94 lines
3.1 KiB
Python
94 lines
3.1 KiB
Python
import mlx.core as mx
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import mlx.nn as nn
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from .attention import WanCrossAttention, WanLayerNorm, WanSelfAttention, _linear_dtype
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class WanAttentionBlock(nn.Module):
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"""Wan transformer block with learned modulation, self-attn, cross-attn, and FFN."""
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def __init__(
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self,
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dim: int,
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ffn_dim: int,
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num_heads: int,
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window_size: tuple = (-1, -1),
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qk_norm: bool = True,
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cross_attn_norm: bool = False,
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eps: float = 1e-6,
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):
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super().__init__()
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# Self-attention
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self.norm1 = WanLayerNorm(dim, eps)
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self.self_attn = WanSelfAttention(dim, num_heads, window_size, qk_norm, eps)
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# Cross-attention (with optional norm on context)
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self.norm3 = (
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WanLayerNorm(dim, eps, elementwise_affine=True)
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if cross_attn_norm
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else None
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)
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self.cross_attn = WanCrossAttention(dim, num_heads, qk_norm, eps)
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# Feed-forward
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self.norm2 = WanLayerNorm(dim, eps)
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self.ffn = WanFFN(dim, ffn_dim)
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# Learned modulation: 6 vectors for scale/shift/gate (kept in float32 for precision)
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self.modulation = (mx.random.normal((1, 6, dim)) * (dim**-0.5)).astype(mx.float32)
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def __call__(
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self,
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x: mx.array,
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e: mx.array,
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seq_lens: list,
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grid_sizes: list,
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freqs: mx.array,
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context: mx.array,
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context_lens: list | None = None,
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cross_kv_cache: tuple | None = None,
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rope_cos_sin: tuple | None = None,
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attn_mask: mx.array | None = None,
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) -> mx.array:
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# Modulation in float32 (e is already float32 from model forward)
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mod = self.modulation + e
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e0 = mod[:, :, 0, :] # shift for self-attn
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e1 = mod[:, :, 1, :] # scale for self-attn
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e2 = mod[:, :, 2, :] # gate for self-attn
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e3 = mod[:, :, 3, :] # shift for ffn
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e4 = mod[:, :, 4, :] # scale for ffn
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e5 = mod[:, :, 5, :] # gate for ffn
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# Self-attention with modulation
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# Type promotion handles bf16→f32 automatically when multiplied with f32 modulation
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x_mod = self.norm1(x) * (1 + e1) + e0
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y = self.self_attn(x_mod, seq_lens, grid_sizes, freqs, rope_cos_sin=rope_cos_sin, attn_mask=attn_mask)
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x = x + y * e2
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# Cross-attention (no modulation, just norm)
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x_cross = self.norm3(x) if self.norm3 is not None else x
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x = x + self.cross_attn(x_cross, context, context_lens, kv_cache=cross_kv_cache)
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# FFN with modulation
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x_mod = self.norm2(x) * (1 + e4) + e3
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y = self.ffn(x_mod)
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x = x + y * e5
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return x
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class WanFFN(nn.Module):
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"""Gated feed-forward network with GELU(tanh) activation."""
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def __init__(self, dim: int, ffn_dim: int):
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super().__init__()
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self.fc1 = nn.Linear(dim, ffn_dim)
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self.act = nn.GELU(approx="tanh")
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self.fc2 = nn.Linear(ffn_dim, dim)
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def __call__(self, x: mx.array) -> mx.array:
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# Cast to compute dtype for efficient matmul (bfloat16 matching official autocast)
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x_w = x.astype(_linear_dtype(self.fc1))
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return self.fc2(self.act(self.fc1(x_w)))
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