1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
|
/* Copyright 2018 The TensorFlow 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 "tensorflow/contrib/tensorrt/kernels/trt_calib_op.h"
#include "tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.h"
#include "tensorflow/contrib/tensorrt/resources/trt_resource_manager.h"
#include "tensorflow/contrib/tensorrt/resources/trt_resources.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/framework/tensor_types.h"
#include "tensorflow/core/framework/types.h"
#if GOOGLE_CUDA
#if GOOGLE_TENSORRT
#include "cuda_runtime_api.h"
#include "tensorrt/include/NvInfer.h"
namespace tensorflow {
namespace tensorrt {
TRTCalibOp::TRTCalibOp(OpKernelConstruction* context) : OpKernel(context) {
OP_REQUIRES_OK(context, context->GetAttr("segment_nodes", &segment_nodes_));
OP_REQUIRES_OK(context, context->GetAttr("input_names", &input_names_));
OP_REQUIRES_OK(context, context->GetAttr("resource_name", &resource_name_));
};
#define TYPECASE(dt, X, Y) \
case dt: { \
return (void*)X->flat<tensorflow::EnumToDataType<dt>::Type>().data(); \
}
void* GetTensorAddress(const Tensor* tensor_ptr) {
auto tensor_type = tensor_ptr->dtype();
switch (tensor_type) {
TYPECASE(tensorflow::DT_FLOAT, tensor_ptr, dest_ptr);
TYPECASE(tensorflow::DT_HALF, tensor_ptr, dest_ptr);
TYPECASE(tensorflow::DT_INT8, tensor_ptr, dest_ptr);
default: {
LOG(FATAL) << "Unsupported Data type "
<< tensorflow::DataTypeString(tensor_type);
return nullptr;
}
}
}
void TRTCalibOp::Compute(tensorflow::OpKernelContext* ctx) {
// TODO(aaroey): make sure ctx->resource_mgr() is used in future PR.
auto trt_rm = tensorflow::tensorrt::TRTResourceManager::instance();
auto res_mgr = trt_rm->getManager("TRTCalibOps");
tensorflow::tensorrt::TRTCalibrationResource* calib_res = nullptr;
auto status = res_mgr->Lookup(resource_name_, resource_name_, &calib_res);
if (!status.ok()) {
ctx->SetStatus(status);
return;
}
int num_inputs = ctx->num_inputs();
// first run instantiate calibrator
if (calib_res->calibrator_ == nullptr) {
dev_tensors_.resize(num_inputs);
int batch_size = ctx->input(0).dim_size(0);
VLOG(1) << " Constructing calibrator";
for (int i = 0; i < num_inputs; i++) {
// allocate workspace on device for inputs
const tensorflow::Tensor& t = ctx->input(i);
OP_REQUIRES_OK(ctx,
ctx->allocate_persistent(t.dtype(), t.shape(),
&dev_tensors_.at(i), nullptr));
const auto device_tensor = dev_tensors_.at(i).AccessTensor(ctx);
CHECK_EQ(t.TotalBytes(), device_tensor->TotalBytes());
void* device_address = GetTensorAddress(device_tensor);
device_buffers_.emplace(input_names_.at(i),
std::pair<void*, size_t>(
device_address, device_tensor->TotalBytes()));
}
calib_res->calibrator_ =
new TRTInt8Calibrator(device_buffers_, batch_size, resource_name_);
string label(resource_name_);
calib_res->thr_ = new std::thread([calib_res, label]() {
VLOG(1) << "Starting calibration thread, Calibration Resource @ "
<< calib_res;
calib_res->builder_->setInt8Calibrator(calib_res->calibrator_);
calib_res->builder_->setInt8Mode(true);
calib_res->engine_ = calib_res->builder_->buildCudaEngine(
*calib_res->network_); // will loop until we terminate calibrator
VLOG(1) << "Calibration loop terminated " << label;
});
VLOG(1) << "initialized calibrator resource";
} // calibrator initialized
// Pass input data to calibrator
std::unordered_map<string, void*> input_data;
for (int i = 0; i < num_inputs; i++) {
const Tensor& t = ctx->input(i);
void* data_address = GetTensorAddress(&t);
const auto device_tensor = dev_tensors_.at(i).AccessTensor(ctx);
CHECK_EQ(t.TotalBytes(),
device_tensor->TotalBytes()); // use the tensor so FW keeps it
input_data.emplace(input_names_.at(i), data_address);
ctx->set_output(i, t);
}
VLOG(2) << "Filled map for sending";
calib_res->calibrator_->setBatch(input_data);
VLOG(2) << "Passed calibration data";
// TODO(aaroey): make sure we wait for the completion of calibration on the
// last batch in future PR.
};
#undef TYPECASE
REGISTER_KERNEL_BUILDER(Name("TRTCalibOp").Device(DEVICE_GPU), TRTCalibOp);
} // namespace tensorrt
} // namespace tensorflow
#endif
#endif
|
