cutlass/tools/profiler/src/device_context.cu
Andrew Kerr c53f3339bb
CUTLASS 2.3 initial commit (#134)
CUTLASS 2.3 adds GEMMs targeting Sparse Tensor Cores on the NVIDIA Ampere Architecture, fast SGEMM, and small matrix classes, bug fixes, and performance enhancements.
2020-09-23 14:00:58 -07:00

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/***************************************************************************************************
* Copyright (c) 2017-2020, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted
* provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright notice, this list of
* conditions and the following disclaimer in the documentation and/or other materials
* provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
* to endorse or promote products derived from this software without specific prior written
* permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
/* \file
\brief
*/
#include "device_context.h"
namespace cutlass {
namespace profiler {
/////////////////////////////////////////////////////////////////////////////////////////////////
/// Allocates memory of a given type, capacity (elements), and name
DeviceAllocation *DeviceContext::allocate_block(
std::string const &name,
library::NumericTypeID type,
size_t capacity) {
device_memory_.emplace_back(type, capacity);
DeviceAllocation *allocation = &device_memory_.back();
allocations_[name] = allocation;
return allocation;
}
/// Allocates memory of a given type, capacity (elements), and name
DeviceAllocation *DeviceContext::allocate_tensor(
std::string const &name,
library::NumericTypeID type,
library::LayoutTypeID layout_id,
std::vector<int> const &extent,
std::vector<int> const &stride,
int batch_count) {
device_memory_.emplace_back(type, layout_id, extent, stride, batch_count);
DeviceAllocation *allocation = &device_memory_.back();
allocations_[name] = allocation;
return allocation;
}
/// Allocates memory of a given type, capacity (elements), and name
DeviceAllocation *DeviceContext::allocate_tensor(
Options const &options,
std::string const &name,
library::NumericTypeID type,
library::LayoutTypeID layout_id,
std::vector<int> const &extent,
std::vector<int> const &stride,
int batch_count) {
DeviceAllocation *allocation =
allocate_tensor(name, type, layout_id, extent, stride, batch_count);
if (options.initialization.enabled) {
Distribution data_distribution = options.initialization.data_distribution;
// check if data distribution is allowed to change
if(!options.initialization.fix_data_distribution) {
// change data distribution based on bit width
switch(type) {
case library::NumericTypeID::kB1:
data_distribution.set_uniform(0, 1, 0);
break;
case library::NumericTypeID::kS2:
data_distribution.set_uniform(-1, 1, 0);
break;
case library::NumericTypeID::kS4:
data_distribution.set_uniform(-2, 2, 0);
break;
case library::NumericTypeID::kU2:
data_distribution.set_uniform(0, 2, 0);
break;
case library::NumericTypeID::kU4:
data_distribution.set_uniform(0, 2, 0);
break;
case library::NumericTypeID::kS8:
data_distribution.set_uniform(-3, 3, 0);
break;
case library::NumericTypeID::kU8:
data_distribution.set_uniform(0, 4, 0);
break;
default: break;
}
}
if (options.initialization.provider == library::Provider::kReferenceDevice) {
allocation->initialize_random_device(
options.initialization.seed,
data_distribution);
}
else if (options.initialization.provider == library::Provider::kReferenceHost) {
allocation->initialize_random_host(
options.initialization.seed,
data_distribution);
}
}
return allocation;
}
/// Allocates memory for sparse meta data
DeviceAllocation *DeviceContext::allocate_sparsemeta_tensor(
Options const &options,
std::string const &name,
library::NumericTypeID type,
library::LayoutTypeID layout_id,
library::NumericTypeID type_a,
std::vector<int> const &extent,
std::vector<int> const &stride,
int batch_count) {
DeviceAllocation *allocation =
allocate_tensor(name, type, layout_id, extent, stride, batch_count);
if (options.initialization.enabled) {
// TF32 has 4bit meta data. The rest has 2bit.
int MetaSizeInBits = (cutlass::library::sizeof_bits(type_a) == 32) ? 4 : 2;
if (options.initialization.provider == library::Provider::kReferenceDevice) {
allocation->initialize_random_sparsemeta_device(
options.initialization.seed,
MetaSizeInBits);
}
else if (options.initialization.provider == library::Provider::kReferenceHost) {
allocation->initialize_random_sparsemeta_host(
options.initialization.seed,
MetaSizeInBits);
}
}
return allocation;
}
/// Clears named allocations (but does not necessarily free memory)
void DeviceContext::clear() {
allocations_.clear();
}
/// Frees all device memory allocations
void DeviceContext::free() {
allocations_.clear();
device_memory_.clear();
}
/// Gets the allocation by name
DeviceAllocation &DeviceContext::at(std::string const &name) {
return *allocations_.at(name);
}
size_t DeviceContext::size() const {
return allocations_.size();
}
DeviceContext::AllocationMap::iterator DeviceContext::begin() {
return allocations_.begin();
}
DeviceContext::AllocationMap::iterator DeviceContext::end() {
return allocations_.end();
}
/////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace profiler
} // namespace cutlass