""" Utils shared by different modes of quantization (eager/graph) """ import warnings import functools import torch from torch.ao.quantization.quant_type import QuantType from typing import Tuple, Any, Union, Callable, Dict, Optional from torch.nn.utils.parametrize import is_parametrized from collections import OrderedDict from inspect import signature from inspect import getfullargspec # Type for fusion patterns, it can be more complicated than the following actually, # see pattern.md for docs # TODO: not sure if typing supports recursive data types Pattern = Union[Callable, Tuple[Callable, Callable], Tuple[Callable, Tuple[Callable, Callable]], Any] Pattern.__module__ = "torch.ao.quantization.utils" # TODO: maybe rename this to MatchInputNode class MatchAllNode: """ A node pattern that matches all nodes, used in defining fusion patterns in FX Graph Mode Quantization """ pass module_type_list = { torch.nn.ReLU, torch.nn.ReLU6, torch.nn.AdaptiveAvgPool1d, torch.nn.AdaptiveAvgPool2d, torch.nn.AdaptiveAvgPool3d, torch.nn.AvgPool1d, torch.nn.AvgPool2d, torch.nn.AvgPool3d, torch.nn.MaxPool1d, torch.nn.MaxPool2d, torch.nn.MaxPool3d, torch.nn.Identity, torch.nn.Hardsigmoid, torch.nn.Sigmoid, torch.nn.Tanh, } func_list = { torch.nn.functional.adaptive_avg_pool1d, torch.nn.functional.adaptive_avg_pool2d, torch.nn.functional.adaptive_avg_pool3d, torch.nn.functional.elu, torch.nn.functional.hardswish, torch.nn.functional.instance_norm, torch.nn.functional.layer_norm, torch.nn.functional.leaky_relu, torch.nn.functional.silu, torch.nn.functional.mish, torch.nn.functional.dropout, torch.nn.functional.max_pool1d, torch.nn.functional.max_pool2d, torch.nn.functional.max_pool3d, torch.nn.functional.relu, torch.nn.functional.hardtanh, torch.nn.functional.hardtanh_, torch.nn.functional.hardsigmoid, torch.nn.functional.sigmoid, torch.transpose, torch.repeat_interleave, torch.sigmoid, torch.squeeze, torch.stack, torch.sum, torch.tanh, torch.unsqueeze, torch.cat, } method_list = { torch.mean, 'relu', 'relu_', 'contiguous', 'detach', 'detach_', 'hardsigmoid', 'hardsigmoid_', 'permute', 'repeat', 'repeat_interleave', 'reshape', 'resize_', 'shape', 'sigmoid', 'sigmoid_', 'size', 'squeeze', 'squeeze_', 'tanh', 'tanh_', 'transpose', 'unsqueeze', 'unsqueeze_', 'view', } # TODO: not used now, remove def check_node(node, modules): # TODO: reuse is_fixed_qparam_node after we move this function to _lower_to_native_backend.py is_call_function = node.op == "call_function" and node.target in func_list is_call_method = node.op == "call_method" and node.target in method_list is_call_module = node.op == "call_module" and type(modules[str(node.target)]) in module_type_list return is_call_function, is_call_method, is_call_module def get_combined_dict(default_dict, additional_dict): d = default_dict.copy() d.update(additional_dict) return d def is_per_tensor(qscheme): return qscheme == torch.per_tensor_affine or \ qscheme == torch.per_tensor_symmetric def is_per_channel(qscheme): return qscheme in [torch.per_channel_affine, torch.per_channel_affine_float_qparams, torch.per_channel_symmetric] def getattr_from_fqn(obj: Any, fqn: str) -> Any: """ Given an obj and a fqn such as "foo.bar.baz", returns gm.foo.bar.baz. """ return functools.reduce(getattr, fqn.split("."), obj) def get_qparam_dict(observer_or_fake_quant): qscheme = observer_or_fake_quant.qscheme if hasattr(observer_or_fake_quant, "qscheme") else None dtype = observer_or_fake_quant.dtype qparams = {"qscheme": qscheme, "dtype": dtype} if not qscheme: return qparams if is_per_tensor(qscheme): qscheme = torch.per_tensor_affine elif is_per_channel(qscheme): # change symmetric to affine since we do not have symmetric # quantized Tensor if qscheme == torch.per_channel_symmetric: qscheme = torch.per_channel_affine qparams["axis"] = observer_or_fake_quant.ch_axis else: raise RuntimeError(f"Unrecognized qscheme: {qscheme}") # update qscheme, since we don't have symmetric quant qscheme # in quantized Tensor qparams["qscheme"] = qscheme scale, zero_point = observer_or_fake_quant.calculate_qparams() qparams["scale"] = scale qparams["zero_point"] = zero_point return qparams def get_swapped_custom_module_class(custom_module, custom_module_class_mapping, qconfig): """ Get the observed/quantized custom module class that we need to swap `custom_module` to Input: custom_module: input, can be an instance of either a float or observed custom module custom_module_class_mapping: the float to observed or observed to quantized custom module class mapping qconfig: qconfig configured for the custom module Output: corresponding observed/quantized custom module class for input custom module instance """ quant_type = get_quant_type(qconfig) class_mapping = custom_module_class_mapping.get(quant_type, {}) assert type(custom_module) in class_mapping, "did not find corresponding observed " \ "module class for {} in mapping: {}".format(type(custom_module), class_mapping) return class_mapping[type(custom_module)] def activation_dtype(qconfig): assert qconfig is not None activation = qconfig.activation() return activation.dtype def weight_dtype(qconfig): assert qconfig is not None weight = qconfig.weight() return weight.dtype def activation_is_statically_quantized(qconfig): """ Given a qconfig, decide if the activation needs to be quantized or not, this includes quantizing to quint8, qint8 and float16 """ return ( activation_dtype(qconfig) in [torch.quint8, torch.qint8, torch.float16] and (not activation_is_dynamically_quantized(qconfig)) ) def activation_is_dynamically_quantized(qconfig): """ Given a qconfig, decide if the activation needs to be dynamically quantized or not, this includes dynamically quantizing to quint8, qint8 and float16 """ activation_dtype, _, activation_compute_dtype = \ get_qconfig_dtypes(qconfig) return activation_compute_dtype in [torch.quint8, torch.qint8, torch.float16] def activation_is_int8_quantized(qconfig): """ Given a qconfig, decide if the activation needs to be quantized to int8 or not, this includes quantizing to quint8, qint8 """ return activation_dtype(qconfig) in [torch.quint8, torch.qint8] def activation_is_int32_quantized(qconfig): """ Given a qconfig, decide if the activation needs to be quantized to int32 or not """ return activation_dtype(qconfig) == torch.qint32 def weight_is_quantized(qconfig): """ Given a qconfig, decide if the weight needs to be quantized or not """ return weight_dtype(qconfig) in [torch.quint8, torch.qint8, torch.float16, torch.quint4x2] def weight_is_statically_quantized(qconfig): """ Given a qconfig, decide if the weight needs to be statically quantized or not """ return weight_dtype(qconfig) in [torch.quint8, torch.qint8] def op_is_int8_dynamically_quantized(qconfig) -> bool: """ Given a qconfig, returns True if this op is using int8 dynamic quantization """ activation_dtype, weight_dtype, activation_compute_dtype = \ get_qconfig_dtypes(qconfig) return ( activation_dtype is torch.quint8 and # for now, the lines below assume fbgemm or qnnpack weight_dtype is torch.qint8 and activation_compute_dtype is torch.quint8 # TODO(future PR): add is_dynamic ) def get_qconfig_dtypes(qconfig): r""" returns the qconfig tuple for qconfig: (activation_dtype, weight_dtype, activation_compute_dtype) """ assert qconfig is not None activation = qconfig.activation() weight = qconfig.weight() compute_dtype = activation.compute_dtype if hasattr(activation, 'compute_dtype') else None return (activation.dtype, weight.dtype, compute_dtype) def get_quant_type(qconfig): assert qconfig is not None activation = qconfig.activation() weight = qconfig.weight() static_dtypes = [torch.quint8, torch.qint8, torch.quint4x2] if weight.dtype in static_dtypes: if hasattr(activation, 'compute_dtype') and activation.compute_dtype in static_dtypes: return QuantType.DYNAMIC elif activation.dtype in static_dtypes: return QuantType.STATIC else: return QuantType.WEIGHT_ONLY if weight.dtype == torch.float16: if hasattr(activation, 'compute_dtype') and activation.compute_dtype in static_dtypes: return QuantType.DYNAMIC elif activation.dtype == torch.float16: return QuantType.STATIC raise Exception("Unrecognized dtype combination in get_quant_type: activation({})," "weight({})".format(activation.dtype, weight.dtype)) def check_min_max_valid(min_val: torch.Tensor, max_val: torch.Tensor) -> bool: """ Checks if the given minimum and maximum values are valid, meaning that they exist and the min value is less than the max value. """ if min_val.numel() == 0 or max_val.numel() == 0: warnings.warn( "must run observer before calling calculate_qparams. " + "Returning default values." ) return False if min_val.dim() == 0 or max_val.dim() == 0: if min_val == float("inf") and max_val == float("-inf"): warnings.warn( "must run observer before calling calculate_qparams. " + "Returning default values." ) return False assert min_val <= max_val, "min {} should be less than max {}".format( min_val, max_val ) else: assert torch.all( min_val <= max_val ), "min {} should be less than max {}".format(min_val, max_val) return True def calculate_qmin_qmax(quant_min: int, quant_max: int, has_customized_qrange: bool, dtype: torch.dtype, reduce_range: bool) -> Tuple[int, int]: r"""Calculates actual qmin and qmax based on the quantization range, observer datatype and if range is reduced. """ # TODO(jerryzh): Figure out why custom quant_min/quant_max are still adjusted. if has_customized_qrange: # This initialization here is to be resolve TorchScript compilation issues and allow # using of refinement to decouple initial_qmin and initial_qmax from quantization range. # The actual values of initial_qmin and initial_qmax will be reset below. if dtype == torch.qint32: initial_quant_min, initial_quant_max = 0, 2**31 - 1 else: initial_quant_min, initial_quant_max = 0, 255 # The following assignment of self.qmin and self.qmax to the local variables and the if check refine the # attribute from Optional valid integers for use, based on TorchScript's requirements. custom_quant_min, custom_quant_max = quant_min, quant_max if custom_quant_min is not None and custom_quant_max is not None: initial_quant_min, initial_quant_max = ( custom_quant_min, custom_quant_max, ) qrange_len = initial_quant_max - initial_quant_min + 1 if dtype == torch.qint8: assert ( 0 < qrange_len <= 256 ), "quantization range should be positive and not exceed the maximum bit range (=256)." elif dtype == torch.qint32: assert ( 0 < qrange_len <= 2**31 ), "quantization range should be positive and not exceed the maximum bit range (=4294967296)." if reduce_range: quant_min, quant_max = quant_min // 2, quant_max // 2 else: # Fallback onto default 8-bit qmin and qmax calculation if dynamic range is not used. if dtype == torch.qint8: if reduce_range: quant_min, quant_max = -64, 63 else: quant_min, quant_max = -128, 127 elif dtype == torch.quint8: if reduce_range: quant_min, quant_max = 0, 127 else: quant_min, quant_max = 0, 255 elif dtype == torch.qint32: quant_min, quant_max = -1 * (2 ** 31), (2 ** 31) - 1 else: quant_min, quant_max = 0, 15 return quant_min, quant_max def _parent_name(target): """ Turn 'foo.bar' into ['foo', 'bar'] """ r = target.rsplit('.', 1) if len(r) == 1: return '', r[0] else: return r[0], r[1] def has_no_children_ignoring_parametrizations(module): """ Checks if module._modules is empty or if module is a parametrization, checks that module._modules only has the 'parametrizations' module """ if len(module._modules) == 0: return True elif is_parametrized(module): return len(module._modules) == 1 and 'parametrizations' in module._modules else: return False def _get_path_of_module(root: torch.nn.Module, submodule: torch.nn.Module) -> Optional[str]: """ Get the path (fully qualified name) of a submodule Example:: >> class M(torch.nn.Module): def __init__(self): self.linear = torch.nn.Linear(5, 5) def forward(self, x): return self.linear(x) >> m = M() >> l = m.linear >> _get_path_of_module(m, l) "linear" """ for n, p in root.named_modules(): if submodule is p: return n return None def _get_signature_locals(f: Callable, loc: Dict[str, Any]) -> Dict[str, Any]: """ Get local keyword arguments Example:: >> def f(self, a, b=9): pass >> loc = {"a": 6, "c": 7} >> _get_signature_locals(f, loc) {"a": 6} """ return {k: v for k, v in loc.items() if k in signature(f).parameters} def _get_default_kwargs(f: Callable) -> "OrderedDict[str, Any]": """ Get all default keyword arguments from function signature Example:: >> def f(self, a, b=9): pass >> _get_default_kwargs(f) {"b": 9} """ kwargs = {} for name, param in signature(f).parameters.items(): if param.default is not param.empty: kwargs[name] = param.default elif param.kind is param.VAR_POSITIONAL: kwargs[name] = () elif param.kind is param.VAR_KEYWORD: kwargs[name] = {} return OrderedDict(kwargs) def _normalize_kwargs(func: Callable, loc: Dict[str, Any]) -> "OrderedDict[str, Any]": """ Given a function and local function arguments, normalize the keyword arguments by filling in default arguments from function signature Example:: >> def f(self, key1=3, key2=3): pass >> loc = {"key2": 6} >> _normalize_kwargs(f, loc) {"key1": 3, "key2": 6} """ default_kwargs = _get_default_kwargs(func) local_kwargs = _get_signature_locals(func, loc) normalized_kwargs = default_kwargs.copy() for attr, val in local_kwargs.items(): if attr in normalized_kwargs: # override the default keyword arguments normalized_kwargs[attr] = val return normalized_kwargs def _get_num_pos_args(f: Callable) -> int: """ Get number of positional args for a function Example:: >> def f(self, key1=3, key2=3): pass >> _get_num_pos_args(f) 3 """ return len(getfullargspec(f).args) def get_fqn_to_example_inputs( model: torch.nn.Module, example_inputs: Tuple[Any, ...] ) -> Dict[str, Tuple[Any, ...]]: """ Given a model and its example inputs, return a dictionary from fully qualified name of submodules to example_inputs for that submodule, e.g. {"linear1": (tensor1,), "linear2": (tensor2,), "sub": (tensor3,), "sub.linear1": (tensor4,), ...} Used to make quantizing submodules easier now that FX Graph Mode Quantization requries example inputs. Also works for keyword arguments with default values, we would flatten keyword arguments as positional arguments and fill in the missing keyword args with default values, e.g. if we have a forward function: def forward(self, x, key1=3, key2=3): ... and we call it with self.submodule(x, key2=6) we'll get example_inputs: (x, 3, 6) user can also override `key1` with positional arguments as well: for self.submodule(x, 5, key2=6) we'll get: (x, 5, 6) variable positional arguments and variable positional keyword arguments in forward function are not supported currently, so please make sure no submodules is using them. """ root = model fqn_to_example_inputs = {} def _patched_module_call(self, *args, **kwargs): submodule_example_inputs = list(args).copy() normalized_kwargs = _normalize_kwargs(self.forward, kwargs) # minus 1 to skipping counting `self` num_args = _get_num_pos_args(self.forward) - 1 num_to_pop = num_args - len(submodule_example_inputs) while num_to_pop and normalized_kwargs: normalized_kwargs.popitem(last=False) num_to_pop -= 1 submodule_example_inputs.extend(normalized_kwargs.values()) submodule_example_inputs_tuple = tuple(submodule_example_inputs) fqn = _get_path_of_module(root, self) if fqn is not None: fqn_to_example_inputs[fqn] = submodule_example_inputs_tuple return orig_module_call(self, *args, **kwargs) orig_module_call = torch.nn.Module.__call__ torch.nn.Module.__call__ = _patched_module_call try: model(*example_inputs) finally: # restore the module call even if there is an exception torch.nn.Module.__call__ = orig_module_call return fqn_to_example_inputs __all__ = [ "Pattern", "MatchAllNode", "check_node", "get_combined_dict", "is_per_tensor", "is_per_channel", "getattr_from_fqn", "get_qparam_dict", "get_swapped_custom_module_class", "activation_dtype", "weight_dtype", "activation_is_statically_quantized", "activation_is_dynamically_quantized", "activation_is_int8_quantized", "activation_is_int32_quantized", "weight_is_quantized", "weight_is_statically_quantized", "op_is_int8_dynamically_quantized", "get_qconfig_dtypes", "get_quant_type", "check_min_max_valid", "calculate_qmin_qmax", "has_no_children_ignoring_parametrizations", "get_fqn_to_example_inputs", ]