110 lines
3.8 KiB
Python
110 lines
3.8 KiB
Python
#################################################################################################
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#
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# Copyright (c) 2017 - 2025 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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from __future__ import annotations
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from cutlass_cppgen.utils.lazy_import import lazy_import
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cuda = lazy_import("cuda.cuda")
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import numpy as np
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from cutlass_cppgen.backend.memory_manager import device_mem_alloc, todevice
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from cutlass_cppgen.utils.datatypes import is_cupy_tensor, is_numpy_tensor, is_torch_tensor
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class NumpyFrontend:
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"""
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Frontend node for numpy
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"""
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@staticmethod
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def argument(np_tensor: "np.ndarray", is_output: "bool") -> cuda.CUdeviceptr:
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"""Convert the input numpy tensor to CUDA device pointer
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:param np_tensor: input numpy nd array
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:param is_output: whether the tensor is output
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:return: CUDA device pointer
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"""
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# copy the data to device
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if is_output:
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return device_mem_alloc(np_tensor.size * np_tensor.itemsize)
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else:
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return todevice(np_tensor)
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class TorchFrontend:
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"""
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Frontend node for torch
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"""
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@staticmethod
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def argument(torch_tensor: "torch.Tensor") -> cuda.CUdeviceptr:
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"""Convert the input torch tensor to CUDA device pointer
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:param torch_tensor: input torch tensor
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:param is_output: whether the tensor is output
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:return: CUDA device pointer
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"""
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# check the device of torch_tensor
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if not torch_tensor.is_cuda:
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torch_tensor = torch_tensor.to("cuda")
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return cuda.CUdeviceptr(torch_tensor.data_ptr())
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class CupyFrontend:
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"""
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Frontend node for cupy
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"""
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@staticmethod
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def argument(cupy_ndarray: "cp.ndarray"):
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return cuda.CUdeviceptr(int(cupy_ndarray.data.ptr))
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class TensorFrontend:
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"""
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Universal Frontend for client-provide tensors
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"""
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@staticmethod
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def argument(tensor, is_output=False):
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if is_numpy_tensor(tensor):
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return NumpyFrontend.argument(tensor, is_output)
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elif is_torch_tensor(tensor):
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return TorchFrontend.argument(tensor)
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elif is_cupy_tensor(tensor):
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return CupyFrontend.argument(tensor)
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else:
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raise NotImplementedError("Unknown Tensor Type")
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