From 69ccdd35cda4c8482e189fa350fbcb83997872f2 Mon Sep 17 00:00:00 2001
From: wanchen.swc <wanchen.swc@alibaba-inc.com>
Date: 星期一, 06 三月 2023 18:18:31 +0800
Subject: [PATCH] [Quantization] model quantization for inference
---
funasr/export/export_model.py | 163 +++++++++++++++++-------------------------------------
1 files changed, 52 insertions(+), 111 deletions(-)
diff --git a/funasr/export/export_model.py b/funasr/export/export_model.py
index e1d5fdb..1c677c9 100644
--- a/funasr/export/export_model.py
+++ b/funasr/export/export_model.py
@@ -7,13 +7,17 @@
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):
+ def __init__(
+ self, cache_dir: Union[Path, str] = None, onnx: bool = True, quant: bool = True
+ ):
assert check_argument_types()
self.set_all_random_seed(0)
if cache_dir is None:
@@ -26,11 +30,12 @@
)
print("output dir: {}".format(self.cache_dir))
self.onnx = onnx
+ self.quant = quant
def _export(
self,
- model: Speech2Text,
+ model,
tag_name: str = None,
verbose: bool = False,
):
@@ -54,15 +59,43 @@
print("output dir: {}".format(export_dir))
+ def _torch_quantize(self, model):
+ from torch_quant.module import ModuleFilter
+ from torch_quant.observer import HistogramObserver
+ 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,
+ act_ob_ctr=HistogramObserver,
+ )
+ model.eval()
+ calib_model = quantizer.calib(model)
+ # run calibration data
+ # using dummy inputs for a example
+ dummy_input = model.get_dummy_inputs()
+ _ = calib_model(*dummy_input)
+ 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)
else:
- dummy_input = model.get_dummy_inputs_txt()
+ dummy_input = model.get_dummy_inputs()
# 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'))
+
+ 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)
@@ -105,121 +138,27 @@
# 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=12,
+ 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()
- )
+ if self.quant:
+ from onnxruntime.quantization import QuantType, quantize_dynamic
+ quant_model_path = os.path.join(path, f'{model.model_name}_quant.onnx')
+ quantize_dynamic(
+ model_input=model_path,
+ model_output=quant_model_path,
+ weight_type=QuantType.QUInt8,
+ )
if __name__ == '__main__':
@@ -228,10 +167,12 @@
model_path = sys.argv[1]
output_dir = sys.argv[2]
onnx = sys.argv[3]
+ quant = sys.argv[4]
onnx = onnx.lower()
onnx = onnx == 'true'
+ quant = quant == '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 = ASRModelExportParaformer(cache_dir=output_dir, onnx=onnx, quant=quant)
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
+ # export_model.export('/root/cache/export/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch')
--
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