From d783b24ba7d8a03dabfa2139fcbf40c216e0ea3d Mon Sep 17 00:00:00 2001
From: zhifu gao <zhifu.gzf@alibaba-inc.com>
Date: 星期四, 16 三月 2023 19:34:52 +0800
Subject: [PATCH] Merge pull request #199 from alibaba-damo-academy/dev_xw
---
funasr/bin/vad_inference_launch.py | 3
funasr/export/models/modules/encoder_layer.py | 6
funasr/export/README.md | 28 +++-
funasr/export/models/modules/multihead_att.py | 28 ++-
funasr/runtime/python/utils/test_rtf.sh | 74 ++++++++++++
funasr/export/export_model.py | 144 +++++++++++++++++++++--
funasr/runtime/python/utils/test_rtf.py | 47 +++++++
7 files changed, 293 insertions(+), 37 deletions(-)
diff --git a/funasr/bin/vad_inference_launch.py b/funasr/bin/vad_inference_launch.py
index 42c5c1e..18eba33 100644
--- a/funasr/bin/vad_inference_launch.py
+++ b/funasr/bin/vad_inference_launch.py
@@ -110,7 +110,8 @@
if mode == "offline":
from funasr.bin.vad_inference import inference_modelscope
return inference_modelscope(**kwargs)
- elif mode == "online":
+ # elif mode == "online":
+ if "param_dict" in kwargs and kwargs["param_dict"]["online"]:
from funasr.bin.vad_inference_online import inference_modelscope
return inference_modelscope(**kwargs)
else:
diff --git a/funasr/export/README.md b/funasr/export/README.md
index c44ad33..33ab22e 100644
--- a/funasr/export/README.md
+++ b/funasr/export/README.md
@@ -11,31 +11,43 @@
`Tips`: torch>=1.11.0
```shell
- python -m funasr.export.export_model [model_name] [export_dir] [onnx]
+ python -m funasr.export.export_model \
+ --model-name [model_name] \
+ --export-dir [export_dir] \
+ --type [onnx, torch] \
+ --quantize \
+ --fallback-num [fallback_num]
```
- `model_name`: the model is to export. It could be the models from modelscope, or local finetuned model(named: model.pb).
- `export_dir`: the dir where the onnx is export.
- `onnx`: `true`, export onnx format model; `false`, export torchscripts format model.
+ `model-name`: the model is to export. It could be the models from modelscope, or local finetuned model(named: model.pb).
+
+ `export-dir`: the dir where the onnx is export.
+
+ `type`: `onnx` or `torch`, export onnx format model or torchscript format model.
+
+ `quantize`: `true`, export quantized model at the same time; `false`, export fp32 model only.
+
+ `fallback-num`: specify the number of fallback layers to perform automatic mixed precision quantization.
+
## For example
### Export onnx format model
Export model from modelscope
```shell
-python -m funasr.export.export_model 'damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch' "./export" true
+python -m funasr.export.export_model --model-name damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch --export-dir ./export --type onnx
```
Export model from local path, the model'name must be `model.pb`.
```shell
-python -m funasr.export.export_model '/mnt/workspace/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch' "./export" true
+python -m funasr.export.export_model --model-name /mnt/workspace/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch --export-dir ./export --type onnx
```
### Export torchscripts format model
Export model from modelscope
```shell
-python -m funasr.export.export_model 'damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch' "./export" false
+python -m funasr.export.export_model --model-name damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch --export-dir ./export --type torch
```
Export model from local path, the model'name must be `model.pb`.
```shell
-python -m funasr.export.export_model '/mnt/workspace/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch' "./export" false
+python -m funasr.export.export_model --model-name /mnt/workspace/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch --export-dir ./export --type torch
```
diff --git a/funasr/export/export_model.py b/funasr/export/export_model.py
index 3cbf6d2..9a1ef96 100644
--- a/funasr/export/export_model.py
+++ b/funasr/export/export_model.py
@@ -15,7 +15,15 @@
# assert torch_version > 1.9
class ASRModelExportParaformer:
- def __init__(self, cache_dir: Union[Path, str] = None, onnx: bool = True):
+ def __init__(
+ self,
+ cache_dir: Union[Path, str] = None,
+ onnx: bool = True,
+ quant: bool = True,
+ fallback_num: int = 0,
+ audio_in: str = None,
+ calib_num: int = 200,
+ ):
assert check_argument_types()
self.set_all_random_seed(0)
if cache_dir is None:
@@ -28,6 +36,11 @@
)
print("output dir: {}".format(self.cache_dir))
self.onnx = onnx
+ self.quant = quant
+ self.fallback_num = fallback_num
+ self.frontend = None
+ self.audio_in = audio_in
+ self.calib_num = calib_num
def _export(
@@ -56,6 +69,43 @@
print("output dir: {}".format(export_dir))
+ def _torch_quantize(self, model):
+ def _run_calibration_data(m):
+ # using dummy inputs for a example
+ if self.audio_in is not None:
+ feats, feats_len = self.load_feats(self.audio_in)
+ for i, (feat, len) in enumerate(zip(feats, feats_len)):
+ with torch.no_grad():
+ m(feat, len)
+ else:
+ dummy_input = model.get_dummy_inputs()
+ m(*dummy_input)
+
+
+ from torch_quant.module import ModuleFilter
+ from torch_quant.quantizer import Backend, Quantizer
+ from funasr.export.models.modules.decoder_layer import DecoderLayerSANM
+ from funasr.export.models.modules.encoder_layer import EncoderLayerSANM
+ module_filter = ModuleFilter(include_classes=[EncoderLayerSANM, DecoderLayerSANM])
+ module_filter.exclude_op_types = [torch.nn.Conv1d]
+ quantizer = Quantizer(
+ module_filter=module_filter,
+ backend=Backend.FBGEMM,
+ )
+ model.eval()
+ calib_model = quantizer.calib(model)
+ _run_calibration_data(calib_model)
+ if self.fallback_num > 0:
+ # perform automatic mixed precision quantization
+ amp_model = quantizer.amp(model)
+ _run_calibration_data(amp_model)
+ quantizer.fallback(amp_model, num=self.fallback_num)
+ print('Fallback layers:')
+ print('\n'.join(quantizer.module_filter.exclude_names))
+ quant_model = quantizer.quantize(model)
+ return quant_model
+
+
def _export_torchscripts(self, model, verbose, path, enc_size=None):
if enc_size:
dummy_input = model.get_dummy_inputs(enc_size)
@@ -66,10 +116,49 @@
model_script = torch.jit.trace(model, dummy_input)
model_script.save(os.path.join(path, f'{model.model_name}.torchscripts'))
+ if self.quant:
+ quant_model = self._torch_quantize(model)
+ model_script = torch.jit.trace(quant_model, dummy_input)
+ model_script.save(os.path.join(path, f'{model.model_name}_quant.torchscripts'))
+
+
def set_all_random_seed(self, seed: int):
random.seed(seed)
np.random.seed(seed)
torch.random.manual_seed(seed)
+
+ def parse_audio_in(self, audio_in):
+
+ wav_list, name_list = [], []
+ if audio_in.endswith(".scp"):
+ f = open(audio_in, 'r')
+ lines = f.readlines()[:self.calib_num]
+ for line in lines:
+ name, path = line.strip().split()
+ name_list.append(name)
+ wav_list.append(path)
+ else:
+ wav_list = [audio_in,]
+ name_list = ["test",]
+ return wav_list, name_list
+
+ def load_feats(self, audio_in: str = None):
+ import torchaudio
+
+ wav_list, name_list = self.parse_audio_in(audio_in)
+ feats = []
+ feats_len = []
+ for line in wav_list:
+ path = line.strip()
+ waveform, sampling_rate = torchaudio.load(path)
+ if sampling_rate != self.frontend.fs:
+ waveform = torchaudio.transforms.Resample(orig_freq=sampling_rate,
+ new_freq=self.frontend.fs)(waveform)
+ fbank, fbank_len = self.frontend(waveform, [waveform.size(1)])
+ feats.append(fbank)
+ feats_len.append(fbank_len)
+ return feats, feats_len
+
def export(self,
tag_name: str = 'damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch',
mode: str = 'paraformer',
@@ -96,6 +185,7 @@
model, asr_train_args = ASRTask.build_model_from_file(
asr_train_config, asr_model_file, cmvn_file, 'cpu'
)
+ self.frontend = model.frontend
self._export(model, tag_name)
@@ -107,11 +197,12 @@
# model_script = torch.jit.script(model)
model_script = model #torch.jit.trace(model)
+ model_path = os.path.join(path, f'{model.model_name}.onnx')
torch.onnx.export(
model_script,
dummy_input,
- os.path.join(path, f'{model.model_name}.onnx'),
+ model_path,
verbose=verbose,
opset_version=14,
input_names=model.get_input_names(),
@@ -119,17 +210,42 @@
dynamic_axes=model.get_dynamic_axes()
)
+ if self.quant:
+ from onnxruntime.quantization import QuantType, quantize_dynamic
+ import onnx
+ quant_model_path = os.path.join(path, f'{model.model_name}_quant.onnx')
+ onnx_model = onnx.load(model_path)
+ nodes = [n.name for n in onnx_model.graph.node]
+ nodes_to_exclude = [m for m in nodes if 'output' in m]
+ quantize_dynamic(
+ model_input=model_path,
+ model_output=quant_model_path,
+ op_types_to_quantize=['MatMul'],
+ per_channel=True,
+ reduce_range=False,
+ weight_type=QuantType.QUInt8,
+ nodes_to_exclude=nodes_to_exclude,
+ )
+
if __name__ == '__main__':
- import sys
-
- model_path = sys.argv[1]
- output_dir = sys.argv[2]
- onnx = sys.argv[3]
- onnx = onnx.lower()
- onnx = onnx == 'true'
- # model_path = 'damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch'
- # output_dir = "../export"
- export_model = ASRModelExportParaformer(cache_dir=output_dir, onnx=onnx)
- export_model.export(model_path)
- # export_model.export('/root/cache/export/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch')
\ No newline at end of file
+ import argparse
+ parser = argparse.ArgumentParser()
+ parser.add_argument('--model-name', type=str, required=True)
+ parser.add_argument('--export-dir', type=str, required=True)
+ parser.add_argument('--type', type=str, default='onnx', help='["onnx", "torch"]')
+ parser.add_argument('--quantize', action='store_true', help='export quantized model')
+ parser.add_argument('--fallback-num', type=int, default=0, help='amp fallback number')
+ parser.add_argument('--audio_in', type=str, default=None, help='["wav", "wav.scp"]')
+ parser.add_argument('--calib_num', type=int, default=200, help='calib max num')
+ args = parser.parse_args()
+
+ export_model = ASRModelExportParaformer(
+ cache_dir=args.export_dir,
+ onnx=args.type == 'onnx',
+ quant=args.quantize,
+ fallback_num=args.fallback_num,
+ audio_in=args.audio_in,
+ calib_num=args.calib_num,
+ )
+ export_model.export(args.model_name)
diff --git a/funasr/export/models/modules/encoder_layer.py b/funasr/export/models/modules/encoder_layer.py
index d132574..7d01397 100644
--- a/funasr/export/models/modules/encoder_layer.py
+++ b/funasr/export/models/modules/encoder_layer.py
@@ -16,6 +16,7 @@
self.feed_forward = model.feed_forward
self.norm1 = model.norm1
self.norm2 = model.norm2
+ self.in_size = model.in_size
self.size = model.size
def forward(self, x, mask):
@@ -23,13 +24,12 @@
residual = x
x = self.norm1(x)
x = self.self_attn(x, mask)
- if x.size(2) == residual.size(2):
+ if self.in_size == self.size:
x = x + residual
residual = x
x = self.norm2(x)
x = self.feed_forward(x)
- if x.size(2) == residual.size(2):
- x = x + residual
+ x = x + residual
return x, mask
diff --git a/funasr/export/models/modules/multihead_att.py b/funasr/export/models/modules/multihead_att.py
index 7d685f5..0a56676 100644
--- a/funasr/export/models/modules/multihead_att.py
+++ b/funasr/export/models/modules/multihead_att.py
@@ -64,6 +64,21 @@
return self.linear_out(context_layer) # (batch, time1, d_model)
+def preprocess_for_attn(x, mask, cache, pad_fn):
+ x = x * mask
+ x = x.transpose(1, 2)
+ if cache is None:
+ x = pad_fn(x)
+ else:
+ x = torch.cat((cache[:, :, 1:], x), dim=2)
+ cache = x
+ return x, cache
+
+
+import torch.fx
+torch.fx.wrap('preprocess_for_attn')
+
+
class MultiHeadedAttentionSANMDecoder(nn.Module):
def __init__(self, model):
super().__init__()
@@ -73,16 +88,7 @@
self.attn = None
def forward(self, inputs, mask, cache=None):
- # b, t, d = inputs.size()
- # mask = torch.reshape(mask, (b, -1, 1))
- inputs = inputs * mask
-
- x = inputs.transpose(1, 2)
- if cache is None:
- x = self.pad_fn(x)
- else:
- x = torch.cat((cache[:, :, 1:], x), dim=2)
- cache = x
+ x, cache = preprocess_for_attn(inputs, mask, cache, self.pad_fn)
x = self.fsmn_block(x)
x = x.transpose(1, 2)
@@ -232,4 +238,4 @@
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(new_context_layer_shape)
return self.linear_out(context_layer) # (batch, time1, d_model)
-
\ No newline at end of file
+
diff --git a/funasr/runtime/python/utils/test_rtf.py b/funasr/runtime/python/utils/test_rtf.py
new file mode 100644
index 0000000..3394e8a
--- /dev/null
+++ b/funasr/runtime/python/utils/test_rtf.py
@@ -0,0 +1,47 @@
+
+import time
+import sys
+import librosa
+backend=sys.argv[1]
+model_dir=sys.argv[2]
+wav_file=sys.argv[3]
+
+from torch_paraformer import Paraformer
+if backend == "onnxruntime":
+ from rapid_paraformer import Paraformer
+
+model = Paraformer(model_dir, batch_size=1, device_id="-1")
+
+wav_file_f = open(wav_file, 'r')
+wav_files = wav_file_f.readlines()
+
+# warm-up
+total = 0.0
+num = 100
+wav_path = wav_files[0].split("\t")[1].strip() if "\t" in wav_files[0] else wav_files[0].split(" ")[1].strip()
+for i in range(num):
+ beg_time = time.time()
+ result = model(wav_path)
+ end_time = time.time()
+ duration = end_time-beg_time
+ total += duration
+ print(result)
+ print("num: {}, time, {}, avg: {}, rtf: {}".format(len(wav_path), duration, total/(i+1), (total/(i+1))/5.53))
+
+# infer time
+beg_time = time.time()
+for i, wav_path_i in enumerate(wav_files):
+ wav_path = wav_path_i.split("\t")[1].strip() if "\t" in wav_path_i else wav_path_i.split(" ")[1].strip()
+ result = model(wav_path)
+end_time = time.time()
+duration = (end_time-beg_time)*1000
+print("total_time_comput_ms: {}".format(int(duration)))
+
+duration_time = 0.0
+for i, wav_path_i in enumerate(wav_files):
+ wav_path = wav_path_i.split("\t")[1].strip() if "\t" in wav_path_i else wav_path_i.split(" ")[1].strip()
+ waveform, _ = librosa.load(wav_path, sr=16000)
+ duration_time += len(waveform)/16.0
+print("total_time_wav_ms: {}".format(int(duration_time)))
+
+print("total_rtf: {:.5}".format(duration/duration_time))
\ No newline at end of file
diff --git a/funasr/runtime/python/utils/test_rtf.sh b/funasr/runtime/python/utils/test_rtf.sh
new file mode 100644
index 0000000..fe13da7
--- /dev/null
+++ b/funasr/runtime/python/utils/test_rtf.sh
@@ -0,0 +1,74 @@
+
+nj=64
+
+#:<<!
+backend=libtorch
+model_dir="/nfs/zhifu.gzf/export/damo/amp_int8/libtorch"
+tag=${backend}_fp32
+!
+
+:<<!
+backend=libtorch
+model_dir="/nfs/zhifu.gzf/export/damo/amp_int8/libtorch_fb20"
+tag=${backend}_amp_fb20
+!
+
+:<<!
+backend=onnxruntime
+model_dir="/nfs/zhifu.gzf/export/damo/amp_int8/onnx"
+tag=${backend}_fp32
+!
+
+:<<!
+backend=onnxruntime
+model_dir="/nfs/zhifu.gzf/export/damo/amp_int8/onnx_dynamic"
+tag=${backend}_fp32
+!
+
+#scp=/nfs/haoneng.lhn/funasr_data/aishell-1/data/test/wav.scp
+scp="/nfs/zhifu.gzf/data_debug/test/wav_1500.scp"
+local_scp_dir=/nfs/zhifu.gzf/data_debug/test/${tag}/split$nj
+
+rtf_tool=test_rtf.py
+
+mkdir -p ${local_scp_dir}
+echo ${local_scp_dir}
+
+split_scps=""
+for JOB in $(seq ${nj}); do
+ split_scps="$split_scps $local_scp_dir/wav.$JOB.scp"
+done
+
+perl ../../../egs/aishell/transformer/utils/split_scp.pl $scp ${split_scps}
+
+
+for JOB in $(seq ${nj}); do
+ {
+ core_id=`expr $JOB - 1`
+ taskset -c ${core_id} python ${rtf_tool} ${backend} ${model_dir} ${local_scp_dir}/wav.$JOB.scp &> ${local_scp_dir}/log.$JOB.txt
+ }&
+
+done
+wait
+
+
+rm -rf ${local_scp_dir}/total_time_comput.txt
+rm -rf ${local_scp_dir}/total_time_wav.txt
+rm -rf ${local_scp_dir}/total_rtf.txt
+for JOB in $(seq ${nj}); do
+ {
+ cat ${local_scp_dir}/log.$JOB.txt | grep "total_time_comput" | awk -F ' ' '{print $2}' >> ${local_scp_dir}/total_time_comput.txt
+ cat ${local_scp_dir}/log.$JOB.txt | grep "total_time_wav" | awk -F ' ' '{print $2}' >> ${local_scp_dir}/total_time_wav.txt
+ cat ${local_scp_dir}/log.$JOB.txt | grep "total_rtf" | awk -F ' ' '{print $2}' >> ${local_scp_dir}/total_rtf.txt
+ }
+
+done
+
+total_time_comput=`cat ${local_scp_dir}/total_time_comput.txt | awk 'BEGIN {max = 0} {if ($1+0>max+0) max=$1 fi} END {print max}'`
+total_time_wav=`cat ${local_scp_dir}/total_time_wav.txt | awk '{sum +=$1};END {print sum}'`
+rtf=`awk 'BEGIN{printf "%.5f\n",'$total_time_comput'/'$total_time_wav'}'`
+speed=`awk 'BEGIN{printf "%.2f\n",1/'$rtf'}'`
+
+echo "total_time_comput_ms: $total_time_comput"
+echo "total_time_wav: $total_time_wav"
+echo "total_rtf: $rtf, speech: $speed"
\ No newline at end of file
--
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