wip add ckpt shampoo

src/zeroband/checkpoint.py

@@ -38,6 +38,7 @@
     send_state_dict,
     send_tensor_and_state_dict,
 )
+from distributed_shampoo import DistributedShampoo
 
 from zeroband.utils.world_info import get_world_info
 
@@ -80,17 +81,23 @@ def __init__(
         self.optim = optim
 
     def state_dict(self) -> dict[str, Any]:
-        return get_optimizer_state_dict(
-            model=self.model, optimizers=self.optim, options=StateDictOptions(flatten_optimizer_state_dict=True)
-        )
+        if isinstance(self.optim, DistributedShampoo):
+            return self.optim.distributed_state_dict(key_to_param=self.model.named_parameters())
+        else:
+            return get_optimizer_state_dict(
+                model=self.model, optimizers=self.optim, options=StateDictOptions(flatten_optimizer_state_dict=True)
+            )
 
     def load_state_dict(self, state_dict: dict[str, Any]) -> None:
-        set_optimizer_state_dict(
-            model=self.model,
-            optimizers=self.optim,
-            optim_state_dict=state_dict,
-            options=StateDictOptions(flatten_optimizer_state_dict=True),
-        )
+        if isinstance(self.optim, DistributedShampoo):
+            self.optim.load_distributed_state_dict(state_dict, key_to_param=self.model.named_parameters())
+        else:
+            set_optimizer_state_dict(
+                model=self.model,
+                optimizers=self.optim,
+                optim_state_dict=state_dict,
+                options=StateDictOptions(flatten_optimizer_state_dict=True),
+            )
 
 
 def cast_dtensor_to_tensor(state_dict: dict[str, Any]) -> dict[str, Any]:

src/zeroband/utils/__init__.py

@@ -4,6 +4,7 @@
 import torch
 from torch.distributed.fsdp import ShardingStrategy
 from torch.distributed._tensor.api import DTensor
+from distributed_shampoo import DistributedShampoo
 
 
 __all__ = ["get_sharding_strategy", "get_peak_flops", "get_num_flop_per_token", "get_num_params"]
@@ -138,6 +139,9 @@ def get_optimizer_signature(optimizer: torch.optim.Optimizer, compress: bool = T
     Get the optimizer signature
     """
 
+    if isinstance(optimizer, DistributedShampoo):
+        return "mocked signature because shampoo does not support state_dict()"
+
     def unwrap_tensor(state_dict: dict) -> dict:
         new_dict = {}
         for key, value in state_dict.items():

tests/test_torchrun/test_train.py

@@ -127,7 +127,8 @@ def test_soap(diloco: bool):
     )
 
 
-def test_ckpt(tmp_path: Path):
+@pytest.mark.parametrize("soap", [False, True])
+def test_ckpt(tmp_path: Path, soap: bool):
     num_gpus = [1, 2]
     v1_file = tmp_path / "v1.log"
     v2_file = tmp_path / "v2.log"
@@ -157,7 +158,8 @@ def test_ckpt(tmp_path: Path):
             "--no-train.sequence_packing",
             "--train.attn_fn",
             "math",
-        ],
+        ]
+        + (["--optim.optim.precondition_frequency", "1"] if soap else []),
         diloco=True,
     )
     _test_multi_gpu(
@@ -178,7 +180,8 @@ def test_ckpt(tmp_path: Path):
             "--no-train.sequence_packing",
             "--train.attn_fn",
             "math",
-        ],
+        ]
+        + (["--optim.optim.precondition_frequency", "1"] if soap else []),
         diloco=True,
     )
     # _test_multi_gpu(