| import torch |
| from torch.nn import Module |
| from .observer import MovingAverageMinMaxObserver, HistogramObserver, MovingAveragePerChannelMinMaxObserver, _with_args |
| import re |
| from abc import ABC, abstractmethod |
| |
| def _is_per_channel(qscheme: 'torch.qscheme') -> bool: |
| return qscheme in [torch.per_channel_symmetric, torch.per_channel_affine] |
| |
| def _is_per_tensor(qscheme: 'torch.qscheme') -> bool: |
| return qscheme in [torch.per_tensor_symmetric, torch.per_tensor_affine] |
| |
| class FakeQuantizeBase(ABC, Module): |
| r""" Base fake quantize module |
| Any fake quantize implementation should derive from this class. |
| |
| Concrete fake quantize module should follow the same API. In forward, they will update |
| the statistics of the observed Tensor and fake quantize the input. They should also provide a |
| `calculate_qparams` function that computes the quantization parameters given |
| the collected statistics. |
| |
| """ |
| |
| fake_quant_enabled: torch.Tensor |
| observer_enabled: torch.Tensor |
| |
| def __init__(self): |
| super().__init__() |
| # fake_quant_enabled and observer_enabled are buffers to support their |
| # replication in DDP. Data type is uint8 because NCCL does not support |
| # bool tensors. |
| self.register_buffer('fake_quant_enabled', torch.tensor([1], dtype=torch.uint8)) |
| self.register_buffer('observer_enabled', torch.tensor([1], dtype=torch.uint8)) |
| |
| @abstractmethod |
| def forward(self, x): |
| pass |
| |
| @abstractmethod |
| def calculate_qparams(self, **kwargs): |
| pass |
| |
| @torch.jit.export |
| def enable_fake_quant(self, enabled: bool = True) -> None: |
| self.fake_quant_enabled[0] = 1 if enabled else 0 |
| |
| @torch.jit.export |
| def disable_fake_quant(self): |
| self.enable_fake_quant(False) |
| |
| @torch.jit.export |
| def enable_observer(self, enabled: bool = True) -> None: |
| self.observer_enabled[0] = 1 if enabled else 0 |
| |
| @torch.jit.export |
| def disable_observer(self): |
| self.enable_observer(False) |
| |
| with_args = classmethod(_with_args) |
| |
| class FakeQuantize(FakeQuantizeBase): |
| r""" Simulate the quantize and dequantize operations in training time. |
| The output of this module is given by |
| |
| x_out = (clamp(round(x/scale + zero_point), quant_min, quant_max)-zero_point)*scale |
| |
| |
| |
| * :attr:`scale` defines the scale factor used for quantization. |
| |
| * :attr:`zero_point` specifies the quantized value to which 0 in floating point maps to |
| |
| * :attr:`quant_min` specifies the minimum allowable quantized value. |
| |
| * :attr:`quant_max` specifies the maximum allowable quantized value. |
| |
| * :attr:`fake_quant_enable` controls the application of fake quantization on tensors, note that |
| statistics can still be updated. |
| |
| * :attr:`observer_enable` controls statistics collection on tensors |
| |
| * :attr:`dtype` specifies the quantized dtype that is being emulated with fake-quantization, |
| allowable values are torch.qint8 and torch.quint8. The values of quant_min and |
| quant_max should be chosen to be consistent with the dtype |
| |
| |
| Args: |
| observer (module): Module for observing statistics on input tensors and calculating scale |
| and zero-point. |
| quant_min (int): The minimum allowable quantized value. |
| quant_max (int): The maximum allowable quantized value. |
| observer_kwargs (optional): Arguments for the observer module |
| |
| Attributes: |
| observer (Module): User provided module that collects statistics on the input tensor and |
| provides a method to calculate scale and zero-point. |
| |
| """ |
| |
| scale: torch.Tensor |
| zero_point: torch.Tensor |
| |
| def __init__(self, observer=MovingAverageMinMaxObserver, quant_min=0, quant_max=255, **observer_kwargs): |
| super().__init__() |
| assert quant_min <= quant_max, \ |
| 'quant_min must be less than or equal to quant_max' |
| self.quant_min = quant_min |
| self.quant_max = quant_max |
| self.activation_post_process = observer(**observer_kwargs) |
| assert torch.iinfo(self.activation_post_process.dtype).min <= quant_min, 'quant_min out of bound' |
| assert quant_max <= torch.iinfo(self.activation_post_process.dtype).max, 'quant_max out of bound' |
| self.register_buffer('scale', torch.tensor([1.0])) |
| self.register_buffer('zero_point', torch.tensor([0])) |
| self.dtype = self.activation_post_process.dtype |
| self.qscheme = self.activation_post_process.qscheme |
| self.ch_axis = self.activation_post_process.ch_axis \ |
| if hasattr(self.activation_post_process, 'ch_axis') else -1 |
| assert _is_per_channel(self.qscheme) or \ |
| _is_per_tensor(self.qscheme), \ |
| 'Only per channel and per tensor quantization are supported in fake quantize' + \ |
| ' got qscheme: ' + str(self.qscheme) |
| self.is_per_channel = _is_per_channel(self.qscheme) |
| |
| @torch.jit.export |
| def calculate_qparams(self): |
| return self.activation_post_process.calculate_qparams() |
| |
| def forward(self, X): |
| if self.observer_enabled[0] == 1: |
| self.activation_post_process(X.detach()) |
| _scale, _zero_point = self.calculate_qparams() |
| _scale, _zero_point = _scale.to(self.scale.device), _zero_point.to(self.zero_point.device) |
| self.scale.resize_(_scale.shape) |
| self.scale.copy_(_scale) |
| self.zero_point.resize_(_zero_point.shape) |
| self.zero_point.copy_(_zero_point) |
| |
| if self.fake_quant_enabled[0] == 1: |
| if self.is_per_channel: |
| X = torch.fake_quantize_per_channel_affine( |
| X, self.scale, self.zero_point, |
| self.ch_axis, self.quant_min, self.quant_max) |
| else: |
| X = torch.fake_quantize_per_tensor_affine( |
| X, float(self.scale), int(self.zero_point), |
| self.quant_min, self.quant_max) |
| return X |
| |
| @torch.jit.export |
| def extra_repr(self): |
| return 'fake_quant_enabled={}, observer_enabled={}, ' \ |
| 'quant_min={}, quant_max={}, dtype={}, qscheme={}, ch_axis={}, ' \ |
| 'scale={}, zero_point={}'.format( |
| self.fake_quant_enabled, self.observer_enabled, |
| self.quant_min, self.quant_max, |
| self.dtype, self.qscheme, self.ch_axis, self.scale, self.zero_point) |
| |
| def _save_to_state_dict(self, destination, prefix, keep_vars): |
| # We cannot currently register scalar values as buffers, so need to manually |
| # specify serialization here. |
| super(FakeQuantize, self)._save_to_state_dict(destination, prefix, keep_vars) |
| destination[prefix + 'scale'] = self.scale |
| destination[prefix + 'zero_point'] = self.zero_point |
| |
| def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, |
| missing_keys, unexpected_keys, error_msgs): |
| # Removing this function throws an error that the the size of the loaded tensor does not match the original size |
| # i.e., These buffers start out with numel 0 and become numel 1 once they have their first forward pass. |
| local_state = ['scale', 'zero_point'] |
| for name in local_state: |
| key = prefix + name |
| if key in state_dict: |
| val = state_dict[key] |
| # Custom handling to allow loading scale and zero_point |
| # of size N into uninitialized buffers of size 0. The |
| # buffers are resized here, and the values are copied in |
| # the default state_dict loading code of the parent. |
| if name == 'scale': |
| self.scale.resize_(val.shape) |
| else: |
| assert name == 'zero_point' |
| self.zero_point.resize_(val.shape) |
| # For torchscript module we need to update the attributes here since we do not |
| # call the `_load_from_state_dict` function defined module.py |
| if torch.jit.is_scripting(): |
| if name == 'scale': |
| self.scale.copy_(val) |
| else: |
| assert name == 'zero_point' |
| self.zero_point.copy_(val) |
| elif strict: |
| missing_keys.append(key) |
| super(FakeQuantize, self)._load_from_state_dict(state_dict, prefix, local_metadata, strict, |
| missing_keys, unexpected_keys, error_msgs) |
| |
| class FixedQParamsFakeQuantize(FakeQuantizeBase): |
| """ Simulate quantize and dequantize with fixed quantization |
| parameters in training time. Only per tensor quantization |
| is supported. |
| Args: |
| `scale` (float): fixed scale for the fake quantize module |
| `zero_point` (int): fixed zero point for the fake quantize module |
| `dtype`, `qscheme`, `quant_min`, `quant_max` |
| """ |
| |
| scale: torch.Tensor |
| zero_point: torch.Tensor |
| |
| def __init__(self, |
| scale, |
| zero_point, |
| dtype=torch.quint8, |
| qscheme=torch.per_tensor_affine, |
| quant_min=0, |
| quant_max=255): |
| super().__init__() |
| assert quant_min <= quant_max, 'quant_min should be less than or equal to quant_max' |
| self.quant_min = quant_min |
| self.quant_max = quant_max |
| self.register_buffer('scale', torch.tensor([scale])) |
| self.register_buffer('zero_point', torch.tensor([zero_point])) |
| self.dtype = dtype |
| self.qscheme = qscheme |
| assert _is_per_tensor(self.qscheme), 'Only per tensor quantization is supported' + \ |
| ' FixedQParamsFakeQuantize module, got qscheme:' + str(self.qscheme) |
| |
| def forward(self, X): |
| if self.fake_quant_enabled[0] == 1: |
| X = torch.fake_quantize_per_tensor_affine(X, float(self.scale), |
| int(self.zero_point), self.quant_min, |
| self.quant_max) |
| return X |
| |
| @torch.jit.export |
| def calculate_qparams(self): |
| return self.scale, self.zero_point |
| |
| @torch.jit.export |
| def extra_repr(self): |
| return 'fake_quant_enabled={}, observer_enabled={}, scale={}, zero_point={}, ' \ |
| 'dtype={}, quant_min={}, quant_max={}, qscheme={}'.format( |
| self.fake_quant_enabled, self.observer_enabled, |
| self.scale, self.zero_point, self.dtype, |
| self.quant_min, self.quant_max, self.qscheme) |
| |
| |
| default_fake_quant = FakeQuantize.with_args(observer=MovingAverageMinMaxObserver, quant_min=0, quant_max=255, |
| dtype=torch.quint8, qscheme=torch.per_tensor_affine, reduce_range=True) |
| default_weight_fake_quant = FakeQuantize.with_args(observer=MovingAverageMinMaxObserver, quant_min=-128, quant_max=127, |
| dtype=torch.qint8, qscheme=torch.per_tensor_symmetric, reduce_range=False) |
| |
| # TODO(future PR): remove these defaults and enforce activation functions |
| # to explicitly specify their output range |
| default_symmetric_fixed_qparams_fake_quant = FixedQParamsFakeQuantize.with_args( |
| scale=2.0 / 256.0, zero_point=128, dtype=torch.quint8, quant_min=0, quant_max=255) |
| default_affine_fixed_qparams_fake_quant = FixedQParamsFakeQuantize.with_args( |
| scale=1.0 / 256.0, zero_point=0, dtype=torch.quint8, quant_min=0, quant_max=255) |
| |
| default_per_channel_weight_fake_quant = FakeQuantize.with_args(observer=MovingAveragePerChannelMinMaxObserver, |
| quant_min=-128, |
| quant_max=127, |
| dtype=torch.qint8, |
| qscheme=torch.per_channel_symmetric, |
| reduce_range=False, |
| ch_axis=0) |
| default_histogram_fake_quant = FakeQuantize.with_args(observer=HistogramObserver, |
| quant_min=0, |
| quant_max=255, |
| dtype=torch.quint8, |
| qscheme=torch.per_tensor_affine, |
| reduce_range=True) |
| |
| def _is_fake_quant_script_module(mod): |
| ''' Returns true if given mod is an instance of FakeQuantize script module. |
| ''' |
| if isinstance(mod, torch.jit.RecursiveScriptModule): |
| # qualified name looks like '__torch__.torch.quantization.fake_quantize.___torch_mangle_2.FakeQuantize' |
| suffix = mod._c.qualified_name.split('.', 1)[1] |
| name = re.sub(r'\.___torch_mangle_\d+', '', suffix) |
| return name == 'torch.quantization.fake_quantize.FakeQuantize' |
| return False |
| |
| def disable_fake_quant(mod): |
| if isinstance(mod, FakeQuantizeBase) or _is_fake_quant_script_module(mod): |
| mod.disable_fake_quant() |
| |
| def enable_fake_quant(mod): |
| if isinstance(mod, FakeQuantizeBase) or _is_fake_quant_script_module(mod): |
| mod.enable_fake_quant() |
| |
| def disable_observer(mod): |
| if isinstance(mod, FakeQuantizeBase) or _is_fake_quant_script_module(mod): |
| mod.disable_observer() |
| |
| def enable_observer(mod): |
| if isinstance(mod, FakeQuantizeBase) or _is_fake_quant_script_module(mod): |
| mod.enable_observer() |
