wehub-resource-sync
237 lines
8.5 KiB
Python
237 lines
8.5 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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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 TrtConvertActivationTest(TrtLayerAutoScanTest):
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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 < 8415:
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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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self.trt_param.workspace_size = 1073741824
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def generate_input1(dims, batch):
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if dims == 1:
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return np.zeros(batch).astype(np.float32)
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elif dims == 2:
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return np.ones((batch, 4)).astype(np.float32)
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elif dims == 3:
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return np.ones((batch, 4, 6)).astype(np.float32)
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else:
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return np.ones((batch, 4, 6, 8)).astype(np.float32)
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def generate_input2(dims, batch):
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if dims == 1:
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return np.zeros(batch).astype(np.float32)
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elif dims == 2:
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return np.ones((batch, 4)).astype(np.float32)
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elif dims == 3:
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return np.ones((batch, 4, 6)).astype(np.float32)
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else:
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return np.ones((batch, 4, 6, 8)).astype(np.float32)
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def generate_input3(dims, batch):
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if dims == 1:
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return np.zeros(batch).astype(np.float32)
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elif dims == 2:
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return np.ones((batch, 4)).astype(np.float32)
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elif dims == 3:
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return np.ones((batch, 4, 6)).astype(np.float32)
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else:
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return np.ones((batch, 4, 6, 8)).astype(np.float32)
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for dims in [1, 2, 3, 4]:
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for batch in [1, 2]:
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self.dims = dims
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dics = [{}]
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ops_config = [
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{
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"op_type": "cast",
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"op_inputs": {"X": ["condition_data"]},
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"op_outputs": {"Out": ["condition_data_bool"]},
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"op_attrs": {"in_dtype": 5, "out_dtype": 0},
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"outputs_dtype": {"condition_data_bool": np.bool_},
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},
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{
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"op_type": "where",
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"op_inputs": {
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"Condition": ["condition_data_bool"],
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"X": ["input_x_data"],
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"Y": ["input_y_data"],
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},
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"op_outputs": {"Out": ["output_data"]},
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"op_attrs": dics[0],
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"outputs_dtype": {"condition_data_bool": np.bool_},
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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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inputs={
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"condition_data": TensorConfig(
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data_gen=partial(generate_input1, dims, batch)
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),
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"input_x_data": TensorConfig(
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data_gen=partial(generate_input2, dims, batch)
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),
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"input_y_data": TensorConfig(
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data_gen=partial(generate_input3, dims, batch)
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),
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},
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outputs=["output_data"],
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)
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yield program_config
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def generate_dynamic_shape(self, attrs):
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if self.dims == 1:
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self.dynamic_shape.min_input_shape = {
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"condition_data": [1],
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"input_x_data": [1],
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"input_y_data": [1],
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}
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self.dynamic_shape.max_input_shape = {
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"condition_data": [2],
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"input_x_data": [2],
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"input_y_data": [2],
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}
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self.dynamic_shape.opt_input_shape = {
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"condition_data": [1],
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"input_x_data": [1],
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"input_y_data": [1],
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}
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elif self.dims == 2:
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self.dynamic_shape.min_input_shape = {
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"condition_data": [1, 4],
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"input_x_data": [1, 4],
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"input_y_data": [1, 4],
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}
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self.dynamic_shape.max_input_shape = {
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"condition_data": [2, 4],
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"input_x_data": [2, 4],
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"input_y_data": [2, 4],
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}
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self.dynamic_shape.opt_input_shape = {
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"condition_data": [1, 4],
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"input_x_data": [1, 4],
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"input_y_data": [1, 4],
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}
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elif self.dims == 3:
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self.dynamic_shape.min_input_shape = {
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"condition_data": [1, 4, 6],
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"input_x_data": [1, 4, 6],
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"input_y_data": [1, 4, 6],
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}
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self.dynamic_shape.max_input_shape = {
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"condition_data": [2, 4, 6],
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"input_x_data": [2, 4, 6],
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"input_y_data": [2, 4, 6],
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}
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self.dynamic_shape.opt_input_shape = {
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"condition_data": [1, 4, 6],
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"input_x_data": [1, 4, 6],
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"input_y_data": [1, 4, 6],
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}
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elif self.dims == 4:
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self.dynamic_shape.min_input_shape = {
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"condition_data": [1, 4, 6, 8],
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"input_x_data": [1, 4, 6, 8],
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"input_y_data": [1, 4, 6, 8],
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}
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self.dynamic_shape.max_input_shape = {
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"condition_data": [2, 4, 6, 8],
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"input_x_data": [2, 4, 6, 8],
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"input_y_data": [2, 4, 6, 8],
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}
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self.dynamic_shape.opt_input_shape = {
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"condition_data": [1, 4, 6, 8],
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"input_x_data": [1, 4, 6, 8],
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"input_y_data": [1, 4, 6, 8],
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}
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return self.dynamic_shape
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def sample_predictor_configs(
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self, program_config, run_pir=False
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) -> tuple[paddle_infer.Config, list[int], float]:
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def clear_dynamic_shape():
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self.dynamic_shape.min_input_shape = {}
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self.dynamic_shape.max_input_shape = {}
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self.dynamic_shape.opt_input_shape = {}
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def generate_trt_nodes_num(attrs, dynamic_shape):
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if not dynamic_shape:
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return 0, 6
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return 1, 4
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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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if not run_pir:
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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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yield (
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self.create_inference_config(),
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generate_trt_nodes_num(attrs, False),
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1e-5,
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)
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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 (
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self.create_inference_config(),
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generate_trt_nodes_num(attrs, False),
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1e-5,
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)
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# for dynamic_shape
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self.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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yield (
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self.create_inference_config(),
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generate_trt_nodes_num(attrs, True),
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1e-5,
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)
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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 (
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self.create_inference_config(),
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generate_trt_nodes_num(attrs, True),
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1e-5,
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)
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def test(self):
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self.run_test(run_pir=True)
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if __name__ == "__main__":
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unittest.main()
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