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Add gelu convert test (PaddlePaddle#35529)
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Co-authored-by: xiaoxiaohehe001 <hiteezsf@163.com>
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2 people authored and AnnaTrainingG committed Sep 29, 2021
1 parent a690c42 commit 9ce6dcd
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13 changes: 13 additions & 0 deletions paddle/fluid/inference/tensorrt/op_teller.cc
Original file line number Diff line number Diff line change
Expand Up @@ -770,6 +770,19 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8,
<< desc.Output("Out").size();
return false;
}

if (desc.HasAttr("approximate")) {
if (BOOST_GET_CONST(bool, desc.GetAttr("approximate"))) return false;
}

auto* block = desc.Block();
auto x_var_name = desc.Input("X")[0];
auto* x_var_desc = block->FindVar(x_var_name);
const auto x_shape = x_var_desc->GetShape();
if (x_shape.size() == 1) {
VLOG(3) << "gelu op does not support input's dim is 1 in tensorrt.";
return false;
}
}

if (op_type == "layer_norm") {
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@@ -0,0 +1,144 @@
# 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


class TrtConvertGeluTest(TrtLayerAutoScanTest):
def is_program_valid(self, program_config: ProgramConfig) -> bool:
return True

def sample_program_configs(self):
def generate_input1(dims, attrs: List[Dict[str, Any]]):
if dims == 1:
return np.ones([64]).astype(np.float32)
elif dims == 2:
return np.ones([3, 64]).astype(np.float32)
elif dims == 3:
return np.ones([3, 64, 64]).astype(np.float32)
else:
return np.ones([1, 3, 64, 64]).astype(np.float32)

for dims in [1, 2, 3, 4]:
for approximate in [True, False]:
self.dims = dims
dics = [{"approximate": approximate}]

ops_config = [{
"op_type": "gelu",
"op_inputs": {
"X": ["input_data"]
},
"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, dims, dics))
},
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 self.dims == 1:
self.dynamic_shape.min_input_shape = {"input_data": [1]}
self.dynamic_shape.max_input_shape = {"input_data": [128]}
self.dynamic_shape.opt_input_shape = {"input_data": [64]}
elif self.dims == 2:
self.dynamic_shape.min_input_shape = {"input_data": [1, 32]}
self.dynamic_shape.max_input_shape = {"input_data": [4, 64]}
self.dynamic_shape.opt_input_shape = {"input_data": [3, 64]}
elif self.dims == 3:
self.dynamic_shape.min_input_shape = {"input_data": [1, 32, 32]}
self.dynamic_shape.max_input_shape = {
"input_data": [10, 64, 64]
}
self.dynamic_shape.opt_input_shape = {"input_data": [3, 64, 64]}
else:
self.dynamic_shape.min_input_shape = {
"input_data": [1, 3, 32, 32]
}
self.dynamic_shape.max_input_shape = {
"input_data": [4, 3, 64, 64]
}
self.dynamic_shape.opt_input_shape = {
"input_data": [1, 3, 64, 64]
}

def clear_dynamic_shape():
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):
if attrs[0]['approximate'] == True or self.dims == 1:
return 0, 3
else:
return 1, 2

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(
attrs, False), 1e-5
self.trt_param.precision = paddle_infer.PrecisionType.Half
yield self.create_inference_config(), generate_trt_nodes_num(
attrs, 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(attrs,
True), 1e-5
self.trt_param.precision = paddle_infer.PrecisionType.Half
yield self.create_inference_config(), generate_trt_nodes_num(attrs,
True), 1e-5

def add_skip_trt_case(self):
def teller1(program_config, predictor_config):
if self.dims == 2:
return True
return False

self.add_skip_case(
teller1, SkipReasons.TRT_NOT_IMPLEMENTED,
"When input dims is 2, pulgin will product a 4 dims output.")

def test(self):
self.add_skip_trt_case()
self.run_test()


if __name__ == "__main__":
unittest.main()

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