import torch from torch.ao.quantization.qconfig import QConfig from torch.ao.quantization.quant_type import QuantType from torch.jit._recursive import wrap_cpp_module def _check_is_script_module(model): if not isinstance(model, torch.jit.ScriptModule): raise ValueError('input must be a script module, got: ' + str(type(model))) def _check_forward_method(model): if not model._c._has_method('forward'): raise ValueError('input script module does not have forward method') def script_qconfig(qconfig): r"""Instantiate the activation and weight observer modules and script them, these observer module instances will be deepcopied during prepare_jit step. """ return QConfig( activation=torch.jit.script(qconfig.activation())._c, weight=torch.jit.script(qconfig.weight())._c) def script_qconfig_dict(qconfig_dict): r"""Helper function used by `prepare_jit`. Apply `script_qconfig` for all entries in `qconfig_dict` that is not None. """ return {k: script_qconfig(v) if v else None for k, v in qconfig_dict.items()} def fuse_conv_bn_jit(model, inplace=False): r""" Fuse conv - bn module Works for eval model only. Args: model: TorchScript model from scripting or tracing """ torch._C._log_api_usage_once("quantization_api.quantize_jit.fuse_conv_bn_jit") model_c = model._c model_c = torch._C._jit_pass_fold_convbn(model_c) if inplace: model._reconstruct(model_c) else: model = wrap_cpp_module(model_c) return model def _prepare_jit(model, qconfig_dict, inplace=False, quant_type=QuantType.STATIC): _check_is_script_module(model) _check_forward_method(model) if not all(isinstance(x, str) for x in qconfig_dict.keys()): raise ValueError('qconfig_dict should only contain names(str) as keys.') scripted_qconfig_dict = script_qconfig_dict(qconfig_dict) model = fuse_conv_bn_jit(model, inplace) model_c = torch._C._jit_pass_insert_observers(model._c, 'forward', scripted_qconfig_dict, inplace, quant_type) if inplace: model._reconstruct(model_c) else: model = wrap_cpp_module(model_c) return model def prepare_jit(model, qconfig_dict, inplace=False): torch._C._log_api_usage_once("quantization_api.quantize_jit.prepare_jit") return _prepare_jit(model, qconfig_dict, inplace, quant_type=QuantType.STATIC) def prepare_dynamic_jit(model, qconfig_dict, inplace=False): torch._C._log_api_usage_once("quantization_api.quantize_jit.prepare_dynamic_jit") return _prepare_jit(model, qconfig_dict, inplace, quant_type=QuantType.DYNAMIC) def _convert_jit(model, inplace=False, debug=False, quant_type=QuantType.STATIC, preserved_attrs=None): _check_is_script_module(model) model.eval() model_c = model._c model_c = torch._C._jit_pass_insert_quant_dequant(model_c, 'forward', inplace, debug, quant_type) if not debug: is_xpu = all(p.device.type == 'xpu' for p in model.parameters()) if not is_xpu: # Moving model parameters to CPU since quantized operators # are only supported on CPU and XPU right now model.cpu() if preserved_attrs is None: preserved_attrs = [] model_c = torch._C._jit_pass_quant_finalize(model_c, quant_type, preserved_attrs) if inplace: model._reconstruct(model_c) else: model = wrap_cpp_module(model_c) torch._C._jit_pass_constant_propagation(model.graph) torch._C._jit_pass_dce(model.graph) return model def convert_jit(model, inplace=False, debug=False, preserved_attrs=None): torch._C._log_api_usage_once("quantization_api.quantize_jit.convert_jit") return _convert_jit(model, inplace, debug, quant_type=QuantType.STATIC, preserved_attrs=preserved_attrs) def convert_dynamic_jit(model, inplace=False, debug=False, preserved_attrs=None): torch._C._log_api_usage_once("quantization_api.quantize_jit.convert_dynamic_jit") return _convert_jit(model, inplace, debug, quant_type=QuantType.DYNAMIC, preserved_attrs=preserved_attrs) def _quantize_jit(model, qconfig_dict, run_fn=None, run_args=None, inplace=False, debug=False, quant_type=QuantType.STATIC): # Always do inplace convert because the Tensor is already # copied in prepare_jit when inplace is False if quant_type == QuantType.DYNAMIC: model = prepare_dynamic_jit(model, qconfig_dict, inplace) model = convert_dynamic_jit(model, True, debug) else: assert run_fn, "Must provide calibration function for post training static quantization" assert run_args, "Must provide calibration dataset for post training static quantization" model = prepare_jit(model, qconfig_dict, inplace) run_fn(model, *run_args) model = convert_jit(model, True, debug) torch._C._jit_pass_constant_propagation(model.graph) torch._C._jit_pass_dce(model.graph) return model def quantize_jit(model, qconfig_dict, run_fn, run_args, inplace=False, debug=False): r"""Quantize the input float TorchScript model with post training static quantization. First it will prepare the model for calibration, then it calls `run_fn` which will run the calibration step, after that we will convert the model to a quantized model. Args: `model`: input float TorchScript model `qconfig_dict`: qconfig_dict is a dictionary with names of sub modules as key and qconfig for that module as value, empty key means the qconfig will be applied to whole model unless it's overwritten by more specific configurations, the qconfig for each module is either found in the dictionary or fallback to the qconfig of parent module. Right now qconfig_dict is the only way to configure how the model is quantized, and it is done in the granularity of module, that is, we only support one type of qconfig for each torch.nn.Module, and the qconfig for sub module will override the qconfig for parent module, empty string means global configuration. `run_fn`: a calibration function for calibrating the prepared model `run_args`: positional arguments for `run_fn` `inplace`: carry out model transformations in-place, the original module is mutated `debug`: flag for producing a debug friendly model (preserve weight attribute) Return: Quantized TorchSciprt model. Example: ```python import torch from torch.ao.quantization import get_default_qconfig from torch.ao.quantization import quantize_jit ts_model = torch.jit.script(float_model.eval()) # or torch.jit.trace(float_model, input) qconfig = get_default_qconfig('fbgemm') def calibrate(model, data_loader): model.eval() with torch.no_grad(): for image, target in data_loader: model(image) quantized_model = quantize_jit( ts_model, {'': qconfig}, calibrate, [data_loader_test]) ``` """ torch._C._log_api_usage_once("quantization_api.quantize_jit.quantize_jit") return _quantize_jit(model, qconfig_dict, run_fn, run_args, inplace, debug, quant_type=QuantType.STATIC) def quantize_dynamic_jit(model, qconfig_dict, inplace=False, debug=False): r"""Quantize the input float TorchScript model with post training dynamic quantization. Currently only qint8 quantization of torch.nn.Linear is supported. Args: `model`: input float TorchScript model `qconfig_dict`: qconfig_dict is a dictionary with names of sub modules as key and qconfig for that module as value, please see detailed descriptions in :func:`~torch.ao.quantization.quantize_jit` `inplace`: carry out model transformations in-place, the original module is mutated `debug`: flag for producing a debug friendly model (preserve weight attribute) Return: Quantized TorchSciprt model. Example: ```python import torch from torch.ao.quantization import per_channel_dynamic_qconfig from torch.ao.quantization import quantize_dynmiac_jit ts_model = torch.jit.script(float_model.eval()) # or torch.jit.trace(float_model, input) qconfig = get_default_qconfig('fbgemm') def calibrate(model, data_loader): model.eval() with torch.no_grad(): for image, target in data_loader: model(image) quantized_model = quantize_dynamic_jit( ts_model, {'': qconfig}, calibrate, [data_loader_test]) ``` """ torch._C._log_api_usage_once("quantization_api.quantize_jit.quantize_dynamic_jit") return _quantize_jit(model, qconfig_dict, inplace=inplace, debug=debug, quant_type=QuantType.DYNAMIC)