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Merged
jczaja merged 2 commits into from
Jan 2, 2023
Merged

Rewrite scale matmul mkldnn fuse pass tester #49105

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d6dc071
Scale Matmul Fuse pass rewritten
HulekJakub Dec 15, 2022
fa37d7b
Merge branch 'PaddlePaddle:develop' into Rewrite_scale_matmul_fuse_pa…
HulekJakub Dec 16, 2022
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4 changes: 0 additions & 4 deletions paddle/fluid/framework/ir/CMakeLists.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -429,10 +429,6 @@ if(WITH_MKLDNN)
test_conv_batch_norm_mkldnn_fuse_pass
SRCS mkldnn/mkldnn_conv_bn_fuse_pass_tester.cc
DEPS ${TEST_CONV_BN_PASS_DEPS})
cc_test(
test_scale_matmul_fuse_pass
SRCS mkldnn/scale_matmul_fuse_pass_tester.cc
DEPS scale_matmul_fuse_pass)
cc_test(
test_mkldnn_placement_pass
SRCS mkldnn/mkldnn_placement_pass_tester.cc
Expand Down
117 changes: 0 additions & 117 deletions paddle/fluid/framework/ir/mkldnn/scale_matmul_fuse_pass_tester.cc
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155 changes: 69 additions & 86 deletions python/paddle/fluid/tests/unittests/ir/inference/test_mkldnn_scale_matmul_fuse_pass.py
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Original file line number Diff line number Diff line change
Expand Up @@ -37,74 +37,55 @@ def sample_program_config(self, draw):
input_dim = draw(st.sampled_from([1, 32, 64]))

def generate_input(attrs, type):
if attrs[1]['transpose_X'] and attrs[1]['transpose_Y']:
shape_x = [
attrs[2]['batch_size'],
attrs[2]['channel'],
attrs[2]['input_dim'],
32,
]
shape_y = [
attrs[2]['batch_size'],
attrs[2]['channel'],
64,
attrs[2]['input_dim'],
]
elif attrs[1]['transpose_X']:
shape_x = [
attrs[2]['batch_size'],
attrs[2]['channel'],
attrs[2]['input_dim'],
32,
]
shape_y = [
attrs[2]['batch_size'],
attrs[2]['channel'],
attrs[2]['input_dim'],
64,
]
elif attrs[1]['transpose_Y']:
shape_x = [
attrs[2]['batch_size'],
attrs[2]['channel'],
32,
attrs[2]['input_dim'],
]
shape_y = [
attrs[2]['batch_size'],
attrs[2]['channel'],
8,
attrs[2]['input_dim'],
]
is_transpose_X = attrs[1]['transpose_X']
is_transpose_Y = attrs[1]['transpose_Y']

if is_transpose_X:
shape_x_3 = attrs[2]['input_dim']
shape_x_4 = 32
else:
shape_x = [
attrs[2]['batch_size'],
attrs[2]['channel'],
32,
attrs[2]['input_dim'],
]
shape_y = [
attrs[2]['batch_size'],
attrs[2]['channel'],
attrs[2]['input_dim'],
16,
]

if type == "x":
return np.random.random(shape_x).astype(np.float32)
shape_x_3 = 32
shape_x_4 = attrs[2]['input_dim']

if is_transpose_X and is_transpose_Y:
shape_y_3 = 64
shape_y_4 = attrs[2]['input_dim']
elif is_transpose_X:
shape_y_3 = attrs[2]['input_dim']
shape_y_4 = 64
elif is_transpose_Y:
shape_y_3 = 8
shape_y_4 = attrs[2]['input_dim']
else:
return np.random.random(shape_y).astype(np.float32)
shape_y_3 = attrs[2]['input_dim']
shape_y_4 = 16

shape_x = [
attrs[2]['batch_size'],
attrs[2]['channel'],
shape_x_3,
shape_x_4,
]
shape_y = [
attrs[2]['batch_size'],
attrs[2]['channel'],
shape_y_3,
shape_y_4,
]

shape = shape_x if type == 'x' else shape_y
return np.random.random(shape).astype(np.float32)

attrs = [
{
"scale": scale,
"bias": bias,
"bias_after_scale": bias_after_scale,
'scale': scale,
'bias': bias,
'bias_after_scale': bias_after_scale,
},
{
"transpose_X": transpose_X,
"transpose_Y": transpose_Y,
"alpha": alpha,
'transpose_X': transpose_X,
'transpose_Y': transpose_Y,
'alpha': alpha,
},
{
'batch_size': batch_size,
Expand All @@ -115,29 +96,29 @@ def generate_input(attrs, type):

ops_config = [
{
"op_type": "scale",
"op_inputs": {"X": ["input_data1"]},
"op_outputs": {"Out": ["scale_output"]},
"op_attrs": {
"scale": attrs[0]['scale'],
"bias": attrs[0]['bias'],
"bias_after_scale": attrs[0]['bias_after_scale'],
'op_type': 'scale',
'op_inputs': {'X': ['input_data1']},
'op_outputs': {'Out': ['scale_output']},
'op_attrs': {
'scale': attrs[0]['scale'],
'bias': attrs[0]['bias'],
'bias_after_scale': attrs[0]['bias_after_scale'],
},
},
{
"op_type": "matmul",
"op_inputs": {"X": ["scale_output"], "Y": ["input_data2"]},
"op_outputs": {"Out": ["matmul_output"]},
"op_attrs": {
'op_type': 'matmul',
'op_inputs': {'X': ['scale_output'], 'Y': ['input_data2']},
'op_outputs': {'Out': ['matmul_output']},
'op_attrs': {
'transpose_X': attrs[1]['transpose_X'],
'transpose_Y': attrs[1]['transpose_Y'],
'alpha': attrs[1]['alpha'],
"fused_reshape_X": [],
"fused_reshape_Y": [],
"fused_transpose_X": [],
"fused_transpose_Y": [],
"fused_reshape_Out": [],
"fused_transpose_Out": [],
'fused_reshape_X': [],
'fused_reshape_Y': [],
'fused_transpose_X': [],
'fused_transpose_Y': [],
'fused_reshape_Out': [],
'fused_transpose_Out': [],
},
},
]
Expand All @@ -148,25 +129,27 @@ def generate_input(attrs, type):
ops=ops,
weights={},
inputs={
"input_data1": TensorConfig(
data_gen=partial(generate_input, attrs, "x")
'input_data1': TensorConfig(
data_gen=partial(generate_input, attrs, 'x')
),
"input_data2": TensorConfig(
data_gen=partial(generate_input, attrs, "y")
'input_data2': TensorConfig(
data_gen=partial(generate_input, attrs, 'y')
),
},
outputs=["matmul_output"],
outputs=['matmul_output'],
)

return program_config

def sample_predictor_configs(self, program_config):
config = self.create_inference_config(use_mkldnn=True)
config = self.create_inference_config(
use_mkldnn=True, passes=['scale_matmul_fuse_pass']
)
yield config, ['matmul'], (1e-5, 1e-5)

def test(self):
self.run_and_statis(quant=False, passes=["scale_matmul_fuse_pass"])
self.run_and_statis(quant=False, passes=['scale_matmul_fuse_pass'])


if __name__ == "__main__":
if __name__ == '__main__':
unittest.main()