Add qwen_fa3_processor.py

qwenimage/qwen_fa3_processor.py

@@ -1,233 +1,3 @@
-"""
-Paired with a good language model. Thanks!
-
-FA3 is currently broken on Blackwell (sm_100) GPUs; this module detects that
-at import time and falls back to PyTorch scaled-dot-product attention (SDPA)
-automatically.  The public class name / call signature are unchanged.
-"""
-
-import torch
-import torch.nn.functional as F
-from typing import Optional, Tuple
-from diffusers.models.transformers.transformer_qwenimage import apply_rotary_emb_qwen
-
-
-# ---------------------------------------------------------------------------
-# FA3 availability check
-# ---------------------------------------------------------------------------
-
-def _is_blackwell() -> bool:
-    """Return True when the current default CUDA device is an sm_100 (Blackwell) GPU."""
-    if not torch.cuda.is_available():
-        return False
-    cap = torch.cuda.get_device_capability()
-    # Blackwell → compute capability 10.x  (sm_100)
-    return cap[0] >= 10
-
-
-_fa3_available: bool = False
-_fa3_unavailable_reason: str = ""
-_flash_attn_func = None
-
-if _is_blackwell():
-    _fa3_unavailable_reason = (
-        "FlashAttention-3 is not yet supported on Blackwell (sm_100) GPUs. "
-        "Falling back to scaled-dot-product attention (SDPA)."
-    )
-else:
-    try:
-        from kernels import get_kernel
-        _k = get_kernel("kernels-community/vllm-flash-attn3")
-        _flash_attn_func = _k.flash_attn_func
-        _fa3_available = True
-    except Exception as e:
-        _fa3_unavailable_reason = (
-            "FlashAttention-3 via Hugging Face `kernels` is unavailable. "
-            "Tried `get_kernel('kernels-community/vllm-flash-attn3')` and failed with:\n{e}\n"
-            "Falling back to scaled-dot-product attention (SDPA)."
-        )
-
-
-# ---------------------------------------------------------------------------
-# FA3 custom op (registered only when the kernel loaded successfully)
-# ---------------------------------------------------------------------------
-
-if _fa3_available:
-    @torch.library.custom_op("flash::flash_attn_func", mutates_args=())
-    def flash_attn_func(
-        q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, causal: bool = False
-    ) -> torch.Tensor:
-        # _flash_attn_func returns (output, softmax_lse); we only need output.
-        output, _lse = _flash_attn_func(q, k, v, causal=causal)
-        return output
-
-    @flash_attn_func.register_fake
-    def _flash_attn_func_fake(q, k, v, causal=False):
-        # output shape mirrors q: (batch, seq_len, num_heads, head_dim)
-        return torch.empty_like(q).contiguous()
-
-else:
-    # Provide a stub so call-sites that import the symbol don't break at
-    # module load; the processor will route around it at runtime.
-    def flash_attn_func(
-        q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, causal: bool = False
-    ) -> torch.Tensor:
-        raise RuntimeError(_fa3_unavailable_reason)
-
-
-# ---------------------------------------------------------------------------
-# SDPA fallback helper
-# ---------------------------------------------------------------------------
-
-def _sdpa_attention(
-    q: torch.Tensor,
-    k: torch.Tensor,
-    v: torch.Tensor,
-    causal: bool = False,
-) -> torch.Tensor:
-    """
-    Scaled dot-product attention using torch.nn.functional.scaled_dot_product_attention.
-
-    Input / output layout: (B, S, H, D_h)  — same as the FA3 kernel.
-    """
-    # SDPA expects (B, H, S, D_h)
-    q = q.transpose(1, 2)
-    k = k.transpose(1, 2)
-    v = v.transpose(1, 2)
-
-    out = F.scaled_dot_product_attention(q, k, v, is_causal=causal)
-
-    # Back to (B, S, H, D_h)
-    return out.transpose(1, 2)
-
-
-# ---------------------------------------------------------------------------
-# Attention processor
-# ---------------------------------------------------------------------------
-
-class QwenDoubleStreamAttnProcessorFA3:
-    """
-    Attention processor for the Qwen double-stream architecture.
-
-    Preferred backend: vLLM FlashAttention-3 via Hugging Face ``kernels``.
-    Automatic fallback: PyTorch ``scaled_dot_product_attention`` (SDPA) when
-    FA3 is unavailable — e.g. on Blackwell (sm_100) GPUs where FA3 is not yet
-    supported, or when the ``kernels`` package is absent.
-
-    Notes / limitations
-    -------------------
-    - Arbitrary attention masks are not supported on the FA3 path.  Pass
-      ``attention_mask=None`` (the default) to stay on the fast path.
-    - On the SDPA path, ``attention_mask`` is likewise ignored; add explicit
-      support here if you need it.
-    - ``encoder_hidden_states`` (text stream) is required.
-    """
-
-    _attention_backend: str  # set in __init__ after capability detection
-
-    def __init__(self):
-        if _fa3_available:
-            self._attention_backend = "fa3"
-        else:
-            import warnings
-            warnings.warn(
-                f"QwenDoubleStreamAttnProcessorFA3: {_fa3_unavailable_reason}",
-                stacklevel=2,
-            )
-            self._attention_backend = "sdpa"
-
-    def _attend(
-        self,
-        q: torch.Tensor,
-        k: torch.Tensor,
-        v: torch.Tensor,
-        causal: bool = False,
-    ) -> torch.Tensor:
-        """Dispatch to FA3 or SDPA depending on what is available."""
-        if self._attention_backend == "fa3":
-            return flash_attn_func(q, k, v, causal=causal)
-        return _sdpa_attention(q, k, v, causal=causal)
-
-    @torch.no_grad()
-    def __call__(
-        self,
-        attn,
-        hidden_states: torch.FloatTensor,                          # (B, S_img, D_model)
-        encoder_hidden_states: torch.FloatTensor = None,           # (B, S_txt, D_model)
-        encoder_hidden_states_mask: torch.FloatTensor = None,      # unused
-        attention_mask: Optional[torch.FloatTensor] = None,        # unsupported on FA3 path
-        image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
-    ) -> Tuple[torch.FloatTensor, torch.FloatTensor]:
-
-        if encoder_hidden_states is None:
-            raise ValueError(
-                "QwenDoubleStreamAttnProcessorFA3 requires encoder_hidden_states (text stream)."
-            )
-        if attention_mask is not None and self._attention_backend == "fa3":
-            raise NotImplementedError(
-                "attention_mask is not supported on the FA3 path. "
-                "Either drop the mask or let the processor fall back to SDPA."
-            )
-
-        B, S_img, _ = hidden_states.shape
-        S_txt = encoder_hidden_states.shape[1]
-
-        # ---- QKV projections ----
-        img_q = attn.to_q(hidden_states)
-        img_k = attn.to_k(hidden_states)
-        img_v = attn.to_v(hidden_states)
-
-        txt_q = attn.add_q_proj(encoder_hidden_states)
-        txt_k = attn.add_k_proj(encoder_hidden_states)
-        txt_v = attn.add_v_proj(encoder_hidden_states)
-
-        # ---- Reshape to (B, S, H, D_h) ----
-        H = attn.heads
-        img_q = img_q.unflatten(-1, (H, -1))
-        img_k = img_k.unflatten(-1, (H, -1))
-        img_v = img_v.unflatten(-1, (H, -1))
-
-        txt_q = txt_q.unflatten(-1, (H, -1))
-        txt_k = txt_k.unflatten(-1, (H, -1))
-        txt_v = txt_v.unflatten(-1, (H, -1))
-
-        # ---- Q/K normalization ----
-        if getattr(attn, "norm_q", None) is not None:
-            img_q = attn.norm_q(img_q)
-        if getattr(attn, "norm_k", None) is not None:
-            img_k = attn.norm_k(img_k)
-        if getattr(attn, "norm_added_q", None) is not None:
-            txt_q = attn.norm_added_q(txt_q)
-        if getattr(attn, "norm_added_k", None) is not None:
-            txt_k = attn.norm_added_k(txt_k)
-
-        # ---- RoPE (Qwen variant) ----
-        if image_rotary_emb is not None:
-            img_freqs, txt_freqs = image_rotary_emb
-            img_q = apply_rotary_emb_qwen(img_q, img_freqs, use_real=False)
-            img_k = apply_rotary_emb_qwen(img_k, img_freqs, use_real=False)
-            txt_q = apply_rotary_emb_qwen(txt_q, txt_freqs, use_real=False)
-            txt_k = apply_rotary_emb_qwen(txt_k, txt_freqs, use_real=False)
-
-        # ---- Joint attention over [text, image] along sequence axis ----
-        q = torch.cat([txt_q, img_q], dim=1)  # (B, S_txt + S_img, H, D_h)
-        k = torch.cat([txt_k, img_k], dim=1)
-        v = torch.cat([txt_v, img_v], dim=1)
-
-        out = self._attend(q, k, v, causal=False)  # (B, S_total, H, D_h)
-
-        # ---- Back to (B, S, D_model) ----
-        out = out.flatten(2, 3).to(q.dtype)
-
-        # ---- Split text / image segments ----
-        txt_attn_out = out[:, :S_txt, :]
-        img_attn_out = out[:, S_txt:, :]
-
-        # ---- Output projections ----
-        img_attn_out = attn.to_out[0](img_attn_out)
-        if len(attn.to_out) > 1:
-            img_attn_out = attn.to_out[1](img_attn_out)  # dropout if present
-
-        txt_attn_out = attn.to_add_out(txt_attn_out)
-
-        return img_attn_out, txt_attn_out
+version https://git-lfs.github.com/spec/v1
+oid sha256:41db072a00d1ea673fd179975be5dc7a73936845e1bceb0442d7d98376ff3c03
+size 8823