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update mkldnn batch_norm_activation fuse pass ut (PaddlePaddle#37402)
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* update mkldnn batch_norm_activation fuse pass ut

* update ut

* update mkldnn batch_norm_act_fuse_pass ut

* update batch_norm_act_fuse_pass ut

* update ut
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@baoachun @zmxdream
baoachun authored and zmxdream committed Dec 25, 2021
1 parent 133ea42 commit a75fc26
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Showing 4 changed files with 126 additions and 84 deletions.
23 changes: 11 additions & 12 deletions paddle/fluid/framework/ir/mkldnn/batch_norm_act_fuse_pass.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -67,6 +67,12 @@ FuseBatchNormActOneDNNPass::FuseBatchNormActOneDNNPass() {
.AddAttr("epsilon")
.IsNumGE(0.0f)
.IsNumLE(0.001f)
.End()
.AddAttr("trainable_statistics")
.IsBoolEQ(false)
.End()
.AddAttr("is_test")
.IsBoolEQ(true)
.End();

AddOpCompat(OpCompat("relu"))
Expand Down Expand Up @@ -108,7 +114,6 @@ void FuseBatchNormActOneDNNPass::FuseBatchNormAct(
GET_IR_NODE_FROM_SUBGRAPH(act, act, bn_act_pattern);

auto *bn_op = batch_norm->Op();

if (bn_op->HasAttr("use_mkldnn")) {
PADDLE_ENFORCE(
BOOST_GET_CONST(bool, bn_op->GetAttr("use_mkldnn")),
Expand All @@ -117,19 +122,13 @@ void FuseBatchNormActOneDNNPass::FuseBatchNormAct(
"is used."));
}

if (bn_op->HasAttr("trainable_statistics")) {
PADDLE_ENFORCE(
!BOOST_GET_CONST(bool, bn_op->GetAttr("trainable_statistics")),
platform::errors::PreconditionNotMet(
"The BatchNorm+Act fusion may happen only when mean and variance "
"are not calculated by current batch statistics."));
}

if (bn_op->HasAttr("is_test")) {
auto *act_op = act->Op();
if (act_op->HasAttr("use_mkldnn")) {
PADDLE_ENFORCE(
BOOST_GET_CONST(bool, bn_op->GetAttr("is_test")),
BOOST_GET_CONST(bool, bn_op->GetAttr("use_mkldnn")),
platform::errors::PreconditionNotMet(
"The BatchNorm+Act fusion may happen only during inference."));
"The BatchNorm+Act fusion may happen only when oneDNN library "
"is used."));
}

bn_op->SetAttr("use_mkldnn", true);
Expand Down
24 changes: 12 additions & 12 deletions paddle/fluid/framework/ir/mkldnn/batch_norm_act_fuse_pass_tester.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -65,9 +65,9 @@ TEST(FuseBatchNormActOneDNNPass, ThrowIsTestTrainableStats) {
// No fusion in this attribute configuration
constexpr int removed_nodes_count = 0;

EXPECT_THROW(test::RunPassAndAssert(&graph, "batch_norm_act_fuse_pass", "x",
"act_y", removed_nodes_count),
paddle::platform::EnforceNotMet);
EXPECT_TRUE(test::RunPassAndAssert(&graph, "batch_norm_act_fuse_pass", "x",
"act_y", removed_nodes_count));
EXPECT_TRUE(test::AssertOpsCount(graph, {{"batch_norm", 1}, {"relu", 1}}));
}

TEST(FuseBatchNormActOneDNNPass, FuseIsTest) {
Expand Down Expand Up @@ -123,9 +123,9 @@ TEST(FuseBatchNormActOneDNNPass, ThrowTrainableStats) {
// No fusion in this attribute configuration
constexpr int removed_nodes_count = 0;

EXPECT_THROW(test::RunPassAndAssert(&graph, "batch_norm_act_fuse_pass", "x",
"act_y", removed_nodes_count),
paddle::platform::EnforceNotMet);
EXPECT_TRUE(test::RunPassAndAssert(&graph, "batch_norm_act_fuse_pass", "x",
"act_y", removed_nodes_count));
EXPECT_TRUE(test::AssertOpsCount(graph, {{"batch_norm", 1}, {"relu", 1}}));
}

TEST(FuseBatchNormActOneDNNPass, AllAttrsFalse) {
Expand All @@ -149,9 +149,9 @@ TEST(FuseBatchNormActOneDNNPass, AllAttrsFalse) {
// No fusion in this attribute configuration
constexpr int removed_nodes_count = 0;

EXPECT_THROW(test::RunPassAndAssert(&graph, "batch_norm_act_fuse_pass", "x",
"act_y", removed_nodes_count),
paddle::platform::EnforceNotMet);
EXPECT_TRUE(test::RunPassAndAssert(&graph, "batch_norm_act_fuse_pass", "x",
"act_y", removed_nodes_count));
EXPECT_TRUE(test::AssertOpsCount(graph, {{"batch_norm", 1}, {"relu", 1}}));
}

TEST(FuseBatchNormActOneDNNPass, ThrowUseMkldnn) {
Expand All @@ -176,9 +176,9 @@ TEST(FuseBatchNormActOneDNNPass, ThrowUseMkldnn) {
// No fusion in this attribute configuration
constexpr int removed_nodes_count = 0;

EXPECT_THROW(test::RunPassAndAssert(&graph, "batch_norm_act_fuse_pass", "x",
"act_y", removed_nodes_count),
paddle::platform::EnforceNotMet);
EXPECT_TRUE(test::RunPassAndAssert(&graph, "batch_norm_act_fuse_pass", "x",
"act_y", removed_nodes_count));
EXPECT_TRUE(test::AssertOpsCount(graph, {{"batch_norm", 1}, {"relu", 1}}));
}

TEST(FuseBatchNormActOneDNNPass, pass_op_version_check) {
Expand Down
1 change: 1 addition & 0 deletions python/paddle/fluid/tests/unittests/ir/inference/CMakeLists.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -94,6 +94,7 @@ if (WITH_MKLDNN)
set_tests_properties(test_conv_act_mkldnn_fuse_pass PROPERTIES TIMEOUT 120)
set_tests_properties(test_conv_transpose_eltwiseadd_bn_fuse_pass PROPERTIES TIMEOUT 250)
set_tests_properties(test_conv_transpose_bn_fuse_pass PROPERTIES TIMEOUT 300)
set_tests_properties(test_mkldnn_batch_norm_act_fuse_pass PROPERTIES TIMEOUT 100)
set_tests_properties(test_mkldnn_conv_transpose_bias_fuse_pass PROPERTIES TIMEOUT 100)
set_tests_properties(test_conv_eltwiseadd_bn_fuse_pass PROPERTIES TIMEOUT 300)
endif()
Expand Down
162 changes: 102 additions & 60 deletions python/paddle/fluid/tests/unittests/ir/inference/test_mkldnn_batch_norm_act_fuse_pass.py
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Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
# 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.
Expand All @@ -11,68 +11,110 @@
# 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.
"""Test for fusion of batch norm and activation."""
from __future__ import print_function

import unittest
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


class TestScaleMatmulMkldnnFusePass(PassAutoScanTest):
def is_program_valid(self, program_config: ProgramConfig) -> bool:
return True

def sample_program_config(self, draw):
data_layout = draw(st.sampled_from(["NCHW", "NHWC"]))
epsilon = draw(st.floats(min_value=0.0, max_value=0.001))
fuse_with_relu = draw(st.booleans())
is_test = draw(st.sampled_from([True]))
momentum = draw(st.floats(min_value=0.0, max_value=5))
trainable_statistics = False
use_global_stats = draw(st.booleans())
use_mkldnn1 = draw(st.sampled_from([True]))
use_cudnn = draw(st.booleans())
use_mkldnn2 = draw(st.sampled_from([True]))
batch_size = draw(st.integers(min_value=1, max_value=4))
channel = draw(st.integers(min_value=1, max_value=64))
input_dim1 = draw(st.integers(min_value=1, max_value=512))
input_dim2 = draw(st.integers(min_value=1, max_value=512))

def generate_input():
shape = [input_dim1, input_dim2]
if data_layout == "NCHW":
shape.insert(0, channel)
shape.insert(0, batch_size)
else:
shape.append(channel)
shape.insert(0, batch_size)
return np.random.random(shape).astype(np.float32)

def generate_weight():
return np.random.random(channel).astype(np.float32)

batch_norm_op = OpConfig(
type="batch_norm",
inputs={
"X": ["input_data"],
"Bias": ["Bias"],
"Mean": ["Mean"],
"Scale": ["Scale"],
"Variance": ["Variance"]
},
outputs={
"Y": ["norm_output"],
"MeanOut": ["Mean"],
"VarianceOut": ["Variance"],
"SavedMean": ["SavedMean"],
"SavedVariance": ["SavedVariance"]
},
attrs={
"data_layout": data_layout,
"epsilon": epsilon,
"fuse_with_relu": fuse_with_relu,
"is_test": is_test,
"momentum": momentum,
"trainable_statistics": trainable_statistics,
"use_global_stats": use_global_stats,
"use_mkldnn": use_mkldnn1
})

relu_op = OpConfig(
type="relu",
inputs={"X": ["norm_output"]},
outputs={"Out": ["relu_output"]},
attrs={"use_cudnn": use_cudnn,
"use_mkldnn": use_mkldnn2})

model_net = [batch_norm_op, relu_op]

program_config = ProgramConfig(
ops=model_net,
weights={
"Bias": TensorConfig(data_gen=partial(generate_weight)),
"Mean": TensorConfig(data_gen=partial(generate_weight)),
"Scale": TensorConfig(data_gen=partial(generate_weight)),
"Variance": TensorConfig(data_gen=partial(generate_weight))
},
inputs={
"input_data": TensorConfig(data_gen=partial(generate_input))
},
outputs=["relu_output"])

return program_config

def sample_predictor_configs(self, program_config):
config = self.create_inference_config(use_mkldnn=True)
yield config, ["batch_norm"], (1e-5, 1e-5)

import paddle.fluid as fluid
from inference_pass_test import InferencePassTest
from paddle import enable_static
from paddle.fluid.core import PassVersionChecker

enable_static()


class BnReluOneDnnFusePassTest(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")
bn_out = fluid.layers.batch_norm(
input=data, is_test=True, use_global_stats=self.global_stats)
relu_out = fluid.layers.relu(bn_out)

self.feeds = {
"data": np.random.random((1, 3, 100, 100)).astype("float32")
}
self.fetch_list = [relu_out]
self.enable_mkldnn = True

def set_params(self):
self.global_stats = False
self.pass_name = "batch_norm_act_fuse_pass"

def test_check_output(self):
self.check_output()
self.assertTrue(PassVersionChecker.IsCompatible(self.pass_name))


class BnReluGlobalStatsOneDnnFusePassTest(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")
bn_out = fluid.layers.batch_norm(
input=data, is_test=True, use_global_stats=self.global_stats)
relu_out = fluid.layers.relu(bn_out)

self.feeds = {
"data": np.random.random((1, 3, 100, 100)).astype("float32")
}
self.fetch_list = [relu_out]
self.enable_mkldnn = True

def set_params(self):
self.global_stats = True
self.pass_name = "batch_norm_act_fuse_pass"

def test_check_output(self):
self.check_output()
self.assertTrue(PassVersionChecker.IsCompatible(self.pass_name))
def test(self):
self.run_and_statis(quant=False, passes=["batch_norm_act_fuse_pass"])


if __name__ == "__main__":
Expand Down

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