Fixed examples test error

Signed-off-by: Cheng, Penghui <penghui.cheng@intel.com>
intel · Dec 21, 2022 · cd40ec3 · cd40ec3
1 parent 642c2a5
commit cd40ec3
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Showing 6 changed files with 8 additions and 30 deletions.
diff --git a/...es/pytorch/nlp/huggingface_models/language-modeling/quantization/ptq_static/fx/run_clm.py b/...es/pytorch/nlp/huggingface_models/language-modeling/quantization/ptq_static/fx/run_clm.py
@@ -561,7 +561,7 @@ def eval_func_for_nc(model_tuned):
         q_model = quantization.fit(model,
                                    conf,
                                    calib_dataloader=trainer.get_eval_dataloader(),
-                                   calib_func=eval_func_for_nc)
+                                   eval_func=eval_func_for_nc)
         q_model.save(training_args.output_dir)
         exit(0)
 

diff --git a/...stion-answering/optimization_pipeline/prune_once_for_all/fx/run_qa_no_trainer_pruneOFA.py b/...stion-answering/optimization_pipeline/prune_once_for_all/fx/run_qa_no_trainer_pruneOFA.py
@@ -907,10 +907,9 @@ def get_logits(teacher_model, train_dataset):
 
     confs = []
     if args.do_prune:
-        # Pruning!
-        from neural_compressor.config import Pruner, PruningConfig
-        pruner = Pruner(prune_type="pattern_lock")
-        p_conf = PruningConfig(pruners=[pruner])
+        from neural_compressor.config import WeightPruningConfig
+        import pdb;pdb.set_trace()
+        p_conf = WeightPruningConfig(pruning_type="pattern_lock")
         confs.append(p_conf)
 
     if args.do_quantization:

diff --git a/...ples/pytorch/nlp/huggingface_models/question-answering/pruning/eager/run_qa_no_trainer.py b/...ples/pytorch/nlp/huggingface_models/question-answering/pruning/eager/run_qa_no_trainer.py
@@ -534,7 +534,7 @@ def main():
             "You are instantiating a new tokenizer from scratch. This is not supported by this script."
             "You can do it from another script, save it, and load it from here, using --tokenizer_name."
         )
-        
+
     if args.distill_loss_weight > 0:
         teacher_path = args.teacher_model_name_or_path 
         if teacher_path is None:
@@ -988,11 +988,9 @@ def create_and_fill_np_array(start_or_end_logits, dataset, max_len):
         start_step=pruning_start,
         end_step=pruning_end
     )
-    # pruner = Pruning(config)
-    # pruner.model = model
-    # pruner.on_train_begin()
     compression_manager = prepare_compression(model=model, confs=config)
     compression_manager.callbacks.on_train_begin()
+    model = compression_manager.model
 
     for epoch in range(starting_epoch, args.num_train_epochs):
         model.train()
@@ -1019,10 +1017,8 @@ def create_and_fill_np_array(start_or_end_logits, dataset, max_len):
                 loss = loss / args.gradient_accumulation_steps
                 accelerator.backward(loss)
                 if step % args.gradient_accumulation_steps == 0 or step == len(train_dataloader) - 1:
-                    # pruner.on_before_optimizer_step()
                     compression_manager.callbacks.on_before_optimizer_step()
                     optimizer.step()
-                    # pruner.on_after_optimizer_step()
                     compression_manager.callbacks.on_after_optimizer_step()
                     lr_scheduler.step()
                     optimizer.zero_grad()

diff --git a/...es/pytorch/nlp/huggingface_models/question-answering/quantization/ptq_static/fx/run_qa.py b/...es/pytorch/nlp/huggingface_models/question-answering/quantization/ptq_static/fx/run_qa.py
@@ -642,7 +642,7 @@ def eval_func(model):
         q_model = quantization.fit(model,
                                    conf,
                                    calib_dataloader=eval_dataloader,
-                                   calib_func=eval_func)
+                                   eval_func=eval_func)
         q_model.save(training_args.output_dir)
         return
 

diff --git a/.../pytorch/nlp/huggingface_models/question-answering/quantization/ptq_static/ipex/run_qa.py b/.../pytorch/nlp/huggingface_models/question-answering/quantization/ptq_static/ipex/run_qa.py
@@ -643,7 +643,7 @@ def eval_func(model):
         q_model = quantization.fit(model,
                                    conf,
                                    calib_dataloader=eval_dataloader,
-                                   calib_func=eval_func)
+                                   eval_func=eval_func)
         q_model.save(training_args.output_dir)
         return
 

diff --git a/...orch/nlp/huggingface_models/question-answering/quantization/ptq_static/ipex/run_tuning.sh b/...orch/nlp/huggingface_models/question-answering/quantization/ptq_static/ipex/run_tuning.sh
@@ -62,23 +62,6 @@ function run_tuning {
            --output_dir $tuned_checkpoint \
            ${extra_cmd}
     fi
-    if [[ "${topology}" == "bert_large_1_10_ipex" ]]; then
-        pip install transformers==3.0.2
-        python run_qa_1_10.py \
-            --model_type bert \
-            --model_name_or_path $input_model \
-            --do_lower_case \
-            --predict_file $dataset_location \
-            --tokenizer_name $tokenizer_name \
-            --do_eval \
-            --max_seq_length 384 \
-            --doc_stride 128 \
-            --no_cuda \
-            --tune \
-            --output_dir $tuned_checkpoint \
-            --int8 \
-            --int8_fp32
-    fi 
 }
 
 main "$@"