[Update] Conv3d && Fix bugs. (#146)

* [Update] Conv3d && Fix bugs.

* [REQ] Update torch version to 1.10.0.

* [REQ] Update torchvision version to 0.11.1.

* [Update] Update observer.

* [Test] Update test.

Co-authored-by: zhangqi3 <[email protected]>
Co-authored-by: fanyunqian <[email protected]>

Author: 3 people

.github/workflows/python-package-conda.yml renamed to .github/workflows/lint-and-test.yml

@@ -1,9 +1,9 @@
-name: Lint and test.
+name: Lint and test
 
 on: [push]
 
 jobs:
-  build-linux:
+  Lint-and-test:
     runs-on: ubuntu-latest
     strategy:
       max-parallel: 5
@@ -22,6 +22,9 @@ jobs:
       run: |
         conda install flake8
         flake8 .
+    - name: Install onnxruntime and onnxsim
+      run:
+        pip install onnxruntime onnx-simplifier
     - name: Install Protobuf
       run:
         conda install protobuf=3.20.1

mqbench/custom_quantizer/model_quantizer.py

@@ -263,10 +263,6 @@ def _convert(self, module, mapping=None, inplace=False, scope=''):
             if not isinstance(mod, _FusedModule):
                 self._convert(mod, mapping, True, new_scope)
             reassign[name] = swap_module(mod, mapping, {})
-            if isinstance(mod, torch.nn.ConvTranspose2d):
-                if hasattr(reassign[name], "weight_fake_quant") and reassign[name].weight_fake_quant.ch_axis != -1:
-                    reassign[name].weight_fake_quant.ch_axis = 1
-                    reassign[name].weight_fake_quant.activation_post_process.ch_axis = 1
         for key, value in reassign.items():
             module._modules[key] = value
 

mqbench/custom_quantizer/onnx_qnn_quantizer.py

@@ -7,8 +7,8 @@
 from torch.quantization.utils import get_combined_dict
 
 
-import mqbench.nn as qnn 
-import mqbench.nn.intrinsic as qnni 
+import mqbench.nn as qnn
+import mqbench.nn.intrinsic as qnni
 from mqbench.utils.registry import register_model_quantizer
 from mqbench.prepare_by_platform import BackendType
 from mqbench.custom_quantizer import ModelQuantizer
@@ -57,7 +57,6 @@ def _qat_swap_modules(self, root: GraphModule, additional_qat_module_mapping: Di
             get_default_qat_module_mappings(), additional_qat_module_mapping)
         # There is no QLinearFC in ONNX for now.
         del(all_mappings[torch.nn.modules.linear.Linear])
-        del(all_mappings[torch.nn.modules.linear._LinearWithBias])
         del(all_mappings[torch.nn.intrinsic.modules.fused.LinearReLU])
         del(all_mappings[qnni.modules.fused.LinearBn1d])
         root = self._convert(root, all_mappings, inplace=True)
@@ -97,4 +96,4 @@ def implicit_merge_patterns(self) -> list:
         # In reversed order!
         return [
             (torch.nn.ReLU, operator.add)
-        ]
+        ]

mqbench/custom_quantizer/tensorrt_quantizer.py

@@ -128,9 +128,6 @@ def _find_act_quants(self, model: GraphModule) -> List:
                 if node.op == "call_function" and node.target == operator.add and \
                         self._is_skiped_add(node, modules, input_node_list):
                     continue
-                if node.op == "call_function" and node.target == operator.add:
-                    import pdb
-                    pdb.set_trace()
                 for _node in input_node_list:
                     if self._is_implicit_merge(modules, (node, _node)):
                         logger.info("Implicit merge: {} + {}".format(_node.name, node.name))

mqbench/deploy/deploy_tengine.py

@@ -1,10 +1,6 @@
 import os
 from collections import OrderedDict
 
-import onnx
-from onnx import numpy_helper
-from onnxsim import simplify
-
 from ..utils.logger import logger
 from .deploy_linear import (
     LinearQuantizer_process,
@@ -20,6 +16,14 @@
     get_constant_inputs
 )
 
+import onnx
+from onnx import numpy_helper
+try:
+    from onnxsim import simplify
+except ModuleNotFoundError:
+    logger.warn('onnxsim not found, if you want to use deploy_tengine, please install it.')
+
+
 
 class Tengine_process(LinearQuantizer_process):
 

mqbench/fuser_method_mappings.py

@@ -33,6 +33,7 @@ def __init__(self, quantizer: QuantizerCls, node: Node):
         self.conv_node = node
         self.conv = quantizer.modules[self.conv_node.target]
 
+
 def fuse_linear_bn(linear, bn):
     r"""Given the linear and bn modules, fuses them and returns the fused module
 
@@ -83,7 +84,6 @@ def fuse_deconv_bn_relu(deconv, bn, relu):
         return qnni.ConvTransposeReLU2d(fuse_deconv_bn_eval(deconv, bn), relu)
 
 
-
 def fuse_conv_freezebn(conv, bn):
     assert(bn.training is False), "Freezebn must be eval."
 
@@ -100,6 +100,7 @@ def fuse_conv_freezebn(conv, bn):
     else:
         return nn.utils.fuse_conv_bn_eval(conv, bn)
 
+
 def fuse_conv_freezebn_relu(conv, bn, relu):
     assert(conv.training == relu.training and bn.training is False), "Conv and relu both must be in the same mode (train or eval) and bn must be eval."
     fused_module : Optional[Type[nn.Sequential]] = None

mqbench/fusion_method.py

@@ -42,20 +42,37 @@ def convert_qnniqat_linearbn(model, fused_node):
 
 @register_convert_function(qnniqat.ConvFreezebn2d)
 @register_convert_function(nniqat.ConvBn2d)
+@register_convert_function(nniqat.ConvBn3d)
 def convert_nniqat_convbn(model, fused_node):
+    """nniqat.ConvBn2d ----> nn.Conv2d ----> nniqat.Conv2d
+    """
+    fused_module_class_map = {
+        qnniqat.ConvFreezebn2d: torch.nn.Conv2d,
+        qnniqat.ConvFreezebnReLU2d: torch.nn.Conv2d,
+        nniqat.ConvBn2d: torch.nn.Conv2d,
+        nniqat.ConvBnReLU2d: torch.nn.Conv2d,
+        nniqat.ConvBn3d: torch.nn.Conv3d,
+        nniqat.ConvBnReLU3d: torch.nn.Conv3d,
+    }
+    fused_qat_module_class_map = {
+        torch.nn.Conv2d: torch.nn.qat.Conv2d,
+        torch.nn.Conv3d: torch.nn.qat.Conv3d,
+    }
     modules = dict(model.named_modules())
     fused_module = modules[fused_node.target]
     # Create a Conv2d from FusedModule.
-    conv = torch.nn.Conv2d(fused_module.in_channels, fused_module.out_channels, fused_module.kernel_size, 
-                           fused_module.stride, fused_module.padding, fused_module.dilation,
-                           fused_module.groups, fused_module.bias is not None, fused_module.padding_mode)
+    conv = fused_module_class_map[type(fused_module)](fused_module.in_channels, fused_module.out_channels,
+                                                      fused_module.kernel_size, fused_module.stride,
+                                                      fused_module.padding, fused_module.dilation,
+                                                      fused_module.groups, fused_module.bias is not None,
+                                                      fused_module.padding_mode)
     conv.weight = fused_module.weight
     if fused_module.bias is not None:
         conv.bias = fused_module.bias
     fused_conv = fuse_conv_bn_eval(conv.eval(), fused_module.bn)
     # We need nn.qat.conv here to export weight quantize node.
     fused_conv.qconfig = fused_module.qconfig
-    fused_conv = torch.nn.qat.Conv2d.from_float(fused_conv)
+    fused_conv = fused_qat_module_class_map[type(conv)].from_float(fused_conv)
     # Attach weight fake quantize params.
     fused_conv.weight_fake_quant = fused_module.weight_fake_quant
     conv_parent_name, conv_name = _parent_name(fused_node.target)
@@ -64,7 +81,8 @@ def convert_nniqat_convbn(model, fused_node):
 
 @register_convert_function(qnniqat.ConvFreezebnReLU2d)
 @register_convert_function(nniqat.ConvBnReLU2d)
-def convert_nniqat_convbnrelu(model, fused_node):
+@register_convert_function(nniqat.ConvBnReLU3d)
+def convert_nniqat_convbnrelu(model, fused_node):    
     convert_nniqat_convbn(model, fused_node)
     modules = dict(model.named_modules())
     fused_module = modules[fused_node.target]
@@ -196,6 +214,9 @@ def convert_qnniqat_deconvbnrelu(model, fused_node):
 
 @register_convert_function(qnniqat.ConvBn2d)
 def convert_qnniqat_convbn(model, fused_node):
+    """mqbench.nn.intrinsic.qat module add bias quant.
+    That is the difference between torch.nn.intrinsic.qat module.
+    """
     modules = dict(model.named_modules())
     fused_module = modules[fused_node.target]
     # Create a Conv2d from FusedModule.
@@ -222,6 +243,9 @@ def convert_qnniqat_convbn(model, fused_node):
 
 @register_convert_function(qnniqat.ConvBnReLU2d)
 def convert_qnniqat_convbnrelu(model, fused_node):
+    """mqbench.nn.intrinsic.qat module add bias quant.
+    That is the difference between torch.nn.intrinsic.qat module.
+    """
     convert_qnniqat_convbn(model, fused_node)
     modules = dict(model.named_modules())
     fused_module = modules[fused_node.target]

mqbench/observer.py

@@ -1,7 +1,7 @@
 import math
 from functools import partial
 from typing import Tuple
-from copy import deepcopy
+
 import torch
 from torch.quantization.observer import _ObserverBase
 
@@ -28,12 +28,14 @@ class ObserverBase(_ObserverBase):
     def __init__(self, dtype=torch.quint8, qscheme=torch.per_tensor_affine,
                  reduce_range=False, quant_min=None, quant_max=None, ch_axis=-1, pot_scale=False,
                  factory_kwargs=None):
-        factory_kwargs = deepcopy(factory_kwargs)
-        self.not_calc_quant_min_max = factory_kwargs.pop('not_calc_quant_min_max', False) if isinstance(factory_kwargs, dict) else False
+        # Since torch 1.10, function calculate_qmin_qmax is not a member function of observer,
+        # but import from utils. It is hard to control. We use try...except here.
+        stored_min, sotred_max = quant_min, quant_max
+        if quant_max is not None and quant_min is not None and (quant_max - quant_min + 1 > 256):
+            quant_min, quant_max = -128, 127
         super(ObserverBase, self).__init__(dtype, qscheme, reduce_range, quant_min, quant_max)
-        # for compatibility with 1.10, prevent the value of self.quant_min,self.quant_max being modified
-        self.quant_min = quant_min
-        self.quant_max = quant_max
+        self.quant_min = stored_min
+        self.quant_max = sotred_max
         self.quant_min, self.quant_max = self._calculate_qmin_qmax()
         self.ch_axis = ch_axis
         self.pot_scale = pot_scale
@@ -79,28 +81,7 @@ def _calculate_qmin_qmax(self) -> Tuple[int, int]:
         observer datatype and if range is reduced.
         """
         if self.has_customized_qrange:
-            # This initialization here is to be resolve TorchScript compilation issues and allow
-            # using of refinement to decouple initial_qmin and initial_qmax from quantization range.
-            # The actual values of initial_qmin and initial_qmax will be reset below.
-            initial_quant_min, initial_quant_max = 0, 255
-            # The following assignment of self.qmin and self.qmax to the local variables and the if check refine the
-            # attribute from Optional valid integers for use, based on TorchScript's requirements.
-            custom_quant_min, custom_quant_max = self.quant_min, self.quant_max
-            if custom_quant_min is not None and custom_quant_max is not None:
-                initial_quant_min, initial_quant_max = (
-                    custom_quant_min,
-                    custom_quant_max,
-                )
-
-            qrange_len = initial_quant_max - initial_quant_min + 1
-            if is_symmetric_quant(self.qscheme):
-                quant_min, quant_max = -qrange_len // 2, qrange_len // 2 - 1
-            else:
-                quant_min, quant_max = 0, qrange_len - 1
-            if self.reduce_range:
-                quant_min, quant_max = quant_min // 2, quant_max // 2
-            if self.not_calc_quant_min_max:
-                quant_min, quant_max = self.quant_min, self.quant_max
+            quant_min, quant_max = self.quant_min, self.quant_max
         else:
             # Fallback onto default 8-bit qmin and qmax calculation if dynamic range is not used.
             if self.dtype == torch.qint8:

requirements.txt

@@ -1,4 +1,3 @@
-torch==1.8.1
-torchvision==0.9.1
-onnx-simplifier
+torch==1.10.0
+torchvision==0.11.1
 onnx

test/model/test_model.py

@@ -11,11 +11,12 @@
 
 class TestQuantizeModel(unittest.TestCase):
     def test_model_ppl(self):
-        exclude_list = ['googlenet', 'deeplabv3_mobilenet_v3_large', 'inception_v3', 'lraspp_mobilenet_v3_large',
-                        'mobilenet_v3_large', 'mobilenet_v3_small']
+        test_model_list = ['alexnet', 'deeplabv3_resnet50', 'densenet121', 'fcn_resnet50', 'mnasnet0_5',
+                           'mobilenet_v2', 'resnet18', 'resnext50_32x4d', 'shufflenet_v2_x0_5', 'squeezenet1_0',
+                           'vgg11', 'vgg11_bn', 'wide_resnet50_2', 'regnet_x_400mf']
         entrypoints = torch.hub.list(GITHUB_RES, force_reload=False)
         for entrypoint in entrypoints:
-            if entrypoint in exclude_list:
+            if entrypoint not in test_model_list:
                 continue
             logger.info(f'testing {entrypoint}')
             if 'deeplab' in entrypoint or 'fcn' in entrypoint:

test/version.py

@@ -1,2 +1,2 @@
-TORCHVISION_VERSION = 'v0.9.0'
-GITHUB_RES = 'pytorch/vision:{}'.format(TORCHVISION_VERSION)
+TORCHVISION_VERSION = 'v0.11.1'
+GITHUB_RES = 'pytorch/vision:{}'.format(TORCHVISION_VERSION)