Fix quantization tutorials (imports, syntax, and style)

Summary: This commit fixes the quantization tutorials such that
they can be run smoothly by the user.

Test Plan: Ran the updated tutorials without problem.

Reviewers: jerryzh168

Subscribers: jerryzh168, supriyar

ghstack-source-id: 196719d
Pull Request resolved: #1763

Author: andrewor14

advanced_source/dynamic_quantization_tutorial.py

@@ -98,9 +98,9 @@ def __len__(self):
 class Corpus(object):
     def __init__(self, path):
         self.dictionary = Dictionary()
-        self.train = self.tokenize(os.path.join(path, 'train.txt'))
-        self.valid = self.tokenize(os.path.join(path, 'valid.txt'))
-        self.test = self.tokenize(os.path.join(path, 'test.txt'))
+        self.train = self.tokenize(os.path.join(path, 'wiki.train.token'))
+        self.valid = self.tokenize(os.path.join(path, 'wiki.valid.token'))
+        self.test = self.tokenize(os.path.join(path, 'wiki.test.token'))
 
     def tokenize(self, path):
         """Tokenizes a text file."""

advanced_source/static_quantization_tutorial.rst

@@ -32,7 +32,7 @@ We'll start by doing the necessary imports:
     import sys  
     import torch.quantization 
 
-    # # Setup warnings  
+    # Set up warnings
     import warnings 
     warnings.filterwarnings(  
         action='ignore',  
@@ -196,9 +196,7 @@ Note: this code is taken from
                     nn.init.zeros_(m.bias)  
 
         def forward(self, x): 
-
             x = self.quant(x) 
-
             x = self.features(x)  
             x = x.mean([2, 3])  
             x = self.classifier(x)  
@@ -314,25 +312,22 @@ in this data. These functions mostly come from
 .. code:: python
 
     def prepare_data_loaders(data_path):  
-
         normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],  
                                          std=[0.229, 0.224, 0.225])
         dataset = torchvision.datasets.ImageNet(
-               data_path, split="train",
-             transforms.Compose([  
-                       transforms.RandomResizedCrop(224),  
-                       transforms.RandomHorizontalFlip(),  
-                       transforms.ToTensor(),  
-                       normalize,  
-                   ]))  
+            data_path, split="train", transform=transforms.Compose([
+                transforms.RandomResizedCrop(224),
+                transforms.RandomHorizontalFlip(),
+                transforms.ToTensor(),
+                normalize,
+            ]))
         dataset_test = torchvision.datasets.ImageNet(
-              data_path, split="val", 
-                  transforms.Compose([  
-                      transforms.Resize(256), 
-                      transforms.CenterCrop(224), 
-                      transforms.ToTensor(),  
-                      normalize,  
-                  ])) 
+            data_path, split="val", transform=transforms.Compose([
+                transforms.Resize(256),
+                transforms.CenterCrop(224),
+                transforms.ToTensor(),
+                normalize,
+            ]))
 
         train_sampler = torch.utils.data.RandomSampler(dataset) 
         test_sampler = torch.utils.data.SequentialSampler(dataset_test) 
@@ -630,4 +625,4 @@ and quantization-aware training - describing what they do "under the hood" and h
 them in PyTorch.  
 
 Thanks for reading! As always, we welcome any feedback, so please create an issue 
-`here <https://github.com/pytorch/pytorch/issues>`_ if you have any.
+`here <https://github.com/pytorch/pytorch/issues>`_ if you have any.

prototype_source/fx_graph_mode_ptq_static.rst

@@ -75,7 +75,7 @@ Download the `torchvision resnet18 model <https://github.com/pytorch/vision/blob
     import sys  
     import torch.quantization   
 
-    # Setup warnings    
+    # Set up warnings
     import warnings 
     warnings.filterwarnings(    
         action='ignore',    
@@ -168,25 +168,22 @@ Download the `torchvision resnet18 model <https://github.com/pytorch/vision/blob
         os.remove("temp.p") 
 
     def prepare_data_loaders(data_path):    
-
         normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],  
                                          std=[0.229, 0.224, 0.225])
         dataset = torchvision.datasets.ImageNet(
-               data_path, split="train",
-             transforms.Compose([  
-                       transforms.RandomResizedCrop(224),  
-                       transforms.RandomHorizontalFlip(),  
-                       transforms.ToTensor(),  
-                       normalize,  
-                   ]))  
+            data_path, split="train", transform=transforms.Compose([
+                transforms.RandomResizedCrop(224),
+                transforms.RandomHorizontalFlip(),
+                transforms.ToTensor(),
+                normalize,
+            ]))
         dataset_test = torchvision.datasets.ImageNet(
-              data_path, split="val", 
-                  transforms.Compose([  
-                      transforms.Resize(256), 
-                      transforms.CenterCrop(224), 
-                      transforms.ToTensor(),  
-                      normalize,  
-                  ]))
+            data_path, split="val", transform=transforms.Compose([
+                transforms.Resize(256),
+                transforms.CenterCrop(224),
+                transforms.ToTensor(),
+                normalize,
+            ]))
 
         train_sampler = torch.utils.data.RandomSampler(dataset) 
         test_sampler = torch.utils.data.SequentialSampler(dataset_test) 
@@ -239,7 +236,7 @@ of the observers for activation and weight. ``qconfig_dict`` is a dictionary wit
 .. code:: python  
   
   qconfig = { 
-      " : qconfig_global,
+      "" : qconfig_global,
       "sub" : qconfig_sub,    
       "sub.fc" : qconfig_fc,  
       "sub.conv": None    
@@ -294,6 +291,7 @@ Utility functions related to ``qconfig`` can be found in the `qconfig <https://g
 
 .. code:: python  
   
+    from torch.ao.quantization.quantize_fx import prepare_fx
     prepared_model = prepare_fx(model_to_quantize, qconfig_dict)  
   
 prepare_fx folds BatchNorm modules into previous Conv2d modules, and insert observers     
@@ -326,6 +324,7 @@ the statistics of the Tensors and we can later use this information to calculate
 
 .. code:: python
 
+    from torch.ao.quantization.quantize_fx import convert_fx
     quantized_model = convert_fx(prepared_model)
     print(quantized_model)
 
@@ -377,6 +376,7 @@ Note that ``fuse_fx`` only works in eval mode.
 
 .. code:: python
 
+    from torch.ao.quantization.quantize_fx import fuse_fx
     fused = fuse_fx(float_model)    
 
     conv1_weight_after_fuse = fused.conv1[0].weight[0]