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"""Unsloth checkpointing"""
# Copyright 2023-present Daniel Han-Chen & the Unsloth team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
class Unsloth_Offloaded_Gradient_Checkpointer( # pylint: disable=invalid-name
torch.autograd.Function
):
"""
Saves VRAM by smartly offloading to RAM.
Tiny hit to performance, since we mask the movement via non blocking calls.
"""
@staticmethod
@torch.cuda.amp.custom_fwd
def forward(ctx, forward_function, hidden_states, *args):
saved_hidden_states = hidden_states.to("cpu", non_blocking=True)
with torch.no_grad():
output = forward_function(hidden_states, *args)
ctx.save_for_backward(saved_hidden_states)
ctx.forward_function = forward_function
ctx.args = args
return output
@staticmethod
@torch.cuda.amp.custom_bwd
def backward(ctx, dY):
(hidden_states,) = ctx.saved_tensors
hidden_states = hidden_states.to("cuda", non_blocking=True).detach()
hidden_states.requires_grad = True
with torch.enable_grad():
(output,) = ctx.forward_function(hidden_states, *ctx.args)
torch.autograd.backward(output, dY)
return (
None,
hidden_states.grad,
) + (
None,
) * len(ctx.args)
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