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enable --compress_to_fp16=True by default in MO (#15488)

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* enable --compress_to_fp16 by default in MO

* corrected docs, added warning if user did't specify --compress_to_fp16 explicitly

* fix failing MO unit-tests

* do not wipe out data_type if user defined it explicitly by cli argument

* updated warning message and docs

* corrected phrasing

* corrected phrasing in FP16_Compression.md

* set compress_to_fp16=False for convert tests

* leftover: set compress_to_fp16=False for convert tests

* minor correction

* print info message in main.py, som minor changes

* typos fix

* fix losing information whether arguments set by user or got from defaults

* returned back default values instead of None

* more selective correcting of test_mo_convert_pytorch.py; added test for cases when compression is enabled/disabled or left by default

* fix test_mo_convert_pytorch.py
This commit is contained in:
Pavel Esir
2023-02-21 13:07:43 +01:00
committed by GitHub
parent 94b64fed79
commit 15973fd2da
7 changed files with 154 additions and 21 deletions
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104
tests/layer_tests/mo_python_api_tests/test_mo_convert_pytorch.py
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@@ -421,10 +421,60 @@ def create_pytorch_nn_module_mean_list(tmp_dir):
parameter_list = [param1, param2]
ref_model = Model([sigm], parameter_list, "test")
return pt_model, ref_model, {'input_shape': [shape, shape], 'mean_values': [[0, 0, 0], [0, 0, 0]],
'onnx_opset_version': 11, 'compress_to_fp16': False, "use_legacy_frontend": True}
def create_pytorch_nn_module_mean_list_default_compression(tmp_dir):
# by default compression should be enabled (same as setting 'compress_to_fp16': True)
# therefore decompression Converts will be present
pt_model = make_pt_model_two_inputs()
shape = [1, 10, 10, 3]
shape = PartialShape(shape)
param1 = ov.opset8.parameter(shape)
param2 = ov.opset8.parameter(shape)
const1 = ov.opset8.constant([[[[0, 0, 0]]]], dtype=np.float16)
const1_decompressed = ov.opset8.convert(const1, destination_type=np.float32)
const2 = ov.opset8.constant([[[[0, 0, 0]]]], dtype=np.float16)
const2_decompressed = ov.opset8.convert(const2, destination_type=np.float32)
sub1 = ov.opset8.subtract(param1, const1_decompressed)
sub2 = ov.opset8.subtract(param2, const2_decompressed)
add = ov.opset8.add(sub1, sub2)
relu = ov.opset8.relu(add)
sigm = ov.opset8.sigmoid(relu)
parameter_list = [param1, param2]
ref_model = Model([sigm], parameter_list, "test")
return pt_model, ref_model, {'input_shape': [shape, shape], 'mean_values': [[0, 0, 0], [0, 0, 0]],
'onnx_opset_version': 11, "use_legacy_frontend": True}
def create_pytorch_nn_module_mean_list_compressin_enabled(tmp_dir):
pt_model = make_pt_model_two_inputs()
shape = [1, 10, 10, 3]
shape = PartialShape(shape)
param1 = ov.opset8.parameter(shape)
param2 = ov.opset8.parameter(shape)
const1 = ov.opset8.constant([[[[0, 0, 0]]]], dtype=np.float16)
const1_decompressed = ov.opset8.convert(const1, destination_type=np.float32)
const2 = ov.opset8.constant([[[[0, 0, 0]]]], dtype=np.float16)
const2_decompressed = ov.opset8.convert(const2, destination_type=np.float32)
sub1 = ov.opset8.subtract(param1, const1_decompressed)
sub2 = ov.opset8.subtract(param2, const2_decompressed)
add = ov.opset8.add(sub1, sub2)
relu = ov.opset8.relu(add)
sigm = ov.opset8.sigmoid(relu)
parameter_list = [param1, param2]
ref_model = Model([sigm], parameter_list, "test")
return pt_model, ref_model, {'input_shape': [shape, shape], 'mean_values': [[0, 0, 0], [0, 0, 0]],
'onnx_opset_version': 11, 'compress_to_fp16': True, "use_legacy_frontend": True}
def create_pytorch_nn_module_scale_list(tmp_dir):
pt_model = make_pt_model_two_inputs()
shape = [1, 10, 10, 3]
@@ -443,10 +493,60 @@ def create_pytorch_nn_module_scale_list(tmp_dir):
parameter_list = [param1, param2]
ref_model = Model([sigm], parameter_list, "test")
return pt_model, ref_model, {'input_shape': [shape, shape], 'scale_values': [[1, 1, 1], [1, 1, 1]],
'onnx_opset_version': 11, 'compress_to_fp16': False, "use_legacy_frontend": True}
def create_pytorch_nn_module_scale_list_default_compression(tmp_dir):
# by default compression should be enabled (same as setting 'compress_to_fp16': True)
# therefore decompression Converts will be present
pt_model = make_pt_model_two_inputs()
shape = [1, 10, 10, 3]
shape = PartialShape(shape)
param1 = ov.opset8.parameter(shape)
param2 = ov.opset8.parameter(shape)
const1 = ov.opset8.constant([[[[1, 1, 1]]]], dtype=np.float16)
const1_decompressed = ov.opset8.convert(const1, destination_type=np.float32)
const2 = ov.opset8.constant([[[[1, 1, 1]]]], dtype=np.float16)
const2_decompressed = ov.opset8.convert(const2, destination_type=np.float32)
sub1 = ov.opset8.multiply(param1, const1_decompressed)
sub2 = ov.opset8.multiply(param2, const2_decompressed)
add = ov.opset8.add(sub1, sub2)
relu = ov.opset8.relu(add)
sigm = ov.opset8.sigmoid(relu)
parameter_list = [param1, param2]
ref_model = Model([sigm], parameter_list, "test")
return pt_model, ref_model, {'input_shape': [shape, shape], 'scale_values': [[1, 1, 1], [1, 1, 1]],
'onnx_opset_version': 11, "use_legacy_frontend": True}
def create_pytorch_nn_module_scale_list_compression_enabled(tmp_dir):
pt_model = make_pt_model_two_inputs()
shape = [1, 10, 10, 3]
shape = PartialShape(shape)
param1 = ov.opset8.parameter(shape)
param2 = ov.opset8.parameter(shape)
const1 = ov.opset8.constant([[[[1, 1, 1]]]], dtype=np.float16)
const1_decompressed = ov.opset8.convert(const1, destination_type=np.float32)
const2 = ov.opset8.constant([[[[1, 1, 1]]]], dtype=np.float16)
const2_decompressed = ov.opset8.convert(const2, destination_type=np.float32)
sub1 = ov.opset8.multiply(param1, const1_decompressed)
sub2 = ov.opset8.multiply(param2, const2_decompressed)
add = ov.opset8.add(sub1, sub2)
relu = ov.opset8.relu(add)
sigm = ov.opset8.sigmoid(relu)
parameter_list = [param1, param2]
ref_model = Model([sigm], parameter_list, "test")
return pt_model, ref_model, {'input_shape': [shape, shape], 'scale_values': [[1, 1, 1], [1, 1, 1]],
'onnx_opset_version': 11, 'compress_to_fp16': True, "use_legacy_frontend": True}
def create_pytorch_nn_module_shapes_list_static(tmp_dir):
pt_model = make_pt_model_two_inputs()
ref_model = make_ref_pt_model_two_inputs([1, 3, 20, 20])
@@ -607,7 +707,11 @@ class TestMoConvertPyTorch(CommonMOConvertTest):
create_pytorch_nn_module_layout_list,
create_pytorch_nn_module_layout_list_case2,
create_pytorch_nn_module_mean_list,
create_pytorch_nn_module_mean_list_default_compression,
create_pytorch_nn_module_mean_list_compressin_enabled,
create_pytorch_nn_module_scale_list,
create_pytorch_nn_module_scale_list_default_compression,
create_pytorch_nn_module_scale_list_compression_enabled,
create_pytorch_nn_module_shapes_list_static,
create_pytorch_nn_module_shapes_list_dynamic,
create_pytorch_nn_module_shapes_list_dynamic_single_input,