110 lines
4.8 KiB
Python
110 lines
4.8 KiB
Python
"""Direct H3 self-attention using packed NVFP4 linears and SageAttention3."""
|
|
|
|
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")
|
|
PLANNED_BACKENDS = ("flash4", "easycache", "h3_cache", "sol_attn", "kj_sage", "kj_chunked_ffn", "kj_head_sliced")
|
|
|
|
|
|
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({"flash4": "planned: exact Blackwell kernel adapter"})
|
|
status.update({"easycache": "planned: approximate denoiser cache"})
|
|
status.update({"h3_cache": "planned: approximate H3-specific cache"})
|
|
status.update({"sol_attn": "experimental: prior H3-tested sparse Triton attention; standalone adapter pending"})
|
|
status.update({"kj_sage": "experimental: prior H3-tested Sage patch; standalone adapter pending"})
|
|
status.update({"kj_chunked_ffn": "planned: exact memory-lifetime adapter"})
|
|
status.update({"kj_head_sliced": "planned: exact memory-lifetime adapter"})
|
|
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 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)
|
|
|
|
|
|
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 = "sage2",
|
|
):
|
|
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 = "sage2"):
|
|
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)
|
|
|
|
from comfy_kitchen import rms_rope_split_half_
|
|
|
|
rms_rope_split_half_(q, k, rope_rotation, self.q_norm_weight, self.k_norm_weight, epsilon=self.eps, rot_dim=rope_rotation.shape[-3] * 2)
|
|
q = q.transpose(1, 2).contiguous()
|
|
k = k.transpose(1, 2).contiguous()
|
|
v = v.transpose(1, 2).contiguous()
|
|
|
|
if self.backend == "sage2":
|
|
from sageattention import sageattn
|
|
|
|
out = sageattn(q, k, v, is_causal=False, tensor_layout="HND", smooth_k=False)
|
|
elif self.backend == "sage3":
|
|
from sageattn3 import sageattn3_blackwell
|
|
|
|
out = sageattn3_blackwell(q, k, v, is_causal=False)
|
|
else:
|
|
out = functional.scaled_dot_product_attention(q, k, v, is_causal=False)
|
|
return self.out_proj(out.transpose(1, 2).reshape(sequence, inner).contiguous())
|