set adamw_mode default true (follows FusedAdam and < 0.3.11 logic) (#844)

deepspeed/ops/adam/cpu_adam.py

@@ -74,7 +74,7 @@ class DeepSpeedCPUAdam(torch.optim.Optimizer):
 
         self.opt_id = DeepSpeedCPUAdam.optimizer_id
         DeepSpeedCPUAdam.optimizer_id = DeepSpeedCPUAdam.optimizer_id + 1
-
+        self.adam_w_mode = adamw_mode
         self.ds_opt_adam = CPUAdamBuilder().load()
 
         self.ds_opt_adam.create_adam(self.opt_id,

deepspeed/runtime/config.py

@@ -40,6 +40,10 @@ DEEPSPEED_OPTIMIZERS = [
 # extra optimizer parameters for adam/adamw
 TORCH_ADAM_PARAM = "torch_adam"
 
+# default to adamw logic for adam/adamw optimizers unless user explictly opts out
+ADAM_W_MODE = "adam_w_mode"
+ADAM_W_MODE_DEFAULT = True
+
 
 class DeepSpeedConfigError(Exception):
     pass

deepspeed/runtime/engine.py

@@ -22,7 +22,7 @@ from deepspeed.runtime.fp16.fused_optimizer import FP16_Optimizer
 from deepspeed.runtime.fp16.unfused_optimizer import FP16_UnfusedOptimizer
 from deepspeed.runtime.config import DeepSpeedConfig, DEEPSPEED_OPTIMIZERS, \
     ADAM_OPTIMIZER, ADAMW_OPTIMIZER, LAMB_OPTIMIZER, ONEBIT_ADAM_OPTIMIZER, \
-    TORCH_ADAM_PARAM
+    TORCH_ADAM_PARAM, ADAM_W_MODE, ADAM_W_MODE_DEFAULT
 
 from deepspeed.runtime.dataloader import DeepSpeedDataLoader
 from deepspeed.runtime.constants import \
@@ -640,26 +640,30 @@ class DeepSpeedEngine(Module):
 
         if self.optimizer_name() in [ADAM_OPTIMIZER, ADAMW_OPTIMIZER]:
             torch_adam = optimizer_parameters.pop(TORCH_ADAM_PARAM, False)
-            adam_w_mode = self.optimizer_name() == ADAMW_OPTIMIZER
-            # zero-offload  torch-adam  adam_w_mode optimizer
-            # T|F           T           T           torch.optim.AdamW
-            # T|F           T           F           torch.optim.Adam
-            # T             F           T|F         DeepSpeedCPUAdam(adam_w_mode)
-            # F             F           T|F         FusedAdam(adam_w_mode)
+            adam_w_mode = optimizer_parameters.pop(ADAM_W_MODE, ADAM_W_MODE_DEFAULT)
+
+            # Optimizer name of Adam forces AdamW logic unless adam_w_mode is explictly set
+            effective_adam_w_mode = self.optimizer_name(
+            ) == ADAMW_OPTIMIZER or adam_w_mode
+
             if torch_adam:
-                if adam_w_mode:
-                    optimizer = torch.optim.AdamW(model_parameters,
+                if not effective_adam_w_mode:
+                    optimizer = torch.optim.Adam(model_parameters,
                                                  **optimizer_parameters)
                 else:
-                    optimizer = torch.optim.Adam(model_parameters,
+                    optimizer = torch.optim.AdamW(model_parameters,
                                                   **optimizer_parameters)
-            elif self.zero_cpu_offload():
+            else:
+                if self.zero_cpu_offload():
+                    from deepspeed.ops.adam import DeepSpeedCPUAdam
                     optimizer = DeepSpeedCPUAdam(model_parameters,
                                                  **optimizer_parameters,
-                                             adamw_mode=adam_w_mode)
+                                                 adamw_mode=effective_adam_w_mode)
                 else:
-                optimizer_parameters['adam_w_mode'] = adam_w_mode
-                optimizer = FusedAdam(model_parameters, **optimizer_parameters)
+                    from deepspeed.ops.adam import FusedAdam
+                    optimizer = FusedAdam(model_parameters,
+                                          **optimizer_parameters,
+                                          adam_w_mode=effective_adam_w_mode)
 
         elif self.optimizer_name() == LAMB_OPTIMIZER:
             from deepspeed.ops.lamb import FusedLamb

tests/unit/test_adamw.py

+import deepspeed
+import torch
+import pytest
+
+from common import distributed_test
+from deepspeed.ops.adam import FusedAdam
+from deepspeed.ops.adam import DeepSpeedCPUAdam
+from simple_model import SimpleModel, args_from_dict
+
+# yapf: disable
+#'optimizer, zero_offload, torch_adam, adam_w_mode, resulting_optimizer
+adam_configs = [["AdamW", False, False, False, (FusedAdam, True)],
+                ["AdamW", False, True,  False, (torch.optim.AdamW, None)],
+                ["AdamW", True,  False, False, (DeepSpeedCPUAdam, True)],
+                ["AdamW", True,  True,  False, (torch.optim.AdamW, None)],
+                ["AdamW", False, False, True,  (FusedAdam, True)],
+                ["AdamW", False, True,  True,  (torch.optim.AdamW, None)],
+                ["AdamW", True,  False, True,  (DeepSpeedCPUAdam, True)],
+                ["AdamW", True,  True,  True,  (torch.optim.AdamW, None)],
+                ["Adam",  False, False, False, (FusedAdam, False)],
+                ["Adam",  False, True,  False, (torch.optim.Adam, None)],
+                ["Adam",  True,  False, False, (DeepSpeedCPUAdam, False)],
+                ["Adam",  True,  True,  False, (torch.optim.Adam, None)],
+                ["Adam",  False, False, True,  (FusedAdam, True)],
+                ["Adam",  False, True,  True,  (torch.optim.AdamW, None)],
+                ["Adam",  True,  False, True,  (DeepSpeedCPUAdam, True)],
+                ["Adam",  True,  True,  True,  (torch.optim.AdamW, None)]]
+
+@pytest.mark.parametrize(
+    'optimizer, zero_offload, torch_adam, adam_w_mode, resulting_optimizer',
+    adam_configs)
+def test_adam_configs(tmpdir,
+                      optimizer,
+                      zero_offload,
+                      torch_adam,
+                      adam_w_mode,
+                      resulting_optimizer):
+    config_dict = {
+        "train_batch_size": 2,
+        "steps_per_print": 1,
+        "optimizer": {
+            "type": optimizer,
+            "params": {
+                "lr": 0.00015,
+                "torch_adam": torch_adam,
+                "adam_w_mode": adam_w_mode
+            }
+        },
+        "gradient_clipping": 1.0,
+        "fp16": {
+            "enabled": True
+        },
+        "zero_optimization": {
+            "stage": 2,
+            "cpu_offload": zero_offload
+        }
+    }
+    args = args_from_dict(tmpdir, config_dict)
+
+    @distributed_test(world_size=[1])
+    def helper(args):
+        model = SimpleModel(10)
+        model, _, _, _ = deepspeed.initialize(args=args,
+                                              model=model,
+                                              model_parameters=model.parameters())
+        # get base optimizer under zero
+        ds_optimizer = model.optimizer.optimizer
+        opt_class, adam_w_mode = resulting_optimizer
+        assert isinstance(ds_optimizer, opt_class)
+        if adam_w_mode in [True, False]:
+            assert ds_optimizer.adam_w_mode == adam_w_mode
+
+    helper(args)