hello, the Chinese onnx model inference seems not right. about sherpa-onnx HOT 12 CLOSED

/onnx_pretrained.py", line 177, in greedy_search
    logits = joiner.run(
  File "/Users/xx/miniforge3/lib/python3.9/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py", line 192, in run
    return self._sess.run(output_names, input_feed, run_options)
onnxruntime.capi.onnxruntime_pybind11_state.InvalidArgument: [ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Invalid rank for input: encoder_out Got: 2 Expected: 4 Please fix either the inputs or the model.

python check_onnx.py --jit-filename weights/icefall_asr_wenetspeech_pruned_transducer_stateless2/exp/cpu_jit_epoch_10_avg_2_torch_1.11.0.pt --onnx-encoder-filename weights/onnx/encoder.onnx --onnx-decoder-filename weights/onnx/decoder.onnx --onnx-joiner-filename weights/onnx/joiner.onnx --onnx-joiner-encoder-proj-filename weights/onnx/joiner_encoder_proj.onnx --onnx-joiner-decoder-proj-filename weights/onnx/joiner_decoder_proj.onnx
2022-10-04 20:13:40,783 INFO [check_onnx.py:201] {'jit_filename': 'weights/icefall_asr_wenetspeech_pruned_transducer_stateless2/exp/cpu_jit_epoch_10_avg_2_torch_1.11.0.pt', 'onnx_encoder_filename': 'weights/onnx/encoder.onnx', 'onnx_decoder_filename': 'weights/onnx/decoder.onnx', 'onnx_joiner_filename': 'weights/onnx/joiner.onnx', 'onnx_joiner_encoder_proj_filename': 'weights/onnx/joiner_encoder_proj.onnx', 'onnx_joiner_decoder_proj_filename': 'weights/onnx/joiner_decoder_proj.onnx'}
2022-10-04 20:13:40,966 INFO [check_onnx.py:209] Test encoder
N, T 1 12
Traceback (most recent call last):
  File "/check_onnx.py", line 252, in <module>
    main()
  File "/Users/xx/miniforge3/lib/python3.9/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
    return func(*args, **kwargs)
  File "\
    test_encoder(model, encoder_session)
  File "//check_onnx.py", line 90, in test_encoder
    assert torch.allclose(encoder_out, torch_encoder_out, atol=1e-05), (
AssertionError: tensor(2.7750)

def get_parser():
    parser = argparse.ArgumentParser(
        formatter_class=argparse.ArgumentDefaultsHelpFormatter
    )

    parser.add_argument("-p", "--pretrained_model", type=str, help="pretrained model")
    parser.add_argument(
        "--bpe_model",
        type=str,
        default="data/lang_bpe_500/bpe.model",
        help="Path to the BPE model",
    )
    parser.add_argument(
        "--jit",
        type=str2bool,
        default=False,
        help="""True to save a model after applying torch.jit.script.
        It will generate 4 files:
         - encoder_jit_script.pt
         - decoder_jit_script.pt
         - joiner_jit_script.pt
         - cpu_jit.pt (which combines the above 3 files)
        Check ./jit_pretrained.py for how to use them.
        """,
    )
    parser.add_argument(
        "--jit-trace",
        type=str2bool,
        default=False,
        help="""True to save a model after applying torch.jit.trace.
        It will generate 3 files:
         - encoder_jit_trace.pt
         - decoder_jit_trace.pt
         - joiner_jit_trace.pt
        Check ./jit_pretrained.py for how to use them.
        """,
    )
    parser.add_argument(
        "--onnx",
        type=str2bool,
        default=True,
        help="""If True, --jit is ignored and it exports the model
        to onnx format. Three files will be generated:
            - encoder.onnx
            - decoder.onnx
            - joiner.onnx
        Check ./onnx_check.py and ./onnx_pretrained.py for how to use them.
        """,
    )
    parser.add_argument(
        "--context-size",
        type=int,
        default=2,
        help="The context size in the decoder. 1 means bigram; " "2 means tri-gram",
    )
    parser.add_argument(
        "--streaming-model",
        type=str2bool,
        default=False,
        help="""Whether to export a streaming model, if the models in exp-dir
        are streaming model, this should be True.
        """,
    )
    add_model_arguments(parser)
    return parser


def export_encoder_model_jit_script(
    encoder_model: nn.Module,
    encoder_filename: str,
) -> None:
    """Export the given encoder model with torch.jit.script()
    Args:
      encoder_model:
        The input encoder model
      encoder_filename:
        The filename to save the exported model.
    """
    script_model = torch.jit.script(encoder_model)
    script_model.save(encoder_filename)
    logging.info(f"Saved to {encoder_filename}")


def export_decoder_model_jit_script(
    decoder_model: nn.Module,
    decoder_filename: str,
) -> None:
    """Export the given decoder model with torch.jit.script()
    Args:
      decoder_model:
        The input decoder model
      decoder_filename:
        The filename to save the exported model.
    """
    script_model = torch.jit.script(decoder_model)
    script_model.save(decoder_filename)
    logging.info(f"Saved to {decoder_filename}")


def export_joiner_model_jit_script(
    joiner_model: nn.Module,
    joiner_filename: str,
) -> None:
    """Export the given joiner model with torch.jit.trace()
    Args:
      joiner_model:
        The input joiner model
      joiner_filename:
        The filename to save the exported model.
    """
    script_model = torch.jit.script(joiner_model)
    script_model.save(joiner_filename)
    logging.info(f"Saved to {joiner_filename}")


def export_encoder_model_jit_trace(
    encoder_model: nn.Module,
    encoder_filename: str,
) -> None:
    """Export the given encoder model with torch.jit.trace()
    Note: The warmup argument is fixed to 1.
    Args:
      encoder_model:
        The input encoder model
      encoder_filename:
        The filename to save the exported model.
    """
    x = torch.zeros(1, 100, 80, dtype=torch.float32)
    x_lens = torch.tensor([100], dtype=torch.int64)

    traced_model = torch.jit.trace(encoder_model, (x, x_lens))
    traced_model.save(encoder_filename)
    logging.info(f"Saved to {encoder_filename}")


def export_decoder_model_jit_trace(
    decoder_model: nn.Module,
    decoder_filename: str,
) -> None:
    """Export the given decoder model with torch.jit.trace()
    Note: The argument need_pad is fixed to False.
    Args:
      decoder_model:
        The input decoder model
      decoder_filename:
        The filename to save the exported model.
    """
    y = torch.zeros(10, decoder_model.context_size, dtype=torch.int64)
    need_pad = torch.tensor([False])

    traced_model = torch.jit.trace(decoder_model, (y, need_pad))
    traced_model.save(decoder_filename)
    logging.info(f"Saved to {decoder_filename}")


def export_joiner_model_jit_trace(
    joiner_model: nn.Module,
    joiner_filename: str,
) -> None:
    """Export the given joiner model with torch.jit.trace()
    Note: The argument project_input is fixed to True. A user should not
    project the encoder_out/decoder_out by himself/herself. The exported joiner
    will do that for the user.
    Args:
      joiner_model:
        The input joiner model
      joiner_filename:
        The filename to save the exported model.
    """
    encoder_out_dim = joiner_model.encoder_proj.weight.shape[1]
    decoder_out_dim = joiner_model.decoder_proj.weight.shape[1]
    encoder_out = torch.rand(1, encoder_out_dim, dtype=torch.float32)
    decoder_out = torch.rand(1, decoder_out_dim, dtype=torch.float32)

    traced_model = torch.jit.trace(joiner_model, (encoder_out, decoder_out))
    traced_model.save(joiner_filename)
    logging.info(f"Saved to {joiner_filename}")


def export_encoder_model_onnx(
    encoder_model: nn.Module,
    encoder_filename: str,
    opset_version: int = 11,
) -> None:
    """Export the given encoder model to ONNX format.
    The exported model has two inputs:
        - x, a tensor of shape (N, T, C); dtype is torch.float32
        - x_lens, a tensor of shape (N,); dtype is torch.int64
    and it has two outputs:
        - encoder_out, a tensor of shape (N, T, C)
        - encoder_out_lens, a tensor of shape (N,)
    Note: The warmup argument is fixed to 1.
    Args:
      encoder_model:
        The input encoder model
      encoder_filename:
        The filename to save the exported ONNX model.
      opset_version:
        The opset version to use.
    """
    x = torch.zeros(1, 100, 80, dtype=torch.float32)
    x_lens = torch.tensor([100], dtype=torch.int64)

    #  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
    # 11 is not supported. Please feel free to request support or
    # submit a pull request on PyTorch GitHub.
    #
    # I cannot find which statement causes the above error.
    # torch.onnx.export() will use torch.jit.trace() internally, which
    # works well for the current reworked model
    warmup = 1.0
    torch.onnx.export(
        encoder_model,
        (x, x_lens, warmup),
        encoder_filename,
        verbose=False,
        opset_version=opset_version,
        input_names=["x", "x_lens", "warmup"],
        output_names=["encoder_out", "encoder_out_lens"],
        dynamic_axes={
            "x": {0: "N", 1: "T"},
            "x_lens": {0: "N"},
            "encoder_out": {0: "N", 1: "T"},
            "encoder_out_lens": {0: "N"},
        },
    )
    logging.info(f"Saved to {encoder_filename}")


def export_decoder_model_onnx(
    decoder_model: nn.Module,
    decoder_filename: str,
    opset_version: int = 11,
) -> None:
    """Export the decoder model to ONNX format.
    The exported model has one input:
        - y: a torch.int64 tensor of shape (N, decoder_model.context_size)
    and has one output:
        - decoder_out: a torch.float32 tensor of shape (N, 1, C)
    Note: The argument need_pad is fixed to False.
    Args:
      decoder_model:
        The decoder model to be exported.
      decoder_filename:
        Filename to save the exported ONNX model.
      opset_version:
        The opset version to use.
    """
    y = torch.zeros(10, decoder_model.context_size, dtype=torch.int64)
    need_pad = False  # Always False, so we can use torch.jit.trace() here
    # Note(fangjun): torch.jit.trace() is more efficient than torch.jit.script()
    # in this case
    torch.onnx.export(
        decoder_model,
        (y, need_pad),
        decoder_filename,
        verbose=False,
        opset_version=opset_version,
        input_names=["y", "need_pad"],
        output_names=["decoder_out"],
        dynamic_axes={
            "y": {0: "N"},
            "decoder_out": {0: "N"},
        },
    )
    logging.info(f"Saved to {decoder_filename}")


def export_joiner_model_onnx(
    joiner_model: nn.Module,
    joiner_filename: str,
    opset_version: int = 11,
) -> None:
    """Export the joiner model to ONNX format.
    The exported model has two inputs:
        - encoder_out: a tensor of shape (N, encoder_out_dim)
        - decoder_out: a tensor of shape (N, decoder_out_dim)
    and has one output:
        - joiner_out: a tensor of shape (N, vocab_size)
    Note: The argument project_input is fixed to True. A user should not
    project the encoder_out/decoder_out by himself/herself. The exported joiner
    will do that for the user.
    """
    encoder_out_dim = joiner_model.encoder_proj.weight.shape[1]
    decoder_out_dim = joiner_model.decoder_proj.weight.shape[1]
    encoder_out = torch.rand(1, 1, 1, encoder_out_dim, dtype=torch.float32)
    decoder_out = torch.rand(1, 1, 1, decoder_out_dim, dtype=torch.float32)

    project_input = True
    # Note: It uses torch.jit.trace() internally
    torch.onnx.export(
        joiner_model,
        (encoder_out, decoder_out, project_input),
        joiner_filename,
        verbose=False,
        opset_version=opset_version,
        input_names=["encoder_out", "decoder_out", "project_input"],
        output_names=["logit"],
        dynamic_axes={
            "encoder_out": {0: "N"},
            "decoder_out": {0: "N"},
            "logit": {0: "N"},
        },
    )
    torch.onnx.export(
        joiner_model.encoder_proj,
        (encoder_out.squeeze(0).squeeze(0)),
        str(joiner_filename).replace(".onnx", "_encoder_proj.onnx"),
        verbose=False,
        opset_version=opset_version,
        input_names=["encoder_out"],
        output_names=["encoder_proj"],
        dynamic_axes={
            "encoder_out": {0: "N"},
            "encoder_proj": {0: "N"},
        },
    )
    torch.onnx.export(
        joiner_model.decoder_proj,
        (decoder_out.squeeze(0).squeeze(0)),
        str(joiner_filename).replace(".onnx", "_decoder_proj.onnx"),
        verbose=False,
        opset_version=opset_version,
        input_names=["decoder_out"],
        output_names=["decoder_proj"],
        dynamic_axes={
            "decoder_out": {0: "N"},
            "decoder_proj": {0: "N"},
        },
    )
    logging.info(f"Saved to {joiner_filename}")


def export_all_in_one_onnx(
    encoder_filename: str,
    decoder_filename: str,
    joiner_filename: str,
    all_in_one_filename: str,
):
    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}")


@torch.no_grad()
def main():
    args = get_parser().parse_args()

    params = get_default_params()
    params.update({"exp_dir": "weights/onnx"})
    params.exp_dir = Path(params.exp_dir)
    params.update(vars(args))

    os.makedirs(params.exp_dir, exist_ok=True)

    device = torch.device("cpu")
    if torch.cuda.is_available():
        device = torch.device("cuda", 0)

    logging.info(f"device: {device}")

    if "bpe" in args.bpe_model:
        sp = spm.SentencePieceProcessor()
        sp.load(args.bpe_model)
        # <blk> is defined in local/train_bpe_model.py
        params.blank_id = sp.piece_to_id("<blk>")
        params.vocab_size = sp.get_piece_size()
    else:
        lexions = Lexicon(args.bpe_model)
        # vc  = max(token_table) + 1
        # <blk> is defined in local/train_bpe_model.py
        params.blank_id = lexions.token_table["<blk>"]
        params.vocab_size = max(lexions.tokens) + 1
        logging.info("Reading Lexions...")

        sp = lexions

    if params.streaming_model:
        assert params.causal_convolution

    logging.info(params)

    logging.info("About to create model")
    # model = get_transducer_model(params, enable_giga=False)
    model = build_conformer_transducer_model(sp, params)
    model.to(device)

    model.load_state_dict(
        torch.load(args.pretrained_model, "cpu"),
        strict=False,
    )
    model.to("cpu")
    model.eval()

    if params.onnx is True:
        convert_scaled_to_non_scaled(model, inplace=True)
        opset_version = 11
        logging.info("Exporting to onnx format")
        encoder_filename = params.exp_dir / "encoder.onnx"
        export_encoder_model_onnx(
            model.encoder,
            encoder_filename,
            opset_version=opset_version,
        )

        decoder_filename = params.exp_dir / "decoder.onnx"
        export_decoder_model_onnx(
            model.decoder,
            decoder_filename,
            opset_version=opset_version,
        )

        joiner_filename = params.exp_dir / "joiner.onnx"
        export_joiner_model_onnx(
            model.joiner,
            joiner_filename,
            opset_version=opset_version,
        )

        all_in_one_filename = params.exp_dir / "all_in_one.onnx"
        export_all_in_one_onnx(
            encoder_filename,
            decoder_filename,
            joiner_filename,
            all_in_one_filename,
        )
    elif params.jit is True:
        convert_scaled_to_non_scaled(model, inplace=True)
        logging.info("Using torch.jit.script()")
        # We won't use the forward() method of the model in C++, so just ignore
        # it here.
        # Otherwise, one of its arguments is a ragged tensor and is not
        # torch scriptabe.
        model.__class__.forward = torch.jit.ignore(model.__class__.forward)
        logging.info("Using torch.jit.script")
        model = torch.jit.script(model)
        filename = params.exp_dir / "cpu_jit.pt"
        model.save(str(filename))
        logging.info(f"Saved to {filename}")

        # Also export encoder/decoder/joiner separately
        encoder_filename = params.exp_dir / "encoder_jit_script.pt"
        export_encoder_model_jit_script(model.encoder, encoder_filename)

        decoder_filename = params.exp_dir / "decoder_jit_script.pt"
        export_decoder_model_jit_script(model.decoder, decoder_filename)

        joiner_filename = params.exp_dir / "joiner_jit_script.pt"
        export_joiner_model_jit_script(model.joiner, joiner_filename)

    elif params.jit_trace is True:
        convert_scaled_to_non_scaled(model, inplace=True)
        logging.info("Using torch.jit.trace()")
        encoder_filename = params.exp_dir / "encoder_jit_trace.pt"
        export_encoder_model_jit_trace(model.encoder, encoder_filename)

        decoder_filename = params.exp_dir / "decoder_jit_trace.pt"
        export_decoder_model_jit_trace(model.decoder, decoder_filename)

        joiner_filename = params.exp_dir / "joiner_jit_trace.pt"
        export_joiner_model_jit_trace(model.joiner, joiner_filename)


if __name__ == "__main__":
    main()