fix examples/rag imports, tests (#7712)

examples/rag/README.md

@@ -65,26 +65,41 @@ Does He Love You	Does He Love You	Red Sandy Spika dress of Reba McEntire	Greates
 We demonstrate how to evaluate retrieval against DPR evaluation data. You can download respective files from links listed [here](https://github.com/facebookresearch/DPR/blob/master/data/download_data.py#L39-L45).
 
 1. Download and unzip the gold data file. We use the `biencoder-nq-dev` from https://dl.fbaipublicfiles.com/dpr/data/retriever/biencoder-nq-dev.json.gz.
+    ```bash
+    wget https://dl.fbaipublicfiles.com/dpr/data/retriever/biencoder-nq-dev.json.gz && gzip -d biencoder-nq-dev.json.gz
+   ```
+
 2. Parse the unziped file using the `parse_dpr_relevance_data.py`
     ```bash
+    mkdir output # or wherever you want to save this
     python examples/rag/parse_dpr_relevance_data.py \
-        --src_path path/to/unziped/biencoder-nq-dev.json \
+        --src_path biencoder-nq-dev.json \
-        --evaluation_set path/to/output/biencoder-nq-dev.questions \
+        --evaluation_set output/biencoder-nq-dev.questions \
-        --gold_data_path path/to/output/biencoder-nq-dev.pages
+        --gold_data_path output/biencoder-nq-dev.pages
     ```
 3. Run evaluation:
     ```bash    
+    python examples/rag/eval_rag.py \
+        --model_name_or_path facebook/rag-sequence-nq \
+        --model_type rag_sequence \
+        --evaluation_set output/biencoder-nq-dev.questions \
+        --gold_data_path output/biencoder-nq-dev.pages \
+        --predictions_path output/retrieval_preds.tsv  \
+        --eval_mode retrieval \
+        --k 1
+    ```
+   ```bash
+   # EXPLANATION
     python examples/rag/eval_rag.py \
         --model_name_or_path facebook/rag-sequence-nq \ # model name or path of the model we're evaluating
         --model_type rag_sequence \ # RAG model type (rag_token or rag_sequence)
-        --evaluation_set path/to/output/biencoder-nq-dev.questions \ # an input dataset for evaluation
+        --evaluation_set output/biencoder-nq-dev.questions \ # an input dataset for evaluation
-        --gold_data_path path/to/output/biencoder-nq-dev.pages \ # a dataset containing ground truth answers for samples from the evaluation_set
+        --gold_data_path poutput/biencoder-nq-dev.pages \ # a dataset containing ground truth answers for samples from the evaluation_set
-        --predictions_path path/to/retrieval_preds.tsv  \ # name of file where predictions will be stored
+        --predictions_path output/retrieval_preds.tsv  \ # name of file where predictions will be stored
         --eval_mode retrieval \ # indicates whether we're performing retrieval evaluation or e2e evaluation
         --k 1 # parameter k for the precision@k metric
+   
     ```
-
-
 ## End-to-end evaluation
 
 We support two formats of the gold data file (controlled by the `gold_data_mode` parameter):
@@ -97,7 +112,9 @@ who is the owner of reading football club	['Xiu Li Dai', 'Dai Yongge', 'Dai Xiul
 Xiu Li Dai
 ```
 
-Predictions of the model for the samples from the `evaluation_set` will be saved under the path specified by the `predictions_path` parameter. If this path already exists, the script will use saved predictions to calculate metrics. Add `--recalculate` parameter to force the script to perform inference from scratch.
+Predictions of the model for the samples from the `evaluation_set` will be saved under the path specified by the `predictions_path` parameter. 
+If this path already exists, the script will use saved predictions to calculate metrics. 
+Add `--recalculate` parameter to force the script to perform inference from scratch.
 
 An example e2e evaluation run could look as follows:
 ```bash

examples/rag/__init__.py

@@ -0,0 +1,5 @@
+import os
+import sys
+
+
+sys.path.insert(1, os.path.dirname(os.path.realpath(__file__)))

examples/rag/eval_rag.py

@@ -15,7 +15,7 @@ from transformers import logging as transformers_logging
 
 
 sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
-from examples.rag.utils import exact_match_score, f1_score  # noqa: E402 # isort:skip
+from utils import exact_match_score, f1_score  # noqa: E402 # isort:skip
 
 
 logger = logging.getLogger(__name__)

examples/rag/finetune.py

@@ -31,16 +31,13 @@ from transformers import (
 from transformers import logging as transformers_logging
 
 
-sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # noqa: E402 # isort:skip
+from callbacks import (  # noqa: E402 # isort:skipq
-
-from examples.lightning_base import BaseTransformer, add_generic_args, generic_train  # noqa: E402 # isort:skip
-from examples.rag.callbacks import (  # noqa: E402 # isort:skip
     get_checkpoint_callback,
     get_early_stopping_callback,
     Seq2SeqLoggingCallback,
 )
-from examples.rag.distributed_retriever import RagPyTorchDistributedRetriever  # noqa: E402 # isort:skip
+from distributed_retriever import RagPyTorchDistributedRetriever  # noqa: E402 # isort:skip
-from examples.rag.utils import (  # noqa: E402 # isort:skip
+from utils import (  # noqa: E402 # isort:skip
     calculate_exact_match,
     flatten_list,
     get_git_info,
@@ -53,6 +50,11 @@ from examples.rag.utils import (  # noqa: E402 # isort:skip
     Seq2SeqDataset,
 )
 
+# need the parent dir module
+sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
+from lightning_base import BaseTransformer, add_generic_args, generic_train  # noqa
+
+
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 

examples/rag/test_distributed_retriever.py

@@ -23,7 +23,7 @@ from transformers.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FI
 
 sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # noqa: E402 # isort:skip
 
-from examples.rag.distributed_retriever import RagPyTorchDistributedRetriever  # noqa: E402 # isort:skip
+from distributed_retriever import RagPyTorchDistributedRetriever  # noqa: E402 # isort:skip
 
 
 def require_distributed_retrieval(test_case):