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@ -36,7 +36,7 @@ def eval_with_progress(self, tokens: Sequence[int]):
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progress_bar = range(0, len(tokens), self.n_batch)
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for i in progress_bar:
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batch = tokens[i : min(len(tokens), i + self.n_batch)]
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batch = tokens[i: min(len(tokens), i + self.n_batch)]
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n_past = self.n_tokens
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n_tokens = len(batch)
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self._batch.set_batch(
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@ -44,16 +44,16 @@ def eval_with_progress(self, tokens: Sequence[int]):
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)
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self._ctx.decode(self._batch)
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# Save tokens
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self.input_ids[n_past : n_past + n_tokens] = batch
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self.input_ids[n_past: n_past + n_tokens] = batch
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# Save logits
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rows = n_tokens
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cols = self._n_vocab
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offset = (
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0 if self.context_params.logits_all else n_tokens - 1
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) # NOTE: Only save the last token logits if logits_all is False
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self.scores[n_past + offset : n_past + n_tokens, :].reshape(-1)[
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self.scores[n_past + offset: n_past + n_tokens, :].reshape(-1)[
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:
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] = self._ctx.get_logits()[offset * cols : rows * cols]
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] = self._ctx.get_logits()[offset * cols: rows * cols]
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# Update n_tokens
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self.n_tokens += n_tokens
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@ -125,7 +125,7 @@ def random_preset(state):
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for cat in params_and_values:
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choices = list(params_and_values[cat].keys())
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if shared.args.loader is not None:
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choices = [x for x in choices if loader_contains(sampler)]
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choices = [x for x in choices if loader_contains(x)]
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if len(choices) > 0:
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choice = random.choice(choices)
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