def __init__(self, config):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(
config, "embedding_size"):
raise ValueError(
"The hidden size (%d) is not a multiple of the number of attention "
"heads (%d)" %
(config.hidden_size, config.num_attention_heads))
self.output_attentions = config.output_attentions
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size /
config.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
# Quantized implementations of torch.nn.Linear modules
self.query = quant_nn.QuantLinear(config.hidden_size,
self.all_head_size)
self.key = quant_nn.QuantLinear(config.hidden_size, self.all_head_size)
self.value = quant_nn.QuantLinear(config.hidden_size,
self.all_head_size)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
# Additional quantizers that will be needed to quantize the inputs to the torch.matmul() operation in the
# forward method. Since it's a simple operation and no quantized version of it exists, the inputs to this
# operation could be manually quantized to realize a quantized mat-mul operation.
self.matmul_q_input_quantizer = TensorQuantizer(
quant_nn.QuantLinear.default_quant_desc_input)
self.matmul_k_input_quantizer = TensorQuantizer(
quant_nn.QuantLinear.default_quant_desc_input)
self.matmul_v_input_quantizer = TensorQuantizer(
quant_nn.QuantLinear.default_quant_desc_input)
self.matmul_a_input_quantizer = TensorQuantizer(
quant_nn.QuantLinear.default_quant_desc_input)
def __init__(
self,
channels: int,
reduction_ratio: int,
context_window: int = -1,
interpolation_mode: str = 'nearest',
activation: Optional[Callable] = None,
quantize: bool = False,
):
"""
Squeeze-and-Excitation sub-module.
Args:
channels: Input number of channels.
reduction_ratio: Reduction ratio for "squeeze" layer.
context_window: Integer number of timesteps that the context
should be computed over, using stride 1 average pooling.
If value < 1, then global context is computed.
interpolation_mode: Interpolation mode of timestep dimension.
Used only if context window is > 1.
The modes available for resizing are: `nearest`, `linear` (3D-only),
`bilinear`, `area`
activation: Intermediate activation function used. Must be a
callable activation function.
"""
super(SqueezeExcite, self).__init__()
self.interpolation_mode = interpolation_mode
self._quantize = quantize
self.pool = None # prepare a placeholder which will be updated
if activation is None:
activation = nn.ReLU(inplace=True)
if PYTORCH_QUANTIZATION_AVAILABLE and quantize:
self.fc = nn.Sequential(
quant_nn.QuantLinear(channels, channels // reduction_ratio, bias=False),
activation,
quant_nn.QuantLinear(channels // reduction_ratio, channels, bias=False),
)
elif not PYTORCH_QUANTIZATION_AVAILABLE and quantize:
raise ImportError(
"pytorch-quantization is not installed. Install from "
"https://github.com/NVIDIA/TensorRT/tree/master/tools/pytorch-quantization."
)
else:
self.fc = nn.Sequential(
nn.Linear(channels, channels // reduction_ratio, bias=False),
activation,
nn.Linear(channels // reduction_ratio, channels, bias=False),
)
self.gap = nn.AdaptiveAvgPool1d(1)
# Set default context window
self.change_context_window(context_window=context_window)
# Set default max sequence length
self.set_max_len(16)
def __init__(self):
super(Net, self).__init__()
#self.conv1 = torch.nn.Conv2d(1, 32, (5, 5), padding=(2, 2), bias=True) # 换成对应的 Quantize 系列的 API
self.conv1 = qnn.QuantConv2d(1, 32, (5, 5), padding=(2, 2), bias=True)
#self.conv2 = torch.nn.Conv2d(32, 64, (5, 5), padding=(2, 2), bias=True)
self.conv2 = qnn.QuantConv2d(32, 64, (5, 5), padding=(2, 2), bias=True)
#self.fc1 = torch.nn.Linear(64 * 7 * 7, 1024, bias=True)
self.fc1 = qnn.QuantLinear(64 * 7 * 7, 1024, bias=True)
#self.fc2 = torch.nn.Linear(1024, 10, bias=True)
self.fc2 = qnn.QuantLinear(1024, 10, bias=True)
def test_initialize_deactivate(self):
no_replace_list = ["Linear"]
custom_quant_modules = [(torch.nn, "Linear", quant_nn.QuantLinear)]
quant_modules.initialize(no_replace_list, custom_quant_modules)
assert (type(quant_nn.QuantLinear(16, 256, 3)) == type(
torch.nn.Linear(16, 256, 3)))
assert (type(quant_nn.QuantConv2d(16, 256, 3)) == type(
torch.nn.Conv2d(16, 256, 3)))
quant_modules.deactivate()
def test_simple_default_args(self):
replacement_helper = QuantModuleReplacementHelper()
replacement_helper.prepare_state()
replacement_helper.apply_quant_modules()
# Linear module should not be replaced with its quantized version
assert (type(quant_nn.QuantLinear(16, 256, 3)) == type(
torch.nn.Linear(16, 256, 3)))
assert (type(quant_nn.QuantConv2d(16, 256, 3)) == type(
torch.nn.Conv2d(16, 256, 3)))
replacement_helper.restore_float_modules()
def test_with_no_replace_list(self):
no_replace_list = ["Linear"]
custom_quant_modules = None
replacement_helper = QuantModuleReplacementHelper()
replacement_helper.prepare_state(no_replace_list, custom_quant_modules)
replacement_helper.apply_quant_modules()
# Linear module should not be replaced with its quantized version
assert (type(quant_nn.QuantLinear(16, 256, 3)) != type(
torch.nn.Linear(16, 256, 3)))
assert (type(quant_nn.QuantConv2d(16, 256, 3)) == type(
torch.nn.Conv2d(16, 256, 3)))
replacement_helper.restore_float_modules()
def test_with_custom_quant_modules(self):
no_replace_list = ["Linear"]
custom_quant_modules = [(torch.nn, "Linear", quant_nn.QuantLinear)]
replacement_helper = QuantModuleReplacementHelper()
replacement_helper.prepare_state(no_replace_list, custom_quant_modules)
replacement_helper.apply_quant_modules()
# Although no replace list indicates Linear module should not be replaced with its
# quantized version, since the custom_quant_modules still contains the Linear module's
# mapping, it will replaced.
assert (type(quant_nn.QuantLinear(16, 256, 3)) == type(
torch.nn.Linear(16, 256, 3)))
assert (type(quant_nn.QuantConv2d(16, 256, 3)) == type(
torch.nn.Conv2d(16, 256, 3)))
replacement_helper.restore_float_modules()
def __init__(
self,
block: Type[Union[BasicBlock, Bottleneck]],
layers: List[int],
quantize: bool = False,
num_classes: int = 1000,
zero_init_residual: bool = False,
groups: int = 1,
width_per_group: int = 64,
replace_stride_with_dilation: Optional[List[bool]] = None,
norm_layer: Optional[Callable[..., nn.Module]] = None) -> None:
super(ResNet, self).__init__()
self._quantize = quantize
if norm_layer is None:
norm_layer = nn.BatchNorm2d
self._norm_layer = norm_layer
self.inplanes = 64
self.dilation = 1
if replace_stride_with_dilation is None:
# each element in the tuple indicates if we should replace
# the 2x2 stride with a dilated convolution instead
replace_stride_with_dilation = [False, False, False]
if len(replace_stride_with_dilation) != 3:
raise ValueError("replace_stride_with_dilation should be None "
"or a 3-element tuple, got {}".format(
replace_stride_with_dilation))
self.groups = groups
self.base_width = width_per_group
if quantize:
self.conv1 = quant_nn.QuantConv2d(3,
self.inplanes,
kernel_size=7,
stride=2,
padding=3,
bias=False)
else:
self.conv1 = nn.Conv2d(3,
self.inplanes,
kernel_size=7,
stride=2,
padding=3,
bias=False)
self.bn1 = norm_layer(self.inplanes)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0], quantize=quantize)
self.layer2 = self._make_layer(block,
128,
layers[1],
stride=2,
dilate=replace_stride_with_dilation[0],
quantize=quantize)
self.layer3 = self._make_layer(block,
256,
layers[2],
stride=2,
dilate=replace_stride_with_dilation[1],
quantize=quantize)
self.layer4 = self._make_layer(block,
512,
layers[3],
stride=2,
dilate=replace_stride_with_dilation[2],
quantize=quantize)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
if quantize:
self.fc = quant_nn.QuantLinear(512 * block.expansion, num_classes)
else:
self.fc = nn.Linear(512 * block.expansion, num_classes)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight,
mode='fan_out',
nonlinearity='relu')
elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
# Zero-initialize the last BN in each residual branch,
# so that the residual branch starts with zeros, and each residual block behaves like an identity.
# This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
if zero_init_residual:
for m in self.modules():
if isinstance(m, Bottleneck):
nn.init.constant_(m.bn3.weight,
0) # type: ignore[arg-type]
elif isinstance(m, BasicBlock):
nn.init.constant_(m.bn2.weight,
0) # type: ignore[arg-type]
