# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from trt_layer_auto_scan_test import TrtLayerAutoScanTest, SkipReasons from program_config import TensorConfig, ProgramConfig import numpy as np import paddle.inference as paddle_infer from functools import partial from typing import Optional, List, Callable, Dict, Any, Set import unittest class TrtConvertMatmulTest_static(TrtLayerAutoScanTest): def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): def generate_input(shape): return np.random.random(shape).astype(np.float32) for batch in [1, 4]: for trans_x in [True, False]: for trans_y in [True, False]: if trans_x and trans_y: input1_shape = [batch, 6, 11] input2_shape = [batch, 32, 6] if trans_x and not trans_y: input1_shape = [batch, 6, 11] input2_shape = [batch, 6, 32] if not trans_x and trans_y: input1_shape = [batch, 32, 6] input2_shape = [batch, 11, 6] if not trans_x and not trans_y: input1_shape = [batch, 32, 6] input2_shape = [batch, 6, 11] for alpha in [0.3, 1.0]: dics = [{ "transpose_X": trans_x, "transpose_Y": trans_y, "alpha": alpha, "fused_reshape_X": [], "fused_reshape_Y": [], "fused_transpose_X": [], "fused_transpose_Y": [], "fused_reshape_Out": [], "fused_transpose_Out": [] }] ops_config = [{ "op_type": "matmul", "op_inputs": { "X": ["input1_data"], "Y": ["input2_data"] }, "op_outputs": { "Out": ["output_data"] }, "op_attrs": dics[0] }] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ "input1_data": TensorConfig(data_gen=partial( generate_input, input1_shape)), "input2_data": TensorConfig(data_gen=partial( generate_input, input2_shape)) }, outputs=["output_data"]) yield program_config def sample_predictor_configs( self, program_config) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): pass def clear_dynamic_shape(): self.dynamic_shape.max_input_shape = {} self.dynamic_shape.min_input_shape = {} self.dynamic_shape.opt_input_shape = {} # for static_shape clear_dynamic_shape() self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), (1, 3), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), (1, 3), 1e-5 def test(self): self.run_test() class TrtConvertMatmulTest_dynamic(TrtLayerAutoScanTest): def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): def generate_input(shape): return np.random.random(shape).astype(np.float32) for trans_x in [True]: for trans_y in [True]: if trans_x and trans_y: input1_shape = [4, 4, 4] input2_shape = [4, 4, 4] # if trans_x and not trans_y: # input1_shape = [4, 4, 4] # input2_shape = [4, 4, 4] # if not trans_x and trans_y: # input1_shape = [batch, 32, 6] # input2_shape = [batch, 11, 6] # if not trans_x and not trans_y: # input1_shape = [batch, 32, 6] # input2_shape = [batch, 6, 11] for alpha in [0.3, 1.0]: dics = [{ "transpose_X": trans_x, "transpose_Y": trans_y, "alpha": alpha, "fused_reshape_X": [], "fused_reshape_Y": [], "fused_transpose_X": [], "fused_transpose_Y": [], "fused_reshape_Out": [], "fused_transpose_Out": [] }] ops_config = [{ "op_type": "matmul", "op_inputs": { "X": ["input1_data"], "Y": ["input2_data"] }, "op_outputs": { "Out": ["output_data"] }, "op_attrs": dics[0] }] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ "input1_data": TensorConfig( data_gen=partial(generate_input, input1_shape)), "input2_data": TensorConfig( data_gen=partial(generate_input, input2_shape)) }, outputs=["output_data"]) yield program_config def sample_predictor_configs( self, program_config) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): self.dynamic_shape.min_input_shape = { "input1_data": [1, 4, 4], "input2_data": [1, 4, 4] } self.dynamic_shape.max_input_shape = { "input1_data": [16, 4, 4], "input2_data": [16, 4, 128] } self.dynamic_shape.opt_input_shape = { "input1_data": [8, 4, 4], "input2_data": [8, 4, 16] } attrs = [ program_config.ops[i].attrs for i in range(len(program_config.ops)) ] # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), (1, 3), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), (1, 3), 1e-5 def add_skip_trt_case(self): def teller1(program_config, predictor_config): if len( self.dynamic_shape.min_input_shape ) != 0 and self.trt_param.precision == paddle_infer.PrecisionType.Half: return True return False self.add_skip_case( teller1, SkipReasons.TRT_NOT_IMPLEMENTED, "Tensorrt MatrixMultiply layer will get error when dynamic shape fp16 mode." ) def test(self): self.add_skip_trt_case() self.run_test() if __name__ == "__main__": unittest.main()