add warmup steps

Author: samsja

open_diloco/train_fsdp.py

@@ -103,6 +103,7 @@ class HvConfig(BaseConfig):
     skip_load_from_peers: bool = False
     world_rank: int
     galaxy_size: int
+    warmup_outerstep: int = 10
 
     @model_validator(mode="before")
     def cast_str_to_list(cls, values: dict[str, Any]) -> dict[str, Any]:
@@ -188,8 +189,18 @@ def get_model(config: Config) -> LlamaForCausalLM:
 
 
 def _get_cosine_schedule_with_warmup_lr_lambda(
-    current_step: int, *, num_warmup_steps: int, num_training_steps: int, num_cycles: float, min_lr_rate: float = 0.0
+    current_step: int,
+    *,
+    num_warmup_steps: int,
+    num_training_steps: int,
+    num_inner_steps: int,
+    warmup_outerstep: int | None,
+    num_cycles: float,
+    min_lr_rate: float = 0.0,
 ):
+    if warmup_outerstep is not None and current_step % num_inner_steps < warmup_outerstep:
+        return 0
+
     if current_step < num_warmup_steps:
         return float(current_step) / float(max(1, num_warmup_steps))
     progress = float(current_step - num_warmup_steps) / float(max(1, num_training_steps - num_warmup_steps))
@@ -198,11 +209,13 @@ def _get_cosine_schedule_with_warmup_lr_lambda(
     return max(0, factor)
 
 
-def get_cosine_schedule_with_warmup(optimizer, num_warmup_steps, num_training_steps, num_inner_steps):
+def get_cosine_schedule_with_warmup(optimizer, config: Config):
     lambda_lr = partial(
         _get_cosine_schedule_with_warmup_lr_lambda,
-        num_warmup_steps=num_warmup_steps,
-        num_training_steps=num_training_steps,
+        num_warmup_steps=config.warmup_steps,
+        num_training_steps=config.total_steps,
+        num_inner_steps=config.hv.local_steps,
+        warmup_outerstep=config.hv.warmup_outerstep,
         num_cycles=0.5,
     )
     return LambdaLR(optimizer, lambda_lr, -1)
@@ -299,9 +312,7 @@ def train(config: Config):
     def scheduler_fn(opt):
         return get_cosine_schedule_with_warmup(
             opt,
-            num_warmup_steps=config.warmup_steps,
-            num_training_steps=config.total_steps,
-            num_inner_steps=config.hv.local_steps,
+            config=config,
         )
 
     if config.hv is not None: