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Keval Morabia
4c611e47a6
Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
89 lines
3.6 KiB
Python
89 lines
3.6 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 peft LoRA linear layers."""
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import torch.nn.functional as F
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from peft.tuners.lora.layer import Linear as LoraLinear
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from modelopt.torch.quantization.qtensor.base_qtensor import QTensorWrapper
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from ..nn import QuantModule, QuantModuleRegistry, TensorQuantizer
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__all__ = []
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@QuantModuleRegistry.register({LoraLinear: "LoraLinear"})
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class _QuantLoraLinear(QuantModule):
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def _setup(self):
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self.input_quantizer = TensorQuantizer()
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self.weight_quantizer = TensorQuantizer()
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self.output_quantizer = TensorQuantizer()
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def forward(self, x, *args, **kwargs):
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adapter_names = kwargs.pop("adapter_names", None)
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if self.disable_adapters or adapter_names is not None or self.merged:
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return super().forward(x, args, kwargs)
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x = self.input_quantizer(x)
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weight = self.base_layer.weight
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is_compressed = isinstance(weight, QTensorWrapper)
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if not is_compressed:
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for active_adapter in self.active_adapters:
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if active_adapter not in self.lora_A.keys(): # noqa: SIM118
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continue
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lora_a = self.lora_A[active_adapter]
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lora_b = self.lora_B[active_adapter]
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scaling = self.scaling[active_adapter]
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if not self.use_dora[active_adapter]:
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weight = weight + scaling * lora_b.weight @ lora_a.weight
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else:
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raise NotImplementedError("dora not implemented")
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weight = self.weight_quantizer(weight)
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output = F.linear(x, weight, self.base_layer.bias)
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else:
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# For compressed weights, compute base output and LoRA outputs separately
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base_output = self.base_layer(x)
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# Only compute LoRA outputs if there are active adapters
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if self.active_adapters:
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# Start with zero LoRA output
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lora_output = None
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for active_adapter in self.active_adapters:
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if active_adapter not in self.lora_A.keys(): # noqa: SIM118
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continue
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lora_a = self.lora_A[active_adapter]
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lora_b = self.lora_B[active_adapter]
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scaling = self.scaling[active_adapter]
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if not self.use_dora[active_adapter]:
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# Compute LoRA output step by step to maintain gradient flow
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lora_a_output = F.linear(x, lora_a.weight)
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lora_b_output = F.linear(lora_a_output, lora_b.weight)
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adapter_output = scaling * lora_b_output
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lora_output = (
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adapter_output if lora_output is None else lora_output + adapter_output
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)
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output = base_output + lora_output
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else:
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output = base_output
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output = self.output_quantizer(output)
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return output
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