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Keval Morabia
37085354c9
Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
53 lines
2.0 KiB
Python
53 lines
2.0 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2025 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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"""Support quantization for Transformers."""
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import torch.nn as nn
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from modelopt.torch.quantization.nn.modules.tensor_quantizer import TensorQuantizer
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from .custom import CUSTOM_POST_CONVERSION_PLUGINS
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def make_deepspeed_compatible(model: nn.Module):
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"""Make the model compatible with DeepSpeed."""
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try:
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from deepspeed.runtime.zero.parameter_offload import ZeROOrderedDict
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except ImportError:
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return
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is_deepspeed_zero3_enabled = any(
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hasattr(module, "_parameters") and isinstance(module._parameters, ZeROOrderedDict)
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for module in model.modules()
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)
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if is_deepspeed_zero3_enabled:
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# For zero stage 3, the _parameters is a ZeROOrderedDict, tensor_quantizer._parameters
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# is usually a dict, so we need to check if it is a ZeROOrderedDict if the model is wrapped
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# by deepspeed.
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def _make_deepspeed_compatible(module):
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"""Make a module's _parameters DeepSpeed compatible."""
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if isinstance(module, TensorQuantizer) and not isinstance(
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module._parameters, ZeROOrderedDict
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):
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module._parameters = ZeROOrderedDict(module._parameters)
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# Make all modules DeepSpeed compatible
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for module in model.modules():
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_make_deepspeed_compatible(module)
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CUSTOM_POST_CONVERSION_PLUGINS.add(make_deepspeed_compatible)
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