wehub-resource-sync
238 lines
7.6 KiB
Python
238 lines
7.6 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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from functools import partial
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import numpy as np
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from auto_scan_test import PassAutoScanTest
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from program_config import OpConfig, ProgramConfig, TensorConfig
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class TestMultiheadMatmulFusePass(PassAutoScanTest):
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def sample_predictor_configs(self, program_config):
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# gpu
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config = self.create_inference_config(use_gpu=True)
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yield config, ["multihead_matmul", "mul"], (1e-2, 1e-3)
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def sample_program_config(self, draw):
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def generate_mul_input():
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return np.random.random([1, 128, 768]).astype(np.float32) - 0.5
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def generate_elewise_input():
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return np.random.random([1, 12, 128, 128]).astype(np.float32)
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def generate_weight(shape):
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return np.random.random(shape).astype(np.float32)
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mul_0 = OpConfig(
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"mul",
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inputs={"X": ["mul_x"], "Y": ["mul_0_w"]},
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outputs={"Out": ["mul_0_out"]},
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x_num_col_dims=2,
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y_num_col_dims=1,
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)
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mul_1 = OpConfig(
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"mul",
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inputs={"X": ["mul_x"], "Y": ["mul_1_w"]},
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outputs={"Out": ["mul_1_out"]},
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x_num_col_dims=2,
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y_num_col_dims=1,
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)
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mul_2 = OpConfig(
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"mul",
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inputs={"X": ["mul_x"], "Y": ["mul_2_w"]},
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outputs={"Out": ["mul_2_out"]},
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x_num_col_dims=2,
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y_num_col_dims=1,
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)
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ele_0 = OpConfig(
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"elementwise_add",
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inputs={"X": [mul_0.outputs["Out"][0]], "Y": ["ele_0_w"]},
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outputs={"Out": ["ele_0_out"]},
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axis=-1,
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)
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ele_1 = OpConfig(
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"elementwise_add",
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inputs={"X": [mul_1.outputs["Out"][0]], "Y": ["ele_1_w"]},
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outputs={"Out": ["ele_1_out"]},
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axis=-1,
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)
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ele_2 = OpConfig(
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"elementwise_add",
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inputs={"X": [mul_2.outputs["Out"][0]], "Y": ["ele_2_w"]},
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outputs={"Out": ["ele_2_out"]},
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axis=-1,
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)
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reshape_0 = OpConfig(
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"reshape2",
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inputs={"X": [ele_0.outputs["Out"][0]]},
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outputs={"Out": ["reshape_0_out"], "XShape": ["reshape_0_Xout"]},
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shape=(1, 128, 12, 64),
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)
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reshape_1 = OpConfig(
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"reshape2",
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inputs={"X": [ele_1.outputs["Out"][0]]},
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outputs={"Out": ["reshape_1_out"], "XShape": ["reshape_1_Xout"]},
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shape=(1, 128, 12, 64),
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)
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reshape_2 = OpConfig(
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"reshape2",
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inputs={"X": [ele_2.outputs["Out"][0]]},
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outputs={"Out": ["reshape_2_out"], "XShape": ["reshape_2_Xout"]},
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shape=(1, 128, 12, 64),
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)
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transpose_0 = OpConfig(
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"transpose2",
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inputs={"X": [reshape_0.outputs["Out"][0]]},
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outputs={"Out": ["transpose_0_out"]},
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axis=(0, 2, 1, 3),
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)
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transpose_1 = OpConfig(
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"transpose2",
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inputs={"X": [reshape_1.outputs["Out"][0]]},
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outputs={"Out": ["transpose_1_out"]},
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axis=(0, 2, 3, 1),
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)
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transpose_2 = OpConfig(
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"transpose2",
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inputs={"X": [reshape_2.outputs["Out"][0]]},
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outputs={"Out": ["transpose_2_out"]},
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axis=(0, 2, 1, 3),
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)
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matmul_0 = OpConfig(
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"matmul",
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inputs={
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"X": [transpose_0.outputs["Out"][0]],
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"Y": [transpose_1.outputs["Out"][0]],
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},
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outputs={"Out": ["matmul_0_out"]},
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alpha=0.125,
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transpose_X=False,
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transpose_Y=False,
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)
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ele_3 = OpConfig(
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"elementwise_add",
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inputs={
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"X": [matmul_0.outputs["Out"][0]],
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"Y": ["eltadd_qk_b_var"],
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},
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outputs={"Out": ["ele_3_out"]},
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axis=-1,
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)
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softmax_op = OpConfig(
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"softmax",
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inputs={"X": [ele_3.outputs["Out"][0]]},
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outputs={"Out": ["softmax_out"]},
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axis=3,
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is_test=True,
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)
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matmul_1 = OpConfig(
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"matmul",
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inputs={
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"X": [softmax_op.outputs["Out"][0]],
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"Y": [transpose_2.outputs["Out"][0]],
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},
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outputs={"Out": ["matmul_1_out"]},
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alpha=1.0,
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transpose_X=False,
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transpose_Y=False,
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)
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transpose_3 = OpConfig(
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"transpose2",
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inputs={"X": [matmul_1.outputs["Out"][0]]},
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outputs={"Out": ["transpose_3_out"]},
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axis=(0, 2, 1, 3),
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)
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reshape_3 = OpConfig(
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"reshape2",
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inputs={"X": [transpose_3.outputs["Out"][0]]},
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outputs={"Out": ["reshape_3_out"], "XShape": ["reshape_3_Xout"]},
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shape=(1, 128, 768),
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)
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mul_3 = OpConfig(
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"mul",
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inputs={"X": [reshape_3.outputs["Out"][0]], "Y": ["mul_3_w"]},
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outputs={"Out": ["mul_3_out"]},
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x_num_col_dims=2,
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y_num_col_dims=1,
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)
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ops = [
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mul_0,
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mul_1,
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mul_2,
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ele_0,
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ele_1,
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ele_2,
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reshape_0,
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reshape_1,
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reshape_2,
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transpose_0,
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transpose_1,
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transpose_2,
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matmul_0,
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ele_3,
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softmax_op,
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matmul_1,
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transpose_3,
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reshape_3,
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mul_3,
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]
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program_config = ProgramConfig(
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ops=ops,
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inputs={
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"mul_x": TensorConfig(data_gen=partial(generate_mul_input)),
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"eltadd_qk_b_var": TensorConfig(
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data_gen=partial(generate_elewise_input)
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),
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},
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weights={
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"mul_0_w": TensorConfig(
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data_gen=partial(generate_weight, [768, 768])
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),
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"mul_1_w": TensorConfig(
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data_gen=partial(generate_weight, [768, 768])
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),
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"mul_2_w": TensorConfig(
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data_gen=partial(generate_weight, [768, 768])
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),
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"mul_3_w": TensorConfig(
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data_gen=partial(generate_weight, [768, 768])
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),
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"ele_0_w": TensorConfig(
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data_gen=partial(generate_weight, [768])
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),
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"ele_1_w": TensorConfig(
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data_gen=partial(generate_weight, [768])
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),
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"ele_2_w": TensorConfig(
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data_gen=partial(generate_weight, [768])
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),
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},
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outputs=[ops[-1].outputs["Out"][0]],
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)
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return program_config
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def test(self):
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self.run_and_statistics(
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quant=False,
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max_examples=100,
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min_success_num=1,
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passes=["multihead_matmul_fuse_pass_v3"],
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)
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if __name__ == "__main__":
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unittest.main()
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