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#################################################################################################
#
# Copyright (c) 2023 - 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: BSD-3-Clause
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. 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.
#
# 3. Neither the name of the copyright holder 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 THE COPYRIGHT HOLDER OR CONTRIBUTORS 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 TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
#################################################################################################
"""
Utilities for expressing shapes
"""
from cutlass_library import (
ConvMode,
ConvKind,
LayoutType
)
from cutlass.backend.c_types import (
Conv2DProblemSize_,
GemmCoord_,
GemmCoordBatched_
)
class MatrixCoord:
def __init__(self, row, col):
self._row = row
self._col = col
@property
def row(self):
return self._row
@property
def column(self):
return self._col
def leading_dimension(self, layout: LayoutType) -> int:
"""
Returns the leading dimension for a matrix with layout ``layout`` and shape provided by the MatrixCoord.
:param layout: layout of matrix
:type layout: cutlass_library.LayoutType
:returns: leading dimension
:rtype: int
"""
if layout == LayoutType.RowMajor:
return self._col
elif layout == LayoutType.ColumnMajor:
return self._row
else:
raise Exception(f'Unsupported layout for leading dimension calculation: {layout}')
class GemmCoord:
def __init__(self, m: int, n: int, k: int):
self._m = m
self._n = n
self._k = k
@property
def m(self) -> int:
return self._m
@property
def n(self) -> int:
return self._n
@property
def k(self) -> int:
return self._k
@property
def mk(self) -> MatrixCoord:
return MatrixCoord(self._m, self._k)
@property
def mn(self) -> MatrixCoord:
return MatrixCoord(self._m, self._n)
@property
def kn(self) -> MatrixCoord:
return MatrixCoord(self._k, self._n)
@property
def ctype(self) -> GemmCoord_:
return GemmCoord_(self._m, self._n, self._k)
def batched_ctype(self, batch_count: int) -> GemmCoordBatched_:
return GemmCoordBatched_(self._m, self._n, self._k, batch_count)
class Conv2DProblemSize:
def __init__(
self, n: int, h: int, w: int, c: int,
k: int, r: int, s: int, c_: int,
pad_h: int, pad_w: int, stride_h: int, stride_w: int,
dilation_h: int, dilation_w: int, mode: ConvMode=ConvMode.CrossCorrelation,
split_k_slices: int=1, groups: int=1):
self.N = n
self.H = h
self.W = w
self.C = c
self.K = k
self.R = r
self.S = s
self.pad_h = pad_h
self.pad_w = pad_w
self.stride_h = stride_h
self.stride_w = stride_w
self.dilation_h = dilation_h
self.dilation_w = dilation_w
self.mode = int(mode)
self.split_k_slices = split_k_slices
self.groups = groups
self.P = ((h + pad_h * 2 - r * dilation_h) // stride_h) + 1
self.Q = ((w + pad_w * 2 - s * dilation_w) // stride_w) + 1
@property
def ctype(self) -> Conv2DProblemSize_:
return Conv2DProblemSize_(self)
def implicit_gemm_size(self, kind: ConvKind):
if kind == ConvKind.Fprop:
return GemmCoord(
self.N * self.P * self.Q,
self.K,
self.R * self.S * self.C // self.groups
)
elif kind == ConvKind.Dgrad:
return GemmCoord(
self.N * self.H * self.W,
self.C,
self.R * self.S * self.K
)
elif kind == ConvKind.Wgrad:
return GemmCoord(
self.K,
self.R * self.S * self.C,
self.N * self.P * self.Q
)
@staticmethod
def from_sizes(input_size, weight_size):
K, R, S, _ = weight_size
pad_h = R // 2
pad_w = S // 2
stride_h = 1
stride_w = 1
dilation_h = 1
dilation_w = 1
return Conv2DProblemSize(
*input_size,
*weight_size,
pad_h, pad_w,
stride_h, stride_w,
dilation_h, dilation_w
)
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