mirror of
https://github.com/NVIDIA/Model-Optimizer.git
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68 lines
2.5 KiB
Python
68 lines
2.5 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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"""Customization for Nemo Megatron GPT."""
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from collections.abc import Callable, Iterator
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from functools import partial
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from types import ModuleType
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import torch
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# New nemo version should depend on megatron.core
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from nemo.collections.nlp.modules.common.megatron.attention import CoreAttention
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from ..nn import QuantInputBase, QuantModuleRegistry, TensorQuantizer
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from .custom import _QuantFunctionalMixin
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__all__ = []
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def _quantized_bmm(self, input, mat2, *args, **kwargs):
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"""Quantized version of BMM2 in nemo CoreAttention."""
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attn, v = input, mat2
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return torch._bmm(attn, self.v_bmm_quantizer(v), *args, **kwargs)
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def _quantized_baddbmm(self, input, batch1, batch2, *args, **kwargs):
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"""Quantized version of BMM1 in nemo CoreAttention."""
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q, k = batch1, batch2
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return torch._baddbmm(input, self.q_bmm_quantizer(q), self.k_bmm_quantizer(k), *args, **kwargs)
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class _QuantCoreAttention(_QuantFunctionalMixin):
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"""Quantized base class for CoreAttention."""
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_functionals_to_replace = [
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(torch, "bmm", _quantized_bmm),
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(torch, "baddbmm", _quantized_baddbmm),
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]
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@property
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def functionals_to_replace(self) -> Iterator[tuple[ModuleType, str, Callable]]:
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for package, func_name, quantized_func in self._functionals_to_replace:
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if not hasattr(package, func_name):
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continue
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quantized_func = partial(quantized_func, self)
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yield package, func_name, quantized_func
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def _setup(self):
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self.q_bmm_quantizer = TensorQuantizer(QuantInputBase.default_quant_desc_input)
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self.k_bmm_quantizer = TensorQuantizer(QuantInputBase.default_quant_desc_input)
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self.v_bmm_quantizer = TensorQuantizer(QuantInputBase.default_quant_desc_input)
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QuantModuleRegistry.register({CoreAttention: "nemo_core_attention"})(_QuantCoreAttention)
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