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未验证 提交 3cfb2e1a 编写于 作者: H Hulek 提交者： GitHub
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Deleted mkldnn_inplace_pass code (#47818) 

* Deleted mkldnn_inplace_pass code

* Fixed error with cmake

* Resolve conflicts
上级 f7514772
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paddle/fluid/framework/ir/CMakeLists.txt
浏览文件 @ 3cfb2e1a
......@@ -151,19 +151,6 @@ endif()
if(WITH_MKLDNN)
pass_library(mkldnn_placement_pass base DEPS placement_pass_base DIR mkldnn)
pass_library(
mkldnn_inplace_pass
inference
DEPS
mkldnn_placement_pass
op_registry
elementwise_add_op
generated_op
activation_op
softmax_op
softmax
DIR
mkldnn)
pass_library(depthwise_conv_mkldnn_pass base DIR mkldnn)
pass_library(conv_affine_channel_mkldnn_fuse_pass inference DIR mkldnn)
pass_library(conv_bias_mkldnn_fuse_pass inference DIR mkldnn)
......@@ -450,10 +437,6 @@ if(WITH_MKLDNN)
test_mkldnn_placement_pass
SRCS mkldnn/mkldnn_placement_pass_tester.cc
DEPS mkldnn_placement_pass)
cc_test(
test_mkldnn_inplace_pass
SRCS mkldnn/mkldnn_inplace_pass_tester.cc
DEPS mkldnn_inplace_pass)
cc_test(
test_compute_propagate_scales_mkldnn_pass
SRCS mkldnn/compute_propagate_scales_mkldnn_pass_tester.cc
......
paddle/fluid/framework/ir/mkldnn/mkldnn_inplace_pass.cc 已删除 100644 → 0
浏览文件 @ f7514772
// Copyright (c) 2018 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 "paddle/fluid/framework/ir/mkldnn/mkldnn_inplace_pass.h"
#include <algorithm>
#include <memory>
#include <string>
#include <unordered_map>
#include <unordered_set>
#include <vector>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/op_info.h"
#include "paddle/fluid/framework/op_version_registry.h"
#include "paddle/fluid/platform/enforce.h"
namespace paddle {
namespace framework {
namespace ir {
void MKLDNNInPlacePass::ApplyImpl(ir::Graph* graph) const {
PADDLE_ENFORCE_NOT_NULL(graph,
platform::errors::InvalidArgument(
"Pointer to graph argument should not be NULL."));
std::unordered_map<std::string, std::string> original_output_names;
std::unordered_set<std::string> inplaced_vars;
GraphPatternDetector gpd;
patterns::MKLDNNInPlace mkldnn_inplace{gpd.mutable_pattern(),
"mkldnn_inplace"};
mkldnn_inplace();
int found_inplace_count = 0;
auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
Graph* g) {
VLOG(3) << "Start to handle MKL-DNN In-Place pass";
GET_IR_NODE_FROM_SUBGRAPH(current_op, inplace_to_be_op, mkldnn_inplace);
GET_IR_NODE_FROM_SUBGRAPH(
current_op_in, inplace_to_be_op_in, mkldnn_inplace);
GET_IR_NODE_FROM_SUBGRAPH(
current_op_out, inplace_to_be_op_out, mkldnn_inplace);
GET_IR_NODE_FROM_SUBGRAPH(next_op, next_op, mkldnn_inplace);
GET_IR_NODE_FROM_SUBGRAPH(next_op_out, next_op_out, mkldnn_inplace);
if ((current_op->Op()->HasAttr("use_mkldnn") == false) ||
(PADDLE_GET_CONST(bool, current_op->Op()->GetAttr("use_mkldnn")) ==
false)) {
VLOG(3) << "do not perform mkl-dnn inplace: use_mkldnn missing or set to "
"false";
return;
}
auto& infer_inplace =
OpInfoMap::Instance().Get(current_op->Op()->Type()).infer_inplace_;
if (!infer_inplace) {
VLOG(3) << "do not perform mkl-dnn inplace: missing InplaceInferer";
return;
}
VLOG(3) << "oneDNN Inplace op(" << current_op->id() << ") "
<< "Curr Node In: " << current_op_in->Name()
<< " Curr Node out: " << current_op_out->Name();
VLOG(3) << "oneDNN Inplace next op(" << next_op->id() << ") "
<< " next Node out: " << next_op_out->Name();
auto inputs = current_op->Op()->Inputs();
auto outputs = current_op->Op()->Outputs();
auto in_to_outs = infer_inplace(false); // strictly no CUDA for MKL-DNN
VLOG(3) << "oneDNN InplaceInferer op(" << current_op->id() << ") "
<< in_to_outs.begin()->first << ": "
<< inputs[in_to_outs.begin()->first][0] << " "
<< in_to_outs.begin()->second << ": "
<< outputs[in_to_outs.begin()->second][0];
// If InferInplace pattern does not contain input node then skip
auto inplace_input_vec = inputs[in_to_outs.begin()->first];
if (std::find(inplace_input_vec.begin(),
inplace_input_vec.end(),
current_op_in->Name()) == inplace_input_vec.end()) {
VLOG(3) << "oneDNN in-place pass SKIP pattern ";
return;
}
// Checking if this particular node (to be inplaced, overwritten)
// is used anywhere else apart from inplaced op
auto input_consumers = current_op_in->outputs;
if (input_consumers.size() > 1) {
VLOG(3) << "oneDNN in-place pass FAIL: in-place var cannot "
"be an input to multiple operators";
return;
} else {
// We will prevent in-place when
// input is used in other part of graph, unless it was a result of
// inplacing
// Allow to next op out reuse inpuit var, as this is the same chaing
if (inplaced_vars.find(current_op_in->Name()) == inplaced_vars.end()) {
for (const Node* n : graph->Nodes()) {
if ((n->id() != current_op_in->id()) &&
(n->id() != next_op_out->id()) &&
(n->Name() == current_op_in->Name())) {
VLOG(3) << "oneDNN in-place pass FAIL var used in diffrent part of "
"graph ";
return;
}
}
}
}
// If this op was alrady inplaced in previous pass placements
// then we need to update input of next op
// but original name to be changed is gone, so we need to remember it
// on first time given op is to be inplaced
if (current_op_in->Name() != current_op_out->Name()) {
original_output_names[current_op->Name() + current_op_in->Name()] =
current_op_out->Name();
} else {
VLOG(3) << "oneDNN Inplace: Current op already inplaced! ";
}
// It may be that next op is reusing some of vars, we need to
// make sure that unwanted inplace is not created
for (auto& n : current_op_out->outputs) {
auto& n_op_infer_inplace =
OpInfoMap::Instance().Get(n->Op()->Type()).infer_inplace_;
if ((n_op_infer_inplace == nullptr)) {
for (auto& m : n->outputs) {
if (m->Name() == current_op_in->Name()) {
VLOG(3) << "oneDNN in-place pass FAIL: in-place var cannot "
"be an output to non-inplaced next op";
return;
}
}
}
}
auto original_name =
original_output_names[current_op->Name() + current_op_in->Name()];
current_op_out->RenameVar(current_op_in->Name());
// Get mapping of input to output
auto out_name = in_to_outs.begin()->second;
current_op->Op()->SetOutput(
out_name, std::vector<std::string>({current_op_out->Name()}));
// Record var name
inplaced_vars.insert(current_op_out->Name());
// If next op in a line is doing inplace
// then we need to update its output as well
// Get inferer of next op
// If no inferer then we are done
auto& next_op_infer_inplace =
OpInfoMap::Instance().Get(next_op->Op()->Type()).infer_inplace_;
if (next_op_infer_inplace) {
auto in_to_outs = next_op_infer_inplace(false);
auto out_name = in_to_outs.begin()->second;
auto* op = next_op->Op();
auto inputs = op->Inputs();
auto outputs = op->Outputs();
// Check if in-place happened
// for variable we changed (original name)
// TODO(jczaja): make recursive propagation of inplace
auto next_op_inplace_inputs = inputs[in_to_outs.begin()->first];
if ((next_op_inplace_inputs == outputs[in_to_outs.begin()->second]) &&
(std::find(next_op_inplace_inputs.begin(),
next_op_inplace_inputs.end(),
original_name) != next_op_inplace_inputs.end())) {
VLOG(3) << "oneDNN InPlace: Next Op is in-placed , updating its "
"input "
"and output var!";
next_op->Op()->SetOutput(
out_name, std::vector<std::string>({current_op_out->Name()}));
next_op_out->RenameVar(current_op_in->Name());
// Get ops that next_op_out is linked to and update their input
auto next_op_out_consumers = next_op_out->outputs; // Has to be ops
for (auto& c : next_op_out_consumers) {
c->Op()->RenameInput(original_name, current_op_out->Name());
}
}
}
next_op->Op()->RenameInput(original_name, current_op_out->Name());
found_inplace_count++;
VLOG(3) << "oneDNN InPlace applied!";
};
// TODO(jczaja): inplace pass does not influece ops inside block ops
auto should_inplace = [&](Graph* g) {
std::unordered_set<std::string> unwanted_ops(
{"conditional_block", "While", "while_loop"});
for (auto& node : g->Nodes()) {
if (node->IsOp() &&
unwanted_ops.find(node->Name()) != unwanted_ops.end()) {
VLOG(3) << "oneDNN InPlace FAILED: unsupported op: " << node->Name();
return false;
}
}
return true;
};
if (should_inplace(graph)) gpd(graph, handler);
}
} // namespace ir
} // namespace framework
} // namespace paddle
REGISTER_PASS(mkldnn_inplace_pass, paddle::framework::ir::MKLDNNInPlacePass);
REGISTER_PASS_CAPABILITY(mkldnn_inplace_pass)
.AddCombination(
paddle::framework::compatible::OpVersionComparatorCombination()
.EQ("softmax", 0)
.LE("elementwise_add", 1)
.EQ("tanh", 0));
paddle/fluid/framework/ir/mkldnn/mkldnn_inplace_pass.h 已删除 100644 → 0
浏览文件 @ f7514772
// 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.
#pragma once
#include <memory>
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
#include "paddle/fluid/framework/ir/pass.h"
namespace paddle {
namespace framework {
namespace ir {
/*
* Transpose weights of FC to comply with MKL-DNN interface
*/
class MKLDNNInPlacePass : public Pass {
public:
virtual ~MKLDNNInPlacePass() {}
protected:
void ApplyImpl(ir::Graph* graph) const;
private:
#if PADDLE_WITH_TESTING
friend class MKLDNNInPlacePassTest;
#endif
};
} // namespace ir
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/mkldnn/mkldnn_inplace_pass_tester.cc 已删除 100644 → 0
浏览文件 @ f7514772
// Copyright (c) 2019 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 <unordered_set>
#include "paddle/utils/tribool.h"
#include "paddle/fluid/framework/ir/mkldnn/mkldnn_inplace_pass.h"
#include "paddle/fluid/framework/ir/pass_tester_helper.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/phi/core/kernel_registry.h"
USE_OP_ITSELF(softmax);
PD_DECLARE_KERNEL(softmax, OneDNN, ONEDNN);
USE_OP_ITSELF(elementwise_add);
PD_DECLARE_KERNEL(add_raw, OneDNN, ONEDNN);
USE_OP_ITSELF(leaky_relu);
PD_DECLARE_KERNEL(leaky_relu, OneDNN, ONEDNN);
USE_OP_ITSELF(gelu);
USE_OP_ITSELF(relu);
USE_OP_ITSELF(tanh);
PD_DECLARE_KERNEL(tanh, OneDNN, ONEDNN);
PD_DECLARE_ARG_MAPPING_FN(gelu);
namespace paddle {
namespace framework {
namespace ir {
class MKLDNNInplacePassTest {
private:
void SetOp(ProgramDesc* prog,
const std::string& type,
const std::string& name,
const std::vector<std::string>& inputs,
const std::vector<std::string>& outputs,
paddle::tribool use_mkldnn) {
auto* op = prog->MutableBlock(0)->AppendOp();
op->SetType(type);
if (!paddle::indeterminate(use_mkldnn))
op->SetAttr("use_mkldnn", use_mkldnn);
if (type == "conv2d") {
op->SetAttr("name", name);
op->SetInput("Input", {inputs[0]});
op->SetInput("Filter", {inputs[1]});
op->SetInput("Bias", {inputs[2]});
} else if (std::unordered_set<std::string>{
"gelu", "leaky_relu", "relu", "tanh"}
.count(type)) {
op->SetInput("X", inputs);
} else if (type == "softmax") {
op->SetAttr("axis", -1);
op->SetInput("X", inputs);
} else if (type == "elementwise_add") {
op->SetInput("X", {inputs[0]});
op->SetInput("Y", {inputs[1]});
} else {
FAIL() << "Unexpected operator type.";
}
op->SetOutput("Out", {outputs[0]});
}
ProgramDesc BuildProgramDesc(const std::string& mkldnn_enabled_op,
bool branched) {
ProgramDesc prog;
for (auto& v : std::vector<std::string>({"a",
"weights",
"bias",
"f",
"g",
"h",
"i",
"j",
"k",
"l",
"m",
"n",
"z"})) {
auto* var = prog.MutableBlock(0)->Var(v);
var->SetType(proto::VarType::SELECTED_ROWS);
if (v == "weights" || v == "bias") {
var->SetPersistable(true);
}
}
SetOp(&prog,
"conv2d",
"conv1",
std::vector<std::string>({"a", "weights", "bias"}),
std::vector<std::string>({"f"}),
paddle::indeterminate);
SetOp(&prog,
"relu",
"relu1",
std::vector<std::string>({"f"}),
std::vector<std::string>({"g"}),
mkldnn_enabled_op.compare("relu") == 0);
SetOp(&prog,
"softmax",
"softmax1",
std::vector<std::string>({"g"}),
std::vector<std::string>({"h"}),
mkldnn_enabled_op.compare("softmax") == 0);
SetOp(&prog,
"elementwise_add",
"elementwise_add1",
std::vector<std::string>({"h", "i"}),
std::vector<std::string>({"j"}),
mkldnn_enabled_op.compare("elementwise_add") == 0);
SetOp(&prog,
"relu",
"relu2",
std::vector<std::string>({"j"}),
std::vector<std::string>({"k"}),
mkldnn_enabled_op.compare("relu") == 0);
SetOp(&prog,
"tanh",
"tanh1",
std::vector<std::string>({"k"}),
std::vector<std::string>({"l"}),
mkldnn_enabled_op.compare("tanh") == 0);
SetOp(&prog,
"relu",
"relu3",
std::vector<std::string>({"l"}),
std::vector<std::string>({"m"}),
mkldnn_enabled_op.compare("relu") == 0);
SetOp(&prog,
"leaky_relu",
"leaky_relu1",
std::vector<std::string>({"m"}),
std::vector<std::string>({"n"}),
mkldnn_enabled_op.compare("leaky_relu") == 0);
SetOp(&prog,
"gelu",
"gelu1",
std::vector<std::string>({"n"}),
std::vector<std::string>({"m"}),
mkldnn_enabled_op.compare("gelu") == 0);
if (branched == true) {
SetOp(&prog,
"softmax",
"softmax2",
std::vector<std::string>({"g"}),
std::vector<std::string>({"z"}),
mkldnn_enabled_op.compare("softmax") == 0);
}
return prog;
}
public:
void MainTest(const std::string& mkldnn_enabled_op,
bool branched,
unsigned expected_use_mkldnn_true_count) {
auto prog = BuildProgramDesc(mkldnn_enabled_op, branched);
std::unique_ptr<ir::Graph> graph(new ir::Graph(prog));
auto pass = PassRegistry::Instance().Get("mkldnn_inplace_pass");
graph.reset(pass->Apply(graph.release()));
unsigned use_mkldnn_true_count = 0;
std::unordered_map<std::string, std::string> input_names;
std::unordered_map<std::string, std::string> output_names;
VLOG(3) << DebugString(graph);
for (auto* node : graph->Nodes()) {
if (node->IsOp()) {
auto* op = node->Op();
if (op->Type() == mkldnn_enabled_op) {
auto ins = op->Inputs();
auto outs = op->Outputs();
// Input and output are the same var
// All inplace ops are inplacing input named: X
// and output : Out
if (ins["X"] == outs["Out"]) {
++use_mkldnn_true_count;
}
}
}
}
EXPECT_EQ(use_mkldnn_true_count, expected_use_mkldnn_true_count);
}
};
TEST(MKLDNNInplacePass, inplace_softmax) {
// softmax to be mkl-dnn enabled and made in-place
MKLDNNInplacePassTest().MainTest("softmax", false, 1);
}
TEST(MKLDNNInplacePass, inplace_softmax_branched) {
// softmax's input is shared by two branches. so no in-place
MKLDNNInplacePassTest().MainTest("softmax", true, 0);
}
TEST(MKLDNNInplacePass, inplace_elementwise_add) {
// Two elementwise_add mkl-dnn enabled op instances to be made inplace
MKLDNNInplacePassTest().MainTest("elementwise_add", false, 0);
}
TEST(MKLDNNInplacePass, inplace_tanh) {
MKLDNNInplacePassTest().MainTest("tanh", false, 1);
}
TEST(MKLDNNInplacePass, inplace_leaky_relu) {
// Input of leaky_relu is used as output of subsequent gelu, so no inplace
// cannot be done
MKLDNNInplacePassTest().MainTest("leaky_relu", false, 0);
}
} // namespace ir
} // namespace framework
} // namespace paddle
USE_PASS(mkldnn_inplace_pass);
paddle/fluid/inference/api/paddle_pass_builder.cc
浏览文件 @ 3cfb2e1a
......@@ -355,11 +355,6 @@ void CpuPassStrategy::EnableMKLDNN() {
"operator_scale_onednn_fuse_pass", //
"operator_unsqueeze2_onednn_fuse_pass", //
"operator_reshape2_onednn_fuse_pass", //
// TODO(intel): Please fix the bug on windows.
// https://github.com/PaddlePaddle/Paddle/issues/29710
// "mkldnn_inplace_pass", // This pass should be activated after
// fuses. Disabled by default due to
// little gain and lots of problems
})) {
passes_.push_back(pass);
}
......@@ -459,7 +454,6 @@ void CpuPassStrategy::EnableMkldnnInt8() {
passes_.push_back("cpu_quantize_squash_pass");
passes_.push_back("int8_scale_calculation_mkldnn_pass");
passes_.push_back("params_quantization_mkldnn_pass");
passes_.push_back("mkldnn_inplace_pass");
}
use_mkldnn_int8_ = true;
#else
......
python/paddle/fluid/contrib/slim/quantization/quant2_int8_mkldnn_pass.py
浏览文件 @ 3cfb2e1a
......@@ -111,7 +111,6 @@ class Quant2Int8MkldnnPass:
# graph = self._update_relu_output_scales(graph)
graph = self._propagate_scales(graph)
graph = self._quantize_fp32_graph(graph)
graph = self._final_optimizations(graph)
graph = self._cleanup(graph)
return graph
......@@ -122,7 +121,6 @@ class Quant2Int8MkldnnPass:
self._reset_pass_idx_and_group('fp32')
graph = self._optimize_fp32_graph(graph)
graph = self._final_optimizations(graph)
graph = self._cleanup(graph)
return graph
......@@ -521,11 +519,6 @@ class Quant2Int8MkldnnPass:
self._pass_idx += 1
return graph
def _final_optimizations(self, graph):
# make some MKL-DNN ops working inplace
graph = self._apply_pass(graph, 'mkldnn_inplace_pass')
return graph
def _cleanup(self, graph):
graph = self._remove_unused_var_nodes(graph)
graph = self._set_op_role_forward(graph)
......
python/paddle/fluid/tests/unittests/ir/inference/test_mkldnn_inplace_fuse_pass.py 已删除 100644 → 0
浏览文件 @ f7514772
# 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.
import unittest
import numpy as np
from inference_pass_test import InferencePassTest
import paddle
import paddle.fluid as fluid
import paddle.nn.functional as F
from paddle.fluid.core import PassVersionChecker
class MkldnnInplacePassTest(InferencePassTest):
def setUp(self):
with fluid.program_guard(self.main_program, self.startup_program):
paddle.enable_static()
data = fluid.data(
name="data", shape=[-1, 3, 100, 100], dtype="float32"
)
conv_out_1 = fluid.layers.conv2d(
data, num_filters=3, filter_size=3, bias_attr=False
)
softmax_out = paddle.nn.functional.softmax(conv_out_1)
relu_out = F.relu(conv_out_1)
eltwise_out = paddle.add(softmax_out, relu_out)
self.pass_name = 'mkldnn_inplace_pass'
self.feeds = {
"data": np.random.random((1, 3, 100, 100)).astype("float32")
}
self.fetch_list = [softmax_out, relu_out, eltwise_out]
self.enable_mkldnn = True
def test_check_output(self):
use_gpu = False
self.check_output_with_option(use_gpu)
def test_pass_compatible(self):
self.assertTrue(PassVersionChecker.IsCompatible(self.pass_name))
if __name__ == "__main__":
unittest.main()
tools/parallel_UT_rule.py
浏览文件 @ 3cfb2e1a
......@@ -112,7 +112,6 @@ HIGH_PARALLEL_JOB_NEW = [
'test_fc_lstm_fuse_pass_cc',
'test_version',
'gather_test',
'test_mkldnn_inplace_fuse_pass',
'test_reshape_bf16_op',
'test_compat',
'test_data_feeder',
......@@ -237,7 +236,6 @@ HIGH_PARALLEL_JOB_NEW = [
'test_selected_rows',
'test_fleet_sharding_meta_optimizer',
'test_inference_api',
'test_mkldnn_inplace_pass',
'test_data_generator',
'test_deprecated_memory_optimize_interfaces',
'test_ir_skip_layernorm_pass',
......@@ -1819,8 +1817,6 @@ CPU_PARALLEL_JOB = [
'test_mkldnn_op_inplace',
'test_mkldnn_matmul_transpose_reshape_fuse_pass',
'test_mkldnn_matmul_op_output_fuse_pass',
'test_mkldnn_inplace_pass',
'test_mkldnn_inplace_fuse_pass',
'test_mkldnn_cpu_bfloat16_pass',
'test_mkldnn_conv_concat_relu_mkldnn_fuse_pass',
'test_mkldnn_conv_bias_fuse_pass',
......
tools/static_mode_white_list.py
浏览文件 @ 3cfb2e1a
......@@ -655,7 +655,6 @@ STATIC_MODE_TESTING_LIST = [
'test_mkldnn_matmul_op_output_fuse_pass',
'test_mkldnn_matmul_transpose_reshape_fuse_pass',
'test_mkldnn_scale_matmul_fuse_pass',
'test_mkldnn_inplace_fuse_pass',
'test_mkldnn_conv_affine_channel_fuse_pass',
'test_batch_fc_op',
'test_c_comm_init_all_op',
......
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