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[cherry pick][Inference]Enhance gpu multihead matmul v3 fuse pass #43765

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Jun 23, 2022
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[cherry pick][Inference]Enhance gpu multihead matmul v3 fuse pass #43765

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3 changes: 2 additions & 1 deletion paddle/fluid/framework/ir/multihead_matmul_fuse_pass.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -423,7 +423,8 @@ PDNode* MultiHeadMatmulPattern::operator()() {
}

PDNode* MultiHeadMatmulV3Pattern::operator()() {
std::unordered_set<std::string> matmul_ops{"matmul", "matmul_v2"};
// Add mul op to support huggingface onnx model convertsion by x2paddle
std::unordered_set<std::string> matmul_ops{"mul", "matmul", "matmul_v2"};
auto* input0 = pattern->NewNode(input0_repr());
input0->assert_is_ops_input(matmul_ops);

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2 changes: 2 additions & 0 deletions paddle/fluid/inference/api/paddle_pass_builder.cc
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Expand Up @@ -145,6 +145,8 @@ GpuPassStrategy::GpuPassStrategy() : PassStrategy({}) {
"gpu_cpu_flatten2_matmul_fuse_pass", //
"gpu_cpu_map_matmul_v2_to_mul_pass", //
"gpu_cpu_map_matmul_v2_to_matmul_pass", //
"matmul_scale_fuse_pass", //
"multihead_matmul_fuse_pass_v3", //
"gpu_cpu_map_matmul_to_mul_pass", //
"fc_fuse_pass", //
"fc_elementwise_layernorm_fuse_pass", //
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6 changes: 6 additions & 0 deletions python/paddle/fluid/tests/unittests/ir/inference/test_matmul_scale_fuse_pass.py
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Expand Up @@ -43,6 +43,12 @@ def sample_predictor_configs(self, program_config):
config = self.create_inference_config(use_mkldnn=True)
yield config, ["matmul", ], (1e-5, 1e-5)

# gpu
config = self.create_inference_config(use_gpu=True)
yield config, [
"matmul",
], (1e-5, 1e-5)

def sample_program_config(self, draw):
# 1. Generate shape and attr of matmul
x_shape = draw(
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215 changes: 215 additions & 0 deletions python/paddle/fluid/tests/unittests/ir/inference/test_multihead_matmul_fuse_pass_v3.py
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Original file line number Diff line number Diff line change
@@ -0,0 +1,215 @@
# Copyright (c) 2022 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, reproduce_failure
import hypothesis.strategies as st


class TestMultiheadMatmulFusePass(PassAutoScanTest):

def sample_predictor_configs(self, program_config):
# gpu
config = self.create_inference_config(use_gpu=True)
yield config, ["multihead_matmul", "mul"], (1e-2, 1e-3)

def sample_program_config(self, draw):

def generate_mul_input():
return np.random.random([1, 128, 768]).astype(np.float32) - 0.5

def generate_elewise_input():
return np.random.random([1, 12, 128, 128]).astype(np.float32)

mul_0 = OpConfig("mul",
inputs={
"X": ["mul_x"],
"Y": ["mul_0_w"]
},
outputs={"Out": ["mul_0_out"]},
x_num_col_dims=2,
y_num_col_dims=1)
mul_1 = OpConfig("mul",
inputs={
"X": ["mul_x"],
"Y": ["mul_1_w"]
},
outputs={"Out": ["mul_1_out"]},
x_num_col_dims=2,
y_num_col_dims=1)
mul_2 = OpConfig("mul",
inputs={
"X": ["mul_x"],
"Y": ["mul_2_w"]
},
outputs={"Out": ["mul_2_out"]},
x_num_col_dims=2,
y_num_col_dims=1)
ele_0 = OpConfig("elementwise_add",
inputs={
"X": [mul_0.outputs["Out"][0]],
"Y": ["ele_0_w"]
},
outputs={"Out": ["ele_0_out"]},
axis=-1)
ele_1 = OpConfig("elementwise_add",
inputs={
"X": [mul_1.outputs["Out"][0]],
"Y": ["ele_1_w"]
},
outputs={"Out": ["ele_1_out"]},
axis=-1)
ele_2 = OpConfig("elementwise_add",
inputs={
"X": [mul_2.outputs["Out"][0]],
"Y": ["ele_2_w"]
},
outputs={"Out": ["ele_2_out"]},
axis=-1)
reshape_0 = OpConfig("reshape2",
inputs={"X": [ele_0.outputs["Out"][0]]},
outputs={
"Out": ["reshape_0_out"],
"XShape": ["reshape_0_Xout"]
},
shape=(1, 128, 12, 64))
reshape_1 = OpConfig("reshape2",
inputs={"X": [ele_1.outputs["Out"][0]]},
outputs={
"Out": ["reshape_1_out"],
"XShape": ["reshape_1_Xout"]
},
shape=(1, 128, 12, 64))
reshape_2 = OpConfig("reshape2",
inputs={"X": [ele_2.outputs["Out"][0]]},
outputs={
"Out": ["reshape_2_out"],
"XShape": ["reshape_2_Xout"]
},
shape=(1, 128, 12, 64))
transpose_0 = OpConfig("transpose2",
inputs={"X": [reshape_0.outputs["Out"][0]]},
outputs={"Out": ["transpose_0_out"]},
axis=(0, 2, 1, 3))
transpose_1 = OpConfig("transpose2",
inputs={"X": [reshape_1.outputs["Out"][0]]},
outputs={"Out": ["transpose_1_out"]},
axis=(0, 2, 3, 1))
transpose_2 = OpConfig("transpose2",
inputs={"X": [reshape_2.outputs["Out"][0]]},
outputs={"Out": ["transpose_2_out"]},
axis=(0, 2, 1, 3))
matmul_0 = OpConfig("matmul",
inputs={
"X": [transpose_0.outputs["Out"][0]],
"Y": [transpose_1.outputs["Out"][0]]
},
outputs={"Out": ["matmul_0_out"]},
alpha=0.125,
transpose_X=False,
transpose_Y=False,
fused_reshape_Out=[],
fused_reshape_X=[],
fused_reshape_Y=[],
fused_transpose_Out=[],
fused_transpose_X=[],
fused_transpose_Y=[])
ele_3 = OpConfig("elementwise_add",
inputs={
"X": [matmul_0.outputs["Out"][0]],
"Y": ["eltadd_qk_b_var"]
},
outputs={"Out": ["ele_3_out"]},
axis=-1)
softmax_op = OpConfig("softmax",
inputs={"X": [ele_3.outputs["Out"][0]]},
outputs={"Out": ["softmax_out"]},
axis=3,
is_test=True)
matmul_1 = OpConfig("matmul",
inputs={
"X": [softmax_op.outputs["Out"][0]],
"Y": [transpose_2.outputs["Out"][0]]
},
outputs={"Out": ["matmul_1_out"]},
alpha=1.0,
transpose_X=False,
transpose_Y=False,
fused_reshape_Out=[],
fused_reshape_X=[],
fused_reshape_Y=[],
fused_transpose_Out=[],
fused_transpose_X=[],
fused_transpose_Y=[])
transpose_3 = OpConfig("transpose2",
inputs={"X": [matmul_1.outputs["Out"][0]]},
outputs={"Out": ["transpose_3_out"]},
axis=(0, 2, 1, 3))
reshape_3 = OpConfig("reshape2",
inputs={"X": [transpose_3.outputs["Out"][0]]},
outputs={
"Out": ["reshape_3_out"],
"XShape": ["reshape_3_Xout"]
},
shape=(1, 128, 768))
mul_3 = OpConfig("mul",
inputs={
"X": [reshape_3.outputs["Out"][0]],
"Y": ["mul_3_w"]
},
outputs={"Out": ["mul_3_out"]},
x_num_col_dims=2,
y_num_col_dims=1)
ops = [
mul_0, mul_1, mul_2, ele_0, ele_1, ele_2, reshape_0, reshape_1,
reshape_2, transpose_0, transpose_1, transpose_2, matmul_0, ele_3,
softmax_op, matmul_1, transpose_3, reshape_3, mul_3
]
program_config = ProgramConfig(
ops=ops,
inputs={
"mul_x":
TensorConfig(data_gen=partial(generate_mul_input)),
"eltadd_qk_b_var":
TensorConfig(data_gen=partial(generate_elewise_input))
},
weights={
"mul_0_w": TensorConfig(shape=[768, 768]),
"mul_1_w": TensorConfig(shape=[768, 768]),
"mul_2_w": TensorConfig(shape=[768, 768]),
"mul_3_w": TensorConfig(shape=[768, 768]),
"ele_0_w": TensorConfig(shape=[768]),
"ele_1_w": TensorConfig(shape=[768]),
"ele_2_w": TensorConfig(shape=[768])
},
outputs=[ops[-1].outputs["Out"][0]])
return program_config

def test(self):
self.run_and_statis(quant=False,
max_examples=100,
min_success_num=1,
passes=["multihead_matmul_fuse_pass_v3"])


if __name__ == "__main__":
unittest.main()