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cacfe76 | 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 | """Processor and collator for the Lapras Reasoning Datasets, in the style of a VLM data pipeline."""
import torch
class TimeSeriesProcessor:
"""Tokenizes text and packs time series into tensors, as a VLM processor does for images."""
def __init__(self, tokenizer):
self.tokenizer = tokenizer
self.tokenizer.padding_side = "right"
def apply_chat_template(self, prompt, response=None):
messages = [{"role": "user", "content": prompt}]
text = self.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
return text if response is None else text + response + self.tokenizer.eos_token
def __call__(self, text, timeseries):
batch = self.tokenizer(text, padding=True, add_special_tokens=False, return_tensors="pt")
n_ch = max(len(x) for x in timeseries)
n_t = max(len(ch) for x in timeseries for ch in x)
values = torch.zeros(len(timeseries), n_ch, n_t)
mask = torch.zeros(len(timeseries), n_ch, n_t, dtype=torch.bool)
for i, x in enumerate(timeseries):
for c, ch in enumerate(x):
values[i, c, : len(ch)] = torch.tensor(ch)
mask[i, c, : len(ch)] = True
batch["timeseries"] = values
batch["timeseries_mask"] = mask
return batch
class TimeSeriesCollator:
"""Builds a training batch in which only the response is supervised (labels are -100 elsewhere)."""
def __init__(self, processor):
self.processor = processor
def __call__(self, examples):
prompts = [self.processor.apply_chat_template(ex["input"]) for ex in examples]
texts = [self.processor.apply_chat_template(ex["input"], ex["output"]) for ex in examples]
batch = self.processor(texts, [ex["timeseries"] for ex in examples])
labels = batch["input_ids"].clone()
labels[batch["attention_mask"] == 0] = -100
for i, prompt in enumerate(prompts):
labels[i, : len(self.processor.tokenizer(prompt, add_special_tokens=False)["input_ids"])] = -100
batch["labels"] = labels
return batch
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