Demonstrate dcp checkpoint save resume with context parallel

bionemo-recipes/models/esm2/src/esm/collator.py

@@ -415,6 +415,40 @@ def _send_data_to_cp_ranks(self):
 
         return batch_on_this_rank
 
+    def state_dict(self):
+        """Get the state dict by delegating to the dataloader."""
+        if self.cp_rank != 0:
+            return {}
+        elif hasattr(self.dataloader, "state_dict"):
+            return {"dataloader": self.dataloader.state_dict()}
+        else:
+            logger.warning(
+                "Attempting to get the state dict of the dataloader, but the dataloader does not support state_dict, "
+                "returning empty dict"
+            )
+            return {"dataloader": {}}
+
+    def load_state_dict(self, state_dict):
+        """Load the state dict by delegating to the dataloader."""
+        if self.cp_rank != 0:
+            return
+        elif hasattr(self.dataloader, "load_state_dict"):
+            self.dataloader.load_state_dict(state_dict["dataloader"])
+        else:
+            logger.warning(
+                "Attempting to load the state dict of the dataloader, but the dataloader does not support "
+                "load_state_dict, returning without loading the state dict."
+            )
+            return
+
+    @property
+    def num_workers(self):
+        """Get the number of workers of the dataloader."""
+        if self.cp_rank != 0:
+            return 0
+        else:
+            return self.dataloader.num_workers
+
 
 def _split_sample_by_num_tokens(sample: dict[str, Any], num_tokens: int) -> tuple[dict[str, Any], dict[str, Any]]:
     """Split a sample dictionary at a specified number of tokens.

bionemo-recipes/models/llama3/collator.py

@@ -415,6 +415,40 @@ def _send_data_to_cp_ranks(self):
 
         return batch_on_this_rank
 
+    def state_dict(self):
+        """Get the state dict by delegating to the dataloader."""
+        if self.cp_rank != 0:
+            return {}
+        elif hasattr(self.dataloader, "state_dict"):
+            return {"dataloader": self.dataloader.state_dict()}
+        else:
+            logger.warning(
+                "Attempting to get the state dict of the dataloader, but the dataloader does not support state_dict, "
+                "returning empty dict"
+            )
+            return {"dataloader": {}}
+
+    def load_state_dict(self, state_dict):
+        """Load the state dict by delegating to the dataloader."""
+        if self.cp_rank != 0:
+            return
+        elif hasattr(self.dataloader, "load_state_dict"):
+            self.dataloader.load_state_dict(state_dict["dataloader"])
+        else:
+            logger.warning(
+                "Attempting to load the state dict of the dataloader, but the dataloader does not support "
+                "load_state_dict, returning without loading the state dict."
+            )
+            return
+
+    @property
+    def num_workers(self):
+        """Get the number of workers of the dataloader."""
+        if self.cp_rank != 0:
+            return 0
+        else:
+            return self.dataloader.num_workers
+
 
 def _split_sample_by_num_tokens(sample: dict[str, Any], num_tokens: int) -> tuple[dict[str, Any], dict[str, Any]]:
     """Split a sample dictionary at a specified number of tokens.

bionemo-recipes/recipes/esm2_native_te/collator.py

@@ -415,6 +415,40 @@ def _send_data_to_cp_ranks(self):
 
         return batch_on_this_rank
 
+    def state_dict(self):
+        """Get the state dict by delegating to the dataloader."""
+        if self.cp_rank != 0:
+            return {}
+        elif hasattr(self.dataloader, "state_dict"):
+            return {"dataloader": self.dataloader.state_dict()}
+        else:
+            logger.warning(
+                "Attempting to get the state dict of the dataloader, but the dataloader does not support state_dict, "
+                "returning empty dict"
+            )
+            return {"dataloader": {}}
+
+    def load_state_dict(self, state_dict):
+        """Load the state dict by delegating to the dataloader."""
+        if self.cp_rank != 0:
+            return
+        elif hasattr(self.dataloader, "load_state_dict"):
+            self.dataloader.load_state_dict(state_dict["dataloader"])
+        else:
+            logger.warning(
+                "Attempting to load the state dict of the dataloader, but the dataloader does not support "
+                "load_state_dict, returning without loading the state dict."
+            )
+            return
+
+    @property
+    def num_workers(self):
+        """Get the number of workers of the dataloader."""
+        if self.cp_rank != 0:
+            return 0
+        else:
+            return self.dataloader.num_workers
+
 
 def _split_sample_by_num_tokens(sample: dict[str, Any], num_tokens: int) -> tuple[dict[str, Any], dict[str, Any]]:
     """Split a sample dictionary at a specified number of tokens.

bionemo-recipes/recipes/llama3_native_te/collator.py

@@ -415,6 +415,40 @@ def _send_data_to_cp_ranks(self):
 
         return batch_on_this_rank
 
+    def state_dict(self):
+        """Get the state dict by delegating to the dataloader."""
+        if self.cp_rank != 0:
+            return {}
+        elif hasattr(self.dataloader, "state_dict"):
+            return {"dataloader": self.dataloader.state_dict()}
+        else:
+            logger.warning(
+                "Attempting to get the state dict of the dataloader, but the dataloader does not support state_dict, "
+                "returning empty dict"
+            )
+            return {"dataloader": {}}
+
+    def load_state_dict(self, state_dict):
+        """Load the state dict by delegating to the dataloader."""
+        if self.cp_rank != 0:
+            return
+        elif hasattr(self.dataloader, "load_state_dict"):
+            self.dataloader.load_state_dict(state_dict["dataloader"])
+        else:
+            logger.warning(
+                "Attempting to load the state dict of the dataloader, but the dataloader does not support "
+                "load_state_dict, returning without loading the state dict."
+            )
+            return
+
+    @property
+    def num_workers(self):
+        """Get the number of workers of the dataloader."""
+        if self.cp_rank != 0:
+            return 0
+        else:
+            return self.dataloader.num_workers
+
 
 def _split_sample_by_num_tokens(sample: dict[str, Any], num_tokens: int) -> tuple[dict[str, Any], dict[str, Any]]:
     """Split a sample dictionary at a specified number of tokens.

bionemo-recipes/recipes/llama3_native_te/dataset.py

@@ -17,15 +17,12 @@
 
 import datasets
 import datasets.distributed
-import torch
 from torch.utils.data import DataLoader, DistributedSampler
 from torchdata.stateful_dataloader import StatefulDataLoader
 from transformers import AutoTokenizer
 from transformers.data.data_collator import DataCollatorForLanguageModeling
 
 from collator import (
-    ContextParallelDataLoaderWrapper,
-    DataCollatorForContextParallel,
     DataCollatorWithFlattening,
     TokenPackingDataset,
 )
@@ -102,6 +99,11 @@ def tokenize_with_windowing(examples):
         remove_columns=[text_column],
     )
 
+    # Even in THD mode, we use a base MLM collator that requires a padding token to be set.
+    if tokenizer.pad_token is None:
+        logger.warning(f"Tokenizer does not have a padding token. Setting it to the EOS token: {tokenizer.eos_token}")
+        tokenizer.pad_token = tokenizer.eos_token
+
     return tokenized_dataset, tokenizer
 
 
@@ -120,7 +122,7 @@ def create_bshd_dataloader(
     text_column: str = "text",
     uppercase_labels: bool = False,
     mask_degenerate_bases: bool = False,
-    pad_to_multiple_of: int | None = None,
+    pad_sequences_to_be_divisible_by: int | None = None,
 ):
     """Create a BSHD dataloader for llama3 pre-training.
 
@@ -139,7 +141,8 @@ def create_bshd_dataloader(
         text_column: Name of the column containing text sequences (default: "text").
         uppercase_labels: Whether to uppercase labels (genomic masking). Default: False.
         mask_degenerate_bases: Whether to mask non-ACGT bases (genomic masking). Default: False.
-        pad_to_multiple_of: The number to pad sequences to be divisible by, required for FP8 training. Default: 16.
+        pad_sequences_to_be_divisible_by: The number to pad sequences to be divisible by, required for FP8 training.
+            Default: None.
 
     Returns:
         A tuple of (dataloader, dataset_or_sampler).
@@ -169,7 +172,7 @@ def create_bshd_dataloader(
     base_collator = DataCollatorForLanguageModeling(
         tokenizer=tokenizer,
         mlm=False,  # Causal language modeling
-        pad_to_multiple_of=pad_to_multiple_of,
+        pad_to_multiple_of=pad_sequences_to_be_divisible_by,
     )
 
     # Wrap with genomic collator if masking options are enabled
@@ -300,40 +303,3 @@ def create_thd_dataloader(
     )
 
     return train_dataloader, tokenized_dataset
-
-
-def create_cp_dataloader(
-    *args,
-    cp_mesh: torch.distributed.device_mesh.DeviceMesh,
-    **kwargs,
-):
-    """Create a Context-parallel aware dataloader that automatically handles sharding between ranks.
-
-    Wraps the output of `create_thd_dataloader` to make it context parallel aware.
-
-    Args:
-        *args: Arguments to pass to `create_thd_dataloader`.
-        cp_mesh: The context parallel mesh.
-        **kwargs: Keyword arguments to pass to `create_thd_dataloader`.
-
-    Returns:
-        A tuple of (dataloader, dataset_or_sampler).
-    """
-    # Ensure pad_sequences_to_be_divisible_by is passed to create_thd_dataloader
-    if kwargs.get("pad_sequences_to_be_divisible_by", None) is None:
-        logger.info("pad_sequences_to_be_divisible_by is not provided, using cp_mesh.size() * 2")
-        kwargs["pad_sequences_to_be_divisible_by"] = cp_mesh.size() * 2
-
-    if cp_mesh.get_local_rank() == 0:
-        train_dataloader, tokenized_dataset = create_thd_dataloader(*args, **kwargs)
-
-        train_dataloader.collate_fn = DataCollatorForContextParallel(
-            collator=train_dataloader.collate_fn,
-            cp_world_size=cp_mesh.size(),
-        )
-
-    else:
-        train_dataloader = None
-        tokenized_dataset = None
-
-    return ContextParallelDataLoaderWrapper(train_dataloader, cp_mesh), tokenized_dataset

bionemo-recipes/recipes/llama3_native_te/hydra_config/L0_sanity_cp.yaml

@@ -4,6 +4,8 @@ defaults:
 
 cp_size: 1
 
+use_sequence_packing: false
+
 config_kwargs:
-  attn_input_format: "thd"
-  self_attn_mask_type: "padding_causal"
+  attn_input_format: "bshd" # Alternatively "thd" on datacenter hardware.
+  self_attn_mask_type: "causal" # Alternatively "padding_causal" for THD inputs.

bionemo-recipes/recipes/llama3_native_te/hydra_config/defaults.yaml

@@ -22,6 +22,7 @@ dataset:
   stride: 200 # Overlap for windowing
   buffer_size: 500_000 # Shuffle buffer size
   use_stateful_dataloader: false # Until https://github.com/pytorch/pytorch/pull/163102 is resolved with torchdata.
+  pad_sequences_to_be_divisible_by: null
   load_dataset_kwargs:
     path: ???
     split: "train"

bionemo-recipes/recipes/llama3_native_te/tests/test_dataset.py

@@ -24,7 +24,8 @@
 from hydra import compose, initialize_config_dir
 from torch.distributed.device_mesh import init_device_mesh
 
-from dataset import create_bshd_dataloader, create_cp_dataloader, create_thd_dataloader, create_tokenized_dataset
+from collator import ContextParallelDataLoaderWrapper, DataCollatorForContextParallel
+from dataset import create_bshd_dataloader, create_thd_dataloader, create_tokenized_dataset
 from distributed_config import DistributedConfig
 
 
@@ -703,15 +704,28 @@ def test_cp_dataloader(tokenizer_path):
     torch.cuda.set_device(dist_config.local_rank)
     device_mesh = init_device_mesh("cuda", mesh_shape=(1, 1), mesh_dim_names=("dp", "cp"))
 
-    dataloader, _ = create_cp_dataloader(
-        distributed_config=dist_config,
-        cp_mesh=device_mesh["cp"],
-        tokenizer_name_or_path=tokenizer_path,
-        load_dataset_kwargs=load_dataset_kwargs,
-        text_column="text",
-        micro_batch_size=1,
-        max_seq_length=1024,
-    )
+    cp_mesh = device_mesh["cp"]
+
+    # Create the context-parallel dataloader directly following the pattern in train_fsdp2_cp.py
+    if cp_mesh.get_local_rank() == 0:
+        train_dataloader, _ = create_thd_dataloader(
+            distributed_config=dist_config,
+            tokenizer_name_or_path=tokenizer_path,
+            load_dataset_kwargs=load_dataset_kwargs,
+            text_column="text",
+            micro_batch_size=1,
+            max_seq_length=1024,
+            pad_sequences_to_be_divisible_by=cp_mesh.size() * 2,
+        )
+
+        train_dataloader.collate_fn = DataCollatorForContextParallel(
+            collator=train_dataloader.collate_fn,
+            cp_world_size=cp_mesh.size(),
+        )
+    else:
+        train_dataloader = None
+
+    dataloader = ContextParallelDataLoaderWrapper(train_dataloader, cp_mesh)
 
     batches = list(dataloader)
     assert len(batches) > 1
@@ -775,30 +789,39 @@ def test_cp_dataloader_multi_gpu(recipe_path, dataset_path):
     parser.add_argument("--dataset_path", type=str, default="dlcm_sanity_dataset.parquet")
     args = parser.parse_args()
 
-    from torch.distributed.device_mesh import init_device_mesh
-
-    from dataset import create_cp_dataloader
-
     dist_config = DistributedConfig()
     device = torch.device(f"cuda:{dist_config.local_rank}")
     torch.distributed.init_process_group(backend="nccl", device_id=device)
     torch.cuda.set_device(dist_config.local_rank)
     device_mesh = init_device_mesh("cuda", mesh_shape=(1, 2), mesh_dim_names=("dp", "cp"))
 
-    dataloader, _ = create_cp_dataloader(
-        distributed_config=dist_config,
-        cp_mesh=device_mesh["cp"],
-        tokenizer_name_or_path="nvidia/Llama-3.1-8B-Instruct-FP8",
-        micro_batch_size=1,
-        text_column="text" if args.dataset_path == "dlcm_sanity_dataset.parquet" else "sequence",
-        load_dataset_kwargs={
-            "path": "parquet",
-            "split": "train",
-            "data_files": args.dataset_path,
-            "streaming": True,
-        },
-        num_workers=1,
-    )
+    cp_mesh = device_mesh["cp"]
+
+    # Create the context-parallel dataloader directly following the pattern in train_fsdp2_cp.py
+    if cp_mesh.get_local_rank() == 0:
+        train_dataloader, _ = create_thd_dataloader(
+            distributed_config=dist_config,
+            tokenizer_name_or_path="nvidia/Llama-3.1-8B-Instruct-FP8",
+            micro_batch_size=1,
+            text_column="text" if args.dataset_path == "dlcm_sanity_dataset.parquet" else "sequence",
+            load_dataset_kwargs={
+                "path": "parquet",
+                "split": "train",
+                "data_files": args.dataset_path,
+                "streaming": True,
+            },
+            num_workers=1,
+            pad_sequences_to_be_divisible_by=cp_mesh.size() * 2,
+        )
+
+        train_dataloader.collate_fn = DataCollatorForContextParallel(
+            collator=train_dataloader.collate_fn,
+            cp_world_size=cp_mesh.size(),
+        )
+    else:
+        train_dataloader = None
+
+    dataloader = ContextParallelDataLoaderWrapper(train_dataloader, cp_mesh)
 
     batches = list(itertools.islice(dataloader, 10))