Update CI to test ONNX models.

.github/scripts/run-librispeech-pruned-transducer-stateless3-2022-05-13.sh

@@ -22,8 +22,35 @@ ls -lh $repo/test_wavs/*.wav
 
 pushd $repo/exp
 ln -s pretrained-iter-1224000-avg-14.pt pretrained.pt
+ln -s pretrained-iter-1224000-avg-14.pt epoch-99.pt
 popd
 
+log "Test exporting to ONNX format"
+
+./pruned_transducer_stateless3/export.py \
+  --exp-dir $repo/exp \
+  --bpe-model $repo/data/lang_bpe_500/bpe.model \
+  --epoch 99 \
+  --avg 1 \
+  --onnx 1
+
+log "Export to torchscript model"
+./pruned_transducer_stateless3/export.py \
+  --exp-dir $repo/exp \
+  --bpe-model $repo/data/lang_bpe_500/bpe.model \
+  --epoch 99 \
+  --avg 1 \
+  --jit 1
+
+ls -lh $repo/exp/*.onnx
+ls -lh $repo/exp/*.pt
+
+./pruned_transducer_stateless3/onnx_check.py \
+  --jit-filename $repo/exp/cpu_jit.pt \
+  --onnx-encoder-filename $repo/exp/encoder.onnx \
+  --onnx-decoder-filename $repo/exp/decoder.onnx \
+  --onnx-joiner-filename $repo/exp/joiner.onnx
+
 for sym in 1 2 3; do
   log "Greedy search with --max-sym-per-frame $sym"
 

.github/workflows/run-librispeech-pruned-transducer-stateless3-2022-05-13.yml

@@ -35,7 +35,7 @@ on:
 
 jobs:
   run_librispeech_pruned_transducer_stateless3_2022_05_13:
-    if: github.event.label.name == 'ready' || github.event.label.name == 'run-decode' || github.event_name == 'push' || github.event_name == 'schedule'
+    if: github.event.label.name == 'onnx' || github.event.label.name == 'ready' || github.event.label.name == 'run-decode' || github.event_name == 'push' || github.event_name == 'schedule'
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:

egs/librispeech/ASR/pruned_transducer_stateless3/export.py

@@ -45,8 +45,8 @@ are on CPU. You can use `to("cuda")` to move them to a CUDA device.
   --avg 10 \
   --onnx 1
 
-It will generate the following files in the given `exp_dir`.
-See `onnx_check.py` to see how to use it.
+It will generate the following three files in the given `exp_dir`.
+Check `onnx_check.py` for how to use them.
 
     - encoder.onnx
     - decoder.onnx
@@ -82,7 +82,6 @@ you can do:
 
 import argparse
 import logging
-import onnx
 from pathlib import Path
 
 import sentencepiece as spm
@@ -219,7 +218,7 @@ def export_encoder_model_onnx(
     x = torch.zeros(1, 100, 80, dtype=torch.float32)
     x_lens = torch.tensor([100], dtype=torch.int64)
 
-    #  encoder_model = torch.jit.script(model.encoder)
+    #  encoder_model = torch.jit.script(encoder_model)
     # It throws the following error for the above statement
     #
     # RuntimeError: Exporting the operator __is_ to ONNX opset version
@@ -257,7 +256,7 @@ def export_decoder_model_onnx(
 
     The exported model has one input:
 
-        - y: a torch.int64 tensor of shape (N, 2)
+        - y: a torch.int64 tensor of shape (N, decoder_model.context_size)
 
     and has one output:
 
@@ -316,7 +315,7 @@ def export_joiner_model_onnx(
     decoder_out = torch.rand(1, decoder_out_dim, dtype=torch.float32)
 
     project_input = True
-    # Note: We use torch.jit.trace() here
+    # Note: It uses torch.jit.trace() internally
     torch.onnx.export(
         joiner_model,
         (encoder_out, decoder_out, project_input),

egs/librispeech/ASR/pruned_transducer_stateless3/onnx_check.py

@@ -25,12 +25,10 @@ import argparse
 import logging
 
 import onnxruntime as ort
+import torch
 
 ort.set_default_logger_severity(3)
 
-import numpy as np
-import torch
-
 
 def get_parser():
     parser = argparse.ArgumentParser(
@@ -188,6 +186,7 @@ def main():
         sess_options=options,
     )
     test_joiner(model, joiner_session)
+    logging.info("Finished checking ONNX models")
 
 
 if __name__ == "__main__":

requirements-ci.txt

@@ -20,3 +20,6 @@ sentencepiece==0.1.96
 tensorboard==2.8.0
 typeguard==2.13.3
 multi_quantization
+
+onnx
+onnxruntime

requirements.txt

@@ -4,3 +4,5 @@ sentencepiece>=0.1.96
 tensorboard
 typeguard
 multi_quantization
+onnx
+onnxruntime