From f98c4bf6d2bb5202488cd4243efdbca65288c313 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期一, 27 二月 2023 14:26:32 +0800
Subject: [PATCH] onnx export
---
/dev/null | 0
.gitignore | 3
funasr/export/models/predictor/cif.py | 53 ++++++++++++++++-
funasr/runtime/python/onnxruntime/rapid_paraformer/utils/utils.py | 5 +
funasr/export/export_model.py | 110 +-----------------------------------
5 files changed, 57 insertions(+), 114 deletions(-)
diff --git a/.gitignore b/.gitignore
index 7a43407..8258377 100644
--- a/.gitignore
+++ b/.gitignore
@@ -6,4 +6,5 @@
.DS_Store
init_model/
*.tar.gz
-test_local/
\ No newline at end of file
+test_local/
+RapidASR
\ No newline at end of file
diff --git a/funasr/export/export_model.py b/funasr/export/export_model.py
index 933a927..8d41462 100644
--- a/funasr/export/export_model.py
+++ b/funasr/export/export_model.py
@@ -7,10 +7,12 @@
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):
@@ -30,7 +32,7 @@
def _export(
self,
- model: Speech2Text,
+ model,
tag_name: str = None,
verbose: bool = False,
):
@@ -112,110 +114,6 @@
os.path.join(path, f'{model.model_name}.onnx'),
verbose=verbose,
opset_version=14,
- input_names=model.get_input_names(),
- output_names=model.get_output_names(),
- dynamic_axes=model.get_dynamic_axes()
- )
-
-
-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()
diff --git a/funasr/export/models/predictor/cif.py b/funasr/export/models/predictor/cif.py
index c8df7f3..cb26862 100644
--- a/funasr/export/models/predictor/cif.py
+++ b/funasr/export/models/predictor/cif.py
@@ -77,6 +77,53 @@
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 @@
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
diff --git a/funasr/runtime/python/onnxruntime/rapid_paraformer/utils/utils.py b/funasr/runtime/python/onnxruntime/rapid_paraformer/utils/utils.py
index 8e220e0..7943abb 100644
--- a/funasr/runtime/python/onnxruntime/rapid_paraformer/utils/utils.py
+++ b/funasr/runtime/python/onnxruntime/rapid_paraformer/utils/utils.py
@@ -148,6 +148,7 @@
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 @@
}
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 @@
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: '
diff --git a/scan.py b/scan.py
deleted file mode 100644
index e69de29..0000000
--- a/scan.py
+++ /dev/null
--
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