fix style

egs/librispeech/ASR/tdnn_lstm_ctc/asr_datamodule.py

@@ -484,13 +484,9 @@ class LibriSpeechAsrDataModule:
     @lru_cache()
     def gigaspeech_dev_cuts(self) -> CutSet:
         logging.info("About to get Gigaspeech dev cuts")
-        return load_manifest_lazy(
+        return load_manifest_lazy(self.args.manifest_dir / "cuts_DEV.jsonl.gz")
-            self.args.manifest_dir / "cuts_DEV.jsonl.gz"
-        )
 
     @lru_cache()
     def gigaspeech_test_cuts(self) -> CutSet:
         logging.info("About to get Gigaspeech test cuts")
-        return load_manifest_lazy(
+        return load_manifest_lazy(self.args.manifest_dir / "cuts_TEST.jsonl.gz")
-            self.args.manifest_dir / "cuts_TEST.jsonl.gz"
-        )

egs/librispeech/ASR/zipformer_lora/decode_gigaspeech.py

@@ -121,7 +121,7 @@ from beam_search import (
     modified_beam_search_lm_shallow_fusion,
     modified_beam_search_LODR,
 )
-from finetune import add_model_arguments, add_finetune_arguments, get_model, get_params
+from finetune import add_finetune_arguments, add_model_arguments, get_model, get_params
 
 from icefall import ContextGraph, LmScorer, NgramLm
 from icefall.checkpoint import (

egs/librispeech/ASR/zipformer_lora/export.py

@@ -165,9 +165,9 @@ from typing import List, Tuple
 
 import k2
 import torch
+from finetune import add_finetune_arguments, add_model_arguments, get_model, get_params
 from scaling_converter import convert_scaled_to_non_scaled
 from torch import Tensor, nn
-from finetune import add_model_arguments, add_finetune_arguments, get_model, get_params
 
 from icefall.checkpoint import (
     average_checkpoints,

egs/librispeech/ASR/zipformer_lora/finetune.py

@@ -147,17 +147,11 @@ def add_finetune_arguments(parser: argparse.ArgumentParser):
     )
 
     parser.add_argument(
-        "--use-lora",
+        "--use-lora", type=str2bool, default=True, help="If use LoRA for fine-tune"
-        type=str2bool,
-        default=True,
-        help="If use LoRA for fine-tune"
     )
 
     parser.add_argument(
-        "--lora-r",
+        "--lora-r", type=int, default=0, help="The bottleneck dimension of LoRA"
-        type=int,
-        default=0,
-        help="The bottleneck dimension of LoRA"
     )
 
     parser.add_argument(
@@ -1287,7 +1281,11 @@ def run(rank, world_size, args):
         else:
             p.requires_grad = False
 
-    logging.info("A total of {} trainable parameters ({:.3f}% of the whole model)".format(num_trainable, num_trainable/num_param * 100))
+    logging.info(
+        "A total of {} trainable parameters ({:.3f}% of the whole model)".format(
+            num_trainable, num_trainable / num_param * 100
+        )
+    )
 
     model.to(device)
     if world_size > 1:

egs/librispeech/ASR/zipformer_lora/scaling.py

@@ -15,16 +15,17 @@
 # limitations under the License.
 
 
-from typing import Optional, Tuple, Union
 import logging
-import k2
-from torch.cuda.amp import custom_fwd, custom_bwd
-import random
-import torch
 import math
+import random
+from typing import Optional, Tuple, Union
+
+import k2
+import torch
 import torch.nn as nn
 import torch.nn.functional as F
 from torch import Tensor
+from torch.cuda.amp import custom_bwd, custom_fwd
 
 
 def logaddexp_onnx(x: Tensor, y: Tensor) -> Tensor:
@@ -518,6 +519,7 @@ def ScaledLinear(*args, initial_scale: float = 1.0, **kwargs) -> nn.Linear:
             torch.nn.init.uniform_(ans.bias, -0.1 * initial_scale, 0.1 * initial_scale)
     return ans
 
+
 class LoRALayer:
     def __init__(
         self,
@@ -529,7 +531,7 @@ class LoRALayer:
         self.r = r
         self.lora_alpha = lora_alpha
         # Optional dropout
-        if lora_dropout > 0.:
+        if lora_dropout > 0.0:
             self.lora_dropout = nn.Dropout(p=lora_dropout)
         else:
             self.lora_dropout = lambda x: x
@@ -537,6 +539,7 @@ class LoRALayer:
         self.merged = False
         self.merge_weights = merge_weights
 
+
 class ScaledLinear_lora(nn.Linear, LoRALayer):
     def __init__(
         self,
@@ -551,8 +554,13 @@ class ScaledLinear_lora(nn.Linear, LoRALayer):
         **kwargs,
     ):
         nn.Linear.__init__(self, in_features, out_features, **kwargs)
-        LoRALayer.__init__(self, r=r, lora_alpha=lora_alpha, lora_dropout=lora_dropout,
+        LoRALayer.__init__(
-                           merge_weights=merge_weights)
+            self,
+            r=r,
+            lora_alpha=lora_alpha,
+            lora_dropout=lora_dropout,
+            merge_weights=merge_weights,
+        )
 
         self.initial_scale = initial_scale
         self.fan_in_fan_out = fan_in_fan_out
@@ -572,8 +580,10 @@ class ScaledLinear_lora(nn.Linear, LoRALayer):
             with torch.no_grad():
                 self.weight[:] *= initial_scale
                 if self.bias is not None:
-                    nn.init.uniform_(self.bias, -0.1 * initial_scale, 0.1 * initial_scale)
+                    nn.init.uniform_(
-                if hasattr(self, 'lora_A'):
+                        self.bias, -0.1 * initial_scale, 0.1 * initial_scale
+                    )
+                if hasattr(self, "lora_A"):
                     # initialize B the same way as the default for nn.Linear and A to zero
                     # this is different than what is described in the paper but should not affect performance
                     nn.init.kaiming_uniform_(self.lora_A, a=math.sqrt(5))
@@ -582,6 +592,7 @@ class ScaledLinear_lora(nn.Linear, LoRALayer):
     def train(self, mode: bool = True):
         def T(w):
             return w.transpose(0, 1) if self.fan_in_fan_out else w
+
         nn.Linear.train(self, mode)
         if mode:
             # We don't want the weights to be merged in training mode
@@ -600,13 +611,19 @@ class ScaledLinear_lora(nn.Linear, LoRALayer):
     def forward(self, x: torch.Tensor):
         def T(w):
             return w.transpose(0, 1) if self.fan_in_fan_out else w
+
         if self.r > 0 and not self.merged:
             result = F.linear(x, T(self.weight), bias=self.bias)
-            delta_result = self.lora_dropout(x) @ self.lora_A.transpose(0, 1) @ self.lora_B.transpose(0, 1)
+            delta_result = (
+                self.lora_dropout(x)
+                @ self.lora_A.transpose(0, 1)
+                @ self.lora_B.transpose(0, 1)
+            )
             return result + delta_result * self.scaling
         else:
             return F.linear(x, T(self.weight), bias=self.bias)
 
+
 def ScaledConv1d(*args, initial_scale: float = 1.0, **kwargs) -> nn.Conv1d:
     """
     Behaves like a constructor of a modified version of nn.Conv1d
@@ -1740,6 +1757,7 @@ class ActivationDropoutAndLinear(torch.nn.Module):
             self.dropout_shared_dim,
         )
 
+
 class ActivationDropoutAndLinear_lora(torch.nn.Module):
     def __init__(
         self,

egs/librispeech/ASR/zipformer_lora/zipformer.py

@@ -30,7 +30,6 @@ from scaling import (
 )
 from scaling import (
     ScaledLinear,  # not as in other dirs.. just scales down initial parameter values.
-    ScaledLinear_lora
 )
 from scaling import (
     ActivationDropoutAndLinear,
@@ -40,6 +39,7 @@ from scaling import (
     ChunkCausalDepthwiseConv1d,
     Dropout2,
     FloatLike,
+    ScaledLinear_lora,
     ScheduledFloat,
     Whiten,
     convert_num_channels,
@@ -1566,7 +1566,7 @@ class RelPositionMultiheadAttentionWeights(nn.Module):
         pos_emb_skip_rate: FloatLike = ScheduledFloat((0.0, 0.5), (4000.0, 0.0)),
         lora_r: int = 0,
         lora_alpha: int = 4,
-        lora_dropout: float=0.0
+        lora_dropout: float = 0.0,
     ) -> None:
         super().__init__()
         self.embed_dim = embed_dim
@@ -1935,7 +1935,7 @@ class SelfAttention(nn.Module):
         value_head_dim: int,
         lora_r: int = 0,
         lora_alpha: int = 4,
-        lora_dropout: float=0.0
+        lora_dropout: float = 0.0,
     ) -> None:
         super().__init__()
         self.in_proj = ScaledLinear_lora(
@@ -2064,7 +2064,7 @@ class FeedforwardModule(nn.Module):
         dropout: FloatLike,
         lora_r: int = 0,
         lora_alpha: int = 4,
-        lora_dropout: float=0.0
+        lora_dropout: float = 0.0,
     ):
         super(FeedforwardModule, self).__init__()
         self.in_proj = ScaledLinear_lora(