Modify ActivationBalancer for speed

.github/scripts/run-pre-trained-conformer-ctc.sh

@@ -12,7 +12,6 @@ cd egs/librispeech/ASR
 
 repo_url=https://github.com/csukuangfj/icefall-asr-conformer-ctc-bpe-500
 git lfs install
-git clone $repo
 
 log "Downloading pre-trained model from $repo_url"
 git clone $repo_url

egs/librispeech/ASR/pruned_transducer_stateless2/conformer.py

@@ -932,7 +932,7 @@ def forward(
         value: Tensor,
         pos_emb: Tensor,
         key_padding_mask: Optional[Tensor] = None,
-        need_weights: bool = True,
+        need_weights: bool = False,
         attn_mask: Optional[Tensor] = None,
         left_context: int = 0,
     ) -> Tuple[Tensor, Optional[Tensor]]:
@@ -1059,7 +1059,7 @@ def multi_head_attention_forward(
         out_proj_bias: Tensor,
         training: bool = True,
         key_padding_mask: Optional[Tensor] = None,
-        need_weights: bool = True,
+        need_weights: bool = False,
         attn_mask: Optional[Tensor] = None,
         left_context: int = 0,
     ) -> Tuple[Tensor, Optional[Tensor]]:

egs/librispeech/ASR/pruned_transducer_stateless2/scaling.py

@@ -16,6 +16,7 @@
 
 
 import collections
+import random
 from itertools import repeat
 from typing import Optional, Tuple
 
@@ -636,6 +637,7 @@ class ActivationBalancer(torch.nn.Module):
            max_abs:  the maximum average-absolute-value per channel, which
                we allow, before we start to modify the derivatives to prevent
                this.
+           balance_prob: the probability to apply the ActivationBalancer.
     """
 
     def __init__(
@@ -646,6 +648,7 @@ def __init__(
         max_factor: float = 0.01,
         min_abs: float = 0.2,
         max_abs: float = 100.0,
+        balance_prob: float = 0.25,
     ):
         super(ActivationBalancer, self).__init__()
         self.channel_dim = channel_dim
@@ -654,17 +657,19 @@ def __init__(
         self.max_factor = max_factor
         self.min_abs = min_abs
         self.max_abs = max_abs
+        assert 0 < balance_prob <= 1, balance_prob
+        self.balance_prob = balance_prob
 
     def forward(self, x: Tensor) -> Tensor:
-        if torch.jit.is_scripting() or is_jit_tracing():
+        if random.random() >= self.balance_prob:
             return x
         else:
             return ActivationBalancerFunction.apply(
                 x,
                 self.channel_dim,
                 self.min_positive,
                 self.max_positive,
-                self.max_factor,
+                self.max_factor / self.balance_prob,
                 self.min_abs,
                 self.max_abs,
             )

egs/librispeech/ASR/pruned_transducer_stateless3/scaling_converter.py

@@ -30,6 +30,7 @@
 import torch
 import torch.nn as nn
 from scaling import (
+    ActivationBalancer,
     BasicNorm,
     ScaledConv1d,
     ScaledConv2d,
@@ -294,6 +295,8 @@ def convert_scaled_to_non_scaled(model: nn.Module, inplace: bool = False):
             d[name] = convert_basic_norm(m)
         elif isinstance(m, ScaledLSTM):
             d[name] = scaled_lstm_to_lstm(m)
+        elif isinstance(m, ActivationBalancer):
+            d[name] = nn.Identity()
 
     for k, v in d.items():
         if "." in k: