onnx export

.gitignore

@@ -6,4 +6,5 @@
 .DS_Store
 init_model/
 *.tar.gz
-test_local/
+test_local/
+RapidASR

funasr/export/export_model.py

@@ -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
     

funasr/export/models/predictor/cif.py

@@ -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

funasr/runtime/python/onnxruntime/rapid_paraformer/utils/utils.py

@@ -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: '