init scion

Author: wang55

torchtitan/components/optimizer.py

@@ -21,6 +21,7 @@
 from torchtitan.components.ft import FTManager, has_torchft
 from torchtitan.config import Optimizer as OptimizerConfig
 from torchtitan.distributed import ParallelDims
+from torchtitan.experiments import distributed_scion
 
 __all__ = [
     "OptimizersContainer",
@@ -75,7 +76,12 @@ def __init__(
         self.optimizers = []
         self.model_parts = model_parts
         for model in self.model_parts:
-            params = [p for p in model.parameters() if p.requires_grad]
+            if issubclass(optimizer_cls, (distributed_scion.DistributedScion)):
+                params, optimizer_kwargs = distributed_scion.create_scion_param_groups(
+                    model, optimizer_kwargs
+                )
+            else:
+                params = [p for p in model.parameters() if p.requires_grad]
             self.optimizers.append(optimizer_cls(params, **optimizer_kwargs))
             all_params.extend(params)
         self._validate_length(len(self.model_parts))
@@ -302,9 +308,17 @@ def build_optimizers(
         "foreach": foreach,
     }
 
+    if name in ["DistributedScion"]:
+        optimizer_kwargs = (
+            distributed_scion.create_scion_optimizer_kwargs_from_optimizer_config(
+                optimizer_config, parallel_dims
+            )
+        )
+
     optimizer_classes = {
         "Adam": torch.optim.Adam,
         "AdamW": torch.optim.AdamW,
+        "DistributedScion": distributed_scion.DistributedScion,
     }
     if name not in optimizer_classes:
         raise NotImplementedError(f"Optimizer {name} not added.")

torchtitan/config/job_config.py

@@ -122,6 +122,27 @@ class Optimizer:
     weight_decay: float = 0.1
     """Weight decay to use"""
 
+    is_light: bool = False
+    """Whether to use Scion's light (memory-saving) version"""
+
+    norm_factor: str = "spectral"
+    """Which norm factor to use"""
+
+    zeropower_backend: str = "newtonschulz5"
+    "Which `zeropower_backend` to use."
+
+    backend_steps: int = 5
+    """Number of steps for the Scion backend"""
+
+    momentum: float = 0.95
+    """Scion momentum to use"""
+
+    nesterov: bool = False
+    """Whether to use Nesterov momentum in Scion"""
+
+    extra_splits_rules: list[dict[str, Any]] | None = None
+    """Extra parameter group splitting rules for Scion optimizers"""
+
     implementation: Literal["for-loop", "foreach", "fused"] = "fused"
     """
     Specify which optimizer implementation to use:

torchtitan/distributed/utils.py

@@ -388,7 +388,8 @@ def clip_grad_norm_(
             dist.all_reduce(total_norm, op=dist.ReduceOp.SUM, group=pp_mesh.get_group())
             total_norm **= 1.0 / norm_type
 
-    torch.nn.utils.clip_grads_with_norm_(parameters, max_norm, total_norm, foreach)
+    if max_norm > 0:
+        torch.nn.utils.clip_grads_with_norm_(parameters, max_norm, total_norm, foreach)
     return total_norm
 
 
@@ -444,7 +445,10 @@ def _clip_grad_norm_with_ep(
             dist.all_reduce(total_norm, op=dist.ReduceOp.SUM, group=pp_mesh.get_group())
             total_norm **= 1.0 / norm_type
 
-    torch.nn.utils.clip_grads_with_norm_(ep_params, max_norm, total_norm, foreach)
-    torch.nn.utils.clip_grads_with_norm_(non_ep_params, max_norm, total_norm, foreach)
+    if max_norm > 0:
+        torch.nn.utils.clip_grads_with_norm_(ep_params, max_norm, total_norm, foreach)
+        torch.nn.utils.clip_grads_with_norm_(
+            non_ep_params, max_norm, total_norm, foreach
+        )
 
     return total_norm

torchtitan/experiments/distributed_scion/__init__.py

@@ -0,0 +1,12 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from .distributed_scion import DistributedScion  # noqa: F401
+from .utils import (  # noqa: F401  # noqa: F401  # noqa: F401
+    create_scion_optimizer_kwargs_from_optimizer_config,
+    create_scion_param_groups,
+    remove_orig_mod_and_weight_for_p_name,
+)