Skip to content

P
Paddle

PaddlePaddle / Paddle
11 个月 前同步成功

通知 2291
Star 20931
Fork 5422
P
Paddle

体验新版 GitCode，发现更多精彩内容 >>

未验证 提交 18c0a002 编写于 作者: H Hulek 提交者： GitHub
浏览文件
操作

Scale Matmul Fuse pass rewritten (#49105)

上级 aa96ddc3
隐藏空白更改
内联 并排
Showing with 69 addition and 207 deletion
paddle/fluid/framework/ir/CMakeLists.txt
浏览文件 @ 18c0a002
...@@ -429,10 +429,6 @@ if(WITH_MKLDNN) ...@@ -429,10 +429,6 @@ if(WITH_MKLDNN)
test_conv_batch_norm_mkldnn_fuse_pass test_conv_batch_norm_mkldnn_fuse_pass
SRCS mkldnn/mkldnn_conv_bn_fuse_pass_tester.cc SRCS mkldnn/mkldnn_conv_bn_fuse_pass_tester.cc
DEPS ${TEST_CONV_BN_PASS_DEPS}) 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( cc_test(
test_mkldnn_placement_pass test_mkldnn_placement_pass
SRCS mkldnn/mkldnn_placement_pass_tester.cc SRCS mkldnn/mkldnn_placement_pass_tester.cc
......
paddle/fluid/framework/ir/mkldnn/scale_matmul_fuse_pass_tester.cc 已删除 100644 → 0
浏览文件 @ aa96ddc3
// Copyright (c) 2020 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.
#include <gtest/gtest.h>
#include "paddle/fluid/framework/ir/mkldnn/scale_matmul_fuse_pass.h"
namespace paddle {
namespace framework {
namespace ir {
void SetOp(ProgramDesc* prog,
const std::string& type,
const std::vector<std::string>& inputs,
const std::vector<std::string>& outputs,
float scale = 1.0f,
float bias = 0.0f) {
auto* op = prog->MutableBlock(0)->AppendOp();
op->SetType(type);
if (type == "scale") {
op->SetInput("X", {inputs[0]});
op->SetAttr("scale", scale);
op->SetAttr("bias", bias);
} else if (type == "matmul") {
op->SetAttr("transpose_X", false);
op->SetAttr("transpose_Y", false);
op->SetInput("X", {inputs[0]});
if (inputs.size() > 1) op->SetInput("Y", {inputs[1]});
op->SetAttr("alpha", scale);
} else {
FAIL() << "Unexpected operator type.";
}
op->SetOutput("Out", {outputs[0]});
}
// a->scale->b
// (b,c)->matmul->d
ProgramDesc BuildProgramDesc(float scale, float bias, float alpha) {
ProgramDesc prog;
for (auto& v : std::vector<std::string>({"a", "b", "c", "d"})) {
prog.MutableBlock(0)->Var(v);
}
SetOp(&prog, "scale", {"a"}, {"b"}, scale, bias);
SetOp(&prog, "matmul", {"b", "c"}, {"d"}, alpha);
return prog;
}
void MainTest(const ProgramDesc& prog,
int removed_nodes_count,
const std::vector<std::string> scale_in_out,
const std::vector<std::string> matmul_in_out,
float alpha) {
std::unique_ptr<ir::Graph> graph(new ir::Graph(prog));
int original_nodes_num = graph->Nodes().size();
auto pass = PassRegistry::Instance().Get("scale_matmul_fuse_pass");
graph.reset(pass->Apply(graph.release()));
int current_nodes_num = graph->Nodes().size();
for (auto* node : graph->Nodes()) {
if (node->IsOp()) {
auto* op = node->Op();
if (op->Type() == "scale") {
EXPECT_EQ(op->Input("X")[0], scale_in_out[0]);
EXPECT_EQ(op->Output("Out")[0], scale_in_out[1]);
} else if (op->Type() == "matmul") {
EXPECT_EQ(op->Input("X")[0], matmul_in_out[0]);
EXPECT_EQ(op->Input("Y")[0], matmul_in_out[1]);
EXPECT_EQ(op->Output("Out")[0], matmul_in_out[2]);
EXPECT_EQ(op->GetAttrIfExists<float>("alpha"), alpha);
}
}
}
EXPECT_EQ(original_nodes_num - removed_nodes_count, current_nodes_num);
}
TEST(ScaleMatmulFusePass, scale_matmul_with_no_bias) {
auto bias = 0.0f;
auto scale = 2.34f;
auto alpha = 3.45f;
int removed_nodes_count = 2;
MainTest(BuildProgramDesc(scale, bias, alpha),
removed_nodes_count,
{},
{"a", "c", "d"},
scale * alpha);
}
TEST(ScaleMatmulFusePass, scale_matmul_with_bias) {
auto bias = 1.0f;
auto scale = 2.34f;
auto alpha = 3.45f;
int removed_nodes_count = 0;
MainTest(BuildProgramDesc(scale, bias, alpha),
removed_nodes_count,
{"a", "b"},
{"b", "c", "d"},
alpha);
}
} // namespace ir
} // namespace framework
} // namespace paddle
USE_PASS(scale_matmul_fuse_pass);
python/paddle/fluid/tests/unittests/ir/inference/test_mkldnn_scale_matmul_fuse_pass.py
浏览文件 @ 18c0a002
...@@ -37,74 +37,55 @@ class TestScaleMatmulMkldnnFusePass(PassAutoScanTest): ...@@ -37,74 +37,55 @@ class TestScaleMatmulMkldnnFusePass(PassAutoScanTest):
input_dim = draw(st.sampled_from([1, 32, 64])) input_dim = draw(st.sampled_from([1, 32, 64]))
def generate_input(attrs, type): def generate_input(attrs, type):
if attrs[1]['transpose_X'] and attrs[1]['transpose_Y']: is_transpose_X = attrs[1]['transpose_X']
shape_x = [ is_transpose_Y = attrs[1]['transpose_Y']
attrs[2]['batch_size'],
attrs[2]['channel'], if is_transpose_X:
attrs[2]['input_dim'], shape_x_3 = attrs[2]['input_dim']
32, shape_x_4 = 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: else:
shape_x = [ shape_x_3 = 32
attrs[2]['batch_size'], shape_x_4 = attrs[2]['input_dim']
attrs[2]['channel'],
32, if is_transpose_X and is_transpose_Y:
attrs[2]['input_dim'], shape_y_3 = 64
] shape_y_4 = attrs[2]['input_dim']
shape_y = [ elif is_transpose_X:
attrs[2]['batch_size'], shape_y_3 = attrs[2]['input_dim']
attrs[2]['channel'], shape_y_4 = 64
attrs[2]['input_dim'], elif is_transpose_Y:
16, shape_y_3 = 8
] shape_y_4 = attrs[2]['input_dim']
if type == "x":
return np.random.random(shape_x).astype(np.float32)
else: 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 = [ attrs = [
{ {
"scale": scale, 'scale': scale,
"bias": bias, 'bias': bias,
"bias_after_scale": bias_after_scale, 'bias_after_scale': bias_after_scale,
}, },
{ {
"transpose_X": transpose_X, 'transpose_X': transpose_X,
"transpose_Y": transpose_Y, 'transpose_Y': transpose_Y,
"alpha": alpha, 'alpha': alpha,
}, },
{ {
'batch_size': batch_size, 'batch_size': batch_size,
...@@ -115,29 +96,29 @@ class TestScaleMatmulMkldnnFusePass(PassAutoScanTest): ...@@ -115,29 +96,29 @@ class TestScaleMatmulMkldnnFusePass(PassAutoScanTest):
ops_config = [ ops_config = [
{ {
"op_type": "scale", 'op_type': 'scale',
"op_inputs": {"X": ["input_data1"]}, 'op_inputs': {'X': ['input_data1']},
"op_outputs": {"Out": ["scale_output"]}, 'op_outputs': {'Out': ['scale_output']},
"op_attrs": { 'op_attrs': {
"scale": attrs[0]['scale'], 'scale': attrs[0]['scale'],
"bias": attrs[0]['bias'], 'bias': attrs[0]['bias'],
"bias_after_scale": attrs[0]['bias_after_scale'], 'bias_after_scale': attrs[0]['bias_after_scale'],
}, },
}, },
{ {
"op_type": "matmul", 'op_type': 'matmul',
"op_inputs": {"X": ["scale_output"], "Y": ["input_data2"]}, 'op_inputs': {'X': ['scale_output'], 'Y': ['input_data2']},
"op_outputs": {"Out": ["matmul_output"]}, 'op_outputs': {'Out': ['matmul_output']},
"op_attrs": { 'op_attrs': {
'transpose_X': attrs[1]['transpose_X'], 'transpose_X': attrs[1]['transpose_X'],
'transpose_Y': attrs[1]['transpose_Y'], 'transpose_Y': attrs[1]['transpose_Y'],
'alpha': attrs[1]['alpha'], 'alpha': attrs[1]['alpha'],
"fused_reshape_X": [], 'fused_reshape_X': [],
"fused_reshape_Y": [], 'fused_reshape_Y': [],
"fused_transpose_X": [], 'fused_transpose_X': [],
"fused_transpose_Y": [], 'fused_transpose_Y': [],
"fused_reshape_Out": [], 'fused_reshape_Out': [],
"fused_transpose_Out": [], 'fused_transpose_Out': [],
}, },
}, },
] ]
...@@ -148,25 +129,27 @@ class TestScaleMatmulMkldnnFusePass(PassAutoScanTest): ...@@ -148,25 +129,27 @@ class TestScaleMatmulMkldnnFusePass(PassAutoScanTest):
ops=ops, ops=ops,
weights={}, weights={},
inputs={ inputs={
"input_data1": TensorConfig( 'input_data1': TensorConfig(
data_gen=partial(generate_input, attrs, "x") data_gen=partial(generate_input, attrs, 'x')
), ),
"input_data2": TensorConfig( 'input_data2': TensorConfig(
data_gen=partial(generate_input, attrs, "y") data_gen=partial(generate_input, attrs, 'y')
), ),
}, },
outputs=["matmul_output"], outputs=['matmul_output'],
) )
return program_config return program_config
def sample_predictor_configs(self, 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) yield config, ['matmul'], (1e-5, 1e-5)
def test(self): 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() unittest.main()
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或