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add gather trt converter test case (PaddlePaddle#35523)
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‎paddle/fluid/inference/tensorrt/op_teller.cc‎

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@@ -319,6 +319,17 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8,
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if (op_type == "gather") {
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if (!with_dynamic_shape) return false;
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if (with_dynamic_shape) {
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auto* block = desc.Block();
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auto* x_var_desc = block->FindVar(desc.Input("X")[0]);
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const auto x_shape = x_var_desc->GetShape();
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if (x_shape.size() == 1) {
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VLOG(3) << "Gather does not support 1-dimensional input in tensorrt";
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return false;
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}
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}
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auto inputs = desc.InputArgumentNames();
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for (auto& input : inputs) {
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if (input == "Axis" && desc.Input("Axis").size() > 0) return false;

‎python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_gather.py‎

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# 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 trt_layer_auto_scan_test import TrtLayerAutoScanTest, SkipReasons
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from program_config import TensorConfig, ProgramConfig
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import numpy as np
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import paddle.inference as paddle_infer
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from functools import partial
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from typing import Optional, List, Callable, Dict, Any, Set
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import logging
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class TrtConvertGatherTest(TrtLayerAutoScanTest):
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def is_program_valid(self, program_config: ProgramConfig) -> bool:
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inputs = program_config.inputs
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attrs = [
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program_config.ops[i].attrs
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for i in range(len(program_config.ops))
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]
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if len(inputs['input_data'].shape) <= attrs[0]['axis']:
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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(shape):
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return np.random.random(shape).astype(np.float32)
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def generate_input2(index):
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return np.array(index).astype(np.int32)
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def generate_input3(axis):
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return np.array([axis]).astype(np.int32)
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for shape in [[32], [16, 64], [32, 16, 16], [32, 64, 16, 32]]:
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for index in [[1, 4], [4, 8]]:
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for axis in [0, 1, 2, 3]:
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for overwrite in [True, False]:
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for input in [{
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"X": ["input_data"],
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"Index": ["index_data"]
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}, {
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"X": ["input_data"],
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"Index": ["index_data"],
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"Axis": ["axis_data"]
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}]:
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self.shape = shape
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self.axis = axis
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self.input_num = len(input)
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dics = [{"overwrite": overwrite, "axis": axis}]
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ops_config = [{
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"op_type": "gather",
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"op_inputs": input,
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"op_outputs": {
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"Out": ["output_data"]
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},
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"op_attrs": dics[0]
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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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"input_data": TensorConfig(data_gen=partial(
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generate_input1, shape)),
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"index_data": TensorConfig(data_gen=partial(
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generate_input2, index)),
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} if len(input) == 2 else {
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"input_data": TensorConfig(data_gen=partial(
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generate_input1, shape)),
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"index_data": TensorConfig(data_gen=partial(
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generate_input2, index)),
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"axis_data": TensorConfig(data_gen=partial(
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generate_input3, axis)),
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},
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outputs=["output_data"])
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yield program_config
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def sample_predictor_configs(
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self, program_config) -> (paddle_infer.Config, List[int], float):
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def generate_dynamic_shape(attrs):
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if len(self.shape) == 1:
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self.dynamic_shape.min_input_shape = {
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"input_data": [4],
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"index_data": [1]
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}
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self.dynamic_shape.max_input_shape = {
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"input_data": [128],
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"index_data": [4]
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}
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self.dynamic_shape.opt_input_shape = {
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"input_data": [16],
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"index_data": [2]
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}
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elif len(self.shape) == 2:
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self.dynamic_shape.min_input_shape = {
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"input_data": [2, 4],
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"index_data": [1]
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}
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self.dynamic_shape.max_input_shape = {
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"input_data": [256, 256],
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"index_data": [4]
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}
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self.dynamic_shape.opt_input_shape = {
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"input_data": [64, 32],
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"index_data": [2]
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}
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elif len(self.shape) == 3:
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self.dynamic_shape.min_input_shape = {
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"input_data": [2, 4, 4],
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"index_data": [1]
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}
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self.dynamic_shape.max_input_shape = {
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"input_data": [128, 256, 256],
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"index_data": [4]
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}
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self.dynamic_shape.opt_input_shape = {
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"input_data": [16, 64, 32],
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"index_data": [2]
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}
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elif len(self.shape) == 4:
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self.dynamic_shape.min_input_shape = {
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"input_data": [2, 4, 4, 2],
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"index_data": [1]
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}
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self.dynamic_shape.max_input_shape = {
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"input_data": [128, 256, 128, 256],
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"index_data": [4]
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}
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self.dynamic_shape.opt_input_shape = {
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"input_data": [16, 64, 16, 32],
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"index_data": [2]
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}
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def clear_dynamic_shape():
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self.dynamic_shape.max_input_shape = {}
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self.dynamic_shape.min_input_shape = {}
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self.dynamic_shape.opt_input_shape = {}
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def generate_trt_nodes_num(dynamic_shape):
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if self.input_num == 3:
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return 0, 5
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else:
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if dynamic_shape and self.axis == 0:
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return 1, 3
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else:
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return 0, 4
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attrs = [
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program_config.ops[i].attrs
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for i in range(len(program_config.ops))
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]
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# for static_shape
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clear_dynamic_shape()
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self.trt_param.precision = paddle_infer.PrecisionType.Float32
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yield self.create_inference_config(), generate_trt_nodes_num(
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False), 1e-5
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self.trt_param.precision = paddle_infer.PrecisionType.Half
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yield self.create_inference_config(), generate_trt_nodes_num(
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False), 1e-5
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# for dynamic_shape
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generate_dynamic_shape(attrs)
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self.trt_param.precision = paddle_infer.PrecisionType.Float32
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yield self.create_inference_config(), generate_trt_nodes_num(True), 1e-5
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self.trt_param.precision = paddle_infer.PrecisionType.Half
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yield self.create_inference_config(), generate_trt_nodes_num(True), 1e-5
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def add_skip_trt_case(self):
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def teller1(program_config, predictor_config):
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if len(self.dynamic_shape.min_input_shape) != 0:
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inputs = program_config.inputs
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if len(inputs['input_data'].shape) == 1 or len(inputs[
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'index_data'].shape) == 1:
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return True
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return False
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self.add_skip_case(
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teller1, SkipReasons.TRT_NOT_SUPPORT,
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"Need to repair the case: trt reshape out failed for dynamic shape mode when inputs' dims==1."
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)
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def teller2(program_config, predictor_config):
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inputs = program_config.inputs
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if "axis_data" in inputs.keys():
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return True
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return False
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self.add_skip_case(
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teller2, SkipReasons.TRT_NOT_SUPPORT,
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"Need to repair the case: trt do not support axis tensor input.")
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def test(self):
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self.add_skip_trt_case()
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self.run_test()
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if __name__ == "__main__":
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unittest.main()

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