WIP: Support torchscript.

egs/librispeech/ASR/conformer_ctc/export.py

@@ -0,0 +1,161 @@
+#!/usr/bin/env python3
+#
+# Copyright 2021 Xiaomi Corporation (Author: Fangjun Kuang)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# This script converts several saved checkpoints
+# to one using model averaging.
+
+import argparse
+import logging
+from pathlib import Path
+
+import torch
+from conformer import Conformer
+
+from icefall.checkpoint import average_checkpoints, load_checkpoint
+from icefall.utils import str2bool, AttributeDict
+from icefall.lexicon import Lexicon
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--epoch",
+        type=int,
+        default=34,
+        help="It specifies the checkpoint to use for decoding."
+        "Note: Epoch counts from 0.",
+    )
+
+    parser.add_argument(
+        "--avg",
+        type=int,
+        default=20,
+        help="Number of checkpoints to average. Automatically select "
+        "consecutive checkpoints before the checkpoint specified by "
+        "'--epoch'. ",
+    )
+
+    parser.add_argument(
+        "--exp-dir",
+        type=str,
+        default="conformer_ctc/exp",
+        help="""It specifies the directory where all training related
+        files, e.g., checkpoints, log, etc, are saved
+        """,
+    )
+
+    parser.add_argument(
+        "--lang-dir",
+        type=str,
+        default="data/lang_bpe",
+        help="""It contains language related input files such as "lexicon.txt"
+        """,
+    )
+
+    parser.add_argument(
+        "--jit",
+        type=str2bool,
+        default=True,
+        help="""True to save a model after using torch.jit.script.
+        """,
+    )
+
+    return parser
+
+
+def get_params() -> AttributeDict:
+    params = AttributeDict(
+        {
+            "a": 1,
+            "b": 10,
+            "feature_dim": 80,
+            "subsampling_factor": 4,
+            "use_feat_batchnorm": True,
+            "attention_dim": 512,
+            "nhead": 8,
+            "num_decoder_layers": 6,
+        }
+    )
+    return params
+
+
+def main():
+    args = get_parser().parse_args()
+    args.exp_dir = Path(args.exp_dir)
+    args.lang_dir = Path(args.lang_dir)
+
+    params = get_params()
+    params.update(vars(args))
+
+    logging.info(params)
+
+    lexicon = Lexicon(params.lang_dir)
+    max_token_id = max(lexicon.tokens)
+    num_classes = max_token_id + 1  # +1 for the blank
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", 0)
+
+    logging.info(f"device: {device}")
+
+    model = Conformer(
+        num_features=params.feature_dim,
+        nhead=params.nhead,
+        d_model=params.attention_dim,
+        num_classes=num_classes,
+        subsampling_factor=params.subsampling_factor,
+        num_decoder_layers=params.num_decoder_layers,
+        vgg_frontend=False,
+        use_feat_batchnorm=params.use_feat_batchnorm,
+    )
+
+    if params.avg == 1:
+        load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
+    else:
+        start = params.epoch - params.avg + 1
+        filenames = []
+        for i in range(start, params.epoch + 1):
+            if start >= 0:
+                filenames.append(f"{params.exp_dir}/epoch-{i}.pt")
+        logging.info(f"averaging {filenames}")
+        model.load_state_dict(average_checkpoints(filenames))
+
+    model.to("cpu")
+
+    if params.jit:
+        logging.info("Using torch.jit.script")
+        model = torch.jit.script(model)
+        model.save(f"{params.exp_dir}/cpu_jit.pt")
+    else:
+        logging.info("Not using torch.jit.script")
+        torch.save(
+            {"model": model.state_dict()}, f"{params.exp_dir}/pretrained.pt"
+        )
+
+
+if __name__ == "__main__":
+    formatter = (
+        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    )
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+    main()

egs/librispeech/ASR/conformer_ctc/transformer.py

@@ -236,6 +236,7 @@ class Transformer(nn.Module):
         x = nn.functional.log_softmax(x, dim=-1)  # (N, T, C)
         return x
 
+    @torch.jit.export
     def decoder_forward(
         self,
         memory: torch.Tensor,
@@ -264,11 +265,11 @@ class Transformer(nn.Module):
         """
         ys_in = add_sos(token_ids, sos_id=sos_id)
         ys_in = [torch.tensor(y) for y in ys_in]
-        ys_in_pad = pad_sequence(ys_in, batch_first=True, padding_value=eos_id)
+        ys_in_pad = pad_sequence(ys_in, batch_first=True, padding_value=float(eos_id))
 
         ys_out = add_eos(token_ids, eos_id=eos_id)
         ys_out = [torch.tensor(y) for y in ys_out]
-        ys_out_pad = pad_sequence(ys_out, batch_first=True, padding_value=-1)
+        ys_out_pad = pad_sequence(ys_out, batch_first=True, padding_value=float(-1))
 
         device = memory.device
         ys_in_pad = ys_in_pad.to(device)
@@ -301,6 +302,7 @@ class Transformer(nn.Module):
 
         return decoder_loss
 
+    @torch.jit.export
     def decoder_nll(
         self,
         memory: torch.Tensor,
@@ -331,11 +333,11 @@ class Transformer(nn.Module):
 
         ys_in = add_sos(token_ids, sos_id=sos_id)
         ys_in = [torch.tensor(y) for y in ys_in]
-        ys_in_pad = pad_sequence(ys_in, batch_first=True, padding_value=eos_id)
+        ys_in_pad = pad_sequence(ys_in, batch_first=True, padding_value=float(eos_id))
 
         ys_out = add_eos(token_ids, eos_id=eos_id)
         ys_out = [torch.tensor(y) for y in ys_out]
-        ys_out_pad = pad_sequence(ys_out, batch_first=True, padding_value=-1)
+        ys_out_pad = pad_sequence(ys_out, batch_first=True, padding_value=float(-1))
 
         device = memory.device
         ys_in_pad = ys_in_pad.to(device, dtype=torch.int64)
@@ -649,7 +651,8 @@ class PositionalEncoding(nn.Module):
         self.d_model = d_model
         self.xscale = math.sqrt(self.d_model)
         self.dropout = nn.Dropout(p=dropout)
-        self.pe = None
+        # not doing: self.pe = None because of errors thrown by torchscript
+        self.pe = torch.zeros(0, self.d_model, dtype=torch.float32)
 
     def extend_pe(self, x: torch.Tensor) -> None:
         """Extend the time t in the positional encoding if required.
@@ -666,8 +669,7 @@ class PositionalEncoding(nn.Module):
         """
         if self.pe is not None:
             if self.pe.size(1) >= x.size(1):
-                if self.pe.dtype != x.dtype or self.pe.device != x.device:
+                self.pe = self.pe.to(dtype=x.dtype, device=x.device)
-                    self.pe = self.pe.to(dtype=x.dtype, device=x.device)
                 return
         pe = torch.zeros(x.size(1), self.d_model, dtype=torch.float32)
         position = torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1)
@@ -972,10 +974,7 @@ def add_sos(token_ids: List[List[int]], sos_id: int) -> List[List[int]]:
       Return a new list-of-list, where each sublist starts
       with SOS ID.
     """
-    ans = []
+    return [[sos_id] + utt for utt in token_ids]
-    for utt in token_ids:
-        ans.append([sos_id] + utt)
-    return ans
 
 
 def add_eos(token_ids: List[List[int]], eos_id: int) -> List[List[int]]:
@@ -992,7 +991,4 @@ def add_eos(token_ids: List[List[int]], eos_id: int) -> List[List[int]]:
       Return a new list-of-list, where each sublist ends
       with EOS ID.
     """
-    ans = []
+    return [utt + [eos_id] for utt in token_ids]
-    for utt in token_ids:
-        ans.append(utt + [eos_id])
-    return ans