未验证 提交 75d5e3bf 编写于 作者: B baoachun 提交者: GitHub

add gather trt converter test case (#35523)

上级 42559f72
...@@ -319,6 +319,17 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8, ...@@ -319,6 +319,17 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8,
if (op_type == "gather") { if (op_type == "gather") {
if (!with_dynamic_shape) return false; if (!with_dynamic_shape) return false;
if (with_dynamic_shape) {
auto* block = desc.Block();
auto* x_var_desc = block->FindVar(desc.Input("X")[0]);
const auto x_shape = x_var_desc->GetShape();
if (x_shape.size() == 1) {
VLOG(3) << "Gather does not support 1-dimensional input in tensorrt";
return false;
}
}
auto inputs = desc.InputArgumentNames(); auto inputs = desc.InputArgumentNames();
for (auto& input : inputs) { for (auto& input : inputs) {
if (input == "Axis" && desc.Input("Axis").size() > 0) return false; if (input == "Axis" && desc.Input("Axis").size() > 0) return false;
......
# 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 logging
class TrtConvertGatherTest(TrtLayerAutoScanTest):
def is_program_valid(self, program_config: ProgramConfig) -> bool:
inputs = program_config.inputs
attrs = [
program_config.ops[i].attrs
for i in range(len(program_config.ops))
]
if len(inputs['input_data'].shape) <= attrs[0]['axis']:
return False
return True
def sample_program_configs(self):
def generate_input1(shape):
return np.random.random(shape).astype(np.float32)
def generate_input2(index):
return np.array(index).astype(np.int32)
def generate_input3(axis):
return np.array([axis]).astype(np.int32)
for shape in [[32], [16, 64], [32, 16, 16], [32, 64, 16, 32]]:
for index in [[1, 4], [4, 8]]:
for axis in [0, 1, 2, 3]:
for overwrite in [True, False]:
for input in [{
"X": ["input_data"],
"Index": ["index_data"]
}, {
"X": ["input_data"],
"Index": ["index_data"],
"Axis": ["axis_data"]
}]:
self.shape = shape
self.axis = axis
self.input_num = len(input)
dics = [{"overwrite": overwrite, "axis": axis}]
ops_config = [{
"op_type": "gather",
"op_inputs": input,
"op_outputs": {
"Out": ["output_data"]
},
"op_attrs": dics[0]
}]
ops = self.generate_op_config(ops_config)
program_config = ProgramConfig(
ops=ops,
weights={},
inputs={
"input_data": TensorConfig(data_gen=partial(
generate_input1, shape)),
"index_data": TensorConfig(data_gen=partial(
generate_input2, index)),
} if len(input) == 2 else {
"input_data": TensorConfig(data_gen=partial(
generate_input1, shape)),
"index_data": TensorConfig(data_gen=partial(
generate_input2, index)),
"axis_data": TensorConfig(data_gen=partial(
generate_input3, axis)),
},
outputs=["output_data"])
yield program_config
def sample_predictor_configs(
self, program_config) -> (paddle_infer.Config, List[int], float):
def generate_dynamic_shape(attrs):
if len(self.shape) == 1:
self.dynamic_shape.min_input_shape = {
"input_data": [4],
"index_data": [1]
}
self.dynamic_shape.max_input_shape = {
"input_data": [128],
"index_data": [4]
}
self.dynamic_shape.opt_input_shape = {
"input_data": [16],
"index_data": [2]
}
elif len(self.shape) == 2:
self.dynamic_shape.min_input_shape = {
"input_data": [2, 4],
"index_data": [1]
}
self.dynamic_shape.max_input_shape = {
"input_data": [256, 256],
"index_data": [4]
}
self.dynamic_shape.opt_input_shape = {
"input_data": [64, 32],
"index_data": [2]
}
elif len(self.shape) == 3:
self.dynamic_shape.min_input_shape = {
"input_data": [2, 4, 4],
"index_data": [1]
}
self.dynamic_shape.max_input_shape = {
"input_data": [128, 256, 256],
"index_data": [4]
}
self.dynamic_shape.opt_input_shape = {
"input_data": [16, 64, 32],
"index_data": [2]
}
elif len(self.shape) == 4:
self.dynamic_shape.min_input_shape = {
"input_data": [2, 4, 4, 2],
"index_data": [1]
}
self.dynamic_shape.max_input_shape = {
"input_data": [128, 256, 128, 256],
"index_data": [4]
}
self.dynamic_shape.opt_input_shape = {
"input_data": [16, 64, 16, 32],
"index_data": [2]
}
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(dynamic_shape):
if self.input_num == 3:
return 0, 5
else:
if dynamic_shape and self.axis == 0:
return 1, 3
else:
return 0, 4
attrs = [
program_config.ops[i].attrs
for i in range(len(program_config.ops))
]
# for static_shape
clear_dynamic_shape()
self.trt_param.precision = paddle_infer.PrecisionType.Float32
yield self.create_inference_config(), generate_trt_nodes_num(
False), 1e-5
self.trt_param.precision = paddle_infer.PrecisionType.Half
yield self.create_inference_config(), generate_trt_nodes_num(
False), 1e-5
# for dynamic_shape
generate_dynamic_shape(attrs)
self.trt_param.precision = paddle_infer.PrecisionType.Float32
yield self.create_inference_config(), generate_trt_nodes_num(True), 1e-5
self.trt_param.precision = paddle_infer.PrecisionType.Half
yield self.create_inference_config(), generate_trt_nodes_num(True), 1e-5
def add_skip_trt_case(self):
def teller1(program_config, predictor_config):
if len(self.dynamic_shape.min_input_shape) != 0:
inputs = program_config.inputs
if len(inputs['input_data'].shape) == 1 or len(inputs[
'index_data'].shape) == 1:
return True
return False
self.add_skip_case(
teller1, SkipReasons.TRT_NOT_SUPPORT,
"Need to repair the case: trt reshape out failed for dynamic shape mode when inputs' dims==1."
)
def teller2(program_config, predictor_config):
inputs = program_config.inputs
if "axis_data" in inputs.keys():
return True
return False
self.add_skip_case(
teller2, SkipReasons.TRT_NOT_SUPPORT,
"Need to repair the case: trt do not support axis tensor input.")
def test(self):
self.add_skip_trt_case()
self.run_test()
if __name__ == "__main__":
unittest.main()
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