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|
import torch
from torch.fx.graph import (
Node,
)
from .utils import (
all_node_args_have_no_tensors,
)
from torch.ao.quantization.quantization_types import (
Pattern,
NodePattern,
)
from abc import ABC
from typing import Any, Callable, Dict, Optional
def _default_root_node_getter(node_pattern):
if node_pattern is None:
return node_pattern
while not isinstance(node_pattern, Node):
node_pattern = node_pattern[-1]
return node_pattern
# TODO: move to backend_config_utils.py
# Base Pattern Handler
class QuantizeHandler(ABC):
""" Base handler class for the quantizer patterns
"""
def __init__(
self,
node_pattern: NodePattern,
modules: Dict[str, torch.nn.Module],
root_node_getter: Callable = None,
is_custom_module=False,
is_standalone_module=False):
""" Records pattern information in __init__, which will be used
in convert
"""
self.node_pattern = node_pattern
self.modules = modules
if root_node_getter is None:
root_node_getter = _default_root_node_getter
self.root_node = root_node_getter(node_pattern)
self.is_custom_module_ = is_custom_module
self.is_standalone_module_ = is_standalone_module
self.num_tensor_args = 0
# determine how many of the first two args are Tensors (versus scalars)
# this distinguishes things like "x + y" from "x + 2" or "2 + x"
if isinstance(self.root_node, Node):
cache_for_no_tensor_check: Dict[Node, bool] = {}
for arg_idx in range(len(self.root_node.args)):
arg = self.root_node.args[arg_idx]
if isinstance(arg, Node) and (
not all_node_args_have_no_tensors(
arg, self.modules, cache_for_no_tensor_check)):
self.num_tensor_args += 1
# TODO: can remove after the is_dynamic flag is defined, so that we can
# move embedding op to backend_config_dict
def input_output_observed(self) -> bool:
"""
Returns True if the pattern matched to this qhandler could be
be observed, and False it it should not be observed.
"""
return True
def is_general_tensor_value_op(self) -> bool:
"""
Returns True if the operator works for both floating point and
quantized input, and does some computation based on the input Tensor,
or the ops that only re-arranges the Tensor values or query some metadata
about the Tensor
so we need to insert observer/fake_quant for the output of the
operator (same observer instance as input)
since the distribution of values is different for input and output
Tensors (for HistogramObserver) while they share the same quantization
parameters
Example operator: avgpool2d, reshape, transpose, maxpool2d
Example observed operator:
observer_0 - avgpool2d - observer_0 (same observer instance as input)
"""
return False
def get_activation_ctr(
self,
qconfig: Any,
pattern: Pattern,
is_training: bool,
) -> Optional[Callable]:
"""
Returns the constructor for the activation observer which should be
used for the pattern matched to this handler. Some handlers override
this to a different value than what is specified in the qconfig.
"""
return qconfig.activation
def is_custom_module(self):
return self.is_custom_module_
def is_standalone_module(self):
return self.is_standalone_module_
# TODO: remove this class, this is still exposed in torch.quantization
# but we should be able to break bc
class BinaryOpQuantizeHandler(QuantizeHandler):
pass
class CatQuantizeHandler(QuantizeHandler):
pass
# TODO: remove this class
class ConvReluQuantizeHandler(QuantizeHandler):
pass
# TODO: remove this class
class LinearReLUQuantizeHandler(QuantizeHandler):
pass
# TODO: remove this class
class BatchNormQuantizeHandler(QuantizeHandler):
pass
# TODO: remove this class
class EmbeddingQuantizeHandler(QuantizeHandler):
pass
# TODO: remove this class
class RNNDynamicQuantizeHandler(QuantizeHandler):
pass
# TODO: remove this class
class DefaultNodeQuantizeHandler(QuantizeHandler):
""" Common quantized op, first input and first output will be quantized
"""
pass
# TODO: remove this class
class FixedQParamsOpQuantizeHandler(QuantizeHandler):
pass
# TODO: remove
class CopyNodeQuantizeHandler(QuantizeHandler):
pass
# TODO: remove
class GeneralTensorShapeOpQuantizeHandler(QuantizeHandler):
pass
# TODO: not used, can be removed after torch.quantization namespace is deprecated
class CustomModuleQuantizeHandler(QuantizeHandler):
pass
# TODO: not used, can be removed after torch.quantization namespace is deprecated
class StandaloneModuleQuantizeHandler(QuantizeHandler):
pass
|
