Merge branch 'megatron-lm_dtk24.04' into 'main'

Megatron lm dtk24.04

See merge request !1

megatron/core/dist_checkpointing/strategies/__init__.py

+# Copyright (c) 2022-2023, NVIDIA CORPORATION.  All rights reserved.
+
+""" Various loading and saving strategies """
+
+import logging
+
+logger = logging.getLogger(__name__)
+
+try:
+    import tensorstore
+    import zarr
+
+    from .tensorstore import _import_trigger
+    from .zarr import _import_trigger
+except ImportError:
+    logger.warning('Zarr-based strategies will not be registered because of missing packages')

megatron/core/dist_checkpointing/strategies/base.py

+# Copyright (c) 2022-2023, NVIDIA CORPORATION.  All rights reserved.
+
+from abc import ABC, abstractmethod
+from collections import defaultdict
+from enum import Enum
+from pathlib import Path
+from typing import Dict, List, Optional
+
+from ..mapping import CheckpointingException, ShardedStateDict, ShardedTensor, StateDict
+
+
+class StrategyAction(Enum):
+    LOAD_COMMON = 'load_common'
+    LOAD_SHARDED = 'load_sharded'
+    SAVE_COMMON = 'save_common'
+    SAVE_SHARDED = 'save_sharded'
+
+
+default_strategies = defaultdict(dict)
+
+
+def get_default_strategy(action: StrategyAction, backend: str, version: int):
+    try:
+        return default_strategies[action.value][(backend, version)]
+    except KeyError as e:
+        raise CheckpointingException(
+            f'Cannot find default strategy for: {(action, backend, version)}'
+        ) from e
+
+
+class LoadStrategyBase(ABC):
+    @abstractmethod
+    def check_backend_compatibility(self, loaded_version):
+        raise NotImplementedError
+
+    @abstractmethod
+    def check_version_compatibility(self, loaded_version):
+        raise NotImplementedError
+
+
+class SaveStrategyBase(ABC):
+    def __init__(self, backend: str, version: int):
+        self.backend = backend
+        self.version = version
+
+
+class LoadCommonStrategy(LoadStrategyBase):
+    @abstractmethod
+    def load(self, checkpoint_dir: Path):
+        raise NotImplementedError
+
+
+class LoadShardedStrategy(LoadStrategyBase):
+    @abstractmethod
+    def load(self, sharded_state_dict: ShardedStateDict, checkpoint_dir: Path):
+        raise NotImplementedError
+
+
+class SaveCommonStrategy(SaveStrategyBase):
+    @abstractmethod
+    def save(self, common_state_dict: StateDict, checkpoint_dir: Path):
+        raise NotImplementedError
+
+
+class SaveShardedStrategy(SaveStrategyBase):
+    @abstractmethod
+    def save(self, sharded_tensors: List[ShardedTensor], checkpoint_dir: Path):
+        raise NotImplementedError

megatron/core/dist_checkpointing/strategies/tensorstore.py

+# Copyright (c) 2022-2023, NVIDIA CORPORATION.  All rights reserved.
+
+""" Strategies using TensorStore to load and save Zarr arrays. """
+
+from functools import partial
+from itertools import starmap
+from pathlib import Path
+
+import tensorstore as ts
+import torch
+
+from ..core import CheckpointingException
+from ..dict_utils import dict_list_map_inplace
+from ..mapping import ShardedStateDict, ShardedTensor
+from .base import LoadShardedStrategy, StrategyAction, default_strategies
+from .zarr import postprocess_numpy_array
+
+_import_trigger = None
+
+
+class TensorStoreLoadShardedStrategy(LoadShardedStrategy):
+    def __init__(self, load_directly_on_device: bool = False):
+        super().__init__()
+        self.load_directly_on_device = load_directly_on_device
+
+    def load(self, sharded_state_dict: ShardedStateDict, checkpoint_dir: Path):
+        if torch.distributed.get_rank() == 0:
+            print(f'Loading distributed checkpoint with {self.__class__.__name__}')
+            if self.load_directly_on_device:
+                print(f'Loading distributed checkpoint directly on the GPU')
+        load_fn = partial(
+            _load_from_array,
+            checkpoint_dir=checkpoint_dir,
+            load_directly_on_device=self.load_directly_on_device,
+        )
+        dict_list_map_inplace(load_fn, sharded_state_dict)
+        return sharded_state_dict
+
+    def check_backend_compatibility(self, loaded_version):
+        pass  # TODO
+
+    def check_version_compatibility(self, loaded_version):
+        pass  # TODO
+
+
+def merge_global_slice_with_shape(global_slice, actual_shape, key):
+    def _merge_slice(dim_slice, dim_size):
+        if isinstance(dim_slice, slice):
+            assert (
+                dim_slice.start < dim_size
+            ), f'Got empty slice for ShardedTensor {key} ({dim_slice}, {dim_size})'
+            if dim_slice.stop > dim_size:
+                dim_slice = slice(dim_slice.start, dim_size, dim_slice.step)
+        return dim_slice
+
+    assert len(global_slice) == len(actual_shape), (global_slice, actual_shape, key)
+    return tuple(starmap(_merge_slice, zip(global_slice, actual_shape)))
+
+
+def _load_from_array(
+    sharded_tensor: ShardedTensor,
+    checkpoint_dir: Path,
+    load_directly_on_device: bool = False,
+    apply_flattened_range: bool = True,
+):
+    x = _load_regular_chunk(sharded_tensor, checkpoint_dir)
+    ten = postprocess_numpy_array(x, sharded_tensor, apply_flattened_range)
+    if load_directly_on_device:
+        sharded_tensor.data.data.copy_(ten)
+        return sharded_tensor.data
+    else:
+        return ten
+
+
+def _load_regular_chunk(sharded_tensor: ShardedTensor, checkpoint_dir: Path):
+    assert isinstance(sharded_tensor, ShardedTensor), type(sharded_tensor)
+    spec = {'driver': 'zarr', 'metadata_key': '.zarray', 'kvstore': {}}
+    spec['kvstore'] = {
+        'driver': 'file',
+        'path': str(checkpoint_dir / sharded_tensor.key),
+    }
+    try:
+        arr = ts.open(ts.Spec(spec), open=True).result()
+    except Exception as e:
+        raise CheckpointingException(
+            f'Array {checkpoint_dir / sharded_tensor.key} could not be loaded. Error: {e}'
+        ) from e
+
+    if sharded_tensor.global_shape == arr.shape:
+        x = (
+            arr[sharded_tensor.global_slice()].read().result()
+        )  # flattened tensors loading is delayed
+    elif sharded_tensor.allow_shape_mismatch:
+        global_slice = merge_global_slice_with_shape(
+            sharded_tensor.global_slice(), arr.shape, sharded_tensor.key
+        )
+        x = arr[global_slice].read().result()  # flattened tensors loading is delayed
+    else:
+        _msg = (
+            f'Global shape mismatch for loaded ({arr.shape})'
+            f' and expected ({sharded_tensor.global_shape}) tensor'
+            f' for key {sharded_tensor.key}'
+        )
+        raise CheckpointingException(_msg)
+    return x
+
+
+default_strategies[StrategyAction.LOAD_SHARDED.value][
+    ('zarr', 1)
+] = TensorStoreLoadShardedStrategy()

megatron/core/dist_checkpointing/strategies/two_stage.py

+# Copyright (c) 2022-2023, NVIDIA CORPORATION.  All rights reserved.
+
+""" 2-stage checkpoint loading. """
+import os
+import time
+from collections import defaultdict
+from dataclasses import dataclass
+from functools import partial, wraps
+from itertools import chain
+from logging import DEBUG, INFO, StreamHandler, getLogger
+from operator import attrgetter, itemgetter
+from pathlib import Path
+from typing import Iterable, List, NamedTuple, Optional, Tuple, Union
+
+import torch
+
+from ..dict_utils import dict_list_map_inplace, map_reduce, nested_values
+from ..mapping import ShardedStateDict, ShardedTensor, StateDict
+from .base import LoadShardedStrategy
+from .tensorstore import _load_from_array
+from .zarr import flatten_range
+
+_import_trigger = None
+
+
+timers = defaultdict(list)
+
+logger = getLogger(__name__)
+
+
+def timed(verbose=True):
+    def timed_dec(fn):
+        name = fn.__name__
+
+        @wraps(fn)
+        def wrapped(*args, **kwargs):
+            if verbose:
+                logger.debug(f'{name} init')
+            start = time.time()
+            ret = fn(*args, **kwargs)
+            took = time.time() - start
+            if verbose:
+                logger.debug(f'{name} took {took}s')
+            timers[name].append(took)
+            return ret
+
+        return wrapped
+
+    return timed_dec
+
+
+@dataclass
+class _ShardedTensorMetadata:
+    global_rank: int
+    sharded_tensor_no_data: ShardedTensor
+    dist_group_rank: Tuple[int]  # id of distributed group
+    dist_group_ranks: Tuple[int]  # id of distributed group
+    data_size: Optional[int] = None  # bytes
+
+
+def sharded_tensor_chunk_id(sharded_tensor: ShardedTensor):
+    return (
+        sharded_tensor.key,
+        sharded_tensor.global_offset,
+    )
+
+
+class TwoStageDataParallelLoadShardedStrategy(LoadShardedStrategy):
+    """ Loads one checkpoint replica from storage and broadcasts to other nodes.
+
+    This strategy loads checkpoint from storage on minimal set of nodes
+    and distributes the checkpoint to other nodes with torch.distributed.
+    Loading is performed with tensorstore.
+
+    Steps:
+    0. (optional) create Gloo distributed groups
+    1. Exchange ShardedTensors metadata between all nodes
+    2. Align needed tensors within DP groups
+    3. For each globally unique tensor:
+      a) on one of the ranks load it from storage to CPU and move to CUDA
+      b) allocate CUDA tensor on other ranks
+      c) broadcast within DP group
+      d) copy tensor content to the model param location
+      e) free tensor buffers from a) and b)
+
+    Notes:
+    1. Loading and broadcasting is done sequentially to avoid both host and device OOMs
+    2. There is a lot of overlap potential between all three steps done for each tensor:
+      a) loading from storage to numpy
+      b) moving CPU tensors to CUDA
+      c) broadcast
+
+    """
+
+    def __init__(self, data_parallel_group, cpu_transfer=True):
+        super().__init__()
+
+        self.cpu_transfer = cpu_transfer
+        self.data_parallel_group_orig = data_parallel_group
+        self.data_parallel_group = None if cpu_transfer else data_parallel_group
+        self.dp_group_ranks = tuple(
+            sorted(torch.distributed.get_process_group_ranks(data_parallel_group))
+        )
+        self.dp_group_rank = torch.distributed.get_rank(self.data_parallel_group_orig)
+        self.global_rank = torch.distributed.get_rank()
+
+    def load(self, sharded_state_dict: ShardedStateDict, checkpoint_dir: Path):
+        self.maybe_init_gloo_group()
+        all_tensors_sorted = self._build_load_plan(sharded_state_dict)
+        self._exchange_loaded_tensors(all_tensors_sorted, sharded_state_dict, checkpoint_dir)
+        self.summarize_load_times()
+        return sharded_state_dict
+
+    def summarize_load_times(self):
+        torch.distributed.barrier()
+        logger.info('Checkpoint loading finished. Summary:')
+        for key, times in sorted(timers.items()):
+            times_sum = sum(times)
+            max_times = torch.tensor([times_sum], device='cuda')
+            avg_times = torch.tensor([times_sum], device='cuda')
+            torch.distributed.all_reduce(max_times, op=torch.distributed.ReduceOp.MAX)
+            torch.distributed.all_reduce(avg_times, op=torch.distributed.ReduceOp.SUM)
+            avg_times /= torch.distributed.get_world_size()
+            if torch.distributed.get_rank() == 0:
+                logger.info(f'{key}: max {max_times[0]}, avg {avg_times[0]}')
+
+    @timed(verbose=False)
+    def load_tensor_from_storage(self, checkpoint_dir, ten_meta: _ShardedTensorMetadata):
+        logger.debug(f'_load_from_array({ten_meta.sharded_tensor_no_data.key}) init')
+        ret = _load_from_array(
+            ten_meta.sharded_tensor_no_data,
+            checkpoint_dir,
+            load_directly_on_device=False,
+            apply_flattened_range=False,
+        )
+        logger.debug(f'_load_from_array({ten_meta.sharded_tensor_no_data.key}) DONE')
+        return ret
+
+    @timed()
+    def maybe_init_gloo_group(self):
+        if not self.cpu_transfer:
+            return
+        all_groups = [None] * torch.distributed.get_world_size()
+        torch.distributed.all_gather_object(all_groups, self.dp_group_ranks)
+        all_groups = set(tuple(sorted(gr)) for gr in all_groups)
+        for group_ranks in sorted(all_groups):
+            gloo_pg = torch.distributed.new_group(ranks=group_ranks, backend='gloo')
+            if self.global_rank in group_ranks:
+                self.data_parallel_group = gloo_pg
+                assert self.dp_group_rank == torch.distributed.get_rank(self.data_parallel_group)
+
+    def check_backend_compatibility(self, loaded_version):
+        pass  # TODO
+
+    def check_version_compatibility(self, loaded_version):
+        pass  # TODO
+
+    @timed()
+    def _build_load_plan(
+        self, sharded_state_dict: ShardedStateDict
+    ) -> List[_ShardedTensorMetadata]:
+        local_meta = [
+            _ShardedTensorMetadata(
+                self.global_rank,
+                sharded_ten.without_data(),
+                self.dp_group_rank,
+                self.dp_group_ranks,
+            )
+            for sharded_ten in nested_values(sharded_state_dict)
+        ]
+        all_meta = [None] * torch.distributed.get_world_size(group=self.data_parallel_group)
+        torch.distributed.all_gather_object(all_meta, local_meta, group=self.data_parallel_group)
+        all_meta = list(chain.from_iterable(all_meta))
+        all_tensors_sorted = self.deduplicate_chunks(all_meta)
+        return all_tensors_sorted
+
+    @timed()
+    def deduplicate_chunks(self, ten_metas: List[_ShardedTensorMetadata]):
+        """ Group tensors by chunk and then pick the tensor with the lowest rank.
+
+        NOTE: with proper loading overlap, loading from randomized ranks
+         (instead of the smallest one) could be beneficial here.
+        """
+        ten_metas = map_reduce(
+            ten_metas,
+            key_fn=lambda meta: sharded_tensor_chunk_id(meta.sharded_tensor_no_data),
+            reduce_fn=partial(min, key=attrgetter('dist_group_rank')),
+        )
+        all_metas_sorted = list(map(itemgetter(1), sorted(ten_metas.items())))
+        return all_metas_sorted
+
+    @timed()
+    def _exchange_loaded_tensors(
+        self, ten_metas: List[_ShardedTensorMetadata], sharded_state_dict, checkpoint_dir
+    ):
+        logger.debug(f'_exchange_loaded_tensors, num ten_metas: {len(ten_metas)}')
+        for ten_meta in ten_metas:
+
+            src_rank = torch.distributed.get_global_rank(
+                self.data_parallel_group, ten_meta.dist_group_rank
+            )
+
+            if self.dp_group_rank == ten_meta.dist_group_rank:
+                exchange_tensor = self.load_tensor_from_storage(checkpoint_dir, ten_meta)
+                if not self.cpu_transfer:
+                    exchange_tensor = exchange_tensor.cuda()
+            else:
+                # TODO: for non-flattened ranges we could reuse the buffer from the start here
+                exchange_tensor = torch.empty(
+                    ten_meta.sharded_tensor_no_data.local_shape,
+                    device='cpu' if self.cpu_transfer else 'cuda',
+                    dtype=ten_meta.sharded_tensor_no_data.dtype,
+                )
+
+            logger.debug(
+                f'exchange {ten_meta.sharded_tensor_no_data.key}, {exchange_tensor.shape}({exchange_tensor.numel()}), broadcast({src_rank} -> {self.dp_group_ranks})'
+            )
+            torch.distributed.broadcast(
+                exchange_tensor, group=self.data_parallel_group, src=src_rank
+            )
+            self._distribute_data_to_state_dict(ten_meta, exchange_tensor, sharded_state_dict)
+            logger.debug(f'exchange {ten_meta.sharded_tensor_no_data.key} done')
+
+            # free buffer memory
+            exchange_tensor = None
+
+    @timed(verbose=False)
+    def _distribute_data_to_state_dict(
+        self,
+        ten_meta: _ShardedTensorMetadata,
+        loaded_ten: torch.Tensor,
+        sharded_state_dict: ShardedStateDict,
+    ):
+        tensor_key = sharded_tensor_chunk_id(ten_meta.sharded_tensor_no_data)
+
+        def _fill_in_data(t: Union[ShardedTensor, torch.Tensor]):
+            if not isinstance(t, ShardedTensor) or sharded_tensor_chunk_id(t) != tensor_key:
+                # already filled-in or key not matching
+                return t
+            sharded_tensor: ShardedTensor = t
+            x = loaded_ten
+            if sharded_tensor.flattened_range is not None:
+                x = flatten_range(sharded_tensor, x)
+
+            # Reuse existing buffer
+            sharded_tensor.data.data.copy_(x)
+            return sharded_tensor.data
+
+        dict_list_map_inplace(_fill_in_data, sharded_state_dict)

megatron/core/dist_checkpointing/strategies/zarr.py

+# Copyright (c) 2022-2023, NVIDIA CORPORATION.  All rights reserved.
+
+""" Strategies using Zarr as an underlying format. """
+import os
+from functools import partial
+from pathlib import Path
+from typing import List
+
+import numpy as np
+import torch
+import zarr
+
+from ..core import CheckpointingException
+from ..dict_utils import dict_list_map_inplace
+from ..mapping import ShardedStateDict, ShardedTensor, is_main_replica
+from .base import LoadShardedStrategy, SaveShardedStrategy, StrategyAction, default_strategies
+
+numpy_to_torch_dtype_dict = {
+    np.bool_: torch.bool,
+    np.uint8: torch.uint8,
+    np.int8: torch.int8,
+    np.int16: torch.int16,
+    np.int32: torch.int32,
+    np.int64: torch.int64,
+    np.float16: torch.float16,
+    np.float32: torch.float32,
+    np.float64: torch.float64,
+    np.complex64: torch.complex64,
+    np.complex128: torch.complex128,
+}
+
+torch_to_numpy_dtype_dict = {v: k for k, v in numpy_to_torch_dtype_dict.items()}
+
+
+try:
+    import tensorstore
+
+    HAS_BFLOAT16 = True
+    numpy_to_torch_dtype_dict[np.dtype('bfloat16')] = torch.bfloat16
+    torch_to_numpy_dtype_dict[torch.bfloat16] = np.dtype('bfloat16')
+except ImportError:
+    HAS_BFLOAT16 = False
+
+_import_trigger = None
+
+
+class ZarrSaveShardedStrategy(SaveShardedStrategy):
+    def save(self, sharded_tensors: List[ShardedTensor], checkpoint_dir: Path):
+        arrays = _create_or_open_zarr_arrays(sharded_tensors, checkpoint_dir)
+        for ten, arr in zip(sharded_tensors, arrays):
+            _save_to_existing_array(ten, arr)
+        torch.distributed.barrier()
+
+
+def _create_or_open_zarr_arrays(
+    sharded_tensors: List[ShardedTensor], checkpoint_dir: Path
+) -> List[zarr.Array]:
+    arrays = []
+    for ten in sharded_tensors:
+        if _should_create_array(ten):
+            _create_zarr_array(ten, checkpoint_dir)
+            # TODO: maybe reuse the opened arrays
+
+    torch.distributed.barrier()
+    for ten in sharded_tensors:
+        # if is_main_replica(ten.replica_id) and set(ten.global_offset) == {0}:
+        #     continue
+        open_kwargs = {}
+        if ten.flattened_range is not None:
+            open_kwargs['synchronizer'] = zarr.ProcessSynchronizer(
+                str(checkpoint_dir / f'{ten.key}.sync')
+            )
+        arr = zarr.open(checkpoint_dir / ten.key, 'r+', **open_kwargs)
+        arrays.append(arr)
+    return arrays
+
+
+def _should_create_array(ten: ShardedTensor):
+    return (
+        is_main_replica(ten.replica_id)
+        and set(ten.global_offset) == {0}
+        and (ten.flattened_range is None or ten.flattened_range.start == 0)
+    )
+
+
+def _save_to_existing_array(sharded_tensor: ShardedTensor, arr: zarr.Array):
+    if not is_main_replica(sharded_tensor.replica_id):
+        return
+    x = sharded_tensor.data
+    x = x.detach().cpu()
+    torch.cuda.synchronize()
+    if x.dtype == torch.bfloat16:
+        x = x.float()
+        x = x.numpy()
+        x = x.astype('bfloat16')
+    else:
+        x = x.numpy()
+
+    if sharded_tensor.flattened_range is None:
+        arr[sharded_tensor.global_slice()] = x
+    else:
+        arr.set_coordinate_selection(sharded_tensor.global_coordinates(), x)
+
+
+def _create_zarr_array(sharded_tensor: ShardedTensor, checkpoint_dir: Path):
+    np_dtype = torch_to_numpy_dtype_dict[sharded_tensor.dtype]
+    try:
+        arr = zarr.create(
+            sharded_tensor.global_shape,
+            dtype=np_dtype,
+            store=checkpoint_dir / sharded_tensor.key,
+            chunks=sharded_tensor.max_allowed_chunks(),
+            compressor=None,
+            fill_value=None,
+            write_empty_chunks=True,
+        )
+    except zarr.errors.ContainsArrayError as e:
+        raise CheckpointingException(
+            f'Array {checkpoint_dir / sharded_tensor.key} already exists'
+        ) from e
+
+    if HAS_BFLOAT16 and np_dtype == np.dtype('bfloat16'):
+        arr._dtype = np_dtype
+        zarray = arr.store['.zarray']
+        arr.store['.zarray'] = zarray.replace(b'<V2', b'bfloat16')
+    return arr
+
+
+class ZarrLoadShardedStrategy(LoadShardedStrategy):
+    def load(self, sharded_state_dict: ShardedStateDict, checkpoint_dir: Path):
+        dict_list_map_inplace(
+            partial(_load_from_array, checkpoint_dir=checkpoint_dir), sharded_state_dict
+        )
+        return sharded_state_dict
+
+    def check_backend_compatibility(self, loaded_version):
+        pass  # TODO
+
+    def check_version_compatibility(self, loaded_version):
+        pass  # TODO
+
+
+def _load_from_array(sharded_tensor: ShardedTensor, checkpoint_dir: Path):
+    assert isinstance(sharded_tensor, ShardedTensor), type(sharded_tensor)
+    try:
+        arr = zarr.open(checkpoint_dir / sharded_tensor.key, 'r')
+    except zarr.errors.PathNotFoundError as e:
+        raise CheckpointingException(
+            f'Array {checkpoint_dir / sharded_tensor.key} not found'
+        ) from e
+
+    if not sharded_tensor.allow_shape_mismatch and sharded_tensor.global_shape != arr.shape:
+        _msg = (
+            f'Global shape mismatch for loaded ({arr.shape})'
+            f' and expected ({sharded_tensor.global_shape}) tensor'
+            f' for key {sharded_tensor.key}'
+        )
+        raise CheckpointingException(_msg)
+
+    x = arr[sharded_tensor.global_slice()]  # flattened tensors loading is delayed
+    return postprocess_numpy_array(x, sharded_tensor)
+
+
+def postprocess_numpy_array(loaded_array, sharded_tensor, apply_flattened_range=True):
+    x = loaded_array
+    if HAS_BFLOAT16 and x.dtype == np.dtype('bfloat16'):
+        x = x.astype(np.dtype('float32'))
+        x = torch.from_numpy(x)
+        x = x.bfloat16()
+    else:
+        x = torch.from_numpy(x)
+    # TODO: consider some other consistency checks
+    if x.shape != sharded_tensor.local_shape:
+        if sharded_tensor.allow_shape_mismatch:
+            x = pad_to_expected_shape(x, sharded_tensor)
+        else:
+            _msg = (
+                f'Local shape mismatch for loaded ({x.shape})'
+                f' and expected ({sharded_tensor.local_shape}) tensor'
+                f' for key {sharded_tensor.key}'
+            )
+            raise CheckpointingException(_msg)
+
+    if apply_flattened_range and sharded_tensor.flattened_range is not None:
+        x = flatten_range(sharded_tensor, x)
+
+    # TODO: consider cuda() tensors support
+    return x
+
+
+def flatten_range(sharded_tensor, x):
+    return x.flatten()[sharded_tensor.flattened_range]
+
+
+def pad_to_expected_shape(x: torch.Tensor, expected_sharded_ten: ShardedTensor):
+    pad_args = []
+    assert len(x.shape) == len(expected_sharded_ten.local_shape)
+    # Reversed iteration order because F.pad expects so
+    for x_sh, exp_sh, axis_fragm in reversed(
+        list(
+            zip(x.shape, expected_sharded_ten.local_shape, expected_sharded_ten.axis_fragmentations)
+        )
+    ):
+        if x_sh == exp_sh:
+            pad_args.extend((0, 0))
+        elif x_sh > exp_sh:
+            assert (
+                False
+            ), f'Expected shape ({exp_sh}) smaller than actual ({x_sh}) for {repr(expected_sharded_ten)}'
+        else:
+            pad_args.extend((0, exp_sh - x_sh))
+    # TODO: behavior control with envvar is for testing purposes only, remove it
+    if not int(os.environ.get('DIST_CKPT_PAD_REPLICATE', 0)):
+        return torch.nn.functional.pad(x, pad_args)
+
+    # unsqueeze and squeeze to get shapes supported by cudnn
+    print(f'Replicating last row for {expected_sharded_ten.key}')
+    if x.dtype == torch.bfloat16:
+        return (
+            torch.nn.functional.pad(x.float().unsqueeze(0), pad_args, mode='replicate')
+            .squeeze(0)
+            .bfloat16()
+        )
+    return torch.nn.functional.pad(x.unsqueeze(0), pad_args, mode='replicate').squeeze(0)
+
+
+# default_strategies[StrategyAction.LOAD_SHARDED.value][('zarr', 1)] = ZarrLoadShardedStrategy()
+default_strategies[StrategyAction.SAVE_SHARDED.value][('zarr', 1)] = ZarrSaveShardedStrategy(
+    'zarr', 1
+)

megatron/core/dist_checkpointing/utils.py

+# Copyright (c) 2022-2023, NVIDIA CORPORATION.  All rights reserved.
+
+from typing import Tuple
+
+from .dict_utils import dict_list_map_inplace, extract_matching_values
+from .mapping import (
+    LocalNonpersitentObject,
+    ShardedStateDict,
+    ShardedTensor,
+    ShardedTensorFactory,
+    StateDict,
+)
+
+
+def extract_sharded_tensors(
+    sharded_state_dict: ShardedStateDict,
+) -> Tuple[ShardedStateDict, StateDict]:
+    return extract_matching_values(sharded_state_dict, lambda v: isinstance(v, ShardedTensor))
+
+
+def extract_sharded_tensors_and_factories(
+    sharded_state_dict: ShardedStateDict,
+) -> Tuple[ShardedStateDict, StateDict]:
+    return extract_matching_values(
+        sharded_state_dict, lambda v: isinstance(v, (ShardedTensor, ShardedTensorFactory))
+    )
+
+
+def extract_sharded_tensors_or_nonpersistent(
+    sharded_state_dict: ShardedStateDict,
+) -> Tuple[ShardedStateDict, StateDict]:
+    return extract_matching_values(
+        sharded_state_dict,
+        lambda v: isinstance(v, (ShardedTensor, LocalNonpersitentObject, ShardedTensorFactory)),
+    )
+
+
+def add_prefix_for_sharding(sharded_state_dict: ShardedStateDict, prefix: str):
+    def add_prefix(t):
+        if isinstance(t, ShardedTensor):
+            t.key = f'{prefix}.{t.key}'
+        return t
+
+    dict_list_map_inplace(add_prefix, sharded_state_dict)

megatron/core/enums.py

-# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
 
 import enum
 
+
 class ModelType(enum.Enum):
     encoder_or_decoder = 1
     encoder_and_decoder = 2
+    retro_encoder = 3
+    retro_decoder = 4

megatron/core/fusions/fused_bias_dropout.py

+# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
+
+from typing import Optional, Tuple
+
+import torch
+
+
+def _bias_dropout_add_func(x, bias, residual, prob, training):
+    # type: (Tensor, Optional[Tensor], Tensor, float, bool) -> Tensor
+    # NOTE: Previously, the argument `bias` used to be passed as
+    # `bias.expand_as(residual)` when the `bias_dropout_func` is called from the
+    # transformer layer but broadcasting should automatically take care of that.
+    # Also, looking at broadcasting semantics, `expand_as` and broadcasting
+    # seem to be identical performance-wise (both just change the view).
+
+    # If we want to train mixed precision, then the output of this function
+    # should be half precision. However, in AMP O1, the input (residual) is
+    # in fp32, and it will up-cast the result to fp32, causing pipeline parallel
+    # GPU communication to hang. Therefore, we need to cast residual to the same
+    # dtype as x.
+    residual = residual if residual.dtype == x.dtype else residual.to(x.dtype)
+    if bias is not None:
+        x = x + bias
+    out = torch.nn.functional.dropout(x, p=prob, training=training)
+    out = residual + out
+    return out
+
+
+@torch.jit.script
+def bias_dropout_add_fused_train(
+    x_with_bias: Tuple[torch.Tensor, Optional[torch.Tensor]], residual: torch.Tensor, prob: float,
+) -> torch.Tensor:
+    x, bias = x_with_bias  # unpack
+    return _bias_dropout_add_func(x, bias, residual, prob, True)
+
+
+@torch.jit.script
+def bias_dropout_add_fused_inference(
+    x_with_bias: Tuple[torch.Tensor, Optional[torch.Tensor]], residual: torch.Tensor, prob: float,
+) -> torch.Tensor:
+    x, bias = x_with_bias  # unpack
+    return _bias_dropout_add_func(x, bias, residual, prob, False)
+
+
+def get_bias_dropout_add(training, fused):
+    def unfused_bias_dropout_add(x_with_bias, residual, prob):
+        x, bias = x_with_bias  # unpack
+        return _bias_dropout_add_func(x, bias, residual, prob, training)
+
+    if fused:
+        # jit scripting for a nn.module (with dropout) is not
+        # triggering the fusion kernel. For now, we use two
+        # different nn.functional routines to account for varying
+        # dropout semantics during training and inference phases.
+        if training:
+            return bias_dropout_add_fused_train
+        else:
+            return bias_dropout_add_fused_inference
+    else:
+        return unfused_bias_dropout_add

megatron/core/fusions/fused_bias_gelu.py

+# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
+
+import torch
+
+###### BIAS GELU FUSION/ NO AUTOGRAD ################
+# 1/sqrt(2*pi)-> 0.3989423
+# 1/sqrt(2)   -> 0.70710678
+# sqrt(2/pi)  -> 0.79788456
+# this function is tanh approximation of gelu
+# actual gelu is:
+# x * 0.5 * (1.0 + torch.erf(x * 0.70710678))
+
+
+@torch.jit.script
+def bias_gelu(bias, y):
+    x = bias + y
+    return x * 0.5 * (1.0 + torch.tanh(0.79788456 * x * (1 + 0.044715 * x * x)))
+
+
+# gradient of tanh approximation of gelu
+# gradient of actual gelu is:
+# 0.5 * (1. + torch.erf(x * 0.70710678)) + 0.3989423 * x * torch.exp(-0.5 * x * x)
+@torch.jit.script
+def bias_gelu_back(g, bias, y):
+    x = bias + y
+    tanh_out = torch.tanh(0.79788456 * x * (1 + 0.044715 * x * x))
+    # sqrt(2/pi) * 3 * 0.044715 -> 0.1070322243
+    ff = 0.5 * x * ((1 - tanh_out * tanh_out) * (0.79788456 + 0.1070322243 * x * x)) + 0.5 * (
+        1 + tanh_out
+    )
+    return ff * g
+
+
+class GeLUFunction(torch.autograd.Function):
+    @staticmethod
+    # bias is an optional argument
+    def forward(ctx, input, bias):
+        ctx.save_for_backward(input, bias)
+        return bias_gelu(bias, input)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        input, bias = ctx.saved_tensors
+        tmp = bias_gelu_back(grad_output, bias, input)
+        return tmp, tmp
+
+
+bias_gelu_impl = GeLUFunction.apply

megatron/core/fusions/fused_layer_norm.py

+# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
+
+import importlib
+import numbers
+
+import torch
+from torch.nn import init
+from torch.nn.parameter import Parameter
+
+from megatron.core.utils import make_viewless_tensor
+
+try:
+    from apex.contrib.layer_norm.layer_norm import FastLayerNormFN
+
+    HAVE_PERSIST_LAYER_NORM = True
+except:
+    HAVE_PERSIST_LAYER_NORM = False
+
+try:
+    from apex.normalization.fused_layer_norm import FusedLayerNormAffineFunction
+
+    HAVE_FUSED_LAYER_NORM = True
+except:
+    HAVE_FUSED_LAYER_NORM = False
+
+
+class FusedLayerNorm(torch.nn.Module):
+    def __init__(
+        self,
+        hidden_size,
+        eps=1e-5,
+        persist_layer_norm=True,
+        sequence_parallel=False,
+        zero_centered_gamma=False,
+    ):
+        super().__init__()
+
+        self.zero_centered_gamma = zero_centered_gamma
+
+        # List of hiddens sizes supported in the persistent layer norm kernel
+        # If the hidden size is not supported, fall back to the non-persistent
+        # kernel.
+        persist_ln_hidden_sizes = [
+            1024,
+            1536,
+            2048,
+            2304,
+            3072,
+            3840,
+            4096,
+            5120,
+            6144,
+            8192,
+            10240,
+            12288,
+            12800,
+            15360,
+            16384,
+            18432,
+            20480,
+            24576,
+            25600,
+            30720,
+            32768,
+            40960,
+            49152,
+            65536,
+        ]
+        if hidden_size not in persist_ln_hidden_sizes or not HAVE_PERSIST_LAYER_NORM:
+            persist_layer_norm = False
+
+        if not persist_layer_norm and not HAVE_FUSED_LAYER_NORM:
+            # TODO: Add pytorch only layer norm
+            raise ValueError(f'Apex must currently be installed to use megatron core.')
+
+        if isinstance(hidden_size, numbers.Integral):
+            hidden_size = (hidden_size,)
+        self.hidden_size = torch.Size(hidden_size)
+        self.eps = eps
+        self.weight = Parameter(torch.Tensor(*hidden_size))
+        self.bias = Parameter(torch.Tensor(*hidden_size))
+        self.reset_parameters()
+        self.persist_layer_norm = persist_layer_norm
+        self.sequence_parallel = sequence_parallel
+
+        # set sequence parallelism flag on weight and bias parameters
+        setattr(self.weight, 'sequence_parallel', self.sequence_parallel)
+        setattr(self.bias, 'sequence_parallel', self.sequence_parallel)
+
+    def reset_parameters(self):
+
+        if self.zero_centered_gamma:
+            init.zeros_(self.weight)
+            init.zeros_(self.bias)
+        else:
+            init.ones_(self.weight)
+            init.zeros_(self.bias)
+
+    def forward(self, input):
+
+        weight = self.weight + 1 if self.zero_centered_gamma else self.weight
+
+        if self.persist_layer_norm:
+            output = FastLayerNormFN.apply(input, weight, self.bias, self.eps)
+
+            # Apex's fast layer norm function outputs a 'view' tensor (i.e., has
+            # a populated '_base' field). This will result in schedule.py's
+            # deallocate_output_tensor() throwing an error, so a viewless tensor is
+            # created to prevent this.
+            output = make_viewless_tensor(
+                inp=output, requires_grad=input.requires_grad, keep_graph=True
+            )
+
+        else:
+            output = FusedLayerNormAffineFunction.apply(
+                input, weight, self.bias, self.hidden_size, self.eps
+            )
+
+        return output

megatron/core/fusions/fused_softmax.py

+# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
+
+
+import torch
+import torch.nn as nn
+
+from megatron.core.transformer.enums import AttnMaskType
+
+
+class ScaledUpperTriangMaskedSoftmax(torch.autograd.Function):
+    """
+    Fused operation which performs following three operations in sequence
+    1. Scale the tensor.
+    2. Apply upper triangular mask (typically used in gpt models).
+    3. Perform softmax.
+    """
+
+    @staticmethod
+    def forward(ctx, inputs, scale):
+        import scaled_upper_triang_masked_softmax_cuda
+
+        scale_t = torch.tensor([scale])
+        softmax_results = scaled_upper_triang_masked_softmax_cuda.forward(inputs, scale_t[0])
+
+        ctx.save_for_backward(softmax_results, scale_t)
+        return softmax_results
+
+    @staticmethod
+    def backward(ctx, output_grads):
+        import scaled_upper_triang_masked_softmax_cuda
+
+        softmax_results, scale_t = ctx.saved_tensors
+        input_grads = scaled_upper_triang_masked_softmax_cuda.backward(
+            output_grads, softmax_results, scale_t[0]
+        )
+
+        return input_grads, None
+
+
+class ScaledMaskedSoftmax(torch.autograd.Function):
+    """
+    Fused operation which performs following three operations in sequence
+    1. Scale the tensor.
+    2. Apply the mask.
+    3. Perform softmax.
+    """
+
+    @staticmethod
+    def forward(ctx, inputs, mask, scale):
+        import scaled_masked_softmax_cuda
+
+        scale_t = torch.tensor([scale])
+
+        softmax_results = scaled_masked_softmax_cuda.forward(inputs, mask, scale_t[0])
+        ctx.save_for_backward(softmax_results, scale_t)
+        return softmax_results
+
+    @staticmethod
+    def backward(ctx, output_grads):
+        import scaled_masked_softmax_cuda
+
+        softmax_results, scale_t = ctx.saved_tensors
+
+        input_grads = scaled_masked_softmax_cuda.backward(output_grads, softmax_results, scale_t[0])
+        return input_grads, None, None
+
+
+class ScaledSoftmax(torch.autograd.Function):
+    """
+    Fused operation which performs following two operations in sequence
+    1. Scale the tensor.
+    2. Perform softmax.
+    """
+
+    @staticmethod
+    def forward(ctx, inputs, scale):
+        import scaled_softmax_cuda
+
+        scale_t = torch.tensor([scale])
+
+        softmax_results = scaled_softmax_cuda.forward(inputs, scale_t[0])
+        ctx.save_for_backward(softmax_results, scale_t)
+        return softmax_results
+
+    @staticmethod
+    def backward(ctx, output_grads):
+        import scaled_softmax_cuda
+
+        softmax_results, scale_t = ctx.saved_tensors
+
+        input_grads = scaled_softmax_cuda.backward(output_grads, softmax_results, scale_t[0])
+        return input_grads, None, None
+
+
+class FusedScaleMaskSoftmax(nn.Module):
+    """
+    fused operation: scaling + mask + softmax
+
+    Arguments:
+        input_in_fp16: flag to indicate if input in fp16 data format.
+        input_in_bf16: flag to indicate if input in bf16 data format.
+        attn_mask_type: attention mask type (pad or causal)
+        scaled_masked_softmax_fusion: flag to indicate user want to use softmax fusion
+        mask_func: mask function to be applied.
+        softmax_in_fp32: if true, softmax in performed at fp32 precision.
+        scale: scaling factor used in input tensor scaling.
+    """
+
+    def __init__(
+        self,
+        input_in_fp16,
+        input_in_bf16,
+        attn_mask_type,
+        scaled_masked_softmax_fusion,
+        mask_func,
+        softmax_in_fp32,
+        scale,
+    ):
+        super(FusedScaleMaskSoftmax, self).__init__()
+        self.input_in_fp16 = input_in_fp16
+        self.input_in_bf16 = input_in_bf16
+        assert not (
+            self.input_in_fp16 and self.input_in_bf16
+        ), "both fp16 and bf16 flags cannot be active at the same time."
+        self.input_in_float16 = self.input_in_fp16 or self.input_in_bf16
+        self.attn_mask_type = attn_mask_type
+        self.scaled_masked_softmax_fusion = scaled_masked_softmax_fusion
+        self.mask_func = mask_func
+        self.softmax_in_fp32 = softmax_in_fp32
+        self.scale = scale
+
+        assert self.scale is None or softmax_in_fp32, "softmax should be in fp32 when scaled"
+
+    def forward(self, input, mask):
+        # [b, np, sq, sk]
+        assert input.dim() == 4
+
+        if self.is_kernel_available(mask, *input.size()):
+            return self.forward_fused_softmax(input, mask)
+        else:
+            return self.forward_torch_softmax(input, mask)
+
+    def is_kernel_available(self, mask, b, np, sq, sk):
+        attn_batches = b * np
+
+        if (
+            self.scaled_masked_softmax_fusion  # user want to fuse
+            and self.input_in_float16  # input must be fp16
+            and 16 < sk <= 4096  # sk must be 16 ~ 2048
+            and sq % 4 == 0  # sq must be divisor of 4
+            and sk % 4 == 0  # sk must be divisor of 4
+            and attn_batches % 4 == 0  # np * b must be divisor of 4
+        ):
+            if 0 <= sk <= 4096:
+                batch_per_block = self.get_batch_per_block(sq, sk, b, np)
+
+                if self.attn_mask_type == AttnMaskType.causal:
+                    if attn_batches % batch_per_block == 0:
+                        return True
+                else:
+                    if sq % batch_per_block == 0:
+                        return True
+        return False
+
+    def forward_fused_softmax(self, input, mask):
+        b, np, sq, sk = input.size()
+        scale = self.scale if self.scale is not None else 1.0
+
+        if self.attn_mask_type == AttnMaskType.causal:
+            assert sq == sk, "causal mask is only for self attention"
+
+            # input is 3D tensor (attn_batches, sq, sk)
+            input = input.view(-1, sq, sk)
+            probs = ScaledUpperTriangMaskedSoftmax.apply(input, scale)
+            return probs.view(b, np, sq, sk)
+        else:
+            # input is 4D tensor (b, np, sq, sk)
+            if mask is not None:
+                return ScaledMaskedSoftmax.apply(input, mask, scale)
+            else:
+                return ScaledSoftmax.apply(input, scale)
+
+    def forward_torch_softmax(self, input, mask):
+        if self.input_in_float16 and self.softmax_in_fp32:
+            input = input.float()
+
+        if self.scale is not None:
+            input = input * self.scale
+        mask_output = self.mask_func(input, mask) if mask is not None else input
+        probs = torch.nn.Softmax(dim=-1)(mask_output)
+
+        if self.input_in_float16 and self.softmax_in_fp32:
+            if self.input_in_fp16:
+                probs = probs.half()
+            else:
+                probs = probs.bfloat16()
+
+        return probs
+
+    @staticmethod
+    def get_batch_per_block(sq, sk, b, np):
+        import scaled_masked_softmax_cuda
+
+        return scaled_masked_softmax_cuda.get_batch_per_block(sq, sk, b, np)

megatron/core/inference_params.py

+class InferenceParams:
+    """Inference parameters that are passed to the main model in order
+    to efficienly calculate and store the context during inference."""
+
+    def __init__(self, max_batch_size, max_sequence_length):
+        self.max_sequence_length = max_sequence_length
+        self.max_batch_size = max_batch_size
+        self.sequence_len_offset = 0
+        self.batch_size_offset = 0
+        self.key_value_memory_dict = {}
+
+    def swap_key_value_dict(self, batch_idx):
+        "swap between batches"
+        if len(self.key_value_memory_dict) == 0:
+            raise ValueError("should not swap when dict in empty")
+
+        for layer_number in self.key_value_memory_dict.keys():
+            inference_key_memory, inference_value_memory = self.key_value_memory_dict[layer_number]
+            assert (
+                len(batch_idx) == inference_key_memory.shape[1]
+            )  # make sure batch size is the same
+            new_inference_key_memory = inference_key_memory[:, batch_idx]
+            new_inference_value_memory = inference_value_memory[:, batch_idx]
+            self.key_value_memory_dict[layer_number] = (
+                new_inference_key_memory,
+                new_inference_value_memory,
+            )

megatron/core/model_parallel_config.py

+# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
+
+from dataclasses import dataclass
+from typing import Callable, Optional
+
+import torch
+
+
+@dataclass
+class ModelParallelConfig:
+    """Base configuration for Megatron Core
+
+    Model Parallelism
+    -----------------
+
+    tensor_model_parallel_size (int): Intra-layer model parallelism. Splits tensors across GPU ranks. Defaults to 1.
+
+    pipeline_model_parallel_size (int): Inter-layer model parallelism. Splits transformer layers across GPU
+        ranks. Defaults to 1.
+
+    virtual_pipeline_model_parallel_size (int): Interleaved pipeline parallelism is used to improve performance by
+        reducing the pipeline bubble.  Considers a transformer block as a list of smaller transformer (virtual) blocks.
+        The number of virtual blocks per pipeline model parallel rank is the virtual model parallel size.  See Efficient
+        Large-Scale Language Model Training on GPU Clusters Using Megatron-LM: https://arxiv.org/pdf/2104.04473.pdf for
+        more details.  Defaults to None.
+
+    sequence_parallel (bool): Makes tensor parallelism more memory efficient for LLMs (20B+) by
+        parallelizing layer norms and dropout sequentially.  See Reducing Activation Recomputation in Large Transformer
+        Models: https://arxiv.org/abs/2205.05198 for more details. Defaults to False.
+
+    Initialization
+    --------------
+
+    perform_initialization (bool, default=True): If true, weights are initialized. This option can be useful when you
+        know you are going to load values from a checkpoint.
+
+    use_cpu_initialization: (bool, default=False): When set to False, we initialize the weights directly on the GPU.
+        Transferring weights from CPU to GPU can take a significant amount of time for large models. Defaults to False.
+
+    Training
+    --------
+
+    fp16 (bool): If true, train with fp16 mixed precision training. Defaults to False.
+
+    bf16 (bool): If true, train with bf16 mixed precision training. Defaults to False.
+
+    params_dtype (torch.dtype): dtype used when intializing the weights. Defaults to torch.float32
+
+    timers (optional, default=None): TODO
+
+    Optimizations
+    -------------
+
+    gradient_accumulation_fusion (bool): If true, fuses weight gradient accumulation to GEMMs. Requires the custom CUDA
+        extension fused_weight_gradient_mlp_cuda module. To use gradient_accumulation_fusion you must install APEX with
+        --cpp_ext and --cuda_ext. For example: "pip install --global-option=\"--cpp_ext\" --global-option=\"--cuda_ext\"
+        ". Note that the extension requires CUDA>=11. Otherwise, you must turn off gradient accumulation fusion.
+        Defaults to False.
+
+    async_tensor_model_parallel_allreduce (bool, default=True): If true, enables asynchronous execution of
+        tensor-model-parallel all-reduce with weight gradient compuation of a column-linear layer.  Defaults to False.
+
+    Pipeline Parallelism
+    --------------------
+
+    pipeline_dtype (required): dtype used in p2p communication, usually params_dtype
+
+    grad_scale_func (optional, default=None): If using loss scaling, this function should take the loss and return the
+        scaled loss. If None, no function is called on the loss.
+
+    enable_autocast (bool): If true runs the forward step function inside torch.autocast context. Default is False.
+
+    autocast_dtype (torch.dtype): dtype to pass to torch.amp.autocast when enabled. Default is pipeline_dtype.
+    
+    variable_seq_lengths (bool, default=False): Support for variable sequence lengths across microbatches. Setting this
+        communicates the size of tensors during pipeline parallelism communication, because of this extra overhead it
+        should only be set if the sequence length varies by microbatch within a global batch.
+
+    num_microbatches_with_partial_activation_checkpoints (int, default=None): If int, set the number of microbatches
+        where not all of the layers will be checkpointed and recomputed. The rest of the microbatches within the window
+        of maximum outstanding microbatches will recompute all layers (either full recompute or selective recompute). If
+        None, the checkpoint and recompute will be left up to the forward_step function.
+
+    overlap_p2p_comm (bool, optional, default=False): When True some of the peer to peer communication for pipeline
+        parallelism will overlap with computation. Must be False if batch_p2p_comm is true.
+
+    batch_p2p_comm (bool, default=True): Use batch_isend_irecv instead of individual isend/irecv calls. Must be False
+        if overlap_p2p_comm is True.
+
+    batch_p2p_sync (bool, default=True): When using batch_isend_irecv, do a cuda.device.synchronize afterward to work
+        around a bug in older version of PyTorch.
+
+    use_ring_exchange_p2p (bool, default = False): Use custom ring_exchange kernel instead of
+        torch.distributed.batch_isend_irecv(). Requires custom built torch with torch.distributed.ring_exchange.
+
+    deallocate_pipeline_outputs (optional, default=False): If True, output data is deallocated after the tensor is sent
+        to the next pipeline stage.  Helps with saving memory, does nothing when pipeline parallel is not used.
+
+    no_sync_func (optional): Function that creates a context that suppresses asynchronous data-parallel
+        communication. If the model is an instance of torch.nn.DistributedDataParallel, the default is to use
+        torch.nn.DistributedDataParallel.no_sync.
+
+    grad_sync_func (optional): Function that launches asynchronous gradient reductions (e.g. distributed optimizer
+        gradient reduce-scatters). The function should take one argument: an iterable of parameters whose gradients are
+        to be synchronized.
+
+    param_sync_func (optional): Function that launches asynchronous parameter synchronizations (e.g. distributed
+        optimizer parameter all-gathers). The function should take one argument: an iterable of parameters to be
+        synchronized.
+
+    """
+
+    # Model parallelism
+    tensor_model_parallel_size: int = 1
+    pipeline_model_parallel_size: int = 1
+    virtual_pipeline_model_parallel_size: Optional[int] = None
+    sequence_parallel: bool = False
+
+    # Initialization
+    perform_initialization: bool = True
+    use_cpu_initialization: bool = False
+
+    # Training
+    fp16: bool = False
+    bf16: bool = False
+    params_dtype: torch.dtype = torch.float32
+    timers: Callable = None
+
+    # Optimizations
+    gradient_accumulation_fusion: bool = False
+    async_tensor_model_parallel_allreduce: bool = False
+
+    # Pipeline Parallel
+    pipeline_dtype: torch.dtype = None
+    grad_scale_func: Callable = None
+    enable_autocast: bool = False
+    autocast_dtype: torch.dtype = None
+    variable_seq_lengths: bool = False
+    num_microbatches_with_partial_activation_checkpoints: Optional[int] = None
+    overlap_p2p_comm: bool = False
+    batch_p2p_comm: bool = True
+    batch_p2p_sync: bool = True
+    use_ring_exchange_p2p: bool = False
+    deallocate_pipeline_outputs: bool = False
+    no_sync_func: Callable = None
+    grad_sync_func: Callable = None
+    param_sync_func: Callable = None
+
+    def __post_init__(self):
+        """ Python dataclass method that is used to modify attributes after initialization.
+            See https://docs.python.org/3/library/dataclasses.html#post-init-processing for more details.
+        """
+        if self.sequence_parallel:
+            if self.tensor_model_parallel_size <= 1:
+                raise ValueError("Can not use sequence paralllelism without tensor parallelism")
+            if self.async_tensor_model_parallel_allreduce:
+                # sequence_parallelism already does this async
+                self.async_tensor_model_parallel_allreduce = False
+
+        if self.pipeline_model_parallel_size > 1:
+            if self.pipeline_dtype is None:
+                raise ValueError(
+                    "When using pipeline parallelism, pipeline_dtype must be specified"
+                )
+
+        if self.autocast_dtype is None:
+            self.autocast_dtype = self.params_dtype

megatron/model/rotary_pos_embedding.py → megatron/core/models/common/rotary_pos_embedding.py

-# coding=utf-8
-
-# The following code has been taken from https://github.com/NVIDIA/NeMo/blob/ \
-# 782b4e1652aaa43c8be390d9db0dc89544afa080/nemo/collections/nlp/modules/ \
-# common/megatron/rotary_pos_embedding.py
+# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
 
 import importlib.util
-import torch
 
+import torch
 from torch import einsum, nn
 
 __all__ = ['RotaryEmbedding', 'apply_rotary_pos_emb']
 
+
 class RotaryEmbedding(nn.Module):
-    def __init__(self, dim):
+    def __init__(self, dim, seq_len_interpolation_factor=None):
         super().__init__()
+        self.seq_len_interpolation_factor = seq_len_interpolation_factor
         inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2).float() / dim))
-        self.register_buffer('inv_freq', inv_freq)
-        if importlib.util.find_spec('einops') is None:
-            raise RuntimeError("einops is required for Rotary Embedding")
+        self.register_buffer('inv_freq', inv_freq, persistent=False)
 
     def forward(self, max_seq_len, offset=0):
         seq = torch.arange(max_seq_len, device=self.inv_freq.device) + offset
+        if self.seq_len_interpolation_factor is not None:
+            seq = seq.type_as(self.inv_freq)
+            seq *= 1 / self.seq_len_interpolation_factor
         freqs = einsum('i , j -> i j', seq.type_as(self.inv_freq), self.inv_freq)
         # first part even vector components, second part odd vector components,
         #  2 * dim in dimension size
         emb = torch.cat((freqs, freqs), dim=-1)
         # emb [seq_length, .., dim]
-        from einops import rearrange
-        return rearrange(emb, 'n d -> n 1 1 d')
+        return emb[:, None, None, :]
+
+    def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs):
+        state_dict.pop(f'{prefix}inv_freq', None)
+        return super()._load_from_state_dict(state_dict, prefix, *args, **kwargs)
 
 
 def _rotate_half(x):
     """
     change sign so the last dimension becomes [-odd, +even]
     """
-    from einops import rearrange
-    x = rearrange(x, '... (j d) -> ... j d', j=2)
-    x1, x2 = x.unbind(dim=-2)
+    x1, x2 = torch.chunk(x, 2, dim=-1)
     return torch.cat((-x2, x1), dim=-1)
 
 

megatron/core/models/gpt/__init__.py

+from .gpt_model import GPTModel

megatron/core/models/gpt/gpt_embedding.py

+# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
+
+import torch
+
+from megatron.core import tensor_parallel
+from megatron.core.transformer.module import MegatronModule
+from megatron.core.transformer.transformer_config import TransformerConfig
+from megatron.core.utils import (
+    make_sharded_tensor_for_checkpoint,
+    make_tp_sharded_tensor_for_checkpoint,
+)
+
+
+class GPTEmbedding(MegatronModule):
+    """Language model embeddings.
+
+    Arguments:
+        config (TransformerConfig): config object with all necessary configs for TransformerBlock
+        vocab_size (int): vocabulary size
+        max_sequence_length (int): maximum size of sequence. This
+                             is used for positional embedding
+        add_position_embedding (bool): Add a position embedding.
+        embedding_dropout_prob float): dropout probability for embeddings
+    """
+
+    def __init__(
+        self,
+        config: TransformerConfig,
+        vocab_size: int,
+        max_sequence_length: int,
+        add_position_embedding: bool,
+    ):
+        super().__init__(config=config)
+
+        self.config: TransformerConfig = config
+        self.vocab_size: int = vocab_size
+        self.max_sequence_length: int = max_sequence_length
+        self.add_position_embedding: bool = add_position_embedding
+
+        # Word embeddings (parallel).
+        self.word_embeddings = tensor_parallel.VocabParallelEmbedding(
+            num_embeddings=self.vocab_size,
+            embedding_dim=self.config.hidden_size,
+            init_method=self.config.init_method,
+            config=self.config,
+        )
+
+        # Position embedding (serial).
+        if self.add_position_embedding:
+            self.position_embeddings = torch.nn.Embedding(
+                self.max_sequence_length, self.config.hidden_size
+            )
+
+            # Initialize the position embeddings.
+            if self.config.perform_initialization:
+                self.config.init_method(self.position_embeddings.weight)
+
+        # Embeddings dropout
+        self.embedding_dropout = torch.nn.Dropout(self.config.hidden_dropout)
+
+    def zero_parameters(self):
+        """Zero out all parameters in embedding."""
+        self.word_embeddings.weight.data.fill_(0)
+        self.word_embeddings.weight.shared = True
+        self.position_embeddings.weight.data.fill_(0)
+        self.position_embeddings.weight.shared = True
+
+    def forward(self, input_ids, position_ids):
+        # Embeddings.
+        word_embeddings = self.word_embeddings(input_ids)
+        if self.add_position_embedding:
+            position_embeddings = self.position_embeddings(position_ids)
+            embeddings = word_embeddings + position_embeddings
+        else:
+            embeddings = word_embeddings
+
+        # Data format change to avoid explicit tranposes : [b s h] --> [s b h].
+        embeddings = embeddings.transpose(0, 1).contiguous()
+
+        # If the input flag for fp32 residual connection is set, convert for float.
+        if self.config.fp32_residual_connection:
+            embeddings = embeddings.float()
+
+        # Dropout.
+        if self.config.sequence_parallel:
+            embeddings = tensor_parallel.scatter_to_sequence_parallel_region(embeddings)
+            with tensor_parallel.get_cuda_rng_tracker().fork():
+                embeddings = self.embedding_dropout(embeddings)
+        else:
+            embeddings = self.embedding_dropout(embeddings)
+
+        return embeddings
+
+    def sharded_state_dict(self, prefix=''):
+
+        sharded_state_dict = {}
+
+        word_embeddings_prefix = f'{prefix}word_embeddings.'
+        word_embeddings_state_dict = self.word_embeddings.state_dict(
+            prefix=word_embeddings_prefix, keep_vars=True
+        )
+
+        sharded_word_embeddings_key = f'{word_embeddings_prefix}weight'
+        sharded_word_embeddings_tensor = make_tp_sharded_tensor_for_checkpoint(
+            tensor=word_embeddings_state_dict[sharded_word_embeddings_key],
+            key=sharded_word_embeddings_key,
+            allow_shape_mismatch=True,
+        )
+        sharded_state_dict[sharded_word_embeddings_key] = sharded_word_embeddings_tensor
+
+        if self.add_position_embedding:
+            position_embeddings_prefix = f'{prefix}position_embeddings.'
+            position_embeddings_state_dict = self.position_embeddings.state_dict(
+                prefix=position_embeddings_prefix, keep_vars=True
+            )
+            sharded_position_embeddings_key = f'{position_embeddings_prefix}weight'
+            sharded_position_embeddings_tensor = make_sharded_tensor_for_checkpoint(
+                tensor=position_embeddings_state_dict[sharded_position_embeddings_key],
+                key=sharded_position_embeddings_key,
+            )
+            sharded_state_dict[sharded_position_embeddings_key] = sharded_position_embeddings_tensor
+
+        return sharded_state_dict