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61 lines
2.1 KiB
Python
61 lines
2.1 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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"""Support quantization for Transformer Engine layers."""
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import torch
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import transformer_engine as te
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import transformer_engine.pytorch.module.linear as te_linear
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from ..nn import QuantModuleRegistry
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from .custom import _ParallelLinear
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@QuantModuleRegistry.register({te.pytorch.Linear: "te_Linear"})
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class _QuantTELinear(_ParallelLinear):
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_functionals_to_replace = [
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(
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te_linear._Linear,
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"apply" if torch.is_grad_enabled() else "forward",
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),
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]
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@staticmethod
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def te_quantized_linear_fn(package, func_name, self, *args, **kwargs):
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"""Quantized version specifically for TE with weight first, then input."""
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if te.__version__ >= "2.0":
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weight, inputs = args[0], args[1]
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remaining_args = args[2:]
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output = getattr(package, func_name)(
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self.weight_quantizer(weight),
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self.input_quantizer(inputs),
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*remaining_args,
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**kwargs,
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)
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else:
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weight, weight_fp8, inputs = args[0], args[1], args[2]
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remaining_args = args[3:]
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output = getattr(package, func_name)(
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self.weight_quantizer(weight),
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weight_fp8,
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self.input_quantizer(inputs),
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*remaining_args,
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**kwargs,
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)
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return self.output_quantizer(output)
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# Override the quantized linear function
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_quantized_linear_fn = te_quantized_linear_fn
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