File size: 19,201 Bytes
95671cf | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 | #!/usr/bin/env python3
# /// script
# requires-python = ">=3.11,<3.12"
# dependencies = [
# "accelerate==1.2.1",
# "coremltools==8.0",
# "huggingface-hub==0.27.1",
# "numpy==1.26.4",
# "safetensors==0.4.5",
# "torch==2.4.0",
# "transformers==4.47.1",
# ]
# ///
"""Export Dolphin 3.0 Llama 3.2 as a stateful Core ML language model.
The exported ML Program keeps its key/value cache in Core ML state. It accepts
a prompt chunk for prefill and one or more continuation tokens for decode:
inputIds: int32 [1, 1...max_query_length]
causalMask: fp16 [1, 1, query_length, 1...max_context_length]
keyCache: fp16 Core ML state
valueCache: fp16 Core ML state
logits: fp16 [1, query_length, vocabulary_size]
The causal-mask final dimension is the absolute end position of the submitted
chunk. Callers must create a fresh Core ML state for each conversation.
"""
from __future__ import annotations
import argparse
import hashlib
import json
import os
import shutil
from pathlib import Path
from typing import Any, Optional, Sequence, Tuple
import coremltools as ct
import numpy as np
import torch
from huggingface_hub import HfApi
from transformers.cache_utils import Cache
from transformers.models.llama.modeling_llama import (
LLAMA_ATTENTION_CLASSES,
LlamaAttention,
LlamaConfig,
LlamaForCausalLM,
apply_rotary_pos_emb,
repeat_kv,
)
DEFAULT_MODEL_ID = "dphn/Dolphin3.0-Llama3.2-3B"
DEFAULT_REVISION = "392a6f57223e7ccfe6ef4ebdb2ff101a42d57364"
DEFAULT_OUTPUT = "Dolphin3.0-Llama3.2-3B-stateful-int4.mlpackage"
TOKENIZER_METADATA_KEY = "co.huggingface.exporters.name"
class SliceUpdateKeyValueCache(Cache):
"""Fixed-size cache whose slices lower to Core ML state updates."""
def __init__(
self,
shape: Tuple[int, ...],
*,
device: str | torch.device = "cpu",
dtype: torch.dtype = torch.float16,
) -> None:
super().__init__()
self.past_seen_tokens = 0
self.maximum_length = shape[-2]
self.k_cache = torch.zeros(shape, dtype=dtype, device=device)
self.v_cache = torch.zeros(shape, dtype=dtype, device=device)
def update(
self,
key_states: torch.Tensor,
value_states: torch.Tensor,
layer_idx: int,
cache_kwargs: Optional[dict[str, Any]] = None,
*,
slice_indices: Optional[Tuple[int, int]] = None,
) -> Tuple[torch.Tensor, torch.Tensor]:
del cache_kwargs
if slice_indices is None:
begin = self.past_seen_tokens
end = begin + key_states.shape[-2]
else:
begin, end = slice_indices
self.k_cache[layer_idx, :, : key_states.shape[1], begin:end, :] = key_states
self.v_cache[layer_idx, :, : value_states.shape[1], begin:end, :] = value_states
return (
self.k_cache[layer_idx, :, :, :end, :],
self.v_cache[layer_idx, :, :, :end, :],
)
def get_seq_length(self, layer_idx: Optional[int] = 0) -> int:
del layer_idx
return self.past_seen_tokens
def get_max_cache_shape(self) -> int:
return self.maximum_length
class SliceUpdateLlamaAttention(LlamaAttention):
"""Llama SDPA attention with traceable in-place KV-cache updates."""
@torch.no_grad()
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: bool = False,
use_cache: bool = False,
cache_position: Optional[torch.LongTensor] = None,
position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
**kwargs: Any,
) -> Tuple[torch.Tensor, None, Optional[Cache]]:
del output_attentions, use_cache, cache_position, kwargs
batch_size, query_length, _ = hidden_states.size()
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
query_states = query_states.view(
batch_size, query_length, self.num_heads, self.head_dim
).transpose(1, 2)
key_states = key_states.view(
batch_size, query_length, self.num_key_value_heads, self.head_dim
).transpose(1, 2)
value_states = value_states.view(
batch_size, query_length, self.num_key_value_heads, self.head_dim
).transpose(1, 2)
if position_embeddings is None:
cos, sin = self.rotary_emb(value_states, position_ids)
else:
cos, sin = position_embeddings
query_states, key_states = apply_rotary_pos_emb(
query_states, key_states, cos, sin
)
if attention_mask is None:
raise ValueError("causalMask is required for stateful generation")
if past_key_value is None:
raise ValueError("KV cache is required for stateful generation")
end_step = attention_mask.shape[-1]
key_states, value_states = past_key_value.update(
key_states,
value_states,
self.layer_idx,
slice_indices=(end_step - query_length, end_step),
)
# Core ML Tools may materialize a state read as FP32 even when the
# declared StateType is FP16. Keep attention operands explicitly
# aligned with the projected query dtype.
key_states = key_states.to(dtype=query_states.dtype)
value_states = value_states.to(dtype=query_states.dtype)
key_states = repeat_kv(key_states, self.num_key_value_groups)
value_states = repeat_kv(value_states, self.num_key_value_groups)
attention_output = torch.nn.functional.scaled_dot_product_attention(
query_states,
key_states,
value_states,
attn_mask=attention_mask,
dropout_p=0.0,
is_causal=False,
)
attention_output = attention_output.transpose(1, 2).contiguous()
attention_output = attention_output.view(
batch_size, query_length, self.num_heads * self.head_dim
)
attention_output = self.o_proj(attention_output)
return attention_output, None, past_key_value
class StatefulLlamaForCausalLM(torch.nn.Module):
"""Wrap a Transformers Llama model and expose its KV cache as buffers."""
def __init__(
self,
model_id: str,
revision: str,
*,
max_context_length: int,
batch_size: int = 1,
torch_dtype: torch.dtype = torch.float16,
tiny_test: bool = False,
) -> None:
super().__init__()
LLAMA_ATTENTION_CLASSES["sdpa"] = SliceUpdateLlamaAttention
if tiny_test:
config = LlamaConfig(
vocab_size=512,
hidden_size=64,
intermediate_size=128,
num_hidden_layers=2,
num_attention_heads=4,
num_key_value_heads=2,
max_position_embeddings=max_context_length,
head_dim=16,
use_cache=True,
)
config._attn_implementation = "sdpa"
self.model = LlamaForCausalLM(config).to(dtype=torch_dtype)
else:
self.model = LlamaForCausalLM.from_pretrained(
model_id,
revision=revision,
torch_dtype=torch_dtype,
low_cpu_mem_usage=True,
attn_implementation="sdpa",
)
self.model.config.use_cache = True
config: LlamaConfig = self.model.config
self.kv_cache_shape = (
config.num_hidden_layers,
batch_size,
config.num_key_value_heads,
max_context_length,
config.head_dim,
)
self.kv_cache = SliceUpdateKeyValueCache(
self.kv_cache_shape, dtype=torch_dtype
)
self.register_buffer("keyCache", self.kv_cache.k_cache)
self.register_buffer("valueCache", self.kv_cache.v_cache)
@torch.no_grad()
def forward(
self, input_ids: torch.LongTensor, causal_mask: torch.Tensor
) -> torch.Tensor:
self.kv_cache.past_seen_tokens = causal_mask.shape[-1] - input_ids.shape[-1]
return self.model(
input_ids=input_ids,
attention_mask=causal_mask,
past_key_values=self.kv_cache,
use_cache=True,
return_dict=True,
).logits
@torch.no_grad()
def reset_cache(self) -> None:
self.kv_cache.k_cache.zero_()
self.kv_cache.v_cache.zero_()
self.kv_cache.past_seen_tokens = 0
def causal_mask(
query_length: int,
end_step: int,
*,
dtype: torch.dtype = torch.float16,
) -> torch.Tensor:
"""Return an additive mask for a chunk ending at ``end_step``."""
if query_length < 1 or end_step < query_length:
raise ValueError("Expected 1 <= query_length <= end_step")
past_length = end_step - query_length
columns = torch.arange(end_step).view(1, end_step)
rows = past_length + torch.arange(query_length).view(query_length, 1)
allowed = columns <= rows
minimum = torch.finfo(dtype).min
mask = torch.where(
allowed,
torch.zeros((), dtype=dtype),
torch.full((), minimum, dtype=dtype),
)
return mask.view(1, 1, query_length, end_step)
@torch.no_grad()
def verify_torch_cache(model: StatefulLlamaForCausalLM) -> dict[str, float]:
"""Check cached decode against a one-pass full-prefix calculation."""
vocab_size = model.model.config.vocab_size
prompt = torch.tensor([[128000, 128257, 882, 198]], dtype=torch.int32)
prompt = torch.remainder(prompt, vocab_size)
continuation = torch.tensor([[42 % vocab_size]], dtype=torch.int32)
model.reset_cache()
prompt_logits = model(prompt, causal_mask(prompt.shape[-1], prompt.shape[-1]))
cached_logits = model(
continuation,
causal_mask(continuation.shape[-1], prompt.shape[-1] + continuation.shape[-1]),
)
model.reset_cache()
full_input = torch.cat((prompt, continuation), dim=-1)
full_logits = model(
full_input, causal_mask(full_input.shape[-1], full_input.shape[-1])
)
cached_last = cached_logits[:, -1, :].float()
full_last = full_logits[:, -1, :].float()
maximum_error = float(torch.max(torch.abs(cached_last - full_last)).item())
mean_error = float(torch.mean(torch.abs(cached_last - full_last)).item())
if not torch.allclose(cached_last, full_last, rtol=5e-3, atol=5e-3):
raise RuntimeError(
"KV-cache parity failed: "
f"max_abs_error={maximum_error}, mean_abs_error={mean_error}"
)
if not torch.isfinite(prompt_logits).all():
raise RuntimeError("Prompt logits contain non-finite values")
return {"max_abs_error": maximum_error, "mean_abs_error": mean_error}
def package_inventory(package_path: Path) -> dict[str, Any]:
files = []
total_bytes = 0
for path in sorted(item for item in package_path.rglob("*") if item.is_file()):
digest = hashlib.sha256()
with path.open("rb") as handle:
for chunk in iter(lambda: handle.read(8 * 1024 * 1024), b""):
digest.update(chunk)
size = path.stat().st_size
total_bytes += size
files.append(
{
"path": str(path.relative_to(package_path)),
"bytes": size,
"sha256": digest.hexdigest(),
}
)
return {"bytes": total_bytes, "files": files}
def export_model(args: argparse.Namespace) -> dict[str, Any]:
output_path = Path(args.output).resolve()
if output_path.exists():
if not args.overwrite:
raise FileExistsError(f"Output already exists: {output_path}")
shutil.rmtree(output_path)
torch.manual_seed(0)
model = StatefulLlamaForCausalLM(
args.model_id,
args.revision,
max_context_length=args.max_context_length,
tiny_test=args.tiny_test,
).eval()
parity = verify_torch_cache(model)
model.reset_cache()
trace_ids = torch.tensor([[128000, 128257]], dtype=torch.int32)
trace_ids = torch.remainder(trace_ids, model.model.config.vocab_size)
trace_mask = causal_mask(trace_ids.shape[-1], trace_ids.shape[-1])
traced = torch.jit.trace(
model, (trace_ids, trace_mask), check_trace=False, strict=False
)
kv_cache_shape = model.kv_cache_shape
del model
query_length = ct.RangeDim(
lower_bound=1,
upper_bound=args.max_query_length,
default=1,
symbol="query_length",
)
end_step = ct.RangeDim(
lower_bound=1,
upper_bound=args.max_context_length,
default=1,
symbol="end_step",
)
inputs = [
ct.TensorType(
shape=(1, query_length), dtype=np.int32, name="inputIds"
),
ct.TensorType(
shape=(1, 1, query_length, end_step),
dtype=np.float16,
name="causalMask",
),
]
states = [
ct.StateType(
wrapped_type=ct.TensorType(shape=kv_cache_shape, dtype=np.float16),
name="keyCache",
),
ct.StateType(
wrapped_type=ct.TensorType(shape=kv_cache_shape, dtype=np.float16),
name="valueCache",
),
]
converted = ct.convert(
traced,
inputs=inputs,
outputs=[ct.TensorType(dtype=np.float16, name="logits")],
states=states,
minimum_deployment_target=ct.target.iOS18,
compute_units=ct.ComputeUnit.CPU_AND_GPU,
skip_model_load=True,
)
del traced
if args.quantize == "int4":
op_config = ct.optimize.coreml.OpLinearQuantizerConfig(
mode="linear_symmetric",
dtype="int4",
granularity="per_block",
block_size=32,
)
optimization = ct.optimize.coreml.OptimizationConfig(
global_config=op_config
)
converted = ct.optimize.coreml.linear_quantize_weights(
converted, config=optimization
)
metadata = {
TOKENIZER_METADATA_KEY: args.tokenizer_repo,
"com.ales27pm.dolphin.source_model": args.model_id,
"com.ales27pm.dolphin.source_revision": args.revision,
"com.ales27pm.dolphin.max_context_length": str(args.max_context_length),
"com.ales27pm.dolphin.max_query_length": str(args.max_query_length),
"com.ales27pm.dolphin.stop_token_ids": "128256,128001,128008,128009",
"com.ales27pm.dolphin.cache": "stateful-key-value",
"com.ales27pm.dolphin.quantization": args.quantize,
}
converted._spec.description.metadata.author = "ales27pm; source model by dphn"
converted._spec.description.metadata.license = "Llama 3.2 Community License"
converted._spec.description.metadata.shortDescription = (
"Stateful Core ML conversion of Dolphin 3.0 Llama 3.2 3B"
)
converted._spec.description.metadata.versionString = "2.0.0"
converted._spec.description.metadata.userDefined.update(metadata)
converted.input_description["inputIds"] = (
"Token IDs for prompt prefill or continuation decode."
)
converted.input_description["causalMask"] = (
"Additive FP16 causal mask; final dimension is the absolute end position."
)
converted.output_description["logits"] = (
"FP16 next-token scores with shape [1, query_length, 128258]."
)
converted.save(str(output_path))
spec = converted.get_spec()
state_names = [state.name for state in spec.description.state]
if state_names != ["keyCache", "valueCache"]:
raise RuntimeError(f"Unexpected state schema: {state_names}")
report = {
"schema_version": 1,
"source": {"repo_id": args.model_id, "revision": args.revision},
"tiny_test": args.tiny_test,
"artifact": output_path.name,
"quantization": args.quantize,
"minimum_deployment": {"ios": "18.0", "macos": "15.0"},
"max_context_length": args.max_context_length,
"max_query_length": args.max_query_length,
"state_names": state_names,
"torch_kv_cache_parity": parity,
"inventory": package_inventory(output_path),
"versions": {
"coremltools": ct.__version__,
"torch": torch.__version__,
},
}
report_path = output_path.with_suffix(".export-report.json")
rendered_report = json.dumps(report, indent=2, sort_keys=True)
report_path.write_text(rendered_report + "\n")
# Emit the evidence before any optional network mutation so an upload or
# authorization failure cannot hide a successfully completed conversion.
print(rendered_report, flush=True)
if args.upload_repo:
if args.tiny_test:
raise RuntimeError("Refusing to upload a synthetic tiny-test artifact")
token = os.environ.get("HF_TOKEN")
if not token:
raise RuntimeError("HF_TOKEN is required when --upload-repo is set")
api = HfApi(token=token)
api.upload_folder(
repo_id=args.upload_repo,
folder_path=str(output_path),
path_in_repo=output_path.name,
revision=args.upload_revision,
commit_message=args.commit_message,
)
api.upload_file(
repo_id=args.upload_repo,
path_or_fileobj=str(report_path),
path_in_repo=f"validation/{report_path.name}",
revision=args.upload_revision,
commit_message=f"Add export report for {output_path.name}",
)
return report
def build_parser() -> argparse.ArgumentParser:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--model-id", default=DEFAULT_MODEL_ID)
parser.add_argument("--revision", default=DEFAULT_REVISION)
parser.add_argument(
"--tokenizer-repo", default="ales27pm/Dolphin3.0-CoreML"
)
parser.add_argument("--output", default=DEFAULT_OUTPUT)
parser.add_argument("--max-context-length", type=int, default=2048)
parser.add_argument("--max-query-length", type=int, default=512)
parser.add_argument("--quantize", choices=("none", "int4"), default="int4")
parser.add_argument("--overwrite", action="store_true")
parser.add_argument(
"--tiny-test",
action="store_true",
help="Use a random two-layer Llama to smoke-test the export pipeline.",
)
parser.add_argument("--upload-repo")
parser.add_argument("--upload-revision", default="main")
parser.add_argument(
"--commit-message", default="Add functional stateful INT4 Core ML model"
)
return parser
def main(argv: Optional[Sequence[str]] = None) -> int:
args = build_parser().parse_args(argv)
if args.max_query_length > args.max_context_length:
raise ValueError("max-query-length cannot exceed max-context-length")
export_model(args)
return 0
if __name__ == "__main__":
raise SystemExit(main())
|