/wheel_linux_x86_64.yml +++ b/.github/workflows/wheel_linux_x86_64.yml @@ -1,7 +1,7 @@ name: Wheel::Linux::x86_64 # **What it does**: Builds python wheels for Linux (ubuntu-latest) architecture x86_64 and store it as artifacts. -# Python versions: 3.9, 3.10, 3.11, 3.12. +# Python versions: 3.10, 3.11, 3.12. # **Why we have it**: To build wheels for pennylane-lightning installation. # **Who does it impact**: Wheels to be uploaded to PyPI. @@ -10,6 +10,11 @@ env: on: pull_request: + types: + - opened + - reopened + - synchronize + - ready_for_review push: branches: - master @@ -25,21 +30,14 @@ jobs: set_wheel_build_matrix: if: | github.event_name != 'pull_request' || - contains(github.event.pull_request.labels.*.name, 'ci:build_wheels') || - github.event_name == 'workflow_dispatch' + (github.event.pull_request.draft == false && contains(github.event.pull_request.labels.*.name, 'ci:build_wheels')) name: "Set wheel build matrix" uses: ./.github/workflows/set_wheel_build_matrix.yml with: event_name: ${{ github.event_name }} - determine_runner: - name: Determine runner type to use - uses: ./.github/workflows/determine-workflow-runner.yml - with: - default_runner: ubuntu-latest - build_dependencies: - needs: [set_wheel_build_matrix, determine_runner] + needs: [set_wheel_build_matrix] strategy: matrix: exec_model: ${{ fromJson(needs.set_wheel_build_matrix.outputs.exec_model) }} @@ -47,13 +45,13 @@ jobs: container_img: ["quay.io/pypa/manylinux_2_28_x86_64"] timeout-minutes: 30 name: Kokkos core (${{ matrix.exec_model }}) - runs-on: ${{ needs.determine_runner.outputs.runner_group }} + runs-on: pl-4-core-large-runner container: ${{ matrix.container_img }} steps: - name: Cache installation directories id: kokkos-cache - uses: actions/cache@v3 + uses: actions/cache@v4 with: path: /root/Kokkos_install/${{ matrix.exec_model }} key: ${{ matrix.container_img }}-kokkos${{ matrix.kokkos_version }}-${{ matrix.exec_model }} @@ -95,7 +93,7 @@ jobs: cd - linux-wheels-x86-64: - needs: [set_wheel_build_matrix, build_dependencies, determine_runner] + needs: [set_wheel_build_matrix, build_dependencies] strategy: fail-fast: false matrix: @@ -106,8 +104,8 @@ jobs: kokkos_version: ${{ fromJson(needs.set_wheel_build_matrix.outputs.kokkos_version) }} container_img: ["quay.io/pypa/manylinux_2_28_x86_64"] timeout-minutes: 30 - name: Linux::${{ matrix.arch }} - ${{ matrix.pl_backend }} (Python ${{ fromJson('{ "cp39-*":"3.9","cp310-*":"3.10","cp311-*":"3.11","cp312-*":"3.12" }')[matrix.cibw_build] }}) - runs-on: ${{ needs.determine_runner.outputs.runner_group }} + name: Linux::${{ matrix.arch }} - ${{ matrix.pl_backend }} (Python ${{ fromJson('{ "cp310-*":"3.10","cp311-*":"3.11","cp312-*":"3.12" }')[matrix.cibw_build] }}) + runs-on: pl-4-core-large-runner container: image: ${{ matrix.container_img }} options: --privileged @@ -115,13 +113,13 @@ jobs: steps: - name: Restoring cached dependencies id: kokkos-cache - uses: actions/cache@v3 + uses: actions/cache@v4 with: path: /root/Kokkos_install/${{ matrix.exec_model }} key: ${{ matrix.container_img }}-kokkos${{ matrix.kokkos_version }}-${{ matrix.exec_model }} - name: Checkout PennyLane-Lightning - uses: actions/checkout@v3 + uses: actions/checkout@v4 - name: Copy cached libraries if: steps.kokkos-cache.outputs.cache-hit == 'true' @@ -134,8 +132,11 @@ jobs: run: | dnf update -y && dnf install -y podman - - name: Install cibuildwheel - run: python3.9 -m pip install cibuildwheel~=2.16.0 + - name: Install dependencies + run: python3.10 -m pip install cibuildwheel~=2.20.0 tomlkit + + - name: Configure pyproject.toml file + run: PL_BACKEND="${{ matrix.pl_backend }}" python3.10 scripts/configure_pyproject_toml.py - name: Build wheels env: @@ -150,19 +151,22 @@ jobs: # Python build settings CIBW_BEFORE_BUILD: | cat /etc/dnf.conf | sed "s/\[main\]/\[main\]\ntimeout=5/g" > /etc/dnf.conf - python -m pip install ninja cmake~=3.24.0 scipy + python -m pip install ninja cmake~=3.27.0 dnf clean all -y dnf install gcc-toolset-13 -y source /opt/rh/gcc-toolset-13/enable -y PATH="/opt/rh/gcc-toolset-13/root/usr/bin:$PATH" CIBW_ENVIRONMENT: | - PATH="/opt/rh/gcc-toolset-13/root/usr/bin:$PATH" \ - PL_BACKEND="${{ matrix.pl_backend }}" + PATH="/opt/rh/gcc-toolset-13/root/usr/bin:$PATH" CMAKE_ARGS="-DCMAKE_BUILD_TYPE=Release" CIBW_BEFORE_TEST: | python -m pip install -r requirements-tests.txt - if ${{ matrix.pl_backend == 'lightning_kokkos'}}; then SKIP_COMPILATION=True PL_BACKEND="lightning_qubit" python -m pip install . -vv; fi + if ${{ matrix.pl_backend == 'lightning_kokkos'}} + then + PL_BACKEND="lightning_qubit" python scripts/configure_pyproject_toml.py + SKIP_COMPILATION=True python -m pip install . -vv + fi CIBW_TEST_COMMAND: | DEVICENAME=`echo ${{ matrix.pl_backend }} | sed "s/_/./g"` @@ -172,12 +176,12 @@ jobs: CIBW_BUILD_VERBOSITY: 3 - run: python3.9 -m cibuildwheel --output-dir wheelhouse + run: python3.10 -m cibuildwheel --output-dir wheelhouse - name: Validate wheels run: | - python3.9 -m pip install twine - python3.9 -m twine check ./wheelhouse/*.whl + python3.10 -m pip install twine + python3.10 -m twine check ./wheelhouse/*.whl - uses: actions-ecosystem/action-regex-match@main id: rc_build @@ -185,36 +189,40 @@ jobs: text: ${{ github.event.pull_request.head.ref }} regex: '.*[0-9]+.[0-9]+.[0-9]+[-_]?rc[0-9]+' - - uses: actions/upload-artifact@v3 + - uses: actions/upload-artifact@v4 if: | github.event_name == 'release' || github.event_name == 'workflow_dispatch' || github.ref == 'refs/heads/master' || steps.rc_build.outputs.match != '' with: - name: ${{ runner.os }}-wheels-${{ matrix.pl_backend }}-${{ matrix.arch }}.zip + name: ${{ runner.os }}-wheels-${{ matrix.pl_backend }}-${{ fromJson('{ "cp310-*":"py310","cp311-*":"py311","cp312-*":"py312" }')[matrix.cibw_build] }}-${{ matrix.arch }}.zip path: ./wheelhouse/*.whl + retention-days: 1 + include-hidden-files: true upload-pypi: - needs: linux-wheels-x86-64 + needs: [set_wheel_build_matrix, linux-wheels-x86-64] strategy: matrix: arch: [x86_64] pl_backend: ["lightning_qubit", "lightning_kokkos"] + cibw_build: ${{ fromJson(needs.set_wheel_build_matrix.outputs.python_version) }} runs-on: ubuntu-latest + permissions: + id-token: write if: | github.event_name == 'release' || github.ref == 'refs/heads/master' steps: - - uses: actions/download-artifact@v3 + - uses: actions/download-artifact@v4 with: - name: Linux-wheels-${{ matrix.pl_backend }}-${{ matrix.arch }}.zip + name: ${{ runner.os }}-wheels-${{ matrix.pl_backend }}-${{ fromJson('{ "cp310-*":"py310","cp311-*":"py311","cp312-*":"py312" }')[matrix.cibw_build] }}-${{ matrix.arch }}.zip path: dist - - name: Upload wheels to PyPI + - name: Upload wheels to TestPyPI uses: pypa/gh-action-pypi-publish@release/v1 with: - user: __token__ - password: ${{ ( matrix.pl_backend == 'lightning_qubit' ) && secrets.TEST_PYPI_API_TOKEN || secrets.TEST_PYPI_LKOKKOS_TOKEN }} + verbose: true repository-url: https://test.pypi.org/legacy/ diff --git a/.github/workflows/wheel_linux_x86_64_cuda.yml b/.github/workflows/wheel_linux_x86_64_cuda.yml index 6a5a7a42c2..19487778f7 100644 --- a/.github/workflows/wheel_linux_x86_64_cuda.yml +++ b/.github/workflows/wheel_linux_x86_64_cuda.yml @@ -1,15 +1,16 @@ name: Wheel::Linux::x86_64::CUDA # **What it does**: Builds python wheels for Linux (ubuntu-latest) architecture x86_64 and store it as artifacts. -# Python versions: 3.9, 3.10, 3.11, 3.12. +# Python versions: 3.10, 3.11, 3.12. # **Why we have it**: To build wheels for pennylane-lightning-gpu installation. # **Who does it impact**: Wheels to be uploaded to PyPI. - -env: - GCC_VERSION: 12 - on: pull_request: + types: + - opened + - reopened + - synchronize + - ready_for_review push: branches: - master @@ -24,49 +25,44 @@ concurrency: jobs: set_wheel_build_matrix: if: | - github.event_name != 'pull_request' || - contains(github.event.pull_request.labels.*.name, 'ci:build_wheels') || - github.event_name == 'workflow_dispatch' + github.event_name != 'pull_request' || + (github.event.pull_request.draft == false && contains(github.event.pull_request.labels.*.name, 'ci:build_wheels')) name: "Set wheel build matrix" uses: ./.github/workflows/set_wheel_build_matrix.yml with: event_name: ${{ github.event_name }} - determine_runner: - name: Determine runner type to use - uses: ./.github/workflows/determine-workflow-runner.yml - with: - default_runner: ubuntu-latest - - linux-wheels-x86-64: - needs: [set_wheel_build_matrix, determine_runner] + needs: [set_wheel_build_matrix] strategy: fail-fast: false matrix: arch: [x86_64] - pl_backend: ["lightning_gpu"] + pl_backend: ["lightning_gpu", "lightning_tensor"] cuda_version: ["12"] cibw_build: ${{ fromJson(needs.set_wheel_build_matrix.outputs.python_version) }} container_img: ["quay.io/pypa/manylinux_2_28_x86_64"] timeout-minutes: 30 - name: Linux::${{ matrix.arch }} - ${{ matrix.pl_backend }} CUDA ${{ matrix.cuda_version }} (Python ${{ fromJson('{ "cp39-*":"3.9","cp310-*":"3.10","cp311-*":"3.11","cp312-*":"3.12" }')[matrix.cibw_build] }}) - runs-on: ${{ needs.determine_runner.outputs.runner_group }} + name: Linux::${{ matrix.arch }} - ${{ matrix.pl_backend }} CUDA ${{ matrix.cuda_version }} (Python ${{ fromJson('{"cp310-*":"3.10","cp311-*":"3.11","cp312-*":"3.12" }')[matrix.cibw_build] }}) + runs-on: pl-4-core-large-runner container: image: ${{ matrix.container_img }} options: --privileged steps: - name: Checkout PennyLane-Lightning - uses: actions/checkout@v3 + uses: actions/checkout@v4 - name: Install dependencies (AlmaLinux) if: ${{ (matrix.container_img == 'quay.io/pypa/manylinux_2_28_x86_64') }} run: | dnf update -y && dnf install -y podman - - name: Install cibuildwheel - run: python3.9 -m pip install cibuildwheel~=2.16.0 + - name: Install dependencies + run: python3.10 -m pip install cibuildwheel~=2.20.0 tomlkit + + - name: Configure pyproject.toml file + run: PL_BACKEND="${{ matrix.pl_backend }}" python3.10 scripts/configure_pyproject_toml.py - name: Build wheels env: @@ -78,23 +74,21 @@ jobs: CIBW_SKIP: "*-musllinux*" - CIBW_CONFIG_SETTINGS: --global-option=build_ext --global-option=--define="CMAKE_CXX_COMPILER=$(which g++);CMAKE_C_COMPILER=$(which gcc);LIGHTNING_RELEASE_TAG=master" - # Python build settings CIBW_BEFORE_BUILD: | - python -m pip install ninja cmake~=3.24.3 auditwheel~=5.0 custatevec-cu${{ matrix.cuda_version }} scipy + python -m pip install ninja cmake~=3.27.0 auditwheel~=5.0 custatevec-cu${{ matrix.cuda_version }} cutensornet-cu${{ matrix.cuda_version }} dnf clean all -y dnf install gcc-toolset-12 dnf-utils -y source /opt/rh/gcc-toolset-12/enable -y yum-config-manager --add-repo http://developer.download.nvidia.com/compute/cuda/repos/rhel8/x86_64/cuda-rhel8.repo -y - dnf -y install cuda-toolkit-${{ matrix.cuda_version }}-0 git openssh wget + dnf -y install cuda-toolkit-${{ matrix.cuda_version }}-0 git # ensure nvcc is available CIBW_ENVIRONMENT: | PATH=/opt/rh/gcc-toolset-12/root/usr/bin:$PATH:/usr/local/cuda-${{ matrix.cuda_version }}/bin \ LD_LIBRARY_PATH=/opt/rh/gcc-toolset-12/root/usr/lib64:/opt/rh/gcc-toolset-12/root/usr/lib:/opt/rh/gcc-toolset-12/root/usr/lib64/dyninst:/opt/rh/gcc-toolset-12/root/usr/lib/dyninst:$LD_LIBRARY_PATH:/usr/local/cuda-${{ matrix.cuda_version }}/lib64 \ - PL_BACKEND="${{ matrix.pl_backend }}" \ - PKG_CONFIG_PATH=/opt/rh/gcc-toolset-12/root/usr/lib64/pkgconfig:$PKG_CONFIG_PATH + PKG_CONFIG_PATH=/opt/rh/gcc-toolset-12/root/usr/lib64/pkgconfig:$PKG_CONFIG_PATH \ + CMAKE_ARGS="-DCMAKE_BUILD_TYPE=Release" CIBW_REPAIR_WHEEL_COMMAND_LINUX: "./bin/auditwheel repair -w {dest_dir} {wheel}" @@ -102,12 +96,12 @@ jobs: CIBW_BUILD_VERBOSITY: 3 - run: python3.9 -m cibuildwheel --output-dir wheelhouse + run: python3.10 -m cibuildwheel --output-dir wheelhouse - name: Validate wheels run: | - python3.9 -m pip install twine - python3.9 -m twine check ./wheelhouse/*.whl + python3.10 -m pip install twine + python3.10 -m twine check ./wheelhouse/*.whl - uses: actions-ecosystem/action-regex-match@main id: rc_build @@ -115,37 +109,41 @@ jobs: text: ${{ github.event.pull_request.head.ref }} regex: '.*[0-9]+.[0-9]+.[0-9]+[-_]?rc[0-9]+' - - uses: actions/upload-artifact@v3 + - uses: actions/upload-artifact@v4 if: | github.event_name == 'release' || github.event_name == 'workflow_dispatch' || github.ref == 'refs/heads/master' || steps.rc_build.outputs.match != '' with: - name: ${{ runner.os }}-wheels-${{ matrix.pl_backend }}-${{ matrix.arch }}-cu${{ matrix.cuda_version }} + name: ${{ runner.os }}-wheels-${{ matrix.pl_backend }}-${{ fromJson('{ "cp310-*":"py310","cp311-*":"py311","cp312-*":"py312" }')[matrix.cibw_build] }}-${{ matrix.arch }}-cu${{ matrix.cuda_version }}.zip path: ./wheelhouse/*.whl + retention-days: 1 + include-hidden-files: true upload-pypi: - needs: linux-wheels-x86-64 + needs: [set_wheel_build_matrix, linux-wheels-x86-64] strategy: matrix: arch: [x86_64] - pl_backend: ["lightning_gpu"] + pl_backend: ["lightning_gpu", "lightning_tensor"] cuda_version: ["12"] + cibw_build: ${{ fromJson(needs.set_wheel_build_matrix.outputs.python_version) }} runs-on: ubuntu-latest + permissions: + id-token: write if: | github.event_name == 'release' || github.ref == 'refs/heads/master' steps: - - uses: actions/download-artifact@v3 + - uses: actions/download-artifact@v4 with: - name: ${{ runner.os }}-wheels-${{ matrix.pl_backend }}-${{ matrix.arch }}-cu${{ matrix.cuda_version }} + name: ${{ runner.os }}-wheels-${{ matrix.pl_backend }}-${{ fromJson('{ "cp310-*":"py310","cp311-*":"py311","cp312-*":"py312" }')[matrix.cibw_build] }}-${{ matrix.arch }}-cu${{ matrix.cuda_version }}.zip path: dist - - name: Upload wheels to PyPI + - name: Upload wheels to TestPyPI uses: pypa/gh-action-pypi-publish@release/v1 with: - user: __token__ - password: ${{ secrets.TEST_PYPI_LGPU_TOKEN }} - repository_url: https://test.pypi.org/legacy/ + verbose: true + repository-url: https://test.pypi.org/legacy/ diff --git a/.github/workflows/wheel_macos_arm64.yml b/.github/workflows/wheel_macos_arm64.yml index 5b1b20d3d0..092ce88441 100644 --- a/.github/workflows/wheel_macos_arm64.yml +++ b/.github/workflows/wheel_macos_arm64.yml @@ -1,12 +1,17 @@ name: Wheel::MacOS::ARM # **What it does**: Builds python wheels for MacOS (11) architecture ARM 64 and store it as artifacts. -# Python versions: 3.9, 3.10, 3.11, 3.12. +# Python versions: 3.10, 3.11, 3.12. # **Why we have it**: To build wheels for pennylane-lightning installation. # **Who does it impact**: Wheels to be uploaded to PyPI. on: pull_request: + types: + - opened + - reopened + - synchronize + - ready_for_review push: branches: - master @@ -16,8 +21,9 @@ on: env: ARCHS: 'arm64' - PYTHON3_MIN_VERSION: "9" + PYTHON3_MIN_VERSION: "10" PYTHON3_MAX_VERSION: "12" + MACOSX_DEPLOYMENT_TARGET: 13.0 concurrency: group: wheel_macos_arm64-${{ github.ref }} @@ -27,8 +33,7 @@ jobs: mac-set-matrix-arm: if: | github.event_name != 'pull_request' || - contains(github.event.pull_request.labels.*.name, 'ci:build_wheels') || - github.event_name == 'workflow_dispatch' + (github.event.pull_request.draft == false && contains(github.event.pull_request.labels.*.name, 'ci:build_wheels')) timeout-minutes: 30 name: Set builder matrix runs-on: ubuntu-latest @@ -58,12 +63,12 @@ jobs: strategy: fail-fast: false matrix: - os: [macos-12] + os: [macos-13] arch: [arm64] pl_backend: ["lightning_kokkos", "lightning_qubit"] cibw_build: ${{fromJson(needs.mac-set-matrix-arm.outputs.python_version)}} timeout-minutes: 30 - name: macos-latest::arm64 - ${{ matrix.pl_backend }} (Python ${{ fromJson('{ "cp39-*":"3.9","cp310-*":"3.10","cp311-*":"3.11","cp312-*":"3.12" }')[matrix.cibw_build] }}) + name: macos-latest::arm64 - ${{ matrix.pl_backend }} (Python ${{ fromJson('{ "cp310-*":"3.10","cp311-*":"3.11","cp312-*":"3.12" }')[matrix.cibw_build] }}) runs-on: ${{ matrix.os }} steps: @@ -73,10 +78,13 @@ jobs: - uses: actions/setup-python@v5 name: Install Python with: - python-version: '3.9' + python-version: '3.10' - - name: Install cibuildwheel - run: python -m pip install cibuildwheel~=2.16.0 + - name: Install dependencies + run: python -m pip install cibuildwheel~=2.20.0 tomlkit + + - name: Configure pyproject.toml file + run: PL_BACKEND="${{ matrix.pl_backend }}" python scripts/configure_pyproject_toml.py - name: Build wheels env: @@ -88,16 +96,18 @@ jobs: # Python build settings CIBW_BEFORE_BUILD: | - python -m pip install pybind11 ninja cmake~=3.24.0 setuptools scipy + python -m pip install pybind11 ninja cmake~=3.27.0 setuptools CIBW_ENVIRONMENT: | - CMAKE_ARGS="-DCMAKE_CXX_COMPILER_TARGET=arm64-apple-macos11 -DCMAKE_SYSTEM_NAME=Darwin -DCMAKE_SYSTEM_PROCESSOR=ARM64 -DENABLE_OPENMP=OFF" \ - PL_BACKEND="${{ matrix.pl_backend }}" + CMAKE_ARGS="-DCMAKE_CXX_COMPILER_TARGET=arm64-apple-macos11 -DCMAKE_SYSTEM_NAME=Darwin -DCMAKE_SYSTEM_PROCESSOR=ARM64 -DENABLE_OPENMP=OFF -DCMAKE_BUILD_TYPE=Release" CIBW_BEFORE_TEST: | python -m pip install -r requirements-tests.txt - if ${{ matrix.pl_backend == 'lightning_kokkos'}}; then SKIP_COMPILATION=True PL_BACKEND="lightning_qubit" python -m pip install . -vv; fi - + if ${{ matrix.pl_backend == 'lightning_kokkos'}} + then + PL_BACKEND="lightning_qubit" python scripts/configure_pyproject_toml.py + SKIP_COMPILATION=True python -m pip install . -vv + fi CIBW_TEST_COMMAND: | DEVICENAME=`echo ${{ matrix.pl_backend }} | sed "s/_/./g"` pl-device-test --device=${DEVICENAME} --skip-ops -x --tb=short --no-flaky-report @@ -114,35 +124,39 @@ jobs: python -m pip install twine python -m twine check ./wheelhouse/*.whl - - uses: actions/upload-artifact@v3 + - uses: actions/upload-artifact@v4 if: | github.event_name == 'release' || github.event_name == 'workflow_dispatch' || github.ref == 'refs/heads/master' with: - name: ${{ runner.os }}-wheels-${{ matrix.pl_backend }}-${{ matrix.arch }}.zip + name: ${{ runner.os }}-wheels-${{ matrix.pl_backend }}-${{ fromJson('{ "cp310-*":"py310","cp311-*":"py311","cp312-*":"py312" }')[matrix.cibw_build] }}-${{ matrix.arch }}.zip path: ./wheelhouse/*.whl + retention-days: 1 + include-hidden-files: true upload-pypi: - needs: mac-wheels-arm64 + needs: [mac-set-matrix-arm, mac-wheels-arm64] strategy: matrix: arch: [arm64] pl_backend: ["lightning_qubit", "lightning_kokkos"] + cibw_build: ${{ fromJson(needs.mac-set-matrix-arm.outputs.python_version) }} runs-on: ubuntu-latest + permissions: + id-token: write if: | github.event_name == 'release' || github.ref == 'refs/heads/master' steps: - - uses: actions/download-artifact@v3 + - uses: actions/download-artifact@v4 with: - name: macOS-wheels-${{ matrix.pl_backend }}-${{ matrix.arch }}.zip + name: macOS-wheels-${{ matrix.pl_backend }}-${{ fromJson('{ "cp310-*":"py310","cp311-*":"py311","cp312-*":"py312" }')[matrix.cibw_build] }}-${{ matrix.arch }}.zip path: dist - - name: Upload wheels to PyPI + - name: Upload wheels to TestPyPI uses: pypa/gh-action-pypi-publish@release/v1 with: - user: __token__ - password: ${{ ( matrix.pl_backend == 'lightning_qubit' ) && secrets.TEST_PYPI_API_TOKEN || secrets.TEST_PYPI_LKOKKOS_TOKEN }} + verbose: true repository-url: https://test.pypi.org/legacy/ diff --git a/.github/workflows/wheel_macos_x86_64.yml b/.github/workflows/wheel_macos_x86_64.yml index f3aa0d9008..74a35f5aa9 100644 --- a/.github/workflows/wheel_macos_x86_64.yml +++ b/.github/workflows/wheel_macos_x86_64.yml @@ -1,12 +1,17 @@ name: Wheel::MacOS::Intel # **What it does**: Builds python wheels for MacOS (10.15) architecture x86_64 and store it as artifacts. -# Python versions: 3.9, 3.10, 3.11, 3.12. +# Python versions: 3.10, 3.11, 3.12. # **Why we have it**: To build wheels for pennylane-lightning installation. # **Who does it impact**: Wheels to be uploaded to PyPI. on: pull_request: + types: + - opened + - reopened + - synchronize + - ready_for_review push: branches: - master @@ -15,7 +20,7 @@ on: workflow_dispatch: env: - MACOSX_DEPLOYMENT_TARGET: 10.15 + MACOSX_DEPLOYMENT_TARGET: 13.0 concurrency: group: wheel_macos_x86_64-${{ github.ref }} @@ -25,8 +30,7 @@ jobs: set_wheel_build_matrix: if: | github.event_name != 'pull_request' || - contains(github.event.pull_request.labels.*.name, 'ci:build_wheels') || - github.event_name == 'workflow_dispatch' + (github.event.pull_request.draft == false && contains(github.event.pull_request.labels.*.name, 'ci:build_wheels')) name: "Set wheel build matrix" uses: ./.github/workflows/set_wheel_build_matrix.yml with: @@ -36,7 +40,7 @@ jobs: needs: [set_wheel_build_matrix] strategy: matrix: - os: [macos-12] + os: [macos-13] arch: [x86_64] exec_model: ${{ fromJson(needs.set_wheel_build_matrix.outputs.exec_model) }} kokkos_version: ${{ fromJson(needs.set_wheel_build_matrix.outputs.kokkos_version) }} @@ -65,7 +69,7 @@ jobs: - uses: actions/setup-python@v5 name: Install Python with: - python-version: '3.9' + python-version: '3.10' - name: Build Kokkos core library if: steps.kokkos-cache.outputs.cache-hit != 'true' @@ -93,14 +97,14 @@ jobs: strategy: fail-fast: false matrix: - os: [macos-12] + os: [macos-13] arch: [x86_64] pl_backend: ["lightning_kokkos", "lightning_qubit"] cibw_build: ${{fromJson(needs.set_wheel_build_matrix.outputs.python_version)}} exec_model: ${{ fromJson(needs.set_wheel_build_matrix.outputs.exec_model) }} kokkos_version: ${{ fromJson(needs.set_wheel_build_matrix.outputs.kokkos_version) }} timeout-minutes: 30 - name: ${{ matrix.os }} - ${{ matrix.pl_backend }} (Python ${{ fromJson('{ "cp39-*":"3.9","cp310-*":"3.10","cp311-*":"3.11","cp312-*":"3.12" }')[matrix.cibw_build] }}) + name: ${{ matrix.os }} - ${{ matrix.pl_backend }} (Python ${{ fromJson('{ "cp310-*":"3.10","cp311-*":"3.11","cp312-*":"3.12" }')[matrix.cibw_build] }}) runs-on: ${{ matrix.os }} steps: @@ -122,10 +126,13 @@ jobs: - uses: actions/setup-python@v5 name: Install Python with: - python-version: '3.9' + python-version: '3.10' - - name: Install cibuildwheel - run: python -m pip install cibuildwheel~=2.16.0 + - name: Install dependencies + run: python -m pip install cibuildwheel~=2.20.0 tomlkit + + - name: Configure pyproject.toml file + run: PL_BACKEND="${{ matrix.pl_backend }}" python scripts/configure_pyproject_toml.py - name: Build wheels env: @@ -138,13 +145,20 @@ jobs: # Python build settings CIBW_BEFORE_BUILD: | - python -m pip install pybind11 ninja cmake~=3.24.0 setuptools scipy + python -m pip install pybind11 ninja cmake~=3.27.0 setuptools + + CIBW_ENVIRONMENT: | + CMAKE_ARGS="-DCMAKE_BUILD_TYPE=Release" PL_BACKEND: ${{ matrix.pl_backend }} CIBW_BEFORE_TEST: | python -m pip install -r requirements-tests.txt - if ${{ matrix.pl_backend == 'lightning_kokkos'}}; then SKIP_COMPILATION=True PL_BACKEND="lightning_qubit" python -m pip install . -vv; fi + if ${{ matrix.pl_backend == 'lightning_kokkos'}} + then + PL_BACKEND="lightning_qubit" python scripts/configure_pyproject_toml.py + SKIP_COMPILATION=True python -m pip install . -vv + fi CIBW_TEST_COMMAND: | DEVICENAME=`echo ${{ matrix.pl_backend }} | sed "s/_/./g"` @@ -168,36 +182,40 @@ jobs: text: ${{ github.event.pull_request.head.ref }} regex: '.*[0-9]+.[0-9]+.[0-9]+[-_]?rc[0-9]+' - - uses: actions/upload-artifact@v3 + - uses: actions/upload-artifact@v4 if: | github.event_name == 'release' || github.event_name == 'workflow_dispatch' || github.ref == 'refs/heads/master' || steps.rc_build.outputs.match != '' with: - name: ${{ runner.os }}-wheels-${{ matrix.pl_backend }}-${{ matrix.arch }}.zip + name: ${{ runner.os }}-wheels-${{ matrix.pl_backend }}-${{ fromJson('{ "cp310-*":"py310","cp311-*":"py311","cp312-*":"py312" }')[matrix.cibw_build] }}-${{ matrix.arch }}.zip path: ./wheelhouse/*.whl + retention-days: 1 + include-hidden-files: true upload-pypi: - needs: mac-wheels-x86 + needs: [set_wheel_build_matrix, mac-wheels-x86] strategy: matrix: arch: [x86_64] pl_backend: ["lightning_qubit", "lightning_kokkos"] + cibw_build: ${{ fromJson(needs.set_wheel_build_matrix.outputs.python_version) }} runs-on: ubuntu-latest + permissions: + id-token: write if: | github.event_name == 'release' || github.ref == 'refs/heads/master' steps: - - uses: actions/download-artifact@v3 + - uses: actions/download-artifact@v4 with: - name: macOS-wheels-${{ matrix.pl_backend }}-${{ matrix.arch }}.zip + name: macOS-wheels-${{ matrix.pl_backend }}-${{ fromJson('{ "cp310-*":"py310","cp311-*":"py311","cp312-*":"py312" }')[matrix.cibw_build] }}-${{ matrix.arch }}.zip path: dist - - name: Upload wheels to PyPI + - name: Upload wheels to TestPyPI uses: pypa/gh-action-pypi-publish@release/v1 with: - user: __token__ - password: ${{ ( matrix.pl_backend == 'lightning_qubit' ) && secrets.TEST_PYPI_API_TOKEN || secrets.TEST_PYPI_LKOKKOS_TOKEN }} + verbose: true repository-url: https://test.pypi.org/legacy/ diff --git a/.github/workflows/wheel_noarch.yml b/.github/workflows/wheel_noarch.yml index 55c62b0287..21dc6b8d3e 100644 --- a/.github/workflows/wheel_noarch.yml +++ b/.github/workflows/wheel_noarch.yml @@ -1,12 +1,17 @@ name: Wheel::Any::None # **What it does**: Builds a pure python wheel for Linux (ubuntu-latest) and store it as an artifact. -# Python version: 3.9. +# Python version: 3.10. # **Why we have it**: To test the wheel build in the python layer, with no compilation. # **Who does it impact**: Wheels to be uploaded to PyPI. on: pull_request: + types: + - opened + - reopened + - synchronize + - ready_for_review push: branches: - master @@ -19,81 +24,65 @@ concurrency: cancel-in-progress: true jobs: - determine_runner: - name: Determine runner type to use - uses: ./.github/workflows/determine-workflow-runner.yml - with: - default_runner: ubuntu-latest - - build-pure-python-wheel: if: | github.event_name != 'pull_request' || contains(github.event.pull_request.labels.*.name, 'ci:build_wheels') || github.event_name == 'workflow_dispatch' - needs: [determine_runner] strategy: matrix: pl_backend: ["lightning_gpu", "lightning_kokkos", "lightning_qubit"] timeout-minutes: 30 - name: Linux - Pure Python wheels - ${{ matrix.pl_backend }} (Python 3.9) - runs-on: ${{ needs.determine_runner.outputs.runner_group }} + name: Linux - Pure Python wheels - ${{ matrix.pl_backend }} (Python 3.10) + runs-on: pl-4-core-large-runner steps: - name: Checkout PennyLane-Lightning + if: ${{ matrix.pl_backend == 'lightning_qubit'}} uses: actions/checkout@v4 - with: - path: main - uses: actions/setup-python@v5 + if: ${{ matrix.pl_backend == 'lightning_qubit'}} with: - python-version: '3.9' + python-version: '3.10' - name: Upgrade pip + if: ${{ matrix.pl_backend == 'lightning_qubit'}} run: | python -m pip install --upgrade pip - - name: Install CMake and ninja + - name: Install dependencies + if: ${{ matrix.pl_backend == 'lightning_qubit'}} run: | - python -m pip install --upgrade cmake ninja + python -m pip install tomlkit - - name: Build wheels + - name: Configure pyproject.toml file + if: ${{ matrix.pl_backend == 'lightning_qubit'}} + run: PL_BACKEND="${{ matrix.pl_backend }}" python scripts/configure_pyproject_toml.py + + - name: Build wheels and source dist if: ${{ matrix.pl_backend == 'lightning_qubit'}} - run: | - python -m pip install --upgrade pip wheel - cd main - PL_BACKEND="${{ matrix.pl_backend }}" python setup.py bdist_wheel env: SKIP_COMPILATION: True + run: | + python -m pip install --upgrade pip wheel build + python -m build - name: Validate wheels if: ${{ matrix.pl_backend == 'lightning_qubit'}} run: | python -m pip install twine - python -m twine check main/dist/*.whl + python -m twine check dist/*.whl - - uses: actions/upload-artifact@v3 + - uses: actions/upload-artifact@v4 if: | matrix.pl_backend == 'lightning_qubit' && (github.event_name == 'release' || github.event_name == 'workflow_dispatch' || github.ref == 'refs/heads/master') with: name: pure-python-wheels-${{ matrix.pl_backend }}.zip - path: main/dist/*.whl - - - name: Build source dist - if: ${{ matrix.pl_backend != 'lightning_qubit'}} - run: | - python -m pip install --upgrade pip wheel - cd main - PL_BACKEND="${{ matrix.pl_backend }}" python setup.py sdist - env: - SKIP_COMPILATION: True - - - uses: actions/upload-artifact@v3 - if: ${{ matrix.pl_backend != 'lightning_qubit' && (github.event_name == 'release' || github.ref == 'refs/heads/master') }} - with: - name: pure-source-dist-${{ matrix.pl_backend }}.tar.gz - path: main/dist/*.tar.gz + path: dist/*.whl + retention-days: 1 + include-hidden-files: true upload-pypi: needs: build-pure-python-wheel @@ -101,12 +90,10 @@ jobs: matrix: pl_backend: ["lightning_gpu", "lightning_kokkos", "lightning_qubit"] runs-on: ubuntu-latest - if: | - matrix.pl_backend == 'lightning_qubit' && - github.event_name == 'release' steps: - - uses: actions/download-artifact@v3 + - uses: actions/download-artifact@v4 + if: ${{ matrix.pl_backend == 'lightning_qubit' && github.event_name == 'release' }} with: name: pure-python-wheels-${{ matrix.pl_backend }}.zip path: dist @@ -118,9 +105,3 @@ jobs: user: __token__ password: ${{ secrets.TEST_PYPI_API_TOKEN }} repository-url: https://test.pypi.org/legacy/ - - - uses: actions/download-artifact@v3 - if: ${{ matrix.pl_backend != 'lightning_qubit' && github.event_name == 'release' }} - with: - name: pure-source-dist-${{ matrix.pl_backend }}.tar.gz - path: dist diff --git a/.github/workflows/wheel_win_x86_64.yml b/.github/workflows/wheel_win_x86_64.yml index ca5b6a3b76..d2464ce9cf 100644 --- a/.github/workflows/wheel_win_x86_64.yml +++ b/.github/workflows/wheel_win_x86_64.yml @@ -1,12 +1,17 @@ name: Wheel::Windows::x86_64 # **What it does**: Builds python wheels for Windows (windows-latest) and store it as artifacts. -# Python versions: 3.9, 3.10, 3.11, 3.12. +# Python versions: 3.10, 3.11, 3.12. # **Why we have it**: To build wheels for pennylane-lightning installation. # **Who does it impact**: Wheels to be uploaded to PyPI. on: pull_request: + types: + - opened + - reopened + - synchronize + - ready_for_review push: branches: - master @@ -26,8 +31,7 @@ jobs: set_wheel_build_matrix: if: | github.event_name != 'pull_request' || - contains(github.event.pull_request.labels.*.name, 'ci:build_wheels') || - github.event_name == 'workflow_dispatch' + (github.event.pull_request.draft == false && contains(github.event.pull_request.labels.*.name, 'ci:build_wheels')) name: "Set wheel build matrix" uses: ./.github/workflows/set_wheel_build_matrix.yml with: @@ -51,7 +55,7 @@ jobs: uses: actions/cache@v4 with: path: D:\a\install_dir\${{ matrix.exec_model }} - key: ${{ matrix.os }}-kokkos${{ matrix.kokkos_version }}-${{ matrix.exec_model }}-RelWithDebInfo + key: ${{ matrix.os }}-kokkos${{ matrix.kokkos_version }}-${{ matrix.exec_model }}-Release - name: Clone Kokkos libs if: steps.kokkos-cache.outputs.cache-hit != 'true' @@ -76,10 +80,10 @@ jobs: -DKokkos_ENABLE_DEPRECATION_WARNINGS=OFF ` -DCMAKE_CXX_STANDARD=20 ` -DCMAKE_POSITION_INDEPENDENT_CODE=ON ` - -DCMAKE_BUILD_TYPE=RelWithDebInfo ` + -DCMAKE_BUILD_TYPE=Release ` -T clangcl - cmake --build ./Build --config RelWithDebInfo --verbose - cmake --install ./Build --config RelWithDebInfo --verbose + cmake --build ./Build --config Release --verbose + cmake --install ./Build --config Release --verbose win-wheels: needs: [set_wheel_build_matrix, build_dependencies] @@ -93,7 +97,7 @@ jobs: exec_model: ["SERIAL"] kokkos_version: ${{ fromJson(needs.set_wheel_build_matrix.outputs.kokkos_version) }} timeout-minutes: 30 - name: ${{ matrix.os }} - ${{ matrix.pl_backend }} (Python ${{ fromJson('{ "cp39-*":"3.9","cp310-*":"3.10","cp311-*":"3.11","cp312-*":"3.12" }')[matrix.cibw_build] }}) + name: ${{ matrix.os }} - ${{ matrix.pl_backend }} (Python ${{ fromJson('{ "cp310-*":"3.10","cp311-*":"3.11","cp312-*":"3.12" }')[matrix.cibw_build] }}) runs-on: ${{ matrix.os }} steps: @@ -113,8 +117,13 @@ jobs: Copy-Item -Path "D:\a\install_dir\${{ matrix.exec_model }}\" ` -Destination "D:\a\pennylane-lightning\pennylane-lightning\Kokkos" -Recurse -Force - - name: Install cibuildwheel - run: python -m pip install cibuildwheel~=2.16.0 wheel + - name: Install dependencies + run: python -m pip install cibuildwheel~=2.20.0 wheel toml + + - name: Configure pyproject.toml file + env: + PL_BACKEND: "${{ matrix.pl_backend }}" + run: python scripts/configure_pyproject_toml.py - name: Build wheels env: @@ -124,11 +133,10 @@ jobs: # Python build settings CIBW_BEFORE_BUILD: | - python -m pip install pybind11 cmake~=3.24.0 build + python -m pip install pybind11 cmake~=3.27.0 build CIBW_ENVIRONMENT: | - CMAKE_ARGS="-DENABLE_LAPACK=OFF" - PL_BACKEND="${{ matrix.pl_backend }}" + CMAKE_ARGS="-DCMAKE_BUILD_TYPE=Release" CIBW_MANYLINUX_X86_64_IMAGE: manylinux2014 @@ -173,7 +181,8 @@ jobs: python -m pip install setuptools python -m pip install -r requirements-tests.txt if (${{ matrix.pl_backend == 'lightning_kokkos'}}) { - SKIP_COMPILATION=True PL_BACKEND="lightning_qubit" python -m pip install . -vv + PL_BACKEND="lightning_qubit" python scripts/configure_pyproject_toml.py + SKIP_COMPILATION=True python -m pip install . -vv } pushd wheelhouse $wheels = Get-ChildItem "./" -Filter *.whl @@ -195,31 +204,34 @@ jobs: text: ${{ github.event.pull_request.head.ref }} regex: '.*[0-9]+.[0-9]+.[0-9]+[-_]?rc[0-9]+' - - uses: actions/upload-artifact@v3 + - uses: actions/upload-artifact@v4 if: | github.event_name == 'release' || github.event_name == 'workflow_dispatch' || github.ref == 'refs/heads/master' || steps.rc_build.outputs.match != '' with: - name: ${{ runner.os }}-wheels-${{ matrix.pl_backend }}-${{ matrix.arch }}.zip + name: ${{ runner.os }}-wheels-${{ matrix.pl_backend }}-${{ fromJson('{ "cp310-*":"py310","cp311-*":"py311","cp312-*":"py312" }')[matrix.cibw_build] }}-${{ matrix.arch }}.zip path: ./wheelhouse/*.whl + retention-days: 1 + include-hidden-files: true upload-pypi: - needs: win-wheels + needs: [set_wheel_build_matrix, win-wheels] strategy: matrix: arch: [AMD64] pl_backend: ["lightning_qubit"] + cibw_build: ${{ fromJson(needs.set_wheel_build_matrix.outputs.python_version) }} runs-on: ubuntu-latest if: | github.event_name == 'release' || github.ref == 'refs/heads/master' steps: - - uses: actions/download-artifact@v3 + - uses: actions/download-artifact@v4 with: - name: Windows-wheels-${{ matrix.pl_backend }}-${{ matrix.arch }}.zip + name: Windows-wheels-${{ matrix.pl_backend }}-${{ fromJson('{ "cp310-*":"py310","cp311-*":"py311","cp312-*":"py312" }')[matrix.cibw_build] }}-${{ matrix.arch }}.zip path: dist - name: Upload wheels to PyPI diff --git a/.gitignore b/.gitignore index 0f80526ee0..dedcf8aa58 100644 --- a/.gitignore +++ b/.gitignore @@ -32,5 +32,7 @@ kokkos/ PennyLane_Lightning_Kokkos.egg-info/ PennyLane_Lightning.egg-info/ prototypes/ +pyproject.toml tests/__pycache__/ venv/ +wheelhouse/ \ No newline at end of file diff --git a/.readthedocs.yml b/.readthedocs.yml index fd7c32576b..51c5b4ffc9 100644 --- a/.readthedocs.yml +++ b/.readthedocs.yml @@ -3,32 +3,17 @@ version: 2 sphinx: configuration: doc/conf.py -python: - install: - - requirements: ci_build_requirements.txt - - requirements: doc/requirements.txt - - requirements: requirements-dev.txt - - method: pip - path: . - build: os: ubuntu-22.04 tools: - python: "3.9" + python: "3.10" apt_packages: - - cmake - - build-essential - - libopenblas-base - - libopenblas-dev - graphviz - - wget jobs: - pre_install: - - wget https://developer.download.nvidia.com/compute/cuda/12.3.2/local_installers/cuda_12.3.2_545.23.08_linux.run - - sh cuda_12.3.2_545.23.08_linux.run --silent --toolkit --toolkitpath=${READTHEDOCS_VIRTUALENV_PATH}/cuda-12.3 || cat /tmp/cuda-installer.log - - echo "setuptools~=66.0\npip~=22.0" >> ci_build_requirements.txt - post_install: - - rm -rf ./build && export PATH=${READTHEDOCS_VIRTUALENV_PATH}/cuda-12.3/bin${PATH:+:${PATH}} && export LD_LIBRARY_PATH=${READTHEDOCS_VIRTUALENV_PATH}/cuda-12.3/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}} && PL_BACKEND="lightning_gpu" python setup.py build_ext --define="PL_DISABLE_CUDA_SAFETY=1" && PL_BACKEND="lightning_gpu" python setup.py bdist_wheel - - rm -rf ./build && PL_BACKEND="lightning_kokkos" python setup.py bdist_wheel - - rm -rf ./build && export PATH=${READTHEDOCS_VIRTUALENV_PATH}/cuda-12.3/bin${PATH:+:${PATH}} && export LD_LIBRARY_PATH=${READTHEDOCS_VIRTUALENV_PATH}/cuda-12.3/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}} && PL_BACKEND="lightning_tensor" python setup.py build_ext --define="PL_DISABLE_CUDA_SAFETY=1" && PL_BACKEND="lightning_tensor" python setup.py bdist_wheel + post_install: + - python -m pip install --exists-action=w --no-cache-dir -r doc/requirements.txt + - PL_BACKEND="lightning_qubit" python scripts/configure_pyproject_toml.py && SKIP_COMPILATION=True python -m build + - rm -rf ./build && PL_BACKEND="lightning_gpu" python scripts/configure_pyproject_toml.py && SKIP_COMPILATION=True python -m build + - rm -rf ./build && PL_BACKEND="lightning_kokkos" python scripts/configure_pyproject_toml.py && SKIP_COMPILATION=True python -m build + - rm -rf ./build && PL_BACKEND="lightning_tensor" python scripts/configure_pyproject_toml.py && SKIP_COMPILATION=True python -m build - python -m pip install ./dist/*.whl diff --git a/CITATION.cff b/CITATION.cff new file mode 100644 index 0000000000..4256080288 --- /dev/null +++ b/CITATION.cff @@ -0,0 +1,59 @@ +cff-version: "1.2.0" +authors: +- family-names: Asadi + given-names: Ali + orcid: "https://orcid.org/0009-0004-6010-2110" +- family-names: Dusko + given-names: Amintor + orcid: "https://orcid.org/0000-0002-0910-7715" +- family-names: Park + given-names: Chae-Yeun + orcid: "https://orcid.org/0000-0003-4430-1000" +- family-names: Michaud-Rioux + given-names: Vincent + orcid: "https://orcid.org/0000-0003-4367-5940" +- family-names: Schoch + given-names: Isidor +- family-names: Shu + given-names: Shuli + orcid: "https://orcid.org/0000-0001-6528-8584" +- family-names: Vincent + given-names: Trevor +- family-names: O'Riordan + given-names: Lee J. + orcid: "https://orcid.org/0000-0002-6758-9433" +contact: +- family-names: O'Riordan + given-names: Lee J. + orcid: "https://orcid.org/0000-0002-6758-9433" +message: If you use this software, please cite as follows. +preferred-citation: + authors: + - family-names: Asadi + given-names: Ali + orcid: "https://orcid.org/0009-0004-6010-2110" + - family-names: Dusko + given-names: Amintor + orcid: "https://orcid.org/0000-0002-0910-7715" + - family-names: Park + given-names: Chae-Yeun + orcid: "https://orcid.org/0000-0003-4430-1000" + - family-names: Michaud-Rioux + given-names: Vincent + orcid: "https://orcid.org/0000-0003-4367-5940" + - family-names: Schoch + given-names: Isidor + - family-names: Shu + given-names: Shuli + orcid: "https://orcid.org/0000-0001-6528-8584" + - family-names: Vincent + given-names: Trevor + - family-names: O'Riordan + given-names: Lee J. + orcid: "https://orcid.org/0000-0002-6758-9433" + - url: "https://arxiv.org/abs/2403.02512" + - type: other + value: "arXiv:2403.02512" + title: "Hybrid quantum programming with PennyLane Lightning on HPC platforms" + type: article +title: "Hybrid quantum programming with PennyLane Lightning on HPC platforms" diff --git a/CMakeLists.txt b/CMakeLists.txt index c7674d90b8..030de959a8 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -12,7 +12,7 @@ set(CMAKE_OSX_DEPLOYMENT_TARGET "11" CACHE STRING "Minimum OS X deployment versi set(CMAKE_CXX_STANDARD 20) # At least C++20 is required project(pennylane_lightning - DESCRIPTION "C++ suite of state-vector simulators bindings for PennyLane. " + DESCRIPTION "C++ suite of state-vector and tensor network simulators bindings for PennyLane. " LANGUAGES CXX ) @@ -20,16 +20,16 @@ project(pennylane_lightning function(set_pennylane_lightning_version VERSION_FILE_PATH) file(STRINGS ${VERSION_FILE_PATH} VERSION_FILE_STR) foreach (LINE IN LISTS VERSION_FILE_STR) - if("${LINE}" MATCHES "__version__.*") + if("${LINE}" MATCHES "version.*") set(VERSION_LINE_STR "${LINE}") endif() endforeach() - string(REGEX REPLACE "__version__ = \"(.*)\"" "\\1" VERSION_STRING ${VERSION_LINE_STR}) + string(REGEX REPLACE "version = \"(.*)\"" "\\1" VERSION_STRING ${VERSION_LINE_STR}) set(VERSION_STRING ${VERSION_STRING} PARENT_SCOPE) endfunction() -set_pennylane_lightning_version(${PROJECT_SOURCE_DIR}/pennylane_lightning/core/_version.py) +set_pennylane_lightning_version(${PROJECT_SOURCE_DIR}/pyproject.toml) message(STATUS "pennylane_lightning version ${VERSION_STRING}") set(PROJECT_VERSION ${VERSION_STRING}) @@ -47,8 +47,6 @@ option(ENABLE_WARNINGS "Enable warnings" ON) option(ENABLE_NATIVE "Enable native CPU build tuning" OFF) option(ENABLE_PYTHON "Enable compilation of the Python module" ON) -option(ENABLE_LAPACK "Enable compilation with scipy/LAPACK" ON) - # OpenMP find_package(OpenMP) if (OpenMP_CXX_FOUND) @@ -74,9 +72,8 @@ endif() foreach(BACKEND ${PL_BACKEND}) message(STATUS "PL_BACKEND: ${BACKEND}") if("${BACKEND}" STREQUAL "lightning_tensor") - set(PL_TENSOR_METHOD "mps" CACHE STRING "PennyLane LightningTensor MPS simulator.") set(PL_TENSOR_BACKEND "cutensornet" CACHE STRING "PennyLane LightningTensor backed by cutensornet") - set(PL_TENSOR "${PL_BACKEND}_${PL_TENSOR_METHOD}_${PL_TENSOR_BACKEND}") + set(PL_TENSOR "${PL_BACKEND}_${PL_TENSOR_BACKEND}") endif() endforeach() @@ -88,34 +85,11 @@ set(CMAKE_POLICY_DEFAULT_CMP0127 NEW) # To suppress pybind11 CMP0127 warning # Add pybind11 include(FetchContent) -if(ENABLE_LAPACK) - find_package(Python COMPONENTS Interpreter Development) - set(SCIPYLIBS ${Python_SITELIB}) - - if(CMAKE_SYSTEM_NAME MATCHES "Darwin") - set(SCIPYLIBS "/System/Library/Frameworks/Accelerate.framework/Versions/Current/Frameworks/vecLib.framework/libLAPACK.dylib") - elseif(CMAKE_SYSTEM_NAME MATCHES "Linux") - if(EXISTS ${SCIPYLIBS}/scipy.libs) - set(SCIPYLIBS ${SCIPYLIBS}/scipy.libs) - else() - # Fallback to the lib path of Python for `conda` support - set(SCIPYLIBS ${SCIPYLIBS}/../..) - endif() - elseif(CMAKE_SYSTEM_NAME MATCHES "Windows") - else() - message(FATAL_ERROR "Unsupported platform: ${CMAKE_SYSTEM_NAME}") - endif() - - add_compile_definitions(SCIPY_LIBS_PATH="${SCIPYLIBS}") - - message(STATUS "Python scipy-lib path: ${SCIPYLIBS}") -endif() - if(ENABLE_PYTHON) find_package(Python COMPONENTS Interpreter Development) FetchContent_Declare(pybind11 GIT_REPOSITORY https://github.com/pybind/pybind11.git - GIT_TAG v2.11.1 + GIT_TAG v2.13.5 ) FetchContent_MakeAvailable(pybind11) endif() @@ -123,6 +97,27 @@ endif() # Print Python site-packages directory for reference message("Python site-packages directory: ${Python_SITELIB}") +if(DEFINED PY_INSTALL) + # Note the following setting is only for pyenv and not for conda + # TODO: Add support for conda + if(APPLE) + set(SCIPY_OPENBLAS32_RUNTIME_LIB_PATH "@loader_path/../scipy_openblas32/lib") + else() + set(SCIPY_OPENBLAS32_RUNTIME_LIB_PATH "$ORIGIN/../scipy_openblas32/lib") + endif() +else() + include("${CMAKE_CURRENT_SOURCE_DIR}/cmake/FindSCIPY_OPENBLAS32.cmake") + if(NOT DEFINED SCIPY_OPENBLAS32_LIB_PATH AND NOT EXISTS ${SCIPY_OPENBLAS32_LIB_PATH}) + set(SCIPY_OPENBLAS32_LIB_PATH "") + find_path_to_openblas(SCIPY_OPENBLAS32_LIB_PATH) + add_compile_definitions(SCIPY_OPENBLAS32_LIB="${SCIPY_OPENBLAS32_LIB_PATH}") + message(STATUS "SCIPY_OPENBLAS32_LIB_PATH: ${SCIPY_OPENBLAS32_LIB_PATH}") + else() + add_compile_definitions(SCIPY_OPENBLAS32_LIB="${SCIPY_OPENBLAS32_LIB_PATH}") + endif() + set(SCIPY_OPENBLAS32_RUNTIME_LIB_PATH "${SCIPY_OPENBLAS32_LIB_PATH}") +endif() + set (CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}) # All CMakeLists.txt in subdirectories use pennylane_lightning_compile_options and pennylane_lightning_external_libs @@ -146,7 +141,7 @@ foreach(BACKEND ${PL_BACKEND}) "${BACKEND}_measurements" ) endforeach() - + target_include_directories(pennylane_lightning INTERFACE "$") ##################################################### @@ -154,10 +149,13 @@ if(ENABLE_PYTHON) message(STATUS "ENABLE_PYTHON is ON.") pybind11_add_module("${PL_BACKEND}_ops" "pennylane_lightning/core/src/bindings/Bindings.cpp") - # Allow pip installation of cuQuantum & CUDA 12 libs to be accessible without setting LD_LIBRARY_PATH for lightning_gpu + set(CMAKE_BUILD_RPATH_USE_ORIGIN ON) if("${PL_BACKEND}" STREQUAL "lightning_gpu" OR "${PL_BACKEND}" STREQUAL "lightning_tensor") - set(CMAKE_BUILD_RPATH_USE_ORIGIN ON) - set_target_properties("${PL_BACKEND}_ops" PROPERTIES BUILD_RPATH "$ORIGIN/../cuquantum/lib:$ORIGIN/../nvidia/cuda_runtime/lib:$ORIGIN/../nvidia/cublas/lib:$ORIGIN/../nvidia/cusparse/lib:$ORIGIN") + # Allow pip installation of cuQuantum & CUDA 12 libs to be accessible without setting LD_LIBRARY_PATH for lightning_gpu + # BUILD_RPATH only works for the last call + set_target_properties("${PL_BACKEND}_ops" PROPERTIES BUILD_RPATH "$ORIGIN/../cuquantum/lib:$ORIGIN/../nvidia/cuda_runtime/lib:$ORIGIN/../nvidia/cublas/lib:$ORIGIN/../nvidia/cusparse/lib:${SCIPY_OPENBLAS32_RUNTIME_LIB_PATH}:$ORIGIN") + else() + set_target_properties("${PL_BACKEND}_ops" PROPERTIES BUILD_RPATH "${SCIPY_OPENBLAS32_RUNTIME_LIB_PATH}") endif() target_link_libraries("${PL_BACKEND}_ops" PRIVATE lightning_compile_options diff --git a/MANIFEST.in b/MANIFEST.in index db5d3a35bb..23ba93b561 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -1,5 +1,9 @@ +include CMakeLists.txt +include cmake/* include requirements.txt include .github/CHANGELOG.md include pennylane_lightning/lightning_qubit/lightning_qubit.toml -include pennylane_lightning/lightning_qpu/lightning_gpu.toml +include pennylane_lightning/lightning_gpu/lightning_gpu.toml include pennylane_lightning/lightning_kokkos/lightning_kokkos.toml +include pennylane_lightning/core/_version.py +graft pennylane_lightning/core/src/ diff --git a/Makefile b/Makefile index 0456de8341..1fe991bb64 100644 --- a/Makefile +++ b/Makefile @@ -3,6 +3,7 @@ COVERAGE := --cov=pennylane_lightning --cov-report term-missing --cov-report=htm TESTRUNNER := -m pytest tests --tb=short PL_BACKEND ?= "$(if $(backend:-=),$(backend),lightning_qubit)" +SCIPY_OPENBLAS :=$(shell $(PYTHON) -c "import scipy_openblas32; print(scipy_openblas32.get_lib_dir())") ifdef check CHECK := --check --diff @@ -35,8 +36,12 @@ help: @echo " test-cpp [verbose=1] to run the C++ test suite (requires CMake)" @echo " use with 'verbose=1' for building with verbose flag" @echo " test-cpp [target=?] to run a specific C++ test target (requires CMake)." + @echo " test-cpp-mpi [backend=?] to run the C++ test suite with MPI (requires CMake and MPI)" + @echo " Default: lightning_gpu" @echo " test-python [device=?] to run the Python test suite" @echo " Default: lightning.qubit" + @echo " wheel [backend=?] to configure and build Python wheels" + @echo " Default: lightning_qubit" @echo " coverage [device=?] to generate a coverage report for python interface" @echo " Default: lightning.qubit" @echo " coverage-cpp [backend=?] to generate a coverage report for C++ interface" @@ -61,6 +66,20 @@ clean: rm -rf pennylane_lightning/*_ops* rm -rf *.egg-info +.PHONY: python python-skip-compile +python: + PL_BACKEND=$(PL_BACKEND) python scripts/configure_pyproject_toml.py + pip install -e . --config-settings editable_mode=compat -vv + +python-skip-compile: + PL_BACKEND=$(PL_BACKEND) python scripts/configure_pyproject_toml.py + SKIP_COMPILATION=True pip install -e . --config-settings editable_mode=compat -vv + +.PHONY: wheel +wheel: + PL_BACKEND=$(PL_BACKEND) python scripts/configure_pyproject_toml.py + python -m build + .PHONY: coverage coverage-cpp coverage: @echo "Generating coverage report for $(if $(device:-=),$(device),lightning.qubit) device:" @@ -77,12 +96,12 @@ coverage-cpp: -DENABLE_COVERAGE=ON \ -DPL_BACKEND=$(PL_BACKEND) \ $(OPTIONS) - cmake --build ./BuildCov + cmake --build ./BuildCov $(VERBOSE) --target $(target) cd ./BuildCov; for file in *runner ; do ./$file; done; \ - lcov --directory . -b ../pennylane_lightning/core/src --capture --output-file coverage.info; \ + lcov --directory . -b ../pennylane_lightning/core/src/ --capture --output-file coverage.info; \ genhtml coverage.info --output-directory out -.PHONY: test-python test-builtin test-suite test-cpp +.PHONY: test-python test-builtin test-suite test-cpp test-cpp-mpi test-python: test-builtin test-suite test-builtin: @@ -99,6 +118,7 @@ test-cpp: -DBUILD_TESTS=ON \ -DENABLE_WARNINGS=ON \ -DPL_BACKEND=$(PL_BACKEND) \ + -DSCIPY_OPENBLAS=$(SCIPY_OPENBLAS) \ $(OPTIONS) ifdef target cmake --build ./BuildTests $(VERBOSE) --target $(target) @@ -108,6 +128,28 @@ else cmake --build ./BuildTests $(VERBOSE) --target test endif +test-cpp-mpi: + rm -rf ./BuildTests + cmake -BBuildTests -G Ninja \ + -DCMAKE_BUILD_TYPE=Debug \ + -DBUILD_TESTS=ON \ + -DENABLE_WARNINGS=ON \ + -DPL_BACKEND=lightning_gpu \ + -DSCIPY_OPENBLAS=$(SCIPY_OPENBLAS) \ + -DENABLE_MPI=ON \ + $(OPTIONS) +ifdef target + cmake --build ./BuildTests $(VERBOSE) --target $(target) + mpirun -np 2 ./BuildTests/$(target) +else + cmake --build ./BuildTests $(VERBOSE) + for file in ./BuildTests/*_test_runner_mpi; do \ + echo "Running $$file"; \ + mpirun -np 2 $$file ; \ + done +endif + + .PHONY: format format-cpp format-python format: format-cpp format-python @@ -115,8 +157,8 @@ format-cpp: ./bin/format $(CHECK) ./pennylane_lightning format-python: - isort --py 311 --profile black -l 100 -p pennylane_lightning ./pennylane_lightning/ ./mpitests ./tests $(ICHECK) $(VERBOSE) - black -l 100 ./pennylane_lightning/ ./mpitests ./tests $(CHECK) $(VERBOSE) + isort --py 312 --profile black -l 100 -p pennylane_lightning ./pennylane_lightning ./mpitests ./tests ./scripts $(ICHECK) $(VERBOSE) + black -t py310 -t py311 -t py312 -l 100 ./pennylane_lightning ./mpitests ./tests ./scripts $(CHECK) $(VERBOSE) .PHONY: check-tidy check-tidy: diff --git a/README.rst b/README.rst index 653407514b..54b86896d7 100644 --- a/README.rst +++ b/README.rst @@ -35,11 +35,11 @@ Lightning Plugins .. header-start-inclusion-marker-do-not-remove -The Lightning plugin ecosystem provides fast state-vector simulators written in C++. +The Lightning plugin ecosystem provides fast state-vector and tensor network simulators written in C++. `PennyLane `_ is a cross-platform Python library for quantum machine learning, automatic differentiation, and optimization of hybrid quantum-classical computations. -PennyLane supports Python 3.9 and above. +PennyLane supports Python 3.10 and above. Features ******** @@ -49,7 +49,7 @@ PennyLane-Lightning high performance simulators include the following backends: * ``lightning.qubit``: is a fast state-vector simulator written in C++. * ``lightning.gpu``: is a state-vector simulator based on the `NVIDIA cuQuantum SDK `_. It notably implements a distributed state-vector simulator based on MPI. * ``lightning.kokkos``: is a state-vector simulator written with `Kokkos `_. It can exploit the inherent parallelism of modern processing units supporting the `OpenMP `_, `CUDA `_ or `HIP `_ programming models. -* ``lightning.tensor``: is a tensor network simulator based on the `NVIDIA cuQuantum SDK `_ (requires NVIDIA GPUs with SM 7.0 or greater). This device is designed to simulate large-scale quantum circuits using tensor networks. For small circuits, state-vector simulator plugins may be more suitable. The supported method is Matrix Product State (MPS). This device does not currently support finite shots. Currently, the supported measurement types are expectation values and the supported differentiation methods are ``parameter-shift`` and ``finite-diff``. Note that only 1,2-wire gates and gates that can be decomposed by PennyLane into 1,2-wire gates are supported. +* ``lightning.tensor``: is a tensor network simulator based on the `NVIDIA cuQuantum SDK `_ (requires NVIDIA GPUs with SM 7.0 or greater). The supported method is Matrix Product State (MPS). .. header-end-inclusion-marker-do-not-remove @@ -58,11 +58,11 @@ The following table summarizes the supported platforms and the primary installat +-----------+---------+--------+-------------+----------------+-----------------+----------------+----------------+ | | L-Qubit | L-GPU | L-GPU (MPI) | L-Kokkos (OMP) | L-Kokkos (CUDA) | L-Kokkos (HIP) | L-Tensor | +===========+=========+========+=============+================+=================+================+================+ -| Linux x86 | pip | pip | source | pip | source | source | source | +| Linux x86 | pip | pip | source | pip | source | source | pip | +-----------+---------+--------+-------------+----------------+-----------------+----------------+----------------+ -| Linux ARM | pip | source | | pip | source | source | | +| Linux ARM | pip | pip | | pip | source | source | pip | +-----------+---------+--------+-------------+----------------+-----------------+----------------+----------------+ -| Linux PPC | pip | source | | pip | source | source | | +| Linux PPC | pip | source | | source | source | source | | +-----------+---------+--------+-------------+----------------+-----------------+----------------+----------------+ | MacOS x86 | pip | | | pip | | | | +-----------+---------+--------+-------------+----------------+-----------------+----------------+----------------+ @@ -77,83 +77,69 @@ The following table summarizes the supported platforms and the primary installat Lightning-Qubit installation **************************** -PyPI wheels (pip) -================= - -Lightning plugins can be installed using ``pip`` as follows - -.. code-block:: console - - $ pip install pennylane-lightning - -The above command will install the Lightning-Qubit plugin (the default since it is most broadly supported). -In order to install the Lightning-GPU and Lightning-Kokkos (OpenMP) backends, you can respectively use the following commands: - -.. code-block:: console - - $ pip install pennylane-lightning[gpu] - $ pip install pennylane-lightning[kokkos] - +Standard installation +===================== +| **Lightning-Qubit comes pre-installed with PennyLane.** +| Please follow our `installation instructions `_ to install PennyLane. Install from source =================== +.. note:: + + The below contains instructions for installing Lightning-Qubit ***from source***. For most cases, *this is not required* and one can simply use the installation instructions at `pennylane.ai/install `__. + If those instructions do not work for you, or you have a more complex build environment that requires building from source, then consider reading on. + To build Lightning plugins from source you can run -.. code-block:: console +.. code-block:: bash - $ PL_BACKEND=${PL_BACKEND} pip install pybind11 pennylane-lightning --no-binary :all: + PL_BACKEND=${PL_BACKEND} pip install pybind11 pennylane-lightning --no-binary :all: -where ``${PL_BACKEND}`` can be ``lightning_qubit`` (default), ``lightning_gpu`` or ``lightning_kokkos``. +where ``${PL_BACKEND}`` can be ``lightning_qubit`` (default), ``lightning_gpu``, ``lightning_kokkos``, or ``lightning_tensor``. The `pybind11 `_ library is required to bind the C++ functionality to Python. A C++ compiler such as ``g++``, ``clang++``, or ``MSVC`` is required. On Debian-based systems, this can be installed via ``apt``: -.. code-block:: console +.. code-block:: bash - $ sudo apt -y update && - $ sudo apt install g++ libomp-dev + sudo apt -y update && sudo apt install -y g++ libomp-dev where ``libomp-dev`` is included to also install OpenMP. On MacOS, we recommend using the latest version of ``clang++`` and ``libomp``: -.. code-block:: console +.. code-block:: bash - $ brew install llvm libomp + brew install llvm libomp -The Lightning-GPU backend has several dependencies (e.g. ``CUDA``, ``custatevec-cu12``, etc.), and hence we recommend referring to Lightning-GPU installation section. -Similarly, for Lightning-Kokkos it is recommended to configure and install Kokkos independently as prescribed in the Lightning-Kokkos installation section. +The Lightning-GPU backend has several dependencies (e.g. ``CUDA``, ``custatevec-cu12``, etc.), and hence we recommend referring to `Lightning-GPU installation `_ section. +Similarly, for Lightning-Kokkos it is recommended to configure and install Kokkos independently as prescribed in the `Lightning-Kokkos installation `_ section. Development installation ======================== For development and testing, you can install by cloning the repository: -.. code-block:: console +.. code-block:: bash - $ git clone https://github.com/PennyLaneAI/pennylane-lightning.git - $ cd pennylane-lightning - $ pip install -r requirements.txt - $ PL_BACKEND=${PL_BACKEND} pip install -e . -vv + git clone https://github.com/PennyLaneAI/pennylane-lightning.git + cd pennylane-lightning + pip install -r requirements.txt + PL_BACKEND=${PL_BACKEND} python scripts/configure_pyproject_toml.py + pip install -e . --config-settings editable_mode=compat -vv Note that subsequent calls to ``pip install -e .`` will use cached binaries stored in the -``build`` folder. Run ``make clean`` if you would like to recompile from scratch. +``build`` folder, and the ``pyproject.toml`` file defined by the configuration script. Run ``make clean`` if you would like to recompile from scratch. You can also pass ``cmake`` options with ``CMAKE_ARGS`` as follows: -.. code-block:: console - - $ CMAKE_ARGS="-DENABLE_OPENMP=OFF -DENABLE_BLAS=OFF" pip install -e . -vv - -or with ``build_ext`` and the ``--define`` flag as follows: +.. code-block:: bash -.. code-block:: console + CMAKE_ARGS="-DENABLE_OPENMP=OFF -DENABLE_BLAS=OFF" pip install -e . --config-settings editable_mode=compat -vv - $ python3 setup.py build_ext -i --define="ENABLE_OPENMP=OFF;ENABLE_BLAS=OFF" - $ python3 setup.py develop -where ``-D`` must not be included before ``;``-separated options. +Supported options are ``-DENABLE_WARNINGS``, ``-DENABLE_NATIVE`` (for ``-march=native``) ``-DENABLE_BLAS``, ``-DENABLE_OPENMP``, and ``-DENABLE_CLANG_TIDY``. Compile MSVC (Windows) ====================== @@ -166,116 +152,123 @@ You need `cmake `_ and appropriate Python environme We recommend using ``[x64 (or x86)] Native Tools Command Prompt for VS [version]`` to compile the library. Be sure that ``cmake`` and ``python`` can be called within the prompt. -.. code-block:: console +.. code-block:: bash - $ cmake --version - $ python --version + cmake --version + python --version Then a common command will work. -.. code-block:: console +.. code-block:: bash - $ pip install -r requirements.txt - $ pip install -e . + pip install -r requirements.txt + pip install -e . Note that OpenMP and BLAS are disabled on this platform. -CMake support -============= -One can also build the plugin using CMake: +Testing +======= -.. code-block:: console +To test that a plugin is working correctly, one can check both Python and C++ unit tests for each device. - $ cmake -S. -B build - $ cmake --build build +Python Test +^^^^^^^^^^^ -Supported options are +Test the Python code with: -- ``-DENABLE_WARNINGS:BOOL=ON`` -- ``-DENABLE_NATIVE:BOOL=ON`` (for ``-march=native``) -- ``-DENABLE_BLAS:BOOL=ON`` -- ``-DENABLE_OPENMP:BOOL=ON`` -- ``-DENABLE_CLANG_TIDY:BOOL=ON`` +.. code-block:: bash -Testing -======= + make test-python device=${PL.DEVICE} -To test that a plugin is working correctly, test the Python code with: +where ``${PL.DEVICE}`` differ from ``${PL_BACKEND}`` by replacing the underscore by a dot. And can be -.. code-block:: console +- ``lightning.qubit`` (default) +- ``lightning.gpu`` +- ``lightning.kokkos`` +- ``lightning.tensor`` - $ make test-python device=${PL_DEVICE} +C++ Test +^^^^^^^^ -where ``${PL_DEVICE}`` can be ``lightning.qubit`` (default), ``lightning.gpu`` or ``lightning.kokkos``. -These differ from ``${PL_BACKEND}`` by replacing the underscore by a dot. The C++ code can be tested with -.. code-block:: console +.. code-block:: bash - $ PL_BACKEND=${PL_BACKEND} make test-cpp + PL_BACKEND=${PL_BACKEND} make test-cpp .. installation_LQubit-end-inclusion-marker-do-not-remove .. installation_LGPU-start-inclusion-marker-do-not-remove + Lightning-GPU installation ************************** -Lightning-GPU can be installed using ``pip``: - -.. code-block:: console - - pip install pennylane-lightning[gpu] - -Lightning-GPU requires CUDA 12 and the `cuQuantum SDK `_ (only the `cuStateVec `_ library is required). -The SDK may be installed within the Python environment ``site-packages`` directory using ``pip`` or ``conda`` or the SDK library path appended to the ``LD_LIBRARY_PATH`` environment variable. -Please see the `cuQuantum SDK`_ install guide for more information. +Standard installation +===================== +| For the majority of cases, +| Lightning-GPU can be installed by following our installation instructions at `pennylane.ai/install `__. Install Lightning-GPU from source ================================= -To install Lightning-GPU from the package sources using the direct SDK path, Lightning-Qubit should be install before Lightning-GPU: +.. note:: + + The below contains instructions for installing Lightning-GPU ***from source***. For most cases, *this is not required* and one can simply use the installation instructions at `pennylane.ai/install `__. If those instructions do not work for you, or you have a more complex build environment that requires building from source, then consider reading on. -.. code-block:: console +To install Lightning-GPU from the package sources using the direct SDK path, Lightning-Qubit should be install before Lightning-GPU (compilation is not necessary): + +.. code-block:: bash git clone https://github.com/PennyLaneAI/pennylane-lightning.git cd pennylane-lightning pip install -r requirements.txt - PL_BACKEND="lightning_qubit" pip install -e . -vv + pip install custatevec-cu12 + PL_BACKEND="lightning_qubit" python scripts/configure_pyproject_toml.py + SKIP_COMPILATION=True pip install -e . --config-settings editable_mode=compat -vv -Then the `cuStateVec`_ library can be installed and set a ``CUQUANTUM_SDK`` environment variable. +Then a ``CUQUANTUM_SDK`` environment variable can be set: -.. code-block:: console +.. code-block:: bash - python -m pip install wheel custatevec-cu12 - export CUQUANTUM_SDK=$(python -c "import site; print( f'{site.getsitepackages()[0]}/cuquantum/lib')") + export CUQUANTUM_SDK=$(python -c "import site; print( f'{site.getsitepackages()[0]}/cuquantum')") The Lightning-GPU can then be installed with ``pip``: -.. code-block:: console +.. code-block:: bash - PL_BACKEND="lightning_gpu" python -m pip install -e . + PL_BACKEND="lightning_gpu" python scripts/configure_pyproject_toml.py + python -m pip install -e . --config-settings editable_mode=compat -vv To simplify the build, we recommend using the containerized build process described in Docker support section. Install Lightning-GPU with MPI ============================== -Building Lightning-GPU with MPI also requires the ``NVIDIA cuQuantum SDK`` (currently supported version: `custatevec-cu12 `_), ``mpi4py`` and ``CUDA-aware MPI`` (Message Passing Interface). -``CUDA-aware MPI`` allows data exchange between GPU memory spaces of different nodes without the need for CPU-mediated transfers. -Both the ``MPICH`` and ``OpenMPI`` libraries are supported, provided they are compiled with CUDA support. -The path to ``libmpi.so`` should be found in ``LD_LIBRARY_PATH``. -It is recommended to install the ``NVIDIA cuQuantum SDK`` and ``mpi4py`` Python package within ``pip`` or ``conda`` inside a virtual environment. -Please consult the `cuQuantum SDK`_ , `mpi4py `_, -`MPICH `_, or `OpenMPI `_ install guide for more information. +.. note:: + + Building Lightning-GPU with MPI also requires the ``NVIDIA cuQuantum SDK`` (currently supported version: `custatevec-cu12 `_), ``mpi4py`` and ``CUDA-aware MPI`` (Message Passing Interface). + ``CUDA-aware MPI`` allows data exchange between GPU memory spaces of different nodes without the need for CPU-mediated transfers. + Both the ``MPICH`` and ``OpenMPI`` libraries are supported, provided they are compiled with CUDA support. + It is recommended to install the ``NVIDIA cuQuantum SDK`` and ``mpi4py`` Python package within ``pip`` or ``conda`` inside a virtual environment. + Please consult the `cuQuantum SDK`_ , `mpi4py `_, + `MPICH `_, or `OpenMPI `_ install guide for more information. + +**Before installing Lightning-GPU with MPI support using the direct SDK path, please ensure that:** -Before installing Lightning-GPU with MPI support using the direct SDK path, please ensure Lightning-Qubit, ``CUDA-aware MPI`` and ``custatevec`` are installed and the environment variable ``CUQUANTUM_SDK`` is set properly. -Then Lightning-GPU with MPI support can then be installed with ``pip``: +.. note:: -.. code-block:: console + - **Lightning-Qubit**, ``CUDA-aware MPI`` **and** ``custatevec`` **are installed**. + - **The environment variable** ``CUQUANTUM_SDK`` **is set properly**. + - **Add the** ``path/to/libmpi.so`` **to** ``LD_LIBRARY_PATH``. - CMAKE_ARGS="-DENABLE_MPI=ON" PL_BACKEND="lightning_gpu" python -m pip install -e . +Then Lightning-GPU with MPI support can then be installed in the *editable* mode: + +.. code-block:: bash + + PL_BACKEND="lightning_gpu" python scripts/configure_pyproject_toml.py + CMAKE_ARGS="-DENABLE_MPI=ON" python -m pip install -e . --config-settings editable_mode=compat -vv Test Lightning-GPU with MPI @@ -283,13 +276,13 @@ Test Lightning-GPU with MPI You may test the Python layer of the MPI enabled plugin as follows: -.. code-block:: console +.. code-block:: bash mpirun -np 2 python -m pytest mpitests --tb=short The C++ code is tested with -.. code-block:: console +.. code-block:: bash rm -rf ./BuildTests cmake . -BBuildTests -DBUILD_TESTS=1 -DBUILD_TESTS=1 -DENABLE_MPI=ON -DCUQUANTUM_SDK= @@ -304,25 +297,42 @@ The C++ code is tested with Lightning-Kokkos installation ***************************** -On linux systems, ``lightning.kokkos`` with the OpenMP backend can be installed by providing the optional ``[kokkos]`` tag: - -.. code-block:: console - - $ pip install pennylane-lightning[kokkos] +Standard installation +===================== +| On most Linux systems, +| Lightning-Kokkos can be installed via Spack or Docker by following our installation instructions at `pennylane.ai/install `__. Install Lightning-Kokkos from source ==================================== +.. note:: + + The below contains instructions for installing Lightning-Kokkos ***from source***. For most cases, one can install Lightning-Kokkos via Spack or Docker by the installation instructions at `pennylane.ai/install `__. If those instructions do not work for you, or you have a more complex build environment that requires building from source, then consider reading on. + As Kokkos enables support for many different HPC-targeted hardware platforms, ``lightning.kokkos`` can be built to support any of these platforms when building from source. +Install Kokkos (Optional) +^^^^^^^^^^^^^^^^^^^^^^^^^ + We suggest first installing Kokkos with the wanted configuration following the instructions found in the `Kokkos documentation `_. For example, the following will build Kokkos for NVIDIA A100 cards -.. code-block:: console +Download the `Kokkos code `_. Lightning Kokkos was tested with Kokkos version <= 4.5.0 + +.. code-block:: bash + + # Replace x, y, and z by the correct version + wget https://github.com/kokkos/kokkos/archive/refs/tags/4.x.yz.tar.gz + tar -xvf 4.x.y.z.tar.gz + cd kokkos-4.x.y.z + +Build Kokkos for NVIDIA A100 cards (``SM80`` architecture) + +.. code-block:: bash cmake -S . -B build -G Ninja \ -DCMAKE_BUILD_TYPE=RelWithDebugInfo \ - -DCMAKE_INSTALL_PREFIX=/opt/kokkos/4.1.00/AMPERE80 \ + -DCMAKE_INSTALL_PREFIX=/opt/kokkos/4.x.y.z/AMPERE80 \ -DCMAKE_CXX_STANDARD=20 \ -DBUILD_SHARED_LIBS:BOOL=ON \ -DBUILD_TESTING:BOOL=OFF \ @@ -333,66 +343,96 @@ For example, the following will build Kokkos for NVIDIA A100 cards -DKokkos_ENABLE_TESTS:BOOL=OFF \ -DKokkos_ENABLE_LIBDL:BOOL=OFF cmake --build build && cmake --install build - echo export CMAKE_PREFIX_PATH=/opt/kokkos/4.1.00/AMPERE80:\$CMAKE_PREFIX_PATH + export CMAKE_PREFIX_PATH=/opt/kokkos/4.x.y.z/AMPERE80:$CMAKE_PREFIX_PATH + Next, append the install location to ``CMAKE_PREFIX_PATH``. Note that the C++20 standard is required (``-DCMAKE_CXX_STANDARD=20`` option), and hence CUDA v12 is required for the CUDA backend. -If an installation is not found, our builder will clone and install it during the build process. + +Install Lightning-Kokkos +^^^^^^^^^^^^^^^^^^^^^^^^ + +If an installation of Kokkos is not found, then our builder will clone and install it during the build process. Lightning-Qubit should be installed (compilation is not necessary): The simplest way to install Lightning-Kokkos (OpenMP backend) through ``pip``. -.. code-block:: console +.. code-block:: bash - CMAKE_ARGS="-DKokkos_ENABLE_OPENMP=ON" PL_BACKEND="lightning_kokkos" python -m pip install . + git clone https://github.com/PennyLaneAI/pennylane-lightning.git + cd pennylane-lightning + PL_BACKEND="lightning_qubit" python scripts/configure_pyproject_toml.py + SKIP_COMPILATION=True pip install -e . --config-settings editable_mode=compat + PL_BACKEND="lightning_kokkos" python scripts/configure_pyproject_toml.py + CMAKE_ARGS="-DKokkos_ENABLE_OPENMP=ON" python -m pip install -e . --config-settings editable_mode=compat -vv -To build the plugin directly with CMake as above: +The supported backend options are -.. code-block:: console +.. list-table:: + :align: center + :width: 100 % + :widths: 20 20 20 20 20 + :header-rows: 0 - cmake -B build -DKokkos_ENABLE_OPENMP=ON -DPL_BACKEND=lightning_kokkos -G Ninja - cmake --build build + * - ``SERIAL`` + - ``OPENMP`` + - ``THREADS`` + - ``HIP`` + - ``CUDA`` +and the corresponding build options are ``-DKokkos_ENABLE_XXX=ON``, where ``XXX`` needs be replaced by the backend name, for instance ``OPENMP``. -The supported backend options are ``SERIAL``, ``OPENMP``, ``THREADS``, ``HIP`` and ``CUDA`` and the corresponding build options are ``-DKokkos_ENABLE_XXX=ON``, where ``XXX`` needs be replaced by the backend name, for instance ``OPENMP``. One can activate simultaneously one serial, one parallel CPU host (e.g. ``OPENMP``, ``THREADS``) and one parallel GPU device backend (e.g. ``HIP``, ``CUDA``), but not two of any category at the same time. For ``HIP`` and ``CUDA``, the appropriate software stacks are required to enable compilation and subsequent use. Similarly, the CMake option ``-DKokkos_ARCH_{...}=ON`` must also be specified to target a given architecture. -A list of the architectures is found on the `Kokkos wiki `_. +A list of the architectures is found on the `Kokkos wiki `_. Note that ``THREADS`` backend is not recommended since `Kokkos does not guarantee its safety `_. .. installation_LKokkos-end-inclusion-marker-do-not-remove .. installation_LTensor-start-inclusion-marker-do-not-remove + Lightning-Tensor installation ***************************** Lightning-Tensor requires CUDA 12 and the `cuQuantum SDK `_ (only the `cutensornet `_ library is required). The SDK may be installed within the Python environment ``site-packages`` directory using ``pip`` or ``conda`` or the SDK library path appended to the ``LD_LIBRARY_PATH`` environment variable. Please see the `cuQuantum SDK `_ install guide for more information. +Standard installation +===================== +| For the majority of cases, +| Lightning-Tensor can be installed by following our installation instructions at `pennylane.ai/install `__. + Install Lightning-Tensor from source ==================================== -Lightning-Qubit should be installed before Lightning-Tensor: -.. code-block:: console +.. note:: + + The below contains instructions for installing Lightning-Tensor ***from source***. For most cases, *this is not required* and one can simply use the installation instructions at `pennylane.ai/install `__. If those instructions do not work for you, or you have a more complex build environment that requires building from source, then consider reading on. + +Lightning-Qubit should be installed before Lightning-Tensor (compilation is not necessary): + +.. code-block:: bash git clone https://github.com/PennyLaneAI/pennylane-lightning.git cd pennylane-lightning pip install -r requirements.txt - PL_BACKEND="lightning_qubit" pip install . + pip install cutensornet-cu12 + PL_BACKEND="lightning_qubit" python scripts/configure_pyproject_toml.py + SKIP_COMPILATION=True pip install -e . --config-settings editable_mode=compat -Then the `cutensornet`_ library can be installed and set a ``CUQUANTUM_SDK`` environment variable. +Then a ``CUQUANTUM_SDK`` environment variable can be set: -.. code-block:: console +.. code-block:: bash - pip install cutensornet-cu12 - export CUQUANTUM_SDK=$(python -c "import site; print( f'{site.getsitepackages()[0]}/cuquantum/lib')") + export CUQUANTUM_SDK=$(python -c "import site; print( f'{site.getsitepackages()[0]}/cuquantum')") The Lightning-Tensor can then be installed with ``pip``: -.. code-block:: console +.. code-block:: bash - PL_BACKEND="lightning_tensor" pip install -e . + PL_BACKEND="lightning_tensor" python scripts/configure_pyproject_toml.py + pip install -e . --config-settings editable_mode=compat -vv .. installation_LTensor-end-inclusion-marker-do-not-remove @@ -402,18 +442,19 @@ well as to the `PennyLane documentation `_ for furth .. docker-start-inclusion-marker-do-not-remove + Docker support ************** Docker images for the various backends are found on the -`PennyLane Docker Hub `_ page, where there is also a detailed description about PennyLane Docker support. +`PennyLane Docker Hub `_ page, where there is also a detailed description about PennyLane Docker support. Briefly, one can build the Docker Lightning images using: -.. code-block:: console +.. code-block:: bash - $ git clone https://github.com/PennyLaneAI/pennylane-lightning.git - $ cd pennylane-lightning - $ docker build -f docker/Dockerfile --target ${TARGET} . + git clone https://github.com/PennyLaneAI/pennylane-lightning.git + cd pennylane-lightning + docker build -f docker/Dockerfile --target ${TARGET} . where ``${TARGET}`` is one of the following @@ -448,15 +489,26 @@ Please make your best effort to comply with `black` and `pylint` before using di Authors ******* +.. citation-start-inclusion-marker-do-not-remove + Lightning is the work of `many contributors `_. -If you are doing research using PennyLane and Lightning, please cite `our paper `_: +If you are using Lightning for research, please cite: + +.. code-block:: bibtex - Ville Bergholm, Josh Izaac, Maria Schuld, Christian Gogolin, M. Sohaib Alam, Shahnawaz Ahmed, - Juan Miguel Arrazola, Carsten Blank, Alain Delgado, Soran Jahangiri, Keri McKiernan, Johannes Jakob Meyer, - Zeyue Niu, Antal Száva, and Nathan Killoran. - *PennyLane: Automatic differentiation of hybrid quantum-classical computations.* 2018. arXiv:1811.04968 + @misc{ + asadi2024, + title={{Hybrid quantum programming with PennyLane Lightning on HPC platforms}}, + author={Ali Asadi and Amintor Dusko and Chae-Yeun Park and Vincent Michaud-Rioux and Isidor Schoch and Shuli Shu and Trevor Vincent and Lee James O'Riordan}, + year={2024}, + eprint={2403.02512}, + archivePrefix={arXiv}, + primaryClass={quant-ph}, + url={https://arxiv.org/abs/2403.02512}, + } +.. citation-end-inclusion-marker-do-not-remove .. support-start-inclusion-marker-do-not-remove Support @@ -491,6 +543,7 @@ PennyLane Lightning makes use of the following libraries and tools, which are un - **pybind11:** https://github.com/pybind/pybind11 - **Kokkos Core:** https://github.com/kokkos/kokkos - **NVIDIA cuQuantum:** https://developer.nvidia.com/cuquantum-sdk +- **scipy-openblas32:** https://pypi.org/project/scipy-openblas32/ - **Xanadu JET:** https://github.com/XanaduAI/jet .. acknowledgements-end-inclusion-marker-do-not-remove diff --git a/bin/auditwheel b/bin/auditwheel index 99acbb069b..1f697b0216 100755 --- a/bin/auditwheel +++ b/bin/auditwheel @@ -18,12 +18,17 @@ libs = [ "libcublas.so", "libcublas.so.11", "libcublas.so.12", + "libcusolver.so.11", + "libcusolver.so.12", "libcusparse.so", "libcusparse.so.11", "libcusparse.so.12", "libcustatevec.so", "libcustatevec.so.0", "libcustatevec.so.1", + "libcutensor.so.2", + "libcutensornet.so.2", + "libnvJitLink.so.12", ] print(f"Excluding {libs}") diff --git a/cmake/FindSCIPY_OPENBLAS32.cmake b/cmake/FindSCIPY_OPENBLAS32.cmake new file mode 100644 index 0000000000..dd6d40a5a3 --- /dev/null +++ b/cmake/FindSCIPY_OPENBLAS32.cmake @@ -0,0 +1,28 @@ +############################################################################################### +# This file provides macros to process scipy-openblas32 external libraries. +############################################################################################### + +# Include this only once +include_guard() + +macro(find_path_to_openblas SCIPY_OPENBLASE_LIB_PATH) + set(SCIPY_OPENBLAS_LIB_NAME "libscipy_openblas${CMAKE_SHARED_LIBRARY_SUFFIX}") + + find_file(SCIPY_OPENBLAS_LIB_FILE + NAMES ${SCIPY_OPENBLAS_LIB_NAME} + HINTS ${SCIPY_OPENBLAS}/lib + $ENV{SCIPY_OPENBLAS32}/lib + ${Python_SITELIB}/scipy_openblas32/lib + ${SCIPY_OPENBLAS} + $ENV{SCIPY_OPENBLAS32} + ENV LD_LIBRARY_PATH + ) + + if(NOT SCIPY_OPENBLAS_LIB_FILE) + message(FATAL_ERROR "\nUnable to find ${SCIPY_OPENBLAS_LIB_NAME}. Please set a SCIPY_OPENBLAS32 env variable to provide the path to scipy_openblas32.") + else() + cmake_path(GET SCIPY_OPENBLAS_LIB_FILE PARENT_PATH SCIPY_OPENBLAS_LIB_DIR) + set(${SCIPY_OPENBLASE_LIB_PATH} ${SCIPY_OPENBLAS_LIB_DIR}) + endif() +endmacro() + diff --git a/cmake/process_options.cmake b/cmake/process_options.cmake index ada5eb711b..9ded54c2a3 100644 --- a/cmake/process_options.cmake +++ b/cmake/process_options.cmake @@ -100,7 +100,3 @@ if (UNIX AND (${CMAKE_SYSTEM_PROCESSOR} MATCHES "(AMD64)|(X64)|(x64)|(x86_64)")) message(STATUS "ENABLE AVX for X64 on UNIX compatible system.") target_compile_options(lightning_compile_options INTERFACE -mavx) endif() - -if(ENABLE_LAPACK) - target_compile_options(lightning_compile_options INTERFACE "-DPL_USE_LAPACK=1") -endif() diff --git a/cmake/support_catalyst.cmake b/cmake/support_catalyst.cmake new file mode 100644 index 0000000000..9e8c8b6730 --- /dev/null +++ b/cmake/support_catalyst.cmake @@ -0,0 +1,74 @@ +############################################################################################### +# This file provides macros to process Catalyst. +############################################################################################### + +# Include this only once +include_guard() + +macro(FindCatalyst target_name) + if(LIGHTNING_CATALYST_SRC_PATH) + if(NOT IS_ABSOLUTE ${LIGHTNING_CATALYST_SRC_PATH}) + message(FATAL_ERROR " LIGHTNING_CATALYST_SRC_PATH=${LIGHTNING_CATALYST_SRC_PATH} must be set to an absolute path") + endif() + if(CATALYST_GIT_TAG) + message(WARN " Setting `LIGHTNING_CATALYST_SRC_PATH=${LIGHTNING_CATALYST_SRC_PATH}` overrides `CATALYST_GIT_TAG=${CATALYST_GIT_TAG}`") + endif() + + # Acquire local git hash and use for CATALYST_GIT_TAG + execute_process(COMMAND git rev-parse --short HEAD + WORKING_DIRECTORY ${LIGHTNING_CATALYST_SRC_PATH} + OUTPUT_VARIABLE CATALYST_GIT_TAG + ) + message(INFO " Building against local Catalyst - path: ${LIGHTNING_CATALYST_SRC_PATH} - GIT TAG: ${CATALYST_GIT_TAG}") + + target_include_directories(${target_name} PUBLIC ${LIGHTNING_CATALYST_SRC_PATH}/runtime/lib/backend/common) + target_include_directories(${target_name} PUBLIC ${LIGHTNING_CATALYST_SRC_PATH}/runtime/include) + + else() + if(NOT CATALYST_GIT_TAG) + set(CATALYST_GIT_TAG "main" CACHE STRING "GIT_TAG value to build Catalyst") + endif() + message(INFO " Building against Catalyst GIT TAG ${CATALYST_GIT_TAG}") + + # Fetching /lib/backend/common hpp headers + set(LIB_BACKEND_COMMON_HEADERS CacheManager.hpp + QubitManager.hpp + Utils.hpp + ) + + foreach(HEADER ${LIB_BACKEND_COMMON_HEADERS}) + string(REGEX REPLACE "\\.[^.]*$" "" HEADER_NAME ${HEADER}) + FetchContent_Declare( + ${HEADER_NAME} + URL https://raw.githubusercontent.com/PennyLaneAI/catalyst/${CATALYST_GIT_TAG}/runtime/lib/backend/common/${HEADER} + DOWNLOAD_NO_EXTRACT True + SOURCE_DIR ../../include + ) + + FetchContent_MakeAvailable(${HEADER_NAME}) + endforeach() + + # Fetching include hpp headers + set(INCLUDE_HEADERS DataView.hpp + Exception.hpp + QuantumDevice.hpp + RuntimeCAPI.h + Types.h + ) + + foreach(HEADER ${INCLUDE_HEADERS}) + string(REGEX REPLACE "\\.[^.]*$" "" HEADER_NAME ${HEADER}) + FetchContent_Declare( + ${HEADER_NAME} + URL https://raw.githubusercontent.com/PennyLaneAI/catalyst/${CATALYST_GIT_TAG}/runtime/include/${HEADER} + DOWNLOAD_NO_EXTRACT True + SOURCE_DIR ../../include + ) + + FetchContent_MakeAvailable(${HEADER_NAME}) + endforeach() + + target_include_directories(${target_name} PUBLIC ${CMAKE_CURRENT_BINARY_DIR}/../../include) + + endif() +endmacro() diff --git a/cmake/support_kokkos.cmake b/cmake/support_kokkos.cmake index 57978cdfd0..41a9087129 100644 --- a/cmake/support_kokkos.cmake +++ b/cmake/support_kokkos.cmake @@ -5,7 +5,7 @@ # Include this file only once include_guard() -set(KOKKOS_VERSION 4.3.01) +set(KOKKOS_VERSION 4.5.00) # Macro to aid in finding Kokkos with 3 potential install options: # 1. Fully integrated Kokkos packages and CMake module files diff --git a/cmake/support_pllgpu.cmake b/cmake/support_pllgpu.cmake index 490e02c075..3822040b23 100644 --- a/cmake/support_pllgpu.cmake +++ b/cmake/support_pllgpu.cmake @@ -47,6 +47,7 @@ endmacro() # Macro to aid in finding cuStateVec lib macro(findCustatevec external_libs) + set(CUQUANTUM_ENV "$ENV{CUQUANTUM_SDK}") find_library(CUSTATEVEC_LIB NAMES libcustatevec.so.1 custatevec.so.1 HINTS /usr/lib @@ -58,6 +59,8 @@ macro(findCustatevec external_libs) lib64 ${CUQUANTUM_SDK}/lib ${CUQUANTUM_SDK}/lib64 + ${CUQUANTUM_ENV}/lib + ${CUQUANTUM_ENV}/lib64 ${CUDAToolkit_LIBRARY_DIR} ${CUDA_TOOLKIT_ROOT_DIR}/lib ${CUDA_TOOLKIT_ROOT_DIR}/lib64 @@ -74,6 +77,7 @@ macro(findCustatevec external_libs) /opt/cuda include ${CUQUANTUM_SDK}/include + ${CUQUANTUM_ENV}/include ${CUDAToolkit_INCLUDE_DIRS} ${CUDA_TOOLKIT_ROOT_DIR}/include ${Python_SITELIB}/cuquantum/include diff --git a/doc/benchmark.rst b/doc/benchmark.rst index 132862c38c..92384c67dd 100644 --- a/doc/benchmark.rst +++ b/doc/benchmark.rst @@ -30,9 +30,6 @@ One can also choose a specific datatype by providing an option: $ ./benchmarks/plot_gate_benchmark.py --precision float ./bench_result.json # Results for std::complex $ ./benchmarks/plot_gate_benchmark.py --precision double ./bench_result.json # Results for std::complex - -Currently, we have two different kernels in ``Lightning Qubit`` named ``PI`` and ``LM``. For difference between two kernels, see the documents :cpp:class:`Pennylane::Gates::GateImplementationsPI` and :cpp:class:`Pennylane::Gates::GateImplementationsLM`. - Here are some example plots: .. image:: ./_static/benchmark/PauliX.png diff --git a/doc/conf.py b/doc/conf.py index b5e4c5bc18..25890ae756 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -21,6 +21,7 @@ # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. sys.path.insert(0, os.path.abspath("")) +sys.path.insert(0, os.path.abspath(".")) sys.path.insert(0, os.path.abspath("_ext")) sys.path.insert(0, os.path.join(os.path.dirname(os.path.abspath("doc")), "doc")) @@ -189,6 +190,8 @@ def __getattr__(cls, name): # This patterns also effect to html_static_path and html_extra_path exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"] +nbsphinx_execute = "never" + # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. show_authors = True diff --git a/doc/dev/installation.rst b/doc/dev/installation.rst index 54ac4cb73e..703010f8eb 100644 --- a/doc/dev/installation.rst +++ b/doc/dev/installation.rst @@ -1,26 +1,37 @@ Installation ############ -Each device in the Lightning ecosystem is a separate Python package. Select the device below for installation instructions: +Lightning Qubit comes with installing PennyLane. Please follow our `installation instructions on pennylane.ai `_ to install PennyLane. + +In most cases, installing Lightning GPU and Lightning Kokkos can be done by following our `high-performance computing installation instructions `_. + +Install from source +********************** + +.. note:: + In cases where our `general installation instructions `_ + do not suffice, please consider reading on. + +Select the device below for detailed source-installation instructions: .. title-card:: :name: Lightning Qubit - :description: Guidelines to installing and testing the Lightning Qubit device. + :description: Detailed guidelines to installing and testing the Lightning Qubit device. :link: ../lightning_qubit/installation.html .. title-card:: :name: Lightning GPU - :description: Guidelines to installing and testing the Lightning GPU device + :description: Detailed guidelines to installing and testing the Lightning GPU device :link: ../lightning_gpu/installation.html .. title-card:: :name: Lightning Kokkos - :description: Guidelines to installing and testing the Lightning Kokkos device + :description: Detailed guidelines to installing and testing the Lightning Kokkos device :link: ../lightning_kokkos/installation.html .. title-card:: :name: Lightning Tensor - :description: Guidelines to installing and testing the Lightning Tensor device + :description: Detailed guidelines to installing and testing the Lightning Tensor device :link: ../lightning_tensor/installation.html .. raw:: html diff --git a/doc/index.rst b/doc/index.rst index d73ddad326..b89f369e94 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -12,7 +12,6 @@ Lightning plugins :start-after: header-start-inclusion-marker-do-not-remove :end-before: header-end-inclusion-marker-do-not-remove - Devices ******* @@ -35,7 +34,7 @@ The Lightning ecosystem provides the following devices: .. title-card:: :name: 'lightning.tensor' - :description: A heterogeneous backend tensor network simulator with NVIDIA cuQuantum library support. + :description: A tensor network simulator with NVIDIA cuQuantum library support. :link: lightning_tensor/device.html .. raw:: html @@ -43,6 +42,18 @@ The Lightning ecosystem provides the following devices:

+Authors +******* + +.. include:: ../README.rst + :start-after: citation-start-inclusion-marker-do-not-remove + :end-before: citation-end-inclusion-marker-do-not-remove + +.. raw:: html + +
+
+ .. toctree:: :maxdepth: 2 :titlesonly: diff --git a/doc/lightning_gpu/device.rst b/doc/lightning_gpu/device.rst index 1f0d6d92d6..8d8d74a755 100644 --- a/doc/lightning_gpu/device.rst +++ b/doc/lightning_gpu/device.rst @@ -11,9 +11,9 @@ A ``lightning.gpu`` device can be loaded using: import pennylane as qml dev = qml.device("lightning.gpu", wires=2) -If the NVIDIA cuQuantum libraries are available, the above device will allow all operations to be performed on a CUDA capable GPU of generation SM 7.0 (Volta) and greater. If the libraries are not correctly installed, or available on path, the device will fall-back to ``lightning.qubit`` and perform all simulation on the CPU. +If the NVIDIA cuQuantum libraries are available, the above device will allow all operations to be performed on a CUDA capable GPU of generation SM 7.0 (Volta) and greater. If the libraries are not correctly installed, or available on path, the device will raise an error. -The ``lightning.gpu`` device also directly supports quantum circuit gradients using the adjoint differentiation method. This can be enabled at the PennyLane QNode level with: +The ``lightning.gpu`` device supports quantum circuit gradients using the adjoint differentiation method by default. This can be enabled at the PennyLane QNode level with: .. code-block:: python @@ -68,7 +68,6 @@ Supported operations and observables ~pennylane.PSWAP ~pennylane.QFT ~pennylane.QubitCarry - ~pennylane.QubitStateVector ~pennylane.QubitSum ~pennylane.QubitUnitary ~pennylane.Rot @@ -99,17 +98,17 @@ Supported operations and observables .. autosummary:: :nosignatures: - ~pennylane.ops.op_math.Exp - ~pennylane.Hadamard - ~pennylane.Hamiltonian - ~pennylane.Hermitian ~pennylane.Identity + ~pennylane.Hadamard ~pennylane.PauliX ~pennylane.PauliY ~pennylane.PauliZ - ~pennylane.ops.op_math.Prod ~pennylane.Projector + ~pennylane.Hermitian + ~pennylane.Hamiltonian ~pennylane.SparseHamiltonian + ~pennylane.ops.op_math.Exp + ~pennylane.ops.op_math.Prod ~pennylane.ops.op_math.SProd ~pennylane.ops.op_math.Sum @@ -281,3 +280,6 @@ To enable the memory-optimized adjoint method with MPI support, ``batch_obs`` sh dev = qml.device('lightning.gpu', wires= n_wires, mpi=True, batch_obs=True) For the adjoint method, each MPI process will provide the overall simulation results. + +.. note:: + The observable ``Projector``` does not have support with the multi-GPU backend. diff --git a/doc/lightning_kokkos/device.rst b/doc/lightning_kokkos/device.rst index 2016d27c31..60b09c9bfc 100644 --- a/doc/lightning_kokkos/device.rst +++ b/doc/lightning_kokkos/device.rst @@ -66,7 +66,6 @@ Supported operations and observables ~pennylane.PSWAP ~pennylane.QFT ~pennylane.QubitCarry - ~pennylane.QubitStateVector ~pennylane.QubitSum ~pennylane.QubitUnitary ~pennylane.Rot @@ -97,16 +96,17 @@ Supported operations and observables .. autosummary:: :nosignatures: - ~pennylane.ops.op_math.Exp - ~pennylane.Hadamard - ~pennylane.Hamiltonian - ~pennylane.Hermitian ~pennylane.Identity + ~pennylane.Hadamard ~pennylane.PauliX ~pennylane.PauliY ~pennylane.PauliZ - ~pennylane.ops.op_math.Prod + ~pennylane.Projector + ~pennylane.Hermitian + ~pennylane.Hamiltonian ~pennylane.SparseHamiltonian + ~pennylane.ops.op_math.Exp + ~pennylane.ops.op_math.Prod ~pennylane.ops.op_math.SProd ~pennylane.ops.op_math.Sum diff --git a/doc/lightning_qubit/development/add_gate_kernel.rst b/doc/lightning_qubit/development/add_gate_kernel.rst index c4785ed358..09f9177bf6 100644 --- a/doc/lightning_qubit/development/add_gate_kernel.rst +++ b/doc/lightning_qubit/development/add_gate_kernel.rst @@ -21,12 +21,12 @@ In this case, you may first create a file and add a class: /* This defines the required alignment for this kernel. If there is no special requirement, using std::alignment_of_v is sufficient. */ template - constexpr static size_t required_alignment = std::alignment_of_v; + constexpr static std::size_t required_alignment = std::alignment_of_v; template static void applyPauliX(std::complex* data, - size_t num_qubits, - const std::vector& wires, + std::size_t num_qubits, + const std::vector& wires, [[maybe_unused]] bool inverse) { /* Write your implementation */ ... @@ -40,7 +40,7 @@ This can be done by modifying two files: // file: gates/KernelType.hpp namespace Pennylane { - enum class KernelType { PI, LM, MyKernel /* This is added */, None }; + enum class KernelType { LM, MyKernel /* This is added */, None }; /* Rest of the file */ @@ -53,7 +53,6 @@ and // file: gates/AvailableKernels.hpp namespace Pennylane { using AvailableKernels = Util::TypeList; } // namespace Pennylane @@ -111,7 +110,6 @@ To test your own kernel implementations, you can go to ``tests/TestKernels.hpp`` .. code-block:: cpp using TestKernels = Pennylane::Util::TypeList; It will automatically test your gate implementation. diff --git a/doc/lightning_qubit/development/avx_kernels/build_system.rst b/doc/lightning_qubit/development/avx_kernels/build_system.rst index 575d9c9795..a630e33471 100644 --- a/doc/lightning_qubit/development/avx_kernels/build_system.rst +++ b/doc/lightning_qubit/development/avx_kernels/build_system.rst @@ -8,11 +8,10 @@ when the library is loaded, and it is used at runtime when it is the most suitab To support AVX2 and AVX512 kernels, we always compile those kernels if the target system is UNIX on x86-64. Specifically, we made separate C++ files for AVX2 and AVX512 kernels and build them as a static library with the corresponding compile options. This is handled by CMake. One can check ``gates/CMakeLists.txt`` file for details. -One caveat is that we want to make sure that default kernels (``KernelType::PI`` and ``KernelType::LM``) are only instantiated once with specific compiler flags during the compile process. +One caveat is that we want to ensure that default ``KernelType::LM`` kernels are only instantiated once with specific compiler flags during the compile process. This is important as the linker sometimes cannot choose the right instantiation when there are multiple instantiations of the same template class. This problem does not arise when all instantiations are compiled with the same options, but with the AVX2/512 kernels, we use different compile options for each translation unit. We solve this problem by adding explicit instantiation declarations in the header files for these kernels -(:ref:`file_pennylane_lightning_core_src_simulators_lightning_qubit_gates_cpu_kernels_GateImplementationsLM.hpp` and -:ref:`file_pennylane_lightning_core_src_simulators_lightning_qubit_gates_cpu_kernels_GateImplementationsPI.hpp`) +(:ref:`file_pennylane_lightning_core_src_simulators_lightning_qubit_gates_cpu_kernels_GateImplementationsLM.hpp`) and compile them as a separate static library. With this, the AVX2/512 kernels are always included in the binary when compiled for UNIX-compatible OSs on x86-64 architecture. diff --git a/doc/lightning_qubit/development/avx_kernels/implementation.rst b/doc/lightning_qubit/development/avx_kernels/implementation.rst index 12bdf6f8e6..2d762124f0 100644 --- a/doc/lightning_qubit/development/avx_kernels/implementation.rst +++ b/doc/lightning_qubit/development/avx_kernels/implementation.rst @@ -42,9 +42,9 @@ The following simple (C++ style) pseudocode shows how the algorithm is implement .. code-block:: - template + template class ApplyPauliX { - template + template void applyInternal(...) { // Within a row permutation = compute a permutation within a row for a given wire @@ -54,7 +54,7 @@ The following simple (C++ style) pseudocode shows how the algorithm is implement save row to the memory } } - void applyExternal(size_t wire, ...) { + void applyExternal(std::size_t wire, ...) { // Between rows for proper index k { row1 = load k-th row diff --git a/doc/lightning_qubit/device.rst b/doc/lightning_qubit/device.rst index 4a3e3535e2..43140350f0 100644 --- a/doc/lightning_qubit/device.rst +++ b/doc/lightning_qubit/device.rst @@ -58,7 +58,6 @@ Supported operations and observables ~pennylane.PSWAP ~pennylane.QFT ~pennylane.QubitCarry - ~pennylane.QubitStateVector ~pennylane.QubitSum ~pennylane.QubitUnitary ~pennylane.Rot @@ -89,17 +88,17 @@ Supported operations and observables .. autosummary:: :nosignatures: - ~pennylane.ops.op_math.Exp - ~pennylane.Hadamard - ~pennylane.Hamiltonian - ~pennylane.Hermitian ~pennylane.Identity + ~pennylane.Hadamard ~pennylane.PauliX ~pennylane.PauliY ~pennylane.PauliZ - ~pennylane.ops.op_math.Prod ~pennylane.Projector + ~pennylane.Hermitian + ~pennylane.Hamiltonian ~pennylane.SparseHamiltonian + ~pennylane.ops.op_math.Exp + ~pennylane.ops.op_math.Prod ~pennylane.ops.op_math.SProd ~pennylane.ops.op_math.Sum diff --git a/doc/lightning_tensor/device.rst b/doc/lightning_tensor/device.rst index 109e909408..d1881498c8 100644 --- a/doc/lightning_tensor/device.rst +++ b/doc/lightning_tensor/device.rst @@ -1,37 +1,27 @@ Lightning Tensor device ======================= -The ``lightning.tensor`` device is a tensor network simulator device. The device is built on top of the `cutensornet `__ from the NVIDIA cuQuantum SDK, enabling GPU-accelerated simulation of quantum tensor network evolution. +The ``lightning.tensor`` device is a tensor network simulator, supporting both the Matrix Product State (MPS) and Exact Tensor Network methods. The device is built on top of the `cutensornet `__ from the NVIDIA cuQuantum SDK, enabling GPU-accelerated simulation of quantum tensor network evolution. This device is designed to simulate large-scale quantum circuits using tensor networks. For small circuits, state-vector simulator plugins may be more suitable. -A ``lightning.tensor`` device can be loaded simply using: +The ``lightning.tensor`` device defaults to the Matrix Product State (MPS) method, and can be loaded using: .. code-block:: python import pennylane as qml dev = qml.device("lightning.tensor", wires=100) -By default, the device represents the quantum state approximated as a Matrix Product State (MPS). +By default, the device approximates the quantum state using an MPS. The default setup for the MPS tensor network approximation is: - ``max_bond_dim`` (maximum bond dimension) defaults to ``128`` . - ``cutoff`` (singular value truncation threshold) defaults to ``0`` . - - ``cutoff_mode`` (singular value truncation mode) defaults to ``abs`` , considering the absolute values of the singular values; Alternatively, users can opt to set ``cutoff_mode`` to ``rel`` to consider the relative values of the singular values. + - ``cutoff_mode`` (singular value truncation mode) defaults to ``abs`` , considering the absolute values of the singular values; Alternatively, users can opt to set ``cutoff_mode`` to ``rel`` to consider the relative values of the singular values. +Note that the ``cutensornet`` will automatically determine the reduced extent of the bond dimension based on the lowest among the multiple truncation cutoffs (``max_bond_dim``, ``cutoff-abs`` and ``cutoff-rel``). For more details on how the ``cutoff`` works, please check the `cuQuantum documentation `__. -The ``lightning.tensor`` device dispatches all operations to be performed on a CUDA-capable GPU of generation SM 7.0 (Volta) -and greater. - -.. note:: - Given the inherent parallelism of GPUs, simulations with intensive parallel computation, such as those with larger maximum - bond dimensions, stand to gain the most from the computational power offered by GPU and those simulations can benifit from the - ``lightning.tensor`` device. It's worth noting that if the bond dimension used in the simulation is small, the ``lightning.tensor`` - device with ``MPS`` running a GPU may perform slower compared to a ``default.tensor`` device with ``MPS`` running on a CPU. For more details - on how bond dimension affects the simulation performance, please refer to the ``Approximate Tensor Network Methods`` section in the `cuQuantum SDK `__. - -Users also have the flexibility to customize these parameters according to their specific needs with: +Users also have the flexibility to customize MPS parameters according to their specific needs with: .. code-block:: python import pennylane as qml - import numpy as np num_qubits = 100 @@ -43,7 +33,21 @@ Users also have the flexibility to customize these parameters according to their dev = qml.device("lightning.tensor", wires=num_qubits, method="mps", **device_kwargs_mps) -The ``lightning.tensor`` device allows users to get quantum circuit gradients using the ``parameter-shift`` method. This can be enabled at the PennyLane ``QNode`` level with: +Users can also run the ``lightning.tensor`` device in the **Exact Tensor Network** mode by setting the ``method`` argument to ``"tn"``: + +.. code-block:: python + + import pennylane as qml + dev = qml.device("lightning.tensor", wires=100, method="tn") + +The lightning.tensor device dispatches all operations to be performed on a CUDA-capable GPU of generation SM 7.0+ (Volta and later) +and greater. This device supports both exact and finite shots measurements. Currently, the supported differentiation methods are parameter-shift and finite-diff. Note that the MPS backend of ``lightning.tensor`` supports multi-wire gates via Matrix Product Operators (MPO). + +The ``lightning.tensor`` device is designed for expectation value calculations. Measurements of :func:`~pennylane.probs` or :func:`~pennylane.state` return dense vectors of dimension :math:`2^{\text{n_qubits}}`, so they should only be used for small systems. + +.. note:: Currently only single-wire :class:`~pennylane.Hermitian` observables are supported. You can use :func:`~pennylane.pauli_decompose` on smaller matrices to obtain a compatible Pauli decomposition in the meantime. + +The ``lightning.tensor`` device allows users to get quantum circuit gradients using the :func:`~pennylane.gradients.param_shift` method. This can be enabled at the PennyLane :class:`~pennylane.QNode` level with: .. code-block:: python @@ -55,10 +59,13 @@ Check out the :doc:`/lightning_tensor/installation` guide for more information. .. seealso:: `DefaultTensor `__ for a CPU only tensor network simulator device. +Note that as ``lightning.tensor`` cannot be cleaned up like other state-vector devices because the data is attached to the graph. It is recommended to create a new ``lightning.tensor`` device per circuit to ensure resources are correctly handled. + + Operations and observables support ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -The "lightning.tensor" supports 1- and 2-wire gate operations and all other operations that can be decomposed to that by PennyLane. +The ``lightning.tensor`` supports all gate operations supported by PennyLane, with the exception of :class:`~pennylane.StatePrep`, which is *not supported* by the *Exact Tensor Network* method. **Supported operations:** @@ -83,7 +90,10 @@ The "lightning.tensor" supports 1- and 2-wire gate operations and all other oper ~pennylane.CZ ~pennylane.DiagonalQubitUnitary ~pennylane.DoubleExcitation + ~pennylane.DoubleExcitationMinus + ~pennylane.DoubleExcitationPlus ~pennylane.ECR + ~pennylane.GlobalPhase ~pennylane.Hadamard ~pennylane.Identity ~pennylane.IsingXX @@ -109,6 +119,7 @@ The "lightning.tensor" supports 1- and 2-wire gate operations and all other oper ~pennylane.SingleExcitation ~pennylane.SingleExcitationMinus ~pennylane.SingleExcitationPlus + ~pennylane.StatePrep ~pennylane.SISWAP ~pennylane.SQISW ~pennylane.SWAP @@ -120,30 +131,12 @@ The "lightning.tensor" supports 1- and 2-wire gate operations and all other oper -**Unsupported operations:** - -.. raw:: html - -
- -.. autosummary:: - :nosignatures: - - ~pennylane.StatePrep - ~pennylane.QubitStateVector - ~pennylane.DoubleExcitationMinus - ~pennylane.DoubleExcitationPlus - ~pennylane.GlobalPhase - -.. raw:: html - -
**Supported observables:** -The ``lightning.tensor`` supports all observables supported by the Lightning state-vector simulators, besides ``qml.SparseHamiltonian``, ``qml.Projector`` and limited support to ``qml.Hamiltonian``, ``qml.Prod``. +The ``lightning.tensor`` supports all observables supported by the Lightning state-vector simulators, besides :class:`~pennylane.SparseHamiltonian`, :class:`~pennylane.Projector` and limited support to :class:`~pennylane.ops.op_math.Sum`, :class:`~pennylane.ops.op_math.Prod` since ``lightning.tensor`` only supports 1-wire :class:`~pennylane.Hermitian` observables. -Users can not create a ``Hamiltonian`` or ``Prod`` observable from ``Hamiltonian`` observables. +Users cannot create a :class:`~pennylane.ops.op_math.Sum` observable or a :class:`~pennylane.ops.op_math.Prod` observable from :class:`~pennylane.ops.op_math.Sum` observables. @@ -156,7 +149,6 @@ Users can not create a ``Hamiltonian`` or ``Prod`` observable from ``Hamiltonian ~pennylane.ops.op_math.Exp ~pennylane.Hadamard - ~pennylane.Hamiltonian ~pennylane.Hermitian ~pennylane.Identity ~pennylane.PauliX @@ -184,4 +176,4 @@ Users can not create a ``Hamiltonian`` or ``Prod`` observable from ``Hamiltonian .. raw:: html - \ No newline at end of file + diff --git a/doc/requirements.txt b/doc/requirements.txt index 94b2a1df82..70f89d4032 100644 --- a/doc/requirements.txt +++ b/doc/requirements.txt @@ -1,3 +1,5 @@ +tomlkit +build breathe docutils==0.16 exhale>=0.3.3 @@ -16,3 +18,4 @@ cutensornet-cu12 wheel sphinxext-opengraph matplotlib +git+https://github.com/PennyLaneAI/pennylane.git@master diff --git a/docker/Dockerfile b/docker/Dockerfile index cff4ffdf50..a538aba390 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -73,14 +73,14 @@ RUN python3 -m venv $VIRTUAL_ENV ENV PATH="$VIRTUAL_ENV/bin:$PATH" RUN rm -rf tmp && git clone --depth 1 --branch ${LIGHTNING_VERSION} https://github.com/PennyLaneAI/pennylane-lightning.git tmp\ && mv tmp/* /opt/pennylane-lightning && rm -rf tmp -RUN pip install --no-cache-dir cmake ninja pybind11 wheel +RUN pip install --no-cache-dir build cmake ninja pybind11 toml wheel # Download Lightning release and build lightning-qubit backend FROM base-build-python AS build-wheel-lightning-qubit WORKDIR /opt/pennylane-lightning RUN pip uninstall -y pennylane-lightning -RUN python setup.py build_ext -RUN python setup.py bdist_wheel +RUN python scripts/configure_pyproject_toml.py || true +RUN python -m build --wheel # Install lightning-qubit backend FROM base-runtime AS wheel-lightning-qubit @@ -93,8 +93,8 @@ FROM base-build-python AS build-wheel-lightning-kokkos-openmp WORKDIR /opt/pennylane-lightning ENV PL_BACKEND=lightning_kokkos RUN pip uninstall -y pennylane-lightning -RUN python setup.py build_ext -i --define="Kokkos_ENABLE_SERIAL:BOOL=ON;Kokkos_ENABLE_OPENMP:BOOL=ON" -RUN python setup.py bdist_wheel +RUN python scripts/configure_pyproject_toml.py || true +RUN CMAKE_ARGS="-DKokkos_ENABLE_SERIAL:BOOL=ON -DKokkos_ENABLE_OPENMP:BOOL=ON" python -m build --wheel # Install lightning-kokkos OpenMP backend FROM base-runtime AS wheel-lightning-kokkos-openmp @@ -118,8 +118,8 @@ WORKDIR /opt/pennylane-lightning ENV PL_BACKEND=lightning_kokkos RUN pip uninstall -y pennylane-lightning RUN echo >> cmake/support_kokkos.cmake && echo "find_package(CUDAToolkit REQUIRED)" >> cmake/support_kokkos.cmake -RUN python setup.py build_ext -i --define="Kokkos_ENABLE_SERIAL:BOOL=ON;Kokkos_ENABLE_OPENMP:BOOL=ON;Kokkos_ENABLE_CUDA:BOOL=ON;Kokkos_ARCH_${CUDA_ARCH}=ON" -RUN python setup.py bdist_wheel +RUN python scripts/configure_pyproject_toml.py || true +RUN CMAKE_ARGS="-DKokkos_ENABLE_SERIAL:BOOL=ON -DKokkos_ENABLE_OPENMP:BOOL=ON -DKokkos_ENABLE_CUDA:BOOL=ON -DKokkos_ARCH_${CUDA_ARCH}=ON" python -m build --wheel # Install python3 and setup runtime virtual env in CUDA-12-runtime image (includes CUDA runtime and math libraries) # Install lightning-kokkos CUDA backend @@ -148,8 +148,9 @@ WORKDIR /opt/pennylane-lightning ENV PL_BACKEND=lightning_gpu RUN pip install --no-cache-dir wheel custatevec-cu12 RUN pip uninstall -y pennylane-lightning -RUN CUQUANTUM_SDK=$(python -c "import site; print( f'{site.getsitepackages()[0]}/cuquantum/lib')") python setup.py build_ext -RUN python setup.py bdist_wheel +RUN python scripts/configure_pyproject_toml.py || true +RUN CUQUANTUM_SDK=$(python -c "import site; print( f'{site.getsitepackages()[0]}/cuquantum/lib')") python -m build --wheel + # Install python3 and setup runtime virtual env in CUDA-12-runtime image (includes CUDA runtime and math libraries) # Install lightning-kokkos CUDA backend @@ -192,8 +193,8 @@ ENV CMAKE_PREFIX_PATH=/opt/rocm:$CMAKE_PREFIX_PATH ENV CXX=hipcc ENV PL_BACKEND=lightning_kokkos RUN pip uninstall -y pennylane-lightning -RUN python setup.py build_ext -i --define="Kokkos_ENABLE_SERIAL:BOOL=ON;Kokkos_ENABLE_OPENMP:BOOL=ON;Kokkos_ENABLE_HIP:BOOL=ON;Kokkos_ARCH_${AMD_ARCH}=ON" -RUN python setup.py bdist_wheel +RUN python scripts/configure_pyproject_toml.py || true +RUN CMAKE_ARGS="-DKokkos_ENABLE_SERIAL:BOOL=ON -DKokkos_ENABLE_OPENMP:BOOL=ON -DKokkos_ENABLE_HIP:BOOL=ON -DKokkos_ARCH_${AMD_ARCH}=ON" python -m build --wheel # Install lightning-kokkos HIP backend FROM rocm/dev-ubuntu-22.04:5.7 AS wheel-lightning-kokkos-rocm diff --git a/mpitests/conftest.py b/mpitests/conftest.py index a2084f2a5d..552cf9f330 100644 --- a/mpitests/conftest.py +++ b/mpitests/conftest.py @@ -98,6 +98,13 @@ def get_device(): # Device specification if device_name == "lightning.gpu": from pennylane_lightning.lightning_gpu import LightningGPU as LightningDevice + from pennylane_lightning.lightning_gpu._measurements import ( + LightningGPUMeasurements as LightningMeasurements, + ) + from pennylane_lightning.lightning_gpu._state_vector import ( + LightningGPUStateVector as LightningStateVector, + ) + else: raise qml.DeviceError(f"The MPI tests do not apply to the {device_name} device.") diff --git a/mpitests/test_adjoint_jacobian.py b/mpitests/test_adjoint_jacobian.py index 4b6eef51e3..b1c768abaa 100644 --- a/mpitests/test_adjoint_jacobian.py +++ b/mpitests/test_adjoint_jacobian.py @@ -26,17 +26,15 @@ from pennylane import QNode from pennylane import numpy as np from pennylane import qnode +from pennylane.devices import ExecutionConfig +from pennylane.tape import QuantumScript from scipy.stats import unitary_group +from pennylane_lightning.lightning_gpu_ops import LightningException + if not ld._CPP_BINARY_AVAILABLE: pytest.skip("No binary module found. Skipping.", allow_module_level=True) -I, X, Y, Z = ( - np.eye(2), - qml.PauliX.compute_matrix(), - qml.PauliY.compute_matrix(), - qml.PauliZ.compute_matrix(), -) # Tuple passed to distributed device ctor # np.complex for data type and True or False @@ -59,265 +57,253 @@ def fixture_dev(request): ) -def Rx(theta): - r"""One-qubit rotation about the x axis. - - Args: - theta (float): rotation angle - Returns: - array: unitary 2x2 rotation matrix :math:`e^{-i \sigma_x \theta/2}` - """ - return math.cos(theta / 2) * I + 1j * math.sin(-theta / 2) * X - - -def Ry(theta): - r"""One-qubit rotation about the y axis. - - Args: - theta (float): rotation angle - Returns: - array: unitary 2x2 rotation matrix :math:`e^{-i \sigma_y \theta/2}` - """ - return math.cos(theta / 2) * I + 1j * math.sin(-theta / 2) * Y - - -def Rz(theta): - r"""One-qubit rotation about the z axis. - - Args: - theta (float): rotation angle - Returns: - array: unitary 2x2 rotation matrix :math:`e^{-i \sigma_z \theta/2}` - """ - return math.cos(theta / 2) * I + 1j * math.sin(-theta / 2) * Z - - class TestAdjointJacobian: # pylint: disable=too-many-public-methods """Tests for the adjoint_jacobian method""" - def test_not_expval(self, dev): + @pytest.mark.parametrize("batch_obs", [True, False]) + def test_not_expval(self, dev, batch_obs): """Test if a QuantumFunctionError is raised for a tape with measurements that are not expectation values""" - with qml.tape.QuantumTape() as tape: - qml.RX(0.1, wires=0) - qml.var(qml.PauliZ(0)) + qs = QuantumScript([qml.RX(1.23, 0)], [qml.var(qml.PauliZ(0))], trainable_params=[0]) + config = ExecutionConfig(gradient_method="adjoint", device_options={"batch_obs": batch_obs}) with pytest.raises( qml.QuantumFunctionError, match="Adjoint differentiation method does not" ): - dev.adjoint_jacobian(tape) + dev.compute_derivatives(qs, config) - with qml.tape.QuantumTape() as tape: - qml.RX(0.1, wires=0) - qml.state() + qs = QuantumScript([qml.RX(1.23, 0)], [qml.state()], trainable_params=[0]) - if device_name == "lightning.gpu": - message = "Adjoint differentiation does not support State measurements." - else: - message = "Adjoint differentiation method does not support measurement StateMP." with pytest.raises( qml.QuantumFunctionError, - match=message, + match="Adjoint differentiation method does not support measurement StateMP.", ): - dev.adjoint_jacobian(tape) + dev.compute_derivatives(qs, config) - def test_finite_shots_warns(self): + @pytest.mark.parametrize("batch_obs", [True, False]) + def test_finite_shots_warns(self, dev, batch_obs): """Tests warning raised when finite shots specified""" - dev = qml.device(device_name, wires=8, mpi=True, shots=1) - - with qml.tape.QuantumTape() as tape: - qml.expval(qml.PauliZ(0)) + qs = QuantumScript( + [qml.RX(1.23, 0)], [qml.expval(qml.Z(0))], shots=10, trainable_params=[0] + ) + config = ExecutionConfig(gradient_method="adjoint", device_options={"batch_obs": batch_obs}) - with pytest.warns( - UserWarning, + with pytest.raises( + qml.QuantumFunctionError, match="Requested adjoint differentiation to be computed with finite shots.", ): - dev.adjoint_jacobian(tape) + dev.compute_derivatives(qs, config) def test_empty_measurements(self, dev): """Tests if an empty array is returned when the measurements of the tape is empty.""" - with qml.tape.QuantumTape() as tape: + def circuit(): qml.RX(0.4, wires=[0]) + return qml.expval(qml.PauliZ(0)) + + result = QNode(circuit, dev, diff_method="adjoint") + + jac = qml.grad(result)() - jac = dev.adjoint_jacobian(tape) assert len(jac) == 0 - def test_unsupported_op(self, dev): + @pytest.mark.parametrize("batch_obs", [True, False]) + def test_unsupported_op(self, batch_obs, dev): """Test if a QuantumFunctionError is raised for an unsupported operation, i.e., multi-parameter operations that are not qml.Rot""" - with qml.tape.QuantumTape() as tape: - qml.CRot(0.1, 0.2, 0.3, wires=[0, 1]) - qml.expval(qml.PauliZ(0)) + qs = QuantumScript( + [qml.CRot(0.1, 0.2, 0.3, wires=[0, 1])], + [qml.expval(qml.PauliZ(0))], + trainable_params=[0], + ) + config = ExecutionConfig(gradient_method="adjoint", device_options={"batch_obs": batch_obs}) with pytest.raises( - qml.QuantumFunctionError, - match="The CRot operation is not supported using the", + LightningException, + match="The operation is not supported using the adjoint differentiation method", ): - dev.adjoint_jacobian(tape) + dev.compute_derivatives(qs, config) - def test_proj_unsupported(self, dev): + @pytest.mark.skip("WIP: Need a deep review if LGPU accept Projector") + @pytest.mark.parametrize("batch_obs", [True, False]) + def test_proj_unsupported(self, batch_obs, dev): """Test if a QuantumFunctionError is raised for a Projector observable""" - with qml.tape.QuantumTape() as tape: - qml.CRX(0.1, wires=[0, 1]) - qml.expval(qml.Projector([0, 1], wires=[0, 1])) + + config = ExecutionConfig(gradient_method="adjoint", device_options={"batch_obs": batch_obs}) + + qs = QuantumScript( + [qml.CRX(0.1, wires=[0, 1])], + [qml.expval(qml.Projector([0, 1], wires=[0, 1]))], + trainable_params=[0], + ) with pytest.raises( qml.QuantumFunctionError, match="differentiation method does not support the Projector", ): - dev.adjoint_jacobian(tape) + dev.compute_derivatives(qs, config) - with qml.tape.QuantumTape() as tape: - qml.CRX(0.1, wires=[0, 1]) - qml.expval(qml.Projector([0], wires=[0]) @ qml.PauliZ(0)) + qs = QuantumScript( + [qml.CRX(0.1, wires=[0, 1])], + [qml.expval(qml.Projector([0], wires=[0]) @ qml.PauliZ(0))], + trainable_params=[0], + ) with pytest.raises( qml.QuantumFunctionError, match="differentiation method does not support the Projector", ): - dev.adjoint_jacobian(tape) + dev.compute_derivatives(qs, config) + + @staticmethod + def tol_for_allclose(c_dtype): + """Compute the tolerance for allclose""" + return 1e-3 if c_dtype == np.complex64 else 1e-7 @pytest.mark.parametrize("theta", np.linspace(-2 * np.pi, 2 * np.pi, 7)) @pytest.mark.parametrize("G", [qml.RX, qml.RY, qml.RZ]) - @pytest.mark.parametrize("stateprep", [qml.QubitStateVector, qml.StatePrep]) - def test_pauli_rotation_gradient(self, stateprep, G, theta, dev): + @pytest.mark.parametrize("batch_obs", [True, False]) + def test_pauli_rotation_gradient( + self, G, theta, batch_obs, dev + ): # pylint: disable=too-many-arguments """Tests that the automatic gradients of Pauli rotations are correct.""" random_state = np.array( [0.43593284 - 0.02945156j, 0.40812291 + 0.80158023j], requires_grad=False ) - tape = qml.tape.QuantumScript( - [stateprep(random_state, 0), G(theta, 0)], [qml.expval(qml.PauliZ(0))] + qs = QuantumScript( + [qml.StatePrep(random_state, 0), G(theta, 0)], + [qml.expval(qml.PauliZ(0))], + trainable_params=[1], ) + config = ExecutionConfig(gradient_method="adjoint", device_options={"batch_obs": batch_obs}) - tape.trainable_params = {1} - - calculated_val = dev.adjoint_jacobian(tape) + calculated_val = dev.compute_derivatives(qs, config) - tol = 1e-3 if dev.R_DTYPE == np.float32 else 1e-7 + tol = self.tol_for_allclose(dev.c_dtype) # compare to finite differences - tapes, fn = qml.gradients.param_shift(tape) + tapes, fn = qml.gradients.param_shift(qs) numeric_val = fn(qml.execute(tapes, dev, None)) assert np.allclose(calculated_val, numeric_val, atol=tol, rtol=0) @pytest.mark.parametrize("theta", np.linspace(-2 * np.pi, 2 * np.pi, 7)) - @pytest.mark.parametrize("stateprep", [qml.QubitStateVector, qml.StatePrep]) - def test_Rot_gradient(self, stateprep, theta, dev): + @pytest.mark.parametrize("batch_obs", [True, False]) + def test_Rot_gradient(self, theta, batch_obs, dev): """Tests that the device gradient of an arbitrary Euler-angle-parameterized gate is correct.""" params = np.array([theta, theta**3, np.sqrt(2) * theta]) - with qml.tape.QuantumTape() as tape: - stateprep(np.array([1.0, -1.0], requires_grad=False) / np.sqrt(2), wires=0) - qml.Rot(*params, wires=[0]) - qml.expval(qml.PauliZ(0)) + qs = QuantumScript( + [ + qml.StatePrep(np.array([1.0, -1.0], requires_grad=False) / np.sqrt(2), wires=0), + qml.Rot(*params, wires=[0]), + ], + [qml.expval(qml.PauliZ(0))], + trainable_params=[1, 2, 3], + ) - tape.trainable_params = {1, 2, 3} + config = ExecutionConfig(gradient_method="adjoint", device_options={"batch_obs": batch_obs}) - calculated_val = dev.adjoint_jacobian(tape) + calculated_val = dev.compute_derivatives(qs, config) - tol = 1e-3 if dev.R_DTYPE == np.float32 else 1e-7 + tol = self.tol_for_allclose(dev.c_dtype) # compare to finite differences - tapes, fn = qml.gradients.param_shift(tape) + tapes, fn = qml.gradients.param_shift(qs) numeric_val = fn(qml.execute(tapes, dev, None)) assert np.allclose(calculated_val, numeric_val, atol=tol, rtol=0) - @pytest.mark.parametrize("par", [1, -2, 1.623, -0.051, 0]) # integers, floats, zero - def test_ry_gradient(self, par, tol, dev): - """Test that the gradient of the RY gate matches the exact analytic formula.""" - with qml.tape.QuantumTape() as tape: - qml.RY(par, wires=[0]) - qml.expval(qml.PauliX(0)) - - tape.trainable_params = {0} + @pytest.mark.parametrize("param", [1, -2, 1.623, -0.051, 0]) # integers, floats, zero + @pytest.mark.parametrize( + "rotation, meas, expected_func", + [ + (qml.RY, qml.PauliX, lambda x: np.cos(x)), # pylint: disable=unnecessary-lambda + (qml.RX, qml.PauliZ, lambda x: -np.sin(x)), # pylint: disable=unnecessary-lambda + ], + ) + @pytest.mark.parametrize("batch_obs", [True, False]) + def test_r_gradient( + self, tol, param, rotation, meas, expected_func, batch_obs, dev + ): # pylint: disable=too-many-arguments + """Test for the gradient of the rotation gate matches the known formula.""" - # gradients - exact = np.cos(par) - grad_A = dev.adjoint_jacobian(tape) + qs = QuantumScript( + [rotation(param, wires=0)], + [qml.expval(meas(0))], + trainable_params=[0], + ) - # different methods must agree - assert np.allclose(grad_A, exact, atol=tol, rtol=0) + config = ExecutionConfig(gradient_method="adjoint", device_options={"batch_obs": batch_obs}) - def test_rx_gradient(self, tol, dev): - """Test that the gradient of the RX gate matches the known formula.""" - a = 0.7418 + # circuit jacobians + dev_jacobian = dev.compute_derivatives(qs, config) + expected_jacobian = expected_func(param) + assert np.allclose(dev_jacobian, expected_jacobian, atol=tol, rtol=0) - with qml.tape.QuantumTape() as tape: - qml.RX(a, wires=0) - qml.expval(qml.PauliZ(0)) + @staticmethod + def process_and_execute_multiple_rx(dev, params, meas, batch_obs): + """Compute the circuit with multiple RX gates""" + qs = QuantumScript( + [qml.RX(params[0], wires=0), qml.RX(params[1], wires=1), qml.RX(params[2], wires=2)], + meas, + trainable_params=[0, 1, 2], + ) + config = ExecutionConfig(gradient_method="adjoint", device_options={"batch_obs": batch_obs}) # circuit jacobians - dev_jacobian = dev.adjoint_jacobian(tape) - expected_jacobian = -np.sin(a) - assert np.allclose(dev_jacobian, expected_jacobian, atol=tol, rtol=0) + dev_jacobian = dev.compute_derivatives(qs, config) + + return dev_jacobian - def test_multiple_rx_gradient_pauliz(self, tol, dev): + @pytest.mark.parametrize("batch_obs", [True, False]) + def test_multiple_rx_gradient_pauliz(self, tol, batch_obs, dev): """Tests that the gradient of multiple RX gates in a circuit yields the correct result.""" params = np.array([np.pi, np.pi / 2, np.pi / 3]) - with qml.tape.QuantumTape() as tape: - qml.RX(params[0], wires=0) - qml.RX(params[1], wires=1) - qml.RX(params[2], wires=2) - - for idx in range(3): - qml.expval(qml.PauliZ(idx)) + meas = [qml.expval(qml.PauliZ(idx)) for idx in range(3)] # circuit jacobians - dev_jacobian = dev.adjoint_jacobian(tape) + dev_jacobian = self.process_and_execute_multiple_rx(dev, params, meas, batch_obs) expected_jacobian = -np.diag(np.sin(params)) assert np.allclose(dev_jacobian, expected_jacobian, atol=tol, rtol=0) - def test_multiple_rx_gradient_hermitian(self, tol, dev): + @pytest.mark.parametrize("batch_obs", [True, False]) + def test_multiple_rx_gradient_hermitian(self, tol, batch_obs, dev): """Tests that the gradient of multiple RX gates in a circuit yields the correct result with Hermitian observable """ - params = np.array([np.pi, np.pi / 2, np.pi / 3]) - with qml.tape.QuantumTape() as tape: - qml.RX(params[0], wires=0) - qml.RX(params[1], wires=1) - qml.RX(params[2], wires=2) + params = np.array([np.pi, np.pi / 2, np.pi / 3]) - for idx in range(3): - qml.expval(qml.Hermitian([[1, 0], [0, -1]], wires=[idx])) + meas = [qml.expval(qml.Hermitian([[1, 0], [0, -1]], wires=[idx])) for idx in range(3)] - tape.trainable_params = {0, 1, 2} # circuit jacobians - dev_jacobian = dev.adjoint_jacobian(tape) + dev_jacobian = self.process_and_execute_multiple_rx(dev, params, meas, batch_obs) expected_jacobian = -np.diag(np.sin(params)) assert np.allclose(dev_jacobian, expected_jacobian, atol=tol, rtol=0) - qubit_ops = [getattr(qml, name) for name in qml.ops._qubit__ops__] # pylint: disable=no-member - ops = {qml.RX, qml.RY, qml.RZ, qml.PhaseShift, qml.CRX, qml.CRY, qml.CRZ, qml.Rot} - - def test_multiple_rx_gradient_expval_hermitian(self, tol, dev): + @pytest.mark.parametrize("batch_obs", [True, False]) + def test_multiple_rx_gradient_expval_hermitian(self, tol, batch_obs, dev): """Tests that the gradient of multiple RX gates in a circuit yields the correct result with Hermitian observable """ params = np.array([np.pi / 3, np.pi / 4, np.pi / 5]) - with qml.tape.QuantumTape() as tape: - qml.RX(params[0], wires=0) - qml.RX(params[1], wires=1) - qml.RX(params[2], wires=2) - + meas = [ qml.expval( qml.Hermitian( [[1, 0, 0, 0], [0, -1, 0, 0], [0, 0, -1, 0], [0, 0, 0, 1]], wires=[0, 2], ) ) + ] - tape.trainable_params = {0, 1, 2} - dev_jacobian = dev.adjoint_jacobian(tape) + dev_jacobian = self.process_and_execute_multiple_rx(dev, params, meas, batch_obs) expected_jacobian = np.array( [ -np.sin(params[0]) * np.cos(params[2]), @@ -328,37 +314,31 @@ def test_multiple_rx_gradient_expval_hermitian(self, tol, dev): assert np.allclose(dev_jacobian, expected_jacobian, atol=tol, rtol=0) - qubit_ops = [getattr(qml, name) for name in qml.ops._qubit__ops__] # pylint: disable=no-member - ops = {qml.RX, qml.RY, qml.RZ, qml.PhaseShift, qml.CRX, qml.CRY, qml.CRZ, qml.Rot} - - def test_multiple_rx_gradient_expval_hamiltonian(self, tol, dev): + @pytest.mark.parametrize("batch_obs", [True, False]) + def test_multiple_rx_gradient_expval_hamiltonian(self, tol, batch_obs, dev): """Tests that the gradient of multiple RX gates in a circuit yields the correct result with Hermitian observable """ params = np.array([np.pi / 3, np.pi / 4, np.pi / 5]) - ham = qml.Hamiltonian( - [1.0, 0.3, 0.3, 0.4], - [ - qml.PauliX(0) @ qml.PauliX(1), - qml.PauliZ(0), - qml.PauliZ(1), - qml.Hermitian( - [[1, 0, 0, 0], [0, -1, 0, 0], [0, 0, -1, 0], [0, 0, 0, 1]], - wires=[0, 2], - ), - ], - ) - - with qml.tape.QuantumTape() as tape: - qml.RX(params[0], wires=0) - qml.RX(params[1], wires=1) - qml.RX(params[2], wires=2) - - qml.expval(ham) + meas = [ + qml.expval( + qml.Hamiltonian( + [1.0, 0.3, 0.3, 0.4], + [ + qml.PauliX(0) @ qml.PauliX(1), + qml.PauliZ(0), + qml.PauliZ(1), + qml.Hermitian( + [[1, 0, 0, 0], [0, -1, 0, 0], [0, 0, -1, 0], [0, 0, 0, 1]], + wires=[0, 2], + ), + ], + ) + ) + ] - tape.trainable_params = {0, 1, 2} - dev_jacobian = dev.adjoint_jacobian(tape) + dev_jacobian = self.process_and_execute_multiple_rx(dev, params, meas, batch_obs) expected_jacobian = ( 0.3 * np.array([-np.sin(params[0]), 0, 0]) + 0.3 * np.array([0, -np.sin(params[1]), 0]) @@ -374,51 +354,21 @@ def test_multiple_rx_gradient_expval_hamiltonian(self, tol, dev): assert np.allclose(dev_jacobian, expected_jacobian, atol=tol, rtol=0) - qubit_ops = [getattr(qml, name) for name in qml.ops._qubit__ops__] # pylint: disable=no-member - ops = {qml.RX, qml.RY, qml.RZ, qml.PhaseShift, qml.CRX, qml.CRY, qml.CRZ, qml.Rot} - - @pytest.mark.parametrize("obs", [qml.PauliX, qml.PauliY]) @pytest.mark.parametrize( - "op", + "meas", [ - qml.RX(0.4, wires=0), - qml.RY(0.6, wires=0), - qml.RZ(0.8, wires=0), - qml.CRX(1.0, wires=[0, 1]), - qml.CRY(2.0, wires=[0, 1]), - qml.CRZ(3.0, wires=[0, 1]), - qml.Rot(0.2, -0.1, 0.2, wires=0), + [qml.expval(qml.PauliX(wires=0)), qml.expval(qml.PauliZ(wires=1))], + [qml.expval(qml.PauliY(wires=0)), qml.expval(qml.PauliZ(wires=1))], + [ + qml.expval( + qml.Hermitian( + [[0, 0, 1, 1], [0, 1, 2, 1], [1, 2, 1, 0], [1, 1, 0, 0]], + wires=[0, 1], + ) + ) + ], ], ) - def test_gradients_pauliz(self, op, obs, dev): - """Tests that the gradients of circuits match between the finite difference and device - methods.""" - # op.num_wires and op.num_params must be initialized a priori - with qml.tape.QuantumTape() as tape: - qml.Hadamard(wires=0) - qml.RX(0.543, wires=0) - qml.CNOT(wires=[0, 1]) - - op # pylint: disable=pointless-statement - - qml.Rot(1.3, -2.3, 0.5, wires=[0]) - qml.RZ(-0.5, wires=0) - qml.adjoint(qml.RY(0.5, wires=1), lazy=False) - qml.CNOT(wires=[0, 1]) - - qml.expval(obs(wires=0)) - qml.expval(qml.PauliZ(wires=1)) - - tape.trainable_params = set(range(1, 1 + op.num_params)) - - tol = 1e-3 if dev.R_DTYPE == np.float32 else 1e-7 - - # pylint: disable=unnecessary-direct-lambda-call - grad_F = (lambda t, fn: fn(qml.execute(t, dev, None)))(*qml.gradients.param_shift(tape)) - grad_D = dev.adjoint_jacobian(tape) - - assert np.allclose(grad_D, grad_F, atol=tol, rtol=0) - @pytest.mark.parametrize( "op", [ @@ -431,119 +381,72 @@ def test_gradients_pauliz(self, op, obs, dev): qml.Rot(0.2, -0.1, 0.2, wires=0), ], ) - def test_gradients_hermitian(self, op, dev): + @pytest.mark.parametrize("batch_obs", [True, False]) + def test_gradients_pauliz_hermitian(self, op, meas, batch_obs, dev): """Tests that the gradients of circuits match between the finite difference and device methods.""" # op.num_wires and op.num_params must be initialized a priori - with qml.tape.QuantumTape() as tape: - qml.Hadamard(wires=0) - qml.RX(0.543, wires=0) - qml.CNOT(wires=[0, 1]) - - op.queue() - - qml.Rot(1.3, -2.3, 0.5, wires=[0]) - qml.RZ(-0.5, wires=0) - qml.adjoint(qml.RY(0.5, wires=1), lazy=False) - qml.CNOT(wires=[0, 1]) - - qml.expval( - qml.Hermitian( - [[0, 0, 1, 1], [0, 1, 2, 1], [1, 2, 1, 0], [1, 1, 0, 0]], - wires=[0, 1], - ) - ) - - tape.trainable_params = set(range(1, 1 + op.num_params)) - - tol = 1e-3 if dev.R_DTYPE == np.float32 else 1e-7 - - # pylint: disable=unnecessary-direct-lambda-call - grad_F = (lambda t, fn: fn(qml.execute(t, dev, None)))(*qml.gradients.param_shift(tape)) - grad_D = dev.adjoint_jacobian(tape) - - assert np.allclose(grad_D, grad_F, atol=tol, rtol=0) - - def test_gradient_gate_with_multiple_parameters_pauliz(self, dev): - """Tests that gates with multiple free parameters yield correct gradients.""" - x, y, z = [0.5, 0.3, -0.7] - - tape = qml.tape.QuantumScript( + qs = QuantumScript( [ - qml.RX(0.4, wires=[0]), - qml.Rot(x, y, z, wires=[0]), - qml.RY(-0.2, wires=[0]), + qml.Hadamard(wires=0), + qml.RX(0.543, wires=0), + qml.CNOT(wires=[0, 1]), + op, + qml.Rot(1.3, -2.3, 0.5, wires=[0]), + qml.RZ(-0.5, wires=0), + qml.adjoint(qml.RY(0.5, wires=1), lazy=False), + qml.CNOT(wires=[0, 1]), ], - [qml.expval(qml.PauliZ(0))], + meas, + trainable_params=list(range(1, 1 + op.num_params)), ) + config = ExecutionConfig(gradient_method="adjoint", device_options={"batch_obs": batch_obs}) - tape.trainable_params = {1, 2, 3} - - tol = 1e-3 if dev.R_DTYPE == np.float32 else 1e-7 + tol = self.tol_for_allclose(dev.c_dtype) - grad_D = dev.adjoint_jacobian(tape) - tapes, fn = qml.gradients.param_shift(tape) + tapes, fn = qml.gradients.param_shift(qs) grad_F = fn(qml.execute(tapes, dev, None)) - # gradient has the correct shape and every element is nonzero - assert len(grad_D) == 3 - assert all(isinstance(v, np.ndarray) for v in grad_D) - assert np.count_nonzero(grad_D) == 3 - # the different methods agree + # circuit jacobians + grad_D = dev.compute_derivatives(qs, config) assert np.allclose(grad_D, grad_F, atol=tol, rtol=0) - def test_gradient_gate_with_multiple_parameters_hermitian(self, dev): - """Tests that gates with multiple free parameters yield correct gradients.""" - x, y, z = [0.5, 0.3, -0.7] - - tape = qml.tape.QuantumScript( + @pytest.mark.parametrize( + "meas", + [ + [qml.expval(qml.PauliZ(0))], + [qml.expval(qml.Hermitian([[0, 1], [1, 1]], wires=0))], [ - qml.RX(0.4, wires=[0]), - qml.Rot(x, y, z, wires=[0]), - qml.RY(-0.2, wires=[0]), + qml.expval( + qml.Hamiltonian( + [1.0, 0.3, 0.3], + [qml.PauliX(0) @ qml.PauliX(1), qml.PauliZ(0), qml.PauliZ(1)], + ) + ) ], - [qml.expval(qml.Hermitian([[0, 1], [1, 1]], wires=0))], - ) - - tape.trainable_params = {1, 2, 3} - - tol = 1e-3 if dev.R_DTYPE == np.float32 else 1e-7 - - grad_D = dev.adjoint_jacobian(tape) - tapes, fn = qml.gradients.param_shift(tape) - grad_F = fn(qml.execute(tapes, dev, None)) - - # gradient has the correct shape and every element is nonzero - assert len(grad_D) == 3 - assert all(isinstance(v, np.ndarray) for v in grad_D) - assert np.count_nonzero(grad_D) == 3 - # the different methods agree - assert np.allclose(grad_D, grad_F, atol=tol, rtol=0) - - def test_gradient_gate_with_multiple_parameters_hamiltonian(self, dev): + ], + ) + @pytest.mark.parametrize("batch_obs", [True, False]) + def test_gradient_gate_with_multiple_parameters(self, meas, batch_obs, dev): """Tests that gates with multiple free parameters yield correct gradients.""" x, y, z = [0.5, 0.3, -0.7] - ham = qml.Hamiltonian( - [1.0, 0.3, 0.3], - [qml.PauliX(0) @ qml.PauliX(1), qml.PauliZ(0), qml.PauliZ(1)], - ) - - tape = qml.tape.QuantumScript( + qs = QuantumScript( [ qml.RX(0.4, wires=[0]), qml.Rot(x, y, z, wires=[0]), qml.RY(-0.2, wires=[0]), ], - [qml.expval(ham)], + meas, + trainable_params=[1, 2, 3], ) + config = ExecutionConfig(gradient_method="adjoint", device_options={"batch_obs": batch_obs}) - tape.trainable_params = {1, 2, 3} + tol = self.tol_for_allclose(dev.c_dtype) - tol = 1e-3 if dev.R_DTYPE == np.float32 else 1e-7 - - grad_D = dev.adjoint_jacobian(tape) - tapes, fn = qml.gradients.param_shift(tape) + # circuit jacobians + grad_D = dev.compute_derivatives(qs, config) + tapes, fn = qml.gradients.param_shift(qs) grad_F = fn(qml.execute(tapes, dev, None)) # gradient has the correct shape and every element is nonzero @@ -553,110 +456,53 @@ def test_gradient_gate_with_multiple_parameters_hamiltonian(self, dev): # the different methods agree assert np.allclose(grad_D, grad_F, atol=tol, rtol=0) - def test_use_device_state(self, tol, dev): - """Tests that when using the device state, the correct answer is still returned.""" - - x, y, z = [0.5, 0.3, -0.7] - - with qml.tape.QuantumTape() as tape: - qml.RX(0.4, wires=[0]) - qml.Rot(x, y, z, wires=[0]) - qml.RY(-0.2, wires=[0]) - qml.expval(qml.PauliZ(0)) - - tape.trainable_params = {1, 2, 3} - - dM1 = dev.adjoint_jacobian(tape) - - qml.execute([tape], dev, None) - dM2 = dev.adjoint_jacobian(tape, use_device_state=True) - - assert np.allclose(dM1, dM2, atol=tol, rtol=0) - - def test_provide_starting_state(self, tol, dev): - """Tests provides correct answer when provided starting state.""" - comm = MPI.COMM_WORLD - - x, y, z = [0.5, 0.3, -0.7] - - with qml.tape.QuantumTape() as tape: - qml.RX(0.4, wires=[0]) - qml.Rot(x, y, z, wires=[0]) - qml.RY(-0.2, wires=[0]) - qml.expval(qml.PauliZ(0)) - - tape.trainable_params = {1, 2, 3} - - dM1 = dev.adjoint_jacobian(tape) - - if device_name == "lightning.gpu": - local_state_vector = dev.state - complex_type = np.complex128 if dev.R_DTYPE == np.float64 else np.complex64 - state_vector = np.zeros(1 << 8).astype(complex_type) - comm.Allgather(local_state_vector, state_vector) - qml.execute([tape], dev, None) - dM2 = dev.adjoint_jacobian(tape, starting_state=state_vector) - assert np.allclose(dM1, dM2, atol=tol, rtol=0) - - def test_provide_wrong_starting_state(self, dev): - """Tests raise an exception when provided starting state mismatches.""" - x, y, z = [0.5, 0.3, -0.7] - - with qml.tape.QuantumTape() as tape: - qml.RX(0.4, wires=[0]) - qml.Rot(x, y, z, wires=[0]) - qml.RY(-0.2, wires=[0]) - qml.expval(qml.PauliZ(0)) - tape.trainable_params = {1, 2, 3} +class TestAdjointJacobianQNode: + """Test QNode integration with the adjoint_jacobian method""" - with pytest.raises( - qml.QuantumFunctionError, - match="The number of qubits of starting_state must be the same as", - ): - dev.adjoint_jacobian(tape, starting_state=np.ones(7)) + # def analytic_rotation(self): + I = np.eye(2) + X = qml.PauliX.compute_matrix() + Y = qml.PauliY.compute_matrix() + Z = qml.PauliZ.compute_matrix() - @pytest.mark.skipif( - device_name == "lightning.gpu", - reason="Adjoint differentiation does not support State measurements.", - ) - def test_state_return_type(self, dev): - """Tests raise an exception when the return type is State""" - with qml.tape.QuantumTape() as tape: - qml.RX(0.4, wires=[0]) - qml.state() + def Rx(self, theta): + r"""One-qubit rotation about the x axis. - tape.trainable_params = {0} + Args: + theta (float): rotation angle + Returns: + array: unitary 2x2 rotation matrix :math:`e^{-i \sigma_x \theta/2}` + """ + return math.cos(theta / 2) * self.I + 1j * math.sin(-theta / 2) * self.X - with pytest.raises( - qml.QuantumFunctionError, - match="Adjoint differentiation method does not support measurement StateMP.", - ): - dev.adjoint_jacobian(tape) + def Ry(self, theta): + r"""One-qubit rotation about the y axis. + Args: + theta (float): rotation angle + Returns: + array: unitary 2x2 rotation matrix :math:`e^{-i \sigma_y \theta/2}` + """ + return math.cos(theta / 2) * self.I + 1j * math.sin(-theta / 2) * self.Y -class TestAdjointJacobianQNode: - """Test QNode integration with the adjoint_jacobian method""" + def Rz(self, theta): + r"""One-qubit rotation about the z axis. - @pytest.fixture(params=fixture_params) - def dev(self, request): - """Returns a PennyLane device.""" - return qml.device( - device_name, - wires=8, - mpi=True, - c_dtype=request.param[0], - batch_obs=request.param[1], - ) + Args: + theta (float): rotation angle + Returns: + array: unitary 2x2 rotation matrix :math:`e^{-i \sigma_z \theta/2}` + """ + return math.cos(theta / 2) * self.I + 1j * math.sin(-theta / 2) * self.Z - def test_finite_shots_warning(self): - """Tests that a warning is raised when computing the adjoint diff on a device with finite shots""" + def test_finite_shots_error(self): + """Tests that an error is raised when computing the adjoint diff on a device with finite shots""" dev = qml.device(device_name, wires=8, mpi=True, shots=1) - with pytest.warns( - UserWarning, - match="Requested adjoint differentiation to be computed with finite shots.", + with pytest.raises( + qml.QuantumFunctionError, match="does not support adjoint with requested circuit." ): @qml.qnode(dev, diff_method="adjoint") @@ -664,12 +510,13 @@ def circ(x): qml.RX(x, wires=0) return qml.expval(qml.PauliZ(0)) - with pytest.warns( - UserWarning, - match="Requested adjoint differentiation to be computed with finite shots.", - ): qml.grad(circ)(0.1) + @staticmethod + def tol_for_allclose(c_dtype): + """Compute the tolerance for allclose""" + return 1e-3 if c_dtype == np.complex64 else 1e-7 + def test_qnode(self, mocker, dev): """Test that specifying diff_method allows the adjoint method to be selected""" args = np.array([0.54, 0.1, 0.5], requires_grad=True) @@ -689,15 +536,15 @@ def circuit(x, y, z): return qml.expval(qml.PauliX(0) @ qml.PauliZ(1)) qnode1 = QNode(circuit, dev, diff_method="adjoint") - spy = mocker.spy(dev, "adjoint_jacobian") + spy = mocker.spy(dev, "LightningAdjointJacobian") grad_fn = qml.grad(qnode1) grad_A = grad_fn(*args) spy.assert_called() - h = 1e-3 if dev.R_DTYPE == np.float32 else 1e-7 - tol = 1e-3 if dev.R_DTYPE == np.float32 else 1e-7 + h = self.tol_for_allclose(dev.c_dtype) + tol = self.tol_for_allclose(dev.c_dtype) qnode2 = QNode(circuit, dev, diff_method="finite-diff", h=h) grad_fn = qml.grad(qnode2) @@ -731,7 +578,7 @@ def cost(p1, p2): zero_state = np.array([1.0, 0.0]) cost(reused_p, other_p) - spy = mocker.spy(dev, "adjoint_jacobian") + spy = mocker.spy(dev, "LightningAdjointJacobian") # analytic gradient grad_fn = qml.grad(cost) @@ -742,18 +589,34 @@ def cost(p1, p2): # manual gradient grad_true0 = ( expZ( - Rx(reused_p) @ Rz(other_p) @ Ry(reused_p + np.pi / 2) @ Rx(extra_param) @ zero_state + self.Rx(reused_p) + @ self.Rz(other_p) + @ self.Ry(reused_p + np.pi / 2) + @ self.Rx(extra_param) + @ zero_state ) - expZ( - Rx(reused_p) @ Rz(other_p) @ Ry(reused_p - np.pi / 2) @ Rx(extra_param) @ zero_state + self.Rx(reused_p) + @ self.Rz(other_p) + @ self.Ry(reused_p - np.pi / 2) + @ self.Rx(extra_param) + @ zero_state ) ) / 2 grad_true1 = ( expZ( - Rx(reused_p + np.pi / 2) @ Rz(other_p) @ Ry(reused_p) @ Rx(extra_param) @ zero_state + self.Rx(reused_p + np.pi / 2) + @ self.Rz(other_p) + @ self.Ry(reused_p) + @ self.Rx(extra_param) + @ zero_state ) - expZ( - Rx(reused_p - np.pi / 2) @ Rz(other_p) @ Ry(reused_p) @ Rx(extra_param) @ zero_state + self.Rx(reused_p - np.pi / 2) + @ self.Rz(other_p) + @ self.Ry(reused_p) + @ self.Rx(extra_param) + @ zero_state ) ) / 2 expected = grad_true0 + grad_true1 # product rule @@ -770,10 +633,10 @@ def circuit(params): qml.Rot(params[1], params[0], 2 * params[0], wires=[0]) return qml.expval(qml.PauliX(0)) - spy_analytic = mocker.spy(dev, "adjoint_jacobian") + spy_analytic = mocker.spy(dev, "LightningAdjointJacobian") - h = 1e-3 if dev.R_DTYPE == np.float32 else 1e-7 - tol = 1e-3 if dev.R_DTYPE == np.float32 else 1e-7 + h = self.tol_for_allclose(dev.c_dtype) + tol = self.tol_for_allclose(dev.c_dtype) cost = QNode(circuit, dev, diff_method="finite-diff", h=h) @@ -803,7 +666,7 @@ def f(params1, params2): qml.RY(tf.cos(params2), wires=[0]) return qml.expval(qml.PauliZ(0)) - if dev.R_DTYPE == np.float32: + if dev.r_dtype == np.float32: tf_r_dtype = tf.float32 else: tf_r_dtype = tf.float64 @@ -811,8 +674,8 @@ def f(params1, params2): params1 = tf.Variable(0.3, dtype=tf_r_dtype) params2 = tf.Variable(0.4, dtype=tf_r_dtype) - h = 2e-3 if dev.R_DTYPE == np.float32 else 1e-7 - tol = 1e-3 if dev.R_DTYPE == np.float32 else 1e-7 + h = self.tol_for_allclose(dev.c_dtype) + tol = self.tol_for_allclose(dev.c_dtype) qnode1 = QNode(f, dev, interface="tf", diff_method="adjoint") qnode2 = QNode(f, dev, interface="tf", diff_method="finite-diff", h=h) @@ -844,7 +707,7 @@ def f(params1, params2): params1 = torch.tensor(0.3, requires_grad=True) params2 = torch.tensor(0.4, requires_grad=True) - h = 2e-3 if dev.R_DTYPE == np.float32 else 1e-7 + h = self.tol_for_allclose(dev.c_dtype) qnode1 = QNode(f, dev, interface="torch", diff_method="adjoint") qnode2 = QNode(f, dev, interface="torch", diff_method="finite-diff", h=h) @@ -866,7 +729,7 @@ def test_interface_jax(self, dev): jax interface""" jax = pytest.importorskip("jax") - if dev.R_DTYPE == np.float64: + if dev.c_dtype == np.complex128: from jax import config # pylint: disable=import-outside-toplevel config.update("jax_enable_x64", True) @@ -877,11 +740,13 @@ def f(params1, params2): qml.RY(jax.numpy.cos(params2), wires=[0]) return qml.expval(qml.PauliZ(0)) - params1 = jax.numpy.array(0.3, dev.R_DTYPE) - params2 = jax.numpy.array(0.4, dev.R_DTYPE) + r_dtype = np.float32 if dev.c_dtype == np.complex64 else np.float64 + + params1 = jax.numpy.array(0.3, r_dtype) + params2 = jax.numpy.array(0.4, r_dtype) - h = 2e-3 if dev.R_DTYPE == np.float32 else 1e-7 - tol = 1e-3 if dev.R_DTYPE == np.float32 else 1e-7 + h = self.tol_for_allclose(dev.c_dtype) + tol = self.tol_for_allclose(dev.c_dtype) qnode_adjoint = QNode(f, dev, interface="jax", diff_method="adjoint") qnode_fd = QNode(f, dev, interface="jax", diff_method="finite-diff", h=h) @@ -894,7 +759,7 @@ def f(params1, params2): def circuit_ansatz(params, wires): """Circuit ansatz containing all the parametrized gates""" - qml.QubitStateVector(unitary_group.rvs(2**8, random_state=0)[0], wires=wires) + qml.StatePrep(unitary_group.rvs(2**8, random_state=0)[0], wires=wires) qml.RX(params[0], wires=wires[0]) qml.RY(params[1], wires=wires[1]) qml.adjoint(qml.RX(params[2], wires=wires[2])) @@ -1383,9 +1248,9 @@ def circuit(params): def test_qubit_unitary(dev, n_targets): """Tests that ``qml.QubitUnitary`` can be included in circuits differentiated with the adjoint method.""" n_wires = len(dev.wires) - dev_def = qml.device("default.qubit.legacy", wires=n_wires) - h = 1e-3 if dev.R_DTYPE == np.float32 else 1e-7 - c_dtype = np.complex64 if dev.R_DTYPE == np.float32 else np.complex128 + dev_def = qml.device("default.qubit", wires=n_wires) + h = 1e-3 if dev.c_dtype == np.complex64 else 1e-7 + c_dtype = dev.c_dtype np.random.seed(1337) par = 2 * np.pi * np.random.rand(n_wires) @@ -1401,7 +1266,7 @@ def test_qubit_unitary(dev, n_targets): init_state = np.array(init_state, requires_grad=False, dtype=c_dtype) U = np.array(U, requires_grad=False, dtype=c_dtype) - obs = qml.operation.Tensor(*(qml.PauliZ(i) for i in range(n_wires))) + obs = qml.prod(*(qml.PauliZ(i) for i in range(n_wires))) def circuit(x): qml.StatePrep(init_state, wires=range(n_wires)) @@ -1432,8 +1297,8 @@ def test_diff_qubit_unitary(dev, n_targets): """Tests that ``qml.QubitUnitary`` can be differentiated with the adjoint method.""" n_wires = len(dev.wires) dev_def = qml.device("default.qubit", wires=n_wires) - h = 1e-3 if dev.R_DTYPE == np.float32 else 1e-7 - c_dtype = np.complex64 if dev.R_DTYPE == np.float32 else np.complex128 + h = 1e-3 if dev.c_dtype == np.complex64 else 1e-7 + c_dtype = dev.c_dtype np.random.seed(1337) par = 2 * np.pi * np.random.rand(n_wires) @@ -1449,7 +1314,7 @@ def test_diff_qubit_unitary(dev, n_targets): init_state = np.array(init_state, requires_grad=False, dtype=c_dtype) U = np.array(U, requires_grad=False, dtype=c_dtype) - obs = qml.operation.Tensor(*(qml.PauliZ(i) for i in range(n_wires))) + obs = qml.prod(*(qml.PauliZ(i) for i in range(n_wires))) def circuit(x, u_mat): qml.StatePrep(init_state, wires=range(n_wires)) diff --git a/mpitests/test_apply.py b/mpitests/test_apply.py index 8bffcc6b29..4fe12703bc 100644 --- a/mpitests/test_apply.py +++ b/mpitests/test_apply.py @@ -34,14 +34,17 @@ ) -def create_random_init_state(numWires, R_DTYPE, seed_value=48): +def create_random_init_state(numWires, c_dtype, seed_value=48): """Returns a random initial state of a certain type.""" np.random.seed(seed_value) - num_elements = 1 << numWires - init_state = np.random.rand(num_elements).astype(R_DTYPE) + 1j * np.random.rand( + + r_dtype = np.float64 if c_dtype == np.complex128 else np.float32 + + num_elements = 2**numWires + init_state = np.random.rand(num_elements).astype(r_dtype) + 1j * np.random.rand( num_elements - ).astype(R_DTYPE) - scale_sum = np.sqrt(np.sum(np.abs(init_state) ** 2)).astype(R_DTYPE) + ).astype(r_dtype) + scale_sum = np.sqrt(np.sum(np.abs(init_state) ** 2)).astype(r_dtype) init_state = init_state / scale_sum return init_state @@ -54,16 +57,13 @@ def apply_operation_gates_qnode_param(tol, dev_mpi, operation, par, Wires): num_global_wires = commSize.bit_length() - 1 num_local_wires = num_wires - num_global_wires - if dev_mpi.R_DTYPE == np.float32: - c_dtype = np.complex64 - else: - c_dtype = np.complex128 + c_dtype = dev_mpi.c_dtype - expected_output_cpu = np.zeros(1 << num_wires).astype(c_dtype) - local_state_vector = np.zeros(1 << num_local_wires).astype(c_dtype) - local_expected_output_cpu = np.zeros(1 << num_local_wires).astype(c_dtype) + expected_output_cpu = np.zeros(2**num_wires).astype(c_dtype) + local_state_vector = np.zeros(2**num_local_wires).astype(c_dtype) + local_expected_output_cpu = np.zeros(2**num_local_wires).astype(c_dtype) - state_vector = create_random_init_state(num_wires, dev_mpi.R_DTYPE) + state_vector = create_random_init_state(num_wires, dev_mpi.c_dtype) comm.Bcast(state_vector, root=0) comm.Scatter(state_vector, local_state_vector, root=0) @@ -84,45 +84,6 @@ def circuit(*params): assert np.allclose(local_state_vector, local_expected_output_cpu, atol=tol, rtol=0) -def apply_operation_gates_apply_param(tol, dev_mpi, operation, par, Wires): - """Wrapper applying a parametric gate with the apply method.""" - num_wires = numQubits - comm = MPI.COMM_WORLD - commSize = comm.Get_size() - num_global_wires = commSize.bit_length() - 1 - num_local_wires = num_wires - num_global_wires - - if dev_mpi.R_DTYPE == np.float32: - c_dtype = np.complex64 - else: - c_dtype = np.complex128 - - expected_output_cpu = np.zeros(1 << num_wires).astype(c_dtype) - local_state_vector = np.zeros(1 << num_local_wires).astype(c_dtype) - local_expected_output_cpu = np.zeros(1 << num_local_wires).astype(c_dtype) - - state_vector = create_random_init_state(num_wires, dev_mpi.R_DTYPE) - comm.Bcast(state_vector, root=0) - - comm.Scatter(state_vector, local_state_vector, root=0) - dev_cpu = qml.device("lightning.qubit", wires=num_wires, c_dtype=c_dtype) - - @qml.qnode(dev_cpu) - def circuit(*params): - qml.StatePrep(state_vector, wires=range(num_wires)) - operation(*params, wires=Wires) - return qml.state() - - expected_output_cpu = np.array(circuit(*par)).astype(c_dtype) - comm.Scatter(expected_output_cpu, local_expected_output_cpu, root=0) - - dev_mpi.syncH2D(local_state_vector) - dev_mpi.apply([operation(*par, wires=Wires)]) - dev_mpi.syncD2H(local_state_vector) - - assert np.allclose(local_state_vector, local_expected_output_cpu, atol=tol, rtol=0) - - def apply_operation_gates_qnode_nonparam(tol, dev_mpi, operation, Wires): """Wrapper applying a non-parametric gate with QNode function.""" num_wires = numQubits @@ -131,16 +92,13 @@ def apply_operation_gates_qnode_nonparam(tol, dev_mpi, operation, Wires): num_global_wires = commSize.bit_length() - 1 num_local_wires = num_wires - num_global_wires - if dev_mpi.R_DTYPE == np.float32: - c_dtype = np.complex64 - else: - c_dtype = np.complex128 + c_dtype = dev_mpi.c_dtype - expected_output_cpu = np.zeros(1 << num_wires).astype(c_dtype) - local_state_vector = np.zeros(1 << num_local_wires).astype(c_dtype) - local_expected_output_cpu = np.zeros(1 << num_local_wires).astype(c_dtype) + expected_output_cpu = np.zeros(2**num_wires).astype(c_dtype) + local_state_vector = np.zeros(2**num_local_wires).astype(c_dtype) + local_expected_output_cpu = np.zeros(2**num_local_wires).astype(c_dtype) - state_vector = create_random_init_state(num_wires, dev_mpi.R_DTYPE) + state_vector = create_random_init_state(num_wires, dev_mpi.c_dtype) comm.Bcast(state_vector, root=0) comm.Scatter(state_vector, local_state_vector, root=0) @@ -161,45 +119,6 @@ def circuit(): assert np.allclose(local_state_vector, local_expected_output_cpu, atol=tol, rtol=0) -def apply_operation_gates_apply_nonparam(tol, dev_mpi, operation, Wires): - """Wrapper applying a non-parametric gate with the apply method.""" - num_wires = numQubits - comm = MPI.COMM_WORLD - commSize = comm.Get_size() - num_global_wires = commSize.bit_length() - 1 - num_local_wires = num_wires - num_global_wires - - if dev_mpi.R_DTYPE == np.float32: - c_dtype = np.complex64 - else: - c_dtype = np.complex128 - - expected_output_cpu = np.zeros(1 << num_wires).astype(c_dtype) - local_state_vector = np.zeros(1 << num_local_wires).astype(c_dtype) - local_expected_output_cpu = np.zeros(1 << num_local_wires).astype(c_dtype) - - state_vector = create_random_init_state(num_wires, dev_mpi.R_DTYPE) - comm.Bcast(state_vector, root=0) - - comm.Scatter(state_vector, local_state_vector, root=0) - dev_cpu = qml.device("lightning.qubit", wires=num_wires, c_dtype=c_dtype) - - @qml.qnode(dev_cpu) - def circuit(): - qml.StatePrep(state_vector, wires=range(num_wires)) - operation(wires=Wires) - return qml.state() - - expected_output_cpu = np.array(circuit()).astype(c_dtype) - comm.Scatter(expected_output_cpu, local_expected_output_cpu, root=0) - - dev_mpi.syncH2D(local_state_vector) - dev_mpi.apply([operation(wires=Wires)]) - dev_mpi.syncD2H(local_state_vector) - - assert np.allclose(local_state_vector, local_expected_output_cpu, atol=tol, rtol=0) - - class TestApply: # pylint: disable=missing-function-docstring,too-many-arguments """Tests whether the device can apply supported quantum gates.""" @@ -220,13 +139,11 @@ def dev_mpi(self, request): @pytest.mark.parametrize("Wires", [0, 1, numQubits - 2, numQubits - 1]) def test_apply_operation_single_wire_nonparam(self, tol, operation, Wires, dev_mpi): apply_operation_gates_qnode_nonparam(tol, dev_mpi, operation, Wires) - apply_operation_gates_apply_nonparam(tol, dev_mpi, operation, Wires) @pytest.mark.parametrize("operation", [qml.CNOT, qml.SWAP, qml.CY, qml.CZ]) @pytest.mark.parametrize("Wires", [[0, 1], [numQubits - 2, numQubits - 1], [0, numQubits - 1]]) def test_apply_operation_two_wire_nonparam(self, tol, operation, Wires, dev_mpi): apply_operation_gates_qnode_nonparam(tol, dev_mpi, operation, Wires) - apply_operation_gates_apply_nonparam(tol, dev_mpi, operation, Wires) @pytest.mark.parametrize("operation", [qml.CSWAP, qml.Toffoli]) @pytest.mark.parametrize( @@ -240,7 +157,6 @@ def test_apply_operation_two_wire_nonparam(self, tol, operation, Wires, dev_mpi) ) def test_apply_operation_three_wire_nonparam(self, tol, operation, Wires, dev_mpi): apply_operation_gates_qnode_nonparam(tol, dev_mpi, operation, Wires) - apply_operation_gates_apply_nonparam(tol, dev_mpi, operation, Wires) @pytest.mark.parametrize("operation", [qml.CSWAP, qml.Toffoli]) @pytest.mark.parametrize( @@ -254,7 +170,6 @@ def test_apply_operation_three_wire_nonparam(self, tol, operation, Wires, dev_mp ) def test_apply_operation_three_wire_qnode_nonparam(self, tol, operation, Wires, dev_mpi): apply_operation_gates_qnode_nonparam(tol, dev_mpi, operation, Wires) - apply_operation_gates_apply_nonparam(tol, dev_mpi, operation, Wires) @pytest.mark.parametrize("operation", [qml.PhaseShift, qml.RX, qml.RY, qml.RZ]) @pytest.mark.parametrize("par", [[0.1], [0.2], [0.3]]) @@ -263,7 +178,6 @@ def test_apply_operation_1gatequbit_1param_gate_qnode_param( self, tol, operation, par, Wires, dev_mpi ): apply_operation_gates_qnode_param(tol, dev_mpi, operation, par, Wires) - apply_operation_gates_apply_param(tol, dev_mpi, operation, par, Wires) @pytest.mark.parametrize("operation", [qml.Rot]) @pytest.mark.parametrize("par", [[0.1, 0.2, 0.3], [0.2, 0.3, 0.4]]) @@ -272,7 +186,6 @@ def test_apply_operation_1gatequbit_3param_gate_qnode_param( self, tol, operation, par, Wires, dev_mpi ): apply_operation_gates_qnode_param(tol, dev_mpi, operation, par, Wires) - apply_operation_gates_apply_param(tol, dev_mpi, operation, par, Wires) @pytest.mark.parametrize("operation", [qml.CRot]) @pytest.mark.parametrize("par", [[0.1, 0.2, 0.3], [0.2, 0.3, 0.4]]) @@ -281,7 +194,6 @@ def test_apply_operation_1gatequbit_3param_cgate_qnode_param( self, tol, operation, par, Wires, dev_mpi ): apply_operation_gates_qnode_param(tol, dev_mpi, operation, par, Wires) - apply_operation_gates_apply_param(tol, dev_mpi, operation, par, Wires) @pytest.mark.parametrize( "operation", @@ -304,7 +216,6 @@ def test_apply_operation_2gatequbit_1param_gate_qnode_param( self, tol, operation, par, Wires, dev_mpi ): apply_operation_gates_qnode_param(tol, dev_mpi, operation, par, Wires) - apply_operation_gates_apply_param(tol, dev_mpi, operation, par, Wires) @pytest.mark.parametrize( "operation", @@ -323,7 +234,17 @@ def test_apply_operation_4gatequbit_1param_gate_qnode_param( self, tol, operation, par, Wires, dev_mpi ): apply_operation_gates_qnode_param(tol, dev_mpi, operation, par, Wires) - apply_operation_gates_apply_param(tol, dev_mpi, operation, par, Wires) + + @pytest.mark.parametrize( + "operation", + [qml.GlobalPhase], + ) + @pytest.mark.parametrize("par", [[0.13], [0.2], [0.3]]) + def test_apply_global_phase(self, tol, operation, par, dev_mpi): + """Test applying the GlobalPhase operation.""" + Wires = range(numQubits) + + apply_operation_gates_qnode_param(tol, dev_mpi, operation, par, Wires) # BasisState test @pytest.mark.parametrize("operation", [qml.BasisState]) @@ -337,17 +258,17 @@ def test_state_prep(self, tol, operation, index, dev_mpi): num_global_wires = commSize.bit_length() - 1 num_local_wires = num_wires - num_global_wires - if dev_mpi.R_DTYPE == np.float32: + if dev_mpi.c_dtype == np.float32: c_dtype = np.complex64 else: c_dtype = np.complex128 - state_vector = np.zeros(1 << num_wires).astype(c_dtype) - expected_output_cpu = np.zeros(1 << num_wires).astype(c_dtype) - local_state_vector = np.zeros(1 << num_local_wires).astype(c_dtype) - local_expected_output_cpu = np.zeros(1 << num_local_wires).astype(c_dtype) + state_vector = np.zeros(2**num_wires).astype(c_dtype) + expected_output_cpu = np.zeros(2**num_wires).astype(c_dtype) + local_state_vector = np.zeros(2**num_local_wires).astype(c_dtype) + local_expected_output_cpu = np.zeros(2**num_local_wires).astype(c_dtype) - state_vector = create_random_init_state(num_wires, dev_mpi.R_DTYPE) + state_vector = create_random_init_state(num_wires, dev_mpi.c_dtype) comm.Scatter(state_vector, local_state_vector, root=0) dev_cpu = qml.device("lightning.qubit", wires=num_wires, c_dtype=c_dtype) @@ -399,17 +320,17 @@ def test_qubit_state_prep(self, tol, par, Wires, dev_mpi): num_global_wires = commSize.bit_length() - 1 num_local_wires = num_wires - num_global_wires - if dev_mpi.R_DTYPE == np.float32: + if dev_mpi.c_dtype == np.float32: c_dtype = np.complex64 else: c_dtype = np.complex128 - state_vector = np.zeros(1 << num_wires).astype(c_dtype) - expected_output_cpu = np.zeros(1 << num_wires).astype(c_dtype) - local_state_vector = np.zeros(1 << num_local_wires).astype(c_dtype) - local_expected_output_cpu = np.zeros(1 << num_local_wires).astype(c_dtype) + state_vector = np.zeros(2**num_wires).astype(c_dtype) + expected_output_cpu = np.zeros(2**num_wires).astype(c_dtype) + local_state_vector = np.zeros(2**num_local_wires).astype(c_dtype) + local_expected_output_cpu = np.zeros(2**num_local_wires).astype(c_dtype) - state_vector = create_random_init_state(num_wires, dev_mpi.R_DTYPE) + state_vector = create_random_init_state(num_wires, dev_mpi.c_dtype) comm.Scatter(state_vector, local_state_vector, root=0) dev_cpu = qml.device("lightning.qubit", wires=num_wires, c_dtype=c_dtype) @@ -435,17 +356,17 @@ def test_dev_reset(self, tol, dev_mpi): num_global_wires = commSize.bit_length() - 1 num_local_wires = num_wires - num_global_wires - if dev_mpi.R_DTYPE == np.float32: + if dev_mpi.c_dtype == np.float32: c_dtype = np.complex64 else: c_dtype = np.complex128 - state_vector = np.zeros(1 << num_wires).astype(c_dtype) - expected_output_cpu = np.zeros(1 << num_wires).astype(c_dtype) - local_state_vector = np.zeros(1 << num_local_wires).astype(c_dtype) - local_expected_output_cpu = np.zeros(1 << num_local_wires).astype(c_dtype) + state_vector = np.zeros(2**num_wires).astype(c_dtype) + expected_output_cpu = np.zeros(2**num_wires).astype(c_dtype) + local_state_vector = np.zeros(2**num_local_wires).astype(c_dtype) + local_expected_output_cpu = np.zeros(2**num_local_wires).astype(c_dtype) - state_vector = create_random_init_state(num_wires, dev_mpi.R_DTYPE) + state_vector = create_random_init_state(num_wires, dev_mpi.c_dtype) comm.Scatter(state_vector, local_state_vector, root=0) dev_cpu = qml.device("lightning.qubit", wires=num_wires, c_dtype=c_dtype) @@ -462,10 +383,10 @@ def circuit(): expected_output_cpu = cpu_qnode().astype(c_dtype) comm.Scatter(expected_output_cpu, local_expected_output_cpu, root=0) - dev_mpi.reset() + dev_mpi._statevector.reset_state() gpumpi_qnode = qml.QNode(circuit, dev_mpi) - dev_mpi.reset() + dev_mpi._statevector.reset_state() local_state_vector = gpumpi_qnode() assert np.allclose(local_state_vector, local_expected_output_cpu, atol=tol, rtol=0) @@ -474,8 +395,8 @@ def circuit(): class TestSparseHamExpval: # pylint: disable=too-few-public-methods,missing-function-docstring """Tests sparse hamiltonian expectation values.""" - @pytest.mark.parametrize("C_DTYPE", [np.complex128, np.complex64]) - def test_sparse_hamiltonian_expectation(self, C_DTYPE): + @pytest.mark.parametrize("c_dtype", [np.complex128, np.complex64]) + def test_sparse_hamiltonian_expectation(self, c_dtype): comm = MPI.COMM_WORLD commSize = comm.Get_size() num_global_wires = commSize.bit_length() - 1 @@ -496,32 +417,38 @@ def test_sparse_hamiltonian_expectation(self, C_DTYPE): 0.3 + 0.3j, 0.3 + 0.5j, ], - dtype=C_DTYPE, + dtype=c_dtype, ) - local_state_vector = np.zeros(1 << num_local_wires).astype(C_DTYPE) + state_vector /= np.linalg.norm(state_vector) + + local_state_vector = np.zeros(2**num_local_wires).astype(c_dtype) comm.Scatter(state_vector, local_state_vector, root=0) - dev_gpu = qml.device("lightning.gpu", wires=3, mpi=False, c_dtype=C_DTYPE) - dev_mpi = qml.device("lightning.gpu", wires=3, mpi=True, c_dtype=C_DTYPE) + H_sparse = qml.SparseHamiltonian(Hmat, wires=range(3)) - dev_mpi.syncH2D(local_state_vector) - dev_gpu.syncH2D(state_vector) + def circuit(): + qml.StatePrep(state_vector, wires=range(3)) + return qml.expval(H_sparse) - H_sparse = qml.SparseHamiltonian(Hmat, wires=range(3)) + dev_gpu = qml.device("lightning.gpu", wires=3, mpi=False, c_dtype=c_dtype) + gpu_qnode = qml.QNode(circuit, dev_gpu) + expected_output_gpu = gpu_qnode() + comm.Bcast(np.array(expected_output_gpu), root=0) - comm.Barrier() + dev_mpi = qml.device("lightning.gpu", wires=3, mpi=True, c_dtype=c_dtype) + mpi_qnode = qml.QNode(circuit, dev_mpi) + expected_output_mpi = mpi_qnode() - res = dev_mpi.expval(H_sparse) - expected = dev_gpu.expval(H_sparse) + comm.Barrier() - assert np.allclose(res, expected) + assert np.allclose(expected_output_mpi, expected_output_gpu) class TestExpval: """Tests that expectation values are properly calculated or that the proper errors are raised.""" - @pytest.mark.parametrize("C_DTYPE", [np.complex128, np.complex64]) + @pytest.mark.parametrize("c_dtype", [np.complex128, np.complex64]) @pytest.mark.parametrize( "operation", [ @@ -533,7 +460,7 @@ class TestExpval: ], ) @pytest.mark.parametrize("wires", [0, 1, 2, numQubits - 3, numQubits - 2, numQubits - 1]) - def test_expval_single_wire_no_parameters(self, tol, operation, wires, C_DTYPE): + def test_expval_single_wire_no_parameters(self, tol, operation, wires, c_dtype): """Tests that expectation values are properly calculated for single-wire observables without parameters.""" num_wires = numQubits comm = MPI.COMM_WORLD @@ -541,14 +468,14 @@ def test_expval_single_wire_no_parameters(self, tol, operation, wires, C_DTYPE): num_global_wires = commSize.bit_length() - 1 num_local_wires = num_wires - num_global_wires - dev_mpi = qml.device("lightning.gpu", wires=numQubits, mpi=True, c_dtype=C_DTYPE) + dev_mpi = qml.device("lightning.gpu", wires=numQubits, mpi=True, c_dtype=c_dtype) - state_vector = create_random_init_state(num_wires, dev_mpi.R_DTYPE) + state_vector = create_random_init_state(num_wires, dev_mpi.c_dtype) comm.Bcast(state_vector, root=0) - local_state_vector = np.zeros(1 << num_local_wires).astype(C_DTYPE) + local_state_vector = np.zeros(2**num_local_wires).astype(c_dtype) comm.Scatter(state_vector, local_state_vector, root=0) - dev_cpu = qml.device("lightning.qubit", wires=num_wires, c_dtype=C_DTYPE) + dev_cpu = qml.device("lightning.qubit", wires=num_wires, c_dtype=c_dtype) def circuit(): qml.StatePrep(state_vector, wires=range(num_wires)) @@ -563,7 +490,7 @@ def circuit(): assert np.allclose(expected_output_mpi, expected_output_cpu, atol=tol, rtol=0) - @pytest.mark.parametrize("C_DTYPE", [np.complex128, np.complex64]) + @pytest.mark.parametrize("c_dtype", [np.complex128, np.complex64]) @pytest.mark.parametrize( "obs", [ @@ -575,12 +502,12 @@ def circuit(): qml.PauliZ(numQubits - 2) @ qml.PauliZ(numQubits - 1), ], ) - def test_expval_multiple_obs(self, obs, tol, C_DTYPE): + def test_expval_multiple_obs(self, obs, tol, c_dtype): """Test expval with Hamiltonian""" num_wires = numQubits - dev_cpu = qml.device("lightning.qubit", wires=num_wires, c_dtype=C_DTYPE) - dev_mpi = qml.device("lightning.gpu", wires=num_wires, mpi=True, c_dtype=C_DTYPE) + dev_cpu = qml.device("lightning.qubit", wires=num_wires, c_dtype=c_dtype) + dev_mpi = qml.device("lightning.gpu", wires=num_wires, mpi=True, c_dtype=c_dtype) def circuit(): qml.RX(0.4, wires=[0]) @@ -592,7 +519,7 @@ def circuit(): assert np.allclose(cpu_qnode(), mpi_qnode(), atol=tol, rtol=0) - @pytest.mark.parametrize("C_DTYPE", [np.complex128, np.complex64]) + @pytest.mark.parametrize("c_dtype", [np.complex128, np.complex64]) @pytest.mark.parametrize( "obs, coeffs", [ @@ -620,14 +547,14 @@ def circuit(): ), ], ) - def test_expval_hamiltonian(self, obs, coeffs, tol, C_DTYPE): + def test_expval_hamiltonian(self, obs, coeffs, tol, c_dtype): """Test expval with Hamiltonian""" num_wires = numQubits ham = qml.Hamiltonian(coeffs, obs) - dev_cpu = qml.device("lightning.qubit", wires=num_wires, c_dtype=C_DTYPE) - dev_mpi = qml.device("lightning.gpu", wires=num_wires, mpi=True, c_dtype=C_DTYPE) + dev_cpu = qml.device("lightning.qubit", wires=num_wires, c_dtype=c_dtype) + dev_mpi = qml.device("lightning.gpu", wires=num_wires, mpi=True, c_dtype=c_dtype) def circuit(): qml.RX(0.4, wires=[0]) @@ -665,67 +592,68 @@ def circuit(): class TestGenerateSample: """Tests that samples are properly calculated.""" - @pytest.mark.parametrize("C_DTYPE", [np.complex128, np.complex64]) - def test_sample_dimensions(self, C_DTYPE): + @pytest.mark.parametrize("c_dtype", [np.complex128, np.complex64]) + def test_sample_dimensions(self, c_dtype): """Tests if the samples returned by sample have the correct dimensions """ num_wires = numQubits - dev = qml.device("lightning.gpu", wires=num_wires, mpi=True, shots=1000, c_dtype=C_DTYPE) + dev = qml.device("lightning.gpu", wires=num_wires, mpi=True, c_dtype=c_dtype) + + ops = [qml.RX(1.5708, wires=[0]), qml.RX(1.5708, wires=[1])] + + shots = 10 + obs = qml.PauliZ(wires=[0]) + tape = qml.tape.QuantumScript(ops, [qml.sample(op=obs)], shots=shots) + s1 = dev.execute(tape) + + assert np.array_equal(s1.shape, (shots,)) - dev.apply([qml.RX(1.5708, wires=[0]), qml.RX(1.5708, wires=[1])]) + shots = 12 + obs = qml.PauliZ(wires=[1]) + tape = qml.tape.QuantumScript(ops, [qml.sample(op=obs)], shots=shots) + s2 = dev.execute(tape) - dev.shots = 10 - dev._wires_measured = {0} - dev._samples = dev.generate_samples() - s1 = dev.sample(qml.PauliZ(wires=[0])) - assert np.array_equal(s1.shape, (10,)) + assert np.array_equal(s2.shape, (shots,)) - dev.reset() - dev.shots = 12 - dev._wires_measured = {1} - dev._samples = dev.generate_samples() - s2 = dev.sample(qml.PauliZ(wires=[1])) - assert np.array_equal(s2.shape, (12,)) + shots = 17 + obs = qml.PauliX(0) @ qml.PauliZ(1) + tape = qml.tape.QuantumScript(ops, [qml.sample(op=obs)], shots=shots) + s3 = dev.execute(tape) - dev.reset() - dev.shots = 17 - dev._wires_measured = {0, 1} - dev._samples = dev.generate_samples() - s3 = dev.sample(qml.PauliX(0) @ qml.PauliZ(1)) - assert np.array_equal(s3.shape, (17,)) + assert np.array_equal(s3.shape, (shots,)) - @pytest.mark.parametrize("C_DTYPE", [np.complex128, np.complex64]) - def test_sample_values(self, tol, C_DTYPE): + @pytest.mark.parametrize("c_dtype", [np.complex128, np.complex64]) + def test_sample_values(self, tol, c_dtype): """Tests if the samples returned by sample have the correct values """ num_wires = numQubits - dev = qml.device("lightning.gpu", wires=num_wires, mpi=True, shots=1000, c_dtype=C_DTYPE) - dev.reset() - dev.apply([qml.RX(1.5708, wires=[0])]) - dev._wires_measured = {0} - dev._samples = dev.generate_samples() + dev = qml.device("lightning.gpu", wires=num_wires, mpi=True, c_dtype=c_dtype) - s1 = dev.sample(qml.PauliZ(0)) + shots = qml.measurements.Shots(1000) + ops = [qml.RX(1.5708, wires=[0])] + obs = qml.PauliZ(0) + tape = qml.tape.QuantumScript(ops, [qml.sample(op=obs)], shots=shots) + s1 = dev.execute(tape) # s1 should only contain 1 and -1, which is guaranteed if # they square to 1 assert np.allclose(s1**2, 1, atol=tol, rtol=0) - @pytest.mark.parametrize("C_DTYPE", [np.complex128, np.complex64]) - def test_sample_values_qnode(self, tol, C_DTYPE): + @pytest.mark.parametrize("c_dtype", [np.complex128, np.complex64]) + def test_sample_values_qnode(self, tol, c_dtype): """Tests if the samples returned by sample have the correct values """ num_wires = numQubits dev_mpi = qml.device( - "lightning.gpu", wires=num_wires, mpi=True, shots=1000, c_dtype=C_DTYPE + "lightning.gpu", wires=num_wires, mpi=True, shots=1000, c_dtype=c_dtype ) - dev_mpi.reset() + dev_mpi._statevector.reset_state() @qml.qnode(dev_mpi) def circuit(): @@ -736,15 +664,15 @@ def circuit(): # they square to 1 assert np.allclose(circuit() ** 2, 1, atol=tol, rtol=0) - @pytest.mark.parametrize("C_DTYPE", [np.complex128, np.complex64]) - def test_multi_samples_return_correlated_results(self, C_DTYPE): + @pytest.mark.parametrize("c_dtype", [np.complex128, np.complex64]) + def test_multi_samples_return_correlated_results(self, c_dtype): """Tests if the samples returned by the sample function have the correct dimensions """ num_wires = 3 dev_gpumpi = qml.device( - "lightning.gpu", wires=num_wires, mpi=True, shots=1000, c_dtype=C_DTYPE + "lightning.gpu", wires=num_wires, mpi=True, shots=1000, c_dtype=c_dtype ) @qml.qnode(dev_gpumpi) @@ -757,13 +685,13 @@ def circuit(): assert np.array_equal(outcomes[0], outcomes[1]) - @pytest.mark.parametrize("C_DTYPE", [np.complex128, np.complex64]) - def test_paulix_pauliy(self, C_DTYPE, tol=TOL_STOCHASTIC): + @pytest.mark.parametrize("c_dtype", [np.complex128, np.complex64]) + def test_paulix_pauliy(self, c_dtype, tol=TOL_STOCHASTIC): """Test that a tensor product involving PauliX and PauliY works correctly""" num_wires = 3 dev_gpumpi = qml.device( - "lightning.gpu", wires=num_wires, mpi=True, shots=1000, c_dtype=C_DTYPE + "lightning.gpu", wires=num_wires, mpi=True, shots=1000, c_dtype=c_dtype ) theta = 0.432 @@ -799,13 +727,13 @@ def circuit(): ) / 16 assert np.allclose(var, expected, atol=tol) - @pytest.mark.parametrize("C_DTYPE", [np.complex128, np.complex64]) - def test_pauliz_hadamard(self, C_DTYPE, tol=TOL_STOCHASTIC): + @pytest.mark.parametrize("c_dtype", [np.complex128, np.complex64]) + def test_pauliz_hadamard(self, c_dtype, tol=TOL_STOCHASTIC): """Test that a tensor product involving PauliZ and PauliY and hadamard works correctly""" num_wires = 3 dev_gpumpi = qml.device( - "lightning.gpu", wires=num_wires, mpi=True, shots=1000, c_dtype=C_DTYPE + "lightning.gpu", wires=num_wires, mpi=True, shots=1000, c_dtype=c_dtype ) theta = 0.432 @@ -845,13 +773,13 @@ def circuit(): class TestTensorVar: """Test tensor variance measurements.""" - @pytest.mark.parametrize("C_DTYPE", [np.complex128, np.complex64]) - def test_paulix_pauliy(self, C_DTYPE, tol=TOL_STOCHASTIC): + @pytest.mark.parametrize("c_dtype", [np.complex128, np.complex64]) + def test_paulix_pauliy(self, c_dtype, tol=TOL_STOCHASTIC): """Test that a tensor product involving PauliX and PauliY works correctly""" num_wires = 3 dev_gpumpi = qml.device( - "lightning.gpu", wires=num_wires, mpi=True, shots=1000, c_dtype=C_DTYPE + "lightning.gpu", wires=num_wires, mpi=True, shots=1000, c_dtype=c_dtype ) theta = 0.432 @@ -879,12 +807,12 @@ def circuit(): ) / 16 assert np.allclose(res, expected, atol=tol) - @pytest.mark.parametrize("C_DTYPE", [np.complex128, np.complex64]) - def test_pauliz_hadamard(self, C_DTYPE, tol=TOL_STOCHASTIC): + @pytest.mark.parametrize("c_dtype", [np.complex128, np.complex64]) + def test_pauliz_hadamard(self, c_dtype, tol=TOL_STOCHASTIC): """Test that a tensor product involving PauliZ and PauliY and hadamard works correctly""" num_wires = 3 dev_gpumpi = qml.device( - "lightning.gpu", wires=num_wires, mpi=True, shots=1000, c_dtype=C_DTYPE + "lightning.gpu", wires=num_wires, mpi=True, shots=1000, c_dtype=c_dtype ) theta = 0.432 diff --git a/mpitests/test_device.py b/mpitests/test_device.py index 03a1880114..666b7602b0 100644 --- a/mpitests/test_device.py +++ b/mpitests/test_device.py @@ -21,6 +21,8 @@ from conftest import LightningDevice as ld from conftest import device_name from mpi4py import MPI +from pennylane import DeviceError +from pennylane.tape import QuantumScript if not ld._CPP_BINARY_AVAILABLE: pytest.skip("No binary module found. Skipping.", allow_module_level=True) @@ -38,13 +40,13 @@ def test_create_device(): def test_unsupported_mpi_buf_size(): - with pytest.raises(TypeError, match="Unsupported mpi_buf_size value"): + with pytest.raises(ValueError, match="Unsupported mpi_buf_size value"): dev = qml.device(device_name, mpi=True, wires=4, mpi_buf_size=-1) - with pytest.raises(TypeError, match="Unsupported mpi_buf_size value"): + with pytest.raises(ValueError, match="Unsupported mpi_buf_size value"): dev = qml.device(device_name, mpi=True, wires=4, mpi_buf_size=3) - with pytest.warns( - RuntimeWarning, - match="The MPI buffer size is larger than the local state vector size", + with pytest.raises( + RuntimeError, + match="The MPI buffer size is larger than the local state vector size.", ): dev = qml.device(device_name, mpi=True, wires=4, mpi_buf_size=2**4) with pytest.raises( @@ -52,3 +54,15 @@ def test_unsupported_mpi_buf_size(): match="Number of processes should be smaller than the number of statevector elements", ): dev = qml.device(device_name, mpi=True, wires=1) + + +def test_unsupported_gate(): + comm = MPI.COMM_WORLD + dev = qml.device(device_name, mpi=True, wires=4) + op = qml.ctrl(qml.GlobalPhase(0.1, wires=[1, 2, 3]), [0], control_values=[True]) + tape = QuantumScript([op]) + with pytest.raises( + DeviceError, match="Lightning-GPU-MPI does not support Controlled GlobalPhase gates" + ): + dev.execute(tape) + comm.Barrier() diff --git a/mpitests/test_expval.py b/mpitests/test_expval.py index d020471c03..3ca73cd82e 100644 --- a/mpitests/test_expval.py +++ b/mpitests/test_expval.py @@ -22,114 +22,260 @@ from conftest import PHI, THETA, VARPHI, device_name from mpi4py import MPI +numQubits = 8 -@pytest.mark.parametrize("theta, phi", list(zip(THETA, PHI))) -class TestExpval: - """Test expectation values""" - def test_identity_expectation(self, theta, phi, tol): - """Test that identity expectation value (i.e. the trace) is 1""" - dev = qml.device(device_name, mpi=True, wires=3) +def create_random_init_state(numWires, c_dtype, seed_value=48): + """Returns a random initial state of a certain type.""" + np.random.seed(seed_value) - O1 = qml.Identity(wires=[0]) - O2 = qml.Identity(wires=[1]) + r_dtype = np.float64 if c_dtype == np.complex128 else np.float32 - dev.apply( - [qml.RX(theta, wires=[0]), qml.RX(phi, wires=[1]), qml.CNOT(wires=[0, 1])], - rotations=[*O1.diagonalizing_gates(), *O2.diagonalizing_gates()], - ) + num_elements = 2**numWires + init_state = np.random.rand(num_elements).astype(r_dtype) + 1j * np.random.rand( + num_elements + ).astype(r_dtype) - res = np.array([dev.expval(O1), dev.expval(O2)]) - assert np.allclose(res, np.array([1, 1]), tol) + init_state = init_state / np.linalg.norm(init_state) + return init_state - def test_pauliz_expectation(self, theta, phi, tol): - """Test that PauliZ expectation value is correct""" - dev = qml.device(device_name, mpi=True, wires=3) - O1 = qml.PauliZ(wires=[0]) - O2 = qml.PauliZ(wires=[1]) +def apply_operation_gates_qnode_param(tol, dev_mpi, operation, par, Wires): + """Wrapper applying a parametric gate with QNode function.""" + num_wires = numQubits + comm = MPI.COMM_WORLD + commSize = comm.Get_size() + num_global_wires = commSize.bit_length() - 1 + num_local_wires = num_wires - num_global_wires - dev.apply( - [qml.RX(theta, wires=[0]), qml.RX(phi, wires=[1]), qml.CNOT(wires=[0, 1])], - rotations=[*O1.diagonalizing_gates(), *O2.diagonalizing_gates()], - ) + c_dtype = dev_mpi.c_dtype - res = np.array([dev.expval(O1), dev.expval(O2)]) - assert np.allclose(res, np.array([np.cos(theta), np.cos(theta) * np.cos(phi)]), tol) + expected_output_cpu = np.zeros(2**num_wires).astype(c_dtype) + local_state_vector = np.zeros(2**num_local_wires).astype(c_dtype) + local_expected_output_cpu = np.zeros(2**num_local_wires).astype(c_dtype) - def test_paulix_expectation(self, theta, phi, tol): - """Test that PauliX expectation value is correct""" - dev = qml.device(device_name, mpi=True, wires=3) + state_vector = create_random_init_state(num_wires, dev_mpi.c_dtype) + comm.Bcast(state_vector, root=0) - O1 = qml.PauliX(wires=[0]) - O2 = qml.PauliX(wires=[1]) + comm.Scatter(state_vector, local_state_vector, root=0) + dev_cpu = qml.device("lightning.qubit", wires=num_wires, c_dtype=c_dtype) - dev.apply( - [qml.RY(theta, wires=[0]), qml.RY(phi, wires=[1]), qml.CNOT(wires=[0, 1])], - rotations=[*O1.diagonalizing_gates(), *O2.diagonalizing_gates()], - ) + def circuit(*params): + qml.StatePrep(state_vector, wires=range(num_wires)) + operation(*params, wires=Wires) + return qml.state() + + cpu_qnode = qml.QNode(circuit, dev_cpu) + expected_output_cpu = cpu_qnode(*par).astype(c_dtype) + comm.Scatter(expected_output_cpu, local_expected_output_cpu, root=0) + + mpi_qnode = qml.QNode(circuit, dev_mpi) + local_state_vector = mpi_qnode(*par) + + assert np.allclose(local_state_vector, local_expected_output_cpu, atol=tol, rtol=0) + + +def apply_operation_gates_qnode_nonparam(tol, dev_mpi, operation, Wires): + """Wrapper applying a non-parametric gate with QNode function.""" + num_wires = numQubits + comm = MPI.COMM_WORLD + commSize = comm.Get_size() + num_global_wires = commSize.bit_length() - 1 + num_local_wires = num_wires - num_global_wires + + c_dtype = dev_mpi.c_dtype + + expected_output_cpu = np.zeros(2**num_wires).astype(c_dtype) + local_state_vector = np.zeros(2**num_local_wires).astype(c_dtype) + local_expected_output_cpu = np.zeros(2**num_local_wires).astype(c_dtype) + + state_vector = create_random_init_state(num_wires, dev_mpi.c_dtype) + comm.Bcast(state_vector, root=0) - res = np.array([dev.expval(O1), dev.expval(O2)], dtype=dev.C_DTYPE) - assert np.allclose( - res, - np.array([np.sin(theta) * np.sin(phi), np.sin(phi)], dtype=dev.C_DTYPE), - tol * 10, + comm.Scatter(state_vector, local_state_vector, root=0) + dev_cpu = qml.device("lightning.qubit", wires=num_wires, c_dtype=c_dtype) + + def circuit(): + qml.StatePrep(state_vector, wires=range(num_wires)) + operation(wires=Wires) + return qml.state() + + cpu_qnode = qml.QNode(circuit, dev_cpu) + expected_output_cpu = cpu_qnode().astype(c_dtype) + comm.Scatter(expected_output_cpu, local_expected_output_cpu, root=0) + + mpi_qnode = qml.QNode(circuit, dev_mpi) + local_state_vector = mpi_qnode() + + assert np.allclose(local_state_vector, local_expected_output_cpu, atol=tol, rtol=0) + + +@pytest.mark.parametrize("c_dtype", [np.complex128, np.complex64]) +@pytest.mark.parametrize("batch_obs", [True, False]) +class TestExpval: + """Tests that expectation values are properly calculated or that the proper errors are raised.""" + + @pytest.mark.parametrize( + "operation", + [ + qml.PauliX, + qml.PauliY, + qml.PauliZ, + qml.Hadamard, + qml.Identity, + ], + ) + @pytest.mark.parametrize("wires", [0, 1, 2, numQubits - 2, numQubits - 1]) + def test_expval_single_wire_no_parameters(self, tol, operation, wires, c_dtype, batch_obs): + """Tests that expectation values are properly calculated for single-wire observables without parameters.""" + num_wires = numQubits + comm = MPI.COMM_WORLD + + dev_mpi = qml.device( + "lightning.gpu", wires=numQubits, mpi=True, c_dtype=c_dtype, batch_obs=batch_obs ) - def test_pauliy_expectation(self, theta, phi, tol): - """Test that PauliY expectation value is correct""" - dev = qml.device(device_name, mpi=True, wires=3) + dev_cpu = qml.device("lightning.qubit", wires=num_wires, c_dtype=c_dtype) - O1 = qml.PauliY(wires=[0]) - O2 = qml.PauliY(wires=[1]) + state_vector = create_random_init_state(num_wires, dev_mpi.c_dtype) + comm.Bcast(state_vector, root=0) - dev.apply( - [qml.RX(theta, wires=[0]), qml.RX(phi, wires=[1]), qml.CNOT(wires=[0, 1])], - rotations=[*O1.diagonalizing_gates(), *O2.diagonalizing_gates()], + def circuit(): + qml.StatePrep(state_vector, wires=range(num_wires)) + return qml.expval(operation(wires)) + + cpu_qnode = qml.QNode(circuit, dev_cpu) + expected_output_cpu = cpu_qnode() + comm.Bcast(np.array(expected_output_cpu), root=0) + + mpi_qnode = qml.QNode(circuit, dev_mpi) + expected_output_mpi = mpi_qnode() + + assert np.allclose(expected_output_mpi, expected_output_cpu, atol=tol, rtol=0) + + @pytest.mark.parametrize( + "obs", + [ + qml.PauliX(0) @ qml.PauliZ(1), + qml.PauliX(0) @ qml.PauliZ(numQubits - 1), + qml.PauliX(numQubits - 2) @ qml.PauliZ(numQubits - 1), + qml.PauliZ(0) @ qml.PauliZ(1), + qml.PauliZ(0) @ qml.PauliZ(numQubits - 1), + qml.PauliZ(numQubits - 2) @ qml.PauliZ(numQubits - 1), + ], + ) + def test_expval_multiple_obs(self, obs, tol, c_dtype, batch_obs): + """Test expval with Hamiltonian""" + num_wires = numQubits + + dev_cpu = qml.device("lightning.qubit", wires=num_wires, c_dtype=c_dtype) + dev_mpi = qml.device( + "lightning.gpu", wires=num_wires, mpi=True, c_dtype=c_dtype, batch_obs=batch_obs ) - res = np.array([dev.expval(O1), dev.expval(O2)]) - assert np.allclose(res, np.array([0, -np.cos(theta) * np.sin(phi)]), tol) + def circuit(): + qml.RX(0.4, wires=[0]) + qml.RY(-0.2, wires=[num_wires - 1]) + return qml.expval(obs) - def test_hadamard_expectation(self, theta, phi, tol): - """Test that Hadamard expectation value is correct""" - dev = qml.device(device_name, mpi=True, wires=3) + cpu_qnode = qml.QNode(circuit, dev_cpu) + mpi_qnode = qml.QNode(circuit, dev_mpi) + + assert np.allclose(cpu_qnode(), mpi_qnode(), atol=tol, rtol=0) + + @pytest.mark.parametrize( + "obs, coeffs", + [ + ([qml.PauliX(0) @ qml.PauliZ(1)], [0.314]), + ([qml.PauliX(0) @ qml.PauliZ(numQubits - 1)], [0.314]), + ([qml.PauliZ(0) @ qml.PauliZ(1)], [0.314]), + ([qml.PauliZ(0) @ qml.PauliZ(numQubits - 1)], [0.314]), + ( + [qml.PauliX(0) @ qml.PauliZ(1), qml.PauliZ(0) @ qml.PauliZ(1)], + [0.314, 0.2], + ), + ( + [ + qml.PauliX(0) @ qml.PauliZ(numQubits - 1), + qml.PauliZ(0) @ qml.PauliZ(1), + ], + [0.314, 0.2], + ), + ( + [ + qml.PauliX(numQubits - 2) @ qml.PauliZ(numQubits - 1), + qml.PauliZ(0) @ qml.PauliZ(1), + ], + [0.314, 0.2], + ), + ], + ) + def test_expval_hamiltonian(self, obs, coeffs, tol, c_dtype, batch_obs): + """Test expval with Hamiltonian""" + num_wires = numQubits + + ham = qml.Hamiltonian(coeffs, obs) + + dev_cpu = qml.device("lightning.qubit", wires=num_wires, c_dtype=c_dtype) + dev_mpi = qml.device( + "lightning.gpu", wires=num_wires, mpi=True, c_dtype=c_dtype, batch_obs=batch_obs + ) + + def circuit(): + qml.RX(0.4, wires=[0]) + qml.RY(-0.2, wires=[numQubits - 1]) + return qml.expval(ham) - O1 = qml.Hadamard(wires=[0]) - O2 = qml.Hadamard(wires=[1]) + cpu_qnode = qml.QNode(circuit, dev_cpu) + mpi_qnode = qml.QNode(circuit, dev_mpi) - dev.apply( - [qml.RY(theta, wires=[0]), qml.RY(phi, wires=[1]), qml.CNOT(wires=[0, 1])], - rotations=[*O1.diagonalizing_gates(), *O2.diagonalizing_gates()], + assert np.allclose(cpu_qnode(), mpi_qnode(), atol=tol, rtol=0) + + def test_expval_non_pauli_word_hamiltionian(self, tol, c_dtype, batch_obs): + """Tests expectation values of non-Pauli word Hamiltonians.""" + dev_mpi = qml.device( + "lightning.gpu", wires=3, mpi=True, c_dtype=c_dtype, batch_obs=batch_obs ) + dev_cpu = qml.device("lightning.qubit", wires=3) + + theta = 0.432 + phi = 0.123 + varphi = -0.543 + + def circuit(): + qml.RX(theta, wires=[0]) + qml.RX(phi, wires=[1]) + qml.RX(varphi, wires=[2]) + qml.CNOT(wires=[0, 1]) + qml.CNOT(wires=[1, 2]) + return qml.expval(0.5 * qml.Hadamard(2)) + + cpu_qnode = qml.QNode(circuit, dev_cpu) + mpi_qnode = qml.QNode(circuit, dev_mpi) + + assert np.allclose(cpu_qnode(), mpi_qnode(), atol=tol, rtol=0) - res = np.array([dev.expval(O1), dev.expval(O2)]) - expected = np.array( - [ - np.sin(theta) * np.sin(phi) + np.cos(theta), - np.cos(theta) * np.cos(phi) + np.sin(phi), - ] - ) / np.sqrt(2) - assert np.allclose(res, expected, tol) - - @pytest.mark.parametrize("n_wires", range(1, 8)) - def test_hermitian_expectation(self, n_wires, theta, phi, tol): + @pytest.mark.parametrize("theta, phi", list(zip(THETA, PHI))) + @pytest.mark.parametrize("n_wires", range(1, numQubits)) + def test_hermitian_expectation(self, n_wires, theta, phi, tol, c_dtype, batch_obs): """Test that Hadamard expectation value is correct""" - n_qubits = 7 + n_qubits = numQubits - 1 dev_def = qml.device("default.qubit", wires=n_qubits) - dev = qml.device(device_name, mpi=True, wires=n_qubits) + dev = qml.device( + device_name, mpi=True, wires=n_qubits, c_dtype=c_dtype, batch_obs=batch_obs + ) comm = MPI.COMM_WORLD m = 2**n_wires U = np.random.rand(m, m) + 1j * np.random.rand(m, m) U = U + np.conj(U.T) - U = U.astype(dev.C_DTYPE) + U = U.astype(dev.c_dtype) comm.Bcast(U, root=0) obs = qml.Hermitian(U, wires=range(n_wires)) init_state = np.random.rand(2**n_qubits) + 1j * np.random.rand(2**n_qubits) - init_state /= np.sqrt(np.dot(np.conj(init_state), init_state)) - init_state = init_state.astype(dev.C_DTYPE) + init_state = init_state / np.linalg.norm(init_state) + init_state = init_state.astype(dev.c_dtype) comm.Bcast(init_state, root=0) def circuit(): @@ -250,69 +396,39 @@ def circuit(x, y): class TestTensorExpval: """Test tensor expectation values""" - def test_paulix_pauliy(self, theta, phi, varphi, tol): + @pytest.mark.parametrize( + "obs,expected", + [ + (qml.PauliX(0) @ qml.PauliY(2), "PXPY"), + (qml.PauliZ(0) @ qml.Identity(1) @ qml.PauliZ(2), "PZIPZ"), + (qml.PauliZ(0) @ qml.Hadamard(1) @ qml.PauliY(2), "PZHPY"), + ], + ) + def test_tensor(self, theta, phi, varphi, obs, expected, tol): """Test that a tensor product involving PauliX and PauliY works correctly""" dev = qml.device(device_name, mpi=True, wires=3) - obs = qml.PauliX(0) @ qml.PauliY(2) - - dev.apply( - [ - qml.RX(theta, wires=[0]), - qml.RX(phi, wires=[1]), - qml.RX(varphi, wires=[2]), - qml.CNOT(wires=[0, 1]), - qml.CNOT(wires=[1, 2]), - ], - rotations=obs.diagonalizing_gates(), - ) - res = dev.expval(obs) - - expected = np.sin(theta) * np.sin(phi) * np.sin(varphi) - - assert np.allclose(res, expected, atol=tol) - - def test_pauliz_identity(self, theta, phi, varphi, tol): - """Test that a tensor product involving PauliZ and Identity works - correctly""" - dev = qml.device(device_name, mpi=True, wires=3) - obs = qml.PauliZ(0) @ qml.Identity(1) @ qml.PauliZ(2) - - dev.apply( - [ - qml.RX(theta, wires=[0]), - qml.RX(phi, wires=[1]), - qml.RX(varphi, wires=[2]), - qml.CNOT(wires=[0, 1]), - qml.CNOT(wires=[1, 2]), - ], - rotations=obs.diagonalizing_gates(), - ) - - res = dev.expval(obs) - - expected = np.cos(varphi) * np.cos(phi) - assert np.allclose(res, expected, tol) - - def test_pauliz_hadamard_pauliy(self, theta, phi, varphi, tol): - """Test that a tensor product involving PauliZ and PauliY and Hadamard - works correctly""" - dev = qml.device(device_name, mpi=True, wires=3) - obs = qml.PauliZ(0) @ qml.Hadamard(1) @ qml.PauliY(2) - - dev.apply( - [ - qml.RX(theta, wires=[0]), - qml.RX(phi, wires=[1]), - qml.RX(varphi, wires=[2]), - qml.CNOT(wires=[0, 1]), - qml.CNOT(wires=[1, 2]), - ], - rotations=obs.diagonalizing_gates(), - ) + def circuit(): + qml.RX(theta, wires=[0]) + qml.RX(phi, wires=[1]) + qml.RX(varphi, wires=[2]) + qml.CNOT(wires=[0, 1]) + qml.CNOT(wires=[1, 2]) + return qml.expval(obs) - res = dev.expval(obs) - expected = -(np.cos(varphi) * np.sin(phi) + np.sin(varphi) * np.cos(theta)) / np.sqrt(2) + mpi_qnode = qml.QNode(circuit, dev) + res = mpi_qnode() + + if expected == "PXPY": + expected_val = np.sin(theta) * np.sin(phi) * np.sin(varphi) + elif expected == "PZIPZ": + expected_val = np.cos(varphi) * np.cos(phi) + elif expected == "PZHPY": + expected_val = -( + np.cos(varphi) * np.sin(phi) + np.sin(varphi) * np.cos(theta) + ) / np.sqrt(2) + else: + expected_val = 0 - assert np.allclose(res, expected, tol) + assert np.allclose(res, expected_val, atol=tol) diff --git a/mpitests/test_native_mcm.py b/mpitests/test_native_mcm.py new file mode 100644 index 0000000000..78bde9872a --- /dev/null +++ b/mpitests/test_native_mcm.py @@ -0,0 +1,43 @@ +# Copyright 2024 Xanadu Quantum Technologies Inc. + +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at + +# http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Tests for default qubit preprocessing.""" +import numpy as np +import pennylane as qml +import pytest +from conftest import LightningDevice, device_name +from mpi4py import MPI + +if not LightningDevice._CPP_BINARY_AVAILABLE: # pylint: disable=protected-access + pytest.skip("No binary module found. Skipping.", allow_module_level=True) + + +def test_unspported_mid_measurement(): + """Test unsupported mid_measurement for Lightning-GPU-MPI.""" + comm = MPI.COMM_WORLD + dev = qml.device(device_name, wires=2, mpi=True, shots=1000) + params = np.pi / 4 * np.ones(2) + + @qml.qnode(dev) + def func(x, y): + qml.RX(x, wires=0) + m0 = qml.measure(0) + qml.cond(m0, qml.RY)(y, wires=1) + return qml.probs(wires=0) + + comm.Barrier() + + with pytest.raises( + qml.DeviceError, match="Lightning-GPU-MPI does not support Mid-circuit measurements." + ): + func(*params) diff --git a/mpitests/test_probs.py b/mpitests/test_probs.py index b2f57f733a..ed9ab9b9c8 100644 --- a/mpitests/test_probs.py +++ b/mpitests/test_probs.py @@ -23,27 +23,31 @@ numQubits = 8 -def create_random_init_state(numWires, R_DTYPE, seed_value=48): +def create_random_init_state(numWires, c_dtype, seed_value=48): + """Returns a random initial state of a certain type.""" np.random.seed(seed_value) - num_elements = 1 << numWires - init_state = np.random.rand(num_elements).astype(R_DTYPE) + 1j * np.random.rand( + + r_dtype = np.float64 if c_dtype == np.complex128 else np.float32 + + num_elements = 2**numWires + init_state = np.random.rand(num_elements).astype(r_dtype) + 1j * np.random.rand( num_elements - ).astype(R_DTYPE) - scale_sum = np.sqrt(np.sum(np.abs(init_state) ** 2)).astype(R_DTYPE) - init_state = init_state / scale_sum + ).astype(r_dtype) + + init_state = init_state / np.linalg.norm(init_state) return init_state -def apply_probs_nonparam(tol, operation, GateWires, Wires, C_DTYPE): +def apply_probs_nonparam(tol, operation, GateWires, Wires, c_dtype): num_wires = numQubits comm = MPI.COMM_WORLD rank = comm.Get_rank() commSize = comm.Get_size() - dev_cpu = qml.device("lightning.qubit", wires=num_wires, c_dtype=C_DTYPE) - dev_mpi = qml.device(device_name, wires=num_wires, mpi=True, c_dtype=C_DTYPE) + dev_cpu = qml.device("lightning.qubit", wires=num_wires, c_dtype=c_dtype) + dev_mpi = qml.device(device_name, wires=num_wires, mpi=True, c_dtype=c_dtype) - state_vector = create_random_init_state(num_wires, dev_mpi.R_DTYPE) + state_vector = create_random_init_state(num_wires, dev_mpi.c_dtype) comm.Bcast(state_vector, root=0) def circuit(): @@ -58,15 +62,16 @@ def circuit(): local_probs = mpi_qnode() recv_counts = comm.gather(len(local_probs), root=0) - comm.Barrier() + r_dtype = np.float64 if c_dtype == np.complex128 else np.float32 + if rank == 0: - probs_mpi = np.zeros(1 << len(Wires)).astype(dev_mpi.R_DTYPE) - displacements = [i for i in range(commSize)] + probs_mpi = np.zeros(2 ** len(Wires)).astype(r_dtype) else: probs_mpi = None probs_cpu = None + comm.Barrier() comm.Gatherv(local_probs, [probs_mpi, recv_counts], root=0) @@ -75,16 +80,16 @@ def circuit(): comm.Barrier() -def apply_probs_param(tol, operation, par, GateWires, Wires, C_DTYPE): +def apply_probs_param(tol, operation, par, GateWires, Wires, c_dtype): num_wires = numQubits comm = MPI.COMM_WORLD rank = comm.Get_rank() commSize = comm.Get_size() - dev_cpu = qml.device("lightning.qubit", wires=num_wires, c_dtype=C_DTYPE) - dev_mpi = qml.device(device_name, wires=num_wires, mpi=True, c_dtype=C_DTYPE) + dev_cpu = qml.device("lightning.qubit", wires=num_wires, c_dtype=c_dtype) + dev_mpi = qml.device(device_name, wires=num_wires, mpi=True, c_dtype=c_dtype) - state_vector = create_random_init_state(num_wires, dev_mpi.R_DTYPE) + state_vector = create_random_init_state(num_wires, dev_mpi.c_dtype) comm.Bcast(state_vector, root=0) def circuit(): @@ -102,8 +107,10 @@ def circuit(): comm.Barrier() + r_dtype = np.float64 if c_dtype == np.complex128 else np.float32 + if rank == 0: - probs_mpi = np.zeros(1 << len(Wires)).astype(dev_mpi.R_DTYPE) + probs_mpi = np.zeros(2 ** len(Wires)).astype(r_dtype) else: probs_mpi = None probs_cpu = None @@ -116,6 +123,19 @@ def circuit(): comm.Barrier() +@pytest.mark.parametrize( + "Wires", + [ + [0], + [1], + [0, 1], + [0, 2], + [0, numQubits - 1], + [numQubits - 2, numQubits - 1], + range(numQubits), + ], +) +@pytest.mark.parametrize("c_dtype", [np.complex128]) class TestProbs: """Tests for the probability method.""" @@ -123,41 +143,15 @@ class TestProbs: "operation", [qml.PauliX, qml.PauliY, qml.PauliZ, qml.Hadamard, qml.S, qml.T] ) @pytest.mark.parametrize("GateWires", [[0], [numQubits - 1]]) - @pytest.mark.parametrize( - "Wires", - [ - [0], - [1], - [0, 1], - [0, 2], - [0, numQubits - 1], - [numQubits - 2, numQubits - 1], - range(numQubits), - ], - ) - @pytest.mark.parametrize("C_DTYPE", [np.complex128]) - def test_prob_single_wire_nonparam(self, tol, operation, GateWires, Wires, C_DTYPE): - apply_probs_nonparam(tol, operation, GateWires, Wires, C_DTYPE) + def test_prob_single_wire_nonparam(self, tol, operation, GateWires, Wires, c_dtype): + apply_probs_nonparam(tol, operation, GateWires, Wires, c_dtype) @pytest.mark.parametrize("operation", [qml.CNOT, qml.SWAP, qml.CY, qml.CZ]) @pytest.mark.parametrize( "GateWires", [[0, 1], [numQubits - 2, numQubits - 1], [0, numQubits - 1]] ) - @pytest.mark.parametrize( - "Wires", - [ - [0], - [1], - [0, 1], - [0, 2], - [0, numQubits - 1], - [numQubits - 2, numQubits - 1], - range(numQubits), - ], - ) - @pytest.mark.parametrize("C_DTYPE", [np.complex128]) - def test_prob_two_wire_nonparam(self, tol, operation, GateWires, Wires, C_DTYPE): - apply_probs_nonparam(tol, operation, GateWires, Wires, C_DTYPE) + def test_prob_two_wire_nonparam(self, tol, operation, GateWires, Wires, c_dtype): + apply_probs_nonparam(tol, operation, GateWires, Wires, c_dtype) @pytest.mark.parametrize("operation", [qml.CSWAP, qml.Toffoli]) @pytest.mark.parametrize( @@ -169,80 +163,28 @@ def test_prob_two_wire_nonparam(self, tol, operation, GateWires, Wires, C_DTYPE) [0, numQubits - 2, numQubits - 1], ], ) - @pytest.mark.parametrize( - "Wires", - [ - [0], - [1], - [0, 1], - [0, 2], - [0, numQubits - 1], - [numQubits - 2, numQubits - 1], - range(numQubits), - ], - ) - @pytest.mark.parametrize("C_DTYPE", [np.complex128]) - def test_prob_three_wire_nonparam(self, tol, operation, GateWires, Wires, C_DTYPE): - apply_probs_nonparam(tol, operation, GateWires, Wires, C_DTYPE) + def test_prob_three_wire_nonparam(self, tol, operation, GateWires, Wires, c_dtype): + apply_probs_nonparam(tol, operation, GateWires, Wires, c_dtype) @pytest.mark.parametrize("operation", [qml.PhaseShift, qml.RX, qml.RY, qml.RZ]) @pytest.mark.parametrize("par", [[0.1], [0.2], [0.3]]) @pytest.mark.parametrize("GateWires", [0, numQubits - 1]) - @pytest.mark.parametrize( - "Wires", - [ - [0], - [1], - [0, 1], - [0, 2], - [0, numQubits - 1], - [numQubits - 2, numQubits - 1], - range(numQubits), - ], - ) - @pytest.mark.parametrize("C_DTYPE", [np.complex128]) - def test_prob_single_wire_param(self, tol, operation, par, GateWires, Wires, C_DTYPE): - apply_probs_param(tol, operation, par, GateWires, Wires, C_DTYPE) + def test_prob_single_wire_param(self, tol, operation, par, GateWires, Wires, c_dtype): + apply_probs_param(tol, operation, par, GateWires, Wires, c_dtype) @pytest.mark.parametrize("operation", [qml.Rot]) @pytest.mark.parametrize("par", [[0.1, 0.2, 0.3], [0.2, 0.3, 0.4]]) @pytest.mark.parametrize("GateWires", [0, numQubits - 1]) - @pytest.mark.parametrize( - "Wires", - [ - [0], - [1], - [0, 1], - [0, 2], - [0, numQubits - 1], - [numQubits - 2, numQubits - 1], - range(numQubits), - ], - ) - @pytest.mark.parametrize("C_DTYPE", [np.complex128]) - def test_prob_single_wire_3param(self, tol, operation, par, GateWires, Wires, C_DTYPE): - apply_probs_param(tol, operation, par, GateWires, Wires, C_DTYPE) + def test_prob_single_wire_3param(self, tol, operation, par, GateWires, Wires, c_dtype): + apply_probs_param(tol, operation, par, GateWires, Wires, c_dtype) @pytest.mark.parametrize("operation", [qml.CRot]) @pytest.mark.parametrize("par", [[0.1, 0.2, 0.3], [0.2, 0.3, 0.4]]) @pytest.mark.parametrize( "GateWires", [[0, numQubits - 1], [0, 1], [numQubits - 2, numQubits - 1]] ) - @pytest.mark.parametrize( - "Wires", - [ - [0], - [1], - [0, 1], - [0, 2], - [0, numQubits - 1], - [numQubits - 2, numQubits - 1], - range(numQubits), - ], - ) - @pytest.mark.parametrize("C_DTYPE", [np.complex128]) - def test_prob_two_wire_3param(self, tol, operation, par, GateWires, Wires, C_DTYPE): - apply_probs_param(tol, operation, par, GateWires, Wires, C_DTYPE) + def test_prob_two_wire_3param(self, tol, operation, par, GateWires, Wires, c_dtype): + apply_probs_param(tol, operation, par, GateWires, Wires, c_dtype) @pytest.mark.parametrize( "operation", @@ -263,21 +205,8 @@ def test_prob_two_wire_3param(self, tol, operation, par, GateWires, Wires, C_DTY @pytest.mark.parametrize( "GateWires", [[0, numQubits - 1], [0, 1], [numQubits - 2, numQubits - 1]] ) - @pytest.mark.parametrize( - "Wires", - [ - [0], - [1], - [0, 1], - [0, 2], - [0, numQubits - 1], - [numQubits - 2, numQubits - 1], - range(numQubits), - ], - ) - @pytest.mark.parametrize("C_DTYPE", [np.complex128]) - def test_prob_two_wire_param(self, tol, operation, par, GateWires, Wires, C_DTYPE): - apply_probs_param(tol, operation, par, GateWires, Wires, C_DTYPE) + def test_prob_two_wire_param(self, tol, operation, par, GateWires, Wires, c_dtype): + apply_probs_param(tol, operation, par, GateWires, Wires, c_dtype) @pytest.mark.parametrize( "operation", @@ -292,18 +221,5 @@ def test_prob_two_wire_param(self, tol, operation, par, GateWires, Wires, C_DTYP [numQubits - 4, numQubits - 3, numQubits - 2, numQubits - 1], ], ) - @pytest.mark.parametrize( - "Wires", - [ - [0], - [1], - [0, 1], - [0, 2], - [0, numQubits - 1], - [numQubits - 2, numQubits - 1], - range(numQubits), - ], - ) - @pytest.mark.parametrize("C_DTYPE", [np.complex128]) - def test_prob_four_wire_param(self, tol, operation, par, GateWires, Wires, C_DTYPE): - apply_probs_param(tol, operation, par, GateWires, Wires, C_DTYPE) + def test_prob_four_wire_param(self, tol, operation, par, GateWires, Wires, c_dtype): + apply_probs_param(tol, operation, par, GateWires, Wires, c_dtype) diff --git a/pennylane_lightning/core/__init__.py b/pennylane_lightning/core/__init__.py index d3a37803e8..30e036e210 100644 --- a/pennylane_lightning/core/__init__.py +++ b/pennylane_lightning/core/__init__.py @@ -1,4 +1,4 @@ -# Copyright 2018-2023 Xanadu Quantum Technologies Inc. +# Copyright 2018-2024 Xanadu Quantum Technologies Inc. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/pennylane_lightning/core/_adjoint_jacobian_base.py b/pennylane_lightning/core/_adjoint_jacobian_base.py new file mode 100644 index 0000000000..a779c0cc4c --- /dev/null +++ b/pennylane_lightning/core/_adjoint_jacobian_base.py @@ -0,0 +1,289 @@ +# Copyright 2018-2024 Xanadu Quantum Technologies Inc. + +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at + +# http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +r""" +Internal methods for adjoint Jacobian differentiation method. +""" + +from abc import ABC, abstractmethod +from typing import Any, Callable, List + +import numpy as np +import pennylane as qml +from pennylane import BasisState, QuantumFunctionError, StatePrep +from pennylane.measurements import Expectation, MeasurementProcess, State +from pennylane.operation import Operation +from pennylane.tape import QuantumTape + +from pennylane_lightning.core._serialize import QuantumScriptSerializer + + +class LightningBaseAdjointJacobian(ABC): + """Lightning [Device] Adjoint Jacobian class + + A class that serves as a base class for Lightning state-vector simulators. + Check and execute the adjoint Jacobian differentiation method. + + Args: + qubit_state(Lightning [Device] StateVector): State Vector to calculate the adjoint Jacobian with. + batch_obs(bool): If serialized tape is to be batched or not. + """ + + def __init__(self, qubit_state: Any, batch_obs: bool) -> None: + self._qubit_state = qubit_state + self._batch_obs = batch_obs + + # Dummy for the C++ bindings + self._jacobian_lightning: Callable = None + self._create_ops_list_lightning: Callable = None + + @property + def qubit_state(self): + """Returns a handle to the Lightning [Device] StateVector object.""" + return self._qubit_state + + @property + def state(self): + """Returns a handle to the Lightning internal data object.""" + return self._qubit_state.state_vector + + @property + def dtype(self): + """Returns the simulation data type.""" + return self._qubit_state.dtype + + @abstractmethod + def _adjoint_jacobian_dtype(self): + """Binding to Lightning [Device] Adjoint Jacobian C++ class. + + Returns: the AdjointJacobian class + """ + + @staticmethod + def _get_return_type( + measurements: List[MeasurementProcess], + ): + """Get the measurement return type. + + Args: + measurements (List[MeasurementProcess]): a list of measurement processes to check. + + Returns: + None, Expectation or State: a common return type of measurements. + """ + if not measurements: + return None + + if len(measurements) == 1 and measurements[0].return_type is State: + return State + + return Expectation + + def _process_jacobian_tape(self, tape: QuantumTape, split_obs: bool = False): + """Process a tape, serializing and building a dictionary proper for + the adjoint Jacobian calculation in the C++ layer. + + Args: + tape (QuantumTape): Operations and measurements that represent instructions for execution on Lightning. + split_obs (bool, optional): If splitting the observables in a list. Defaults to False. + + Returns: + dictionary: dictionary providing serialized data for Jacobian calculation. + """ + use_csingle = self._qubit_state.dtype == np.complex64 + + use_mpi = False + obs_serialized, obs_indices = QuantumScriptSerializer( + self._qubit_state.device_name, use_csingle, use_mpi, split_obs + ).serialize_observables(tape) + + ops_serialized, use_sp = QuantumScriptSerializer( + self._qubit_state.device_name, use_csingle, use_mpi, split_obs + ).serialize_ops(tape) + + # pylint: disable=not-callable + ops_serialized = self._create_ops_list_lightning(*ops_serialized) + + # We need to filter out indices in trainable_params which do not + # correspond to operators. + trainable_params = sorted(tape.trainable_params) + if len(trainable_params) == 0: + return None + + tp_shift = [] + record_tp_rows = [] + all_params = 0 + + for op_idx, trainable_param in enumerate(trainable_params): + # get op_idx-th operator among differentiable operators + operation, _, _ = tape.get_operation(op_idx) + if isinstance(operation, Operation) and not isinstance( + operation, (BasisState, StatePrep) + ): + # We now just ignore non-op or state preps + tp_shift.append(trainable_param) + record_tp_rows.append(all_params) + all_params += 1 + + if use_sp: + # When the first element of the tape is state preparation. Still, I am not sure + # whether there must be only one state preparation... + tp_shift = [i - 1 for i in tp_shift] + + return { + "state_vector": self.state, + "obs_serialized": obs_serialized, + "ops_serialized": ops_serialized, + "tp_shift": tp_shift, + "record_tp_rows": record_tp_rows, + "all_params": all_params, + "obs_indices": obs_indices, + } + + @staticmethod + def _adjoint_jacobian_processing(jac): + """ + Post-process the Jacobian matrix returned by ``adjoint_jacobian`` for + the new return type system. + """ + jac = np.squeeze(jac) + + if jac.ndim == 0: + return np.array(jac) + + if jac.ndim == 1: + return tuple(np.array(j) for j in jac) + + # must be 2-dimensional + return tuple(tuple(np.array(j_) for j_ in j) for j in jac) + + def _handle_raises(self, tape: QuantumTape, is_jacobian: bool, grad_vec=None): + """Handle the raises related with the tape for computing the Jacobian with the adjoint method or the vector-Jacobian products.""" + + if tape.shots: + raise QuantumFunctionError( + "Requested adjoint differentiation to be computed with finite shots. " + "The derivative is always exact when using the adjoint " + "differentiation method." + ) + + tape_return_type = self._get_return_type(tape.measurements) + + if is_jacobian: + if not tape_return_type: + # the tape does not have measurements + return True + + if tape_return_type is State: + raise QuantumFunctionError( + "Adjoint differentiation method does not support measurement StateMP." + ) + + if not is_jacobian: + if qml.math.allclose(grad_vec, 0.0) or not tape_return_type: + # the tape does not have measurements or the gradient is 0.0 + return True + + if tape_return_type is State: + raise QuantumFunctionError( + "Adjoint differentiation does not support State measurements." + ) + + if any(m.return_type is not Expectation for m in tape.measurements): + raise QuantumFunctionError( + "Adjoint differentiation method does not support expectation return type " + "mixed with other return types" + ) + + return False + + @abstractmethod + def calculate_jacobian(self, tape: QuantumTape): + """Computes the Jacobian with the adjoint method. + + .. code-block:: python + + statevector = Lightning [Device] StateVector(num_wires=num_wires) + statevector = statevector.get_final_state(tape) + jacobian = Lightning [Device] AdjointJacobian(statevector).calculate_jacobian(tape) + + Args: + tape (QuantumTape): Operations and measurements that represent instructions for execution on Lightning. + + Returns: + The Jacobian of a tape. + """ + + # pylint: disable=inconsistent-return-statements + def calculate_vjp(self, tape: QuantumTape, grad_vec): + """Compute the vector-Jacobian products of a tape. + + .. code-block:: python + + statevector = Lightning [Device] StateVector(num_wires=num_wires) + statevector = statevector.get_final_state(tape) + vjp = Lightning [Device] AdjointJacobian(statevector).calculate_vjp(tape, grad_vec) + + computes :math:`\\pmb{w} = (w_1,\\cdots,w_m)` where + + .. math:: + + w_k = dy_k \\cdot J_{k,j} + + Here, :math:`dy` is the workflow cotangent (grad_vec), and :math:`J` the Jacobian. + + Args: + tape (QuantumTape): Operations and measurements that represent instructions for execution on Lightning. + grad_vec (tensor_like): Gradient-output vector, also called `dy` or cotangent. Must have shape matching the output + shape of the corresponding tape, i.e. number of measurements if the return type is expectation. + + Returns: + The vector-Jacobian products of a tape. + """ + + empty_array = self._handle_raises(tape, is_jacobian=False, grad_vec=grad_vec) + + if empty_array: + return qml.math.convert_like(np.zeros(len(tape.trainable_params)), grad_vec) + + # Proceed, because tape_return_type is Expectation. + if qml.math.ndim(grad_vec) == 0: + grad_vec = (grad_vec,) + + if len(grad_vec) != len(tape.measurements): + raise ValueError( + "Number of observables in the tape must be the same as the " + "length of grad_vec in the vjp method" + ) + + if np.iscomplexobj(grad_vec): + raise ValueError( + "The vjp method only works with a real-valued grad_vec when the " + "tape is returning an expectation value" + ) + + ham = qml.simplify(qml.dot(grad_vec, [m.obs for m in tape.measurements])) + + num_params = len(tape.trainable_params) + + if num_params == 0: + return np.array([], dtype=self.qubit_state.dtype) + + new_tape = qml.tape.QuantumScript( + tape.operations, + [qml.expval(ham)], + shots=tape.shots, + trainable_params=tape.trainable_params, + ) + + return self.calculate_jacobian(new_tape) diff --git a/pennylane_lightning/core/_measurements_base.py b/pennylane_lightning/core/_measurements_base.py new file mode 100644 index 0000000000..08e4053b16 --- /dev/null +++ b/pennylane_lightning/core/_measurements_base.py @@ -0,0 +1,391 @@ +# Copyright 2018-2024 Xanadu Quantum Technologies Inc. + +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at + +# http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Class implementation for state vector measurements. +""" + +from abc import ABC, abstractmethod +from typing import Any, Callable, List, Union + +import numpy as np +import pennylane as qml +from pennylane.devices.qubit.sampling import _group_measurements +from pennylane.measurements import ( + ClassicalShadowMP, + ExpectationMP, + MeasurementProcess, + ProbabilityMP, + SampleMeasurement, + ShadowExpvalMP, + Shots, + StateMeasurement, + VarianceMP, +) +from pennylane.ops import SparseHamiltonian, Sum +from pennylane.tape import QuantumScript +from pennylane.typing import Result, TensorLike +from pennylane.wires import Wires + +from pennylane_lightning.core._serialize import QuantumScriptSerializer + + +class LightningBaseMeasurements(ABC): + """Lightning [Device] Measurements class + + A class that serves as a base class for Lightning state-vector simulators. + Measures the state provided by the Lightning [Device] StateVector class. + + Args: + qubit_state(Lightning [Device] StateVector): Lightning state-vector class containing the state vector to be measured. + """ + + def __init__( + self, + qubit_state: Any, + ) -> None: + self._qubit_state = qubit_state + + self._use_mpi = False + # Dummy for the C++ bindings + self._measurement_lightning = None + + @property + def qubit_state(self): + """Returns a handle to the Lightning [Device] StateVector object.""" + return self._qubit_state + + @property + def dtype(self): + """Returns the simulation data type.""" + return self._qubit_state.dtype + + @abstractmethod + def _measurement_dtype(self): + """Binding to Lightning [Device] Measurements C++ class. + + Returns: the Measurements class + """ + + def state_diagonalizing_gates(self, measurementprocess: StateMeasurement) -> TensorLike: + """Apply a measurement to state when the measurement process has an observable with diagonalizing gates. + This method is bypassing the measurement process to default.qubit implementation. + + Args: + measurementprocess (StateMeasurement): measurement to apply to the state + + Returns: + TensorLike: the result of the measurement + """ + diagonalizing_gates = measurementprocess.diagonalizing_gates() + self._qubit_state.apply_operations(diagonalizing_gates) + state_array = self._qubit_state.state + wires = Wires(range(self._qubit_state.num_wires)) + result = measurementprocess.process_state(state_array, wires) + self._qubit_state.apply_operations([qml.adjoint(g) for g in reversed(diagonalizing_gates)]) + return result + + def expval(self, measurementprocess: MeasurementProcess): + """Expectation value of the supplied observable contained in the MeasurementProcess. + + Args: + measurementprocess (StateMeasurement): measurement to apply to the state + + Returns: + Expectation value of the observable + """ + + if isinstance(measurementprocess.obs, qml.SparseHamiltonian): + # ensuring CSR sparse representation. + CSR_SparseHamiltonian = measurementprocess.obs.sparse_matrix( + wire_order=list(range(self._qubit_state.num_wires)) + ).tocsr(copy=False) + return self._measurement_lightning.expval( + CSR_SparseHamiltonian.indptr, + CSR_SparseHamiltonian.indices, + CSR_SparseHamiltonian.data, + ) + + if ( + isinstance(measurementprocess.obs, qml.Hermitian) + or (measurementprocess.obs.arithmetic_depth > 0) + or isinstance(measurementprocess.obs.name, List) + ): + # pylint: disable=protected-access + ob_serialized = QuantumScriptSerializer( + self._qubit_state.device_name, self.dtype == np.complex64, self._use_mpi + )._ob(measurementprocess.obs) + return self._measurement_lightning.expval(ob_serialized) + + return self._measurement_lightning.expval( + measurementprocess.obs.name, measurementprocess.obs.wires + ) + + def probs(self, measurementprocess: MeasurementProcess): + """Probabilities of the supplied observable or wires contained in the MeasurementProcess. + + Args: + measurementprocess (StateMeasurement): measurement to apply to the state. + + Returns: + Probabilities of the supplied observable or wires. + """ + diagonalizing_gates = measurementprocess.diagonalizing_gates() + + if diagonalizing_gates: + self._qubit_state.apply_operations(diagonalizing_gates) + + results = self._measurement_lightning.probs(measurementprocess.wires.tolist()) + + if diagonalizing_gates: + self._qubit_state.apply_operations( + [qml.adjoint(g, lazy=False) for g in reversed(diagonalizing_gates)] + ) + + return results + + def var(self, measurementprocess: MeasurementProcess): + """Variance of the supplied observable contained in the MeasurementProcess. + + Args: + measurementprocess (StateMeasurement): measurement to apply to the state + + Returns: + Variance of the observable + """ + + if isinstance(measurementprocess.obs, qml.SparseHamiltonian): + # ensuring CSR sparse representation. + CSR_SparseHamiltonian = measurementprocess.obs.sparse_matrix( + wire_order=list(range(self._qubit_state.num_wires)) + ).tocsr(copy=False) + return self._measurement_lightning.var( + CSR_SparseHamiltonian.indptr, + CSR_SparseHamiltonian.indices, + CSR_SparseHamiltonian.data, + ) + + if ( + isinstance(measurementprocess.obs, qml.Hermitian) + or (measurementprocess.obs.arithmetic_depth > 0) + or isinstance(measurementprocess.obs.name, List) + ): + # pylint: disable=protected-access + ob_serialized = QuantumScriptSerializer( + self._qubit_state.device_name, self.dtype == np.complex64, self._use_mpi + )._ob(measurementprocess.obs) + return self._measurement_lightning.var(ob_serialized) + + return self._measurement_lightning.var( + measurementprocess.obs.name, measurementprocess.obs.wires + ) + + def get_measurement_function( + self, measurementprocess: MeasurementProcess + ) -> Callable[[MeasurementProcess, TensorLike], TensorLike]: + # pylint: disable=too-many-return-statements + """Get the appropriate method for performing a measurement. + + Args: + measurementprocess (MeasurementProcess): measurement process to apply to the state + + Returns: + Callable: function that returns the measurement result + """ + if isinstance(measurementprocess, StateMeasurement): + if isinstance(measurementprocess, ExpectationMP): + if self._use_mpi: + if isinstance(measurementprocess.obs, (qml.Projector)): + return self.state_diagonalizing_gates + else: + if isinstance(measurementprocess.obs, (qml.Identity, qml.Projector)): + return self.state_diagonalizing_gates + return self.expval + + if isinstance(measurementprocess, ProbabilityMP): + return self.probs + + if isinstance(measurementprocess, VarianceMP): + if self._use_mpi: + if isinstance(measurementprocess.obs, (qml.Projector)): + return self.state_diagonalizing_gates + else: + if isinstance(measurementprocess.obs, (qml.Identity, qml.Projector)): + return self.state_diagonalizing_gates + return self.var + if measurementprocess.obs is None or measurementprocess.obs.has_diagonalizing_gates: + return self.state_diagonalizing_gates + + raise NotImplementedError + + def measurement(self, measurementprocess: MeasurementProcess) -> TensorLike: + """Apply a measurement process to a state. + + Args: + measurementprocess (MeasurementProcess): measurement process to apply to the state + + Returns: + TensorLike: the result of the measurement + """ + return self.get_measurement_function(measurementprocess)(measurementprocess) + + def measure_final_state(self, circuit: QuantumScript, mid_measurements=None) -> Result: + """ + Perform the measurements required by the circuit on the provided state. + + This is an internal function that will be called by the successor to ``lightning.[device]``. + + Args: + circuit (QuantumScript): The single circuit to simulate + mid_measurements (None, dict): Dictionary of mid-circuit measurements + + Returns: + Tuple[TensorLike]: The measurement results + """ + + if not circuit.shots: + # analytic case + if len(circuit.measurements) == 1: + return self.measurement(circuit.measurements[0]) + + return tuple(self.measurement(mp) for mp in circuit.measurements) + + # finite-shot case + results = self.measure_with_samples( + circuit.measurements, + shots=circuit.shots, + mid_measurements=mid_measurements, + ) + + if len(circuit.measurements) == 1: + if circuit.shots.has_partitioned_shots: + return tuple(res[0] for res in results) + + return results[0] + + return results + + def measure_with_samples( + self, + measurements: List[Union[SampleMeasurement, ClassicalShadowMP, ShadowExpvalMP]], + shots: Shots, + mid_measurements: dict = None, + ) -> List[TensorLike]: + """ + Returns the samples of the measurement process performed on the given state. + This function assumes that the user-defined wire labels in the measurement process + have already been mapped to integer wires used in the device. + + Args: + measurements (List[Union[SampleMeasurement, ClassicalShadowMP, ShadowExpvalMP]]): + The sample measurements to perform + shots (Shots): The number of samples to take + mid_measurements (None, dict): Dictionary of mid-circuit measurements + + Returns: + List[TensorLike[Any]]: Sample measurement results + """ + # last N measurements are sampling MCMs in ``dynamic_one_shot`` execution mode + mps = measurements[0 : -len(mid_measurements)] if mid_measurements else measurements + groups, indices = _group_measurements(mps) + + all_res = [] + for group in groups: + if isinstance(group[0], (ExpectationMP, VarianceMP)) and isinstance( + group[0].obs, SparseHamiltonian + ): + raise TypeError( + "ExpectationMP/VarianceMP(SparseHamiltonian) cannot be computed with samples." + ) + if isinstance(group[0], VarianceMP) and isinstance(group[0].obs, Sum): + raise TypeError("VarianceMP(Sum) cannot be computed with samples.") + if isinstance(group[0], (ClassicalShadowMP, ShadowExpvalMP)): + raise TypeError( + "ExpectationMP(ClassicalShadowMP, ShadowExpvalMP) cannot be computed with samples." + ) + if isinstance(group[0], ExpectationMP) and isinstance(group[0].obs, Sum): + all_res.extend(self._measure_sum_with_samples(group, shots)) + else: + all_res.extend(self._measure_with_samples_diagonalizing_gates(group, shots)) + + # reorder results + flat_indices = [] + for row in indices: + flat_indices += row + sorted_res = tuple( + res for _, res in sorted(list(enumerate(all_res)), key=lambda r: flat_indices[r[0]]) + ) + + # append MCM samples + if mid_measurements: + sorted_res += tuple(mid_measurements.values()) + + # put the shot vector axis before the measurement axis + if shots.has_partitioned_shots: + sorted_res = tuple(zip(*sorted_res)) + + return sorted_res + + def _apply_diagonalizing_gates(self, mps: List[SampleMeasurement], adjoint: bool = False): + if len(mps) == 1: + diagonalizing_gates = mps[0].diagonalizing_gates() + elif all(mp.obs for mp in mps): + diagonalizing_gates = qml.pauli.diagonalize_qwc_pauli_words([mp.obs for mp in mps])[0] + else: + diagonalizing_gates = [] + + if adjoint: + diagonalizing_gates = [ + qml.adjoint(g, lazy=False) for g in reversed(diagonalizing_gates) + ] + + self._qubit_state.apply_operations(diagonalizing_gates) + + @abstractmethod + def _measure_with_samples_diagonalizing_gates( + self, + mps: List[SampleMeasurement], + shots: Shots, + ) -> TensorLike: + """ + Returns the samples of the measurement process performed on the given state, + by rotating the state into the measurement basis using the diagonalizing gates + given by the measurement process. + + Args: + mps (~.measurements.SampleMeasurement): The sample measurements to perform + shots (~.measurements.Shots): The number of samples to take + + Returns: + TensorLike[Any]: Sample measurement results + """ + + def _measure_sum_with_samples( + self, + mp: List[SampleMeasurement], + shots: Shots, + ): + # the list contains only one element based on how we group measurements + mp = mp[0] + + # if the measurement process involves a Sum, measure each + # of the terms separately and sum + def _sum_for_single_shot(s): + results = self.measure_with_samples( + [ExpectationMP(t) for t in mp.obs], + s, + ) + return sum(results) + + unsqueezed_results = tuple(_sum_for_single_shot(type(shots)(s)) for s in shots) + return [unsqueezed_results] if shots.has_partitioned_shots else [unsqueezed_results[0]] diff --git a/pennylane_lightning/core/_serialize.py b/pennylane_lightning/core/_serialize.py index b056e7fba8..40338b991f 100644 --- a/pennylane_lightning/core/_serialize.py +++ b/pennylane_lightning/core/_serialize.py @@ -33,8 +33,7 @@ matrix, ) from pennylane.math import unwrap -from pennylane.operation import Tensor -from pennylane.ops import Hamiltonian, LinearCombination, Prod, SProd, Sum +from pennylane.ops import LinearCombination, Prod, SProd, Sum from pennylane.tape import QuantumTape NAMED_OBS = (Identity, PauliX, PauliY, PauliZ, Hadamard) @@ -54,13 +53,19 @@ class QuantumScriptSerializer: device_name: device shortname. use_csingle (bool): whether to use np.complex64 instead of np.complex128 use_mpi (bool, optional): If using MPI to accelerate calculation. Defaults to False. - split_obs (bool, optional): If splitting the observables in a list. Defaults to False. + split_obs (Union[bool, int], optional): If splitting the observables in a list. Defaults to False. + tensor_backend (str): If using `lightning.tensor` and select the TensorNetwork backend, mps or exact. Default to '' """ - # pylint: disable=import-outside-toplevel, too-many-instance-attributes, c-extension-no-member, too-many-branches, too-many-statements + # pylint: disable=import-outside-toplevel, too-many-instance-attributes, c-extension-no-member, too-many-branches, too-many-statements too-many-positional-arguments too-many-arguments def __init__( - self, device_name, use_csingle: bool = False, use_mpi: bool = False, split_obs: bool = False + self, + device_name, + use_csingle: bool = False, + use_mpi: bool = False, + split_obs: bool = False, + tensor_backend: str = str(), ): self.use_csingle = use_csingle self.device_name = device_name @@ -96,43 +101,14 @@ def __init__( else: raise DeviceError(f'The device name "{device_name}" is not a valid option.') - if device_name == "lightning.tensor": - self.tensornetwork_c64 = lightning_ops.TensorNetC64 - self.tensornetwork_c128 = lightning_ops.TensorNetC128 - else: - self.statevector_c64 = lightning_ops.StateVectorC64 - self.statevector_c128 = lightning_ops.StateVectorC128 - - self.named_obs_c64 = lightning_ops.observables.NamedObsC64 - self.named_obs_c128 = lightning_ops.observables.NamedObsC128 - self.hermitian_obs_c64 = lightning_ops.observables.HermitianObsC64 - self.hermitian_obs_c128 = lightning_ops.observables.HermitianObsC128 - self.tensor_prod_obs_c64 = lightning_ops.observables.TensorProdObsC64 - self.tensor_prod_obs_c128 = lightning_ops.observables.TensorProdObsC128 - self.hamiltonian_c64 = lightning_ops.observables.HamiltonianC64 - self.hamiltonian_c128 = lightning_ops.observables.HamiltonianC128 - - if device_name != "lightning.tensor": - self.sparse_hamiltonian_c64 = lightning_ops.observables.SparseHamiltonianC64 - self.sparse_hamiltonian_c128 = lightning_ops.observables.SparseHamiltonianC128 - self._use_mpi = use_mpi - if self._use_mpi: - self.statevector_mpi_c64 = lightning_ops.StateVectorMPIC64 - self.statevector_mpi_c128 = lightning_ops.StateVectorMPIC128 - self.named_obs_mpi_c64 = lightning_ops.observablesMPI.NamedObsMPIC64 - self.named_obs_mpi_c128 = lightning_ops.observablesMPI.NamedObsMPIC128 - self.hermitian_obs_mpi_c64 = lightning_ops.observablesMPI.HermitianObsMPIC64 - self.hermitian_obs_mpi_c128 = lightning_ops.observablesMPI.HermitianObsMPIC128 - self.tensor_prod_obs_mpi_c64 = lightning_ops.observablesMPI.TensorProdObsMPIC64 - self.tensor_prod_obs_mpi_c128 = lightning_ops.observablesMPI.TensorProdObsMPIC128 - self.hamiltonian_mpi_c64 = lightning_ops.observablesMPI.HamiltonianMPIC64 - self.hamiltonian_mpi_c128 = lightning_ops.observablesMPI.HamiltonianMPIC128 - self.sparse_hamiltonian_mpi_c64 = lightning_ops.observablesMPI.SparseHamiltonianMPIC64 - self.sparse_hamiltonian_mpi_c128 = lightning_ops.observablesMPI.SparseHamiltonianMPIC128 - - self._mpi_manager = lightning_ops.MPIManager + if device_name in ["lightning.qubit", "lightning.kokkos", "lightning.gpu"]: + assert tensor_backend == str() + self._set_lightning_state_bindings(lightning_ops) + else: + self._tensor_backend = tensor_backend + self._set_lightning_tensor_bindings(tensor_backend, lightning_ops) @property def ctype(self): @@ -194,6 +170,75 @@ def sparse_hamiltonian_obs(self): ) return self.sparse_hamiltonian_c64 if self.use_csingle else self.sparse_hamiltonian_c128 + def _set_lightning_state_bindings(self, lightning_ops): + """Define the variables needed to access the modules from the C++ bindings for state vector.""" + + self.statevector_c64 = lightning_ops.StateVectorC64 + self.statevector_c128 = lightning_ops.StateVectorC128 + + self.named_obs_c64 = lightning_ops.observables.NamedObsC64 + self.named_obs_c128 = lightning_ops.observables.NamedObsC128 + self.hermitian_obs_c64 = lightning_ops.observables.HermitianObsC64 + self.hermitian_obs_c128 = lightning_ops.observables.HermitianObsC128 + self.tensor_prod_obs_c64 = lightning_ops.observables.TensorProdObsC64 + self.tensor_prod_obs_c128 = lightning_ops.observables.TensorProdObsC128 + self.hamiltonian_c64 = lightning_ops.observables.HamiltonianC64 + self.hamiltonian_c128 = lightning_ops.observables.HamiltonianC128 + + self.sparse_hamiltonian_c64 = lightning_ops.observables.SparseHamiltonianC64 + self.sparse_hamiltonian_c128 = lightning_ops.observables.SparseHamiltonianC128 + + if self._use_mpi: + self.statevector_mpi_c64 = lightning_ops.StateVectorMPIC64 + self.statevector_mpi_c128 = lightning_ops.StateVectorMPIC128 + + self.named_obs_mpi_c64 = lightning_ops.observablesMPI.NamedObsMPIC64 + self.named_obs_mpi_c128 = lightning_ops.observablesMPI.NamedObsMPIC128 + self.hermitian_obs_mpi_c64 = lightning_ops.observablesMPI.HermitianObsMPIC64 + self.hermitian_obs_mpi_c128 = lightning_ops.observablesMPI.HermitianObsMPIC128 + self.tensor_prod_obs_mpi_c64 = lightning_ops.observablesMPI.TensorProdObsMPIC64 + self.tensor_prod_obs_mpi_c128 = lightning_ops.observablesMPI.TensorProdObsMPIC128 + self.hamiltonian_mpi_c64 = lightning_ops.observablesMPI.HamiltonianMPIC64 + self.hamiltonian_mpi_c128 = lightning_ops.observablesMPI.HamiltonianMPIC128 + + self.sparse_hamiltonian_mpi_c64 = lightning_ops.observablesMPI.SparseHamiltonianMPIC64 + self.sparse_hamiltonian_mpi_c128 = lightning_ops.observablesMPI.SparseHamiltonianMPIC128 + + self._mpi_manager = lightning_ops.MPIManager + + def _set_lightning_tensor_bindings(self, tensor_backend, lightning_ops): + """Define the variables needed to access the modules from the C++ bindings for tensor network.""" + if tensor_backend == "mps": + self.tensornetwork_c64 = lightning_ops.mpsTensorNetC64 + self.tensornetwork_c128 = lightning_ops.mpsTensorNetC128 + + self.named_obs_c64 = lightning_ops.observables.mpsNamedObsC64 + self.named_obs_c128 = lightning_ops.observables.mpsNamedObsC128 + self.hermitian_obs_c64 = lightning_ops.observables.mpsHermitianObsC64 + self.hermitian_obs_c128 = lightning_ops.observables.mpsHermitianObsC128 + self.tensor_prod_obs_c64 = lightning_ops.observables.mpsTensorProdObsC64 + self.tensor_prod_obs_c128 = lightning_ops.observables.mpsTensorProdObsC128 + self.hamiltonian_c64 = lightning_ops.observables.mpsHamiltonianC64 + self.hamiltonian_c128 = lightning_ops.observables.mpsHamiltonianC128 + + elif tensor_backend == "tn": + self.tensornetwork_c64 = lightning_ops.exactTensorNetC64 + self.tensornetwork_c128 = lightning_ops.exactTensorNetC128 + + self.named_obs_c64 = lightning_ops.observables.exactNamedObsC64 + self.named_obs_c128 = lightning_ops.observables.exactNamedObsC128 + self.hermitian_obs_c64 = lightning_ops.observables.exactHermitianObsC64 + self.hermitian_obs_c128 = lightning_ops.observables.exactHermitianObsC128 + self.tensor_prod_obs_c64 = lightning_ops.observables.exactTensorProdObsC64 + self.tensor_prod_obs_c128 = lightning_ops.observables.exactTensorProdObsC128 + self.hamiltonian_c64 = lightning_ops.observables.exactHamiltonianC64 + self.hamiltonian_c128 = lightning_ops.observables.exactHamiltonianC128 + + else: + raise ValueError( + f"Unsupported method: {tensor_backend}. Supported methods are 'mps' (Matrix Product State) and 'tn' (Exact Tensor Network)." + ) + def _named_obs(self, observable, wires_map: dict = None): """Serializes a Named observable""" wires = [wires_map[w] for w in observable.wires] if wires_map else observable.wires.tolist() @@ -205,24 +250,48 @@ def _hermitian_ob(self, observable, wires_map: dict = None): """Serializes a Hermitian observable""" wires = [wires_map[w] for w in observable.wires] if wires_map else observable.wires.tolist() + if self.device_name == "lightning.tensor" and len(wires) > 1: + raise ValueError("The number of Hermitian observables target wires should be 1.") return self.hermitian_obs(matrix(observable).ravel().astype(self.ctype), wires) def _tensor_ob(self, observable, wires_map: dict = None): """Serialize a tensor observable""" - obs = observable.obs if isinstance(observable, Tensor) else observable.operands - return self.tensor_obs([self._ob(o, wires_map) for o in obs]) + return self.tensor_obs([self._ob(o, wires_map) for o in observable.operands]) + + def _chunk_ham_terms(self, coeffs, ops, split_num: int = 1) -> List: + "Create split_num sub-Hamiltonians from a single high term-count Hamiltonian" + num_terms = len(coeffs) + iperm = np.argsort(np.array([len(op.get_wires()) for op in ops])) + coeffs = [coeffs[i] for i in iperm] + ops = [ops[i] for i in iperm] + c_coeffs = [ + tuple(coeffs[slice(i, num_terms, split_num)]) for i in range(min(num_terms, split_num)) + ] + c_ops = [ + tuple(ops[slice(i, num_terms, split_num)]) for i in range(min(num_terms, split_num)) + ] + return c_coeffs, c_ops def _hamiltonian(self, observable, wires_map: dict = None): coeffs, ops = observable.terms() coeffs = np.array(unwrap(coeffs)).astype(self.rtype) + if self.split_obs: + ops_l = [] + for t in ops: + term_cpp = self._ob(t, wires_map) + if isinstance(term_cpp, Sequence): + ops_l.extend(term_cpp) + else: + ops_l.append(term_cpp) + c, o = self._chunk_ham_terms(coeffs, ops_l, self.split_obs) + hams = [self.hamiltonian_obs(c_coeffs, c_obs) for (c_coeffs, c_obs) in zip(c, o)] + return hams + terms = [self._ob(t, wires_map) for t in ops] # TODO: This is in case `_hamiltonian` is called recursively which would cause a list # to be passed where `_ob` expects an observable. terms = [t[0] if isinstance(t, Sequence) and len(t) == 1 else t for t in terms] - if self.split_obs: - return [self.hamiltonian_obs([c], [t]) for (c, t) in zip(coeffs, terms)] - return self.hamiltonian_obs(coeffs, terms) def _sparse_hamiltonian(self, observable, wires_map: dict = None): @@ -237,7 +306,7 @@ def _sparse_hamiltonian(self, observable, wires_map: dict = None): """ if self._use_mpi: - Hmat = Hamiltonian([1.0], [Identity(0)]).sparse_matrix() + Hmat = Identity(0).sparse_matrix() H_sparse = SparseHamiltonian(Hmat, wires=range(1)) spm = H_sparse.sparse_matrix() # Only root 0 needs the overall sparse matrix data @@ -260,6 +329,9 @@ def _pauli_word(self, observable, wires_map: dict = None): def map_wire(wire: int): return wires_map[wire] if wires_map else wire + if len(observable) == 0: + return self.named_obs(PAULI_NAME_MAP["I"], [0]) + if len(observable) == 1: wire, pauli = list(observable.items())[0] return self.named_obs(PAULI_NAME_MAP[pauli], [map_wire(wire)]) @@ -280,11 +352,14 @@ def _pauli_sentence(self, observable, wires_map: dict = None): terms = [self._pauli_word(pw, wires_map) for pw in pwords] coeffs = np.array(coeffs).astype(self.rtype) + if self.split_obs: + c, o = self._chunk_ham_terms(coeffs, terms, self.split_obs) + psentences = [self.hamiltonian_obs(c_coeffs, c_obs) for (c_coeffs, c_obs) in zip(c, o)] + return psentences + if len(terms) == 1 and coeffs[0] == 1.0: return terms[0] - if self.split_obs: - return [self.hamiltonian_obs([c], [t]) for (c, t) in zip(coeffs, terms)] return self.hamiltonian_obs(coeffs, terms) # pylint: disable=protected-access, too-many-return-statements @@ -292,11 +367,9 @@ def _ob(self, observable, wires_map: dict = None): """Serialize a :class:`pennylane.operation.Observable` into an Observable.""" if isinstance(observable, NAMED_OBS): return self._named_obs(observable, wires_map) - if isinstance(observable, Hamiltonian): - return self._hamiltonian(observable, wires_map) if observable.pauli_rep is not None: return self._pauli_sentence(observable.pauli_rep, wires_map) - if isinstance(observable, (Tensor, Prod)): + if isinstance(observable, Prod): if isinstance(observable, Prod) and observable.has_overlapping_wires: return self._hermitian_ob(observable, wires_map) return self._tensor_ob(observable, wires_map) @@ -324,17 +397,17 @@ def serialize_observables(self, tape: QuantumTape, wires_map: dict = None) -> Li """ serialized_obs = [] - offset_indices = [0] + obs_indices = [] - for observable in tape.observables: + for i, observable in enumerate(tape.observables): ser_ob = self._ob(observable, wires_map) if isinstance(ser_ob, list): serialized_obs.extend(ser_ob) - offset_indices.append(offset_indices[-1] + len(ser_ob)) + obs_indices.extend([i] * len(ser_ob)) else: serialized_obs.append(ser_ob) - offset_indices.append(offset_indices[-1] + 1) - return serialized_obs, offset_indices + obs_indices.append(i) + return serialized_obs, obs_indices def serialize_ops(self, tape: QuantumTape, wires_map: dict = None) -> Tuple[ List[List[str]], @@ -365,6 +438,7 @@ def serialize_ops(self, tape: QuantumTape, wires_map: dict = None) -> Tuple[ controlled_values = [] wires = [] mats = [] + inverses = [] uses_stateprep = False @@ -402,10 +476,12 @@ def get_wires(operation, single_op): uses_stateprep = True continue if isinstance(operation, Rot): - op_list = operation.expand().operations + op_list = operation.decomposition() else: op_list = [operation] + inverse = isinstance(operation, qml.ops.op_math.Adjoint) + for single_op in op_list: ( single_op, @@ -414,18 +490,20 @@ def get_wires(operation, single_op): controlled_wires_list, controlled_values_list, ) = get_wires(operation, single_op) - names.append(name) + inverses.append(inverse) + names.append(single_op.base.name if inverse else name) # QubitUnitary is a special case, it has a parameter which is not differentiable. # We thus pass a dummy 0.0 parameter which will not be referenced if isinstance(single_op, qml.QubitUnitary): params.append([0.0]) mats.append(matrix(single_op)) - elif not hasattr(self.sv_type, name): - params.append([]) - mats.append(matrix(single_op)) else: - params.append(single_op.parameters) - mats.append([]) + if hasattr(self.sv_type, single_op.base.name if inverse else name): + params.append(single_op.parameters) + mats.append([]) + else: + params.append([]) + mats.append(matrix(single_op)) controlled_values.append(controlled_values_list) controlled_wires.append( @@ -435,7 +513,6 @@ def get_wires(operation, single_op): ) wires.append([wires_map[w] for w in wires_list] if wires_map else wires_list) - inverses = [False] * len(names) return ( names, params, diff --git a/pennylane_lightning/core/_state_vector_base.py b/pennylane_lightning/core/_state_vector_base.py new file mode 100644 index 0000000000..8815e13a04 --- /dev/null +++ b/pennylane_lightning/core/_state_vector_base.py @@ -0,0 +1,220 @@ +# Copyright 2018-2024 Xanadu Quantum Technologies Inc. + +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at + +# http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Class implementation for state-vector manipulation. +""" + +from abc import ABC, abstractmethod +from typing import Optional, Union + +import numpy as np +from pennylane import BasisState, StatePrep +from pennylane.measurements import MidMeasureMP +from pennylane.tape import QuantumScript +from pennylane.wires import Wires + + +class LightningBaseStateVector(ABC): + """Lightning [Device] state-vector class. + + A class that serves as a base class for Lightning state-vector simulators. + Interfaces with C++ python binding methods for state-vector manipulation. + + Args: + num_wires(int): the number of wires to initialize the device with + dtype: Datatypes for state-vector representation. Must be one of + ``np.complex64`` or ``np.complex128``. Default is ``np.complex128`` + """ + + def __init__(self, num_wires: int, dtype: Union[np.complex128, np.complex64]): + if dtype not in [np.complex64, np.complex128]: + raise TypeError(f"Unsupported complex type: {dtype}") + + self._num_wires = num_wires + self._wires = Wires(range(num_wires)) + self._dtype = dtype + + # Dummy for the device name + self._device_name = None + # Dummy for the C++ bindings + self._qubit_state = None + + @property + def dtype(self): + """Returns the state vector data type.""" + return self._dtype + + @property + def device_name(self): + """Returns the state vector device name.""" + return self._device_name + + @property + def wires(self): + """All wires that can be addressed on this device""" + return self._wires + + @property + def num_wires(self): + """Number of wires addressed on this device""" + return self._num_wires + + @property + def state_vector(self): + """Returns a handle to the state vector.""" + return self._qubit_state + + @property + @abstractmethod + def state(self): + """Copy the state vector data to a numpy array. + + **Example** + + >>> dev = qml.device('lightning.[Device]', wires=1) + >>> dev.apply([qml.PauliX(wires=[0])]) + >>> print(dev.state) + [0.+0.j 1.+0.j] + """ + + @abstractmethod + def _state_dtype(self): + """Binding to Lightning Managed state vector C++ class. + + Returns: the state vector class + """ + + def reset_state(self): + """Reset the device's state""" + # init the state vector to |00..0> + self._qubit_state.resetStateVector() + + @abstractmethod + def _apply_state_vector(self, state, device_wires: Wires, sync: Optional[bool] = None): + """Initialize the internal state vector in a specified state. + Args: + state (array[complex]): normalized input state of length ``2**len(wires)`` + or broadcasted state of shape ``(batch_size, 2**len(wires))`` + device_wires (Wires): wires that get initialized in the state + """ + + def _apply_basis_state(self, state, wires): + """Initialize the state vector in a specified computational basis state. + + Args: + state (array[int]): computational basis state of shape ``(wires,)`` + consisting of 0s and 1s. + wires (Wires): wires that the provided computational state should be + initialized on + use_async(Optional[bool]): immediately sync with host-sv after applying operation. + + Note: This function does not support broadcasted inputs yet. + """ + if not set(state.tolist()).issubset({0, 1}): + raise ValueError("BasisState parameter must consist of 0 or 1 integers.") + + if len(state) != len(wires): + raise ValueError("BasisState parameter and wires must be of equal length.") + + # Return a computational basis state over all wires. + self._qubit_state.setBasisState(list(state), list(wires)) + + @abstractmethod + def _apply_lightning_controlled(self, operation): + """Apply an arbitrary controlled operation to the state tensor. + + Args: + operation (~pennylane.operation.Operation): controlled operation to apply + + Returns: + None + """ + + @abstractmethod + def _apply_lightning_midmeasure( + self, operation: MidMeasureMP, mid_measurements: dict, postselect_mode: str + ): + """Execute a MidMeasureMP operation and return the sample in mid_measurements. + + Args: + operation (~pennylane.operation.Operation): mid-circuit measurement + mid_measurements (None, dict): Dictionary of mid-circuit measurements + postselect_mode (str): Configuration for handling shots with mid-circuit measurement + postselection. Use ``"hw-like"`` to discard invalid shots and ``"fill-shots"`` to + keep the same number of shots. + + Returns: + None + """ + + @abstractmethod + def _apply_lightning( + self, operations, mid_measurements: dict = None, postselect_mode: str = None + ): + """Apply a list of operations to the state tensor. + + Args: + operations (list[~pennylane.operation.Operation]): operations to apply + mid_measurements (None, dict): Dictionary of mid-circuit measurements + postselect_mode (str): Configuration for handling shots with mid-circuit measurement + postselection. Use ``"hw-like"`` to discard invalid shots and ``"fill-shots"`` to + keep the same number of shots. Default is ``None``. + + Returns: + None + """ + + def apply_operations( + self, operations, mid_measurements: dict = None, postselect_mode: str = None + ): + """Applies operations to the state vector.""" + # State preparation is currently done in Python + if operations: # make sure operations[0] exists + if isinstance(operations[0], StatePrep): + self._apply_state_vector(operations[0].parameters[0].copy(), operations[0].wires) + operations = operations[1:] + elif isinstance(operations[0], BasisState): + self._apply_basis_state(operations[0].parameters[0], operations[0].wires) + operations = operations[1:] + self._apply_lightning( + operations, mid_measurements=mid_measurements, postselect_mode=postselect_mode + ) + + def get_final_state( + self, + circuit: QuantumScript, + mid_measurements: dict = None, + postselect_mode: str = None, + ): + """ + Get the final state that results from executing the given quantum script. + + This is an internal function that will be called by the successor to ``lightning.[Device]``. + + Args: + circuit (QuantumScript): The single circuit to simulate + mid_measurements (None, dict): Dictionary of mid-circuit measurements + postselect_mode (str): Configuration for handling shots with mid-circuit measurement + postselection. Use ``"hw-like"`` to discard invalid shots and ``"fill-shots"`` to + keep the same number of shots. Default is ``None``. + + Returns: + Lightning [Device] StateVector: Lightning final state class. + + """ + self.apply_operations( + circuit.operations, mid_measurements=mid_measurements, postselect_mode=postselect_mode + ) + + return self diff --git a/pennylane_lightning/core/_version.py b/pennylane_lightning/core/_version.py index f695fbccc5..30f24595a9 100644 --- a/pennylane_lightning/core/_version.py +++ b/pennylane_lightning/core/_version.py @@ -16,4 +16,4 @@ Version number (major.minor.patch[-label]) """ -__version__ = "0.38.0-dev7" +__version__ = "0.40.0-dev42" diff --git a/pennylane_lightning/core/lightning_base.py b/pennylane_lightning/core/lightning_base.py index 9ee19e275c..3abc87301d 100644 --- a/pennylane_lightning/core/lightning_base.py +++ b/pennylane_lightning/core/lightning_base.py @@ -16,14 +16,15 @@ This module contains the base class for all PennyLane Lightning simulator devices, and interfaces with C++ for improved performance. """ -from itertools import islice, product -from typing import List +from itertools import product +from typing import List, Union import numpy as np import pennylane as qml -from pennylane import BasisState, QubitDevice, StatePrep +from pennylane import BasisState, StatePrep +from pennylane.devices import QubitDevice from pennylane.measurements import Expectation, MeasurementProcess, State -from pennylane.operation import Operation, Tensor +from pennylane.operation import Operation from pennylane.ops import Prod, Projector, SProd, Sum from pennylane.wires import Wires @@ -31,12 +32,6 @@ from ._version import __version__ -def _chunk_iterable(iteration, num_chunks): - "Lazy-evaluated chunking of given iterable from https://stackoverflow.com/a/22045226" - iteration = iter(iteration) - return iter(lambda: tuple(islice(iteration, num_chunks)), ()) - - class LightningBase(QubitDevice): """PennyLane Lightning Base device. @@ -55,7 +50,7 @@ class LightningBase(QubitDevice): OpenMP. """ - pennylane_requires = ">=0.36" + pennylane_requires = ">=0.38" version = __version__ author = "Xanadu Inc." short_name = "lightning.base" @@ -74,7 +69,7 @@ def __init__( raise ImportError( f"Pre-compiled binaries for {self.short_name} are not available. " "To manually compile from source, follow the instructions at " - "https://pennylane-lightning.readthedocs.io/en/latest/installation.html." + "https://docs.pennylane.ai/projects/lightning/en/stable/dev/installation.html." ) if c_dtype is np.complex64: r_dtype = np.float32 @@ -99,10 +94,6 @@ def stopping_condition(self): and observable) and returns ``True`` if supported by the device.""" def accepts_obj(obj): - if isinstance(obj, qml.QFT): - return len(obj.wires) < 10 - if isinstance(obj, qml.GroverOperator): - return len(obj.wires) < 13 is_not_tape = not isinstance(obj, qml.tape.QuantumTape) is_supported = getattr(self, "supports_operation", lambda name: False)(obj.name) return is_not_tape and is_supported @@ -187,9 +178,7 @@ def probability(self, wires=None, shot_range=None, bin_size=None): def _get_diagonalizing_gates(self, circuit: qml.tape.QuantumTape) -> List[Operation]: # pylint: disable=no-member, protected-access def skip_diagonalizing(obs): - return isinstance(obs, qml.Hamiltonian) or ( - isinstance(obs, qml.ops.Sum) and obs._pauli_rep is not None - ) + return isinstance(obs, qml.ops.Sum) and obs._pauli_rep is not None meas_filtered = list( filter(lambda m: m.obs is None or not skip_diagonalizing(m.obs), circuit.measurements) @@ -261,12 +250,18 @@ def _get_basis_state_index(self, state, wires): return int(qml.math.dot(state, basis_states)) # pylint: disable=too-many-function-args, assignment-from-no-return, too-many-arguments + # pylint: disable=too-many-positional-arguments def _process_jacobian_tape( - self, tape, starting_state, use_device_state, use_mpi: bool = False, split_obs: bool = False + self, + tape, + starting_state, + use_device_state, + use_mpi: bool = False, + split_obs: Union[bool, int] = False, ): state_vector = self._init_process_jacobian_tape(tape, starting_state, use_device_state) - obs_serialized, obs_idx_offsets = QuantumScriptSerializer( + obs_serialized, obs_indices = QuantumScriptSerializer( self.short_name, self.use_csingle, use_mpi, split_obs ).serialize_observables(tape, self.wire_map) @@ -309,7 +304,7 @@ def _process_jacobian_tape( "tp_shift": tp_shift, "record_tp_rows": record_tp_rows, "all_params": all_params, - "obs_idx_offsets": obs_idx_offsets, + "obs_indices": obs_indices, } @staticmethod @@ -323,18 +318,12 @@ def _assert_adjdiff_no_projectors(observable): Raises: ~pennylane.QuantumFunctionError: if a ``Projector`` is found. """ - if isinstance(observable, Tensor): - if any(isinstance(o, Projector) for o in observable.non_identity_obs): - raise qml.QuantumFunctionError( - "Adjoint differentiation method does not support the Projector observable" - ) - - elif isinstance(observable, Projector): + if isinstance(observable, Projector): raise qml.QuantumFunctionError( "Adjoint differentiation method does not support the Projector observable" ) - elif isinstance(observable, SProd): + if isinstance(observable, SProd): LightningBase._assert_adjdiff_no_projectors(observable.base) elif isinstance(observable, (Sum, Prod)): diff --git a/pennylane_lightning/core/lightning_interpreter.py b/pennylane_lightning/core/lightning_interpreter.py new file mode 100644 index 0000000000..b7d4668b65 --- /dev/null +++ b/pennylane_lightning/core/lightning_interpreter.py @@ -0,0 +1,182 @@ +# Copyright 2024 Xanadu Quantum Technologies Inc. + +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at + +# http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +This module contains a class for executing plxpr using default qubit tools. +""" +from copy import copy + +import jax +from pennylane.capture import disable, enable +from pennylane.capture.base_interpreter import PlxprInterpreter +from pennylane.capture.primitives import ( + adjoint_transform_prim, + cond_prim, + ctrl_transform_prim, + for_loop_prim, + measure_prim, + while_loop_prim, +) +from pennylane.measurements import MidMeasureMP, Shots + +from ._measurements_base import LightningBaseMeasurements +from ._state_vector_base import LightningBaseStateVector + + +class LightningInterpreter(PlxprInterpreter): + """A class that can interpret pennylane variant JAXPR. + + Args: + state (LightningBaseStateVector): the class containing the statevector + measurement_class (type[LightningBaseMeasurement]): The type to use to perform + measurements on the statevector + shots (Shots): the number of shots to use. Shot vectors are not yet supported. + + .. code-block:: python + + import pennylane as qml + import jax + qml.capture.enable() + + statevector = LightningStateVector(num_wires=3, dtype=np.complex128) + measurement_class = LightningMeasurements + + interpreter = LightningInterpreter(statevector, measurement_class, shots=Shots(None)) + + def f(x): + @qml.for_loop(3) + def loop(i, y): + qml.RX(y, i) + return y + 0.5 + loop(x) + return [qml.expval(qml.Z(i)) for i in range(3)] + + jaxpr = jax.make_jaxpr(f)(1.2) + + interpreter(f)(0.0), interpreter.eval(jaxpr.jaxpr, jaxpr.consts, 0.0) + + .. code-block:: + + ([1.0, 0.8775825618903728, 0.5403023058681395], [1.0, 0.8775825618903728, 0.5403023058681395]) + """ + + def __init__( + self, + state: LightningBaseStateVector, + measurement_class: type[LightningBaseMeasurements], + shots: Shots = Shots(None), + ): + self.state = state + self.measurement_class = measurement_class + self.shots = shots + if self.shots.has_partitioned_shots: + raise NotImplementedError("LightningInterpreter does not support partitioned shots.") + self.reset = True + super().__init__() + + def setup(self) -> None: + """Reset the state if necessary.""" + if self.reset: + self.state.reset_state() + self.reset = False # copies will have reset=False and wont reset state + + def cleanup(self) -> None: + """Indicate that the state will need to be reset if this instance is reused.""" + self.reset = True + + def interpret_operation(self, op): + """Apply an operation to the state.""" + self.state.apply_operations([op]) + + def interpret_measurement_eqn(self, eqn: "jax.core.JaxprEqn"): + """Interpret a given measurement equation.""" + if "mcm" == eqn.primitive.name[-3:]: + raise NotImplementedError( + "LightningInterpreter does not yet support postprocessing mcms" + ) + return super().interpret_measurement_eqn(eqn) + + def interpret_measurement(self, measurement): + """Apply a measurement to the state and return numerical results.""" + # measurements can sometimes create intermediary mps, but those intermediaries will not work with capture enabled + disable() + try: + if self.shots: + return self.measurement_class(self.state).measure_with_samples( + [measurement], self.shots + ) + return self.measurement_class(self.state).measurement(measurement) + finally: + enable() + + +@LightningInterpreter.register_primitive(measure_prim) +def _(self, *invals, reset, postselect): + mp = MidMeasureMP(invals, reset=reset, postselect=postselect) + mcms = {} + self.state.apply_operations([mp], mid_measurements=mcms) + return mcms[mp] + + +# pylint: disable=unused-argument +@LightningInterpreter.register_primitive(adjoint_transform_prim) +def _(self, *invals, jaxpr, n_consts, lazy=True): + # TODO: requires jaxpr -> list of ops first + raise NotImplementedError + + +# pylint: disable=too-many-arguments +@LightningInterpreter.register_primitive(ctrl_transform_prim) +def _(self, *invals, n_control, jaxpr, control_values, work_wires, n_consts): + # TODO: requires jaxpr -> list of ops first + raise NotImplementedError + + +# pylint: disable=too-many-arguments +@LightningInterpreter.register_primitive(for_loop_prim) +def _(self, start, stop, step, *invals, jaxpr_body_fn, consts_slice, args_slice): + consts = invals[consts_slice] + init_state = invals[args_slice] + + res = init_state + for i in range(start, stop, step): + res = copy(self).eval(jaxpr_body_fn, consts, i, *res) + + return res + + +# pylint: disable=too-many-arguments +@LightningInterpreter.register_primitive(while_loop_prim) +def _(self, *invals, jaxpr_body_fn, jaxpr_cond_fn, body_slice, cond_slice, args_slice): + consts_body = invals[body_slice] + consts_cond = invals[cond_slice] + init_state = invals[args_slice] + + fn_res = init_state + while copy(self).eval(jaxpr_cond_fn, consts_cond, *fn_res)[0]: + fn_res = copy(self).eval(jaxpr_body_fn, consts_body, *fn_res) + + return fn_res + + +@LightningInterpreter.register_primitive(cond_prim) +def _(self, *invals, jaxpr_branches, consts_slices, args_slice): + n_branches = len(jaxpr_branches) + conditions = invals[:n_branches] + args = invals[args_slice] + + for pred, jaxpr, const_slice in zip(conditions, jaxpr_branches, consts_slices): + consts = invals[const_slice] + if pred and jaxpr is not None: + return copy(self).eval(jaxpr, consts, *args) + return () diff --git a/pennylane_lightning/core/lightning_newAPI_base.py b/pennylane_lightning/core/lightning_newAPI_base.py new file mode 100644 index 0000000000..d070204aca --- /dev/null +++ b/pennylane_lightning/core/lightning_newAPI_base.py @@ -0,0 +1,464 @@ +# Copyright 2018-2024 Xanadu Quantum Technologies Inc. + +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at + +# http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +r""" +This module contains the :class:`~.LightningBase` class, that serves as a base class for Lightning simulator devices that +interfaces with C++ for fast linear algebra calculations. +""" +from abc import abstractmethod +from numbers import Number +from typing import Callable, List, Optional, Sequence, Tuple, Union + +import numpy as np +import pennylane as qml +from pennylane.devices import DefaultExecutionConfig, Device, ExecutionConfig +from pennylane.devices.modifiers import simulator_tracking, single_tape_support +from pennylane.tape import QuantumScript, QuantumTape +from pennylane.typing import Result, ResultBatch + +from ._measurements_base import LightningBaseMeasurements + +Result_or_ResultBatch = Union[Result, ResultBatch] +QuantumTapeBatch = Sequence[QuantumTape] +QuantumTape_or_Batch = Union[QuantumTape, QuantumTapeBatch] +PostprocessingFn = Callable[[ResultBatch], Result_or_ResultBatch] + + +@simulator_tracking +@single_tape_support +class LightningBase(Device): + """PennyLane Lightning Base device. + + A class that serves as a base class for Lightning state-vector simulators. + + Args: + wires (int or list): number or list of wires to initialize the device with + sync (bool): immediately sync with host-sv after applying operations + c_dtype: Datatypes for statevector representation. Must be one of + ``np.complex64`` or ``np.complex128``. + shots (int or list): How many times the circuit should be evaluated (or sampled) to estimate + the expectation values. Defaults to ``None`` if not specified. Setting + to ``None`` results in computing statistics like expectation values and + variances analytically. + """ + + # pylint: disable=too-many-instance-attributes + + # General device options + _new_API = True + + def __init__( # pylint: disable=too-many-arguments + self, + wires: Union[int, List], + *, + c_dtype: Union[np.complex64, np.complex128], + shots: Union[int, List], + batch_obs: bool, + ): + super().__init__(wires=wires, shots=shots) + + self._c_dtype = c_dtype + self._batch_obs = batch_obs + + if isinstance(wires, int): + self._wire_map = None # should just use wires as is + else: + self._wire_map = {w: i for i, w in enumerate(self.wires)} + + # Dummy for LightningStateVector, LightningMeasurements, LightningAdjointJacobian + self.LightningStateVector: Callable = None + self.LightningMeasurements: type[LightningBaseMeasurements] = None + self.LightningAdjointJacobian: Callable = None + + @property + def c_dtype(self): + """State vector complex data type.""" + return self._c_dtype + + dtype = c_dtype + + @abstractmethod + def _set_lightning_classes(self): + """Load the LightningStateVector, LightningMeasurements, LightningAdjointJacobian as class attribute""" + + @abstractmethod + def _setup_execution_config(self, config): + """ + Update the execution config with choices for how the device should be used and the device options. + """ + + @abstractmethod + def preprocess(self, execution_config: ExecutionConfig = DefaultExecutionConfig): + """This function defines the device transform program to be applied and an updated device configuration. + + Args: + execution_config (Union[ExecutionConfig, Sequence[ExecutionConfig]]): A data structure describing the + parameters needed to fully describe the execution. + + Returns: + TransformProgram, ExecutionConfig: A transform program that when called returns :class:`~.QuantumTape`'s that the + device can natively execute as well as a postprocessing function to be called after execution, and a configuration + with unset specifications filled in. + + This device: + + * Supports any qubit operations that provide a matrix + * Currently does not support finite shots + * Currently does not intrinsically support parameter broadcasting + + """ + + @abstractmethod + def execute( + self, + circuits: QuantumTape_or_Batch, + execution_config: ExecutionConfig = DefaultExecutionConfig, + ) -> Result_or_ResultBatch: + """Execute a circuit or a batch of circuits and turn it into results. + + Args: + circuits (Union[QuantumTape, Sequence[QuantumTape]]): the quantum circuits to be executed + execution_config (ExecutionConfig): a datastructure with additional information required for execution + + Returns: + TensorLike, tuple[TensorLike], tuple[tuple[TensorLike]]: A numeric result of the computation. + """ + + @abstractmethod + def simulate( + self, + circuit: QuantumScript, + state, # Lightning [Device] StateVector + postselect_mode: str = None, + ) -> Result: + """Simulate a single quantum script. + + Args: + circuit (QuantumTape): The single circuit to simulate + state (Lightning [Device] StateVector): handle to Lightning state vector + postselect_mode (str): Configuration for handling shots with mid-circuit measurement + postselection. Use ``"hw-like"`` to discard invalid shots and ``"fill-shots"`` to + keep the same number of shots. Default is ``None``. + + Returns: + Tuple[TensorLike]: The results of the simulation + + Note that this function can return measurements for non-commuting observables simultaneously. + """ + + @abstractmethod + def supports_derivatives( + self, + execution_config: Optional[ExecutionConfig] = None, + circuit: Optional[qml.tape.QuantumTape] = None, + ) -> bool: + """Check whether or not derivatives are available for a given configuration and circuit. + + ``LightningGPU`` supports adjoint differentiation with analytic results. + + Args: + execution_config (ExecutionConfig): The configuration of the desired derivative calculation + circuit (QuantumTape): An optional circuit to check derivatives support for. + + Returns: + Bool: Whether or not a derivative can be calculated provided the given information + + """ + + def jacobian( + self, + circuit: QuantumTape, + state, # Lightning [Device] StateVector + batch_obs: bool = False, + wire_map: dict = None, + ): + """Compute the Jacobian for a single quantum script. + + Args: + circuit (QuantumTape): The single circuit to simulate + state (Lightning [Device] StateVector): handle to the Lightning state vector + batch_obs (bool): Determine whether we process observables in parallel when + computing the jacobian. Default is False. + wire_map (Optional[dict]): a map from wire labels to simulation indices + + Returns: + TensorLike: The Jacobian of the quantum script + """ + if wire_map is not None: + [circuit], _ = qml.map_wires(circuit, wire_map) + state.reset_state() + final_state = state.get_final_state(circuit) + # pylint: disable=not-callable + return self.LightningAdjointJacobian(final_state, batch_obs=batch_obs).calculate_jacobian( + circuit + ) + + def simulate_and_jacobian( + self, + circuit: QuantumTape, + state, # Lightning [Device] StateVector + batch_obs: bool = False, + wire_map: dict = None, + ) -> Tuple: + """Simulate a single quantum script and compute its Jacobian. + + Args: + circuit (QuantumTape): The single circuit to simulate + state (Lightning [Device] StateVector): handle to the Lightning state vector + batch_obs (bool): Determine whether we process observables in parallel when + computing the jacobian. Default is False. + wire_map (Optional[dict]): a map from wire labels to simulation indices + + Returns: + Tuple[TensorLike]: The results of the simulation and the calculated Jacobian + + Note that this function can return measurements for non-commuting observables simultaneously. + """ + if wire_map is not None: + [circuit], _ = qml.map_wires(circuit, wire_map) + res = self.simulate(circuit, state) + # pylint: disable=not-callable + jac = self.LightningAdjointJacobian(state, batch_obs=batch_obs).calculate_jacobian(circuit) + return res, jac + + def vjp( # pylint: disable=too-many-arguments, too-many-positional-arguments + self, + circuit: QuantumTape, + cotangents: Tuple[Number], + state, # Lightning [Device] StateVector + batch_obs: bool = False, + wire_map: dict = None, + ): + """Compute the Vector-Jacobian Product (VJP) for a single quantum script. + Args: + circuit (QuantumTape): The single circuit to simulate + cotangents (Tuple[Number, Tuple[Number]]): Gradient-output vector. Must + have shape matching the output shape of the corresponding circuit. If + the circuit has a single output, ``cotangents`` may be a single number, + not an iterable of numbers. + state (Lightning [Device] StateVector): handle to the Lightning state vector + batch_obs (bool): Determine whether we process observables in parallel when + computing the VJP. + wire_map (Optional[dict]): a map from wire labels to simulation indices + + Returns: + TensorLike: The VJP of the quantum script + """ + if wire_map is not None: + [circuit], _ = qml.map_wires(circuit, wire_map) + state.reset_state() + final_state = state.get_final_state(circuit) + # pylint: disable=not-callable + return self.LightningAdjointJacobian(final_state, batch_obs=batch_obs).calculate_vjp( + circuit, cotangents + ) + + def simulate_and_vjp( # pylint: disable=too-many-arguments, too-many-positional-arguments + self, + circuit: QuantumTape, + cotangents: Tuple[Number], + state, + batch_obs: bool = False, + wire_map: dict = None, + ) -> Tuple: + """Simulate a single quantum script and compute its Vector-Jacobian Product (VJP). + Args: + circuit (QuantumTape): The single circuit to simulate + cotangents (Tuple[Number, Tuple[Number]]): Gradient-output vector. Must + have shape matching the output shape of the corresponding circuit. If + the circuit has a single output, ``cotangents`` may be a single number, + not an iterable of numbers. + state (Lightning [Device] StateVector): handle to the Lightning state vector + batch_obs (bool): Determine whether we process observables in parallel when + computing the jacobian. + wire_map (Optional[dict]): a map from wire labels to simulation indices + + Returns: + Tuple[TensorLike]: The results of the simulation and the calculated VJP + Note that this function can return measurements for non-commuting observables simultaneously. + """ + if wire_map is not None: + [circuit], _ = qml.map_wires(circuit, wire_map) + res = self.simulate(circuit, state) + # pylint: disable=not-callable + _vjp = self.LightningAdjointJacobian(state, batch_obs=batch_obs).calculate_vjp( + circuit, cotangents + ) + return res, _vjp + + def compute_derivatives( + self, + circuits: QuantumTape_or_Batch, + execution_config: ExecutionConfig = DefaultExecutionConfig, + ): + """Calculate the jacobian of either a single or a batch of circuits on the device. + + Args: + circuits (Union[QuantumTape, Sequence[QuantumTape]]): the circuits to calculate derivatives for + execution_config (ExecutionConfig): a datastructure with all additional information required for execution + + Returns: + Tuple: The jacobian for each trainable parameter + """ + batch_obs = execution_config.device_options.get("batch_obs", self._batch_obs) + + return tuple( + self.jacobian(circuit, self._statevector, batch_obs=batch_obs, wire_map=self._wire_map) + for circuit in circuits + ) + + def execute_and_compute_derivatives( + self, + circuits: QuantumTape_or_Batch, + execution_config: ExecutionConfig = DefaultExecutionConfig, + ) -> Tuple: + """Compute the results and jacobians of circuits at the same time. + + Args: + circuits (Union[QuantumTape, Sequence[QuantumTape]]): the circuits or batch of circuits + execution_config (ExecutionConfig): a datastructure with all additional information required for execution + + Returns: + Tuple: A numeric result of the computation and the gradient. + """ + batch_obs = execution_config.device_options.get("batch_obs", self._batch_obs) + results = tuple( + self.simulate_and_jacobian( + c, self._statevector, batch_obs=batch_obs, wire_map=self._wire_map + ) + for c in circuits + ) + return tuple(zip(*results)) + + def supports_vjp( + self, + execution_config: Optional[ExecutionConfig] = None, + circuit: Optional[QuantumTape] = None, + ) -> bool: + """Whether or not this device defines a custom vector jacobian product. + ``Lightning[Device]`` will check if supports adjoint differentiation with analytic results. + Args: + execution_config (ExecutionConfig): The configuration of the desired derivative calculation + circuit (QuantumTape): An optional circuit to check derivatives support for. + Returns: + Bool: Whether or not a derivative can be calculated provided the given information + """ + return self.supports_derivatives(execution_config, circuit) + + def compute_vjp( + self, + circuits: QuantumTape_or_Batch, + cotangents: Tuple[Number], + execution_config: ExecutionConfig = DefaultExecutionConfig, + ) -> Tuple: + r"""The vector jacobian product used in reverse-mode differentiation. ``Lightning[Device]`` uses the + adjoint differentiation method to compute the VJP. + Args: + circuits (Union[QuantumTape, Sequence[QuantumTape]]): the circuit or batch of circuits + cotangents (Tuple[Number, Tuple[Number]]): Gradient-output vector. Must have shape matching the output shape of the + corresponding circuit. If the circuit has a single output, ``cotangents`` may be a single number, not an iterable + of numbers. + execution_config (ExecutionConfig): a datastructure with all additional information required for execution + Returns: + tensor-like: A numeric result of computing the vector jacobian product + **Definition of vjp:** + If we have a function with jacobian: + .. math:: + \vec{y} = f(\vec{x}) \qquad J_{i,j} = \frac{\partial y_i}{\partial x_j} + The vector jacobian product is the inner product of the derivatives of the output ``y`` with the + Jacobian matrix. The derivatives of the output vector are sometimes called the **cotangents**. + .. math:: + \text{d}x_i = \Sigma_{i} \text{d}y_i J_{i,j} + **Shape of cotangents:** + The value provided to ``cotangents`` should match the output of :meth:`~.execute`. For computing the full Jacobian, + the cotangents can be batched to vectorize the computation. In this case, the cotangents can have the following + shapes. ``batch_size`` below refers to the number of entries in the Jacobian: + * For a state measurement, the cotangents must have shape ``(batch_size, 2 ** n_wires)`` + * For ``n`` expectation values, the cotangents must have shape ``(n, batch_size)``. If ``n = 1``, + then the shape must be ``(batch_size,)``. + """ + batch_obs = execution_config.device_options.get("batch_obs", self._batch_obs) + return tuple( + self.vjp(circuit, cots, self._statevector, batch_obs=batch_obs, wire_map=self._wire_map) + for circuit, cots in zip(circuits, cotangents) + ) + + def execute_and_compute_vjp( + self, + circuits: QuantumTape_or_Batch, + cotangents: Tuple[Number], + execution_config: ExecutionConfig = DefaultExecutionConfig, + ) -> Tuple: + """Calculate both the results and the vector jacobian product used in reverse-mode differentiation. + Args: + circuits (Union[QuantumTape, Sequence[QuantumTape]]): the circuit or batch of circuits to be executed + cotangents (Tuple[Number, Tuple[Number]]): Gradient-output vector. Must have shape matching the output shape of the + corresponding circuit. If the circuit has a single output, ``cotangents`` may be a single number, not an iterable + of numbers. + execution_config (ExecutionConfig): a datastructure with all additional information required for execution + Returns: + Tuple, Tuple: the result of executing the scripts and the numeric result of computing the vector jacobian product + """ + batch_obs = execution_config.device_options.get("batch_obs", self._batch_obs) + results = tuple( + self.simulate_and_vjp( + circuit, cots, self._statevector, batch_obs=batch_obs, wire_map=self._wire_map + ) + for circuit, cots in zip(circuits, cotangents) + ) + return tuple(zip(*results)) + + # pylint: disable=import-outside-toplevel + def eval_jaxpr(self, jaxpr, consts, *args): + """Execute pennylane variant jaxpr using C++ simulation tools. + + Args: + jaxpr (jax.core.Jaxpr): jaxpr containing quantum operations + consts (list[TensorLike]): List of constants for the jaxpr closure variables + *args (TensorLike): The arguments to the jaxpr. + + Returns: + list(TensorLike): the results of the execution + + .. code-block:: python + + import pennylane as qml + import jax + qml.capture.enable() + + def f(x): + @qml.for_loop(3) + def loop(i, y): + qml.RX(y, i) + return y + 0.5 + loop(x) + return [qml.expval(qml.Z(i)) for i in range(3)] + + jaxpr = jax.make_jaxpr(f)(0.5) + + dev = qml.device('lightning.qubit', wires=3) + dev.eval_jaxpr(jaxpr.jaxpr, jaxpr.consts, 0.0) + + .. code-block:: + + [1.0, 0.8775825618903728, 0.5403023058681395] + + """ + # has jax dependency, so can't import up top + from .lightning_interpreter import LightningInterpreter + + interpreter = LightningInterpreter( + self._statevector, self.LightningMeasurements, shots=self.shots + ) + return interpreter.eval(jaxpr, consts, *args) diff --git a/pennylane_lightning/core/src/algorithms/JacobianData.hpp b/pennylane_lightning/core/src/algorithms/JacobianData.hpp index c408167dbe..fba007789e 100644 --- a/pennylane_lightning/core/src/algorithms/JacobianData.hpp +++ b/pennylane_lightning/core/src/algorithms/JacobianData.hpp @@ -249,7 +249,7 @@ template class OpsData { [[nodiscard]] auto getTotalNumParams() const -> std::size_t { return std::accumulate( ops_params_.begin(), ops_params_.end(), std::size_t{0U}, - [](size_t acc, auto ¶ms) { return acc + params.size(); }); + [](std::size_t acc, auto ¶ms) { return acc + params.size(); }); } }; @@ -306,7 +306,8 @@ template class JacobianData { * (e.g. StatePrep) or Hamiltonian coefficients. * @endrst */ - JacobianData(size_t num_params, std::size_t num_elem, const CFP_t *sv_ptr, + JacobianData(std::size_t num_params, std::size_t num_elem, + const CFP_t *sv_ptr, std::vector>> obs, OpsData ops, std::vector trainP) : num_parameters(num_params), num_elements(num_elem), psi(sv_ptr), diff --git a/pennylane_lightning/core/src/algorithms/tests/Test_AdjointJacobian.cpp b/pennylane_lightning/core/src/algorithms/tests/Test_AdjointJacobian.cpp index 0f56ca388c..a18221c145 100644 --- a/pennylane_lightning/core/src/algorithms/tests/Test_AdjointJacobian.cpp +++ b/pennylane_lightning/core/src/algorithms/tests/Test_AdjointJacobian.cpp @@ -25,7 +25,6 @@ using namespace Pennylane::Util; #ifdef _ENABLE_PLQUBIT constexpr bool BACKEND_FOUND = true; -constexpr bool SUPPORTS_CTRL = true; #include "AdjointJacobianLQubit.hpp" #include "ObservablesLQubit.hpp" @@ -41,7 +40,6 @@ using namespace Pennylane::LightningQubit::Observables; #elif _ENABLE_PLKOKKOS == 1 constexpr bool BACKEND_FOUND = true; -constexpr bool SUPPORTS_CTRL = false; #include "AdjointJacobianKokkos.hpp" #include "ObservablesKokkos.hpp" @@ -57,7 +55,6 @@ using namespace Pennylane::LightningKokkos::Observables; #elif _ENABLE_PLGPU == 1 constexpr bool BACKEND_FOUND = true; -constexpr bool SUPPORTS_CTRL = false; #include "AdjointJacobianGPU.hpp" #include "ObservablesGPU.hpp" #include "TestHelpersStateVectors.hpp" @@ -72,7 +69,6 @@ using namespace Pennylane::LightningGPU::Observables; #else constexpr bool BACKEND_FOUND = false; -constexpr bool SUPPORTS_CTRL = false; using TestStateVectorBackends = Pennylane::Util::TypeList; template struct StateVectorToName {}; @@ -140,41 +136,38 @@ template void testAdjointJacobian() { DYNAMIC_SECTION("Op=PhaseShift, Obs=Y - " << StateVectorToName::name) { - if (SUPPORTS_CTRL) { - const std::vector tp{0}; - const std::size_t num_qubits = GENERATE(2, 3, 4); - - const std::size_t num_params = 3; - const std::size_t num_obs = 1; - const auto obs = std::make_shared>( - "PauliY", std::vector{num_qubits - 1}); - std::vector jacobian(num_obs * tp.size(), 0); + const std::vector tp{0}; + const std::size_t num_qubits = GENERATE(2, 3, 4); + + const std::size_t num_params = 3; + const std::size_t num_obs = 1; + const auto obs = std::make_shared>( + "PauliY", std::vector{num_qubits - 1}); + std::vector jacobian(num_obs * tp.size(), 0); + + for (const auto &p : param) { + std::vector> controls{ + std::vector(num_qubits - 1)}; + std::iota(controls[0].begin(), controls[0].end(), 0); + std::vector> control_values{ + std::vector(num_qubits - 1, true)}; + auto ops = OpsData( + {"PhaseShift"}, {{p}}, {{num_qubits - 1}}, {false}, {{}}, + controls, control_values); - for (const auto &p : param) { - std::vector> controls{ - std::vector(num_qubits - 1)}; - std::iota(controls[0].begin(), controls[0].end(), 0); - std::vector> control_values{ - std::vector(num_qubits - 1, true)}; - auto ops = OpsData( - {"PhaseShift"}, {{p}}, {{num_qubits - 1}}, {false}, - {{}}, controls, control_values); - - std::vector cdata(1U << num_qubits); - cdata[cdata.size() - 2] = - Pennylane::Util::INVSQRT2(); - cdata[cdata.size() - 1] = - Pennylane::Util::INVSQRT2(); - - StateVectorT psi(cdata.data(), cdata.size()); - JacobianData tape{ - num_params, psi.getLength(), psi.getData(), {obs}, ops, - tp}; - adj.adjointJacobian(std::span{jacobian}, tape, psi, true); - - CAPTURE(jacobian); - CHECK(cos(p) == Approx(jacobian[0])); - } + std::vector cdata(1U << num_qubits); + cdata[cdata.size() - 2] = + Pennylane::Util::INVSQRT2(); + cdata[cdata.size() - 1] = + Pennylane::Util::INVSQRT2(); + + StateVectorT psi(cdata.data(), cdata.size()); + JacobianData tape{ + num_params, psi.getLength(), psi.getData(), {obs}, ops, tp}; + adj.adjointJacobian(std::span{jacobian}, tape, psi, true); + + CAPTURE(jacobian); + CHECK(cos(p) == Approx(jacobian[0])); } } @@ -453,7 +446,7 @@ template void testAdjointJacobian() { const auto obs = std::make_shared>( "PauliZ", std::vector{0}); - for (size_t i = 0; i < thetas.size(); i++) { + for (std::size_t i = 0; i < thetas.size(); i++) { const PrecisionT theta = thetas[i]; std::vector local_params{ theta, std::pow(theta, (PrecisionT)3), @@ -671,4 +664,4 @@ TEST_CASE("Algorithms::adjointJacobian", "[Algorithms]") { if constexpr (BACKEND_FOUND) { testAdjointJacobian(); } -} \ No newline at end of file +} diff --git a/pennylane_lightning/core/src/algorithms/tests/mpi/Test_AdjointJacobianMPI.cpp b/pennylane_lightning/core/src/algorithms/tests/mpi/Test_AdjointJacobianMPI.cpp index b4e617eec4..9953b218f0 100644 --- a/pennylane_lightning/core/src/algorithms/tests/mpi/Test_AdjointJacobianMPI.cpp +++ b/pennylane_lightning/core/src/algorithms/tests/mpi/Test_AdjointJacobianMPI.cpp @@ -87,7 +87,6 @@ template void testAdjointJacobian() { StateVectorT psi(mpi_manager, dt_local, mpi_buffersize, nGlobalIndexBits, nLocalIndexBits); - psi.initSV(); const auto obs1 = std::make_shared>( "PauliZ", std::vector{0}); @@ -138,7 +137,6 @@ template void testAdjointJacobian() { StateVectorT psi(mpi_manager, dt_local, mpi_buffersize, nGlobalIndexBits, nLocalIndexBits); - psi.initSV(); const auto obs1 = std::make_shared>( "PauliZ", std::vector{0}); @@ -189,7 +187,6 @@ template void testAdjointJacobian() { StateVectorT psi(mpi_manager, dt_local, mpi_buffersize, nGlobalIndexBits, nLocalIndexBits); - psi.initSV(); const auto obs = std::make_shared>( std::make_shared>( @@ -240,7 +237,6 @@ template void testAdjointJacobian() { StateVectorT psi(mpi_manager, dt_local, mpi_buffersize, nGlobalIndexBits, nLocalIndexBits); - psi.initSV(); const auto obs = std::make_shared>( std::make_shared>( @@ -310,7 +306,6 @@ template void testAdjointJacobian() { StateVectorT psi(mpi_manager, dt_local, mpi_buffersize, nGlobalIndexBits, nLocalIndexBits); - psi.initSV(); auto obs1 = std::make_shared>( "PauliZ", std::vector{0}); @@ -362,7 +357,6 @@ template void testAdjointJacobian() { StateVectorT psi(mpi_manager, dt_local, mpi_buffersize, nGlobalIndexBits, nLocalIndexBits); - psi.initSV(); auto obs1 = std::make_shared>( std::make_shared>( diff --git a/pennylane_lightning/core/src/bindings/Bindings.cpp b/pennylane_lightning/core/src/bindings/Bindings.cpp index 2733e6f5e0..596eef765e 100644 --- a/pennylane_lightning/core/src/bindings/Bindings.cpp +++ b/pennylane_lightning/core/src/bindings/Bindings.cpp @@ -85,6 +85,6 @@ PYBIND11_MODULE( // Register bindings for backend-specific info: registerBackendSpecificInfo(m); - registerLightningTensorClassBindings(m); + registerLightningTensorClassBindings(m); } #endif diff --git a/pennylane_lightning/core/src/bindings/Bindings.hpp b/pennylane_lightning/core/src/bindings/Bindings.hpp index fe72f1bb86..e3b7811116 100644 --- a/pennylane_lightning/core/src/bindings/Bindings.hpp +++ b/pennylane_lightning/core/src/bindings/Bindings.hpp @@ -189,7 +189,7 @@ auto alignedNumpyArray(CPUMemoryModel memory_model, std::size_t size, * @param size Size of the array to create * @param dt Pybind11's datatype object */ -auto allocateAlignedArray(size_t size, const py::dtype &dt, +auto allocateAlignedArray(std::size_t size, const py::dtype &dt, bool zeroInit = false) -> py::array { // TODO: Move memset operations to here to reduce zeroInit pass-throughs. auto memory_model = bestCPUMemoryModel(); @@ -480,7 +480,7 @@ void registerBackendAgnosticMeasurements(PyClass &pyclass) { auto &&result = M.generate_samples(num_shots); const std::size_t ndim = 2; const std::vector shape{num_shots, num_wires}; - constexpr auto sz = sizeof(size_t); + constexpr auto sz = sizeof(std::size_t); const std::vector strides{sz * num_wires, sz}; // return 2-D NumPy array return py::array(py::buffer_info( @@ -559,7 +559,7 @@ void registerBackendAgnosticAlgorithms(py::module_ &m) { .def("__repr__", [](const OpsData &ops) { using namespace Pennylane::Util; std::ostringstream ops_stream; - for (size_t op = 0; op < ops.getSize(); op++) { + for (std::size_t op = 0; op < ops.getSize(); op++) { ops_stream << "{'name': " << ops.getOpsName()[op]; ops_stream << ", 'params': " << ops.getOpsParams()[op]; ops_stream << ", 'inv': " << ops.getOpsInverses()[op]; @@ -591,7 +591,7 @@ void registerBackendAgnosticAlgorithms(py::module_ &m) { const std::vector> &ops_controlled_values) { std::vector> conv_matrices( ops_matrices.size()); - for (size_t op = 0; op < ops_name.size(); op++) { + for (std::size_t op = 0; op < ops_name.size(); op++) { const auto m_buffer = ops_matrices[op].request(); if (m_buffer.size) { const auto m_ptr = @@ -727,31 +727,196 @@ template void registerLightningTensorBackendAgnosticMeasurements(PyClass &pyclass) { using MeasurementsT = MeasurementsTNCuda; using ObservableT = ObservableTNCuda; - pyclass.def( - "expval", - [](MeasurementsT &M, const std::shared_ptr &ob) { - return M.expval(*ob); - }, - "Expected value of an observable object."); + pyclass + .def( + "expval", + [](MeasurementsT &M, const std::shared_ptr &ob) { + return M.expval(*ob); + }, + "Expected value of an observable object.") + .def( + "probs", + [](MeasurementsT &M, const std::vector &wires) { + return py::array_t( + py::cast(M.probs(wires))); + }, + "Probabilities of a set of wires.") + .def( + "var", + [](MeasurementsT &M, const std::shared_ptr &ob) { + return M.var(*ob); + }, + "Variance of an observable object.") + .def("generate_samples", [](MeasurementsT &M, + const std::vector &wires, + std::size_t num_shots) { + constexpr auto sz = sizeof(std::size_t); + const std::size_t num_wires = wires.size(); + const std::size_t ndim = 2; + const std::vector shape{num_shots, num_wires}; + auto &&result = M.generate_samples(wires, num_shots); + + const std::vector strides{sz * num_wires, sz}; + // return 2-D NumPy array + return py::array(py::buffer_info( + result.data(), /* data as contiguous array */ + sz, /* size of one scalar */ + py::format_descriptor::format(), /* data type */ + ndim, /* number of dimensions */ + shape, /* shape of the matrix */ + strides /* strides for each axis */ + )); + }); +} + +/** + * @brief Register observable classes for TensorNetwork. + * + * @tparam LightningBackendT + * @param m Pybind module + * @param name backend name of TN (mps, tn) + */ +template +void registerBackendAgnosticObservablesTensor(py::module_ &m, + const std::string &name) { + using PrecisionT = + typename LightningBackendT::PrecisionT; // LightningBackendT's's + // precision. + using ComplexT = + typename LightningBackendT::ComplexT; // LightningBackendT's + // complex type. + using ParamT = PrecisionT; // Parameter's data precision + + const std::string bitsize = + std::to_string(sizeof(std::complex) * 8); + + using np_arr_c = py::array_t, py::array::c_style>; + using np_arr_r = py::array_t; + + using ObservableT = ObservableTNCuda; + using NamedObsT = NamedObsTNCuda; + using HermitianObsT = HermitianObsTNCuda; + using TensorProdObsT = TensorProdObsTNCuda; + using HamiltonianT = HamiltonianTNCuda; + + std::string class_name; + + class_name = std::string(name) + "ObservableC" + bitsize; + py::class_>(m, class_name.c_str(), + py::module_local()); + + class_name = std::string(name) + "NamedObsC" + bitsize; + py::class_, ObservableT>( + m, class_name.c_str(), py::module_local()) + .def(py::init( + [](const std::string &name, const std::vector &wires) { + return NamedObsT(name, wires); + })) + .def("__repr__", &NamedObsT::getObsName) + .def("get_wires", &NamedObsT::getWires, "Get wires of observables") + .def( + "__eq__", + [](const NamedObsT &self, py::handle other) -> bool { + if (!py::isinstance(other)) { + return false; + } + auto &&other_cast = other.cast(); + return self == other_cast; + }, + "Compare two observables"); + + class_name = std::string(name) + "HermitianObsC" + bitsize; + py::class_, ObservableT>( + m, class_name.c_str(), py::module_local()) + .def(py::init([](const np_arr_c &matrix, + const std::vector &wires) { + auto const &buffer = matrix.request(); + const auto ptr = static_cast(buffer.ptr); + return HermitianObsT(std::vector(ptr, ptr + buffer.size), + wires); + })) + .def("__repr__", &HermitianObsT::getObsName) + .def("get_wires", &HermitianObsT::getWires, "Get wires of observables") + .def("get_matrix", &HermitianObsT::getMatrix, + "Get matrix representation of Hermitian operator") + .def( + "__eq__", + [](const HermitianObsT &self, py::handle other) -> bool { + if (!py::isinstance(other)) { + return false; + } + auto &&other_cast = other.cast(); + return self == other_cast; + }, + "Compare two observables"); + + class_name = std::string(name) + "TensorProdObsC" + bitsize; + py::class_, ObservableT>( + m, class_name.c_str(), py::module_local()) + .def(py::init([](const std::vector> &obs) { + return TensorProdObsT(obs); + })) + .def("__repr__", &TensorProdObsT::getObsName) + .def("get_wires", &TensorProdObsT::getWires, "Get wires of observables") + .def("get_ops", &TensorProdObsT::getObs, "Get operations list") + .def( + "__eq__", + [](const TensorProdObsT &self, py::handle other) -> bool { + if (!py::isinstance(other)) { + return false; + } + auto &&other_cast = other.cast(); + return self == other_cast; + }, + "Compare two observables"); + + class_name = std::string(name) + "HamiltonianC" + bitsize; + using ObsPtr = std::shared_ptr; + py::class_, ObservableT>( + m, class_name.c_str(), py::module_local()) + .def(py::init( + [](const np_arr_r &coeffs, const std::vector &obs) { + auto const &buffer = coeffs.request(); + const auto ptr = static_cast(buffer.ptr); + return HamiltonianT{std::vector(ptr, ptr + buffer.size), + obs}; + })) + .def("__repr__", &HamiltonianT::getObsName) + .def("get_wires", &HamiltonianT::getWires, "Get wires of observables") + .def("get_ops", &HamiltonianT::getObs, + "Get operations contained by Hamiltonian") + .def("get_coeffs", &HamiltonianT::getCoeffs, + "Get Hamiltonian coefficients") + .def( + "__eq__", + [](const HamiltonianT &self, py::handle other) -> bool { + if (!py::isinstance(other)) { + return false; + } + auto &&other_cast = other.cast(); + return self == other_cast; + }, + "Compare two observables"); } /** * @brief Templated class to build lightning.tensor class bindings. * - * @tparam TensorNetT Tensor network type + * @tparam TensorNetT Tensor network type. * @param m Pybind11 module. */ template void lightningTensorClassBindings(py::module_ &m) { using PrecisionT = typename TensorNetT::PrecisionT; // TensorNet's precision. // Enable module name to be based on size of complex datatype + auto name = TensorNetT::method; // TensorNet's backend name [mps, exact]. const std::string bitsize = std::to_string(sizeof(std::complex) * 8); //***********************************************************************// // TensorNet //***********************************************************************// - std::string class_name = "TensorNetC" + bitsize; + std::string class_name = std::string(name) + "TensorNetC" + bitsize; auto pyclass = py::class_(m, class_name.c_str(), py::module_local()); @@ -763,12 +928,12 @@ template void lightningTensorClassBindings(py::module_ &m) { /* Observables submodule */ py::module_ obs_submodule = m.def_submodule("observables", "Submodule for observables classes."); - registerBackendAgnosticObservables(obs_submodule); + registerBackendAgnosticObservablesTensor(obs_submodule, name); //***********************************************************************// // Measurements //***********************************************************************// - class_name = "MeasurementsC" + bitsize; + class_name = std::string(name) + "MeasurementsC" + bitsize; auto pyclass_measurements = py::class_>( m, class_name.c_str(), py::module_local()); diff --git a/pennylane_lightning/core/src/bindings/BindingsCudaUtils.hpp b/pennylane_lightning/core/src/bindings/BindingsCudaUtils.hpp new file mode 100644 index 0000000000..2a04ad4d63 --- /dev/null +++ b/pennylane_lightning/core/src/bindings/BindingsCudaUtils.hpp @@ -0,0 +1,101 @@ +// Copyright 2024 Xanadu Quantum Technologies Inc. + +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at + +// http://www.apache.org/licenses/LICENSE-2.0 + +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +/** + * @file BindingsCudaUtils.hpp + * Defines CUDA device - specific operations to export to Python, other + * utility functions interfacing with Pybind11 and support to agnostic bindings. + */ + +#pragma once + +#include "BindingsBase.hpp" +#include "DevTag.hpp" +#include "DevicePool.hpp" +#include "cuda_helpers.hpp" + +/// @cond DEV +namespace { +using namespace Pennylane; +using namespace Pennylane::Bindings; +} // namespace +/// @endcond + +namespace py = pybind11; + +namespace Pennylane::LightningGPU::Util { +/** + * @brief Register bindings for CUDA utils. + * + * @param m Pybind11 module. + */ +void registerCudaUtils(py::module_ &m) { + m.def("device_reset", &deviceReset, "Reset all GPU devices and contexts."); + m.def("allToAllAccess", []() { + for (int i = 0; i < static_cast(getGPUCount()); i++) { + cudaDeviceEnablePeerAccess(i, 0); + } + }); + + m.def("is_gpu_supported", &isCuQuantumSupported, + py::arg("device_number") = 0, + "Checks if the given GPU device meets the minimum architecture " + "support for the PennyLane-Lightning-GPU device."); + + m.def("get_gpu_arch", &getGPUArch, py::arg("device_number") = 0, + "Returns the given GPU major and minor GPU support."); + py::class_>(m, "DevPool") + .def(py::init<>()) + .def("getActiveDevices", &DevicePool::getActiveDevices) + .def("isActive", &DevicePool::isActive) + .def("isInactive", &DevicePool::isInactive) + .def("acquireDevice", &DevicePool::acquireDevice) + .def("releaseDevice", &DevicePool::releaseDevice) + .def("syncDevice", &DevicePool::syncDevice) + .def("refresh", &DevicePool::refresh) + .def_static("getTotalDevices", &DevicePool::getTotalDevices) + .def_static("getDeviceUIDs", &DevicePool::getDeviceUIDs) + .def_static("setDeviceID", &DevicePool::setDeviceIdx) + .def(py::pickle( + []([[maybe_unused]] const DevicePool &self) { // __getstate__ + return py::make_tuple(); + }, + [](py::tuple &t) { // __setstate__ + if (t.size() != 0) { + throw std::runtime_error("Invalid state!"); + } + return DevicePool{}; + })); + + py::class_>(m, "DevTag") + .def(py::init<>()) + .def(py::init()) + .def(py::init([](int device_id, void *stream_id) { + // Note, streams must be handled externally for now. + // Binding support provided through void* conversion to cudaStream_t + return new DevTag(device_id, + static_cast(stream_id)); + })) + .def(py::init &>()) + .def("getDeviceID", &DevTag::getDeviceID) + .def("getStreamID", + [](DevTag &dev_tag) { + // default stream points to nullptr, so just return void* as + // type + return static_cast(dev_tag.getStreamID()); + }) + .def("refresh", &DevTag::refresh); +} + +} // namespace Pennylane::LightningGPU::Util diff --git a/pennylane_lightning/core/src/bindings/BindingsMPI.hpp b/pennylane_lightning/core/src/bindings/BindingsMPI.hpp index bad6e9e1da..8c6d1bb5b1 100644 --- a/pennylane_lightning/core/src/bindings/BindingsMPI.hpp +++ b/pennylane_lightning/core/src/bindings/BindingsMPI.hpp @@ -282,7 +282,7 @@ void registerBackendAgnosticMeasurementsMPI(PyClass &pyclass) { auto &&result = M.generate_samples(num_shots); const std::size_t ndim = 2; const std::vector shape{num_shots, num_wires}; - constexpr auto sz = sizeof(size_t); + constexpr auto sz = sizeof(std::size_t); const std::vector strides{sz * num_wires, sz}; // return 2-D NumPy array return py::array(py::buffer_info( @@ -351,7 +351,7 @@ void registerBackendAgnosticAlgorithmsMPI(py::module_ &m) { .def("__repr__", [](const OpsData &ops) { using namespace Pennylane::Util; std::ostringstream ops_stream; - for (size_t op = 0; op < ops.getSize(); op++) { + for (std::size_t op = 0; op < ops.getSize(); op++) { ops_stream << "{'name': " << ops.getOpsName()[op]; ops_stream << ", 'params': " << ops.getOpsParams()[op]; ops_stream << ", 'inv': " << ops.getOpsInverses()[op]; @@ -378,7 +378,7 @@ void registerBackendAgnosticAlgorithmsMPI(py::module_ &m) { const std::vector> &ops_controlled_values) { std::vector> conv_matrices( ops_matrices.size()); - for (size_t op = 0; op < ops_name.size(); op++) { + for (std::size_t op = 0; op < ops_name.size(); op++) { const auto m_buffer = ops_matrices[op].request(); if (m_buffer.size) { const auto m_ptr = diff --git a/pennylane_lightning/core/src/measurements/MeasurementsBase.hpp b/pennylane_lightning/core/src/measurements/MeasurementsBase.hpp index 167e8c75ae..6aaec8d676 100644 --- a/pennylane_lightning/core/src/measurements/MeasurementsBase.hpp +++ b/pennylane_lightning/core/src/measurements/MeasurementsBase.hpp @@ -17,6 +17,7 @@ */ #pragma once +#include #include #include #include @@ -56,7 +57,9 @@ template class MeasurementsBase { #else const StateVectorT &_statevector; #endif - std::mt19937 rng; + std::optional _deviceseed{std::nullopt}; + + std::mt19937 _rng; public: #ifdef _ENABLE_PLGPU @@ -72,16 +75,22 @@ template class MeasurementsBase { * * @param seed Seed */ - void setSeed(const std::size_t seed) { rng.seed(seed); } + void setSeed(const std::optional &seed = std::nullopt) { + if (seed.has_value()) { + _rng.seed(seed.value()); + this->_deviceseed = seed; + } else { + setRandomSeed(); + } + } /** * @brief Randomly set the seed of the internal random generator * - * @param seed Seed */ void setRandomSeed() { std::random_device rd; - setSeed(rd()); + _rng.seed(rd()); } /** @@ -134,7 +143,7 @@ template class MeasurementsBase { * @return 1-D vector of samples in binary with each sample * separated by a stride equal to the number of qubits. */ - auto generate_samples(size_t num_samples) -> std::vector { + auto generate_samples(std::size_t num_samples) -> std::vector { return static_cast(this)->generate_samples(num_samples); }; @@ -160,7 +169,7 @@ template class MeasurementsBase { } else if (obs.getObsName().find("Hamiltonian") != std::string::npos) { auto coeffs = obs.getCoeffs(); auto obsTerms = obs.getObs(); - for (size_t obs_term_idx = 0; obs_term_idx < coeffs.size(); + for (std::size_t obs_term_idx = 0; obs_term_idx < coeffs.size(); obs_term_idx++) { result += coeffs[obs_term_idx] * expval(*obsTerms[obs_term_idx], num_shots, shot_range); @@ -202,11 +211,11 @@ template class MeasurementsBase { std::vector eigenVals = eigenValues[0]; - for (size_t i = 1; i < eigenValues.size(); i++) { + for (std::size_t i = 1; i < eigenValues.size(); i++) { eigenVals = kronProd(eigenVals, eigenValues[i]); } - for (size_t i = 0; i < num_samples; i++) { + for (std::size_t i = 0; i < num_samples; i++) { std::size_t idx = 0; std::size_t wire_idx = 0; for (auto &obs_wire : obs_wires) { @@ -289,6 +298,7 @@ template class MeasurementsBase { sv.updateData(data_storage.data(), data_storage.size()); obs.applyInPlaceShots(sv, eigenvalues, obs_wires); Derived measure(sv); + measure.setSeed(this->_deviceseed); if (num_shots > std::size_t{0}) { return measure.probs(obs_wires, num_shots); } @@ -297,6 +307,7 @@ template class MeasurementsBase { StateVectorT sv(_statevector); obs.applyInPlaceShots(sv, eigenvalues, obs_wires); Derived measure(sv); + measure.setSeed(this->_deviceseed); if (num_shots > std::size_t{0}) { return measure.probs(obs_wires, num_shots); } @@ -319,11 +330,11 @@ template class MeasurementsBase { std::size_t num_wires = _statevector.getTotalNumQubits(); - std::vector prob_shots(size_t{1} << wires.size(), 0.0); + std::vector prob_shots(std::size_t{1} << wires.size(), 0.0); for (auto &it : counts_map) { std::size_t bitVal = 0; - for (size_t bit = 0; bit < wires.size(); bit++) { + for (std::size_t bit = 0; bit < wires.size(); bit++) { // Mapping the value of wires[bit]th bit to local [bit]th bit of // the output bitVal += @@ -346,12 +357,12 @@ template class MeasurementsBase { * * @return Floating point std::vector with probabilities. */ - auto probs(size_t num_shots) -> std::vector { + auto probs(std::size_t num_shots) -> std::vector { auto counts_map = counts(num_shots); std::size_t num_wires = _statevector.getTotalNumQubits(); - std::vector prob_shots(size_t{1} << num_wires, 0.0); + std::vector prob_shots(std::size_t{1} << num_wires, 0.0); for (auto &it : counts_map) { prob_shots[it.first] = @@ -389,6 +400,7 @@ template class MeasurementsBase { */ auto sample(const std::size_t &num_shots) -> std::vector { Derived measure(_statevector); + measure.setSeed(this->_deviceseed); return measure.generate_samples(num_shots); } @@ -407,7 +419,7 @@ template class MeasurementsBase { -> std::unordered_map { std::unordered_map outcome_map; auto sample_data = sample(obs, num_shots); - for (size_t i = 0; i < num_shots; i++) { + for (std::size_t i = 0; i < num_shots; i++) { auto key = sample_data[i]; auto it = outcome_map.find(key); if (it != outcome_map.end()) { @@ -425,18 +437,18 @@ template class MeasurementsBase { * * @param num_shots Number of wires the sampled observable was performed on * - * @return std::unordered_map with format ``{'outcome': - * num_occurences}`` + * @return std::unordered_map with format + * ``{'outcome': num_occurences}`` */ auto counts(const std::size_t &num_shots) - -> std::unordered_map { - std::unordered_map outcome_map; + -> std::unordered_map { + std::unordered_map outcome_map; auto sample_data = sample(num_shots); std::size_t num_wires = _statevector.getTotalNumQubits(); - for (size_t i = 0; i < num_shots; i++) { + for (std::size_t i = 0; i < num_shots; i++) { std::size_t key = 0; - for (size_t j = 0; j < num_wires; j++) { + for (std::size_t j = 0; j < num_wires; j++) { key += sample_data[i * num_wires + j] << (num_wires - 1 - j); } @@ -480,11 +492,13 @@ template class MeasurementsBase { StateVectorT sv(data_storage.data(), data_storage.size()); obs.applyInPlaceShots(sv, eigenValues, obs_wires); Derived measure(sv); + measure.setSeed(this->_deviceseed); samples = measure.generate_samples(num_shots); } else { StateVectorT sv(_statevector); obs.applyInPlaceShots(sv, eigenValues, obs_wires); Derived measure(sv); + measure.setSeed(this->_deviceseed); samples = measure.generate_samples(num_shots); } @@ -494,7 +508,8 @@ template class MeasurementsBase { // Get a slice of samples based on the shot_range vector std::size_t shot_idx = 0; for (const auto &i : shot_range) { - for (size_t j = i * num_qubits; j < (i + 1) * num_qubits; j++) { + for (std::size_t j = i * num_qubits; j < (i + 1) * num_qubits; + j++) { // TODO some extra work to make it cache-friendly sub_samples[shot_idx * num_qubits + j - i * num_qubits] = samples[j]; diff --git a/pennylane_lightning/core/src/measurements/tests/Test_MeasurementsBase.cpp b/pennylane_lightning/core/src/measurements/tests/Test_MeasurementsBase.cpp index 0f54df968b..623570919b 100644 --- a/pennylane_lightning/core/src/measurements/tests/Test_MeasurementsBase.cpp +++ b/pennylane_lightning/core/src/measurements/tests/Test_MeasurementsBase.cpp @@ -20,6 +20,7 @@ using Pennylane::Util::isApproxEqual; } // namespace /// @endcond #include +#include #include #ifdef _ENABLE_PLQUBIT @@ -84,47 +85,33 @@ template void testProbabilities() { // Expected results calculated with Pennylane default.qubit: std::vector< std::pair, std::vector>> - input = { -#if defined(_ENABLE_PLGPU) - // Bit index reodering conducted in the python layer - // for L-GPU. Also L-GPU backend doesn't support - // out of order wires for probability calculation - {{2, 1, 0}, - {0.67078706, 0.03062806, 0.0870997, 0.00397696, 0.17564072, - 0.00801973, 0.02280642, 0.00104134}} -#else -#if defined(_ENABLE_PLQUBIT) - // LightningQubit currently supports arbitrary wire index - // ordering. - {{0, 2, 1}, - {0.67078706, 0.0870997, 0.03062806, 0.00397696, 0.17564072, - 0.02280642, 0.00801973, 0.00104134}}, - {{1, 0, 2}, - {0.67078706, 0.03062806, 0.17564072, 0.00801973, 0.0870997, - 0.00397696, 0.02280642, 0.00104134}}, - {{1, 2, 0}, - {0.67078706, 0.17564072, 0.03062806, 0.00801973, 0.0870997, - 0.02280642, 0.00397696, 0.00104134}}, - {{2, 0, 1}, - {0.67078706, 0.0870997, 0.17564072, 0.02280642, 0.03062806, - 0.00397696, 0.00801973, 0.00104134}}, - {{2, 1, 0}, - {0.67078706, 0.17564072, 0.0870997, 0.02280642, 0.03062806, - 0.00801973, 0.00397696, 0.00104134}}, - {{2, 1}, {0.84642778, 0.10990612, 0.0386478, 0.0050183}}, - -#endif - {{0, 1, 2}, - {0.67078706, 0.03062806, 0.0870997, 0.00397696, 0.17564072, - 0.00801973, 0.02280642, 0.00104134}}, - {{0, 1}, {0.70141512, 0.09107666, 0.18366045, 0.02384776}}, - {{0, 2}, {0.75788676, 0.03460502, 0.19844714, 0.00906107}}, - {{1, 2}, {0.84642778, 0.0386478, 0.10990612, 0.0050183}}, - {{0}, {0.79249179, 0.20750821}}, - {{1}, {0.88507558, 0.11492442}}, - {{2}, {0.9563339, 0.0436661}} -#endif - }; + input = {// LightningQubit currently supports arbitrary wire index + // ordering. + {{0, 2, 1}, + {0.67078706, 0.0870997, 0.03062806, 0.00397696, + 0.17564072, 0.02280642, 0.00801973, 0.00104134}}, + {{1, 0, 2}, + {0.67078706, 0.03062806, 0.17564072, 0.00801973, + 0.0870997, 0.00397696, 0.02280642, 0.00104134}}, + {{1, 2, 0}, + {0.67078706, 0.17564072, 0.03062806, 0.00801973, + 0.0870997, 0.02280642, 0.00397696, 0.00104134}}, + {{2, 0, 1}, + {0.67078706, 0.0870997, 0.17564072, 0.02280642, + 0.03062806, 0.00397696, 0.00801973, 0.00104134}}, + {{2, 1, 0}, + {0.67078706, 0.17564072, 0.0870997, 0.02280642, + 0.03062806, 0.00801973, 0.00397696, 0.00104134}}, + {{2, 1}, {0.84642778, 0.10990612, 0.0386478, 0.0050183}}, + {{0, 1, 2}, + {0.67078706, 0.03062806, 0.0870997, 0.00397696, + 0.17564072, 0.00801973, 0.02280642, 0.00104134}}, + {{0, 1}, {0.70141512, 0.09107666, 0.18366045, 0.02384776}}, + {{0, 2}, {0.75788676, 0.03460502, 0.19844714, 0.00906107}}, + {{1, 2}, {0.84642778, 0.0386478, 0.10990612, 0.0050183}}, + {{0}, {0.79249179, 0.20750821}}, + {{1}, {0.88507558, 0.11492442}}, + {{2}, {0.9563339, 0.0436661}}}; // Defining the Statevector that will be measured. auto statevector_data = createNonTrivialState(); @@ -233,7 +220,7 @@ template void testProbabilitiesObs() { DYNAMIC_SECTION("Test PauliX" << StateVectorToName::name) { - for (size_t i = 0; i < num_qubits; i++) { + for (std::size_t i = 0; i < num_qubits; i++) { NamedObs obs("PauliX", {i}); Measurements Measurer_obs(statevector); @@ -250,7 +237,7 @@ template void testProbabilitiesObs() { DYNAMIC_SECTION("Test PauliY" << StateVectorToName::name) { - for (size_t i = 0; i < num_qubits; i++) { + for (std::size_t i = 0; i < num_qubits; i++) { NamedObs obs("PauliY", {i}); Measurements Measurer_obs(statevector); @@ -268,7 +255,7 @@ template void testProbabilitiesObs() { DYNAMIC_SECTION("Test PauliZ" << StateVectorToName::name) { - for (size_t i = 0; i < num_qubits; i++) { + for (std::size_t i = 0; i < num_qubits; i++) { NamedObs obs("PauliZ", {i}); Measurements Measurer_obs(statevector); @@ -283,7 +270,7 @@ template void testProbabilitiesObs() { DYNAMIC_SECTION("Test Hadamard" << StateVectorToName::name) { - for (size_t i = 0; i < num_qubits; i++) { + for (std::size_t i = 0; i < num_qubits; i++) { NamedObs obs("Hadamard", {i}); Measurements Measurer_obs(statevector); const PrecisionT theta = -M_PI / 4.0; @@ -300,7 +287,7 @@ template void testProbabilitiesObs() { DYNAMIC_SECTION("Test Identity" << StateVectorToName::name) { - for (size_t i = 0; i < num_qubits; i++) { + for (std::size_t i = 0; i < num_qubits; i++) { NamedObs obs("Identity", {i}); Measurements Measurer_obs(statevector); @@ -406,11 +393,7 @@ template void testProbabilitiesObsShots() { std::size_t num_shots = 10000; auto prob_obs_shots = Measurer_obs_shots.probs(*obs, num_shots); -#ifdef _ENABLE_PLGPU - auto prob = Measurer.probs(std::vector({2, 1, 0})); -#else auto prob = Measurer.probs(std::vector({0, 1, 2})); -#endif REQUIRE_THAT(prob_obs_shots, Catch::Approx(prob).margin(5e-2)); } @@ -436,11 +419,7 @@ template void testProbabilitiesObsShots() { std::size_t num_shots = 10000; auto prob_obs_shots = Measurer_obs_shots.probs(*obs, num_shots); -#ifdef _ENABLE_PLGPU - auto prob = Measurer.probs(std::vector({2, 1, 0})); -#else auto prob = Measurer.probs(std::vector({0, 1, 2})); -#endif REQUIRE_THAT(prob_obs_shots, Catch::Approx(prob).margin(5e-2)); } @@ -477,11 +456,11 @@ template void testNamedObsExpval() { {-0.64421768, -0.47942553, -0.29552020}, {0.58498357, 0.77015115, 0.91266780}}; - for (size_t ind_obs = 0; ind_obs < obs_name.size(); ind_obs++) { + for (std::size_t ind_obs = 0; ind_obs < obs_name.size(); ind_obs++) { DYNAMIC_SECTION(obs_name[ind_obs] << " - Varying wires" << StateVectorToName::name) { - for (size_t ind_wires = 0; ind_wires < wires_list.size(); + for (std::size_t ind_wires = 0; ind_wires < wires_list.size(); ind_wires++) { NamedObs obs(obs_name[ind_obs], wires_list[ind_wires]); @@ -525,13 +504,13 @@ template void testNamedObsExpvalShot() { {0.58498357, 0.77015115, 0.91266780}, {0.7620549436, 0.8420840225, 0.8449848566}, {1.0, 1.0, 1.0}}; - for (size_t ind_obs = 0; ind_obs < obs_name.size(); ind_obs++) { + for (std::size_t ind_obs = 0; ind_obs < obs_name.size(); ind_obs++) { DYNAMIC_SECTION(obs_name[ind_obs] << " - Varying wires" << StateVectorToName::name) { std::size_t num_shots = 20000; std::vector shots_range = {}; - for (size_t ind_wires = 0; ind_wires < wires_list.size(); + for (std::size_t ind_wires = 0; ind_wires < wires_list.size(); ind_wires++) { NamedObs obs(obs_name[ind_obs], wires_list[ind_wires]); @@ -545,16 +524,16 @@ template void testNamedObsExpvalShot() { } } - for (size_t ind_obs = 0; ind_obs < obs_name.size(); ind_obs++) { + for (std::size_t ind_obs = 0; ind_obs < obs_name.size(); ind_obs++) { DYNAMIC_SECTION(obs_name[ind_obs] << " - Varying wires-with shots_range" << StateVectorToName::name) { std::size_t num_shots = 20000; std::vector shots_range; - for (size_t i = 0; i < num_shots; i += 2) { + for (std::size_t i = 0; i < num_shots; i += 2) { shots_range.push_back(i); } - for (size_t ind_wires = 0; ind_wires < wires_list.size(); + for (std::size_t ind_wires = 0; ind_wires < wires_list.size(); ind_wires++) { NamedObs obs(obs_name[ind_obs], wires_list[ind_wires]); @@ -577,7 +556,6 @@ TEST_CASE("Expval Shot- NamedObs", "[MeasurementsBase][Observables]") { } } -#ifdef PL_USE_LAPACK template void testHermitianObsExpvalShot() { if constexpr (!std::is_same_v) { using StateVectorT = typename TypeList::Type; @@ -680,7 +658,6 @@ TEST_CASE("Expval Shot - HermitianObs ", "[MeasurementsBase][Observables]") { testHermitianObsExpvalShot(); } } -#endif template void testHermitianObsExpval() { if constexpr (!std::is_same_v) { @@ -712,7 +689,7 @@ template void testHermitianObsExpval() { MatrixT Hermitian_matrix{real_term, ComplexT{0, imag_term}, ComplexT{0, -imag_term}, real_term}; - for (size_t ind_wires = 0; ind_wires < wires_list.size(); + for (std::size_t ind_wires = 0; ind_wires < wires_list.size(); ind_wires++) { HermitianObs obs(Hermitian_matrix, wires_list[ind_wires]); @@ -739,7 +716,7 @@ template void testHermitianObsExpval() { Hermitian_matrix[10] = ComplexT{1.0, 0}; Hermitian_matrix[15] = ComplexT{1.0, 0}; - for (size_t ind_wires = 0; ind_wires < wires_list.size(); + for (std::size_t ind_wires = 0; ind_wires < wires_list.size(); ind_wires++) { HermitianObs obs(Hermitian_matrix, wires_list[ind_wires]); @@ -812,7 +789,7 @@ template void testTensorProdObsExpvalShot() { << StateVectorToName::name) { std::size_t num_shots = 20000; std::vector shots_range; - for (size_t i = 0; i < num_shots; i += 2) { + for (std::size_t i = 0; i < num_shots; i += 2) { shots_range.push_back(i); } auto X0 = std::make_shared>( @@ -831,7 +808,7 @@ template void testTensorProdObsExpvalShot() { << StateVectorToName::name) { std::size_t num_shots = 20000; std::vector shots_range; - for (size_t i = 0; i < num_shots; i += 2) { + for (std::size_t i = 0; i < num_shots; i += 2) { shots_range.push_back(i); } auto X0 = std::make_shared>( @@ -846,7 +823,6 @@ template void testTensorProdObsExpvalShot() { expected, static_cast(0.20))); } -#ifdef PL_USE_LAPACK DYNAMIC_SECTION(" With Identity and shots_range" << StateVectorToName::name) { std::size_t num_shots = 80000; @@ -867,7 +843,6 @@ template void testTensorProdObsExpvalShot() { REQUIRE_THAT(result, Catch::Matchers::WithinRel( expected, static_cast(0.20))); } -#endif testTensorProdObsExpvalShot(); } @@ -901,11 +876,11 @@ template void testNamedObsVar() { {0.5849835, 0.7701511, 0.9126678}, {0.6577942, 0.4068672, 0.1670374}}; - for (size_t ind_obs = 0; ind_obs < obs_name.size(); ind_obs++) { + for (std::size_t ind_obs = 0; ind_obs < obs_name.size(); ind_obs++) { DYNAMIC_SECTION(obs_name[ind_obs] << " - Varying wires" << StateVectorToName::name) { - for (size_t ind_wires = 0; ind_wires < wires_list.size(); + for (std::size_t ind_wires = 0; ind_wires < wires_list.size(); ind_wires++) { NamedObs obs(obs_name[ind_obs], wires_list[ind_wires]); @@ -918,7 +893,7 @@ template void testNamedObsVar() { DYNAMIC_SECTION(obs_name[ind_obs] << " Shots - Varying wires" << StateVectorToName::name) { - for (size_t ind_wires = 0; ind_wires < wires_list.size(); + for (std::size_t ind_wires = 0; ind_wires < wires_list.size(); ind_wires++) { NamedObs obs(obs_name[ind_obs], wires_list[ind_wires]); @@ -972,7 +947,7 @@ template void testHermitianObsVar() { MatrixT Hermitian_matrix{real_term, ComplexT{0, imag_term}, ComplexT{0, -imag_term}, real_term}; - for (size_t ind_wires = 0; ind_wires < wires_list.size(); + for (std::size_t ind_wires = 0; ind_wires < wires_list.size(); ind_wires++) { HermitianObs obs(Hermitian_matrix, wires_list[ind_wires]); @@ -998,7 +973,7 @@ template void testHermitianObsVar() { Hermitian_matrix[10] = ComplexT{1.0, 0}; Hermitian_matrix[15] = ComplexT{1.0, 0}; - for (size_t ind_wires = 0; ind_wires < wires_list.size(); + for (std::size_t ind_wires = 0; ind_wires < wires_list.size(); ind_wires++) { HermitianObs obs(Hermitian_matrix, wires_list[ind_wires]); @@ -1018,7 +993,6 @@ TEST_CASE("Var - HermitianObs", "[MeasurementsBase][Observables]") { } } -#ifdef PL_USE_LAPACK template void testHermitianObsShotVar() { if constexpr (!std::is_same_v) { using StateVectorT = typename TypeList::Type; @@ -1049,7 +1023,7 @@ template void testHermitianObsShotVar() { MatrixT Hermitian_matrix{real_term, ComplexT{0, imag_term}, ComplexT{0, -imag_term}, real_term}; - for (size_t ind_wires = 0; ind_wires < wires_list.size(); + for (std::size_t ind_wires = 0; ind_wires < wires_list.size(); ind_wires++) { HermitianObs obs(Hermitian_matrix, wires_list[ind_wires]); @@ -1079,7 +1053,7 @@ template void testHermitianObsShotVar() { Hermitian_matrix[10] = ComplexT{1.0, 0}; Hermitian_matrix[15] = ComplexT{1.0, 0}; - for (size_t ind_wires = 0; ind_wires < wires_list.size(); + for (std::size_t ind_wires = 0; ind_wires < wires_list.size(); ind_wires++) { HermitianObs obs(Hermitian_matrix, wires_list[ind_wires]); @@ -1103,7 +1077,6 @@ TEST_CASE("Var - HermitianObs Shot", "[MeasurementsBase][Observables]") { testHermitianObsShotVar(); } } -#endif template void testTensorProdObsVarShot() { if constexpr (!std::is_same_v) { @@ -1154,7 +1127,6 @@ template void testTensorProdObsVarShot() { expected, static_cast(0.20))); } -#ifdef PL_USE_LAPACK DYNAMIC_SECTION("With Hermitian and NameObs" << StateVectorToName::name) { using MatrixT = std::vector; @@ -1204,7 +1176,6 @@ template void testTensorProdObsVarShot() { REQUIRE_THAT(result, Catch::Matchers::WithinRel( expected, static_cast(0.20))); } -#endif DYNAMIC_SECTION(" full wires with apply operations" << StateVectorToName::name) { @@ -1254,7 +1225,9 @@ TEST_CASE("Var Shot- TensorProdObs", "[MeasurementsBase][Observables]") { testTensorProdObsVarShot(); } } -template void testSamples() { + +template +void testSamples(const std::optional &seed = std::nullopt) { if constexpr (!std::is_same_v) { using StateVectorT = typename TypeList::Type; using PrecisionT = typename StateVectorT::PrecisionT; @@ -1276,6 +1249,7 @@ template void testSamples() { // This object attaches to the statevector allowing several // measurements. Measurements Measurer(statevector); + Measurer.setSeed(seed); std::vector expected_probabilities = { 0.67078706, 0.03062806, 0.0870997, 0.00397696, @@ -1290,8 +1264,8 @@ template void testSamples() { std::vector samples_decimal(num_samples, 0); // convert samples to decimal and then bin them in counts - for (size_t i = 0; i < num_samples; i++) { - for (size_t j = 0; j < num_qubits; j++) { + for (std::size_t i = 0; i < num_samples; i++) { + for (std::size_t j = 0; j < num_qubits; j++) { if (samples[i * num_qubits + j] != 0) { samples_decimal[i] += twos[(num_qubits - 1 - j)]; } @@ -1301,7 +1275,7 @@ template void testSamples() { // compute estimated probabilities from histogram std::vector probabilities(counts.size()); - for (size_t i = 0; i < counts.size(); i++) { + for (std::size_t i = 0; i < counts.size(); i++) { probabilities[i] = counts[i] / (PrecisionT)num_samples; } @@ -1310,7 +1284,7 @@ template void testSamples() { REQUIRE_THAT(probabilities, Catch::Approx(expected_probabilities).margin(.05)); } - testSamples(); + testSamples(seed); } } @@ -1320,6 +1294,12 @@ TEST_CASE("Samples", "[MeasurementsBase]") { } } +TEST_CASE("Seeded samples", "[MeasurementsBase]") { + if constexpr (BACKEND_FOUND) { + testSamples(37); + } +} + template void testSamplesCountsObs() { if constexpr (!std::is_same_v) { using StateVectorT = typename TypeList::Type; @@ -1352,12 +1332,12 @@ template void testSamplesCountsObs() { {0.58498357, 0.77015115, 0.91266780}, {0.7620549436, 0.8420840225, 0.8449848566}, {1.0, 1.0, 1.0}}; - for (size_t ind_obs = 0; ind_obs < obs_name.size(); ind_obs++) { + for (std::size_t ind_obs = 0; ind_obs < obs_name.size(); ind_obs++) { DYNAMIC_SECTION(obs_name[ind_obs] << " Sample Obs - Varying wires" << StateVectorToName::name) { std::size_t num_shots = 20000; - for (size_t ind_wires = 0; ind_wires < wires_list.size(); + for (std::size_t ind_wires = 0; ind_wires < wires_list.size(); ind_wires++) { NamedObs obs(obs_name[ind_obs], wires_list[ind_wires]); @@ -1380,7 +1360,7 @@ template void testSamplesCountsObs() { << " Counts Obs - Varying wires" << StateVectorToName::name) { std::size_t num_shots = 20000; - for (size_t ind_wires = 0; ind_wires < wires_list.size(); + for (std::size_t ind_wires = 0; ind_wires < wires_list.size(); ind_wires++) { NamedObs obs(obs_name[ind_obs], wires_list[ind_wires]); @@ -1415,8 +1395,8 @@ template void testSamplesCountsObs() { std::vector samples_decimal(num_samples, 0); // convert samples to decimal and then bin them in counts - for (size_t i = 0; i < num_samples; i++) { - for (size_t j = 0; j < num_qubits; j++) { + for (std::size_t i = 0; i < num_samples; i++) { + for (std::size_t j = 0; j < num_qubits; j++) { if (samples[i * num_qubits + j] != 0) { samples_decimal[i] += twos[(num_qubits - 1 - j)]; } @@ -1426,7 +1406,7 @@ template void testSamplesCountsObs() { // compute estimated probabilities from histogram std::vector probabilities(counts.size()); - for (size_t i = 0; i < counts.size(); i++) { + for (std::size_t i = 0; i < counts.size(); i++) { probabilities[i] = counts[i] / (PrecisionT)num_samples; } @@ -1460,7 +1440,7 @@ template void testSamplesCountsObs() { // compute estimated probabilities from histogram std::vector probabilities(counts.size()); - for (size_t i = 0; i < counts.size(); i++) { + for (std::size_t i = 0; i < counts.size(); i++) { probabilities[i] = counts[i] / (PrecisionT)num_samples; } @@ -1525,7 +1505,7 @@ template void testHamiltonianObsExpvalShot() { << StateVectorToName::name) { std::size_t num_shots = 20000; std::vector shots_range; - for (size_t i = 0; i < num_shots; i += 2) { + for (std::size_t i = 0; i < num_shots; i += 2) { shots_range.push_back(i); } @@ -1562,7 +1542,7 @@ template void testHamiltonianObsExpvalShot() { REQUIRE_THAT(res, Catch::Matchers::WithinRel( expected, static_cast(0.20))); } -#ifdef PL_USE_LAPACK + DYNAMIC_SECTION("YHer" << StateVectorToName::name) { auto Y0 = std::make_shared>( "PauliY", std::vector{0}); @@ -1583,7 +1563,6 @@ template void testHamiltonianObsExpvalShot() { REQUIRE_THAT(res, Catch::Matchers::WithinRel( expected, static_cast(0.20))); } -#endif testHamiltonianObsExpvalShot(); } @@ -1643,7 +1622,6 @@ template void testHamiltonianObsVarShot() { expected, static_cast(0.20))); } -#ifdef PL_USE_LAPACK DYNAMIC_SECTION("YHer" << StateVectorToName::name) { using ComplexT = typename StateVectorT::ComplexT; auto Y0 = std::make_shared>( @@ -1672,7 +1650,6 @@ template void testHamiltonianObsVarShot() { REQUIRE_THAT(res, Catch::Matchers::WithinRel( expected, static_cast(0.20))); } -#endif testHamiltonianObsVarShot(); } @@ -1732,4 +1709,4 @@ TEST_CASE("Measure Shot - SparseHObs ", "[MeasurementsBase][Observables]") { if constexpr (BACKEND_FOUND) { testSparseHObsMeasureShot(); } -} \ No newline at end of file +} diff --git a/pennylane_lightning/core/src/measurements/tests/mpi/Test_MeasurementsBaseMPI.cpp b/pennylane_lightning/core/src/measurements/tests/mpi/Test_MeasurementsBaseMPI.cpp index 7c834201af..7f6411263e 100644 --- a/pennylane_lightning/core/src/measurements/tests/mpi/Test_MeasurementsBaseMPI.cpp +++ b/pennylane_lightning/core/src/measurements/tests/mpi/Test_MeasurementsBaseMPI.cpp @@ -58,7 +58,7 @@ template void testProbabilities() { input = {// Bit index reodering conducted in the python layer // for L-GPU. Also L-GPU backend doesn't support // out of order wires for probability calculation - {{2, 1, 0}, + {{0, 1, 2}, {0.67078706, 0.03062806, 0.0870997, 0.00397696, 0.17564072, 0.00801973, 0.02280642, 0.00104134}}}; @@ -185,7 +185,7 @@ template void testProbabilitiesObs() { DYNAMIC_SECTION("Test PauliX" << StateVectorMPIToName::name) { - for (size_t i = 0; i < num_qubits; i++) { + for (std::size_t i = 0; i < num_qubits; i++) { NamedObsMPI obs("PauliX", {i}); MeasurementsMPI Measurer_obs(statevector); @@ -202,7 +202,7 @@ template void testProbabilitiesObs() { DYNAMIC_SECTION("Test PauliY" << StateVectorMPIToName::name) { - for (size_t i = 0; i < num_qubits; i++) { + for (std::size_t i = 0; i < num_qubits; i++) { NamedObsMPI obs("PauliY", {i}); MeasurementsMPI Measurer_obs(statevector); @@ -220,7 +220,7 @@ template void testProbabilitiesObs() { DYNAMIC_SECTION("Test PauliZ" << StateVectorMPIToName::name) { - for (size_t i = 0; i < num_qubits; i++) { + for (std::size_t i = 0; i < num_qubits; i++) { NamedObsMPI obs("PauliZ", {i}); MeasurementsMPI Measurer_obs(statevector); @@ -235,7 +235,7 @@ template void testProbabilitiesObs() { DYNAMIC_SECTION("Test Hadamard" << StateVectorMPIToName::name) { - for (size_t i = 0; i < num_qubits; i++) { + for (std::size_t i = 0; i < num_qubits; i++) { NamedObsMPI obs("Hadamard", {i}); MeasurementsMPI Measurer_obs(statevector); const PrecisionT theta = -M_PI / 4.0; @@ -252,7 +252,7 @@ template void testProbabilitiesObs() { DYNAMIC_SECTION("Test Identity" << StateVectorMPIToName::name) { - for (size_t i = 0; i < num_qubits; i++) { + for (std::size_t i = 0; i < num_qubits; i++) { NamedObsMPI obs("Identity", {i}); MeasurementsMPI Measurer_obs(statevector); @@ -386,7 +386,7 @@ template void testProbabilitiesObsShots() { std::size_t num_shots = 10000; auto prob_obs_shots = Measurer_obs_shots.probs(*obs, num_shots); - auto prob = Measurer.probs(std::vector({2, 1, 0})); + auto prob = Measurer.probs(std::vector({0, 1, 2})); auto prob_all = mpi_manager.allgather(prob); REQUIRE_THAT(prob_obs_shots, Catch::Approx(prob_all).margin(5e-2)); } @@ -473,11 +473,11 @@ template void testNamedObsExpval() { {-0.64421768, -0.47942553, -0.29552020}, {0.58498357, 0.77015115, 0.91266780}}; - for (size_t ind_obs = 0; ind_obs < obs_name.size(); ind_obs++) { + for (std::size_t ind_obs = 0; ind_obs < obs_name.size(); ind_obs++) { DYNAMIC_SECTION(obs_name[ind_obs] << " - Varying wires" << StateVectorMPIToName::name) { - for (size_t ind_wires = 0; ind_wires < wires_list.size(); + for (std::size_t ind_wires = 0; ind_wires < wires_list.size(); ind_wires++) { NamedObsMPI obs(obs_name[ind_obs], wires_list[ind_wires]); @@ -548,11 +548,11 @@ template void testNamedObsExpvalShot() { std::size_t num_shots = 10000; std::vector shots_range = {}; - for (size_t ind_obs = 0; ind_obs < obs_name.size(); ind_obs++) { + for (std::size_t ind_obs = 0; ind_obs < obs_name.size(); ind_obs++) { DYNAMIC_SECTION(obs_name[ind_obs] << " - Varying wires" << StateVectorMPIToName::name) { - for (size_t ind_wires = 0; ind_wires < wires_list.size(); + for (std::size_t ind_wires = 0; ind_wires < wires_list.size(); ind_wires++) { NamedObsMPI obs(obs_name[ind_obs], wires_list[ind_wires]); @@ -628,7 +628,7 @@ template void testHermitianObsExpval() { MatrixT Hermitian_matrix{real_term, ComplexT{0, imag_term}, ComplexT{0, -imag_term}, real_term}; - for (size_t ind_wires = 0; ind_wires < wires_list.size(); + for (std::size_t ind_wires = 0; ind_wires < wires_list.size(); ind_wires++) { HermitianObsMPI obs(Hermitian_matrix, wires_list[ind_wires]); @@ -655,7 +655,7 @@ template void testHermitianObsExpval() { Hermitian_matrix[10] = ComplexT{1.0, 0}; Hermitian_matrix[15] = ComplexT{1.0, 0}; - for (size_t ind_wires = 0; ind_wires < wires_list.size(); + for (std::size_t ind_wires = 0; ind_wires < wires_list.size(); ind_wires++) { HermitianObsMPI obs(Hermitian_matrix, wires_list[ind_wires]); @@ -747,7 +747,7 @@ template void testTensorProdObsExpvalShot() { << StateVectorMPIToName::name) { std::size_t num_shots = 10000; std::vector shots_range; - for (size_t i = 0; i < num_shots; i += 2) { + for (std::size_t i = 0; i < num_shots; i += 2) { shots_range.push_back(i); } auto X0 = std::make_shared>( @@ -764,7 +764,7 @@ template void testTensorProdObsExpvalShot() { << StateVectorMPIToName::name) { std::size_t num_shots = 10000; std::vector shots_range; - for (size_t i = 0; i < num_shots; i += 2) { + for (std::size_t i = 0; i < num_shots; i += 2) { shots_range.push_back(i); } auto X0 = std::make_shared>( @@ -861,7 +861,7 @@ template void testHamiltonianObsExpvalShot() { << StateVectorMPIToName::name) { std::size_t num_shots = 10000; std::vector shots_range; - for (size_t i = 0; i < num_shots; i += 2) { + for (std::size_t i = 0; i < num_shots; i += 2) { shots_range.push_back(i); } auto X0 = std::make_shared>( @@ -879,7 +879,7 @@ template void testHamiltonianObsExpvalShot() { << StateVectorMPIToName::name) { std::size_t num_shots = 10000; std::vector shots_range; - for (size_t i = 0; i < num_shots; i += 2) { + for (std::size_t i = 0; i < num_shots; i += 2) { shots_range.push_back(i); } auto X0 = std::make_shared>( @@ -950,11 +950,11 @@ template void testNamedObsVar() { {0.5849835, 0.7701511, 0.9126678}, {0.6577942, 0.4068672, 0.1670374}}; - for (size_t ind_obs = 0; ind_obs < obs_name.size(); ind_obs++) { + for (std::size_t ind_obs = 0; ind_obs < obs_name.size(); ind_obs++) { DYNAMIC_SECTION(obs_name[ind_obs] << " - Varying wires" << StateVectorMPIToName::name) { - for (size_t ind_wires = 0; ind_wires < wires_list.size(); + for (std::size_t ind_wires = 0; ind_wires < wires_list.size(); ind_wires++) { NamedObsMPI obs(obs_name[ind_obs], wires_list[ind_wires]); @@ -967,7 +967,7 @@ template void testNamedObsVar() { DYNAMIC_SECTION(obs_name[ind_obs] << " Shots - Varying wires" << StateVectorMPIToName::name) { - for (size_t ind_wires = 0; ind_wires < wires_list.size(); + for (std::size_t ind_wires = 0; ind_wires < wires_list.size(); ind_wires++) { NamedObsMPI obs(obs_name[ind_obs], wires_list[ind_wires]); @@ -1043,7 +1043,7 @@ template void testHermitianObsVar() { MatrixT Hermitian_matrix{real_term, ComplexT{0, imag_term}, ComplexT{0, -imag_term}, real_term}; - for (size_t ind_wires = 0; ind_wires < wires_list.size(); + for (std::size_t ind_wires = 0; ind_wires < wires_list.size(); ind_wires++) { HermitianObsMPI obs(Hermitian_matrix, wires_list[ind_wires]); @@ -1069,7 +1069,7 @@ template void testHermitianObsVar() { Hermitian_matrix[10] = ComplexT{1.0, 0}; Hermitian_matrix[15] = ComplexT{1.0, 0}; - for (size_t ind_wires = 0; ind_wires < wires_list.size(); + for (std::size_t ind_wires = 0; ind_wires < wires_list.size(); ind_wires++) { HermitianObsMPI obs(Hermitian_matrix, wires_list[ind_wires]); @@ -1225,8 +1225,8 @@ template void testSamples() { std::vector samples_decimal(num_samples, 0); // convert samples to decimal and then bin them in counts - for (size_t i = 0; i < num_samples; i++) { - for (size_t j = 0; j < num_qubits; j++) { + for (std::size_t i = 0; i < num_samples; i++) { + for (std::size_t j = 0; j < num_qubits; j++) { if (samples[i * num_qubits + j] != 0) { samples_decimal[i] += twos[(num_qubits - 1 - j)]; } @@ -1236,7 +1236,7 @@ template void testSamples() { // compute estimated probabilities from histogram std::vector probabilities(counts.size()); - for (size_t i = 0; i < counts.size(); i++) { + for (std::size_t i = 0; i < counts.size(); i++) { probabilities[i] = counts[i] / (PrecisionT)num_samples; } @@ -1312,12 +1312,12 @@ template void testSamplesCountsObs() { {0.58498357, 0.77015115, 0.91266780}, {0.7620549436, 0.8420840225, 0.8449848566}, {1.0, 1.0, 1.0}}; - for (size_t ind_obs = 0; ind_obs < obs_name.size(); ind_obs++) { + for (std::size_t ind_obs = 0; ind_obs < obs_name.size(); ind_obs++) { DYNAMIC_SECTION(obs_name[ind_obs] << " Sample Obs - Varying wires" << StateVectorMPIToName::name) { std::size_t num_shots = 10000; - for (size_t ind_wires = 0; ind_wires < wires_list.size(); + for (std::size_t ind_wires = 0; ind_wires < wires_list.size(); ind_wires++) { NamedObsMPI obs(obs_name[ind_obs], wires_list[ind_wires]); @@ -1338,7 +1338,7 @@ template void testSamplesCountsObs() { << " Counts Obs - Varying wires" << StateVectorMPIToName::name) { std::size_t num_shots = 10000; - for (size_t ind_wires = 0; ind_wires < wires_list.size(); + for (std::size_t ind_wires = 0; ind_wires < wires_list.size(); ind_wires++) { NamedObsMPI obs(obs_name[ind_obs], wires_list[ind_wires]); @@ -1371,8 +1371,8 @@ template void testSamplesCountsObs() { std::vector samples_decimal(num_samples, 0); // convert samples to decimal and then bin them in counts - for (size_t i = 0; i < num_samples; i++) { - for (size_t j = 0; j < num_qubits; j++) { + for (std::size_t i = 0; i < num_samples; i++) { + for (std::size_t j = 0; j < num_qubits; j++) { if (samples[i * num_qubits + j] != 0) { samples_decimal[i] += twos[(num_qubits - 1 - j)]; } @@ -1382,7 +1382,7 @@ template void testSamplesCountsObs() { // compute estimated probabilities from histogram std::vector probabilities(counts.size()); - for (size_t i = 0; i < counts.size(); i++) { + for (std::size_t i = 0; i < counts.size(); i++) { probabilities[i] = counts[i] / (PrecisionT)num_samples; } @@ -1415,7 +1415,7 @@ template void testSamplesCountsObs() { // compute estimated probabilities from histogram std::vector probabilities(counts.size()); - for (size_t i = 0; i < counts.size(); i++) { + for (std::size_t i = 0; i < counts.size(); i++) { probabilities[i] = counts[i] / (PrecisionT)num_samples; } diff --git a/pennylane_lightning/core/src/observables/Observables.hpp b/pennylane_lightning/core/src/observables/Observables.hpp index d10cb4b62a..dfccfa6c7b 100644 --- a/pennylane_lightning/core/src/observables/Observables.hpp +++ b/pennylane_lightning/core/src/observables/Observables.hpp @@ -23,9 +23,7 @@ #include "Error.hpp" #include "Util.hpp" -#ifdef PL_USE_LAPACK #include "UtilLinearAlg.hpp" -#endif namespace Pennylane::Observables { /** @@ -220,10 +218,8 @@ class HermitianObsBase : public Observable { MatrixT matrix_; std::vector wires_; -#ifdef PL_USE_LAPACK - std::vector eigenVals_; - MatrixT unitary_; -#endif + mutable std::vector eigenVals_; + mutable MatrixT unitary_; private: [[nodiscard]] auto isEqual(const Observable &other) const @@ -234,18 +230,10 @@ class HermitianObsBase : public Observable { return (matrix_ == other_cast.matrix_) && (wires_ == other_cast.wires_); } - public: /** - * @brief Create an Hermitian observable - * - * @param matrix Matrix in row major format. - * @param wires Wires the observable applies to. + * @brief Decompose the Hermitian matrix into diagonalizing gates. */ - HermitianObsBase(MatrixT matrix, std::vector wires) - : matrix_{std::move(matrix)}, wires_{std::move(wires)} { - PL_ASSERT(matrix_.size() == Util::exp2(2 * wires_.size())); - -#ifdef PL_USE_LAPACK + void decompose_() const { std::vector> mat(matrix_.size()); std::transform(matrix_.begin(), matrix_.end(), mat.begin(), @@ -253,17 +241,36 @@ class HermitianObsBase : public Observable { return static_cast>(value); }); + PL_ABORT_IF_NOT( + Pennylane::Util::is_Hermitian(Util::exp2(wires_.size()), + Util::exp2(wires_.size()), + mat) == true, + "The matrix passed to HermitianObs is not a Hermitian matrix."); + std::vector> unitary(matrix_.size()); + std::vector eigenVals; Pennylane::Util::compute_diagonalizing_gates( Util::exp2(wires_.size()), Util::exp2(wires_.size()), mat, - eigenVals_, unitary); + eigenVals, unitary); unitary_.resize(unitary.size()); std::transform( unitary.begin(), unitary.end(), unitary_.begin(), [](ComplexT value) { return static_cast(value); }); -#endif + eigenVals_ = eigenVals; + } + + public: + /** + * @brief Create an Hermitian observable + * + * @param matrix Matrix in row major format. + * @param wires Wires the observable applies to. + */ + HermitianObsBase(MatrixT matrix, std::vector wires) + : matrix_{std::move(matrix)}, wires_{std::move(wires)} { + PL_ASSERT(matrix_.size() == Util::exp2(2 * wires_.size())); } [[nodiscard]] auto getMatrix() const -> const MatrixT & { return matrix_; } @@ -284,30 +291,14 @@ class HermitianObsBase : public Observable { [[maybe_unused]] StateVectorT &sv, [[maybe_unused]] std::vector> &eigenValues, [[maybe_unused]] std::vector &ob_wires) const override { -#ifdef PL_USE_LAPACK - std::vector> mat(matrix_.size()); - - std::transform(matrix_.begin(), matrix_.end(), mat.begin(), - [](ComplexT value) { - return static_cast>(value); - }); - - PL_ABORT_IF_NOT( - Pennylane::Util::is_Hermitian(Util::exp2(wires_.size()), - Util::exp2(wires_.size()), - mat) == true, - "The matrix passed to HermitianObs is not a Hermitian matrix."); + if (eigenVals_.empty() && unitary_.empty()) { + decompose_(); + } eigenValues.clear(); ob_wires = wires_; sv.applyMatrix(unitary_, wires_); eigenValues.push_back(eigenVals_); -#else - PL_ABORT( - "Hermitian observables with shot measurement are not supported; " - "consider building the device with `ENABLE_LAPACK=ON` compilation " - "flag."); -#endif } }; @@ -332,7 +323,7 @@ class TensorProdObsBase : public Observable { return false; } - for (size_t i = 0; i < obs_.size(); i++) { + for (std::size_t i = 0; i < obs_.size(); i++) { if (*obs_[i] != *other_cast.obs_[i]) { return false; } @@ -460,7 +451,7 @@ class TensorProdObsBase : public Observable { using Util::operator<<; std::ostringstream obs_stream; const auto obs_size = obs_.size(); - for (size_t idx = 0; idx < obs_size; idx++) { + for (std::size_t idx = 0; idx < obs_size; idx++) { obs_stream << obs_[idx]->getObsName(); if (idx != obs_size - 1) { obs_stream << " @ "; @@ -495,7 +486,7 @@ class HamiltonianBase : public Observable { return false; } - for (size_t i = 0; i < obs_.size(); i++) { + for (std::size_t i = 0; i < obs_.size(); i++) { if (*obs_[i] != *other_cast.obs_[i]) { return false; } @@ -566,7 +557,7 @@ class HamiltonianBase : public Observable { std::ostringstream ss; ss << "Hamiltonian: { 'coeffs' : " << coeffs_ << ", 'observables' : ["; const auto term_size = coeffs_.size(); - for (size_t t = 0; t < term_size; t++) { + for (std::size_t t = 0; t < term_size; t++) { ss << obs_[t]->getObsName(); if (t != term_size - 1) { ss << ", "; diff --git a/pennylane_lightning/core/src/observables/tests/Test_Observables.cpp b/pennylane_lightning/core/src/observables/tests/Test_Observables.cpp index 5da1f458a7..b8aa79646d 100644 --- a/pennylane_lightning/core/src/observables/tests/Test_Observables.cpp +++ b/pennylane_lightning/core/src/observables/tests/Test_Observables.cpp @@ -207,7 +207,6 @@ template void testHermitianObsBase() { REQUIRE(ob2 != ob3); } -#ifdef PL_USE_LAPACK DYNAMIC_SECTION("Failed to create a HermitianObs- " << StateVectorToName::name) { std::mt19937_64 re{1337}; @@ -227,27 +226,6 @@ template void testHermitianObsBase() { Catch::Matchers::Contains("The matrix passed to HermitianObs " "is not a Hermitian matrix.")); } -#else - DYNAMIC_SECTION("Failed for HermitianObs for applyInPlaceShots - " - << StateVectorToName::name) { - std::mt19937_64 re{1337}; - constexpr std::size_t num_qubits = 3; - auto init_state = - createRandomStateVectorData(re, num_qubits); - - StateVectorT state_vector(init_state.data(), init_state.size()); - auto obs = - HermitianObsT{std::vector{1.0, 0.0, -1.0, 0.0}, {0}}; - - std::vector> eigenValues; - std::vector ob_wires; - - REQUIRE_THROWS_WITH( - obs.applyInPlaceShots(state_vector, eigenValues, ob_wires), - Catch::Matchers::Contains("Hermitian observables with shot " - "measurement are not supported")); - } -#endif testHermitianObsBase(); } diff --git a/pennylane_lightning/core/src/observables/tests/mpi/Test_ObservablesMPI.cpp b/pennylane_lightning/core/src/observables/tests/mpi/Test_ObservablesMPI.cpp index eb39b57f5c..30f7262349 100644 --- a/pennylane_lightning/core/src/observables/tests/mpi/Test_ObservablesMPI.cpp +++ b/pennylane_lightning/core/src/observables/tests/mpi/Test_ObservablesMPI.cpp @@ -522,7 +522,6 @@ template void testHamiltonianBase() { StateVectorT sv_mpi(mpi_manager, dt_local, mpi_buffersize, nGlobalIndexBits, nLocalIndexBits); - sv_mpi.initSV(); REQUIRE_THROWS_AS(ham->applyInPlace(sv_mpi), LightningException); diff --git a/pennylane_lightning/core/src/simulators/base/StateVectorBase.hpp b/pennylane_lightning/core/src/simulators/base/StateVectorBase.hpp index df5dedf4af..2ac156a88c 100644 --- a/pennylane_lightning/core/src/simulators/base/StateVectorBase.hpp +++ b/pennylane_lightning/core/src/simulators/base/StateVectorBase.hpp @@ -55,7 +55,8 @@ template class StateVectorBase { * * @param num_qubits Number of qubits */ - explicit StateVectorBase(size_t num_qubits) : num_qubits_{num_qubits} {} + explicit StateVectorBase(std::size_t num_qubits) + : num_qubits_{num_qubits} {} /** * @brief Get the number of qubits represented by the statevector data. @@ -141,7 +142,7 @@ template class StateVectorBase { numOperations != ops_params.size(), "Invalid arguments: number of operations, wires, inverses, and " "parameters must all be equal"); - for (size_t i = 0; i < numOperations; i++) { + for (std::size_t i = 0; i < numOperations; i++) { this->applyOperation(ops[i], ops_wires[i], ops_adjoint[i], ops_params[i]); } @@ -168,7 +169,7 @@ template class StateVectorBase { numOperations == ops_adjoint.size(), "Invalid arguments: number of operations, wires and inverses" "must all be equal"); - for (size_t i = 0; i < numOperations; i++) { + for (std::size_t i = 0; i < numOperations; i++) { this->applyOperation(ops[i], ops_wires[i], ops_adjoint[i], {}); } } diff --git a/pennylane_lightning/core/src/simulators/base/tests/Test_StateVectorBase.cpp b/pennylane_lightning/core/src/simulators/base/tests/Test_StateVectorBase.cpp index 6388c13481..405fcdc757 100644 --- a/pennylane_lightning/core/src/simulators/base/tests/Test_StateVectorBase.cpp +++ b/pennylane_lightning/core/src/simulators/base/tests/Test_StateVectorBase.cpp @@ -115,9 +115,8 @@ template void testApplyOperations() { state_vector_2.applyOperation("PauliX", {0}, false); state_vector_2.applyOperation("PauliY", {1}, false); - REQUIRE(isApproxEqual( - state_vector_1.getData(), state_vector_1.getLength(), - state_vector_2.getData(), state_vector_2.getLength())); + REQUIRE(isApproxEqual(state_vector_1.getDataVector(), + state_vector_2.getDataVector())); } DYNAMIC_SECTION("Apply 0-controlled operations without parameters - " @@ -139,9 +138,8 @@ template void testApplyOperations() { state_vector_2.applyOperation("PauliX", {}, {}, {0}, false); state_vector_2.applyOperation("PauliY", {}, {}, {1}, false); - REQUIRE(isApproxEqual( - state_vector_1.getData(), state_vector_1.getLength(), - state_vector_2.getData(), state_vector_2.getLength())); + REQUIRE(isApproxEqual(state_vector_1.getDataVector(), + state_vector_2.getDataVector())); } DYNAMIC_SECTION("Apply operations with parameters - " @@ -163,9 +161,8 @@ template void testApplyOperations() { state_vector_2.applyOperation("RX", {0}, false, {0.1}); state_vector_2.applyOperation("RY", {1}, false, {0.2}); - REQUIRE(isApproxEqual( - state_vector_1.getData(), state_vector_1.getLength(), - state_vector_2.getData(), state_vector_2.getLength())); + REQUIRE(isApproxEqual(state_vector_1.getDataVector(), + state_vector_2.getDataVector())); } testApplyOperations(); } diff --git a/pennylane_lightning/core/src/simulators/lightning_gpu/CMakeLists.txt b/pennylane_lightning/core/src/simulators/lightning_gpu/CMakeLists.txt index 618ea7c329..f2fa032454 100644 --- a/pennylane_lightning/core/src/simulators/lightning_gpu/CMakeLists.txt +++ b/pennylane_lightning/core/src/simulators/lightning_gpu/CMakeLists.txt @@ -74,6 +74,7 @@ endif() ############################################################################### set(COMPONENT_SUBDIRS algorithms bindings + catalyst measurements gates observables diff --git a/pennylane_lightning/core/src/simulators/lightning_gpu/MPIWorker.hpp b/pennylane_lightning/core/src/simulators/lightning_gpu/MPIWorker.hpp index 6e524b61fe..3ba33cdf28 100644 --- a/pennylane_lightning/core/src/simulators/lightning_gpu/MPIWorker.hpp +++ b/pennylane_lightning/core/src/simulators/lightning_gpu/MPIWorker.hpp @@ -124,7 +124,7 @@ make_shared_mpi_worker(custatevecHandle_t handle, MPIManager &mpi_manager, if (nP2PDeviceBits != 0) { std::size_t local_device_id = mpi_manager.getRank() % nDevices; - for (size_t devId = 0; devId < nDevices; ++devId) { + for (std::size_t devId = 0; devId < nDevices; ++devId) { if (devId != local_device_id) { int accessEnabled; PL_CUDA_IS_SUCCESS(cudaDeviceCanAccessPeer( @@ -175,8 +175,15 @@ make_shared_mpi_worker(custatevecHandle_t handle, MPIManager &mpi_manager, communicatorType, nullptr); if (err != CUSTATEVEC_STATUS_SUCCESS) { communicator = nullptr; - PL_CUSTATEVEC_IS_SUCCESS(custatevecCommunicatorCreate( - handle, &communicator, communicatorType, "libmpi.so")); + // If communicator creation failed, try to load the MPI library + // dynamically. This requires the MPI library to be in the + // search/runtime path, like LD_LIBRARY_PATH, RPATH. An error message + // will be printed if the library is not found in the runtime path. + auto py_err = custatevecCommunicatorCreate( + handle, &communicator, communicatorType, "libmpi.so"); + PL_ABORT_IF_NOT(py_err == CUSTATEVEC_STATUS_SUCCESS, + "MPI communicator creation failed. Please add " + "'/path/to/libmpi.so' to LD_LIBRARY_PATH."); } // LCOV_EXCL_STOP mpi_manager.Barrier(); @@ -267,7 +274,7 @@ make_shared_mpi_worker(custatevecHandle_t handle, MPIManager &mpi_manager, std::size_t p2pSubSVIndexBegin = (mpi_manager.getRank() / nSubSVsP2P) * nSubSVsP2P; std::size_t p2pSubSVIndexEnd = p2pSubSVIndexBegin + nSubSVsP2P; - for (size_t p2pSubSVIndex = p2pSubSVIndexBegin; + for (std::size_t p2pSubSVIndex = p2pSubSVIndexBegin; p2pSubSVIndex < p2pSubSVIndexEnd; p2pSubSVIndex++) { if (static_cast(mpi_manager.getRank()) == p2pSubSVIndex) diff --git a/pennylane_lightning/core/src/simulators/lightning_gpu/StateVectorCudaBase.hpp b/pennylane_lightning/core/src/simulators/lightning_gpu/StateVectorCudaBase.hpp index d4bec462a6..772d1b6a2c 100644 --- a/pennylane_lightning/core/src/simulators/lightning_gpu/StateVectorCudaBase.hpp +++ b/pennylane_lightning/core/src/simulators/lightning_gpu/StateVectorCudaBase.hpp @@ -198,29 +198,20 @@ class StateVectorCudaBase : public StateVectorBase { data_buffer_ = std::move(other); } - /** - * @brief Initialize the statevector data to the |0...0> state - * - */ - void initSV(bool async = false) { - std::size_t index = 0; - const std::complex value(1, 0); - static_cast(this)->setBasisState(value, index, async); - }; - protected: using ParFunc = std::function &, bool, const std::vector &)>; using FMap = std::unordered_map; - StateVectorCudaBase(size_t num_qubits, int device_id = 0, + StateVectorCudaBase(std::size_t num_qubits, int device_id = 0, cudaStream_t stream_id = 0, bool device_alloc = true) : StateVectorBase(num_qubits), data_buffer_{std::make_unique>( Pennylane::Util::exp2(num_qubits), device_id, stream_id, device_alloc)} {} - StateVectorCudaBase(size_t num_qubits, LightningGPU::DevTag dev_tag, + StateVectorCudaBase(std::size_t num_qubits, + LightningGPU::DevTag dev_tag, bool device_alloc = true) : StateVectorBase(num_qubits), data_buffer_{std::make_unique>( diff --git a/pennylane_lightning/core/src/simulators/lightning_gpu/StateVectorCudaMPI.hpp b/pennylane_lightning/core/src/simulators/lightning_gpu/StateVectorCudaMPI.hpp index 21192a4064..7117f9b1c4 100644 --- a/pennylane_lightning/core/src/simulators/lightning_gpu/StateVectorCudaMPI.hpp +++ b/pennylane_lightning/core/src/simulators/lightning_gpu/StateVectorCudaMPI.hpp @@ -119,6 +119,7 @@ class StateVectorCudaMPI final handle_.get(), mpi_manager_, mpi_buf_size, BaseType::getData(), num_local_qubits, localStream_.get())), gate_cache_(true, dev_tag) { + resetStateVector(); PL_CUDA_IS_SUCCESS(cudaDeviceSynchronize()); mpi_manager_.Barrier(); }; @@ -137,6 +138,7 @@ class StateVectorCudaMPI final handle_.get(), mpi_manager_, mpi_buf_size, BaseType::getData(), num_local_qubits, localStream_.get())), gate_cache_(true, dev_tag) { + resetStateVector(); PL_CUDA_IS_SUCCESS(cudaDeviceSynchronize()); mpi_manager_.Barrier(); }; @@ -155,6 +157,7 @@ class StateVectorCudaMPI final handle_.get(), mpi_manager_, mpi_buf_size, BaseType::getData(), num_local_qubits, localStream_.get())), gate_cache_(true, dev_tag) { + resetStateVector(); PL_CUDA_IS_SUCCESS(cudaDeviceSynchronize()); mpi_manager_.Barrier(); }; @@ -193,7 +196,7 @@ class StateVectorCudaMPI final handle_.get(), mpi_manager_, 0, BaseType::getData(), num_local_qubits, localStream_.get())), gate_cache_(true, dev_tag) { - BaseType::initSV(); + resetStateVector(); PL_CUDA_IS_SUCCESS(cudaDeviceSynchronize()); mpi_manager_.Barrier(); } @@ -251,91 +254,83 @@ class StateVectorCudaMPI final } /** - * @brief Set value for a single element of the state-vector on device. This - * method is implemented by cudaMemcpy. - * - * @param value Value to be set for the target element. - * @param index Index of the target element. - * @param async Use an asynchronous memory copy. + * @brief the statevector data to the |0...0> state. + * @param use_async Use an asynchronous memory copy or not. Default is + * false. */ - void setBasisState(const std::complex &value, - const std::size_t index, const bool async = false) { - std::size_t rankId = index >> BaseType::getNumQubits(); - - std::size_t local_index = - static_cast( - rankId * std::pow(2.0, static_cast( - BaseType::getNumQubits()))) ^ - index; + void resetStateVector(bool use_async = false) { BaseType::getDataBuffer().zeroInit(); + std::size_t index = 0; + ComplexT value(1.0, 0.0); + setBasisState_(value, index, use_async); + }; - CFP_t value_cu = cuUtil::complexToCu>(value); - auto stream_id = localStream_.get(); + /** + * @brief Prepare a single computational basis state. + * + * @param state Binary number representing the index + * @param wires Wires. + * @param use_async Use an asynchronous memory copy. + */ + void setBasisState(const std::vector &state, + const std::vector &wires, + const bool use_async) { + PL_ABORT_IF_NOT(state.size() == wires.size(), + "state and wires must have equal dimensions."); - if (mpi_manager_.getRank() == rankId) { - setBasisState_CUDA(BaseType::getData(), value_cu, local_index, - async, stream_id); + const auto n_wires = this->getTotalNumQubits(); + + std::size_t index{0U}; + for (std::size_t k = 0; k < n_wires; k++) { + index |= state[k] << (n_wires - 1 - wires[k]); } - PL_CUDA_IS_SUCCESS(cudaDeviceSynchronize()); - mpi_manager_.Barrier(); + + const std::complex value(1.0, 0.0); + BaseType::getDataBuffer().zeroInit(); + setBasisState_(value, index, use_async); } /** - * @brief Set values for a batch of elements of the state-vector. This - * method is implemented by the customized CUDA kernel defined in the - * DataBuffer class. + * @brief Set values for a batch of elements of the state-vector. * - * @param num_indices Number of elements to be passed to the state vector. - * @param values Pointer to values to be set for the target elements. - * @param indices Pointer to indices of the target elements. - * @param async Use an asynchronous memory copy. + * @param state_ptr Pointer to initial state data. + * @param num_states Length of initial state data. + * @param wires Wires. + * @param use_async Use an asynchronous memory copy. Default is false. */ - template - void setStateVector(const index_type num_indices, - const std::complex *values, - const index_type *indices, const bool async = false) { - BaseType::getDataBuffer().zeroInit(); - - std::vector indices_local; - std::vector> values_local; - - for (size_t i = 0; i < static_cast(num_indices); i++) { - int index = indices[i]; - PL_ASSERT(index >= 0); - std::size_t rankId = - static_cast(index) >> BaseType::getNumQubits(); - - if (rankId == mpi_manager_.getRank()) { - int local_index = - static_cast( - rankId * std::pow(2.0, static_cast( - BaseType::getNumQubits()))) ^ - index; - indices_local.push_back(local_index); - values_local.push_back(values[i]); + void setStateVector(const ComplexT *state_ptr, const std::size_t num_states, + const std::vector &wires, + bool use_async = false) { + PL_ABORT_IF_NOT(num_states == Pennylane::Util::exp2(wires.size()), + "Inconsistent state and wires dimensions."); + + const auto num_qubits = this->getTotalNumQubits(); + + PL_ABORT_IF_NOT(std::find_if(wires.begin(), wires.end(), + [&num_qubits](const auto i) { + return i >= num_qubits; + }) == wires.end(), + "Invalid wire index."); + + using index_type = + typename std::conditional::value, + int32_t, int64_t>::type; + + // Calculate the indices of the state-vector to be set. + // TODO: Could move to GPU/MPI calculation if the state size is large. + std::vector indices(num_states); + const std::size_t num_wires = wires.size(); + constexpr std::size_t one{1U}; + for (std::size_t i = 0; i < num_states; i++) { + std::size_t index{0U}; + for (std::size_t j = 0; j < num_wires; j++) { + const std::size_t bit = (i & (one << j)) >> j; + index |= bit << (num_qubits - 1 - wires[num_wires - 1 - j]); } + indices[i] = static_cast(index); } - - auto device_id = BaseType::getDataBuffer().getDevTag().getDeviceID(); - auto stream_id = BaseType::getDataBuffer().getDevTag().getStreamID(); - - index_type num_elements = indices_local.size(); - - DataBuffer d_indices{ - static_cast(num_elements), device_id, stream_id, true}; - - DataBuffer d_values{static_cast(num_elements), - device_id, stream_id, true}; - - d_indices.CopyHostDataToGpu(indices_local.data(), d_indices.getLength(), - async); - d_values.CopyHostDataToGpu(values_local.data(), d_values.getLength(), - async); - - setStateVector_CUDA(BaseType::getData(), num_elements, - d_values.getData(), d_indices.getData(), - thread_per_block, stream_id); - PL_CUDA_IS_SUCCESS(cudaDeviceSynchronize()); + setStateVector_(num_states, state_ptr, indices.data(), + use_async); mpi_manager_.Barrier(); } @@ -391,30 +386,29 @@ class StateVectorCudaMPI final if (opName == "Identity") { return; + } else if (opName == "GlobalPhase") { + PrecisionT param = adjoint ? -params[0] : params[0]; + CFP_t scale_factor{std::cos(param), -std::sin(param)}; + scaleC_CUDA( + scale_factor, BaseType::getDataBuffer().getData(), + BaseType::getDataBuffer().getLength(), + BaseType::getDataBuffer().getDevTag().getDeviceID(), + BaseType::getDataBuffer().getDevTag().getStreamID(), + getCublasCaller()); } else if (native_gates_.find(opName) != native_gates_.end()) { applyParametricPauliGate({opName}, ctrls, tgts, params.front(), adjoint); } else if (opName == "Rot" || opName == "CRot") { - if (adjoint) { - auto rot_matrix = - cuGates::getRot(params[2], params[1], params[0]); - applyDeviceMatrixGate(rot_matrix.data(), ctrls, tgts, true); - } else { - auto rot_matrix = - cuGates::getRot(params[0], params[1], params[2]); - applyDeviceMatrixGate(rot_matrix.data(), ctrls, tgts, false); - } + auto rot_matrix = + cuGates::getRot(params[0], params[1], params[2]); + applyDeviceMatrixGate(rot_matrix.data(), ctrls, tgts, adjoint); + } else if (opName == "Matrix") { + applyDeviceMatrixGate(gate_matrix.data(), ctrls, tgts, adjoint); } else if (par_gates_.find(opName) != par_gates_.end()) { par_gates_.at(opName)(wires, adjoint, params); } else { // No offloadable function call; defer to matrix passing auto &&par = (params.empty()) ? std::vector{0.0} : params; - // ensure wire indexing correctly preserved for tensor-observables - const std::vector ctrls_local{ctrls.rbegin(), - ctrls.rend()}; - const std::vector tgts_local{tgts.rbegin(), - tgts.rend()}; - if (!gate_cache_.gateExists(opName, par[0]) && gate_matrix.empty()) { std::string message = "Currently unsupported gate: " + opName; @@ -423,8 +417,8 @@ class StateVectorCudaMPI final gate_cache_.add_gate(opName, par[0], gate_matrix); } applyDeviceMatrixGate( - gate_cache_.get_gate_device_ptr(opName, par[0]), ctrls_local, - tgts_local, adjoint); + gate_cache_.get_gate_device_ptr(opName, par[0]), ctrls, tgts, + adjoint); } } @@ -483,7 +477,7 @@ class StateVectorCudaMPI final const std::vector &wires, bool adjoint = false) { PL_ABORT_IF(wires.empty(), "Number of wires must be larger than 0"); - const std::string opName = {}; + const std::string opName = "Matrix"; std::size_t n = std::size_t{1} << wires.size(); const std::vector> matrix(gate_matrix, gate_matrix + n * n); @@ -1173,7 +1167,7 @@ class StateVectorCudaMPI final auto threshold = WiresSwapStatus::Swappable; bool isAllTargetsLocal = std::all_of( tgtsSwapStatus.begin(), tgtsSwapStatus.end(), - [&threshold](size_t status) { return status < threshold; }); + [&threshold](std::size_t status) { return status < threshold; }); mpi_manager_.Barrier(); @@ -1181,7 +1175,7 @@ class StateVectorCudaMPI final expvalOnPauliBasis(pauli_words, localTgts, expect_local); } else { std::size_t wirePairsIdx = 0; - for (size_t i = 0; i < pauli_words.size(); i++) { + for (std::size_t i = 0; i < pauli_words.size(); i++) { if (tgtsSwapStatus[i] == WiresSwapStatus::UnSwappable) { auto opsNames = pauliStringToOpNames(pauli_words[i]); StateVectorCudaMPI tmp( @@ -1189,7 +1183,8 @@ class StateVectorCudaMPI final this->getNumGlobalQubits(), this->getNumLocalQubits(), this->getData()); - for (size_t opsIdx = 0; opsIdx < tgts[i].size(); opsIdx++) { + for (std::size_t opsIdx = 0; opsIdx < tgts[i].size(); + opsIdx++) { std::vector wires = {tgts[i][opsIdx]}; tmp.applyOperation({opsNames[opsIdx]}, {tgts[i][opsIdx]}, {false}); @@ -1526,6 +1521,88 @@ class StateVectorCudaMPI final return t_indices; } + /** + * @brief Set values for a batch of elements of the state-vector. This + * method is implemented by the customized CUDA kernel defined in the + * DataBuffer class. + * + * @param num_indices Number of elements to be passed to the state vector. + * @param values Pointer to values to be set for the target elements. + * @param indices Pointer to indices of the target elements. + * @param async Use an asynchronous memory copy. + */ + template + void setStateVector_(const index_type num_indices, + const std::complex *values, + const index_type *indices, const bool async = false) { + BaseType::getDataBuffer().zeroInit(); + + std::vector indices_local; + std::vector> values_local; + + for (std::size_t i = 0; i < static_cast(num_indices); + i++) { + int index = indices[i]; + PL_ASSERT(index >= 0); + std::size_t rankId = + static_cast(index) >> BaseType::getNumQubits(); + + if (rankId == mpi_manager_.getRank()) { + int local_index = static_cast( + compute_local_index(static_cast(index), + this->getNumLocalQubits())); + indices_local.push_back(local_index); + values_local.push_back(values[i]); + } + } + + auto device_id = BaseType::getDataBuffer().getDevTag().getDeviceID(); + auto stream_id = BaseType::getDataBuffer().getDevTag().getStreamID(); + + index_type num_elements = indices_local.size(); + + DataBuffer d_indices{ + static_cast(num_elements), device_id, stream_id, true}; + + DataBuffer d_values{static_cast(num_elements), + device_id, stream_id, true}; + + d_indices.CopyHostDataToGpu(indices_local.data(), d_indices.getLength(), + async); + d_values.CopyHostDataToGpu(values_local.data(), d_values.getLength(), + async); + + setStateVector_CUDA(BaseType::getData(), num_elements, + d_values.getData(), d_indices.getData(), + thread_per_block, stream_id); + } + + /** + * @brief Set value for a single element of the state-vector on device. This + * method is implemented by cudaMemcpy. + * + * @param value Value to be set for the target element. + * @param index Index of the target element. + * @param async Use an asynchronous memory copy. + */ + void setBasisState_(const std::complex &value, + const std::size_t index, const bool async = false) { + const std::size_t rankId = index >> this->getNumLocalQubits(); + + const std::size_t local_index = + compute_local_index(index, this->getNumLocalQubits()); + + CFP_t value_cu = cuUtil::complexToCu>(value); + auto stream_id = localStream_.get(); + + if (mpi_manager_.getRank() == rankId) { + setBasisState_CUDA(BaseType::getData(), value_cu, local_index, + async, stream_id); + } + PL_CUDA_IS_SUCCESS(cudaDeviceSynchronize()); + mpi_manager_.Barrier(); + } + /** * @brief Get expectation value for a sum of Pauli words. * @@ -1589,8 +1666,8 @@ class StateVectorCudaMPI final } /** - * @brief Apply parametric Pauli gates to local statevector using custateVec - * calls. + * @brief Apply parametric Pauli gates to local statevector using + * custateVec calls. * * @param pauli_words List of Pauli words representing operation. * @param ctrls Control wires @@ -1660,16 +1737,17 @@ class StateVectorCudaMPI final }); // Initialize a vector to store the status of wires and default its - // elements as zeros, which assumes there is no target and control wire. + // elements as zeros, which assumes there is no target and control + // wire. std::vector statusWires(this->getTotalNumQubits(), WireStatus::Default); // Update wire status based on the gate information - for (size_t i = 0; i < ctrlsInt.size(); i++) { + for (std::size_t i = 0; i < ctrlsInt.size(); i++) { statusWires[ctrlsInt[i]] = WireStatus::Control; } // Update wire status based on the gate information - for (size_t i = 0; i < tgtsInt.size(); i++) { + for (std::size_t i = 0; i < tgtsInt.size(); i++) { statusWires[tgtsInt[i]] = WireStatus::Target; } @@ -1725,9 +1803,8 @@ class StateVectorCudaMPI final * @param tgts Target qubits. * @param use_adjoint Use adjoint of given gate. */ - void applyCuSVDeviceMatrixGate(const CFP_t *matrix, - const std::vector &ctrls, - const std::vector &tgts, + void applyCuSVDeviceMatrixGate(const CFP_t *matrix, std::vector &ctrls, + std::vector &tgts, bool use_adjoint = false) { void *extraWorkspace = nullptr; std::size_t extraWorkspaceSizeInBytes = 0; @@ -1745,6 +1822,9 @@ class StateVectorCudaMPI final compute_type = CUSTATEVEC_COMPUTE_32F; } + std::reverse(tgts.begin(), tgts.end()); + std::reverse(ctrls.begin(), ctrls.end()); + // check the size of external workspace PL_CUSTATEVEC_IS_SUCCESS(custatevecApplyMatrixGetWorkspaceSize( /* custatevecHandle_t */ handle_.get(), @@ -1820,16 +1900,17 @@ class StateVectorCudaMPI final }); // Initialize a vector to store the status of wires and default its - // elements as zeros, which assumes there is no target and control wire. + // elements as zeros, which assumes there is no target and control + // wire. std::vector statusWires(this->getTotalNumQubits(), WireStatus::Default); // Update wire status based on the gate information - for (size_t i = 0; i < ctrlsInt.size(); i++) { + for (std::size_t i = 0; i < ctrlsInt.size(); i++) { statusWires[ctrlsInt[i]] = WireStatus::Control; } // Update wire status based on the gate information - for (size_t i = 0; i < tgtsInt.size(); i++) { + for (std::size_t i = 0; i < tgtsInt.size(); i++) { statusWires[tgtsInt[i]] = WireStatus::Target; } @@ -1961,12 +2042,13 @@ class StateVectorCudaMPI final }); // Initialize a vector to store the status of wires and default its - // elements as zeros, which assumes there is no target and control wire. + // elements as zeros, which assumes there is no target and control + // wire. std::vector statusWires(this->getTotalNumQubits(), WireStatus::Default); // Update wire status based on the gate information - for (size_t i = 0; i < tgtsInt.size(); i++) { + for (std::size_t i = 0; i < tgtsInt.size(); i++) { statusWires[tgtsInt[i]] = WireStatus::Target; } @@ -2055,7 +2137,7 @@ class StateVectorCudaMPI final // the main loop of index bit swaps // constexpr std::size_t nLoops = 2; - for (size_t loop = 0; loop < nLoops; loop++) { + for (std::size_t loop = 0; loop < nLoops; loop++) { for (int swapBatchIndex = 0; swapBatchIndex < static_cast(nSwapBatches); swapBatchIndex++) { diff --git a/pennylane_lightning/core/src/simulators/lightning_gpu/StateVectorCudaManaged.hpp b/pennylane_lightning/core/src/simulators/lightning_gpu/StateVectorCudaManaged.hpp index 153dd6a782..deab3ce122 100644 --- a/pennylane_lightning/core/src/simulators/lightning_gpu/StateVectorCudaManaged.hpp +++ b/pennylane_lightning/core/src/simulators/lightning_gpu/StateVectorCudaManaged.hpp @@ -41,6 +41,7 @@ #include "cuStateVec_helpers.hpp" #include "LinearAlg.hpp" +#include "Util.hpp" /// @cond DEV namespace { @@ -67,25 +68,6 @@ extern void setBasisState_CUDA(cuDoubleComplex *sv, cuDoubleComplex &value, const std::size_t index, bool async, cudaStream_t stream_id); -extern void globalPhaseStateVector_CUDA(cuComplex *sv, std::size_t num_sv, - cuComplex phase, - std::size_t thread_per_block, - cudaStream_t stream_id); -extern void globalPhaseStateVector_CUDA(cuDoubleComplex *sv, std::size_t num_sv, - cuDoubleComplex phase, - std::size_t thread_per_block, - cudaStream_t stream_id); - -extern void cGlobalPhaseStateVector_CUDA(cuComplex *sv, std::size_t num_sv, - bool adjoint, cuComplex *phase, - std::size_t thread_per_block, - cudaStream_t stream_id); -extern void cGlobalPhaseStateVector_CUDA(cuDoubleComplex *sv, - std::size_t num_sv, bool adjoint, - cuDoubleComplex *phase, - std::size_t thread_per_block, - cudaStream_t stream_id); - /** * @brief Managed memory CUDA state-vector class using custateVec backed * gate-calls. @@ -107,11 +89,14 @@ class StateVectorCudaManaged using MemoryStorageT = Pennylane::Util::MemoryStorageLocation::Undefined; StateVectorCudaManaged() = delete; - StateVectorCudaManaged(size_t num_qubits) + StateVectorCudaManaged(std::size_t num_qubits) : StateVectorCudaBase>( num_qubits), handle_(make_shared_cusv_handle()), - cublascaller_(make_shared_cublas_caller()), gate_cache_(true){}; + cublascaller_(make_shared_cublas_caller()), gate_cache_(true) { + resetStateVector(); + PL_CUDA_IS_SUCCESS(cudaDeviceSynchronize()); + }; StateVectorCudaManaged( std::size_t num_qubits, const DevTag &dev_tag, bool alloc = true, @@ -124,7 +109,8 @@ class StateVectorCudaManaged cublascaller_(std::move(cublascaller_in)), cusparsehandle_(std::move(cusparsehandle_in)), gate_cache_(true, dev_tag) { - BaseType::initSV(); + resetStateVector(); + PL_CUDA_IS_SUCCESS(cudaDeviceSynchronize()); }; StateVectorCudaManaged(const CFP_t *gpu_data, std::size_t length) @@ -167,92 +153,90 @@ class StateVectorCudaManaged ~StateVectorCudaManaged() = default; /** - * @brief Set value for a single element of the state-vector on device. This - * method is implemented by cudaMemcpy. - * - * @param value Value to be set for the target element. - * @param index Index of the target element. - * @param async Use an asynchronous memory copy. + * @brief the statevector data to the |0...0> state. + * @param use_async Use an asynchronous memory copy or not. Default is + * false. */ - void setBasisState(const std::complex &value, - const std::size_t index, const bool async = false) { + void resetStateVector(bool use_async = false) { BaseType::getDataBuffer().zeroInit(); - - CFP_t value_cu = cuUtil::complexToCu>(value); - auto stream_id = BaseType::getDataBuffer().getDevTag().getStreamID(); - setBasisState_CUDA(BaseType::getData(), value_cu, index, async, - stream_id); - } + std::size_t index = 0; + ComplexT value(1.0, 0.0); + setBasisState_(value, index, use_async); + }; /** - * @brief Set values for a batch of elements of the state-vector. This - * method is implemented by the customized CUDA kernel defined in the - * DataBuffer class. + * @brief Prepare a single computational basis state. * - * @param num_indices Number of elements to be passed to the state vector. - * @param values Pointer to values to be set for the target elements. - * @param indices Pointer to indices of the target elements. - * @param async Use an asynchronous memory copy. + * @param state Binary number representing the index + * @param wires Wires. + * @param use_async(Optional[bool]): immediately sync with host-sv after + * applying operation. */ - template - void setStateVector(const index_type num_indices, - const std::complex *values, - const index_type *indices, const bool async = false) { - BaseType::getDataBuffer().zeroInit(); - - auto device_id = BaseType::getDataBuffer().getDevTag().getDeviceID(); - auto stream_id = BaseType::getDataBuffer().getDevTag().getStreamID(); + void setBasisState(const std::vector &state, + const std::vector &wires, + const bool use_async = false) { + PL_ABORT_IF_NOT(state.size() == wires.size(), + "state and wires must have equal dimensions."); + const auto num_qubits = BaseType::getNumQubits(); + PL_ABORT_IF_NOT( + std::find_if(wires.begin(), wires.end(), + [&num_qubits](const auto i) { + return i >= num_qubits; + }) == wires.end(), + "wires must take values lower than the number of qubits."); + const auto n_wires = wires.size(); + std::size_t index{0U}; + for (std::size_t k = 0; k < n_wires; k++) { + index |= state[k] << (num_qubits - 1 - wires[k]); + } - index_type num_elements = num_indices; - DataBuffer d_indices{ - static_cast(num_elements), device_id, stream_id, true}; - DataBuffer d_values{static_cast(num_elements), - device_id, stream_id, true}; + const std::complex value(1.0, 0.0); - d_indices.CopyHostDataToGpu(indices, d_indices.getLength(), async); - d_values.CopyHostDataToGpu(values, d_values.getLength(), async); - - setStateVector_CUDA(BaseType::getData(), num_elements, - d_values.getData(), d_indices.getData(), - thread_per_block, stream_id); - } - - /** - * @brief Multiplies the state-vector by a global phase. - * - * @param adjoint Indicates whether to use adjoint of gate. - * @param param Complex phase generator. - */ - template - void globalPhaseStateVector(const bool adjoint, const Precision param) { - auto stream_id = BaseType::getDataBuffer().getDevTag().getStreamID(); - std::complex phase = - std::exp(std::complex{0, (adjoint) ? param : -param}); - auto cuPhase = complexToCu(phase); - globalPhaseStateVector_CUDA(BaseType::getData(), BaseType::getLength(), - cuPhase, thread_per_block, stream_id); + BaseType::getDataBuffer().zeroInit(); + setBasisState_(value, index, use_async); } /** - * @brief Multiplies the state-vector by a controlled global phase. + * @brief Set values for a batch of elements of the state-vector. * - * @param phase Controlled complex phase vector. + * @param state_ptr Pointer to the initial state data. + * @param num_states Length of the initial state data. + * @param wires Wires. + * @param use_async Use an asynchronous memory copy. Default is false. */ - template - void cGlobalPhaseStateVector(const bool adjoint, - const std::vector &phase, - const bool async = false) { - PL_ABORT_IF_NOT(BaseType::getLength() == phase.size(), - "The state-vector data must have the same size as the " - "controlled-phase data.") - auto device_id = BaseType::getDataBuffer().getDevTag().getDeviceID(); - auto stream_id = BaseType::getDataBuffer().getDevTag().getStreamID(); - DataBuffer d_phase{phase.size(), device_id, stream_id, - true}; - d_phase.CopyHostDataToGpu(phase.data(), d_phase.getLength(), async); - cGlobalPhaseStateVector_CUDA(BaseType::getData(), BaseType::getLength(), - adjoint, d_phase.getData(), - thread_per_block, stream_id); + void setStateVector(const ComplexT *state_ptr, const std::size_t num_states, + const std::vector &wires, + bool use_async = false) { + PL_ABORT_IF_NOT(num_states == Pennylane::Util::exp2(wires.size()), + "Inconsistent state and wires dimensions."); + + const auto num_qubits = BaseType::getNumQubits(); + + PL_ABORT_IF_NOT(std::find_if(wires.begin(), wires.end(), + [&num_qubits](const auto i) { + return i >= num_qubits; + }) == wires.end(), + "Invalid wire index."); + + using index_type = + typename std::conditional::value, + int32_t, int64_t>::type; + + // Calculate the indices of the state-vector to be set. + // TODO: Could move to GPU calculation if the state size is large. + std::vector indices(num_states); + const std::size_t num_wires = wires.size(); + constexpr std::size_t one{1U}; + for (std::size_t i = 0; i < num_states; i++) { + std::size_t index{0U}; + for (std::size_t j = 0; j < num_wires; j++) { + const std::size_t bit = (i & (one << j)) >> j; + index |= bit << (num_qubits - 1 - wires[num_wires - 1 - j]); + } + indices[i] = static_cast(index); + } + setStateVector_(num_states, state_ptr, indices.data(), + use_async); } /** @@ -307,72 +291,182 @@ class StateVectorCudaManaged wires.end()}; if (opName == "Identity") { return; - } else if (opName == "C(GlobalPhase)") { - cGlobalPhaseStateVector(adjoint, gate_matrix); } else if (opName == "GlobalPhase") { - globalPhaseStateVector(adjoint, params[0]); + PrecisionT param = adjoint ? -params[0] : params[0]; + CFP_t scale_factor{std::cos(param), -std::sin(param)}; + scaleC_CUDA( + scale_factor, BaseType::getDataBuffer().getData(), + BaseType::getDataBuffer().getLength(), + BaseType::getDataBuffer().getDevTag().getDeviceID(), + BaseType::getDataBuffer().getDevTag().getStreamID(), + getCublasCaller()); } else if (native_gates_.find(opName) != native_gates_.end()) { - applyParametricPauliGate({opName}, ctrls, tgts, params.front(), - adjoint); + applyParametricPauliGate_({opName}, ctrls, tgts, params.front(), + adjoint); } else if (opName == "Rot" || opName == "CRot") { - if (adjoint) { - auto rot_matrix = - cuGates::getRot(params[2], params[1], params[0]); - applyDeviceMatrixGate(rot_matrix.data(), ctrls, tgts, true); - } else { - auto rot_matrix = - cuGates::getRot(params[0], params[1], params[2]); - applyDeviceMatrixGate(rot_matrix.data(), ctrls, tgts, false); - } + auto rot_matrix = + cuGates::getRot(params[0], params[1], params[2]); + applyDeviceMatrixGate_(rot_matrix.data(), ctrls, tgts, adjoint); + } else if (opName == "Matrix") { + applyDeviceMatrixGate_(gate_matrix.data(), ctrls, tgts, adjoint); } else if (par_gates_.find(opName) != par_gates_.end()) { par_gates_.at(opName)(wires, adjoint, params); } else { // No offloadable function call; defer to matrix passing auto &&par = (params.empty()) ? std::vector{0.0} : params; - // ensure wire indexing correctly preserved for tensor-observables - const std::vector ctrls_local{ctrls.rbegin(), - ctrls.rend()}; - const std::vector tgts_local{tgts.rbegin(), - tgts.rend()}; - if (!gate_cache_.gateExists(opName, par[0]) && gate_matrix.empty()) { - std::string message = "Currently unsupported gate: " + opName; + std::string message = "Currently unsupported gate: " + opName + + "and no matrix is provided."; throw LightningException(message); } else if (!gate_cache_.gateExists(opName, par[0])) { gate_cache_.add_gate(opName, par[0], gate_matrix); } - applyDeviceMatrixGate( - gate_cache_.get_gate_device_ptr(opName, par[0]), ctrls_local, - tgts_local, adjoint); + applyDeviceMatrixGate_( + gate_cache_.get_gate_device_ptr(opName, par[0]), ctrls, tgts, + adjoint); } } + /** + * @brief Apply a single gate to the state-vector. + * + * @param opName Name of gate to apply. + * @param controlled_wires Control wires. + * @param controlled_values Control values (false or true). + * @param tgt_wires Wires to apply gate to. + * @param adjoint Indicates whether to use adjoint of gate. + * @param params Optional parameter list for parametric gates. + * @param matrix Gate data (in row-major format). + */ + void applyOperation(const std::string &opName, + const std::vector &controlled_wires, + const std::vector &controlled_values, + const std::vector &tgt_wires, bool adjoint, + const std::vector ¶ms, + const std::vector &matrix) { + std::vector matrix_cu(matrix.size()); + std::transform(matrix.begin(), matrix.end(), matrix_cu.begin(), + [](const std::complex &x) { + return cuUtil::complexToCu>( + x); + }); + applyOperation(opName, controlled_wires, controlled_values, tgt_wires, + adjoint, params, matrix_cu); + } + /** * @brief Apply a single gate to the state vector. * * @param opName Name of gate to apply. * @param controlled_wires Control wires. * @param controlled_values Control values (false or true). - * @param wires Wires to apply gate to. - * @param inverse Indicates whether to use adjoint of gate. + * @param tgt_wires Wires to apply gate to. + * @param adjoint Indicates whether to use adjoint of gate. * @param params Optional parameter list for parametric gates. - * @param params Optional std gate matrix if opName doesn't exist. + * @param gate_matrix Gate data (in row-major format). */ - template