# coding=utf-8 r"""Quantized convolution modules.""" from typing import Optional, List, TypeVar import torch import torch.nn as nn import torch.nn.functional as F import torch.ao.nn.intrinsic as nni import torch.ao.nn.intrinsic.qat as nniqat from torch._ops import ops from torch.nn.common_types import _size_1_t from torch.nn.modules.utils import _single, _pair, _triple from torch.nn.utils import fuse_conv_bn_weights from .utils import _quantize_weight, WeightedQuantizedModule __all__ = ['Conv1d', 'Conv2d', 'Conv3d', 'ConvTranspose1d', 'ConvTranspose2d', 'ConvTranspose3d'] _SUPPORTED_PADDING = { 'zeros', 'reflect' } def _reverse_repeat_padding(padding: List[int]) -> List[int]: _reversed_padding_repeated_twice: List[int] = [] N = len(padding) for idx in range(N): for _ in range(2): _reversed_padding_repeated_twice.append(padding[N - idx - 1]) return _reversed_padding_repeated_twice class _ConvNd(WeightedQuantizedModule): def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode='zeros', device=None, dtype=None): # All subclasses have this signature - See PR #49702s raise NotImplementedError def _init(self, in_channels, out_channels, kernel_size, stride, padding, dilation, transposed, output_padding, groups, bias, padding_mode='zeros', device=None, dtype=None) -> None: factory_kwargs = {'device': device, 'dtype': dtype} super().__init__() if in_channels % groups != 0: raise ValueError('in_channels must be divisible by groups') if out_channels % groups != 0: raise ValueError('out_channels must be divisible by groups') self.in_channels = in_channels self.out_channels = out_channels self.kernel_size = kernel_size self.stride = stride self.padding = padding self.dilation = dilation self.transposed = transposed self.output_padding = output_padding self.groups = groups if padding_mode not in _SUPPORTED_PADDING: raise ValueError("'padding_mode' {} is not supported by quantized convolution".format(padding_mode)) self.padding_mode = padding_mode # Initialize as NCHW. set_weight will internally transpose to NHWC. if self.transposed: weight_shape = [in_channels, out_channels // self.groups] else: weight_shape = [out_channels, in_channels // self.groups] qweight = torch._empty_affine_quantized( weight_shape + list(kernel_size), scale=1, zero_point=0, dtype=torch.qint8, **{k: v for k, v in factory_kwargs.items() if k != 'dtype'}) bias_float = ( torch.zeros(out_channels, dtype=torch.float, **{k: v for k, v in factory_kwargs.items() if k != 'dtype'}) if bias else None) self.set_weight_bias(qweight, bias_float) self.scale = 1.0 self.zero_point = 0 def set_weight_bias(self, qweight, bias_float): raise NotImplementedError def bias(self): raise NotImplementedError def _weight_bias(self): raise NotImplementedError def extra_repr(self): s = ('{in_channels}, {out_channels}, kernel_size={kernel_size}' ', stride={stride}, scale={scale}, zero_point={zero_point}') if self.padding != (0,) * len(self.padding): s += ', padding={padding}' if self.dilation != (1,) * len(self.dilation): s += ', dilation={dilation}' if self.output_padding != (0,) * len(self.output_padding): s += ', output_padding={output_padding}' if self.groups != 1: s += ', groups={groups}' if self.bias() is None: s += ', bias=False' return s.format(**self.__dict__) # ===== Serialization methods ===== # The special consideration here is that we have to unpack the weights into # their regular QTensor form for serialization. Packed weights should not # live outside the process in which they were created, rather they should be # derived from the QTensor weight. # self # |--- weight : Tensor # |--- bias : Tensor # # TODO: maybe change to this when https://github.com/pytorch/pytorch/pull/32958 is landed # self # |--- _packed_params : Conv2dPackedParamsBase or Conv3dPackedParamsBase def _save_to_state_dict(self, destination, prefix, keep_vars): super()._save_to_state_dict(destination, prefix, keep_vars) (w, b) = self._weight_bias() destination[prefix + 'weight'] = w destination[prefix + 'bias'] = b destination[prefix + 'scale'] = torch.tensor(self.scale) destination[prefix + 'zero_point'] = torch.tensor(self.zero_point) @torch.jit.export def __getstate__(self): (w, b) = self._weight_bias() return ( self.in_channels, self.out_channels, self.kernel_size, self.stride, self.padding, self.dilation, self.transposed, self.output_padding, self.groups, self.padding_mode, w, b, self.scale, self.zero_point, self.training ) # ===== Deserialization methods ===== # Counterpart to the serialization methods, we must pack the serialized # QTensor weight into its packed format for use by the FBGEMM ops. def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs): self.set_weight_bias( state_dict[prefix + 'weight'], state_dict[prefix + 'bias']) state_dict.pop(prefix + 'weight') state_dict.pop(prefix + 'bias') self.scale = float(state_dict[prefix + 'scale']) state_dict.pop(prefix + 'scale') self.zero_point = int(state_dict[prefix + 'zero_point']) state_dict.pop(prefix + 'zero_point') super()._load_from_state_dict( state_dict, prefix, local_metadata, False, missing_keys, unexpected_keys, error_msgs) @torch.jit.export def __setstate__(self, state): self.in_channels = state[0] self.out_channels = state[1] self.kernel_size = state[2] self.stride = state[3] self.padding = state[4] self.dilation = state[5] self.transposed = state[6] self.output_padding = state[7] self.groups = state[8] self.padding_mode = state[9] self.set_weight_bias(state[10], state[11]) self.scale = state[12] self.zero_point = state[13] self.training = state[14] def __deepcopy__(self, memo): new_instance = type(self).__new__(type(self)) torch.nn.Module.__init__(new_instance) state = self.__getstate__() new_instance.__setstate__(state) return new_instance def __copy__(self): return self.__deepcopy__({}) @classmethod def get_qconv(cls, mod, activation_post_process, weight_post_process=None): r"""Creates a qconv object and returns it. """ if weight_post_process is None: weight_post_process = mod.qconfig.weight() weight_post_process(mod.weight) assert weight_post_process.dtype == torch.qint8, \ 'Weight observer must have a dtype of qint8' qweight = _quantize_weight(mod.weight.float(), weight_post_process) # the __init__ call used is the one from derived classes and not the one from _ConvNd qconv = cls(mod.in_channels, mod.out_channels, mod.kernel_size, mod.stride, mod.padding, mod.dilation, mod.groups, mod.bias is not None, mod.padding_mode) qconv.set_weight_bias(qweight, mod.bias) if activation_post_process is None or activation_post_process.dtype == torch.float: return qconv # dynamic quantization doesn't need scale/zero_point else: act_scale, act_zp = activation_post_process.calculate_qparams() qconv.scale = float(act_scale) qconv.zero_point = int(act_zp) return qconv @staticmethod def from_float(cls, mod): if hasattr(mod, "weight_fake_quant"): # assert type(mod) == cls.__QAT_MODULE, " nnq." + cls.__name__ + \ # ".from_float only works for " + cls.__QAT_MODULE.__name__ if type(mod) == cls._NNIQAT_CONV_BN_MODULE: mod.weight, mod.bias = fuse_conv_bn_weights( mod.weight, mod.bias, mod.bn.running_mean, mod.bn.running_var, mod.bn.eps, mod.bn.weight, mod.bn.bias) assert hasattr(mod, "activation_post_process"), \ "Input QAT module must have observer attached" weight_post_process = mod.weight_fake_quant activation_post_process = mod.activation_post_process else: assert type(mod) == cls._FLOAT_MODULE, \ " nnq." + cls.__name__ + ".from_float only works for " + \ cls._FLOAT_MODULE.__name__ + " but got:" + str(type(mod)) assert hasattr(mod, "qconfig"), \ "Input float module must have qconfig defined." activation_post_process = None if not hasattr( mod, "activation_post_process") else mod.activation_post_process if type(mod) in [cls._NNI_CONV_RELU_MODULE, cls._NNI_CONV_ADD_MODULE, cls._NNI_CONV_ADD_RELU_MODULE]: mod = mod[0] weight_post_process = mod.qconfig.weight() return cls.get_qconv(mod, activation_post_process, weight_post_process) @classmethod def from_reference(cls, ref_qconv, output_scale, output_zero_point): r"""Create a (fbgemm/qnnpack) quantized module from a reference quantized module Args: ref_qconv (Module): a reference quantized module, either produced by torch.ao.quantization utilities or provided by the user output_scale (float): scale for output Tensor output_zero_point (int): zero point for output Tensor """ qconv = cls( ref_qconv.in_channels, ref_qconv.out_channels, ref_qconv.kernel_size, # type: ignore[arg-type] ref_qconv.stride, # type: ignore[arg-type] ref_qconv.padding, # type: ignore[arg-type] ref_qconv.dilation, # type: ignore[arg-type] ref_qconv.groups, ref_qconv.bias is not None, # type: ignore[arg-type] ref_qconv.padding_mode, device=ref_qconv.weight.device, dtype=ref_qconv.weight.dtype) qweight = ref_qconv.get_quantized_weight() qconv.set_weight_bias(qweight, ref_qconv.bias) qconv.scale = float(output_scale) qconv.zero_point = int(output_zero_point) return qconv class Conv1d(_ConvNd): r"""Applies a 1D convolution over a quantized input signal composed of several quantized input planes. For details on input arguments, parameters, and implementation see :class:`~torch.nn.Conv1d`. .. note:: Only `zeros` is supported for the :attr:`padding_mode` argument. .. note:: Only `torch.quint8` is supported for the input data type. Attributes: weight (Tensor): packed tensor derived from the learnable weight parameter. scale (Tensor): scalar for the output scale zero_point (Tensor): scalar for the output zero point See :class:`~torch.nn.Conv1d` for other attributes. Examples:: >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE) >>> m = nn.quantized.Conv1d(16, 33, 3, stride=2) >>> input = torch.randn(20, 16, 100) >>> # quantize input to quint8 >>> # xdoctest: +SKIP >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, ... dtype=torch.quint8) >>> output = m(q_input) """ _FLOAT_MODULE = nn.Conv1d _NNIQAT_CONV_BN_MODULE = nniqat.ConvBn1d _NNI_CONV_RELU_MODULE = nni.ConvReLU1d _NNI_CONV_ADD_MODULE = None _NNI_CONV_ADD_RELU_MODULE = None def __init__(self, in_channels: int, out_channels: int, kernel_size: _size_1_t, stride: _size_1_t = 1, padding: _size_1_t = 0, dilation: _size_1_t = 1, groups: int = 1, bias: bool = True, padding_mode: str = 'zeros', device=None, dtype=None): factory_kwargs = {'device': device, 'dtype': dtype} kernel_size = _single(kernel_size) stride = _single(stride) padding = padding if isinstance(padding, str) else _single(padding) dilation = _single(dilation) # Subclasses of _ConvNd needs to call _init rather than __init__. See # discussion on PR #49702 super()._init( in_channels, out_channels, kernel_size, stride, padding, dilation, False, _single(0), groups, bias, padding_mode, **factory_kwargs) def _get_name(self): return 'QuantizedConv1d' def set_weight_bias(self, w: torch.Tensor, b: Optional[torch.Tensor]) -> None: if self.padding_mode == 'zeros': self._packed_params = torch.ops.quantized.conv1d_prepack( w, b, self.stride, self.padding, self.dilation, self.groups) else: self._packed_params = torch.ops.quantized.conv1d_prepack( w, b, self.stride, _pair(0), self.dilation, self.groups) def _weight_bias(self): w, b = torch.ops.quantized.conv1d_unpack(self._packed_params) return w, b def weight(self): return self._weight_bias()[0] def bias(self): return self._weight_bias()[1] def forward(self, input): # Temporarily using len(shape) instead of ndim due to JIT issue # https://github.com/pytorch/pytorch/issues/23890 if len(input.shape) != 3: raise ValueError("Input shape must be `(N, C, L)`!") if self.padding_mode != 'zeros': # Padding in Conv1d is stored as (p, p), need to get (p,) _reversed_padding_repeated_twice = _reverse_repeat_padding(self.padding[:1]) input = F.pad(input, _reversed_padding_repeated_twice, mode=self.padding_mode) return ops.quantized.conv1d(input, self._packed_params, self.scale, self.zero_point) @classmethod def from_float(cls, mod): r"""Creates a quantized module from a float module or qparams_dict. Args: mod (Module): a float module, either produced by torch.ao.quantization utilities or provided by the user """ return _ConvNd.from_float(cls, mod) class Conv2d(_ConvNd): r"""Applies a 2D convolution over a quantized input signal composed of several quantized input planes. For details on input arguments, parameters, and implementation see :class:`~torch.nn.Conv2d`. .. note:: Only `zeros` is supported for the :attr:`padding_mode` argument. .. note:: Only `torch.quint8` is supported for the input data type. Attributes: weight (Tensor): packed tensor derived from the learnable weight parameter. scale (Tensor): scalar for the output scale zero_point (Tensor): scalar for the output zero point See :class:`~torch.nn.Conv2d` for other attributes. Examples:: >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE) >>> # With square kernels and equal stride >>> m = nn.quantized.Conv2d(16, 33, 3, stride=2) >>> # non-square kernels and unequal stride and with padding >>> m = nn.quantized.Conv2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2)) >>> # non-square kernels and unequal stride and with padding and dilation >>> m = nn.quantized.Conv2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2), dilation=(3, 1)) >>> input = torch.randn(20, 16, 50, 100) >>> # quantize input to quint8 >>> # xdoctest: +SKIP >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, dtype=torch.quint8) >>> output = m(q_input) """ _FLOAT_MODULE = nn.Conv2d _NNIQAT_CONV_BN_MODULE = nniqat.ConvBn2d _NNI_CONV_RELU_MODULE = nni.ConvReLU2d _NNI_CONV_ADD_MODULE = nni.ConvAdd2d _NNI_CONV_ADD_RELU_MODULE = nni.ConvAddReLU2d def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode='zeros', device=None, dtype=None): factory_kwargs = {'device': device, 'dtype': dtype} kernel_size = _pair(kernel_size) stride = _pair(stride) padding = _pair(padding) dilation = _pair(dilation) # Subclasses of _ConvNd need to call _init rather than __init__. See # discussion on PR #49702 super()._init( in_channels, out_channels, kernel_size, stride, padding, dilation, False, _pair(0), groups, bias, padding_mode, **factory_kwargs) def _get_name(self): return 'QuantizedConv2d' def set_weight_bias(self, w: torch.Tensor, b: Optional[torch.Tensor]) -> None: if self.padding_mode == 'zeros': self._packed_params = torch.ops.quantized.conv2d_prepack( w, b, self.stride, self.padding, self.dilation, self.groups) else: self._packed_params = torch.ops.quantized.conv2d_prepack( w, b, self.stride, _pair(0), self.dilation, self.groups) def _weight_bias(self): return self._packed_params.unpack() def weight(self): return self._weight_bias()[0] def bias(self): return self._weight_bias()[1] def forward(self, input): # Temporarily using len(shape) instead of ndim due to JIT issue # https://github.com/pytorch/pytorch/issues/23890 if len(input.shape) != 4: raise ValueError("Input shape must be `(N, C, H, W)`!") if self.padding_mode != 'zeros': _reversed_padding_repeated_twice = _reverse_repeat_padding(self.padding) input = F.pad(input, _reversed_padding_repeated_twice, mode=self.padding_mode) return ops.quantized.conv2d( input, self._packed_params, self.scale, self.zero_point) @classmethod def from_float(cls, mod): r"""Creates a quantized module from a float module or qparams_dict. Args: mod (Module): a float module, either produced by torch.ao.quantization utilities or provided by the user """ return _ConvNd.from_float(cls, mod) class Conv3d(_ConvNd): r"""Applies a 3D convolution over a quantized input signal composed of several quantized input planes. For details on input arguments, parameters, and implementation see :class:`~torch.nn.Conv3d`. .. note:: Only `zeros` is supported for the :attr:`padding_mode` argument. .. note:: Only `torch.quint8` is supported for the input data type. Attributes: weight (Tensor): packed tensor derived from the learnable weight parameter. scale (Tensor): scalar for the output scale zero_point (Tensor): scalar for the output zero point See :class:`~torch.nn.Conv3d` for other attributes. Examples:: >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE) >>> # With square kernels and equal stride >>> m = nn.quantized.Conv3d(16, 33, 3, stride=2) >>> # non-square kernels and unequal stride and with padding >>> m = nn.quantized.Conv3d(16, 33, (3, 5, 5), stride=(1, 2, 2), padding=(1, 2, 2)) >>> # non-square kernels and unequal stride and with padding and dilation >>> m = nn.quantized.Conv3d(16, 33, (3, 5, 5), stride=(1, 2, 2), padding=(1, 2, 2), dilation=(1, 2, 2)) >>> input = torch.randn(20, 16, 56, 56, 56) >>> # quantize input to quint8 >>> # xdoctest: +SKIP >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, dtype=torch.quint8) >>> output = m(q_input) """ _FLOAT_MODULE = nn.Conv3d _NNIQAT_CONV_BN_MODULE = nniqat.ConvBn3d _NNI_CONV_RELU_MODULE = nni.ConvReLU3d _NNI_CONV_ADD_MODULE = None _NNI_CONV_ADD_RELU_MODULE = None def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode='zeros', device=None, dtype=None): assert padding_mode != 'reflect', "Conv3d does not support reflection padding" factory_kwargs = {'device': device, 'dtype': dtype} kernel_size = _triple(kernel_size) stride = _triple(stride) padding = _triple(padding) dilation = _triple(dilation) # Subclasses of _ConvNd need to call _init rather than __init__. See # discussion on PR #49702 super()._init( in_channels, out_channels, kernel_size, stride, padding, dilation, False, _triple(0), groups, bias, padding_mode, **factory_kwargs) def _get_name(self): return 'QuantizedConv3d' def set_weight_bias(self, w: torch.Tensor, b: Optional[torch.Tensor]) -> None: if self.padding_mode == 'zeros': self._packed_params = torch.ops.quantized.conv3d_prepack( w, b, self.stride, self.padding, self.dilation, self.groups) else: self._packed_params = torch.ops.quantized.conv3d_prepack( w, b, self.stride, _triple(0), self.dilation, self.groups) def _weight_bias(self): return self._packed_params.unpack() def weight(self): return self._weight_bias()[0] def bias(self): return self._weight_bias()[1] def forward(self, input): # Temporarily using len(shape) instead of ndim due to JIT issue # https://github.com/pytorch/pytorch/issues/23890 if len(input.shape) != 5: raise ValueError("Input shape must be `(N, C, D, H, W)`!") if self.padding_mode != 'zeros': _reversed_padding_repeated_twice = _reverse_repeat_padding(self.padding) input = F.pad(input, _reversed_padding_repeated_twice, mode=self.padding_mode) return ops.quantized.conv3d( input, self._packed_params, self.scale, self.zero_point) @classmethod def from_float(cls, mod): r"""Creates a quantized module from a float module or qparams_dict. Args: mod (Module): a float module, either produced by torch.ao.quantization utilities or provided by the user """ return _ConvNd.from_float(cls, mod) # === Transposed Convolutions === MOD = TypeVar('MOD', bound=nn.modules.conv._ConvNd) class _ConvTransposeNd(_ConvNd): _FLOAT_MODULE = MOD def __init__(self, in_channels, out_channels, kernel_size, stride, padding, dilation, transposed, output_padding, groups, bias, padding_mode, device=None, dtype=None): if padding_mode != 'zeros': raise ValueError('Only "zeros" padding mode is supported for {}'.format(self.__class__.__name__)) factory_kwargs = {'device': device, 'dtype': dtype} # Subclasses of _ConvNd need to call _init rather than __init__. See # discussion on PR #49702 super()._init( in_channels, out_channels, kernel_size, stride, padding, dilation, transposed, output_padding, groups, bias, padding_mode, **factory_kwargs) def _input_padding(self, kernel_size: List[int], dilation: List[int], padding: List[int]) -> List[int]: res = torch.jit.annotate(List[int], []) for kdx in range(len(kernel_size)): pad = (dilation[kdx] * (kernel_size[kdx] - 1) - padding[kdx]) res.append(pad) return res @classmethod def from_float(cls, mod): r"""Creates a quantized module from a float module or qparams_dict. Args: mod (Module): a float module, either produced by torch.ao.quantization utilities or provided by the user """ # derived classes override cls._FLOAT_MODULE attribute msg = ' nnq.' + cls.__name__ + '.from_float only works for ' + \ cls._FLOAT_MODULE.__name__ # type: ignore[attr-defined] assert type(mod) == cls._FLOAT_MODULE, msg assert hasattr(mod, 'qconfig'), \ 'Input float module must have qconfig defined.' weight_post_process = mod.qconfig.weight() weight_post_process(mod.weight) assert weight_post_process.dtype == torch.qint8, \ 'Weight observer must have a dtype of qint8' qweight = _quantize_weight(mod.weight.float(), weight_post_process) # the __init__ call used is the one from derived classes and not the one from _ConvTransposeNd qconv = cls(mod.in_channels, mod.out_channels, mod.kernel_size, # type: ignore[call-arg] mod.stride, mod.padding, mod.output_padding, mod.groups, mod.bias is not None, mod.dilation, mod.padding_mode) qconv.set_weight_bias(qweight, mod.bias) if not hasattr(mod, "activation_post_process") or mod.activation_post_process.dtype == torch.float: return qconv # dynamic quantization doesn't need scale/zero_point else: act_scale, act_zp = mod.activation_post_process.calculate_qparams() qconv.scale = float(act_scale) qconv.zero_point = int(act_zp) return qconv @staticmethod def from_reference(cls, ref_qconvt, output_scale, output_zero_point): r"""Create a (fbgemm/qnnpack) quantized module from a reference quantized module Args: ref_qconvt (Module): a reference quantized module, either produced by torch.ao.quantization utilities or provided by the user output_scale (float): scale for output Tensor output_zero_point (int): zero point for output Tensor """ qconv = cls( ref_qconvt.in_channels, ref_qconvt.out_channels, ref_qconvt.kernel_size, # type: ignore[arg-type] ref_qconvt.stride, # type: ignore[arg-type] ref_qconvt.padding, # type: ignore[arg-type] ref_qconvt.output_padding, # type: ignore[arg-type] ref_qconvt.groups, ref_qconvt.bias is not None, # type: ignore[arg-type] ref_qconvt.dilation, # type: ignore[arg-type] ref_qconvt.padding_mode, device=ref_qconvt.weight.device, dtype=ref_qconvt.weight.dtype) qweight = ref_qconvt.get_quantized_weight() qconv.set_weight_bias(qweight, ref_qconvt.bias) qconv.scale = float(output_scale) qconv.zero_point = int(output_zero_point) return qconv class ConvTranspose1d(_ConvTransposeNd): r"""Applies a 1D transposed convolution operator over an input image composed of several input planes. For details on input arguments, parameters, and implementation see :class:`~torch.nn.ConvTranspose1d`. .. note:: Currently only the QNNPACK engine is implemented. Please, set the `torch.backends.quantized.engine = 'qnnpack'` For special notes, please, see :class:`~torch.ao.nn.quantized.Conv1d` Attributes: weight (Tensor): packed tensor derived from the learnable weight parameter. scale (Tensor): scalar for the output scale zero_point (Tensor): scalar for the output zero point See :class:`~torch.nn.ConvTranspose2d` for other attributes. Examples:: >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE) >>> torch.backends.quantized.engine = 'qnnpack' >>> from torch.ao.nn import quantized as nnq >>> # With square kernels and equal stride >>> m = nnq.ConvTranspose1d(16, 33, 3, stride=2) >>> # non-square kernels and unequal stride and with padding >>> m = nnq.ConvTranspose1d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2)) >>> input = torch.randn(20, 16, 50) >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, dtype=torch.quint8) >>> output = m(q_input) >>> # exact output size can be also specified as an argument >>> input = torch.randn(1, 16, 12) >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, dtype=torch.quint8) >>> downsample = nnq.Conv1d(16, 16, 3, stride=2, padding=1) >>> upsample = nnq.ConvTranspose1d(16, 16, 3, stride=2, padding=1) >>> h = downsample(q_input) >>> h.size() torch.Size([1, 16, 6]) >>> # xdoctest: +SKIP("FIXME: output_size is not a parameter) >>> output = upsample(h, output_size=input.size()) >>> output.size() torch.Size([1, 16, 12]) """ _FLOAT_MODULE = nn.ConvTranspose1d def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, output_padding=0, groups=1, bias=True, dilation=1, padding_mode='zeros', device=None, dtype=None): factory_kwargs = {'device': device, 'dtype': dtype} kernel_size = _single(kernel_size) stride = _single(stride) padding = _single(padding) dilation = _single(dilation) output_padding = _single(output_padding) super().__init__( in_channels, out_channels, kernel_size, stride, padding, dilation, True, output_padding, groups, bias, padding_mode, **factory_kwargs) def _get_name(self): return 'QuantizedConvTranpose1d' def set_weight_bias(self, w: torch.Tensor, b: Optional[torch.Tensor]) -> None: self._packed_params = torch.ops.quantized.conv_transpose1d_prepack( w, b, self.stride, self.padding, self.output_padding, self.dilation, self.groups) def _weight_bias(self): w, b = torch.ops.quantized.conv_transpose1d_unpack(self._packed_params) return w, b def weight(self): (w, _) = self._weight_bias() return w def bias(self): (_, b) = self._weight_bias() return b def forward(self, input): # Temporarily using len(shape) instead of ndim due to JIT issue # https://github.com/pytorch/pytorch/issues/23890 if len(input.shape) != 3: raise ValueError("Input shape must be `(N, C, L)`!") return torch.ops.quantized.conv_transpose1d( input, self._packed_params, self.scale, self.zero_point) @classmethod def from_reference(cls, ref_qconvt, output_scale, output_zero_point): return _ConvTransposeNd.from_reference(cls, ref_qconvt, output_scale, output_zero_point) class ConvTranspose2d(_ConvTransposeNd): r"""Applies a 2D transposed convolution operator over an input image composed of several input planes. For details on input arguments, parameters, and implementation see :class:`~torch.nn.ConvTranspose2d`. For special notes, please, see :class:`~torch.ao.nn.quantized.Conv2d` Attributes: weight (Tensor): packed tensor derived from the learnable weight parameter. scale (Tensor): scalar for the output scale zero_point (Tensor): scalar for the output zero point See :class:`~torch.nn.ConvTranspose2d` for other attributes. Examples:: >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE) >>> # QNNPACK or FBGEMM as backend >>> torch.backends.quantized.engine = 'qnnpack' >>> # With square kernels and equal stride >>> import torch.ao.nn.quantized as nnq >>> m = nnq.ConvTranspose2d(16, 33, 3, stride=2) >>> # non-square kernels and unequal stride and with padding >>> m = nnq.ConvTranspose2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2)) >>> input = torch.randn(20, 16, 50, 100) >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, dtype=torch.quint8) >>> output = m(q_input) >>> # exact output size can be also specified as an argument >>> input = torch.randn(1, 16, 12, 12) >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, dtype=torch.quint8) >>> downsample = nnq.Conv2d(16, 16, 3, stride=2, padding=1) >>> upsample = nnq.ConvTranspose2d(16, 16, 3, stride=2, padding=1) >>> h = downsample(q_input) >>> h.size() torch.Size([1, 16, 6, 6]) >>> # xdoctest: +SKIP("FIXME: output_size is not a parameter) >>> output = upsample(h, output_size=input.size()) >>> output.size() torch.Size([1, 16, 12, 12]) """ _FLOAT_MODULE = nn.ConvTranspose2d def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, output_padding=0, groups=1, bias=True, dilation=1, padding_mode='zeros', device=None, dtype=None): factory_kwargs = {'device': device, 'dtype': dtype} kernel_size = _pair(kernel_size) stride = _pair(stride) padding = _pair(padding) dilation = _pair(dilation) output_padding = _pair(output_padding) super().__init__( in_channels, out_channels, kernel_size, stride, padding, dilation, True, output_padding, groups, bias, padding_mode, **factory_kwargs) def _get_name(self): return 'QuantizedConvTranpose2d' def set_weight_bias(self, w: torch.Tensor, b: Optional[torch.Tensor]) -> None: self._packed_params = torch.ops.quantized.conv_transpose2d_prepack( w, b, self.stride, self.padding, self.output_padding, self.dilation, self.groups) def _weight_bias(self): w, b = torch.ops.quantized.conv2d_unpack(self._packed_params) return w, b def weight(self): (w, _) = self._weight_bias() return w def bias(self): (_, b) = self._weight_bias() return b def forward(self, input): # Temporarily using len(shape) instead of ndim due to JIT issue # https://github.com/pytorch/pytorch/issues/23890 if len(input.shape) != 4: raise ValueError("Input shape must be `(N, C, H, W)`!") return ops.quantized.conv_transpose2d( input, self._packed_params, self.scale, self.zero_point) @classmethod def from_reference(cls, ref_qconvt, output_scale, output_zero_point): return _ConvTransposeNd.from_reference(cls, ref_qconvt, output_scale, output_zero_point) class ConvTranspose3d(_ConvTransposeNd): r"""Applies a 3D transposed convolution operator over an input image composed of several input planes. For details on input arguments, parameters, and implementation see :class:`~torch.nn.ConvTranspose3d`. .. note:: Currently only the FBGEMM engine is implemented. Please, set the `torch.backends.quantized.engine = 'fbgemm'` For special notes, please, see :class:`~torch.ao.nn.quantized.Conv3d` Attributes: weight (Tensor): packed tensor derived from the learnable weight parameter. scale (Tensor): scalar for the output scale zero_point (Tensor): scalar for the output zero point See :class:`~torch.nn.ConvTranspose3d` for other attributes. Examples:: >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE) >>> torch.backends.quantized.engine = 'fbgemm' >>> from torch.ao.nn import quantized as nnq >>> # With cubic kernels and equal stride >>> m = nnq.ConvTranspose3d(16, 33, 3, stride=2) >>> # non-cubic kernels and unequal stride and with padding >>> m = nnq.ConvTranspose3d(16, 33, (3, 3, 5), stride=(2, 1, 1), padding=(4, 2, 2)) >>> input = torch.randn(20, 16, 50, 100, 100) >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, dtype=torch.quint8) >>> output = m(q_input) >>> # exact output size can be also specified as an argument >>> input = torch.randn(1, 16, 12, 12, 12) >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, dtype=torch.quint8) >>> downsample = nnq.Conv3d(16, 16, 3, stride=2, padding=1) >>> upsample = nnq.ConvTranspose3d(16, 16, 3, stride=2, padding=1) >>> h = downsample(q_input) >>> h.size() torch.Size([1, 16, 6, 6, 6]) >>> # xdoctest: +SKIP("FIXME: output_size is not a parameter) >>> output = upsample(h, output_size=input.size()) >>> output.size() torch.Size([1, 16, 12, 12, 12]) """ _FLOAT_MODULE = nn.ConvTranspose3d def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, output_padding=0, groups=1, bias=True, dilation=1, padding_mode='zeros', device=None, dtype=None): factory_kwargs = {'device': device, 'dtype': dtype} kernel_size = _triple(kernel_size) stride = _triple(stride) padding = _triple(padding) dilation = _triple(dilation) output_padding = _triple(output_padding) super().__init__( in_channels, out_channels, kernel_size, stride, padding, dilation, True, output_padding, groups, bias, padding_mode, **factory_kwargs) def _get_name(self): return 'QuantizedConvTranpose3d' def set_weight_bias(self, w: torch.Tensor, b: Optional[torch.Tensor]) -> None: self._packed_params = torch.ops.quantized.conv_transpose3d_prepack( w, b, self.stride, self.padding, self.output_padding, self.dilation, self.groups) def _weight_bias(self): w, b = torch.ops.quantized.conv3d_unpack(self._packed_params) return w, b def weight(self): (w, _) = self._weight_bias() return w def bias(self): (_, b) = self._weight_bias() return b def forward(self, input): # Temporarily using len(shape) instead of ndim due to JIT issue # https://github.com/pytorch/pytorch/issues/23890 if len(input.shape) != 5: raise ValueError("Input shape must be `(N, C, T, H, W)`!") return ops.quantized.conv_transpose3d( input, self._packed_params, self.scale, self.zero_point) @classmethod def from_reference(cls, ref_qconvt, output_scale, output_zero_point): return _ConvTransposeNd.from_reference(cls, ref_qconvt, output_scale, output_zero_point)