85 lines
3.3 KiB
Python
85 lines
3.3 KiB
Python
import collections
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from enum import Enum
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import torch
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from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import (checkpoint_wrapper)
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TRANSFORMER_BLOCK_NAMES = [
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"blocks",
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"double_blocks",
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"single_blocks",
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"transformer_blocks",
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"temporal_transformer_blocks",
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"transformer_double_blocks",
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"transformer_single_blocks",
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]
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class CheckpointType(str, Enum):
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FULL = "full"
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OPS = "ops"
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BLOCK_SKIP = "block_skip"
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_SELECTIVE_ACTIVATION_CHECKPOINTING_OPS = {
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torch.ops.aten.mm.default,
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torch.ops.aten._scaled_dot_product_efficient_attention.default,
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torch.ops.aten._scaled_dot_product_flash_attention.default,
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torch.ops._c10d_functional.reduce_scatter_tensor.default,
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}
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def apply_activation_checkpointing(module: torch.nn.Module,
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checkpointing_type: str = CheckpointType.FULL,
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n_layer: int = 1) -> torch.nn.Module:
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if checkpointing_type == CheckpointType.FULL:
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module = _apply_activation_checkpointing_blocks(module)
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elif checkpointing_type == CheckpointType.OPS:
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module = _apply_activation_checkpointing_ops(module, _SELECTIVE_ACTIVATION_CHECKPOINTING_OPS)
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elif checkpointing_type == CheckpointType.BLOCK_SKIP:
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module = _apply_activation_checkpointing_blocks(module, n_layer)
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else:
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raise ValueError(
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f"Checkpointing type '{checkpointing_type}' not supported. Supported types are {CheckpointType.__members__.keys()}"
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)
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return module
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def _apply_activation_checkpointing_blocks(module: torch.nn.Module, n_layer: int | None = None) -> torch.nn.Module:
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applied = False
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for transformer_block_name in TRANSFORMER_BLOCK_NAMES:
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blocks: torch.nn.Module = getattr(module, transformer_block_name, None)
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if blocks is None:
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continue
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for index, (layer_id, block) in enumerate(blocks.named_children()):
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if n_layer is None or index % n_layer == 0:
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block = checkpoint_wrapper(block, preserve_rng_state=False)
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blocks.register_module(layer_id, block)
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applied = True
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if not applied:
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raise ValueError("Activation checkpointing is not applied successfully")
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return module
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def _apply_activation_checkpointing_ops(module: torch.nn.Module, ops) -> torch.nn.Module:
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from torch.utils.checkpoint import (CheckpointPolicy, create_selective_checkpoint_contexts)
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def _get_custom_policy(meta: dict[str, int]) -> CheckpointPolicy:
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def _custom_policy(ctx, func, *args, **kwargs):
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mode = "recompute" if ctx.is_recompute else "forward"
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mm_count_key = f"{mode}_mm_count"
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if func == torch.ops.aten.mm.default:
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meta[mm_count_key] += 1
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# Saves output of all compute ops, except every second mm
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to_save = func in ops and not (func == torch.ops.aten.mm.default and meta[mm_count_key] % 2 == 0)
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return CheckpointPolicy.MUST_SAVE if to_save else CheckpointPolicy.PREFER_RECOMPUTE
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return _custom_policy
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def selective_checkpointing_context_fn():
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meta: dict[str, int] = collections.defaultdict(int)
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return create_selective_checkpoint_contexts(_get_custom_policy(meta))
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return checkpoint_wrapper(module, context_fn=selective_checkpointing_context_fn, preserve_rng_state=False)
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