98 lines
3.6 KiB
Python
98 lines
3.6 KiB
Python
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# lifted from https://gist.github.com/pszemraj/e88ff24ab296b6d89057376b299b368a
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# to-do: make this work with LoRAs, it complains
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# Copyright 2023-present Daniel Han-Chen & the Unsloth team. All rights reserved.
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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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import torch
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import transformers
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import inspect
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class Unsloth_Offloaded_Gradient_Checkpointer(torch.autograd.Function):
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"""
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Saves VRAM by smartly offloading to RAM.
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Tiny hit to performance, since we mask the movement via non blocking calls.
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"""
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@staticmethod
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@torch.cuda.amp.custom_fwd
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def forward(ctx, forward_function, hidden_states, *args):
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saved_hidden_states = hidden_states.to("cpu", non_blocking=True)
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with torch.no_grad():
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output = forward_function(hidden_states, *args)
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ctx.save_for_backward(saved_hidden_states)
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ctx.forward_function = forward_function
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ctx.args = args
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return output
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pass
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@staticmethod
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@torch.cuda.amp.custom_bwd
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def backward(ctx, dY):
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(hidden_states,) = ctx.saved_tensors
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hidden_states = hidden_states.to("cuda", non_blocking=True).detach()
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hidden_states.requires_grad = True
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with torch.enable_grad():
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(output,) = ctx.forward_function(hidden_states, *ctx.args)
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torch.autograd.backward(output, dY)
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return (
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None,
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hidden_states.grad,
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) + (
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None,
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) * len(ctx.args)
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pass
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pass
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def new_gradient_checkpointing_enable(self, gradient_checkpointing_kwargs=None):
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#assert gradient_checkpointing_kwargs == None
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gradient_checkpointing_kwargs = None
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if not self.supports_gradient_checkpointing:
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raise ValueError(
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f"{self.__class__.__name__} does not support gradient checkpointing."
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)
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gradient_checkpointing_func = Unsloth_Offloaded_Gradient_Checkpointer.apply
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# For old GC format (transformers < 4.35.0) for models that live on the Hub
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# we will fall back to the overwritten `_set_gradient_checkpointing` method
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_is_using_old_format = (
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"value" in inspect.signature(self._set_gradient_checkpointing).parameters
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)
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if not _is_using_old_format:
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self._set_gradient_checkpointing(
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enable=True, gradient_checkpointing_func=gradient_checkpointing_func
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)
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else:
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raise NotImplementedError()
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if getattr(self, "_hf_peft_config_loaded", False):
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# When using PEFT + gradient checkpointing + Trainer we need to make sure the input has requires_grad=True
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# we do it also on PEFT: https://github.com/huggingface/peft/blob/85013987aa82aa1af3da1236b6902556ce3e483e/src/peft/peft_model.py#L334
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# When training with PEFT, only LoRA layers will have requires grad set to True, but the output of frozen layers need to propagate
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# the gradients to make sure the gradient flows.
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self.enable_input_require_grads()
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def apply_unsloth_offloaded_gradient_checkpoint_monkey_patch():
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transformers.modeling_utils.PreTrainedModel.gradient_checkpointing_enable = (
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new_gradient_checkpointing_enable
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)
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