Refactoing names for consistency

Zafar Takhirov · facebook-github-bot · commit dc8785a022bf · 2019-10-16T12:18:26.000-07:00
Summary: Pull Request resolved: #27670 Test Plan: Imported from OSS Differential Revision: D17846269 Pulled By: z-a-f fbshipit-source-id: ed3c7441c185bf11b2e62879aa3ecbc654aa2d4e
diff --git a/docs/source/quantization.rst b/docs/source/quantization.rst
@@ -190,10 +190,10 @@ Layers for the quantization-aware training
     * :class:`~torch.quantization.Observer` — Abstract base class for observers
 * Quantization configurations
     * :class:`~torch.quantization.QConfig` — Quantization configuration class
-    * ``default_qconfig`` — Same as ``QConfig(activation=default_observer, weight=default_weight_observer)`` (See :class:`~torch.quantization.QConfig.QConfig`)
-    * :attr:`~torch.quantization.default_qat_qconfig` — Same as ``QConfig(activation=default_fake_quant, weight=default_weight_fake_quant)`` (See :class:`~torch.quantization.QConfig.QConfig`)
-    * :attr:`~torch.quantization.default_dynamic_qconfig` — Same as ``QConfigDynamic(weight=default_weight_observer)`` (See :class:`~torch.quantization.QConfig.QConfigDynamic`)
-    * :attr:`~torch.quantization.float16_dynamic_qconfig` — Same as ``QConfigDynamic(weight=NoopObserver.with_args(dtype=torch.float16))`` (See :class:`~torch.quantization.QConfig.QConfigDynamic`)
+    * :attr:`~torch.quantization.default_qconfig` — Same as ``QConfig(activation=default_observer, weight=default_weight_observer)`` (See :class:`~torch.quantization.qconfig.QConfig`)
+    * :attr:`~torch.quantization.default_qat_qconfig` — Same as ``QConfig(activation=default_fake_quant, weight=default_weight_fake_quant)`` (See :class:`~torch.quantization.qconfig.QConfig`)
+    * :attr:`~torch.quantization.default_dynamic_qconfig` — Same as ``QConfigDynamic(weight=default_weight_observer)`` (See :class:`~torch.quantization.qconfig.QConfigDynamic`)
+    * :attr:`~torch.quantization.float16_dynamic_qconfig` — Same as ``QConfigDynamic(weight=NoopObserver.with_args(dtype=torch.float16))`` (See :class:`~torch.quantization.qconfig.QConfigDynamic`)
 * Stubs
     * :class:`~torch.quantization.DeQuantStub` - placeholder module for dequantize() operation in float-valued models
     * :class:`~torch.quantization.QuantStub` - placeholder module for quantize() operation in float-valued models
diff --git a/test/test_qat.py b/test/test_qat.py
@@ -6,7 +6,7 @@
 import torch
 from torch.nn import Conv2d, BatchNorm2d, ReLU
 from torch.nn.intrinsic.qat import ConvBn2d, ConvBnReLU2d
-from torch.quantization.QConfig import default_qat_qconfig
+from torch.quantization.qconfig import default_qat_qconfig
 import torch.backends.mkldnn
 from common_utils import TestCase, run_tests
 from hypothesis import given
diff --git a/test/test_quantized_models.py b/test/test_quantized_models.py
@@ -102,8 +102,8 @@ def test_weight_only_activation_only_fakequant(self):
                 torch.manual_seed(67)
                 calib_data = torch.rand(2048, 3, 15, 15, dtype=torch.float32)
                 eval_data = torch.rand(10, 3, 15, 15, dtype=torch.float32)
-                qconfigset = set([torch.quantization.default_weight_only_quant_qconfig,
-                                  torch.quantization.default_activation_only_quant_qconfig])
+                qconfigset = set([torch.quantization.default_weight_only_qconfig,
+                                  torch.quantization.default_activation_only_qconfig])
                 SQNRTarget = [35, 45]
                 for idx, qconfig in enumerate(qconfigset):
                     my_model = ModelMultipleOpsNoAvgPool().to(torch.float32)
diff --git a/torch/nn/quantized/dynamic/modules/linear.py b/torch/nn/quantized/dynamic/modules/linear.py
@@ -61,7 +61,7 @@ def from_float(cls, mod):
             # We have the circular import issues if we import the qconfig in the beginning of this file:
             # https://github.com/pytorch/pytorch/pull/24231. The current workaround is to postpone the
             # import until we need it.
-            from torch.quantization.QConfig import default_dynamic_qconfig
+            from torch.quantization.qconfig import default_dynamic_qconfig
             weight_observer = default_dynamic_qconfig.weight()
         assert weight_observer.dtype == torch.qint8, 'Weight observer must have dtype torch.qint8'
         weight_observer(mod.weight)
diff --git a/torch/nn/quantized/dynamic/modules/rnn.py b/torch/nn/quantized/dynamic/modules/rnn.py
@@ -227,7 +227,7 @@ def from_float(cls, mod):
             # We have the circular import issues if we import the qconfig in the beginning of this file:
             # https://github.com/pytorch/pytorch/pull/24231. The current workaround is to postpone the
             # import until we need it.
-            from torch.quantization.QConfig import default_dynamic_qconfig
+            from torch.quantization.qconfig import default_dynamic_qconfig
             weight_observer = default_dynamic_qconfig.weight()
 
         dtype = weight_observer.dtype
diff --git a/torch/quantization/__init__.py b/torch/quantization/__init__.py
@@ -1,7 +1,7 @@
 from __future__ import absolute_import, division, print_function, unicode_literals
 from .quantize import *  # noqa: F401
 from .observer import *  # noqa: F401
-from .QConfig import *  # noqa: F401
+from .qconfig import *  # noqa: F401
 from .fake_quantize import *  # noqa: F401
 from .fuse_modules import fuse_modules  # noqa: F401
 from .stubs import *  # noqa: F401
@@ -24,7 +24,7 @@ def default_eval_fn(model, calib_data):
     'propagate_qconfig_', 'add_quant_dequant', 'add_observer_', 'swap_module',
     'default_eval_fn', 'get_observer_dict',
     # Observers
-    'Observer', 'WeightObserver', 'observer', 'default_observer',
+    'ObserverBase', 'WeightObserver', 'observer', 'default_observer',
     'default_weight_observer',
     # QConfig
     'QConfig', 'default_qconfig', 'default_dynamic_qconfig', 'float16_dynamic_qconfig',
diff --git a/torch/quantization/_quantize_script.py b/torch/quantization/_quantize_script.py
@@ -1,7 +1,7 @@
 from __future__ import absolute_import, division, print_function, unicode_literals
 
 import torch
-from .QConfig import QConfig
+from .qconfig import QConfig
 
 class PackedParams(torch.nn.Module):
     def __init__(self):
diff --git a/torch/quantization/observer.py b/torch/quantization/observer.py
@@ -39,7 +39,7 @@ def _with_args(cls_or_self, **kwargs):
 ABC = ABCMeta(str("ABC"), (object,), {})  # compatible with Python 2 *and* 3:
 
 
-class Observer(ABC, nn.Module):
+class ObserverBase(ABC, nn.Module):
     r"""
     Observer base Module. Any observer implementation should derive from this class.
 
@@ -49,7 +49,7 @@ class Observer(ABC, nn.Module):
     the collected statistics.
     """
     def __init__(self, dtype):
-        super(Observer, self).__init__()
+        super(ObserverBase, self).__init__()
         self.dtype = dtype
 
     @abstractmethod
@@ -63,7 +63,7 @@ def calculate_qparams(self, **kwargs):
     with_args = classmethod(_with_args)
 
 
-class _ObserverBase(Observer):
+class _ObserverBase(ObserverBase):
     r"""
     Common base for all qint/quint8 observers
     """
@@ -636,7 +636,7 @@ def get_tensor_value(self):
         return self.tensor_val
 
 
-class NoopObserver(Observer):
+class NoopObserver(ObserverBase):
     r"""
     Observer that doesn't do anything and just passes its configuration to the
     quantized module's ``.from_float()``.
diff --git a/torch/quantization/qconfig.py b/torch/quantization/qconfig.py
@@ -67,10 +67,10 @@ def __new__(cls, weight):
 default_qat_qconfig = QConfig(activation=default_fake_quant,
                               weight=default_weight_fake_quant)
 
-default_weight_only_quant_qconfig = QConfig(activation=torch.nn.Identity,
-                                            weight=default_weight_fake_quant)
-default_activation_only_quant_qconfig = QConfig(activation=default_fake_quant,
-                                                weight=torch.nn.Identity)
+default_weight_only_qconfig = QConfig(activation=torch.nn.Identity,
+                                      weight=default_weight_fake_quant)
+default_activation_only_qconfig = QConfig(activation=default_fake_quant,
+                                          weight=torch.nn.Identity)
 
 def get_default_qconfig(backend='fbgemm'):
     if backend == 'fbgemm':
diff --git a/torch/quantization/quantize.py b/torch/quantization/quantize.py
@@ -14,7 +14,7 @@
                                DEFAULT_QAT_MODULE_MAPPING,
                                DEFAULT_QCONFIG_PROPAGATE_WHITE_LIST)
 from .stubs import DeQuantStub, QuantWrapper
-from .QConfig import default_dynamic_qconfig, float16_dynamic_qconfig
+from .qconfig import default_dynamic_qconfig, float16_dynamic_qconfig
 
 def _propagate_qconfig_helper(module, qconfig_dict, white_list=None,
                               qconfig_parent=None, prefix=''):
