feat(quant): support nnie quant

GitOrigin-RevId: 8ca3f828bdd55e9bfcec072831aa161e8b961fa8

python_module/megengine/module/qat/module.py

@@ -49,6 +49,8 @@ class QATModule(Module):
     def _apply_fakequant_with_observer(
         self, target: Tensor, fake_quant: FakeQuantize, observer: Observer
     ):
+        if observer is None:
+            return target
         oup = observer(target)
         if fake_quant is None:
             return oup
@@ -76,7 +78,7 @@ class QATModule(Module):
         r"""
         Get weight's quantization dtype as the method from ``qconfig``.
         """
-        if hasattr(self.act_fake_quant, "get_dtype"):
+        if hasattr(self.weight_fake_quant, "get_dtype"):
             return self.weight_fake_quant.get_dtype()
         else:
             return self.weight_observer.get_dtype()

python_module/megengine/quantization/__init__.py

@@ -5,7 +5,9 @@
 # Unless required by applicable law or agreed to in writing,
 # software distributed under the License is distributed on an
 # "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+
 from .fake_quant import FakeQuantize
+from .internal_fake_quant import *
 from .observer import HistogramObserver, Observer, ObserverMode
 from .qconfig import (
     QConfig,

python_module/megengine/quantization/fake_quant.py

@@ -19,6 +19,15 @@ from .observer import ObserverMode, Round
 
 
 class _FakeQuantize(Module):
+    r"""
+    A Basic Fake Quant module.
+
+    :param dtype: A string indicating the target quantization type of input.
+    :param narrow_range: Whether the absolute value of ``qmin`` is the same as ``qmax``,
+        instead of 1 greater. Usually True for weight and False for activation.
+    :param enable: Whether do ``normal_forward`` or ``fake_quant_forward``.
+    """
+
     def __init__(self, dtype: str, narrow_range: bool = False, enable: bool = True):
         super().__init__()
         if not dtype in _metadata_dict.keys():
@@ -92,9 +101,9 @@ class TQT_Function(Function):
 
 
 class TQT(_FakeQuantize):
-    """
+    r"""
     TQT: https://arxiv.org/abs/1903.08066 Trained Quantization Thresholds
-    for Accurate and Efficient Fixed-Point Inference of Deep Neural Networks
+    for Accurate and Efficient Fixed-Point Inference of Deep Neural Networks.
     """
 
     def __init__(self, dtype: str, narrow_range: bool = False, enable: bool = True):
@@ -119,11 +128,6 @@ class TQT(_FakeQuantize):
 class FakeQuantize(_FakeQuantize):
     r"""
     A module to do quant and dequant according to observer's scale and zero_point.
-
-    :param dtype: A string indicating the target quantization type of input.
-    :param narrow_range: Whether the absolute value of ``qmin`` is the same as ``qmax``,
-        instead of 1 greater. Usually True for weight and False for activation.
-    :param enable: Whether do ``normal_forward`` or ``fake_quant_forward``.
     """
 
     def fake_quant_forward(self, inp, q_dict):

python_module/megengine/quantization/internal_fake_quant.py

+# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
+#
+# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+import copy
+import math
+from functools import partial
+
+import numpy as np
+
+from .. import functional as F
+from ..core import Function
+from .fake_quant import _FakeQuantize
+from .observer import MinMaxObserver
+from .qconfig import QConfig
+

python_module/test/unit/quantization/test_TQT.py → python_module/test/unit/quantization/test_fake_quant.py

@@ -13,6 +13,7 @@ import megengine as mge
 import megengine._internal as mgb
 from megengine.core import tensor
 from megengine.quantization.fake_quant import TQT_Function
+from megengine.quantization.internal_fake_quant import *
 from megengine.test import assertTensorClose
 
 
@@ -75,3 +76,5 @@ def test_TQT():
     a.set_value(a_np)
     b.set_value(b_np)
     check_inp(a, b, b, a_np, b_np, b_np)
+
+