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Merge pull request #156 from alibaba-damo-academy/dev_gzf

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onnx export
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zhifu gao 2023-02-27 14:30:00 +08:00 committed by GitHub
commit 2432fc9c39
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5 changed files with 57 additions and 114 deletions
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3
.gitignore vendored
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@ -6,4 +6,5 @@
.DS_Store
init_model/
*.tar.gz
test_local/
test_local/
RapidASR

110
funasr/export/export_model.py
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@ -7,11 +7,13 @@ import os
import logging
import torch
from funasr.bin.asr_inference_paraformer import Speech2Text
from funasr.export.models import get_model
import numpy as np
import random
torch_version = float(".".join(torch.__version__.split(".")[:2]))
assert torch_version > 1.9
class ASRModelExportParaformer:
def __init__(self, cache_dir: Union[Path, str] = None, onnx: bool = True):
assert check_argument_types()
@ -30,7 +32,7 @@ class ASRModelExportParaformer:
def _export(
self,
model: Speech2Text,
model,
tag_name: str = None,
verbose: bool = False,
):
@ -118,110 +120,6 @@ class ASRModelExportParaformer:
)
class ASRModelExport:
def __init__(self, cache_dir: Union[Path, str] = None, onnx: bool = True):
assert check_argument_types()
self.set_all_random_seed(0)
if cache_dir is None:
cache_dir = Path.home() / ".cache" / "export"
self.cache_dir = Path(cache_dir)
self.export_config = dict(
feats_dim=560,
onnx=False,
)
print("output dir: {}".format(self.cache_dir))
self.onnx = onnx
def _export(
self,
model: Speech2Text,
tag_name: str = None,
verbose: bool = False,
):
export_dir = self.cache_dir / tag_name.replace(' ', '-')
os.makedirs(export_dir, exist_ok=True)
# export encoder1
self.export_config["model_name"] = "model"
model = get_model(
model,
self.export_config,
)
model.eval()
# self._export_onnx(model, verbose, export_dir)
if self.onnx:
self._export_onnx(model, verbose, export_dir)
else:
self._export_torchscripts(model, verbose, export_dir)
print("output dir: {}".format(export_dir))
def _export_torchscripts(self, model, verbose, path, enc_size=None):
if enc_size:
dummy_input = model.get_dummy_inputs(enc_size)
else:
dummy_input = model.get_dummy_inputs_txt()
# model_script = torch.jit.script(model)
model_script = torch.jit.trace(model, dummy_input)
model_script.save(os.path.join(path, f'{model.model_name}.torchscripts'))
def set_all_random_seed(self, seed: int):
random.seed(seed)
np.random.seed(seed)
torch.random.manual_seed(seed)
def export(self,
tag_name: str = 'damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch',
mode: str = 'paraformer',
):
model_dir = tag_name
if model_dir.startswith('damo/'):
from modelscope.hub.snapshot_download import snapshot_download
model_dir = snapshot_download(model_dir, cache_dir=self.cache_dir)
asr_train_config = os.path.join(model_dir, 'config.yaml')
asr_model_file = os.path.join(model_dir, 'model.pb')
cmvn_file = os.path.join(model_dir, 'am.mvn')
json_file = os.path.join(model_dir, 'configuration.json')
if mode is None:
import json
with open(json_file, 'r') as f:
config_data = json.load(f)
mode = config_data['model']['model_config']['mode']
if mode.startswith('paraformer'):
from funasr.tasks.asr import ASRTaskParaformer as ASRTask
elif mode.startswith('uniasr'):
from funasr.tasks.asr import ASRTaskUniASR as ASRTask
model, asr_train_args = ASRTask.build_model_from_file(
asr_train_config, asr_model_file, cmvn_file, 'cpu'
)
self._export(model, tag_name)
def _export_onnx(self, model, verbose, path, enc_size=None):
if enc_size:
dummy_input = model.get_dummy_inputs(enc_size)
else:
dummy_input = model.get_dummy_inputs()
# model_script = torch.jit.script(model)
model_script = model # torch.jit.trace(model)
torch.onnx.export(
model_script,
dummy_input,
os.path.join(path, f'{model.model_name}.onnx'),
verbose=verbose,
opset_version=12,
input_names=model.get_input_names(),
output_names=model.get_output_names(),
dynamic_axes=model.get_dynamic_axes()
)
if __name__ == '__main__':
import sys

53
funasr/export/models/predictor/cif.py
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@ -77,6 +77,53 @@ class CifPredictorV2(nn.Module):
return hidden, alphas, token_num_floor
# @torch.jit.script
# def cif(hidden, alphas, threshold: float):
# batch_size, len_time, hidden_size = hidden.size()
# threshold = torch.tensor([threshold], dtype=alphas.dtype).to(alphas.device)
#
# # loop varss
# integrate = torch.zeros([batch_size], device=hidden.device)
# frame = torch.zeros([batch_size, hidden_size], device=hidden.device)
# # intermediate vars along time
# list_fires = []
# list_frames = []
#
# for t in range(len_time):
# alpha = alphas[:, t]
# distribution_completion = torch.ones([batch_size], device=hidden.device) - integrate
#
# integrate += alpha
# list_fires.append(integrate)
#
# fire_place = integrate >= threshold
# integrate = torch.where(fire_place,
# integrate - torch.ones([batch_size], device=hidden.device),
# integrate)
# cur = torch.where(fire_place,
# distribution_completion,
# alpha)
# remainds = alpha - cur
#
# frame += cur[:, None] * hidden[:, t, :]
# list_frames.append(frame)
# frame = torch.where(fire_place[:, None].repeat(1, hidden_size),
# remainds[:, None] * hidden[:, t, :],
# frame)
#
# fires = torch.stack(list_fires, 1)
# frames = torch.stack(list_frames, 1)
# list_ls = []
# len_labels = torch.floor(alphas.sum(-1)).int()
# max_label_len = len_labels.max()
# for b in range(batch_size):
# fire = fires[b, :]
# l = torch.index_select(frames[b, :, :], 0, torch.nonzero(fire >= threshold).squeeze())
# pad_l = torch.zeros([int(max_label_len - l.size(0)), int(hidden_size)], device=hidden.device)
# list_ls.append(torch.cat([l, pad_l], 0))
# return torch.stack(list_ls, 0), fires
@torch.jit.script
def cif(hidden, alphas, threshold: float):
batch_size, len_time, hidden_size = hidden.size()
@ -113,15 +160,11 @@ def cif(hidden, alphas, threshold: float):
fires = torch.stack(list_fires, 1)
frames = torch.stack(list_frames, 1)
# list_ls = []
len_labels = torch.round(alphas.sum(-1)).type(torch.int32)
# max_label_len = int(torch.max(len_labels).item())
# print("type: {}".format(type(max_label_len)))
fire_idxs = fires >= threshold
frame_fires = torch.zeros_like(hidden)
max_label_len = frames[0, fire_idxs[0]].size(0)
for b in range(batch_size):
# fire = fires[b, :]
frame_fire = frames[b, fire_idxs[b]]
frame_len = frame_fire.size(0)
frame_fires[b, :frame_len, :] = frame_fire

5
funasr/runtime/python/onnxruntime/rapid_paraformer/utils/utils.py
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@ -148,6 +148,7 @@ class ONNXRuntimeError(Exception):
class OrtInferSession():
def __init__(self, model_file, device_id=-1):
device_id = str(device_id)
sess_opt = SessionOptions()
sess_opt.log_severity_level = 4
sess_opt.enable_cpu_mem_arena = False
@ -166,7 +167,7 @@ class OrtInferSession():
}
EP_list = []
if device_id != -1 and get_device() == 'GPU' \
if device_id != "-1" and get_device() == 'GPU' \
and cuda_ep in get_available_providers():
EP_list = [(cuda_ep, cuda_provider_options)]
EP_list.append((cpu_ep, cpu_provider_options))
@ -176,7 +177,7 @@ class OrtInferSession():
sess_options=sess_opt,
providers=EP_list)
if device_id != -1 and cuda_ep not in self.session.get_providers():
if device_id != "-1" and cuda_ep not in self.session.get_providers():
warnings.warn(f'{cuda_ep} is not avaiable for current env, the inference part is automatically shifted to be executed under {cpu_ep}.\n'
'Please ensure the installed onnxruntime-gpu version matches your cuda and cudnn version, '
'you can check their relations from the offical web site: '

0
scan.py
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