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feat(engine.py): Sample unique tokens per row in generation stream

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Before, when initiating a batch generation, the first sampled token was broadcasted to all rows. This change updates the logic to ensure that a unique token is sampled for each row, improving the diversity and independence of generated sequences within a batch.
This commit is contained in:
Azekowka 2025-10-14 17:33:47 +05:00
parent 9a08bb4edb
commit 557b2d5840
1 changed files with 2 additions and 2 deletions
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nanochat/engine.py
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@ -185,7 +185,7 @@ class Engine:
seq_len=len(tokens),
**kv_model_kwargs,
)
ids = torch.tensor([tokens], dtype=torch.long, device=device)
ids = torch.tensor([tokens.copy() for _ in range(num_samples)], dtype=torch.long, device=device)
logits = self.model.forward(ids, kv_cache=kv_cache_prefill)
logits = logits[:, -1, :]
next_ids = sample_next_token(logits, rng, temperature, top_k) # (B, 1)
@ -218,7 +218,7 @@ class Engine:
# Get sampled tokens - either from prefill or from forward pass
if first_iteration:
# Use the tokens we already sampled from prefill
sampled_tokens = [sampled_tokens[0]] * num_samples # Broadcast first token to all rows
# sampled_tokens = [sampled_tokens[0]] * num_samples # Broadcast first token to all rows
# TODO: we should sample a token for each row instead of broadcasting
first_iteration = False
else: