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stream.cc
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/* Copyright 2015 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/stream_executor/stream.h"
#include "tensorflow/stream_executor/platform/port.h"
#include "absl/strings/str_cat.h"
#include "third_party/eigen3/Eigen/Core"
#include "tensorflow/stream_executor/blas.h"
#include "tensorflow/stream_executor/host_or_device_scalar.h"
#include "tensorflow/stream_executor/lib/stacktrace.h"
#include "tensorflow/stream_executor/platform.h"
#include "tensorflow/stream_executor/platform/logging.h"
#include "tensorflow/stream_executor/rng.h"
#include "tensorflow/stream_executor/stream_executor_internal.h"
#include "tensorflow/stream_executor/stream_executor_pimpl.h"
namespace stream_executor {
namespace {
// Code to turn parameters to functions on stream into strings that
// will be VLOG'ed. We need overloads, instead of
// e.g. BatchDescriptorToVlogString(), as the code that calls these
// functions does not know what the type of the parameter is.
string ToVlogString(const dnn::BatchDescriptor &descriptor) {
return descriptor.ToShortString();
}
string ToVlogString(const dnn::FilterDescriptor &descriptor) {
return descriptor.ToShortString();
}
string ToVlogString(const dnn::ConvolutionDescriptor &descriptor) {
return descriptor.ToShortString();
}
string ToVlogString(const dnn::PoolingDescriptor &descriptor) {
return descriptor.ToShortString();
}
string ToVlogString(const dnn::NormalizeDescriptor &descriptor) {
return descriptor.ToShortString();
}
string ToVlogString(dnn::ActivationMode mode) {
return dnn::ActivationModeString(mode);
}
string ToVlogString(const dnn::AlgorithmConfig &algo_config) {
return algo_config.ToString();
}
std::string ToVlogString(const dnn::ExecutionPlanConfig &plan_config) {
return plan_config.ToString();
}
string ToVlogString(dnn::ElementwiseOperation op) {
return dnn::ElementwiseOperationString(op);
}
string ToVlogString(dnn::QuantizedActivationMode mode) {
return dnn::QuantizedActivationModeString(mode);
}
string ToVlogString(blas::Transpose t) { return blas::TransposeString(t); }
string ToVlogString(blas::UpperLower ul) { return blas::UpperLowerString(ul); }
string ToVlogString(blas::Diagonal d) { return blas::DiagonalString(d); }
string ToVlogString(blas::Side s) { return blas::SideString(s); }
string ToVlogString(blas::ComputationType ty) {
return blas::ComputationTypeString(ty);
}
string ToVlogString(const void *ptr) {
if (ptr == nullptr) {
return "null";
}
// StrCat does not convert pointers to text.
std::ostringstream out;
out << ptr;
return out.str();
}
template <class T>
string ToVlogString(const std::complex<T> &c) {
// StrCat does not convert std::complex to text.
std::ostringstream out;
out << c;
return out.str();
}
template <class T>
string ToVlogString(const std::function<T> &f) {
return f == nullptr ? "null" : "<non-null function>";
}
string ToVlogString(const DeviceMemoryBase &memory) {
return ToVlogString(memory.opaque());
}
string ToVlogString(const DeviceMemoryBase *memory) {
return memory == nullptr ? "null" : ToVlogString(*memory);
}
string ToVlogString(const Eigen::half &h) {
return absl::StrCat(static_cast<float>(h));
}
string ToVlogString(int i) { return absl::StrCat(i); }
string ToVlogString(uint32 i) { return absl::StrCat(i); }
string ToVlogString(uint64 i) { return absl::StrCat(i); }
string ToVlogString(int64 i) { return absl::StrCat(i); }
string ToVlogString(float f) { return absl::StrCat(f); }
string ToVlogString(double d) { return absl::StrCat(d); }
template <typename T>
string ToVlogString(const HostOrDeviceScalar<T> &memory_or_constant) {
if (memory_or_constant.is_pointer()) {
return ToVlogString(memory_or_constant.pointer());
}
return ToVlogString(memory_or_constant.value());
}
template <class T>
string ToVlogString(port::ArraySlice<T> elements) {
string str = absl::StrCat(
ToVlogString(reinterpret_cast<const void *>(elements.data())), "[",
elements.size(), "]{");
const char *separator = "";
size_t max_to_show = std::numeric_limits<size_t>::max();
if (!VLOG_IS_ON(2)) {
max_to_show = 5;
} else if (!VLOG_IS_ON(3)) {
max_to_show = 20;
} else if (!VLOG_IS_ON(11)) {
max_to_show = 1000;
}
for (size_t i = 0; i < elements.size(); ++i) {
if (i == max_to_show) {
str += ", ...";
break;
}
absl::StrAppend(&str, separator, ToVlogString(elements[i]));
separator = ", ";
}
str += "}";
return str;
}
template <class T>
string ToVlogString(port::MutableArraySlice<T> elements) {
return ToVlogString(port::ArraySlice<T>(elements));
}
string ToVlogString(dnn::DepthToSpaceLayout depth_to_space_layout) {
switch (depth_to_space_layout) {
case dnn::DepthToSpaceLayout::DepthHeightWidth:
return "DepthToSpaceLayout::DepthHeightWidth";
}
return "unknown DepthToSpaceLayout";
}
string ToVlogString(dnn::DataType data_type) {
switch (data_type) {
case dnn::DataType::kFloat:
return "dnn::DataType::kFloat";
case dnn::DataType::kDouble:
return "dnn::DataType::kDouble";
case dnn::DataType::kHalf:
return "dnn::DataType::kHalf";
case dnn::DataType::kInt8:
return "dnn::DataType::kInt8";
case dnn::DataType::kInt32:
return "dnn::DataType::kInt32";
default:
return "unknown DataType";
}
}
string ToVlogString(stream_executor::BatchNormalizationKind kind) {
switch (kind) {
case stream_executor::BatchNormalizationKind::kBatchnormForward:
return "stream_executor::BatchNormalizationKind::kBatchnormForward";
case stream_executor::BatchNormalizationKind::kBatchnormBackward:
return "stream_executor::BatchNormalizationKind::kBatchnormBackward";
default:
LOG(FATAL) << "Unknown BatchNormalizationKind "
<< static_cast<int32>(kind);
}
return "Unknown BatchNormalizationKind";
}
string ToVlogString(bool b) {
if (b) {
return "True";
}
return "false";
}
string ToVlogString(string str) { return str; }
// Used together with PARAM to VLOG calls made to the stream. Intended
// to be used like this:
//
// VLOG(1) << CallStr("MyFunction", this, {PARAM(a), PARAM(b)});
//
// where a and b are the parameters to MyFunction.
//
// See VLOG_CALL for a short-hand for this. This way of doing it saves
// a tremendous amount of boilerplate code given how many functions
// there are on Stream and how many parameters they each have.
string CallStr(const char *function_name, Stream *stream,
std::vector<std::pair<const char *, string>> params) {
// Do not call this function unless VLOG is on since just
// constructing all the strings in params is expensive.
CHECK(VLOG_IS_ON(1));
string str = absl::StrCat(stream->DebugStreamPointers(),
" Called Stream::", function_name, "(");
const char *separator = "";
for (const auto ¶m : params) {
absl::StrAppend(&str, separator, param.first, "=", param.second);
separator = ", ";
}
absl::StrAppend(&str, ")");
if (VLOG_IS_ON(10)) {
absl::StrAppend(&str, " ", port::CurrentStackTrace(), "\n");
}
return str;
}
// Use this macro to avoid having to type every parameter twice to log
// it with VLOG and CallStr.
#define PARAM(parameter) \
{ #parameter, ToVlogString(parameter) }
// Use this macro to avoid having to type out the name of each
// function and to save some boilerplate. Intended to be used like this:
//
// VLOG_CALL(PARAM(a), PARAM(b))
//
// This saves a tremendous amount of boilerplate compared to the alternative:
//
// VLOG(1) << "Calling MyFunction(a=" << ToVlogString(a)
// << ", b=" << ToVlogString(b);
//
// Note here that most of the parameter names are not short and that
// most of the functions take many more than 2 parameters.
#define VLOG_CALL(...) VLOG(1) << CallStr(__func__, this, {__VA_ARGS__})
} // namespace
Stream::Stream(StreamExecutor *parent)
: parent_(parent),
implementation_(parent->implementation()->GetStreamImplementation()),
allocated_(false),
ok_(false),
temporary_memory_manager_(this) {
VLOG_CALL(PARAM(parent));
}
Stream::Stream(StreamExecutor *parent,
internal::StreamInterface *implementation)
: parent_(parent),
implementation_(implementation),
allocated_(false),
ok_(false),
temporary_memory_manager_(this) {
VLOG_CALL(PARAM(parent), PARAM(implementation));
}
Stream::~Stream() {
VLOG_CALL();
// Ensure the stream is completed.
auto status = BlockHostUntilDone();
if (!status.ok()) {
LOG(WARNING) << "Error blocking host until done in stream destructor: "
<< status;
}
temporary_memory_manager_.ForceDeallocateAll();
RunAfterBlockHostUntilDoneCallbacks();
if (allocated_) {
parent_->DeallocateStream(this);
}
}
port::Status Stream::RefreshStatus() {
port::Status status = parent_->GetStatus(this);
CheckStatus(status);
return status;
}
Stream &Stream::Init() {
VLOG_CALL();
absl::MutexLock lock(&mu_);
CHECK_EQ(false, allocated_)
<< "stream appears to already have been initialized";
CHECK(!ok_) << "stream should be in !ok() state pre-initialization";
if (parent_->AllocateStream(this)) {
// Successful initialization!
allocated_ = true;
ok_ = true;
} else {
LOG(ERROR) << "failed to allocate stream during initialization";
}
return *this;
}
Stream &Stream::InitTimer(Timer *timer) {
VLOG_CALL(PARAM(timer));
if (ok()) {
CheckError(parent_->AllocateTimer(timer));
} else {
LOG(INFO) << "did not allocate timer: " << timer;
}
return *this;
}
Stream &Stream::InitWithTimer(Timer *timer) {
VLOG_CALL(PARAM(timer));
return Init().InitTimer(timer);
}
Stream &Stream::ThenRecordEvent(Event *event) {
VLOG_CALL(PARAM(event));
port::Status status = parent_->RecordEvent(this, event);
if (!status.ok()) {
LOG(ERROR) << "Error recording event in stream: " << status.error_message()
<< "; not marking stream as bad, as the Event object may be "
<< "at fault. Monitor for further errors.";
}
return *this;
}
Stream &Stream::ThenLaunchGraph(void *exec_graph) {
VLOG_CALL(PARAM(exec_graph));
DCHECK(parent_ != nullptr);
port::Status status = parent_->LaunchExecutableGraph(this, exec_graph);
if (!status.ok()) {
LOG(ERROR) << "Failed to launch task graph " << status.error_message();
}
return *this;
}
Stream &Stream::ThenBeginGraphCapture() {
VLOG_CALL();
DCHECK(parent_ != nullptr);
port::Status status = parent_->BeginGraphCapture(this);
if (!status.ok()) {
LOG(ERROR) << "Failed to begin capturing task graph "
<< status.error_message();
}
return *this;
}
Stream &Stream::ThenEndGraphCapture(void *&graph) {
VLOG_CALL();
DCHECK(parent_ != nullptr);
port::StatusOr<void *> status = parent_->EndGraphCapture(this, graph);
if (!status.ok()) {
LOG(ERROR) << "Failed to end capturing task graph "
<< status.status().error_message();
}
graph = status.ValueOrDie();
return *this;
}
Stream &Stream::ThenSoftmax(
const DeviceMemory<float> &x, const dnn::BatchDescriptor &x_desc,
bool log , DeviceMemory<float> *y)
{
VLOG_CALL(PARAM(x), PARAM(x_desc), PARAM(log), PARAM(y));
if (ok()) {
if (dnn::DnnSupport *dnn = parent_->AsDnn()) {
CheckError(dnn->DoSoftmax(this, x, x_desc, log, y));
} else {
SetErrorAndLogNoDnnSupport();
}
}
return *this;
}
Stream &Stream::ThenSoftmax(
const DeviceMemory<Eigen::half> &x, const dnn::BatchDescriptor &x_desc,
bool log , DeviceMemory<Eigen::half> *y)
{
VLOG_CALL(PARAM(x), PARAM(x_desc), PARAM(log), PARAM(y));
if (ok()) {
if (dnn::DnnSupport *dnn = parent_->AsDnn()) {
CheckError(dnn->DoSoftmax(this, x, x_desc, log, y));
} else {
SetErrorAndLogNoDnnSupport();
}
}
return *this;
}
Stream &Stream::ThenFindBatchNormalizationTrainingExReserveSpaceSize(
int64 batch_size, int64 feature_count, int64 y_size,
const std::string &layout, dnn::DataType input_type,
size_t *reserve_space_size, dnn::ActivationMode activation_mode,
bool apply_side_input) {
VLOG_CALL(PARAM(batch_size), PARAM(feature_count), PARAM(y_size),
PARAM(input_type), PARAM(layout), PARAM(activation_mode),
PARAM(apply_side_input));
stream_executor::dnn::DataLayout data_layout;
if (layout == "NHWC" || layout == "NHWC_VECT_W") {
data_layout = stream_executor::dnn::DataLayout::kBatchYXDepth;
} else if (layout == "NCHW" || "NCHW_VECT_C") {
data_layout = stream_executor::dnn::DataLayout::kBatchDepthYX;
} else {
LOG(ERROR) << "Invalid or unimplemented data type while computing "
"BatchNormalization training reserve space size";
}
stream_executor::dnn::BatchDescriptor input_desc;
input_desc.set_layout(data_layout)
.set_count(batch_size)
.set_feature_map_count(feature_count)
.set_height(y_size)
.set_width(1 /*width*/);
if (ok()) {
if (dnn::DnnSupport *dnn = parent_->AsDnn()) {
CheckError(dnn->GetBatchNormalizationReserveSpaceSize(
this, input_type, input_desc, reserve_space_size, activation_mode,
apply_side_input));
} else {
SetErrorAndLogNoDnnSupport();
}
}
return *this;
}
Stream &Stream::ThenFindBatchNormWorkspaceSize(
dnn::DataType input_data_type, dnn::DataType scale_data_type,
const dnn::BatchDescriptor &x_desc,
const dnn::BatchDescriptor &scale_offset_desc,
size_t *workspace_size_in_bytes,
stream_executor::BatchNormalizationKind kind,
dnn::ActivationMode activation_mode, bool apply_side_input) {
VLOG_CALL(PARAM(input_data_type), PARAM(scale_data_type), PARAM(x_desc),
PARAM(scale_offset_desc), PARAM(kind), PARAM(activation_mode),
PARAM(apply_side_input));
if (ok()) {
if (dnn::DnnSupport *dnn = parent_->AsDnn()) {
CheckError(dnn->GetBatchNormalizationWorkspaceSize(
this, input_data_type, scale_data_type, x_desc, scale_offset_desc,
workspace_size_in_bytes, kind, activation_mode, apply_side_input));
} else {
SetErrorAndLogNoDnnSupport();
}
}
return *this;
}
Stream &Stream::ThenBatchNormalizationForward(
const DeviceMemory<float> &x, const DeviceMemory<float> &scale,
const DeviceMemory<float> &offset,
const DeviceMemory<float> &estimated_mean,
const DeviceMemory<float> &estimated_variance,
const DeviceMemory<float> &side_input, const dnn::BatchDescriptor &x_desc,
const dnn::BatchDescriptor &scale_offset_desc, const double epsilon,
const double exponential_average_factor,
dnn::ActivationMode activation_mode, DeviceMemory<float> *y,
DeviceMemory<float> *batch_mean, DeviceMemory<float> *batch_var,
DeviceMemory<float> *saved_mean, DeviceMemory<float> *saved_inv_var,
bool is_training,
ScratchAllocator *reserve_space_allocator,
ScratchAllocator *workspace_allocator) {
VLOG_CALL(PARAM(x), PARAM(scale), PARAM(offset), PARAM(x_desc),
PARAM(side_input), PARAM(activation_mode), PARAM(scale_offset_desc),
PARAM(epsilon), PARAM(y));
if (ok()) {
if (dnn::DnnSupport *dnn = parent_->AsDnn()) {
CheckError(dnn->DoBatchNormalizationForward(
this, x, scale, offset, estimated_mean, estimated_variance,
side_input, x_desc, scale_offset_desc, epsilon,
exponential_average_factor, activation_mode, y, batch_mean, batch_var,
saved_mean, saved_inv_var, is_training, reserve_space_allocator,
workspace_allocator));
} else {
SetErrorAndLogNoDnnSupport();
}
}
return *this;
}
Stream &Stream::ThenBatchNormalizationBackward(
const DeviceMemory<float> &y_backprop, const DeviceMemory<float> &x,
const DeviceMemory<float> &scale, const DeviceMemory<float> &mean,
const DeviceMemory<float> &inv_var, const dnn::BatchDescriptor &x_desc,
const dnn::BatchDescriptor &scale_offset_desc, const double epsilon,
DeviceMemory<float> *x_backprop, DeviceMemory<float> *scale_backprop,
DeviceMemory<float> *offset_backprop,
DeviceMemory<uint8> *reserve_space_data,
ScratchAllocator *workspace_allocator) {
VLOG_CALL(PARAM(y_backprop), PARAM(x), PARAM(scale), PARAM(x_desc),
PARAM(scale_offset_desc), PARAM(epsilon), PARAM(x_backprop),
PARAM(scale_backprop), PARAM(offset_backprop));
if (ok()) {
if (dnn::DnnSupport *dnn = parent_->AsDnn()) {
CheckError(dnn->DoBatchNormalizationBackward(
this, y_backprop, x, scale, mean, inv_var, x_desc, scale_offset_desc,
epsilon, x_backprop, scale_backprop, offset_backprop,
reserve_space_data, workspace_allocator));
} else {
SetErrorAndLogNoDnnSupport();
}
}
return *this;
}
Stream &Stream::ThenBatchNormalizationForward(
const DeviceMemory<Eigen::half> &x, const DeviceMemory<float> &scale,
const DeviceMemory<float> &offset,
const DeviceMemory<float> &estimated_mean,
const DeviceMemory<float> &estimated_variance,
const DeviceMemory<Eigen::half> &side_input,
const dnn::BatchDescriptor &x_desc,
const dnn::BatchDescriptor &scale_offset_desc, const double epsilon,
const double exponential_average_factor,
dnn::ActivationMode activation_mode, DeviceMemory<Eigen::half> *y,
DeviceMemory<float> *batch_mean, DeviceMemory<float> *batch_var,
DeviceMemory<float> *saved_mean, DeviceMemory<float> *saved_inv_var,
bool is_training, ScratchAllocator *reserve_space_allocator,
ScratchAllocator *workspace_allocator) {
VLOG_CALL(PARAM(x), PARAM(scale), PARAM(offset), PARAM(x_desc),
PARAM(scale_offset_desc), PARAM(epsilon), PARAM(y));
if (ok()) {
if (dnn::DnnSupport *dnn = parent_->AsDnn()) {
CheckError(dnn->DoBatchNormalizationForward(
this, x, scale, offset, estimated_mean, estimated_variance,
side_input, x_desc, scale_offset_desc, epsilon,
exponential_average_factor, activation_mode, y, batch_mean, batch_var,
saved_mean, saved_inv_var, is_training, reserve_space_allocator,
workspace_allocator));
} else {
SetErrorAndLogNoDnnSupport();
}
}
return *this;
}
Stream &Stream::ThenBatchNormalizationBackward(
const DeviceMemory<Eigen::half> &y_backprop,
const DeviceMemory<Eigen::half> &x, const DeviceMemory<float> &scale,
const DeviceMemory<float> &mean, const DeviceMemory<float> &inv_var,
const dnn::BatchDescriptor &x_desc,
const dnn::BatchDescriptor &scale_offset_desc, const double epsilon,
DeviceMemory<Eigen::half> *x_backprop, DeviceMemory<float> *scale_backprop,
DeviceMemory<float> *offset_backprop,
DeviceMemory<uint8> *reserve_space_data,
ScratchAllocator *workspace_allocator) {
VLOG_CALL(PARAM(y_backprop), PARAM(x), PARAM(scale), PARAM(x_desc),
PARAM(scale_offset_desc), PARAM(epsilon), PARAM(x_backprop),
PARAM(scale_backprop), PARAM(offset_backprop));
if (ok()) {
if (dnn::DnnSupport *dnn = parent_->AsDnn()) {
CheckError(dnn->DoBatchNormalizationBackward(
this, y_backprop, x, scale, mean, inv_var, x_desc, scale_offset_desc,
epsilon, x_backprop, scale_backprop, offset_backprop,
reserve_space_data, workspace_allocator));
} else {
SetErrorAndLogNoDnnSupport();
}
}
return *this;
}
Stream &Stream::ThenFusedConvolveWithAlgorithm(
const dnn::BatchDescriptor &conv_input_descriptor,
const DeviceMemory<double> &conv_input_data, double conv_input_scale,
const dnn::FilterDescriptor &filter_descriptor,
const DeviceMemory<double> &filter_data,
const dnn::ConvolutionDescriptor &convolution_descriptor,
const DeviceMemory<double> &side_input_data, double side_input_scale,
const dnn::BatchDescriptor &bias_descriptor,
const DeviceMemory<double> &biases, dnn::ActivationMode activation_mode,
const dnn::BatchDescriptor &output_descriptor, DeviceMemory<double> *output,
ScratchAllocator *scratch_allocator,
const dnn::AlgorithmConfig &algorithm_config,
dnn::ProfileResult *output_profile_result) {
VLOG_CALL(PARAM(conv_input_descriptor), PARAM(conv_input_data),
PARAM(conv_input_scale), PARAM(filter_descriptor),
PARAM(filter_data), PARAM(convolution_descriptor), PARAM(biases),
PARAM(side_input_data), PARAM(side_input_scale),
PARAM(activation_mode), PARAM(output_descriptor), PARAM(output),
PARAM(algorithm_config));
if (ok()) {
if (dnn::DnnSupport *dnn = parent_->AsDnn()) {
auto status = dnn->DoFusedConvolve(
this, conv_input_descriptor, conv_input_data, conv_input_scale,
filter_descriptor, filter_data, convolution_descriptor,
side_input_data, side_input_scale, bias_descriptor, biases,
activation_mode, output_descriptor, output, scratch_allocator,
algorithm_config, output_profile_result);
if (!status && !output_profile_result) {
SetError();
}
} else {
SetErrorAndLogNoDnnSupport();
}
}
return *this;
}
Stream &Stream::ThenFusedConvolveWithAlgorithm(
const dnn::BatchDescriptor &conv_input_descriptor,
const DeviceMemory<float> &conv_input_data, float conv_input_scale,
const dnn::FilterDescriptor &filter_descriptor,
const DeviceMemory<float> &filter_data,
const dnn::ConvolutionDescriptor &convolution_descriptor,
const DeviceMemory<float> &side_input_data, float side_input_scale,
const dnn::BatchDescriptor &bias_descriptor,
const DeviceMemory<float> &biases, dnn::ActivationMode activation_mode,
const dnn::BatchDescriptor &output_descriptor, DeviceMemory<float> *output,
ScratchAllocator *scratch_allocator,
const dnn::AlgorithmConfig &algorithm_config,
dnn::ProfileResult *output_profile_result) {
VLOG_CALL(PARAM(conv_input_descriptor), PARAM(conv_input_data),
PARAM(conv_input_scale), PARAM(filter_descriptor),
PARAM(filter_data), PARAM(convolution_descriptor), PARAM(biases),
PARAM(side_input_data), PARAM(side_input_scale),
PARAM(activation_mode), PARAM(output_descriptor), PARAM(output),
PARAM(algorithm_config));
if (ok()) {
if (dnn::DnnSupport *dnn = parent_->AsDnn()) {
auto status = dnn->DoFusedConvolve(
this, conv_input_descriptor, conv_input_data, conv_input_scale,
filter_descriptor, filter_data, convolution_descriptor,
side_input_data, side_input_scale, bias_descriptor, biases,
activation_mode, output_descriptor, output, scratch_allocator,
algorithm_config, output_profile_result);
if (!status && !output_profile_result) {
SetError();
}
} else {
SetErrorAndLogNoDnnSupport();
}
}
return *this;
}
Stream &Stream::ThenFusedConvolveWithAlgorithm(
const dnn::BatchDescriptor &conv_input_descriptor,
const DeviceMemory<Eigen::half> &conv_input_data, float conv_input_scale,
const dnn::FilterDescriptor &filter_descriptor,
const DeviceMemory<Eigen::half> &filter_data,
const dnn::ConvolutionDescriptor &convolution_descriptor,
const DeviceMemory<Eigen::half> &side_input_data, float side_input_scale,
const dnn::BatchDescriptor &bias_descriptor,
const DeviceMemory<Eigen::half> &biases,
dnn::ActivationMode activation_mode,
const dnn::BatchDescriptor &output_descriptor,
DeviceMemory<Eigen::half> *output, ScratchAllocator *scratch_allocator,
const dnn::AlgorithmConfig &algorithm_config,
dnn::ProfileResult *output_profile_result) {
VLOG_CALL(PARAM(conv_input_descriptor), PARAM(conv_input_data),
PARAM(conv_input_scale), PARAM(filter_descriptor),
PARAM(filter_data), PARAM(convolution_descriptor), PARAM(biases),
PARAM(side_input_data), PARAM(side_input_scale),
PARAM(bias_descriptor), PARAM(biases), PARAM(activation_mode),
PARAM(output_descriptor), PARAM(output), PARAM(algorithm_config));
if (ok()) {
if (dnn::DnnSupport *dnn = parent_->AsDnn()) {
auto status = dnn->DoFusedConvolve(
this, conv_input_descriptor, conv_input_data, conv_input_scale,
filter_descriptor, filter_data, convolution_descriptor,
side_input_data, side_input_scale, bias_descriptor, biases,
activation_mode, output_descriptor, output, scratch_allocator,
algorithm_config, output_profile_result);
if (!status && !output_profile_result) {
SetError();
}
} else {
SetErrorAndLogNoDnnSupport();
}
}
return *this;
}
Stream &Stream::ThenFusedConvolveWithAlgorithm(
const dnn::BatchDescriptor &conv_input_descriptor,
const DeviceMemory<int8> &conv_input_data, float conv_input_scale,
const dnn::FilterDescriptor &filter_descriptor,
const DeviceMemory<int8> &filter_data,
const dnn::ConvolutionDescriptor &convolution_descriptor,
const DeviceMemory<int8> &side_input_data, float side_input_scale,
const dnn::BatchDescriptor &bias_descriptor,
const DeviceMemory<float> &biases, dnn::ActivationMode activation_mode,
const dnn::BatchDescriptor &output_descriptor, DeviceMemory<int8> *output,
ScratchAllocator *scratch_allocator,
const dnn::AlgorithmConfig &algorithm_config,
dnn::ProfileResult *output_profile_result) {
VLOG_CALL(PARAM(conv_input_descriptor), PARAM(conv_input_data),
PARAM(conv_input_scale), PARAM(filter_descriptor),
PARAM(filter_data), PARAM(convolution_descriptor), PARAM(biases),
PARAM(side_input_data), PARAM(side_input_scale),
PARAM(bias_descriptor), PARAM(biases), PARAM(activation_mode),
PARAM(output_descriptor), PARAM(output), PARAM(algorithm_config));
if (ok()) {
if (dnn::DnnSupport *dnn = parent_->AsDnn()) {
auto status = dnn->DoFusedConvolve(
this, conv_input_descriptor, conv_input_data, conv_input_scale,
filter_descriptor, filter_data, convolution_descriptor,
side_input_data, side_input_scale, bias_descriptor, biases,
activation_mode, output_descriptor, output, scratch_allocator,
algorithm_config, output_profile_result);
if (!status && !output_profile_result) {
SetError();
}
} else {
SetErrorAndLogNoDnnSupport();
}
}
return *this;
}
Stream &Stream::ThenFusedConvolveWithAlgorithm(
const dnn::BatchDescriptor &conv_input_descriptor,
const DeviceMemory<int8> &conv_input_data, float conv_input_scale,
const dnn::FilterDescriptor &filter_descriptor,
const DeviceMemory<int8> &filter_data,
const dnn::ConvolutionDescriptor &convolution_descriptor,
const DeviceMemory<float> &side_input_data, float side_input_scale,
const dnn::BatchDescriptor &bias_descriptor,
const DeviceMemory<float> &biases, dnn::ActivationMode activation_mode,
const dnn::BatchDescriptor &output_descriptor, DeviceMemory<float> *output,
ScratchAllocator *scratch_allocator,
const dnn::AlgorithmConfig &algorithm_config,
dnn::ProfileResult *output_profile_result) {
VLOG_CALL(PARAM(conv_input_descriptor), PARAM(conv_input_data),
PARAM(conv_input_scale), PARAM(filter_descriptor),
PARAM(filter_data), PARAM(convolution_descriptor), PARAM(biases),
PARAM(side_input_data), PARAM(side_input_scale),
PARAM(bias_descriptor), PARAM(biases), PARAM(activation_mode),
PARAM(output_descriptor), PARAM(output), PARAM(algorithm_config));
if (ok()) {
if (dnn::DnnSupport *dnn = parent_->AsDnn()) {
auto status = dnn->DoFusedConvolve(
this, conv_input_descriptor, conv_input_data, conv_input_scale,
filter_descriptor, filter_data, convolution_descriptor,
side_input_data, side_input_scale, bias_descriptor, biases,
activation_mode, output_descriptor, output, scratch_allocator,
algorithm_config, output_profile_result);
if (!status && !output_profile_result) {
SetError();
}
} else {
SetErrorAndLogNoDnnSupport();
}
}
return *this;
}
port::Status Stream::FusedConvolveWithExecutionPlan(
const dnn::BatchDescriptor &conv_input_descriptor,
const DeviceMemory<double> &conv_input_data, double conv_input_scale,
const dnn::FilterDescriptor &filter_descriptor,
const DeviceMemory<double> &filter_data,
const dnn::ConvolutionDescriptor &convolution_descriptor,
const DeviceMemory<double> &side_input_data, double side_input_scale,
const dnn::BatchDescriptor &bias_descriptor,
const DeviceMemory<double> &biases, dnn::ActivationMode activation_mode,
const dnn::BatchDescriptor &output_descriptor, DeviceMemory<double> *output,
ScratchAllocator *scratch_allocator,
const dnn::ExecutionPlanConfig &plan_config,
dnn::ProfileExecutionPlanResult *output_profile_result) {
VLOG_CALL(PARAM(conv_input_descriptor), PARAM(conv_input_data),
PARAM(conv_input_scale), PARAM(filter_descriptor),
PARAM(filter_data), PARAM(convolution_descriptor), PARAM(biases),
PARAM(side_input_data), PARAM(side_input_scale),
PARAM(activation_mode), PARAM(output_descriptor), PARAM(output));
if (dnn::DnnSupport *dnn = parent_->AsDnn()) {
return dnn->DoFusedConvolve(
this, conv_input_descriptor, conv_input_data, conv_input_scale,
filter_descriptor, filter_data, convolution_descriptor, side_input_data,
side_input_scale, bias_descriptor, biases, activation_mode,
output_descriptor, output, scratch_allocator, plan_config,
output_profile_result);
}
return port::UnimplementedError("DNN library is not found.");
}
port::Status Stream::FusedConvolveWithExecutionPlan(
const dnn::BatchDescriptor &conv_input_descriptor,
const DeviceMemory<float> &conv_input_data, float conv_input_scale,
const dnn::FilterDescriptor &filter_descriptor,
const DeviceMemory<float> &filter_data,
const dnn::ConvolutionDescriptor &convolution_descriptor,
const DeviceMemory<float> &side_input_data, float side_input_scale,
const dnn::BatchDescriptor &bias_descriptor,
const DeviceMemory<float> &biases, dnn::ActivationMode activation_mode,
const dnn::BatchDescriptor &output_descriptor, DeviceMemory<float> *output,
ScratchAllocator *scratch_allocator,
const dnn::ExecutionPlanConfig &plan_config,
dnn::ProfileExecutionPlanResult *output_profile_result) {
VLOG_CALL(PARAM(conv_input_descriptor), PARAM(conv_input_data),
PARAM(conv_input_scale), PARAM(filter_descriptor),
PARAM(filter_data), PARAM(convolution_descriptor), PARAM(biases),
PARAM(side_input_data), PARAM(side_input_scale),
PARAM(activation_mode), PARAM(output_descriptor), PARAM(output));
if (dnn::DnnSupport *dnn = parent_->AsDnn()) {
return dnn->DoFusedConvolve(
this, conv_input_descriptor, conv_input_data, conv_input_scale,
filter_descriptor, filter_data, convolution_descriptor, side_input_data,
side_input_scale, bias_descriptor, biases, activation_mode,
output_descriptor, output, scratch_allocator, plan_config,
output_profile_result);
}
return port::UnimplementedError("DNN library is not found.");
}
port::Status Stream::FusedConvolveWithExecutionPlan(
const dnn::BatchDescriptor &conv_input_descriptor,
const DeviceMemory<Eigen::half> &conv_input_data, float conv_input_scale,
const dnn::FilterDescriptor &filter_descriptor,
const DeviceMemory<Eigen::half> &filter_data,
const dnn::ConvolutionDescriptor &convolution_descriptor,
const DeviceMemory<Eigen::half> &side_input_data, float side_input_scale,
const dnn::BatchDescriptor &bias_descriptor,
const DeviceMemory<Eigen::half> &biases,
dnn::ActivationMode activation_mode,
const dnn::BatchDescriptor &output_descriptor,
DeviceMemory<Eigen::half> *output, ScratchAllocator *scratch_allocator,
const dnn::ExecutionPlanConfig &plan_config,
dnn::ProfileExecutionPlanResult *output_profile_result) {
VLOG_CALL(PARAM(conv_input_descriptor), PARAM(conv_input_data),
PARAM(conv_input_scale), PARAM(filter_descriptor),
PARAM(filter_data), PARAM(convolution_descriptor), PARAM(biases),
PARAM(side_input_data), PARAM(side_input_scale),
PARAM(bias_descriptor), PARAM(biases), PARAM(activation_mode),
PARAM(output_descriptor), PARAM(output));
if (dnn::DnnSupport *dnn = parent_->AsDnn()) {
return dnn->DoFusedConvolve(
this, conv_input_descriptor, conv_input_data, conv_input_scale,
filter_descriptor, filter_data, convolution_descriptor, side_input_data,
side_input_scale, bias_descriptor, biases, activation_mode,
output_descriptor, output, scratch_allocator, plan_config,
output_profile_result);
}
return port::UnimplementedError("DNN library is not found.");
}
port::Status Stream::FusedConvolveWithExecutionPlan(
const dnn::BatchDescriptor &conv_input_descriptor,
const DeviceMemory<int8> &conv_input_data, float conv_input_scale,
const dnn::FilterDescriptor &filter_descriptor,
const DeviceMemory<int8> &filter_data,
const dnn::ConvolutionDescriptor &convolution_descriptor,
const DeviceMemory<int8> &side_input_data, float side_input_scale,
const dnn::BatchDescriptor &bias_descriptor,
const DeviceMemory<float> &biases, dnn::ActivationMode activation_mode,
const dnn::BatchDescriptor &output_descriptor, DeviceMemory<int8> *output,
ScratchAllocator *scratch_allocator,
const dnn::ExecutionPlanConfig &plan_config,
dnn::ProfileExecutionPlanResult *output_profile_result) {
VLOG_CALL(PARAM(conv_input_descriptor), PARAM(conv_input_data),
PARAM(conv_input_scale), PARAM(filter_descriptor),
PARAM(filter_data), PARAM(convolution_descriptor), PARAM(biases),
PARAM(side_input_data), PARAM(side_input_scale),
PARAM(bias_descriptor), PARAM(biases), PARAM(activation_mode),
PARAM(output_descriptor), PARAM(output));
if (dnn::DnnSupport *dnn = parent_->AsDnn()) {
return dnn->DoFusedConvolve(
this, conv_input_descriptor, conv_input_data, conv_input_scale,
filter_descriptor, filter_data, convolution_descriptor, side_input_data,
side_input_scale, bias_descriptor, biases, activation_mode,
output_descriptor, output, scratch_allocator, plan_config,
output_profile_result);
}
return port::UnimplementedError("DNN library is not found.");
}
port::Status Stream::FusedConvolveWithExecutionPlan(
const dnn::BatchDescriptor &conv_input_descriptor,
const DeviceMemory<int8> &conv_input_data, float conv_input_scale,
const dnn::FilterDescriptor &filter_descriptor,
const DeviceMemory<int8> &filter_data,
const dnn::ConvolutionDescriptor &convolution_descriptor,
const DeviceMemory<float> &side_input_data, float side_input_scale,
const dnn::BatchDescriptor &bias_descriptor,
const DeviceMemory<float> &biases, dnn::ActivationMode activation_mode,
const dnn::BatchDescriptor &output_descriptor, DeviceMemory<float> *output,
ScratchAllocator *scratch_allocator,
const dnn::ExecutionPlanConfig &plan_config,
dnn::ProfileExecutionPlanResult *output_profile_result) {
VLOG_CALL(PARAM(conv_input_descriptor), PARAM(conv_input_data),
PARAM(conv_input_scale), PARAM(filter_descriptor),
PARAM(filter_data), PARAM(convolution_descriptor), PARAM(biases),
PARAM(side_input_data), PARAM(side_input_scale),
PARAM(bias_descriptor), PARAM(biases), PARAM(activation_mode),
PARAM(output_descriptor), PARAM(output));
if (dnn::DnnSupport *dnn = parent_->AsDnn()) {
return dnn->DoFusedConvolve(
this, conv_input_descriptor, conv_input_data, conv_input_scale,
filter_descriptor, filter_data, convolution_descriptor, side_input_data,
side_input_scale, bias_descriptor, biases, activation_mode,
output_descriptor, output, scratch_allocator, plan_config,
output_profile_result);
}
return port::UnimplementedError("DNN library is not found.");
}
Stream &Stream::ThenConvolveWithAlgorithm(
const dnn::BatchDescriptor &input_descriptor,
const DeviceMemory<double> &input_data,
const dnn::FilterDescriptor &filter_descriptor,
const DeviceMemory<double> &filter_data,
const dnn::ConvolutionDescriptor &convolution_descriptor,
const dnn::BatchDescriptor &output_descriptor, DeviceMemory<double> *output,
ScratchAllocator *scratch_allocator,
const dnn::AlgorithmConfig &algorithm_config,
dnn::ProfileResult *output_profile_result) {
VLOG_CALL(PARAM(input_descriptor), PARAM(input_data),
PARAM(filter_descriptor), PARAM(filter_data),
PARAM(convolution_descriptor), PARAM(output_descriptor),
PARAM(output), PARAM(algorithm_config));
if (ok()) {
if (dnn::DnnSupport *dnn = parent_->AsDnn()) {
DeviceMemory<uint8> scratch_memory;
dnn::AlgorithmDesc algorithm_desc;
auto status =
dnn->PrepareForConvolution(
dnn::ConvolutionKind::FORWARD, this, input_descriptor,
input_data, filter_descriptor, filter_data, output_descriptor,
*output, convolution_descriptor, algorithm_config,
scratch_allocator, &algorithm_desc, &scratch_memory)
.ok();
if (status) {
status = dnn->DoConvolve(
this, input_descriptor, input_data, filter_descriptor, filter_data,
convolution_descriptor, output_descriptor, output, algorithm_desc,
&scratch_memory, output_profile_result);
}
if (!status && !output_profile_result) {
SetError();
}
} else {
SetErrorAndLogNoDnnSupport();
}
}
return *this;
}
Stream &Stream::ThenConvolveWithAlgorithm(
const dnn::BatchDescriptor &input_descriptor,
const DeviceMemory<float> &input_data,
const dnn::FilterDescriptor &filter_descriptor,
const DeviceMemory<float> &filter_data,
const dnn::ConvolutionDescriptor &convolution_descriptor,
const dnn::BatchDescriptor &output_descriptor, DeviceMemory<float> *output,
ScratchAllocator *scratch_allocator,
const dnn::AlgorithmConfig &algorithm_config,
dnn::ProfileResult *output_profile_result) {
VLOG_CALL(PARAM(input_descriptor), PARAM(input_data),
PARAM(filter_descriptor), PARAM(filter_data),
PARAM(convolution_descriptor), PARAM(output_descriptor),