QDQBert example update (#16395)

* update Dockerfile and utils_qa

* Update README.md

examples/research_projects/quantization-qdqbert/Dockerfile

@@ -12,7 +12,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-FROM nvcr.io/nvidia/pytorch:21.07-py3
+FROM nvcr.io/nvidia/pytorch:22.02-py3
 LABEL maintainer="Hugging Face"
 LABEL repository="transformers"
 
@@ -20,11 +20,7 @@ RUN apt-get update
 RUN apt-get install sudo
 
 RUN python3 -m pip install --no-cache-dir --upgrade pip
-RUN python3 -m pip install --no-cache-dir --ignore-installed ruamel.yaml \
+RUN python3 -m pip install --no-cache-dir --ignore-installed pycuda
-    mkl \
-    absl-py \
-    yamlpy \
-    tensorboardX
 RUN python3 -m pip install --no-cache-dir \
     pytorch-quantization --extra-index-url https://pypi.ngc.nvidia.com
 
@@ -34,4 +30,4 @@ RUN cd transformers/ && \
     python3 -m pip install --no-cache-dir .
 
 RUN python3 -m pip install --no-cache-dir datasets \
-    accelerate
+    accelerate

examples/research_projects/quantization-qdqbert/README.md

@@ -39,9 +39,6 @@ Run the docker:
 docker run --gpus all --privileged --rm -it --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 bert_quantization:latest
 ```
 
-*Note that the current NGC pytorch container (pytorch:21.07-py3) has TensorRT 8.0 which doesn't meet the requiremnt of TensorRT >= 8.2. One can either update the Dockerfile with the latest [NGC pytorch container](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch) once it supports TensorRT 8.2, or manually download and install [TensorRT >= 8.2](https://developer.nvidia.com/nvidia-tensorrt-download) in the container.*
-
-
 In the container:
 ```
 cd transformers/examples/research_projects/quantization-qdqbert/

examples/research_projects/quantization-qdqbert/utils_qa.py

@@ -73,10 +73,12 @@ def postprocess_qa_predictions(
         log_level (:obj:`int`, `optional`, defaults to ``logging.WARNING``):
             ``logging`` log level (e.g., ``logging.WARNING``)
     """
-    assert len(predictions) == 2, "`predictions` should be a tuple with two elements (start_logits, end_logits)."
+    if len(predictions) != 2:
+        raise ValueError("`predictions` should be a tuple with two elements (start_logits, end_logits).")
     all_start_logits, all_end_logits = predictions
 
-    assert len(predictions[0]) == len(features), f"Got {len(predictions[0])} predictions and {len(features)} features."
+    if len(predictions[0]) != len(features):
+        raise ValueError(f"Got {len(predictions[0])} predictions and {len(features)} features.")
 
     # Build a map example to its corresponding features.
     example_id_to_index = {k: i for i, k in enumerate(examples["id"])}
@@ -135,7 +137,9 @@ def postprocess_qa_predictions(
                         start_index >= len(offset_mapping)
                         or end_index >= len(offset_mapping)
                         or offset_mapping[start_index] is None
+                        or len(offset_mapping[start_index]) < 2
                         or offset_mapping[end_index] is None
+                        or len(offset_mapping[end_index]) < 2
                     ):
                         continue
                     # Don't consider answers with a length that is either < 0 or > max_answer_length.
@@ -145,6 +149,7 @@ def postprocess_qa_predictions(
                     # provided).
                     if token_is_max_context is not None and not token_is_max_context.get(str(start_index), False):
                         continue
+
                     prelim_predictions.append(
                         {
                             "offsets": (offset_mapping[start_index][0], offset_mapping[end_index][1]),
@@ -212,7 +217,8 @@ def postprocess_qa_predictions(
 
     # If we have an output_dir, let's save all those dicts.
     if output_dir is not None:
-        assert os.path.isdir(output_dir), f"{output_dir} is not a directory."
+        if not os.path.isdir(output_dir):
+            raise EnvironmentError(f"{output_dir} is not a directory.")
 
         prediction_file = os.path.join(
             output_dir, "predictions.json" if prefix is None else f"{prefix}_predictions.json"
@@ -283,12 +289,12 @@ def postprocess_qa_predictions_with_beam_search(
         log_level (:obj:`int`, `optional`, defaults to ``logging.WARNING``):
             ``logging`` log level (e.g., ``logging.WARNING``)
     """
-    assert len(predictions) == 5, "`predictions` should be a tuple with five elements."
+    if len(predictions) != 5:
+        raise ValueError("`predictions` should be a tuple with five elements.")
     start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits = predictions
 
-    assert len(predictions[0]) == len(
+    if len(predictions[0]) != len(features):
-        features
+        raise ValueError(f"Got {len(predictions[0])} predictions and {len(features)} features.")
-    ), f"Got {len(predictions[0])} predicitions and {len(features)} features."
 
     # Build a map example to its corresponding features.
     example_id_to_index = {k: i for i, k in enumerate(examples["id"])}
@@ -400,7 +406,8 @@ def postprocess_qa_predictions_with_beam_search(
 
     # If we have an output_dir, let's save all those dicts.
     if output_dir is not None:
-        assert os.path.isdir(output_dir), f"{output_dir} is not a directory."
+        if not os.path.isdir(output_dir):
+            raise EnvironmentError(f"{output_dir} is not a directory.")
 
         prediction_file = os.path.join(
             output_dir, "predictions.json" if prefix is None else f"{prefix}_predictions.json"