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import typing_extensions |
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import torch |
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from torch._export.passes.constant_folding import constant_fold |
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from torch.ao.quantization.pt2e.duplicate_dq_pass import DuplicateDQPass |
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from torch.ao.quantization.pt2e.port_metadata_pass import PortNodeMetaForQDQ |
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from torch.ao.quantization.quantizer import ( |
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DerivedQuantizationSpec, |
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FixedQParamsQuantizationSpec, |
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QuantizationAnnotation, |
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QuantizationSpec, |
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QuantizationSpecBase, |
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Quantizer, |
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SharedQuantizationSpec, |
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) |
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from torch.fx import GraphModule, Node |
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from torch.fx.passes.infra.pass_manager import PassManager |
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from .pt2e.prepare import prepare |
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from .pt2e.qat_utils import _fold_conv_bn_qat, _fuse_conv_bn_qat |
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from .pt2e.representation import reference_representation_rewrite |
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from .pt2e.utils import _disallow_eval_train, _fuse_conv_bn_, _get_node_name_to_scope |
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from .quantize_fx import _convert_to_reference_decomposed_fx |
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from .utils import DEPRECATION_WARNING |
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__all__ = [ |
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"prepare_pt2e", |
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"prepare_qat_pt2e", |
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"convert_pt2e", |
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] |
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@typing_extensions.deprecated(DEPRECATION_WARNING) |
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def prepare_pt2e( |
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model: GraphModule, |
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quantizer: Quantizer, |
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) -> GraphModule: |
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"""Prepare a model for post training quantization |
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Args: |
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* `model` (torch.fx.GraphModule): a model captured by `torch.export.export_for_training` API. |
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* `quantizer`: A backend specific quantizer that conveys how user want the |
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model to be quantized. Tutorial for how to write a quantizer can be found here: |
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https://pytorch.org/tutorials/prototype/pt2e_quantizer.html |
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Return: |
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A GraphModule with observer (based on quantizer annotation), ready for calibration |
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Example:: |
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import torch |
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from torch.ao.quantization.quantize_pt2e import prepare_pt2e |
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from torch.ao.quantization.quantizer import ( |
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XNNPACKQuantizer, |
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get_symmetric_quantization_config, |
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) |
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class M(torch.nn.Module): |
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def __init__(self) -> None: |
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super().__init__() |
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self.linear = torch.nn.Linear(5, 10) |
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def forward(self, x): |
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return self.linear(x) |
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# initialize a floating point model |
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float_model = M().eval() |
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# define calibration function |
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def calibrate(model, data_loader): |
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model.eval() |
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with torch.no_grad(): |
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for image, target in data_loader: |
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model(image) |
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# Step 1. program capture |
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# NOTE: this API will be updated to torch.export API in the future, but the captured |
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# result should mostly stay the same |
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m = torch.export.export_for_training(m, *example_inputs).module() |
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# we get a model with aten ops |
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# Step 2. quantization |
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# backend developer will write their own Quantizer and expose methods to allow |
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# users to express how they |
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# want the model to be quantized |
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quantizer = XNNPACKQuantizer().set_global(get_symmetric_quantization_config()) |
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m = prepare_pt2e(m, quantizer) |
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# run calibration |
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# calibrate(m, sample_inference_data) |
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""" |
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torch._C._log_api_usage_once("quantization_api.quantize_pt2e.prepare_pt2e") |
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original_graph_meta = model.meta |
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node_name_to_scope = _get_node_name_to_scope(model) |
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_fuse_conv_bn_(model) |
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model = quantizer.transform_for_annotation(model) |
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quantizer.annotate(model) |
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quantizer.validate(model) |
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model = prepare( |
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model, |
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node_name_to_scope, |
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is_qat=False, |
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obs_or_fq_callback=quantizer.prepare_obs_or_fq_callback, |
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) |
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model.meta.update(original_graph_meta) |
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model = _disallow_eval_train(model) |
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return model |
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@typing_extensions.deprecated(DEPRECATION_WARNING) |
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def prepare_qat_pt2e( |
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model: GraphModule, |
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quantizer: Quantizer, |
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) -> GraphModule: |
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"""Prepare a model for quantization aware training |
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Args: |
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* `model` (torch.fx.GraphModule): see :func:`~torch.ao.quantization.quantize_pt2e.prepare_pt2e` |
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* `quantizer`: see :func:`~torch.ao.quantization.quantize_pt2e.prepare_pt2e` |
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Return: |
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A GraphModule with fake quant modules (based on quantizer annotation), ready for |
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quantization aware training |
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Example:: |
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import torch |
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from torch.ao.quantization.quantize_pt2e import prepare_qat_pt2e |
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from torch.ao.quantization.quantizer import ( |
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XNNPACKQuantizer, |
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get_symmetric_quantization_config, |
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) |
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class M(torch.nn.Module): |
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def __init__(self) -> None: |
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super().__init__() |
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self.linear = torch.nn.Linear(5, 10) |
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def forward(self, x): |
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return self.linear(x) |
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# initialize a floating point model |
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float_model = M().eval() |
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# define the training loop for quantization aware training |
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def train_loop(model, train_data): |
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model.train() |
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for image, target in data_loader: |
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... |
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# Step 1. program capture |
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# NOTE: this API will be updated to torch.export API in the future, but the captured |
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# result should mostly stay the same |
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m = torch.export.export_for_training(m, *example_inputs).module() |
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# we get a model with aten ops |
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# Step 2. quantization |
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# backend developer will write their own Quantizer and expose methods to allow |
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# users to express how they |
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# want the model to be quantized |
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quantizer = XNNPACKQuantizer().set_global(get_symmetric_quantization_config()) |
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m = prepare_qat_pt2e(m, quantizer) |
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# run quantization aware training |
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train_loop(prepared_model, train_loop) |
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""" |
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torch._C._log_api_usage_once("quantization_api.quantize_pt2e.prepare_qat_pt2e") |
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original_graph_meta = model.meta |
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node_name_to_scope = _get_node_name_to_scope(model) |
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model = quantizer.transform_for_annotation(model) |
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quantizer.annotate(model) |
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quantizer.validate(model) |
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_fuse_conv_bn_qat(model) |
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model = prepare( |
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model, |
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node_name_to_scope, |
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is_qat=True, |
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obs_or_fq_callback=quantizer.prepare_obs_or_fq_callback, |
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) |
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model.meta.update(original_graph_meta) |
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model = _disallow_eval_train(model) |
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return model |
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_QUANT_OPS = [ |
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torch.ops.quantized_decomposed.quantize_per_tensor.default, |
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torch.ops.quantized_decomposed.quantize_per_tensor.tensor, |
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torch.ops.quantized_decomposed.quantize_per_channel.default, |
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torch.ops.pt2e_quant.quantize_affine, |
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] |
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def _quant_node_constraint(n: Node) -> bool: |
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"""If there is any pure ops between get_attr and quantize op they will be const propagated |
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e.g. get_attr(weight) -> transpose -> quantize -> dequantize* |
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(Note: dequantize op is not going to be constant propagated) |
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This filter is added because we don't want to constant fold the things that are not |
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related to quantization |
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""" |
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return n.op == "call_function" and n.target in _QUANT_OPS |
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@typing_extensions.deprecated(DEPRECATION_WARNING) |
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def convert_pt2e( |
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model: GraphModule, |
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use_reference_representation: bool = False, |
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fold_quantize: bool = True, |
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) -> GraphModule: |
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"""Convert a calibrated/trained model to a quantized model |
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Args: |
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* `model` (torch.fx.GraphModule): calibrated/trained model |
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* `use_reference_representation` (bool): boolean flag to indicate whether to produce reference representation or not |
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* `fold_quantize` (bool): boolean flag for whether fold the quantize op or not |
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Returns: |
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quantized model, either in q/dq representation or reference representation |
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Example:: |
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# prepared_model: the model produced by `prepare_pt2e`/`prepare_qat_pt2e` and calibration/training |
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# `convert_pt2e` produces a quantized model that represents quantized computation with |
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# quantize dequantize ops and fp32 ops by default. |
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# Please refer to |
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# https://pytorch.org/tutorials/prototype/pt2e_quant_ptq_static.html#convert-the-calibrated-model-to-a-quantized-model |
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# for detailed explanation of output quantized model |
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quantized_model = convert_pt2e(prepared_model) |
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""" |
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torch._C._log_api_usage_once("quantization_api.quantize_pt2e.convert_pt2e") |
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if not isinstance(use_reference_representation, bool): |
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raise ValueError( |
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"Unexpected argument type for `use_reference_representation`, " |
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f"please make sure you intend to pass argument {use_reference_representation} to convert_pt2e" |
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) |
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original_graph_meta = model.meta |
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model = _convert_to_reference_decomposed_fx(model) |
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model = _fold_conv_bn_qat(model) |
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pm = PassManager([DuplicateDQPass()]) |
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model = pm(model).graph_module |
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pm = PassManager([PortNodeMetaForQDQ()]) |
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model = pm(model).graph_module |
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if fold_quantize: |
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constant_fold(model, _quant_node_constraint) |
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if use_reference_representation: |
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model = reference_representation_rewrite(model) |
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model.meta.update(original_graph_meta) |
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model = _disallow_eval_train(model) |
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return model |
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