c53f3339bb
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.
218 lines
6.9 KiB
C++
218 lines
6.9 KiB
C++
/***************************************************************************************************
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* Copyright (c) 2017-2020, NVIDIA CORPORATION. All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without modification, are permitted
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* provided that the following conditions are met:
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* * Redistributions of source code must retain the above copyright notice, this list of
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* conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above copyright notice, this list of
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* conditions and the following disclaimer in the documentation and/or other materials
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* provided with the distribution.
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* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
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* to endorse or promote products derived from this software without specific prior written
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* permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
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* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
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* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
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* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
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* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
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* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*
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**************************************************************************************************/
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/* \file
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\brief Execution environment
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*/
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#pragma once
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#include <stdexcept>
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#include <list>
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#include <vector>
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#include "cutlass/library/library.h"
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#include "cutlass/util/distribution.h"
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#include "enumerated_types.h"
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/////////////////////////////////////////////////////////////////////////////////////////////////
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namespace cutlass {
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namespace profiler {
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/////////////////////////////////////////////////////////////////////////////////////////////////
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/// Device memory allocation
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class DeviceAllocation {
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private:
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/// Data type of contained elements
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library::NumericTypeID type_;
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/// Gets the stride between elements
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size_t batch_stride_;
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/// Capacity in elements of device allocation
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size_t capacity_;
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/// Pointer to device memory
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void *pointer_;
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/// Layout type ID
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library::LayoutTypeID layout_;
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/// Stride vector
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std::vector<int> stride_;
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/// Extent vector
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std::vector<int> extent_;
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/// Support allocating a 'batch' of non-overlapping tensors in contiguous memory
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int batch_count_;
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/// Buffer holding TensorRef instance to recently allocated memory
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std::vector<uint8_t> tensor_ref_buffer_;
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public:
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//
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// Static member functions
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//
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/// Determines the number of bytes needed to represent this numeric type
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static size_t bytes(library::NumericTypeID type, size_t capacity);
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/// Returns the stride of a packed layout
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static std::vector<int> get_packed_layout(
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library::LayoutTypeID layout_id,
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std::vector<int> const &extent);
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/// returns the capacity needed
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static size_t construct_layout(
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void *bytes,
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library::LayoutTypeID layout_id,
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std::vector<int> const &extent,
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std::vector<int> &stride);
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/// Returns true if two blocks have exactly the same value
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static bool block_compare_equal(
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library::NumericTypeID numeric_type,
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void const *ptr_A,
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void const *ptr_B,
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size_t capacity);
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/// Returns true if two blocks have approximately the same value
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static bool block_compare_relatively_equal(
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library::NumericTypeID numeric_type,
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void const *ptr_A,
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void const *ptr_B,
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size_t capacity,
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double epsilon,
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double nonzero_floor);
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public:
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//
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// Methods
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//
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DeviceAllocation();
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DeviceAllocation(library::NumericTypeID type, size_t capacity);
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DeviceAllocation(
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library::NumericTypeID type,
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library::LayoutTypeID layout_id,
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std::vector<int> const &extent,
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std::vector<int> const &stride = std::vector<int>(),
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int batch_count = 1);
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~DeviceAllocation();
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DeviceAllocation &reset();
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/// Allocates device memory of a given type and capacity
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DeviceAllocation &reset(library::NumericTypeID type, size_t capacity);
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/// Allocates memory for a given layout and tensor
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DeviceAllocation &reset(
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library::NumericTypeID type,
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library::LayoutTypeID layout_id,
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std::vector<int> const &extent,
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std::vector<int> const &stride = std::vector<int>(),
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int batch_count = 1);
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/// Returns a buffer owning the tensor reference
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std::vector<uint8_t> &tensor_ref() {
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return tensor_ref_buffer_;
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}
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bool good() const;
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/// Data type of contained elements
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library::NumericTypeID type() const;
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/// Pointer to start of device memory allocation
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void *data() const;
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/// Pointer to the first element of a batch
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void *batch_data(int batch_idx) const;
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/// Gets the layout type
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library::LayoutTypeID layout() const;
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/// Gets the stride vector
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std::vector<int> const & stride() const;
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/// Gets the extent vector
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std::vector<int> const & extent() const;
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/// Gets the number of adjacent tensors in memory
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int batch_count() const;
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/// Gets the stride (in units of elements) beteween items
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int64_t batch_stride() const;
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/// Gets the stride (in units of bytes) beteween items
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int64_t batch_stride_bytes() const;
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/// Capacity of allocation in number of elements
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size_t capacity() const;
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/// Capacity of allocation in bytes
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size_t bytes() const;
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/// Initializes a device allocation to a random distribution using cuRAND
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void initialize_random_device(int seed, Distribution dist);
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/// Initializes a host allocation to a random distribution using std::cout
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void initialize_random_host(int seed, Distribution dist);
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/// Initializes a device allocation to a random distribution using cuRAND
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void initialize_random_sparsemeta_device(int seed, int MetaSizeInBits);
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/// Initializes a host allocation to a random distribution using std::cout
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void initialize_random_sparsemeta_host(int seed, int MetaSizeInBits);
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/// Copies from an equivalent-sized tensor in device memory
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void copy_from_device(void const *ptr);
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/// Copies from an equivalent-sized tensor in device memory
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void copy_from_host(void const *ptr);
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/// Copies from an equivalent-sized tensor in device memory
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void copy_to_host(void *ptr);
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/// Writes a tensor to csv
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void write_tensor_csv(std::ostream &out);
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};
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using DeviceAllocationList = std::list<DeviceAllocation>;
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/////////////////////////////////////////////////////////////////////////////////////////////////
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} // namespace profiler
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} // namespace cutlass
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/////////////////////////////////////////////////////////////////////////////////////////////////
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