h3-blackwell-runtime/src/h3_blackwell_runtime/attention.py

"""Direct H3 self-attention using packed NVFP4 linears and SageAttention3."""

import os
import torch
import torch.nn.functional as functional
from torch import nn

from .checkpoint import H3Checkpoint
from .nvfp4 import Nvfp4Linear


AVAILABLE_BACKENDS = ("sage2", "sdpa", "sage3", "sage3_mean", "kj_sage_cuda", "kj_sage_triton", "kj_sage_fp8", "kj_sage_fp8pp", "kj_head_sliced", "sol_attn")
PLANNED_BACKENDS = ("flash4", "easycache", "h3_cache", "kj_chunked_ffn")
DEFAULT_ATTENTION_BACKEND = os.getenv("H3_DEFAULT_ATTENTION", "sol_attn")


def attention_backend_status() -> dict[str, str]:
    """Report direct-runtime attention choices without importing ComfyUI nodes."""
    status = {name: "available" for name in AVAILABLE_BACKENDS}
    status.update({"sol_attn": "experimental: sparse Triton attention for eligible non-causal H3 attention calls; falls back below H3_SOL_MIN_TOKENS unless H3_SOL_STRICT=1"})
    status.update({"flash4": "planned: exact Blackwell kernel adapter"})
    status.update({"easycache": "planned: approximate denoiser cache"})
    status.update({"h3_cache": "planned: approximate H3-specific cache"})
    status.update({"kj_chunked_ffn": "available: exact H3 MLP row chunking via H3_MLP_CHUNKS or runtime args"})
    return status


def rms_norm(x: torch.Tensor, weight: torch.Tensor, eps: float) -> torch.Tensor:
    return torch.nn.functional.rms_norm(x, (x.shape[-1],), weight.to(x.dtype), eps)


def run_sol_attention_bshd(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, *, is_causal: bool) -> torch.Tensor:
    """Run Sol-Attn on `[batch, sequence, heads, dim]` tensors and return the same layout."""
    try:
        from sol_kernel import sol_attn

        tau = float(os.getenv("H3_SOL_TAU", "1.3"))
        min_tokens = int(os.getenv("H3_SOL_MIN_TOKENS", "4096"))
        thresh_type = os.getenv("H3_SOL_THRESH_TYPE", "diag")
        int8_qk = os.getenv("H3_SOL_INT8_QK", "").lower() in {"1", "true", "yes", "on"}
        int8_pv = os.getenv("H3_SOL_INT8_PV", "").lower() in {"1", "true", "yes", "on"}
        if is_causal:
            raise ValueError("Sol-Attn backend only supports non-causal H3 attention")
        if q.shape[-1] != 128:
            raise ValueError(f"Sol-Attn requires head dim 128, got {q.shape[-1]}")
        if q.shape[1] < min_tokens:
            raise ValueError(f"{q.shape[1]} tokens < H3_SOL_MIN_TOKENS={min_tokens}")
        return sol_attn(
            q.contiguous(),
            k.contiguous(),
            v.contiguous(),
            tau=tau,
            thresh_type=thresh_type,
            int8_qk=int8_qk,
            int8_pv=int8_pv,
        )
    except Exception:
        if os.getenv("H3_SOL_STRICT", "").lower() in {"1", "true", "yes", "on"}:
            raise
        fallback = os.getenv("H3_SOL_FALLBACK", "sage2")
        hnd = run_attention(q.transpose(1, 2).contiguous(), k.transpose(1, 2).contiguous(), v.transpose(1, 2).contiguous(), backend=fallback, is_causal=is_causal)
        return hnd.transpose(1, 2)


def qkv_to_bshd(qkv: torch.Tensor, heads: int, head_dim: int) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
    """Split `[S, 3*H*D]` QKV into contiguous Sol-native `[1,S,H,D]` tensors."""
    if os.getenv("H3_SOL_QKV_LAYOUT", "native").lower() != "fused":
        raise RuntimeError("fused QKV layout disabled")
    try:
        from .nvfp4_quant import _vortex_scale_extension

        return tuple(_vortex_scale_extension().qkv_to_bshd(qkv, heads, head_dim))
    except Exception:
        if os.getenv("H3_SOL_QKV_LAYOUT_STRICT", "").lower() in {"1", "true", "yes", "on"}:
            raise
        inner = heads * head_dim
        sequence = qkv.shape[0]
        q, k, v = qkv.split(inner, dim=-1)
        return (
            q.view(1, sequence, heads, head_dim).contiguous(),
            k.view(1, sequence, heads, head_dim).contiguous(),
            v.view(1, sequence, heads, head_dim).contiguous(),
        )


def run_attention(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, *, backend: str, is_causal: bool) -> torch.Tensor:
    """Run one `[batch, heads, sequence, dim]` attention operation."""
    if backend == "sol_attn":
        return run_sol_attention_bshd(q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2), is_causal=is_causal).transpose(1, 2)
    if backend == "kj_head_sliced":
        head_slice_size = int(os.getenv("H3_HEAD_SLICE_SIZE", "8"))
        base_backend = os.getenv("H3_HEAD_SLICE_BACKEND", "sage2")
        if head_slice_size <= 0:
            raise ValueError("H3_HEAD_SLICE_SIZE must be positive")
        outputs = [
            run_attention(q[:, start:start + head_slice_size], k[:, start:start + head_slice_size], v[:, start:start + head_slice_size], backend=base_backend, is_causal=is_causal)
            for start in range(0, q.shape[1], head_slice_size)
        ]
        return torch.cat(outputs, dim=1)
    if backend == "sage2":
        from sageattention import sageattn

        return sageattn(q, k, v, is_causal=is_causal, tensor_layout="HND", smooth_k=False)
    if backend == "sage3":
        from sageattn3 import sageattn3_blackwell

        return sageattn3_blackwell(q, k, v, is_causal=is_causal)
    if backend == "sage3_mean":
        from sageattn3 import sageattn3_blackwell

        return sageattn3_blackwell(q, k, v, is_causal=is_causal, per_block_mean=True)
    if backend == "kj_sage_cuda":
        from sageattention import sageattn_qk_int8_pv_fp16_cuda

        return sageattn_qk_int8_pv_fp16_cuda(q, k, v, is_causal=is_causal, pv_accum_dtype="fp32", tensor_layout="HND")
    if backend == "kj_sage_triton":
        from sageattention import sageattn_qk_int8_pv_fp16_triton

        return sageattn_qk_int8_pv_fp16_triton(q, k, v, is_causal=is_causal, tensor_layout="HND")
    if backend == "kj_sage_fp8":
        from sageattention import sageattn_qk_int8_pv_fp8_cuda

        return sageattn_qk_int8_pv_fp8_cuda(q, k, v, is_causal=is_causal, pv_accum_dtype="fp32+fp32", tensor_layout="HND")
    if backend == "kj_sage_fp8pp":
        from sageattention import sageattn_qk_int8_pv_fp8_cuda

        return sageattn_qk_int8_pv_fp8_cuda(q, k, v, is_causal=is_causal, pv_accum_dtype="fp32+fp16", tensor_layout="HND")
    if backend == "sdpa":
        return functional.scaled_dot_product_attention(q, k, v, is_causal=is_causal)
    raise ValueError(f"Unsupported H3 attention backend: {backend}")


def apply_split_half_rope(x: torch.Tensor, rotation: torch.Tensor) -> torch.Tensor:
    """Apply H3's split-half rotary table to `[batch, sequence, heads, dim]`."""
    rotated_width = rotation.shape[-3] * 2
    half = rotated_width // 2
    if rotated_width > x.shape[-1]:
        raise ValueError("RoPE rotation width exceeds the attention head dimension.")

    pair = torch.stack((x[..., :half], x[..., half:rotated_width]), dim=-1)
    pair = torch.matmul(rotation.to(x.dtype), pair.unsqueeze(-1)).squeeze(-1)
    return torch.cat((pair[..., 0], pair[..., 1], x[..., rotated_width:]), dim=-1)


def rms_rope_split_half_(
    q: torch.Tensor,
    k: torch.Tensor,
    rotation: torch.Tensor,
    q_weight: torch.Tensor,
    k_weight: torch.Tensor,
    eps: float,
) -> tuple[torch.Tensor, torch.Tensor]:
    """Run Comfy Kitchen's standalone fused H3 Q/K normalization and RoPE."""
    import comfy_kitchen  # Registers the independent CUDA extension operators.

    del comfy_kitchen
    torch.ops.comfy_kitchen.rms_rope_split_half_(
        q, k, rotation, q_weight, k_weight, eps, rotation.shape[-3] * 2
    )
    return q, k


class H3SageAttention(nn.Module):
    """One MiniMax H3 attention module, independent of ComfyUI and Raylight."""

    def __init__(
        self,
        qkv_proj: Nvfp4Linear,
        out_proj: Nvfp4Linear,
        q_norm_weight: torch.Tensor,
        k_norm_weight: torch.Tensor,
        *,
        heads: int = 56,
        head_dim: int = 128,
        eps: float = 1e-5,
        backend: str = DEFAULT_ATTENTION_BACKEND,
    ):
        super().__init__()
        self.qkv_proj = qkv_proj
        self.out_proj = out_proj
        self.heads = heads
        self.head_dim = head_dim
        self.eps = eps
        if backend in PLANNED_BACKENDS:
            raise ValueError(f"H3 attention backend '{backend}' needs a standalone adapter and is not installed.")
        if backend not in AVAILABLE_BACKENDS:
            raise ValueError(f"Unsupported H3 attention backend: {backend}")
        self.backend = backend
        self.register_buffer("q_norm_weight", q_norm_weight, persistent=False)
        self.register_buffer("k_norm_weight", k_norm_weight, persistent=False)

    @classmethod
    def from_checkpoint(cls, checkpoint: H3Checkpoint, prefix: str, *, output_dtype=torch.bfloat16, backend: str = DEFAULT_ATTENTION_BACKEND):
        return cls(
            checkpoint.nvfp4_linear(f"{prefix}.qkv_proj", output_dtype=output_dtype),
            checkpoint.nvfp4_linear(f"{prefix}.out_proj", output_dtype=output_dtype),
            checkpoint.tensor(f"{prefix}.q_norm.weight", dtype=output_dtype),
            checkpoint.tensor(f"{prefix}.k_norm.weight", dtype=output_dtype),
            backend=backend,
        )

    def forward(self, x: torch.Tensor, rope_rotation: torch.Tensor) -> torch.Tensor:
        if x.ndim != 2:
            raise ValueError("H3 attention expects `[sequence, hidden]` input.")
        sequence = x.shape[0]
        inner = self.heads * self.head_dim
        q, k, v = self.qkv_proj(x).split(inner, dim=-1)
        q = q.view(1, sequence, self.heads, self.head_dim)
        k = k.view(1, sequence, self.heads, self.head_dim)
        v = v.view(1, sequence, self.heads, self.head_dim)

        q, k = rms_rope_split_half_(q, k, rope_rotation, self.q_norm_weight, self.k_norm_weight, self.eps)
        if self.backend == "sol_attn":
            if os.getenv("H3_SOL_QKV_LAYOUT", "native").lower() == "fused":
                q, k, v = qkv_to_bshd(qkv, self.heads, self.head_dim)
            else:
                q, k, v = q.contiguous(), k.contiguous(), v.contiguous()
            out = run_sol_attention_bshd(q, k, v, is_causal=False)
            return self.out_proj(out.reshape(sequence, inner).contiguous())

        q = q.transpose(1, 2).contiguous()
        k = k.transpose(1, 2).contiguous()
        v = v.transpose(1, 2).contiguous()

        out = run_attention(q, k, v, backend=self.backend, is_causal=False)
        return self.out_proj(out.transpose(1, 2).reshape(sequence, inner).contiguous())