Rename python/cutlass to python/cutlass_cppgen (#2652)

python/cutlass_cppgen/shape.py

@@ -0,0 +1,184 @@
+#################################################################################################
+#
+# Copyright (c) 2023 - 2025 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_cppgen.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
+        )