more-train-fixes

spacy/cli/ray_param_server.py

@@ -21,24 +21,24 @@ class OptimizerWorker:
 
         if self.waiting < self.world_size - 1:
             if self.waiting == 0:
-                self.gradient[key] = gradient.copy()
+                self.grad_dict[key] = gradient.copy()
                 self.weights_dict[key] = weights.copy()
             else:
-                self.gradient[key] += gradient
+                self.grad_dict[key] += gradient
             self.waiting = self.barrier.n_waiting + 1
             self.lock.release()
             self.barrier.wait()
         else:
-            self.gradient[key] += gradient
+            self.grad_dict[key] += gradient
             self.lock.release()
-            self.gradient[key] /= self.world_size
+            self.grad_dict[key] /= self.world_size
             new_weights, new_grads = self.optimizer(
-                key, self.weights_dict[key], self.gradient[key], lr_scale=lr_scale)
+                key, self.weights_dict[key], self.grad_dict[key], lr_scale=lr_scale)
             self.weights_dict[key] = new_weights
-            self.gradient[key] = new_grads
+            self.grad_dict[key] = new_grads
             self.waiting = 0
             self.barrier.wait()
-        return self.weights_dict[key], self.gradient[key]
+        return self.weights_dict[key], self.grad_dict[key]
 
     def fetch(self):
         return self.optimizer
@@ -49,12 +49,13 @@ class OptimizerWorker:
 class RayOptimizer:
     local_optimizer = None
 
-    def __init__(self, config_path, use_gpu, rank):
+    def __init__(self, config_path, use_gpu, world_size):
         RemoteOptimizer = ray.remote(OptimizerWorker)
+        options = {"max_concurrency": world_size}
         if use_gpu >= 0:
-            RemoteOptimizer = RemoteOptimizer.options(num_gpus=0.1)
-        self.optimizer = RemoteOptimizer.remote(config_path)
-        self.rank = rank
+            options["num_gpus"] = 0.1
+        RemoteOptimizer = RemoteOptimizer.options(**options)
+        self.optimizer = RemoteOptimizer.remote(config_path, world_size)
         self.sync()
 
     def sync(self):

spacy/cli/ray_utils.py

@@ -1,6 +1,8 @@
 """Allreduce distributed training with Ray."""
 
+import random
 import ray
+import numpy
 from wasabi import msg
 from .. import util
 
@@ -10,10 +12,11 @@ nccl = None
 from typing import Dict, Optional, Union, Tuple, List, cast
 from thinc.types import FloatsXd
 
+
 def create_optimizer(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"])  # Fix this.
+    util.fix_random_seed(config["training"]["seed"])
     config = util.load_config(config_path, create_objects=True)
     training = config["training"]
     return training["optimizer"]

spacy/cli/train_from_config.py

@@ -198,7 +198,7 @@ def train_cli(
 
     if num_workers and num_workers > 1:
         import ray
-        ray.init(address="auto")
+        ray.init()
         if strategy == "ps":
             from spacy.cli.ray_param_server import RayOptimizer
             remote_train = ray.remote(setup_and_train)
@@ -206,7 +206,8 @@ def train_cli(
                 msg.info("Enabling GPU with Ray")
                 remote_train = remote_train.options(num_gpus=0.9)
 
-            train_args["remote_optimizer"] = RayOptimizer(config_path, use_gpu=use_gpu)
+            train_args["remote_optimizer"] = RayOptimizer(
+                config_path, use_gpu=use_gpu, world_size=num_workers)
             ray.get([remote_train.remote(
                 use_gpu,
                 train_args,

spacy/util.py

@@ -898,11 +898,17 @@ def escape_html(text):
 def use_gpu(gpu_id):
     return require_gpu(gpu_id)
 
+def gpu_is_available():
+    try:
+        cupy.cuda.runtime.getDeviceCount()
+        return True
+    except cupy.cuda.runtime.CUDARuntimeError:
+        return False
 
 def fix_random_seed(seed=0):
     random.seed(seed)
     numpy.random.seed(seed)
-    if cupy is not None:
+    if cupy is not None and gpu_is_available():
         cupy.random.seed(seed)