mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-06 12:20:52 +01:00
7094c09ff7
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/46095 Adds logging on usage of public quantization APIs. This only works in FB codebase and is a no-op in OSS. Test Plan: The test plan is fb-only Reviewed By: raghuramank100 Differential Revision: D24220817 fbshipit-source-id: a2cc957b5a077a70c318242f4a245426e48f75e5
245 lines
9.5 KiB
Python
245 lines
9.5 KiB
Python
import torch
|
|
from torch.fx import GraphModule # type: ignore
|
|
from torch.fx import symbolic_trace # type: ignore
|
|
from torch.fx.symbolic_trace import Tracer # type: ignore
|
|
from .fx import Fuser # noqa: F401
|
|
from .fx import Quantizer # noqa: F401
|
|
from .fx.utils import graph_pretty_str # noqa: F401
|
|
|
|
def _check_is_graph_module(model):
|
|
if not isinstance(model, GraphModule):
|
|
raise ValueError(
|
|
'input model must be a GraphModule, ' +
|
|
'Got type:' + str(type(model)) + ' Please make ' +
|
|
'sure to follow the tutorials.')
|
|
|
|
def _fuse_fx(graph_module, inplace=False):
|
|
r""" Internal helper function to fuse modules in preparation for quantization
|
|
|
|
Args:
|
|
graph_module: GraphModule object from symbolic tracing (torch.fx.symbolic_trace)
|
|
"""
|
|
_check_is_graph_module(graph_module)
|
|
fuser = Fuser()
|
|
return fuser.fuse(graph_module, inplace)
|
|
|
|
class CustomTracer(Tracer):
|
|
def __init__(self, standalone_modules):
|
|
super().__init__()
|
|
self.standalone_modules = standalone_modules
|
|
|
|
def is_leaf_module(self, m, module_qualified_name):
|
|
return (m.__module__.startswith('torch.nn') and
|
|
not isinstance(m, torch.nn.Sequential)) or \
|
|
module_qualified_name in self.standalone_modules
|
|
|
|
|
|
def _prepare_fx(model, qconfig_dict, inplace, is_standalone_module=False):
|
|
r""" Internal helper function for prepare_fx
|
|
Args:
|
|
`model`, `qconfig_dict`, `inplace`: see docs for :func:`~torch.quantization.prepare_fx`
|
|
`is_standalone_module`: a boolean flag indicates whether we are
|
|
quantizing a standalone module or not, a standalone module
|
|
is a submodule of the parent module that is not inlined in the
|
|
forward graph of the parent module,
|
|
the way we quantize standalone module is described in:
|
|
:func:`~torch.quantization._prepare_standalone_module_fx`
|
|
"""
|
|
# symbolically trace the model
|
|
if is_standalone_module:
|
|
# standlone module is traced before quantizing standalone modules
|
|
graph_module = symbolic_trace(model)
|
|
else:
|
|
standalone_modules = qconfig_dict.get('standalone_module_name', [])
|
|
# skipping tracing standalone modules when tracing top level module
|
|
graph_module = GraphModule(model, CustomTracer(standalone_modules).trace(model))
|
|
graph_module = _fuse_fx(graph_module, inplace)
|
|
quantizer = Quantizer()
|
|
return quantizer.prepare(graph_module, qconfig_dict, inplace=True, is_standalone_module=is_standalone_module)
|
|
|
|
def _prepare_standalone_module_fx(model, qconfig_dict, inplace=False):
|
|
r""" [Internal use only] Prepare a standalone module, so that it can be used when quantizing the
|
|
parent module.
|
|
standalone_module means it a submodule that is not inlined in parent module,
|
|
and will be quantized separately as one unit.
|
|
|
|
input of the module is quantized in parent module, output of the module
|
|
is quantized in the standalone module.
|
|
Extra attributes in output GraphModule while preparing a standalone module:
|
|
_standalone_module_observed_input_idxs(List[Int]): a list of indexs for the graph inputs that
|
|
needs to be observed in parent module
|
|
_output_is_observed(Bool): a boolean variable indicate whether the output of the
|
|
custom module is observed or not
|
|
|
|
"""
|
|
torch._C._log_api_usage_once("quantization_api.quantize_fx._prepare_standalone_module_fx")
|
|
return _prepare_fx(model, qconfig_dict, inplace, is_standalone_module=True)
|
|
|
|
|
|
def fuse_fx(model, inplace=False):
|
|
r""" Fuse modules like conv+bn, conv+bn+relu etc, model must be in eval mode.
|
|
Fusion rules are defined in torch.quantization.fx.fusion_pattern.py
|
|
Args:
|
|
`model`: a torch.nn.Module model
|
|
`inplace`: flag for whether we fuse modules inplace or out of place
|
|
|
|
Example:
|
|
```python
|
|
from torch.quantization import fuse_fx
|
|
m = Model().eval()
|
|
m = fuse_fx(m)
|
|
```
|
|
"""
|
|
torch._C._log_api_usage_once("quantization_api.quantize_fx.fuse_fx")
|
|
assert not model.training, 'fuse_fx only works on models in eval mode'
|
|
graph_module = torch.fx.symbolic_trace(model)
|
|
return _fuse_fx(graph_module, inplace)
|
|
|
|
def prepare_fx(model, qconfig_dict, inplace=False):
|
|
r""" Prepare a model for post training static quantization
|
|
|
|
Args:
|
|
`model`: torch.nn.Module model, must be in eval mode
|
|
`qconfig_dict`: qconfig_dict is a dictionary with the following configurations:
|
|
qconfig_dict = {
|
|
# optional, global config
|
|
"": qconfig?,
|
|
|
|
# optional, used for module and function types
|
|
# could also be split into module_types and function_types if we prefer
|
|
"object_type": [
|
|
(torch.nn.Conv2d, qconfig?),
|
|
(torch.nn.functional.add, qconfig?),
|
|
...,
|
|
],
|
|
|
|
# optional, used for module names
|
|
"module_name": [
|
|
("foo.bar", qconfig?)
|
|
...,
|
|
],
|
|
|
|
# optional, matched in order, first match takes precedence
|
|
"module_name_regex": [
|
|
("foo.*bar.*conv[0-9]+", qconfig?)
|
|
...,
|
|
],
|
|
# priority (in increasing order): global, object_type, module_name_regex, module_name
|
|
# qconfig == None means fusion and quantization should be skipped for anything
|
|
# matching the rule
|
|
|
|
# optional: specify the path for standalone modules
|
|
# These modules are symbolically traced and quantized as one unit
|
|
"standalone_module_name": [
|
|
"submodule.standalone"
|
|
]
|
|
}
|
|
`inplace`: flag for carry out model transformations in-place,
|
|
the original module is mutated
|
|
|
|
|
|
Return:
|
|
A GraphModule with observer (configured by qconfig_dict), ready for calibration
|
|
|
|
Example:
|
|
```python
|
|
import torch
|
|
from torch.quantization import get_default_qconfig
|
|
from torch.quantization import prepare_fx
|
|
|
|
float_model.eval()
|
|
graph_module = torch.fx.symbolic_trace(float_model)
|
|
qconfig = get_default_qconfig('fbgemm')
|
|
def calibrate(model, data_loader):
|
|
model.eval()
|
|
with torch.no_grad():
|
|
for image, target in data_loader:
|
|
model(image)
|
|
|
|
qconfig_dict = {"": qconfig}
|
|
prepared_model = prepare_fx(graph_module, qconfig_dict)
|
|
# Run calibration
|
|
calibrate(prepared_model, sample_inference_data)
|
|
```
|
|
"""
|
|
torch._C._log_api_usage_once("quantization_api.quantize_fx.prepare_fx")
|
|
assert not model.training, 'prepare_fx only works for models in' + \
|
|
'eval mode'
|
|
return _prepare_fx(model, qconfig_dict, inplace)
|
|
|
|
def prepare_qat_fx(model, qconfig_dict, inplace=False):
|
|
r""" Prepare a model for quantization aware training
|
|
Args:
|
|
`model`: torch.nn.Module model, must be in train mode
|
|
`qconfig_dict`: see :func:`~torch.quantization.prepare_fx`
|
|
`inplace`: flag for carry out model transformations in-place,
|
|
the original module is mutated
|
|
|
|
Return:
|
|
A GraphModule with fake quant modules (configured by qconfig_dict), ready for
|
|
quantization aware training
|
|
|
|
Example:
|
|
```python
|
|
import torch
|
|
from torch.quantization import get_default_qat_qconfig
|
|
from torch.quantization import prepare_fx
|
|
|
|
qconfig = get_default_qat_qconfig('fbgemm')
|
|
def train_loop(model, train_data):
|
|
model.train()
|
|
for image, target in data_loader:
|
|
...
|
|
|
|
float_model.train()
|
|
qconfig_dict = {"": qconfig}
|
|
prepared_model = prepare_fx(float_model, qconfig_dict)
|
|
# Run calibration
|
|
train_loop(prepared_model, train_loop)
|
|
```
|
|
"""
|
|
torch._C._log_api_usage_once("quantization_api.quantize_fx.prepare_qat_fx")
|
|
assert model.training, 'prepare_qat_fx only works for models in ' + \
|
|
'train mode'
|
|
return _prepare_fx(model, qconfig_dict, inplace)
|
|
|
|
def _convert_fx(graph_module, inplace, debug, is_standalone_module=False):
|
|
""" `is_standalone_module`: see docs in :func:`~torch.quantization.prepare_standalone_module_fx`
|
|
"""
|
|
_check_is_graph_module(graph_module)
|
|
quantizer = Quantizer()
|
|
return quantizer.convert(graph_module, inplace, debug, is_standalone_module)
|
|
|
|
def convert_fx(graph_module, inplace=False, debug=False):
|
|
r""" Convert a calibrated or trained model to a quantized model
|
|
Args:
|
|
`graph_module`: A prepared and calibrated/trained model (GraphModule)
|
|
`inplace`: flag for carry out model transformations in-place,
|
|
the original module is mutated
|
|
`debug`: flag for producing a debug friendly model (preserve weight attribute)
|
|
Return:
|
|
A quantized model (GraphModule)
|
|
|
|
Example:
|
|
```python
|
|
# prepared_model: the model after prepare_fx/prepare_qat_fx and calibration/training
|
|
quantized_model = convert_fx(prepared_model)
|
|
```
|
|
"""
|
|
torch._C._log_api_usage_once("quantization_api.quantize_fx.convert_fx")
|
|
return _convert_fx(graph_module, inplace, debug)
|
|
|
|
def _convert_standalone_module_fx(graph_module, inplace=False, debug=False):
|
|
r""" [Internal use only] Convert a model produced by :func:`~torch.quantization.prepare_standalone_module_fx`
|
|
and convert it to a quantized model
|
|
|
|
The inputs will be quantized by parent module, checks `_standalone_module_observed_input_idxs` of
|
|
input model and will treat these inputs as quantized
|
|
also will not dequantize the final output
|
|
Return:
|
|
A quantized standalone module which accepts quantized input(if needed)
|
|
and produces quantized output (if needed).
|
|
"""
|
|
torch._C._log_api_usage_once("quantization_api.quantize_fx._convert_standalone_module_fx")
|
|
return _convert_fx(graph_module, inplace, debug, is_standalone_module=True)
|