ray-init

spacy/cli/ray_utils.py

@@ -0,0 +1,51 @@
+import ray
+from wasabi import msg
+from .. import util
+
+
+class OptimizerWorker:
+    def __init__(self, config_path):
+        msg.info(f"Loading config from: {config_path}")
+        config = util.load_config(config_path, create_objects=False)
+        util.fix_random_seed(config["training"]["seed"])
+        config = util.load_config(config_path, create_objects=True)
+        training = config["training"]
+        optimizer = training["optimizer"]
+        self.optimizer = optimizer
+        self.weights_dict = {}
+
+    def call(self, key, weights, gradient, *, lr_scale=1.0):
+        if key not in self.weights_dict:
+            self.weights_dict[key] = weights.copy()
+        new_weights, new_grads = self.optimizer(
+            key, self.weights_dict[key], gradient.copy(), lr_scale=lr_scale)
+        self.weights_dict[key] = new_weights
+        return new_weights, new_grads
+
+    def fetch(self):
+        return self.optimizer
+
+    def step_schedules(self):
+        self.optimizer.step_schedules()
+
+class RayOptimizer:
+    local_optimizer = None
+
+    def __init__(self, config_path):
+        RemoteOptimizer = ray.remote(OptimizerWorker)
+        self.optimizer = RemoteOptimizer.remote(config_path)
+        self.sync()
+
+    def sync(self):
+        self.local_optimizer = ray.get(self.optimizer.fetch.remote())
+
+    def __call__(self, *args, **kwargs):
+        weights, grads = ray.get(self.optimizer.call.remote(*args, **kwargs))
+        return weights.copy(), grads.copy()
+
+    def __getattr__(self, name):
+        return getattr(self.local_optimizer, name)
+
+    def step_schedules(self):
+        self.optimizer.step_schedules.remote()
+        self.sync()

spacy/cli/train_from_config.py

@@ -126,6 +126,7 @@ class ConfigSchema(BaseModel):
     raw_text=("Path to jsonl file with unlabelled text documents.", "option", "rt", Path),
     verbose=("Display more information for debugging purposes", "flag", "VV", bool),
     use_gpu=("Use GPU", "option", "g", int),
+    num_workers=("Parallel Workers", "option", "j", int),
     tag_map_path=("Location of JSON-formatted tag map", "option", "tm", Path),
     omit_extra_lookups=("Don't include extra lookups in model", "flag", "OEL", bool),
     # fmt: on
@@ -139,6 +140,7 @@ def train_cli(
     raw_text=None,
     verbose=False,
     use_gpu=-1,
+    num_workers=1,
     tag_map_path=None,
     omit_extra_lookups=False,
 ):
@@ -181,22 +183,33 @@ def train_cli(
         with init_tok2vec.open("rb") as file_:
             weights_data = file_.read()
 
+    train_args = dict(
+        config_path=config_path,
+        data_paths={"train": train_path, "dev": dev_path},
+        output_path=output_path,
+        raw_text=raw_text,
+        tag_map=tag_map,
+        weights_data=weights_data,
+        omit_extra_lookups=omit_extra_lookups
+    )
+
+    if num_workers and num_workers > 1:
+        from spacy.cli.ray_utils import RayOptimizer
+        import ray
+        ray.init()
+        remote_train = ray.remote(setup_and_train)
+        train_args["remote_optimizer"] = RayOptimizer(config_path)
+        ray.get([remote_train.remote(use_gpu, train_args) for _ in range(num_workers)])
+    else:
+        setup_and_train(use_gpu, train_args)
+
+def setup_and_train(use_gpu, train_args):
     if use_gpu >= 0:
         msg.info("Using GPU: {use_gpu}")
         util.use_gpu(use_gpu)
     else:
         msg.info("Using CPU")
-
+    train(**train_args)
-    train(
-        config_path,
-        {"train": train_path, "dev": dev_path},
-        output_path=output_path,
-        raw_text=raw_text,
-        tag_map=tag_map,
-        weights_data=weights_data,
-        omit_extra_lookups=omit_extra_lookups,
-    )
-
 
 def train(
     config_path,
@@ -206,6 +219,7 @@ def train(
     tag_map=None,
     weights_data=None,
     omit_extra_lookups=False,
+    remote_optimizer=None
 ):
     msg.info(f"Loading config from: {config_path}")
     # Read the config first without creating objects, to get to the original nlp_config
@@ -220,6 +234,8 @@ def train(
     msg.info("Creating nlp from config")
     nlp = util.load_model_from_config(nlp_config)
     optimizer = training["optimizer"]
+    if remote_optimizer:
+        optimizer = remote_optimizer
     limit = training["limit"]
     msg.info("Loading training corpus")
     corpus = GoldCorpus(data_paths["train"], data_paths["dev"], limit=limit)
@@ -332,7 +348,6 @@ def train(
         eval_frequency=training["eval_frequency"],
         raw_text=raw_text,
     )
-
     msg.info(f"Training. Initial learn rate: {optimizer.learn_rate}")
     print_row = setup_printer(training, nlp)