未验证 提交 06cf314a 编写于 作者: B baoachun 提交者: GitHub

add seqpool_cvm_concat_fuse_pass ut (#37902)

* add seqpool_cvm_concat_fuse_pass ut

* rename ut name
上级 e0fd3bbf
......@@ -16,6 +16,7 @@
#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
#include "paddle/fluid/framework/ir/pass.h"
#include "paddle/fluid/framework/op_version_registry.h"
namespace paddle {
namespace framework {
......@@ -65,7 +66,7 @@ SeqPoolCVMConcatFusePass::SeqPoolCVMConcatFusePass() {
.IsOptional()
.End()
.AddAttr("pooltype")
.IsStringIn({"AVERAGE", "SUM", "SQRT", "LAST", "FIRST", "MAX"})
.IsStringEQ("SUM")
.End()
.AddAttr("pad_value")
.End();
......@@ -198,3 +199,9 @@ void SeqPoolCVMConcatFusePass::ApplyImpl(ir::Graph* graph) const {
REGISTER_PASS(seqpool_cvm_concat_fuse_pass,
paddle::framework::ir::SeqPoolCVMConcatFusePass);
REGISTER_PASS_CAPABILITY(seqpool_cvm_concat_fuse_pass)
.AddCombination(
paddle::framework::compatible::OpVersionComparatorCombination()
.EQ("sequence_pool", 0)
.EQ("cvm", 0)
.EQ("concat", 0));
# 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.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from auto_scan_test import PassAutoScanTest, SkipReasons
from program_config import TensorConfig, ProgramConfig, OpConfig
import numpy as np
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
from functools import reduce
class TestSeqpoolCvmConcatFusePass(PassAutoScanTest):
def is_program_valid(self, program_config: ProgramConfig) -> bool:
return True
def sample_program_config(self, draw):
is_test = True
pooltype = "SUM"
pad_value1 = draw(st.floats())
pad_value2 = draw(st.floats())
pad_value3 = draw(st.floats())
use_cvm = True
axis = draw(st.sampled_from([1]))
batch_size = draw(st.integers(min_value=1, max_value=4))
def generate_input1():
shape = [batch_size, 128, 6, 120]
return np.random.random(shape).astype(np.float32)
def generate_input2():
shape = [batch_size, 2]
return np.random.random(shape).astype(np.float32)
def generate_input3():
return np.random.random([1, 768]).astype(np.float32)
im2sequence_op = OpConfig(
type="im2sequence",
inputs={"X": ["input_data1"]},
outputs={"Out": ["seq_out"]},
attrs={
"kernels": [6, 1],
"out_stride": [1, 1],
"paddings": [0, 0, 0, 0],
"strides": [1, 1]
})
sequence_pool_op1 = OpConfig(
type="sequence_pool",
inputs={"X": ["seq_out"]},
outputs={"Out": ["seq_pool1_out"],
"MaxIndex": ["index1_out"]},
attrs={
"is_test": is_test,
"pooltype": pooltype,
"pad_value": pad_value1
})
sequence_pool_op2 = OpConfig(
type="sequence_pool",
inputs={"X": ["seq_out"]},
outputs={"Out": ["seq_pool2_out"],
"MaxIndex": ["index2_out"]},
attrs={
"is_test": is_test,
"pooltype": pooltype,
"pad_value": pad_value2
})
sequence_pool_op3 = OpConfig(
type="sequence_pool",
inputs={"X": ["seq_out"]},
outputs={"Out": ["seq_pool3_out"],
"MaxIndex": ["index3_out"]},
attrs={
"is_test": is_test,
"pooltype": pooltype,
"pad_value": pad_value3
})
cvm_op1 = OpConfig(
type="cvm",
inputs={"X": ["seq_pool1_out"],
"CVM": ["input_data2"]},
outputs={"Y": ["cvm1_out"]},
attrs={"use_cvm": use_cvm})
cvm_op2 = OpConfig(
type="cvm",
inputs={"X": ["seq_pool2_out"],
"CVM": ["input_data2"]},
outputs={"Y": ["cvm2_out"]},
attrs={"use_cvm": use_cvm})
cvm_op3 = OpConfig(
type="cvm",
inputs={"X": ["seq_pool3_out"],
"CVM": ["input_data2"]},
outputs={"Y": ["cvm3_out"]},
attrs={"use_cvm": use_cvm})
concat_op = OpConfig(
type="concat",
inputs={"X": ["cvm1_out", "cvm2_out", "cvm3_out"]},
outputs={"Out": ["concat_output"]},
attrs={'axis': axis})
model_net = [
im2sequence_op, sequence_pool_op1, sequence_pool_op2,
sequence_pool_op3, cvm_op1, cvm_op2, cvm_op3, concat_op
]
program_config = ProgramConfig(
ops=model_net,
weights={},
inputs={
"input_data1": TensorConfig(data_gen=partial(generate_input1)),
"input_data2": TensorConfig(data_gen=partial(generate_input2)),
"input_data3": TensorConfig(data_gen=partial(generate_input3))
},
outputs=["concat_output"])
return program_config
def sample_predictor_configs(self, program_config):
config = self.create_inference_config()
yield config, ["im2sequence", "fusion_seqpool_cvm_concat"], (1e-5, 1e-5)
def test(self):
self.run_and_statis(
quant=False, passes=["seqpool_cvm_concat_fuse_pass"])
if __name__ == "__main__":
unittest.main()
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