Revert merging three onnx models into a single one.

It's quite time consuming to extract a sub-graph from the combined
model. For instance, it takes more than one hour to extract
the encoder model.

egs/librispeech/ASR/pruned_transducer_stateless3/export.py

@@ -34,7 +34,7 @@ It will generate a file `cpu_jit.pt` in the given `exp_dir`. You can later
 load it by `torch.jit.load("cpu_jit.pt")`.
 
 Note `cpu` in the name `cpu_jit.pt` means the parameters when loaded into Python
-are on CPU. You can use `to("cuda")` to move it to a CUDA device.
+are on CPU. You can use `to("cuda")` to move them to a CUDA device.
 
 (2) Export to ONNX format
 
@@ -51,10 +51,7 @@ See `onnx_check.py` to see how to use it.
     - encoder.onnx
     - decoder.onnx
     - joiner.onnx
-    - all_in_one.onnx
 
-The file all_in_one.onnx combines `encoder.onnx`, `decoder.onnx`, and
-`joiner.onnx`. You can use `onnx.utils.Extractor` to extract them.
 
 (3) Export `model.state_dict()`
 
@@ -427,25 +424,6 @@ def main():
             joiner_filename,
             opset_version=opset_version,
         )
-
-        all_in_one_filename = params.exp_dir / "all_in_one.onnx"
-        encoder_onnx = onnx.load(encoder_filename)
-        decoder_onnx = onnx.load(decoder_filename)
-        joiner_onnx = onnx.load(joiner_filename)
-
-        encoder_onnx = onnx.compose.add_prefix(encoder_onnx, prefix="encoder/")
-        decoder_onnx = onnx.compose.add_prefix(decoder_onnx, prefix="decoder/")
-        joiner_onnx = onnx.compose.add_prefix(joiner_onnx, prefix="joiner/")
-
-        combined_model = onnx.compose.merge_models(
-            encoder_onnx, decoder_onnx, io_map={}
-        )
-        combined_model = onnx.compose.merge_models(
-            combined_model, joiner_onnx, io_map={}
-        )
-        onnx.save(combined_model, all_in_one_filename)
-        logging.info(f"Saved to {all_in_one_filename}")
-
     elif params.jit is True:
         # We won't use the forward() method of the model in C++, so just ignore
         # it here.

egs/librispeech/ASR/pruned_transducer_stateless3/onnx_check.py

@@ -17,18 +17,18 @@
 # limitations under the License.
 
 """
-This script checks that exported onnx models produces the same output
-as the given torchscript model for the same input.
+This script checks that exported onnx models produce the same output
+with the given torchscript model for the same input.
 """
 
 import argparse
 import logging
+
 import onnxruntime as ort
 
 ort.set_default_logger_severity(3)
 
 import numpy as np
-
 import torch
 
 
@@ -85,7 +85,10 @@ def test_encoder(
             x_lens = torch.randint(low=10, high=T + 1, size=(N,))
             x_lens[0] = T
 
-            encoder_inputs = {"x": x.numpy(), "x_lens": x_lens.numpy()}
+            encoder_inputs = {
+                "x": x.numpy(),
+                "x_lens": x_lens.numpy(),
+            }
             encoder_out, encoder_out_lens = encoder_session.run(
                 ["encoder_out", "encoder_out_lens"],
                 encoder_inputs,
@@ -141,7 +144,7 @@ def test_joiner(
             "encoder_out": encoder_out.numpy(),
             "decoder_out": decoder_out.numpy(),
         }
-        decoder_out = joiner_session.run(["logit"], joiner_inputs)[0]
+        joiner_out = joiner_session.run(["logit"], joiner_inputs)[0]
         joiner_out = torch.from_numpy(joiner_out)
 
         torch_joiner_out = model.joiner(