pytorch/torch/ao/quantization/quantize_pt2e.py

import torch
from torch.fx import GraphModule
from torch.fx import Node

from .pt2e.prepare import prepare
from .pt2e.qat_utils import (
    _fuse_conv_bn_qat,
    _fold_conv_bn_qat,
)
from .pt2e.utils import (
    _get_node_name_to_scope,
    _fuse_conv_bn_,
    _disallow_eval_train,
)
from .pt2e.representation import reference_representation_rewrite
from .quantize_fx import _convert_to_reference_decomposed_fx
from torch.ao.quantization.quantizer import (  # noqa: F401
    Quantizer,
    QuantizationSpecBase,
    QuantizationSpec,
    FixedQParamsQuantizationSpec,
    SharedQuantizationSpec,
    DerivedQuantizationSpec,
    QuantizationAnnotation,
)
from torch.fx.passes.infra.pass_manager import PassManager
from torch.ao.quantization.pt2e.duplicate_dq_pass import DuplicateDQPass
from torch.ao.quantization.pt2e.port_metadata_pass import PortNodeMetaForQDQ
from torch._inductor.constant_folding import constant_fold

__all__ = [
    "prepare_pt2e",
    "prepare_qat_pt2e",
    "convert_pt2e",
]


def prepare_pt2e(
    model: GraphModule,
    quantizer: Quantizer,
) -> GraphModule:
    """Prepare a model for post training quantization

    Args:
      * `model` (torch.fx.GraphModule): a model captured by `torch.export` API
        in the short term we are using `torch._export.capture_pre_autograd_graph`,
        in the long term we'll migrate to some `torch.export` API
      * `quantizer`: A backend specific quantizer that conveys how user want the
        model to be quantized. Tutorial for how to write a quantizer can be found here:
        https://pytorch.org/tutorials/prototype/pt2e_quantizer.html

    Return:
      A GraphModule with observer (based on quantizer annotation), ready for calibration

    Example::

        import torch
        from torch.ao.quantization.quantize_pt2e import prepare_pt2e
        from torch._export import capture_pre_autograd_graph
        from torch.ao.quantization.quantizer import (
            XNNPACKQuantizer,
            get_symmetric_quantization_config,
        )

        class M(torch.nn.Module):
            def __init__(self):
                super().__init__()
                self.linear = torch.nn.Linear(5, 10)

           def forward(self, x):
               return self.linear(x)

        # initialize a floating point model
        float_model = M().eval()

        # define calibration function
        def calibrate(model, data_loader):
            model.eval()
            with torch.no_grad():
                for image, target in data_loader:
                    model(image)

        # Step 1. program capture
        # NOTE: this API will be updated to torch.export API in the future, but the captured
        # result shoud mostly stay the same
        m = capture_pre_autograd_graph(m, *example_inputs)
        # we get a model with aten ops

        # Step 2. quantization
        # backend developer will write their own Quantizer and expose methods to allow
        # users to express how they
        # want the model to be quantized
        quantizer = XNNPACKQuantizer().set_global(get_symmetric_quantization_config())
        m = prepare_pt2e(m, quantizer)

        # run calibration
        # calibrate(m, sample_inference_data)
    """
    torch._C._log_api_usage_once("quantization_api.quantize_pt2e.prepare_pt2e")
    original_graph_meta = model.meta
    node_name_to_scope = _get_node_name_to_scope(model)
    # TODO: check qconfig_mapping to make sure conv and bn are both configured
    # to be quantized before fusion
    # TODO: (maybe) rewrite this with subgraph_rewriter
    _fuse_conv_bn_(model)
    quantizer.transform_for_annotation(model)
    quantizer.annotate(model)
    quantizer.validate(model)
    model = prepare(model, node_name_to_scope, is_qat=False)
    model.meta.update(original_graph_meta)
    model = _disallow_eval_train(model)
    return model

def prepare_qat_pt2e(
    model: GraphModule,
    quantizer: Quantizer,
) -> GraphModule:
    """Prepare a model for quantization aware training

    Args:
      * `model` (torch.fx.GraphModule): see :func:`~torch.ao.quantization.quantize_pt2e.prepare_pt2e`
      * `quantizer`: see :func:`~torch.ao.quantization.quantize_pt2e.prepare_pt2e`

    Return:
      A GraphModule with fake quant modules (based on quantizer annotation), ready for
      quantization aware training

    Example::
        import torch
        from torch.ao.quantization.quantize_pt2e import prepare_qat_pt2e
        from torch._export import capture_pre_autograd_graph
        from torch.ao.quantization.quantizer import (
            XNNPACKQuantizer,
            get_symmetric_quantization_config,
        )

        class M(torch.nn.Module):
            def __init__(self):
                super().__init__()
                self.linear = torch.nn.Linear(5, 10)

           def forward(self, x):
               return self.linear(x)

        # initialize a floating point model
        float_model = M().eval()

        # define the training loop for quantization aware training
        def train_loop(model, train_data):
            model.train()
            for image, target in data_loader:
                ...

        # Step 1. program capture
        # NOTE: this API will be updated to torch.export API in the future, but the captured
        # result shoud mostly stay the same
        m = capture_pre_autograd_graph(m, *example_inputs)
        # we get a model with aten ops

        # Step 2. quantization
        # backend developer will write their own Quantizer and expose methods to allow
        # users to express how they
        # want the model to be quantized
        quantizer = XNNPACKQuantizer().set_global(get_symmetric_quantization_config())
        m = prepare_qat_pt2e(m, quantizer)

        # run quantization aware training
        train_loop(prepared_model, train_loop)

    """
    torch._C._log_api_usage_once("quantization_api.quantize_pt2e.prepare_qat_pt2e")
    original_graph_meta = model.meta
    node_name_to_scope = _get_node_name_to_scope(model)
    quantizer.transform_for_annotation(model)
    quantizer.annotate(model)
    quantizer.validate(model)
    # Perform fusion after annotate to avoid quantizing ops in the new
    # subgraph that don't need to be quantized
    # TODO: only fuse if conv and bn are both configured to be quantized
    _fuse_conv_bn_qat(model)
    model = prepare(model, node_name_to_scope, is_qat=True)
    model.meta.update(original_graph_meta)
    model = _disallow_eval_train(model)
    return model

_QUANT_OPS = [
    torch.ops.quantized_decomposed.quantize_per_tensor.default,
    torch.ops.quantized_decomposed.quantize_per_tensor.tensor,
    torch.ops.quantized_decomposed.quantize_per_channel.default,
]
def _quant_node_constraint(n: Node) -> bool:
    """If there is any pure ops between get_attr and quantize op they will be const propagated
    e.g. get_attr(weight) -> transpose -> quantize -> dequantize*
    (Note: dequantize op is not going to be constant propagated)

    This filter is added because we don't want to constant fold the things that are not
    related to quantization
    """
    return n.op == "call_function" and n.target in _QUANT_OPS

def convert_pt2e(
    model: GraphModule,
    use_reference_representation: bool = False,
    fold_quantize: bool = False,
) -> GraphModule:
    """Convert a calibrated/trained model to a quantized model

    Args:
      * `model` (torch.fx.GraphModule): calibrated/trained model
      * `use_reference_representation` (bool): boolean flag to indicate whether to produce referece representation or not
      * `fold_quantize` (bool): boolean flag to indicate whether fold the quantize op or not

    Note: please set `fold_quantize` to True whenever you can, we'll deprecate this flag and
    make True the default option in the future, to make sure the change doesn't break BC for you, it's
    better to set the flag to True now.

    Returns:
        quantized model, either in q/dq representation or reference representation

    Example::

        # prepared_model: the model produced by `prepare_pt2e`/`prepare_qat_pt2e` and calibration/training
        # `convert_pt2e` produces a quantized model that represents quantized computation with
        # quantize dequantize ops and fp32 ops by default.
        # Please refer to
        # https://pytorch.org/tutorials/prototype/pt2e_quant_ptq_static.html#convert-the-calibrated-model-to-a-quantized-model
        # for detailed explanation of output quantized model
        quantized_model = convert_pt2e(prepared_model)

    """  # flake8: noqa
    torch._C._log_api_usage_once("quantization_api.quantize_pt2e.convert_pt2e")
    if not isinstance(use_reference_representation, bool):
        raise ValueError(
            "Unexpected argument type for `use_reference_representation`, "
            f"please make sure you intend to pass argument {use_reference_representation} to convert_pt2e")
    original_graph_meta = model.meta
    model = _convert_to_reference_decomposed_fx(model)
    model = _fold_conv_bn_qat(model)

    pm = PassManager([DuplicateDQPass()])
    model = pm(model).graph_module

    pm = PassManager([PortNodeMetaForQDQ()])
    model = pm(model).graph_module

    if fold_quantize:
        constant_fold(model, _quant_node_constraint)

    if use_reference_representation:
        model = reference_representation_rewrite(model)

    model.meta.update(original_graph_meta)
    model = _disallow_eval_train(model)
    return model