[RAG] Add Ray implementation for distributed retrieval

examples/legacy/pytorch-lightning/requirements.txt

@@ -19,3 +19,4 @@ pytest
 conllu
 sentencepiece != 0.1.92
 protobuf
+ray

examples/research_projects/rag/README.md

@@ -50,6 +50,44 @@ python examples/rag/consolidate_rag_checkpoint.py \
 ```
 You will then be able to pass `path/to/checkpoint` as `model_name_or_path` to the `finetune_rag.py` script.
 
+## Document Retrieval
+When running distributed fine-tuning, each training worker needs to retrieve contextual documents
+for its input by querying a index loaded into memory. RAG provides two implementations for document retrieval, 
+one with [`torch.distributed`](https://pytorch.org/docs/stable/distributed.html) communication package and the other 
+with [`Ray`](https://docs.ray.io/en/master/).
+
+This option can be configured with the `--distributed_retriever` flag which can either be set to `pytorch` or `ray`.
+By default this flag is set to `pytorch`.
+
+For the Pytorch implementation, only training worker 0 loads the index into CPU memory, and a gather/scatter pattern is used
+to collect the inputs from the other training workers and send back the corresponding document embeddings.
+
+For the Ray implementation, the index is loaded in *separate* process(es). The training workers randomly select which 
+retriever worker to query. To use Ray for distributed retrieval, you have to set the `--distributed_retriever` arg to `ray`.
+To configure the number of retrieval workers (the number of processes that load the index), you can set the `num_retrieval_workers` flag.
+Also make sure to start the Ray cluster before running fine-tuning.
+
+```bash
+# Start a single-node Ray cluster.
+ray start --head
+
+python examples/rag/finetune_rag.py \
+    --data_dir $DATA_DIR \
+    --output_dir $OUTPUT_DIR \
+    --model_name_or_path $MODEL_NAME_OR_PATH \
+    --model_type rag_sequence \
+    --fp16 \
+    --gpus 8
+    --distributed_retriever ray \
+    --num_retrieval_workers 4
+
+# Stop the ray cluster once fine-tuning has finished.
+ray stop
+```
+
+Using Ray can lead to retrieval speedups on multi-GPU settings since multiple processes load the index rather than
+just the rank 0 training worker. Using Ray also allows you to load the index on GPU since the index is loaded on a separate
+processes than the model, while with pytorch distributed retrieval, both are loaded in the same process potentially leading to GPU OOM.
 
 # Evaluation
 Our evaluation script enables two modes of evaluation (controlled by the `eval_mode` argument): `e2e` - end2end evaluation, returns EM (exact match) and F1 scores calculated for the downstream task and `retrieval` - which returns precision@k of the documents retrieved for provided inputs.

examples/research_projects/rag/_test_finetune_rag.py

@@ -9,6 +9,7 @@
 from transformers.testing_utils import (
     TestCasePlus,
     execute_subprocess_async,
+    require_ray,
     require_torch_gpu,
     require_torch_multi_gpu,
 )
@@ -29,7 +30,7 @@ def _create_dummy_data(self, data_dir):
                 with open(os.path.join(data_dir, f"{split}.{field}"), "w") as f:
                     f.write(content)
 
-    def _run_finetune(self, gpus: int):
+    def _run_finetune(self, gpus: int, distributed_retriever: str = "pytorch"):
         stream_handler = logging.StreamHandler(sys.stdout)
         logger.addHandler(stream_handler)
 
@@ -66,6 +67,7 @@ def _run_finetune(self, gpus: int):
                 --gradient_accumulation_steps 1 \
                 --distributed-port 8787 \
                 --use_dummy_dataset 1 \
+                --distributed_retriever {distributed_retriever} \
             """.split()
 
         if gpus > 0:
@@ -94,3 +96,15 @@ def test_finetune_gpu(self):
     def test_finetune_multigpu(self):
         result = self._run_finetune(gpus=2)
         self.assertGreaterEqual(result["test"][0]["test_avg_em"], 0.2)
+
+    @require_torch_gpu
+    @require_ray
+    def test_finetune_gpu_ray_retrieval(self):
+        result = self._run_finetune(gpus=1, distributed_retriever="ray")
+        self.assertGreaterEqual(result["test"][0]["test_avg_em"], 0.2)
+
+    @require_torch_multi_gpu
+    @require_ray
+    def test_finetune_multigpu_ray_retrieval(self):
+        result = self._run_finetune(gpus=1, distributed_retriever="ray")
+        self.assertGreaterEqual(result["test"][0]["test_avg_em"], 0.2)

examples/research_projects/rag/distributed_retriever.py → examples/research_projects/rag/distributed_pytorch_retriever.py

@@ -31,14 +31,13 @@ class RagPyTorchDistributedRetriever(RagRetriever):
             If specified, use this index instead of the one built using the configuration
     """
 
-    _init_retrieval = False
-
     def __init__(self, config, question_encoder_tokenizer, generator_tokenizer, index=None):
         super().__init__(
             config,
             question_encoder_tokenizer=question_encoder_tokenizer,
             generator_tokenizer=generator_tokenizer,
             index=index,
+            init_retrieval=False,
         )
         self.process_group = None
 

examples/research_projects/rag/distributed_ray_retriever.py

@@ -0,0 +1,144 @@
+import logging
+import random
+
+import ray
+from transformers import RagRetriever
+
+
+logger = logging.getLogger(__name__)
+
+
+class RayRetriever:
+    def __init__(self):
+        self.initialized = False
+
+    def create_rag_retriever(self, config, question_encoder_tokenizer, generator_tokenizer, index):
+        if not self.initialized:
+            self.retriever = RagRetriever(
+                config,
+                question_encoder_tokenizer=question_encoder_tokenizer,
+                generator_tokenizer=generator_tokenizer,
+                index=index,
+                init_retrieval=False,
+            )
+            self.initialized = True
+
+    def init_retrieval(self):
+        self.retriever.index.init_index()
+
+    def retrieve(self, question_hidden_states, n_docs):
+        doc_ids, retrieved_doc_embeds = self.retriever._main_retrieve(question_hidden_states, n_docs)
+        return doc_ids, retrieved_doc_embeds
+
+
+class RagRayDistributedRetriever(RagRetriever):
+    """
+    A distributed retriever built on top of the ``Ray`` API, a library
+    for building distributed applications (https://docs.ray.io/en/master/).
+    package. During training, all training workers initialize their own
+    instance of a `RagRayDistributedRetriever`, and each instance of
+    this distributed retriever shares a common set of Retrieval Ray
+    Actors (https://docs.ray.io/en/master/walkthrough.html#remote
+    -classes-actors) that load the index on separate processes. Ray
+    handles the communication between the `RagRayDistributedRetriever`
+    instances and the remote Ray actors. If training is done in a
+    non-distributed setup, the index will simply be loaded in the same
+    process as the training worker and Ray will not be used.
+
+    Args:
+        config (:class:`~transformers.RagConfig`):
+            The configuration of the RAG model this Retriever is used with. Contains parameters indicating which ``Index`` to build.
+        question_encoder_tokenizer (:class:`~transformers.PretrainedTokenizer`):
+            The tokenizer that was used to tokenize the question.
+            It is used to decode the question and then use the generator_tokenizer.
+        generator_tokenizer (:class:`~transformers.PretrainedTokenizer`):
+            The tokenizer used for the generator part of the RagModel.
+        retrieval_workers (:obj:`List[ray.ActorClass(RayRetriever)]`): A list of already initialized `RayRetriever` actors.
+            These actor classes run on remote processes and are responsible for performing the index lookup.
+        index (:class:`~transformers.retrieval_rag.Index`, optional, defaults to the one defined by the configuration):
+            If specified, use this index instead of the one built using the configuration
+    """
+
+    def __init__(self, config, question_encoder_tokenizer, generator_tokenizer, retrieval_workers, index=None):
+        if index is not None and index.is_initialized() and len(retrieval_workers) > 0:
+            raise ValueError(
+                "When using Ray for distributed fine-tuning, "
+                "you'll need to provide the paths instead, "
+                "as the dataset and the index are loaded "
+                "separately. More info in examples/rag/use_own_knowledge_dataset.py "
+            )
+        super().__init__(
+            config,
+            question_encoder_tokenizer=question_encoder_tokenizer,
+            generator_tokenizer=generator_tokenizer,
+            index=index,
+            init_retrieval=False,
+        )
+        self.retrieval_workers = retrieval_workers
+        if len(self.retrieval_workers) > 0:
+            ray.get(
+                [
+                    worker.create_rag_retriever.remote(config, question_encoder_tokenizer, generator_tokenizer, index)
+                    for worker in self.retrieval_workers
+                ]
+            )
+
+    def init_retrieval(self):
+        """
+        Retriever initialization function, needs to be called from the
+        training process. This function triggers retrieval initialization
+        for all retrieval actors if using distributed setting, or loads
+        index into current process if training is not distributed.
+        """
+        logger.info("initializing retrieval")
+
+        if len(self.retrieval_workers) > 0:
+            ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers])
+        else:
+            # Non-distributed training. Load index into this same process.
+            self.index.init_index()
+
+    def retrieve(self, question_hidden_states, n_docs):
+        """
+        Retrieves documents for specified ``question_hidden_states``. If
+        running training with multiple workers, a random retrieval actor is
+        selected to perform the index lookup and return the result.
+
+        Args:
+            question_hidden_states (:obj:`np.ndarray` of shape :obj:`(batch_size, vector_size)`):
+                A batch of query vectors to retrieve with.
+            n_docs (:obj:`int`):
+                The number of docs retrieved per query.
+
+        Output:
+            retrieved_doc_embeds (:obj:`np.ndarray` of shape :obj:`(batch_size, n_docs, dim)`
+                The retrieval embeddings of the retrieved docs per query.
+            doc_ids (:obj:`np.ndarray` of shape :obj:`batch_size, n_docs`)
+                The ids of the documents in the index
+            doc_dicts (:obj:`List[dict]`):
+                The retrieved_doc_embeds examples per query.
+        """
+        if len(self.retrieval_workers) > 0:
+            # Select a random retrieval actor.
+            random_worker = self.retrieval_workers[random.randint(0, len(self.retrieval_workers) - 1)]
+            doc_ids, retrieved_doc_embeds = ray.get(random_worker.retrieve.remote(question_hidden_states, n_docs))
+        else:
+            doc_ids, retrieved_doc_embeds = self._main_retrieve(question_hidden_states, n_docs)
+        return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(doc_ids)
+
+    @classmethod
+    def get_tokenizers(cls, retriever_name_or_path, indexed_dataset=None, **kwargs):
+        return super(RagRayDistributedRetriever, cls).get_tokenizers(retriever_name_or_path, indexed_dataset, **kwargs)
+
+    @classmethod
+    def from_pretrained(cls, retriever_name_or_path, actor_handles, indexed_dataset=None, **kwargs):
+        config, question_encoder_tokenizer, generator_tokenizer, index = cls.get_tokenizers(
+            retriever_name_or_path, indexed_dataset, **kwargs
+        )
+        return cls(
+            config,
+            question_encoder_tokenizer=question_encoder_tokenizer,
+            generator_tokenizer=generator_tokenizer,
+            retrieval_workers=actor_handles,
+            index=index,
+        )