Spaces:
Running
on
Zero
Running
on
Zero
File size: 6,037 Bytes
727a943 9eee006 727a943 9eee006 727a943 9eee006 727a943 9eee006 727a943 9eee006 727a943 9eee006 727a943 9eee006 727a943 9eee006 727a943 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 |
"""
Adapted from
https://github.com/huggingface/flux-fast/blob/156281514e2725782ffab9431d4004840f7e3b4d/utils/pipeline_utils.py#L87
"""
import torch
from typing import List, Optional
import inspect
import torch
from kernels import get_kernel
_flash_attn_func = get_kernel("kernels-community/vllm-flash-attn3").flash_attn_func
@torch.library.custom_op("flash::flash_attn_func", mutates_args=())
def flash_attn_func(
q: torch.Tensor,
k: torch.Tensor,
v: torch.Tensor,
softmax_scale: Optional[float] = None,
causal: bool = False,
# probably wrong type for these 4
qv: Optional[float] = None,
q_descale: Optional[float] = None,
k_descale: Optional[float] = None,
v_descale: Optional[float] = None,
window_size: Optional[List[int]] = None,
sink_token_length: int = 0,
softcap: float = 0.0,
num_splits: int = 1,
# probably wrong type for this too
pack_gqa: Optional[float] = None,
deterministic: bool = False,
sm_margin: int = 0,
) -> torch.Tensor: # Tuple[torch.Tensor, torch.Tensor]:
if window_size is None:
window_size = (-1, -1)
else:
window_size = tuple(window_size)
sig = inspect.signature(_flash_attn_func)
accepted = set(sig.parameters)
all_kwargs = {
"softmax_scale": softmax_scale,
"causal": causal,
"qv": qv,
"q_descale": q_descale,
"k_descale": k_descale,
"v_descale": v_descale,
"window_size": window_size,
"sink_token_length": sink_token_length,
"softcap": softcap,
"num_splits": num_splits,
"pack_gqa": pack_gqa,
"deterministic": deterministic,
"sm_margin": sm_margin,
}
kwargs = {k: v for k, v in all_kwargs.items() if k in accepted}
outputs = _flash_attn_func(q, k, v, **kwargs)
return outputs[0]
@flash_attn_func.register_fake
def _(q, k, v, **kwargs):
# two outputs:
# 1. output: (batch, seq_len, num_heads, head_dim)
# 2. softmax_lse: (batch, num_heads, seq_len) with dtype=torch.float32
meta_q = torch.empty_like(q).contiguous()
return meta_q # , q.new_empty((q.size(0), q.size(2), q.size(1)), dtype=torch.float32)
class FlashFluxAttnProcessor3_0:
"""Attention processor used typically in processing the SD3-like self-attention projections."""
def __call__(
self,
attn,
hidden_states: torch.FloatTensor,
encoder_hidden_states: torch.FloatTensor = None,
attention_mask: Optional[torch.FloatTensor] = None,
image_rotary_emb: Optional[torch.Tensor] = None,
) -> torch.FloatTensor:
batch_size, _, _ = hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
# `sample` projections.
query = attn.to_q(hidden_states)
key = attn.to_k(hidden_states)
value = attn.to_v(hidden_states)
inner_dim = key.shape[-1]
head_dim = inner_dim // attn.heads
query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
if attn.norm_q is not None:
query = attn.norm_q(query)
if attn.norm_k is not None:
key = attn.norm_k(key)
# the attention in FluxSingleTransformerBlock does not use `encoder_hidden_states`
# `context` projections.
if encoder_hidden_states is not None:
encoder_hidden_states_query_proj = attn.add_q_proj(encoder_hidden_states)
encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)
encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)
encoder_hidden_states_query_proj = encoder_hidden_states_query_proj.view(
batch_size, -1, attn.heads, head_dim
).transpose(1, 2)
encoder_hidden_states_key_proj = encoder_hidden_states_key_proj.view(
batch_size, -1, attn.heads, head_dim
).transpose(1, 2)
encoder_hidden_states_value_proj = encoder_hidden_states_value_proj.view(
batch_size, -1, attn.heads, head_dim
).transpose(1, 2)
if attn.norm_added_q is not None:
encoder_hidden_states_query_proj = attn.norm_added_q(encoder_hidden_states_query_proj)
if attn.norm_added_k is not None:
encoder_hidden_states_key_proj = attn.norm_added_k(encoder_hidden_states_key_proj)
# attention
query = torch.cat([encoder_hidden_states_query_proj, query], dim=2)
key = torch.cat([encoder_hidden_states_key_proj, key], dim=2)
value = torch.cat([encoder_hidden_states_value_proj, value], dim=2)
if image_rotary_emb is not None:
from diffusers.models.embeddings import apply_rotary_emb
query = apply_rotary_emb(query, image_rotary_emb)
key = apply_rotary_emb(key, image_rotary_emb)
# NB: transposes are necessary to match expected SDPA input shape
hidden_states = flash_attn_func(query.transpose(1, 2), key.transpose(1, 2), value.transpose(1, 2))[
0
].transpose(1, 2)
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
hidden_states = hidden_states.to(query.dtype)
if encoder_hidden_states is not None:
encoder_hidden_states, hidden_states = (
hidden_states[:, : encoder_hidden_states.shape[1]],
hidden_states[:, encoder_hidden_states.shape[1] :],
)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
encoder_hidden_states = attn.to_add_out(encoder_hidden_states)
return hidden_states, encoder_hidden_states
else:
return hidden_states |