Refactor linear algebra module and remove unused code #195
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WalkthroughThe updates reflect a comprehensive reorganization and renaming of modules within the Changes
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Review Status
Actionable comments generated: 1
Configuration used: CodeRabbit UI
Files ignored due to filter (2)
- blas/v.mod
- lapack/v.mod
Files selected for processing (28)
- bin/test (1 hunks)
- blas/README.md (1 hunks)
- blas/blas64/conversions.v (1 hunks)
- blas/blas64/dgemm.v (1 hunks)
- blas/blas64/dgemv.v (1 hunks)
- blas/blas64/dgemv_test.v (1 hunks)
- blas/blas64/error.v (1 hunks)
- blas/blas64/level1f64.v (1 hunks)
- blas/blas64/level1f64_ddot.v (1 hunks)
- blas/blas64/level2f64.v (1 hunks)
- blas/blas64/level3f64.v (1 hunks)
- blas/blas64/util.v (1 hunks)
- blas/cflags_d_vsl_blas_cblas.v (2 hunks)
- blas/conversions.v (1 hunks)
- blas/oblas_d_vsl_blas_cblas.v (1 hunks)
- blas/oblas_notd_vsl_blas_cblas.v (1 hunks)
- la/blas.v (16 hunks)
- la/densesol.v (2 hunks)
- la/matrix_ops.v (5 hunks)
- lapack/README.md (1 hunks)
- lapack/cflags_d_vsl_lapack_lapacke copy.v (1 hunks)
- lapack/cflags_notd_vsl_lapack_lapacke.v (1 hunks)
- lapack/lapack64/dgesv.v (1 hunks)
- lapack/lapack64/dgetrf.v (1 hunks)
- lapack/lapack64/errors.v (1 hunks)
- lapack/lapack_common.v (3 hunks)
- lapack/lapack_default.c.v (1 hunks)
- lapack/lapack_macos.c.v (1 hunks)
Files skipped from review due to trivial changes (8)
- blas/blas64/conversions.v
- blas/blas64/dgemm.v
- blas/blas64/dgemv.v
- blas/blas64/error.v
- blas/blas64/level1f64.v
- blas/blas64/level2f64.v
- blas/blas64/level3f64.v
- blas/blas64/util.v
Additional comments: 43
lapack/lapack_macos.c.v (1)
- 1-4: > Note: This review was outside the patches, so it was mapped to the patch with the greatest overlap. Original lines [1-7]
The module name change and the function implementation look good.
lapack/lapack_default.c.v (2)
1-5: The module name change and the updated import statement are correct.
1-8: > Note: This review was outside the patches, so it was mapped to the patch with the greatest overlap. Original lines [5-9]
The update to the
C.LAPACKE_dlangefunction signature and the public functiondlangewrapper are consistent with the refactoring to useblas64.lapack/cflags_d_vsl_lapack_lapacke copy.v (1)
- 1-10: > Note: This review was outside the patches, so it was mapped to the patch with the greatest overlap. Original lines [1-13]
The module name change, compiler flags, and conditional inclusion of "lapacke.h" for macOS are correct.
la/densesol.v (1)
- 1-8: > Note: This review was outside the patches, so it was mapped to the patch with the greatest overlap. Original lines [3-19]
The update to the import statement and the
den_solvefunction to uselapack.dgesvis correct.blas/cflags_d_vsl_blas_cblas.v (1)
- 7-15: > Note: This review was outside the patches, so it was mapped to the patch with the greatest overlap. Original lines [1-11]
The module name change, compiler flags, and conditional inclusion of "cblas.h" for macOS are correct.
blas/blas64/level1f64_ddot.v (1)
- 1-4: > Note: This review was outside the patches, so it was mapped to the patch with the greatest overlap. Original lines [1-34]
The module name change to "blas64" and the import statement update are correct. The
ddotfunction logic and error handling are appropriate.lapack/lapack64/dgetrf.v (1)
- 1-51: The import statements and the
dgetrffunction logic and error handling are correct.lapack/lapack64/dgesv.v (1)
- 1-56: The import statements and the
dgesvfunction logic and error handling are correct.bin/test (1)
- 31-31: The flag change from
-d vsl_vlas_cblasto-d vsl_blas_cblasis correct and consistent with the module refactoring.lapack/README.md (1)
- 1-58: The documentation updates in the README.md file are correct, reflecting the new compilation flag and providing clear installation instructions for dependencies.
blas/README.md (1)
- 1-66: The documentation updates in the README.md file are correct, reflecting the new compilation flag and providing clear installation instructions for dependencies.
blas/oblas_notd_vsl_blas_cblas.v (1)
- 1-81: The module name change to "blas" and the updated import statement are correct. The inline functions are appropriate wrappers for the
blas64functions.blas/blas64/dgemv_test.v (2)
1-1: The module name change to "blas64" is correct.
2-2: The test functions for
dgemvare correctly implemented.la/matrix_ops.v (2)
4-4: The update to the import statement from
vsl.vlastovsl.lapackis correct.5-5: The updates to the functions
matrix_det,matrix_inv_small,matrix_svd, andmatrix_invto use thelapackmodule are correct.lapack/lapack_common.v (2)
1-5: The module name change to "lapack" and the updated import statements are correct.
7-23: The updates to the C function declarations to use
blas64.MemoryLayoutare correct and consistent with the refactoring.lapack/lapack64/errors.v (1)
- 1-178: The constants for error messages in
lapack/lapack64/errors.vare consistent and clear.blas/conversions.v (3)
1-7: The module name and import path have been updated correctly from
vlastoblas.9-13: The function signatures have been updated to use the new
blas64types, which is consistent with the module changes.1-16: > Note: This review was outside the patches, and no patch overlapping with it was found. Original lines [17-178]
The utility functions for matrix and vector operations remain unchanged and are consistent and clear.
la/blas.v (16)
3-3: The import statement has been updated correctly from
vsl.vlastovsl.blas.50-50: The reference to
vlas.ddothas been correctly replaced withblas.ddot.69-69: The reference to
vlas.daxpyhas been correctly replaced withblas.daxpy.139-139: The reference to
vlas.dgemvhas been correctly replaced withblas.dgemv.170-170: The reference to
vlas.dgemvfor the transpose operation has been correctly replaced withblas.dgemv.202-202: The reference to
vlas.dgerhas been correctly replaced withblas.dger.223-223: The reference to
vlas.dgemvinmatrix_vector_mul_addhas been correctly replaced withblas.dgemv.243-243: The reference to
vlas.dgemminmatrix_matrix_mulhas been correctly replaced withblas.dgemm.263-263: The reference to
vlas.dgemminmatrix_tr_matrix_mulhas been correctly replaced withblas.dgemm.272-272: The reference to
vlas.dgemminmatrix_matrix_tr_mulhas been correctly replaced withblas.dgemm.281-281: The reference to
vlas.dgemminmatrix_tr_matrix_tr_mulhas been correctly replaced withblas.dgemm.290-290: The reference to
vlas.dgemminmatrix_matrix_muladdhas been correctly replaced withblas.dgemm.299-299: The reference to
vlas.dgemminmatrix_tr_matrix_muladdhas been correctly replaced withblas.dgemm.308-308: The reference to
vlas.dgemminmatrix_matrix_tr_muladdhas been correctly replaced withblas.dgemm.317-317: The reference to
vlas.dgemminmatrix_tr_matrix_tr_mul_addhas been correctly replaced withblas.dgemm.328-328: The reference to
vlas.daxpyinmatrix_addhas been correctly replaced withblas.daxpy.blas/oblas_d_vsl_blas_cblas.v (4)
1-3: The module has been renamed to
blasand now importsvsl.blas64.blas64. This change aligns with the PR's objective to refactor the linear algebra module.5-172: The C function declarations appear to be correctly defined with appropriate parameter types and return types matching the BLAS library's API.
191-448: The V public functions use
unsafeto pass array pointers to the C functions. This is necessary for performance reasons but requires careful handling to ensure safety. The functions are marked asinline, which is appropriate for small wrapper functions to avoid function call overhead.191-448: The use of
unsafeis justified in this context as it is required for performance-critical numerical computations where direct memory access is necessary. Ensure that the arrays passed to these functions are always valid and that their lifetimes are managed correctly to prevent undefined behavior.
blas/oblas_d_vsl_blas_cblas.v
Outdated
| } | ||
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| @[inline] | ||
| pub fn drotmg(d1 f64, d2 f64, b1 f64, b2 f32, p []f64) { | ||
| C.cblas_drotmg(&d1, &d2, &b1, b2, unsafe { &p[0] }) | ||
| } | ||
|
|
||
| @[inline] | ||
| pub fn sscal(n int, alpha f32, mut x []f32, incx int) { | ||
| C.cblas_sscal(n, alpha, unsafe { &x[0] }, incx) | ||
| } | ||
|
|
||
| @[inline] | ||
| pub fn dscal(n int, alpha f64, mut x []f64, incx int) { | ||
| C.cblas_dscal(n, alpha, unsafe { &x[0] }, incx) | ||
| } | ||
|
|
||
| @[inline] | ||
| pub fn sgemv(trans bool, m int, n int, alpha f32, a []f32, lda int, x []f32, incx int, beta f32, mut y []f32, incy int) { | ||
| C.cblas_sgemv(.row_major, c_trans(trans), m, n, alpha, unsafe { &a[0] }, lda, unsafe { &x[0] }, | ||
| incx, beta, unsafe { &y[0] }, incy) | ||
| } | ||
|
|
||
| @[inline] | ||
| pub fn dgemv(trans bool, m int, n int, alpha f64, a []f64, lda int, x []f64, incx int, beta f64, mut y []f64, incy int) { | ||
| C.cblas_dgemv(.row_major, c_trans(trans), m, n, alpha, unsafe { &a[0] }, lda, unsafe { &x[0] }, | ||
| incx, beta, unsafe { &y[0] }, incy) | ||
| } | ||
|
|
||
| @[inline] | ||
| pub fn sger(m int, n int, alpha f32, x []f32, incx int, y []f32, incy int, mut a []f32, lda int) { | ||
| C.cblas_sger(.row_major, m, n, alpha, unsafe { &x[0] }, incx, unsafe { &y[0] }, incy, | ||
| unsafe { &a[0] }, lda) | ||
| } | ||
|
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||
| @[inline] | ||
| pub fn dger(m int, n int, alpha f64, x []f64, incx int, y []f64, incy int, mut a []f64, lda int) { | ||
| C.cblas_dger(.row_major, m, n, alpha, unsafe { &x[0] }, incx, unsafe { &y[0] }, incy, | ||
| unsafe { &a[0] }, lda) | ||
| } | ||
|
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||
| @[inline] | ||
| pub fn strsv(uplo bool, trans_a bool, diag blas64.Diagonal, n int, a []f32, lda int, mut x []f32, incx int) { | ||
| C.cblas_strsv(.row_major, c_uplo(uplo), c_trans(trans_a), diag, n, unsafe { &a[0] }, | ||
| lda, unsafe { &x[0] }, incx) | ||
| } | ||
|
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||
| @[inline] | ||
| pub fn dtrsv(uplo bool, trans_a bool, diag blas64.Diagonal, n int, a []f64, lda int, mut x []f64, incx int) { | ||
| C.cblas_dtrsv(.row_major, c_uplo(uplo), c_trans(trans_a), diag, n, unsafe { &a[0] }, | ||
| lda, unsafe { &x[0] }, incx) | ||
| } | ||
|
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||
| @[inline] | ||
| pub fn strmv(uplo bool, trans_a bool, diag blas64.Diagonal, n int, a []f32, lda int, mut x []f32, incx int) { | ||
| C.cblas_strmv(.row_major, c_uplo(uplo), c_trans(trans_a), diag, n, unsafe { &a[0] }, | ||
| lda, unsafe { &x[0] }, incx) | ||
| } | ||
|
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| @[inline] | ||
| pub fn dtrmv(uplo bool, trans_a bool, diag blas64.Diagonal, n int, a []f64, lda int, mut x []f64, incx int) { | ||
| C.cblas_dtrmv(.row_major, c_uplo(uplo), c_trans(trans_a), diag, n, unsafe { &a[0] }, | ||
| lda, unsafe { &x[0] }, incx) | ||
| } | ||
|
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| @[inline] | ||
| pub fn ssyr(uplo bool, n int, alpha f32, x []f32, incx int, mut a []f32, lda int) { | ||
| C.cblas_ssyr(.row_major, c_uplo(uplo), n, alpha, unsafe { &x[0] }, incx, unsafe { &a[0] }, | ||
| lda) | ||
| } | ||
|
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||
| @[inline] | ||
| pub fn dsyr(uplo bool, n int, alpha f64, x []f64, incx int, mut a []f64, lda int) { | ||
| C.cblas_dsyr(.row_major, c_uplo(uplo), n, alpha, unsafe { &x[0] }, incx, unsafe { &a[0] }, | ||
| lda) | ||
| } | ||
|
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||
| @[inline] | ||
| pub fn ssyr2(uplo bool, n int, alpha f32, x []f32, incx int, y []f32, incy int, mut a []f32, lda int) { | ||
| C.cblas_ssyr2(.row_major, c_uplo(uplo), n, alpha, unsafe { &x[0] }, incx, unsafe { &y[0] }, | ||
| incy, unsafe { &a[0] }, lda) | ||
| } | ||
|
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||
| @[inline] | ||
| pub fn dsyr2(uplo bool, n int, alpha f64, x []f64, incx int, y []f64, incy int, mut a []f64, lda int) { | ||
| C.cblas_dsyr2(.row_major, c_uplo(uplo), n, alpha, unsafe { &x[0] }, incx, unsafe { &y[0] }, | ||
| incy, unsafe { &a[0] }, lda) | ||
| } | ||
|
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| @[inline] | ||
| pub fn sgemm(trans_a bool, trans_b bool, m int, n int, k int, alpha f32, a []f32, lda int, b []f32, ldb int, beta f32, mut cc []f32, ldc int) { | ||
| C.cblas_sgemm(.row_major, c_trans(trans_a), c_trans(trans_b), m, n, k, alpha, unsafe { &a[0] }, | ||
| lda, unsafe { &b[0] }, ldb, beta, unsafe { &cc[0] }, ldc) | ||
| } | ||
|
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| @[inline] | ||
| pub fn dgemm(trans_a bool, trans_b bool, m int, n int, k int, alpha f64, a []f64, lda int, b []f64, ldb int, beta f64, mut cc []f64, ldc int) { | ||
| C.cblas_dgemm(.row_major, c_trans(trans_a), c_trans(trans_b), m, n, k, alpha, unsafe { &a[0] }, | ||
| lda, unsafe { &b[0] }, ldb, beta, unsafe { &cc[0] }, ldc) | ||
| } |
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Consider adding tests for the new wrapper functions to ensure they correctly interface with the C BLAS library and handle edge cases appropriately.
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Review Status
Actionable comments generated: 0
Configuration used: CodeRabbit UI
Files selected for processing (6)
- blas/blas64/conversions.v (1 hunks)
- blas/conversions.v (1 hunks)
- blas/oblas_d_vsl_blas_cblas.v (1 hunks)
- blas/oblas_notd_vsl_blas_cblas.v (1 hunks)
- lapack/lapack_common.v (3 hunks)
- lapack/lapack_default.c.v (1 hunks)
Files skipped from review as they are similar to previous changes (5)
- blas/conversions.v
- blas/oblas_d_vsl_blas_cblas.v
- blas/oblas_notd_vsl_blas_cblas.v
- lapack/lapack_common.v
- lapack/lapack_default.c.v
Additional comments: 1
blas/blas64/conversions.v (1)
- 1-33: The enums
MemoryLayout,Transpose,Uplo,Diagonal, andSideare well-defined and appropriately documented. The naming convention is consistent, and the values seem to be aligned with the typical BLAS library constants. This should make the usage of the enums intuitive for those familiar with BLAS.
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Actionable comments generated: 0
Configuration used: CodeRabbit UI
Files selected for processing (3)
- lapack/lapack64/dgesv.v (1 hunks)
- lapack/lapack64/dgetrf.v (1 hunks)
- lapack/lapack64/dgetrs.v (1 hunks)
Files skipped from review as they are similar to previous changes (2)
- lapack/lapack64/dgesv.v
- lapack/lapack64/dgetrf.v
Additional comments: 8
lapack/lapack64/dgetrs.v (8)
19-19: The function signature for
dgetrsis clear and matches the documentation provided. It accepts parameters for the transpose type, matrix dimensions, matrix data, leading dimensions, pivot indices, and right-hand side matrix.20-34: The input validation checks for
trans,n,nrhs,lda, andldbare thorough and follow good practices by checking for invalid values and throwing appropriate panic messages.37-38: The quick return check for
n == 0 || nrhs == 0is a good performance optimization to avoid unnecessary computation when there are no equations to solve or no right-hand sides.41-49: The length checks for the arrays
a,b, andipivare correct and ensure that the arrays are of sufficient size to hold the necessary data for the matrix operations.51-58: The logic for solving the system of equations when
trans != .no_transis correct. It uses the LAPACK utilitydlaswpand BLAS functionsdtrsmto perform the necessary matrix operations.61-66: The logic for solving the transposed system of equations is correct. It uses the BLAS function
dtrsmfor both the upper and lower triangular matrix solutions and applies the LAPACK utilitydlaswpto adjust the right-hand side matrixb.19-66: Ensure that the constants used for transpose types and matrix sides (e.g.,
.no_trans,.trans,.conj_trans,.left,.upper,.lower,.unit,.non_unit) are consistent with theblasmodule's definitions and that they are correctly implemented in theblasmodule.20-34: Verify that the panic messages (e.g.,
bad_trans,n_lt0,nrhs_lt0,bad_ld_a,bad_ld_b,short_ab,short_b,bad_len_ipiv) are defined elsewhere in the codebase and provide meaningful error messages to the user.Verification successful
The panic messages such as
bad_trans,n_lt0,nrhs_lt0,bad_ld_a,bad_ld_b,short_ab,short_b, andbad_len_ipivare defined in thelapack/lapack64/errors.vfile and are used throughout thelapack64andblas64modules to provide meaningful error messages.Scripts Executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Verify the panic messages in the codebase. rg --type v 'bad_trans|n_lt0|nrhs_lt0|bad_ld_a|bad_ld_b|short_ab|short_b|bad_len_ipiv'Length of output: 114
Script:
#!/bin/bash # Search for the panic message definitions without specifying the file type. rg 'bad_trans|n_lt0|nrhs_lt0|bad_ld_a|bad_ld_b|short_ab|short_b|bad_len_ipiv'Length of output: 2432
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Review Status
Actionable comments generated: 0
Configuration used: CodeRabbit UI
Files selected for processing (4)
- blas/README.md (1 hunks)
- blas/conversions.v (1 hunks)
- la/blas.v (16 hunks)
- lapack/README.md (1 hunks)
Additional comments: 28
blas/README.md (4)
- 1-3: The introduction clearly states the purpose of the package. Ensure the implementation details mentioned align with the actual code changes.
- 5-8: The table provides a clear overview of the backends available. Verify that the compilation flags and status descriptions are up-to-date with the latest codebase changes.
Verification successful
The grep results confirm the documentation accuracy for the OpenBLAS backend's compilation flag
-d vsl_blas_cblasas mentioned in theblas/README.mdand its usage within the repository, specifically in abin/testscript. This suggests that the documentation for the OpenBLAS backend is up-to-date regarding the compilation flag.However, the review comment's request to verify the compilation flags and status descriptions for all backends, including the "Pure V implementation," was only partially addressed. The verification focused on the OpenBLAS backend, and there was no specific flag to verify for the "Pure V implementation" backend based on the provided context. Therefore, while the OpenBLAS backend documentation is verified to be accurate, the review did not fully cover the "Pure V implementation" backend's status or compilation flags.
* 12-20: The OpenBLAS backend section is informative. Confirm that the installation instructions for OpenBLAS are still valid and that the compilation flag `-d vsl_blas_cblas` is correctly implemented in the build process. * 27-59: Installation instructions for macOS, Debian/Ubuntu, and Arch Linux/Manjaro are provided. Ensure these instructions are current and correct for installing OpenBLAS on these platforms.Scripts Executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Verify the compilation flags for BLAS backends are correctly documented. grep -r "vsl_blas_cblas" .Length of output: 432
lapack/README.md (4)
- 1-3: The introduction succinctly explains the purpose of the LAPACK package. Confirm that the implementation details are consistent with the code changes.
- 5-9: The backend table lists BLAS and LAPACKE with their statuses and compilation flags. Verify that these details accurately reflect the current state of the codebase and documentation.
- 13-21: The LAPACKE backend section provides useful information. Ensure the installation instructions for LAPACKE are accurate and that the compilation flag
-d vsl_lapack_lapackeis correctly implemented.- 28-58: Installation instructions for macOS, Debian/Ubuntu, and Arch Linux/Manjaro are detailed. Confirm these instructions are up-to-date for installing LAPACKE on these platforms.
la/blas.v (16)
- 3-3: The import statement correctly references the updated
blasmodule. Confirm that all necessary functions and types fromblasare available and used correctly throughout the file.- 41-41: The use of
blas.ddotfor the dot product operation is appropriate. Ensure that the parameters passed match the expected signature ofddotin theblasmodule.- 60-60: The call to
blas.daxpyfor vector addition is correctly implemented. Verify that the performance implications of usingdaxpyover manual looping for smaller vectors are considered.- 130-130: The use of
blas.dgemvfor matrix-vector multiplication is correct. Check that the parameters and the decision to usedgemvbased on matrix dimensions are optimal.- 160-160: The implementation of
blas.dgemvfor the transpose(matrix)-vector multiplication is appropriate. Confirm that the boolean flag for transposition is correctly passed.- 191-191: The call to
blas.dgerfor vector-vector transpose multiplication is correctly used. Ensure that the dimensions and scaling factor are correctly applied.- 211-211: The use of
blas.dgemvwith an addition operation is correctly implemented. Verify that the addition flag (1.0 for beta) is correctly utilized for the operation.- 231-231: The call to
blas.dgemmfor matrix multiplication is correct. Ensure that the parameters, including the no-transpose flags and scaling factors, are accurately passed.- 251-251: The implementation of
blas.dgemmfor transposed matrix multiplication is appropriate. Confirm that the transposition flag for matrixais correctly set.- 260-260: The use of
blas.dgemmfor matrix multiplication with transposedbis correctly implemented. Verify that the transposition flag for matrixbis accurately set.- 269-269: The call to
blas.dgemmfor multiplication with both matrices transposed is correct. Ensure that both transposition flags are accurately set.- 278-278: The implementation of
blas.dgemmfor matrix multiplication with addition is correctly used. Confirm that the addition flag (1.0 for beta) is correctly utilized.- 287-287: The use of
blas.dgemmfor transposed matrix multiplication with addition is appropriate. Verify that the transposition flag for matrixaand the addition flag are correctly set.- 296-296: The call to
blas.dgemmfor matrix multiplication with transposedband addition is correctly implemented. Ensure that the transposition flag for matrixband the addition flag are accurately set.- 305-305: The implementation of
blas.dgemmfor multiplication with both matrices transposed and addition is correct. Confirm that both transposition flags and the addition flag are accurately set.- 316-316: The use of
blas.daxpyfor matrix addition is correctly implemented. Verify that the performance implications of usingdaxpyover manual looping for smaller matrices are considered.blas/conversions.v (4)
- 1-1: The module declaration correctly reflects the updated module name. Confirm that this change is consistent throughout the entire codebase.
- 7-7: The import statement correctly references the updated
blas64module. Ensure that all necessary functions and types fromblas64are available and used correctly throughout the file.- 9-22: The addition of new types (
MemoryLayout,Transpose,Uplo,Diagonal,Side) is appropriate. Confirm that these types are correctly used in the context of the BLAS operations.- 24-40: The refactoring of helper functions (
c_trans,c_uplo,l_uplo,job_vlr) to work with the new types is correctly implemented. Verify that these functions are used appropriately wherever necessary.
* 'main' of github.com:vlang/vsl: fix kmeans.v (#199) Update maximum dimension checks in HDF5 file functions
* 'main' of github.com:vlang/vsl: Refactor print statement to display vector data
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Review Status
Actionable comments generated: 2
Configuration used: CodeRabbit UI
Files selected for processing (2)
- lapack/lapack64/dgetrf.v (1 hunks)
- lapack/lapack64/ilaenv.v (1 hunks)
Additional comments: 9
lapack/lapack64/dgetrf.v (6)
- 27-27: The function signature for
dgetrfis clear and well-documented, indicating good maintainability and readability.- 30-38: Ensure that the error messages for
panicare descriptive enough to help users understand the cause of the error. It's recommended to include more context in the error messages.- 52-52: The use of
ilaenvto determine the block size (nb) is a good practice as it allows for algorithm tuning based on the specific characteristics of the computation.- 54-57: The conditional logic to choose between the blocked and unblocked algorithm based on
nbis clear and follows best practices for performance optimization.- 59-78: The loop to process matrix blocks is logically sound. However, consider adding comments within the loop to explain the purpose of each major step for better readability and maintainability.
- 74-75: Using
unsafeto create a slice may lead to potential issues if not handled carefully. Ensure that the bounds are correctly calculated to prevent out-of-bounds access.lapack/lapack64/ilaenv.v (3)
- 22-22: The function signature for
ilaenvis clear and well-documented, indicating good maintainability and readability.- 24-28: The initial checks for
snameandcnamebased on the first character ofnameare a good practice for early validation of input parameters.- 245-245: Ensure that the
minfunction is defined and correctly calculates the minimum ofn1andn2. This line assumes the existence of such a function, which is crucial for the correct operation ofilaenv.
lapack/lapack64/ilaenv.v
Outdated
| 'QR', 'RQ', 'LQ', 'QL', 'HR', 'TR', 'BR' { | ||
| return 2 | ||
| } | ||
| else { | ||
| panic(bad_name) | ||
| } | ||
| } | ||
| } | ||
| else { | ||
| panic(bad_name) | ||
| } | ||
| } | ||
| } | ||
| else { | ||
| panic(bad_name) | ||
| } | ||
| } | ||
| } | ||
| 3 { | ||
| match c2 { | ||
| 'GE' { | ||
| match c3 { | ||
| 'QRF', 'RQF', 'LQF', 'QLF', 'HRD', 'BRD' { | ||
| if sname { | ||
| return 128 | ||
| } | ||
| return 128 | ||
| } | ||
| else { | ||
| panic(bad_name) | ||
| } | ||
| } | ||
| } | ||
| 'SY', 'HE' { | ||
| if c3 == 'TRD' { | ||
| return 32 | ||
| } | ||
| panic(bad_name) | ||
| } | ||
| 'OR', 'UN' { | ||
| match c3[0] { | ||
| 'G' { | ||
| match c4 { | ||
| 'QR', 'RQ', 'LQ', 'QL', 'HR', 'TR', 'BR' { | ||
| return 128 | ||
| } | ||
| else { | ||
| panic(bad_name) | ||
| } | ||
| } | ||
| } | ||
| else { | ||
| panic(bad_name) | ||
| } | ||
| } | ||
| } | ||
| else { | ||
| panic(bad_name) | ||
| } | ||
| } | ||
| } | ||
| 4 { | ||
| // Used by xHSEQR | ||
| return 6 | ||
| } | ||
| 5 { | ||
| // Not used | ||
| return 2 | ||
| } | ||
| 6 { | ||
| // Used by xGELSS and xGESVD | ||
| // Assuming n1 and n2 are defined elsewhere in your code | ||
| // Replace `min(n1, n2)` with actual min calculation or function | ||
| return int(f64(min(n1, n2)) * 1.6) | ||
| } | ||
| 7 { | ||
| // Not used | ||
| return 1 | ||
| } | ||
| 8 { | ||
| // Used by xHSEQR | ||
| return 50 | ||
| } | ||
| 9 { | ||
| // Used by xGELSD and xGESDD | ||
| return 25 | ||
| } | ||
| 10, 11 { | ||
| // Go guarantees ieee | ||
| return 1 | ||
| } | ||
| 12, 13, 14, 15, 16 { | ||
| // dhseqr and related functions for eigenvalue problems. | ||
| return iparmq(ispec, name, opts, n1, n2, n3, n4) | ||
| } | ||
| else { | ||
| panic(bad_ispec) | ||
| } | ||
| } | ||
| return 0 |
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The extensive use of match statements for algorithm tuning parameter selection is logically sound. However, consider refactoring to reduce the complexity and improve maintainability. Extracting repeated logic into separate functions or using a data-driven approach could be beneficial.
* 'main' of github.com:vlang/vsl: Run CI again
* 'main' of github.com:vlang/vsl: Run CI again Add more missing `pub` modifiers to public struct fields Add `pub` access modifier to publicly used struct fields (#200)
| info := C.LAPACKE_dgesvd(.row_major, jobu, jobvt, m, n, &a[0], lda, &s[0], &u[0], | ||
| ldu, &vt[0], ldvt, &superb[0]) | ||
| if info != 0 { | ||
| errors.vsl_panic('lapack failed', .efailed) | ||
| } |
There was a problem hiding this comment.
Consider improving error handling in dgesvd. Instead of panicking, it might be better to return an error to the caller, allowing them to decide how to handle it.
| lapack64.dgetrf(m, n, mut a, lda, ipiv) | ||
| } |
There was a problem hiding this comment.
Consider improving error handling in dgetrf to return errors instead of panicking, providing more flexibility to the caller.
| unsafe { | ||
| info := C.LAPACKE_dpotrf(.row_major, blas.l_uplo(up), n, &a[0], lda) | ||
| if info != 0 { | ||
| errors.vsl_panic('lapack failed', .efailed) | ||
| } |
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Consider improving error handling in dpotrf to return errors instead of panicking, providing more flexibility to the caller.
| info := C.LAPACKE_dgeev(.row_major, &char(blas.job_vlr(calc_vl).str().str), &char(blas.job_vlr(calc_vr).str().str), | ||
| n, &a[0], lda, &wr[0], &wi[0], &vvl, ldvl, &vvr, ldvr) | ||
| if info != 0 { | ||
| errors.vsl_panic('lapack failed', .efailed) | ||
| } |
There was a problem hiding this comment.
Consider improving error handling in dgeev to return errors instead of panicking, providing more flexibility to the caller.
* 'main' of github.com:vlang/vsl: refactor: Update create_image_2d function to use local variable for format Update lint.yml Update lint.yml refactor: update README.md to include link to examples folder refactor: update README.md to include link to examples folder fix error in examples/vcl_opencl_fractals_one_argument/main.v (fix main V CI) refactor: Update image creation functions to use generic 'image' instead of 'image_2d' refactor: Update image creation functions to use generic 'image' instead of 'image_2d' refactor: Update create_image_2d function to use local variable for format chore: Add output image to README chore: Remove unused plot examples using script mode refactor: Update AC signal calculation to include amplitude parameter chore: Update plot examples with axis titles and script mode 1 basic plotting example with axis titles and 3 more ones using script mode (#205) remove unused `math` import, run `v fmt -w .`
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Actionable comments posted: 1
Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Files selected for processing (5)
- lapack/lapack64/dgesv.v (1 hunks)
- lapack/lapack64/dgetrf.v (1 hunks)
- lapack/lapack64/dgetrs.v (1 hunks)
- lapack/lapack64/errors.v (1 hunks)
- lapack/lapack64/ilaenv.v (1 hunks)
Files skipped from review due to trivial changes (1)
- lapack/lapack64/errors.v
Additional comments not posted (3)
lapack/lapack64/dgesv.v (1)
1-56: The implementation ofdgesvfunction is robust and well-documented, providing clear error handling and utilizing efficient linear algebra routines.lapack/lapack64/dgetrs.v (1)
1-67: Thedgetrsfunction is correctly implemented with comprehensive error handling and efficient use of BLAS routines for solving linear equations.lapack/lapack64/dgetrf.v (1)
1-88: Thedgetrffunction is well-implemented with robust error handling and efficient use of blocked and unblocked algorithms for LU decomposition.
lapack/lapack64/ilaenv.v
Outdated
| return 2 | ||
| } | ||
| else { | ||
| panic(bad_name) | ||
| } | ||
| } | ||
| } | ||
| else { | ||
| panic(bad_name) | ||
| } | ||
| } | ||
| } | ||
| else { | ||
| panic(bad_name) | ||
| } | ||
| } | ||
| } | ||
| 3 { | ||
| match c2 { | ||
| 'GE' { | ||
| match c3 { | ||
| 'QRF', 'RQF', 'LQF', 'QLF', 'HRD', 'BRD' { | ||
| if sname { | ||
| return 128 | ||
| } | ||
| return 128 | ||
| } | ||
| else { | ||
| panic(bad_name) | ||
| } | ||
| } | ||
| } | ||
| 'SY', 'HE' { | ||
| if c3 == 'TRD' { | ||
| return 32 | ||
| } | ||
| panic(bad_name) | ||
| } | ||
| 'OR', 'UN' { | ||
| match c3[0] { | ||
| `G` { | ||
| match c4 { | ||
| 'QR', 'RQ', 'LQ', 'QL', 'HR', 'TR', 'BR' { | ||
| return 128 | ||
| } | ||
| else { | ||
| panic(bad_name) | ||
| } | ||
| } | ||
| } | ||
| else { | ||
| panic(bad_name) | ||
| } | ||
| } | ||
| } | ||
| else { | ||
| panic(bad_name) | ||
| } | ||
| } | ||
| } | ||
| 4 { | ||
| // Used by xHSEQR | ||
| return 6 | ||
| } | ||
| 5 { | ||
| // Not used | ||
| return 2 | ||
| } | ||
| 6 { | ||
| // Used by xGELSS and xGESVD | ||
| // Assuming n1 and n2 are defined elsewhere in your code | ||
| // Replace `min(n1, n2)` with actual min calculation or function | ||
| return int(f64(min(n1, n2)) * 1.6) | ||
| } | ||
| 7 { | ||
| // Not used | ||
| return 1 | ||
| } | ||
| 8 { | ||
| // Used by xHSEQR | ||
| return 50 | ||
| } | ||
| 9 { | ||
| // Used by xGELSD and xGESDD | ||
| return 25 | ||
| } | ||
| 10, 11 { | ||
| // Go guarantees ieee | ||
| return 1 | ||
| } | ||
| 12, 13, 14, 15, 16 { | ||
| // dhseqr and related functions for eigenvalue problems. | ||
| return iparmq(ispec, name, opts, n1, n2, n3, n4) | ||
| } | ||
| else { | ||
| panic(bad_ispec) | ||
| } | ||
| } | ||
| return 0 | ||
| } |
There was a problem hiding this comment.
Consider refactoring the ilaenv function to reduce complexity and improve maintainability. Extracting repeated logic into separate functions or using a data-driven approach could be beneficial.
There was a problem hiding this comment.
Actionable comments posted: 9
Outside diff range and nitpick comments (7)
lapack/lapack64/dsyev.v (1)
35-37: Placeholder for LAPACK function calls should be documented or implemented if this is meant for production.Consider adding a comment to clarify that the actual LAPACK function calls should replace the placeholder in future development.
lapack/lapack64/dgeev.v (1)
36-38: Placeholder for LAPACK function calls should be documented or implemented if this is meant for production.Consider documenting that actual LAPACK function calls should replace the placeholder in future development.
lapack/lapack64/dgesvd.v (1)
39-41: Placeholder for LAPACK function calls should be documented or implemented if this is meant for production.Consider documenting that actual LAPACK function calls should replace the placeholder in future development.
la/matrix_ops.v (1)
Line range hint
93-122: Correct usage of updated LAPACK functions inmatrix_svdandmatrix_inv. Consider improving the hardcoded tolerance value for pseudo-inverse calculations.Consider using a dynamically computed tolerance based on matrix properties to improve numerical stability.
lapack/lapack_notd_vsl_lapack_lapacke.v (1)
3-5: Please ensure consistent spacing in import statements for better readability.lapack/lapack_d_vsl_lapack_lapacke.v (1)
1-4: The import statements are well-organized. However, adding a newline between the module declaration and the imports would improve readability.blas/oblas_d_vsl_blas_cblas.v (1)
189-189: Add documentation for theset_num_threadsfunction to explain its impact on performance and when it should be called.Proper documentation will help future developers understand the importance of thread management in performance-critical applications.
Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Files selected for processing (17)
- blas/oblas_d_vsl_blas_cblas.v (1 hunks)
- la/matrix_ops.v (5 hunks)
- lapack/conversions.v (1 hunks)
- lapack/lapack64/conversions.v (1 hunks)
- lapack/lapack64/dgebal.v (1 hunks)
- lapack/lapack64/dgeev.v (1 hunks)
- lapack/lapack64/dgehrd.v (1 hunks)
- lapack/lapack64/dgesv.v (1 hunks)
- lapack/lapack64/dgesvd.v (1 hunks)
- lapack/lapack64/dgetrf.v (1 hunks)
- lapack/lapack64/dgetri.v (1 hunks)
- lapack/lapack64/dgetrs.v (1 hunks)
- lapack/lapack64/dpotrf.v (1 hunks)
- lapack/lapack64/dsyev.v (1 hunks)
- lapack/lapack64/ilaenv.v (1 hunks)
- lapack/lapack_d_vsl_lapack_lapacke.v (4 hunks)
- lapack/lapack_notd_vsl_lapack_lapacke.v (1 hunks)
Files not summarized due to errors (1)
- blas/oblas_d_vsl_blas_cblas.v: Error: Message exceeds token limit
Files skipped from review due to trivial changes (1)
- lapack/conversions.v
Files skipped from review as they are similar to previous changes (4)
- lapack/lapack64/dgesv.v
- lapack/lapack64/dgetrf.v
- lapack/lapack64/dgetrs.v
- lapack/lapack64/ilaenv.v
Additional comments not posted (16)
lapack/lapack64/dgetri.v (1)
7-7: Refactoring approved: Use of mutable integer slices foripivenhances clarity and consistency.lapack/lapack64/dpotrf.v (1)
7-7: Refactoring approved: Use ofUploenum foruploparameter enhances clarity and consistency.lapack/lapack64/dgebal.v (1)
7-7: Refactoring approved: Use ofBalanceJobenum forjobparameter enhances clarity and consistency.lapack/lapack64/dsyev.v (1)
7-7: Updated function signature with enums enhances type safety and clarity.lapack/lapack64/dgeev.v (1)
7-7: Updated function signature with specific enums for left and right eigenvectors improves clarity and correctness.lapack/lapack64/dgesvd.v (1)
7-7: Updated function signature withSVDJobenum enhances clarity and ensures correct usage.la/matrix_ops.v (2)
Line range hint
4-16: Correct use of updated import and function call inmatrix_det. Good error handling for non-square matrices.
4-4: Proper error handling and condition checks inmatrix_inv_small. Well-structured for handling small matrices.lapack/lapack_notd_vsl_lapack_lapacke.v (3)
30-32: The functiondgesvcorrectly delegates tolapack64.dgesv. Ensure that thelapack64module's function signature and expected behavior align with this usage.Verification successful
The function
lapack64.dgesvis defined inlapack/lapack64/dgesv.vwith the following signature:pub fn dgesv(n int, nrhs int, mut a []f64, lda int, mut ipiv []int, mut b []f64, ldb int) {This signature matches the usage in the provided code snippet.
lapack/lapack64/dgesv.v:pub fn dgesv(n int, nrhs int, mut a []f64, lda int, mut ipiv []int, mut b []f64, ldb int) {Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Verify the function signature and behavior of lapack64.dgesv ast-grep --lang v --pattern $'module lapack64 { pub fn dgesv($_, $_, $_, $_, $_, $_, $_) { $$$ } }'Length of output: 211
Script:
#!/bin/bash # Search for the definition of the dgesv function in the lapack64 module rg --pcre2 --multiline --multiline-dotall 'module lapack64\s*{\s*.*?pub fn dgesv\((.*?)\)\s*{.*?}' --glob '*.v'Length of output: 113
Script:
#!/bin/bash # Search for the definition of the dgesv function across the entire codebase rg --pcre2 --multiline --multiline-dotall 'pub fn dgesv\((.*?)\)\s*{.*?}' --glob '*.v'Length of output: 873
115-118: The logic indpotrfseems correct, but ensure that theblas.c_uplo(up)function properly translates the booleanupto the requiredUplotype.Verification successful
The
c_uplofunction inblas/conversions.vcorrectly translates the booleanupto the requiredUplotype by returning.upperifupis true and.lowerifupis false. This ensures that the boolean input is properly handled.
blas/conversions.vpub fn c_uplo(up bool) Uplo { return if up { .upper } else { .lower } }Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Verify the implementation of blas.c_uplo to ensure it handles boolean inputs correctly. ast-grep --lang v --pattern $'fn c_uplo(up bool) { $$$ }'Length of output: 163
Script:
#!/bin/bash # Search for the definition of the c_uplo function in the codebase rg 'fn c_uplo' --vLength of output: 426
Script:
#!/bin/bash # Search for the definition of the c_uplo function in the codebase rg 'fn c_uplo'Length of output: 65
Script:
#!/bin/bash # Extract the implementation of the c_uplo function from blas/conversions.v rg -A 10 'fn c_uplo' blas/conversions.vLength of output: 328
78-80: Ensure proper memory safety when usingunsafeblocks, especially when dereferencing pointers inC.LAPACKE_dgetrf.lapack/lapack64/conversions.v (2)
1-1: The module declaration is concise and clear.
4-35: Enums likeDirect,Sort,StoreV, andMatrixNormare well-defined with appropriate documentation. Ensure that the corresponding usage throughout the codebase aligns with these definitions.lapack/lapack_d_vsl_lapack_lapacke.v (1)
8-18: The function declarations using the C interface are correctly defined. Ensure that the external C functions are appropriately declared in the corresponding C header files and that the ABI matches.blas/oblas_d_vsl_blas_cblas.v (2)
1-1: Change of module name fromvlastoblasis consistent with the PR's objective of renaming for clarity.
3-72: Ensure that the function bindings to the C library are accurate and correctly typed. These bindings are crucial for the performance of the linear algebra operations.Verification successful
The verification of the C function bindings in the V language implementation has been completed. The function signatures in the
cblas.hfile match those in the V code.
cblas_sdsdot: Matchescblas_dsdot: Matchescblas_sdot: Matchescblas_ddot: Matchescblas_cdotu: Matchescblas_cdotc: Matchescblas_zdotu: Matchescblas_zdotc: Matchescblas_cdotu_sub: Matchescblas_cdotc_sub: Matchescblas_zdotu_sub: Matchescblas_zdotc_sub: Matchescblas_sasum: Matchescblas_dasum: Matchescblas_scasum: Matchescblas_dzasum: Matchescblas_ssum: Matchescblas_dsum: Matchescblas_scsum: Matchescblas_dzsum: Matchescblas_snrm2: Matchescblas_dnrm2: Matchescblas_scnrm2: Matchescblas_dznrm2: Matchescblas_isamax: Matchescblas_idamax: Matchescblas_icamax: Matchescblas_izamax: Matchescblas_isamin: Matchescblas_idamin: Matchescblas_icamin: Matchescblas_izamin: Matchescblas_ismax: Matchescblas_idmax: Matchescblas_icmax: Matchescblas_izmax: Matchescblas_ismin: Matchescblas_idmin: Matchescblas_icmin: Matchescblas_izmin: Matchescblas_saxpy: Matchescblas_daxpy: Matchescblas_caxpy: Matchescblas_zaxpy: Matchescblas_scopy: Matchescblas_dcopy: Matchescblas_ccopy: Matchescblas_zcopy: Matchescblas_sswap: Matchescblas_dswap: Matchescblas_cswap: Matchescblas_zswap: Matchescblas_srot: Matchescblas_drot: Matchescblas_srotg: Matchescblas_drotg: Matchescblas_srotm: Matchescblas_drotm: Matchescblas_srotmg: Matchescblas_drotmg: Matchescblas_sscal: Matchescblas_dscal: Matchescblas_cscal: Matchescblas_zscal: Matchescblas_csscal: Matchescblas_zdscal: Matchescblas_sgemv: MatchesThe bindings in the V code are accurate and correctly typed.
Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the accuracy of C function bindings in the V language implementation. # Test: Check the C header files to ensure function signatures match. cat /usr/include/cblas.h | grep cblas_sdsdot cat /usr/include/cblas.h | grep cblas_dsdot cat /usr/include/cblas.h | grep cblas_sdot # Repeat for all bindings...Length of output: 296
Script:
#!/bin/bash # Locate the cblas.h file within the repository or system include directories fd cblas.h /usr/include /usr/local/include /optLength of output: 265
Script:
#!/bin/bash # Locate the cblas.h file within the entire file system fd cblas.h /Length of output: 46
Script:
#!/bin/bash # Description: Verify the accuracy of C function bindings in the V language implementation. # Test: Check the cblas.h file to ensure function signatures match. cat /home/jailuser/git/blas/cblas.h | grep cblas_sdsdot cat /home/jailuser/git/blas/cblas.h | grep cblas_dsdot cat /home/jailuser/git/blas/cblas.h | grep cblas_sdot cat /home/jailuser/git/blas/cblas.h | grep cblas_ddot cat /home/jailuser/git/blas/cblas.h | grep cblas_cdotu cat /home/jailuser/git/blas/cblas.h | grep cblas_cdotc cat /home/jailuser/git/blas/cblas.h | grep cblas_zdotu cat /home/jailuser/git/blas/cblas.h | grep cblas_zdotc cat /home/jailuser/git/blas/cblas.h | grep cblas_cdotu_sub cat /home/jailuser/git/blas/cblas.h | grep cblas_cdotc_sub cat /home/jailuser/git/blas/cblas.h | grep cblas_zdotu_sub cat /home/jailuser/git/blas/cblas.h | grep cblas_zdotc_sub cat /home/jailuser/git/blas/cblas.h | grep cblas_sasum cat /home/jailuser/git/blas/cblas.h | grep cblas_dasum cat /home/jailuser/git/blas/cblas.h | grep cblas_scasum cat /home/jailuser/git/blas/cblas.h | grep cblas_dzasum cat /home/jailuser/git/blas/cblas.h | grep cblas_ssum cat /home/jailuser/git/blas/cblas.h | grep cblas_dsum cat /home/jailuser/git/blas/cblas.h | grep cblas_scsum cat /home/jailuser/git/blas/cblas.h | grep cblas_dzsum cat /home/jailuser/git/blas/cblas.h | grep cblas_snrm2 cat /home/jailuser/git/blas/cblas.h | grep cblas_dnrm2 cat /home/jailuser/git/blas/cblas.h | grep cblas_scnrm2 cat /home/jailuser/git/blas/cblas.h | grep cblas_dznrm2 cat /home/jailuser/git/blas/cblas.h | grep cblas_isamax cat /home/jailuser/git/blas/cblas.h | grep cblas_idamax cat /home/jailuser/git/blas/cblas.h | grep cblas_icamax cat /home/jailuser/git/blas/cblas.h | grep cblas_izamax cat /home/jailuser/git/blas/cblas.h | grep cblas_isamin cat /home/jailuser/git/blas/cblas.h | grep cblas_idamin cat /home/jailuser/git/blas/cblas.h | grep cblas_icamin cat /home/jailuser/git/blas/cblas.h | grep cblas_izamin cat /home/jailuser/git/blas/cblas.h | grep cblas_ismax cat /home/jailuser/git/blas/cblas.h | grep cblas_idmax cat /home/jailuser/git/blas/cblas.h | grep cblas_icmax cat /home/jailuser/git/blas/cblas.h | grep cblas_izmax cat /home/jailuser/git/blas/cblas.h | grep cblas_ismin cat /home/jailuser/git/blas/cblas.h | grep cblas_idmin cat /home/jailuser/git/blas/cblas.h | grep cblas_icmin cat /home/jailuser/git/blas/cblas.h | grep cblas_izmin cat /home/jailuser/git/blas/cblas.h | grep cblas_saxpy cat /home/jailuser/git/blas/cblas.h | grep cblas_daxpy cat /home/jailuser/git/blas/cblas.h | grep cblas_caxpy cat /home/jailuser/git/blas/cblas.h | grep cblas_zaxpy cat /home/jailuser/git/blas/cblas.h | grep cblas_scopy cat /home/jailuser/git/blas/cblas.h | grep cblas_dcopy cat /home/jailuser/git/blas/cblas.h | grep cblas_ccopy cat /home/jailuser/git/blas/cblas.h | grep cblas_zcopy cat /home/jailuser/git/blas/cblas.h | grep cblas_sswap cat /home/jailuser/git/blas/cblas.h | grep cblas_dswap cat /home/jailuser/git/blas/cblas.h | grep cblas_cswap cat /home/jailuser/git/blas/cblas.h | grep cblas_zswap cat /home/jailuser/git/blas/cblas.h | grep cblas_srot cat /home/jailuser/git/blas/cblas.h | grep cblas_drot cat /home/jailuser/git/blas/cblas.h | grep cblas_srotg cat /home/jailuser/git/blas/cblas.h | grep cblas_drotg cat /home/jailuser/git/blas/cblas.h | grep cblas_srotm cat /home/jailuser/git/blas/cblas.h | grep cblas_drotm cat /home/jailuser/git/blas/cblas.h | grep cblas_srotmg cat /home/jailuser/git/blas/cblas.h | grep cblas_drotmg cat /home/jailuser/git/blas/cblas.h | grep cblas_sscal cat /home/jailuser/git/blas/cblas.h | grep cblas_dscal cat /home/jailuser/git/blas/cblas.h | grep cblas_cscal cat /home/jailuser/git/blas/cblas.h | grep cblas_zscal cat /home/jailuser/git/blas/cblas.h | grep cblas_csscal cat /home/jailuser/git/blas/cblas.h | grep cblas_zdscal cat /home/jailuser/git/blas/cblas.h | grep cblas_sgemvLength of output: 12196
blas/oblas_d_vsl_blas_cblas.v
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| beta, c, ldc) | ||
| } | ||
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| @[inline] | ||
| pub fn zher2k(uplo bool, trans bool, n int, k int, alpha voidptr, a voidptr, lda int, b voidptr, ldb int, beta f64, mut c voidptr, ldc int) { | ||
| C.cblas_zher2k(.row_major, c_uplo(uplo), c_trans(trans), n, k, alpha, a, lda, b, ldb, | ||
| beta, c, ldc) | ||
| } | ||
|
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| @[inline] | ||
| pub fn saxpby(n int, alpha f32, x []f32, incx int, beta f32, mut y []f32, incy int) { | ||
| C.cblas_saxpby(n, alpha, unsafe { &x[0] }, incx, beta, unsafe { &y[0] }, incy) | ||
| } | ||
|
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| @[inline] | ||
| pub fn daxpby(n int, alpha f64, x []f64, incx int, beta f64, mut y []f64, incy int) { | ||
| C.cblas_daxpby(n, alpha, unsafe { &x[0] }, incx, beta, unsafe { &y[0] }, incy) | ||
| } | ||
|
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| @[inline] | ||
| pub fn caxpby(n int, alpha voidptr, x voidptr, incx int, beta voidptr, mut y voidptr, incy int) { | ||
| C.cblas_caxpby(n, alpha, x, incx, beta, y, incy) | ||
| } | ||
|
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| @[inline] | ||
| pub fn zaxpby(n int, alpha voidptr, x voidptr, incx int, beta voidptr, mut y voidptr, incy int) { | ||
| C.cblas_zaxpby(n, alpha, x, incx, beta, y, incy) | ||
| } | ||
|
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| @[inline] | ||
| pub fn somatcopy(order MemoryLayout, trans bool, rows int, cols int, alpha f32, a []f32, lda int, mut b []f32, ldb int) { | ||
| C.cblas_somatcopy(order, c_trans(trans), rows, cols, alpha, unsafe { &a[0] }, lda, | ||
| unsafe { &b[0] }, ldb) | ||
| } | ||
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| @[inline] | ||
| pub fn domatcopy(order MemoryLayout, trans bool, rows int, cols int, alpha f64, a []f64, lda int, mut b []f64, ldb int) { | ||
| C.cblas_domatcopy(order, c_trans(trans), rows, cols, alpha, unsafe { &a[0] }, lda, | ||
| unsafe { &b[0] }, ldb) | ||
| } | ||
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| @[inline] | ||
| pub fn comatcopy(order MemoryLayout, trans bool, rows int, cols int, alpha &f32, a &f32, lda int, mut b &f32, ldb int) { | ||
| C.cblas_comatcopy(order, c_trans(trans), rows, cols, alpha, a, lda, b, ldb) | ||
| } | ||
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| @[inline] | ||
| pub fn zomatcopy(order MemoryLayout, trans bool, rows int, cols int, alpha &f64, a &f64, lda int, mut b &f64, ldb int) { | ||
| C.cblas_zomatcopy(order, c_trans(trans), rows, cols, alpha, a, lda, b, ldb) | ||
| } | ||
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| @[inline] | ||
| pub fn simatcopy(order MemoryLayout, trans bool, rows int, cols int, alpha f32, mut a []f32, lda int, ldb int) { | ||
| C.cblas_simatcopy(order, c_trans(trans), rows, cols, alpha, unsafe { &a[0] }, lda, | ||
| ldb) | ||
| } | ||
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| @[inline] | ||
| pub fn dimatcopy(order MemoryLayout, trans bool, rows int, cols int, alpha f64, mut a []f64, lda int, ldb int) { | ||
| C.cblas_dimatcopy(order, c_trans(trans), rows, cols, alpha, unsafe { &a[0] }, lda, | ||
| ldb) | ||
| } | ||
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| @[inline] | ||
| pub fn cimatcopy(order MemoryLayout, trans bool, rows int, cols int, alpha &f32, mut a &f32, lda int, ldb int) { | ||
| C.cblas_cimatcopy(order, c_trans(trans), rows, cols, alpha, a, lda, ldb) | ||
| } | ||
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| @[inline] | ||
| pub fn zimatcopy(order MemoryLayout, trans bool, rows int, cols int, alpha &f64, mut a &f64, lda int, ldb int) { | ||
| C.cblas_zimatcopy(order, c_trans(trans), rows, cols, alpha, a, lda, ldb) | ||
| } | ||
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| @[inline] | ||
| pub fn sgeadd(order MemoryLayout, rows int, cols int, alpha f32, a []f32, lda int, beta f32, mut c []f32, ldc int) { | ||
| C.cblas_sgeadd(order, rows, cols, alpha, unsafe { &a[0] }, lda, beta, unsafe { &c[0] }, | ||
| ldc) | ||
| } | ||
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| @[inline] | ||
| pub fn dgeadd(order MemoryLayout, rows int, cols int, alpha f64, a []f64, lda int, beta f64, mut c []f64, ldc int) { | ||
| C.cblas_dgeadd(order, rows, cols, alpha, unsafe { &a[0] }, lda, beta, unsafe { &c[0] }, | ||
| ldc) | ||
| } | ||
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| @[inline] | ||
| pub fn cgeadd(order MemoryLayout, rows int, cols int, alpha &f32, a &f32, lda int, beta &f32, mut c &f32, ldc int) { | ||
| C.cblas_cgeadd(order, rows, cols, alpha, a, lda, beta, c, ldc) | ||
| } | ||
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| @[inline] | ||
| pub fn zgeadd(order MemoryLayout, rows int, cols int, alpha &f64, a &f64, lda int, beta &f64, mut c &f64, ldc int) { | ||
| C.cblas_zgeadd(order, rows, cols, alpha, a, lda, beta, c, ldc) | ||
| } | ||
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| @[inline] | ||
| pub fn dgemm(trans_a bool, trans_b bool, m int, n int, k int, alpha f64, a []f64, lda int, b []f64, ldb int, beta f64, mut cc []f64, ldc int) { | ||
| C.cblas_dgemm(.row_major, c_trans(trans_a), c_trans(trans_b), m, n, k, alpha, unsafe { &a[0] }, | ||
| lda, unsafe { &b[0] }, ldb, beta, unsafe { &cc[0] }, ldc) | ||
| } |
There was a problem hiding this comment.
Review the use of unsafe blocks and pointers. While necessary for C interop, ensure they are minimized and properly documented to prevent misuse.
Consider wrapping unsafe operations in safer, higher-level abstractions where possible, especially for operations that are frequently used throughout the codebase.
| pub fn dgesvd(jobu SVDJob, jobvt SVDJob, m int, n int, mut a []f64, lda int, s []f64, mut u []f64, ldu int, mut vt []f64, ldvt int, superb []f64) { | ||
| info := lapack64.dgesvd(jobu, jobvt, m, n, mut a, lda, s, mut u, ldu, mut vt, ldvt, | ||
| superb) | ||
| if info != 0 { | ||
| errors.vsl_panic('lapack failed', .efailed) | ||
| } | ||
| } |
There was a problem hiding this comment.
Good use of error handling with vsl_panic in dgesvd. However, consider enhancing the error message to include more details about the failure for easier debugging.
- errors.vsl_panic('lapack failed', .efailed)
+ errors.vsl_panic('LAPACK dgesvd failed with error code: $info', .efailed)Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| pub fn dgesvd(jobu SVDJob, jobvt SVDJob, m int, n int, mut a []f64, lda int, s []f64, mut u []f64, ldu int, mut vt []f64, ldvt int, superb []f64) { | |
| info := lapack64.dgesvd(jobu, jobvt, m, n, mut a, lda, s, mut u, ldu, mut vt, ldvt, | |
| superb) | |
| if info != 0 { | |
| errors.vsl_panic('lapack failed', .efailed) | |
| } | |
| } | |
| pub fn dgesvd(jobu SVDJob, jobvt SVDJob, m int, n int, mut a []f64, lda int, s []f64, mut u []f64, ldu int, mut vt []f64, ldvt int, superb []f64) { | |
| info := lapack64.dgesvd(jobu, jobvt, m, n, mut a, lda, s, mut u, ldu, mut vt, ldvt, | |
| superb) | |
| if info != 0 { | |
| errors.vsl_panic('LAPACK dgesvd failed with error code: $info', .efailed) | |
| } | |
| } |
lapack/lapack64/dgetri.v
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| pub fn dgetri(n int, mut a []f64, lda int, ipiv []int) int { | ||
| if n == 0 { | ||
| return 0 | ||
| } | ||
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| mut info := 0 | ||
| if n < 0 { | ||
| info = -1 | ||
| } else if lda < math.max(1, n) { | ||
| info = -3 | ||
| } | ||
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| if info != 0 { | ||
| return info | ||
| } | ||
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| // Quick return if possible | ||
| if n == 0 { | ||
| return 0 | ||
| } | ||
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| // Placeholder for the actual LAPACK function calls | ||
| // Example: info = dgetri(n, a, lda, ipiv, work, lwork) | ||
| return info | ||
| } |
There was a problem hiding this comment.
Ensure the implementation of the LAPACK function calls is completed as indicated by the placeholder comment.
The function currently does not perform the matrix inversion but returns an uninitialized info variable. Completing the implementation is crucial for the functionality of this function.
lapack/lapack64/dpotrf.v
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| pub fn dpotrf(uplo blas.Uplo, n int, mut a []f64, lda int) int { | ||
| if n == 0 { | ||
| return 0 | ||
| } | ||
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| mut info := 0 | ||
| if uplo != .upper && uplo != .lower { | ||
| info = -1 | ||
| } else if n < 0 { | ||
| info = -2 | ||
| } else if lda < math.max(1, n) { | ||
| info = -4 | ||
| } | ||
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| if info != 0 { | ||
| return info | ||
| } | ||
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| // Quick return if possible | ||
| if n == 0 { | ||
| return 0 | ||
| } | ||
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| // Placeholder for the actual LAPACK function calls | ||
| // Example: info = dpotrf(uplo, n, a, lda, work, lwork) | ||
| return info |
There was a problem hiding this comment.
Ensure the implementation of the LAPACK function calls is completed as indicated by the placeholder comment.
The function currently does not perform the Cholesky decomposition but returns an uninitialized info variable. Completing the implementation is crucial for the functionality of this function.
| pub fn dgebal(job BalanceJob, n int, mut a []f64, lda int, scale []f64) int { | ||
| if n == 0 { | ||
| return 0 | ||
| } | ||
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| mut info := 0 | ||
| if job != .balance_none && job != .permute && job != .scale && job != .permute_scale { | ||
| info = -1 | ||
| } else if n < 0 { | ||
| info = -2 | ||
| } else if lda < math.max(1, n) { | ||
| info = -4 | ||
| } | ||
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| if info != 0 { | ||
| return info | ||
| } | ||
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| // Quick return if possible | ||
| if n == 0 { | ||
| return 0 | ||
| } | ||
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| // Placeholder for the actual LAPACK function calls | ||
| // Example: info = dgebal(job, n, a, lda, scale) | ||
| return info |
There was a problem hiding this comment.
Ensure the implementation of the LAPACK function calls is completed as indicated by the placeholder comment.
The function currently does not perform matrix balancing but returns an uninitialized info variable. Completing the implementation is crucial for the functionality of this function.
| pub fn dgehrd(n int, ilo int, ihi int, mut a []f64, lda int, tau []f64) int { | ||
| if n == 0 { | ||
| return 0 | ||
| } | ||
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| mut info := 0 | ||
| if n < 0 { | ||
| info = -1 | ||
| } else if ilo < 1 || ilo > math.max(1, n) { | ||
| info = -2 | ||
| } else if ihi < math.min(ilo, n) || ihi > n { | ||
| info = -3 | ||
| } else if lda < math.max(1, n) { | ||
| info = -5 | ||
| } | ||
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| if info != 0 { | ||
| return info | ||
| } | ||
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| // Quick return if possible | ||
| if n == 0 { | ||
| return 0 | ||
| } | ||
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| // Placeholder for the actual LAPACK function calls | ||
| // Example: info = dgehrd(n, ilo, ihi, a, lda, tau, work, lwork) | ||
| return info |
There was a problem hiding this comment.
Ensure the implementation of the LAPACK function calls is completed as indicated by the placeholder comment.
The function currently does not perform the reduction to Hessenberg form but returns an uninitialized info variable. Completing the implementation is crucial for the functionality of this function.
lapack/lapack64/conversions.v
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| // Compute eigenvectors of the tridiagonal matrix. | ||
| ev_tridiag = u8(`I`) | ||
| // Do not compute eigenvectors. | ||
| ev_comp_none = u8(`N`) | ||
| } | ||
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| // EVJob specifies whether eigenvectors are computed in Dsyev. | ||
| pub enum EVJob as u8 { | ||
| // Compute eigenvectors. | ||
| ev_compute = u8(`V`) | ||
| // Do not compute eigenvectors. | ||
| ev_none = u8(`N`) | ||
| } | ||
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| // LeftEVJob specifies whether left eigenvectors are computed in Dgeev. | ||
| pub enum LeftEVJob as u8 { | ||
| // Compute left eigenvectors. | ||
| left_ev_compute = u8(`V`) | ||
| // Do not compute left eigenvectors. | ||
| left_ev_none = u8(`N`) | ||
| } | ||
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| // RightEVJob specifies whether right eigenvectors are computed in Dgeev. | ||
| pub enum RightEVJob as u8 { | ||
| // Compute right eigenvectors. | ||
| right_ev_compute = u8(`V`) | ||
| // Do not compute right eigenvectors. | ||
| right_ev_none = u8(`N`) | ||
| } | ||
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| // BalanceJob specifies matrix balancing operation. | ||
| pub enum BalanceJob as u8 { | ||
| permute = u8(`P`) | ||
| scale = u8(`S`) | ||
| permute_scale = u8(`B`) | ||
| balance_none = u8(`N`) | ||
| } | ||
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| // SchurJob specifies whether the Schur form is computed in Dhseqr. | ||
| pub enum SchurJob as u8 { | ||
| eigenvalues_only = u8(`E`) | ||
| eigenvalues_and_schur = u8(`S`) | ||
| } | ||
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| // SchurComp specifies whether and how the Schur vectors are computed in Dhseqr. | ||
| pub enum SchurComp as u8 { | ||
| // Compute Schur vectors of the original matrix. | ||
| schur_orig = u8(`V`) | ||
| // Compute Schur vectors of the upper Hessenberg matrix. | ||
| schur_hess = u8(`I`) | ||
| // Do not compute Schur vectors. | ||
| schur_none = u8(`N`) | ||
| } | ||
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| // UpdateSchurComp specifies whether the matrix of Schur vectors is updated in Dtrexc. | ||
| pub enum UpdateSchurComp as u8 { | ||
| // Update the matrix of Schur vectors. | ||
| update_schur = u8(`V`) | ||
| // Do not update the matrix of Schur vectors. | ||
| update_schur_none = u8(`N`) | ||
| } | ||
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| // EVSide specifies what eigenvectors are computed in Dtrevc3. | ||
| pub enum EVSide as u8 { | ||
| // Compute only right eigenvectors. | ||
| ev_right = u8(`R`) | ||
| // Compute only left eigenvectors. | ||
| ev_left = u8(`L`) | ||
| // Compute both right and left eigenvectors. | ||
| ev_both = u8(`B`) | ||
| } | ||
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| // EVHowMany specifies which eigenvectors are computed in Dtrevc3 and how. | ||
| pub enum EVHowMany as u8 { | ||
| // Compute all right and/or left eigenvectors. | ||
| ev_all = u8(`A`) | ||
| // Compute all right and/or left eigenvectors multiplied by an input matrix. | ||
| ev_all_mul_q = u8(`B`) | ||
| // Compute selected right and/or left eigenvectors. | ||
| ev_selected = u8(`S`) | ||
| } | ||
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| // MaximizeNormXJob specifies the heuristic method for computing a contribution to | ||
| // the reciprocal Dif-estimate in Dlatdf. | ||
| pub enum MaximizeNormXJob as u8 { | ||
| // Solve Z*x=h-f where h is a vector of ±1. | ||
| local_look_ahead = 0 | ||
| // Compute an approximate null-vector e of Z, normalize e and solve Z*x=±e-f. | ||
| normalized_null_vector = 2 | ||
| } | ||
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| // OrthoComp specifies whether and how the orthogonal matrix is computed in Dgghrd. | ||
| pub enum OrthoComp as u8 { | ||
| // Do not compute the orthogonal matrix. | ||
| ortho_none = u8(`N`) | ||
| // The orthogonal matrix is formed explicitly and returned in the argument. | ||
| ortho_explicit = u8(`I`) | ||
| // The orthogonal matrix is post-multiplied into the matrix stored in the argument on entry. | ||
| ortho_postmul = u8(`V`) | ||
| } |
There was a problem hiding this comment.
The enums MatrixType, Pivot, ApplyOrtho, etc., are comprehensive and cover a wide range of functionalities. Consider adding unit tests to verify the correctness of each enum value's behavior in functions where they are used.
Would you like me to help by creating some unit tests for these enums?
| pub fn dgesvd(jobu SVDJob, jobvt SVDJob, m int, n int, mut a []f64, lda int, s []f64, mut u []f64, ldu int, mut vt []f64, ldvt int, superb []f64) { | ||
| info := C.LAPACKE_dgesvd(.row_major, jobu, jobvt, m, n, unsafe { &a[0] }, lda, &s[0], | ||
| unsafe { &u[0] }, ldu, unsafe { &vt[0] }, ldvt, &superb[0]) |
There was a problem hiding this comment.
Error handling using vsl_panic is consistent across functions. Consider centralizing error handling to reduce redundancy and improve maintainability.
- errors.vsl_panic('lapack failed', .efailed)
+ handle_lapack_error(info)
// Add a new function to handle LAPACK errors centrally
fn handle_lapack_error(info int) {
if info != 0 {
errors.vsl_panic('LAPACK operation failed with error code: $info', .efailed)
}
}Also applies to: 146-146, 192-193
There was a problem hiding this comment.
Actionable comments posted: 2
Outside diff range and nitpick comments (1)
lapack/lapack64/ilaenv.v (1)
25-25: Consider replacing the placeholder comment with a more concrete plan or remove it if not applicable.The TODO comment suggests replacing the current implementation with a constant lookup or a list of constants, which could potentially improve performance and maintainability. If this change is not planned in the near future, it might be better to remove the comment to avoid confusion.
Review details
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Files selected for processing (1)
- lapack/lapack64/ilaenv.v (1 hunks)
Additional comments not posted (1)
lapack/lapack64/ilaenv.v (1)
247-247: Ensure accurate calculation in the return statement.The expression
int(f64(math.min(n1, n2)) * 1.6)converts the minimum ofn1andn2into a floating-point number, multiplies it by 1.6, and then casts it back to an integer. Verify that this calculation provides the expected results in all cases, particularly under edge conditions wheren1andn2are very large or very small.
| if !sname && !cname { | ||
| panic(bad_name) | ||
| } |
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Ensure proper error management by defining what bad_name is.
It appears that bad_name is used as an error message or identifier, but its definition is not visible in the provided code. Ensure that bad_name is properly defined and handled, possibly as a constant or a localized string that can be logged or displayed when an error occurs.
| // Assuming n4 and n2 are defined elsewhere in your code | ||
| match c3 { | ||
| 'TRF' { | ||
| // Replace `n4` and `n2` with actual variables | ||
| if sname { | ||
| // if n4 <= 64 { | ||
| // return 1 | ||
| // } | ||
| return 32 | ||
| } | ||
| // if n4 <= 64 { | ||
| // return 1 | ||
| // } | ||
| return 32 | ||
| } |
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Address the commented-out code and clarify the logic regarding n4 and n2.
The commented-out code within the match statement is confusing and could lead to maintenance issues. It's important to either implement the logic that's currently commented out or remove the comments if they are no longer relevant. Additionally, the use of n4 and n2 should be clarified as their definitions and values are critical to the logic.
.github/workflows/ci.yml
Outdated
| - name: Execute Tests using Pure V Backend | ||
| run: ~/.vmodules/vsl/bin/test ${{ matrix.flags }} | ||
| # - name: Execute Tests using Pure V Backend | ||
| # run: ~/.vmodules/vsl/bin/test ${{ matrix.flags }} |
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I will revert this as soon as I finish testing
| - name: Execute Tests using Pure V Backend | ||
| run: ~/.vmodules/vsl/bin/test | ||
| # - name: Execute Tests using Pure V Backend | ||
| # run: ~/.vmodules/vsl/bin/test |
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I will revert this as soon as I finish testing
…l_lapack_lapacke.v - Add functions uplo_from_bool and uplo_to_bool to conversions.v - Remove unused dlamch_s function from dgetf2.v - Update dsyev.v to include additional error handling and scaling of matrix - Update dpotrf function in lapack_notd_vsl_lapack_lapacke.v to use uplo_from_bool for uplo parameter
…to use named constant for uplo parameter
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Actionable comments posted: 14
Review details
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Review profile: CHILL
Files selected for processing (32)
- .github/workflows/ci.yml (2 hunks)
- bin/test (2 hunks)
- blas/blas64/dgemm.v (5 hunks)
- blas/conversions.v (1 hunks)
- blas/oblas_d_vsl_blas_cblas.v (1 hunks)
- blas/oblas_notd_vsl_blas_cblas.v (1 hunks)
- la/blas.v (11 hunks)
- la/densesol.v (2 hunks)
- la/matrix_ops.v (5 hunks)
- lapack/lapack64/dgesv.v (1 hunks)
- lapack/lapack64/dgetf2.v (1 hunks)
- lapack/lapack64/dgetrf.v (1 hunks)
- lapack/lapack64/dgetri.v (1 hunks)
- lapack/lapack64/dgetrs.v (1 hunks)
- lapack/lapack64/dlae2.v (1 hunks)
- lapack/lapack64/dlanst.v (1 hunks)
- lapack/lapack64/dlansy.v (1 hunks)
- lapack/lapack64/dlapy2.v (1 hunks)
- lapack/lapack64/dlascl.v (1 hunks)
- lapack/lapack64/dlasrt.v (1 hunks)
- lapack/lapack64/dlassq.v (1 hunks)
- lapack/lapack64/dlaswp.v (1 hunks)
- lapack/lapack64/dorg2l.v (1 hunks)
- lapack/lapack64/dorgql.v (1 hunks)
- lapack/lapack64/dorgtr.v (1 hunks)
- lapack/lapack64/dsterf.v (1 hunks)
- lapack/lapack64/dsyev.v (1 hunks)
- lapack/lapack64/dsytrd.v (1 hunks)
- lapack/lapack64/iparmq.v (1 hunks)
- lapack/lapack64/lapack64.v (1 hunks)
- lapack/lapack_d_vsl_lapack_lapacke.v (7 hunks)
- lapack/lapack_notd_vsl_lapack_lapacke.v (1 hunks)
Files not summarized due to errors (1)
- blas/oblas_d_vsl_blas_cblas.v: Error: Message exceeds token limit
Files skipped from review due to trivial changes (1)
- lapack/lapack64/lapack64.v
Files skipped from review as they are similar to previous changes (6)
- la/matrix_ops.v
- lapack/lapack64/dgesv.v
- lapack/lapack64/dgetrf.v
- lapack/lapack64/dgetri.v
- lapack/lapack64/dgetrs.v
- lapack/lapack_d_vsl_lapack_lapacke.v
Additional comments not posted (38)
lapack/lapack64/dlapy2.v (1)
5-10: Approved - Functiondlapy2implementation and documentationThe implementation of
dlapy2is concise and correct, effectively utilizingmath.hypotfor its intended purpose. The documentation is clear, indicating its use and the reason for its exposure for testing.la/densesol.v (1)
Line range hint
3-19: Approved -den_solvefunction logic and documentationThe logic for solving dense linear systems using LAPACK is correctly implemented. The cloning of matrix data to preserve the original matrix when required is a good practice. Ensure that the LAPACK functions are correctly imported and used as per the module's standards.
Verification successful
Verified - Correct Import and Usage of LAPACK Functions
The import statement and usage of the LAPACK function
dgesvin theden_solvefunction are correctly implemented.
import vsl.lapackis present inla/densesol.vlapack.dgesvis correctly used within the functionScripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the correct import and usage of LAPACK functions in `den_solve`. # Test: Search for LAPACK import and usage in the file. rg --type v 'import vsl.lapack' 'lapack.dgesv' la/densesol.vLength of output: 86
Script:
#!/bin/bash # Description: Verify the correct import and usage of LAPACK functions in `den_solve`. # Test: Search for LAPACK import and usage in the file. rg 'import vsl.lapack' la/densesol.v rg 'lapack.dgesv' la/densesol.vLength of output: 150
lapack/lapack64/dlaswp.v (1)
6-44: Approved - Functiondlaswpimplementation and documentationThe implementation of
dlaswpis robust, correctly handling various edge cases with appropriate error messages. The usage of BLAS'sdswapfor row interchanges is correctly implemented. The documentation is clear, indicating its use and the reason for its exposure for testing.lapack/lapack64/dlae2.v (1)
34-46: Review logic for computing eigenvalues based on the sign ofsm.The calculation of
rt1andrt2is based on the sign ofsmwhich is a sum ofaandc. Ensure that this logic correctly handles all edge cases, especially whensmis zero.lapack/lapack64/dgetf2.v (2)
8-25: Ensure robust error handling and informative error messages.The function uses panics for error handling which is acceptable in system libraries but ensure that the error messages are informative and cover all cases properly.
Verification successful
Ensure robust error handling and informative error messages.
The error messages used in the
dgetf2function are defined inlapack/lapack64/errors.vand are reasonably informative. However, the message forbad_ld_acould be more descriptive to specify the exact issue with the leading dimension.
lapack/lapack64/errors.v:
m_lt0: 'lapack: m < 0'n_lt0: 'lapack: n < 0'bad_ld_a: 'lapack: bad leading dimension of A' (could be more descriptive)short_a: 'lapack: insufficient length of a'bad_len_ipiv: 'lapack: bad length of ipiv'Recommendation: Consider enhancing the
bad_ld_aerror message to specify the expected condition, e.g., "lapack: leading dimension of A is less than max(1, n)".Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify that all error messages in `dgetf2` function are informative and cover the necessary cases. # Test: Check source code comments and documentation for error handling. grep "panic" lapack/lapack64/dgetf2.vLength of output: 163
Script:
#!/bin/bash # Description: Verify the definitions and usage of error messages in `dgetf2` function. # Find where the error constants are defined. grep -E "m_lt0|n_lt0|bad_ld_a|short_a|bad_len_ipiv" -r . # Check for comments related to these error constants to understand their context. grep -E "m_lt0|n_lt0|bad_ld_a|short_a|bad_len_ipiv" -r . --context=2Length of output: 24077
31-57: Validate the correct usage of BLAS functions and parameters.The function calls several BLAS functions like
idamax,dswap, anddscal. Ensure that these functions are used correctly and that all parameters passed to them are valid.lapack/lapack64/dlanst.v (3)
9-23: Enhance input validation and error handling for norm type and matrix dimensions.The function starts by checking the validity of the
normtype and the dimensions of the matrix. These checks are crucial for preventing runtime errors and ensuring correct calculations.
25-67: Ensure that the return statements correctly reflect the computed norms and handle all edge cases.The function returns the computed norms based on the selected type. It's important to ensure that these values are accurate and that all edge cases, such as matrices with zero dimensions, are handled correctly.
25-67: Optimize and verify the correctness of norm calculations.The match statement handles different norm calculations. Each block should be reviewed for mathematical accuracy and optimized for performance where possible.
lapack/lapack64/dorg2l.v (2)
21-52: Strengthen input validation and error handling.The function includes several checks for input validity, such as matrix dimensions and array lengths. These checks are essential for preventing incorrect operations and potential runtime errors.
62-75: Ensure the correct application of elementary reflectors and validate the use of BLAS functions.The application of elementary reflectors is a crucial part of generating the orthonormal matrix. Verify that the reflectors are applied correctly and that all BLAS function calls are appropriate and correctly parameterized.
lapack/lapack64/iparmq.v (1)
5-83: Review ofiparmqfunction:
- Function Complexity: The function has nested conditions and match statements which are somewhat complex. Consider adding comments to explain the logic behind each major block, especially within the
matchstatements.- Error Handling: The use of
panicfor error handling is appropriate given the context (internal utility function), but ensure that these cases are well-documented to avoid misuse.- Potential Optimization: The repeated calculation of
nhin multiple conditions (lines 8-20) could be optimized by calculating its value once and storing it, rather than recalculating it multiple times.Overall, the function is logically sound, but improvements in documentation and slight refactoring could enhance readability and maintainability.
bin/test (1)
Line range hint
13-37: Review of Test Script Modifications:
- New Testing Flag: The addition of the
--use-lapackeflag (line 35) is a positive change, aligning with the updated testing requirements for LAPACKE integration.- Flag Handling: Ensure that the flags are adequately documented in the script's header or help output to inform users about the new testing capabilities.
- Script Robustness: The script robustly handles different configurations and cleanly exits on errors, which is good practice.
No issues found with the changes. The modifications support the new testing requirements effectively.
[APROVED]lapack/lapack64/dlascl.v (1)
5-108: Review ofdlasclfunction:
- Error Handling: The function includes comprehensive checks for input values (lines 19-33), which is crucial for a function that performs low-level matrix operations. This robust error handling is appropriate for the function's purpose.
- Matrix Scaling Logic: The logic for scaling different types of matrices (lines 39-102) is well-structured and clearly handles different matrix types. Consider adding more comments to explain the scaling process for each matrix type.
- Optimization Opportunity: The loop from lines 52-107 performs scaling in multiple steps. Investigate if there's a way to optimize this loop, potentially reducing the number of iterations needed to achieve the desired scaling.
Overall, the function is well-implemented with thorough error handling and clear logic. Minor improvements could be made in documentation and optimization.
lapack/lapack64/dsyev.v (1)
6-102: Review ofdsyevfunction:
- Error Handling and Input Validation: The function includes multiple checks for input validity (lines 7-24), ensuring that the matrix dimensions and work array sizes are appropriate before proceeding with computations. This is crucial for avoiding runtime errors in numerical computations.
- Quick Return Optimization: The quick return conditions (lines 27-28, 52) are well-placed to avoid unnecessary computations for edge cases.
- Numerical Stability and Scaling: The scaling logic (lines 62-76) to handle matrices with values outside the normal floating-point range is a good practice for improving numerical stability in eigenvalue computations.
- Complexity and Maintainability: Given the complexity of the function, consider adding more detailed comments explaining each step of the eigenvalue computation process, especially the use of different LAPACK routines.
Overall, the function is robust and well-implemented with appropriate checks and optimizations. Enhancements in documentation could further improve maintainability.
.github/workflows/ci.yml (2)
68-68: Skip modification as per developer's comment.The developer has explicitly stated that this change is temporary for testing purposes and will be reverted. Therefore, no action is needed at this time.
Also applies to: 114-114
71-71: Ensure compatibility with flags.The addition of
--use-cblasand--use-lapackeflags in the test commands is consistent with the refactoring goals stated in the PR. However, verify that all necessary configurations and dependencies for these flags are correctly set up in the CI environment.Also applies to: 117-117
Verification successful
Dependencies for
--use-cblasand--use-lapackeare correctly set up.The CI configuration includes
liblapacke-devandlibopenblas-dev, ensuring compatibility with the added flags.
.github/workflows/ci.yml:
- Lines containing
liblapacke-devandlibopenblas-dev.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Check if necessary configurations and dependencies for CBLAS and LAPACKE are set up. cat .github/workflows/ci.yml | grep -E 'liblapacke-dev|libopenblas-dev'Length of output: 132
blas/oblas_notd_vsl_blas_cblas.v (1)
1-82: Refactor and inline function implementations reviewed.All functions are correctly defined as inline, which is suitable for these types of small wrapper functions that primarily delegate to another function. The module and import changes from
vlastoblasare correctly applied. Ensure that all calls to these functions across the project are updated to reflect these changes.lapack/lapack64/dlansy.v (1)
1-122: Review ofdlansyfunction.The function
dlansyis well-structured with clear error handling and quick return paths. The use of a match expression to handle different norms is appropriate and maintains readability. Ensure that the constantsbad_norm,bad_uplo,short_a, andshort_workare defined elsewhere in the module as they are used for panic messages. Also, verify that all possible norm values are accounted for in the match expression to avoid potential runtime errors.Verification successful
All constants used in
dlansyare defined elsewhere in the codebase.The constants
bad_norm,bad_uplo,short_a, andshort_workare defined inlapack/lapack64/errors.v, confirming their presence and proper usage.
bad_norm:lapack/lapack64/errors.vbad_uplo:lapack/lapack64/errors.vshort_a:lapack/lapack64/errors.vshort_work:lapack/lapack64/errors.vScripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Check for the definition of constants used in dlansy. rg --type v 'bad_norm|bad_uplo|short_a|short_work' --files-with-matchesLength of output: 99
Script:
#!/bin/bash # Description: Check for the definition of constants used in dlansy. rg --type vlang 'bad_norm|bad_uplo|short_a|short_work' --files-with-matchesLength of output: 107
Script:
#!/bin/bash # Description: Check for the definition of constants used in dlansy without specifying a file type. rg 'bad_norm|bad_uplo|short_a|short_work'Length of output: 2527
lapack/lapack64/dlassq.v (1)
1-120: Review ofdlassqfunction.The
dlassqfunction is implemented with detailed and careful handling of scaling to prevent overflow and underflow, which is crucial for numerical stability. Ensure that constants likedtbig,dsbig,dtsml, anddssmlare defined and appropriately set to handle edge cases in scaling. The logic to combine results from different accumulators is complex but appears correct. Consider adding more comments or documentation to explain the rationale behind these calculations for future maintainers.Verification successful
Verified the definitions of scaling constants.
The constants
dtbig,dsbig,dtsml, anddssmlare defined inlapack/lapack64/lapack64.vand have appropriate values for handling scaling in thedlassqfunction. This ensures that the function can handle edge cases of overflow and underflow correctly.
dtsml = 1.4916681462400413e-154dtbig = 1.997919072202235e+146dssml = 4.4989137945431964e+161dsbig = 1.1113793747425387e-162Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the definition and values of scaling constants. rg --type v 'dtbig|dsbig|dtsml|dssml' --files-with-matchesLength of output: 86
Script:
#!/bin/bash # Description: Verify the definition and values of scaling constants. rg 'dtbig|dsbig|dtsml|dssml'Length of output: 2365
lapack/lapack64/dorgtr.v (3)
60-65: Ensure adequate buffer size for operations.The check for array length
a.len < (n - 1) * lda + ncould potentially lead to out-of-bounds access if not properly handled. It's good to see proactive error handling to prevent such issues.
67-83: Optimize matrix transformations based on 'uplo' value.The transformations based on the
uplovalue are correctly implemented to adjust matrix elements for upper and lower triangular matrices. This conditional handling is crucial for the correctness of the orthogonal matrix generation.Also applies to: 101-104
84-106: Review and optimize the loop operations for performance.The nested loops for shifting vectors and setting matrix values could potentially be optimized or parallelized to improve performance, especially for large matrices.
[PERFORMANCE]lapack/lapack64/dorgql.v (2)
69-74: Ensure adequate buffer size for operations.Proactive error handling for buffer size checks is observed here as well, which is crucial to prevent out-of-bounds access.
76-94: Optimize matrix transformations and loop operations.The handling of matrix transformations and the use of blocked vs. unblocked code based on the workspace size (
lwork) are well implemented. Consider parallelizing the loops to enhance performance for large matrices.
[PERFORMANCE]Also applies to: 110-138
lapack/lapack_notd_vsl_lapack_lapacke.v (2)
30-32: Ensure proper function delegation.The function
dgesvcorrectly delegates the operation tolapack64.dgesv, ensuring that the higher-level function is a simple wrapper, which is good for modularity.
54-60: Enhance error messages for better debugging.Improving the error message in
dgesvdas previously suggested will help in debugging and maintaining the code.- errors.vsl_panic('lapack failed', .efailed) + errors.vsl_panic('LAPACK dgesvd failed with error code: $info', .efailed)Likely invalid or redundant comment.
lapack/lapack64/dsytrd.v (2)
93-104: Ensure adequate buffer size for operations.Proactive error handling for buffer size checks is observed here as well, which is crucial to prevent out-of-bounds access.
137-183: Optimize matrix transformations and loop operations.The handling of matrix transformations and the use of blocked vs. unblocked code based on the workspace size (
lwork) are well implemented. Consider parallelizing the loops to enhance performance for large matrices.
[PERFORMANCE]lapack/lapack64/dsterf.v (1)
17-280: Review of thedsterfFunction
Correctness and Logic: The function implements the QL and QR algorithms for computing eigenvalues of a tridiagonal matrix. The logic appears to follow the standard algorithmic steps for these methods, including scaling, iteration control, and convergence checks.
Error Handling: The function uses panic for error handling, which is aggressive but appropriate for conditions that should never occur if the API is used correctly (e.g., negative dimensions or short arrays).
Performance: The function contains several loops and condition checks that are necessary for the algorithm. The use of
unsafefor array slicing could be risky but is likely used here for performance reasons to avoid bounds checking overhead in tight loops.Maintainability: The function is quite long and complex, which could affect maintainability. Consider refactoring into smaller helper functions to handle specific parts of the algorithm, such as scaling or the QR/QL iterations.
Documentation: The function is well-documented with comments explaining the purpose and the process, which is good for maintainability and understanding the code.
Overall, the implementation seems solid, but refactoring for better readability and maintainability could be beneficial.
blas/blas64/dgemm.v (1)
Line range hint
1-252: Review of thedgemmanddgemm_parallelFunctions
Correctness and Logic: Both functions implement matrix multiplication with options for transposing matrices. The logic includes handling different transposition cases and a parallel computation approach in
dgemm_parallel.Error Handling: The function checks for invalid matrix dimensions and strides, which is crucial to prevent out-of-bounds errors.
Performance: The use of parallel computation in
dgemm_parallelis a significant performance optimization for large matrices. However, ensure that the parallelization overhead does not negate the benefits for smaller matrices.Maintainability: The code is complex due to the many conditions and loops. Consider adding more comments to explain the logic in
dgemm_parallel, especially how submatrices are handled.Security: The use of
unsafein array slicing is a potential risk. Ensure that all indices and lengths are verified before accessing arrays to prevent out-of-bounds errors.Refactoring for clarity and additional comments would improve maintainability and understandability.
la/blas.v (1)
Line range hint
3-316: Review of Vector and Matrix Functions inla/blas.v
Correctness and Logic: The functions correctly implement various linear algebra operations such as dot product, vector addition, and matrix multiplication using the BLAS library. The logic is consistent with standard linear algebra operations.
Error Handling: There is no explicit error handling. Consider adding checks for dimensions and null pointers where applicable.
Performance: The use of BLAS functions (
ddot,daxpy,dgemv,dgemm) is appropriate and should ensure high performance for these operations.Maintainability: The functions are generally well-structured and modular. However, some functions are quite long and could benefit from being broken into smaller sub-functions.
Documentation: More detailed comments on the expected shapes and sizes of the input matrices and vectors would be helpful for users of these functions.
Overall, the implementations are efficient and correct, but some additional error handling and documentation could improve robustness and usability.
[APROVED]blas/conversions.v (3)
1-1: Module declaration updated to reflect new naming convention.The module has been correctly renamed from
vlastoblasto align with the refactor's objectives.
7-7: Correct import statement forblas64.The import path has been updated to
vsl.blas.blas64, which is consistent with the new structure of the linear algebra modules.
10-10: Type aliases have been updated to use newblas64types.These changes correctly reflect the new types defined in the
blas64module, ensuring consistency across the BLAS implementation.Also applies to: 13-13, 16-16, 19-19, 22-22
blas/oblas_d_vsl_blas_cblas.v (3)
195-197: Consistent use of unsafe pointer access.The functions
sdsdot,dsdot,sdot, andddotconsistently use unsafe pointer access to the first element of arrays. This approach is efficient but requires careful handling to ensure safety. Consider adding explicit comments or documentation about the necessity of ensuring the input arrays are non-empty and large enough to prevent out-of-bounds access.Also applies to: 200-202, 205-207, 210-212
189-192: Ensure thread safety when setting the number of threads.The function
set_num_threadsdirectly interfaces with the OpenBLAS library to set the number of threads. It's crucial to ensure that this setting does not introduce any race conditions or affect the state negatively when used in a multi-threaded environment.
1037-1040: Optimize matrix-matrix multiplication for high performance.The
dgemmfunction is critical for performance in many numerical computations. It directly interfaces with the CBLAS library function. Ensure that the leading dimensions (lda,ldb,ldc) and other parameters are correctly set and validated to maximize performance and prevent memory access errors.Verification successful
Ensure optimal settings for matrix-matrix multiplication.
The
dgemmfunction is used extensively in the codebase, indicating its critical role in numerical computations. The search results provide several instances ofdgemmusage, including detailed implementations and optimizations. There are no apparent issues with the current settings for leading dimensions (lda,ldb,ldc) and other parameters.
- The function correctly interfaces with the CBLAS library.
- Leading dimensions and other parameters seem to be validated and used appropriately.
- Parallel and serial implementations are provided, ensuring flexibility and performance optimization.
Recommendations:
- Review and ensure that leading dimensions (
lda,ldb,ldc) are always set correctly to avoid memory access errors.- Consider adding more inline comments or documentation for complex sections to aid future maintenance and optimization efforts.
Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Check for optimal settings and common pitfalls in matrix-matrix multiplication. # Test: Search for discussions or optimizations in the usage of `dgemm`. rg --type v "dgemm" --context 15Length of output: 58
Script:
#!/bin/bash # Description: Check for optimal settings and common pitfalls in matrix-matrix multiplication. # Test: Search for discussions or optimizations in the usage of `dgemm`. rg --type vlang "dgemm" --context 15Length of output: 66
Script:
#!/bin/bash # Description: Check for optimal settings and common pitfalls in matrix-matrix multiplication. # Find all `.v` files and search for `dgemm` usage within them. fd -e v -x rg "dgemm" --context 15 {}Length of output: 16954
| // Side is used to specify whether a matrix is on the left or right side in a matrix-matrix multiplication. | ||
| pub type Side = blas64.Side |
There was a problem hiding this comment.
Consider adding examples for Side.
To enhance understanding, consider adding examples or more detailed explanations on how the Side type is used in operations.
Would you like me to add examples or detailed usage documentation for this type?
Clarify documentation for Side type.
The comment for Side should be more detailed to explain its use in matrix-matrix multiplication contexts.
- // Side is used to specify whether a matrix is on the left or right side in a matrix-matrix multiplication.
+ // Side specifies the side of the operand matrix in a matrix-matrix multiplication, determining if the matrix is multiplied from the left or the right.Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| // Side is used to specify whether a matrix is on the left or right side in a matrix-matrix multiplication. | |
| pub type Side = blas64.Side | |
| // Side specifies the side of the operand matrix in a matrix-matrix multiplication, determining if the matrix is multiplied from the left or the right. | |
| pub type Side = blas64.Side |
Tip
Codebase Verification
Ensure All Utility Functions Have Unit Tests
The review comment suggests verifying that all utility functions in blas/conversions.v are covered by unit tests. The executed script did not find any test functions in blas/conversions.v.
- Please add unit tests for the utility functions in
blas/conversions.vto ensure their correctness, especially for edge cases like zero-sized matrices.
Analysis chain
Review of utility functions for matrix operations.
The utility functions for converting and printing matrices are well-implemented. However, ensure that all functions are covered by unit tests to verify their correctness, especially with edge cases like zero-sized matrices.
Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Verify unit tests cover all utility functions in `blas/conversions.v`.
# Test: Search for unit tests related to utility functions.
rg --type v $'fn test_'
Length of output: 50
Script:
#!/bin/bash
# Description: Verify unit tests cover all utility functions in `blas/conversions.v`.
# Step 1: Find all `.v` files in the repository.
fd -e v -t f > v_files.txt
# Step 2: Search for test functions in the found `.v` files.
while read -r file; do
rg 'fn test_' "$file"
done < v_files.txt
Length of output: 20332
| d.sort(b < a) | ||
| } |
There was a problem hiding this comment.
Fix sorting logic in decreasing order
The lambda function for sorting in decreasing order seems incorrect. It should compare two elements and return a boolean. Consider using the correct syntax for the comparison function.
- d.sort(b < a)
+ d.sort((a, b) => b < a)Committable suggestion was skipped due to low confidence.
| @[inline] | ||
| pub fn sgemv(trans Transpose, m int, n int, alpha f32, a []f32, lda int, x []f32, incx int, beta f32, mut y []f32, incy int) { | ||
| C.cblas_sgemv(.row_major, trans, m, n, alpha, unsafe { &a[0] }, lda, unsafe { &x[0] }, | ||
| incx, beta, unsafe { &y[0] }, incy) |
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Tip
Codebase Verification
Review matrix-vector operations for performance and safety.
The functions sgemv, dgemv, cgemv, and zgemv perform matrix-vector multiplications. These functions translate directly to their respective CBLAS calls. Ensure that the parameters such as order, trans, m, n, lda, incx, and incy are correctly validated before use to prevent any misuse or errors in matrix operations.
- The
dgemvfunction inblas64/dgemv.vincludes validation checks for parametersm,n,lda, andincx. - Other functions (
sgemv,cgemv,zgemv) inblas/oblas_d_vsl_blas_cblas.vdo not show explicit validation for their parameters.
Please review and add necessary validation for the parameters in sgemv, cgemv, and zgemv to ensure robust and safe operations.
Analysis chain
Review matrix-vector operations for performance and safety.
The functions sgemv, dgemv, cgemv, and zgemv perform matrix-vector multiplications. These functions translate directly to their respective CBLAS calls. Ensure that the parameters such as order, trans, m, n, lda, incx, and incy are correctly validated before use to prevent any misuse or errors in matrix operations.
Also applies to: 481-483, 487-489, 492-494
Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Verify parameter validation in matrix-vector operations.
# Test: Search for validation logic around the parameters of matrix-vector operations.
rg --type v "sgemv|dgemv|cgemv|zgemv" --context 10
Length of output: 78
Script:
#!/bin/bash
# Description: Verify parameter validation in matrix-vector operations.
# Test: Search for validation logic around the parameters of matrix-vector operations.
rg "fn sgemv|fn dgemv|fn cgemv|fn zgemv" --context 10
Length of output: 6118
| pub fn sdsdot(n int, alpha f32, x []f32, incx int, y []f32, incy int) f32 { | ||
| return C.cblas_sdsdot(n, alpha, unsafe { &x[0] }, incx, unsafe { &y[0] }, incy) | ||
| } |
There was a problem hiding this comment.
Tip
Codebase Verification
Optimize memory access for dot product functions.
The functions sdsdot, dsdot, sdot, and ddot use unsafe pointers to access array elements. Ensure that there is a boundary check or a clear precondition that the input arrays have at least n elements before these functions are called to prevent potential segmentation faults.
- File:
blas/oblas_d_vsl_blas_cblas.v- Lines: 195-197, 200-202, 205-207, 210-212
No boundary checks or preconditions were found in the provided context. Please add necessary checks to ensure safe memory access.
Analysis chain
Optimize memory access for dot product functions.
The functions sdsdot, dsdot, sdot, and ddot use unsafe pointers to access array elements, which is efficient but potentially unsafe if not handled correctly. Ensure that there is a boundary check or a clear precondition that the input arrays have at least n elements before these functions are called to prevent potential segmentation faults.
Also applies to: 200-202, 205-207, 210-212
Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Verify that all calls to dot product functions ensure the arrays have at least `n` elements.
# Test: Search for function calls and check for boundary conditions.
rg --type v "sdsdot|dsdot|sdot|ddot" --context 5
Length of output: 76
Script:
#!/bin/bash
# Description: Verify that all calls to dot product functions ensure the arrays have at least `n` elements.
# Test: Search for function calls and check for boundary conditions.
rg --type-add 'v:*.v' "sdsdot|dsdot|sdot|ddot" --context 5
Length of output: 6594
| pub fn dlae2(a f64, b f64, c f64) (f64, f64) { | ||
| sm := a + c | ||
| df := a - c | ||
| adf := math.abs(df) | ||
| tb := b + b | ||
| ab := math.abs(tb) | ||
| mut acmx := c | ||
| mut acmn := a | ||
| if math.abs(a) > math.abs(c) { | ||
| acmx = a | ||
| acmn = c | ||
| } |
There was a problem hiding this comment.
Consider optimizing variable assignments.
The variables acmx and acmn are initialized to c and a respectively and might be reassigned based on a condition. This can be optimized by using a ternary operation or a more direct assignment approach to reduce redundancy.
- mut acmx := c
- mut acmn := a
- if math.abs(a) > math.abs(c) {
- acmx = a
- acmn = c
- }
+ mut acmx := if math.abs(a) > math.abs(c) { a } else { c }
+ mut acmn := if math.abs(a) > math.abs(c) { c } else { a }Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| pub fn dlae2(a f64, b f64, c f64) (f64, f64) { | |
| sm := a + c | |
| df := a - c | |
| adf := math.abs(df) | |
| tb := b + b | |
| ab := math.abs(tb) | |
| mut acmx := c | |
| mut acmn := a | |
| if math.abs(a) > math.abs(c) { | |
| acmx = a | |
| acmn = c | |
| } | |
| pub fn dlae2(a f64, b f64, c f64) (f64, f64) { | |
| sm := a + c | |
| df := a - c | |
| adf := math.abs(df) | |
| tb := b + b | |
| ab := math.abs(tb) | |
| mut acmx := if math.abs(a) > math.abs(c) { a } else { c } | |
| mut acmn := if math.abs(a) > math.abs(c) { c } else { a } |
| pub fn dorgql(m int, n int, k int, mut a []f64, lda int, tau []f64, mut work []f64, lwork int) { | ||
| if m < 0 { | ||
| panic(m_lt0) | ||
| } | ||
| if n < 0 { | ||
| panic(n_lt0) | ||
| } | ||
| if n > m { | ||
| panic(n_gtm) | ||
| } | ||
| if k < 0 { | ||
| panic(k_lt0) | ||
| } | ||
| if k > n { | ||
| panic(k_gtn) | ||
| } | ||
| if lda < math.max(1, n) { | ||
| panic(bad_ld_a) | ||
| } | ||
| if lwork < math.max(1, n) && lwork != -1 { | ||
| panic(bad_l_work) | ||
| } | ||
| if work.len < math.max(1, lwork) { | ||
| panic(short_work) | ||
| } |
There was a problem hiding this comment.
Refactor error messages for clarity and consistency.
Similar to dorgtr, the error handling uses generic panic messages. More descriptive messages would enhance maintainability and debugging.
- panic(m_lt0)
+ panic("Parameter 'm' must be non-negative.")
- panic(n_lt0)
+ panic("Parameter 'n' must be non-negative.")
- panic(k_lt0)
+ panic("Parameter 'k' must be non-negative.")Committable suggestion was skipped due to low confidence.
| mut vvl := 0.0 | ||
| mut vvr := 0.0 | ||
| mut ldvl := ldvl_ | ||
| mut ldvr := ldvr_ | ||
| if calc_vl == .left_ev_compute { | ||
| vvl = vl[0] | ||
| } else { | ||
| ldvl = 1 | ||
| } | ||
| if calc_vr == .left_ev_compute { | ||
| vvr = vr[0] | ||
| } else { | ||
| ldvr = 1 | ||
| } | ||
|
|
||
| vl[0] = vvl | ||
| vr[0] = vvr | ||
|
|
||
| info := lapack64.dgeev(calc_vl, calc_vr, n, mut a, lda, wr, wi, mut vl, ldvl, mut | ||
| vr, ldvr) | ||
| if info != 0 { | ||
| errors.vsl_panic('lapack failed', .efailed) | ||
| } | ||
| } |
There was a problem hiding this comment.
Clarify vector length handling in dgeev.
The handling of vector lengths in dgeev can be simplified to avoid manual index manipulation, reducing the potential for errors.
- ldvl = if calc_vl == .left_ev_compute { vl.len } else { 1 }
- ldvr = if calc_vr == .left_ev_compute { vr.len } else { 1 }
+ ldvl = if calc_vl == .left_ev_compute { vl[0] } else { 1 }
+ ldvr = if calc_vr == .left_ev_compute { vr[0] } else { 1 }Committable suggestion was skipped due to low confidence.
| pub fn dsytrd(uplo blas.Uplo, n int, mut a []f64, lda int, mut d []f64, mut e []f64, mut tau []f64, mut work []f64, lwork int) { | ||
| if uplo != .upper && uplo != .lower { | ||
| panic(bad_uplo) | ||
| } | ||
| if n < 0 { | ||
| panic(n_lt0) | ||
| } | ||
| if lda < math.max(1, n) { | ||
| panic(bad_ld_a) | ||
| } | ||
| if lwork < 1 && lwork != -1 { | ||
| panic(bad_l_work) | ||
| } | ||
| if work.len < math.max(1, lwork) { | ||
| panic(short_work) | ||
| } |
There was a problem hiding this comment.
Refactor error messages for clarity and consistency.
The error handling uses generic panic messages. More descriptive messages would enhance maintainability and debugging.
- panic(bad_uplo)
+ panic("Invalid 'uplo' value. Expected .upper or .lower.")
- panic(n_lt0)
+ panic("Parameter 'n' must be non-negative.")
- panic(bad_ld_a)
+ panic("Invalid leading dimension 'lda'.")Committable suggestion was skipped due to low confidence.
| for j := 0; j < mn; j++ { | ||
| // Find a pivot and test for singularity. | ||
| jp := j + blas.idamax(m - j, a[j * lda + j..], lda) | ||
| ipiv[j] = jp | ||
| if a[jp * lda + j] == 0.0 { | ||
| panic('lapack: matrix is singular') | ||
| } else { | ||
| // Swap the rows if necessary. | ||
| if jp != j { | ||
| mut slice1 := unsafe { a[j * lda..] } | ||
| mut slice2 := unsafe { a[jp * lda..] } | ||
| blas.dswap(n, mut slice1, 1, mut slice2, 1) | ||
| } | ||
| if j < m - 1 { | ||
| aj := a[j * lda + j] | ||
| if math.abs(aj) >= sfmin { | ||
| mut slice3 := unsafe { a[(j + 1) * lda + j..] } | ||
| blas.dscal(m - j - 1, 1.0 / aj, mut slice3, lda) | ||
| } else { | ||
| for i := 0; i < m - j - 1; i++ { | ||
| a[(j + 1) * lda + j] /= aj | ||
| } | ||
| } | ||
| } | ||
| } | ||
| if j < mn - 1 { | ||
| mut slice4 := unsafe { a[(j + 1) * lda + j + 1..] } | ||
| blas.dger(m - j - 1, n - j - 1, -1.0, a[(j + 1) * lda + j..], lda, a[j * lda + j + 1..], | ||
| 1, mut slice4, lda) | ||
| } |
There was a problem hiding this comment.
Optimize matrix operations and ensure safety of array accesses.
The function performs several critical matrix operations within loops. Ensure that these operations are optimized and that all array accesses are safe and within bounds.
- for j := 0; j < mn; j++ {
- ...
- if a[jp * lda + j] == 0.0 {
- panic('lapack: matrix is singular')
- }
- ...
- }
+ for j := 0; j < mn; j++ {
+ ...
+ if a[jp * lda + j] == 0.0 {
+ panic('lapack: matrix is singular at position ${jp}, ${j}')
+ }
+ ...
+ }This change adds more specific error information in the panic message.
Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| for j := 0; j < mn; j++ { | |
| // Find a pivot and test for singularity. | |
| jp := j + blas.idamax(m - j, a[j * lda + j..], lda) | |
| ipiv[j] = jp | |
| if a[jp * lda + j] == 0.0 { | |
| panic('lapack: matrix is singular') | |
| } else { | |
| // Swap the rows if necessary. | |
| if jp != j { | |
| mut slice1 := unsafe { a[j * lda..] } | |
| mut slice2 := unsafe { a[jp * lda..] } | |
| blas.dswap(n, mut slice1, 1, mut slice2, 1) | |
| } | |
| if j < m - 1 { | |
| aj := a[j * lda + j] | |
| if math.abs(aj) >= sfmin { | |
| mut slice3 := unsafe { a[(j + 1) * lda + j..] } | |
| blas.dscal(m - j - 1, 1.0 / aj, mut slice3, lda) | |
| } else { | |
| for i := 0; i < m - j - 1; i++ { | |
| a[(j + 1) * lda + j] /= aj | |
| } | |
| } | |
| } | |
| } | |
| if j < mn - 1 { | |
| mut slice4 := unsafe { a[(j + 1) * lda + j + 1..] } | |
| blas.dger(m - j - 1, n - j - 1, -1.0, a[(j + 1) * lda + j..], lda, a[j * lda + j + 1..], | |
| 1, mut slice4, lda) | |
| } | |
| for j := 0; j < mn; j++ { | |
| // Find a pivot and test for singularity. | |
| jp := j + blas.idamax(m - j, a[j * lda + j..], lda) | |
| ipiv[j] = jp | |
| if a[jp * lda + j] == 0.0 { | |
| panic('lapack: matrix is singular at position ${jp}, ${j}') | |
| } else { | |
| // Swap the rows if necessary. | |
| if jp != j { | |
| mut slice1 := unsafe { a[j * lda..] } | |
| mut slice2 := unsafe { a[jp * lda..] } | |
| blas.dswap(n, mut slice1, 1, mut slice2, 1) | |
| } | |
| if j < m - 1 { | |
| aj := a[j * lda + j] | |
| if math.abs(aj) >= sfmin { | |
| mut slice3 := unsafe { a[(j + 1) * lda + j..] } | |
| blas.dscal(m - j - 1, 1.0 / aj, mut slice3, lda) | |
| } else { | |
| for i := 0; i < m - j - 1; i++ { | |
| a[(j + 1) * lda + j] /= aj | |
| } | |
| } | |
| } | |
| } | |
| if j < mn - 1 { | |
| mut slice4 := unsafe { a[(j + 1) * lda + j + 1..] } | |
| blas.dger(m - j - 1, n - j - 1, -1.0, a[(j + 1) * lda + j..], lda, a[j * lda + j + 1..], | |
| 1, mut slice4, lda) | |
| } |
lapack/lapack64/dorg2l.v
Outdated
| // Initialize columns 0:n-k to columns of the unit matrix. | ||
| for j := 0; j < n - k; j++ { | ||
| for l := 0; l < m; l++ { | ||
| a[l * lda + j] = 0 | ||
| } | ||
| a[(m - n + j) * lda + j] = 1 | ||
| } | ||
|
|
||
| for i := 0; i < k; i++ { | ||
| ii := n - k + i | ||
|
|
||
| // Apply H_i to A[0:m-k+i, 0:n-k+i] from the left. | ||
| a[(m - n + ii) * lda + ii] = 1 | ||
| dlarf(.left, m - n + ii + 1, ii, mut a[ii..], lda, tau[i], mut a, lda, mut work) | ||
| blas.dscal(m - n + ii, -tau[i], mut a[ii..], lda) | ||
| a[(m - n + ii) * lda + ii] = 1 - tau[i] | ||
|
|
||
| // Set A[m-k+i:m, n-k+i+1] to zero. | ||
| for l := m - n + ii + 1; l < m; l++ { | ||
| a[l * lda + ii] = 0 | ||
| } | ||
| } |
There was a problem hiding this comment.
Optimize matrix initialization and ensure correct setup for further computations.
The initialization of the matrix columns to unit vectors is critical for the correct functioning of the algorithm. Ensure that this initialization is done efficiently and correctly.
- for j := 0; j < n - k; j++ {
- for l := 0; l < m; l++ {
- a[l * lda + j] = 0
- }
- a[(m - n + j) * lda + j] = 1
- }
+ for j := 0; j < n - k; j++ {
+ memset(mut a[j * lda..(j + 1) * lda], 0, m * sizeof(f64))
+ a[(m - n + j) * lda + j] = 1
+ }This change uses memset to zero out the matrix slice more efficiently.
Committable suggestion was skipped due to low confidence.
…l_lapack_lapacke.v
… to handle errors and use named constants
There was a problem hiding this comment.
Actionable comments posted: 17
Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Files selected for processing (28)
- .github/workflows/ci.yml (2 hunks)
- blas/blas64/conversions.v (1 hunks)
- lapack/conversions.v (1 hunks)
- lapack/lapack64/conversions.v (1 hunks)
- lapack/lapack64/dgeev.v (1 hunks)
- lapack/lapack64/dgesvd.v (1 hunks)
- lapack/lapack64/dlaev2.v (1 hunks)
- lapack/lapack64/dlarf.v (1 hunks)
- lapack/lapack64/dlarfb.v (1 hunks)
- lapack/lapack64/dlarfg.v (1 hunks)
- lapack/lapack64/dlarft.v (1 hunks)
- lapack/lapack64/dlartg.v (1 hunks)
- lapack/lapack64/dlaset.v (1 hunks)
- lapack/lapack64/dlasr.v (1 hunks)
- lapack/lapack64/dlatrd.v (1 hunks)
- lapack/lapack64/dorg2l.v (1 hunks)
- lapack/lapack64/dorg2r.v (1 hunks)
- lapack/lapack64/dorgql.v (1 hunks)
- lapack/lapack64/dorgqr.v (1 hunks)
- lapack/lapack64/dsteqr.v (1 hunks)
- lapack/lapack64/dsyev.v (1 hunks)
- lapack/lapack64/dsytd2.v (1 hunks)
- lapack/lapack64/errors.v (1 hunks)
- lapack/lapack64/iladlc.v (1 hunks)
- lapack/lapack64/iladlr.v (1 hunks)
- lapack/lapack64/ilaenv.v (1 hunks)
- lapack/lapack_d_vsl_lapack_lapacke.v (7 hunks)
- lapack/lapack_notd_vsl_lapack_lapacke.v (1 hunks)
Files not reviewed due to errors (3)
- lapack/lapack64/dlaset.v (no review received)
- lapack/lapack64/dorg2r.v (no review received)
- lapack/lapack64/dlartg.v (no review received)
Files skipped from review due to trivial changes (1)
- blas/blas64/conversions.v
Files skipped from review as they are similar to previous changes (10)
- .github/workflows/ci.yml
- lapack/conversions.v
- lapack/lapack64/conversions.v
- lapack/lapack64/dgeev.v
- lapack/lapack64/dgesvd.v
- lapack/lapack64/dorg2l.v
- lapack/lapack64/errors.v
- lapack/lapack64/ilaenv.v
- lapack/lapack_d_vsl_lapack_lapacke.v
- lapack/lapack_notd_vsl_lapack_lapacke.v
Additional comments not posted (18)
lapack/lapack64/iladlr.v (5)
14-14: Check for validldacondition.The condition
lda < math.max(1, n)is crucial for ensuring that the leading dimension is not less than the number of columns. This is a good practice for memory safety.
18-20: Early exit for zero dimensions.Returning early when either dimension is zero is efficient and prevents unnecessary computation.
22-24: Validate array length.The check for
a.len < (m - 1) * lda + nis essential to ensure the array is large enough to prevent out-of-bounds access.
26-29: Optimization for common cases.Checking the corners of the matrix first for non-zero values to potentially return early is an efficient optimization for matrices that are mostly zero.
30-37: Efficient scan for non-zero elements.The nested loop efficiently scans the matrix from the last row to the first, which is suitable for finding the last non-zero row. This is a good implementation of the specified functionality.
lapack/lapack64/iladlc.v (5)
14-14: Check for validldacondition.The condition
lda < math.max(1, n)is correctly enforced here as well, ensuring the leading dimension is appropriate.
18-20: Early exit for zero dimensions.Efficiently handles cases where matrix dimensions are zero, avoiding unnecessary computations.
22-24: Validate array length.Properly checks that the array length is sufficient to prevent out-of-bounds access, similar to
iladlr.v.
26-29: Optimization for common cases.Checking the corners of the matrix first for non-zero values to potentially return early is an efficient optimization.
31-41: Efficient column scan.The function tracks the highest non-zero column seen, which is an efficient way to find the last non-zero column. This loop structure is well-suited for the task.
lapack/lapack64/dlarfg.v (4)
7-24: Validate input parameters and handle errors robustly.The function correctly checks for invalid input parameters and uses
panicto handle errors. This is a critical safety measure for functions that perform low-level operations.
14-16: Handle trivial cases early.Returning early for
n <= 1is efficient and simplifies the logic for the main computation by removing edge cases.
18-20: Validate vector length.Ensures that the vector
xis long enough for the operations to be performed, preventing out-of-bounds memory access.
22-50: Complex computation of Householder parameters.The function performs complex calculations to generate the Householder vector and coefficient. The use of scaling to avoid overflow and underflow is particularly noteworthy and indicates careful numerical analysis.
lapack/lapack64/dlarft.v (1)
1-1: Module declaration is correct and clear.
The modulelapack64is appropriately declared, aligning with the file's purpose within the LAPACK operations.lapack/lapack64/dsytd2.v (1)
1-1: Module declaration is correct and clear.
The modulelapack64is appropriately declared, aligning with the file's purpose within the LAPACK operations.lapack/lapack64/dlasr.v (1)
1-1: Module declaration is correct and clear.
The modulelapack64is appropriately declared, aligning with the file's purpose within the LAPACK operations.lapack/lapack64/dsteqr.v (1)
30-57: Verify array length conditions to prevent potential runtime panics.The function
dsteqruses several conditions to ensure the input arrays are of the correct length before proceeding with the computations. This is crucial to prevent out-of-bounds access. Ensure that these conditions are thoroughly tested to avoid runtime errors in edge cases.
| work, ldwork) | ||
| for i := 0; i < m; i++ { | ||
| for j := 0; j < k; j++ { | ||
| c[i * ldc + (n - k + j)] -= unsafe { work[i * ldwork + j] } | ||
| } | ||
| } | ||
| return | ||
| } | ||
| if direct == .forward { | ||
| if side == .left { | ||
| for j := 0; j < k; j++ { | ||
| blas.dcopy(n, c[j * ldc..], 1, mut work[j * ldwork..], 1) | ||
| } | ||
| blas.dtrmm(.right, .upper, .trans, .unit, n, k, 1.0, v, ldv, mut work, ldwork) | ||
| if m > k { | ||
| blas.dgemm(.trans, .trans, n, k, m - k, 1.0, c[k * ldc..], ldc, v[k..], | ||
| ldv, 1.0, mut work, ldwork) | ||
| } | ||
| blas.dtrmm(.right, .upper, transt, .non_unit, n, k, 1.0, t, ldt, mut work, | ||
| ldwork) | ||
| if m > k { | ||
| blas.dgemm(.trans, .trans, m - k, n, k, -1.0, v[k..], ldv, work, ldwork, | ||
| 1.0, mut c[k * ldc..], ldc) | ||
| } | ||
| blas.dtrmm(.right, .upper, .no_trans, .unit, n, k, 1.0, v, ldv, mut work, | ||
| ldwork) | ||
| for i := 0; i < n; i++ { | ||
| for j := 0; j < k; j++ { | ||
| c[j * ldc + i] -= unsafe { work[i * ldwork + j] } | ||
| } | ||
| } | ||
| return | ||
| } | ||
| for j := 0; j < k; j++ { | ||
| blas.dcopy(m, c[j..], ldc, mut work[j * ldwork..], 1) | ||
| } | ||
| blas.dtrmm(.right, .upper, .trans, .unit, m, k, 1.0, v, ldv, mut work, ldwork) | ||
| if n > k { | ||
| blas.dgemm(.no_trans, .trans, m, k, n - k, 1.0, c[k..], ldc, v[k..], ldv, | ||
| 1.0, mut work, ldwork) | ||
| } | ||
| blas.dtrmm(.right, .upper, trans, .non_unit, m, k, 1.0, t, ldt, mut work, ldwork) | ||
| if n > k { | ||
| blas.dgemm(.no_trans, .trans, m, n - k, k, -1.0, work, ldwork, v[k..], ldv, | ||
| 1.0, mut c[k..], ldc) | ||
| } | ||
| blas.dtrmm(.right, .upper, .no_trans, .unit, m, k, 1.0, v, ldv, mut work, ldwork) | ||
| for i := 0; i < m; i++ { | ||
| for j := 0; j < k; j++ { | ||
| c[i * ldc + j] -= unsafe { work[i * ldwork + j] } | ||
| } | ||
| } | ||
| return | ||
| } | ||
| if side == .left { | ||
| for j := 0; j < k; j++ { | ||
| blas.dcopy(n, c[(m - k + j) * ldc..], 1, mut work[j * ldwork..], 1) | ||
| } | ||
| blas.dtrmm(.right, .lower, .no_trans, .unit, n, k, 1.0, v[(m - k)..], ldv, mut | ||
| work, ldwork) | ||
| if m > k { | ||
| blas.dgemm(.trans, .no_trans, n, k, m - k, 1.0, c, ldc, v, ldv, 1.0, mut work, | ||
| ldwork) | ||
| } | ||
| blas.dtrmm(.right, .lower, transt, .non_unit, n, k, 1.0, t, ldt, mut work, ldwork) | ||
| if m > k { | ||
| blas.dgemm(.no_trans, .trans, m - k, n, k, -1.0, v, ldv, work, ldwork, 1.0, mut | ||
| c, ldc) | ||
| } | ||
| blas.dtrmm(.right, .lower, .trans, .unit, n, k, 1.0, v[(m - k)..], ldv, mut work, | ||
| ldwork) | ||
| for i := 0; i < n; i++ { | ||
| for j := 0; j < k; j++ { | ||
| c[(m - k + j) * ldc + i] -= unsafe { work[i * ldwork + j] } | ||
| } | ||
| } | ||
| return | ||
| } | ||
| for j := 0; j < k; j++ { | ||
| blas.dcopy(m, c[(n - k + j)..], ldc, mut work[j * ldwork..], 1) | ||
| } | ||
| blas.dtrmm(.right, .lower, .no_trans, .unit, m, k, 1.0, v[(n - k)..], ldv, mut work, | ||
| ldwork) | ||
| if n > k { | ||
| blas.dgemm(.no_trans, .trans, m, k, n - k, 1.0, c, ldc, v, ldv, 1.0, mut work, | ||
| ldwork) | ||
| } | ||
| blas.dtrmm(.right, .lower, trans, .non_unit, m, k, 1.0, t, ldt, mut work, ldwork) | ||
| if n > k { | ||
| blas.dgemm(.no_trans, .no_trans, m, n - k, k, -1.0, work, ldwork, v, ldv, 1.0, mut | ||
| c, ldc) | ||
| } | ||
| blas.dtrmm(.right, .lower, .trans, .unit, m, k, 1.0, v[(n - k)..], ldv, mut work, | ||
| ldwork) | ||
| for i := 0; i < m; i++ { | ||
| for j := 0; j < k; j++ { | ||
| c[i * ldc + (n - k + j)] -= unsafe { work[i * ldwork + j] } | ||
| } | ||
| } | ||
| } |
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Refactor large function for better maintainability.
The function dlarfb is quite large and handles multiple cases within nested conditional and loop structures. This makes it hard to read and maintain. Consider refactoring into smaller, more manageable functions or private methods that handle specific parts of the algorithm, such as handling different side and store conditions.
| pub fn dlarfb(side blas.Side, trans blas.Transpose, direct Direct, store StoreV, m int, n int, k int, v []f64, ldv int, t []f64, ldt int, mut c []f64, ldc int, mut work []f64, ldwork int) { | ||
| if side != .left && side != .right { | ||
| panic(bad_side) | ||
| } | ||
| if trans != .trans && trans != .no_trans { | ||
| panic(bad_trans) | ||
| } | ||
| if direct != .forward && direct != .backward { | ||
| panic(bad_direct) | ||
| } | ||
| if store != .column_wise && store != .row_wise { | ||
| panic(bad_store_v) | ||
| } | ||
| if m < 0 { | ||
| panic(m_lt0) | ||
| } | ||
| if n < 0 { | ||
| panic(n_lt0) | ||
| } | ||
| if k < 0 { | ||
| panic(k_lt0) | ||
| } | ||
| if store == .column_wise && ldv < math.max(1, k) { | ||
| panic(bad_ld_v) | ||
| } | ||
| if store == .row_wise && ldv < math.max(1, m) { | ||
| panic(bad_ld_v) | ||
| } | ||
| if ldt < math.max(1, k) { | ||
| panic(bad_ld_t) | ||
| } | ||
| if ldc < math.max(1, n) { | ||
| panic(bad_ld_c) | ||
| } | ||
| if ldwork < math.max(1, k) { | ||
| panic(bad_ld_work) | ||
| } | ||
|
|
||
| if m == 0 || n == 0 { | ||
| return | ||
| } | ||
|
|
||
| mut nv := m | ||
| if side == .right { | ||
| nv = n | ||
| } | ||
| if store == .column_wise && v.len < (nv - 1) * ldv + k { | ||
| panic(short_v) | ||
| } | ||
| if store == .row_wise && v.len < (k - 1) * ldv + nv { | ||
| panic(short_v) | ||
| } | ||
| if t.len < (k - 1) * ldt + k { | ||
| panic(short_t) | ||
| } | ||
| if c.len < (m - 1) * ldc + n { | ||
| panic(short_c) | ||
| } | ||
| if work.len < (nv - 1) * ldwork + k { | ||
| panic(short_work) | ||
| } |
There was a problem hiding this comment.
Ensure comprehensive error messages for better debugging support.
The function dlarfb uses panics for error handling, which is appropriate in this low-level numerical context. However, the error messages such as bad_side, bad_trans, etc., are quite generic. Consider providing more descriptive error messages that include the function name and the invalid value that caused the error, to aid in debugging.
- panic(bad_side)
+ panic("dlarfb: invalid 'side' value")Committable suggestion was skipped due to low confidence.
| mut p := d[l] | ||
| if m == l { | ||
| // Eigenvalue found | ||
| l-- | ||
| if l < lend { | ||
| break | ||
| } | ||
| continue | ||
| } | ||
|
|
||
| // If remaining matrix is 2×2, use dlae2 to compute its eigenvalues. | ||
| if m == l - 1 { | ||
| if icompz > 0 { | ||
| d[l - 1], d[l], work[m], work[n - 1 + m] = dlaev2(d[l - 1], e[l - 1], | ||
| d[l]) | ||
| dlasr(.right, .variable, .forward, n, 2, work[m..], work[n - 1 + m..], mut | ||
| z[l - 1..], ldz) | ||
| } else { | ||
| d[l - 1], d[l] = dlae2(d[l - 1], e[l - 1], d[l]) | ||
| } | ||
| e[l - 1] = 0 | ||
| l -= 2 | ||
| if l < lend { | ||
| break | ||
| } | ||
| continue | ||
| } | ||
| if jtot == nmaxit { | ||
| break | ||
| } | ||
| jtot++ | ||
|
|
||
| // Form shift. | ||
| mut g := (d[l - 1] - p) / (2 * e[l - 1]) | ||
| mut r := dlapy2(g, 1) | ||
| g = d[m] - p + (e[l - 1]) / (g + math.copysign(r, g)) | ||
| mut s := 1.0 | ||
| mut c := 1.0 | ||
| p = 0.0 | ||
|
|
||
| // Inner loop. | ||
| for i := m; i < l; i++ { | ||
| f := s * e[i] | ||
| b := c * e[i] | ||
| c, s, r = dlartg(g, f) | ||
| if i != m { | ||
| e[i - 1] = r | ||
| } | ||
| g = d[i] - p | ||
| r = (d[i + 1] - g) * s + 2 * c * b | ||
| p = s * r | ||
| d[i] = g + p | ||
| g = c * r - b | ||
|
|
||
| // If eigenvectors are desired, then save rotations. | ||
| if icompz > 0 { | ||
| work[i] = c | ||
| work[n - 1 + i] = s | ||
| } | ||
| } | ||
|
|
||
| // If eigenvectors are desired, then apply saved rotations. | ||
| if icompz > 0 { | ||
| mm := l - m + 1 | ||
| dlasr(.right, .variable, .forward, n, mm, work[m..], work[n - 1 + m..], mut | ||
| z[m..], ldz) | ||
| } | ||
| d[l] -= p | ||
| e[l - 1] = g | ||
| } | ||
| } | ||
|
|
||
| // Undo scaling if necessary. | ||
| match iscale { | ||
| down { | ||
| // Pretend that d and e are matrices with 1 column. | ||
| dlascl(.general, 0, 0, ssfmax, anorm, lendsv - lsv + 1, 1, mut d[lsv..], | ||
| 1) | ||
| dlascl(.general, 0, 0, ssfmax, anorm, lendsv - lsv, 1, mut e[lsv..], 1) | ||
| } | ||
| up { | ||
| dlascl(.general, 0, 0, ssfmin, anorm, lendsv - lsv + 1, 1, mut d[lsv..], | ||
| 1) | ||
| dlascl(.general, 0, 0, ssfmin, anorm, lendsv - lsv, 1, mut e[lsv..], 1) | ||
| } | ||
| else {} | ||
| } | ||
|
|
||
| // Check for no convergence to an eigenvalue after a total of n*maxit iterations. | ||
| if jtot >= nmaxit { | ||
| break | ||
| } | ||
| } | ||
| for i := 0; i < n - 1; i++ { | ||
| if e[i] != 0 { | ||
| return false | ||
| } | ||
| } | ||
| return true | ||
| } |
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Optimize and refactor iterative algorithm for clarity and efficiency.
The main eigenvalue computation loop in dsteqr is quite complex and could benefit from optimization and refactoring for clarity. Consider isolating the inner loops into separate functions and using more descriptive variable names. Additionally, explore algorithmic optimizations that could reduce the number of iterations needed.
| pub fn dlarft(direct Direct, store StoreV, n int, k int, v []f64, ldv int, tau []f64, mut t []f64, ldt int) { | ||
| mv, nv := if store == .row_wise { k, n } else { n, k } | ||
| if direct != .forward && direct != .backward { | ||
| panic(bad_direct) | ||
| } | ||
| if store != .row_wise && store != .column_wise { | ||
| panic(bad_store_v) | ||
| } | ||
| if n < 0 { | ||
| panic(n_lt0) | ||
| } | ||
| if k < 1 { | ||
| panic(k_lt1) | ||
| } | ||
| if ldv < math.max(1, nv) { | ||
| panic(bad_ld_v) | ||
| } | ||
| if tau.len < k { | ||
| panic(short_tau) | ||
| } | ||
| if ldt < math.max(1, k) { | ||
| panic(short_t) | ||
| } | ||
|
|
||
| if n == 0 { | ||
| return | ||
| } | ||
|
|
||
| if v.len < (mv - 1) * ldv + nv { | ||
| panic(short_v) | ||
| } | ||
| if t.len < (k - 1) * ldt + k { | ||
| panic(short_t) | ||
| } |
There was a problem hiding this comment.
Consider refining error messages for clarity and debugging.
The error handling in the function dlarft uses panic statements with generic messages like bad_direct and bad_store_v. These messages could be more descriptive to aid in debugging.
- panic(bad_direct)
+ panic("Invalid 'direct' parameter: must be either .forward or .backward")
- panic(bad_store_v)
+ panic("Invalid 'store' parameter: must be either .row_wise or .column_wise")Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| pub fn dlarft(direct Direct, store StoreV, n int, k int, v []f64, ldv int, tau []f64, mut t []f64, ldt int) { | |
| mv, nv := if store == .row_wise { k, n } else { n, k } | |
| if direct != .forward && direct != .backward { | |
| panic(bad_direct) | |
| } | |
| if store != .row_wise && store != .column_wise { | |
| panic(bad_store_v) | |
| } | |
| if n < 0 { | |
| panic(n_lt0) | |
| } | |
| if k < 1 { | |
| panic(k_lt1) | |
| } | |
| if ldv < math.max(1, nv) { | |
| panic(bad_ld_v) | |
| } | |
| if tau.len < k { | |
| panic(short_tau) | |
| } | |
| if ldt < math.max(1, k) { | |
| panic(short_t) | |
| } | |
| if n == 0 { | |
| return | |
| } | |
| if v.len < (mv - 1) * ldv + nv { | |
| panic(short_v) | |
| } | |
| if t.len < (k - 1) * ldt + k { | |
| panic(short_t) | |
| } | |
| mv, nv := if store == .row_wise { k, n } else { n, k } | |
| if direct != .forward && direct != .backward { | |
| panic("Invalid 'direct' parameter: must be either .forward or .backward") | |
| } | |
| if store != .row_wise && store != .column_wise { | |
| panic("Invalid 'store' parameter: must be either .row_wise or .column_wise") | |
| } | |
| if n < 0 { | |
| panic(n_lt0) | |
| } | |
| if k < 1 { | |
| panic(k_lt1) | |
| } | |
| if ldv < math.max(1, nv) { | |
| panic(bad_ld_v) | |
| } | |
| if tau.len < k { | |
| panic(short_tau) | |
| } | |
| if ldt < math.max(1, k) { | |
| panic(short_t) | |
| } | |
| if n == 0 { | |
| return | |
| } | |
| if v.len < (mv - 1) * ldv + nv { | |
| panic(short_v) | |
| } | |
| if t.len < (k - 1) * ldt + k { | |
| panic(short_t) | |
| } |
| blas.dgemv(.trans, j - i, i, -tau[i], v[(i + 1) * ldv..], ldv, v[(i + 1) * ldv + i..], | ||
| ldv, 1.0, mut t[i..], ldt) | ||
| } else { | ||
| for lastv = n - 1; lastv >= i + 1; lastv-- { | ||
| if v[i * ldv + lastv] != 0 { | ||
| break | ||
| } | ||
| } | ||
| for j := 0; j < i; j++ { | ||
| t[j * ldt + i] = -tau[i] * v[j * ldv + i] | ||
| } | ||
| j := math.min(lastv, prevlastv) | ||
| blas.dgemv(.no_trans, i, j - i, -tau[i], v[i + 1..], ldv, v[i * ldv + i + 1..], | ||
| 1, 1.0, mut t[i..], ldt) | ||
| } | ||
| blas.dtrmv(.upper, .no_trans, .non_unit, i, t, ldt, mut t[i..], ldt) |
There was a problem hiding this comment.
Optimize matrix operations to enhance performance.
The matrix operations within the if store == .column_wise block can be optimized by reducing redundant calculations and enhancing the use of BLAS functions.
- for lastv = n - 1; lastv >= i + 1; lastv-- {
- if v[lastv * ldv + i] != 0 {
- break
- }
- }
+ lastv = n - 1
+ while lastv >= i + 1 && v[lastv * ldv + i] == 0 {
+ lastv--
+ }This change reduces the complexity of finding the last non-zero element in the vector.
Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| blas.dgemv(.trans, j - i, i, -tau[i], v[(i + 1) * ldv..], ldv, v[(i + 1) * ldv + i..], | |
| ldv, 1.0, mut t[i..], ldt) | |
| } else { | |
| for lastv = n - 1; lastv >= i + 1; lastv-- { | |
| if v[i * ldv + lastv] != 0 { | |
| break | |
| } | |
| } | |
| for j := 0; j < i; j++ { | |
| t[j * ldt + i] = -tau[i] * v[j * ldv + i] | |
| } | |
| j := math.min(lastv, prevlastv) | |
| blas.dgemv(.no_trans, i, j - i, -tau[i], v[i + 1..], ldv, v[i * ldv + i + 1..], | |
| 1, 1.0, mut t[i..], ldt) | |
| } | |
| blas.dtrmv(.upper, .no_trans, .non_unit, i, t, ldt, mut t[i..], ldt) | |
| blas.dgemv(.trans, j - i, i, -tau[i], v[(i + 1) * ldv..], ldv, v[(i + 1) * ldv + i..], | |
| ldv, 1.0, mut t[i..], ldt) | |
| } else { | |
| lastv = n - 1 | |
| while lastv >= i + 1 && v[lastv * ldv + i] == 0 { | |
| lastv-- | |
| } | |
| for j := 0; j < i; j++ { | |
| t[j * ldt + i] = -tau[i] * v[j * ldv + i] | |
| } | |
| j := math.min(lastv, prevlastv) | |
| blas.dgemv(.no_trans, i, j - i, -tau[i], v[i + 1..], ldv, v[i * ldv + i + 1..], | |
| 1, 1.0, mut t[i..], ldt) | |
| } | |
| blas.dtrmv(.upper, .no_trans, .non_unit, i, t, ldt, mut t[i..], ldt) |
| pub fn dorgqr(m int, n int, k int, mut a []f64, lda int, tau []f64, mut work []f64, lwork int) { | ||
| if m < 0 { | ||
| panic(m_lt0) | ||
| } | ||
| if n < 0 { | ||
| panic(n_lt0) | ||
| } | ||
| if n > m { | ||
| panic(n_gtm) | ||
| } | ||
| if k < 0 { | ||
| panic(k_lt0) | ||
| } | ||
| if k > n { | ||
| panic(k_gtn) | ||
| } | ||
| if lda < math.max(1, n) && lwork != -1 { | ||
| panic(bad_ld_a) | ||
| } | ||
| if lwork < math.max(1, n) && lwork != -1 { | ||
| panic(bad_l_work) | ||
| } | ||
| if work.len < math.max(1, lwork) { | ||
| panic(short_work) | ||
| } | ||
|
|
||
| if n == 0 { | ||
| work[0] = 1.0 | ||
| return | ||
| } | ||
|
|
||
| mut nb := ilaenv(1, 'DORGQR', ' ', m, n, k, -1) | ||
| if lwork == -1 { | ||
| work[0] = f64(n * nb) | ||
| return | ||
| } | ||
|
|
||
| if a.len < (m - 1) * lda + n { | ||
| panic(short_a) | ||
| } | ||
| if tau.len != k { | ||
| panic(bad_len_tau) | ||
| } | ||
|
|
||
| mut nbmin := 2 | ||
| mut nx := 0 | ||
| mut iws := n | ||
| mut ldwork := 0 | ||
| if 1 < nb && nb < k { | ||
| nx = math.max(0, ilaenv(3, 'DORGQR', ' ', m, n, k, -1)) | ||
| if nx < k { | ||
| ldwork = nb | ||
| iws = n * ldwork | ||
| if lwork < iws { | ||
| nb = lwork / n | ||
| ldwork = nb | ||
| nbmin = math.max(2, ilaenv(2, 'DORGQR', ' ', m, n, k, -1)) | ||
| } | ||
| } | ||
| } | ||
| mut ki := 0 | ||
| mut kk := 0 | ||
| if nbmin <= nb && nb < k && nx < k { | ||
| ki = ((k - nx - 1) / nb) * nb | ||
| kk = math.min(k, ki + nb) | ||
| for i := 0; i < kk; i++ { | ||
| for j := kk; j < n; j++ { | ||
| unsafe { | ||
| a[i * lda + j] = 0.0 | ||
| } | ||
| } | ||
| } | ||
| } | ||
| if kk < n { | ||
| dorg2r(m - kk, n - kk, k - kk, mut a[(kk * lda + kk)..], lda, tau[kk..], mut work) | ||
| } | ||
| if kk > 0 { | ||
| for i := ki; i >= 0; i -= nb { | ||
| ib := math.min(nb, k - i) | ||
| if i + ib < n { | ||
| dlarft(.forward, .column_wise, m - i, ib, a[(i * lda + i)..], lda, tau[i..], mut | ||
| work, ldwork) | ||
|
|
||
| dlarfb(.left, .no_trans, .forward, .column_wise, m - i, n - i - ib, ib, | ||
| a[(i * lda + i)..], lda, work, ldwork, mut a[(i * lda + i + ib)..], | ||
| lda, mut work[(ib * ldwork)..], ldwork) | ||
| } | ||
| dorg2r(m - i, ib, ib, mut a[(i * lda + i)..], lda, tau[i..(i + ib)], mut work) | ||
| for j := i; j < i + ib; j++ { | ||
| for l := 0; l < i; l++ { | ||
| unsafe { | ||
| a[l * lda + j] = 0.0 | ||
| } | ||
| } | ||
| } | ||
| } | ||
| } | ||
| work[0] = f64(iws) | ||
| } |
There was a problem hiding this comment.
Review of comprehensive error handling and performance considerations in dorgqr.
- Error Handling: The function has robust error handling (lines 7-30), ensuring that all input parameters are within acceptable ranges. This is crucial for a mathematical function where input dimensions dictate behavior.
- Performance: The use of blocking (line 37 and onwards) is a good strategy for optimizing matrix operations. However, the calculation of
nb,nx, andldworkcould be encapsulated into a helper function to improve readability and reusability. - Magic Numbers: Similar to the previous file, consider replacing magic numbers with named constants.
- mut nb := ilaenv(1, 'DORGQR', ' ', m, n, k, -1)
+ const block_size_factor = 1
+ mut nb := ilaenv(block_size_factor, 'DORGQR', ' ', m, n, k, -1)Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| pub fn dorgqr(m int, n int, k int, mut a []f64, lda int, tau []f64, mut work []f64, lwork int) { | |
| if m < 0 { | |
| panic(m_lt0) | |
| } | |
| if n < 0 { | |
| panic(n_lt0) | |
| } | |
| if n > m { | |
| panic(n_gtm) | |
| } | |
| if k < 0 { | |
| panic(k_lt0) | |
| } | |
| if k > n { | |
| panic(k_gtn) | |
| } | |
| if lda < math.max(1, n) && lwork != -1 { | |
| panic(bad_ld_a) | |
| } | |
| if lwork < math.max(1, n) && lwork != -1 { | |
| panic(bad_l_work) | |
| } | |
| if work.len < math.max(1, lwork) { | |
| panic(short_work) | |
| } | |
| if n == 0 { | |
| work[0] = 1.0 | |
| return | |
| } | |
| mut nb := ilaenv(1, 'DORGQR', ' ', m, n, k, -1) | |
| if lwork == -1 { | |
| work[0] = f64(n * nb) | |
| return | |
| } | |
| if a.len < (m - 1) * lda + n { | |
| panic(short_a) | |
| } | |
| if tau.len != k { | |
| panic(bad_len_tau) | |
| } | |
| mut nbmin := 2 | |
| mut nx := 0 | |
| mut iws := n | |
| mut ldwork := 0 | |
| if 1 < nb && nb < k { | |
| nx = math.max(0, ilaenv(3, 'DORGQR', ' ', m, n, k, -1)) | |
| if nx < k { | |
| ldwork = nb | |
| iws = n * ldwork | |
| if lwork < iws { | |
| nb = lwork / n | |
| ldwork = nb | |
| nbmin = math.max(2, ilaenv(2, 'DORGQR', ' ', m, n, k, -1)) | |
| } | |
| } | |
| } | |
| mut ki := 0 | |
| mut kk := 0 | |
| if nbmin <= nb && nb < k && nx < k { | |
| ki = ((k - nx - 1) / nb) * nb | |
| kk = math.min(k, ki + nb) | |
| for i := 0; i < kk; i++ { | |
| for j := kk; j < n; j++ { | |
| unsafe { | |
| a[i * lda + j] = 0.0 | |
| } | |
| } | |
| } | |
| } | |
| if kk < n { | |
| dorg2r(m - kk, n - kk, k - kk, mut a[(kk * lda + kk)..], lda, tau[kk..], mut work) | |
| } | |
| if kk > 0 { | |
| for i := ki; i >= 0; i -= nb { | |
| ib := math.min(nb, k - i) | |
| if i + ib < n { | |
| dlarft(.forward, .column_wise, m - i, ib, a[(i * lda + i)..], lda, tau[i..], mut | |
| work, ldwork) | |
| dlarfb(.left, .no_trans, .forward, .column_wise, m - i, n - i - ib, ib, | |
| a[(i * lda + i)..], lda, work, ldwork, mut a[(i * lda + i + ib)..], | |
| lda, mut work[(ib * ldwork)..], ldwork) | |
| } | |
| dorg2r(m - i, ib, ib, mut a[(i * lda + i)..], lda, tau[i..(i + ib)], mut work) | |
| for j := i; j < i + ib; j++ { | |
| for l := 0; l < i; l++ { | |
| unsafe { | |
| a[l * lda + j] = 0.0 | |
| } | |
| } | |
| } | |
| } | |
| } | |
| work[0] = f64(iws) | |
| } | |
| pub fn dorgqr(m int, n int, k int, mut a []f64, lda int, tau []f64, mut work []f64, lwork int) { | |
| if m < 0 { | |
| panic(m_lt0) | |
| } | |
| if n < 0 { | |
| panic(n_lt0) | |
| } | |
| if n > m { | |
| panic(n_gtm) | |
| } | |
| if k < 0 { | |
| panic(k_lt0) | |
| } | |
| if k > n { | |
| panic(k_gtn) | |
| } | |
| if lda < math.max(1, n) && lwork != -1 { | |
| panic(bad_ld_a) | |
| } | |
| if lwork < math.max(1, n) && lwork != -1 { | |
| panic(bad_l_work) | |
| } | |
| if work.len < math.max(1, lwork) { | |
| panic(short_work) | |
| } | |
| if n == 0 { | |
| work[0] = 1.0 | |
| return | |
| } | |
| const block_size_factor = 1 | |
| mut nb := ilaenv(block_size_factor, 'DORGQR', ' ', m, n, k, -1) | |
| if lwork == -1 { | |
| work[0] = f64(n * nb) | |
| return | |
| } | |
| if a.len < (m - 1) * lda + n { | |
| panic(short_a) | |
| } | |
| if tau.len != k { | |
| panic(bad_len_tau) | |
| } | |
| mut nbmin := 2 | |
| mut nx := 0 | |
| mut iws := n | |
| mut ldwork := 0 | |
| if 1 < nb && nb < k { | |
| nx = math.max(0, ilaenv(3, 'DORGQR', ' ', m, n, k, -1)) | |
| if nx < k { | |
| ldwork = nb | |
| iws = n * ldwork | |
| if lwork < iws { | |
| nb = lwork / n | |
| ldwork = nb | |
| nbmin = math.max(2, ilaenv(2, 'DORGQR', ' ', m, n, k, -1)) | |
| } | |
| } | |
| } | |
| mut ki := 0 | |
| mut kk := 0 | |
| if nbmin <= nb && nb < k && nx < k { | |
| ki = ((k - nx - 1) / nb) * nb | |
| kk = math.min(k, ki + nb) | |
| for i := 0; i < kk; i++ { | |
| for j := kk; j < n; j++ { | |
| unsafe { | |
| a[i * lda + j] = 0.0 | |
| } | |
| } | |
| } | |
| } | |
| if kk < n { | |
| dorg2r(m - kk, n - kk, k - kk, mut a[(kk * lda + kk)..], lda, tau[kk..], mut work) | |
| } | |
| if kk > 0 { | |
| for i := ki; i >= 0; i -= nb { | |
| ib := math.min(nb, k - i) | |
| if i + ib < n { | |
| dlarft(.forward, .column_wise, m - i, ib, a[(i * lda + i)..], lda, tau[i..], mut | |
| work, ldwork) | |
| dlarfb(.left, .no_trans, .forward, .column_wise, m - i, n - i - ib, ib, | |
| a[(i * lda + i)..], lda, work, ldwork, mut a[(i * lda + i + ib)..], | |
| lda, mut work[(ib * ldwork)..], ldwork) | |
| } | |
| dorg2r(m - i, ib, ib, mut a[(i * lda + i)..], lda, tau[i..(i + ib)], mut work) | |
| for j := i; j < i + ib; j++ { | |
| for l := 0; l < i; l++ { | |
| unsafe { | |
| a[l * lda + j] = 0.0 | |
| } | |
| } | |
| } | |
| } | |
| } | |
| work[0] = f64(iws) | |
| } |
| pub fn dlarf(side blas.Side, m int, n int, v []f64, incv int, tau f64, mut c []f64, ldc int, mut work []f64) { | ||
| if side != .left && side != .right { | ||
| panic(bad_side) | ||
| } | ||
| if m < 0 { | ||
| panic(m_lt0) | ||
| } | ||
| if n < 0 { | ||
| panic(n_lt0) | ||
| } | ||
| if incv == 0 { | ||
| panic(zero_inc_v) | ||
| } | ||
| if ldc < math.max(1, n) { | ||
| panic(bad_ld_c) | ||
| } | ||
|
|
||
| if m == 0 || n == 0 { | ||
| return | ||
| } | ||
|
|
||
| applyleft := side == .left | ||
| len_v := if applyleft { m } else { n } | ||
|
|
||
| if v.len < 1 + (len_v - 1) * math.abs(incv) { | ||
| panic(short_v) | ||
| } | ||
| if c.len < (m - 1) * ldc + n { | ||
| panic(short_c) | ||
| } | ||
| if (applyleft && work.len < n) || (!applyleft && work.len < m) { | ||
| panic(short_work) | ||
| } | ||
|
|
||
| mut lastv := -1 // last non-zero element of v | ||
| mut lastc := -1 // last non-zero row/column of C | ||
| if tau != 0 { | ||
| lastv = if applyleft { m - 1 } else { n - 1 } | ||
| mut i := if incv > 0 { lastv * incv } else { 0 } | ||
| // Look for the last non-zero row in v. | ||
| for lastv >= 0 && v[i] == 0 { | ||
| lastv-- | ||
| i -= incv | ||
| } | ||
| if applyleft { | ||
| // Scan for the last non-zero column in C[0:lastv, :] | ||
| lastc = iladlc(lastv + 1, n, c, ldc) | ||
| } else { | ||
| // Scan for the last non-zero row in C[:, 0:lastv] | ||
| lastc = iladlr(m, lastv + 1, c, ldc) | ||
| } | ||
| } | ||
| if lastv == -1 || lastc == -1 { | ||
| return | ||
| } | ||
|
|
||
| if applyleft { | ||
| // Form H * C | ||
| // w[0:lastc+1] = c[1:lastv+1, 1:lastc+1]ᵀ * v[1:lastv+1,1] | ||
| blas.dgemv(.trans, lastv + 1, lastc + 1, 1.0, c, ldc, v, incv, 0.0, mut work, | ||
| 1) | ||
| // c[0: lastv, 0: lastc] = c[...] - w[0:lastv, 1] * v[1:lastc, 1]ᵀ | ||
| blas.dger(lastv + 1, lastc + 1, -tau, v, incv, work, 1, mut c, ldc) | ||
| } else { | ||
| // Form C * H | ||
| // w[0:lastc+1,1] := c[0:lastc+1,0:lastv+1] * v[0:lastv+1,1] | ||
| blas.dgemv(.no_trans, lastc + 1, lastv + 1, 1.0, c, ldc, v, incv, 0.0, mut work, | ||
| 1) | ||
| // c[0:lastc+1,0:lastv+1] = c[...] - w[0:lastc+1,0] * v[0:lastv+1,0]ᵀ | ||
| blas.dger(lastc + 1, lastv + 1, -tau, work, 1, v, incv, mut c, ldc) | ||
| } | ||
| } |
There was a problem hiding this comment.
Optimize and clarify the dlarf function.
- Error Handling: The function includes multiple checks for input validity (lines 22-36). These are essential for a function manipulating matrix dimensions.
- Clarity: The comments (lines 6-20) provide a good explanation of the function's purpose and parameters. However, the actual matrix operations (lines 77-91) could be better documented to explain each step's impact on the matrix.
- Performance: The function could benefit from optimizing the matrix operations by checking if any preconditions allow skipping certain calculations.
+ // Check if scaling is needed before applying transformations
+ if (tau == 0) return;
- if tau != 0 {
+ // Apply transformationsCommittable suggestion was skipped due to low confidence.
| pub fn dsyev(jobz EigenVectorsJob, uplo blas.Uplo, n int, mut a []f64, lda int, mut w []f64, mut work []f64, lwork int) { | ||
| if jobz != .ev_none && jobz != .ev_compute { | ||
| panic(bad_ev_job) | ||
| } | ||
| if uplo != .upper && uplo != .lower { | ||
| panic(bad_uplo) | ||
| } | ||
| if n < 0 { | ||
| panic(n_lt0) | ||
| } | ||
| if lda < math.max(1, n) { | ||
| panic(bad_ld_a) | ||
| } | ||
| if lwork < math.max(1, 3 * n - 1) && lwork != -1 { | ||
| panic(bad_l_work) | ||
| } | ||
| if work.len < math.max(1, lwork) { | ||
| panic(short_work) | ||
| } | ||
|
|
||
| // Quick return if possible. | ||
| if n == 0 { | ||
| return | ||
| } | ||
|
|
||
| opts := if uplo == .upper { 'U' } else { 'L' } | ||
| nb := ilaenv(1, 'DSYTRD', opts, n, -1, -1, -1) | ||
| lworkopt := math.max(1, (nb + 2) * n) | ||
| if lwork == -1 { | ||
| work[0] = f64(lworkopt) | ||
| return | ||
| } | ||
|
|
||
| if a.len < (n - 1) * lda + n { | ||
| panic(short_a) | ||
| } | ||
| if w.len < n { | ||
| panic(short_w) | ||
| } | ||
|
|
||
| if n == 1 { | ||
| w[0] = a[0] | ||
| work[0] = 2 | ||
| if jobz == .ev_compute { | ||
| a[0] = 1 | ||
| } | ||
| return | ||
| } | ||
|
|
||
| safmin := dlamch_s | ||
| eps := dlamch_p | ||
| smlnum := safmin / eps | ||
| bignum := 1 / smlnum | ||
| rmin := math.sqrt(smlnum) | ||
| rmax := math.sqrt(bignum) | ||
|
|
||
| // Scale matrix to allowable range, if necessary. | ||
| anrm := dlansy(.max_abs, uplo, n, a, lda, mut work) | ||
| mut scaled := false | ||
| mut sigma := f64(0) | ||
| if anrm > 0 && anrm < rmin { | ||
| scaled = true | ||
| sigma = rmin / anrm | ||
| } else if anrm > rmax { | ||
| scaled = true | ||
| sigma = rmax / anrm | ||
| } | ||
| if scaled { | ||
| kind := if uplo == .upper { MatrixType.upper_tri } else { MatrixType.lower_tri } | ||
| dlascl(kind, 0, 0, 1, sigma, n, n, mut a, lda) | ||
| } | ||
| inde := 0 | ||
| indtau := inde + n | ||
| indwork := indtau + n | ||
| llwork := lwork - indwork | ||
| dsytrd(uplo, n, mut a, lda, mut w, mut work[inde..], mut work[indtau..], mut work[indwork..], | ||
| llwork) | ||
|
|
||
| // For eigenvalues only, call Dsterf. For eigenvectors, first call Dorgtr | ||
| // to generate the orthogonal matrix, then call Dsteqr. | ||
| if jobz == .ev_none { | ||
| if !dsterf(n, mut w, mut work[inde..]) { | ||
| panic('Dsterf failed') | ||
| } | ||
| } else { | ||
| dorgtr(uplo, n, mut a, lda, work[indtau..], mut work[indwork..], llwork) | ||
| if !dsteqr(EigenVectorsComp(jobz), n, mut w, mut work[inde..], mut a, lda, mut | ||
| work[indtau..]) { | ||
| panic('Dsteqr failed') | ||
| } | ||
| } | ||
|
|
||
| // If the matrix was scaled, then rescale eigenvalues appropriately. | ||
| if scaled { | ||
| blas.dscal(n, 1 / sigma, mut w, 1) | ||
| } | ||
| work[0] = f64(lworkopt) | ||
| } |
There was a problem hiding this comment.
Enhance and optimize the dsyev function.
- Correctness: The function correctly handles different scenarios based on the
jobzparameter (lines 86-96). This flexibility is crucial for a function that may be used in various contexts. - Performance: The function performs scaling on the matrix to keep the values within an acceptable range (lines 62-76). This is a good practice to avoid numerical instability in eigenvalue computation.
- Maintainability: The function is quite complex and could benefit from decomposing into smaller, more manageable functions. For instance, the scaling logic and the eigenvalue computation could be separated.
+ // Refactor scaling into a separate function
+ scale_matrix_if_needed(mut a, lda, n, uplo)
- if scaled {
+ // Call refactored functionCommittable suggestion was skipped due to low confidence.
| } | ||
| if n < 0 { | ||
| panic(n_lt0) | ||
| } | ||
| if nb < 0 { | ||
| panic(nb_lt0) | ||
| } | ||
| if nb > n { | ||
| panic(nb_gtn) | ||
| } | ||
| if lda < math.max(1, n) { | ||
| panic(bad_ld_a) | ||
| } | ||
| if ldw < math.max(1, nb) { | ||
| panic(bad_ld_w) | ||
| } | ||
|
|
||
| if n == 0 { | ||
| return | ||
| } | ||
|
|
||
| if a.len < (n - 1) * lda + n { | ||
| panic(short_a) | ||
| } | ||
| if w.len < (n - 1) * ldw + nb { | ||
| panic(short_w) | ||
| } | ||
| if e.len < n - 1 { | ||
| panic(short_e) | ||
| } | ||
| if tau.len < n - 1 { | ||
| panic(short_tau) | ||
| } | ||
|
|
||
| if uplo == .upper { | ||
| for i := n - 1; i >= n - nb; i-- { | ||
| iw := i - n + nb | ||
| if i < n - 1 { | ||
| // Update A(0:i, i). | ||
| blas.dgemv(.no_trans, i + 1, n - i - 1, -1, a[i + 1..], lda, w[i * ldw + iw + 1..], | ||
| 1, 1, mut a[i..], lda) | ||
| blas.dgemv(.no_trans, i + 1, n - i - 1, -1, w[iw + 1..], ldw, a[i * lda + i + 1..], | ||
| 1, 1, mut a[i..], lda) | ||
| } | ||
| if i > 0 { | ||
| // Generate elementary reflector H_i to annihilate A(0:i-2,i). | ||
| e[i - 1], tau[i - 1] = dlarfg(i, a[(i - 1) * lda + i], mut a[i..], lda) | ||
| a[(i - 1) * lda + i] = 1 | ||
|
|
||
| // Compute W(0:i-1, i). | ||
| blas.dsymv(.upper, i, 1, a, lda, a[i..], lda, 0, mut w[iw..], ldw) | ||
| if i < n - 1 { | ||
| blas.dgemv(.trans, i, n - i - 1, 1, w[iw + 1..], ldw, a[i..], lda, | ||
| 0, mut w[(i + 1) * ldw + iw..], ldw) | ||
| blas.dgemv(.no_trans, i, n - i - 1, -1, a[i + 1..], lda, w[(i + 1) * ldw + iw..], | ||
| ldw, 1, mut w[iw..], ldw) | ||
| blas.dgemv(.trans, i, n - i - 1, 1, a[i + 1..], lda, a[i..], lda, | ||
| 0, mut w[(i + 1) * ldw + iw..], ldw) | ||
| blas.dgemv(.no_trans, i, n - i - 1, -1, w[iw + 1..], ldw, w[(i + 1) * ldw + iw..], | ||
| ldw, 1, mut w[iw..], ldw) | ||
| } | ||
| blas.dscal(i, tau[i - 1], mut w[iw..], ldw) | ||
| alpha := -0.5 * tau[i - 1] * blas.ddot(i, w[iw..], ldw, a[i..], lda) | ||
| blas.daxpy(i, alpha, a[i..], lda, mut w[iw..], ldw) | ||
| } | ||
| } | ||
| } else { | ||
| // Reduce first nb columns of lower triangle. | ||
| for i := 0; i < nb; i++ { | ||
| // Update A(i:n, i) | ||
| blas.dgemv(.no_trans, n - i, i, -1, a[i * lda..], lda, w[i * ldw..], 1, 1, mut | ||
| a[i * lda + i..], lda) | ||
| blas.dgemv(.no_trans, n - i, i, -1, w[i * ldw..], ldw, a[i * lda..], 1, 1, mut | ||
| a[i * lda + i..], lda) | ||
| if i < n - 1 { | ||
| // Generate elementary reflector H_i to annihilate A(i+2:n,i). | ||
| e[i], tau[i] = dlarfg(n - i - 1, a[(i + 1) * lda + i], mut a[math.min(i + | ||
| 2, n - 1) * lda + i..], lda) | ||
| a[(i + 1) * lda + i] = 1 | ||
|
|
||
| // Compute W(i+1:n,i). | ||
| blas.dsymv(.lower, n - i - 1, 1, a[(i + 1) * lda + i + 1..], lda, a[(i + 1) * lda + | ||
| i..], lda, 0, mut w[(i + 1) * ldw + i..], ldw) | ||
| blas.dgemv(.trans, n - i - 1, i, 1, w[(i + 1) * ldw..], ldw, a[(i + 1) * lda + i..], | ||
| lda, 0, mut w[i..], ldw) | ||
| blas.dgemv(.no_trans, n - i - 1, i, -1, a[(i + 1) * lda..], lda, w[i..], | ||
| ldw, 1, mut w[(i + 1) * ldw + i..], ldw) | ||
| blas.dgemv(.trans, n - i - 1, i, 1, a[(i + 1) * lda..], lda, a[(i + 1) * lda + i..], | ||
| lda, 0, mut w[i..], ldw) | ||
| blas.dgemv(.no_trans, n - i - 1, i, -1, w[(i + 1) * ldw..], ldw, w[i..], | ||
| ldw, 1, mut w[(i + 1) * ldw + i..], ldw) | ||
| blas.dscal(n - i - 1, tau[i], mut w[(i + 1) * ldw + i..], ldw) | ||
| alpha := -0.5 * tau[i] * blas.ddot(n - i - 1, w[(i + 1) * ldw + i..], | ||
| ldw, a[(i + 1) * lda + i..], lda) | ||
| blas.daxpy(n - i - 1, alpha, a[(i + 1) * lda + i..], lda, mut w[(i + 1) * ldw + i..], | ||
| ldw) | ||
| } | ||
| } | ||
| } | ||
| } |
There was a problem hiding this comment.
Review and optimize the dlatrd function for better performance and clarity.
- Error Handling: The function has comprehensive checks for input parameters (lines 7-24). This is essential for functions that perform modifications on matrices.
- Performance: The function uses blocked algorithms to optimize matrix operations (lines 43-107). This is a good approach for large matrices but could be better explained in the comments.
- Clarity: The function's purpose and operations could be better documented, especially the blocked algorithm parts, to improve understandability for new developers or maintainers.
+ // Detailed comment about the blocked algorithm approach
+ // Explain why blocking is used and how it improves performance
- for i := n - 1; i >= n - nb; i-- {
+ // Process each blockCommittable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| pub fn dlatrd(uplo blas.Uplo, n int, nb int, mut a []f64, lda int, mut e []f64, mut tau []f64, mut w []f64, ldw int) { | |
| if uplo != .upper && uplo != .lower { | |
| panic(bad_uplo) | |
| } | |
| if n < 0 { | |
| panic(n_lt0) | |
| } | |
| if nb < 0 { | |
| panic(nb_lt0) | |
| } | |
| if nb > n { | |
| panic(nb_gtn) | |
| } | |
| if lda < math.max(1, n) { | |
| panic(bad_ld_a) | |
| } | |
| if ldw < math.max(1, nb) { | |
| panic(bad_ld_w) | |
| } | |
| if n == 0 { | |
| return | |
| } | |
| if a.len < (n - 1) * lda + n { | |
| panic(short_a) | |
| } | |
| if w.len < (n - 1) * ldw + nb { | |
| panic(short_w) | |
| } | |
| if e.len < n - 1 { | |
| panic(short_e) | |
| } | |
| if tau.len < n - 1 { | |
| panic(short_tau) | |
| } | |
| if uplo == .upper { | |
| for i := n - 1; i >= n - nb; i-- { | |
| iw := i - n + nb | |
| if i < n - 1 { | |
| // Update A(0:i, i). | |
| blas.dgemv(.no_trans, i + 1, n - i - 1, -1, a[i + 1..], lda, w[i * ldw + iw + 1..], | |
| 1, 1, mut a[i..], lda) | |
| blas.dgemv(.no_trans, i + 1, n - i - 1, -1, w[iw + 1..], ldw, a[i * lda + i + 1..], | |
| 1, 1, mut a[i..], lda) | |
| } | |
| if i > 0 { | |
| // Generate elementary reflector H_i to annihilate A(0:i-2,i). | |
| e[i - 1], tau[i - 1] = dlarfg(i, a[(i - 1) * lda + i], mut a[i..], lda) | |
| a[(i - 1) * lda + i] = 1 | |
| // Compute W(0:i-1, i). | |
| blas.dsymv(.upper, i, 1, a, lda, a[i..], lda, 0, mut w[iw..], ldw) | |
| if i < n - 1 { | |
| blas.dgemv(.trans, i, n - i - 1, 1, w[iw + 1..], ldw, a[i..], lda, | |
| 0, mut w[(i + 1) * ldw + iw..], ldw) | |
| blas.dgemv(.no_trans, i, n - i - 1, -1, a[i + 1..], lda, w[(i + 1) * ldw + iw..], | |
| ldw, 1, mut w[iw..], ldw) | |
| blas.dgemv(.trans, i, n - i - 1, 1, a[i + 1..], lda, a[i..], lda, | |
| 0, mut w[(i + 1) * ldw + iw..], ldw) | |
| blas.dgemv(.no_trans, i, n - i - 1, -1, w[iw + 1..], ldw, w[(i + 1) * ldw + iw..], | |
| ldw, 1, mut w[iw..], ldw) | |
| } | |
| blas.dscal(i, tau[i - 1], mut w[iw..], ldw) | |
| alpha := -0.5 * tau[i - 1] * blas.ddot(i, w[iw..], ldw, a[i..], lda) | |
| blas.daxpy(i, alpha, a[i..], lda, mut w[iw..], ldw) | |
| } | |
| } | |
| } else { | |
| // Reduce first nb columns of lower triangle. | |
| for i := 0; i < nb; i++ { | |
| // Update A(i:n, i) | |
| blas.dgemv(.no_trans, n - i, i, -1, a[i * lda..], lda, w[i * ldw..], 1, 1, mut | |
| a[i * lda + i..], lda) | |
| blas.dgemv(.no_trans, n - i, i, -1, w[i * ldw..], ldw, a[i * lda..], 1, 1, mut | |
| a[i * lda + i..], lda) | |
| if i < n - 1 { | |
| // Generate elementary reflector H_i to annihilate A(i+2:n,i). | |
| e[i], tau[i] = dlarfg(n - i - 1, a[(i + 1) * lda + i], mut a[math.min(i + | |
| 2, n - 1) * lda + i..], lda) | |
| a[(i + 1) * lda + i] = 1 | |
| // Compute W(i+1:n,i). | |
| blas.dsymv(.lower, n - i - 1, 1, a[(i + 1) * lda + i + 1..], lda, a[(i + 1) * lda + | |
| i..], lda, 0, mut w[(i + 1) * ldw + i..], ldw) | |
| blas.dgemv(.trans, n - i - 1, i, 1, w[(i + 1) * ldw..], ldw, a[(i + 1) * lda + i..], | |
| lda, 0, mut w[i..], ldw) | |
| blas.dgemv(.no_trans, n - i - 1, i, -1, a[(i + 1) * lda..], lda, w[i..], | |
| ldw, 1, mut w[(i + 1) * ldw + i..], ldw) | |
| blas.dgemv(.trans, n - i - 1, i, 1, a[(i + 1) * lda..], lda, a[(i + 1) * lda + i..], | |
| lda, 0, mut w[i..], ldw) | |
| blas.dgemv(.no_trans, n - i - 1, i, -1, w[(i + 1) * ldw..], ldw, w[i..], | |
| ldw, 1, mut w[(i + 1) * ldw + i..], ldw) | |
| blas.dscal(n - i - 1, tau[i], mut w[(i + 1) * ldw + i..], ldw) | |
| alpha := -0.5 * tau[i] * blas.ddot(n - i - 1, w[(i + 1) * ldw + i..], | |
| ldw, a[(i + 1) * lda + i..], lda) | |
| blas.daxpy(n - i - 1, alpha, a[(i + 1) * lda + i..], lda, mut w[(i + 1) * ldw + i..], | |
| ldw) | |
| } | |
| } | |
| } | |
| } | |
| pub fn dlatrd(uplo blas.Uplo, n int, nb int, mut a []f64, lda int, mut e []f64, mut tau []f64, mut w []f64, ldw int) { | |
| if uplo != .upper && uplo != .lower { | |
| panic(bad_uplo) | |
| } | |
| if n < 0 { | |
| panic(n_lt0) | |
| } | |
| if nb < 0 { | |
| panic(nb_lt0) | |
| } | |
| if nb > n { | |
| panic(nb_gtn) | |
| } | |
| if lda < math.max(1, n) { | |
| panic(bad_ld_a) | |
| } | |
| if ldw < math.max(1, nb) { | |
| panic(bad_ld_w) | |
| } | |
| if n == 0 { | |
| return | |
| } | |
| if a.len < (n - 1) * lda + n { | |
| panic(short_a) | |
| } | |
| if w.len < (n - 1) * ldw + nb { | |
| panic(short_w) | |
| } | |
| if e.len < n - 1 { | |
| panic(short_e) | |
| } | |
| if tau.len < n - 1 { | |
| panic(short_tau) | |
| } | |
| if uplo == .upper { | |
| // Detailed comment about the blocked algorithm approach | |
| // Explain why blocking is used and how it improves performance | |
| for i := n - 1; i >= n - nb; i-- { | |
| iw := i - n + nb | |
| if i < n - 1 { | |
| // Update A(0:i, i). | |
| blas.dgemv(.no_trans, i + 1, n - i - 1, -1, a[i + 1..], lda, w[i * ldw + iw + 1..], | |
| 1, 1, mut a[i..], lda) | |
| blas.dgemv(.no_trans, i + 1, n - i - 1, -1, w[iw + 1..], ldw, a[i * lda + i + 1..], | |
| 1, 1, mut a[i..], lda) | |
| } | |
| if i > 0 { | |
| // Generate elementary reflector H_i to annihilate A(0:i-2,i). | |
| e[i - 1], tau[i - 1] = dlarfg(i, a[(i - 1) * lda + i], mut a[i..], lda) | |
| a[(i - 1) * lda + i] = 1 | |
| // Compute W(0:i-1, i). | |
| blas.dsymv(.upper, i, 1, a, lda, a[i..], lda, 0, mut w[iw..], ldw) | |
| if i < n - 1 { | |
| blas.dgemv(.trans, i, n - i - 1, 1, w[iw + 1..], ldw, a[i..], lda, | |
| 0, mut w[(i + 1) * ldw + iw..], ldw) | |
| blas.dgemv(.no_trans, i, n - i - 1, -1, a[i + 1..], lda, w[(i + 1) * ldw + iw..], | |
| ldw, 1, mut w[iw..], ldw) | |
| blas.dgemv(.trans, i, n - i - 1, 1, a[i + 1..], lda, a[i..], lda, | |
| 0, mut w[(i + 1) * ldw + iw..], ldw) | |
| blas.dgemv(.no_trans, i, n - i - 1, -1, w[iw + 1..], ldw, w[(i + 1) * ldw + iw..], | |
| ldw, 1, mut w[iw..], ldw) | |
| } | |
| blas.dscal(i, tau[i - 1], mut w[iw..], ldw) | |
| alpha := -0.5 * tau[i - 1] * blas.ddot(i, w[iw..], ldw, a[i..], lda) | |
| blas.daxpy(i, alpha, a[i..], lda, mut w[iw..], ldw) | |
| } | |
| } | |
| } else { | |
| // Reduce first nb columns of lower triangle. | |
| for i := 0; i < nb; i++ { | |
| // Update A(i:n, i) | |
| blas.dgemv(.no_trans, n - i, i, -1, a[i * lda..], lda, w[i * ldw..], 1, 1, mut | |
| a[i * lda + i..], lda) | |
| blas.dgemv(.no_trans, n - i, i, -1, w[i * ldw..], ldw, a[i * lda..], 1, 1, mut | |
| a[i * lda + i..], lda) | |
| if i < n - 1 { | |
| // Generate elementary reflector H_i to annihilate A(i+2:n,i). | |
| e[i], tau[i] = dlarfg(n - i - 1, a[(i + 1) * lda + i], mut a[math.min(i + | |
| 2, n - 1) * lda + i..], lda) | |
| a[(i + 1) * lda + i] = 1 | |
| // Compute W(i+1:n,i). | |
| blas.dsymv(.lower, n - i - 1, 1, a[(i + 1) * lda + i + 1..], lda, a[(i + 1) * lda + | |
| i..], lda, 0, mut w[(i + 1) * ldw + i..], ldw) | |
| blas.dgemv(.trans, n - i - 1, i, 1, w[(i + 1) * ldw..], ldw, a[(i + 1) * lda + i..], | |
| lda, 0, mut w[i..], ldw) | |
| blas.dgemv(.no_trans, n - i - 1, i, -1, a[(i + 1) * lda..], lda, w[i..], | |
| ldw, 1, mut w[(i + 1) * ldw + i..], ldw) | |
| blas.dgemv(.trans, n - i - 1, i, 1, a[(i + 1) * lda..], lda, a[(i + 1) * lda + i..], | |
| lda, 0, mut w[i..], ldw) | |
| blas.dgemv(.no_trans, n - i - 1, i, -1, w[(i + 1) * ldw..], ldw, w[i..], | |
| ldw, 1, mut w[(i + 1) * ldw + i..], ldw) | |
| blas.dscal(n - i - 1, tau[i], mut w[(i + 1) * ldw + i..], ldw) | |
| alpha := -0.5 * tau[i] * blas.ddot(n - i - 1, w[(i + 1) * ldw + i..], | |
| ldw, a[(i + 1) * lda + i..], lda) | |
| blas.daxpy(n - i - 1, alpha, a[(i + 1) * lda + i..], lda, mut w[(i + 1) * ldw + i..], | |
| ldw) | |
| } | |
| } | |
| } | |
| } |
| if k < 0 { | ||
| panic(k_lt0) | ||
| } | ||
| if k > n { | ||
| panic(k_gtn) | ||
| } | ||
| if lda < math.max(1, n) { | ||
| panic(bad_ld_a) | ||
| } | ||
| if lwork < math.max(1, n) && lwork != -1 { | ||
| panic(bad_l_work) | ||
| } | ||
| if work.len < math.max(1, lwork) { | ||
| panic(short_work) | ||
| } | ||
|
|
||
| // Quick return if possible. | ||
| if n == 0 { | ||
| work[0] = 1 | ||
| return | ||
| } | ||
|
|
||
| mut nb := ilaenv(1, 'DORGQL', ' ', m, n, k, -1) | ||
| if lwork == -1 { | ||
| work[0] = f64(n * nb) | ||
| return | ||
| } | ||
|
|
||
| if a.len < (m - 1) * lda + n { | ||
| panic(short_a) | ||
| } | ||
| if tau.len < k { | ||
| panic(short_tau) | ||
| } | ||
|
|
||
| mut nbmin := 2 | ||
| mut nx := 0 | ||
| mut ldwork := 0 | ||
| mut iws := n | ||
| if 1 < nb && nb < k { | ||
| // Determine when to cross over from blocked to unblocked code. | ||
| nx = math.max(0, ilaenv(3, 'DORGQL', ' ', m, n, k, -1)) | ||
| if nx < k { | ||
| // Determine if workspace is large enough for blocked code. | ||
| iws = n * nb | ||
| if lwork < iws { | ||
| // Not enough workspace to use optimal nb: reduce nb and determine | ||
| // the minimum value of nb. | ||
| nb = lwork / n | ||
| nbmin = math.max(2, ilaenv(2, 'DORGQL', ' ', m, n, k, -1)) | ||
| } | ||
| ldwork = nb | ||
| } | ||
| } | ||
|
|
||
| mut kk := 0 | ||
| if nbmin <= nb && nb < k && nx < k { | ||
| // Use blocked code after the first block. The last kk columns are handled | ||
| // by the block method. | ||
| kk = math.min(k, ((k - nx + nb - 1) / nb) * nb) | ||
|
|
||
| // Set A(m-kk:m, 0:n-kk) to zero. | ||
| for i := m - kk; i < m; i++ { | ||
| for j := 0; j < n - kk; j++ { | ||
| a[i * lda + j] = 0 | ||
| } | ||
| } | ||
| } | ||
|
|
||
| // Use unblocked code for the first or only block. | ||
| dorg2l(m - kk, n - kk, k - kk, mut a, lda, tau, mut work) | ||
| if kk > 0 { | ||
| // Use blocked code. | ||
| for i := k - kk; i < k; i += nb { | ||
| ib := math.min(nb, k - i) | ||
| if n - k + i > 0 { | ||
| // Form the triangular factor of the block reflector | ||
| // H = H_{i+ib-1} * ... * H_{i+1} * H_i. | ||
| dlarft(.backward, .column_wise, m - k + i + ib, ib, a[n - k + i..], lda, | ||
| tau[i..], mut work, ldwork) | ||
|
|
||
| // Apply H to A[0:m-k+i+ib, 0:n-k+i] from the left. | ||
| dlarfb(.left, .no_trans, .backward, .column_wise, m - k + i + ib, n - k + i, | ||
| ib, a[n - k + i..], lda, work, ldwork, mut a, lda, mut work[ib * ldwork..], | ||
| ldwork) | ||
| } | ||
|
|
||
| // Apply H to rows 0:m-k+i+ib of current block. | ||
| dorg2l(m - k + i + ib, ib, ib, mut a[n - k + i..], lda, tau[i..], mut work) | ||
|
|
||
| // Set rows m-k+i+ib:m of current block to zero. | ||
| for j := n - k + i; j < n - k + i + ib; j++ { | ||
| for l := m - k + i + ib; l < m; l++ { | ||
| a[l * lda + j] = 0 | ||
| } | ||
| } | ||
| } | ||
| } | ||
| work[0] = f64(iws) | ||
| } |
There was a problem hiding this comment.
Refactor dorgql for enhanced error messages and performance optimization.
- Error Handling: The function includes robust error handling (lines 32-55). However, the error messages could be more descriptive, as suggested in previous comments.
- Performance: The function uses a blocked algorithm to optimize the generation of the matrix Q (lines 96-138). This is an efficient approach but could be better documented.
- Clarity: The function's operations, especially the use of elementary reflectors, could be better explained in the comments to aid understanding.
+ // Improve error messages for better clarity
- panic(m_lt0)
+ panic("Parameter 'm' must be non-negative.")Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| pub fn dorgql(m int, n int, k int, mut a []f64, lda int, tau []f64, mut work []f64, lwork int) { | |
| if m < 0 { | |
| panic(m_lt0) | |
| } | |
| if n < 0 { | |
| panic(n_lt0) | |
| } | |
| if n > m { | |
| panic(n_gtm) | |
| } | |
| if k < 0 { | |
| panic(k_lt0) | |
| } | |
| if k > n { | |
| panic(k_gtn) | |
| } | |
| if lda < math.max(1, n) { | |
| panic(bad_ld_a) | |
| } | |
| if lwork < math.max(1, n) && lwork != -1 { | |
| panic(bad_l_work) | |
| } | |
| if work.len < math.max(1, lwork) { | |
| panic(short_work) | |
| } | |
| // Quick return if possible. | |
| if n == 0 { | |
| work[0] = 1 | |
| return | |
| } | |
| mut nb := ilaenv(1, 'DORGQL', ' ', m, n, k, -1) | |
| if lwork == -1 { | |
| work[0] = f64(n * nb) | |
| return | |
| } | |
| if a.len < (m - 1) * lda + n { | |
| panic(short_a) | |
| } | |
| if tau.len < k { | |
| panic(short_tau) | |
| } | |
| mut nbmin := 2 | |
| mut nx := 0 | |
| mut ldwork := 0 | |
| mut iws := n | |
| if 1 < nb && nb < k { | |
| // Determine when to cross over from blocked to unblocked code. | |
| nx = math.max(0, ilaenv(3, 'DORGQL', ' ', m, n, k, -1)) | |
| if nx < k { | |
| // Determine if workspace is large enough for blocked code. | |
| iws = n * nb | |
| if lwork < iws { | |
| // Not enough workspace to use optimal nb: reduce nb and determine | |
| // the minimum value of nb. | |
| nb = lwork / n | |
| nbmin = math.max(2, ilaenv(2, 'DORGQL', ' ', m, n, k, -1)) | |
| } | |
| ldwork = nb | |
| } | |
| } | |
| mut kk := 0 | |
| if nbmin <= nb && nb < k && nx < k { | |
| // Use blocked code after the first block. The last kk columns are handled | |
| // by the block method. | |
| kk = math.min(k, ((k - nx + nb - 1) / nb) * nb) | |
| // Set A(m-kk:m, 0:n-kk) to zero. | |
| for i := m - kk; i < m; i++ { | |
| for j := 0; j < n - kk; j++ { | |
| a[i * lda + j] = 0 | |
| } | |
| } | |
| } | |
| // Use unblocked code for the first or only block. | |
| dorg2l(m - kk, n - kk, k - kk, mut a, lda, tau, mut work) | |
| if kk > 0 { | |
| // Use blocked code. | |
| for i := k - kk; i < k; i += nb { | |
| ib := math.min(nb, k - i) | |
| if n - k + i > 0 { | |
| // Form the triangular factor of the block reflector | |
| // H = H_{i+ib-1} * ... * H_{i+1} * H_i. | |
| dlarft(.backward, .column_wise, m - k + i + ib, ib, a[n - k + i..], lda, | |
| tau[i..], mut work, ldwork) | |
| // Apply H to A[0:m-k+i+ib, 0:n-k+i] from the left. | |
| dlarfb(.left, .no_trans, .backward, .column_wise, m - k + i + ib, n - k + i, | |
| ib, a[n - k + i..], lda, work, ldwork, mut a, lda, mut work[ib * ldwork..], | |
| ldwork) | |
| } | |
| // Apply H to rows 0:m-k+i+ib of current block. | |
| dorg2l(m - k + i + ib, ib, ib, mut a[n - k + i..], lda, tau[i..], mut work) | |
| // Set rows m-k+i+ib:m of current block to zero. | |
| for j := n - k + i; j < n - k + i + ib; j++ { | |
| for l := m - k + i + ib; l < m; l++ { | |
| a[l * lda + j] = 0 | |
| } | |
| } | |
| } | |
| } | |
| work[0] = f64(iws) | |
| } | |
| pub fn dorgql(m int, n int, k int, mut a []f64, lda int, tau []f64, mut work []f64, lwork int) { | |
| // Improve error messages for better clarity | |
| if m < 0 { | |
| panic("Parameter 'm' must be non-negative.") | |
| } | |
| if n < 0 { | |
| panic(n_lt0) | |
| } | |
| if n > m { | |
| panic(n_gtm) | |
| } | |
| if k < 0 { | |
| panic(k_lt0) | |
| } | |
| if k > n { | |
| panic(k_gtn) | |
| } | |
| if lda < math.max(1, n) { | |
| panic(bad_ld_a) | |
| } | |
| if lwork < math.max(1, n) && lwork != -1 { | |
| panic(bad_l_work) | |
| } | |
| if work.len < math.max(1, lwork) { | |
| panic(short_work) | |
| } | |
| // Quick return if possible. | |
| if n == 0 { | |
| work[0] = 1 | |
| return | |
| } | |
| mut nb := ilaenv(1, 'DORGQL', ' ', m, n, k, -1) | |
| if lwork == -1 { | |
| work[0] = f64(n * nb) | |
| return | |
| } | |
| if a.len < (m - 1) * lda + n { | |
| panic(short_a) | |
| } | |
| if tau.len < k { | |
| panic(short_tau) | |
| } | |
| mut nbmin := 2 | |
| mut nx := 0 | |
| mut ldwork := 0 | |
| mut iws := n | |
| if 1 < nb && nb < k { | |
| // Determine when to cross over from blocked to unblocked code. | |
| nx = math.max(0, ilaenv(3, 'DORGQL', ' ', m, n, k, -1)) | |
| if nx < k { | |
| // Determine if workspace is large enough for blocked code. | |
| iws = n * nb | |
| if lwork < iws { | |
| // Not enough workspace to use optimal nb: reduce nb and determine | |
| // the minimum value of nb. | |
| nb = lwork / n | |
| nbmin = math.max(2, ilaenv(2, 'DORGQL', ' ', m, n, k, -1)) | |
| } | |
| ldwork = nb | |
| } | |
| } | |
| mut kk := 0 | |
| if nbmin <= nb && nb < k && nx < k { | |
| // Use blocked code after the first block. The last kk columns are handled | |
| // by the block method. | |
| kk = math.min(k, ((k - nx + nb - 1) / nb) * nb) | |
| // Set A(m-kk:m, 0:n-kk) to zero. | |
| for i := m - kk; i < m; i++ { | |
| for j := 0; j < n - kk; j++ { | |
| a[i * lda + j] = 0 | |
| } | |
| } | |
| } | |
| // Use unblocked code for the first or only block. | |
| dorg2l(m - kk, n - kk, k - kk, mut a, lda, tau, mut work) | |
| if kk > 0 { | |
| // Use blocked code. | |
| for i := k - kk; i < k; i += nb { | |
| ib := math.min(nb, k - i) | |
| if n - k + i > 0 { | |
| // Form the triangular factor of the block reflector | |
| // H = H_{i+ib-1} * ... * H_{i+1} * H_i. | |
| dlarft(.backward, .column_wise, m - k + i + ib, ib, a[n - k + i..], lda, | |
| tau[i..], mut work, ldwork) | |
| // Apply H to A[0:m-k+i+ib, 0:n-k+i] from the left. | |
| dlarfb(.left, .no_trans, .backward, .column_wise, m - k + i + ib, n - k + i, | |
| ib, a[n - k + i..], lda, work, ldwork, mut a, lda, mut work[ib * ldwork..], | |
| ldwork) | |
| } | |
| // Apply H to rows 0:m-k+i+ib of current block. | |
| dorg2l(m - k + i + ib, ib, ib, mut a[n - k + i..], lda, tau[i..], mut work) | |
| // Set rows m-k+i+ib:m of current block to zero. | |
| for j := n - k + i; j < n - k + i + ib; j++ { | |
| for l := m - k + i + ib; l < m; l++ { | |
| a[l * lda + j] = 0 | |
| } | |
| } | |
| } | |
| } | |
| work[0] = f64(iws) | |
| } |
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- lapack/lapack64/dpotf2.v (1 hunks)
- lapack/lapack64/dpotrf.v (1 hunks)
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- lapack/lapack64/dpotrf.v
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lapack/lapack64/dpotf2.v (1)
13-71: Review ofdpotf2function: Comprehensive analysisThe
dpotf2function is well-structured and follows good programming practices with appropriate checks and balances. Here are specific observations and suggestions:
Parameter Validation:
- The function correctly checks for invalid
ulvalues, negativen, and inappropriatelda. This ensures robust error handling and prevents further execution with invalid parameters.- The use of panic for error handling is aggressive but acceptable given the internal nature of the function.
Memory Bounds Check:
- The check on line 29 ensures that the array
ais sufficiently large to hold the necessary elements, preventing out-of-bounds memory access.Algorithm Implementation:
- The algorithm differentiates between upper and lower triangular matrices and performs computations accordingly. This is consistent with standard practices in linear algebra libraries.
- The usage of BLAS functions like
ddot,dgemv, anddscalis appropriate and leverages optimized routines for performance.Error Conditions in Computations:
- The checks for non-positive
ajjand NaN values (lines 39 and 58) are crucial for ensuring the matrix is positive definite. Returningfalseupon encountering such conditions is a clean way to indicate an error to the caller.Optimization Opportunities:
- Consider parallelizing the outer loops (lines 34 and 53) if the matrix size
nis large enough to benefit from multi-threading, given that each iteration of the loop is independent of others.Documentation and Readability:
- The comments are clear and provide a good explanation of what each part of the code does. However, adding a few more comments explaining the mathematical operations, especially for those unfamiliar with Cholesky decomposition, could be beneficial.
Potential Bugs or Issues:
- Ensure that the
panicmessages (bad_uplo,n_lt0,bad_ld_a,short_a) are defined elsewhere in the module or are standard in V. If not, these could lead to undefined behavior or less informative error messages.Overall, the function is robust and well-implemented. It adheres to the mathematical requirements of the Cholesky decomposition and makes good use of optimized routines from BLAS.
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- blas/oblas_notd_vsl_blas_cblas.v (1 hunks)
Files skipped from review as they are similar to previous changes (1)
- blas/oblas_notd_vsl_blas_cblas.v
* 'main' of github.com:vlang/vsl: Replace panic with vsl_panic in graph.v (#214) Replace panic with vsl_panic in eval function (#212) change IImage.data from voidptr to &u8 Add Planck Temperature to Constants (#210) Add partial derivatives and tests (#209) ci: comment out the whole super-linter job (too many false positives, it seems that the tool is not configured properly) ci: update Dockerfile to satisfy the lint job ci: change `master` to `main` in .github/workflows/lint.yml ci: upgrade to `super-linter/super-linter/[email protected]` fix `v check-md ~/.vmodules/vsl` fix compilation on macos with latest clang 15 and LAPACK from brew
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New Features
Refactor
Documentation