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

python/cutlass_cppgen/backend/arguments.py

@@ -0,0 +1,136 @@
+#################################################################################################
+#
+# Copyright (c) 2017 - 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.
+#
+#################################################################################################
+
+from math import prod
+from typing import Union
+
+from cutlass_cppgen.utils.lazy_import import lazy_import
+
+cuda = lazy_import("cuda.cuda")
+cudart = lazy_import("cuda.cudart")
+import numpy as np
+
+import cutlass_cppgen
+from cutlass_cppgen.backend.frontend import CupyFrontend, NumpyFrontend, TorchFrontend
+from cutlass_cppgen.backend.memory_manager import DevicePtrWrapper
+from cutlass_cppgen.utils.datatypes import is_cupy_tensor, is_numpy_tensor, is_torch_tensor
+
+
+class ArgumentBase:
+    """
+    Base class for operation arguments
+    """
+
+    def __init__(
+        self,
+        A: "Union[cuda.CUdeviceptr, np.ndarray, torch.Tensor, cp.ndarray]",
+        B: "Union[cuda.CUdeviceptr, np.ndarray, torch.Tensor, cp.ndarray]",
+        C: "Union[cuda.CUdeviceptr, np.ndarray, torch.Tensor, cp.ndarray]",
+        D: "Union[cuda.CUdeviceptr, np.ndarray, torch.Tensor, cp.ndarray]",
+        **kwargs,
+    ) -> None:
+        # tensor_C can be interpreted as the bias with bias=True in keyword args
+        self.bias = kwargs.get("bias", False)
+
+        self.stream = kwargs.get("stream", cuda.CUstream(0))
+
+        # RMM buffers used to track tensor lifetime
+        self.buffers = {}
+        # Host tensor to copy the computed result back
+        self.host_tensors = {}
+
+        self.ptr_A = self.tensor_to_ptr(A, "A")
+        self.ptr_B = self.tensor_to_ptr(B, "B")
+        self.ptr_C = self.tensor_to_ptr(C, "C")
+        self.ptr_D = self.tensor_to_ptr(D, "D", is_output=True)
+        if C is not None:
+            if not isinstance(C, cuda.CUdeviceptr):
+                self.tensor_c_numel = prod(C.shape)
+
+    def tensor_to_ptr(self, tensor, name, is_output=False):
+        """
+        Convert and remember the input tensor to cuda.CUdeviceptr used by cuda python
+        For numpy.ndarray, it also remembers the host buffer for synchronization
+        """
+        if tensor is None:
+            return cuda.CUdeviceptr(0)
+        if is_numpy_tensor(tensor):
+            if is_output:
+                assert name
+            self.buffers[name] = NumpyFrontend.argument(tensor, is_output)
+            if is_output:
+                self.host_tensors[name] = tensor
+            return self.buffers[name].ptr
+        elif is_torch_tensor(tensor):
+            return TorchFrontend.argument(tensor)
+        elif isinstance(tensor, cuda.CUdeviceptr):
+            return tensor
+        elif is_cupy_tensor(tensor):
+            return CupyFrontend.argument(tensor)
+        else:
+            raise TypeError("Unsupported Frontend. Only support numpy and torch")
+
+    def sync(self, stream_sync=True):
+        if stream_sync:
+            (err,) = cudart.cudaDeviceSynchronize()
+            if err != cuda.CUresult.CUDA_SUCCESS:
+                raise RuntimeError("CUDA Error %s" % str(err))
+
+        for key in self.host_tensors.keys():
+            host_tensor = self.host_tensors[key]
+            (err,) = cuda.cuMemcpyDtoH(
+                host_tensor,
+                self.buffers[key].ptr,
+                host_tensor.size * host_tensor.itemsize,
+            )
+            if err != cuda.CUresult.CUDA_SUCCESS:
+                raise RuntimeError("CUDA Error %s" % str(err))
+
+        self.free()
+
+    def free(self):
+        """
+        Frees allocated device-side memory
+        """
+        # Free any device memory allocated manually
+        if not cutlass_cppgen.use_rmm:
+            for name, buf in self.buffers.items():
+                if isinstance(buf, DevicePtrWrapper):
+                    err, = cudart.cudaFree(buf.ptr)
+                    if err != cudart.cudaError_t.cudaSuccess:
+                        raise RuntimeError(f"cudaFree failed with error {err}")
+
+            if hasattr(self, "workspace_buffer") and isinstance(self.workspace_buffer, DevicePtrWrapper):
+                err, = cudart.cudaFree(self.workspace_buffer.ptr)
+                if err != cudart.cudaError_t.cudaSuccess:
+                    raise RuntimeError(f"cudaFree failed with error {err}")
+                del self.workspace_buffer