File size: 5,134 Bytes
e2bfccc | 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 | """Generate a few text samples from a saved checkpoint."""
from __future__ import annotations
import argparse
import json
import sys
from pathlib import Path
import torch
REPO_ROOT = Path(__file__).resolve().parents[2]
SRC_ROOT = REPO_ROOT / "src"
if str(SRC_ROOT) not in sys.path:
sys.path.insert(0, str(SRC_ROOT))
from taoTrain.checkpointing.checkpoint import CheckpointManager
from taoTrain.config import ModelConfig
from taoTrain.inference.inferencer import Inferencer
from taoTrain.models import get_model
def clear_kernel_caches(model) -> None:
for module in model.modules():
clear = getattr(module, "clear_kernel_cache", None)
if callable(clear):
clear()
def generate_once(
model,
tokenizer,
prompt: str,
*,
device: torch.device,
max_new_tokens: int,
temperature: float,
top_p: float,
dtype: torch.dtype,
) -> str:
input_ids = tokenizer.encode(prompt, return_tensors="pt").to(device)
generated = []
eos_token_id = getattr(tokenizer, "eos_token_id", None)
model.eval()
device_type = "cuda" if device.type == "cuda" else "cpu"
autocast_enabled = device.type == "cuda" and dtype in {torch.float16, torch.bfloat16}
with torch.inference_mode(), torch.autocast(device_type=device_type, dtype=dtype, enabled=autocast_enabled):
for _ in range(max_new_tokens):
clear_kernel_caches(model)
outputs = model(input_ids=input_ids, attention_mask=torch.ones_like(input_ids), labels=None)
logits = outputs["logits"][:, -1, :] / max(temperature, 1e-6)
if top_p < 1.0:
sorted_logits, sorted_indices = torch.sort(logits, descending=True)
sorted_probs = torch.softmax(sorted_logits, dim=-1)
cumulative = torch.cumsum(sorted_probs, dim=-1)
remove = cumulative > top_p
remove[..., 1:] = remove[..., :-1].clone()
remove[..., 0] = False
indices_to_remove = sorted_indices[remove]
logits[0, indices_to_remove] = float("-inf")
probs = torch.softmax(logits, dim=-1)
next_token = torch.multinomial(probs, num_samples=1)
token_id = int(next_token.item())
if eos_token_id is not None and token_id == eos_token_id:
break
generated.append(token_id)
input_ids = torch.cat([input_ids, next_token], dim=-1)
clear_kernel_caches(model)
return tokenizer.decode(generated, skip_special_tokens=True)
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--checkpoint", required=True)
parser.add_argument("--tokenizer-path", required=True)
parser.add_argument("--output", required=True)
parser.add_argument("--prompt", action="append", default=[])
parser.add_argument("--max-new-tokens", type=int, default=80)
parser.add_argument("--temperature", type=float, default=0.8)
parser.add_argument("--top-p", type=float, default=0.9)
parser.add_argument("--device", default="cuda")
parser.add_argument("--dtype", choices=["float32", "bfloat16", "float16"], default="bfloat16")
args = parser.parse_args()
prompts = args.prompt or [
"The purpose of artificial intelligence is",
"In a small village,",
"<user>Hello, who are you?<assistant>",
]
device = torch.device(args.device if args.device == "cpu" or torch.cuda.is_available() else "cpu")
dtype = {
"float32": torch.float32,
"bfloat16": torch.bfloat16,
"float16": torch.float16,
}[args.dtype]
tokenizer = Inferencer._load_tokenizer(args.tokenizer_path)
checkpoint_path = Path(args.checkpoint)
checkpoint = CheckpointManager(checkpoint_path.parent).load(checkpoint_path, device=device)
model_config = ModelConfig(**checkpoint.get("config", {}).get("model", {}))
model = get_model(model_config, device=device)
model.load_state_dict(checkpoint["model_state"], strict=False)
samples = []
for prompt in prompts:
text = generate_once(
model,
tokenizer,
prompt,
device=device,
max_new_tokens=args.max_new_tokens,
temperature=args.temperature,
top_p=args.top_p,
dtype=dtype,
)
samples.append({"prompt": prompt, "completion": text})
result = {
"checkpoint": args.checkpoint,
"tokenizer_path": args.tokenizer_path,
"device": str(device),
"dtype": str(dtype),
"max_new_tokens": args.max_new_tokens,
"temperature": args.temperature,
"top_p": args.top_p,
"samples": samples,
}
output = Path(args.output)
output.parent.mkdir(parents=True, exist_ok=True)
output.write_text(json.dumps(result, indent=2, ensure_ascii=False), encoding="utf-8")
print(json.dumps(result, indent=2, ensure_ascii=False))
if __name__ == "__main__":
main()
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