mirror of
https://github.com/zebrajr/pytorch.git
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3a77f9aaaf
Summary: moving quantizer to torch.ao.quantization to make it a public api, since pt2e is a folder for implementations Test Plan: CIs sanity check: "buck test //executorch/backends/xnnpack/test:test_xnnpack_quantized_models -- test_resnet18" Differential Revision: D47727838 Pull Request resolved: https://github.com/pytorch/pytorch/pull/105885 Approved by: https://github.com/andrewor14
95 lines
3.3 KiB
Python
95 lines
3.3 KiB
Python
from __future__ import annotations
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import copy
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from typing import List, Set
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import torch
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import torch.nn.functional as F
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from torch.ao.quantization.observer import PerChannelMinMaxObserver
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from torch.ao.quantization.quantizer.quantizer import (
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OperatorConfig,
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OperatorPatternType,
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QuantizationAnnotation,
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QuantizationConfig,
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QuantizationSpec,
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Quantizer,
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)
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__all__ = [
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"get_embedding_operators_config",
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"EmbeddingQuantizer",
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]
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def get_embedding_operators_config() -> OperatorConfig:
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weight_quantization_spec = QuantizationSpec(
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dtype=torch.uint8,
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qscheme=torch.per_channel_affine_float_qparams,
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ch_axis=0,
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observer_or_fake_quant_ctr=PerChannelMinMaxObserver.with_args(eps=2**-12),
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)
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quantization_config = QuantizationConfig(None, None, weight_quantization_spec, None)
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ops: List[OperatorPatternType] = [[torch.nn.Embedding]]
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ops.append([F.embedding])
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supported_config_and_operators = OperatorConfig(
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config=quantization_config, operators=ops
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)
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return copy.deepcopy(supported_config_and_operators)
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class EmbeddingQuantizer(Quantizer):
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def __init__(self):
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super().__init__()
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@classmethod
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def get_supported_quantization_configs(cls) -> List[QuantizationConfig]:
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op_configs: Set[QuantizationConfig] = set({})
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for spec, _ in cls.get_supported_operators():
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op_configs.add(spec)
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return list(op_configs)
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@classmethod
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def get_supported_operator_for_quantization_config(
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cls, quantization_config: QuantizationConfig
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) -> List[OperatorPatternType]:
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for config, ops in cls.get_supported_operators():
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# note: this assumes each entry in cls.supported_spec_and_operators
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# corresponds to one spec, e.g. we don't have
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# [(spec1, op_list1), (spec1, op_list2), (spec2, op_list3)]
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# where the first and second entry have the same spec but did not
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# merge the op list
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if config == quantization_config:
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return ops
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return []
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def annotate(self, model: torch.fx.GraphModule) -> torch.fx.GraphModule:
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"""just handling global spec for now"""
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self._annotate_embedding_ops(model.graph)
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return model
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def _annotate_embedding_ops(self, graph: torch.fx.Graph) -> None:
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embedding_config: OperatorConfig = get_embedding_operators_config()
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for node in graph.nodes:
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# Keep node parsing based annotations instead of module partitioners
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# just as an example of alternate ways of annotating
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if (
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node.op == "call_function"
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and node.target == torch.ops.aten.embedding.default
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):
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if embedding_config.config.weight is None:
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raise ValueError(
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"Embedding config must have a valid weight quantization spec."
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)
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node.meta["quantization_annotation"] = QuantizationAnnotation(
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input_qspec_map={
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node.args[0]: embedding_config.config.weight,
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}
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)
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def validate(self, model: torch.fx.GraphModule) -> None:
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pass
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@classmethod
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def get_supported_operators(cls) -> List[OperatorConfig]:
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return [get_embedding_operators_config()]
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