File size: 11,180 Bytes
ed33fd7 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 |
#!/usr/bin/env python3
"""
Convert Sortformer to CoreML with proper dynamic length handling.
The key issue: Original conversion traced with fixed lengths (spkcache=120, fifo=40),
but at runtime we need to handle empty state (spkcache=0, fifo=0) for first chunk.
Solution: Use scripting instead of tracing, or trace with multiple example lengths.
"""
import torch
import torch.nn as nn
import coremltools as ct
import numpy as np
import os
import sys
SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
sys.path.insert(0, os.path.join(SCRIPT_DIR, 'NeMo'))
from nemo.collections.asr.models import SortformerEncLabelModel
print("=" * 70)
print("CONVERTING SORTFORMER WITH DYNAMIC LENGTH SUPPORT")
print("=" * 70)
# Load model
model_path = os.path.join(SCRIPT_DIR, 'diar_streaming_sortformer_4spk-v2.nemo')
print(f"Loading model: {model_path}")
model = SortformerEncLabelModel.restore_from(model_path, map_location='cpu', strict=False)
model.eval()
# Configure for low-latency streaming
modules = model.sortformer_modules
modules.chunk_len = 6
modules.chunk_left_context = 1
modules.chunk_right_context = 1
modules.fifo_len = 40
modules.spkcache_len = 120
modules.spkcache_update_period = 30
print(f"Config: chunk_len={modules.chunk_len}, left={modules.chunk_left_context}, right={modules.chunk_right_context}")
print(f" fifo_len={modules.fifo_len}, spkcache_len={modules.spkcache_len}")
# Dimensions
chunk_frames = (modules.chunk_len + modules.chunk_left_context + modules.chunk_right_context) * modules.subsampling_factor
fc_d_model = modules.fc_d_model # 512
feat_dim = 128
print(f"Chunk frames: {chunk_frames}")
class DynamicPreEncoderWrapper(nn.Module):
"""Pre-encoder that properly handles dynamic lengths."""
def __init__(self, model, max_spkcache=120, max_fifo=40, max_chunk=8):
super().__init__()
self.model = model
self.max_spkcache = max_spkcache
self.max_fifo = max_fifo
self.max_chunk = max_chunk
self.max_total = max_spkcache + max_fifo + max_chunk
def forward(self, chunk, chunk_lengths, spkcache, spkcache_lengths, fifo, fifo_lengths):
# Pre-encode the chunk
chunk_embs, chunk_emb_lengths = self.model.encoder.pre_encode(x=chunk, lengths=chunk_lengths)
# Get actual lengths as scalars
spk_len = spkcache_lengths[0].item() if spkcache_lengths.numel() > 0 else 0
fifo_len = fifo_lengths[0].item() if fifo_lengths.numel() > 0 else 0
chunk_len = chunk_emb_lengths[0].item()
total_len = spk_len + fifo_len + chunk_len
# Create output tensor (packed at start, rest is zeros)
B, _, D = spkcache.shape
output = torch.zeros(B, self.max_total, D, device=chunk.device, dtype=chunk.dtype)
# Copy valid frames
if spk_len > 0:
output[:, :spk_len, :] = spkcache[:, :spk_len, :]
if fifo_len > 0:
output[:, spk_len:spk_len+fifo_len, :] = fifo[:, :fifo_len, :]
output[:, spk_len+fifo_len:spk_len+fifo_len+chunk_len, :] = chunk_embs[:, :chunk_len, :]
total_length = torch.tensor([total_len], dtype=torch.long)
return output, total_length, chunk_embs, chunk_emb_lengths
class DynamicHeadWrapper(nn.Module):
"""Head that properly handles dynamic lengths with masking."""
def __init__(self, model):
super().__init__()
self.model = model
def forward(self, pre_encoder_embs, pre_encoder_lengths, chunk_embs, chunk_emb_lengths):
# Encode
fc_embs, fc_lengths = self.model.frontend_encoder(
processed_signal=pre_encoder_embs,
processed_signal_length=pre_encoder_lengths,
bypass_pre_encode=True,
)
# Get predictions
preds = self.model.forward_infer(fc_embs, fc_lengths)
# Apply mask based on actual length
# preds shape: [B, T, num_speakers]
max_len = preds.shape[1]
length = pre_encoder_lengths[0]
mask = torch.arange(max_len, device=preds.device) < length
preds = preds * mask.unsqueeze(0).unsqueeze(-1).float()
return preds, chunk_embs, chunk_emb_lengths
# Test with both empty and full state
print("\n" + "=" * 70)
print("TESTING DYNAMIC WRAPPERS")
print("=" * 70)
pre_encoder = DynamicPreEncoderWrapper(model)
head = DynamicHeadWrapper(model)
pre_encoder.eval()
head.eval()
# Test 1: Empty state (like chunk 0)
print("\nTest 1: Empty state (chunk 0)")
chunk = torch.randn(1, 56, 128) # First chunk has fewer frames
chunk_len = torch.tensor([56], dtype=torch.long)
spkcache = torch.zeros(1, 120, 512)
spkcache_len = torch.tensor([0], dtype=torch.long)
fifo = torch.zeros(1, 40, 512)
fifo_len = torch.tensor([0], dtype=torch.long)
with torch.no_grad():
pre_out, pre_len, chunk_embs, chunk_emb_len = pre_encoder(
chunk, chunk_len, spkcache, spkcache_len, fifo, fifo_len
)
preds, _, _ = head(pre_out, pre_len, chunk_embs, chunk_emb_len)
print(f" Pre-encoder output: {pre_out.shape}, length={pre_len.item()}")
print(f" Chunk embeddings: {chunk_embs.shape}, length={chunk_emb_len.item()}")
print(f" Predictions: {preds.shape}")
sums = [f"{preds[0, i, :].sum().item():.4f}" for i in range(min(8, preds.shape[1]))]
print(f" First 8 pred frames sum: {sums}")
# Test 2: Full state
print("\nTest 2: Full state")
chunk = torch.randn(1, 64, 128)
chunk_len = torch.tensor([64], dtype=torch.long)
spkcache = torch.randn(1, 120, 512)
spkcache_len = torch.tensor([120], dtype=torch.long)
fifo = torch.randn(1, 40, 512)
fifo_len = torch.tensor([40], dtype=torch.long)
with torch.no_grad():
pre_out, pre_len, chunk_embs, chunk_emb_len = pre_encoder(
chunk, chunk_len, spkcache, spkcache_len, fifo, fifo_len
)
preds, _, _ = head(pre_out, pre_len, chunk_embs, chunk_emb_len)
print(f" Pre-encoder output: {pre_out.shape}, length={pre_len.item()}")
print(f" Chunk embeddings: {chunk_embs.shape}, length={chunk_emb_len.item()}")
print(f" Predictions: {preds.shape}")
print("\n" + "=" * 70)
print("ISSUE IDENTIFIED")
print("=" * 70)
print("""
The problem is that the current CoreML model was traced with FIXED lengths.
When lengths change at runtime, the traced operations don't adapt.
The fix requires re-tracing with proper dynamic handling OR using coremltools
flexible shapes feature.
For now, let's try a simpler approach: always pad inputs to max size and
use the length parameters only for extracting the correct output slice.
""")
# The issue is that torch.jit.trace captures specific tensor values
# We need to use torch.jit.script for truly dynamic behavior
# But many NeMo operations don't work with script
print("\nATTEMPTING CONVERSION WITH FLEXIBLE SHAPES...")
# Try using coremltools range shapes
try:
# Create wrapper that handles the length masking internally
class SimplePipelineWrapper(nn.Module):
def __init__(self, model):
super().__init__()
self.model = model
def forward(self, chunk, chunk_lengths, spkcache, spkcache_lengths, fifo, fifo_lengths):
# Pre-encode chunk
chunk_embs, chunk_emb_lens = self.model.encoder.pre_encode(x=chunk, lengths=chunk_lengths)
# Get lengths
spk_len = spkcache_lengths[0]
fifo_len = fifo_lengths[0]
chunk_len = chunk_emb_lens[0]
# Concatenate (always use fixed output size, rely on length for valid region)
# This matches what NeMo does internally
B = chunk.shape[0]
max_out = 168 # 120 + 40 + 8
D = 512
concat_embs = torch.zeros(B, max_out, D, device=chunk.device, dtype=chunk.dtype)
# Copy spkcache
for i in range(120):
if i < spk_len:
concat_embs[:, i, :] = spkcache[:, i, :]
# Copy fifo
for i in range(40):
if i < fifo_len:
concat_embs[:, 120 + i, :] = fifo[:, i, :]
# Copy chunk embeddings
for i in range(8):
if i < chunk_len:
concat_embs[:, 120 + 40 + i, :] = chunk_embs[:, i, :]
total_len = spk_len + fifo_len + chunk_len
total_lens = total_len.unsqueeze(0)
# Run through encoder
fc_embs, fc_lens = self.model.frontend_encoder(
processed_signal=concat_embs,
processed_signal_length=total_lens,
bypass_pre_encode=True,
)
# Get predictions
preds = self.model.forward_infer(fc_embs, fc_lens)
return preds, chunk_embs, chunk_emb_lens
wrapper = SimplePipelineWrapper(model)
wrapper.eval()
# Trace with empty state example
print("Tracing with empty state example...")
chunk = torch.randn(1, 64, 128)
chunk_len = torch.tensor([56], dtype=torch.long) # Actual length
spkcache = torch.zeros(1, 120, 512)
spkcache_len = torch.tensor([0], dtype=torch.long)
fifo = torch.zeros(1, 40, 512)
fifo_len = torch.tensor([0], dtype=torch.long)
with torch.no_grad():
traced = torch.jit.trace(wrapper, (chunk, chunk_len, spkcache, spkcache_len, fifo, fifo_len))
print("Converting to CoreML...")
mlmodel = ct.convert(
traced,
inputs=[
ct.TensorType(name="chunk", shape=(1, 64, 128), dtype=np.float32),
ct.TensorType(name="chunk_lengths", shape=(1,), dtype=np.int32),
ct.TensorType(name="spkcache", shape=(1, 120, 512), dtype=np.float32),
ct.TensorType(name="spkcache_lengths", shape=(1,), dtype=np.int32),
ct.TensorType(name="fifo", shape=(1, 40, 512), dtype=np.float32),
ct.TensorType(name="fifo_lengths", shape=(1,), dtype=np.int32),
],
outputs=[
ct.TensorType(name="speaker_preds", dtype=np.float32),
ct.TensorType(name="chunk_pre_encoder_embs", dtype=np.float32),
ct.TensorType(name="chunk_pre_encoder_lengths", dtype=np.int32),
],
minimum_deployment_target=ct.target.iOS16,
compute_precision=ct.precision.FLOAT32,
compute_units=ct.ComputeUnit.CPU_ONLY, # Start with CPU for debugging
)
output_path = os.path.join(SCRIPT_DIR, 'coreml_models', 'SortformerPipeline_Dynamic.mlpackage')
mlmodel.save(output_path)
print(f"Saved to: {output_path}")
# Test the new model
print("\nTesting new CoreML model...")
test_output = mlmodel.predict({
'chunk': chunk.numpy(),
'chunk_lengths': chunk_len.numpy().astype(np.int32),
'spkcache': spkcache.numpy(),
'spkcache_lengths': spkcache_len.numpy().astype(np.int32),
'fifo': fifo.numpy(),
'fifo_lengths': fifo_len.numpy().astype(np.int32),
})
coreml_preds = np.array(test_output['speaker_preds'])
print(f"CoreML predictions shape: {coreml_preds.shape}")
print(f"CoreML first 8 frames:")
for i in range(min(8, coreml_preds.shape[1])):
print(f" Frame {i}: {coreml_preds[0, i, :]}")
except Exception as e:
print(f"Error during conversion: {e}")
import traceback
traceback.print_exc()
|