99 lines
3.3 KiB
Python
99 lines
3.3 KiB
Python
#################################################################################################
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#
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# Copyright (c) 2023 - 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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"""
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Collection of builtin functions used for host reference in EVT
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"""
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import numpy as np
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from cutlass_cppgen.utils.datatypes import is_cupy_tensor, is_numpy_tensor, is_torch_available, is_torch_tensor
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if is_torch_available():
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import torch
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def multiply_add(x, y, z):
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return x * y + z
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def sum(x, dim):
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if is_numpy_tensor(x):
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return x.sum(axis=tuple(dim))
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elif is_torch_tensor(x):
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return torch.sum(x, dim)
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def max(x, dim):
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if is_numpy_tensor(x):
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return x.max(axis=tuple(dim))
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elif is_torch_tensor(x):
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return torch.amax(x, dim)
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def maximum(x, y):
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if is_numpy_tensor(x):
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return np.maximum(x, y)
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elif is_torch_tensor(x):
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return torch.maximum(x, torch.tensor(y))
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def minimum(x, y):
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if is_numpy_tensor(x):
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return np.minimum(x, y)
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elif is_torch_tensor(x):
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return torch.minimum(x, torch.tensor(y))
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def exp(x):
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if is_numpy_tensor(x):
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return np.exp(x)
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elif is_torch_tensor(x):
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return torch.exp(x)
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##############################################################################
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# Layout manipulate nodes
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##############################################################################
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def permute(x, indices: tuple):
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if is_numpy_tensor(x):
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return np.transpose(x, axes=indices)
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elif is_torch_tensor(x):
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return x.permute(*indices)
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def reshape(x, new_shape: tuple):
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if is_numpy_tensor(x):
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return np.reshape(x, newshape=new_shape)
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elif is_torch_tensor(x):
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return x.view(new_shape)
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