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Martina Burlando 2025-08-02 02:00:33 +02:00
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# mypy: allow-untyped-decorators
# mypy: allow-untyped-defs
import enum
import operator
from typing import Callable, Optional, Union
import torch
import torch.ao.nn.intrinsic.quantized as nniq
import torch.ao.nn.quantized as nnq
import torch.nn as nn
from torch.ao.quantization import FakeQuantizeBase, ObserverBase
from torch.ao.quantization.observer import _is_activation_post_process
from torch.ao.quantization.utils import getattr_from_fqn
from torch.fx import GraphModule
from torch.fx.graph import Node
from .ns_types import NSNodeTargetType, NSResultsType
toq = torch.ops.quantized
# TODO(future PR): consider deleting this enum and using the torch types
# directly. This might be tricky because it is not a one to one mapping.
class NodeInputOrOutputType(enum.Enum):
FP32 = enum.auto() # torch.float
INT8 = enum.auto() # torch.qint8 or torch.quint8
FP16 = enum.auto() # torch.float16
UNKNOWN = enum.auto() # we cannot determine input/output dtype
# TODO(future PR): while these functions can support multiple dtypes,
# for the purposes of numerical debugging we want to get the actual
# dtype used in the model. We will likely need some kind of dtype
# propagation to estimate this.
FP32_OR_INT8 = enum.auto() # either torch.float or torch.quint8 or torch.qint8
# TODO(future PRs): dynamic quant, fake quant, etc
def get_node_first_input_and_output_type(
node: Node,
gm: GraphModule,
logger_cls: Callable,
node_type_to_io_type_map: dict[str, set[NSNodeTargetType]],
) -> tuple[NodeInputOrOutputType, NodeInputOrOutputType]:
# TODO(future PR): clean this up
FUNS_IO_TYPE_FP32 = node_type_to_io_type_map["funs_io_type_fp32"]
FUNS_IO_TYPE_FP16 = node_type_to_io_type_map["funs_io_type_fp16"]
FUNS_IO_TYPE_INT8 = node_type_to_io_type_map["funs_io_type_int8"]
FUNS_IO_TYPE_FP32_OR_INT8 = node_type_to_io_type_map["funs_io_type_fp32_or_int8"]
MODS_IO_TYPE_FP32 = node_type_to_io_type_map["mods_io_type_fp32"]
MODS_IO_TYPE_INT8 = node_type_to_io_type_map["mods_io_type_int8"]
MODS_IO_TYPE_FP32_OR_INT8 = node_type_to_io_type_map["mods_io_type_fp32_or_int8"]
METHS_IO_TYPE_FP32_OR_INT8 = node_type_to_io_type_map["meths_io_type_fp32_or_int8"]
if node.op == "call_function":
if node.target in FUNS_IO_TYPE_FP32:
return (NodeInputOrOutputType.FP32, NodeInputOrOutputType.FP32)
if node.target in FUNS_IO_TYPE_FP16:
return (NodeInputOrOutputType.FP16, NodeInputOrOutputType.FP16)
elif node.target in FUNS_IO_TYPE_INT8:
return (NodeInputOrOutputType.INT8, NodeInputOrOutputType.INT8)
elif node.target in FUNS_IO_TYPE_FP32_OR_INT8:
first_arg = get_normalized_nth_input(node, gm, 0)
assert isinstance(first_arg, Node)
(
_prev_node_input_type,
prev_node_output_type,
) = get_node_first_input_and_output_type(
first_arg, gm, logger_cls, node_type_to_io_type_map
)
return (prev_node_output_type, prev_node_output_type)
else:
return (NodeInputOrOutputType.UNKNOWN, NodeInputOrOutputType.UNKNOWN)
elif node.op == "call_module":
assert node.op == "call_module"
assert isinstance(node.target, str)
mod = getattr_from_fqn(gm, node.target)
is_known_fp32_or_int8_input_module = any(
isinstance(mod, target_type) for target_type in MODS_IO_TYPE_FP32_OR_INT8 # type: ignore[arg-type]
)
if (
isinstance(mod, (logger_cls, ObserverBase, FakeQuantizeBase)) # type: ignore[arg-type]
or is_known_fp32_or_int8_input_module
):
# A logger or observer's input and output type is the output
# type of the preceding node.
first_arg = get_normalized_nth_input(node, gm, 0)
assert isinstance(first_arg, Node)
(
_prev_node_input_type,
prev_node_output_type,
) = get_node_first_input_and_output_type(
first_arg, gm, logger_cls, node_type_to_io_type_map
)
return (prev_node_output_type, prev_node_output_type)
is_known_fp32_input_module = any(
isinstance(mod, target_type) for target_type in MODS_IO_TYPE_FP32 # type: ignore[arg-type]
)
is_known_int8_input_module = any(
isinstance(mod, target_type) for target_type in MODS_IO_TYPE_INT8 # type: ignore[arg-type]
)
if is_known_fp32_input_module:
return (NodeInputOrOutputType.FP32, NodeInputOrOutputType.FP32)
elif is_known_int8_input_module:
return (NodeInputOrOutputType.INT8, NodeInputOrOutputType.INT8)
else:
return (NodeInputOrOutputType.UNKNOWN, NodeInputOrOutputType.UNKNOWN)
elif node.op == "call_method":
if node.target == "dequantize":
# Dequantize is a special node because it allows multiple input types.
# So, we look up the output type of the previous node and return that
# as the input type of this node instance.
prev_node = get_normalized_nth_input(node, gm, 0)
assert isinstance(prev_node, Node)
(
_prev_node_input_type,
prev_node_output_type,
) = get_node_first_input_and_output_type(
prev_node, gm, logger_cls, node_type_to_io_type_map
)
return (prev_node_output_type, NodeInputOrOutputType.FP32)
elif node.target == "to":
# to is a special node because it allows multiple input types.
# So, we look up the output type of the previous node and return that
# as the input type of this node instance. We also look up the target
# of to and return the correct output type.
prev_node = get_normalized_nth_input(node, gm, 0)
assert isinstance(prev_node, Node)
(
_prev_node_input_type,
prev_node_output_type,
) = get_node_first_input_and_output_type(
prev_node, gm, logger_cls, node_type_to_io_type_map
)
cur_node_dtype_target = get_normalized_nth_input(node, gm, 1)
assert (
cur_node_dtype_target is torch.float16
), f"{cur_node_dtype_target} handling needs to be added"
return (prev_node_output_type, NodeInputOrOutputType.FP16)
elif node.target in METHS_IO_TYPE_FP32_OR_INT8:
first_arg = get_normalized_nth_input(node, gm, 0)
assert isinstance(first_arg, Node)
(
_prev_node_input_type,
prev_node_output_type,
) = get_node_first_input_and_output_type(
first_arg, gm, logger_cls, node_type_to_io_type_map
)
return (prev_node_output_type, prev_node_output_type)
return (NodeInputOrOutputType.UNKNOWN, NodeInputOrOutputType.UNKNOWN)
else:
return (NodeInputOrOutputType.UNKNOWN, NodeInputOrOutputType.UNKNOWN)
def get_node_input_qparams(
node: Node,
gm: GraphModule,
node_type_to_io_type_map: dict[str, set[NSNodeTargetType]],
) -> Optional[tuple[Union[torch.Tensor, float], Union[torch.Tensor, int]]]:
"""
Returns the qparams (scale, zero_point) of the first input to `node`,
if they can be inferred from the graph.
"""
prev_node = get_normalized_nth_input(node, gm, 0)
if not isinstance(prev_node, Node):
return None
MODS_IO_TYPE_FP32_OR_INT8 = node_type_to_io_type_map["mods_io_type_fp32_or_int8"]
def _get_scale_zp_from_function_args(node, gm, scale_arg_idx, zp_arg_idx):
scale_node = get_normalized_nth_input(node, gm, scale_arg_idx)
zp_node = get_normalized_nth_input(node, gm, zp_arg_idx)
assert isinstance(scale_node, Node) and isinstance(scale_node.target, str)
assert isinstance(zp_node, Node) and isinstance(zp_node.target, str)
scale_obj = getattr_from_fqn(gm, scale_node.target)
zp_obj = getattr_from_fqn(gm, zp_node.target)
return (scale_obj, zp_obj)
if prev_node.op == "call_function":
# quantize - read the args directly
if prev_node.target == torch.quantize_per_tensor:
return _get_scale_zp_from_function_args(prev_node, gm, 1, 2)
elif prev_node.target in (toq.add, toq.add_relu, toq.mul, toq.mul_relu):
return _get_scale_zp_from_function_args(prev_node, gm, 2, 3)
return None
# TODO(future PR): handle more functionals
# TODO(future PR): handle functional ops which inherit qparams from input
elif prev_node.op == "call_module":
# get type of the module
assert isinstance(prev_node.target, str)
module_obj = getattr_from_fqn(gm, prev_node.target)
if isinstance(
module_obj,
(
nnq.Linear,
nnq.Conv1d,
nnq.Conv2d,
nniq.ConvReLU2d,
nnq.Conv3d,
nnq.BatchNorm2d,
nnq.BatchNorm3d,
nnq.ConvTranspose1d,
nnq.ConvTranspose2d,
nnq.ELU,
nnq.GroupNorm,
nnq.InstanceNorm1d,
nnq.InstanceNorm2d,
nnq.InstanceNorm3d,
nnq.LayerNorm,
nnq.Hardswish,
nnq.LeakyReLU,
nnq.ReLU6,
nniq.BNReLU2d,
nniq.BNReLU3d,
nniq.ConvReLU1d,
nniq.ConvReLU2d,
nniq.ConvReLU3d,
nniq.LinearReLU,
),
):
return (module_obj.scale, module_obj.zero_point) # type: ignore[return-value]
is_known_fp32_or_int8_input_module = any(
isinstance(module_obj, target_type) for target_type in MODS_IO_TYPE_FP32_OR_INT8 # type: ignore[arg-type]
)
if is_known_fp32_or_int8_input_module:
return get_node_input_qparams(prev_node, gm, node_type_to_io_type_map)
return None
def return_first_non_observer_node(
node: Node,
gm: GraphModule,
) -> Node:
"""
If node is not an observer, returns it. If node is an observer,
navigates up the graph and returns the first parent which is not an
observer. For example,
graph: (node_non_obs), node = node_non_obs : returns node_non_obs
graph: (node_non_obs -> obs0), node = obs0 : returns node_non_obs
graph: (node_non_obs -> obs0 -> fq0), node = fq0 : returns node_non_obs
"""
if node.op == "call_module":
node_obj = getattr_from_fqn(gm, node.target) # type: ignore[arg-type]
if _is_activation_post_process(node_obj):
assert len(node.args) == 1
assert isinstance(node.args[0], Node)
node = node.args[0]
# code duplication intended, not worth refactoring
assert isinstance(node.target, str)
node_obj = getattr_from_fqn(gm, node.target)
if _is_activation_post_process(node_obj):
assert len(node.args) == 1
assert isinstance(node.args[0], Node)
node = node.args[0]
return node
def get_number_of_non_param_args(
node: Node,
gm: GraphModule,
) -> int:
"""
Assumes that all non-param args occur first. Returns the number of
non-param args expected for a node. For example, for
F.linear(x, weight, bias)
Returns 1, because x is a non-param arg and weight and bias are params.
For
lstm_mod(x, hid)
Returns 2, because both x and hid are non-param args.
"""
if node.op == "call_module":
node_obj = getattr_from_fqn(gm, node.target) # type: ignore[arg-type]
if isinstance(node_obj, nn.LSTM):
return 2
# default is 1
return 1
def get_arg_indices_of_inputs_to_log(node: Node) -> list[int]:
"""
Returns the indices of args of the node which we should attach
loggers to, if input logging is enabled.
For example,
* for (x + y), returns [0, 1]
* for (1 + y), returns [1]
* for (x + 1), returns [0]
* for (linear(x, w, b)) returns [0]
* by default, returns [0]
"""
if len(node.args) == 0:
return []
if node.op == "call_function" and (
# TODO(future PR): use relationship map instead of hardcoding
node.target in (torch.add, torch.ops.quantized.add, operator.add)
or node.target in (torch.mul, torch.ops.quantized.mul, operator.mul)
):
result = [i for i in range(2) if type(node.args[i]) == Node]
return result
return [0]
def get_target_type_str(node: Node, gm: GraphModule) -> str:
"""
Returns a string representation of the type of the function or module
pointed to by this node, or '' for other node types.
"""
target_type = ""
if node.op in ("call_function", "call_method"):
target_type = torch.typename(node.target)
elif node.op == "call_module":
assert isinstance(node.target, str)
target_mod = getattr_from_fqn(gm, node.target)
target_type = torch.typename(target_mod)
return target_type
def rekey_logger_info_on_node_name_of_model(
results: NSResultsType,
model_name: str,
) -> NSResultsType:
"""
Rekeys the layer name of a results dictionary to use node names
from `model_name`.
For example, transforms
{'base_op_1_0': {'node_output': {'model_a':
[{'ref_node_name': 'linear1', ...}]}}}
into
{'linear1': {'node_output': {'model_a':
[{'ref_node_name': 'linear1', ...}]}}}
Note: we cannot use these node names directly because they are not
guaranteed to be consistent across models. This is why we extract
the results first and rekey afterwards.
"""
new_results = {}
for old_layer_name, result_type_to_results in results.items():
new_layer_name = None
for model_name_to_results in result_type_to_results.values():
for cur_model_name, list_of_results in model_name_to_results.items():
if cur_model_name == model_name:
assert len(list_of_results)
new_layer_name = list_of_results[0]["ref_node_name"]
else:
continue
if new_layer_name is not None:
new_results[new_layer_name] = result_type_to_results
else:
new_results[old_layer_name] = result_type_to_results
return new_results
def maybe_add_missing_fqns(results: NSResultsType) -> None:
"""
If `fqn` entries are filled in for one of the models in `results`, copies
them over to any models which do not have them filled out.
A common use case benefitting from this is comparing a model prepared by
quantization to a quantized model. In this case, the model prepared by
quantization would have `fqn` entries, and the quantized model would not.
"""
# Check in the first result to find any model with fqn entries defined.
model_name_with_fqns = None
for result_type_to_results in results.values():
for model_name_to_results in result_type_to_results.values():
for model_name, model_results in model_name_to_results.items():
if len(model_results) > 0:
if model_results[0]["fqn"] is not None:
model_name_with_fqns = model_name
break
break
break
if model_name_with_fqns:
for result_type_to_results in results.values():
for model_name_to_results in result_type_to_results.values():
ref_model_results = model_name_to_results[model_name_with_fqns]
for model_name, model_results in model_name_to_results.items():
if model_name == model_name_with_fqns:
continue
for i in range(len(model_results)):
fqn = ref_model_results[i]["fqn"]
model_results[i]["fqn"] = fqn
def maybe_dequantize_first_two_tensor_args_and_handle_tuples(f):
def inner(*args, **kwargs):
a0, a1, *a_other = args
if (isinstance(a0, tuple) and isinstance(a1, tuple)) or (
isinstance(a0, list) and isinstance(a1, list)
):
results = []
for el0, el1 in zip(a0, a1):
new_args = (el0, el1, *a_other)
results.append(inner(*new_args, **kwargs))
return results
elif isinstance(a0, torch.Tensor) and isinstance(a1, torch.Tensor):
if a0.is_quantized:
a0 = a0.dequantize()
if a1.is_quantized:
a1 = a1.dequantize()
# for the purposes of this util, only handle floats
if a0.dtype != torch.float or a1.dtype != torch.float:
return None
new_args = (a0, a1, *a_other)
return f(*new_args, **kwargs)
return inner
@maybe_dequantize_first_two_tensor_args_and_handle_tuples
def compute_sqnr(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
"""
Computes the SQNR between `x` and `y`.
Args:
x: Tensor or tuple of tensors
y: Tensor or tuple of tensors
Return:
float or tuple of floats
"""
Ps = torch.norm(x)
Pn = torch.norm(x - y)
return 20 * torch.log10(Ps / Pn)
@maybe_dequantize_first_two_tensor_args_and_handle_tuples
def compute_normalized_l2_error(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
"""
Computes the normalized L2 error between `x` and `y`.
Args:
x: Tensor or tuple of tensors
y: Tensor or tuple of tensors
Return:
float or tuple of floats
"""
return torch.sqrt(((x - y) ** 2).sum() / (x**2).sum())
@maybe_dequantize_first_two_tensor_args_and_handle_tuples
def compute_cosine_similarity(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
"""
Computes the cosine similarity between `x` and `y`.
Args:
x: Tensor or tuple of tensors
y: Tensor or tuple of tensors
Return:
float or tuple of floats
"""
# For convolutions, the shape of the quantized weight has one additional
# dimension compared to the shape of the fp32 weight. Match the shapes
# to enable cosine similarity comparison.
x = x.reshape(1, -1)
y = y.reshape(1, -1)
return torch.nn.functional.cosine_similarity(x, y)
def op_type_supports_shadowing(node: Node) -> bool:
if node.op == "call_function":
if node.target in (
torch.add,
torch.mul,
operator.add,
operator.mul,
torch.cat,
torch.stack,
):
# shadowing for ops with multiple tensor inputs is not implemented yet
return False
return True
def get_normalized_nth_input(node: Node, gm: GraphModule, idx: int) -> Node:
"""
Given a node, gets the n'th input to that node, normalizing
args and kwargs to the best of its ability.
"""
try:
norm_args_and_kwargs = node.normalized_arguments(
gm, normalize_to_only_use_kwargs=True
)
if norm_args_and_kwargs is not None:
norm_args, norm_kwargs = norm_args_and_kwargs
assert len(norm_args) + len(norm_kwargs) > idx
if idx < len(norm_args):
return norm_args[idx]
else:
# note: in Python 3.7+ dicts are ordered
return list(norm_kwargs.values())[idx]
else:
assert len(node.args) + len(node.kwargs) > idx
if idx < len(node.args):
return node.args[idx] # type: ignore[return-value]
else:
kwargs_idx = idx + len(node.args)
return list(node.kwargs.values())[kwargs_idx] # type: ignore[return-value]
except RuntimeError:
# this RuntimeError happens when node argument normalization
# requires typehints to proceed, such as for torch.add where
# either the first, second or both arguments could be tensors
assert len(node.args) + len(node.kwargs) > idx
if idx < len(node.args):
return node.args[idx] # type: ignore[return-value]
else:
kwargs_idx = idx + len(node.args)
return list(node.kwargs.values())[kwargs_idx] # type: ignore[return-value]