d42b53c070
* Added Torchscript Backend * First commit for backend with Torch FX Decoder * Merging changes from Torch FX branch * Torch FX initial fixes (Temporary) * Fixed type/shape issues in Torch FX decoder * Added translation for built-in getitem * MaxPool update & Output shape fix (Torch FX) * Torch FX graph outputs fix * Torch FX support for sigmoid and slu_ * Torch FX graph module caching * Torch Fx partitioner cache removed * Torch FX initial getitem replacer added * Index check for torch fx getitem replacer * Debug print removed from partitioner * Added environment variables for pytorch tracing mode and openvino device * FX translation fix for getitem & getitem replacer removed * Added checks for PyTorch tracing mode environment variable * Adding compile mode for fallback * Added more ops for resnet18 * Added a check for environment variable * Generalized addmm to work with torchscript and torchfx * Added the missing batch_norm.default translation * fx_backend: include get_attr ops to the partitions * AddeTODO note t to improvget_attr algorithm * created function for adding get_attr nodes * fx_backend: added aten.mul.Tensor, re-enabled aten.empty.memory_format * fx_backend: Additional op support/improvement for Inception V3 * Added comment for fix 64-bit to 32-bit max int conversion * fx_backend: Update for avg_poolnd to support 3 inputs * Fixed erorr in decoder.py * TorchFX caching fix * Torch backend, op support for Stable Diff. & BERT * Arranged ops in order and added torch tensor mapping * Added support for more ops for super glue * TorchFX: Initial permanent fallback * TorchFX: New ops for improved TorchVision support * TorchFX backend optimizations for partitioning and tmp fallback * working operator updates for superglue * Updates to operators for superglue * Removed max.dim and stack * Cleanup * Cleanup * Fixed a couple of syntax issues * Fixed a couple of syntax issues * Added missing method to TorchFX Decoder * Added missing method to TorchFX Decoder * Removed redundant code for transpose * TorchFX: Initial StableDiffusion support * PyTorch decoder ovtype to ctype fix for int64 * Added ops for distilbert * Fixed few unnecessary include statements * Seperated TorchFX and TorchScript decoders * Modified import statements to reflect two decoders * f64 fix for TorchFX * Import fix for PyTorch backend modules * TorchFX serialize graph for debugging (Temporary) * Serialize and load back feature enabled for TorchFX * Temporary optimization to remove Broadcast * Temporary SoftmaxRehapeElimination pass is added * TorchFX custom model cache directory * PyTorch bitwise translation, conversion checks enabled * Naming fix in make_list_construct * TorchFX: Added comments to Softmax and Slice translations * translate_chunk temporarily removed for TS backend * Fixed linter issues * Addressed clang formatting issues * Fixed few more clang and linter issues * Fixed tests to use ts_decoder * Fixed naming convention issues * Added missing import * Added inlined_inputs to TorchScriptDecoder * Added tests for torch fx backend * Removed magic numbers in PyTorch decoder utils * TorchFX decoder data type fix * Added cast from size_t to int * TorchFX output handling code cleanup * TorchFX: Use detached input tensor * Added missing cast from size_t to int * Added static cast in group_norm * Fixed casting issue in split --------- Co-authored-by: ynimmaga <yamini.nimmagadda@intel.com> Co-authored-by: Cavus Mustafa <mustafa.cavus@intel.com>
236 lines
15 KiB
Python
236 lines
15 KiB
Python
# Copyright (C) 2018-2023 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import pytest
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from pytorch_layer_test_class import PytorchLayerTest
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d2_params = [{'weights_shape': [3, 3, 2, 2], 'strides': [1, 1], 'pads': [0, 0], 'dilations': [1, 1], 'groups': 1,
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'output_padding': [0, 0], 'transposed': True},
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{'weights_shape': [3, 3, 2, 2], 'strides': [1, 1], 'pads': [0, 0], 'dilations': [
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1, 1], 'groups': 1, 'output_padding': [0, 0], 'transposed': False},
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{'weights_shape': [3, 1, 1, 1], 'strides': [1, 1], 'pads': [0, 0], 'dilations': [
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1, 1], 'groups': 3, 'output_padding': [0, 0], 'transposed': True},
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{'weights_shape': [3, 1, 1, 1], 'strides': [1, 1], 'pads': [0, 0], 'dilations': [
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1, 1], 'groups': 3, 'output_padding': [0, 0], 'transposed': False},
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{'weights_shape': [3, 1, 1, 1], 'strides': [1, 1], 'bias_shape': [1], 'pads': [
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1, 1], 'dilations': [1, 1], 'groups': 1, 'output_padding': [0, 0], 'transposed': True},
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{'weights_shape': [3, 3, 1, 1], 'strides': [1, 1], 'pads': [
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1, 1], 'dilations': [1, 1], 'groups': 1, 'output_padding': [0, 0], 'transposed': False},
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{'weights_shape': [3, 1, 1, 1], 'strides': [1, 1], 'bias_shape': [1], 'pads': [
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3, 1], 'dilations': [1, 1], 'groups': 1, 'output_padding': [0, 0], 'transposed': True},
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{'weights_shape': [3, 3, 1, 1], 'strides': [1, 1], 'pads': [
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3, 1], 'dilations': [1, 1], 'groups': 1, 'output_padding': [0, 0], 'transposed': False},
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{'weights_shape': [3, 1, 1, 1], 'strides': [1, 1], 'bias_shape': [1], 'pads': [
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1, 0], 'dilations': [1, 1], 'groups': 1, 'output_padding': [0, 0], 'transposed': True},
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{'weights_shape': [3, 3, 1, 1], 'strides': [1, 1], 'pads': [
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0, 1], 'dilations': [1, 1], 'groups': 1, 'output_padding': [0, 0], 'transposed': False},
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{'weights_shape': [3, 1, 1, 1], 'strides': [1, 1], 'pads': [
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1, 0], 'dilations': [1, 1], 'groups': 3, 'output_padding': [0, 0], 'transposed': True},
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{'weights_shape': [3, 1, 1, 1], 'strides': [1, 1], 'pads': [
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0, 1], 'dilations': [1, 1], 'groups': 3, 'output_padding': [0, 0], 'transposed': False},
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{'weights_shape': [3, 1, 1, 1], 'strides': [1, 1], 'pads': [
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1, 0], 'dilations': [2, 2], 'groups': 3, 'output_padding': [0, 0], 'transposed': True},
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{'weights_shape': [3, 1, 1, 1], 'strides': [1, 1], 'pads': [
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0, 0], 'dilations': [2, 2], 'groups': 3, 'output_padding': [0, 0], 'transposed': False},
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{'weights_shape': [3, 1, 1, 1], 'strides': [2, 1], 'bias_shape': [1], 'pads': [
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1, 0], 'dilations': [1, 1], 'groups': 1, 'output_padding': [0, 0], 'transposed': True},
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{'weights_shape': [3, 3, 1, 1], 'strides': [2, 1], 'pads': [
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0, 0], 'dilations': [1, 1], 'groups': 1, 'output_padding': [0, 0], 'transposed': False},
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{'weights_shape': [3, 1, 1, 1], 'strides': [2, 2], 'bias_shape': [1], 'pads': [
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0, 0], 'dilations': [1, 1], 'groups': 1, 'output_padding': [0, 0], 'transposed': True},
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{'weights_shape': [3, 3, 1, 1], 'strides': [2, 2], 'pads': [
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0, 0], 'dilations': [1, 1], 'groups': 1, 'output_padding': [0, 0], 'transposed': False},
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{'weights_shape': [3, 3, 1, 1], 'strides': [2, 1], 'pads': [
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0, 0], 'dilations': [1, 1], 'groups': 1, 'output_padding': [0, 0], 'transposed': False},
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{'weights_shape': [3, 1, 1, 1], 'strides': [2, 2], 'bias_shape': [1], 'pads': [
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0, 0], 'dilations': [1, 1], 'groups': 1, 'output_padding': [0, 0], 'transposed': True},
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{'weights_shape': [3, 1, 1, 1], 'strides': [2, 2], 'bias_shape': [1], 'pads': [
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1, 1], 'dilations': [2, 2], 'groups': 1, 'output_padding': [1, 1], 'transposed': True},
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]
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d1_params = [
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{'weights_shape': [3, 3, 2], 'strides': [1], 'pads': [0], 'dilations': [1], 'groups': 1, 'output_padding': [0],
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'transposed': True},
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{'weights_shape': [3, 3, 2], 'strides': [1], 'pads': [0], 'dilations': [
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1], 'groups': 1, 'output_padding': [0], 'transposed': False},
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{'weights_shape': [3, 1, 1], 'strides': [1], 'pads': [0], 'dilations': [
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1], 'groups': 3, 'output_padding': [0], 'transposed': True},
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{'weights_shape': [3, 1, 1], 'strides': [1], 'pads': [0], 'dilations': [
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1], 'groups': 3, 'output_padding': [0], 'transposed': False},
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{'weights_shape': [3, 1, 1], 'strides': [1], 'bias_shape': [1], 'pads': [
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1], 'dilations': [1], 'groups': 1, 'output_padding': [0], 'transposed': True},
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{'weights_shape': [3, 3, 1], 'strides': [1], 'pads': [
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1], 'dilations': [1], 'groups': 1, 'output_padding': [0], 'transposed': False},
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{'weights_shape': [3, 1, 1], 'strides': [1], 'bias_shape': [1], 'pads': [
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3], 'dilations': [1], 'groups': 1, 'output_padding': [0], 'transposed': True},
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{'weights_shape': [3, 3, 1], 'strides': [1], 'pads': [
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3], 'dilations': [1], 'groups': 1, 'output_padding': [0], 'transposed': False},
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{'weights_shape': [3, 1, 1], 'strides': [1], 'bias_shape': [1], 'pads': [
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1], 'dilations': [1], 'groups': 1, 'output_padding': [0], 'transposed': True},
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{'weights_shape': [3, 3, 1], 'strides': [1], 'pads': [
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0], 'dilations': [1], 'groups': 1, 'output_padding': [0], 'transposed': False},
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{'weights_shape': [3, 1, 1], 'strides': [1], 'pads': [
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1], 'dilations': [1], 'groups': 3, 'output_padding': [0], 'transposed': True},
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{'weights_shape': [3, 1, 1], 'strides': [1], 'pads': [1], 'dilations': [
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1], 'groups': 3, 'output_padding': [0], 'transposed': False},
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{'weights_shape': [3, 1, 1], 'strides': [1], 'pads': [
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1], 'dilations': [2], 'groups': 3, 'output_padding': [0], 'transposed': True},
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{'weights_shape': [3, 1, 1], 'strides': [1], 'pads': [
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0], 'dilations': [2], 'groups': 3, 'output_padding': [0], 'transposed': False},
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{'weights_shape': [3, 1, 1], 'strides': [2], 'bias_shape': [1], 'pads': [
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1], 'dilations': [1], 'groups': 1, 'output_padding': [0], 'transposed': True},
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{'weights_shape': [3, 3, 1], 'strides': [2], 'pads': [
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0], 'dilations': [1], 'groups': 1, 'output_padding': [0], 'transposed': False},
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{'weights_shape': [3, 1, 1], 'strides': [2], 'bias_shape': [1], 'pads': [
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0], 'dilations': [1], 'groups': 1, 'output_padding': [0], 'transposed': True},
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{'weights_shape': [3, 3, 1], 'strides': [2], 'pads': [
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0], 'dilations': [1], 'groups': 1, 'output_padding': [0], 'transposed': False},
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{'weights_shape': [3, 3, 1], 'strides': [1], 'pads': [0], 'dilations': [
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1], 'groups': 1, 'output_padding': [0], 'transposed': False},
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{'weights_shape': [3, 1, 1], 'strides': [2], 'bias_shape': [1], 'pads': [
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0], 'dilations': [1], 'groups': 1, 'output_padding': [0], 'transposed': True},
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{'weights_shape': [3, 1, 1], 'strides': [2], 'bias_shape': [1], 'pads': [
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1], 'dilations': [2], 'groups': 1, 'output_padding': [1], 'transposed': True},
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]
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d3_params = [
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{'weights_shape': [3, 3, 2, 2, 1], 'strides': [1, 1, 1], 'pads': [0, 0, 0], 'dilations': [1, 1, 1], 'groups': 1,
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'output_padding': [0, 0, 0], 'transposed': True},
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{'weights_shape': [3, 3, 2, 2, 1], 'strides': [1, 1, 1], 'pads': [0, 0, 0], 'dilations': [
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1, 1, 1], 'groups': 1, 'output_padding': [0, 0, 0], 'transposed': False},
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{'weights_shape': [3, 1, 1, 1, 1], 'strides': [1, 1, 1], 'pads': [0, 0, 0], 'dilations': [
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1, 1, 1], 'groups': 3, 'output_padding': [0, 0, 0], 'transposed': True},
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{'weights_shape': [3, 1, 1, 1, 1], 'strides': [1, 1, 1], 'pads': [0, 0, 0], 'dilations': [
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1, 1, 1], 'groups': 3, 'output_padding': [0, 0, 0], 'transposed': False},
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{'weights_shape': [3, 1, 1, 1, 1], 'strides': [1, 1, 1], 'bias_shape': [1], 'pads': [
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1, 1, 1], 'dilations': [1, 1, 1], 'groups': 1, 'output_padding': [0, 0, 0], 'transposed': True},
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{'weights_shape': [3, 3, 1, 1, 1], 'strides': [1, 1, 1], 'pads': [
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1, 1, 1], 'dilations': [1, 1, 1], 'groups': 1, 'output_padding': [0, 0, 0], 'transposed': False},
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{'weights_shape': [3, 1, 1, 1, 1], 'strides': [1, 1, 1], 'bias_shape': [1], 'pads': [
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3, 1, 3], 'dilations': [1, 1, 1], 'groups': 1, 'output_padding': [0, 0, 0], 'transposed': True},
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{'weights_shape': [3, 3, 1, 1, 1], 'strides': [1, 1, 1], 'pads': [
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3, 1, 3], 'dilations': [1, 1, 1], 'groups': 1, 'output_padding': [0, 0, 0], 'transposed': False},
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{'weights_shape': [3, 1, 1, 1, 1], 'strides': [1, 1, 1], 'bias_shape': [1], 'pads': [
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1, 0, 0], 'dilations': [1, 1, 1], 'groups': 1, 'output_padding': [0, 0, 0], 'transposed': True},
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{'weights_shape': [3, 3, 1, 1, 1], 'strides': [1, 1, 1], 'pads': [
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0, 1, 0], 'dilations': [1, 1, 1], 'groups': 1, 'output_padding': [0, 0, 0], 'transposed': False},
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{'weights_shape': [3, 1, 1, 1, 1], 'strides': [1, 1, 1], 'pads': [
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1, 0, 0], 'dilations': [1, 1, 1], 'groups': 3, 'output_padding': [0, 0, 0], 'transposed': True},
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{'weights_shape': [3, 1, 1, 1, 1], 'strides': [1, 1, 1], 'pads': [
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0, 1, 1], 'dilations': [1, 1, 1], 'groups': 3, 'output_padding': [0, 0, 0], 'transposed': False},
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{'weights_shape': [3, 1, 1, 1, 1], 'strides': [1, 1, 1], 'pads': [
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1, 0, 0], 'dilations': [2, 2, 1], 'groups': 3, 'output_padding': [0, 0, 0], 'transposed': True},
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{'weights_shape': [3, 1, 1, 1, 1], 'strides': [1, 1, 1], 'pads': [
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0, 0, 0], 'dilations': [2, 2, 2], 'groups': 3, 'output_padding': [0, 0, 0], 'transposed': False},
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{'weights_shape': [3, 1, 1, 1, 1], 'strides': [2, 1, 1], 'bias_shape': [1], 'pads': [
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1, 0, 0], 'dilations': [1, 1, 1], 'groups': 1, 'output_padding': [0, 0, 0], 'transposed': True},
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{'weights_shape': [3, 3, 1, 1, 1], 'strides': [2, 1, 1], 'pads': [
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0, 0, 0], 'dilations': [1, 1, 1], 'groups': 1, 'output_padding': [0, 0, 0], 'transposed': False},
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{'weights_shape': [3, 1, 1, 1, 1], 'strides': [2, 2, 2], 'bias_shape': [1], 'pads': [
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0, 0, 0], 'dilations': [1, 1, 1], 'groups': 1, 'output_padding': [0, 0, 0], 'transposed': True},
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{'weights_shape': [3, 3, 1, 1, 1], 'strides': [2, 2, 2], 'pads': [
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0, 0, 0], 'dilations': [1, 1, 1], 'groups': 1, 'output_padding': [0, 0, 0], 'transposed': False},
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{'weights_shape': [3, 3, 1, 1, 1], 'strides': [2, 1, 1], 'pads': [
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0, 0, 1], 'dilations': [1, 1, 1], 'groups': 1, 'output_padding': [0, 0, 0], 'transposed': False},
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{'weights_shape': [3, 1, 1, 1, 1], 'strides': [2, 2, 2], 'bias_shape': [1], 'pads': [
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0, 0, 0], 'dilations': [1, 1, 1], 'groups': 1, 'output_padding': [0, 0, 0], 'transposed': True},
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{'weights_shape': [3, 1, 1, 1, 1], 'strides': [2, 2, 2], 'bias_shape': [1], 'pads': [
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1, 1, 1], 'dilations': [2, 2, 2], 'groups': 1, 'output_padding': [1, 1, 1], 'transposed': True},
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]
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class TestConvolution(PytorchLayerTest):
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def _prepare_input(self, ndim=4):
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import numpy as np
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shape = (1, 3, 10, 10, 10)
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return (np.random.randn(*shape[:ndim]).astype(np.float32),)
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def create_model(self, weights_shape, strides, pads, dilations, groups, bias, transposed, output_padding=0,
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bias_shape=None, underscore=True):
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import torch
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bias_dim = 0
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class aten__convolution(torch.nn.Module):
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def __init__(self):
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super(aten__convolution, self).__init__()
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self.weight = torch.randn(weights_shape)
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self.bias_shape = bias_shape
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if self.bias_shape is None:
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self.bias_shape = weights_shape[bias_dim]
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self.bias = torch.randn(self.bias_shape) if bias else None
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self.strides = strides
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self.pads = pads
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self.dilations = dilations
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self.groups = groups
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self.transposed = transposed
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self.output_padding = output_padding
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self._op = torch._convolution
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def forward(self, x):
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return self._op(
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x, self.weight, self.bias, self.strides, self.pads, self.dilations, self.transposed,
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self.output_padding, self.groups, False, False, False, False
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)
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class aten_convolution(torch.nn.Module):
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def __init__(self):
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super(aten_convolution, self).__init__()
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self.weight = torch.randn(weights_shape)
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self.bias_shape = bias_shape
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if self.bias_shape is None:
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self.bias_shape = weights_shape[bias_dim]
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self.bias = torch.randn(self.bias_shape) if bias else None
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self.strides = strides
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self.pads = pads
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self.dilations = dilations
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self.groups = groups
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self.transposed = transposed
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self.output_padding = output_padding
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self._op = torch.convolution
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def forward(self, x):
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return self._op(
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x, self.weight, self.bias, self.strides, self.pads, self.dilations, self.transposed,
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self.output_padding, self.groups
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)
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ref_net = None
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if underscore:
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return aten__convolution(), ref_net, "aten::_convolution"
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return aten_convolution(), ref_net, "aten::convolution"
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@pytest.mark.parametrize("params", d1_params)
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@pytest.mark.parametrize("bias", [True, False])
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@pytest.mark.parametrize("underscore", [True, False])
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@pytest.mark.nightly
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@pytest.mark.precommit
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@pytest.mark.precommit_ts_backend
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@pytest.mark.precommit_fx_backend
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def test_convolution1d(self, params, bias, underscore, ie_device, precision, ir_version):
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self._test(*self.create_model(**params, bias=bias, underscore=underscore),
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ie_device, precision, ir_version, dynamic_shapes=params['groups'] == 1,
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kwargs_to_prepare_input={'ndim': 3})
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@pytest.mark.parametrize("params", d2_params)
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@pytest.mark.parametrize("bias", [True, False])
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@pytest.mark.parametrize("underscore", [True, False])
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@pytest.mark.nightly
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@pytest.mark.precommit
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@pytest.mark.precommit_ts_backend
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@pytest.mark.precommit_fx_backend
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def test_convolution2d(self, params, bias, underscore, ie_device, precision, ir_version):
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self._test(*self.create_model(**params, bias=bias, underscore=underscore),
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ie_device, precision, ir_version, dynamic_shapes=params['groups'] == 1)
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@pytest.mark.parametrize("params", d3_params)
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@pytest.mark.parametrize("bias", [True, False])
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@pytest.mark.parametrize("underscore", [True, False])
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@pytest.mark.nightly
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def test_convolution3d(self, params, bias, underscore, ie_device, precision, ir_version):
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self._test(*self.create_model(**params, bias=bias, underscore=underscore),
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ie_device, precision, ir_version, dynamic_shapes=params['groups'] == 1,
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kwargs_to_prepare_input={'ndim': 5})
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