Tflite backend

.circleci/config.yml

@@ -9,7 +9,7 @@ jobs:
       - run: pip3 install .
       - run: 
           name: Test
-          command: pytest -vv tests
+          command: LC_ALL=C.UTF-8 LANG=C.UTF-8 pytest -vv tests -m 'not slow_test and not deprecated'
           #- run: genhtml main_coverage.info --output-directory /tmp/coverage
           #- store_test_results:
           #    path: /tmp/test_results

.gitignore

@@ -1,3 +1,6 @@
+#system files
+__pycache__
+.DS_Store
 # virtual env
 .venv
 venv
@@ -20,4 +23,4 @@ tests/deep_mlp/data
 .vscode
 # docs
 doc/build
-doc/source/_build
+doc/source/_build

Pipfile

@@ -1,12 +1,13 @@
 [[source]]
 url = "https://pypi.python.org/simple"
-verify_ssl = true
+verify_ssl = false
 name = "pypi"
 
 [packages]
 e1839a8 = {path = ".",editable = true}
 
 [dev-packages]
+matplotlib = "*"
 pylint = "*"
 "flake8" = "*"
 pytest = "*"
@@ -16,3 +17,6 @@ scipy = "*"
 graphviz = "*"
 sphinx = "*"
 sphinx-autoapi = "*"
+
+[pipenv]
+allow_prereleases = true

README.rst

@@ -226,12 +226,32 @@ Use Case: Dropout Layer Removal
 
 \ |cnn-dropout|
 
-.. subgraph-match-end
-
 We use mainly `Tensorflow`_ for declaring the pattern graph for matcher now.
 
 High-level graph builder is on its way, see `Future Works <#future-works>`_ for detail.
 
+.. subgraph-match-end
+
+.. offline-tensor-alloc-start
+
+Offline Tensor Memory Allocation
+--------------------------------
+
+Considering following simple multi layers perceptron (`simple_mnist.pb`_):
+
+\ |mlp-alloc-graph|
+
+Once enabled the optimization transformer, ``tensor_alloc``, an offline tensor memory allocation planner,
+``utensor-cli`` will generate ``uTensor`` runtime codes that use following optimized allocation plan:
+
+\ |mlp-alloc|
+
+- y-axis: tensor names ordered by topological sorting
+- x-axis: these are the memory span occupied by each tensor, that is, the memory address offset and
+the size of the tensor
+
+.. offline-tensor-alloc-end
+
 Tutorials
 =========
 
@@ -313,6 +333,7 @@ Future Works
 .. _Tensorflow: https://www.tensorflow.org
 .. _PyTorch: https://pytorch.org/
 .. _uTensor: https://github.com/uTensor/uTensor
+.. _simple_mnist.pb: https://github.com/uTensor/utensor_cgen/blob/develop/tests/deep_mlp/simple_mnist.pb
 
 .. readme_end
 
@@ -322,7 +343,10 @@ Future Works
     :alt: conv-pool-fuse
 .. |convert-example| image:: doc/source/_images/convert_example.png
     :alt: convert-example
-
+.. |mlp-alloc| image:: doc/source/_images/mlp_alloc.png
+    :alt: mlp-alloc
+.. |mlp-alloc-graph| image:: doc/source/_images/mlp_alloc_graph.png
+    :alt: mlp-alloc-graph
 
 .. TODOs
 .. =====

doc/source/images.rst

@@ -8,3 +8,9 @@
   :height: 300
 .. |convert-example| image:: /_images/convert_example.png
   :alt: convert-example
+.. |mlp-alloc| image:: /_images/mlp_alloc.png
+    :alt: mlp-alloc
+.. |mlp-alloc-graph| image:: /_images/mlp_alloc_graph.png
+    :alt: mlp-alloc-graph
+    :width: 300
+    :height: 450

example_config.toml

@@ -1,15 +1,50 @@
 # https://github.com/toml-lang/toml
 # <target_name>.<component>.<part>
+# we use string 'None' to represent python None value
+# you should convert the string to None if you try to write extension for utensor_cgen
 [utensor.backend]
 legacy-api = true
 
+[utensor.backend.tensor_alloc_planner]
+max_pool_size = 1048576
+include_inputs = false
+out_fname = "None"
+enabled = true
+
 [utensor.backend.legacy_code_generator]
 src_fname = "None"
 params_dir = "data"
 embed_params_dir = "/fs/data"
 model_dir = "models"
-transform_methods = [ "dropout(name_pattern=r\"(dropout[_\\w\\d]*)/.*\")", "linear_reorder", "quantize", "conv_pool", "inline", "biasAdd", "remove_id_op", "fake_gather_v2", "refcnt",]
-save_graph = false
 debug_cmt = false
 
-[utensor.backend.graph_lower]
+[utensor.backend.legacy_graph_lower]
+
+[utensor.backend.rearch_code_generator]
+src_fname = "None"
+header_fname = "None"
+params_dir = "data"
+model_dir = "models"
+meta_data_pool_size = "auto"
+ram_data_pool_size = "auto"
+
+[utensor.backend.rearch_graph_lower]
+
+[utensor.backend.pipeline_transformer]
+save_graph = false
+transform_methods = [ "dropout(name_pattern=r'(dropout[_\\w\\d]*)/.*')", "linear_reorder", "quantize", "conv_pool", "inline", "biasAdd", "remove_id_op", "fake_gather_v2", "refcnt",]
+
+[utensor.backend.tensor_alloc_planner.aesthetic_kwargs]
+split_on_large_graph = true
+num_tensors_per_split = 20
+figsize = "None"
+fontsize = 12
+lw = 12
+rand_seed = 1111
+
+[utensor.backend.tensor_alloc_planner.dtype_size_map]
+float = 4
+double = 8
+uint8 = 1
+int = 4
+long = 8

plugins/dummy_backend/__init__.py

@@ -1,34 +1,33 @@
 from textwrap import wrap
 
-from utensor_cgen.backend.base import Backend
 from utensor_cgen.backend import BackendManager
+from utensor_cgen.backend.base import Backend
 from utensor_cgen.utils import class_property
 
+
 @BackendManager.register
 class DummyBackend(Backend):
-    TARGET = 'dummy-backend'
+  TARGET = 'dummy-backend'
 
-    def __init__(self, config):
-        if not config:
-            config = self.default_config
-        self.output_file = config[self.TARGET][self.COMPONENT]['output-file']
+  def __init__(self, config):
+    self.output_file = self.config[self.TARGET][self.COMPONENT]['output-file']
 
-    def apply(self, ugraph):
-        with open(self.output_file, 'w') as fid:
-            fid.write('#include <stdio.h>\n\n')
-            fid.write('int main(int argc, char* argv[]) {\n')
-            fid.write('    printf("graph name: {}\\n");\n'.format(ugraph.name))
-            fid.write('    printf("ops in topological sorted order:\\n");\n')
-            for op_name in ugraph.topo_order:
-                fid.write('    printf("    {}\\n");\n'.format(op_name))
-            fid.write('    return 0;\n}')
+  def apply(self, ugraph):
+    with open(self.output_file, 'w') as fid:
+      fid.write('#include <stdio.h>\n\n')
+      fid.write('int main(int argc, char* argv[]) {\n')
+      fid.write('    printf("graph name: {}\\n");\n'.format(ugraph.name))
+      fid.write('    printf("ops in topological sorted order:\\n");\n')
+      for op_name in ugraph.topo_order:
+        fid.write('    printf("    {}\\n");\n'.format(op_name))
+      fid.write('    return 0;\n}')
 
-    @class_property
-    def default_config(cls):
-        return {
-            cls.TARGET: {
-                    cls.COMPONENT: {
-                    'output-file': 'list_op.c'
-                }
-            }
+  @class_property
+  def default_config(cls):
+    return {
+      cls.TARGET: {
+          cls.COMPONENT: {
+          'output-file': 'list_op.c'
         }
+      }
+    }