未验证 提交 5c291737 编写于 作者: Z zhoutianzi666 提交者: GitHub

[Paddle-TRT] remove useless code in fc (#44382)

* remove useless code in fc
上级 0fd974b4
......@@ -333,74 +333,6 @@ class FcOpConverter : public OpConverter {
if (!engine_->with_dynamic_shape()) {
x_num_col_dims--;
}
// If use tensorrt'oss, the x_dim and x_num_col_dims need change, and can
// not add Shuffle layer in ernie's multihead.
if (x_dim.nbDims == 4 && x_num_col_dims == 1) {
if (enable_int8 || support_int8) {
// add conv1x1 layer
nvinfer1::DimsHW nv_ksize(1, 1);
auto* fc_layer_int8 = TRT_ENGINE_ADD_LAYER(engine_,
Convolution,
*X,
n_output,
nv_ksize,
weight.get(),
bias.get());
if (activation_type == "relu") {
fc_layer_int8->setName(
("ernie_fc_op_int8: Convolution (Output: " + output_name + ")")
.c_str());
PADDLE_ENFORCE_EQ(
op_desc.HasAttr("out_threshold"),
true,
platform::errors::InvalidArgument(
"must have out threshold in fc layers in int8 mode"));
float out_scale = 0;
if (enable_int8) {
out_scale =
BOOST_GET_CONST(float, op_desc.GetAttr("out_threshold"));
} else {
out_scale = BOOST_GET_CONST(float, op_desc.GetAttr("Out"));
}
engine_->SetTensorDynamicRange(fc_layer_int8->getOutput(0),
out_scale);
nvinfer1::IActivationLayer* relu_layer_int8 =
TRT_ENGINE_ADD_LAYER(engine_,
Activation,
*(fc_layer_int8->getOutput(0)),
nvinfer1::ActivationType::kRELU);
RreplenishLayerAndOutput(relu_layer_int8,
"relu_after_ernie_fc_int8",
{output_name},
test_mode);
} else {
RreplenishLayerAndOutput(fc_layer_int8,
"ernie_fc_op_int8: Convolution",
{output_name},
test_mode);
}
} else {
// add fc layer
auto* fc_layer_float = TRT_ENGINE_ADD_LAYER(
engine_, FullyConnected, *X, n_output, weight.get(), bias.get());
if (activation_type == "relu") {
fc_layer_float->setName(
("ernie_fc_op_float: (Output: " + output_name + ")").c_str());
nvinfer1::IActivationLayer* relu_layer_float =
TRT_ENGINE_ADD_LAYER(engine_,
Activation,
*(fc_layer_float->getOutput(0)),
nvinfer1::ActivationType::kRELU);
RreplenishLayerAndOutput(relu_layer_float,
"relu_after_ernie_fc_float",
{output_name},
test_mode);
} else {
RreplenishLayerAndOutput(
fc_layer_float, "ernie_fc_op_float", {output_name}, test_mode);
}
}
} else { // need reshape input before and after fc
PADDLE_ENFORCE_GT(
x_dim.nbDims,
x_num_col_dims,
......@@ -410,6 +342,7 @@ class FcOpConverter : public OpConverter {
"x_dim.nbDims : %d, x_num_col_dims : %d.",
x_dim.nbDims,
x_num_col_dims));
// need reshape input before and after fc
auto* reshape_before_fc_layer =
reshape_before_fc(X, x_dim, x_num_col_dims, output_name);
auto* reshape_itensor = reshape_before_fc_layer->getOutput(0);
......@@ -418,7 +351,6 @@ class FcOpConverter : public OpConverter {
}
regist_fc(reshape_itensor, n_output, weight, bias);
}
}
};
} // namespace tensorrt
......
# 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 unittest
import paddle.inference as paddle_infer
from functools import partial
from typing import Optional, List, Callable, Dict, Any, Set
import os
class TrtConvertFcTest(TrtLayerAutoScanTest):
def is_program_valid(self, program_config: ProgramConfig) -> bool:
# The output has diff between gpu and trt in CI windows
if (os.name == 'nt'):
return False
return True
def sample_program_configs(self):
self.trt_param.workspace_size = 1073741824
def generate_input1(batch, attrs: List[Dict[str, Any]]):
return np.random.random([batch, 3, 64, (int)(attrs[0]["m"] / 2),
2]).astype(np.float32)
def generate_w(batch, attrs: List[Dict[str, Any]]):
return np.random.random([attrs[0]["m"],
attrs[0]["n"]]).astype(np.float32)
def generate_bias(batch, attrs: List[Dict[str, Any]]):
return np.random.random([attrs[0]["n"]]).astype(np.float32)
for batch in [1, 4]:
for [m, n] in [[32, 23]]:
dics = [
{
"in_num_col_dims": 3,
# for my conveinence
"m": m,
"n": n,
},
{}
]
ops_config = [
{
"op_type": "fc",
"op_inputs": {
"Input": ["input_data"],
"W": ["w_data"],
"Bias": ["bias_data"]
},
"op_outputs": {
"Out": ["output_data"]
},
"op_attrs": dics[0]
},
]
ops = self.generate_op_config(ops_config)
program_config = ProgramConfig(
ops=ops,
weights={
"w_data":
TensorConfig(data_gen=partial(generate_w, batch, dics)),
"bias_data":
TensorConfig(
data_gen=partial(generate_bias, batch, dics))
},
inputs={
"input_data":
TensorConfig(
data_gen=partial(generate_input1, batch, 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):
self.dynamic_shape.min_input_shape = {
"input_data": [1, 3, 32, 16, 2],
}
self.dynamic_shape.max_input_shape = {
"input_data": [4, 3, 64, 16, 2],
}
self.dynamic_shape.opt_input_shape = {
"input_data": [1, 3, 64, 16, 2],
}
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):
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, 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, 1e-5)
def test(self):
self.run_test()
def test_quant(self):
self.run_test(quant=True)
class TrtConvertFcTest2(TrtLayerAutoScanTest):
def is_program_valid(self, program_config: ProgramConfig) -> bool:
# The output has diff between gpu and trt in CI windows
if (os.name == 'nt'):
return False
return True
def sample_program_configs(self):
self.trt_param.workspace_size = 1073741824
def generate_input1(batch, attrs: List[Dict[str, Any]]):
return np.random.random([batch, 3, 64, 14]).astype(np.float32)
def generate_w(batch, attrs: List[Dict[str, Any]]):
return np.random.random([attrs[0]["m"],
attrs[0]["n"]]).astype(np.float32)
def generate_bias(batch, attrs: List[Dict[str, Any]]):
return np.random.random([attrs[0]["n"]]).astype(np.float32)
for batch in [1, 4]:
for [m, n] in [[14, 43]]:
dics = [
{
"in_num_col_dims": 3,
# for my conveinence
"m": m,
"n": n,
},
{}
]
ops_config = [
{
"op_type": "fc",
"op_inputs": {
"Input": ["input_data"],
"W": ["w_data"],
"Bias": ["bias_data"]
},
"op_outputs": {
"Out": ["output_data"]
},
"op_attrs": dics[0]
},
]
ops = self.generate_op_config(ops_config)
program_config = ProgramConfig(
ops=ops,
weights={
"w_data":
TensorConfig(data_gen=partial(generate_w, batch, dics)),
"bias_data":
TensorConfig(
data_gen=partial(generate_bias, batch, dics))
},
inputs={
"input_data":
TensorConfig(
data_gen=partial(generate_input1, batch, dics)),
},
outputs=["output_data"])
yield program_config
def sample_predictor_configs(
self, program_config) -> (paddle_infer.Config, List[int], float):
def generate_dynamic_shape():
self.dynamic_shape.min_input_shape = {
"input_data": [1, 3, 32, 14],
}
self.dynamic_shape.max_input_shape = {
"input_data": [4, 3, 64, 14],
}
self.dynamic_shape.opt_input_shape = {
"input_data": [1, 3, 64, 14],
}
def clear_dynamic_shape():
self.dynamic_shape.min_input_shape = {}
self.dynamic_shape.max_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, 2), 1e-5
self.trt_param.precision = paddle_infer.PrecisionType.Half
yield self.create_inference_config(), (1, 2), (1e-5, 1e-5)
# for dynamic_shape
generate_dynamic_shape()
self.trt_param.precision = paddle_infer.PrecisionType.Float32
yield self.create_inference_config(), (1, 2), 1e-5
self.trt_param.precision = paddle_infer.PrecisionType.Half
yield self.create_inference_config(), (1, 2), (1e-5, 1e-5)
def test(self):
self.run_test()
# this is the special case when x_dim.nbDims == 4 && x_num_col_dims == 1
class TrtConvertFcTest3(TrtLayerAutoScanTest):
def is_program_valid(self, program_config: ProgramConfig) -> bool:
return True
def sample_program_configs(self):
self.trt_param.workspace_size = 1073741824
def generate_input1(batch, attrs: List[Dict[str, Any]]):
return np.ones([batch, 14, 1, 2]).astype(np.float32)
def generate_w(batch, attrs: List[Dict[str, Any]]):
return np.ones([attrs[0]["m"], attrs[0]["n"]]).astype(np.float32)
def generate_bias(batch, attrs: List[Dict[str, Any]]):
return np.ones([attrs[0]["n"]]).astype(np.float32)
for batch in [1, 4]:
for [m, n] in [[28, 43]]:
dics = [
{
"in_num_col_dims": 1,
"Input_scale": 0.1,
"out_threshold": 0.1,
"enable_int8": True,
# for my conveinence
"m": m,
"n": n,
},
{}
]
ops_config = [
{
"op_type": "fc",
"op_inputs": {
"Input": ["input_data"],
"W": ["w_data"],
"Bias": ["bias_data"]
},
"op_outputs": {
"Out": ["output_data"]
},
"op_attrs": dics[0]
},
]
ops = self.generate_op_config(ops_config)
program_config = ProgramConfig(
ops=ops,
weights={
"w_data":
TensorConfig(data_gen=partial(generate_w, batch, dics)),
"bias_data":
TensorConfig(
data_gen=partial(generate_bias, batch, dics))
},
inputs={
"input_data":
TensorConfig(
data_gen=partial(generate_input1, batch, dics)),
},
outputs=["output_data"])
yield program_config
def sample_predictor_configs(
self, program_config) -> (paddle_infer.Config, List[int], float):
def generate_dynamic_shape():
self.dynamic_shape.min_input_shape = {
"input_data": [1, 14, 1, 2],
}
self.dynamic_shape.max_input_shape = {
"input_data": [4, 14, 1, 2],
}
self.dynamic_shape.opt_input_shape = {
"input_data": [1, 14, 1, 2],
}
def clear_dynamic_shape():
self.dynamic_shape.min_input_shape = {}
self.dynamic_shape.max_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, 2), 1e-5
self.trt_param.precision = paddle_infer.PrecisionType.Half
yield self.create_inference_config(), (1, 2), (1e-5, 1e-5)
# for dynamic_shape
generate_dynamic_shape()
self.trt_param.precision = paddle_infer.PrecisionType.Float32
yield self.create_inference_config(), (1, 2), 1e-5
self.trt_param.precision = paddle_infer.PrecisionType.Half
yield self.create_inference_config(), (1, 2), (1e-5, 1e-5)
self.trt_param.precision = paddle_infer.PrecisionType.Int8
yield self.create_inference_config(), (1, 2), (1e-5, 1e-5)
def test(self):
self.run_test()
def test_quant(self):
self.run_test(quant=True)
if __name__ == "__main__":
unittest.main()
Markdown is supported
0% .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑!
想要评论请 注册