328 lines
12 KiB
Python
328 lines
12 KiB
Python
"""IFD (Instruction Following Difficulty) data quality scoring.
|
|
|
|
IFD = conditional_NLL / unconditional_NLL
|
|
|
|
- Messages format: plain text concatenation (no chat template)
|
|
- Plain format: raw instr_key + resp_key fields
|
|
"""
|
|
|
|
import argparse
|
|
import json
|
|
import statistics
|
|
|
|
import torch
|
|
import torch.nn.functional as F
|
|
import tqdm
|
|
|
|
from astrai.model import AutoModel
|
|
from astrai.tokenize import AutoTokenizer
|
|
|
|
|
|
def _pack_bins(pairs, max_len):
|
|
"""BFD bin packing: pack (c+r) into bins of max total length."""
|
|
indexed = sorted(enumerate(pairs), key=lambda x: -(len(x[1][0]) + len(x[1][1])))
|
|
bins = [] # each bin: list of (orig_idx, ctx_ids, resp_ids)
|
|
lengths = []
|
|
for orig_idx, (c, r) in indexed:
|
|
size = len(c) + len(r)
|
|
best_bin = -1
|
|
for bi, rem in enumerate(lengths):
|
|
if rem >= size:
|
|
if best_bin < 0 or rem < lengths[best_bin]:
|
|
best_bin = bi
|
|
if best_bin >= 0:
|
|
bins[best_bin].append((orig_idx, c, r))
|
|
lengths[best_bin] -= size
|
|
else:
|
|
bins.append([(orig_idx, c, r)])
|
|
lengths.append(max_len - size)
|
|
return bins
|
|
|
|
|
|
@torch.inference_mode()
|
|
def _score_batch(pairs, model, device, max_len=2048):
|
|
"""BFD-packed IFD: pack items into bins, one forward pass per bin."""
|
|
if not pairs:
|
|
return []
|
|
bins = _pack_bins(pairs, max_len)
|
|
|
|
result = [None] * len(pairs)
|
|
|
|
for bin_items in bins:
|
|
seq_ids = []
|
|
global_pos = [] # doc-reset position IDs for RoPE
|
|
doc_ids = [] # document index for attention mask
|
|
doc_offsets = []
|
|
|
|
for di, (orig_idx, c, r) in enumerate(bin_items):
|
|
ctx_len = len(c)
|
|
start = len(seq_ids)
|
|
item_len = len(c) + len(r)
|
|
seq_ids.extend(c)
|
|
seq_ids.extend(r)
|
|
end = len(seq_ids)
|
|
global_pos.extend(range(item_len))
|
|
doc_ids.extend([di] * item_len)
|
|
doc_offsets.append((start, end, orig_idx, ctx_len))
|
|
|
|
full_ids = torch.tensor([seq_ids], device=device, dtype=torch.long)
|
|
pos_ids = torch.tensor([global_pos], device=device, dtype=torch.long)
|
|
T = len(seq_ids)
|
|
causal = torch.tril(torch.ones(T, T, dtype=torch.bool, device=device))
|
|
doc_t = torch.tensor([doc_ids], device=device)
|
|
doc_mask = doc_t.unsqueeze(-1) == doc_t.unsqueeze(-2)
|
|
attn_mask = (causal & doc_mask[0]).unsqueeze(0).unsqueeze(0)
|
|
logits_full = model(full_ids, position_ids=pos_ids, input_mask=attn_mask)[
|
|
"logits"
|
|
][0]
|
|
|
|
for start, end, orig_idx, ctx_len in doc_offsets:
|
|
rl = end - start - ctx_len
|
|
if rl < 2:
|
|
continue
|
|
resp_start = start + ctx_len - 1
|
|
resp_logits = logits_full[resp_start : end - 1]
|
|
resp_targets = torch.tensor(
|
|
seq_ids[start + ctx_len : end], device=device, dtype=torch.long
|
|
)
|
|
L_cond = F.cross_entropy(resp_logits, resp_targets, reduction="mean").item()
|
|
result[orig_idx] = (L_cond, rl)
|
|
|
|
# unconditional pass: batch all responses separately (sorted by length)
|
|
resp_seqs = [
|
|
(i, result[i][1], pairs[i][1])
|
|
for i in range(len(pairs))
|
|
if result[i] is not None
|
|
]
|
|
if resp_seqs:
|
|
resp_seqs.sort(key=lambda x: -x[1])
|
|
r_batch = torch.zeros(
|
|
len(resp_seqs),
|
|
max(len(r) for _, _, r in resp_seqs),
|
|
dtype=torch.long,
|
|
device=device,
|
|
)
|
|
for ri, (_, rl, r_ids) in enumerate(resp_seqs):
|
|
r_batch[ri, :rl] = torch.tensor(r_ids, dtype=torch.long)
|
|
logits_resp = model(r_batch)["logits"]
|
|
|
|
for ri, (orig_idx, rl, _) in enumerate(resp_seqs):
|
|
L_cond = result[orig_idx][0]
|
|
unp_logits = logits_resp[ri, : rl - 1]
|
|
unp_targets = r_batch[ri, 1:rl]
|
|
L_uncond = F.cross_entropy(unp_logits, unp_targets, reduction="mean").item()
|
|
ifd = L_cond / L_uncond if L_uncond > 0 else None
|
|
result[orig_idx] = {
|
|
"L_cond": round(L_cond, 6),
|
|
"L_uncond": round(L_uncond, 6),
|
|
"ifd": round(ifd, 6) if ifd is not None else None,
|
|
"resp_len": rl,
|
|
}
|
|
|
|
return result
|
|
|
|
|
|
def _trim(context_ids, resp_ids, max_len):
|
|
"""Truncate to fit max_len, keeping response intact if possible."""
|
|
if len(resp_ids) > max_len // 2:
|
|
resp_ids = resp_ids[: max_len // 2]
|
|
full_ids = context_ids + resp_ids
|
|
if len(full_ids) <= max_len:
|
|
return context_ids, resp_ids
|
|
overflow = len(full_ids) - max_len
|
|
if overflow >= len(context_ids):
|
|
return [], resp_ids[:max_len]
|
|
return context_ids[overflow:], resp_ids
|
|
|
|
|
|
def score_plain(model, tokenizer, instruction, response, device, max_len=2048):
|
|
"""Compute IFD for a single instruction-response pair (plain format)."""
|
|
ctx_ids = tokenizer.encode(instruction, add_special_tokens=False)
|
|
resp_ids = tokenizer.encode(response, add_special_tokens=False)
|
|
ctx_ids, resp_ids = _trim(ctx_ids, resp_ids, max_len)
|
|
if not ctx_ids or not resp_ids:
|
|
return {"L_cond": None, "L_uncond": None, "ifd": None, "error": "empty"}
|
|
return _score_batch([(ctx_ids, resp_ids)], model, device, max_len)[0]
|
|
|
|
|
|
def score_messages(model, tokenizer, messages, device, max_len=2048):
|
|
"""Compute IFD for each assistant turn in a messages array."""
|
|
turns = []
|
|
for i, msg in enumerate(messages):
|
|
if msg.get("role") != "assistant":
|
|
continue
|
|
ctx_text = "\n\n".join(m["content"] for m in messages[:i])
|
|
ctx_ids = tokenizer.encode(ctx_text)
|
|
resp_ids = tokenizer.encode(msg["content"], add_special_tokens=False)
|
|
ctx_ids, resp_ids = _trim(ctx_ids, resp_ids, max_len)
|
|
if ctx_ids and resp_ids:
|
|
turns.append((ctx_ids, resp_ids))
|
|
if not turns:
|
|
return None
|
|
raw_scores = _score_batch(turns, model, device, max_len)
|
|
valid = [s for s in raw_scores if s is not None and s["ifd"] is not None]
|
|
if not valid:
|
|
return {"ifd": None, "ifd_turns": raw_scores}
|
|
avg = sum(s["ifd"] for s in valid) / len(valid)
|
|
return {
|
|
"ifd": avg,
|
|
"ifd_detail": valid[0] if len(valid) == 1 else None,
|
|
"ifd_turns": raw_scores,
|
|
}
|
|
|
|
|
|
def process_file(
|
|
param_path,
|
|
input_file,
|
|
output_file,
|
|
instr_key,
|
|
resp_key,
|
|
max_len=2048,
|
|
data_format="plain",
|
|
batch_size=1,
|
|
device=None,
|
|
):
|
|
if device is None:
|
|
device = "cuda" if torch.cuda.is_available() else "cpu"
|
|
dtype = torch.bfloat16 if "cuda" in device else torch.float32
|
|
|
|
model = AutoModel.from_pretrained(param_path)
|
|
tokenizer = AutoTokenizer.from_pretrained(param_path)
|
|
model.to(device=device, dtype=dtype)
|
|
model.eval()
|
|
|
|
with open(input_file, encoding="utf-8") as f:
|
|
data = [json.loads(line) for line in f if line.strip()]
|
|
|
|
results = []
|
|
all_ifds = []
|
|
buffer = []
|
|
|
|
for item in tqdm.tqdm(data, desc="Computing IFD", unit="sample"):
|
|
if data_format == "messages":
|
|
turns = []
|
|
for i, msg in enumerate(item.get("messages", [])):
|
|
if msg.get("role") != "assistant":
|
|
continue
|
|
ctx_text = "\n\n".join(m["content"] for m in item["messages"][:i])
|
|
ctx_ids = tokenizer.encode(ctx_text)
|
|
resp_ids = tokenizer.encode(msg["content"], add_special_tokens=False)
|
|
ctx_ids, resp_ids = _trim(ctx_ids, resp_ids, max_len)
|
|
if ctx_ids and resp_ids:
|
|
turns.append((ctx_ids, resp_ids))
|
|
if not turns:
|
|
results.append({**item, "ifd": None, "ifd_turns": []})
|
|
continue
|
|
buffer.append((item, turns, "messages"))
|
|
else:
|
|
ctx_ids = tokenizer.encode(item[instr_key], add_special_tokens=False)
|
|
resp_ids = tokenizer.encode(item[resp_key], add_special_tokens=False)
|
|
ctx_ids, resp_ids = _trim(ctx_ids, resp_ids, max_len)
|
|
if not ctx_ids or not resp_ids:
|
|
results.append({**item, "ifd": None, "ifd_detail": {"error": "empty"}})
|
|
continue
|
|
buffer.append((item, [(ctx_ids, resp_ids)], "plain"))
|
|
|
|
if len(buffer) >= batch_size:
|
|
_flush_buffer(buffer, results, all_ifds, model, device, max_len)
|
|
|
|
if buffer:
|
|
_flush_buffer(buffer, results, all_ifds, model, device, max_len)
|
|
|
|
with open(output_file, "w", encoding="utf-8") as f:
|
|
for item in results:
|
|
f.write(json.dumps(item, ensure_ascii=False) + "\n")
|
|
|
|
valid_ifd = [v for v in all_ifds if v is not None]
|
|
if valid_ifd:
|
|
print(f"\n{'=' * 50}")
|
|
print(f" Samples: {len(data)}")
|
|
print(f" Valid IFD: {len(valid_ifd)}")
|
|
print(f" Mean IFD: {statistics.mean(valid_ifd):.4f}")
|
|
print(f" Median IFD: {statistics.median(valid_ifd):.4f}")
|
|
print(f" Stdev IFD: {statistics.stdev(valid_ifd):.4f}")
|
|
print(f" Min IFD: {min(valid_ifd):.4f}")
|
|
print(f" Max IFD: {max(valid_ifd):.4f}")
|
|
print(f"{'=' * 50}")
|
|
|
|
print(f"Results saved to {output_file}")
|
|
|
|
|
|
def _flush_buffer(buffer, results, all_ifds, model, device, max_len=2048):
|
|
all_pairs = []
|
|
indices = []
|
|
for item, turns, fmt in buffer:
|
|
start = len(all_pairs)
|
|
all_pairs.extend(turns)
|
|
indices.append((item, turns, fmt, start, len(all_pairs)))
|
|
|
|
raw = _score_batch(all_pairs, model, device, max_len)
|
|
|
|
for item, turns, fmt, start, end in indices:
|
|
turn_scores = raw[start:end]
|
|
if fmt == "messages":
|
|
valid = [s for s in turn_scores if s is not None and s["ifd"] is not None]
|
|
if not valid:
|
|
results.append({**item, "ifd": None, "ifd_turns": turn_scores})
|
|
else:
|
|
avg = sum(s["ifd"] for s in valid) / len(valid)
|
|
all_ifds.append(avg)
|
|
results.append(
|
|
{
|
|
**item,
|
|
"ifd": avg,
|
|
"ifd_detail": valid[0] if len(valid) == 1 else None,
|
|
"ifd_turns": turn_scores,
|
|
}
|
|
)
|
|
else:
|
|
score = turn_scores[0]
|
|
all_ifds.append(score["ifd"])
|
|
results.append({**item, "ifd": score["ifd"], "ifd_detail": score})
|
|
|
|
buffer.clear()
|
|
|
|
|
|
def main():
|
|
parser = argparse.ArgumentParser(
|
|
description="Compute IFD scores for instruction-response data"
|
|
)
|
|
parser.add_argument("--param_path", type=str, required=True, help="Model directory")
|
|
parser.add_argument("--input", type=str, required=True, help="Input JSONL file")
|
|
parser.add_argument("--output", type=str, required=True, help="Output JSONL file")
|
|
parser.add_argument("--max_len", type=int, default=2048, help="Max token length")
|
|
parser.add_argument(
|
|
"--format",
|
|
type=str,
|
|
default="plain",
|
|
choices=["plain", "messages"],
|
|
help="Input format",
|
|
)
|
|
parser.add_argument(
|
|
"--instr_key", type=str, default="instruction", help="Key for instruction field"
|
|
)
|
|
parser.add_argument(
|
|
"--resp_key", type=str, default="response", help="Key for response field"
|
|
)
|
|
parser.add_argument(
|
|
"--batch_size", type=int, default=8, help="Batch size for model forward passes"
|
|
)
|
|
parser.add_argument("--device", type=str, default=None, help="Device (e.g. cuda:0)")
|
|
args = parser.parse_args()
|
|
|
|
process_file(
|
|
args.param_path,
|
|
args.input,
|
|
args.output,
|
|
args.instr_key,
|
|
args.resp_key,
|
|
args.max_len,
|
|
data_format=args.format,
|
|
batch_size=args.batch_size,
|
|
device=args.device,
|
|
)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|