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#################################################################################################
#
# Copyright (c) 2017 - 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.
#
#################################################################################################
from math import prod
from typing import Union
from cuda import cuda, cudart
import numpy as np
import cutlass
from cutlass.backend.frontend import CupyFrontend, NumpyFrontend, TorchFrontend
from cutlass.backend.memory_manager import DevicePtrWrapper
from cutlass.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.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
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