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