Fix multi gpu training via Dataparallel

haystack/__init__.py

@@ -12,5 +12,6 @@
 logging.getLogger('farm.infer').setLevel(logging.INFO)
 logging.getLogger('transformers').setLevel(logging.WARNING)
 logging.getLogger('farm.eval').setLevel(logging.INFO)
+logging.getLogger('farm.modeling.optimization').setLevel(logging.INFO)
 
 

haystack/reader/farm.py

@@ -10,10 +10,12 @@
 from farm.infer import QAInferencer
 from farm.modeling.optimization import initialize_optimizer
 from farm.modeling.predictions import QAPred, QACandidate
+from farm.modeling.adaptive_model import BaseAdaptiveModel
 from farm.train import Trainer
 from farm.eval import Evaluator
 from farm.utils import set_all_seeds, initialize_device_settings
 from scipy.special import expit
+import shutil
 
 from haystack.database.base import Document
 from haystack.database.elasticsearch import ElasticsearchDocumentStore
@@ -177,9 +179,17 @@ def train(
         # and calculates a few descriptive statistics of our datasets
         data_silo = DataSilo(processor=processor, batch_size=batch_size, distributed=False)
 
+        # Quick-fix until this is fixed upstream in FARM:
+        # We must avoid applying DataParallel twice (once when loading the inferencer,
+        # once when calling initalize_optimizer)
+        self.inferencer.model.save("tmp_model")
+        model = BaseAdaptiveModel.load(load_dir="tmp_model", device=device, strict=True)
+        shutil.rmtree('tmp_model')
+
         # 3. Create an optimizer and pass the already initialized model
         model, optimizer, lr_schedule = initialize_optimizer(
-            model=self.inferencer.model,
+            model=model,
+            # model=self.inferencer.model,
             learning_rate=learning_rate,
             schedule_opts={"name": "LinearWarmup", "warmup_proportion": warmup_proportion},
             n_batches=len(data_silo.loaders["train"]),
@@ -197,6 +207,8 @@ def train(
             evaluate_every=evaluate_every,
             device=device,
         )
+
+
         # 5. Let it grow!
         self.inferencer.model = trainer.train()
         self.save(Path(save_dir))

tutorials/Tutorial2_Finetune_a_model_on_your_data.ipynb

@@ -90,10 +90,10 @@
     }
    ],
    "source": [
-    "reader = FARMReader(model_name_or_path=\"distilbert-base-uncased-distilled-squad\", use_gpu=False)\n",
+    "reader = FARMReader(model_name_or_path=\"distilbert-base-uncased-distilled-squad\", use_gpu=True)\n",
     "train_data = \"data/squad20\"\n",
     "# train_data = \"PATH/TO_YOUR/TRAIN_DATA\" \n",
-    "reader.train(data_dir=train_data, train_filename=\"dev-v2.0.json\", use_gpu=False, n_epochs=1, save_dir=\"my_model\")"
+    "reader.train(data_dir=train_data, train_filename=\"dev-v2.0.json\", use_gpu=True, n_epochs=1, save_dir=\"my_model\")"
    ]
   },
   {

tutorials/Tutorial2_Finetune_a_model_on_your_data.py

@@ -34,10 +34,10 @@
 
 #**Recommendation: Run training on a GPU. To do so change the `use_gpu` arguments below to `True`
 
-reader = FARMReader(model_name_or_path="distilbert-base-uncased-distilled-squad", use_gpu=False)
+reader = FARMReader(model_name_or_path="distilbert-base-uncased-distilled-squad", use_gpu=True)
 train_data = "data/squad20"
 # train_data = "PATH/TO_YOUR/TRAIN_DATA" 
-reader.train(data_dir=train_data, train_filename="dev-v2.0.json", use_gpu=False, n_epochs=1, save_dir="my_model")
+reader.train(data_dir=train_data, train_filename="dev-v2.0.json", use_gpu=True, n_epochs=1, save_dir="my_model")
 
 # Saving the model happens automatically at the end of training into the `save_dir` you specified
 # However, you could also save a reader manually again via: