wehub-resource-sync
532 lines
19 KiB
Python
532 lines
19 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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from typing import Any
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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 TrtConvertReshapeTest(TrtLayerAutoScanTest):
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def is_program_valid(self, program_config: ProgramConfig) -> bool:
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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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if self.dims == 1:
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if len(attrs[0]['shape']) != 1:
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return False
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# To test if the shape contains 0
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if len(attrs[0]['shape']) == 3:
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if attrs[0]['shape'][1] == 0:
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if self.dims != 3:
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return False
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if len(attrs[0]['shape']) == 4:
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if attrs[0]['shape'][2] == 0:
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if self.dims != 4:
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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(attrs: list[dict[str, Any]]):
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if self.dims == 4:
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self.input_shape = [1, 2, 4, 6]
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return np.ones([1, 2, 4, 6]).astype(np.float32)
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elif self.dims == 3:
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self.input_shape = [1, 8, 6]
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return np.ones([1, 8, 6]).astype(np.float32)
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elif self.dims == 2:
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self.input_shape = [1, 48]
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return np.ones([1, 48]).astype(np.float32)
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elif self.dims == 1:
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self.input_shape = [48]
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return np.ones([48]).astype(np.float32)
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def generate_weight1(attrs: list[dict[str, Any]]):
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return np.array([1, 48]).astype(np.int32)
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def generate_shapeT1_data(attrs: list[dict[str, Any]]):
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return np.array([2]).astype(np.int32)
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def generate_shapeT2_data(attrs: list[dict[str, Any]]):
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return np.array([24]).astype(np.int32)
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for dims in [4, 3, 2, 1]:
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for shape in [
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# [1, 6, 8],
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# [1, 2, 4, 6],
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# [1, 1, 0, 12],
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# [1, 0, 6],
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[1, -1, 12],
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[2, -1],
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[3, 16],
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[3, 4, 4],
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[48],
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[-1, 48],
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]:
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dics = [
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{
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"shape": shape,
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},
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]
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self.dims = dims
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dics_input = [{"X": ["reshape_input"]}]
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ops_config = [
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{
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"op_type": "reshape2",
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"op_inputs": dics_input[0],
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"op_outputs": {"Out": ["reshape_out"]},
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"op_attrs": dics[0],
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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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"reshape_input": TensorConfig(
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data_gen=partial(generate_input1, dics)
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)
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},
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outputs=["reshape_out"],
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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 == 4:
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self.dynamic_shape.min_input_shape = {"reshape_input": [1, 2, 4, 6]}
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self.dynamic_shape.max_input_shape = {"reshape_input": [1, 2, 4, 6]}
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self.dynamic_shape.opt_input_shape = {"reshape_input": [1, 2, 4, 6]}
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elif self.dims == 3:
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self.dynamic_shape.min_input_shape = {"reshape_input": [1, 8, 6]}
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self.dynamic_shape.max_input_shape = {"reshape_input": [1, 8, 6]}
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self.dynamic_shape.opt_input_shape = {"reshape_input": [1, 8, 6]}
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elif self.dims == 2:
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self.dynamic_shape.min_input_shape = {"reshape_input": [1, 48]}
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self.dynamic_shape.max_input_shape = {"reshape_input": [1, 48]}
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self.dynamic_shape.opt_input_shape = {"reshape_input": [1, 48]}
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elif self.dims == 1:
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self.dynamic_shape.min_input_shape = {"reshape_input": [48]}
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self.dynamic_shape.max_input_shape = {"reshape_input": [48]}
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self.dynamic_shape.opt_input_shape = {"reshape_input": [48]}
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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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# in static shape mode, here is consistent with op_teller.cc
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if not dynamic_shape:
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if attrs[0]['shape'][0] == 0:
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return 1, 2
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elif len(attrs[0]['shape']) == 1:
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return 0, 3
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elif np.prod(attrs[0]['shape'][1:]) == np.prod(
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self.input_shape[1:]
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):
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return 1, 2
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else:
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return 0, 3
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return 1, 2
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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-3,
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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-3,
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)
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def add_skip_trt_case(self):
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pass
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def test(self):
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self.add_skip_trt_case()
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self.run_test(run_pir=True)
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# reshape having three inputs.
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class TrtConvertReshapeTest2(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_input1(attrs: list[dict[str, Any]]):
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if self.dims == 4:
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return np.random.random([1, 2, 4, 6]).astype(np.float32)
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elif self.dims == 3:
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return np.random.random([1, 8, 6]).astype(np.float32)
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elif self.dims == 2:
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return np.random.random([1, 48]).astype(np.float32)
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elif self.dims == 1:
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return np.random.random([48]).astype(np.float32)
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for dims in [4, 3, 2, 1]:
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for shape in [[-1, 48]]:
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dics = [
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{
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"shape": shape,
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},
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{},
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]
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self.dims = dims
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dics_input = [
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{
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"X": ["reshape_input"],
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"ShapeTensor": ["shapeT1_data", "shapeT2_data"],
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},
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]
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ops_config = [
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{
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"op_type": "fill_constant",
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"op_inputs": {},
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"op_outputs": {"Out": ["shapeT1_data"]},
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"op_attrs": {
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"dtype": 2,
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"str_value": "2",
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"value": 2,
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"shape": [1],
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},
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},
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{
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"op_type": "fill_constant",
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"op_inputs": {},
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"op_outputs": {"Out": ["shapeT2_data"]},
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"op_attrs": {
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"dtype": 2,
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"str_value": "24",
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"value": 24,
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"shape": [1],
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},
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},
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{
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"op_type": "reshape2",
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"op_inputs": dics_input[0],
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"op_outputs": {"Out": ["reshape_out"]},
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"op_attrs": dics[0],
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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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"reshape_input": TensorConfig(
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data_gen=partial(generate_input1, dics)
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)
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},
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outputs=["reshape_out"],
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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 == 4:
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self.dynamic_shape.min_input_shape = {"reshape_input": [1, 2, 4, 6]}
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self.dynamic_shape.max_input_shape = {"reshape_input": [1, 2, 4, 6]}
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self.dynamic_shape.opt_input_shape = {"reshape_input": [1, 2, 4, 6]}
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elif self.dims == 3:
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self.dynamic_shape.min_input_shape = {"reshape_input": [1, 8, 6]}
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self.dynamic_shape.max_input_shape = {"reshape_input": [1, 8, 6]}
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self.dynamic_shape.opt_input_shape = {"reshape_input": [1, 8, 6]}
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elif self.dims == 2:
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self.dynamic_shape.min_input_shape = {"reshape_input": [1, 48]}
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self.dynamic_shape.max_input_shape = {"reshape_input": [1, 48]}
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self.dynamic_shape.opt_input_shape = {"reshape_input": [1, 48]}
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elif self.dims == 1:
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self.dynamic_shape.min_input_shape = {"reshape_input": [48]}
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self.dynamic_shape.max_input_shape = {"reshape_input": [48]}
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self.dynamic_shape.opt_input_shape = {"reshape_input": [48]}
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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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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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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 self.create_inference_config(), (1, 2), 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, 2), 1e-3
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def add_skip_trt_case(self):
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pass
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def test(self):
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self.add_skip_trt_case()
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self.run_test(run_pir=True)
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# reshape having 2 inputs.
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class TrtConvertReshapeTest3(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_input1(attrs: list[dict[str, Any]]):
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if self.dims == 4:
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return np.random.random([1, 2, 12, 6]).astype(np.float32)
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elif self.dims == 3:
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return np.random.random([1, 8, 18]).astype(np.float32)
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elif self.dims == 2:
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return np.random.random([1, 144]).astype(np.float32)
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elif self.dims == 1:
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return np.random.random([144]).astype(np.float32)
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for dims in [4, 3, 2, 1]:
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for shape in [[-1, 144]]:
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dics = [
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{
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"shape": shape,
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},
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{},
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]
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self.dims = dims
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dics_input = [
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{
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"X": ["reshape_input"],
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"shape_data": ["shape_data"],
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},
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]
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ops_config = [
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{
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"op_type": "fill_constant",
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"op_inputs": {},
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"op_outputs": {"Out": ["shape_data"]},
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"op_attrs": {
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"dtype": 2,
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"str_value": "12",
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"shape": [2],
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},
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},
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{
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"op_type": "reshape2",
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"op_inputs": dics_input[0],
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"op_outputs": {"Out": ["reshape_out"]},
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"op_attrs": dics[0],
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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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"reshape_input": TensorConfig(
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data_gen=partial(generate_input1, dics)
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)
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},
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outputs=["reshape_out"],
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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 == 4:
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self.dynamic_shape.min_input_shape = {
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"reshape_input": [1, 2, 12, 6]
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}
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self.dynamic_shape.max_input_shape = {
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"reshape_input": [4, 2, 12, 6]
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}
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self.dynamic_shape.opt_input_shape = {
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"reshape_input": [1, 2, 12, 6]
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}
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elif self.dims == 3:
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self.dynamic_shape.min_input_shape = {"reshape_input": [1, 8, 18]}
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self.dynamic_shape.max_input_shape = {"reshape_input": [4, 8, 18]}
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self.dynamic_shape.opt_input_shape = {"reshape_input": [1, 8, 18]}
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elif self.dims == 2:
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self.dynamic_shape.min_input_shape = {"reshape_input": [1, 144]}
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self.dynamic_shape.max_input_shape = {"reshape_input": [4, 144]}
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self.dynamic_shape.opt_input_shape = {"reshape_input": [1, 144]}
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elif self.dims == 1:
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self.dynamic_shape.min_input_shape = {"reshape_input": [144]}
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self.dynamic_shape.max_input_shape = {"reshape_input": [144]}
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self.dynamic_shape.opt_input_shape = {"reshape_input": [144]}
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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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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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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 self.create_inference_config(), (1, 2), 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, 2), 1e-3
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def add_skip_trt_case(self):
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pass
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def test(self):
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self.add_skip_trt_case()
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self.run_test(run_pir=True)
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class TrtConvertReshapeZeroDimsTest(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_input1(attrs: list[dict[str, Any]]):
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if self.dims > 0:
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self.input_shape = [1] * self.dims
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return np.random.random(self.input_shape).astype(np.float32)
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elif self.dims == 0:
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self.input_shape = []
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return np.random.random([]).astype(np.float32)
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for dims in [0, 1, 2, 3]:
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for shape in [
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[1],
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[1, 1],
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]:
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dics = [
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{
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"shape": shape,
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},
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]
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self.dims = dims
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dics_input = [{"X": ["reshape_input"]}]
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ops_config = [
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{
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"op_type": "reshape2",
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"op_inputs": dics_input[0],
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"op_outputs": {"Out": ["reshape_out"]},
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"op_attrs": dics[0],
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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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"reshape_input": TensorConfig(
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data_gen=partial(generate_input1, dics)
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)
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},
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outputs=["reshape_out"],
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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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self.dynamic_shape.min_input_shape = {"reshape_input": self.input_shape}
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self.dynamic_shape.max_input_shape = {"reshape_input": self.input_shape}
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self.dynamic_shape.opt_input_shape = {"reshape_input": self.input_shape}
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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 = {}
|
|
self.dynamic_shape.max_input_shape = {}
|
|
self.dynamic_shape.opt_input_shape = {}
|
|
|
|
def generate_trt_nodes_num(attrs, dynamic_shape):
|
|
# only test dynamic shape mode
|
|
return 1, 2
|
|
|
|
attrs = [
|
|
program_config.ops[i].attrs for i in range(len(program_config.ops))
|
|
]
|
|
|
|
# for dynamic_shape
|
|
self.generate_dynamic_shape(attrs)
|
|
self.trt_param.precision = paddle_infer.PrecisionType.Float32
|
|
program_config.set_input_type(np.float32)
|
|
yield (
|
|
self.create_inference_config(),
|
|
generate_trt_nodes_num(attrs, True),
|
|
1e-5,
|
|
)
|
|
self.trt_param.precision = paddle_infer.PrecisionType.Half
|
|
program_config.set_input_type(np.float16)
|
|
yield (
|
|
self.create_inference_config(),
|
|
generate_trt_nodes_num(attrs, True),
|
|
1e-3,
|
|
)
|
|
|
|
def add_skip_trt_case(self):
|
|
pass
|
|
|
|
def test(self):
|
|
self.add_skip_trt_case()
|
|
self.run_test(run_pir=True)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|