274 lines
9.2 KiB
Python
274 lines
9.2 KiB
Python
# original source:
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# https://github.com/AminRezaei0x443/memory-efficient-attention/blob/1bc0d9e6ac5f82ea43a375135c4e1d3896ee1694/memory_efficient_attention/attention_torch.py
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# license:
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# MIT
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# credit:
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# Amin Rezaei (original author)
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# Alex Birch (optimized algorithm for 3D tensors, at the expense of removing bias, masking and callbacks)
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# implementation of:
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# Self-attention Does Not Need O(n2) Memory":
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# https://arxiv.org/abs/2112.05682v2
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from functools import partial
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import torch
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from torch import Tensor
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from torch.utils.checkpoint import checkpoint
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import math
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try:
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from typing import Optional, NamedTuple, List, Protocol
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except ImportError:
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from typing import Optional, NamedTuple, List
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from typing_extensions import Protocol
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from torch import Tensor
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from typing import List
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from comfy import model_management
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def dynamic_slice(
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x: Tensor,
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starts: List[int],
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sizes: List[int],
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) -> Tensor:
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slicing = [slice(start, start + size) for start, size in zip(starts, sizes)]
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return x[slicing]
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class AttnChunk(NamedTuple):
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exp_values: Tensor
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exp_weights_sum: Tensor
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max_score: Tensor
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class SummarizeChunk(Protocol):
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@staticmethod
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def __call__(
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query: Tensor,
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key_t: Tensor,
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value: Tensor,
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) -> AttnChunk: ...
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class ComputeQueryChunkAttn(Protocol):
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@staticmethod
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def __call__(
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query: Tensor,
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key_t: Tensor,
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value: Tensor,
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) -> Tensor: ...
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def _summarize_chunk(
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query: Tensor,
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key_t: Tensor,
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value: Tensor,
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scale: float,
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upcast_attention: bool,
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mask,
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) -> AttnChunk:
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if upcast_attention:
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with torch.autocast(enabled=False, device_type = 'cuda'):
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query = query.float()
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key_t = key_t.float()
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attn_weights = torch.baddbmm(
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torch.empty(1, 1, 1, device=query.device, dtype=query.dtype),
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query,
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key_t,
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alpha=scale,
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beta=0,
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)
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else:
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attn_weights = torch.baddbmm(
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torch.empty(1, 1, 1, device=query.device, dtype=query.dtype),
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query,
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key_t,
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alpha=scale,
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beta=0,
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)
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max_score, _ = torch.max(attn_weights, -1, keepdim=True)
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max_score = max_score.detach()
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attn_weights -= max_score
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if mask is not None:
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attn_weights += mask
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torch.exp(attn_weights, out=attn_weights)
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exp_weights = attn_weights.to(value.dtype)
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exp_values = torch.bmm(exp_weights, value)
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max_score = max_score.squeeze(-1)
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return AttnChunk(exp_values, exp_weights.sum(dim=-1), max_score)
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def _query_chunk_attention(
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query: Tensor,
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key_t: Tensor,
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value: Tensor,
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summarize_chunk: SummarizeChunk,
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kv_chunk_size: int,
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mask,
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) -> Tensor:
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batch_x_heads, k_channels_per_head, k_tokens = key_t.shape
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_, _, v_channels_per_head = value.shape
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def chunk_scanner(chunk_idx: int, mask) -> AttnChunk:
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key_chunk = dynamic_slice(
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key_t,
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(0, 0, chunk_idx),
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(batch_x_heads, k_channels_per_head, kv_chunk_size)
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)
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value_chunk = dynamic_slice(
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value,
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(0, chunk_idx, 0),
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(batch_x_heads, kv_chunk_size, v_channels_per_head)
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)
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if mask is not None:
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mask = mask[:,:,chunk_idx:chunk_idx + kv_chunk_size]
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return summarize_chunk(query, key_chunk, value_chunk, mask=mask)
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chunks: List[AttnChunk] = [
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chunk_scanner(chunk, mask) for chunk in torch.arange(0, k_tokens, kv_chunk_size)
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]
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acc_chunk = AttnChunk(*map(torch.stack, zip(*chunks)))
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chunk_values, chunk_weights, chunk_max = acc_chunk
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global_max, _ = torch.max(chunk_max, 0, keepdim=True)
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max_diffs = torch.exp(chunk_max - global_max)
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chunk_values *= torch.unsqueeze(max_diffs, -1)
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chunk_weights *= max_diffs
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all_values = chunk_values.sum(dim=0)
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all_weights = torch.unsqueeze(chunk_weights, -1).sum(dim=0)
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return all_values / all_weights
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# TODO: refactor CrossAttention#get_attention_scores to share code with this
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def _get_attention_scores_no_kv_chunking(
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query: Tensor,
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key_t: Tensor,
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value: Tensor,
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scale: float,
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upcast_attention: bool,
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mask,
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) -> Tensor:
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if upcast_attention:
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with torch.autocast(enabled=False, device_type = 'cuda'):
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query = query.float()
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key_t = key_t.float()
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attn_scores = torch.baddbmm(
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torch.empty(1, 1, 1, device=query.device, dtype=query.dtype),
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query,
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key_t,
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alpha=scale,
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beta=0,
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)
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else:
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attn_scores = torch.baddbmm(
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torch.empty(1, 1, 1, device=query.device, dtype=query.dtype),
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query,
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key_t,
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alpha=scale,
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beta=0,
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)
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if mask is not None:
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attn_scores += mask
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try:
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attn_probs = attn_scores.softmax(dim=-1)
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del attn_scores
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except model_management.OOM_EXCEPTION:
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print("ran out of memory while running softmax in _get_attention_scores_no_kv_chunking, trying slower in place softmax instead")
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attn_scores -= attn_scores.max(dim=-1, keepdim=True).values
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torch.exp(attn_scores, out=attn_scores)
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summed = torch.sum(attn_scores, dim=-1, keepdim=True)
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attn_scores /= summed
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attn_probs = attn_scores
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hidden_states_slice = torch.bmm(attn_probs.to(value.dtype), value)
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return hidden_states_slice
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class ScannedChunk(NamedTuple):
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chunk_idx: int
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attn_chunk: AttnChunk
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def efficient_dot_product_attention(
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query: Tensor,
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key_t: Tensor,
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value: Tensor,
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query_chunk_size=1024,
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kv_chunk_size: Optional[int] = None,
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kv_chunk_size_min: Optional[int] = None,
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use_checkpoint=True,
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upcast_attention=False,
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mask = None,
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):
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"""Computes efficient dot-product attention given query, transposed key, and value.
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This is efficient version of attention presented in
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https://arxiv.org/abs/2112.05682v2 which comes with O(sqrt(n)) memory requirements.
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Args:
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query: queries for calculating attention with shape of
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`[batch * num_heads, tokens, channels_per_head]`.
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key_t: keys for calculating attention with shape of
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`[batch * num_heads, channels_per_head, tokens]`.
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value: values to be used in attention with shape of
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`[batch * num_heads, tokens, channels_per_head]`.
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query_chunk_size: int: query chunks size
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kv_chunk_size: Optional[int]: key/value chunks size. if None: defaults to sqrt(key_tokens)
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kv_chunk_size_min: Optional[int]: key/value minimum chunk size. only considered when kv_chunk_size is None. changes `sqrt(key_tokens)` into `max(sqrt(key_tokens), kv_chunk_size_min)`, to ensure our chunk sizes don't get too small (smaller chunks = more chunks = less concurrent work done).
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use_checkpoint: bool: whether to use checkpointing (recommended True for training, False for inference)
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Returns:
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Output of shape `[batch * num_heads, query_tokens, channels_per_head]`.
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"""
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batch_x_heads, q_tokens, q_channels_per_head = query.shape
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_, _, k_tokens = key_t.shape
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scale = q_channels_per_head ** -0.5
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kv_chunk_size = min(kv_chunk_size or int(math.sqrt(k_tokens)), k_tokens)
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if kv_chunk_size_min is not None:
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kv_chunk_size = max(kv_chunk_size, kv_chunk_size_min)
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if mask is not None and len(mask.shape) == 2:
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mask = mask.unsqueeze(0)
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def get_query_chunk(chunk_idx: int) -> Tensor:
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return dynamic_slice(
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query,
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(0, chunk_idx, 0),
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(batch_x_heads, min(query_chunk_size, q_tokens), q_channels_per_head)
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)
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def get_mask_chunk(chunk_idx: int) -> Tensor:
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if mask is None:
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return None
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chunk = min(query_chunk_size, q_tokens)
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return mask[:,chunk_idx:chunk_idx + chunk]
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summarize_chunk: SummarizeChunk = partial(_summarize_chunk, scale=scale, upcast_attention=upcast_attention)
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summarize_chunk: SummarizeChunk = partial(checkpoint, summarize_chunk) if use_checkpoint else summarize_chunk
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compute_query_chunk_attn: ComputeQueryChunkAttn = partial(
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_get_attention_scores_no_kv_chunking,
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scale=scale,
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upcast_attention=upcast_attention
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) if k_tokens <= kv_chunk_size else (
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# fast-path for when there's just 1 key-value chunk per query chunk (this is just sliced attention btw)
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partial(
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_query_chunk_attention,
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kv_chunk_size=kv_chunk_size,
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summarize_chunk=summarize_chunk,
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)
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)
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if q_tokens <= query_chunk_size:
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# fast-path for when there's just 1 query chunk
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return compute_query_chunk_attn(
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query=query,
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key_t=key_t,
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value=value,
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mask=mask,
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)
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# TODO: maybe we should use torch.empty_like(query) to allocate storage in-advance,
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# and pass slices to be mutated, instead of torch.cat()ing the returned slices
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res = torch.cat([
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compute_query_chunk_attn(
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query=get_query_chunk(i * query_chunk_size),
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key_t=key_t,
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value=value,
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mask=get_mask_chunk(i * query_chunk_size)
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) for i in range(math.ceil(q_tokens / query_chunk_size))
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], dim=1)
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return res
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