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未验证 提交 33fbb66e 编写于 作者: Z zhenlin 提交者： GitHub
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update 3 tests (#37922) 

* update 3 tests

* fix typo error
上级 e7f5d325
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Showing with 485 addition and 259 deletion
paddle/fluid/framework/ir/conv_affine_channel_fuse_pass.cc
浏览文件 @ 33fbb66e
// Copyright (c) 2018 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.
......@@ -130,7 +130,7 @@ ConvAffineChannelFusePass::ConvAffineChannelFusePass() {
.IsType<std::vector<int>>()
.End()
.AddAttr("data_format")
.IsStringIn({"NCHW", "NHWC", "AnyLayout"})
.IsStringIn({"NCHW" /*, "NHWC", "AnyLayout"*/})
.End();
AddOpCompat(OpCompat("affine_channel"))
......@@ -148,7 +148,7 @@ ConvAffineChannelFusePass::ConvAffineChannelFusePass() {
.IsTensor()
.End()
.AddAttr("data_layout")
.IsStringIn({"NCHW", "NHWC", "AnyLayout"})
.IsStringIn({"NCHW" /*, "NHWC", "AnyLayout"*/})
.End();
AddOpCompat(OpCompat("elementwise_add"))
......@@ -197,19 +197,23 @@ void ConvAffineChannelFusePass::ApplyImpl(ir::Graph* graph) const {
GET_CONV_BN_NODES(conv_ac_pattern);
// Get affine_channel bias for resizing eltwise_y!
auto* ac_bias_tensor =
scope->FindVar(ac_bias->Name())->GetMutable<LoDTensor>();
// Create eltwise_y (conv bias) variable
VarDesc eltwise_y_in_desc(
patterns::PDNodeName(name_scope_, "eltwise_y_in"));
// Set shape && datatype manually
eltwise_y_in_desc.SetShape(framework::vectorize(ac_bias_tensor->dims()));
eltwise_y_in_desc.SetDataType(ac_bias_tensor->type());
eltwise_y_in_desc.SetLoDLevel(ac_bias->Var()->GetLoDLevel());
eltwise_y_in_desc.SetPersistable(true);
// Initialize eltwise_y
auto* eltwise_y_in_node = g->CreateVarNode(&eltwise_y_in_desc);
auto* eltwise_y_in_tensor =
scope->Var(eltwise_y_in_node->Name())->GetMutable<LoDTensor>();
// Get affine_channel bias
auto* ac_bias_tensor =
scope->FindVar(ac_bias->Name())->GetMutable<LoDTensor>();
// Initialize eltwise_y
eltwise_y_in_tensor->Resize(ac_bias_tensor->dims());
std::fill_n(eltwise_y_in_tensor->mutable_data<float>(platform::CPUPlace()),
eltwise_y_in_tensor->numel(), 0.0f);
......@@ -278,7 +282,7 @@ ConvEltwiseAddAffineChannelFusePass::ConvEltwiseAddAffineChannelFusePass() {
.IsType<std::vector<int>>()
.End()
.AddAttr("data_format")
.IsStringIn({"NCHW", "NHWC", "AnyLayout"})
.IsStringIn({"NCHW" /*, "NHWC", "AnyLayout"*/})
.End();
AddOpCompat(OpCompat("affine_channel"))
.AddInput("X")
......@@ -295,7 +299,7 @@ ConvEltwiseAddAffineChannelFusePass::ConvEltwiseAddAffineChannelFusePass() {
.IsTensor()
.End()
.AddAttr("data_layout")
.IsStringIn({"NCHW", "NHWC", "AnyLayout"})
.IsStringIn({"NCHW" /*, "NHWC", "AnyLayout"*/})
.End();
AddOpCompat(OpCompat("elementwise_add"))
.AddInput("X")
......
paddle/fluid/framework/ir/conv_elementwise_add_fuse_pass.cc
浏览文件 @ 33fbb66e
// Copyright (c) 2018 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.
......@@ -57,7 +57,7 @@ ConvElementwiseAddFusePass::ConvElementwiseAddFusePass() {
.AddAttr("dilations")
.End()
.AddAttr("data_format")
.IsStringIn({"NCHW", "NHWC", "AnyLayout"})
.IsStringIn({"NCHW" /*, "NHWC", "AnyLayout"*/})
.End();
AddOpCompat(OpCompat("elementwise_add"))
......@@ -87,7 +87,7 @@ void ConvElementwiseAddFusePass::ApplyImpl(ir::Graph* graph) const {
patterns::ConvElementwiseadd pattern(gpd.mutable_pattern(), pattern_name);
pattern(x);
int found_conv_eltwise_count = 0;
auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
Graph* g) {
if (!IsCompat(subgraph, g)) {
......@@ -135,9 +135,12 @@ void ConvElementwiseAddFusePass::ApplyImpl(ir::Graph* graph) const {
// Delete the unneeded nodes.
GraphSafeRemoveNodes(graph, {conv_op, conv_out, elementwise_add_op});
found_conv_eltwise_count++;
};
gpd(graph, handler);
// check if detect conv2d_fusion subgraph!
AddStatis(found_conv_eltwise_count);
}
} // namespace ir
......
python/paddle/fluid/tests/unittests/ir/inference/CMakeLists.txt
浏览文件 @ 33fbb66e
......@@ -80,6 +80,7 @@ if (WITH_MKLDNN AND TENSORRT_FOUND AND WITH_GPU)
set_tests_properties(test_fc_fuse_pass PROPERTIES TIMEOUT 240)
set_tests_properties(test_simplify_with_basic_ops_pass_autoscan PROPERTIES TIMEOUT 60)
set_tests_properties(test_adaptive_pool2d_convert_global_pass_autoscan PROPERTIES TIMEOUT 60)
set_tests_properties(test_conv_eltwiseadd_affine_channel_fuse_pass PROPERTIES TIMEOUT 100)
endif()
if (WITH_MKLDNN)
......
python/paddle/fluid/tests/unittests/ir/inference/test_conv_affine_channel_fuse_pass.py
浏览文件 @ 33fbb66e
# 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.
......@@ -12,216 +12,148 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from auto_scan_test import PassAutoScanTest, IgnoreReasons
from program_config import TensorConfig, ProgramConfig, OpConfig
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 PassVersionChecker
class ConvAffineChannelFusePassExplicitPaddingTest(InferencePassTest):
def setUp(self):
with fluid.program_guard(self.main_program, self.startup_program):
data = fluid.data(
name="data", shape=[-1, 3, 64, 64], dtype="float32")
conv_out = fluid.layers.conv2d(
input=data,
num_filters=3,
filter_size=3,
groups=3,
padding=[1, 1, 1, 1],
bias_attr=False,
act=None)
input_scale = fluid.layers.create_parameter(
shape=[3], dtype="float32")
input_bias = fluid.layers.create_parameter(
shape=[3], dtype="float32")
ac_out = fluid.layers.affine_channel(
x=conv_out, scale=input_scale, bias=input_bias)
self.feeds = {
"data": np.random.random([1, 3, 64, 64]).astype("float32"),
}
self.fetch_list = [ac_out]
def test_check_output(self):
self.check_output()
self.assertTrue(
PassVersionChecker.IsCompatible('conv_affine_channel_fuse_pass'))
class ConvAffineChannelFusePassValidPaddingTest(InferencePassTest):
def setUp(self):
with fluid.program_guard(self.main_program, self.startup_program):
data = fluid.data(
name="data", shape=[-1, 3, 64, 64], dtype="float32")
conv_out = fluid.layers.conv2d(
input=data,
num_filters=3,
filter_size=3,
groups=3,
padding='VALID',
bias_attr=False,
act=None)
input_scale = fluid.layers.create_parameter(
shape=[3], dtype="float32")
input_bias = fluid.layers.create_parameter(
shape=[3], dtype="float32")
ac_out = fluid.layers.affine_channel(
x=conv_out, scale=input_scale, bias=input_bias)
self.feeds = {
"data": np.random.random([1, 3, 64, 64]).astype("float32"),
}
self.fetch_list = [ac_out]
def test_check_output(self):
self.check_output()
self.assertTrue(
PassVersionChecker.IsCompatible('conv_affine_channel_fuse_pass'))
class ConvAffineChannelFusePassSamePaddingTest(InferencePassTest):
def setUp(self):
with fluid.program_guard(self.main_program, self.startup_program):
data = fluid.data(
name="data", shape=[-1, 3, 64, 64], dtype="float32")
conv_out = fluid.layers.conv2d(
input=data,
num_filters=3,
filter_size=3,
groups=3,
padding='SAME',
bias_attr=False,
act=None)
input_scale = fluid.layers.create_parameter(
shape=[3], dtype="float32")
input_bias = fluid.layers.create_parameter(
shape=[3], dtype="float32")
ac_out = fluid.layers.affine_channel(
x=conv_out, scale=input_scale, bias=input_bias)
self.feeds = {
"data": np.random.random([1, 3, 64, 64]).astype("float32"),
}
self.fetch_list = [ac_out]
def test_check_output(self):
self.check_output()
self.assertTrue(
PassVersionChecker.IsCompatible('conv_affine_channel_fuse_pass'))
class ConvEltwiseAddAffineChannelFusePassExplicitPaddingTest(InferencePassTest):
def setUp(self):
with fluid.program_guard(self.main_program, self.startup_program):
data = fluid.data(
name="data", shape=[-1, 3, 64, 64], dtype="float32")
param_attr = fluid.ParamAttr(
initializer=fluid.initializer.Xavier(uniform=False),
learning_rate=0.001)
conv_out = fluid.layers.conv2d(
input=data,
num_filters=3,
filter_size=3,
groups=3,
padding=[1, 1, 1, 1],
bias_attr=param_attr,
act=None)
input_scale = fluid.layers.create_parameter(
shape=[3], dtype="float32")
input_bias = fluid.layers.create_parameter(
shape=[3], dtype="float32")
ac_out = fluid.layers.affine_channel(
x=conv_out, scale=input_scale, bias=input_bias)
self.feeds = {
"data": np.random.random([1, 3, 64, 64]).astype("float32"),
}
self.fetch_list = [ac_out]
def test_check_output(self):
self.check_output()
self.assertTrue(
PassVersionChecker.IsCompatible(
'conv_eltwiseadd_affine_channel_fuse_pass'))
class ConvEltwiseAddAffineChannelFusePassValidPaddingTest(InferencePassTest):
def setUp(self):
with fluid.program_guard(self.main_program, self.startup_program):
data = fluid.data(
name="data", shape=[-1, 3, 64, 64], dtype="float32")
param_attr = fluid.ParamAttr(
initializer=fluid.initializer.Xavier(uniform=False),
learning_rate=0.001)
conv_out = fluid.layers.conv2d(
input=data,
num_filters=3,
filter_size=3,
groups=3,
padding='VALID',
bias_attr=param_attr,
act=None)
input_scale = fluid.layers.create_parameter(
shape=[3], dtype="float32")
input_bias = fluid.layers.create_parameter(
shape=[3], dtype="float32")
ac_out = fluid.layers.affine_channel(
x=conv_out, scale=input_scale, bias=input_bias)
self.feeds = {
"data": np.random.random([1, 3, 64, 64]).astype("float32"),
}
self.fetch_list = [ac_out]
def test_check_output(self):
self.check_output()
self.assertTrue(
PassVersionChecker.IsCompatible(
'conv_eltwiseadd_affine_channel_fuse_pass'))
class ConvEltwiseAddAffineChannelFusePassSamePaddingTest(InferencePassTest):
def setUp(self):
with fluid.program_guard(self.main_program, self.startup_program):
data = fluid.data(
name="data", shape=[-1, 3, 64, 64], dtype="float32")
param_attr = fluid.ParamAttr(
initializer=fluid.initializer.Xavier(uniform=False),
learning_rate=0.001)
conv_out = fluid.layers.conv2d(
input=data,
num_filters=3,
filter_size=3,
groups=3,
padding='Same',
bias_attr=param_attr,
act=None)
input_scale = fluid.layers.create_parameter(
shape=[3], dtype="float32")
input_bias = fluid.layers.create_parameter(
shape=[3], dtype="float32")
ac_out = fluid.layers.affine_channel(
x=conv_out, scale=input_scale, bias=input_bias)
self.feeds = {
"data": np.random.random([1, 3, 64, 64]).astype("float32"),
}
self.fetch_list = [ac_out]
def test_check_output(self):
self.check_output()
self.assertTrue(
PassVersionChecker.IsCompatible(
'conv_eltwiseadd_affine_channel_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, reproduce_failure
import hypothesis.strategies as st
class TestConvAffineChannelFusePass(PassAutoScanTest):
def is_program_valid(self, program_config: ProgramConfig) -> bool:
return True
def sample_program_config(self, draw):
padding_algorithm = draw(st.sampled_from(["EXPLICIT", "SAME", "VALID"]))
groups = draw(st.integers(min_value=1, max_value=3))
data_format = draw(st.sampled_from(["NCHW", "NHWC"]))
axis = draw(st.sampled_from([1]))
filter_channel = draw(st.integers(min_value=1, max_value=16)) * 4
filter_size = draw(st.integers(min_value=1, max_value=4))
in_channel = groups * filter_channel
out_channel_factor = draw(st.integers(min_value=1, max_value=16)) * 4
out_channel = groups * out_channel_factor
batch_size = draw(st.integers(min_value=1, max_value=4))
dilations = draw(
st.lists(
st.integers(
min_value=1, max_value=2), min_size=2, max_size=2))
paddings = draw(
st.lists(
st.integers(
min_value=0, max_value=2), min_size=2, max_size=2))
strides = draw(
st.lists(
st.integers(
min_value=1, max_value=2), min_size=2, max_size=2))
has_bias = draw(st.booleans())
x_shape = [
batch_size, in_channel, 64, 64
] if data_format == "NCHW" else [batch_size, 64, 64, in_channel]
w_shape = [out_channel, filter_channel, filter_size, filter_size]
scale_shape = [out_channel]
bias_shape = [out_channel]
def generate_input():
return np.random.random(x_shape).astype(np.float32)
def generate_weight():
return np.random.random(w_shape).astype(np.float32)
def generate_bias():
return np.random.random(bias_shape).astype(np.float32)
def generate_scale_bias():
return np.random.random(bias_shape).astype(np.float32)
conv2d_op = OpConfig(
"conv2d",
inputs={
"Input": ["input_data"],
"Filter": ["conv2d_weight"],
},
outputs={"Output": ["conv_output"]},
data_format=data_format,
dilations=dilations,
padding_algorithm=padding_algorithm,
groups=groups,
paddings=paddings,
strides=strides,
has_bias=has_bias,
is_test=True)
ac_op = OpConfig(
"affine_channel",
inputs={
"X": ["conv_output"],
"Scale": ["affine_channel_scale"],
"Bias": ["affine_channel_bias"]
},
outputs={"Out": ["affine_channel_ouput"]},
data_layout=data_format)
if has_bias == True:
conv2d_op.inputs["Bias"] = ["conv2d_bias"]
ops = [conv2d_op, ac_op]
program_config = ProgramConfig(
ops=ops,
inputs={
"input_data": TensorConfig(data_gen=partial(generate_input)),
},
weights={
"conv2d_weight":
TensorConfig(data_gen=partial(generate_weight)),
"affine_channel_scale":
TensorConfig(data_gen=partial(generate_scale_bias)),
"affine_channel_bias":
TensorConfig(data_gen=partial(generate_scale_bias)),
},
outputs=["affine_channel_ouput"])
if has_bias == True:
program_config.weights["conv2d_bias"] = TensorConfig(
data_gen=partial(generate_bias))
return program_config
def sample_predictor_configs(self, program_config):
config = self.create_inference_config(use_gpu=True)
yield config, ['conv2d', 'elementwise_add'], (1e-4, 1e-4)
config = self.create_inference_config(use_mkldnn=True)
yield config, ['conv2d', 'elementwise_add'], (1e-4, 1e-4)
def add_ignore_pass_case(self):
# If the problem has been fixed, the judgment
# in is_program_valid needs to be deleted!!!
def teller1(program_config, predictor_config):
if program_config.ops[0].attrs['data_format'] == "NHWC":
return True
return False
# mkldnn Output has diff with bias!
def teller2(program_config, predictor_config):
return predictor_config.mkldnn_enabled() and program_config.ops[
0].attrs['has_bias'] == True
self.add_ignore_check_case(
teller1, IgnoreReasons.PASS_ACCURACY_ERROR,
"The output format of conv2d is wrong when data_format attribute is NHWC, \
because currently its fused op (Conv2DFusion) only supports data format of channel first (NCHW)."
)
self.add_ignore_check_case(
teller2, IgnoreReasons.PASS_ACCURACY_ERROR,
"Currently mkldnn Output has diff with bias!")
def test(self):
self.run_and_statis(
quant=False,
passes=["conv_affine_channel_fuse_pass"], )
if __name__ == "__main__":
......
python/paddle/fluid/tests/unittests/ir/inference/test_conv_elementwise_add_fuse_pass.py
浏览文件 @ 33fbb66e
# 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.
......@@ -12,41 +12,144 @@
# 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, IgnoreReasons
from program_config import TensorConfig, ProgramConfig, OpConfig
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 PassVersionChecker
from paddle.fluid.core import AnalysisConfig
"""Test for fusion of conv and elementwise_add."""
class ConvElementwiseAddFusePassTest(InferencePassTest):
def setUp(self):
with fluid.program_guard(self.main_program, self.startup_program):
data = fluid.data(
name="data", shape=[-1, 3, 100, 100], dtype="float32")
param_attr = fluid.ParamAttr(
initializer=fluid.initializer.Xavier(uniform=False),
learning_rate=0.001)
conv_out = fluid.layers.conv2d(
input=data, num_filters=3, filter_size=3, bias_attr=param_attr)
self.feeds = {
"data": np.random.random((1, 3, 100, 100)).astype("float32")
}
self.fetch_list = [conv_out]
self.enable_mkldnn = False
def test_check_output(self):
if core.is_compiled_with_cuda():
use_gpu = True
self.check_output_with_option(use_gpu)
self.assertTrue(
PassVersionChecker.IsCompatible('conv_elementwise_add_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 TestConvEltwiseAddFusePass(PassAutoScanTest):
def is_program_valid(self, program_config: ProgramConfig) -> bool:
attrs = [
program_config.ops[i].attrs
for i in range(len(program_config.ops))
]
if attrs[0]['data_format'] == "NHWC" and attrs[1]['axis'] != 3:
return False
return True
def sample_program_config(self, draw):
padding_algorithm = draw(st.sampled_from(["EXPLICIT", "SAME", "VALID"]))
groups = draw(st.integers(min_value=1, max_value=3))
data_format = draw(st.sampled_from(["NCHW", "NHWC"]))
axis = draw(st.sampled_from([1]))
filter_channel = draw(st.integers(min_value=1, max_value=16)) * 4
filter_size = draw(st.integers(min_value=1, max_value=4))
in_channel = groups * filter_channel
out_channel_factor = draw(st.integers(min_value=1, max_value=16)) * 4
out_channel = groups * out_channel_factor
batch_size = draw(st.integers(min_value=1, max_value=4))
dilations = draw(
st.lists(
st.integers(
min_value=1, max_value=2), min_size=2, max_size=2))
paddings = draw(
st.lists(
st.integers(
min_value=0, max_value=2), min_size=2, max_size=2))
strides = draw(
st.lists(
st.integers(
min_value=1, max_value=2), min_size=2, max_size=2))
x_shape = [
batch_size, in_channel, 64, 64
] if data_format == "NCHW" else [batch_size, 64, 64, in_channel]
w_shape = [out_channel, filter_channel, filter_size, filter_size]
scale_shape = [out_channel]
bias_shape = [out_channel]
def generate_input():
return np.random.random(x_shape).astype(np.float32)
def generate_weight():
return np.random.random(w_shape).astype(np.float32)
def generate_bias():
return np.random.random(bias_shape).astype(np.float32)
def generate_scale_bias():
return np.random.random(bias_shape).astype(np.float32)
conv2d_op = OpConfig(
"conv2d",
inputs={
"Input": ["input_data"],
"Filter": ["conv2d_weight"],
},
outputs={"Output": ["conv_output"]},
data_format=data_format,
dilations=dilations,
padding_algorithm=padding_algorithm,
groups=groups,
paddings=paddings,
strides=strides,
is_test=True)
eltwise_op = OpConfig(
"elementwise_add",
inputs={"X": ["conv_output"],
"Y": ["conv2d_bias"]},
outputs={"Out": ["elementwise_output"]},
axis=axis)
ops = [conv2d_op, eltwise_op]
program_config = ProgramConfig(
ops=ops,
inputs={
"input_data": TensorConfig(data_gen=partial(generate_input)),
},
weights={
"conv2d_weight":
TensorConfig(data_gen=partial(generate_weight)),
"conv2d_bias":
TensorConfig(data_gen=partial(generate_scale_bias)),
},
outputs=["elementwise_output"])
return program_config
def sample_predictor_configs(self, program_config):
config = self.create_inference_config(use_gpu=True)
yield config, ['conv2d_fusion'], (1e-4, 1e-4)
# # TRT
config = self.create_trt_inference_config()
config.enable_tensorrt_engine(
workspace_size=1 << 20,
max_batch_size=4,
min_subgraph_size=1,
precision_mode=paddle_infer.PrecisionType.Float32,
use_static=False,
use_calib_mode=False)
yield config, ['conv2d_fusion'], (1e-4, 1e-4)
def add_ignore_pass_case(self):
# If the problem has been fixed, the judgment
# in is_program_valid needs to be deleted!!!
def teller1(program_config, predictor_config):
if program_config.ops[0].attrs['data_format'] == "NHWC":
return True
return False
self.add_ignore_check_case(
teller1, IgnoreReasons.PASS_ACCURACY_ERROR,
"The output format of conv2d is wrong when data_format attribute is NHWC, \
it will trigger Broadcast dimension mismatch bug \
when data_format attribute is NHWC and axis of eltwise op is 1 for this pass."
)
def test(self):
self.run_and_statis(
quant=False,
passes=["conv_elementwise_add_fuse_pass"], )
if __name__ == "__main__":
......
python/paddle/fluid/tests/unittests/ir/inference/test_conv_eltwiseadd_affine_channel_fuse_pass.py 0 → 100644
浏览文件 @ 33fbb66e
# 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, IgnoreReasons
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
class TestConvEltwiseAddAffineChannelFusePass(PassAutoScanTest):
def is_program_valid(self, program_config: ProgramConfig) -> bool:
attrs = [
program_config.ops[i].attrs
for i in range(len(program_config.ops))
]
if attrs[0]['data_format'] == "NHWC" and attrs[1]['axis'] != 3:
return False
return True
def sample_program_config(self, draw):
padding_algorithm = draw(st.sampled_from(["EXPLICIT", "SAME", "VALID"]))
groups = draw(st.integers(min_value=1, max_value=3))
data_format = draw(st.sampled_from(["NCHW", "NHWC"]))
axis = draw(st.sampled_from([1]))
filter_channel = draw(st.integers(min_value=1, max_value=16)) * 4
filter_size = draw(st.integers(min_value=1, max_value=4))
in_channel = groups * filter_channel
out_channel_factor = draw(st.integers(min_value=1, max_value=16)) * 4
out_channel = groups * out_channel_factor
batch_size = draw(st.integers(min_value=1, max_value=4))
dilations = draw(
st.lists(
st.integers(
min_value=1, max_value=2), min_size=2, max_size=2))
paddings = draw(
st.lists(
st.integers(
min_value=0, max_value=2), min_size=2, max_size=2))
strides = draw(
st.lists(
st.integers(
min_value=1, max_value=2), min_size=2, max_size=2))
has_bias = draw(st.booleans())
x_shape = [
batch_size, in_channel, 64, 64
] if data_format == "NCHW" else [batch_size, 64, 64, in_channel]
w_shape = [out_channel, filter_channel, filter_size, filter_size]
scale_shape = [out_channel]
bias_shape = [out_channel]
def generate_input():
return np.random.random(x_shape).astype(np.float32)
def generate_weight():
return np.random.random(w_shape).astype(np.float32)
def generate_bias():
return np.random.random(bias_shape).astype(np.float32)
def generate_scale_bias():
return np.random.random(bias_shape).astype(np.float32)
conv2d_op = OpConfig(
"conv2d",
inputs={
"Input": ["input_data"],
"Filter": ["conv2d_weight"],
},
outputs={"Output": ["conv_output"]},
data_format=data_format,
dilations=dilations,
padding_algorithm=padding_algorithm,
groups=groups,
paddings=paddings,
strides=strides,
has_bias=has_bias,
is_test=True)
eltwise_op = OpConfig(
"elementwise_add",
inputs={"X": ["conv_output"],
"Y": ["conv2d_bias"]},
outputs={"Out": ["elementwise_output"]},
axis=axis)
ac_op = OpConfig(
"affine_channel",
inputs={
"X": ["elementwise_output"],
"Scale": ["affine_channel_scale"],
"Bias": ["affine_channel_bias"]
},
outputs={"Out": ["affine_channel_ouput"]},
data_layout=data_format)
if has_bias == True:
conv2d_op.inputs["Bias"] = ["conv2d_bias"]
ops = [conv2d_op, eltwise_op, ac_op]
program_config = ProgramConfig(
ops=ops,
inputs={
"input_data": TensorConfig(data_gen=partial(generate_input)),
},
weights={
"conv2d_weight":
TensorConfig(data_gen=partial(generate_weight)),
"conv2d_bias": TensorConfig(data_gen=partial(generate_bias)),
"affine_channel_scale":
TensorConfig(data_gen=partial(generate_scale_bias)),
"affine_channel_bias":
TensorConfig(data_gen=partial(generate_scale_bias)),
},
outputs=["affine_channel_ouput"])
return program_config
def sample_predictor_configs(self, program_config):
config = self.create_inference_config(use_gpu=True)
yield config, ['conv2d', 'elementwise_add'], (1e-4, 1e-4)
config = self.create_inference_config(use_mkldnn=True)
yield config, ['conv2d', 'elementwise_add'], (1e-4, 1e-4)
# TRT
config = self.create_trt_inference_config()
config.enable_tensorrt_engine(
workspace_size=1 << 20,
max_batch_size=4,
min_subgraph_size=1,
precision_mode=paddle_infer.PrecisionType.Float32,
use_static=False,
use_calib_mode=False)
yield config, ['conv2d', 'elementwise_add'], (1e-4, 1e-4)
def add_ignore_pass_case(self):
# If the problem has been fixed, the judgment
# in is_program_valid needs to be deleted!!!
def teller1(program_config, predictor_config):
if program_config.ops[0].attrs['data_format'] == "NHWC":
return True
return False
# mkldnn Output has diff with bias!
def teller2(program_config, predictor_config):
return predictor_config.mkldnn_enabled() and program_config.ops[
0].attrs['has_bias'] == True
self.add_ignore_check_case(
teller1, IgnoreReasons.PASS_ACCURACY_ERROR,
"The output format of conv2d is wrong when data_format attribute is NHWC, \
it will trigger Broadcast dimension mismatch bug \
when data_format attribute is NHWC and axis of eltwise op is 1 for this pass."
)
self.add_ignore_check_case(
teller2, IgnoreReasons.PASS_ACCURACY_ERROR,
"Currently mkldnn Output has diff with bias!")
def test(self):
self.run_and_statis(
quant=False,
passes=["conv_eltwiseadd_affine_channel_fuse_pass"], )
if __name__ == "__main__":
unittest.main()
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