wehub-resource-sync
1398 lines
65 KiB
Python
1398 lines
65 KiB
Python
# Copyright (c) 2021 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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from __future__ import annotations
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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 program_config import ProgramConfig, TensorConfig
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from trt_layer_auto_scan_test import TrtLayerAutoScanTest
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import paddle.inference as paddle_infer
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class TrtConvertMultiHeadMatmulTest(TrtLayerAutoScanTest):
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def is_program_valid(self, program_config: ProgramConfig) -> bool:
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return True
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def sample_program_configs(self):
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def generate_input(shape):
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return np.full(shape, 0.1).astype(np.float32)
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def generate_weight(shape):
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return (
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np.random.rand(*shape).astype(np.float32).round(decimals=1) / 5
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- 0.1
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)
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for batch in [1, 4]:
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self.batch = batch
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for reshape_shape in [[0, 0, 12, 64]]:
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for dim1 in [128]:
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input2_shapes = [
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[batch, reshape_shape[2], dim1, dim1],
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[batch, 1, 1, dim1],
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]
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for input2_shape in input2_shapes:
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for axis in [0]:
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dics = [
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{"x_num_col_dims": 2, "y_num_col_dims": 1},
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{"axis": 2},
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{"shape": reshape_shape},
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{"axis": [0, 2, 1, 3]},
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{"x_num_col_dims": 2, "y_num_col_dims": 1},
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{"axis": 2},
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{"shape": reshape_shape},
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{"axis": [0, 2, 1, 3]},
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{"x_num_col_dims": 2, "y_num_col_dims": 1},
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{"axis": 2},
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{"shape": reshape_shape},
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{"axis": [0, 2, 1, 3]},
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{
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"scale": 0.125,
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"bias": 0.0,
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"bias_after_scale": True,
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},
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{
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"alpha": 1.0,
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"transpose_X": False,
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"transpose_Y": True,
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},
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{"axis": axis},
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{"axis": -1, "is_test": True},
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{
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"seed": 0,
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"dropout_prob": 0.10000000149011612,
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"dropout_implementation": "upscale_in_train",
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"fix_seed": False,
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"is_test": True,
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},
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{
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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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{"axis": [0, 2, 1, 3]},
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{"shape": [0, 0, 768]},
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{"x_num_col_dims": 2, "y_num_col_dims": 1},
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]
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ops_config = [
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{
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"op_type": "mul",
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"op_inputs": {
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"X": ["input_data1"],
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"Y": ["mul1_weight"],
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},
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"op_outputs": {"Out": ["mul1_output"]},
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"op_attrs": dics[0],
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},
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{
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"op_type": "elementwise_add",
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"op_inputs": {
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"X": ["mul1_output"],
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"Y": ["elementwise_add1_weight"],
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},
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"op_outputs": {
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"Out": ["elementwise_add1_output"]
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},
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"op_attrs": dics[1],
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},
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{
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"op_type": "reshape2",
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"op_inputs": {
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"X": ["elementwise_add1_output"],
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},
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"op_outputs": {
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"Out": ["reshape21_output"],
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"XShape": ["reshape21_output_xshape"],
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},
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"op_attrs": dics[2],
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},
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{
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"op_type": "transpose2",
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"op_inputs": {"X": ["reshape21_output"]},
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"op_outputs": {
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"Out": ["transpose21_output"],
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"XShape": ["transpose21_output_xshape"],
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},
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"op_attrs": dics[3],
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},
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{
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"op_type": "mul",
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"op_inputs": {
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"X": ["input_data1"],
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"Y": ["mul2_weight"],
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},
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"op_outputs": {"Out": ["mul2_output"]},
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"op_attrs": dics[4],
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},
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{
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"op_type": "elementwise_add",
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"op_inputs": {
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"X": ["mul2_output"],
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"Y": ["elementwise_add2_weight"],
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},
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"op_outputs": {
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"Out": ["elementwise_add2_output"]
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},
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"op_attrs": dics[5],
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},
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{
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"op_type": "reshape2",
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"op_inputs": {
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"X": ["elementwise_add2_output"]
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},
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"op_outputs": {
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"Out": ["reshape22_output"],
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"XShape": ["reshape22_output_xshape"],
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},
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"op_attrs": dics[6],
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},
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{
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"op_type": "transpose2",
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"op_inputs": {"X": ["reshape22_output"]},
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"op_outputs": {
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"Out": ["transpose22_output"],
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"XShape": ["transpose22_output_xshape"],
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},
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"op_attrs": dics[7],
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},
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{
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"op_type": "mul",
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"op_inputs": {
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"X": ["input_data1"],
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"Y": ["mul3_weight"],
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},
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"op_outputs": {"Out": ["mul3_output"]},
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"op_attrs": dics[8],
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},
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{
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"op_type": "elementwise_add",
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"op_inputs": {
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"X": ["mul3_output"],
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"Y": ["elementwise_add3_weight"],
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},
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"op_outputs": {
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"Out": ["elementwise_add3_output"]
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},
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"op_attrs": dics[9],
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},
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{
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"op_type": "reshape2",
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"op_inputs": {
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"X": ["elementwise_add3_output"]
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},
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"op_outputs": {
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"Out": ["reshape23_output"],
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"XShape": ["reshape23_output_xshape"],
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},
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"op_attrs": dics[10],
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},
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{
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"op_type": "transpose2",
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"op_inputs": {"X": ["reshape23_output"]},
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"op_outputs": {
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"Out": ["transpose23_output"],
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"XShape": ["transpose23_output_xshape"],
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},
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"op_attrs": dics[11],
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},
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{
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"op_type": "scale",
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"op_inputs": {
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"X": ["transpose23_output"],
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},
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"op_outputs": {"Out": ["scale_output"]},
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"op_attrs": dics[12],
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},
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{
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"op_type": "matmul",
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"op_inputs": {
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"X": ["scale_output"],
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"Y": ["transpose22_output"],
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},
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"op_outputs": {"Out": ["matmul1_output"]},
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"op_attrs": dics[13],
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},
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{
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"op_type": "elementwise_add",
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"op_inputs": {
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"X": ["matmul1_output"],
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"Y": ["input_data2"],
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},
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"op_outputs": {
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"Out": ["elementwise_add4_output"]
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},
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"op_attrs": dics[14],
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},
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{
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"op_type": "softmax",
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"op_inputs": {
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"X": ["elementwise_add4_output"]
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},
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"op_outputs": {"Out": ["softmax_output"]},
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"op_attrs": dics[15],
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},
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{
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"op_type": "dropout",
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"op_inputs": {
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"X": ["softmax_output"],
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},
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"op_outputs": {"Out": ["dropout3_output"]},
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"op_attrs": dics[16],
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},
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{
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"op_type": "matmul",
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"op_inputs": {
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"X": ["dropout3_output"],
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"Y": ["transpose21_output"],
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},
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"op_outputs": {"Out": ["matmul2_output"]},
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"op_attrs": dics[17],
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},
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{
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"op_type": "transpose2",
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"op_inputs": {"X": ["matmul2_output"]},
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"op_outputs": {
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"Out": ["transpose24_output"],
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"XShape": ["transpose24_output_xshape"],
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},
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"op_attrs": dics[18],
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},
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{
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"op_type": "reshape2",
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"op_inputs": {"X": ["transpose24_output"]},
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"op_outputs": {
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"Out": ["reshape24_output"],
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"XShape": ["reshape24_output_xshape"],
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},
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"op_attrs": dics[19],
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},
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# In order to fuse ops with
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# multihead_matmul_fuse_pass_v2, the last op
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# must be mul.
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{
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"op_type": "mul",
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"op_inputs": {
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"X": ["reshape24_output"],
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"Y": ["mul4_weight"],
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},
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"op_outputs": {"Out": ["mul4_output"]},
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"op_attrs": dics[20],
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},
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]
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ops = self.generate_op_config(ops_config)
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program_config = ProgramConfig(
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ops=ops,
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weights={
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"mul1_weight": TensorConfig(
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data_gen=partial(
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generate_weight, (768, 768)
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)
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),
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"mul2_weight": TensorConfig(
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data_gen=partial(
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generate_weight, (768, 768)
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)
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),
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"mul3_weight": TensorConfig(
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data_gen=partial(
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generate_weight, (768, 768)
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)
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),
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"mul4_weight": TensorConfig(
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data_gen=partial(
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generate_weight, (768, 768)
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)
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),
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"elementwise_add1_weight": TensorConfig(
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data_gen=partial(
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generate_weight, (768,)
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)
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),
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"elementwise_add2_weight": TensorConfig(
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data_gen=partial(
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generate_weight, (768,)
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)
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),
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"elementwise_add3_weight": TensorConfig(
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data_gen=partial(
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generate_weight, (768,)
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)
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),
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},
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inputs={
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"input_data1": TensorConfig(
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data_gen=partial(
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generate_input, (batch, dim1, 768)
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)
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),
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"input_data2": TensorConfig(
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data_gen=partial(
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generate_input, input2_shape
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)
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),
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},
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outputs=["mul4_output"],
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)
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yield program_config
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def sample_predictor_configs(
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self, program_config
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) -> tuple[paddle_infer.Config, list[int], float]:
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def generate_dynamic_shape(attrs):
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# The last dim of input1 and input2 should be static.
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self.dynamic_shape.min_input_shape = {
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"input_data1": [1, 8, 768],
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"input_data2": [1, 1, 1, 128],
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"reshape24_output": [1, 128, 768],
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}
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self.dynamic_shape.max_input_shape = {
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"input_data1": [16, 512, 768],
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"input_data2": [16, 256, 512, 128],
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"reshape24_output": [1, 128, 768],
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}
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self.dynamic_shape.opt_input_shape = {
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"input_data1": [8, 128, 768],
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"input_data2": [8, 32, 64, 128],
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"reshape24_output": [1, 128, 768],
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}
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def clear_dynamic_shape():
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self.dynamic_shape.max_input_shape = {}
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self.dynamic_shape.min_input_shape = {}
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self.dynamic_shape.opt_input_shape = {}
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attrs = [
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program_config.ops[i].attrs for i in range(len(program_config.ops))
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]
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# for dynamic_shape
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generate_dynamic_shape(attrs)
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self.trt_param.precision = paddle_infer.PrecisionType.Float32
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program_config.set_input_type(np.float32)
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self.trt_param.workspace_size = 2013265920
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yield self.create_inference_config(), (1, 3), (1e-5, 1e-5)
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self.trt_param.precision = paddle_infer.PrecisionType.Half
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program_config.set_input_type(np.float16)
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yield self.create_inference_config(), (1, 3), (1e-2, 1e-2)
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def test(self):
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self.run_test()
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class TrtConvertMultiHeadMatmulTestInt8(TrtConvertMultiHeadMatmulTest):
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def sample_program_configs(self):
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def generate_input(shape):
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return np.full(shape, 0.1).astype(np.float32)
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def generate_weight(shape):
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return (
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np.random.rand(*shape).astype(np.float32).round(decimals=1) / 5
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- 0.1
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)
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for batch in [4]:
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self.batch = batch
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for reshape_shape in [[0, 0, 12, 64]]:
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for dim1 in [128]:
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input2_shapes = [
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[batch, reshape_shape[2], dim1, dim1],
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[batch, 1, 1, dim1],
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]
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for input2_shape in input2_shapes:
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for axis in [0]:
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dics = [
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{
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"x_num_col_dims": 2,
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"y_num_col_dims": 1,
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"enable_int8": True,
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"Input_scale": 1.0,
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},
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{
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"axis": 2,
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"out_threshold": 1.0,
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},
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{"shape": reshape_shape},
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{"axis": [0, 2, 1, 3]},
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{
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"x_num_col_dims": 2,
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"y_num_col_dims": 1,
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"enable_int8": True,
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"Input_scale": 1.0,
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},
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{
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"axis": 2,
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"out_threshold": 1.0,
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},
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{"shape": reshape_shape},
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{"axis": [0, 2, 1, 3]},
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{
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"x_num_col_dims": 2,
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"y_num_col_dims": 1,
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"enable_int8": True,
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"Input_scale": 1.0,
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},
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{
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"axis": 2,
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"out_threshold": 1.0,
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},
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{"shape": reshape_shape},
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{"axis": [0, 2, 1, 3]},
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{
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"scale": 0.125,
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"bias": 0.0,
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"bias_after_scale": True,
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},
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{
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"alpha": 1.0,
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"transpose_X": False,
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"transpose_Y": True,
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},
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{"axis": axis},
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{"axis": -1, "is_test": True},
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{
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"seed": 0,
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"dropout_prob": 0.10000000149011612,
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"dropout_implementation": "upscale_in_train",
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"fix_seed": False,
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"is_test": True,
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},
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{
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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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{"axis": [0, 2, 1, 3]},
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{"shape": [0, 0, 768]},
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{"x_num_col_dims": 2, "y_num_col_dims": 1},
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]
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|
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ops_config = [
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{
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"op_type": "mul",
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"op_inputs": {
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"X": ["input_data1"],
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"Y": ["mul1_weight"],
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},
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"op_outputs": {"Out": ["mul1_output"]},
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"op_attrs": dics[0],
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},
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{
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"op_type": "elementwise_add",
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"op_inputs": {
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"X": ["mul1_output"],
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"Y": ["elementwise_add1_weight"],
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},
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"op_outputs": {
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"Out": ["elementwise_add1_output"]
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},
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"op_attrs": dics[1],
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},
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{
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"op_type": "reshape2",
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"op_inputs": {
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"X": ["elementwise_add1_output"],
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},
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"op_outputs": {
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"Out": ["reshape21_output"],
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"XShape": ["reshape21_output_xshape"],
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},
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"op_attrs": dics[2],
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},
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{
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"op_type": "transpose2",
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"op_inputs": {"X": ["reshape21_output"]},
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"op_outputs": {
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"Out": ["transpose21_output"],
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"XShape": ["transpose21_output_xshape"],
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},
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"op_attrs": dics[3],
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},
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{
|
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"op_type": "mul",
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"op_inputs": {
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"X": ["input_data1"],
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"Y": ["mul2_weight"],
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},
|
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"op_outputs": {"Out": ["mul2_output"]},
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"op_attrs": dics[4],
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},
|
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{
|
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"op_type": "elementwise_add",
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"op_inputs": {
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"X": ["mul2_output"],
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"Y": ["elementwise_add2_weight"],
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|
},
|
|
"op_outputs": {
|
|
"Out": ["elementwise_add2_output"]
|
|
},
|
|
"op_attrs": dics[5],
|
|
},
|
|
{
|
|
"op_type": "reshape2",
|
|
"op_inputs": {
|
|
"X": ["elementwise_add2_output"]
|
|
},
|
|
"op_outputs": {
|
|
"Out": ["reshape22_output"],
|
|
"XShape": ["reshape22_output_xshape"],
|
|
},
|
|
"op_attrs": dics[6],
|
|
},
|
|
{
|
|
"op_type": "transpose2",
|
|
"op_inputs": {"X": ["reshape22_output"]},
|
|
"op_outputs": {
|
|
"Out": ["transpose22_output"],
|
|
"XShape": ["transpose22_output_xshape"],
|
|
},
|
|
"op_attrs": dics[7],
|
|
},
|
|
{
|
|
"op_type": "mul",
|
|
"op_inputs": {
|
|
"X": ["input_data1"],
|
|
"Y": ["mul3_weight"],
|
|
},
|
|
"op_outputs": {"Out": ["mul3_output"]},
|
|
"op_attrs": dics[8],
|
|
},
|
|
{
|
|
"op_type": "elementwise_add",
|
|
"op_inputs": {
|
|
"X": ["mul3_output"],
|
|
"Y": ["elementwise_add3_weight"],
|
|
},
|
|
"op_outputs": {
|
|
"Out": ["elementwise_add3_output"]
|
|
},
|
|
"op_attrs": dics[9],
|
|
},
|
|
{
|
|
"op_type": "reshape2",
|
|
"op_inputs": {
|
|
"X": ["elementwise_add3_output"]
|
|
},
|
|
"op_outputs": {
|
|
"Out": ["reshape23_output"],
|
|
"XShape": ["reshape23_output_xshape"],
|
|
},
|
|
"op_attrs": dics[10],
|
|
},
|
|
{
|
|
"op_type": "transpose2",
|
|
"op_inputs": {"X": ["reshape23_output"]},
|
|
"op_outputs": {
|
|
"Out": ["transpose23_output"],
|
|
"XShape": ["transpose23_output_xshape"],
|
|
},
|
|
"op_attrs": dics[11],
|
|
},
|
|
{
|
|
"op_type": "scale",
|
|
"op_inputs": {
|
|
"X": ["transpose23_output"],
|
|
},
|
|
"op_outputs": {"Out": ["scale_output"]},
|
|
"op_attrs": dics[12],
|
|
},
|
|
{
|
|
"op_type": "matmul",
|
|
"op_inputs": {
|
|
"X": ["scale_output"],
|
|
"Y": ["transpose22_output"],
|
|
},
|
|
"op_outputs": {"Out": ["matmul1_output"]},
|
|
"op_attrs": dics[13],
|
|
},
|
|
{
|
|
"op_type": "elementwise_add",
|
|
"op_inputs": {
|
|
"X": ["matmul1_output"],
|
|
"Y": ["input_data2"],
|
|
},
|
|
"op_outputs": {
|
|
"Out": ["elementwise_add4_output"]
|
|
},
|
|
"op_attrs": dics[14],
|
|
},
|
|
{
|
|
"op_type": "softmax",
|
|
"op_inputs": {
|
|
"X": ["elementwise_add4_output"]
|
|
},
|
|
"op_outputs": {"Out": ["softmax_output"]},
|
|
"op_attrs": dics[15],
|
|
},
|
|
{
|
|
"op_type": "dropout",
|
|
"op_inputs": {
|
|
"X": ["softmax_output"],
|
|
},
|
|
"op_outputs": {"Out": ["dropout3_output"]},
|
|
"op_attrs": dics[16],
|
|
},
|
|
{
|
|
"op_type": "matmul",
|
|
"op_inputs": {
|
|
"X": ["dropout3_output"],
|
|
"Y": ["transpose21_output"],
|
|
},
|
|
"op_outputs": {"Out": ["matmul2_output"]},
|
|
"op_attrs": dics[17],
|
|
},
|
|
{
|
|
"op_type": "transpose2",
|
|
"op_inputs": {"X": ["matmul2_output"]},
|
|
"op_outputs": {
|
|
"Out": ["transpose24_output"],
|
|
"XShape": ["transpose24_output_xshape"],
|
|
},
|
|
"op_attrs": dics[18],
|
|
},
|
|
{
|
|
"op_type": "reshape2",
|
|
"op_inputs": {"X": ["transpose24_output"]},
|
|
"op_outputs": {
|
|
"Out": ["reshape24_output"],
|
|
"XShape": ["reshape24_output_xshape"],
|
|
},
|
|
"op_attrs": dics[19],
|
|
},
|
|
# In order to fuse ops with
|
|
# multihead_matmul_fuse_pass_v2, the last op
|
|
# must be mul.
|
|
{
|
|
"op_type": "mul",
|
|
"op_inputs": {
|
|
"X": ["reshape24_output"],
|
|
"Y": ["mul4_weight"],
|
|
},
|
|
"op_outputs": {"Out": ["mul4_output"]},
|
|
"op_attrs": dics[20],
|
|
},
|
|
]
|
|
ops = self.generate_op_config(ops_config)
|
|
|
|
program_config = ProgramConfig(
|
|
ops=ops,
|
|
weights={
|
|
"mul1_weight": TensorConfig(
|
|
data_gen=partial(
|
|
generate_weight, (768, 768)
|
|
)
|
|
),
|
|
"mul2_weight": TensorConfig(
|
|
data_gen=partial(
|
|
generate_weight, (768, 768)
|
|
)
|
|
),
|
|
"mul3_weight": TensorConfig(
|
|
data_gen=partial(
|
|
generate_weight, (768, 768)
|
|
)
|
|
),
|
|
"mul4_weight": TensorConfig(
|
|
data_gen=partial(
|
|
generate_weight, (768, 768)
|
|
)
|
|
),
|
|
"elementwise_add1_weight": TensorConfig(
|
|
data_gen=partial(
|
|
generate_weight, (768,)
|
|
)
|
|
),
|
|
"elementwise_add2_weight": TensorConfig(
|
|
data_gen=partial(
|
|
generate_weight, (768,)
|
|
)
|
|
),
|
|
"elementwise_add3_weight": TensorConfig(
|
|
data_gen=partial(
|
|
generate_weight, (768,)
|
|
)
|
|
),
|
|
},
|
|
inputs={
|
|
"input_data1": TensorConfig(
|
|
data_gen=partial(
|
|
generate_input, (batch, dim1, 768)
|
|
)
|
|
),
|
|
"input_data2": TensorConfig(
|
|
data_gen=partial(
|
|
generate_input, input2_shape
|
|
)
|
|
),
|
|
},
|
|
outputs=["mul4_output"],
|
|
)
|
|
|
|
yield program_config
|
|
|
|
|
|
class TrtConvertVitToMultiHeadMatmulTest(TrtLayerAutoScanTest):
|
|
def is_program_valid(self, program_config: ProgramConfig) -> bool:
|
|
return True
|
|
|
|
def sample_program_configs(self):
|
|
def generate_input1(batch, length):
|
|
return np.full((batch, length, 768), 0.1).astype(np.float32)
|
|
|
|
def generate_weight(shape):
|
|
return (
|
|
np.random.rand(*shape).astype(np.float32).round(decimals=1) / 5
|
|
- 0.1
|
|
)
|
|
|
|
for batch in [4]:
|
|
self.batch = batch
|
|
for length in [197]:
|
|
self.length = length
|
|
ops_config = [
|
|
{
|
|
"op_type": "matmul_v2",
|
|
"op_inputs": {
|
|
"X": ["input_data1"],
|
|
"Y": ["matmul1_weight"],
|
|
},
|
|
"op_outputs": {"Out": ["matmul1_output"]},
|
|
"op_attrs": {
|
|
"trans_x": False,
|
|
"trans_y": False,
|
|
"Input_scale_layer": 1.0,
|
|
},
|
|
},
|
|
{
|
|
"op_type": "elementwise_add",
|
|
"op_inputs": {
|
|
"X": ["matmul1_output"],
|
|
"Y": ["elementwise_add1_weight"],
|
|
},
|
|
"op_outputs": {"Out": ["elementwise_add1_output"]},
|
|
"op_attrs": {
|
|
"scale_out": 1.0,
|
|
"scale_x": 1.0,
|
|
"scale_y": 1.0,
|
|
"axis": 2,
|
|
"Out": 1.0,
|
|
},
|
|
},
|
|
{
|
|
"op_type": "reshape2",
|
|
"op_inputs": {
|
|
"X": ["elementwise_add1_output"],
|
|
},
|
|
"op_outputs": {
|
|
"Out": ["reshape1_output"],
|
|
"XShape": ["reshape1_output_xshape"],
|
|
},
|
|
"op_attrs": {"shape": [-1, self.length, 3, 12, 64]},
|
|
},
|
|
{
|
|
"op_type": "transpose2",
|
|
"op_inputs": {"X": ["reshape1_output"]},
|
|
"op_outputs": {
|
|
"Out": ["transpose1_output"],
|
|
"XShape": ["transpose1_output_xshape"],
|
|
},
|
|
"op_attrs": {
|
|
"axis": [2, 0, 3, 1, 4],
|
|
"data_format": "AnyLayout",
|
|
},
|
|
},
|
|
{
|
|
"op_type": "slice",
|
|
"op_inputs": {
|
|
"Input": ["transpose1_output"],
|
|
},
|
|
"op_outputs": {"Out": ["slice1_output"]},
|
|
"op_attrs": {
|
|
"axes": [0],
|
|
"starts": [0],
|
|
"ends": [1],
|
|
"decrease_axis": [0],
|
|
"infer_flags": [1],
|
|
},
|
|
},
|
|
{
|
|
"op_type": "slice",
|
|
"op_inputs": {
|
|
"Input": ["transpose1_output"],
|
|
},
|
|
"op_outputs": {"Out": ["slice2_output"]},
|
|
"op_attrs": {
|
|
"axes": [0],
|
|
"starts": [1],
|
|
"ends": [2],
|
|
"decrease_axis": [0],
|
|
"infer_flags": [1],
|
|
},
|
|
},
|
|
{
|
|
"op_type": "slice",
|
|
"op_inputs": {
|
|
"Input": ["transpose1_output"],
|
|
},
|
|
"op_outputs": {"Out": ["slice3_output"]},
|
|
"op_attrs": {
|
|
"axes": [0],
|
|
"starts": [2],
|
|
"ends": [3],
|
|
"decrease_axis": [0],
|
|
"infer_flags": [1],
|
|
},
|
|
},
|
|
{
|
|
"op_type": "transpose2",
|
|
"op_inputs": {"X": ["slice2_output"]},
|
|
"op_outputs": {
|
|
"Out": ["transpose2_output"],
|
|
},
|
|
"op_attrs": {
|
|
"axis": [0, 1, 3, 2],
|
|
"data_format": "AnyLayout",
|
|
},
|
|
},
|
|
{
|
|
"op_type": "matmul_v2",
|
|
"op_inputs": {
|
|
"X": ["slice1_output"],
|
|
"Y": ["transpose2_output"],
|
|
},
|
|
"op_outputs": {"Out": ["matmul2_output"]},
|
|
"op_attrs": {"trans_x": False, "trans_y": False},
|
|
},
|
|
{
|
|
"op_type": "scale",
|
|
"op_inputs": {
|
|
"X": ["matmul2_output"],
|
|
},
|
|
"op_outputs": {"Out": ["scale_output"]},
|
|
"op_attrs": {
|
|
"scale": 0.125,
|
|
"bias": 0.0,
|
|
"bias_after_scale": True,
|
|
},
|
|
},
|
|
{
|
|
"op_type": "softmax",
|
|
"op_inputs": {"X": ["scale_output"]},
|
|
"op_outputs": {"Out": ["softmax_output"]},
|
|
"op_attrs": {"axis": -1, "data_format": "AnyLayout"},
|
|
},
|
|
{
|
|
"op_type": "matmul_v2",
|
|
"op_inputs": {
|
|
"X": ["softmax_output"],
|
|
"Y": ["slice3_output"],
|
|
},
|
|
"op_outputs": {"Out": ["matmul3_output"]},
|
|
"op_attrs": {"trans_x": False, "trans_y": False},
|
|
},
|
|
{
|
|
"op_type": "transpose2",
|
|
"op_inputs": {"X": ["matmul3_output"]},
|
|
"op_outputs": {
|
|
"Out": ["transpose3_output"],
|
|
"XShape": ["transpose3_output_xshape"],
|
|
},
|
|
"op_attrs": {
|
|
"axis": [0, 2, 1, 3],
|
|
"data_format": "AnyLayout",
|
|
},
|
|
},
|
|
{
|
|
"op_type": "reshape2",
|
|
"op_inputs": {"X": ["transpose3_output"]},
|
|
"op_outputs": {
|
|
"Out": ["reshape2_output"],
|
|
"XShape": ["reshape2_output_xshape"],
|
|
},
|
|
"op_attrs": {"shape": [-1, self.length, 768]},
|
|
},
|
|
]
|
|
|
|
ops = self.generate_op_config(ops_config)
|
|
|
|
program_config = ProgramConfig(
|
|
ops=ops,
|
|
weights={
|
|
"matmul1_weight": TensorConfig(
|
|
data_gen=partial(generate_weight, (768, 2304))
|
|
),
|
|
"elementwise_add1_weight": TensorConfig(
|
|
data_gen=partial(generate_weight, (2304,))
|
|
),
|
|
},
|
|
inputs={
|
|
"input_data1": TensorConfig(
|
|
data_gen=partial(generate_input1, batch, length)
|
|
)
|
|
},
|
|
outputs=["reshape2_output"],
|
|
)
|
|
|
|
yield program_config
|
|
|
|
def sample_predictor_configs(
|
|
self, program_config
|
|
) -> tuple[paddle_infer.Config, list[int], float]:
|
|
def generate_dynamic_shape(attrs):
|
|
# The last dim of input1 and input2 should be static.
|
|
self.dynamic_shape.min_input_shape = {
|
|
"input_data1": [1, 8, 768],
|
|
}
|
|
self.dynamic_shape.max_input_shape = {
|
|
"input_data1": [16, 512, 768],
|
|
}
|
|
self.dynamic_shape.opt_input_shape = {
|
|
"input_data1": [1, 197, 768],
|
|
}
|
|
|
|
def clear_dynamic_shape():
|
|
self.dynamic_shape.max_input_shape = {}
|
|
self.dynamic_shape.min_input_shape = {}
|
|
self.dynamic_shape.opt_input_shape = {}
|
|
|
|
attrs = [
|
|
program_config.ops[i].attrs for i in range(len(program_config.ops))
|
|
]
|
|
|
|
def generate_trt_nodes_num():
|
|
ver = paddle_infer.get_trt_compile_version()
|
|
if ver[0] * 1000 + ver[1] * 100 + ver[2] * 10 < 8000:
|
|
return 0, 3
|
|
return 1, 2
|
|
|
|
# for dynamic_shape
|
|
generate_dynamic_shape(attrs)
|
|
self.trt_param.workspace_size = 2013265920
|
|
self.trt_param.precision = paddle_infer.PrecisionType.Int8
|
|
program_config.set_input_type(np.float32)
|
|
yield (
|
|
self.create_inference_config(),
|
|
generate_trt_nodes_num(),
|
|
(
|
|
1e-3,
|
|
1e-3,
|
|
),
|
|
)
|
|
self.trt_param.precision = paddle_infer.PrecisionType.Half
|
|
program_config.set_input_type(np.float16)
|
|
yield (
|
|
self.create_inference_config(),
|
|
generate_trt_nodes_num(),
|
|
(
|
|
1e-3,
|
|
2e-2,
|
|
),
|
|
)
|
|
self.trt_param.precision = paddle_infer.PrecisionType.Float32
|
|
program_config.set_input_type(np.float32)
|
|
yield (
|
|
self.create_inference_config(),
|
|
generate_trt_nodes_num(),
|
|
(
|
|
1e-5,
|
|
1e-5,
|
|
),
|
|
)
|
|
|
|
def test(self):
|
|
self.run_test()
|
|
|
|
|
|
class TrtConvertMultiHeadMatmulTest_biasqk_seqseq(TrtLayerAutoScanTest):
|
|
def is_program_valid(self, program_config: ProgramConfig) -> bool:
|
|
return True
|
|
|
|
def sample_program_configs(self):
|
|
def generate_input(shape):
|
|
return np.full(shape, 0.1).astype(np.float32)
|
|
|
|
def generate_weight(shape):
|
|
return (
|
|
np.random.rand(*shape).astype(np.float32).round(decimals=1) / 5
|
|
- 0.1
|
|
)
|
|
|
|
for batch in [2]:
|
|
self.batch = batch
|
|
for reshape_shape in [[0, 0, 12, 64]]:
|
|
for dim1 in [128]:
|
|
input2_shapes = [
|
|
[batch, reshape_shape[2], dim1, dim1],
|
|
[batch, 1, 1, dim1],
|
|
]
|
|
for input2_shape in input2_shapes:
|
|
for axis in [0]:
|
|
dics = [
|
|
{"x_num_col_dims": 2, "y_num_col_dims": 1},
|
|
{"axis": 2},
|
|
{"shape": reshape_shape},
|
|
{"axis": [0, 2, 1, 3]},
|
|
{"x_num_col_dims": 2, "y_num_col_dims": 1},
|
|
{"axis": 2},
|
|
{"shape": reshape_shape},
|
|
{"axis": [0, 2, 1, 3]},
|
|
{"x_num_col_dims": 2, "y_num_col_dims": 1},
|
|
{"axis": 2},
|
|
{"shape": reshape_shape},
|
|
{"axis": [0, 2, 1, 3]},
|
|
{
|
|
"scale": 0.125,
|
|
"bias": 0.0,
|
|
"bias_after_scale": True,
|
|
},
|
|
{
|
|
"alpha": 1.0,
|
|
"transpose_X": False,
|
|
"transpose_Y": True,
|
|
},
|
|
{"axis": axis},
|
|
{"axis": -1, "is_test": True},
|
|
{
|
|
"seed": 0,
|
|
"dropout_prob": 0.10000000149011612,
|
|
"dropout_implementation": "upscale_in_train",
|
|
"fix_seed": False,
|
|
"is_test": True,
|
|
},
|
|
{
|
|
"alpha": 1.0,
|
|
"transpose_X": False,
|
|
"transpose_Y": False,
|
|
},
|
|
{"axis": [0, 2, 1, 3]},
|
|
{"shape": [0, 0, 768]},
|
|
{"x_num_col_dims": 2, "y_num_col_dims": 1},
|
|
]
|
|
|
|
ops_config = [
|
|
{
|
|
"op_type": "mul",
|
|
"op_inputs": {
|
|
"X": ["input_data1"],
|
|
"Y": ["mul1_weight"],
|
|
},
|
|
"op_outputs": {"Out": ["mul1_output"]},
|
|
"op_attrs": dics[0],
|
|
},
|
|
{
|
|
"op_type": "elementwise_add",
|
|
"op_inputs": {
|
|
"X": ["mul1_output"],
|
|
"Y": ["elementwise_add1_weight"],
|
|
},
|
|
"op_outputs": {
|
|
"Out": ["elementwise_add1_output"]
|
|
},
|
|
"op_attrs": dics[1],
|
|
},
|
|
{
|
|
"op_type": "reshape2",
|
|
"op_inputs": {
|
|
"X": ["elementwise_add1_output"],
|
|
},
|
|
"op_outputs": {
|
|
"Out": ["reshape21_output"],
|
|
"XShape": ["reshape21_output_xshape"],
|
|
},
|
|
"op_attrs": dics[2],
|
|
},
|
|
{
|
|
"op_type": "transpose2",
|
|
"op_inputs": {"X": ["reshape21_output"]},
|
|
"op_outputs": {
|
|
"Out": ["transpose21_output"],
|
|
"XShape": ["transpose21_output_xshape"],
|
|
},
|
|
"op_attrs": dics[3],
|
|
},
|
|
{
|
|
"op_type": "mul",
|
|
"op_inputs": {
|
|
"X": ["input_data1"],
|
|
"Y": ["mul2_weight"],
|
|
},
|
|
"op_outputs": {"Out": ["mul2_output"]},
|
|
"op_attrs": dics[4],
|
|
},
|
|
{
|
|
"op_type": "elementwise_add",
|
|
"op_inputs": {
|
|
"X": ["mul2_output"],
|
|
"Y": ["elementwise_add2_weight"],
|
|
},
|
|
"op_outputs": {
|
|
"Out": ["elementwise_add2_output"]
|
|
},
|
|
"op_attrs": dics[5],
|
|
},
|
|
{
|
|
"op_type": "reshape2",
|
|
"op_inputs": {
|
|
"X": ["elementwise_add2_output"]
|
|
},
|
|
"op_outputs": {
|
|
"Out": ["reshape22_output"],
|
|
"XShape": ["reshape22_output_xshape"],
|
|
},
|
|
"op_attrs": dics[6],
|
|
},
|
|
{
|
|
"op_type": "transpose2",
|
|
"op_inputs": {"X": ["reshape22_output"]},
|
|
"op_outputs": {
|
|
"Out": ["transpose22_output"],
|
|
"XShape": ["transpose22_output_xshape"],
|
|
},
|
|
"op_attrs": dics[7],
|
|
},
|
|
{
|
|
"op_type": "mul",
|
|
"op_inputs": {
|
|
"X": ["input_data1"],
|
|
"Y": ["mul3_weight"],
|
|
},
|
|
"op_outputs": {"Out": ["mul3_output"]},
|
|
"op_attrs": dics[8],
|
|
},
|
|
{
|
|
"op_type": "elementwise_add",
|
|
"op_inputs": {
|
|
"X": ["mul3_output"],
|
|
"Y": ["elementwise_add3_weight"],
|
|
},
|
|
"op_outputs": {
|
|
"Out": ["elementwise_add3_output"]
|
|
},
|
|
"op_attrs": dics[9],
|
|
},
|
|
{
|
|
"op_type": "reshape2",
|
|
"op_inputs": {
|
|
"X": ["elementwise_add3_output"]
|
|
},
|
|
"op_outputs": {
|
|
"Out": ["reshape23_output"],
|
|
"XShape": ["reshape23_output_xshape"],
|
|
},
|
|
"op_attrs": dics[10],
|
|
},
|
|
{
|
|
"op_type": "transpose2",
|
|
"op_inputs": {"X": ["reshape23_output"]},
|
|
"op_outputs": {
|
|
"Out": ["transpose23_output"],
|
|
"XShape": ["transpose23_output_xshape"],
|
|
},
|
|
"op_attrs": dics[11],
|
|
},
|
|
{
|
|
"op_type": "scale",
|
|
"op_inputs": {
|
|
"X": ["transpose23_output"],
|
|
},
|
|
"op_outputs": {"Out": ["scale_output"]},
|
|
"op_attrs": dics[12],
|
|
},
|
|
{
|
|
"op_type": "matmul",
|
|
"op_inputs": {
|
|
"X": ["scale_output"],
|
|
"Y": ["transpose22_output"],
|
|
},
|
|
"op_outputs": {"Out": ["matmul1_output"]},
|
|
"op_attrs": dics[13],
|
|
},
|
|
{
|
|
"op_type": "elementwise_add",
|
|
"op_inputs": {
|
|
"X": ["matmul1_output"],
|
|
"Y": ["input_data2"],
|
|
},
|
|
"op_outputs": {
|
|
"Out": ["elementwise_add4_output"]
|
|
},
|
|
"op_attrs": dics[14],
|
|
},
|
|
{
|
|
"op_type": "softmax",
|
|
"op_inputs": {
|
|
"X": ["elementwise_add4_output"]
|
|
},
|
|
"op_outputs": {"Out": ["softmax_output"]},
|
|
"op_attrs": dics[15],
|
|
},
|
|
{
|
|
"op_type": "dropout",
|
|
"op_inputs": {
|
|
"X": ["softmax_output"],
|
|
},
|
|
"op_outputs": {"Out": ["dropout3_output"]},
|
|
"op_attrs": dics[16],
|
|
},
|
|
{
|
|
"op_type": "matmul",
|
|
"op_inputs": {
|
|
"X": ["dropout3_output"],
|
|
"Y": ["transpose21_output"],
|
|
},
|
|
"op_outputs": {"Out": ["matmul2_output"]},
|
|
"op_attrs": dics[17],
|
|
},
|
|
{
|
|
"op_type": "transpose2",
|
|
"op_inputs": {"X": ["matmul2_output"]},
|
|
"op_outputs": {
|
|
"Out": ["transpose24_output"],
|
|
"XShape": ["transpose24_output_xshape"],
|
|
},
|
|
"op_attrs": dics[18],
|
|
},
|
|
{
|
|
"op_type": "reshape2",
|
|
"op_inputs": {"X": ["transpose24_output"]},
|
|
"op_outputs": {
|
|
"Out": ["reshape24_output"],
|
|
"XShape": ["reshape24_output_xshape"],
|
|
},
|
|
"op_attrs": dics[19],
|
|
},
|
|
# In order to fuse ops with
|
|
# multihead_matmul_fuse_pass_v2, the last op
|
|
# must be mul.
|
|
{
|
|
"op_type": "mul",
|
|
"op_inputs": {
|
|
"X": ["reshape24_output"],
|
|
"Y": ["mul4_weight"],
|
|
},
|
|
"op_outputs": {"Out": ["mul4_output"]},
|
|
"op_attrs": dics[20],
|
|
},
|
|
]
|
|
ops = self.generate_op_config(ops_config)
|
|
|
|
program_config = ProgramConfig(
|
|
ops=ops,
|
|
weights={
|
|
"mul1_weight": TensorConfig(
|
|
data_gen=partial(
|
|
generate_weight, (768, 768)
|
|
)
|
|
),
|
|
"mul2_weight": TensorConfig(
|
|
data_gen=partial(
|
|
generate_weight, (768, 768)
|
|
)
|
|
),
|
|
"mul3_weight": TensorConfig(
|
|
data_gen=partial(
|
|
generate_weight, (768, 768)
|
|
)
|
|
),
|
|
"mul4_weight": TensorConfig(
|
|
data_gen=partial(
|
|
generate_weight, (768, 768)
|
|
)
|
|
),
|
|
"elementwise_add1_weight": TensorConfig(
|
|
data_gen=partial(
|
|
generate_weight, (768,)
|
|
)
|
|
),
|
|
"elementwise_add2_weight": TensorConfig(
|
|
data_gen=partial(
|
|
generate_weight, (768, 768)
|
|
)
|
|
),
|
|
"elementwise_add3_weight": TensorConfig(
|
|
data_gen=partial(
|
|
generate_weight, (768,)
|
|
)
|
|
),
|
|
},
|
|
inputs={
|
|
"input_data1": TensorConfig(
|
|
data_gen=partial(
|
|
generate_input, (batch, dim1, 768)
|
|
)
|
|
),
|
|
"input_data2": TensorConfig(
|
|
data_gen=partial(
|
|
generate_input, input2_shape
|
|
)
|
|
),
|
|
},
|
|
outputs=["mul4_output"],
|
|
)
|
|
|
|
yield program_config
|
|
|
|
def sample_predictor_configs(
|
|
self, program_config
|
|
) -> tuple[paddle_infer.Config, list[int], float]:
|
|
def generate_dynamic_shape(attrs):
|
|
# The last dim of input1 and input2 should be static.
|
|
self.dynamic_shape.min_input_shape = {
|
|
"input_data1": [1, 8, 768],
|
|
"input_data2": [1, 1, 1, 128],
|
|
"reshape24_output": [1, 128, 768],
|
|
}
|
|
self.dynamic_shape.max_input_shape = {
|
|
"input_data1": [16, 512, 768],
|
|
"input_data2": [16, 256, 512, 128],
|
|
"reshape24_output": [1, 128, 768],
|
|
}
|
|
self.dynamic_shape.opt_input_shape = {
|
|
"input_data1": [8, 128, 768],
|
|
"input_data2": [8, 32, 64, 128],
|
|
"reshape24_output": [1, 128, 768],
|
|
}
|
|
|
|
def clear_dynamic_shape():
|
|
self.dynamic_shape.max_input_shape = {}
|
|
self.dynamic_shape.min_input_shape = {}
|
|
self.dynamic_shape.opt_input_shape = {}
|
|
|
|
attrs = [
|
|
program_config.ops[i].attrs for i in range(len(program_config.ops))
|
|
]
|
|
|
|
# for dynamic_shape
|
|
generate_dynamic_shape(attrs)
|
|
self.trt_param.precision = paddle_infer.PrecisionType.Float32
|
|
program_config.set_input_type(np.float32)
|
|
self.trt_param.workspace_size = 2013265920
|
|
yield self.create_inference_config(), (1, 3), (1e-5, 1e-5)
|
|
self.trt_param.precision = paddle_infer.PrecisionType.Half
|
|
program_config.set_input_type(np.float16)
|
|
yield self.create_inference_config(), (1, 3), (1e-2, 1e-2)
|
|
|
|
def test(self):
|
|
self.run_test()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|