wehub-resource-sync
1443 lines
55 KiB
Python
1443 lines
55 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import annotations
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import unittest
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from functools import partial
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import numpy as np
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from program_config import ProgramConfig, TensorConfig
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from trt_layer_auto_scan_test import TrtLayerAutoScanTest
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import paddle.inference as paddle_infer
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# This is the special test case with weight including batch dimension
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# I don't want to mess up the code written by others, so I wrote a class specifically
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class TrtConvertElementwiseTestOneInputSpecialCase0(TrtLayerAutoScanTest):
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def is_program_valid(self, program_config: ProgramConfig) -> bool:
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return True
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def sample_program_configs(self):
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def generate_input(shape, op_type):
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# elementwise_floordiv is integer only
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if op_type == "elementwise_floordiv":
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return np.random.randint(
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low=1, high=10000, size=shape, dtype=np.int32
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)
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elif op_type == "elementwise_mod":
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return np.random.uniform(low=0.1, high=1.0, size=shape).astype(
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np.float32
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)
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else:
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return np.random.random(shape).astype(np.float32)
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def generate_weight(op_type):
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if op_type == "elementwise_floordiv":
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return np.random.randint(
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low=1, high=10000, size=[1, 32, 1, 1], dtype=np.int32
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)
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elif op_type == "elementwise_mod":
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return np.random.uniform(
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low=0.1, high=1.0, size=[1, 32, 1, 1]
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).astype(np.float32)
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else:
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return np.random.randn(1, 32, 1, 1).astype(np.float32)
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for batch in [1, 4]:
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for shape in [[batch, 32, 16, 32]]:
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for op_type in [
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"elementwise_add",
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"elementwise_mul",
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"elementwise_sub",
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"elementwise_div",
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"elementwise_pow",
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"elementwise_min",
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"elementwise_max",
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"elementwise_floordiv",
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"elementwise_mod",
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]:
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for axis in [-1]:
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self.dims = len(shape)
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dics = [{"axis": axis}]
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ops_config = [
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{
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"op_type": op_type,
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"op_inputs": {
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"X": ["input_data"],
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"Y": ["weight"],
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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": {
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"output_data": (
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np.float32
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if op_type != "elementwise_floordiv"
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else np.int32
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)
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},
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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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"weight": TensorConfig(
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data_gen=partial(generate_weight, op_type)
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)
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},
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inputs={
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"input_data": TensorConfig(
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data_gen=partial(
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generate_input, shape, op_type
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)
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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):
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# The input.dims[1] must be equal to the weight's length.
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if self.dims == 4:
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self.dynamic_shape.min_input_shape = {"input_data": [1, 32, 4, 4]}
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self.dynamic_shape.max_input_shape = {"input_data": [4, 32, 32, 32]}
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self.dynamic_shape.opt_input_shape = {"input_data": [4, 32, 16, 32]}
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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.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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def generate_trt_nodes_num(attrs, dynamic_shape):
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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, 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, 1e-3),
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)
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# for dynamic_shape
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self.generate_dynamic_shape()
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self.trt_param.precision = paddle_infer.PrecisionType.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, 1e-5),
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)
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self.trt_param.precision = paddle_infer.PrecisionType.Half
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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, 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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# # This is the special test case
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class TrtConvertElementwiseTestOneInputSpecialCase1(TrtLayerAutoScanTest):
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def is_program_valid(self, program_config: ProgramConfig) -> bool:
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return True
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def sample_program_configs(self):
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def generate_input(shape, op_type):
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# elementwise_floordiv is integer only
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if op_type == "elementwise_floordiv":
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return np.random.randint(
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low=1, high=10000, size=shape, dtype=np.int32
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)
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if op_type == "elementwise_mod":
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return np.random.uniform(low=0.1, high=1.0, size=shape).astype(
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np.float32
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)
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else:
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return np.random.random(shape).astype(np.float32)
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def generate_weight(op_type):
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# elementwise_floordiv is integer only
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if op_type == "elementwise_floordiv":
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return np.random.randint(
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low=1, high=10000, size=[1], dtype=np.int32
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)
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elif op_type == "elementwise_mod":
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return np.random.uniform(low=0.1, high=1.0, size=[1]).astype(
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np.float32
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)
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else:
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return np.random.randn(1).astype(np.float32)
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for shape in [[4]]:
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for op_type in [
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"elementwise_add",
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"elementwise_mul",
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"elementwise_sub",
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"elementwise_div",
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"elementwise_pow",
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"elementwise_min",
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"elementwise_max",
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"elementwise_floordiv",
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"elementwise_mod",
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]:
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for axis in [-1]:
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self.dims = len(shape)
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dics = [{"axis": axis}]
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ops_config = [
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{
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"op_type": op_type,
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"op_inputs": {"X": ["input_data"], "Y": ["weight"]},
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"op_outputs": {"Out": ["output_data"]},
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"op_attrs": dics[0],
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"outputs_dtype": {
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"output_data": (
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np.float32
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if op_type != "elementwise_floordiv"
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else np.int32
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)
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},
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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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"weight": TensorConfig(
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data_gen=partial(generate_weight, op_type)
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)
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},
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inputs={
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"input_data": TensorConfig(
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data_gen=partial(generate_input, shape, op_type)
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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):
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self.dynamic_shape.min_input_shape = {"input_data": [1]}
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self.dynamic_shape.max_input_shape = {"input_data": [8]}
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self.dynamic_shape.opt_input_shape = {"input_data": [4]}
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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=True
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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.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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def generate_trt_nodes_num(attrs, dynamic_shape):
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if not dynamic_shape:
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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, 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, 1e-3),
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)
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# for dynamic_shape
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self.generate_dynamic_shape()
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self.trt_param.precision = paddle_infer.PrecisionType.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, 1e-5),
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)
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self.trt_param.precision = paddle_infer.PrecisionType.Half
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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, 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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class TrtConvertElementwiseTestOneInput(TrtLayerAutoScanTest):
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def is_program_valid(self, program_config: ProgramConfig) -> bool:
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return True
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def sample_program_configs(self):
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def generate_input(shape, op_type):
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# elementwise_floordiv is integer only
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if op_type == "elementwise_floordiv":
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return np.random.randint(
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low=1, high=10000, size=shape, dtype=np.int32
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)
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elif op_type == "elementwise_mod":
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return np.random.uniform(low=0.1, high=1.0, size=shape).astype(
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np.float32
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)
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else:
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return np.random.random(shape).astype(np.float32)
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def generate_weight(op_type):
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# elementwise_floordiv is integer only
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if op_type == "elementwise_floordiv":
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return np.random.randint(
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low=1, high=10000, size=[32], dtype=np.int32
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)
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elif op_type == "elementwise_mod":
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return np.random.uniform(low=0.1, high=1.0, size=[32]).astype(
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np.float32
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)
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else:
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return np.random.randn(32).astype(np.float32)
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for batch in [1, 4]:
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for shape in [
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[32],
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[batch, 32],
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[batch, 32, 32],
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[batch, 32, 4, 32],
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]:
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for op_type in [
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"elementwise_add",
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"elementwise_mul",
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"elementwise_sub",
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"elementwise_div",
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"elementwise_pow",
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"elementwise_min",
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"elementwise_max",
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"elementwise_floordiv",
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"elementwise_mod",
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]:
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for axis in [-1 if len(shape) == 1 else 1]:
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self.dims = len(shape)
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dics = [{"axis": axis}]
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ops_config = [
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{
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"op_type": op_type,
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"op_inputs": {
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"X": ["input_data"],
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"Y": ["weight"],
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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": {
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"output_data": (
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np.float32
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if op_type != "elementwise_floordiv"
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else np.int32
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)
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},
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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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"weight": TensorConfig(
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data_gen=partial(generate_weight, op_type)
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)
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},
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inputs={
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"input_data": TensorConfig(
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data_gen=partial(
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generate_input, shape, op_type
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)
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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):
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# The input.dims[1] must be equal to the weight's length.
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if self.dims == 1:
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self.dynamic_shape.min_input_shape = {"input_data": [32]}
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self.dynamic_shape.max_input_shape = {"input_data": [32]}
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self.dynamic_shape.opt_input_shape = {"input_data": [32]}
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elif self.dims == 2:
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self.dynamic_shape.min_input_shape = {"input_data": [1, 32]}
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self.dynamic_shape.max_input_shape = {"input_data": [4, 32]}
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self.dynamic_shape.opt_input_shape = {"input_data": [2, 32]}
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elif self.dims == 3:
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self.dynamic_shape.min_input_shape = {"input_data": [1, 32, 32]}
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self.dynamic_shape.max_input_shape = {"input_data": [4, 32, 32]}
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self.dynamic_shape.opt_input_shape = {"input_data": [2, 32, 32]}
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elif self.dims == 4:
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self.dynamic_shape.min_input_shape = {"input_data": [1, 32, 4, 32]}
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self.dynamic_shape.max_input_shape = {"input_data": [4, 32, 32, 32]}
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self.dynamic_shape.opt_input_shape = {"input_data": [4, 32, 16, 32]}
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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.max_input_shape = {}
|
|
self.dynamic_shape.min_input_shape = {}
|
|
self.dynamic_shape.opt_input_shape = {}
|
|
|
|
def generate_trt_nodes_num(attrs, dynamic_shape):
|
|
if self.dims == 1 and not dynamic_shape:
|
|
return 0, 3
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|
return 1, 2
|
|
|
|
attrs = [
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program_config.ops[i].attrs for i in range(len(program_config.ops))
|
|
]
|
|
|
|
# for static_shape
|
|
clear_dynamic_shape()
|
|
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, 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(),
|
|
generate_trt_nodes_num(attrs, False),
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(1e-3, 1e-3),
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|
)
|
|
|
|
# for dynamic_shape
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|
self.generate_dynamic_shape()
|
|
self.trt_param.precision = paddle_infer.PrecisionType.Float32
|
|
yield (
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self.create_inference_config(),
|
|
generate_trt_nodes_num(attrs, True),
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(1e-5, 1e-5),
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)
|
|
self.trt_param.precision = paddle_infer.PrecisionType.Half
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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, 1e-3),
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)
|
|
|
|
def add_skip_trt_case(self):
|
|
pass
|
|
|
|
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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|
|
|
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class TrtConvertElementwiseTestTwoInputWithoutBroadcast(TrtLayerAutoScanTest):
|
|
def is_program_valid(self, program_config: ProgramConfig) -> bool:
|
|
return True
|
|
|
|
def sample_program_configs(self):
|
|
def generate_input(shape, op_type):
|
|
# elementwise_floordiv is integer only
|
|
if op_type == "elementwise_floordiv":
|
|
return np.random.randint(
|
|
low=1, high=10000, size=shape, dtype=np.int32
|
|
)
|
|
elif op_type == "elementwise_mod":
|
|
return np.random.uniform(low=0.1, high=1.0, size=shape).astype(
|
|
np.float32
|
|
)
|
|
else:
|
|
return np.random.random(shape).astype(np.float32)
|
|
|
|
for shape in [[4], [4, 32], [2, 32, 16], [1, 8, 16, 32]]:
|
|
for op_type in [
|
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"elementwise_add",
|
|
"elementwise_mul",
|
|
"elementwise_sub",
|
|
"elementwise_div",
|
|
"elementwise_pow",
|
|
"elementwise_min",
|
|
"elementwise_max",
|
|
"elementwise_floordiv",
|
|
"elementwise_mod",
|
|
]:
|
|
for axis in [0, -1]:
|
|
self.dims = len(shape)
|
|
dics = [{"axis": axis}]
|
|
ops_config = [
|
|
{
|
|
"op_type": op_type,
|
|
"op_inputs": {
|
|
"X": ["input_data1"],
|
|
"Y": ["input_data2"],
|
|
},
|
|
"op_outputs": {"Out": ["output_data"]},
|
|
"op_attrs": dics[0],
|
|
"outputs_dtype": {
|
|
"output_data": (
|
|
np.float32
|
|
if op_type != "elementwise_floordiv"
|
|
else np.int32
|
|
)
|
|
},
|
|
}
|
|
]
|
|
ops = self.generate_op_config(ops_config)
|
|
|
|
program_config = ProgramConfig(
|
|
ops=ops,
|
|
weights={},
|
|
inputs={
|
|
"input_data1": TensorConfig(
|
|
data_gen=partial(generate_input, shape, op_type)
|
|
),
|
|
"input_data2": TensorConfig(
|
|
data_gen=partial(generate_input, shape, op_type)
|
|
),
|
|
},
|
|
outputs=["output_data"],
|
|
)
|
|
|
|
yield program_config
|
|
|
|
def generate_dynamic_shape(self):
|
|
if self.dims == 1:
|
|
self.dynamic_shape.min_input_shape = {
|
|
"input_data1": [1],
|
|
"input_data2": [1],
|
|
}
|
|
self.dynamic_shape.max_input_shape = {
|
|
"input_data1": [128],
|
|
"input_data2": [128],
|
|
}
|
|
self.dynamic_shape.opt_input_shape = {
|
|
"input_data1": [32],
|
|
"input_data2": [32],
|
|
}
|
|
elif self.dims == 2:
|
|
self.dynamic_shape.min_input_shape = {
|
|
"input_data1": [1, 4],
|
|
"input_data2": [1, 4],
|
|
}
|
|
self.dynamic_shape.max_input_shape = {
|
|
"input_data1": [128, 256],
|
|
"input_data2": [128, 256],
|
|
}
|
|
self.dynamic_shape.opt_input_shape = {
|
|
"input_data1": [32, 64],
|
|
"input_data2": [32, 64],
|
|
}
|
|
elif self.dims == 3:
|
|
self.dynamic_shape.min_input_shape = {
|
|
"input_data1": [1, 4, 4],
|
|
"input_data2": [1, 4, 4],
|
|
}
|
|
self.dynamic_shape.max_input_shape = {
|
|
"input_data1": [128, 128, 256],
|
|
"input_data2": [128, 128, 256],
|
|
}
|
|
self.dynamic_shape.opt_input_shape = {
|
|
"input_data1": [2, 32, 16],
|
|
"input_data2": [2, 32, 16],
|
|
}
|
|
elif self.dims == 4:
|
|
self.dynamic_shape.min_input_shape = {
|
|
"input_data1": [1, 4, 4, 4],
|
|
"input_data2": [1, 4, 4, 4],
|
|
}
|
|
self.dynamic_shape.max_input_shape = {
|
|
"input_data1": [8, 128, 64, 128],
|
|
"input_data2": [8, 128, 64, 128],
|
|
}
|
|
self.dynamic_shape.opt_input_shape = {
|
|
"input_data1": [2, 64, 32, 32],
|
|
"input_data2": [2, 64, 32, 32],
|
|
}
|
|
return self.dynamic_shape
|
|
|
|
def sample_predictor_configs(
|
|
self, program_config, run_pir=False
|
|
) -> tuple[paddle_infer.Config, list[int], float]:
|
|
def clear_dynamic_shape():
|
|
self.dynamic_shape.max_input_shape = {}
|
|
self.dynamic_shape.min_input_shape = {}
|
|
self.dynamic_shape.opt_input_shape = {}
|
|
|
|
def generate_trt_nodes_num(attrs, dynamic_shape):
|
|
if self.dims == 1 and not dynamic_shape:
|
|
return 0, 4
|
|
return 1, 3
|
|
|
|
attrs = [
|
|
program_config.ops[i].attrs for i in range(len(program_config.ops))
|
|
]
|
|
|
|
# for static_shape
|
|
clear_dynamic_shape()
|
|
if not run_pir:
|
|
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, False),
|
|
(1e-5, 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, False),
|
|
(1e-3, 1e-3),
|
|
)
|
|
|
|
# for dynamic_shape
|
|
self.generate_dynamic_shape()
|
|
self.trt_param.precision = paddle_infer.PrecisionType.Float32
|
|
yield self.create_inference_config(), (1, 3), (1e-5, 1e-5)
|
|
self.trt_param.precision = paddle_infer.PrecisionType.Half
|
|
yield self.create_inference_config(), (1, 3), (1e-3, 1e-3)
|
|
|
|
def add_skip_trt_case(self):
|
|
pass
|
|
|
|
def test(self):
|
|
self.add_skip_trt_case()
|
|
self.run_test(run_pir=True)
|
|
|
|
|
|
class TrtConvertElementwiseTestTwoInputWithBroadcast(TrtLayerAutoScanTest):
|
|
def is_program_valid(self, program_config: ProgramConfig) -> bool:
|
|
inputs = program_config.inputs
|
|
if len(inputs['input_data1'].shape) != len(inputs['input_data2'].shape):
|
|
return False
|
|
|
|
return True
|
|
|
|
def sample_program_configs(self):
|
|
def generate_input(shape, op_type):
|
|
# elementwise_floordiv is integer only
|
|
if op_type == "elementwise_floordiv":
|
|
return np.random.randint(
|
|
low=1, high=10000, size=shape, dtype=np.int32
|
|
)
|
|
elif op_type == "elementwise_mod":
|
|
return np.random.uniform(low=0.1, high=1.0, size=shape).astype(
|
|
np.float32
|
|
)
|
|
else:
|
|
return np.random.random(shape).astype(np.float32)
|
|
|
|
input1_shape_list = [[4, 32], [2, 4, 32], [4, 2, 4, 32]]
|
|
input2_shape1_list = [[32], [4, 32], [2, 4, 32]]
|
|
input2_shape2_list = [[4, 1], [2, 4, 1], [4, 2, 4, 1]]
|
|
input2_shape3_list = [[32], [2, 1, 1], [4, 2, 1, 32]]
|
|
input2_shape4_list = [[32], [4, 32], [4, 1, 4, 32]]
|
|
input2_shape5_list = [[32], [2, 1, 32], [4, 1, 1, 32]]
|
|
input2_shape6_list = [[1, 32], [1, 32], [1, 1, 1, 32]]
|
|
input2_shape_list = [
|
|
input2_shape1_list,
|
|
input2_shape2_list,
|
|
input2_shape3_list,
|
|
input2_shape4_list,
|
|
input2_shape5_list,
|
|
input2_shape6_list,
|
|
]
|
|
axis1_list = [[-1], [1, -1], [1, -1]]
|
|
axis2_list = [[-1], [0], [0]]
|
|
axis3_list = [[-1], [0], [0]]
|
|
axis4_list = [[-1], [-1], [0]]
|
|
axis5_list = [[-1, 1], [-1, 0], [-1, 0]]
|
|
axis6_list = [[-1, 0], [-1, 1], [-1, 0]]
|
|
axis_list = [
|
|
axis1_list,
|
|
axis2_list,
|
|
axis3_list,
|
|
axis4_list,
|
|
axis5_list,
|
|
axis6_list,
|
|
]
|
|
|
|
for i in range(3):
|
|
input1_shape = input1_shape_list[i]
|
|
for j in range(6):
|
|
input2_shape = input2_shape_list[j][i]
|
|
for op_type in [
|
|
"elementwise_add",
|
|
"elementwise_mul",
|
|
"elementwise_sub",
|
|
"elementwise_div",
|
|
"elementwise_pow",
|
|
"elementwise_min",
|
|
"elementwise_max",
|
|
"elementwise_floordiv",
|
|
"elementwise_mod",
|
|
]:
|
|
for axis in axis_list[j][i]:
|
|
self.shape1 = input1_shape
|
|
self.shape2 = input2_shape
|
|
dics = [{"axis": axis}]
|
|
ops_config = [
|
|
{
|
|
"op_type": op_type,
|
|
"op_inputs": {
|
|
"X": ["input_data1"],
|
|
"Y": ["input_data2"],
|
|
},
|
|
"op_outputs": {"Out": ["output_data"]},
|
|
"op_attrs": dics[0],
|
|
"outputs_dtype": {
|
|
"output_data": (
|
|
np.float32
|
|
if op_type != "elementwise_floordiv"
|
|
else np.int32
|
|
)
|
|
},
|
|
}
|
|
]
|
|
ops = self.generate_op_config(ops_config)
|
|
|
|
program_config = ProgramConfig(
|
|
ops=ops,
|
|
weights={},
|
|
inputs={
|
|
"input_data1": TensorConfig(
|
|
data_gen=partial(
|
|
generate_input, input1_shape, op_type
|
|
)
|
|
),
|
|
"input_data2": TensorConfig(
|
|
data_gen=partial(
|
|
generate_input, input2_shape, op_type
|
|
)
|
|
),
|
|
},
|
|
outputs=["output_data"],
|
|
)
|
|
|
|
yield program_config
|
|
|
|
def generate_dynamic_shape(self):
|
|
max_shape = [
|
|
[128],
|
|
[128, 128],
|
|
[128, 128, 128],
|
|
[128, 128, 128, 128],
|
|
]
|
|
min_shape = [[1], [1, 1], [1, 1, 1], [1, 1, 1, 1]]
|
|
opt_shape = [[32], [32, 32], [32, 32, 32], [32, 32, 32, 32]]
|
|
|
|
self.dynamic_shape.min_input_shape = {
|
|
"input_data1": min_shape[len(self.shape1) - 1],
|
|
"input_data2": min_shape[len(self.shape2) - 1],
|
|
}
|
|
self.dynamic_shape.max_input_shape = {
|
|
"input_data1": max_shape[len(self.shape1) - 1],
|
|
"input_data2": max_shape[len(self.shape2) - 1],
|
|
}
|
|
self.dynamic_shape.opt_input_shape = {
|
|
"input_data1": opt_shape[len(self.shape1) - 1],
|
|
"input_data2": opt_shape[len(self.shape2) - 1],
|
|
}
|
|
return self.dynamic_shape
|
|
|
|
def sample_predictor_configs(
|
|
self, program_config, run_pir=False
|
|
) -> tuple[paddle_infer.Config, list[int], float]:
|
|
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()
|
|
if not run_pir:
|
|
if self.shape1[0] == self.shape2[0]:
|
|
self.trt_param.precision = paddle_infer.PrecisionType.Float32
|
|
program_config.set_input_type(np.float32)
|
|
yield self.create_inference_config(), (1, 3), (1e-5, 1e-5)
|
|
self.trt_param.precision = paddle_infer.PrecisionType.Half
|
|
program_config.set_input_type(np.float16)
|
|
yield self.create_inference_config(), (1, 3), (1e-3, 1e-3)
|
|
|
|
# for dynamic_shape
|
|
self.generate_dynamic_shape()
|
|
self.trt_param.precision = paddle_infer.PrecisionType.Float32
|
|
yield self.create_inference_config(), (1, 3), (1e-5, 1e-5)
|
|
self.trt_param.precision = paddle_infer.PrecisionType.Half
|
|
yield self.create_inference_config(), (1, 3), (1e-3, 1e-3)
|
|
|
|
def add_skip_trt_case(self):
|
|
pass
|
|
|
|
def test(self):
|
|
self.add_skip_trt_case()
|
|
self.run_test(run_pir=True)
|
|
|
|
|
|
class TrtConvertElementwiseTestOneInputCornerCase(TrtLayerAutoScanTest):
|
|
def is_program_valid(self, program_config: ProgramConfig) -> bool:
|
|
return True
|
|
|
|
def sample_program_configs(self):
|
|
def generate_input(shape, op_type):
|
|
# elementwise_floordiv is integer only
|
|
if op_type == "elementwise_floordiv":
|
|
return np.random.randint(
|
|
low=1, high=10000, size=shape, dtype=np.int32
|
|
)
|
|
elif op_type == "elementwise_mod":
|
|
return np.random.uniform(low=0.1, high=1.0, size=shape).astype(
|
|
np.float32
|
|
)
|
|
else:
|
|
return np.random.random(shape).astype(np.float32)
|
|
|
|
# use rand not randn to avoiding pow producing `NAN`
|
|
def generate_weight(op_type):
|
|
if op_type == "elementwise_floordiv":
|
|
return np.random.randint(
|
|
low=1, high=10000, size=[32], dtype=np.int32
|
|
)
|
|
elif op_type == "elementwise_mod":
|
|
return np.random.uniform(low=0.1, high=1.0, size=[32]).astype(
|
|
np.float32
|
|
)
|
|
else:
|
|
return np.random.rand(32).astype(np.float32)
|
|
|
|
for batch in [1, 2, 4]:
|
|
for shape in [
|
|
[32],
|
|
[batch, 32],
|
|
[batch, 32, 32],
|
|
[batch, 32, 16, 32],
|
|
]:
|
|
for op_type in [
|
|
"elementwise_add",
|
|
"elementwise_mul",
|
|
"elementwise_sub",
|
|
"elementwise_div",
|
|
"elementwise_pow",
|
|
"elementwise_min",
|
|
"elementwise_max",
|
|
"elementwise_floordiv",
|
|
"elementwise_mod",
|
|
]:
|
|
self.op_type = op_type
|
|
for axis in [-1 if len(shape) == 1 else 1]:
|
|
self.dims = len(shape)
|
|
dics = [{"axis": axis}]
|
|
ops_config = [
|
|
{
|
|
"op_type": op_type,
|
|
"op_inputs": {
|
|
"X": ["weight"],
|
|
"Y": ["input_data"],
|
|
},
|
|
"op_outputs": {"Out": ["output_data"]},
|
|
"op_attrs": dics[0],
|
|
"outputs_dtype": {
|
|
"output_data": (
|
|
np.float32
|
|
if op_type != "elementwise_floordiv"
|
|
else np.int32
|
|
)
|
|
},
|
|
}
|
|
]
|
|
ops = self.generate_op_config(ops_config)
|
|
|
|
program_config = ProgramConfig(
|
|
ops=ops,
|
|
weights={
|
|
"weight": TensorConfig(
|
|
data_gen=partial(generate_weight, op_type)
|
|
)
|
|
},
|
|
inputs={
|
|
"input_data": TensorConfig(
|
|
data_gen=partial(
|
|
generate_input, shape, op_type
|
|
)
|
|
),
|
|
},
|
|
outputs=["output_data"],
|
|
)
|
|
|
|
yield program_config
|
|
|
|
def generate_dynamic_shape(self):
|
|
# The input.dims[1] must be equal to the weight's length.
|
|
if self.dims == 1:
|
|
self.dynamic_shape.min_input_shape = {"input_data": [32]}
|
|
self.dynamic_shape.max_input_shape = {"input_data": [32]}
|
|
self.dynamic_shape.opt_input_shape = {"input_data": [32]}
|
|
elif self.dims == 2:
|
|
self.dynamic_shape.min_input_shape = {"input_data": [1, 32]}
|
|
self.dynamic_shape.max_input_shape = {"input_data": [4, 32]}
|
|
self.dynamic_shape.opt_input_shape = {"input_data": [2, 32]}
|
|
elif self.dims == 3:
|
|
self.dynamic_shape.min_input_shape = {"input_data": [1, 32, 32]}
|
|
self.dynamic_shape.max_input_shape = {"input_data": [4, 32, 32]}
|
|
self.dynamic_shape.opt_input_shape = {"input_data": [2, 32, 32]}
|
|
elif self.dims == 4:
|
|
self.dynamic_shape.min_input_shape = {"input_data": [1, 32, 4, 32]}
|
|
self.dynamic_shape.max_input_shape = {
|
|
"input_data": [4, 32, 128, 32]
|
|
}
|
|
self.dynamic_shape.opt_input_shape = {"input_data": [2, 32, 32, 32]}
|
|
return self.dynamic_shape
|
|
|
|
def sample_predictor_configs(
|
|
self, program_config, run_pir=False
|
|
) -> tuple[paddle_infer.Config, list[int], float]:
|
|
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()
|
|
if not run_pir:
|
|
self.trt_param.precision = paddle_infer.PrecisionType.Float32
|
|
program_config.set_input_type(np.float32)
|
|
yield self.create_inference_config(), (0, 3), (1e-5, 1e-5)
|
|
self.trt_param.precision = paddle_infer.PrecisionType.Half
|
|
program_config.set_input_type(np.float16)
|
|
yield self.create_inference_config(), (0, 3), (1e-3, 1e-3)
|
|
|
|
# for dynamic_shape
|
|
self.generate_dynamic_shape()
|
|
self.trt_param.precision = paddle_infer.PrecisionType.Float32
|
|
yield self.create_inference_config(), (1, 2), (1e-5, 1e-5)
|
|
self.trt_param.precision = paddle_infer.PrecisionType.Half
|
|
yield self.create_inference_config(), (1, 2), (1e-3, 1e-3)
|
|
|
|
def add_skip_trt_case(self):
|
|
pass
|
|
|
|
def test(self):
|
|
self.add_skip_trt_case()
|
|
self.run_test(run_pir=True)
|
|
|
|
|
|
# class TrtConvertElementwiseTestTwoInputSkipCase(TrtLayerAutoScanTest):
|
|
# def is_program_valid(self, program_config: ProgramConfig) -> bool:
|
|
# # if program_config.ops[0].type in "round":
|
|
# return True
|
|
|
|
# def sample_program_configs(self):
|
|
# def generate_input(shape, op_type):
|
|
# if op_type == "elementwise_pow":
|
|
# return np.random.randint(
|
|
# low=1, high=10000, size=shape, dtype=np.int32
|
|
# )
|
|
# # Paddle mul support bool and TensorRT not
|
|
# if op_type == "elementwise_mul":
|
|
# return np.random.random(shape).astype(np.bool_)
|
|
|
|
# for shape in [[4], [4, 32], [2, 32, 16], [1, 8, 16, 32]]:
|
|
# for op_type in [
|
|
# "elementwise_pow",
|
|
# "elementwise_mul",
|
|
# ]:
|
|
# for axis in [0, -1]:
|
|
# self.dims = len(shape)
|
|
# dics = [{"axis": axis}]
|
|
# ops_config = [
|
|
# {
|
|
# "op_type": op_type,
|
|
# "op_inputs": {
|
|
# "X": ["input_data1"],
|
|
# "Y": ["input_data2"],
|
|
# },
|
|
# "op_outputs": {"Out": ["output_data"]},
|
|
# "op_attrs": dics[0],
|
|
# "outputs_dtype": {
|
|
# "output_data": (
|
|
# np.int32
|
|
# if op_type == "elementwise_pow"
|
|
# else np.bool_
|
|
# )
|
|
# },
|
|
# }
|
|
# ]
|
|
# ops = self.generate_op_config(ops_config)
|
|
|
|
# program_config = ProgramConfig(
|
|
# ops=ops,
|
|
# weights={},
|
|
# inputs={
|
|
# "input_data1": TensorConfig(
|
|
# data_gen=partial(generate_input, shape, op_type)
|
|
# ),
|
|
# "input_data2": TensorConfig(
|
|
# data_gen=partial(generate_input, shape, op_type)
|
|
# ),
|
|
# },
|
|
# outputs=["output_data"],
|
|
# )
|
|
|
|
# yield program_config
|
|
|
|
# def generate_dynamic_shape(self):
|
|
# if self.dims == 1:
|
|
# self.dynamic_shape.min_input_shape = {
|
|
# "input_data1": [1],
|
|
# "input_data2": [1],
|
|
# }
|
|
# self.dynamic_shape.max_input_shape = {
|
|
# "input_data1": [128],
|
|
# "input_data2": [128],
|
|
# }
|
|
# self.dynamic_shape.opt_input_shape = {
|
|
# "input_data1": [32],
|
|
# "input_data2": [32],
|
|
# }
|
|
# elif self.dims == 2:
|
|
# self.dynamic_shape.min_input_shape = {
|
|
# "input_data1": [1, 4],
|
|
# "input_data2": [1, 4],
|
|
# }
|
|
# self.dynamic_shape.max_input_shape = {
|
|
# "input_data1": [128, 256],
|
|
# "input_data2": [128, 256],
|
|
# }
|
|
# self.dynamic_shape.opt_input_shape = {
|
|
# "input_data1": [32, 64],
|
|
# "input_data2": [32, 64],
|
|
# }
|
|
# elif self.dims == 3:
|
|
# self.dynamic_shape.min_input_shape = {
|
|
# "input_data1": [1, 4, 4],
|
|
# "input_data2": [1, 4, 4],
|
|
# }
|
|
# self.dynamic_shape.max_input_shape = {
|
|
# "input_data1": [128, 128, 256],
|
|
# "input_data2": [128, 128, 256],
|
|
# }
|
|
# self.dynamic_shape.opt_input_shape = {
|
|
# "input_data1": [2, 32, 16],
|
|
# "input_data2": [2, 32, 16],
|
|
# }
|
|
# elif self.dims == 4:
|
|
# self.dynamic_shape.min_input_shape = {
|
|
# "input_data1": [1, 4, 4, 4],
|
|
# "input_data2": [1, 4, 4, 4],
|
|
# }
|
|
# self.dynamic_shape.max_input_shape = {
|
|
# "input_data1": [8, 128, 64, 128],
|
|
# "input_data2": [8, 128, 64, 128],
|
|
# }
|
|
# self.dynamic_shape.opt_input_shape = {
|
|
# "input_data1": [2, 64, 32, 32],
|
|
# "input_data2": [2, 64, 32, 32],
|
|
# }
|
|
# return self.dynamic_shape
|
|
|
|
# def sample_predictor_configs(
|
|
# self, program_config, run_pir=False
|
|
# ) -> tuple[paddle_infer.Config, list[int], float]:
|
|
|
|
# def clear_dynamic_shape():
|
|
# self.dynamic_shape.max_input_shape = {}
|
|
# self.dynamic_shape.min_input_shape = {}
|
|
# self.dynamic_shape.opt_input_shape = {}
|
|
|
|
# def generate_trt_nodes_num(attrs, dynamic_shape):
|
|
# return 0, 4
|
|
|
|
# attrs = [
|
|
# program_config.ops[i].attrs for i in range(len(program_config.ops))
|
|
# ]
|
|
|
|
# # for static_shape
|
|
# clear_dynamic_shape()
|
|
# if not run_pir:
|
|
# 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, False
|
|
# ), (1e-5, 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, False
|
|
# ), (1e-3, 1e-3)
|
|
|
|
# # for dynamic_shape
|
|
# self.generate_dynamic_shape()
|
|
# self.trt_param.precision = paddle_infer.PrecisionType.Float32
|
|
# yield self.create_inference_config(), (0, 4), (1e-5, 1e-5)
|
|
# self.trt_param.precision = paddle_infer.PrecisionType.Half
|
|
# yield self.create_inference_config(), (0, 4), (1e-3, 1e-3)
|
|
|
|
# def add_skip_trt_case(self):
|
|
# pass
|
|
|
|
# def test(self):
|
|
# self.add_skip_trt_case()
|
|
# self.run_test(run_pir=True)
|
|
|
|
|
|
class TrtConvertPowOp(TrtLayerAutoScanTest):
|
|
def is_program_valid(self, program_config: ProgramConfig) -> bool:
|
|
return True
|
|
|
|
def sample_program_configs(self):
|
|
def generate_input(shape):
|
|
if len(shape) == 0:
|
|
return np.random.random([]).astype(np.float32)
|
|
return np.random.random(shape).astype(np.float32)
|
|
|
|
for batch in [1, 4]:
|
|
for shape in [
|
|
[],
|
|
[4],
|
|
[batch, 32],
|
|
[batch, 32, 32],
|
|
[batch, 32, 16, 32],
|
|
]:
|
|
for factor in [1.0, 2.0, -1.0, 0.5, -2]:
|
|
self.dims = len(shape)
|
|
dics = [{"factor": factor}]
|
|
ops_config = [
|
|
{
|
|
"op_type": "pow",
|
|
"op_inputs": {
|
|
"X": ["input_data"],
|
|
},
|
|
"op_outputs": {"Out": ["output_data"]},
|
|
"op_attrs": dics[0],
|
|
"outputs_dtype": {"output_data": np.float32},
|
|
}
|
|
]
|
|
ops = self.generate_op_config(ops_config)
|
|
|
|
program_config = ProgramConfig(
|
|
ops=ops,
|
|
weights={},
|
|
inputs={
|
|
"input_data": TensorConfig(
|
|
data_gen=partial(generate_input, shape)
|
|
),
|
|
},
|
|
outputs=["output_data"],
|
|
)
|
|
|
|
yield program_config
|
|
|
|
def generate_dynamic_shape(self):
|
|
if self.dims == 0:
|
|
self.dynamic_shape.min_input_shape = {"input_data": []}
|
|
self.dynamic_shape.max_input_shape = {"input_data": []}
|
|
self.dynamic_shape.opt_input_shape = {"input_data": []}
|
|
elif self.dims == 1:
|
|
self.dynamic_shape.min_input_shape = {"input_data": [1]}
|
|
self.dynamic_shape.max_input_shape = {"input_data": [8]}
|
|
self.dynamic_shape.opt_input_shape = {"input_data": [4]}
|
|
elif self.dims == 2:
|
|
self.dynamic_shape.min_input_shape = {"input_data": [1, 32]}
|
|
self.dynamic_shape.max_input_shape = {"input_data": [4, 32]}
|
|
self.dynamic_shape.opt_input_shape = {"input_data": [2, 32]}
|
|
elif self.dims == 3:
|
|
self.dynamic_shape.min_input_shape = {"input_data": [1, 32, 4]}
|
|
self.dynamic_shape.max_input_shape = {"input_data": [4, 32, 32]}
|
|
self.dynamic_shape.opt_input_shape = {"input_data": [2, 32, 32]}
|
|
elif self.dims == 4:
|
|
self.dynamic_shape.min_input_shape = {"input_data": [1, 32, 4, 4]}
|
|
self.dynamic_shape.max_input_shape = {"input_data": [4, 32, 32, 32]}
|
|
self.dynamic_shape.opt_input_shape = {"input_data": [4, 32, 16, 32]}
|
|
return self.dynamic_shape
|
|
|
|
def sample_predictor_configs(
|
|
self, program_config, run_pir=False
|
|
) -> tuple[paddle_infer.Config, list[int], float]:
|
|
def clear_dynamic_shape():
|
|
self.dynamic_shape.max_input_shape = {}
|
|
self.dynamic_shape.min_input_shape = {}
|
|
self.dynamic_shape.opt_input_shape = {}
|
|
|
|
def generate_trt_nodes_num(attrs, dynamic_shape):
|
|
if (self.dims == 1 or self.dims == 0) and not dynamic_shape:
|
|
return 0, 3
|
|
return 1, 2
|
|
|
|
attrs = [
|
|
program_config.ops[i].attrs for i in range(len(program_config.ops))
|
|
]
|
|
|
|
# for static_shape
|
|
clear_dynamic_shape()
|
|
if not run_pir:
|
|
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, False),
|
|
(1e-5, 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, False),
|
|
(1e-3, 1e-3),
|
|
)
|
|
|
|
# for dynamic_shape
|
|
self.generate_dynamic_shape()
|
|
self.trt_param.precision = paddle_infer.PrecisionType.Float32
|
|
yield (
|
|
self.create_inference_config(),
|
|
generate_trt_nodes_num(attrs, True),
|
|
(1e-5, 1e-5),
|
|
)
|
|
self.trt_param.precision = paddle_infer.PrecisionType.Half
|
|
yield (
|
|
self.create_inference_config(),
|
|
generate_trt_nodes_num(attrs, True),
|
|
(1e-3, 1e-3),
|
|
)
|
|
|
|
def add_skip_trt_case(self):
|
|
pass
|
|
|
|
def test(self):
|
|
self.add_skip_trt_case()
|
|
self.run_test(run_pir=True)
|
|
|
|
|
|
class TrtConvertElementwise0D(TrtLayerAutoScanTest):
|
|
def is_program_valid(self, program_config: ProgramConfig) -> bool:
|
|
return True
|
|
|
|
def sample_program_configs(self):
|
|
def generate_input(dims, op_type):
|
|
shape = []
|
|
if dims == 0:
|
|
shape = []
|
|
elif dims == 1:
|
|
shape = [8]
|
|
elif dims == 2:
|
|
shape = [1, 8]
|
|
elif dims == 3:
|
|
shape = [1, 8, 8]
|
|
else:
|
|
shape = [1, 8, 8, 8]
|
|
|
|
# elementwise_floordiv is integer only
|
|
if op_type == "elementwise_floordiv":
|
|
return np.random.randint(
|
|
low=1, high=10000, size=shape, dtype=np.int32
|
|
)
|
|
elif op_type == "elementwise_mod":
|
|
return np.random.uniform(low=0.1, high=1.0, size=shape).astype(
|
|
np.float32
|
|
)
|
|
else:
|
|
return np.random.random(shape).astype(np.float32)
|
|
|
|
for dims in [[0, 0], [0, 1], [0, 2], [1, 0], [2, 0]]:
|
|
for op_type in [
|
|
"elementwise_add",
|
|
"elementwise_mul",
|
|
"elementwise_sub",
|
|
"elementwise_div",
|
|
"elementwise_pow",
|
|
"elementwise_min",
|
|
"elementwise_max",
|
|
"elementwise_floordiv",
|
|
"elementwise_mod",
|
|
]:
|
|
for axis in [-1 if dims[0] == 1 or dims[0] == 0 else 1]:
|
|
self.dims = dims[0]
|
|
dics = [{"axis": axis}]
|
|
ops_config = [
|
|
{
|
|
"op_type": op_type,
|
|
"op_inputs": {
|
|
"X": ["input_data"],
|
|
"Y": ["weight"],
|
|
},
|
|
"op_outputs": {"Out": ["output_data"]},
|
|
"op_attrs": dics[0],
|
|
"outputs_dtype": {
|
|
"output_data": (
|
|
np.float32
|
|
if op_type != "elementwise_floordiv"
|
|
else np.int32
|
|
)
|
|
},
|
|
}
|
|
]
|
|
ops = self.generate_op_config(ops_config)
|
|
|
|
program_config = ProgramConfig(
|
|
ops=ops,
|
|
weights={
|
|
"weight": TensorConfig(
|
|
data_gen=partial(
|
|
generate_input, dims[1], op_type
|
|
)
|
|
)
|
|
},
|
|
inputs={
|
|
"input_data": TensorConfig(
|
|
data_gen=partial(
|
|
generate_input, dims[0], op_type
|
|
)
|
|
),
|
|
},
|
|
outputs=["output_data"],
|
|
)
|
|
|
|
yield program_config
|
|
|
|
def generate_dynamic_shape(self):
|
|
# The input.dims[1] must be equal to the weight's length.
|
|
if self.dims == 0:
|
|
self.dynamic_shape.min_input_shape = {"input_data": []}
|
|
self.dynamic_shape.max_input_shape = {"input_data": []}
|
|
self.dynamic_shape.opt_input_shape = {"input_data": []}
|
|
if self.dims == 1:
|
|
self.dynamic_shape.min_input_shape = {"input_data": [1]}
|
|
self.dynamic_shape.max_input_shape = {"input_data": [8]}
|
|
self.dynamic_shape.opt_input_shape = {"input_data": [8]}
|
|
elif self.dims == 2:
|
|
self.dynamic_shape.min_input_shape = {"input_data": [1, 8]}
|
|
self.dynamic_shape.max_input_shape = {"input_data": [4, 8]}
|
|
self.dynamic_shape.opt_input_shape = {"input_data": [2, 8]}
|
|
elif self.dims == 3:
|
|
self.dynamic_shape.min_input_shape = {"input_data": [1, 1, 4]}
|
|
self.dynamic_shape.max_input_shape = {"input_data": [4, 16, 16]}
|
|
self.dynamic_shape.opt_input_shape = {"input_data": [2, 8, 8]}
|
|
elif self.dims == 4:
|
|
self.dynamic_shape.min_input_shape = {"input_data": [1, 8, 8, 8]}
|
|
self.dynamic_shape.max_input_shape = {"input_data": [4, 8, 8, 8]}
|
|
self.dynamic_shape.opt_input_shape = {"input_data": [4, 8, 8, 8]}
|
|
return self.dynamic_shape
|
|
|
|
def sample_predictor_configs(
|
|
self, program_config, run_pir=False
|
|
) -> tuple[paddle_infer.Config, list[int], float]:
|
|
def clear_dynamic_shape():
|
|
self.dynamic_shape.max_input_shape = {}
|
|
self.dynamic_shape.min_input_shape = {}
|
|
self.dynamic_shape.opt_input_shape = {}
|
|
|
|
def generate_trt_nodes_num(attrs, dynamic_shape):
|
|
if not dynamic_shape and (self.dims == 1 or self.dims == 0):
|
|
return 0, 3
|
|
return 1, 2
|
|
|
|
attrs = [
|
|
program_config.ops[i].attrs for i in range(len(program_config.ops))
|
|
]
|
|
|
|
# for static_shape
|
|
clear_dynamic_shape()
|
|
if not run_pir:
|
|
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, False),
|
|
(1e-5, 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, False),
|
|
(1e-3, 1e-3),
|
|
)
|
|
|
|
# # for dynamic_shape
|
|
self.generate_dynamic_shape()
|
|
self.trt_param.precision = paddle_infer.PrecisionType.Float32
|
|
yield (
|
|
self.create_inference_config(),
|
|
generate_trt_nodes_num(attrs, True),
|
|
(1e-5, 1e-5),
|
|
)
|
|
self.trt_param.precision = paddle_infer.PrecisionType.Half
|
|
yield (
|
|
self.create_inference_config(),
|
|
generate_trt_nodes_num(attrs, True),
|
|
(1e-3, 1e-3),
|
|
)
|
|
|
|
def test(self):
|
|
self.run_test(run_pir=True)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|