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Use FP32 for log probabilities #19

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Merged
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Apr 1, 2023
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Use FP32 for log probabilities #19

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3 changes: 2 additions & 1 deletion cacheflow/models/sample.py
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Original file line number Diff line number Diff line change
Expand Up @@ -36,10 +36,11 @@ def forward(
# Use in-place division to avoid creating a new tensor.
logits.div_(t.unsqueeze(dim=1))

# We use float32 for probabilities and log probabilities.
# Compute the probabilities.
probs = torch.softmax(logits, dim=-1, dtype=torch.float)
# Compute the log probabilities (before applying top-p).
logprobs = torch.log(probs, out=logits)
logprobs = torch.log(probs)

# Apply top-p truncation.
top_ps = _get_top_ps(input_metadata)
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