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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 | #!/usr/bin/env python3
# /// script
# requires-python = ">=3.11,<3.12"
# dependencies = [
# "coremltools==8.0",
# "jinja2==3.1.5",
# "numpy==1.26.4",
# "transformers==4.47.1",
# ]
# ///
"""Run bounded text generation with the stateful Dolphin Core ML package."""
from __future__ import annotations
import argparse
import json
from pathlib import Path
from typing import Iterable
import coremltools as ct
import numpy as np
from transformers import AutoTokenizer
DEFAULT_MODEL = "Dolphin3.0-Llama3.2-3B-stateful-int4.mlpackage"
DEFAULT_TOKENIZER = "ales27pm/Dolphin3.0-CoreML"
STOP_TOKEN_IDS = frozenset((128256, 128001, 128008, 128009))
def causal_mask(query_length: int, end_step: int) -> np.ndarray:
if query_length < 1 or end_step < query_length:
raise ValueError("Expected 1 <= query_length <= end_step")
past_length = end_step - query_length
columns = np.arange(end_step)[None, :]
rows = past_length + np.arange(query_length)[:, None]
return np.where(columns <= rows, 0.0, -65504.0).astype(np.float16)[
None, None, :, :
]
def sample_token(
logits: np.ndarray,
*,
temperature: float,
top_p: float,
rng: np.random.Generator,
) -> int:
scores = logits[0, -1].astype(np.float32)
if not np.isfinite(scores).all():
raise RuntimeError("Core ML returned non-finite logits")
if temperature <= 0:
return int(np.argmax(scores))
scores /= temperature
scores -= np.max(scores)
probabilities = np.exp(scores)
probabilities /= probabilities.sum()
order = np.argsort(probabilities)[::-1]
ordered = probabilities[order]
# Keep the first token whose inclusion reaches or crosses the requested
# probability mass. Subtracting the current probability makes the test
# equivalent to shifting the cumulative mask one position to the right.
keep = np.cumsum(ordered) - ordered < top_p
selected = order[keep]
selected_probabilities = probabilities[selected]
selected_probabilities /= selected_probabilities.sum()
return int(rng.choice(selected, p=selected_probabilities))
def stop_ids(tokenizer_eos: int | Iterable[int] | None) -> frozenset[int]:
values = set(STOP_TOKEN_IDS)
if isinstance(tokenizer_eos, int):
values.add(tokenizer_eos)
elif tokenizer_eos is not None:
values.update(int(item) for item in tokenizer_eos)
return frozenset(values)
def main() -> int:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("prompt")
parser.add_argument("--model", default=DEFAULT_MODEL)
parser.add_argument("--tokenizer", default=DEFAULT_TOKENIZER)
parser.add_argument(
"--system",
default="You are Dolphin, created by Eric Hartford. You are a helpful assistant.",
)
parser.add_argument("--max-new-tokens", type=int, default=64)
parser.add_argument("--temperature", type=float, default=0.0)
parser.add_argument("--top-p", type=float, default=0.9)
parser.add_argument("--seed", type=int, default=0)
parser.add_argument(
"--compute-units",
choices=("all", "cpu_and_gpu", "cpu_only", "cpu_and_ne"),
default="cpu_and_gpu",
)
args = parser.parse_args()
if not 0 < args.top_p <= 1:
parser.error("--top-p must be in (0, 1]")
if args.max_new_tokens < 1:
parser.error("--max-new-tokens must be positive")
tokenizer = AutoTokenizer.from_pretrained(args.tokenizer, revision="main")
messages = [
{"role": "system", "content": args.system},
{"role": "user", "content": args.prompt},
]
prompt_ids = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="np"
).astype(np.int32)
compute_units = {
"all": ct.ComputeUnit.ALL,
"cpu_and_gpu": ct.ComputeUnit.CPU_AND_GPU,
"cpu_only": ct.ComputeUnit.CPU_ONLY,
"cpu_and_ne": ct.ComputeUnit.CPU_AND_NE,
}[args.compute_units]
model = ct.models.MLModel(args.model, compute_units=compute_units)
metadata = model.user_defined_metadata
max_context = int(
metadata.get("com.ales27pm.dolphin.max_context_length", "2048")
)
max_query = int(metadata.get("com.ales27pm.dolphin.max_query_length", "512"))
if prompt_ids.shape[-1] > max_query:
raise ValueError(
f"Prompt has {prompt_ids.shape[-1]} tokens; model prefill limit is {max_query}"
)
if prompt_ids.shape[-1] + args.max_new_tokens > max_context:
raise ValueError(
"Prompt plus requested output exceeds the model's "
f"{max_context}-token state capacity"
)
state = model.make_state()
rng = np.random.default_rng(args.seed)
generated: list[int] = []
query = prompt_ids
end_step = prompt_ids.shape[-1]
eos_ids = stop_ids(tokenizer.eos_token_id)
for _ in range(args.max_new_tokens):
result = model.predict(
{
"inputIds": query,
"causalMask": causal_mask(query.shape[-1], end_step),
},
state=state,
)
token = sample_token(
result["logits"],
temperature=args.temperature,
top_p=args.top_p,
rng=rng,
)
if token in eos_ids:
break
generated.append(token)
query = np.array([[token]], dtype=np.int32)
end_step += 1
text = tokenizer.decode(generated, skip_special_tokens=True)
print(text)
print(
json.dumps(
{
"prompt_tokens": int(prompt_ids.shape[-1]),
"generated_tokens": len(generated),
"stop_token_ids": sorted(eos_ids),
},
sort_keys=True,
)
)
return 0
if __name__ == "__main__":
raise SystemExit(main())
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