未验证 提交 00b09e86 编写于 作者: P Pei Yang 提交者: GitHub

[Paddle-TRT] support group_norm (#31040)

* add group norm plugin

* fix compile problems

* move concat axis check to trt op teller

* add nbDims for scale and bias nv dims

* add group norm unit test

* fix unittest

* add trt version restriction for group norm op teller

* fix unittest
上级 c209751c
......@@ -1173,6 +1173,7 @@ USE_TRT_CONVERTER(conv2d_transpose);
USE_TRT_CONVERTER(leaky_relu);
USE_TRT_CONVERTER(shuffle_channel);
USE_TRT_CONVERTER(swish);
USE_TRT_CONVERTER(group_norm);
USE_TRT_CONVERTER(instance_norm);
USE_TRT_CONVERTER(layer_norm);
USE_TRT_CONVERTER(gelu);
......
# Add TRT tests
nv_library(tensorrt_converter
SRCS matmul_op.cc conv2d_op.cc fc_op.cc pool2d_op.cc elementwise_op.cc
batch_norm_op.cc activation_op.cc softmax_op.cc concat_op.cc dropout_op.cc
batch_norm_op.cc activation_op.cc softmax_op.cc concat_op.cc dropout_op.cc group_norm_op.cc
pad_op.cc split_op.cc prelu_op.cc leaky_relu_op.cc gelu_op.cc layer_norm_op.cc multihead_matmul_op.cc
shuffle_channel_op.cc swish_op.cc instance_norm_op.cc stack_op.cc transpose_op.cc flatten_op.cc
emb_eltwise_layernorm.cc skip_layernorm.cc scale_op.cc slice_op.cc hard_sigmoid_op.cc hard_swish_op.cc clip_op.cc
......
......@@ -35,7 +35,7 @@ class ConcatOpConverter : public OpConverter {
public:
void operator()(const framework::proto::OpDesc& op,
const framework::Scope& scope, bool test_mode) override {
VLOG(3) << "convert a fluid mul op to tensorrt mul layer without bias";
VLOG(3) << "convert a paddle concat op to tensorrt concat layer";
framework::OpDesc op_desc(op, nullptr);
// Declare inputs
......@@ -44,11 +44,6 @@ class ConcatOpConverter : public OpConverter {
itensors.push_back(engine_->GetITensor(input_name));
}
int axis = BOOST_GET_CONST(int, op_desc.GetAttr("axis"));
PADDLE_ENFORCE_GT(axis, 0, platform::errors::InvalidArgument(
"The axis attr of Concat"
" op should be larger than 0 for trt. "
"But received %d.",
axis));
auto* layer = TRT_ENGINE_ADD_LAYER(engine_, Concatenation, itensors.data(),
itensors.size());
......
/* 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. */
#include <vector>
#include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
namespace paddle {
namespace framework {
class Scope;
namespace proto {
class OpDesc;
} // namespace proto
} // namespace framework
} // namespace paddle
namespace paddle {
namespace inference {
namespace tensorrt {
class GroupNormOpConverter : public OpConverter {
public:
void operator()(const framework::proto::OpDesc& op,
const framework::Scope& scope, bool test_mode) override {
VLOG(3) << "convert a fluid group_norm op";
framework::OpDesc op_desc(op, nullptr);
auto* input_itensor = engine_->GetITensor(op_desc.Input("X").front());
int groups = BOOST_GET_CONST(int, op_desc.GetAttr("groups"));
float epsilon = BOOST_GET_CONST(float, op_desc.GetAttr("epsilon"));
std::string scale_name = op_desc.Input("Scale").front();
std::string bias_name = op_desc.Input("Bias").front();
// get the presistable var's data
auto get_persistable_data = [&](const std::string& var_name,
framework::DDim* dims) -> float* {
auto* temp_var = scope.FindVar(var_name);
auto* temp_tensor = temp_var->GetMutable<framework::LoDTensor>();
(*dims) = temp_tensor->dims();
auto* temp_data = engine_->GetWeightCPUData(var_name, temp_tensor, false);
return temp_data;
};
framework::DDim scale_dims;
framework::DDim bias_dims;
float* scale_data = get_persistable_data(scale_name, &scale_dims);
float* bias_data = get_persistable_data(bias_name, &bias_dims);
int64_t scale_numel = framework::product(scale_dims);
int64_t bias_numel = framework::product(bias_dims);
TensorRTEngine::Weight scale_weights{nvinfer1::DataType::kFLOAT,
static_cast<void*>(scale_data),
static_cast<size_t>(scale_numel)};
TensorRTEngine::Weight bias_weights{nvinfer1::DataType::kFLOAT,
static_cast<void*>(bias_data),
static_cast<size_t>(bias_numel)};
nvinfer1::Dims scale_nv_dims;
nvinfer1::Dims bias_nv_dims;
scale_nv_dims.nbDims = scale_dims.size();
bias_nv_dims.nbDims = bias_dims.size();
for (int i = 0; i < scale_dims.size(); i++) {
scale_nv_dims.d[i] = scale_dims.at(i);
}
for (int i = 0; i < bias_dims.size(); i++) {
bias_nv_dims.d[i] = bias_dims.at(i);
}
auto* scale_layer = TRT_ENGINE_ADD_LAYER(engine_, Constant, scale_nv_dims,
scale_weights.get());
auto* bias_layer = TRT_ENGINE_ADD_LAYER(engine_, Constant, bias_nv_dims,
bias_weights.get());
std::vector<nvinfer1::ITensor*> plugin_inputs;
plugin_inputs.emplace_back(input_itensor);
plugin_inputs.emplace_back(scale_layer->getOutput(0));
plugin_inputs.emplace_back(bias_layer->getOutput(0));
const std::vector<nvinfer1::PluginField> fields{
{"eps", &epsilon, nvinfer1::PluginFieldType::kFLOAT32, 1},
{"num_groups", &groups, nvinfer1::PluginFieldType::kINT32, 1},
};
nvinfer1::PluginFieldCollection* plugin_collections =
static_cast<nvinfer1::PluginFieldCollection*>(
malloc(sizeof(*plugin_collections) +
fields.size() * sizeof(nvinfer1::PluginField)));
plugin_collections->nbFields = static_cast<int>(fields.size());
plugin_collections->fields = fields.data();
auto creator =
GetPluginRegistry()->getPluginCreator("GroupNormalizationPlugin", "1");
auto group_norm_plugin =
creator->createPlugin("GroupNormalizationPlugin", plugin_collections);
free(plugin_collections);
auto group_norm_plugin_layer = engine_->network()->addPluginV2(
plugin_inputs.data(), plugin_inputs.size(), *group_norm_plugin);
auto output_name = op_desc.Output("Y")[0];
RreplenishLayerAndOutput(group_norm_plugin_layer, "group_norm",
{output_name}, test_mode);
}
};
} // namespace tensorrt
} // namespace inference
} // namespace paddle
REGISTER_TRT_OP_CONVERTER(group_norm, GroupNormOpConverter);
......@@ -41,6 +41,9 @@ struct SimpleOpTypeSetTeller : public Teller {
teller_set.insert("multihead_matmul");
teller_set.insert("skip_layernorm");
teller_set.insert("slice");
#endif
#if IS_TRT_VERSION_GE(7130)
teller_set.insert("group_norm");
#endif
}
......@@ -149,6 +152,21 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8,
}
}
}
if (op_type == "group_norm") {
bool has_attrs = (desc.HasAttr("epsilon") && desc.HasAttr("groups"));
if (has_attrs == false) return false;
auto registry = GetPluginRegistry();
if (registry == nullptr) return false;
}
if (op_type == "concat") {
if (!desc.HasAttr("axis")) {
return false;
} else {
int axis = BOOST_GET_CONST(int, desc.GetAttr("axis"));
if (axis <= 0) return false;
}
}
if (op_type == "transpose2" || op_type == "transpose") {
if (!desc.HasAttr("axis")) {
return false;
......
# Copyright (c) 2020 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 __future__ import print_function
import unittest
import numpy as np
from inference_pass_test import InferencePassTest
import paddle.fluid as fluid
import paddle.fluid.core as core
from paddle.fluid.core import PassVersionChecker
from paddle.fluid.core import AnalysisConfig
class TRTGroupNormTest(InferencePassTest):
def setUp(self):
with fluid.program_guard(self.main_program, self.startup_program):
data = fluid.data(
name="data", shape=[-1, 512, 12, 12], dtype="float32")
relu_out = fluid.layers.relu(data)
relu6_out = fluid.layers.relu6(relu_out)
tanh_out = fluid.layers.tanh(relu6_out)
conv_out = fluid.layers.conv2d(
input=tanh_out,
num_filters=512,
filter_size=3,
groups=1,
padding=[1, 1],
bias_attr=False,
act=None)
out = self.append_group_norm(conv_out)
self.feeds = {
"data": np.random.random([1, 512, 12, 12]).astype("float32"),
}
self.enable_trt = True
self.trt_parameters = TRTGroupNormTest.TensorRTParam(
1 << 30, 32, 1, AnalysisConfig.Precision.Float32, False, False)
self.dynamic_shape_params = TRTGroupNormTest.DynamicShapeParam({
'data': [1, 512, 12, 12]
}, {'data': [1, 512, 12, 12]}, {'data': [1, 512, 12, 12]}, False)
self.fetch_list = [out]
def append_group_norm(self, data):
param_attr = fluid.ParamAttr(
name='group_norm_scale',
initializer=fluid.initializer.Constant(value=1.0))
bias_attr = fluid.ParamAttr(
name='group_norm_bias',
initializer=fluid.initializer.Constant(value=0.0))
return fluid.layers.group_norm(
data,
groups=32,
epsilon=0.000009999999747378752,
param_attr=param_attr,
bias_attr=bias_attr)
def test_check_output(self):
if core.is_compiled_with_cuda():
use_gpu = True
self.check_output_with_option(use_gpu)
self.assertTrue(
PassVersionChecker.IsCompatible('tensorrt_subgraph_pass'))
if __name__ == "__main__":
unittest.main()
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