未验证 提交 bce1e572 编写于 作者: B baoachun 提交者: GitHub

update mkldnn scale_matmul fuse pass ut (#37210)

* update mkldnn scale_matmul fuse pass ut

* update mkldnn scale_matmul_fuse_pass ut
上级 0456e003
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
......@@ -12,61 +12,147 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import unittest
from auto_scan_test import PassAutoScanTest, SkipReasons
from program_config import TensorConfig, ProgramConfig
import numpy as np
from inference_pass_test import InferencePassTest
import paddle.fluid as fluid
import paddle.fluid.core as core
from paddle.fluid.core import AnalysisConfig
from paddle.fluid.core import PassVersionChecker
class ScaleMatmulMkldnnFusePassTest(InferencePassTest):
def setUp(self):
self.set_params()
with fluid.program_guard(self.main_program, self.startup_program):
data = fluid.data(
name="data", shape=[1, 3, 100, 100], dtype="float32")
weight = fluid.layers.create_parameter(
shape=[1, 3, 100, 100], dtype="float32")
scale = fluid.layers.scale(data, scale=self.scale_scale)
matmul = fluid.layers.matmul(
scale,
weight,
transpose_x=self.transpose_x,
transpose_y=self.transpose_y)
self.fetch_list = [matmul]
self.enable_mkldnn = True
def set_params(self):
self.feeds = {
"data": np.random.random((1, 3, 100, 100)).astype("float32")
}
self.scale_scale = 2.0
self.transpose_x = False
self.transpose_y = False
self.pass_name = "scale_matmul_fuse_pass"
import paddle.inference as paddle_infer
from functools import partial
from typing import Optional, List, Callable, Dict, Any, Set
import unittest
import hypothesis
from hypothesis import given, settings, seed, example, assume
import hypothesis.strategies as st
class TestScaleMatmulMkldnnFusePass(PassAutoScanTest):
def is_program_valid(self, program_config: ProgramConfig) -> bool:
return True
def test_check_output(self):
use_gpu = False
self.check_output_with_option(use_gpu)
def sample_program_config(self, draw):
scale = draw(st.floats(min_value=0.01, max_value=2))
bias = 0.0
bias_after_scale = draw(st.booleans())
transpose_X = draw(st.booleans())
transpose_Y = draw(st.booleans())
alpha = draw(st.floats(min_value=0.01, max_value=2))
batch_size = draw(st.integers(min_value=1, max_value=4))
channel = draw(st.integers(min_value=1, max_value=64))
input_dim = draw(st.sampled_from([1, 32, 64]))
def test_pass_compatible(self):
self.assertTrue(PassVersionChecker.IsCompatible(self.pass_name))
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']
]
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)
else:
return np.random.random(shape_y).astype(np.float32)
class ScaleMatmulMkldnnFusePassTest_1(ScaleMatmulMkldnnFusePassTest):
def set_params(self):
self.feeds = {
"data": np.random.random((1, 3, 100, 100)).astype("float32")
attrs = [{
"scale": scale,
"bias": bias,
"bias_after_scale": bias_after_scale
}, {
"transpose_X": transpose_X,
"transpose_Y": transpose_Y,
"alpha": alpha
}, {
'batch_size': batch_size,
'channel': channel,
'input_dim': input_dim
}]
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": "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": []
}
self.scale_scale = 5.0
self.transpose_x = True
self.transpose_y = True
self.pass_name = "scale_matmul_fuse_pass"
}]
ops = self.generate_op_config(ops_config)
program_config = ProgramConfig(
ops=ops,
weights={},
inputs={
"input_data1":
TensorConfig(data_gen=partial(generate_input, attrs, "x")),
"input_data2":
TensorConfig(data_gen=partial(generate_input, attrs, "y"))
},
outputs=["matmul_output"])
return program_config
def sample_predictor_configs(self, program_config):
config = self.create_inference_config(use_mkldnn=True)
yield config, ['matmul'], (1e-5, 1e-5)
def test(self):
self.run_and_statis(quant=False, passes=["scale_matmul_fuse_pass"])
if __name__ == "__main__":
......
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