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[Quantization] model quantization for inference

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This commit is contained in:
wanchen.swc 2023-03-06 18:18:31 +08:00
parent 25b88b6f26
commit 69ccdd35cd
3 changed files with 67 additions and 18 deletions
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51
funasr/export/export_model.py
View File

@ -15,7 +15,9 @@ import random
# 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:
@ -28,6 +30,7 @@ class ASRModelExportParaformer:
)
print("output dir: {}".format(self.cache_dir))
self.onnx = onnx
self.quant = quant
def _export(
@ -56,6 +59,28 @@ class ASRModelExportParaformer:
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)
@ -66,6 +91,12 @@ class ASRModelExportParaformer:
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)
@ -107,11 +138,12 @@ class ASRModelExportParaformer:
# 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(),
@ -119,6 +151,15 @@ class ASRModelExportParaformer:
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__':
import sys
@ -126,10 +167,12 @@ if __name__ == '__main__':
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')
# export_model.export('/root/cache/export/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch')

6
funasr/export/models/modules/encoder_layer.py
View File

@ -16,6 +16,7 @@ class EncoderLayerSANM(nn.Module):
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 @@ class EncoderLayerSANM(nn.Module):
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

28
funasr/export/models/modules/multihead_att.py
View File

@ -64,6 +64,21 @@ class MultiHeadedAttentionSANM(nn.Module):
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 @@ class MultiHeadedAttentionSANMDecoder(nn.Module):
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 @@ class OnnxRelPosMultiHeadedAttention(OnnxMultiHeadedAttention):
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)