use docker

.github/scripts/docker/generate_build_matrix.py

@@ -10,7 +10,17 @@ def get_args():
     parser = argparse.ArgumentParser()
     parser.add_argument(
         "--min-torch-version",
-        help="Minimu torch version",
+        help="torch version",
+    )
+
+    parser.add_argument(
+        "--torch-version",
+        help="torch version",
+    )
+
+    parser.add_argument(
+        "--python-version",
+        help="python version",
     )
     return parser.parse_args()
 
@@ -52,7 +62,7 @@ def get_torchaudio_version(torch_version):
         return torch_version
 
 
-def get_matrix(min_torch_version):
+def get_matrix(min_torch_version, specified_torch_version, specified_python_version):
     k2_version = "1.24.4.dev20241029"
     kaldifeat_version = "1.25.5.dev20241029"
     version = "20241218"
@@ -71,6 +81,12 @@ def get_matrix(min_torch_version):
     torch_version += ["2.5.0"]
     torch_version += ["2.5.1"]
 
+    if specified_torch_version:
+        torch_version = [specified_torch_version]
+
+    if specified_python_version:
+        python_version = [specified_python_version]
+
     matrix = []
     for p in python_version:
         for t in torch_version:
@@ -115,7 +131,11 @@ def get_matrix(min_torch_version):
 
 def main():
     args = get_args()
-    matrix = get_matrix(min_torch_version=args.min_torch_version)
+    matrix = get_matrix(
+        min_torch_version=args.min_torch_version,
+        specified_torch_version=args.torch_version,
+        specified_python_version=args.python_version,
+    )
     print(json.dumps({"include": matrix}))
 
 

.github/scripts/librispeech/ASR/run_rknn.sh

@@ -0,0 +1,69 @@
+#!/usr/bin/env bash
+
+set -ex
+
+log() {
+  # This function is from espnet
+  local fname=${BASH_SOURCE[1]##*/}
+  echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
+}
+
+cd egs/librispeech/ASR
+
+
+# https://huggingface.co/csukuangfj/k2fsa-zipformer-chinese-english-mixed
+function export_bilingual_zh_en() {
+  d=exp_zh_en
+
+  mkdir $d
+  pushd $d
+
+  curl -SL -O https://huggingface.co/csukuangfj/k2fsa-zipformer-chinese-english-mixed/resolve/main/exp/pretrained.pt
+  mv pretrained.pt epoch-99.pt
+
+  curl -SL -O https://huggingface.co/csukuangfj/k2fsa-zipformer-chinese-english-mixed/resolve/main/data/lang_char_bpe/tokens.txt
+  curl -SL -O https://huggingface.co/csukuangfj/k2fsa-zipformer-chinese-english-mixed/resolve/main/data/lang_char_bpe/bpe.model
+
+  curl -SL -O https://huggingface.co/csukuangfj/k2fsa-zipformer-chinese-english-mixed/resolve/main/test_wavs/0.wav
+  curl -SL -O https://huggingface.co/csukuangfj/k2fsa-zipformer-chinese-english-mixed/resolve/main/test_wavs/BAC009S0764W0164.wav
+  ls -lh
+  popd
+
+  ./pruned_transducer_stateless7_streaming/export-onnx-zh.py \
+    --dynamic-batch 0 \
+    --enable-int8-quantization 0 \
+    --tokens $d/tokens.txt \
+    --use-averaged-model 0 \
+    --epoch 99 \
+    --avg 1 \
+    --exp-dir $d/ \
+    --decode-chunk-len 32 \
+    --num-encoder-layers "2,4,3,2,4" \
+    --feedforward-dims "1024,1024,1536,1536,1024" \
+    --nhead "8,8,8,8,8" \
+    --encoder-dims "384,384,384,384,384" \
+    --attention-dims "192,192,192,192,192" \
+    --encoder-unmasked-dims "256,256,256,256,256" \
+    --zipformer-downsampling-factors "1,2,4,8,2" \
+    --cnn-module-kernels "31,31,31,31,31" \
+    --decoder-dim 512 \
+    --joiner-dim 512
+
+  ./pruned_transducer_stateless7_streaming/onnx_pretrained.py \
+    --encoder-model-filename $d/encoder-epoch-99-avg-1.onnx \
+    --decoder-model-filename $d/decoder-epoch-99-avg-1.onnx \
+    --joiner-model-filename $d/joiner-epoch-99-avg-1.onnx \
+    --tokens $d/tokens.txt \
+    $d/0.wav
+
+  ./pruned_transducer_stateless7_streaming/onnx_pretrained.py \
+    --encoder-model-filename $d/encoder-epoch-99-avg-1.onnx \
+    --decoder-model-filename $d/decoder-epoch-99-avg-1.onnx \
+    --joiner-model-filename $d/joiner-epoch-99-avg-1.onnx \
+    --tokens $d/tokens.txt \
+    $d/BAC009S0764W0164.wav
+
+  ls -lh $d/
+}
+
+export_bilingual_zh_en

.github/workflows/rknn.yml

@@ -17,24 +17,75 @@ concurrency:
   cancel-in-progress: true
 
 jobs:
+  generate_build_matrix:
+    if: github.repository_owner == 'csukuangfj' || github.repository_owner == 'k2-fsa'
+    # see https://github.com/pytorch/pytorch/pull/50633
+    runs-on: ubuntu-latest
+    outputs:
+      matrix: ${{ steps.set-matrix.outputs.matrix }}
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+      - name: Generating build matrix
+        id: set-matrix
+        run: |
+          # outputting for debugging purposes
+          python ./.github/scripts/docker/generate_build_matrix.py --torch-version=2.4.0 --python-version=3.10
+          MATRIX=$(python ./.github/scripts/docker/generate_build_matrix.py --torch-version=2.4.0 --python-version=3.10)
+          echo "::set-output name=matrix::${MATRIX}"
   rknn:
+    needs: generate_build_matrix
+    name: py${{ matrix.python-version }} torch${{ matrix.torch-version }} v${{ matrix.version }}
     runs-on: ubuntu-latest
     strategy:
       fail-fast: false
       matrix:
-        os: [ubuntu-latest]
-        python-version: ["3.10"]
+        ${{ fromJson(needs.generate_build_matrix.outputs.matrix) }}
+
     steps:
       - uses: actions/checkout@v4
         with:
           fetch-depth: 0
 
       - name: Setup Python
+        if: false
         uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python-version }}
 
+      - name: Export ONNX model
+        uses: addnab/docker-run-action@v3
+        with:
+            image: ghcr.io/${{ github.repository_owner }}/icefall:cpu-py${{ matrix.python-version }}-torch${{ matrix.torch-version }}-v${{ matrix.version }}
+            options: |
+              --volume ${{ github.workspace }}/:/icefall
+            shell: bash
+            run: |
+              cat /etc/*release
+              lsb_release -a
+              uname -a
+              python3 --version
+              export PYTHONPATH=/icefall:$PYTHONPATH
+              cd /icefall
+              git config --global --add safe.directory /icefall
+
+              python3 -m torch.utils.collect_env
+              python3 -m k2.version
+              pip list
+
+              .github/scripts/librispeech/ASR/run_rknn.sh
+
+              # Install rknn
+              curl -SL -O https://huggingface.co/csukuangfj/rknn-toolkit2/resolve/main/rknn_toolkit2-2.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
+              pip install ./*.whl "numpy<=1.26.4"
+              pip list | grep rknn
+              echo "---"
+              pip list
+              echo "---"
+
       - name: Install rknn
+        if: false
         shell: bash
         run: |
           ls
@@ -46,6 +97,7 @@ jobs:
           echo "---"
 
       - name: Run
+        if: false
         shell: bash
         run: |
           ls
@@ -74,11 +126,13 @@ jobs:
           ls -lh ../model
 
       - uses: actions/upload-artifact@v4
+        if: false
         with:
           name: rknn-files
           path: ./rknn_model_zoo/examples/zipformer/model/*.rknn
 
       - uses: actions/upload-artifact@v4
+        if: false
         with:
           name: onnx-files
           path: ./rknn_model_zoo/examples/zipformer/model/*.onnx

egs/librispeech/ASR/pruned_transducer_stateless7_streaming/export-onnx-zh.py

@@ -85,6 +85,20 @@ def get_parser():
         formatter_class=argparse.ArgumentDefaultsHelpFormatter
     )
 
+    parser.add_argument(
+        "--dynamic-batch",
+        type=int,
+        default=1,
+        help="1 to support dynamic batch size. 0 to support only batch size == 1",
+    )
+
+    parser.add_argument(
+        "--enable-int8-quantization",
+        type=int,
+        default=1,
+        help="1 to also export int8 onnx models.",
+    )
+
     parser.add_argument(
         "--epoch",
         type=int,
@@ -257,6 +271,7 @@ def export_encoder_model_onnx(
     encoder_model: OnnxEncoder,
     encoder_filename: str,
     opset_version: int = 11,
+    dynamic_batch: bool = True,
 ) -> None:
     """
     Onnx model inputs:
@@ -274,6 +289,8 @@ def export_encoder_model_onnx(
         The filename to save the exported ONNX model.
       opset_version:
         The opset version to use.
+      dynamic_batch:
+        True to export a model supporting dynamic batch size
     """
 
     encoder_model.encoder.__class__.forward = (
@@ -379,7 +396,9 @@ def export_encoder_model_onnx(
             "encoder_out": {0: "N"},
             **inputs,
             **outputs,
-        },
+        }
+        if dynamic_batch
+        else {},
     )
 
     add_meta_data(filename=encoder_filename, meta_data=meta_data)
@@ -389,6 +408,7 @@ def export_decoder_model_onnx(
     decoder_model: nn.Module,
     decoder_filename: str,
     opset_version: int = 11,
+    dynamic_batch: bool = True,
 ) -> None:
     """Export the decoder model to ONNX format.
 
@@ -412,7 +432,7 @@ def export_decoder_model_onnx(
     """
     context_size = decoder_model.decoder.context_size
     vocab_size = decoder_model.decoder.vocab_size
-    y = torch.zeros(10, context_size, dtype=torch.int64)
+    y = torch.zeros(1, context_size, dtype=torch.int64)
     decoder_model = torch.jit.script(decoder_model)
     torch.onnx.export(
         decoder_model,
@@ -425,7 +445,9 @@ def export_decoder_model_onnx(
         dynamic_axes={
             "y": {0: "N"},
             "decoder_out": {0: "N"},
-        },
+        }
+        if dynamic_batch
+        else {},
     )
     meta_data = {
         "context_size": str(context_size),
@@ -438,6 +460,7 @@ def export_joiner_model_onnx(
     joiner_model: nn.Module,
     joiner_filename: str,
     opset_version: int = 11,
+    dynamic_batch: bool = True,
 ) -> None:
     """Export the joiner model to ONNX format.
     The exported joiner model has two inputs:
@@ -452,8 +475,8 @@ def export_joiner_model_onnx(
     joiner_dim = joiner_model.output_linear.weight.shape[1]
     logging.info(f"joiner dim: {joiner_dim}")
 
-    projected_encoder_out = torch.rand(11, joiner_dim, dtype=torch.float32)
-    projected_decoder_out = torch.rand(11, joiner_dim, dtype=torch.float32)
+    projected_encoder_out = torch.rand(1, joiner_dim, dtype=torch.float32)
+    projected_decoder_out = torch.rand(1, joiner_dim, dtype=torch.float32)
 
     torch.onnx.export(
         joiner_model,
@@ -470,7 +493,9 @@ def export_joiner_model_onnx(
             "encoder_out": {0: "N"},
             "decoder_out": {0: "N"},
             "logit": {0: "N"},
-        },
+        }
+        if dynamic_batch
+        else {},
     )
     meta_data = {
         "joiner_dim": str(joiner_dim),
@@ -629,6 +654,7 @@ def main():
         encoder,
         encoder_filename,
         opset_version=opset_version,
+        dynamic_batch=params.dynamic_batch == 1,
     )
     logging.info(f"Exported encoder to {encoder_filename}")
 
@@ -638,6 +664,7 @@ def main():
         decoder,
         decoder_filename,
         opset_version=opset_version,
+        dynamic_batch=params.dynamic_batch == 1,
     )
     logging.info(f"Exported decoder to {decoder_filename}")
 
@@ -647,37 +674,39 @@ def main():
         joiner,
         joiner_filename,
         opset_version=opset_version,
+        dynamic_batch=params.dynamic_batch == 1,
     )
     logging.info(f"Exported joiner to {joiner_filename}")
 
     # Generate int8 quantization models
     # See https://onnxruntime.ai/docs/performance/model-optimizations/quantization.html#data-type-selection
 
-    logging.info("Generate int8 quantization models")
+    if params.enable_int8_quantization:
+        logging.info("Generate int8 quantization models")
 
-    encoder_filename_int8 = params.exp_dir / f"encoder-{suffix}.int8.onnx"
-    quantize_dynamic(
-        model_input=encoder_filename,
-        model_output=encoder_filename_int8,
-        op_types_to_quantize=["MatMul"],
-        weight_type=QuantType.QInt8,
-    )
+        encoder_filename_int8 = params.exp_dir / f"encoder-{suffix}.int8.onnx"
+        quantize_dynamic(
+            model_input=encoder_filename,
+            model_output=encoder_filename_int8,
+            op_types_to_quantize=["MatMul"],
+            weight_type=QuantType.QInt8,
+        )
 
-    decoder_filename_int8 = params.exp_dir / f"decoder-{suffix}.int8.onnx"
-    quantize_dynamic(
-        model_input=decoder_filename,
-        model_output=decoder_filename_int8,
-        op_types_to_quantize=["MatMul", "Gather"],
-        weight_type=QuantType.QInt8,
-    )
+        decoder_filename_int8 = params.exp_dir / f"decoder-{suffix}.int8.onnx"
+        quantize_dynamic(
+            model_input=decoder_filename,
+            model_output=decoder_filename_int8,
+            op_types_to_quantize=["MatMul", "Gather"],
+            weight_type=QuantType.QInt8,
+        )
 
-    joiner_filename_int8 = params.exp_dir / f"joiner-{suffix}.int8.onnx"
-    quantize_dynamic(
-        model_input=joiner_filename,
-        model_output=joiner_filename_int8,
-        op_types_to_quantize=["MatMul"],
-        weight_type=QuantType.QInt8,
-    )
+        joiner_filename_int8 = params.exp_dir / f"joiner-{suffix}.int8.onnx"
+        quantize_dynamic(
+            model_input=joiner_filename,
+            model_output=joiner_filename_int8,
+            op_types_to_quantize=["MatMul"],
+            weight_type=QuantType.QInt8,
+        )
 
 
 if __name__ == "__main__":

egs/librispeech/ASR/pruned_transducer_stateless7_streaming/onnx_pretrained.py

@@ -132,10 +132,18 @@ class OnnxModel:
             sess_options=self.session_opts,
             providers=["CPUExecutionProvider"],
         )
+        print("==========Encoder input==========")
+        for i in self.encoder.get_inputs():
+            print(i)
+        print("==========Encoder output==========")
+        for i in self.encoder.get_outputs():
+            print(i)
+
         self.init_encoder_states()
 
     def init_encoder_states(self, batch_size: int = 1):
         encoder_meta = self.encoder.get_modelmeta().custom_metadata_map
+        print(encoder_meta)
 
         model_type = encoder_meta["model_type"]
         assert model_type == "zipformer", model_type
@@ -232,6 +240,12 @@ class OnnxModel:
             sess_options=self.session_opts,
             providers=["CPUExecutionProvider"],
         )
+        print("==========Decoder input==========")
+        for i in self.decoder.get_inputs():
+            print(i)
+        print("==========Decoder output==========")
+        for i in self.decoder.get_outputs():
+            print(i)
 
         decoder_meta = self.decoder.get_modelmeta().custom_metadata_map
         self.context_size = int(decoder_meta["context_size"])
@@ -247,6 +261,13 @@ class OnnxModel:
             providers=["CPUExecutionProvider"],
         )
 
+        print("==========Joiner input==========")
+        for i in self.joiner.get_inputs():
+            print(i)
+        print("==========Joiner output==========")
+        for i in self.joiner.get_outputs():
+            print(i)
+
         joiner_meta = self.joiner.get_modelmeta().custom_metadata_map
         self.joiner_dim = int(joiner_meta["joiner_dim"])