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Cosmetic improvements

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This commit is contained in:
Daniel Povey 2022-10-12 19:34:48 +08:00
parent 12323025d7
commit b736bb4840
2 changed files with 3 additions and 3 deletions
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2
egs/librispeech/ASR/pruned_transducer_stateless7/conformer.py
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@ -487,7 +487,7 @@ class ConformerEncoder(nn.Module):
if len(ans) == num_to_drop: if len(ans) == num_to_drop:
break break
if shared_rng.random() < 0.005 or __name__ == "__main__": if shared_rng.random() < 0.005 or __name__ == "__main__":
logging.info(f"warmup_begin={warmup_begin}, warmup_end={warmup_end}, warmup_count={warmup_count:.1f}, num_to_drop={num_to_drop}, layers_to_drop={ans}") logging.info(f"warmup_begin={warmup_begin:.1f}, warmup_end={warmup_end:.1f}, warmup_count={warmup_count:.1f}, num_to_drop={num_to_drop}, layers_to_drop={ans}")
return ans return ans

4
egs/librispeech/ASR/pruned_transducer_stateless7/scaling.py
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@ -444,8 +444,8 @@ class MaxEig(torch.nn.Module):
with torch.cuda.amp.autocast(enabled=False): with torch.cuda.amp.autocast(enabled=False):
eps = 1.0e-20 eps = 1.0e-20
assert x.dtype != torch.float16
orig_x = x orig_x = x
x = x.to(torch.float32)
with torch.no_grad(): with torch.no_grad():
x = x.transpose(self.channel_dim, -1).reshape(-1, self.num_channels) x = x.transpose(self.channel_dim, -1).reshape(-1, self.num_channels)
x = x - x.mean(dim=0) x = x - x.mean(dim=0)
@ -461,7 +461,7 @@ class MaxEig(torch.nn.Module):
# ensure new direction is nonzero even if x == 0, by including `direction`. # ensure new direction is nonzero even if x == 0, by including `direction`.
self._set_direction(0.1 * self.max_eig_direction + new_direction) self._set_direction(0.1 * self.max_eig_direction + new_direction)
if random.random() < 0.0005 or __name__ == "__main__": if random.random() < 0.01 or __name__ == "__main__":
logging.info(f"variance_proportion = {variance_proportion.item()}, shape={tuple(orig_x.shape)}, cur_prob={self.cur_prob}") logging.info(f"variance_proportion = {variance_proportion.item()}, shape={tuple(orig_x.shape)}, cur_prob={self.cur_prob}")
if variance_proportion >= self.max_var_per_eig: if variance_proportion >= self.max_var_per_eig: