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File: mllm/training/tally_tokenwise.py
Summary: Converts token-level tallies into per-token statistics.
"""
import json
import os
from typing import Any, Dict, List, Tuple, Union
import numpy as np
import pandas as pd
import torch
from transformers import AutoTokenizer
class ContextualizedTokenwiseTally:
"""
Collect, store, and save token-level metrics per rollout.
- One DataFrame per rollout_id in `paths`
- Index = timestep (int)
- Columns are added incrementally via `add_contexts()` and `add_data()`
- Cells may contain scalars, strings, or lists (dtype=object)
"""
def __init__(
self,
tokenizer: AutoTokenizer,
paths: List[str],
max_context_length: int = 30,
):
"""
Args:
tokenizer: HuggingFace tokenizer used to convert tids -> tokens
paths: rollout identifiers (parallel to batch dimension)
max_context_length: truncate context token lists to this length
"""
self.tokenizer = tokenizer
self.paths = paths
self.max_context_length = max_context_length
self.tally: Dict[str, pd.DataFrame] = {path: pd.DataFrame() for path in paths}
# set later by setters
self.contexts: torch.Tensor | None = None
self.action_mask: torch.Tensor | None = None
self.range: Tuple[int, int] | None = None
# --------- Utilities ---------
def tids_to_str(self, tids: List[int]) -> List[str]:
"""Convert a list of token IDs to a list of token strings."""
return self.tokenizer.convert_ids_to_tokens(tids)
def _ensure_ready(self):
"""Validate that action mask and range are configured prior to writes."""
assert self.action_mask is not None, "call set_action_mask(mask) first"
assert self.range is not None, "call set_range((start, end)) first"
@staticmethod
def _sanitize_filename(name: Any) -> str:
"""Make a safe filename from any rollout_id."""
s = str(name)
bad = {os.sep, " ", ":", "|", "<", ">", '"', "'"}
if os.altsep is not None:
bad.add(os.altsep)
for ch in bad:
s = s.replace(ch, "_")
return s
@staticmethod
def _pad_left(seq: List[Any], length: int, pad_val: Any = "") -> List[Any]:
"""Left-pad a sequence to `length` with `pad_val`."""
if len(seq) >= length:
return seq[-length:]
return [pad_val] * (length - len(seq)) + list(seq)
# --------- Setters ---------
def set_action_mask(self, action_mask: torch.Tensor):
"""Register the (B, S) mask indicating which tokens correspond to actions."""
self.action_mask = action_mask
def set_range(self, range: Tuple[int, int]):
"""Record which subset of ``paths`` the current mini-batch corresponds to."""
self.range = range
# --------- Column builders ---------
def add_contexts(self, contexts: torch.Tensor):
"""
Add a single 'context' column (list[str]) for valid steps.
Expects `contexts` with shape (B, S): token id at each timestep.
For each valid timestep t, we use the last N tokens up to and including t:
window = contexts[i, max(0, t - N + 1) : t + 1]
The list is left-padded with "" to always be length N.
"""
self._ensure_ready()
current_paths = self.paths[self.range[0] : self.range[1]]
B, S = contexts.shape
N = self.max_context_length
# to CPU ints once
contexts_cpu = contexts.detach().to("cpu")
for i in range(B):
rollout_id = current_paths[i]
df = self.tally.get(rollout_id, pd.DataFrame())
valid_idx = torch.nonzero(
self.action_mask[i].bool(), as_tuple=False
).squeeze(-1)
if valid_idx.numel() == 0:
self.tally[rollout_id] = df
continue
idx_list = valid_idx.tolist()
# ensure index contains valid steps
if df.empty:
df = pd.DataFrame(index=idx_list)
else:
new_index = sorted(set(df.index.tolist()) | set(idx_list))
if list(df.index) != new_index:
df = df.reindex(new_index)
# build context windows
ctx_token_lists = []
for t in idx_list:
start = max(0, t - N + 1)
window_ids = contexts_cpu[i, start : t + 1].tolist()
window_toks = self.tids_to_str([int(x) for x in window_ids])
if len(window_toks) < N:
window_toks = [""] * (N - len(window_toks)) + window_toks
else:
window_toks = window_toks[-N:]
ctx_token_lists.append(window_toks)
# single 'context' column
if "context" not in df.columns:
df["context"] = pd.Series(index=df.index, dtype=object)
df.loc[idx_list, "context"] = pd.Series(
ctx_token_lists, index=idx_list, dtype=object
)
self.tally[rollout_id] = df
def add_data(
self,
metric_id: str,
metrics: torch.Tensor,
to_tids: bool = False,
):
"""
Add a metric column for valid steps.
Args:
metric_id: column name
metrics: shape (B, S) for scalars/ids or (B, S, K) for top-k vectors
to_tids: if True, treat ints/lists of ints as tids and convert to tokens
"""
self._ensure_ready()
current_paths = self.paths[self.range[0] : self.range[1]]
if metrics.dim() == 2:
B, S = metrics.shape
elif metrics.dim() == 3:
B, S, _ = metrics.shape
else:
raise ValueError("metrics must be (B, S) or (B, S, K)")
for i in range(B):
rollout_id = current_paths[i]
df = self.tally.get(rollout_id, pd.DataFrame())
valid_idx = torch.nonzero(
self.action_mask[i].bool(), as_tuple=False
).squeeze(-1)
if valid_idx.numel() == 0:
self.tally[rollout_id] = df
continue
idx_list = valid_idx.detach().cpu().tolist()
# Ensure index contains valid steps
if df.empty:
df = pd.DataFrame(index=idx_list)
else:
new_index = sorted(set(df.index.tolist()) | set(idx_list))
if list(df.index) != new_index:
df = df.reindex(new_index)
# Slice metrics at valid steps
m_valid = metrics[i][valid_idx]
# -> pure python lists (1D list or list-of-lists)
values = m_valid.detach().cpu().tolist()
# optional tids -> tokens
if to_tids:
def _to_tokish(x):
if isinstance(x, list):
return self.tids_to_str([int(v) for v in x])
else:
return self.tids_to_str([int(x)])[0]
values = [_to_tokish(v) for v in values]
# Ensure column exists with object dtype, then assign via aligned Series
if metric_id not in df.columns:
df[metric_id] = pd.Series(index=df.index, dtype=object)
if isinstance(values, np.ndarray):
values = values.tolist()
if len(values) != len(idx_list):
raise ValueError(
f"Length mismatch for '{metric_id}': values={len(values)} vs idx_list={len(idx_list)}"
)
df.loc[idx_list, metric_id] = pd.Series(
values, index=idx_list, dtype=object
)
self.tally[rollout_id] = df
# --------- Saving ---------
def save(self, path: str):
"""
Write a manifest JSON and one CSV per rollout.
- Manifest includes metadata only (safe to JSON).
- Each rollout CSV is written with index label 'timestep'.
- Only a single 'context' column (list[str]).
"""
if not self.tally or all(df.empty for df in self.tally.values()):
return
os.makedirs(path, exist_ok=True)
from datetime import datetime
now = datetime.now()
manifest = {
"created_at": f"{now:%Y-%m-%d %H:%M:%S}",
"max_context_length": self.max_context_length,
"num_rollouts": len(self.tally),
"rollouts": [],
}
for rid, df in self.tally.items():
rid_str = str(rid)
safe_name = self._sanitize_filename(rid_str)
csv_path = os.path.join(path, f"{safe_name}_tokenwise.csv")
# Put 'context' first, then the rest
cols = ["context"] + [c for c in df.columns if c != "context"]
try:
df[cols].to_csv(csv_path, index=True, index_label="timestep")
except Exception as e:
continue
manifest["rollouts"].append(
{
"rollout_id": rid_str,
"csv": csv_path,
"num_rows": int(df.shape[0]),
"columns": cols,
}
)
manifest_path = os.path.join(
path, f"tokenwise_manifest_{now:%Y-%m-%d___%H-%M-%S}.json"
)
with open(manifest_path, "w") as fp:
json.dump(manifest, fp, indent=2)
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