715 lines
26 KiB
Python
715 lines
26 KiB
Python
#################################################################################################
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#
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# Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: BSD-3-Clause
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions are met:
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#
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# 1. Redistributions of source code must retain the above copyright notice, this
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# list of conditions and the following disclaimer.
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#
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# 2. Redistributions in binary form must reproduce the above copyright notice,
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# this list of conditions and the following disclaimer in the documentation
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# and/or other materials provided with the distribution.
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#
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# 3. Neither the name of the copyright holder nor the names of its
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# contributors may be used to endorse or promote products derived from
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# this software without specific prior written permission.
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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# OR TORT (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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"""
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Common data types and string names for them. This file is similar to /tools/library/scripts/library.py,
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but uses the Pybind-bound CUTLASS data types as many keys to the dictionary.
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"""
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import enum
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import cutlass_bindings
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from cutlass import KernelScheduleType
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# The following block implements enum.auto() for Python 3.5 variants that don't include it such
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# as the default 3.5.2 on Ubuntu 16.04.
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#
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# https://codereview.stackexchange.com/questions/177309/reimplementing-pythons-enum-auto-for-compatibility
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try:
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from enum import auto as enum_auto
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except ImportError:
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__cutlass_library_auto_enum = 0
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def enum_auto() -> int:
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global __cutlass_library_auto_enum
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i = __cutlass_library_auto_enum
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__cutlass_library_auto_enum += 1
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return i
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ShortDataTypeNames = {
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cutlass_bindings.int32: "i",
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cutlass_bindings.float16: "h",
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cutlass_bindings.float32: "s",
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cutlass_bindings.float64: "d",
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cutlass_bindings.dtype.cf32: "c",
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cutlass_bindings.dtype.cf64: "z",
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}
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DataTypeNames = {
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cutlass_bindings.dtype.b1: "b1",
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cutlass_bindings.dtype.u4: "u4",
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cutlass_bindings.dtype.u8: "u8",
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cutlass_bindings.dtype.u16: "u16",
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cutlass_bindings.dtype.u32: "u32",
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cutlass_bindings.dtype.u64: "u64",
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cutlass_bindings.dtype.s4: "s4",
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cutlass_bindings.int8: "s8",
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cutlass_bindings.dtype.s16: "s16",
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cutlass_bindings.int32: "s32",
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cutlass_bindings.dtype.s64: "s64",
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cutlass_bindings.float16: "f16",
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cutlass_bindings.bfloat16: "bf16",
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cutlass_bindings.float32: "f32",
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cutlass_bindings.tfloat32: "tf32",
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cutlass_bindings.float64: "f64",
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cutlass_bindings.dtype.cf16: "cf16",
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cutlass_bindings.dtype.cbf16: "cbf16",
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cutlass_bindings.dtype.cf32: "cf32",
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cutlass_bindings.dtype.ctf32: "ctf32",
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cutlass_bindings.dtype.cf64: "cf64",
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cutlass_bindings.dtype.cu4: "cu4",
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cutlass_bindings.dtype.cu8: "cu8",
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cutlass_bindings.dtype.cu16: "cu16",
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cutlass_bindings.dtype.cu32: "cu32",
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cutlass_bindings.dtype.cu64: "cu64",
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cutlass_bindings.dtype.cs4: "cs4",
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cutlass_bindings.dtype.cs8: "cs8",
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cutlass_bindings.dtype.cs16: "cs16",
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cutlass_bindings.dtype.cs32: "cs32",
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cutlass_bindings.dtype.cs64: "cs64",
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}
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DataTypeTag = {
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cutlass_bindings.dtype.b1: "cutlass::uint1b_t",
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cutlass_bindings.dtype.u4: "cutlass::uint4b_t",
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cutlass_bindings.dtype.u8: "uint8_t",
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cutlass_bindings.dtype.u16: "uint16_t",
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cutlass_bindings.dtype.u32: "uint32_t",
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cutlass_bindings.dtype.u64: "uint64_t",
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cutlass_bindings.dtype.s4: "cutlass::int4b_t",
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cutlass_bindings.int8: "int8_t",
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cutlass_bindings.dtype.s16: "int16_t",
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cutlass_bindings.int32: "int32_t",
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cutlass_bindings.dtype.s64: "int64_t",
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cutlass_bindings.float16: "cutlass::half_t",
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cutlass_bindings.bfloat16: "cutlass::bfloat16_t",
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cutlass_bindings.float32: "float",
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cutlass_bindings.tfloat32: "cutlass::tfloat32_t",
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cutlass_bindings.float64: "double",
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cutlass_bindings.dtype.cf16: "cutlass::complex<cutlass::half_t>",
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cutlass_bindings.dtype.cbf16: "cutlass::complex<cutlass::bfloat16_t>",
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cutlass_bindings.dtype.cf32: "cutlass::complex<float>",
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cutlass_bindings.dtype.ctf32: "cutlass::complex<cutlass::tfloat32_t>",
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cutlass_bindings.dtype.cf64: "cutlass::complex<double>",
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cutlass_bindings.dtype.cu4: "cutlass::complex<cutlass::uint4b_t>",
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cutlass_bindings.dtype.cu8: "cutlass::complex<cutlass::uint8_t>",
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cutlass_bindings.dtype.cu16: "cutlass::complex<cutlass::uint16_t>",
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cutlass_bindings.dtype.cu32: "cutlass::complex<cutlass::uint32_t>",
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cutlass_bindings.dtype.cu64: "cutlass::complex<cutlass::uint64_t>",
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cutlass_bindings.dtype.cs4: "cutlass::complex<cutlass::int4b_t>",
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cutlass_bindings.dtype.cs8: "cutlass::complex<cutlass::int8_t>",
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cutlass_bindings.dtype.cs16: "cutlass::complex<cutlass::int16_t>",
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cutlass_bindings.dtype.cs32: "cutlass::complex<cutlass::int32_t>",
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cutlass_bindings.dtype.cs64: "cutlass::complex<cutlass::int64_t>",
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}
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DataTypeSize = {
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cutlass_bindings.dtype.b1: 1,
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cutlass_bindings.dtype.u4: 4,
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cutlass_bindings.dtype.u8: 8,
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cutlass_bindings.dtype.u16: 16,
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cutlass_bindings.dtype.u32: 32,
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cutlass_bindings.dtype.u64: 64,
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cutlass_bindings.dtype.s4: 4,
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cutlass_bindings.int8: 8,
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cutlass_bindings.dtype.s16: 16,
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cutlass_bindings.int32: 32,
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cutlass_bindings.dtype.s64: 64,
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cutlass_bindings.float16: 16,
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cutlass_bindings.bfloat16: 16,
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cutlass_bindings.float32: 32,
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cutlass_bindings.tfloat32: 32,
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cutlass_bindings.float64: 64,
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cutlass_bindings.dtype.cf16: 32,
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cutlass_bindings.dtype.cbf16: 32,
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cutlass_bindings.dtype.cf32: 64,
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cutlass_bindings.dtype.ctf32: 32,
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cutlass_bindings.dtype.cf64: 128,
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cutlass_bindings.dtype.cu4: 8,
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cutlass_bindings.dtype.cu8: 16,
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cutlass_bindings.dtype.cu16: 32,
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cutlass_bindings.dtype.cu32: 64,
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cutlass_bindings.dtype.cu64: 128,
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cutlass_bindings.dtype.cs4: 8,
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cutlass_bindings.dtype.cs8: 16,
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cutlass_bindings.dtype.cs16: 32,
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cutlass_bindings.dtype.cs32: 64,
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cutlass_bindings.dtype.cs64: 128,
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}
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class DataTypeSizeBytes:
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"""
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Static class to mimic the `DataTypeSize` dictionary, but with checks for whether the
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data type key is less than a full byte or a non-integer number of bytes.
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"""
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@staticmethod
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def __class_getitem__(datatype):
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"""
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Returns the number of bytes in size the data type is. Raises an exception if the data type
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is either less than a full byte or a non-integer number of bytes in size.
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:param datatype: data type to query
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:return: number of bytes the data type occupies
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:rtype: int
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"""
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bits = DataTypeSize[datatype]
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if bits < 8:
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raise Exception(
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"Data type {} is less than one byte in size.".format(datatype)
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)
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elif bits % 8 != 0:
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raise Exception(
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"Data type {} is not an integer number of bytes.".format(datatype)
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)
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return bits // 8
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ComplexTransformTag = {
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cutlass_bindings.complex_transform.none: "cutlass::ComplexTransform::kNone",
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cutlass_bindings.complex_transform.conj: "cutlass::ComplexTransform::kConjugate",
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}
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RealComplexBijection = [
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(cutlass_bindings.float16, cutlass_bindings.dtype.cf16),
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(cutlass_bindings.float32, cutlass_bindings.dtype.cf32),
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(cutlass_bindings.float64, cutlass_bindings.dtype.cf64),
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]
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def is_complex(data_type):
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for r, c in RealComplexBijection:
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if data_type == c:
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return True
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return False
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def get_complex_from_real(real_type):
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for r, c in RealComplexBijection:
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if real_type == r:
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return c
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return cutlass_bindings.dtype.invalid
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def get_real_from_complex(complex_type):
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for r, c in RealComplexBijection:
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if complex_type == c:
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return r
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return cutlass_bindings.dtype.invalid
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class ComplexMultiplyOp(enum.Enum):
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multiply_add = enum_auto()
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gaussian = enum_auto()
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class MathOperation(enum.Enum):
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multiply_add = enum_auto()
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multiply_add_saturate = enum_auto()
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xor_popc = enum_auto()
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multiply_add_fast_bf16 = enum_auto()
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multiply_add_fast_f16 = enum_auto()
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multiply_add_fast_f32 = enum_auto()
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multiply_add_complex_fast_f32 = enum_auto()
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multiply_add_complex = enum_auto()
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multiply_add_complex_gaussian = enum_auto()
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MathOperationNames = {
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MathOperation.multiply_add: "multiply_add",
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MathOperation.multiply_add_saturate: "multiply_add_saturate",
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MathOperation.xor_popc: "xor_popc",
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MathOperation.multiply_add_fast_bf16: "multiply_add_fast_bf16",
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MathOperation.multiply_add_fast_f16: "multiply_add_fast_f16",
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MathOperation.multiply_add_fast_f32: "multiply_add_fast_f32",
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MathOperation.multiply_add_complex_fast_f32: "multiply_add_complex_fast_f32",
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MathOperation.multiply_add_complex: "multiply_add_complex",
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MathOperation.multiply_add_complex_gaussian: "multiply_add_complex_gaussian",
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}
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MathOperationTag = {
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MathOperation.multiply_add: "cutlass::arch::OpMultiplyAdd",
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MathOperation.multiply_add_saturate: "cutlass::arch::OpMultiplyAddSaturate",
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MathOperation.xor_popc: "cutlass::arch::OpXorPopc",
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MathOperation.multiply_add_fast_bf16: "cutlass::arch::OpMultiplyAddFastBF16",
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MathOperation.multiply_add_fast_f16: "cutlass::arch::OpMultiplyAddFastF16",
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MathOperation.multiply_add_fast_f32: "cutlass::arch::OpMultiplyAddFastF32",
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MathOperation.multiply_add_complex_fast_f32: "cutlass::arch::OpMultiplyAddComplexFastF32",
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MathOperation.multiply_add_complex: "cutlass::arch::OpMultiplyAddComplex",
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MathOperation.multiply_add_complex_gaussian: "cutlass::arch::OpMultiplyAddGaussianComplex",
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}
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LayoutTag = {
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cutlass_bindings.ColumnMajor: "cutlass::layout::ColumnMajor",
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cutlass_bindings.RowMajor: "cutlass::layout::RowMajor",
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cutlass_bindings.layout.ColumnMajorInterleaved2: "cutlass::layout::ColumnMajorInterleaved<2>",
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cutlass_bindings.layout.RowMajorInterleaved2: "cutlass::layout::RowMajorInterleaved<2>",
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cutlass_bindings.ColumnMajorInterleaved32: "cutlass::layout::ColumnMajorInterleaved<32>",
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cutlass_bindings.RowMajorInterleaved32: "cutlass::layout::RowMajorInterleaved<32>",
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cutlass_bindings.layout.ColumnMajorInterleaved64: "cutlass::layout::ColumnMajorInterleaved<64>",
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cutlass_bindings.layout.RowMajorInterleaved64: "cutlass::layout::RowMajorInterleaved<64>",
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cutlass_bindings.TensorNHWC: "cutlass::layout::TensorNHWC",
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cutlass_bindings.layout.TensorNDHWC: "cutlass::layout::TensorNDHWC",
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cutlass_bindings.layout.TensorNCHW: "cutlass::layout::TensorNCHW",
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cutlass_bindings.layout.TensorNGHWC: "cutlass::layout::TensorNGHWC",
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cutlass_bindings.TensorNC32HW32: "cutlass::layout::TensorNCxHWx<32>",
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cutlass_bindings.TensorC32RSK32: "cutlass::layout::TensorCxRSKx<32>",
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cutlass_bindings.layout.TensorNC64HW64: "cutlass::layout::TensorNCxHWx<64>",
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cutlass_bindings.layout.TensorC64RSK64: "cutlass::layout::TensorCxRSKx<64>",
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}
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TransposedLayout = {
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cutlass_bindings.ColumnMajor: cutlass_bindings.RowMajor,
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cutlass_bindings.RowMajor: cutlass_bindings.ColumnMajor,
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cutlass_bindings.layout.ColumnMajorInterleaved2: cutlass_bindings.layout.RowMajorInterleaved2,
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cutlass_bindings.layout.RowMajorInterleaved2: cutlass_bindings.layout.ColumnMajorInterleaved2,
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cutlass_bindings.ColumnMajorInterleaved32: cutlass_bindings.RowMajorInterleaved32,
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cutlass_bindings.RowMajorInterleaved32: cutlass_bindings.ColumnMajorInterleaved32,
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cutlass_bindings.layout.ColumnMajorInterleaved64: cutlass_bindings.layout.RowMajorInterleaved64,
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cutlass_bindings.layout.RowMajorInterleaved64: cutlass_bindings.layout.ColumnMajorInterleaved64,
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cutlass_bindings.TensorNHWC: cutlass_bindings.TensorNHWC,
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}
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ShortLayoutTypeNames = {
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cutlass_bindings.ColumnMajor: "n",
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cutlass_bindings.layout.ColumnMajorInterleaved2: "n2",
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cutlass_bindings.ColumnMajorInterleaved32: "n32",
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cutlass_bindings.layout.ColumnMajorInterleaved64: "n64",
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cutlass_bindings.RowMajor: "t",
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cutlass_bindings.layout.RowMajorInterleaved2: "t2",
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cutlass_bindings.RowMajorInterleaved32: "t32",
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cutlass_bindings.layout.RowMajorInterleaved64: "t64",
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cutlass_bindings.TensorNHWC: "nhwc",
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cutlass_bindings.layout.TensorNDHWC: "ndhwc",
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cutlass_bindings.layout.TensorNCHW: "nchw",
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cutlass_bindings.layout.TensorNGHWC: "nghwc",
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cutlass_bindings.TensorNC32HW32: "nc32hw32",
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cutlass_bindings.layout.TensorNC64HW64: "nc64hw64",
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cutlass_bindings.TensorC32RSK32: "c32rsk32",
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cutlass_bindings.layout.TensorC64RSK64: "c64rsk64",
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}
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ShortComplexLayoutNames = {
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(cutlass_bindings.ColumnMajor, cutlass_bindings.complex_transform.none): "n",
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(cutlass_bindings.ColumnMajor, cutlass_bindings.complex_transform.conj): "c",
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(cutlass_bindings.RowMajor, cutlass_bindings.complex_transform.none): "t",
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(cutlass_bindings.RowMajor, cutlass_bindings.complex_transform.conj): "h",
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}
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OpcodeClassNames = {
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cutlass_bindings.OpClass.Simt: "simt",
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cutlass_bindings.OpClass.TensorOp: "tensorop",
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cutlass_bindings.OpClass.WmmaTensorOp: "wmma_tensorop",
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cutlass_bindings.OpClass.SparseTensorOp: "sptensorop",
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}
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OpcodeClassTag = {
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cutlass_bindings.OpClass.Simt: "cutlass::arch::OpClassSimt",
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cutlass_bindings.OpClass.TensorOp: "cutlass::arch::OpClassTensorOp",
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cutlass_bindings.OpClass.WmmaTensorOp: "cutlass::arch::OpClassWmmaTensorOp",
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cutlass_bindings.OpClass.SparseTensorOp: "cutlass::arch::OpClassSparseTensorOp",
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}
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class OperationKind(enum.Enum):
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Gemm = enum_auto()
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Conv2d = enum_auto()
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Conv3d = enum_auto()
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OperationKindNames = {
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OperationKind.Gemm: "gemm",
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OperationKind.Conv2d: "conv2d",
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OperationKind.Conv3d: "conv3d",
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}
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ArchitectureNames = {
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50: "maxwell",
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60: "pascal",
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61: "pascal",
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70: "volta",
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75: "turing",
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80: "ampere",
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90: "hopper",
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}
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SharedMemPerCC = {
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70: 96 << 10, # 96KB of SMEM
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72: 96 << 10, # 96KB of SMEM
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75: 64 << 10, # 64KB of SMEM
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80: 160 << 10, # 164KB of SMEM - 4KB reserved for the driver
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86: 100 << 10, # 100KB of SMEM
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87: 160 << 10, # 164KB of SMEM - 4KB reserved for the driver
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89: 100 << 10, # 100KB of SMEM
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90: 227 << 10, # 228KB of SMEM - 1KB reserved for the driver
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}
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class GemmKind(enum.Enum):
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Gemm = enum_auto()
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Sparse = enum_auto()
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Universal = enum_auto()
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PlanarComplex = enum_auto()
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PlanarComplexArray = enum_auto()
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Grouped = enum_auto()
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GemmKindNames = {
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GemmKind.Gemm: "gemm",
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GemmKind.Sparse: "spgemm",
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GemmKind.Universal: "gemm",
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GemmKind.PlanarComplex: "gemm_planar_complex",
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GemmKind.PlanarComplexArray: "gemm_planar_complex_array",
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GemmKind.Grouped: "gemm_grouped",
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}
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class SwizzlingFunctor(enum.Enum):
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Identity1 = enum_auto()
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Identity2 = enum_auto()
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Identity4 = enum_auto()
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Identity8 = enum_auto()
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Horizontal = enum_auto()
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BatchedIdentity1 = enum_auto()
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StridedDgradIdentity1 = enum_auto()
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StridedDgradIdentity4 = enum_auto()
|
|
StridedDgradHorizontal = enum_auto()
|
|
|
|
|
|
SwizzlingFunctorTag = {
|
|
cutlass_bindings.IdentitySwizzle1: "cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>",
|
|
SwizzlingFunctor.Identity2: "cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<2>",
|
|
SwizzlingFunctor.Identity4: "cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<4>",
|
|
SwizzlingFunctor.Identity8: "cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<8>",
|
|
SwizzlingFunctor.Horizontal: "cutlass::gemm::threadblock::GemmHorizontalThreadblockSwizzle",
|
|
SwizzlingFunctor.BatchedIdentity1: "cutlass::gemm::threadblock::GemmBatchedIdentityThreadblockSwizzle",
|
|
SwizzlingFunctor.StridedDgradIdentity1: "cutlass::conv::threadblock::StridedDgradIdentityThreadblockSwizzle<1>",
|
|
SwizzlingFunctor.StridedDgradIdentity4: "cutlass::conv::threadblock::StridedDgradIdentityThreadblockSwizzle<4>",
|
|
SwizzlingFunctor.StridedDgradHorizontal: "cutlass::conv::threadblock::StridedDgradHorizontalThreadblockSwizzle",
|
|
}
|
|
|
|
|
|
class SchedulerMode(enum.Enum):
|
|
Device = (enum_auto(),)
|
|
Host = enum_auto()
|
|
|
|
|
|
SchedulerModeTag = {
|
|
SchedulerMode.Device: "cutlass::gemm::kernel::GroupScheduleMode::kDeviceOnly",
|
|
SchedulerMode.Host: "cutlass::gemm::kernel::GroupScheduleMode::kHostPrecompute",
|
|
}
|
|
|
|
|
|
ShortSchedulerModeNames = {SchedulerMode.Device: "Device", SchedulerMode.Host: "Host"}
|
|
|
|
|
|
ConvKindTag = {
|
|
cutlass_bindings.conv.Operator.fprop: "cutlass::conv::Operator::kFprop",
|
|
cutlass_bindings.conv.Operator.dgrad: "cutlass::conv::Operator::kDgrad",
|
|
cutlass_bindings.conv.Operator.wgrad: "cutlass::conv::Operator::kWgrad",
|
|
}
|
|
|
|
|
|
ConvKindNames = {
|
|
cutlass_bindings.conv.Operator.fprop: "fprop",
|
|
cutlass_bindings.conv.Operator.dgrad: "dgrad",
|
|
cutlass_bindings.conv.Operator.wgrad: "wgrad",
|
|
}
|
|
|
|
|
|
IteratorAlgorithmTag = {
|
|
cutlass_bindings.conv.IteratorAlgorithm.analytic: "cutlass::conv::IteratorAlgorithm::kAnalytic",
|
|
cutlass_bindings.conv.IteratorAlgorithm.optimized: "cutlass::conv::IteratorAlgorithm::kOptimized",
|
|
cutlass_bindings.conv.IteratorAlgorithm.fixed_channels: "cutlass::conv::IteratorAlgorithm::kFixedChannels",
|
|
cutlass_bindings.conv.IteratorAlgorithm.few_channels: "cutlass::conv::IteratorAlgorithm::kFewChannels",
|
|
}
|
|
|
|
|
|
IteratorAlgorithmNames = {
|
|
cutlass_bindings.conv.IteratorAlgorithm.analytic: "analytic",
|
|
cutlass_bindings.conv.IteratorAlgorithm.optimized: "optimized",
|
|
cutlass_bindings.conv.IteratorAlgorithm.fixed_channels: "fixed_channels",
|
|
cutlass_bindings.conv.IteratorAlgorithm.few_channels: "few_channels",
|
|
}
|
|
|
|
|
|
class StrideSupport(enum.Enum):
|
|
Strided = enum_auto()
|
|
Unity = enum_auto()
|
|
|
|
|
|
StrideSupportTag = {
|
|
StrideSupport.Strided: "cutlass::conv::StrideSupport::kStrided",
|
|
StrideSupport.Unity: "cutlass::conv::StrideSupport::kUnity",
|
|
}
|
|
|
|
|
|
StrideSupportNames = {
|
|
StrideSupport.Strided: "",
|
|
StrideSupport.Unity: "unity_stride",
|
|
}
|
|
|
|
|
|
class ConvMode(enum.Enum):
|
|
CrossCorrelation = enum_auto()
|
|
Convolution = enum_auto()
|
|
|
|
|
|
ConvModeTag = {
|
|
ConvMode.CrossCorrelation: "cutlass::conv::Mode::kCrossCorrelation",
|
|
ConvMode.Convolution: "cutlass::conv::Mode::kConvolution",
|
|
}
|
|
|
|
|
|
class MathInstruction:
|
|
"""
|
|
Description of a the lowest-level matrix-multiply-accumulate operation to be used in a kernel
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
instruction_shape,
|
|
element_a,
|
|
element_b,
|
|
element_accumulator,
|
|
opcode_class=cutlass_bindings.OpClass.Simt,
|
|
math_operation=MathOperation.multiply_add,
|
|
):
|
|
"""
|
|
:param instruction_shape: size of the [M, N, K] dimensions of the instruction
|
|
:type instruction_shape: list or tuple
|
|
:param element_a: data type of operand A
|
|
:param element_b: data type of operand B
|
|
:param element_accumulator: data type used in accumulation
|
|
:param opcode_class: higher-level class of the instruction (e.g., SIMT or Tensor Core)
|
|
:type opcode_class: cutlass_bindings.OpClass
|
|
:param math_operation: the type of low-level operation to be performed (e.g., multiply accumulate)
|
|
:type math_operation: MathOperation
|
|
"""
|
|
self.instruction_shape = instruction_shape
|
|
self.element_a = element_a
|
|
self.element_b = element_b
|
|
self.element_accumulator = element_accumulator
|
|
self.opcode_class = opcode_class
|
|
self.math_operation = math_operation
|
|
|
|
|
|
class TileDescription:
|
|
"""
|
|
Description of a tile of computation to be performed in the kernel, encompassing threadblock, cluster, and warp shapes,
|
|
stage count, and math instruction specification
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
threadblock_shape,
|
|
stages,
|
|
warp_count,
|
|
math_instruction,
|
|
cluster_shape=[1, 1, 1],
|
|
kernel_schedule: KernelScheduleType = None
|
|
):
|
|
"""
|
|
:param threadblock_shape: shape of a threadblock tyle
|
|
:type threadblock_shape: list or tuple
|
|
:param stages: number of pipline stages in the operation. For SM90 kernels, this can be set to `None` and the maximum
|
|
number of stages that can be supported for an operation on a given architecture will be computed at a later time
|
|
:type stages: int or None
|
|
:param warp_count: number of warps in each [M, N, K] dimension of a threadblock tile
|
|
:type warp_count: list, tuple, or None
|
|
:param math_instruction: specification of the instruction type and shape to be performed and the types of its operands
|
|
:type math_instruction: MathInstruction
|
|
:param cluster_shape: number of threadblocks in the [X, Y, Z] dimensions of a threadblock cluster
|
|
:param kernel_schedule: type of kernel schedule to use (only available for SM90+)
|
|
:type kernel_schedule: cutlass.backend.KernelScheduleType
|
|
"""
|
|
self.threadblock_shape = threadblock_shape
|
|
self.cluster_shape = cluster_shape
|
|
self.kernel_schedule = kernel_schedule
|
|
self.stages: int = stages
|
|
|
|
self.math_instruction = math_instruction
|
|
|
|
# Number of warps along x, y, z directions
|
|
self.warp_count = warp_count
|
|
|
|
@property
|
|
def num_threads(self):
|
|
"""
|
|
Returns the number of threads in the threadblock
|
|
|
|
:return: number of threads in the threadblock
|
|
:rtype: int or None (if warp count is None)
|
|
"""
|
|
if self.warp_count is not None:
|
|
threads = 32
|
|
for cnt in self.warp_count:
|
|
threads *= cnt
|
|
return threads
|
|
return None
|
|
|
|
def procedural_name(self):
|
|
"""
|
|
Returns a name identifying the tile description
|
|
|
|
:return: name identifying the tile description
|
|
:rtype: int
|
|
"""
|
|
emit_stages = 0 if self.stages is None else self.stages
|
|
name = "%dx%dx%d_%dx%d_%dx%d" % (
|
|
self.cluster_shape[0],
|
|
self.cluster_shape[1],
|
|
self.cluster_shape[2],
|
|
self.threadblock_shape[0],
|
|
self.threadblock_shape[1],
|
|
self.threadblock_shape[2],
|
|
emit_stages
|
|
)
|
|
|
|
return name
|
|
|
|
def __str__(self):
|
|
"""
|
|
Returns a string with containing each of the tile description's values
|
|
|
|
:return: contents of tile description
|
|
:rtype: str
|
|
"""
|
|
schedule = KernelScheduleType.ScheduleAuto
|
|
if self.kernel_schedule is not None:
|
|
schedule = self.kernel_schedule
|
|
return f"""
|
|
{{
|
|
ClusterShape: {self.cluster_shape}
|
|
ThreadblockShape: {self.threadblock_shape}
|
|
WarpCount: {self.warp_count}
|
|
Stages: {self.stages if self.stages is not None else 'Auto'}
|
|
Kernel schedule: {schedule.name}
|
|
}}"""
|
|
|
|
|
|
class TensorDescription:
|
|
def __init__(self, element, layout, alignment=1,
|
|
complex_transform=cutlass_bindings.complex_transform.none):
|
|
self.element = element
|
|
self.layout = layout
|
|
self.alignment = min(128 // DataTypeSize[self.element], alignment)
|
|
self.complex_transform = complex_transform
|
|
|
|
|
|
def CalculateSmemUsagePerStage(operation):
|
|
"""
|
|
Returns the amount of shared memory in bytes consumed in a single stage of a kernel.
|
|
|
|
:param op: operation for which the maximum stages should be computed. If stages are
|
|
set via the `op.tile_description.stages` parameter, this setting is ignored
|
|
in the present calculation
|
|
:type op: cutlass.backend.Operation
|
|
|
|
:return: number of bytes of shared memory consumed by a single stage
|
|
:rtype: int
|
|
"""
|
|
m, n, k = operation.tile_description.threadblock_shape
|
|
|
|
if operation.operation_kind == OperationKind.Gemm:
|
|
stage_barrier_bytes = 32
|
|
return (
|
|
(DataTypeSize[operation.A.element] * m * k // 8)
|
|
+ (DataTypeSize[operation.B.element] * k * n // 8)
|
|
+ stage_barrier_bytes
|
|
)
|
|
else:
|
|
raise Exception("Unsupported operation kind {}.".format(operation.operation_kind))
|
|
|
|
|
|
def CalculateSmemUsage(operation):
|
|
"""
|
|
Returns the amount of shared memory in bytes consumed by a kernel.
|
|
|
|
:param op: operation for which the maximum stages should be computed. If stages are
|
|
set via the `op.tile_description.stages` parameter, this setting is ignored
|
|
in the present calculation
|
|
:type op: cutlass.backend.Operation
|
|
|
|
:return: int
|
|
"""
|
|
return operation.tile_description.stages * CalculateSmemUsagePerStage(operation)
|
|
|
|
|
|
class ApiVersion(enum.Enum):
|
|
"""
|
|
Differentiate between CUTLASS 2.x and 3.x API versions
|
|
"""
|
|
|
|
v2x = enum_auto()
|
|
v3x = enum_auto()
|
|
|
|
|
|
def api_version(arch, opclass, datatype):
|
|
"""
|
|
Returns whether the architecture, opcode class, and datatype in question require using CUTLASS 2.x
|
|
or 3.x for code emission.
|
|
|
|
:param arch: compute capability of device on which to run
|
|
:type arch: int
|
|
:param opclass: class of the operation being performed
|
|
:type opclass: cutlass_bindings.OpClass
|
|
:param datatype: data type to be used in operation (assumes that ElementA and ElementB are the same)
|
|
|
|
:return: API version to be used in code emission
|
|
:rtype: ApiVersion
|
|
"""
|
|
if (arch >= 90 and
|
|
opclass == cutlass_bindings.OpClass.TensorOp and
|
|
(datatype != cutlass_bindings.float64)):
|
|
return ApiVersion.v3x
|
|
else:
|
|
return ApiVersion.v2x
|