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on
Zero
Running
on
Zero
""" | |
""" | |
import torch | |
from kernels import get_kernel | |
_flash_attn_func = get_kernel("kernels-community/vllm-flash-attn3").flash_attn_func | |
def flash_attn_func(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor) -> torch.Tensor: | |
outputs, lse = _flash_attn_func(q, k, v) | |
return outputs | |
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) | |
# Copied FusedFluxAttnProcessor2_0 but using flash v3 instead of SDPA | |
class FlashFusedFluxAttnProcessor3_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 = None, | |
attention_mask: torch.FloatTensor | None = None, | |
image_rotary_emb: torch.Tensor | None = None, | |
) -> torch.FloatTensor: | |
batch_size, _, _ = hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape | |
# `sample` projections. | |
qkv = attn.to_qkv(hidden_states) | |
split_size = qkv.shape[-1] // 3 | |
query, key, value = torch.split(qkv, split_size, dim=-1) | |
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_qkv = attn.to_added_qkv(encoder_hidden_states) | |
split_size = encoder_qkv.shape[-1] // 3 | |
( | |
encoder_hidden_states_query_proj, | |
encoder_hidden_states_key_proj, | |
encoder_hidden_states_value_proj, | |
) = torch.split(encoder_qkv, split_size, dim=-1) | |
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 | |