jgerh
diff --git a/‎.github/workflows/cicd-main.yml‎
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diff --git a/‎nemo/collections/llm/gpt/model/hf_auto_model_for_causal_lm.py‎
Lines changed: 6 additions & 1 deletion b/‎nemo/collections/llm/gpt/model/hf_auto_model_for_causal_lm.py‎
Lines changed: 6 additions & 1 deletion
diff --git a/‎nemo/lightning/__init__.py‎
Lines changed: 2 additions & 1 deletion b/‎nemo/lightning/__init__.py‎
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diff --git a/‎nemo/lightning/pytorch/strategies/__init__.py‎
Lines changed: 2 additions & 1 deletion b/‎nemo/lightning/pytorch/strategies/__init__.py‎
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diff --git a/‎nemo/lightning/pytorch/strategies/fsdp2_strategy.py‎
Lines changed: 276 additions & 0 deletions b/‎nemo/lightning/pytorch/strategies/fsdp2_strategy.py‎
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diff --git a/‎nemo/lightning/pytorch/strategies/megatron_strategy.py‎
Lines changed: 1 addition & 1 deletion b/‎nemo/lightning/pytorch/strategies/megatron_strategy.py‎
Lines changed: 1 addition & 1 deletion
@@ -3701,6 +3701,17 @@ jobs:
         TRANSFORMERS_OFFLINE=1 python tests/collections/llm/hf/sft.py --model /home/TestData/nlp/hf_gemma/hf_gemma_2b --max-steps 10 --devices 2 --strategy ddp
       AFTER_SCRIPT: |
         rm -rf nemo_experiments
+  
+  L2_HF_Transformer_SFT_FSDP2_2gpu:
+    needs: [ cicd-test-container-setup ]
+    uses: ./.github/workflows/_test_template.yml
+    if: contains(fromJSON(needs.cicd-test-container-setup.outputs.test_to_run), 'L2_HF_Transformer_SFT_FSDP2_2gpu') || needs.cicd-test-container-setup.outputs.all == 'true'
+    with:
+      RUNNER: self-hosted-azure
+      SCRIPT: |
+        TRANSFORMERS_OFFLINE=1 python tests/collections/llm/hf/sft_fsdp2.py --model /home/TestData/nlp/hf_gemma/hf_gemma_2b --max-steps 10 --devices 2
+      AFTER_SCRIPT: |
+        rm -rf nemo_experiments
 
   L2_HF_Transformer_PT_2gpu:
     needs: [ cicd-test-container-setup ]
@@ -3723,6 +3734,17 @@ jobs:
         TRANSFORMERS_OFFLINE=1 python tests/collections/llm/hf/sft_nemorun.py --model /home/TestData/nlp/hf_gemma/hf_gemma_2b --max-steps 10 --devices 2 --strategy ddp
       AFTER_SCRIPT: |
         rm -rf nemo_experiments
+  
+  L2_HF_Transformer_SFT_2gpu_nemorun_fsdp2:
+    needs: [ cicd-test-container-setup ]
+    uses: ./.github/workflows/_test_template.yml
+    if: contains(fromJSON(needs.cicd-test-container-setup.outputs.test_to_run), 'L2_HF_Transformer_SFT_2gpu_nemorun_fsdp2') || needs.cicd-test-container-setup.outputs.all == 'true'
+    with:
+      RUNNER: self-hosted-azure
+      SCRIPT: |
+        TRANSFORMERS_OFFLINE=1 python tests/collections/llm/hf/sft_nemorun_fsdp2.py --model /home/TestData/nlp/hf_gemma/hf_gemma_2b --max-steps 10 --devices 2
+      AFTER_SCRIPT: |
+        rm -rf nemo_experiments
 
   L2_HF_Transformer_PT_2gpu_nemorun:
     needs: [ cicd-test-container-setup ]
@@ -5048,6 +5070,8 @@ jobs:
       - L2_NeMo_2_PTQ_Llama2_FP8
       - L2_NeMo_2_jit_callback
       - L2_NeMo_2_LLAVA_NEXT_MOCK_TRAINING
+      - L2_HF_Transformer_SFT_FSDP2_2gpu
+      - L2_HF_Transformer_SFT_2gpu_nemorun_fsdp2
     if: always()
     runs-on: ubuntu-latest
     steps:
 
@@ -20,6 +20,7 @@
 from nemo.collections.common.tokenizers.huggingface.auto_tokenizer import AutoTokenizer
 from nemo.collections.llm import fn
 from nemo.lightning import io
+from nemo.lightning.pytorch.strategies.utils import fsdp2_strategy_parallelize
 from nemo.utils import logging
 
 
@@ -91,6 +92,10 @@ def configure_model(self):
                 config, torch_dtype=dtype, trust_remote_code=self.trust_remote_code
             )
 
+        # Apply FSDP2 and TP to the model
+        if self.device_mesh is not None:
+            fsdp2_strategy_parallelize(self.model, device_mesh=self.device_mesh)
+
         if self.model_accelerator is not None:
             self.model_accelerator(self.model)
 
@@ -99,7 +104,7 @@ def configure_model(self):
     def forward(self, batch):
         return self.model(**batch)
 
-    def training_step(self, batch):
+    def training_step(self, batch, batch_idx=None):
         labels = batch.pop('labels').to(self.model.device)
         loss_mask = batch.pop('loss_mask', None)
 
 
@@ -31,7 +31,7 @@
 from nemo.lightning.pytorch.optim import LRSchedulerModule, MegatronOptimizerModule, OptimizerModule, lr_scheduler
 from nemo.lightning.pytorch.plugins import MegatronDataSampler, MegatronMixedPrecision
 from nemo.lightning.pytorch.plugins import data_sampler as _data_sampler
-from nemo.lightning.pytorch.strategies import FSDPStrategy, MegatronStrategy
+from nemo.lightning.pytorch.strategies import FSDP2Strategy, FSDPStrategy, MegatronStrategy
 from nemo.lightning.pytorch.strategies.utils import RestoreConfig
 from nemo.lightning.pytorch.trainer import Trainer, configure_no_restart_validation_training_loop
 from nemo.lightning.resume import AutoResume
@@ -60,6 +60,7 @@ def _is_slurm_interactive_mode():
     "MegatronMixedPrecision",
     "MegatronOptimizerModule",
     "FSDPStrategy",
+    "FSDP2Strategy",
     "RestoreConfig",
     "lr_scheduler",
     "NeMoLogger",
 
@@ -12,11 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from nemo.lightning.pytorch.strategies.fsdp2_strategy import FSDP2Strategy
 from nemo.lightning.pytorch.strategies.fsdp_strategy import FSDPStrategy
 from nemo.lightning.pytorch.strategies.megatron_strategy import MegatronStrategy
 
-
 __all__ = [
     "FSDPStrategy",
+    "FSDP2Strategy",
     "MegatronStrategy",
 ]
@@ -0,0 +1,276 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import shutil
+from collections import OrderedDict
+from pathlib import Path
+from typing import Any, Dict, Literal, Optional, Union
+
+import lightning.pytorch as pl
+import torch
+from lightning.fabric.plugins import CheckpointIO
+from lightning.fabric.strategies.fsdp import _get_sharded_state_dict_context
+from lightning.pytorch.strategies.model_parallel import ModelParallelStrategy as PLModelParallelStrategy
+from lightning.pytorch.trainer.states import TrainerFn
+from lightning.pytorch.utilities.types import STEP_OUTPUT
+from torch.distributed.checkpoint.state_dict import (  # get_state_dict,
+    StateDictOptions,
+    get_optimizer_state_dict,
+    set_state_dict,
+)
+from torch.utils.data import DataLoader
+from typing_extensions import override
+
+from nemo.lightning import io
+from nemo.lightning.pytorch.strategies.utils import (
+    ckpt_to_dir,
+    create_checkpoint_io,
+    fix_progress_bar,
+    init_model_parallel,
+    mcore_to_pyt_sharded_state_dict,
+    pyt_to_mcore_state_dict,
+    setup_data_sampler,
+    setup_parallel_ranks,
+)
+
+
+class FSDP2Strategy(PLModelParallelStrategy, io.IOMixin):
+    """Megatron plugin for Pytorch Lightning.
+
+    This strategy implements FSDP 2 using PyTorch's native FSDP 2 methods. Comparing with
+    MegatronStrategy, FSDP2Strategy is designed to be more lightweight, with minimal
+    modifications over Lightning's ModelParallelStrategy which supports FSDP2 + TP
+    parallelization but preserves necessary features to be compatible with nemo and mcore.
+    By default, this strategy wraps FSDP2 per TransformerLayer.
+
+    Note:
+        This strategy is designed to work with NVIDIA's Megatron-LM framework and requires
+        specific model implementations that are compatible with Megatron's parallelism techniques.
+    Note:
+        Due to the different optimizer structure (FSDP2 only uses torch native optimizers),
+        MegatronStrategy cannot resume training from checkpoints saved by FSDP2Strategy, and vice
+        versa. However, the model weights structure is made compatible, so switching strategy is
+        possible if users only need the weights not the optimizer states. (E.g. run pretrain with
+        megatron 4D parallelism and run SFT with FSDP2.)
+    """
+
+    def __init__(
+        self,
+        data_parallel_size: Union[Literal["auto"], int] = "auto",
+        tensor_parallel_size: Union[Literal["auto"], int] = "auto",
+        ckpt_load_optimizer: bool = True,
+        ckpt_save_optimizer: bool = True,
+        data_sampler=None,
+        **kwargs,
+    ):
+        super().__init__(data_parallel_size=data_parallel_size, tensor_parallel_size=tensor_parallel_size, **kwargs)
+
+        self.data_sampler = data_sampler
+        self.ckpt_load_optimizer = ckpt_load_optimizer
+        self.ckpt_save_optimizer = ckpt_save_optimizer
+
+    @override
+    def setup_environment(self) -> None:
+        setup_parallel_ranks(self)
+        super().setup_environment()
+        init_model_parallel(self.model)
+
+    @override
+    def setup(self, trainer: pl.Trainer) -> None:
+        self.trainer = trainer
+        setup_data_sampler(self.trainer)
+        fix_progress_bar(trainer)
+        super().setup(trainer)
+
+    def _get_loss_reduction(self, step_type: str):
+        for fn_name in [f"{step_type}_loss_reduction", "loss_reduction"]:
+            if hasattr(self.lightning_module, fn_name):
+                return getattr(self.lightning_module, fn_name)
+        return None
+
+    def _step_proxy(self, step_type, batch, batch_idx=None):
+        method_name = f"{step_type}_step"
+        if self.model != self.lightning_module:
+            loss = self._forward_redirection(self.model, self.lightning_module, method_name, batch, batch_idx)
+        else:
+            loss = getattr(self.lightning_module, method_name)(batch, batch_idx)
+
+        _loss_reduction = self._get_loss_reduction(step_type)
+        if _loss_reduction:
+            return _loss_reduction.forward(batch, loss)
+        return loss, {'avg': loss}
+
+    @override
+    def training_step(self, batch, batch_idx=None) -> STEP_OUTPUT:
+        assert self.lightning_module is not None
+        assert self.model is not None
+        with self.precision_plugin.train_step_context():
+            loss, reduced = self._step_proxy("training", batch, batch_idx)
+
+            self.lightning_module.log(
+                'global_step',
+                self.trainer.global_step,
+                prog_bar=True,
+                rank_zero_only=True,
+                batch_size=1,
+            )
+
+            self.lightning_module.log(
+                'step',
+                self.trainer.global_step,
+            )
+            self.lightning_module.log(
+                'reduced_train_loss', reduced['avg'], prog_bar=True, rank_zero_only=True, batch_size=1
+            )
+
+            # returns unreduced loss for backward
+            return loss
+
+    @override
+    def validation_step(self, batch, batch_idx=None) -> Any:
+        assert self.lightning_module is not None
+        assert self.model is not None
+        with self.precision_plugin.val_step_context():
+            loss, reduced = self._step_proxy("validation", batch, batch_idx)
+            self.lightning_module.log('val_loss', reduced['avg'], rank_zero_only=True, batch_size=1)
+            return loss
+
+    @override
+    def test_step(self, batch, batch_idx=None) -> STEP_OUTPUT:
+        assert self.lightning_module is not None
+        assert self.model is not None
+        with self.precision_plugin.test_step_context():
+            loss, reduced = self._step_proxy("test", batch, batch_idx)
+            self.lightning_module.log('test_loss', reduced['avg'], rank_zero_only=True, batch_size=1)
+
+            return loss
+
+    @override
+    def predict_step(self, batch, batch_idx=None) -> STEP_OUTPUT:
+        assert self.lightning_module is not None
+        assert self.model is not None
+        with self.precision_plugin.predict_step_context():
+            loss, reduced = self._step_proxy("predict", batch, batch_idx)
+            return reduced
+
+    @override
+    def process_dataloader(self, dataloader: DataLoader) -> DataLoader:
+        if self.data_sampler:
+            return self.data_sampler.transform_dataloader(dataloader)
+
+        return dataloader
+
+    @property
+    @override
+    def checkpoint_io(self) -> CheckpointIO:
+        if not self._checkpoint_io:
+            self._checkpoint_io = create_checkpoint_io()
+
+        return self._checkpoint_io
+
+    @checkpoint_io.setter
+    def checkpoint_io(self, io: CheckpointIO) -> None:
+        self._checkpoint_io = io
+
+    @property
+    def current_epoch_step(self) -> int:
+        """
+        Get the value of step within an epoch.
+        """
+        return max(
+            self.trainer.fit_loop.epoch_loop.automatic_optimization.optim_progress.optimizer.step.current.completed,
+            self.trainer.fit_loop.epoch_loop.manual_optimization.optim_step_progress.current.completed,
+        )
+
+    @override
+    def remove_checkpoint(self, filepath: Union[str, Path]) -> None:
+        # Taken from MegatronStrategy
+        ckpt = ckpt_to_dir(filepath)
+        if self.is_global_zero:
+            if os.path.islink(ckpt):
+                os.unlink(ckpt)
+            else:
+                shutil.rmtree(ckpt)
+
+    @override
+    def save_checkpoint(
+        self, checkpoint: Dict[str, Any], filepath: Union[str, Path], storage_options: Optional[Any] = None
+    ) -> None:
+        """Converts PyT checkpoints to MCore format and save using MCore dist ckpt library."""
+        checkpoint["sharded_state_dict"] = pyt_to_mcore_state_dict(
+            checkpoint.pop("state_dict"), device_mesh=self.device_mesh
+        )
+        checkpoint["state_dict"] = OrderedDict([])
+
+        if "optimizer_states" in checkpoint and self.trainer.state.fn == TrainerFn.FITTING:
+            # Clear the optimizer states. This handles the case where ckpt_save_optimizer=False
+            # Ideally, the optimizer state dicts should not be generated in this case
+            checkpoint["optimizer_states"] = {}
+
+            ## replace unsharded optimizer_states with sharded dict.
+            ## note that if trainer.save_checkpoint(path, save_weights_only=True) is called,
+            ## the checkpoint will contain only model weights. Optimizer states will be omitted.
+            if self.ckpt_save_optimizer:
+                checkpoint['optimizer'] = get_optimizer_state_dict(self.model, self.optimizers)
+                pyt_to_mcore_state_dict(
+                    checkpoint['optimizer']['state'], prefix="optimizer.state.", device_mesh=self.device_mesh
+                )
+
+        self.checkpoint_io.save_checkpoint(checkpoint, filepath, storage_options=storage_options)
+
+    @override
+    def load_checkpoint(self, checkpoint_path: str | Path) -> Dict[str, Any]:
+        """PTL method which we override to integrate distributed checkpoints for FSDP models.
+        Different from MegatronStrategy, both model and optimizer states are restore within
+        this method.
+
+        The logic here is slightly more complicated:
+        1. Obtain PyT state dicts (sharded & unflattened) for model and optim -> torch::ShardedTensor
+        2. Convert to MCore state dicts -> mcore::ShardedTensor
+        3. Load from checkpoint using MCore dist ckpt API -> torch::Tensor
+        4. Convert to PyT state dicts (sharded & unflattened) -> torch::ShardedTensor
+        5. Load into model and optim using PyT dist ckpt API
+        6. Return the loaded checkpoint for lightning to load other metadata
+        """
+        path = Path(self.broadcast(checkpoint_path))
+        torch.cuda.empty_cache()
+
+        # TODO: the elegant way to load both state dicts. Need pytorch 2.3.1
+        # msd, osd = get_state_dict(self.model, self.optimizers, options=StateDictOptions(cpu_offload=True))
+        sharded_state_dict = {}
+        with _get_sharded_state_dict_context(self.model):
+            msd = self.model.state_dict()
+            pyt_to_mcore_state_dict(msd, device_mesh=self.device_mesh)
+            sharded_state_dict["sharded_state_dict"] = msd
+
+        if self.ckpt_load_optimizer and self.trainer.state.fn == TrainerFn.FITTING:
+            osd = get_optimizer_state_dict(self.model, self.optimizers, options=StateDictOptions(cpu_offload=True))
+            pyt_to_mcore_state_dict(osd['state'], prefix="optimizer.state.", device_mesh=self.device_mesh)
+            sharded_state_dict["optimizer"] = osd
+
+        checkpoint = self.checkpoint_io.load_checkpoint(path, sharded_state_dict=sharded_state_dict)
+        mcore_to_pyt_sharded_state_dict(checkpoint['sharded_state_dict'], msd)
+
+        if self.ckpt_load_optimizer and self.trainer.state.fn == TrainerFn.FITTING:
+            mcore_to_pyt_sharded_state_dict(checkpoint['optimizer']['state'], osd['state'])
+
+        set_state_dict(
+            self.model,
+            self.optimizers if self.ckpt_load_optimizer else [],
+            model_state_dict=checkpoint['sharded_state_dict'],
+            optim_state_dict=checkpoint['optimizer'] if self.ckpt_load_optimizer else None,
+        )
+
+        return checkpoint
@@ -116,7 +116,7 @@ class MegatronStrategy(DDPStrategy, io.IOMixin):
             across GPU ranks. Defaults to 1.
         virtual_pipeline_model_parallel_size (Optional[int]): Interleaved pipeline parallelism used to
             improve performance by reducing the pipeline bubble. Defaults to None.
-        microbatch_group_size_per_vp_stage（Optional[int]）: the number of micro-batches that are executed
+        microbatch_group_size_per_vp_stage (Optional[int]): the number of micro-batches that are executed
             at a time for a given virtual stage (both forward and backward). Defaults to None and convert
             to pipeline_parallel_size. which specifies a depth-first schedule.
         context_parallel_size (int): Splits network input along sequence dimension across GPU ranks.