export-for-ncnn.py

#!/usr/bin/env python3

"""
Please see
https://k2-fsa.github.io/icefall/model-export/export-ncnn.html
for more details about how to use this file.

We use
https://huggingface.co/TeoWenShen/icefall-asr-csj-pruned-transducer-stateless7-streaming-230208

to demonstrate the usage of this file.

1. Download the pre-trained model

cd egs/csj/ASR

repo_url=https://huggingface.co/TeoWenShen/icefall-asr-csj-pruned-transducer-stateless7-streaming-230208
GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
repo=$(basename $repo_url)

pushd $repo
git lfs pull --include "exp_fluent/pretrained.pt"

cd exp_fluent
ln -s pretrained.pt epoch-99.pt
popd

2. Export to ncnn

./pruned_transducer_stateless7_streaming/export-for-ncnn.py \
  --lang $repo/data/lang_char \
  --exp-dir $repo/exp_fluent/ \
  --epoch 99 \
  --avg 1 \
  --use-averaged-model 0 \
  \
  --decode-chunk-len 32 \
  --num-left-chunks 4 \
  --num-encoder-layers "2,4,3,2,4" \
  --feedforward-dims  "1024,1024,2048,2048,1024" \
  --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

cd $repo/exp_fluent

pnnx encoder_jit_trace-pnnx.pt
pnnx decoder_jit_trace-pnnx.pt
pnnx joiner_jit_trace-pnnx.pt

You can find converted models at
https://huggingface.co/csukuangfj/sherpa-ncnn-streaming-zipformer-ja-fluent-2023-02-14

Please also have a look at
https://huggingface.co/csukuangfj/sherpa-ncnn-streaming-zipformer-ja-fluent-2023-02-14/blob/main/export-for-ncnn-fluent.sh

See ./streaming-ncnn-decode.py
and
https://github.com/k2-fsa/sherpa-ncnn
for usage.
"""

import argparse
import logging
from pathlib import Path

import torch
from do_not_use_it_directly import add_model_arguments, get_params, get_transducer_model
from scaling_converter import convert_scaled_to_non_scaled
from tokenizer import Tokenizer

from icefall.checkpoint import (
    average_checkpoints,
    average_checkpoints_with_averaged_model,
    find_checkpoints,
    load_checkpoint,
)
from icefall.utils import setup_logger, str2bool


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

    parser.add_argument(
        "--epoch",
        type=int,
        default=28,
        help="""It specifies the checkpoint to use for averaging.
        Note: Epoch counts from 0.
        You can specify --avg to use more checkpoints for model averaging.""",
    )

    parser.add_argument(
        "--iter",
        type=int,
        default=0,
        help="""If positive, --epoch is ignored and it
        will use the checkpoint exp_dir/checkpoint-iter.pt.
        You can specify --avg to use more checkpoints for model averaging.
        """,
    )

    parser.add_argument(
        "--avg",
        type=int,
        default=15,
        help="Number of checkpoints to average. Automatically select "
        "consecutive checkpoints before the checkpoint specified by "
        "'--epoch' and '--iter'",
    )

    parser.add_argument(
        "--exp-dir",
        type=str,
        default="pruned_transducer_stateless7_streaming/exp",
        help="""It specifies the directory where all training related
        files, e.g., checkpoints, log, etc, are saved
        """,
    )

    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(
        "--use-averaged-model",
        type=str2bool,
        default=True,
        help="Whether to load averaged model. Currently it only supports "
        "using --epoch. If True, it would decode with the averaged model "
        "over the epoch range from `epoch-avg` (excluded) to `epoch`."
        "Actually only the models with epoch number of `epoch-avg` and "
        "`epoch` are loaded for averaging. ",
    )

    add_model_arguments(parser)

    return parser


def export_encoder_model_jit_trace(
    encoder_model: torch.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.
    """
    encoder_model.__class__.forward = encoder_model.__class__.streaming_forward

    decode_chunk_len = encoder_model.decode_chunk_size * 2
    pad_length = 7
    T = decode_chunk_len + pad_length  # 32 + 7 = 39

    logging.info(f"decode_chunk_len: {decode_chunk_len}")
    logging.info(f"T: {T}")

    x = torch.zeros(1, T, 80, dtype=torch.float32)
    states = encoder_model.get_init_state()

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


def export_decoder_model_jit_trace(
    decoder_model: torch.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: torch.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}")


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

    args.exp_dir = Path(args.exp_dir)

    params = get_params()
    params.update(vars(args))

    device = torch.device("cpu")

    setup_logger(f"{params.exp_dir}/log-export/log-export-ncnn")

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

    sp = Tokenizer.load(args.lang, args.lang_type)

    # <blk> is defined in local/prepare_lang_char.py
    params.blank_id = sp.piece_to_id("<blk>")
    params.vocab_size = sp.get_piece_size()
    assert params.blank_id == 0, params.blank_id

    logging.info(params)

    logging.info("About to create model")
    model = get_transducer_model(params)

    if not params.use_averaged_model:
        if params.iter > 0:
            filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
                : params.avg
            ]
            if len(filenames) == 0:
                raise ValueError(
                    f"No checkpoints found for"
                    f" --iter {params.iter}, --avg {params.avg}"
                )
            elif len(filenames) < params.avg:
                raise ValueError(
                    f"Not enough checkpoints ({len(filenames)}) found for"
                    f" --iter {params.iter}, --avg {params.avg}"
                )
            logging.info(f"averaging {filenames}")
            model.to(device)
            model.load_state_dict(average_checkpoints(filenames, device=device))
        elif 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 i >= 1:
                    filenames.append(f"{params.exp_dir}/epoch-{i}.pt")
            logging.info(f"averaging {filenames}")
            model.to(device)
            model.load_state_dict(average_checkpoints(filenames, device=device))
    else:
        if params.iter > 0:
            filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
                : params.avg + 1
            ]
            if len(filenames) == 0:
                raise ValueError(
                    f"No checkpoints found for"
                    f" --iter {params.iter}, --avg {params.avg}"
                )
            elif len(filenames) < params.avg + 1:
                raise ValueError(
                    f"Not enough checkpoints ({len(filenames)}) found for"
                    f" --iter {params.iter}, --avg {params.avg}"
                )
            filename_start = filenames[-1]
            filename_end = filenames[0]
            logging.info(
                "Calculating the averaged model over iteration checkpoints"
                f" from {filename_start} (excluded) to {filename_end}"
            )
            model.to(device)
            model.load_state_dict(
                average_checkpoints_with_averaged_model(
                    filename_start=filename_start,
                    filename_end=filename_end,
                    device=device,
                )
            )
        else:
            assert params.avg > 0, params.avg
            start = params.epoch - params.avg
            assert start >= 1, start
            filename_start = f"{params.exp_dir}/epoch-{start}.pt"
            filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt"
            logging.info(
                f"Calculating the averaged model over epoch range from "
                f"{start} (excluded) to {params.epoch}"
            )
            model.to(device)
            model.load_state_dict(
                average_checkpoints_with_averaged_model(
                    filename_start=filename_start,
                    filename_end=filename_end,
                    device=device,
                )
            )

    model.to("cpu")
    model.eval()

    convert_scaled_to_non_scaled(model, inplace=True, is_pnnx=True)

    encoder_num_param = sum([p.numel() for p in model.encoder.parameters()])
    decoder_num_param = sum([p.numel() for p in model.decoder.parameters()])
    joiner_num_param = sum([p.numel() for p in model.joiner.parameters()])
    total_num_param = encoder_num_param + decoder_num_param + joiner_num_param
    logging.info(f"encoder parameters: {encoder_num_param}")
    logging.info(f"decoder parameters: {decoder_num_param}")
    logging.info(f"joiner parameters: {joiner_num_param}")
    logging.info(f"total parameters: {total_num_param}")

    logging.info("Using torch.jit.trace()")

    logging.info("Exporting encoder")
    encoder_filename = params.exp_dir / "encoder_jit_trace-pnnx.pt"
    export_encoder_model_jit_trace(model.encoder, encoder_filename)

    logging.info("Exporting decoder")
    decoder_filename = params.exp_dir / "decoder_jit_trace-pnnx.pt"
    export_decoder_model_jit_trace(model.decoder, decoder_filename)

    logging.info("Exporting joiner")
    joiner_filename = params.exp_dir / "joiner_jit_trace-pnnx.pt"
    export_joiner_model_jit_trace(model.joiner, joiner_filename)


if __name__ == "__main__":
    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"

    main()