wehub-resource-sync
638 lines
26 KiB
Python
638 lines
26 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 SkipReasons, TrtLayerAutoScanTest
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import paddle.inference as paddle_infer
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class TrtConvertFlashMultiHeadMatmulTest(TrtLayerAutoScanTest):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.optimization_level = 5
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def is_program_valid(self, program_config: ProgramConfig) -> bool:
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ver = paddle_infer.get_trt_compile_version()
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if ver[0] * 1000 + ver[1] * 100 + ver[2] * 10 < 8520:
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return False
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return True
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def sample_program_configs(self):
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def generate_input1(batch, dim1):
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return (
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np.random.rand(batch, dim1, 320).astype(np.float32) / 10 - 0.05
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)
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def generate_weight1():
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return np.random.rand(320, 320).astype(np.float32) / 10 - 0.05
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for batch in [1, 2]:
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self.batch = batch
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for reshape_shape in [[0, 0, 8, 40]]:
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for dim1 in [4096]:
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dics = [
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{"trans_x": False, "trans_y": False}, # 0,matmul_v2_q
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{"shape": reshape_shape}, # 1,reshape_q
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{
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"axis": [0, 2, 1, 3],
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"data_format": "AnyLayout",
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}, # 2,trans_q
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{"trans_x": False, "trans_y": False}, # 3,matmul_v2_k
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{"shape": reshape_shape}, # 4,reshape_k
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{
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"axis": [0, 2, 1, 3],
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"data_format": "AnyLayout",
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}, # 5,trans_k
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{"trans_x": False, "trans_y": False}, # 6,matmul_v2_q
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{"shape": reshape_shape}, # 7,reshape_q
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{
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"axis": [0, 2, 1, 3],
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"data_format": "AnyLayout",
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}, # 8,trans_q
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{ # 9,matmul_qk
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"trans_x": False,
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"trans_y": True,
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},
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{ # 10,scale
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"scale": 0.15811388194561005,
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"bias": 0.0,
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"bias_after_scale": True,
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},
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{"axis": -1, "is_test": True}, # 11,softmax
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{"trans_x": False, "trans_y": False}, # 12,matmul_qkv
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{
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"axis": [0, 2, 1, 3],
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"data_format": "AnyLayout",
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}, # 13,trans_qkv
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{"shape": [0, 0, 320]}, # 14,reshape_qkv
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]
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ops_config = [
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{
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"op_type": "matmul_v2",
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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": "reshape2",
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"op_inputs": {
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"X": ["mul1_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[1],
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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[2],
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},
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{
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"op_type": "matmul_v2",
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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[3],
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},
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{
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"op_type": "reshape2",
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"op_inputs": {"X": ["mul2_output"]},
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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[4],
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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[5],
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},
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{
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"op_type": "matmul_v2",
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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[6],
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},
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{
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"op_type": "reshape2",
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"op_inputs": {"X": ["mul3_output"]},
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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[7],
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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[8],
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},
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{
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"op_type": "matmul_v2",
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"op_inputs": {
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"X": ["transpose21_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[9],
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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": ["matmul1_output"],
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},
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"op_outputs": {"Out": ["scale_output"]},
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"op_attrs": dics[10],
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},
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{
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"op_type": "softmax",
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"op_inputs": {"X": ["scale_output"]},
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"op_outputs": {"Out": ["softmax_output"]},
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"op_attrs": dics[11],
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},
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{
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"op_type": "matmul_v2",
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"op_inputs": {
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"X": ["softmax_output"],
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"Y": ["transpose23_output"],
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},
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"op_outputs": {"Out": ["matmul2_output"]},
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"op_attrs": dics[12],
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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[13],
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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[14],
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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(generate_weight1)
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),
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"mul2_weight": TensorConfig(
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data_gen=partial(generate_weight1)
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),
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"mul3_weight": TensorConfig(
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data_gen=partial(generate_weight1)
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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(generate_input1, batch, dim1)
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)
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},
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outputs=["reshape24_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, 4096, 320],
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}
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self.dynamic_shape.max_input_shape = {
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"input_data1": [16, 4096, 320],
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}
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self.dynamic_shape.opt_input_shape = {
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"input_data1": [2, 4096, 320],
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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 static_shape
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clear_dynamic_shape()
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self.trt_param.precision = paddle_infer.PrecisionType.Float32
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self.trt_param.workspace_size = 1 << 33
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yield self.create_inference_config(), (1, 2), (1e-5, 1e-5)
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self.trt_param.precision = paddle_infer.PrecisionType.Half
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yield self.create_inference_config(), (1, 2), (2e-2, 5e-3)
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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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self.trt_param.workspace_size = 1 << 33
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yield self.create_inference_config(), (1, 2), (1e-5, 1e-4)
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self.trt_param.precision = paddle_infer.PrecisionType.Half
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yield self.create_inference_config(), (1, 2), (2e-2, 5e-3)
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def add_skip_trt_case(self):
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def teller1(program_config, predictor_config):
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if self.dynamic_shape.min_input_shape == {}:
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return True
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return False
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self.add_skip_case(
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teller1,
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SkipReasons.TRT_NOT_IMPLEMENTED,
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"The flash attention trt oss plugin do not support static shape yet",
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)
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def teller2(program_config, predictor_config):
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if self.trt_param.precision == paddle_infer.PrecisionType.Float32:
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return True
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return False
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self.add_skip_case(
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teller2,
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SkipReasons.TRT_NOT_IMPLEMENTED,
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"The flash attention trt oss plugin do not support fp32 yet",
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)
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def teller3(program_config, predictor_config):
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if self.trt_param.precision == paddle_infer.PrecisionType.Int8:
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return True
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return False
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self.add_skip_case(
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teller3,
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SkipReasons.TRT_NOT_IMPLEMENTED,
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"The flash attention trt oss plugin do not support int8 yet.",
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)
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def test(self):
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self.add_skip_trt_case()
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self.run_test()
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class TrtConvertFlashMultiHeadMatmulWeightInputTest(TrtLayerAutoScanTest):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.optimization_level = 5
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def is_program_valid(self, program_config: ProgramConfig) -> bool:
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ver = paddle_infer.get_trt_compile_version()
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if ver[0] * 1000 + ver[1] * 100 + ver[2] * 10 < 8520:
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return False
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return True
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def sample_program_configs(self):
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def generate_input1(batch, dim1):
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return (
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np.random.rand(batch, dim1, 320).astype(np.float32) / 10 - 0.05
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)
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def generate_weight1():
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return np.random.rand(320, 320).astype(np.float32) / 10 - 0.05
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for batch in [1, 2]:
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self.batch = batch
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for reshape_shape in [[0, 0, 8, 40]]:
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for dim1 in [4096]:
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dics = [
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{"trans_x": False, "trans_y": False}, # 0,matmul_v2_q
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{"shape": reshape_shape}, # 1,reshape_q
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{
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"axis": [0, 2, 1, 3],
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"data_format": "AnyLayout",
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}, # 2,trans_q
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{"trans_x": False, "trans_y": False}, # 3,matmul_v2_k
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{"shape": reshape_shape}, # 4,reshape_k
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{
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"axis": [0, 2, 1, 3],
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"data_format": "AnyLayout",
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}, # 5,trans_k
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{"trans_x": False, "trans_y": False}, # 6,matmul_v2_q
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{"shape": reshape_shape}, # 7,reshape_q
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{
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"axis": [0, 2, 1, 3],
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"data_format": "AnyLayout",
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}, # 8,trans_q
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{ # 9,matmul_qk
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"trans_x": False,
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"trans_y": True,
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},
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{ # 10,scale
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"scale": 0.15811388194561005,
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"bias": 0.0,
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"bias_after_scale": True,
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},
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{"axis": -1, "is_test": True}, # 11,softmax
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{"trans_x": False, "trans_y": False}, # 12,matmul_qkv
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{
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"axis": [0, 2, 1, 3],
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"data_format": "AnyLayout",
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}, # 13,trans_qkv
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{"shape": [0, 0, 320]}, # 14,reshape_qkv
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]
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ops_config = [
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{
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"op_type": "matmul_v2",
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"op_inputs": {
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"X": ["input_data1"],
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"Y": ["weight_query"],
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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": "reshape2",
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"op_inputs": {
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"X": ["mul1_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[1],
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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[2],
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},
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{
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"op_type": "matmul_v2",
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"op_inputs": {
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"X": ["input_data1"],
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"Y": ["weight_key"],
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},
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"op_outputs": {"Out": ["mul2_output"]},
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"op_attrs": dics[3],
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},
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{
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"op_type": "reshape2",
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"op_inputs": {"X": ["mul2_output"]},
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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[4],
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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[5],
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},
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{
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"op_type": "matmul_v2",
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"op_inputs": {
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"X": ["input_data1"],
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"Y": ["weight_value"],
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},
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"op_outputs": {"Out": ["mul3_output"]},
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"op_attrs": dics[6],
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},
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{
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"op_type": "reshape2",
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"op_inputs": {"X": ["mul3_output"]},
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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[7],
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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[8],
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},
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{
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"op_type": "matmul_v2",
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"op_inputs": {
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"X": ["transpose21_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[9],
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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": ["matmul1_output"],
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},
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"op_outputs": {"Out": ["scale_output"]},
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"op_attrs": dics[10],
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},
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{
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"op_type": "softmax",
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"op_inputs": {"X": ["scale_output"]},
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"op_outputs": {"Out": ["softmax_output"]},
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"op_attrs": dics[11],
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},
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{
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"op_type": "matmul_v2",
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"op_inputs": {
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"X": ["softmax_output"],
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"Y": ["transpose23_output"],
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},
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"op_outputs": {"Out": ["matmul2_output"]},
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"op_attrs": dics[12],
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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[13],
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},
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{
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"op_type": "reshape2",
|
|
"op_inputs": {"X": ["transpose24_output"]},
|
|
"op_outputs": {
|
|
"Out": ["reshape24_output"],
|
|
"XShape": ["reshape24_output_xshape"],
|
|
},
|
|
"op_attrs": dics[14],
|
|
},
|
|
]
|
|
ops = self.generate_op_config(ops_config)
|
|
|
|
program_config = ProgramConfig(
|
|
ops=ops,
|
|
weights={},
|
|
inputs={
|
|
"input_data1": TensorConfig(
|
|
data_gen=partial(generate_input1, batch, dim1)
|
|
),
|
|
"weight_query": TensorConfig(
|
|
data_gen=partial(generate_weight1)
|
|
),
|
|
"weight_key": TensorConfig(
|
|
data_gen=partial(generate_weight1)
|
|
),
|
|
"weight_value": TensorConfig(
|
|
data_gen=partial(generate_weight1)
|
|
),
|
|
},
|
|
outputs=["reshape24_output"],
|
|
)
|
|
|
|
yield program_config
|
|
|
|
def sample_predictor_configs(
|
|
self, program_config
|
|
) -> tuple[paddle_infer.Config, list[int], float]:
|
|
def generate_dynamic_shape(attrs):
|
|
self.dynamic_shape.min_input_shape = {
|
|
"input_data1": [1, 4096, 320],
|
|
"weight_query": [320, 320],
|
|
"weight_key": [320, 320],
|
|
"weight_value": [320, 320],
|
|
}
|
|
self.dynamic_shape.max_input_shape = {
|
|
"input_data1": [16, 4096, 320],
|
|
"weight_query": [320, 320],
|
|
"weight_key": [320, 320],
|
|
"weight_value": [320, 320],
|
|
}
|
|
self.dynamic_shape.opt_input_shape = {
|
|
"input_data1": [2, 4096, 320],
|
|
"weight_query": [320, 320],
|
|
"weight_key": [320, 320],
|
|
"weight_value": [320, 320],
|
|
}
|
|
|
|
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 static_shape
|
|
clear_dynamic_shape()
|
|
self.trt_param.precision = paddle_infer.PrecisionType.Float32
|
|
program_config.set_input_type(np.float32)
|
|
self.trt_param.workspace_size = 1 << 33
|
|
yield self.create_inference_config(), (1, 5), (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, 5), (1e-3, 1e-3)
|
|
# 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 = 1 << 33
|
|
yield self.create_inference_config(), (1, 5), (1e-5, 1e-4)
|
|
self.trt_param.precision = paddle_infer.PrecisionType.Half
|
|
program_config.set_input_type(np.float16)
|
|
yield self.create_inference_config(), (1, 5), (1e-2, 1e-3)
|
|
|
|
def add_skip_trt_case(self):
|
|
def teller1(program_config, predictor_config):
|
|
if self.dynamic_shape.min_input_shape == {}:
|
|
return True
|
|
return False
|
|
|
|
self.add_skip_case(
|
|
teller1,
|
|
SkipReasons.TRT_NOT_IMPLEMENTED,
|
|
"The flash attention trt oss plugin do not support static shape yet",
|
|
)
|
|
|
|
def teller2(program_config, predictor_config):
|
|
if self.trt_param.precision == paddle_infer.PrecisionType.Float32:
|
|
return True
|
|
return False
|
|
|
|
self.add_skip_case(
|
|
teller2,
|
|
SkipReasons.TRT_NOT_IMPLEMENTED,
|
|
"The flash attention trt oss plugin do not support fp32 yet",
|
|
)
|
|
|
|
def teller3(program_config, predictor_config):
|
|
if self.trt_param.precision == paddle_infer.PrecisionType.Int8:
|
|
return True
|
|
return False
|
|
|
|
self.add_skip_case(
|
|
teller3,
|
|
SkipReasons.TRT_NOT_IMPLEMENTED,
|
|
"The flash attention trt oss plugin do not support int8 yet.",
|
|
)
|
|
|
|
def test(self):
|
|
self.add_skip_trt_case()
|
|
self.run_test()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|