move-ps

spacy/cli/ray_param_server.py

@@ -0,0 +1,48 @@
+"""Parameter Server distributed training with Ray."""
+
+import ray
+from wasabi import msg
+from .. import util
+
+class OptimizerWorker:
+    def __init__(self, config_path):
+        self.optimizer = _create_optimizer(config_path)
+        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, use_gpu):
+        RemoteOptimizer = ray.remote(OptimizerWorker)
+        if use_gpu >= 0:
+            RemoteOptimizer = RemoteOptimizer.options(num_gpus=0.1)
+        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/ray_utils.py

@@ -1,3 +1,5 @@
+"""Allreduce distributed training with Ray."""
+
 import ray
 from wasabi import msg
 from .. import util
@@ -16,49 +18,6 @@ def _create_optimizer(config_path):
     training = config["training"]
     return training["optimizer"]
 
-class OptimizerWorker:
-    def __init__(self, config_path):
-        self.optimizer = _create_optimizer(config_path)
-        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, use_gpu):
-        RemoteOptimizer = ray.remote(OptimizerWorker)
-        if use_gpu >= 0:
-            RemoteOptimizer = RemoteOptimizer.options(num_gpus=0.1)
-        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()
-
 class RayWorker:
     def __init__(self, rank, world_size):
         global nccl

spacy/cli/train_from_config.py

@@ -143,7 +143,7 @@ def train_cli(
     verbose=False,
     use_gpu=-1,
     num_workers=1,
-    strategy="ps",
+    strategy="allreduce",
     tag_map_path=None,
     omit_extra_lookups=False,
 ):
@@ -197,10 +197,10 @@ def train_cli(
     )
 
     if num_workers and num_workers > 1:
-        from spacy.cli.ray_utils import RayOptimizer
         import ray
         ray.init()
         if strategy == "ps":
+            from spacy.cli.ray_param_server import RayOptimizer
             remote_train = ray.remote(setup_and_train)
             if use_gpu >= 0:
                 msg.info("Enabling GPU with Ray")