mirror of
https://github.com/Wan-Video/Wan2.1.git
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390 lines
12 KiB
Python
390 lines
12 KiB
Python
# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
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import torch
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from importlib.metadata import version
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from mmgp import offload
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import torch.nn.functional as F
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try:
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import flash_attn_interface
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FLASH_ATTN_3_AVAILABLE = True
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except ModuleNotFoundError:
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FLASH_ATTN_3_AVAILABLE = False
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try:
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import flash_attn
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FLASH_ATTN_2_AVAILABLE = True
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except ModuleNotFoundError:
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FLASH_ATTN_2_AVAILABLE = False
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flash_attn = None
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try:
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from sageattention import sageattn_varlen
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def sageattn_varlen_wrapper(
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q,
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k,
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v,
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cu_seqlens_q,
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cu_seqlens_kv,
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max_seqlen_q,
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max_seqlen_kv,
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):
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return sageattn_varlen(q, k, v, cu_seqlens_q, cu_seqlens_kv, max_seqlen_q, max_seqlen_kv)
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except ImportError:
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sageattn_varlen_wrapper = None
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import warnings
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try:
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from sageattention import sageattn
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from .sage2_core import sageattn as alt_sageattn, is_sage_supported
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sage_supported = is_sage_supported()
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except ImportError:
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sageattn = None
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alt_sageattn = None
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sage_supported = False
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# @torch.compiler.disable()
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def sageattn_wrapper(
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qkv_list,
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attention_length
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):
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q,k, v = qkv_list
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padding_length = q.shape[0] -attention_length
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q = q[:attention_length, :, : ].unsqueeze(0)
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k = k[:attention_length, :, : ].unsqueeze(0)
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v = v[:attention_length, :, : ].unsqueeze(0)
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if True:
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qkv_list = [q,k,v]
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del q, k ,v
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o = alt_sageattn(qkv_list, tensor_layout="NHD").squeeze(0)
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else:
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o = sageattn(q, k, v, tensor_layout="NHD").squeeze(0)
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del q, k ,v
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qkv_list.clear()
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if padding_length > 0:
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o = torch.cat([o, torch.empty( (padding_length, *o.shape[-2:]), dtype= o.dtype, device=o.device ) ], 0)
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return o
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# # try:
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# if True:
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# from sageattention import sageattn_qk_int8_pv_fp8_window_cuda
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# @torch.compiler.disable()
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# def sageattn_window_wrapper(
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# qkv_list,
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# attention_length,
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# window
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# ):
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# q,k, v = qkv_list
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# padding_length = q.shape[0] -attention_length
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# q = q[:attention_length, :, : ].unsqueeze(0)
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# k = k[:attention_length, :, : ].unsqueeze(0)
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# v = v[:attention_length, :, : ].unsqueeze(0)
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# o = sageattn_qk_int8_pv_fp8_window_cuda(q, k, v, tensor_layout="NHD", window = window).squeeze(0)
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# del q, k ,v
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# qkv_list.clear()
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# if padding_length > 0:
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# o = torch.cat([o, torch.empty( (padding_length, *o.shape[-2:]), dtype= o.dtype, device=o.device ) ], 0)
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# return o
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# # except ImportError:
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# # sageattn = sageattn_qk_int8_pv_fp8_window_cuda
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@torch.compiler.disable()
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def sdpa_wrapper(
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qkv_list,
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attention_length
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):
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q,k, v = qkv_list
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padding_length = q.shape[0] -attention_length
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q = q[:attention_length, :].transpose(0,1).unsqueeze(0)
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k = k[:attention_length, :].transpose(0,1).unsqueeze(0)
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v = v[:attention_length, :].transpose(0,1).unsqueeze(0)
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o = F.scaled_dot_product_attention(
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q, k, v, attn_mask=None, is_causal=False
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).squeeze(0).transpose(0,1)
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del q, k ,v
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qkv_list.clear()
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if padding_length > 0:
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o = torch.cat([o, torch.empty( (padding_length, *o.shape[-2:]), dtype= o.dtype, device=o.device ) ], 0)
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return o
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def get_attention_modes():
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ret = ["sdpa", "auto"]
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if flash_attn != None:
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ret.append("flash")
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# if memory_efficient_attention != None:
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# ret.append("xformers")
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if sageattn_varlen_wrapper != None:
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ret.append("sage")
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if sageattn != None and version("sageattention").startswith("2") :
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ret.append("sage2")
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return ret
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def get_supported_attention_modes():
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ret = get_attention_modes()
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if not sage_supported:
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if "sage" in ret:
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del ret["sage"]
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if "sage2" in ret:
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del ret["sage2"]
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return ret
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__all__ = [
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'pay_attention',
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'attention',
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]
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def pay_attention(
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qkv_list,
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# q,
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# k,
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# v,
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q_lens=None,
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k_lens=None,
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dropout_p=0.,
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softmax_scale=None,
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q_scale=None,
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causal=False,
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window_size=(-1, -1),
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deterministic=False,
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dtype=torch.bfloat16,
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version=None,
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force_attention= None,
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cross_attn= False
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):
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"""
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q: [B, Lq, Nq, C1].
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k: [B, Lk, Nk, C1].
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v: [B, Lk, Nk, C2]. Nq must be divisible by Nk.
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q_lens: [B].
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k_lens: [B].
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dropout_p: float. Dropout probability.
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softmax_scale: float. The scaling of QK^T before applying softmax.
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causal: bool. Whether to apply causal attention mask.
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window_size: (left right). If not (-1, -1), apply sliding window local attention.
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deterministic: bool. If True, slightly slower and uses more memory.
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dtype: torch.dtype. Apply when dtype of q/k/v is not float16/bfloat16.
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"""
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attn = offload.shared_state["_attention"] if force_attention== None else force_attention
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q,k,v = qkv_list
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qkv_list.clear()
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half_dtypes = (torch.float16, torch.bfloat16)
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assert dtype in half_dtypes
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assert q.device.type == 'cuda' and q.size(-1) <= 256
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# params
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b, lq, lk, out_dtype = q.size(0), q.size(1), k.size(1), q.dtype
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def half(x):
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return x if x.dtype in half_dtypes else x.to(dtype)
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# preprocess query
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if q_lens is None:
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q = half(q.flatten(0, 1))
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q_lens = torch.tensor(
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[lq] * b, dtype=torch.int32).to(
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device=q.device, non_blocking=True)
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else:
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q = half(torch.cat([u[:v] for u, v in zip(q, q_lens)]))
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# preprocess key, value
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if k_lens is None:
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k = half(k.flatten(0, 1))
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v = half(v.flatten(0, 1))
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k_lens = torch.tensor(
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[lk] * b, dtype=torch.int32).to(
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device=k.device, non_blocking=True)
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else:
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k = half(torch.cat([u[:v] for u, v in zip(k, k_lens)]))
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v = half(torch.cat([u[:v] for u, v in zip(v, k_lens)]))
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q = q.to(v.dtype)
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k = k.to(v.dtype)
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if q_scale is not None:
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q = q * q_scale
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if version is not None and version == 3 and not FLASH_ATTN_3_AVAILABLE:
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warnings.warn(
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'Flash attention 3 is not available, use flash attention 2 instead.'
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)
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# apply attention
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if attn=="sage":
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x = sageattn_varlen_wrapper(
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q=q,
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k=k,
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v=v,
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cu_seqlens_q=torch.cat([q_lens.new_zeros([1]), q_lens]).cumsum(
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0, dtype=torch.int32).to(q.device, non_blocking=True),
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cu_seqlens_kv=torch.cat([k_lens.new_zeros([1]), k_lens]).cumsum(
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0, dtype=torch.int32).to(q.device, non_blocking=True),
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max_seqlen_q=lq,
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max_seqlen_kv=lk,
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).unflatten(0, (b, lq))
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elif attn=="sage2":
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import math
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if cross_attn or True:
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qkv_list = [q,k,v]
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del q,k,v
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x = sageattn_wrapper(qkv_list, lq).unsqueeze(0)
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# else:
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# layer = offload.shared_state["layer"]
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# embed_sizes = offload.shared_state["embed_sizes"]
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# current_step = offload.shared_state["step_no"]
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# max_steps = offload.shared_state["max_steps"]
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# nb_latents = embed_sizes[0] * embed_sizes[1]* embed_sizes[2]
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# window = 0
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# start_window_step = int(max_steps * 0.4)
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# start_layer = 10
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# if (layer < start_layer ) or current_step <start_window_step:
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# window = 0
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# else:
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# coef = min((max_steps - current_step)/(max_steps-start_window_step),1)*max(min((25 - layer)/(25-start_layer),1),0) * 0.7 + 0.3
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# print(f"step: {current_step}, layer: {layer}, coef:{coef:0.1f}]")
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# window = math.ceil(coef* nb_latents)
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# invert_spaces = (layer + current_step) % 2 == 0 and window > 0
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# def flip(q):
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# q = q.reshape(*embed_sizes, *q.shape[-2:])
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# q = q.transpose(0,2)
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# q = q.contiguous()
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# q = q.transpose(0,2)
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# q = q.reshape( -1, *q.shape[-2:])
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# return q
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# def flop(q):
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# q = q.reshape(embed_sizes[2], embed_sizes[1], embed_sizes[0] , *q.shape[-2:])
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# q = q.transpose(0,2)
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# q = q.contiguous()
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# q = q.transpose(0,2)
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# q = q.reshape( -1, *q.shape[-2:])
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# return q
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# if invert_spaces:
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# q = flip(q)
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# k = flip(k)
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# v = flip(v)
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# qkv_list = [q,k,v]
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# del q,k,v
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# x = sageattn_window_wrapper(qkv_list, lq, window= window) #.unsqueeze(0)
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# if invert_spaces:
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# x = flop(x)
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# x = x.unsqueeze(0)
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elif attn=="sdpa":
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qkv_list = [q, k, v]
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del q, k , v
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x = sdpa_wrapper( qkv_list, lq).unsqueeze(0)
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elif attn=="flash" and version == 3:
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# Note: dropout_p, window_size are not supported in FA3 now.
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x = flash_attn_interface.flash_attn_varlen_func(
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q=q,
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k=k,
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v=v,
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cu_seqlens_q=torch.cat([q_lens.new_zeros([1]), q_lens]).cumsum(
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0, dtype=torch.int32).to(q.device, non_blocking=True),
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cu_seqlens_k=torch.cat([k_lens.new_zeros([1]), k_lens]).cumsum(
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0, dtype=torch.int32).to(q.device, non_blocking=True),
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seqused_q=None,
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seqused_k=None,
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max_seqlen_q=lq,
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max_seqlen_k=lk,
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softmax_scale=softmax_scale,
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causal=causal,
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deterministic=deterministic)[0].unflatten(0, (b, lq))
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elif attn=="flash":
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x = flash_attn.flash_attn_varlen_func(
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q=q,
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k=k,
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v=v,
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cu_seqlens_q=torch.cat([q_lens.new_zeros([1]), q_lens]).cumsum(
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0, dtype=torch.int32).to(q.device, non_blocking=True),
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cu_seqlens_k=torch.cat([k_lens.new_zeros([1]), k_lens]).cumsum(
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0, dtype=torch.int32).to(q.device, non_blocking=True),
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max_seqlen_q=lq,
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max_seqlen_k=lk,
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dropout_p=dropout_p,
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softmax_scale=softmax_scale,
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causal=causal,
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window_size=window_size,
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deterministic=deterministic).unflatten(0, (b, lq))
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# output
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return x.type(out_dtype)
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def attention(
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q,
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k,
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v,
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q_lens=None,
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k_lens=None,
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dropout_p=0.,
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softmax_scale=None,
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q_scale=None,
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causal=False,
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window_size=(-1, -1),
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deterministic=False,
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dtype=torch.bfloat16,
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fa_version=None,
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):
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if FLASH_ATTN_2_AVAILABLE or FLASH_ATTN_3_AVAILABLE:
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return pay_attention(
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q=q,
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k=k,
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v=v,
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q_lens=q_lens,
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k_lens=k_lens,
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dropout_p=dropout_p,
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softmax_scale=softmax_scale,
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q_scale=q_scale,
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causal=causal,
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window_size=window_size,
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deterministic=deterministic,
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dtype=dtype,
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version=fa_version,
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)
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else:
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if q_lens is not None or k_lens is not None:
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warnings.warn(
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'Padding mask is disabled when using scaled_dot_product_attention. It can have a significant impact on performance.'
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)
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attn_mask = None
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q = q.transpose(1, 2).to(dtype)
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k = k.transpose(1, 2).to(dtype)
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v = v.transpose(1, 2).to(dtype)
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out = torch.nn.functional.scaled_dot_product_attention(
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q, k, v, attn_mask=attn_mask, is_causal=causal, dropout_p=dropout_p)
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out = out.transpose(1, 2).contiguous()
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return out
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