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Keval Morabia
1ef1d72a1b
Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
88 lines
3.3 KiB
Python
88 lines
3.3 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Utility functions for PyTorch tensors."""
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from collections import abc
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import numpy as np
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import torch
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__all__ = [
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"numpy_to_torch",
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"to_empty_if_meta_device",
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"torch_detach",
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"torch_to",
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"torch_to_numpy",
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]
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def torch_to(data, *args, **kwargs):
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"""Try to recursively move the data to the specified args/kwargs."""
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if isinstance(data, torch.Tensor):
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return data.to(*args, **kwargs)
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elif isinstance(data, (tuple, list)):
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return type(data)([torch_to(val, *args, **kwargs) for val in data])
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elif isinstance(data, abc.Mapping):
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return {k: torch_to(val, *args, **kwargs) for k, val in data.items()}
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return data
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def torch_detach(data):
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"""Try to recursively detach the data from the computation graph."""
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if isinstance(data, torch.Tensor):
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return torch.detach(data)
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elif isinstance(data, (tuple, list)):
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return type(data)([torch_detach(val) for val in data])
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elif isinstance(data, abc.Mapping):
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return {k: torch_detach(val) for k, val in data.items()}
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return data
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def torch_to_numpy(inputs: list[torch.Tensor]) -> list[np.ndarray]:
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"""Convert torch tensors to numpy arrays."""
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return [t.detach().cpu().numpy() for t in inputs]
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def numpy_to_torch(np_outputs: list[np.ndarray]) -> list[torch.Tensor]:
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"""Convert numpy arrays to torch tensors."""
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return [torch.from_numpy(arr) for arr in np_outputs]
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def to_empty_if_meta_device(module: torch.nn.Module, *, device: torch.device, recurse=True):
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"""Move tensors to device if not meta device; otherwise materialize with empty_like().
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Officially, torch suggests to_empty() for meta device materialization. Under the hood,
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torch.empty_like() is applied to all parameters or buffers (see _apply). This may
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accidentally overwrite buffers with precomputed values during construction. Given the
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goal is to only materialize those tensors on meta device, this function checks the
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device first and only move the tensor to the destination if it is not on meta device.
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Args:
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module: The target module to apply this transformation.
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device: The desired device of the parameters
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and buffers in this module.
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recurse: Whether parameters and buffers of submodules should
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be recursively moved to the specified device.
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"""
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def _empty_like_if_meta(tensor: torch.Tensor, *, device: torch.device):
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if tensor.device == torch.device("meta"):
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return torch.empty_like(tensor, device=device)
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else:
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return tensor.to(device)
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return module._apply(lambda t: _empty_like_if_meta(t, device=device), recurse=recurse)
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