perf: add BFD bin-packing and custom attention mask to IFD batch scoring

- change position_ids_mode default from "none" to "doc_reset" so SFT preprocessing always generates position_ids (was causing dataset load KeyError)
- generate per-doc position_ids before packing (doc_reset mode), preserving document boundaries for BFD packing (cross-doc attention leak fix)
- change _align_bucket padding from [1] to [0] to avoid accidentally training on loss_mask padding

assets/docs/preprocessing.md

@@ -268,7 +268,7 @@ When `sources` is set, `sections` is ignored.
 | `storage_format` | str | `"bin"` | `"bin"` (mmap) or `"h5"` |
 | `max_tokens_per_shard` | int | `100000000` | Flush threshold in cumulative tokens |
 | `dtype` | dict[str, str] | `{}` | Per-key tensor dtype override (e.g. `{"loss_mask": "bool"}`) |
-| `position_ids_mode` | str | `"none"` | How to compute position_ids: `"none"`, `"doc_reset"`, `"continuous"` |
+| `position_ids_mode` | str | `"doc_reset"` | How to compute position_ids: `"none"`, `"doc_reset"`, `"continuous"` |
 
 ---
 

astrai/config/preprocess_config.py

@@ -96,7 +96,7 @@ class OutputConfig(BaseConfig):
     storage_format: str = "bin"
     max_tokens_per_shard: int = 100_000_000
     dtype: Dict[str, str] = field(default_factory=dict)
-    position_ids_mode: str = "none"
+    position_ids_mode: str = "doc_reset"
 
 
 @dataclass

astrai/preprocessing/pipeline.py

@@ -144,7 +144,7 @@ class Pipeline:
         for key in list(bucket.keys()):
             if key in result:
                 continue
-            bucket[key].append([1] * len(ids))
+            bucket[key].append([0] * len(ids))
 
     def _iter_items(self):
         for path in self.paths:
@@ -160,6 +160,12 @@ class Pipeline:
             idx = shard_idx[domain]
 
             pp = self.config.preprocessing
+            original_sequences = keys.get("sequence", [])
+            mode = self.config.output.position_ids_mode
+
+            if mode == "doc_reset" and original_sequences:
+                keys["position_ids"] = [list(range(len(s))) for s in original_sequences]
+
             keys = self._packer.apply(dict(keys), pp.max_packed_len, pp.truncation_mode)
 
             tensors: Dict[str, List[torch.Tensor]] = {}
@@ -171,9 +177,10 @@ class Pipeline:
                     torch.tensor(list(chain.from_iterable(ids_list)), dtype=dt)
                 ]
 
-            pos_ids = self._position_id.generate(keys.get("sequence", []))
-            if pos_ids:
-                tensors["position_ids"] = [torch.tensor(pos_ids, dtype=torch.int32)]
+            if mode == "continuous" and original_sequences:
+                pos_ids = self._position_id.generate(keys.get("sequence", []))
+                if pos_ids:
+                    tensors["position_ids"] = [torch.tensor(pos_ids, dtype=torch.int32)]
 
             self._writer.save(self.output_dir, domain, idx, tensors)
             shard_idx[domain] = idx + 1

scripts/eval/evaluate_ifd.py

@@ -1,31 +1,14 @@
 """IFD (Instruction Following Difficulty) data quality scoring.
 
-Computes IFD scores for instruction-response pairs to guide data selection.
-IFD = conditional_NLL / unconditional_NLL, where:
+IFD = conditional_NLL / unconditional_NLL
 
-- conditional_NLL: average CE loss on response tokens given instruction context
-- unconditional_NLL: average CE loss on response tokens alone
-
-Higher IFD (close to 1) = instruction provides less help = harder sample.
-Lower IFD (close to 0) = instruction provides strong guidance = easy sample.
-IFD > 1 = instruction misleads the model = likely low-quality data.
-
-Usage::
-
-    python scripts/eval/ifd.py --param_path ./params \
-        --input data.jsonl --output data_with_ifd.jsonl \
-        --instr_key instruction --resp_key response
-
-Disable chat template::
-
-    python scripts/eval/ifd.py --param_path ./params \
-        --input data.jsonl --output data_with_ifd.jsonl \
-        --instr_key instruction --resp_key response \
-        --no_chat_template
+- 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
@@ -35,217 +18,223 @@ from astrai.model import AutoModel
 from astrai.tokenize import AutoTokenizer
 
 
-def compute_ifd(
-    model,
-    tokenizer,
-    instruction: str,
-    response: str,
-    device: str,
-    max_len: int = 2048,
-    use_chat_template: bool = False,
-) -> dict:
-    if use_chat_template:
-        return _compute_ifd_with_template(
-            model, tokenizer, instruction, response, device, max_len
-        )
-    return _compute_ifd_raw(model, tokenizer, instruction, response, device, max_len)
-
-
-def _compute_ifd_raw(model, tokenizer, instruction, response, device, max_len) -> dict:
-    instr_ids = tokenizer.encode(instruction, add_special_tokens=False)
-    resp_ids = tokenizer.encode(response, add_special_tokens=False)
-
-    if len(resp_ids) > max_len:
-        resp_ids = resp_ids[:max_len]
-
-    if not resp_ids:
-        return {
-            "L_cond": None,
-            "L_uncond": None,
-            "ifd": None,
-            "error": "empty response",
-        }
-
-    qa_len = len(instr_ids) + len(resp_ids)
-    if qa_len > max_len:
-        overflow = qa_len - max_len
-        if overflow >= len(instr_ids):
-            resp_ids = resp_ids[:max_len]
-            instr_ids = []
+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:
-            instr_ids = instr_ids[overflow:]
+            bins.append([(orig_idx, c, r)])
+            lengths.append(max_len - size)
+    return bins
 
-    if not instr_ids:
-        return {
-            "L_cond": None,
-            "L_uncond": None,
-            "ifd": None,
-            "error": "response too long for context",
-        }
 
-    instr_len = len(instr_ids)
-    resp_len = len(resp_ids)
+@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)
 
-    qa_ids = instr_ids + resp_ids
+    result = [None] * len(pairs)
 
-    with torch.inference_mode():
-        logits_qa = model(torch.tensor([qa_ids], device=device, dtype=torch.long))[
-            "logits"
-        ][0]
-        logits_resp = model(torch.tensor([resp_ids], device=device, dtype=torch.long))[
+    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]
 
-    resp_logits = logits_qa[instr_len - 1 : -1]
-    resp_targets = logits_resp.new_tensor(resp_ids, dtype=torch.long)
-    L_cond = F.cross_entropy(resp_logits, resp_targets, reduction="mean").item()
+        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)
 
-    unp_logits = logits_resp[:-1]
-    unp_targets = logits_resp.new_tensor(resp_ids[1:], dtype=torch.long)
-    L_uncond = F.cross_entropy(unp_logits, unp_targets, reduction="mean").item()
+    # 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"]
 
-    ifd = L_cond / L_uncond if L_uncond > 0 else None
+        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 {
-        "L_cond": round(L_cond, 6),
-        "L_uncond": round(L_uncond, 6),
-        "ifd": round(ifd, 6) if ifd is not None else None,
-        "instr_len": instr_len,
-        "resp_len": resp_len,
-        "error": None,
-    }
-
-
-def _compute_ifd_with_template(
-    model, tokenizer, instruction, response, device, max_len
-) -> dict:
-    instr_prefix = tokenizer.apply_chat_template(
-        [{"role": "user", "content": instruction}],
-        tokenize=False,
-        add_generation_prompt=True,
-    )
-    full_text = tokenizer.apply_chat_template(
-        [
-            {"role": "user", "content": instruction},
-            {"role": "assistant", "content": response},
-        ],
-        tokenize=False,
-        add_generation_prompt=False,
-    )
-
-    full_ids = tokenizer.encode(full_text)
-    prefix_ids = tokenizer.encode(instr_prefix)
-    resp_ids = tokenizer.encode(response)
-
-    if not resp_ids:
-        return {
-            "L_cond": None,
-            "L_uncond": None,
-            "ifd": None,
-            "error": "empty response",
-        }
-
-    if len(full_ids) > max_len:
-        overflow = len(full_ids) - max_len
-        full_ids = full_ids[overflow:]
-        prefix_len = len(prefix_ids) - overflow
-        prefix_len = max(0, prefix_len)
-    else:
-        prefix_len = len(prefix_ids)
-
-    cond_tensor = torch.tensor([full_ids], device=device, dtype=torch.long)
-
-    with torch.inference_mode():
-        logits_qa = model(cond_tensor)["logits"][0]
-
-    resp_start = prefix_len - 1
-    resp_end = len(full_ids) - 1
-    if resp_end <= resp_start:
-        return {
-            "L_cond": None,
-            "L_uncond": None,
-            "ifd": None,
-            "error": "response truncated entirely",
-        }
-
-    resp_logits = logits_qa[resp_start:resp_end]
-    resp_targets = torch.tensor(full_ids[prefix_len:], device=device, dtype=torch.long)
-    L_cond = F.cross_entropy(resp_logits, resp_targets, reduction="mean").item()
-
-    resp_tensor = torch.tensor([resp_ids], device=device, dtype=torch.long)
-
-    with torch.inference_mode():
-        logits_resp = model(resp_tensor)["logits"][0]
-
-    unp_logits = logits_resp[:-1]
-    unp_targets = resp_tensor[0, 1:]
-    L_uncond = F.cross_entropy(unp_logits, unp_targets, reduction="mean").item()
-
-    ifd = L_cond / L_uncond if L_uncond > 0 else None
-
-    return {
-        "L_cond": round(L_cond, 6),
-        "L_uncond": round(L_uncond, 6),
-        "ifd": round(ifd, 6) if ifd is not None else None,
-        "instr_len": prefix_len,
-        "resp_len": len(resp_ids),
-        "error": None,
+        "ifd": avg,
+        "ifd_detail": valid[0] if len(valid) == 1 else None,
+        "ifd_turns": raw_scores,
     }
 
 
 def process_file(
-    param_path: str,
-    input_file: str,
-    output_file: str,
-    instr_key: str,
-    resp_key: str,
-    max_len: int = 2048,
-    use_chat_template: bool = False,
+    param_path,
+    input_file,
+    output_file,
+    instr_key,
+    resp_key,
+    max_len=2048,
+    data_format="plain",
+    batch_size=1,
+    device=None,
 ):
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    dtype = torch.bfloat16 if device == "cuda" else torch.float32
+    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()
 
-    if use_chat_template and tokenizer._chat_template is None:
-        raise RuntimeError(
-            "--use_chat_template specified but tokenizer has no chat template. "
-            "Add a chat_template to tokenizer_config.json or omit the flag."
-        )
-
-    with open(input_file, "r", encoding="utf-8") as f:
+    with open(input_file, encoding="utf-8") as f:
         data = [json.loads(line) for line in f if line.strip()]
 
     results = []
-    ifd_values = []
+    all_ifds = []
+    buffer = []
 
-    with torch.inference_mode():
-        for item in tqdm.tqdm(data, desc="Computing IFD", unit="sample"):
-            instruction = item[instr_key]
-            response = item[resp_key]
-            scores = compute_ifd(
-                model,
-                tokenizer,
-                instruction,
-                response,
-                device,
-                max_len,
-                use_chat_template=use_chat_template,
-            )
-            ifd_values.append(scores["ifd"])
-            results.append({**item, "ifd": scores["ifd"], "ifd_detail": scores})
+    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 ifd_values if v is not None]
+    valid_ifd = [v for v in all_ifds if v is not None]
     if valid_ifd:
-        import statistics
-
         print(f"\n{'=' * 50}")
         print(f"  Samples:    {len(data)}")
         print(f"  Valid IFD:  {len(valid_ifd)}")
@@ -259,6 +248,41 @@ def process_file(
     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"
@@ -266,30 +290,24 @@ def main():
     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(
-        "--instr_key",
+        "--format",
         type=str,
-        default="instruction",
-        help="Key for instruction field",
+        default="plain",
+        choices=["plain", "messages"],
+        help="Input format",
     )
     parser.add_argument(
-        "--resp_key",
-        type=str,
-        default="response",
-        help="Key for response field",
+        "--instr_key", type=str, default="instruction", help="Key for instruction field"
     )
     parser.add_argument(
-        "--max_len",
-        type=int,
-        default=2048,
-        help="Max token length (instruction truncated to fit)",
+        "--resp_key", type=str, default="response", help="Key for response field"
     )
     parser.add_argument(
-        "--no_chat_template",
-        action="store_true",
-        default=False,
-        help="Disable chat template, use raw text concatenation",
+        "--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(
@@ -299,7 +317,9 @@ def main():
         args.instr_key,
         args.resp_key,
         args.max_len,
-        use_chat_template=not args.no_chat_template,
+        data_format=args.format,
+        batch_size=args.batch_size,
+        device=args.device,
     )