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e7662d1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 | """MLX-native MossAudioEncoder.
Direct port of src/modeling_moss_audio.py:36-155 (MossAudioEncoder).
Adapted from ml-explore/mlx-examples/whisper/mlx_whisper/whisper.py with:
- 3× Conv2d stride-2 stem (instead of Whisper's 2× Conv1d)
- Pre-existing HF Whisper attribute names (q_proj/k_proj/v_proj/out_proj, fc1/fc2,
self_attn_layer_norm/final_layer_norm) so weight remap is near-identity
- DeepStack taps: capture hidden state AFTER layers in deepstack_layer_indexes
- feature_lens-based padding mask
"""
from __future__ import annotations
import math
from dataclasses import dataclass, field
from typing import List, Optional, Tuple
import mlx.core as mx
import mlx.nn as nn
# ---- helpers ----------------------------------------------------------
def sinusoids(length: int, channels: int, max_timescale: float = 10000.0) -> mx.array:
"""Whisper-style sinusoidal position embeddings. Matches mlx-examples whisper."""
assert channels % 2 == 0
log_timescale_increment = math.log(max_timescale) / (channels // 2 - 1)
inv_timescales = mx.exp(-log_timescale_increment * mx.arange(channels // 2))
scaled_time = mx.arange(length)[:, None] * inv_timescales[None, :]
return mx.concatenate([mx.sin(scaled_time), mx.cos(scaled_time)], axis=1)
# ---- attention ------------------------------------------------------
class WhisperAttention(nn.Module):
"""HF-Whisper-style self-attention. Layer-scaling convention (`1/sqrt(head_dim)`
applied to Q, not split between Q and K like mlx-examples does).
Attribute names match HF so weight remap is identity: q_proj/k_proj/v_proj/out_proj.
"""
def __init__(self, d_model: int, n_heads: int):
super().__init__()
self.n_heads = n_heads
self.head_dim = d_model // n_heads
assert d_model == self.head_dim * n_heads
# HF Whisper: q/v/out have bias; k does not
self.q_proj = nn.Linear(d_model, d_model, bias=True)
self.k_proj = nn.Linear(d_model, d_model, bias=False)
self.v_proj = nn.Linear(d_model, d_model, bias=True)
self.out_proj = nn.Linear(d_model, d_model, bias=True)
def __call__(self, x: mx.array, mask: Optional[mx.array] = None) -> mx.array:
B, T, D = x.shape
q = self.q_proj(x).reshape(B, T, self.n_heads, self.head_dim).transpose(0, 2, 1, 3)
k = self.k_proj(x).reshape(B, T, self.n_heads, self.head_dim).transpose(0, 2, 1, 3)
v = self.v_proj(x).reshape(B, T, self.n_heads, self.head_dim).transpose(0, 2, 1, 3)
scale = self.head_dim ** -0.5
attn = (q * scale) @ k.transpose(0, 1, 3, 2) # (B, H, T, T)
if mask is not None:
attn = attn + mask
w = mx.softmax(attn, axis=-1, precise=True)
out = (w @ v).transpose(0, 2, 1, 3).reshape(B, T, D)
return self.out_proj(out)
# ---- encoder layer --------------------------------------------------
class WhisperEncoderBlock(nn.Module):
"""Pre-LN Whisper encoder block. Matches transformers.WhisperEncoderLayer."""
def __init__(self, d_model: int, n_heads: int, ffn_dim: int):
super().__init__()
self.self_attn = WhisperAttention(d_model, n_heads)
self.self_attn_layer_norm = nn.LayerNorm(d_model)
self.fc1 = nn.Linear(d_model, ffn_dim)
self.fc2 = nn.Linear(ffn_dim, d_model)
self.final_layer_norm = nn.LayerNorm(d_model)
def __call__(self, x: mx.array, mask: Optional[mx.array] = None) -> mx.array:
h = self.self_attn_layer_norm(x)
x = x + self.self_attn(h, mask=mask)
h = self.final_layer_norm(x)
x = x + self.fc2(nn.gelu(self.fc1(h)))
return x
# ---- encoder --------------------------------------------------------
@dataclass
class EncoderConfig:
num_mel_bins: int = 128
downsample_hidden_size: int = 480
d_model: int = 1280
n_heads: int = 20
ffn_dim: int = 5120
n_layers: int = 32
max_source_positions: int = 1500
layer_norm_eps: float = 1e-5
output_dim: int = 1280
deepstack_layer_indexes: List[int] = field(default_factory=lambda: [8, 16, 24])
class MossAudioEncoderMLX(nn.Module):
def __init__(self, cfg: EncoderConfig):
super().__init__()
self.cfg = cfg
# Conv2d stem: 1 → 480 → 480 → 480, each stride-2
# MLX Conv2d expects NHWC, weight shape (OC, kH, kW, IC)
self.conv1 = nn.Conv2d(1, cfg.downsample_hidden_size, kernel_size=3, stride=2, padding=1)
self.conv2 = nn.Conv2d(cfg.downsample_hidden_size, cfg.downsample_hidden_size, kernel_size=3, stride=2, padding=1)
self.conv3 = nn.Conv2d(cfg.downsample_hidden_size, cfg.downsample_hidden_size, kernel_size=3, stride=2, padding=1)
# After 3× stride-2 on mel-axis (128→64→32→16): flat dim = 480*16 = 7680
self.stem_proj = nn.Linear(cfg.downsample_hidden_size * 16, cfg.d_model)
# Precomputed sinusoids, will be sliced
self._positions = sinusoids(cfg.max_source_positions, cfg.d_model)
self.layers = [
WhisperEncoderBlock(cfg.d_model, cfg.n_heads, cfg.ffn_dim)
for _ in range(cfg.n_layers)
]
self.layer_norm = nn.LayerNorm(cfg.d_model, eps=cfg.layer_norm_eps)
# MOSS has optional out_proj; for 4B output_dim==d_model, so it's Identity in PyTorch
# We skip it entirely (equivalent).
assert cfg.output_dim == cfg.d_model, "non-identity out_proj not yet implemented"
self._deepstack_set = set(cfg.deepstack_layer_indexes)
def _compute_downsampled_length(self, L: int) -> int:
"""3× stride-2 conv output length: ceil((((L-1)//2+1)-1)//2+1 ... )"""
def step(n): return (n - 1) // 2 + 1
return step(step(step(L)))
def __call__(
self,
input_features: mx.array, # (B, n_mels, T) bf16 mel spectrogram
feature_lens: Optional[mx.array] = None,
return_deepstack: bool = True,
) -> Tuple[mx.array, Optional[List[mx.array]]]:
if input_features.ndim == 2:
input_features = input_features[None]
B, n_mels, T = input_features.shape
if feature_lens is None:
feature_lens = mx.full((B,), T, dtype=mx.int32)
# (B, n_mels, T) → (B, n_mels, T, 1) [NHWC with channels-last = 1 input channel]
# But MLX Conv2d expects input shape (B, H, W, C_in). We map:
# H = n_mels (128), W = T (frames), C_in = 1
x = input_features[..., None] # (B, n_mels, T, 1)
x = nn.gelu(self.conv1(x)) # (B, 64, T/2, 480)
x = nn.gelu(self.conv2(x)) # (B, 32, T/4, 480)
x = nn.gelu(self.conv3(x)) # (B, 16, T/8, 480)
# PyTorch reference: (B, C, F, T) → permute(0,3,1,2) → (B, T, C, F) → flatten → (B, T, C*F)
# MLX is (B, F, T, C) post-conv. Need transpose to (B, T, C, F) to match PT's flatten order.
B_, H_, W_, C_ = x.shape # H_=F, W_=T, C_=C
x = x.transpose(0, 2, 3, 1).reshape(B_, W_, C_ * H_) # (B, T, C*F)
x = self.stem_proj(x) # (B, T', d_model)
# Trim to actual downsampled length (in case input was padded)
max_len = self._compute_downsampled_length(int(feature_lens.max().item()))
if x.shape[1] > max_len:
x = x[:, :max_len, :]
# Add sinusoidal positions
seq_len = x.shape[1]
pos = self._positions[:seq_len].astype(x.dtype)
x = x + pos
# Build attention mask: (B, 1, 1, seq_len) additive
# padding_mask[b, t] = True if t >= downsampled_len[b] (this is where we mask out)
dsl = mx.stack([
mx.array(self._compute_downsampled_length(int(feature_lens[b].item())), dtype=mx.int32)
for b in range(B)
]) # (B,)
ar = mx.arange(seq_len, dtype=mx.int32)
padding = ar[None, :] >= dsl[:, None] # (B, seq_len) bool
neg_inf = mx.array(-1e9, dtype=x.dtype)
mask = mx.where(padding, neg_inf, mx.array(0.0, dtype=x.dtype))
mask = mask[:, None, None, :] # (B, 1, 1, seq_len)
deepstack: List[mx.array] = []
for layer_idx, layer in enumerate(self.layers):
x = layer(x, mask=mask)
if return_deepstack and layer_idx in self._deepstack_set:
# Apply the final layer_norm snapshot at this point, per MOSS's output_deepstack_hidden_states
# Actually, MOSS captures x BEFORE the final layer_norm — matches what PyTorch does.
deepstack.append(x)
x = self.layer_norm(x)
return x, (deepstack if return_deepstack else None)
# ---- GatedMLP (for audio_adapter + deepstack_audio_merger_list) ----
class GatedMLP(nn.Module):
"""MOSS's GatedMLP: down(silu(gate(x)) * up(x)). SwiGLU convention.
Matches MOSS/src/modeling_moss_audio.py:155-164.
All linears are bias=False.
"""
def __init__(self, input_size: int, hidden_size: int, output_size: int):
super().__init__()
self.gate_proj = nn.Linear(input_size, hidden_size, bias=False)
self.up_proj = nn.Linear(input_size, hidden_size, bias=False)
self.down_proj = nn.Linear(hidden_size, output_size, bias=False)
def __call__(self, x: mx.array) -> mx.array:
return self.down_proj(nn.silu(self.gate_proj(x)) * self.up_proj(x))
__all__ = ["sinusoids", "WhisperAttention", "WhisperEncoderBlock",
"EncoderConfig", "MossAudioEncoderMLX", "GatedMLP"]
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