Update Dynamic Quant for BERT tutorial

Author: jianyuh

intermediate_source/dynamic_quantization_bert_tutorial.py

@@ -86,8 +86,9 @@
 #
 # .. code:: shell
 #
-#    !pip install sklearn
-#    !pip install transformers
+#    pip install sklearn
+#    pip install transformers
+#
 
 
 ######################################################################
@@ -98,8 +99,10 @@
 # Mac:
 #
 # .. code:: shell
-#   !yes y | pip uninstall torch tochvision
-#   !yes y | pip install --pre torch -f https://download.pytorch.org/whl/nightly/cu101/torch_nightly.html
+#
+#    yes y | pip uninstall torch tochvision
+#    yes y | pip install --pre torch -f https://download.pytorch.org/whl/nightly/cu101/torch_nightly.html
+#
 
 
 ######################################################################
@@ -155,13 +158,13 @@
 # https://github.com/nyu-mll/GLUE-baselines/blob/master/download_glue_data.py)
 # and unpack it to some directory “glue_data/MRPC”.
 #
-
-# !python download_glue_data.py --data_dir='glue_data' --tasks='MRPC' --test_labels=True
-!pwd
-!ls
-!wget https://gist.githubusercontent.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e/raw/17b8dd0d724281ed7c3b2aeeda662b92809aadd5/download_glue_data.py
-!python download_glue_data.py --data_dir='glue_data' --tasks='MRPC'
-!ls glue_data/MRPC
+#
+# .. code:: shell
+#
+#    wget https://gist.githubusercontent.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e/raw/17b8dd0d724281ed7c3b2aeeda662b92809aadd5/download_glue_data.py
+#    python download_glue_data.py --data_dir='glue_data' --tasks='MRPC'
+#    ls glue_data/MRPC
+#
 
 
 ######################################################################
@@ -255,9 +258,6 @@
 from google.colab import drive
 drive.mount('/content/drive')
 
-!ls
-!pwd
-
 
 ######################################################################
 # Set global configurations
@@ -273,13 +273,9 @@
 configs = Namespace()
 
 # The output directory for the fine-tuned model.
-# configs.output_dir = "/mnt/homedir/jianyuhuang/public/bert/MRPC/"
 configs.output_dir = "/content/drive/My Drive/BERT_Quant_Tutorial/MRPC/"
-# configs.output_dir = "./MRPC/"
 
 # The data directory for the MRPC task in the GLUE benchmark.
-# configs.data_dir = "/mnt/homedir/jianyuhuang/public/bert/glue_data/MRPC"
-# configs.data_dir = "./glue_data/MRPC"
 configs.data_dir = "/content/glue_data/MRPC"
 
 # The model name or path for the pre-trained model.
@@ -493,30 +489,6 @@ def load_and_cache_examples(args, task, tokenizer, evaluate=False):
 print(quantized_model)
 
 
-
-######################################################################
-# In PyTorch 1.4 release, we further add the per-channel quantization
-# support for dynamic quantization.
-#
-# .. figure:: https://drive.google.com/open?id=1N6P70MR6jJ2tcFnFJ2lROLSFqmiOY--g
-#    :alt: Per Tensor Quantization for Weight
-#
-#    Per Tensor Quantization for Weight
-#
-# .. figure:: https://drive.google.com/open?id=1nyjUKP5qtkRCJPKtUaXXwhglLMQQ0Dfs
-#    :alt: Per Channel Quantization for Weight
-#
-#    Per Channel Quantization for Weight
-#
-
-qconfig_dict = {
-    torch.nn.Linear: torch.quantization.per_channel_dynamic_qconfig
-}
-per_channel_quantized_model = torch.quantization.quantize_dynamic(
-    model, qconfig_dict, dtype=torch.qint8
-)
-
-
 ######################################################################
 # Check the model size
 # --------------------
@@ -532,9 +504,6 @@ def print_size_of_model(model):
 
 print_size_of_model(model)
 print_size_of_model(quantized_model)
-# print_size_of_model(per_channel_quantized_model)
-
-
 
 
 ######################################################################
@@ -606,10 +575,6 @@ def time_model_evaluation(model, configs, tokenizer):
 # processing the evaluation of MRPC dataset.
 #
 
-# Evaluate the INT8 BERT model after the per-channel dynamic quantization
-time_model_evaluation(per_channel_quantized_model, configs, tokenizer)
-
-
 
 ######################################################################
 # Serialize the quantized model
@@ -621,7 +586,7 @@ def time_model_evaluation(model, configs, tokenizer):
 quantized_output_dir = configs.output_dir + "quantized/"
 if not os.path.exists(quantized_output_dir):
     os.makedirs(quantized_output_dir)
-quantized_model.save_pretrained(quantized_output_dir)
+    quantized_model.save_pretrained(quantized_output_dir)
 
 
 ######################################################################