Initial commit

megatron/core/optimizer/optimizer_config.py

+# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+
+from dataclasses import dataclass
+from typing import Callable, Optional
+
+import torch
+
+
+@dataclass
+class OptimizerConfig:
+    """Configuration for optimizer."""
+
+    ##############
+    # General
+    ##############
+    optimizer: str = 'adam'
+    """Optimizer to use (one of Adam or SGD)."""
+
+    lr: Optional[float] = None
+    """Initial learning rate. Depending on decay style and initial warmup, the learning rate at each
+       iteration would be different.
+    """
+
+    min_lr: Optional[float] = None
+    """Minumum value for learning rate. The scheduler clip values below this threshold."""
+
+    decoupled_lr: Optional[float] = None
+    """Separate learning rate for the input and output layer."""
+
+    decoupled_min_lr: Optional[float] = None
+    """Minimum value for learning rate for the input and output layer. The scheduler clip values
+       below this threshold.
+    """
+
+    weight_decay: float = 0.01
+    """Weight decay coefficient for L2 regularization."""
+
+    ##############
+    # Precision
+    ##############
+    fp16: bool = False
+    """If true, train with fp16 mixed precision training. Defaults to False."""
+
+    bf16: bool = False
+    """If true, train with bf16 mixed precision training. Defaults to False."""
+
+    params_dtype: torch.dtype = torch.float32
+    """dtype used when intializing the weights. Defaults to torch.float32."""
+
+    ###############
+    # Loss scaling
+    ###############
+    loss_scale: Optional[float] = None
+    """Static loss scaling, positive power of 2 values can improve fp16 convergence. If None,
+       dynamic loss scaling is used.
+    """
+
+    initial_loss_scale: float = 2 ** 32
+    """Initial loss-scale for dynamic loss scaling."""
+
+    min_loss_scale: float = 1.0
+    """Minimum loss scale for dynamic loss scaling."""
+
+    loss_scale_window: float = 1000
+    """Window over which to raise/lower dynamic scale."""
+
+    hysteresis: int = 2
+    """Hysteresis for dynamic loss scaling."""
+
+    ##############
+    # Optimizer
+    ##############
+    # Adam
+    adam_beta1: float = 0.9
+    """First coefficient for computing running averages of gradient and its square in Adam
+    optimizer.
+    """
+
+    adam_beta2: float = 0.999
+    """Second coefficient for computing running averages of gradient and its square in Adam
+    optimizer.
+    """
+
+    adam_eps: float = 1e-08
+    """Term added to the denominator to improve numerical stability in Adam optimizer."""
+
+    # SGD.
+    sgd_momentum: float = 0.9
+    """Momentum factor for SGD optimizer."""
+
+    #######################
+    # Distributed optimizer
+    #######################
+    use_distributed_optimizer: bool = False
+    """Distribute optimizer state over data-parallel replicas."""
+
+    overlap_grad_reduce: bool = False
+    """If true, overlap grad reduce-scatter with backward compute in distributed optimizer."""
+
+    overlap_param_gather: bool = False
+    """If true, overlap param all-gather with forward compute in distributed optimizer."""
+
+    ################
+    # Miscellaneous
+    ################
+    clip_grad: float = 1.0
+    """Gradient clipping based on global L2 norm."""
+
+    log_num_zeros_in_grad: bool = False
+    """If true, calculate and log the number of zeros in gradient."""
+
+    barrier_with_L1_time: bool = False
+    """If true, use barrier with level 1 time measurements."""
+
+    timers: Callable = None
+    """Function to get timers."""

megatron/core/package_info.py

+# Copyright (c) 2023, NVIDIA CORPORATION.  All rights reserved.
+
+
+MAJOR = 0
+MINOR = 8
+PATCH = 0
+PRE_RELEASE = 'rc0'
+
+# Use the following formatting: (major, minor, patch, pre-release)
+VERSION = (MAJOR, MINOR, PATCH, PRE_RELEASE)
+
+__shortversion__ = '.'.join(map(str, VERSION[:3]))
+__version__ = '.'.join(map(str, VERSION[:3])) + ''.join(VERSION[3:])
+
+__package_name__ = 'megatron_core'
+__contact_names__ = 'NVIDIA'
+__contact_emails__ = 'nemo-toolkit@nvidia.com'  # use NeMo Email
+__homepage__ = (
+    'https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/'  # use NeMo homepage
+)
+__repository_url__ = 'https://github.com/NVIDIA/Megatron-LM/megatron/core'
+__download_url__ = 'https://github.com/NVIDIA/Megatron-LM/releases'
+__description__ = (
+    'Megatron Core - a library for efficient and scalable training of transformer based models'
+)
+__license__ = 'BSD-3'
+__keywords__ = (
+    'deep learning, machine learning, gpu, NLP, NLU, language, transformer, nvidia, pytorch, torch'
+)

megatron/core/packed_seq_params.py

+from dataclasses import dataclass
+
+from torch import Tensor
+
+
+@dataclass
+class PackedSeqParams:
+    # parameters to TEDotProductAttention and fused rope kernels for the `thd` (packed) sequence format,
+    qkv_format: str = None
+    cu_seqlens_q: Tensor = None
+    cu_seqlens_kv: Tensor = None
+    max_seqlen_q: Tensor = None
+    max_seqlen_kv: Tensor = None

megatron/core/parallel_state.py

+# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
+
+"""Model and data parallel groups."""
+
+import os
+import warnings
+from datetime import timedelta
+from typing import List, Optional
+
+import torch
+
+from .utils import GlobalMemoryBuffer
+
+# Intra-layer model parallel group that the current rank belongs to.
+_TENSOR_MODEL_PARALLEL_GROUP = None
+# Inter-layer model parallel group that the current rank belongs to.
+_PIPELINE_MODEL_PARALLEL_GROUP = None
+# Model parallel group (both intra- and pipeline) that the current rank belongs to.
+_MODEL_PARALLEL_GROUP = None
+# Model parallel group (both intra-, pipeline, and expert) that the current rank belongs to.
+_MODEL_AND_EXPERT_PARALLEL_GROUP = None
+# Embedding group.
+_EMBEDDING_GROUP = None
+# Position embedding group.
+_POSITION_EMBEDDING_GROUP = None
+# Data parallel group that the current rank belongs to.
+_DATA_PARALLEL_GROUP = None
+_DATA_PARALLEL_GROUP_GLOO = None
+# tensor model parallel group and data parallel group combined
+# used for fp8 and moe training
+_TENSOR_AND_DATA_PARALLEL_GROUP = None
+# Expert parallel group that the current rank belongs to.
+_EXPERT_MODEL_PARALLEL_GROUP = None
+_TENSOR_AND_EXPERT_PARALLEL_GROUP = None
+_DATA_MODULO_EXPERT_PARALLEL_GROUP = None
+_DATA_MODULO_EXPERT_PARALLEL_GROUP_GLOO = None
+
+
+_VIRTUAL_PIPELINE_MODEL_PARALLEL_RANK = None
+_VIRTUAL_PIPELINE_MODEL_PARALLEL_WORLD_SIZE = None
+_PIPELINE_MODEL_PARALLEL_SPLIT_RANK = None
+
+# These values enable us to change the mpu sizes on the fly.
+_MPU_TENSOR_MODEL_PARALLEL_WORLD_SIZE = None
+_MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZE = None
+_MPU_EXPERT_MODEL_PARALLEL_WORLD_SIZE = None
+_MPU_TENSOR_MODEL_PARALLEL_RANK = None
+_MPU_PIPELINE_MODEL_PARALLEL_RANK = None
+_MPU_EXPERT_MODEL_PARALLEL_RANK = None
+
+# A list of ranks that have a copy of the embedding.
+_EMBEDDING_GLOBAL_RANKS = None
+
+# A list of ranks that have a copy of the position embedding.
+_POSITION_EMBEDDING_GLOBAL_RANKS = None
+
+# A list of global ranks for each pipeline group to ease calculation of the source
+# rank when broadcasting from the first or last pipeline stage.
+_PIPELINE_GLOBAL_RANKS = None
+
+# A list of global ranks for each data parallel group to ease calculation of the source
+# rank when broadcasting weights from src to all other data parallel ranks
+_DATA_PARALLEL_GLOBAL_RANKS = None
+
+# A list of global ranks for each tensor model parallel group to ease calculation of
+# the first local rank in the tensor model parallel group
+_TENSOR_MODEL_PARALLEL_GLOBAL_RANKS = None
+
+# Context parallel group that the current rank belongs to
+_CONTEXT_PARALLEL_GROUP = None
+# A list of global ranks for each context parallel group to ease calculation of the
+# destination rank when exchanging KV/dKV between context parallel_ranks
+_CONTEXT_PARALLEL_GLOBAL_RANKS = None
+
+# Data parallel group information with context parallel combined.
+_DATA_PARALLEL_GROUP_WITH_CP = None
+_DATA_PARALLEL_GROUP_WITH_CP_GLOO = None
+_DATA_PARALLEL_GLOBAL_RANKS_WITH_CP = None
+
+# combined parallel group of TP, DP, and CP used for fp8
+_TENSOR_AND_DATA_PARALLEL_GROUP_WITH_CP = None
+
+# Memory buffers to avoid dynamic memory allocation
+_GLOBAL_MEMORY_BUFFER = None
+
+# MOE logging
+_MOE_AUX_LOSSES_LOGGING_TRACKER = {}
+
+
+def get_nccl_options(pg_name, nccl_comm_cfgs):
+    """Set the NCCL process group options.
+
+    Args:
+        pg_name (str): process group name
+        nccl_comm_cfgs (dict): nccl communicator configurations
+
+    When an option (e.g., max_ctas) is not found in the config, use the NCCL default setting.
+    """
+    if pg_name in nccl_comm_cfgs:
+        nccl_options = torch.distributed.ProcessGroupNCCL.Options()
+        nccl_options.config.cga_cluster_size = nccl_comm_cfgs[pg_name].get('cga_cluster_size', 4)
+        nccl_options.config.max_ctas = nccl_comm_cfgs[pg_name].get('max_ctas', 32)
+        nccl_options.config.min_ctas = nccl_comm_cfgs[pg_name].get('min_ctas', 1)
+        return nccl_options
+    else:
+        return None
+
+
+def generate_masked_orthogonal_rank_groups(
+    world_size: int, parallel_size: List[int], mask: List[bool],
+) -> List[List[int]]:
+    """Generate orthogonal parallel groups based on the parallel size and mask.
+
+    Arguments:
+        world_size (int): world size
+
+        parallel_size (List[int]):
+            The parallel size of each orthogonal parallel type. For example, if
+            tensor_parallel_size = 2, pipeline_model_parallel_group = 3, data_parallel_size = 4,
+            and the parallel mapping order is tp-pp-dp, then the parallel_size = [2, 3, 4].
+
+        mask (List[bool]):
+            The mask controls which parallel methods the generated groups represent. If mask[i] is
+            True, it means the generated group contains the i-th parallelism method. For example, 
+            if parallel_size = [tp_size, pp_size, dp_size], and mask = [True, False , True], then 
+            the generated group is the `tp-dp` group, if the mask = [False, True, False], then the 
+            generated group is the `pp` group.
+
+    Algorithm:
+        For orthogonal parallelism, such as tp/dp/pp/cp, the global_rank and
+        local_rank satisfy the following equation:
+            global_rank = tp_rank + dp_rank * tp_size + pp_rank * tp_size * dp_size (1)
+                tp_rank \in [0, tp_size)
+                dp_rank \in [0, dp_size)
+                pp_rank \in [0, pp_size)
+
+        If we want to get the `dp_group` (tp_size * pp_size groups of dp_size ranks each.
+        For example,  if the gpu size is 8 and order is 'tp-pp-dp', size is '2-2-2', and the 
+        dp_group here is [[0, 4], [1, 5], [2, 6], [3, 7]].)
+        The tp_rank and pp_rank will be combined to form the `dp_group_index`.
+            dp_group_index = tp_rank + pp_rank * tp_size (2)
+
+        So, Given that tp_rank and pp_rank satisfy equation (2), and dp_rank in
+        range(0, dp_size), the ranks in dp_group[dp_group_index] satisfies the
+        equation (1).
+        
+        This function solve this math problem.
+
+    For example, if the parallel_size = [tp_size, dp_size, pp_size] = [2, 3, 4],
+    and the mask = [False, True, False]. Then,
+        dp_group_index(0) = tp_rank(0) + pp_rank(0) * 2
+        dp_group_index(1) = tp_rank(1) + pp_rank(0) * 2
+        ...
+        dp_group_index(7) = tp_rank(1) + pp_rank(3) * 2
+
+        dp_group[0] = 0 + range(0, 3) * 2 + 0 = [0, 2, 4]
+        dp_group[1] = 1 + range(0, 3) * 2 + 0 = [1, 3, 5]
+        ...
+        dp_group[7] = 1 + range(0, 3) * 2 + 3 * 2 * 3 = [19, 21, 23]
+    """
+
+    def prefix_product(a: List[int], init=1) -> List[int]:
+        r = [init]
+        for v in a:
+            init = init * v
+            r.append(init)
+        return r
+
+    def inner_product(a: List[int], b: List[int]) -> int:
+        return sum([x * y for x, y in zip(a, b)])
+
+    def decompose(index, shape, stride=None):
+        ''' 
+        This function solve the math problem below:
+            There is an equation: 
+                index = sum(idx[i] * stride[i])
+            And given the value of index, stride.
+            Return the idx.
+        This function will used to get the pp/dp/pp_rank
+        from group_index and rank_in_group.
+        '''
+        if stride is None:
+            stride = prefix_product(shape)
+        idx = [(index // d) % s for s, d in zip(shape, stride)]
+        # stride is a prefix_product result. And the value of stride[-1]
+        # is not used.
+        assert (
+            sum([x * y for x, y in zip(idx, stride[:-1])]) == index
+        ), "idx {} with shape {} mismatch the return idx {}".format(index, shape, idx)
+        return idx
+
+    masked_shape = [s for s, m in zip(parallel_size, mask) if m]
+    unmasked_shape = [s for s, m in zip(parallel_size, mask) if not m]
+
+    global_stride = prefix_product(parallel_size)
+    masked_stride = [d for d, m in zip(global_stride, mask) if m]
+    unmasked_stride = [d for d, m in zip(global_stride, mask) if not m]
+
+    group_size = prefix_product(masked_shape)[-1]
+    num_of_group = world_size // group_size
+
+    ranks = []
+    for group_index in range(num_of_group):
+        # get indices from unmaksed for group_index.
+        decomposed_group_idx = decompose(group_index, unmasked_shape)
+        rank = []
+        for rank_in_group in range(group_size):
+            # get indices from masked for rank_in_group.
+            decomposed_rank_idx = decompose(rank_in_group, masked_shape)
+            rank.append(
+                inner_product(decomposed_rank_idx, masked_stride)
+                + inner_product(decomposed_group_idx, unmasked_stride)
+            )
+        ranks.append(rank)
+    return ranks
+
+
+class RankGenerator(object):
+    def __init__(self, tp: int, ep: int, dp: int, pp: int, cp: int, order: str) -> None:
+        self.tp = tp
+        self.ep = ep
+        self.dp = dp
+        self.pp = pp
+        self.cp = cp
+        self.world_size = tp * dp * pp * cp
+
+        self.name_to_size = {
+            "tp": self.tp,
+            "pp": self.pp,
+            "dp": self.dp,
+            "ep": self.ep,
+            "cp": self.cp,
+        }
+        self.order = order
+        order = order.lower()
+
+        if 'ep' in order:
+            if 'ep-dp' not in order and 'dp-ep' not in order:
+                raise RuntimeError(f"The ep and dp must be adjacent in order ({self.order}).")
+
+        for name in self.name_to_size.keys():
+            if name not in order and self.name_to_size[name] != 1:
+                raise RuntimeError(
+                    f"The size of ({name}) is ({self.name_to_size[name]}), but you haven't specified the order ({self.order})."
+                )
+            elif name not in order:
+                order = order + '-' + name
+
+        self.order_w_ep = order
+        self.order_wo_ep = '-'.join([token for token in order.split('-') if token != 'ep'])
+        self.ordered_size_wo_ep = []
+        self.ordered_size_w_ep = []
+
+        for token in order.split('-'):
+            if token == 'dp':
+                self.ordered_size_w_ep.append(self.dp // self.ep)
+                self.ordered_size_wo_ep.append(self.dp)
+            elif token == 'ep':
+                self.ordered_size_w_ep.append(self.ep)
+            else:
+                self.ordered_size_w_ep.append(self.name_to_size[token])
+                self.ordered_size_wo_ep.append(self.name_to_size[token])
+
+    def get_mask(self, order: str, token: str):
+        ordered_token = order.split('-')
+        token = token.split('-')
+        mask = [False] * len(ordered_token)
+        for t in token:
+            mask[ordered_token.index(t)] = True
+        return mask
+
+    def get_ranks(self, token, independent_ep=False):
+        '''Get rank group by input token.
+
+        Arguments:
+            token (str):
+                Specify the ranks type that want to get. If we want
+                to obtain multiple parallel types, we can use a hyphen
+                '-' to separate them. For example, if we want to obtain
+                the TP_DP group, the token should be 'tp-dp'.
+
+            independent_ep (bool: True):
+                This flag controls whether we treat EP and DP independently.
+                EP shares ranks with DP, if we want to get ranks related to
+                EP, we should set the flag. For example, get_ranks('dp', True)
+                will get DP modulo EP group, and get_ranks('dp', False) will
+                get full DP group.
+        '''
+        if independent_ep:
+            parallel_size = self.ordered_size_w_ep
+            order = self.order_w_ep
+        else:
+            parallel_size = self.ordered_size_wo_ep
+            order = self.order_wo_ep
+        mask = self.get_mask(order, token)
+        ranks = generate_masked_orthogonal_rank_groups(self.world_size, parallel_size, mask)
+        return ranks
+
+
+def initialize_model_parallel(
+    tensor_model_parallel_size: int = 1,
+    pipeline_model_parallel_size: int = 1,
+    virtual_pipeline_model_parallel_size: Optional[int] = None,
+    pipeline_model_parallel_split_rank: Optional[int] = None,
+    use_sharp: bool = False,
+    context_parallel_size: int = 1,
+    expert_model_parallel_size: int = 1,
+    nccl_communicator_config_path: Optional[str] = None,
+    distributed_timeout_minutes: int = 30,
+    order: str = "tp-cp-ep-dp-pp",
+) -> None:
+    """Initialize model data parallel groups.
+
+    Args:
+        tensor_model_parallel_size (int, default = 1):
+            The number of GPUs to split individual tensors across.
+
+        pipeline_model_parallel_size (int, default = 1):
+            The number of tensor parallel GPU groups to split the
+            Transformer layers across. For example, if
+            tensor_model_parallel_size is 4 and
+            pipeline_model_parallel_size is 2, the model will be split
+            into 2 groups of 4 GPUs.
+
+        virtual_pipeline_model_parallel_size (int, optional):
+            The number of stages that each pipeline group will have,
+            interleaving as necessary. If None, no interleaving is
+            performed. For example, if tensor_model_parallel_size is 1,
+            pipeline_model_parallel_size is 4,
+            virtual_pipeline_model_parallel_size is 2, and there are
+            16 transformer layers in the model, the model will be
+            split into 8 stages with two layers each and each GPU
+            would get 2 stages as such (layer number starting with 1):
+
+            GPU 0: [1, 2] [9, 10]
+            GPU 1: [3, 4] [11, 12]
+            GPU 2: [5, 6] [13, 14]
+            GPU 3: [7, 8] [15, 16]
+
+        pipeline_model_parallel_split_rank (int, optional):
+            For models with both an encoder and decoder, the rank in
+            pipeline to switch between encoder and decoder (i.e. the
+            first rank of the decoder). This allows the user to set
+            the pipeline parallel size of the encoder and decoder
+            independently. For example, if
+            pipeline_model_parallel_size is 8 and
+            pipeline_model_parallel_split_rank is 3, then ranks 0-2
+            will be the encoder and ranks 3-7 will be the decoder.
+
+        use_sharp (bool, default = False):
+            Set the use of SHARP for the collective communications of
+            data-parallel process groups. When `True`, run barrier
+            within each data-parallel process group, which specifies
+            the SHARP application target groups.
+
+        context_parallel_size (int, default = 1):
+            The number of tensor parallel GPU groups to split the
+            network input sequence length across. Compute of attention
+            module requires tokens of full sequence length, so GPUs
+            in a context parallel group need to communicate with each
+            other to exchange information of other sequence chunks.
+            Each GPU and its counterparts in other tensor parallel
+            groups compose a context parallel group.
+
+            For example, assume we have 8 GPUs, if tensor model parallel
+            size is 4 and context parallel size is 2, the network input
+            will be split into two sequence chunks, which are processed
+            by 2 different groups of 4 GPUs. One chunk is processed by
+            GPU0-3, the other chunk is processed by GPU4-7. Four groups
+            are build to do context parallel communications: [GPU0, GPU4],
+            [GPU1, GPU5], [GPU2, GPU6], and [GPU3, GPU7].
+
+            Context parallelism partitions sequence length, so it has no
+            impact on weights, which means weights are duplicated among
+            GPUs in a context parallel group. Hence, weight gradients
+            all-reduce is required in backward. For simplicity, we piggyback
+            GPUs of context parallelism on data parallel group for
+            weight gradient all-reduce.
+        
+        expert_model_parallel_size (int, default = 1):
+            The number of Mixture of Experts parallel GPUs in each expert
+            parallel group.
+
+        nccl_communicator_config_path (str, default = None):
+            Path to the yaml file of NCCL communicator configurations.
+            `min_ctas`, `max_ctas`, and `cga_cluster_size` can be set
+            for each communicator.
+
+        distributed_timeout_minutes (int, default = 30): Timeout, in
+            minutes,for operations executed against distributed
+            process groups. See PyTorch documentation at
+            https://pytorch.org/docs/stable/distributed.html for
+            caveats.
+
+        order (str, default=tp-dp-pp):
+            The rank initialization order of parallelism. Now we support
+            tp-dp-pp and tp-pp-dp orders.
+
+    Let's say we have a total of 16 GPUs denoted by g0 ... g15 and we
+    use 2 GPUs to parallelize the model tensor, and 4 GPUs to parallelize
+    the model pipeline. The present function will
+    create 8 tensor model-parallel groups, 4 pipeline model-parallel groups
+    and 8 data-parallel groups as:
+        8 data_parallel groups:
+            [g0, g2], [g1, g3], [g4, g6], [g5, g7], [g8, g10], [g9, g11], [g12, g14], [g13, g15]
+        8 tensor model-parallel groups:
+            [g0, g1], [g2, g3], [g4, g5], [g6, g7], [g8, g9], [g10, g11], [g12, g13], [g14, g15]
+        4 pipeline model-parallel groups:
+            [g0, g4, g8, g12], [g1, g5, g9, g13], [g2, g6, g10, g14], [g3, g7, g11, g15]
+    Note that for efficiency, the caller should make sure adjacent ranks
+    are on the same DGX box. For example if we are using 2 DGX-1 boxes
+    with a total of 16 GPUs, rank 0 to 7 belong to the first box and
+    ranks 8 to 15 belong to the second box.
+
+    """
+    # Get world size and rank. Ensure some consistencies.
+    assert torch.distributed.is_initialized()
+    world_size: int = torch.distributed.get_world_size()
+
+    if (
+        world_size
+        % (tensor_model_parallel_size * pipeline_model_parallel_size * context_parallel_size)
+        != 0
+    ):
+        raise RuntimeError(
+            f"world_size ({world_size}) is not divisible by tensor_model_parallel_size "
+            f"({tensor_model_parallel_size}) x pipeline_model_parallel_size ({pipeline_model_parallel_size}) "
+            f"x context_parallel_size ({context_parallel_size})"
+        )
+
+    data_parallel_size: int = world_size // (
+        tensor_model_parallel_size * pipeline_model_parallel_size * context_parallel_size
+    )
+
+    if data_parallel_size % expert_model_parallel_size != 0:
+        raise RuntimeError(
+            f"data_parallel_size ({data_parallel_size}) is not divisible by expert_model_parallel_size "
+        )
+
+    if expert_model_parallel_size > 1 and context_parallel_size > 1:
+        raise RuntimeError(
+            f"combination of expert model prallellism and context parallelism is not supported"
+        )
+
+    num_tensor_model_parallel_groups: int = world_size // tensor_model_parallel_size
+    num_pipeline_model_parallel_groups: int = world_size // pipeline_model_parallel_size
+
+    if virtual_pipeline_model_parallel_size is not None:
+        if not pipeline_model_parallel_size > 1:
+            raise RuntimeError(
+                "pipeline-model-parallel size should be greater than 1 with interleaved schedule"
+            )
+        global _VIRTUAL_PIPELINE_MODEL_PARALLEL_RANK
+        global _VIRTUAL_PIPELINE_MODEL_PARALLEL_WORLD_SIZE
+        _VIRTUAL_PIPELINE_MODEL_PARALLEL_RANK = 0
+        _VIRTUAL_PIPELINE_MODEL_PARALLEL_WORLD_SIZE = virtual_pipeline_model_parallel_size
+
+    if pipeline_model_parallel_split_rank is not None:
+        global _PIPELINE_MODEL_PARALLEL_SPLIT_RANK
+        _PIPELINE_MODEL_PARALLEL_SPLIT_RANK = pipeline_model_parallel_split_rank
+
+    rank = torch.distributed.get_rank()
+
+    nccl_comm_cfgs = {}
+    if nccl_communicator_config_path is not None:
+        try:
+            import yaml
+        except ImportError:
+            raise RuntimeError(
+                "Cannot import `yaml`. Setting custom nccl communicator configs "
+                "requires the yaml package."
+            )
+
+        with open(nccl_communicator_config_path, "r") as stream:
+            nccl_comm_cfgs = yaml.safe_load(stream)
+
+    rank_generator = RankGenerator(
+        tp=tensor_model_parallel_size,
+        ep=expert_model_parallel_size,
+        dp=data_parallel_size,
+        pp=pipeline_model_parallel_size,
+        cp=context_parallel_size,
+        order=order,
+    )
+    timeout = timedelta(minutes=distributed_timeout_minutes)
+
+    # Build the data-parallel groups.
+    global _DATA_PARALLEL_GROUP
+    global _DATA_PARALLEL_GROUP_GLOO
+    global _DATA_PARALLEL_GLOBAL_RANKS
+    global _DATA_PARALLEL_GROUP_WITH_CP
+    global _DATA_PARALLEL_GROUP_WITH_CP_GLOO
+    global _DATA_PARALLEL_GLOBAL_RANKS_WITH_CP
+    assert _DATA_PARALLEL_GROUP is None, 'data parallel group is already initialized'
+
+    for ranks in rank_generator.get_ranks('dp'):
+        group = torch.distributed.new_group(
+            ranks, timeout=timeout, pg_options=get_nccl_options('dp', nccl_comm_cfgs)
+        )
+        group_gloo = torch.distributed.new_group(ranks, timeout=timeout, backend="gloo")
+        if rank in ranks:
+            _DATA_PARALLEL_GROUP = group
+            _DATA_PARALLEL_GROUP_GLOO = group_gloo
+            _DATA_PARALLEL_GLOBAL_RANKS = ranks
+    for ranks_with_cp in rank_generator.get_ranks('dp-cp'):
+        group_with_cp = torch.distributed.new_group(
+            ranks_with_cp, timeout=timeout, pg_options=get_nccl_options('dp_cp', nccl_comm_cfgs)
+        )
+        group_with_cp_gloo = torch.distributed.new_group(
+            ranks_with_cp, timeout=timeout, backend="gloo"
+        )
+        if rank in ranks_with_cp:
+            _DATA_PARALLEL_GROUP_WITH_CP = group_with_cp
+            _DATA_PARALLEL_GROUP_WITH_CP_GLOO = group_with_cp_gloo
+            _DATA_PARALLEL_GLOBAL_RANKS_WITH_CP = ranks_with_cp
+
+    # Apply SHARP to DP process groups
+    if use_sharp:
+        if rank == 0:
+            print(
+                "The number of process groups to use SHARP with depends on the type "
+                "of the network switch. Nvidia QM1 switch supports SAHRP up to 8 "
+                "process groups and QM2 supports up to 256 process groups. We apply "
+                "SHARP to the communications of the data-parallel domain. If the "
+                "number of data-parallel process groups is larger than the max "
+                "process groups that the network switch supports, the communication "
+                "will fall back to non-SHARP operators. To enable SHARP, "
+                "`#SBATCH_NETWORK=sharp` should be set in the sbatch script."
+            )
+        torch.distributed.barrier(
+            group=get_data_parallel_group(with_context_parallel=True),
+            device_ids=[torch.cuda.current_device()],
+        )
+        # Set `NCCL_COLLNET_ENABLE=0` to restrict SHARP application to DP process groups
+        os.environ["NCCL_COLLNET_ENABLE"] = "0"
+
+    # Build the context-parallel groups.
+    global _CONTEXT_PARALLEL_GROUP
+    global _CONTEXT_PARALLEL_GLOBAL_RANKS
+    assert _CONTEXT_PARALLEL_GROUP is None, 'context parallel group is already initialized'
+    for ranks in rank_generator.get_ranks('cp'):
+        group = torch.distributed.new_group(
+            ranks, timeout=timeout, pg_options=get_nccl_options('cp', nccl_comm_cfgs)
+        )
+        if rank in ranks:
+            _CONTEXT_PARALLEL_GROUP = group
+            _CONTEXT_PARALLEL_GLOBAL_RANKS = ranks
+
+    # Build the model-parallel groups.
+    global _MODEL_PARALLEL_GROUP
+    assert _MODEL_PARALLEL_GROUP is None, 'model parallel group is already initialized'
+    for ranks in rank_generator.get_ranks('tp-pp'):
+        group = torch.distributed.new_group(
+            ranks, timeout=timeout, pg_options=get_nccl_options('mp', nccl_comm_cfgs)
+        )
+        if rank in ranks:
+            _MODEL_PARALLEL_GROUP = group
+
+    # Build the model-parallel groups with expert parallel
+    global _MODEL_AND_EXPERT_PARALLEL_GROUP
+    assert (
+        _MODEL_AND_EXPERT_PARALLEL_GROUP is None
+    ), 'model and expert parallel group is already initialized'
+    for ranks in rank_generator.get_ranks('tp-ep-pp', independent_ep=True):
+        group = torch.distributed.new_group(
+            ranks, timeout=timeout, pg_options=get_nccl_options('mp_exp', nccl_comm_cfgs)
+        )
+        if rank in ranks:
+            _MODEL_AND_EXPERT_PARALLEL_GROUP = group
+
+    # Build the tensor model-parallel groups.
+    global _TENSOR_MODEL_PARALLEL_GROUP
+    global _TENSOR_MODEL_PARALLEL_GLOBAL_RANKS
+    assert (
+        _TENSOR_MODEL_PARALLEL_GROUP is None
+    ), 'tensor model parallel group is already initialized'
+    for ranks in rank_generator.get_ranks('tp'):
+        group = torch.distributed.new_group(
+            ranks, timeout=timeout, pg_options=get_nccl_options('tp', nccl_comm_cfgs)
+        )
+        if rank in ranks:
+            _TENSOR_MODEL_PARALLEL_GROUP = group
+            _TENSOR_MODEL_PARALLEL_GLOBAL_RANKS = ranks
+
+    # Build the pipeline model-parallel groups and embedding groups
+    # (first and last rank in each pipeline model-parallel group).
+    global _PIPELINE_MODEL_PARALLEL_GROUP
+    global _PIPELINE_GLOBAL_RANKS
+    assert (
+        _PIPELINE_MODEL_PARALLEL_GROUP is None
+    ), 'pipeline model parallel group is already initialized'
+    global _EMBEDDING_GROUP
+    global _EMBEDDING_GLOBAL_RANKS
+    assert _EMBEDDING_GROUP is None, 'embedding group is already initialized'
+    global _POSITION_EMBEDDING_GROUP
+    global _POSITION_EMBEDDING_GLOBAL_RANKS
+    assert _POSITION_EMBEDDING_GROUP is None, 'position embedding group is already initialized'
+    for ranks in rank_generator.get_ranks('pp'):
+        group = torch.distributed.new_group(
+            ranks, timeout=timeout, pg_options=get_nccl_options('pp', nccl_comm_cfgs)
+        )
+        if rank in ranks:
+            _PIPELINE_MODEL_PARALLEL_GROUP = group
+            _PIPELINE_GLOBAL_RANKS = ranks
+        # Setup embedding group (to exchange gradients between
+        # first and last stages).
+        if len(ranks) > 1:
+            embedding_ranks = [ranks[0], ranks[-1]]
+            position_embedding_ranks = [ranks[0]]
+            if pipeline_model_parallel_split_rank is not None:
+                if ranks[pipeline_model_parallel_split_rank] not in embedding_ranks:
+                    embedding_ranks = [
+                        ranks[0],
+                        ranks[pipeline_model_parallel_split_rank],
+                        ranks[-1],
+                    ]
+                if ranks[pipeline_model_parallel_split_rank] not in position_embedding_ranks:
+                    position_embedding_ranks = [ranks[0], ranks[pipeline_model_parallel_split_rank]]
+        else:
+            embedding_ranks = ranks
+            position_embedding_ranks = ranks
+
+        group = torch.distributed.new_group(
+            embedding_ranks, timeout=timeout, pg_options=get_nccl_options('embd', nccl_comm_cfgs)
+        )
+        if rank in embedding_ranks:
+            _EMBEDDING_GROUP = group
+        if rank in ranks:
+            _EMBEDDING_GLOBAL_RANKS = embedding_ranks
+
+        group = torch.distributed.new_group(
+            position_embedding_ranks,
+            timeout=timeout,
+            pg_options=get_nccl_options('embd', nccl_comm_cfgs),
+        )
+        if rank in position_embedding_ranks:
+            _POSITION_EMBEDDING_GROUP = group
+        if rank in ranks:
+            _POSITION_EMBEDDING_GLOBAL_RANKS = position_embedding_ranks
+
+    # Build the tensor + data parallel groups.
+    global _TENSOR_AND_DATA_PARALLEL_GROUP
+    global _TENSOR_AND_DATA_PARALLEL_GROUP_WITH_CP
+    assert (
+        _TENSOR_AND_DATA_PARALLEL_GROUP is None
+    ), 'Tensor + data parallel group is already initialized'
+    for ranks in rank_generator.get_ranks('tp-dp-cp'):
+        group = torch.distributed.new_group(
+            ranks, timeout=timeout, pg_options=get_nccl_options('tp_dp_cp', nccl_comm_cfgs)
+        )
+        if rank in ranks:
+            _TENSOR_AND_DATA_PARALLEL_GROUP_WITH_CP = group
+    for ranks in rank_generator.get_ranks('tp-dp'):
+        group = torch.distributed.new_group(
+            ranks, timeout=timeout, pg_options=get_nccl_options('tp_dp', nccl_comm_cfgs)
+        )
+        if rank in ranks:
+            _TENSOR_AND_DATA_PARALLEL_GROUP = group
+
+    # Build the tensor + expert parallel groups
+    global _EXPERT_MODEL_PARALLEL_GROUP
+    assert _EXPERT_MODEL_PARALLEL_GROUP is None, 'Expert parallel group is already initialized'
+    global _TENSOR_AND_EXPERT_PARALLEL_GROUP
+    assert (
+        _TENSOR_AND_EXPERT_PARALLEL_GROUP is None
+    ), 'Tensor + expert parallel group is already initialized'
+    global _DATA_MODULO_EXPERT_PARALLEL_GROUP
+    assert (
+        _DATA_MODULO_EXPERT_PARALLEL_GROUP is None
+    ), 'Data modulo expert group is already initialized'
+    global _DATA_MODULO_EXPERT_PARALLEL_GROUP_GLOO
+
+    for ranks in rank_generator.get_ranks('tp-ep', independent_ep=True):
+        group = torch.distributed.new_group(
+            ranks, timeout=timeout, pg_options=get_nccl_options('tp_exp', nccl_comm_cfgs)
+        )
+        if rank in ranks:
+            _TENSOR_AND_EXPERT_PARALLEL_GROUP = group
+
+    for ranks in rank_generator.get_ranks('ep', independent_ep=True):
+        group = torch.distributed.new_group(
+            ranks, pg_options=get_nccl_options('exp', nccl_comm_cfgs)
+        )
+        if rank in ranks:
+            _EXPERT_MODEL_PARALLEL_GROUP = group
+
+    for ranks in rank_generator.get_ranks('dp', independent_ep=True):
+        group = torch.distributed.new_group(
+            ranks, timeout=timeout, pg_options=get_nccl_options('dp_modulo_exp', nccl_comm_cfgs)
+        )
+        group_gloo = torch.distributed.new_group(ranks, backend="gloo")
+        if rank in ranks:
+            _DATA_MODULO_EXPERT_PARALLEL_GROUP = group
+            _DATA_MODULO_EXPERT_PARALLEL_GROUP_GLOO = group_gloo
+
+    # Initialize global memory buffer
+    # This isn't really "parallel state" but there isn't another good place to
+    # put this. If we end up with a more generic initialization of megatron-core
+    # we could stick it there
+    _set_global_memory_buffer()
+
+
+def is_initialized():
+    """Useful for code segments that may be accessed with or without mpu initialization"""
+    return _DATA_PARALLEL_GROUP is not None
+
+
+def is_unitialized() -> bool:
+    """Check if parallel state has been initialized
+
+    Deprecated. Use is_initialized instead.
+
+    """
+    warnings.warn(
+        "is_unitialized is deprecated, use is_initialized instead", DeprecationWarning,
+    )
+    return not is_initialized()
+
+
+def model_parallel_is_initialized():
+    """Check if model and data parallel groups are initialized."""
+    if (
+        _TENSOR_MODEL_PARALLEL_GROUP is None
+        or _PIPELINE_MODEL_PARALLEL_GROUP is None
+        or _DATA_PARALLEL_GROUP is None
+    ):
+        return False
+    return True
+
+
+def get_model_parallel_group(with_expert_parallel=False):
+    """Get the model parallel group the caller rank belongs to."""
+    if with_expert_parallel:
+        assert (
+            _MODEL_AND_EXPERT_PARALLEL_GROUP is not None
+        ), 'model parallel group is not initialized'
+        return _MODEL_AND_EXPERT_PARALLEL_GROUP
+    assert _MODEL_PARALLEL_GROUP is not None, 'model parallel group is not initialized'
+    return _MODEL_PARALLEL_GROUP
+
+
+def get_tensor_model_parallel_group(check_initialized=True):
+    """Get the tensor model parallel group the caller rank belongs to."""
+    if check_initialized:
+        assert (
+            _TENSOR_MODEL_PARALLEL_GROUP is not None
+        ), 'tensor model parallel group is not initialized'
+    return _TENSOR_MODEL_PARALLEL_GROUP
+
+
+def get_pipeline_model_parallel_group():
+    """Get the pipeline model parallel group the caller rank belongs to."""
+    assert (
+        _PIPELINE_MODEL_PARALLEL_GROUP is not None
+    ), 'pipeline_model parallel group is not initialized'
+    return _PIPELINE_MODEL_PARALLEL_GROUP
+
+
+def get_data_parallel_group(with_context_parallel=False):
+    """Get the data parallel group the caller rank belongs to."""
+    if with_context_parallel:
+        assert (
+            _DATA_PARALLEL_GROUP_WITH_CP is not None
+        ), 'data parallel group with context parallel combined is not initialized'
+        return _DATA_PARALLEL_GROUP_WITH_CP
+    else:
+        assert _DATA_PARALLEL_GROUP is not None, 'data parallel group is not initialized'
+        return _DATA_PARALLEL_GROUP
+
+
+def get_data_parallel_group_gloo(with_context_parallel=False):
+    """Get the data parallel group-gloo the caller rank belongs to."""
+    if with_context_parallel:
+        assert (
+            _DATA_PARALLEL_GROUP_WITH_CP_GLOO is not None
+        ), 'data parallel group-gloo with context parallel combined is not initialized'
+        return _DATA_PARALLEL_GROUP_WITH_CP_GLOO
+    else:
+        assert _DATA_PARALLEL_GROUP_GLOO is not None, 'data parallel group-gloo is not initialized'
+        return _DATA_PARALLEL_GROUP_GLOO
+
+
+def get_context_parallel_group(check_initialized=True):
+    """Get the context parallel group the caller rank belongs to."""
+    if check_initialized:
+        assert _CONTEXT_PARALLEL_GROUP is not None, 'context parallel group is not initialized'
+    return _CONTEXT_PARALLEL_GROUP
+
+
+def get_context_parallel_global_ranks(check_initialized=True):
+    """Get all global ranks of the context parallel group that the caller rank belongs to."""
+    if check_initialized:
+        assert (
+            _CONTEXT_PARALLEL_GLOBAL_RANKS is not None
+        ), 'context parallel group is not initialized'
+    return _CONTEXT_PARALLEL_GLOBAL_RANKS
+
+
+def get_embedding_group():
+    """Get the embedding group the caller rank belongs to."""
+    assert _EMBEDDING_GROUP is not None, 'embedding group is not initialized'
+    return _EMBEDDING_GROUP
+
+
+def get_position_embedding_group():
+    """Get the position embedding group the caller rank belongs to."""
+    assert _POSITION_EMBEDDING_GROUP is not None, 'position embedding group is not initialized'
+    return _POSITION_EMBEDDING_GROUP
+
+
+def get_amax_reduction_group(with_context_parallel=False):
+    """Get the FP8 amax reduction group the caller rank belongs to."""
+    if with_context_parallel:
+        assert (
+            _TENSOR_AND_DATA_PARALLEL_GROUP_WITH_CP is not None
+        ), 'FP8 amax reduction group is not initialized'
+        return _TENSOR_AND_DATA_PARALLEL_GROUP_WITH_CP
+    else:
+        assert (
+            _TENSOR_AND_DATA_PARALLEL_GROUP is not None
+        ), 'FP8 amax reduction group is not initialized'
+        return _TENSOR_AND_DATA_PARALLEL_GROUP
+
+
+def get_tensor_and_data_parallel_group(with_context_parallel=False):
+    """Get the tensor and data parallel group the caller rank belongs to."""
+    if with_context_parallel:
+        assert (
+            _TENSOR_AND_DATA_PARALLEL_GROUP_WITH_CP is not None
+        ), 'tensor and data parallel group is not initialized'
+        return _TENSOR_AND_DATA_PARALLEL_GROUP_WITH_CP
+    else:
+        assert (
+            _TENSOR_AND_DATA_PARALLEL_GROUP is not None
+        ), 'tensor and data parallel group is not initialized'
+        return _TENSOR_AND_DATA_PARALLEL_GROUP
+
+
+def get_expert_model_parallel_group():
+    assert (
+        _EXPERT_MODEL_PARALLEL_GROUP is not None
+    ), 'expert model parallel group is not initialized'
+    return _EXPERT_MODEL_PARALLEL_GROUP
+
+
+def get_tensor_and_expert_parallel_group():
+    assert (
+        _TENSOR_AND_EXPERT_PARALLEL_GROUP is not None
+    ), 'tensor and expert parallel group is not initialized'
+    return _TENSOR_AND_EXPERT_PARALLEL_GROUP
+
+
+def get_data_modulo_expert_parallel_group():
+    assert (
+        _DATA_MODULO_EXPERT_PARALLEL_GROUP is not None
+    ), 'data modulo expert parallel group is not initialized'
+    return _DATA_MODULO_EXPERT_PARALLEL_GROUP
+
+
+def get_data_modulo_expert_parallel_group_gloo():
+    assert (
+        _DATA_MODULO_EXPERT_PARALLEL_GROUP_GLOO is not None
+    ), 'data modulo expert parallel group-gloo is not initialized'
+    return _DATA_MODULO_EXPERT_PARALLEL_GROUP_GLOO
+
+
+def set_expert_model_parallel_world_size(world_size):
+    global _MPU_EXPERT_MODEL_PARALLEL_WORLD_SIZE
+    _MPU_EXPERT_MODEL_PARALLEL_WORLD_SIZE = world_size
+
+
+def set_tensor_model_parallel_world_size(world_size):
+    """Set the tensor model parallel size"""
+    global _MPU_TENSOR_MODEL_PARALLEL_WORLD_SIZE
+    _MPU_TENSOR_MODEL_PARALLEL_WORLD_SIZE = world_size
+
+
+def set_pipeline_model_parallel_world_size(world_size):
+    """Set the pipeline model parallel size"""
+    global _MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZE
+    _MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZE = world_size
+
+
+def set_virtual_pipeline_model_parallel_world_size(world_size):
+    """Set the pipeline model parallel size"""
+    global _VIRTUAL_PIPELINE_MODEL_PARALLEL_WORLD_SIZE
+    _VIRTUAL_PIPELINE_MODEL_PARALLEL_WORLD_SIZE = world_size
+
+
+def get_tensor_model_parallel_world_size():
+    """Return world size for the tensor model parallel group."""
+    global _MPU_TENSOR_MODEL_PARALLEL_WORLD_SIZE
+    if _MPU_TENSOR_MODEL_PARALLEL_WORLD_SIZE is not None:
+        return _MPU_TENSOR_MODEL_PARALLEL_WORLD_SIZE
+    return torch.distributed.get_world_size(group=get_tensor_model_parallel_group())
+
+
+def get_pipeline_model_parallel_world_size():
+    """Return world size for the pipeline model parallel group."""
+    global _MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZE
+    if _MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZE is not None:
+        return _MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZE
+    return torch.distributed.get_world_size(group=get_pipeline_model_parallel_group())
+
+
+def set_expert_model_parallel_rank(rank):
+    """Set expert model parallel rank."""
+    global _MPU_EXPERT_MODEL_PARALLEL_RANK
+    _MPU_EXPERT_MODEL_PARALLEL_RANK = rank
+
+
+def set_tensor_model_parallel_rank(rank):
+    """Set tensor model parallel rank."""
+    global _MPU_TENSOR_MODEL_PARALLEL_RANK
+    _MPU_TENSOR_MODEL_PARALLEL_RANK = rank
+
+
+def set_pipeline_model_parallel_rank(rank):
+    """Set pipeline model parallel rank."""
+    global _MPU_PIPELINE_MODEL_PARALLEL_RANK
+    _MPU_PIPELINE_MODEL_PARALLEL_RANK = rank
+
+
+def set_pipeline_model_parallel_split_rank(rank):
+    """Set pipeline model parallel split rank."""
+    global _PIPELINE_MODEL_PARALLEL_SPLIT_RANK
+    _PIPELINE_MODEL_PARALLEL_SPLIT_RANK = rank
+
+
+def get_tensor_model_parallel_rank():
+    """Return my rank for the tensor model parallel group."""
+    global _MPU_TENSOR_MODEL_PARALLEL_RANK
+    if _MPU_TENSOR_MODEL_PARALLEL_RANK is not None:
+        return _MPU_TENSOR_MODEL_PARALLEL_RANK
+    return torch.distributed.get_rank(group=get_tensor_model_parallel_group())
+
+
+def get_pipeline_model_parallel_rank():
+    """Return my rank for the pipeline model parallel group."""
+    global _MPU_PIPELINE_MODEL_PARALLEL_RANK
+    if _MPU_PIPELINE_MODEL_PARALLEL_RANK is not None:
+        return _MPU_PIPELINE_MODEL_PARALLEL_RANK
+    return torch.distributed.get_rank(group=get_pipeline_model_parallel_group())
+
+
+def get_pipeline_model_parallel_split_rank():
+    """Return pipeline model parallel split rank."""
+    global _PIPELINE_MODEL_PARALLEL_SPLIT_RANK
+    return _PIPELINE_MODEL_PARALLEL_SPLIT_RANK
+
+
+def is_pipeline_first_stage(ignore_virtual=False):
+    """Return True if in the first pipeline model-parallel stage, False otherwise."""
+    if not ignore_virtual:
+        if (
+            get_virtual_pipeline_model_parallel_world_size() is not None
+            and get_virtual_pipeline_model_parallel_rank() != 0
+        ):
+            return False
+    return get_pipeline_model_parallel_rank() == 0
+
+
+def is_pipeline_last_stage(ignore_virtual=False):
+    """Return True if in the last pipeline model-parallel stage, False otherwise."""
+    if not ignore_virtual:
+        virtual_pipeline_model_parallel_world_size = (
+            get_virtual_pipeline_model_parallel_world_size()
+        )
+        if virtual_pipeline_model_parallel_world_size is not None and get_virtual_pipeline_model_parallel_rank() != (
+            virtual_pipeline_model_parallel_world_size - 1
+        ):
+            return False
+    return get_pipeline_model_parallel_rank() == (get_pipeline_model_parallel_world_size() - 1)
+
+
+def is_rank_in_embedding_group(ignore_virtual=False):
+    """Return true if current rank is in embedding group, False otherwise."""
+    rank = torch.distributed.get_rank()
+    global _EMBEDDING_GLOBAL_RANKS
+    if ignore_virtual:
+        return rank in _EMBEDDING_GLOBAL_RANKS
+    if rank in _EMBEDDING_GLOBAL_RANKS:
+        if rank == _EMBEDDING_GLOBAL_RANKS[0]:
+            return is_pipeline_first_stage(ignore_virtual=False)
+        elif rank == _EMBEDDING_GLOBAL_RANKS[-1]:
+            return is_pipeline_last_stage(ignore_virtual=False)
+        else:
+            return True
+    return False
+
+
+def is_rank_in_position_embedding_group():
+    """Return true if current rank is in position embedding group, False otherwise."""
+    rank = torch.distributed.get_rank()
+    global _POSITION_EMBEDDING_GLOBAL_RANKS
+    return rank in _POSITION_EMBEDDING_GLOBAL_RANKS
+
+
+def is_pipeline_stage_before_split(rank=None):
+    """Return True if pipeline stage executes encoder block for a model
+    with both encoder and decoder."""
+    if get_pipeline_model_parallel_world_size() == 1:
+        return True
+    if rank is None:
+        rank = get_pipeline_model_parallel_rank()
+    global _PIPELINE_MODEL_PARALLEL_SPLIT_RANK
+    if _PIPELINE_MODEL_PARALLEL_SPLIT_RANK is None:
+        return True
+    if rank < _PIPELINE_MODEL_PARALLEL_SPLIT_RANK:
+        return True
+    return False
+
+
+def is_pipeline_stage_after_split(rank=None):
+    """Return True if pipeline stage executes decoder block for a model
+    with both encoder and decoder."""
+    if get_pipeline_model_parallel_world_size() == 1:
+        return True
+    if rank is None:
+        rank = get_pipeline_model_parallel_rank()
+    global _PIPELINE_MODEL_PARALLEL_SPLIT_RANK
+    if _PIPELINE_MODEL_PARALLEL_SPLIT_RANK is None:
+        return True
+    if rank >= _PIPELINE_MODEL_PARALLEL_SPLIT_RANK:
+        return True
+    return False
+
+
+def is_pipeline_stage_at_split():
+    """Return true if pipeline stage executes decoder block and next
+    stage executes encoder block for a model with both encoder and
+    decoder."""
+    rank = get_pipeline_model_parallel_rank()
+    return is_pipeline_stage_before_split(rank) and is_pipeline_stage_after_split(rank + 1)
+
+
+def get_virtual_pipeline_model_parallel_rank():
+    """Return the virtual pipeline-parallel rank."""
+    global _VIRTUAL_PIPELINE_MODEL_PARALLEL_RANK
+    return _VIRTUAL_PIPELINE_MODEL_PARALLEL_RANK
+
+
+def set_virtual_pipeline_model_parallel_rank(rank):
+    """Set the virtual pipeline-parallel rank."""
+    global _VIRTUAL_PIPELINE_MODEL_PARALLEL_RANK
+    _VIRTUAL_PIPELINE_MODEL_PARALLEL_RANK = rank
+
+
+def get_virtual_pipeline_model_parallel_world_size():
+    """Return the virtual pipeline-parallel world size."""
+    global _VIRTUAL_PIPELINE_MODEL_PARALLEL_WORLD_SIZE
+    return _VIRTUAL_PIPELINE_MODEL_PARALLEL_WORLD_SIZE
+
+
+def get_tensor_model_parallel_src_rank():
+    """Calculate the global rank corresponding to the first local rank
+    in the tensor model parallel group."""
+    assert (
+        _TENSOR_MODEL_PARALLEL_GLOBAL_RANKS is not None
+    ), "Tensor model parallel group is not initialized"
+    return _TENSOR_MODEL_PARALLEL_GLOBAL_RANKS[0]
+
+
+def get_data_parallel_src_rank(with_context_parallel=False):
+    """Calculate the global rank corresponding to the first local rank
+    in the data parallel group."""
+    if with_context_parallel:
+        assert (
+            _DATA_PARALLEL_GLOBAL_RANKS_WITH_CP is not None
+        ), "Data parallel group with context parallel combined is not initialized"
+        return _DATA_PARALLEL_GLOBAL_RANKS_WITH_CP[0]
+    else:
+        assert _DATA_PARALLEL_GLOBAL_RANKS is not None, "Data parallel group is not initialized"
+        return _DATA_PARALLEL_GLOBAL_RANKS[0]
+
+
+def get_pipeline_model_parallel_first_rank():
+    """Return the global rank of the first process in the pipeline for the
+    current tensor parallel group"""
+    assert _PIPELINE_GLOBAL_RANKS is not None, "Pipeline parallel group is not initialized"
+    return _PIPELINE_GLOBAL_RANKS[0]
+
+
+def get_pipeline_model_parallel_last_rank():
+    """Return the global rank of the last process in the pipeline for the
+    current tensor parallel group"""
+    assert _PIPELINE_GLOBAL_RANKS is not None, "Pipeline parallel group is not initialized"
+    last_rank_local = get_pipeline_model_parallel_world_size() - 1
+    return _PIPELINE_GLOBAL_RANKS[last_rank_local]
+
+
+def get_pipeline_model_parallel_next_rank():
+    """Return the global rank that follows the caller in the pipeline"""
+    assert _PIPELINE_GLOBAL_RANKS is not None, "Pipeline parallel group is not initialized"
+    rank_in_pipeline = get_pipeline_model_parallel_rank()
+    world_size = get_pipeline_model_parallel_world_size()
+    return _PIPELINE_GLOBAL_RANKS[(rank_in_pipeline + 1) % world_size]
+
+
+def get_pipeline_model_parallel_prev_rank():
+    """Return the global rank that preceeds the caller in the pipeline"""
+    assert _PIPELINE_GLOBAL_RANKS is not None, "Pipeline parallel group is not initialized"
+    rank_in_pipeline = get_pipeline_model_parallel_rank()
+    world_size = get_pipeline_model_parallel_world_size()
+    return _PIPELINE_GLOBAL_RANKS[(rank_in_pipeline - 1) % world_size]
+
+
+def get_data_parallel_world_size(with_context_parallel=False):
+    """Return world size for the data parallel group."""
+    if torch.distributed.is_available() and torch.distributed.is_initialized():
+        return torch.distributed.get_world_size(
+            group=get_data_parallel_group(with_context_parallel=with_context_parallel)
+        )
+    else:
+        return 0
+
+
+def get_data_parallel_rank(with_context_parallel=False):
+    """Return my rank for the data parallel group."""
+    if torch.distributed.is_available() and torch.distributed.is_initialized():
+        return torch.distributed.get_rank(
+            group=get_data_parallel_group(with_context_parallel=with_context_parallel)
+        )
+    else:
+        return 0
+
+
+def get_context_parallel_world_size():
+    """Return world size for the context parallel group."""
+    if torch.distributed.is_available() and torch.distributed.is_initialized():
+        return torch.distributed.get_world_size(group=get_context_parallel_group())
+    else:
+        return 0
+
+
+def get_context_parallel_rank():
+    """Return my rank for the context parallel group."""
+    if torch.distributed.is_available() and torch.distributed.is_initialized():
+        return torch.distributed.get_rank(group=get_context_parallel_group())
+    else:
+        return 0
+
+
+def get_expert_model_parallel_world_size():
+    """Return world size for the expert model parallel group"""
+    if _MPU_EXPERT_MODEL_PARALLEL_WORLD_SIZE:
+        return _MPU_EXPERT_MODEL_PARALLEL_WORLD_SIZE
+    if torch.distributed.is_available() and torch.distributed.is_initialized():
+        tensor_and_expert_parallel_world_size = torch.distributed.get_world_size(
+            group=get_tensor_and_expert_parallel_group()
+        )
+        return tensor_and_expert_parallel_world_size // get_tensor_model_parallel_world_size()
+    else:
+        return 0
+
+
+def get_tensor_and_expert_parallel_world_size():
+    """Return world size for the expert model parallel group times model parallel group.
+       Currently, each expert will also be distributed across TP group by default.
+    """
+    if torch.distributed.is_available() and torch.distributed.is_initialized():
+        tensor_and_expert_parallel_world_size = torch.distributed.get_world_size(
+            group=get_tensor_and_expert_parallel_group()
+        )
+        return tensor_and_expert_parallel_world_size
+    else:
+        return 0
+
+
+def get_expert_model_parallel_rank():
+    """Return my rank for the expert parallel group"""
+    if _MPU_EXPERT_MODEL_PARALLEL_RANK:
+        return _MPU_EXPERT_MODEL_PARALLEL_RANK
+    if torch.distributed.is_available() and torch.distributed.is_initialized():
+        tensor_and_expert_parallel_rank = torch.distributed.get_rank(
+            group=get_tensor_and_expert_parallel_group()
+        )
+        return tensor_and_expert_parallel_rank // get_tensor_model_parallel_world_size()
+    else:
+        return 0
+
+
+def get_data_modulo_expert_parallel_rank():
+    """Return my rank for the context parallel group."""
+    if torch.distributed.is_available() and torch.distributed.is_initialized():
+        return torch.distributed.get_rank(group=get_data_modulo_expert_parallel_group())
+    else:
+        return 0
+
+
+def get_tensor_and_expert_parallel_rank():
+    """Return my rank for the tensor and expert parallel group"""
+    if torch.distributed.is_available() and torch.distributed.is_initialized():
+        return torch.distributed.get_rank(group=get_tensor_and_expert_parallel_group())
+    else:
+        return 0
+
+
+def _set_global_memory_buffer():
+    """Initialize global buffer"""
+    global _GLOBAL_MEMORY_BUFFER
+    assert _GLOBAL_MEMORY_BUFFER is None, 'global memory buffer is already initialized'
+    _GLOBAL_MEMORY_BUFFER = GlobalMemoryBuffer()
+
+
+def get_global_memory_buffer():
+    """Return the global GlobalMemoryBuffer object"""
+    assert _GLOBAL_MEMORY_BUFFER is not None, 'global memory buffer is not initialized'
+    return _GLOBAL_MEMORY_BUFFER
+
+
+def destroy_global_memory_buffer():
+    """Sets the global memory buffer to None"""
+    global _GLOBAL_MEMORY_BUFFER
+    _GLOBAL_MEMORY_BUFFER = None
+
+
+def destroy_model_parallel():
+    """Set the groups to none."""
+    global _MODEL_PARALLEL_GROUP
+    _MODEL_PARALLEL_GROUP = None
+    global _MODEL_AND_EXPERT_PARALLEL_GROUP
+    _MODEL_AND_EXPERT_PARALLEL_GROUP = None
+    global _TENSOR_MODEL_PARALLEL_GROUP
+    _TENSOR_MODEL_PARALLEL_GROUP = None
+    global _PIPELINE_MODEL_PARALLEL_GROUP
+    _PIPELINE_MODEL_PARALLEL_GROUP = None
+    global _DATA_PARALLEL_GROUP
+    _DATA_PARALLEL_GROUP = None
+    global _DATA_PARALLEL_GROUP_WITH_CP
+    _DATA_PARALLEL_GROUP_WITH_CP = None
+    global _CONTEXT_PARALLEL_GROUP
+    _CONTEXT_PARALLEL_GROUP = None
+    global _CONTEXT_PARALLEL_GLOBAL_RANKS
+    _CONTEXT_PARALLEL_GLOBAL_RANKS = None
+    global _EMBEDDING_GROUP
+    _EMBEDDING_GROUP = None
+    global _POSITION_EMBEDDING_GROUP
+    _POSITION_EMBEDDING_GROUP = None
+    global _TENSOR_AND_DATA_PARALLEL_GROUP
+    _TENSOR_AND_DATA_PARALLEL_GROUP = None
+    global _TENSOR_AND_DATA_PARALLEL_GROUP_WITH_CP
+    _TENSOR_AND_DATA_PARALLEL_GROUP_WITH_CP = None
+    global _EXPERT_MODEL_PARALLEL_GROUP
+    _EXPERT_MODEL_PARALLEL_GROUP = None
+    global _TENSOR_AND_EXPERT_PARALLEL_GROUP
+    _TENSOR_AND_EXPERT_PARALLEL_GROUP = None
+    global _DATA_MODULO_EXPERT_PARALLEL_GROUP
+    _DATA_MODULO_EXPERT_PARALLEL_GROUP = None
+    global _VIRTUAL_PIPELINE_MODEL_PARALLEL_RANK
+    _VIRTUAL_PIPELINE_MODEL_PARALLEL_RANK = None
+    global _VIRTUAL_PIPELINE_MODEL_PARALLEL_WORLD_SIZE
+    _VIRTUAL_PIPELINE_MODEL_PARALLEL_WORLD_SIZE = None
+    global _MPU_TENSOR_MODEL_PARALLEL_WORLD_SIZE
+    _MPU_TENSOR_MODEL_PARALLEL_WORLD_SIZE = None
+    global _MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZE
+    _MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZE = None
+    global _MPU_TENSOR_MODEL_PARALLEL_RANK
+    _MPU_TENSOR_MODEL_PARALLEL_RANK = None
+    global _MPU_PIPELINE_MODEL_PARALLEL_RANK
+    _MPU_PIPELINE_MODEL_PARALLEL_RANK = None
+    global _GLOBAL_MEMORY_BUFFER
+    _GLOBAL_MEMORY_BUFFER = None
+    global _MPU_EXPERT_MODEL_PARALLEL_WORLD_SIZE
+    _MPU_EXPERT_MODEL_PARALLEL_WORLD_SIZE = None
+    global _MPU_EXPERT_MODEL_PARALLEL_RANK
+    _MPU_EXPERT_MODEL_PARALLEL_RANK = None

megatron/core/pipeline_parallel/__init__.py

+from .schedules import get_forward_backward_func

megatron/core/pipeline_parallel/p2p_communication.py

+# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
+
+import operator
+from functools import reduce
+from typing import Callable, List, Optional, Tuple, Union
+
+import torch
+
+from megatron import core
+from megatron.core import ModelParallelConfig
+from megatron.core.parallel_state import (
+    get_pipeline_model_parallel_group,
+    get_pipeline_model_parallel_next_rank,
+    get_pipeline_model_parallel_prev_rank,
+    get_pipeline_model_parallel_rank,
+    get_pipeline_model_parallel_world_size,
+)
+
+# Types
+Shape = Union[List[int], torch.Size]
+
+
+def _communicate_shapes(tensor_send_next, tensor_send_prev, recv_prev, recv_next, config):
+    """Communicate tensor shapes between stages. Used to communicate
+    tensor shapes before the actual tensor communication happens.
+    This is required when the sequence lengths across micro batches
+    are not uniform.
+
+    Args:
+        tensor_send_next: tensor to send to next rank (no tensor sent if
+                          set to None).
+        tensor_send_prev: tensor to send to prev rank (no tensor sent if
+                          set to None).
+        recv_prev: boolean for whether tensor should be received from
+                   previous rank.
+        recv_next: boolean for whether tensor should be received from
+                   next rank.
+    Returns:
+        (recv_prev_shape, recv_next_shape)
+    """
+
+    recv_prev_shape_tensor = None
+    recv_next_shape_tensor = None
+    send_prev_shape_tensor = None
+    send_next_shape_tensor = None
+    if recv_prev:
+        recv_prev_shape_tensor = torch.empty(
+            (3), device=torch.cuda.current_device(), dtype=torch.int64
+        )
+    if recv_next:
+        recv_next_shape_tensor = torch.empty(
+            (3), device=torch.cuda.current_device(), dtype=torch.int64
+        )
+    if tensor_send_prev is not None:
+        send_prev_shape_tensor = torch.tensor(
+            tensor_send_prev.size(), device=torch.cuda.current_device(), dtype=torch.int64
+        )
+    if tensor_send_next is not None:
+        send_next_shape_tensor = torch.tensor(
+            tensor_send_next.size(), device=torch.cuda.current_device(), dtype=torch.int64
+        )
+
+    if config.use_ring_exchange_p2p:
+        torch.distributed.ring_exchange(
+            tensor_send_prev=send_prev_shape_tensor,
+            tensor_recv_prev=recv_prev_shape_tensor,
+            tensor_send_next=send_next_shape_tensor,
+            tensor_recv_next=recv_next_shape_tensor,
+            group=get_pipeline_model_parallel_group(),
+        )
+    else:
+        ops = []
+        if send_prev_shape_tensor is not None:
+            send_prev_op = torch.distributed.P2POp(
+                torch.distributed.isend,
+                send_prev_shape_tensor,
+                get_pipeline_model_parallel_prev_rank(),
+            )
+            ops.append(send_prev_op)
+        if recv_prev_shape_tensor is not None:
+            recv_prev_op = torch.distributed.P2POp(
+                torch.distributed.irecv,
+                recv_prev_shape_tensor,
+                get_pipeline_model_parallel_prev_rank(),
+            )
+            ops.append(recv_prev_op)
+        if send_next_shape_tensor is not None:
+            send_next_op = torch.distributed.P2POp(
+                torch.distributed.isend,
+                send_next_shape_tensor,
+                get_pipeline_model_parallel_next_rank(),
+            )
+            ops.append(send_next_op)
+        if recv_next_shape_tensor is not None:
+            recv_next_op = torch.distributed.P2POp(
+                torch.distributed.irecv,
+                recv_next_shape_tensor,
+                get_pipeline_model_parallel_next_rank(),
+            )
+            ops.append(recv_next_op)
+        if len(ops) > 0:
+            reqs = torch.distributed.batch_isend_irecv(ops)
+            for req in reqs:
+                req.wait()
+
+        # To protect against race condition when using batch_isend_irecv().
+        # should take this out once the bug with batch_isend_irecv is resolved.
+        torch.cuda.synchronize()
+
+    recv_prev_shape = [0, 0, 0]
+    if recv_prev_shape_tensor is not None:
+        recv_prev_shape = recv_prev_shape_tensor.tolist()
+
+    recv_next_shape = [0, 0, 0]
+    if recv_next_shape_tensor is not None:
+        recv_next_shape = recv_next_shape_tensor.tolist()
+
+    return recv_prev_shape, recv_next_shape
+
+
+def _batched_p2p_ops(
+    *,
+    tensor_send_prev: Optional[torch.Tensor],
+    tensor_recv_prev: Optional[torch.Tensor],
+    tensor_send_next: Optional[torch.Tensor],
+    tensor_recv_next: Optional[torch.Tensor],
+    group: torch.distributed.ProcessGroup
+):
+    ops = []
+    if tensor_send_prev is not None:
+        send_prev_op = torch.distributed.P2POp(
+            torch.distributed.isend,
+            tensor_send_prev,
+            get_pipeline_model_parallel_prev_rank(),
+            group,
+        )
+        ops.append(send_prev_op)
+    if tensor_recv_prev is not None:
+        recv_prev_op = torch.distributed.P2POp(
+            torch.distributed.irecv,
+            tensor_recv_prev,
+            get_pipeline_model_parallel_prev_rank(),
+            group,
+        )
+        ops.append(recv_prev_op)
+    if tensor_send_next is not None:
+        send_next_op = torch.distributed.P2POp(
+            torch.distributed.isend,
+            tensor_send_next,
+            get_pipeline_model_parallel_next_rank(),
+            group,
+        )
+        ops.append(send_next_op)
+    if tensor_recv_next is not None:
+        recv_next_op = torch.distributed.P2POp(
+            torch.distributed.irecv,
+            tensor_recv_next,
+            get_pipeline_model_parallel_next_rank(),
+            group,
+        )
+        ops.append(recv_next_op)
+    if len(ops) > 0:
+        reqs = torch.distributed.batch_isend_irecv(ops)
+    else:
+        reqs = []
+    return reqs
+
+
+def _p2p_ops(
+    *,
+    tensor_send_prev: Optional[torch.Tensor],
+    tensor_recv_prev: Optional[torch.Tensor],
+    tensor_send_next: Optional[torch.Tensor],
+    tensor_recv_next: Optional[torch.Tensor],
+    group: torch.distributed.ProcessGroup
+):
+    reqs = []
+    rank = get_pipeline_model_parallel_rank()
+    even_send_odd_recv_group = group
+    if get_pipeline_model_parallel_world_size() == 2:
+        # Use the global process group for one of the two p2p communications
+        # to allow the overlap of the independent communications.
+        # Using the global process group is compatible because the pipeline-parallel
+        # communications set the source and destination by global rank.
+        even_recv_odd_send_group = torch.distributed.group.WORLD
+    else:
+        even_recv_odd_send_group = group
+    if get_pipeline_model_parallel_rank() % 2 == 0:
+        if tensor_send_next is not None:
+            send_next_req = torch.distributed.isend(
+                tensor=tensor_send_next,
+                dst=get_pipeline_model_parallel_next_rank(),
+                group=even_send_odd_recv_group,
+            )
+            reqs.append(send_next_req)
+
+        if tensor_recv_prev is not None:
+            recv_prev_req = torch.distributed.irecv(
+                tensor=tensor_recv_prev,
+                src=get_pipeline_model_parallel_prev_rank(),
+                group=even_recv_odd_send_group,
+            )
+            reqs.append(recv_prev_req)
+
+        if tensor_send_prev is not None:
+            send_prev_req = torch.distributed.isend(
+                tensor=tensor_send_prev,
+                dst=get_pipeline_model_parallel_prev_rank(),
+                group=even_send_odd_recv_group,
+            )
+            reqs.append(send_prev_req)
+
+        if tensor_recv_next is not None:
+            recv_next_req = torch.distributed.irecv(
+                tensor=tensor_recv_next,
+                src=get_pipeline_model_parallel_next_rank(),
+                group=even_recv_odd_send_group,
+            )
+            reqs.append(recv_next_req)
+
+    else:
+        if tensor_recv_prev is not None:
+            recv_prev_req = torch.distributed.irecv(
+                tensor=tensor_recv_prev,
+                src=get_pipeline_model_parallel_prev_rank(),
+                group=even_send_odd_recv_group,
+            )
+            reqs.append(recv_prev_req)
+
+        if tensor_send_next is not None:
+            send_next_req = torch.distributed.isend(
+                tensor=tensor_send_next,
+                dst=get_pipeline_model_parallel_next_rank(),
+                group=even_recv_odd_send_group,
+            )
+            reqs.append(send_next_req)
+
+        if tensor_recv_next is not None:
+            recv_next_req = torch.distributed.irecv(
+                tensor=tensor_recv_next,
+                src=get_pipeline_model_parallel_next_rank(),
+                group=even_send_odd_recv_group,
+            )
+            reqs.append(recv_next_req)
+
+        if tensor_send_prev is not None:
+            send_prev_req = torch.distributed.isend(
+                tensor=tensor_send_prev,
+                dst=get_pipeline_model_parallel_prev_rank(),
+                group=even_recv_odd_send_group,
+            )
+            reqs.append(send_prev_req)
+    return reqs
+
+
+def _communicate(
+    *,
+    tensor_send_next: Optional[torch.Tensor],
+    tensor_send_prev: Optional[torch.Tensor],
+    recv_prev: bool,
+    recv_next: bool,
+    tensor_shape: Shape,
+    config: ModelParallelConfig,
+    wait_on_reqs: bool = True
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    """Communicate tensors between stages. Used as helper method in other
+    communication methods that are used in megatron/schedules.py.
+
+    Args:
+        tensor_send_next (torch.Tensor, optional):
+            Tensor to send to next rank (no tensor sent if None)
+
+        tensor_send_prev (torch.Tensor, optional):
+            Tensor to send to prev rank (no tensor sent if None)
+
+        recv_prev (boolean, required):
+            whether tensor should be received from previous rank.
+
+        recv_next (boolean, required):
+            whether tensor should be received from next rank.
+
+        tensor_shape (List[int] or torch.Size, required):
+            shape of tensor to receive (this method assumes that all
+            tensors sent and received in a single function call are
+            the same shape).
+
+        wait_on_reqs (boolean, optional, default=False):
+            For non-batched p2p communication, wait on each request
+            before returning.
+
+    Returns:
+        tuple containing
+
+        - tensor_recv_prev: torch.Tensor if recv_prev is True, None otherwise.
+        - tensor_recv_next: torch.Tensor if recv_next is True, None otherwise.
+
+    """
+
+    # Create placeholder tensors for receive in forward and backward directions
+    # if needed.
+    tensor_recv_prev = None
+    tensor_recv_next = None
+
+    if not config.variable_seq_lengths:
+        recv_prev_shape = tensor_shape
+        recv_next_shape = tensor_shape
+    else:
+        recv_prev_shape, recv_next_shape = _communicate_shapes(
+            tensor_send_next, tensor_send_prev, recv_prev, recv_next, config
+        )
+
+    if recv_prev:
+        if config.pipeline_dtype is None:
+            raise RuntimeError("pipeline_dtype must be provided if recv_prev is True")
+        if tensor_shape is None:
+            raise RuntimeError(
+                "tensor_shape must be specified if recv_prev is True. "
+                "Common tensor_shape is (seq_length, micro_batch_size, hidden_size)"
+            )
+        tensor_recv_prev = torch.empty(
+            recv_prev_shape,
+            requires_grad=True,
+            device=torch.cuda.current_device(),
+            dtype=config.pipeline_dtype,
+        )
+    if recv_next:
+        if config.pipeline_dtype is None:
+            raise RuntimeError("dtype must be provided if recv_next is True")
+        if tensor_shape is None:
+            raise RuntimeError(
+                "tensor_shape must be specified if recv_next is True. "
+                "Common tensor_shape is (seq_length, micro_batch_size, hidden_size)"
+            )
+        tensor_recv_next = torch.empty(
+            recv_next_shape,
+            requires_grad=True,
+            device=torch.cuda.current_device(),
+            dtype=config.pipeline_dtype,
+        )
+
+    # Send tensors in both the forward and backward directions as appropriate.
+    if config.use_ring_exchange_p2p:
+
+        def _ring_exchange_wrapper(**kwargs):
+            torch.distributed.ring_exchange(**kwargs)
+            return []
+
+        p2p_func = _ring_exchange_wrapper
+    elif config.batch_p2p_comm:
+        assert wait_on_reqs
+        p2p_func = _batched_p2p_ops
+    else:
+        p2p_func = _p2p_ops
+
+    reqs = p2p_func(
+        tensor_send_prev=tensor_send_prev,
+        tensor_recv_prev=tensor_recv_prev,
+        tensor_send_next=tensor_send_next,
+        tensor_recv_next=tensor_recv_next,
+        group=get_pipeline_model_parallel_group(),
+    )
+
+    if wait_on_reqs and len(reqs) > 0:
+        for req in reqs:
+            req.wait()
+        reqs = None
+
+    if config.batch_p2p_comm and config.batch_p2p_sync:
+        # To protect against race condition when using batch_isend_irecv().
+        # User should assert that we have a modern enough PyTorch to not need this
+        torch.cuda.synchronize()
+
+    return tensor_recv_prev, tensor_recv_next, reqs
+
+
+def recv_forward(tensor_shape: Shape, config: ModelParallelConfig) -> torch.Tensor:
+    """ Receive tensor from previous rank in pipeline (forward receive).
+
+    See _communicate for argument details.
+    """
+
+    if core.parallel_state.is_pipeline_first_stage():
+        input_tensor = None
+    else:
+        if config.timers is not None:
+            config.timers('forward-recv', log_level=2).start()
+        input_tensor, _, _ = _communicate(
+            tensor_send_next=None,
+            tensor_send_prev=None,
+            recv_prev=True,
+            recv_next=False,
+            tensor_shape=tensor_shape,
+            config=config,
+        )
+        if config.timers is not None:
+            config.timers('forward-recv').stop()
+    return input_tensor
+
+
+def recv_backward(tensor_shape: Shape, config: ModelParallelConfig) -> torch.Tensor:
+    """Receive tensor from next rank in pipeline (backward receive).
+
+    See _communicate for argument details.
+    """
+    if core.parallel_state.is_pipeline_last_stage():
+        output_tensor_grad = None
+    else:
+        if config.timers is not None:
+            config.timers('backward-recv', log_level=2).start()
+        _, output_tensor_grad, _ = _communicate(
+            tensor_send_next=None,
+            tensor_send_prev=None,
+            recv_prev=False,
+            recv_next=True,
+            tensor_shape=tensor_shape,
+            config=config,
+        )
+        if config.timers is not None:
+            config.timers('backward-recv').stop()
+    return output_tensor_grad
+
+
+def send_forward(output_tensor: torch.Tensor, config: ModelParallelConfig) -> None:
+    """Send tensor to next rank in pipeline (forward send).
+
+    See _communicate for argument details.
+    """
+
+    if not core.parallel_state.is_pipeline_last_stage():
+        if config.timers is not None:
+            config.timers('forward-send', log_level=2).start()
+        _communicate(
+            tensor_send_next=output_tensor,
+            tensor_send_prev=None,
+            recv_prev=False,
+            recv_next=False,
+            tensor_shape=None,
+            config=config,
+        )
+        if config.timers is not None:
+            config.timers('forward-send').stop()
+
+
+def send_backward(input_tensor_grad: torch.Tensor, config: ModelParallelConfig) -> None:
+    """Send tensor to previous rank in pipeline (backward send).
+
+    See _communicate for argument details.
+    """
+    if not core.parallel_state.is_pipeline_first_stage():
+        if config.timers is not None:
+            config.timers('backward-send', log_level=2).start()
+        _communicate(
+            tensor_send_next=None,
+            tensor_send_prev=input_tensor_grad,
+            recv_prev=False,
+            recv_next=False,
+            tensor_shape=None,
+            config=config,
+        )
+        if config.timers is not None:
+            config.timers('backward-send').stop()
+
+
+def send_forward_recv_backward(
+    output_tensor: torch.Tensor, tensor_shape: Shape, config: ModelParallelConfig
+) -> torch.Tensor:
+    """Batched send and recv with next rank in pipeline.
+
+    See _communicate for argument details.
+    """
+    if core.parallel_state.is_pipeline_last_stage():
+        output_tensor_grad = None
+    else:
+        if config.timers is not None:
+            config.timers('forward-send-backward-recv', log_level=2).start()
+        #print("++++++++++++output_tensor is:",output_tensor.numel())
+        _, output_tensor_grad, _ = _communicate(
+            tensor_send_next=output_tensor,
+            tensor_send_prev=None,
+            recv_prev=False,
+            recv_next=True,
+            tensor_shape=tensor_shape,
+            config=config,
+        )
+        if config.timers is not None:
+            config.timers('forward-send-backward-recv').stop()
+    return output_tensor_grad
+
+
+def send_backward_recv_forward(
+    input_tensor_grad: torch.Tensor, tensor_shape: Shape, config: ModelParallelConfig
+) -> torch.Tensor:
+    """Batched send and recv with previous rank in pipeline.
+
+    See _communicate for argument details.
+    """
+    if core.parallel_state.is_pipeline_first_stage():
+        input_tensor = None
+    else:
+        if config.timers is not None:
+            config.timers('backward-send-forward-recv', log_level=2).start()
+        input_tensor, _, _ = _communicate(
+            tensor_send_next=None,
+            tensor_send_prev=input_tensor_grad,
+            recv_prev=True,
+            recv_next=False,
+            tensor_shape=tensor_shape,
+            config=config,
+        )
+        if config.timers is not None:
+            config.timers('backward-send-forward-recv').stop()
+    return input_tensor
+
+
+def send_forward_recv_forward(
+    output_tensor: torch.Tensor,
+    recv_prev: bool,
+    tensor_shape: Shape,
+    config: ModelParallelConfig,
+    overlap_p2p_comm: bool = False,
+) -> torch.Tensor:
+    """Batched recv from previous rank and send to next rank in pipeline.
+
+    See _communicate for argument details.
+    """
+    if config.timers is not None:
+        config.timers('forward-send-forward-recv', log_level=2).start()
+    input_tensor, _, wait_handles = _communicate(
+        tensor_send_next=output_tensor,
+        tensor_send_prev=None,
+        recv_prev=recv_prev,
+        recv_next=False,
+        tensor_shape=tensor_shape,
+        wait_on_reqs=(not overlap_p2p_comm),
+        config=config,
+    )
+    if config.timers is not None:
+        config.timers('forward-send-forward-recv').stop()
+    if overlap_p2p_comm:
+        return input_tensor, wait_handles
+    return input_tensor
+
+
+def send_backward_recv_backward(
+    input_tensor_grad: torch.Tensor,
+    recv_next: bool,
+    tensor_shape: Shape,
+    config: ModelParallelConfig,
+    overlap_p2p_comm: bool = False,
+) -> torch.Tensor:
+    """Batched recv from next rank and send to previous rank in pipeline.
+
+    See _communicate for argument details.
+    """
+    if config.timers is not None:
+        config.timers('backward-send-backward-recv', log_level=2).start()
+    _, output_tensor_grad, wait_handles = _communicate(
+        tensor_send_next=None,
+        tensor_send_prev=input_tensor_grad,
+        recv_prev=False,
+        recv_next=recv_next,
+        tensor_shape=tensor_shape,
+        wait_on_reqs=(not overlap_p2p_comm),
+        config=config,
+    )
+    if config.timers is not None:
+        config.timers('backward-send-backward-recv').stop()
+    if overlap_p2p_comm:
+        return output_tensor_grad, wait_handles
+    return output_tensor_grad
+
+
+def send_forward_backward_recv_forward_backward(
+    output_tensor: torch.Tensor,
+    input_tensor_grad: torch.Tensor,
+    recv_prev: bool,
+    recv_next: bool,
+    tensor_shape: Shape,
+    config: ModelParallelConfig,
+) -> torch.Tensor:
+    """Batched send and recv with previous and next ranks in pipeline.
+
+    See _communicate for argument details.
+    """
+    if config.timers is not None:
+        config.timers('forward-backward-send-forward-backward-recv', log_level=2).start()
+    input_tensor, output_tensor_grad, _ = _communicate(
+        tensor_send_next=output_tensor,
+        tensor_send_prev=input_tensor_grad,
+        recv_prev=recv_prev,
+        recv_next=recv_next,
+        tensor_shape=tensor_shape,
+        config=config,
+    )
+    if config.timers is not None:
+        config.timers('forward-backward-send-forward-backward-recv').stop()
+    return input_tensor, output_tensor_grad

megatron/core/pipeline_parallel/schedules.py

+# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
+
+import contextlib
+from typing import Callable, Iterator, List, Optional, Union
+
+import torch
+from torch.autograd.variable import Variable
+
+from megatron.core import parallel_state
+from megatron.core.enums import ModelType
+from megatron.core.pipeline_parallel import p2p_communication
+from megatron.core.transformer.moe.router import MoEAuxLossAutoScaler
+from megatron.core.utils import get_attr_wrapped_model, get_model_config, get_model_type
+
+# Types
+Shape = Union[List[int], torch.Size]
+
+
+def get_forward_backward_func():
+    """Retrieves the appropriate forward_backward function given the
+    configuration of parallel_state.
+
+    Returns a function that will perform all of the forward and
+    backward passes of the model given the pipeline model parallel
+    world size and virtual pipeline model parallel world size in the
+    global parallel_state.
+
+    Note that if using sequence parallelism, the sequence length component of
+    the tensor shape is updated to original_sequence_length /
+    tensor_model_parallel_world_size.
+
+    The function returned takes the following arguments:
+
+    forward_step_func (required): A function that takes a data
+        iterator and a model as its arguments and return the model's
+        forward output and the loss function. The loss function should
+        take one torch.Tensor and return a torch.Tensor of loss and a
+        dictionary of string -> torch.Tensor.
+
+        A third argument, checkpoint_activations_microbatch, indicates
+        that the activations for this microbatch should be
+        checkpointed. A None value for this argument indicates that
+        the default from the configuration should be used. This is
+        used when the
+        num_microbatches_with_partial_activation_checkpoints is used.
+
+        For example:
+
+        def loss_func(loss_mask, output_tensor):
+            losses = output_tensor.float()
+            loss_mask = loss_mask.view(-1).float()
+            loss = torch.sum(losses.view(-1) * loss_mask) / loss_mask.sum()
+
+            # Reduce loss for logging.
+            averaged_loss = average_losses_across_data_parallel_group([loss])
+
+            return loss, {'lm loss': averaged_loss[0]}
+
+        def forward_step(data_iterator, model):
+            data, loss_mask = next(data_iterator)
+            output = model(data)
+            return output, partial(loss_func, loss_mask)
+
+
+        forward_backward_func(forward_step_func=forward_step, ...)
+
+
+    data_iterator (required): an iterator over the data, will be
+        passed as is to forward_step_func. Expected to be a list of
+        iterators in the case of interleaved pipeline parallelism.
+
+    model (required): the actual model. Expected to be a list of modules in the case of interleaved
+        pipeline parallelism. Must be a (potentially wrapped) megatron.core.models.MegatronModule.
+
+    num_microbatches (int, required):
+        The number of microbatches to go through
+
+    seq_length (int, required): Sequence length of the current global batch. If this is a dual-stack
+        transformer, this is the encoder's sequence length. This is ignored if variable_seq_lengths
+        in the config is True. Otherwise, each microbatch in the current global batch size must use
+        this sequence length.
+
+    micro_batch_size (int, required): The number of sequences in a microbatch.
+
+    decoder_seq_length (int, optional): The sequence length for the decoder in a dual-stack
+        transformer. This is ignored for a single-stack transformer.
+
+    forward_only (optional, default = False): Perform only the forward step
+
+    collect_non_loss_data (optional, bool, default=False): TODO
+
+    first_val_step (bool, optional): Is the first step of the validation phase. Used by
+        Transformer Engine modules to only update their fp8 weights only on the first validation step.
+
+    """
+    pipeline_model_parallel_size = parallel_state.get_pipeline_model_parallel_world_size()
+    if pipeline_model_parallel_size > 1:
+        if parallel_state.get_virtual_pipeline_model_parallel_world_size() is not None:
+            forward_backward_func = forward_backward_pipelining_with_interleaving
+        else:
+            forward_backward_func = forward_backward_pipelining_without_interleaving
+    else:
+        forward_backward_func = forward_backward_no_pipelining
+    return forward_backward_func
+
+
+def deallocate_output_tensor(out, deallocate_pipeline_outputs=False):
+    '''Pseudo-deallocate (i.e., set to scalar) the output tensor's '.data' field.
+
+    This method should be called right after the output tensor has been
+    sent to the next pipeline stage. At this point, the output tensor is
+    only useful for its '.grad_fn' field, and not its '.data'.
+    '''
+    if (out is None) or (not deallocate_pipeline_outputs):
+        return
+    assert isinstance(out, torch.Tensor), "expected Tensor, found %s." % type(out).__name__
+    assert out._base is None, "counter-productive to free a view of another tensor."
+    out.data = torch.empty((1,), device=out.device, dtype=out.dtype,)
+
+
+def custom_backward(output, grad_output):
+    '''Directly call C++ autograd engine.
+
+    To make the 'deallocate_output_tensor' (above) optimization work, the C++
+    autograd engine must be called directly, bypassing Pytorch's
+    torch.autograd.backward. Pytorch's 'backward' checks that the output and
+    grad have the same shape, while C++'s 'backward' does not.
+    '''
+
+    assert output.numel() == 1, "output should be pseudo-'freed' in schedule, to optimize memory"
+    assert isinstance(output, torch.Tensor), "output == '%s'." % type(output).__name__
+    assert isinstance(grad_output, (torch.Tensor, type(None))), (
+        "grad_output == '%s'." % type(grad_output).__name__
+    )
+
+    # Handle scalar output
+    if grad_output is None:
+        assert output.numel() == 1, "implicit grad requires scalar output."
+        grad_output = torch.ones_like(output, memory_format=torch.preserve_format,)
+
+    # Call c++ engine [ see torch/csrc/autograd/python_engine.cpp ]
+    Variable._execution_engine.run_backward(
+        tensors=(output,),
+        grad_tensors=(grad_output,),
+        keep_graph=False,
+        create_graph=False,
+        inputs=tuple(),
+        allow_unreachable=True,
+        accumulate_grad=True,
+    )
+
+
+def set_current_microbatch(model, microbatch_id):
+    decoder_exists = True
+    decoder = None
+    try:
+        decoder = get_attr_wrapped_model(model, "decoder")
+    except RuntimeError:
+        decoder_exists = False
+    if decoder_exists and decoder is not None:
+        decoder.current_microbatch = microbatch_id
+
+
+def forward_step(
+    forward_step_func,
+    data_iterator,
+    model,
+    num_microbatches,
+    input_tensor,
+    forward_data_store,
+    config,
+    collect_non_loss_data=False,
+    checkpoint_activations_microbatch=None,
+    is_first_microbatch=False,
+    current_microbatch=None,
+):
+
+    """Forward step for passed-in model.
+
+    If first stage, input tensor is obtained from data_iterator, otherwise
+    passed-in input_tensor is used.
+
+    Returns output tensor."""
+    if config.timers is not None:
+        config.timers('forward-compute', log_level=2).start()
+
+    if is_first_microbatch and hasattr(model, 'set_is_first_microbatch'):
+        model.set_is_first_microbatch()
+    if current_microbatch is not None:
+        set_current_microbatch(model, current_microbatch)
+
+    unwrap_output_tensor = False
+    if not isinstance(input_tensor, list):
+        input_tensor = [input_tensor]
+        unwrap_output_tensor = True
+
+    set_input_tensor = get_attr_wrapped_model(model, "set_input_tensor")
+    set_input_tensor(input_tensor)
+
+    if config.enable_autocast:
+        context_manager = torch.autocast("cuda", dtype=config.autocast_dtype)
+    else:
+        context_manager = contextlib.nullcontext()
+    with context_manager:
+        if checkpoint_activations_microbatch is None:
+            output_tensor, loss_func = forward_step_func(data_iterator, model)
+        else:
+            output_tensor, loss_func = forward_step_func(
+                data_iterator, model, checkpoint_activations_microbatch
+            )
+
+    num_tokens = torch.tensor(0, dtype=torch.int)
+    if parallel_state.is_pipeline_last_stage():
+        if not collect_non_loss_data:
+            outputs = loss_func(output_tensor)
+            if len(outputs) == 3:
+                output_tensor, num_tokens, loss_reduced = outputs
+                if not config.calculate_per_token_loss:
+                    output_tensor /= num_tokens
+                    output_tensor /= num_microbatches
+            else:
+                # preserve legacy loss averaging behavior (ie, over the number of microbatches)
+                assert len(outputs) == 2
+                output_tensor, loss_reduced = outputs
+                output_tensor /= num_microbatches
+            forward_data_store.append(loss_reduced)
+        else:
+            data = loss_func(output_tensor, non_loss_data=True)
+            forward_data_store.append(data)
+
+    if config.timers is not None:
+        config.timers('forward-compute').stop()
+
+    # Set the loss scale for the auxiliary loss of the MoE layer.
+    # Since we use a trick to do backward on the auxiliary loss, we need to set the scale explicitly.
+    if hasattr(config, 'num_moe_experts') and config.num_moe_experts is not None:
+        # Calculate the loss scale based on the grad_scale_func if available, else default to 1.
+        loss_scale = (
+            config.grad_scale_func(torch.ones(1, device=output_tensor.device))
+            if config.grad_scale_func is not None
+            else torch.tensor(1.0)
+        )
+        # Set the loss scale
+        MoEAuxLossAutoScaler.set_loss_scale(loss_scale / num_microbatches)
+
+    # If T5 model (or other model with encoder and decoder)
+    # and in decoder stack, then send encoder_hidden_state
+    # downstream as well.
+    model_type = get_model_type(model)
+    if (
+        parallel_state.is_pipeline_stage_after_split()
+        and model_type == ModelType.encoder_and_decoder
+    ):
+        return [output_tensor, input_tensor[-1]], num_tokens
+
+    if unwrap_output_tensor:
+        return output_tensor, num_tokens
+    return [output_tensor], num_tokens
+
+
+def backward_step(input_tensor, output_tensor, output_tensor_grad, model_type, config):
+    """Backward step through passed-in output tensor.
+
+    If last stage, output_tensor_grad is None, otherwise gradient of loss
+    with respect to stage's output tensor.
+
+    Returns gradient of loss with respect to input tensor (None if first
+    stage)."""
+
+    # NOTE: This code currently can handle at most one skip connection. It
+    # needs to be modified slightly to support arbitrary numbers of skip
+    # connections.
+
+    if config.timers is not None:
+        config.timers('backward-compute', log_level=2).start()
+
+    # Retain the grad on the input_tensor.
+    unwrap_input_tensor_grad = False
+    if not isinstance(input_tensor, list):
+        input_tensor = [input_tensor]
+        unwrap_input_tensor_grad = True
+    for x in input_tensor:
+        if x is not None:
+            x.retain_grad()
+
+    if not isinstance(output_tensor, list):
+        output_tensor = [output_tensor]
+    if not isinstance(output_tensor_grad, list):
+        output_tensor_grad = [output_tensor_grad]
+
+    # Backward pass.
+    if output_tensor_grad[0] is None and config.grad_scale_func is not None:
+        output_tensor[0] = config.grad_scale_func(output_tensor[0])
+
+    if config.deallocate_pipeline_outputs:
+        custom_backward(output_tensor[0], output_tensor_grad[0])
+    else:
+        torch.autograd.backward(output_tensor[0], grad_tensors=output_tensor_grad[0])
+
+    # Collect the grad of the input_tensor.
+    input_tensor_grad = [None]
+    if input_tensor is not None:
+        input_tensor_grad = []
+        for x in input_tensor:
+            if x is None:
+                input_tensor_grad.append(None)
+            else:
+                input_tensor_grad.append(x.grad)
+
+    # Handle single skip connection if it exists (encoder_hidden_state in
+    # model with encoder and decoder).
+    if (
+        parallel_state.get_pipeline_model_parallel_world_size() > 1
+        and parallel_state.is_pipeline_stage_after_split()
+        and model_type == ModelType.encoder_and_decoder
+    ):
+        if output_tensor_grad[1] is not None:
+            input_tensor_grad[-1].add_(output_tensor_grad[1])
+    if unwrap_input_tensor_grad:
+        input_tensor_grad = input_tensor_grad[0]
+
+    if config.timers is not None:
+        config.timers('backward-compute').stop()
+
+    return input_tensor_grad
+
+
+def check_first_val_step(first_val_step, forward_only, cond):
+    if (first_val_step is not None) and forward_only:
+        return first_val_step and cond
+    else:
+        return cond
+
+
+def forward_backward_no_pipelining(
+    *,
+    forward_step_func,
+    data_iterator: Union[Iterator, List[Iterator]],
+    model: Union[torch.nn.Module, List[torch.nn.Module]],
+    num_microbatches: int,
+    seq_length: int,  # unused
+    micro_batch_size: int,  # unused
+    decoder_seq_length: int = None,  # unused
+    forward_only: bool = False,
+    collect_non_loss_data: bool = False,
+    first_val_step: bool = None,
+):
+    """Run forward and backward passes with no pipeline parallelism
+    (no inter-stage communication).
+
+    Returns dictionary with losses.
+
+
+    See get_forward_backward_func() for argument details
+    """
+
+    if isinstance(model, list):
+        assert len(model) == 1, "non-pipeline-parallel schedule does not support model chunking"
+        model = model[0]
+    if isinstance(data_iterator, list):
+        assert (
+            len(data_iterator) == 1
+        ), "non-pipeline-parallel schedule does not support model chunking"
+        data_iterator = data_iterator[0]
+
+    config = get_model_config(model)
+    if config.timers is not None:
+        config.timers('forward-backward', log_level=1).start(barrier=config.barrier_with_L1_time)
+
+    no_sync_func = config.no_sync_func
+    if no_sync_func is None:
+        no_sync_func = contextlib.nullcontext
+
+    model_type = get_model_type(model)
+
+    forward_data_store = []
+    input_tensor, output_tensor_grad = None, None
+    total_num_tokens = torch.tensor(0, dtype=torch.int).cuda()
+    with no_sync_func():
+        for i in range(num_microbatches - 1):
+            output_tensor, num_tokens = forward_step(
+                forward_step_func,
+                data_iterator,
+                model,
+                num_microbatches,
+                input_tensor,
+                forward_data_store,
+                config,
+                collect_non_loss_data,
+                is_first_microbatch=check_first_val_step(first_val_step, forward_only, i == 0),
+                current_microbatch=i,
+            )
+            total_num_tokens += num_tokens.item()
+            if not forward_only:
+                backward_step(input_tensor, output_tensor, output_tensor_grad, model_type, config)
+
+    # Run computation for last microbatch out of context handler (want to
+    # synchronize gradients).
+    output_tensor, num_tokens = forward_step(
+        forward_step_func,
+        data_iterator,
+        model,
+        num_microbatches,
+        input_tensor,
+        forward_data_store,
+        config,
+        collect_non_loss_data,
+        is_first_microbatch=check_first_val_step(
+            first_val_step, forward_only, num_microbatches == 1
+        ),
+        current_microbatch=num_microbatches - 1,
+    )
+    total_num_tokens += num_tokens.item()
+
+    if not forward_only:
+        backward_step(input_tensor, output_tensor, output_tensor_grad, model_type, config)
+
+    if config.finalize_model_grads_func is not None and not forward_only:
+        # Finalize model grads (perform full grad all-reduce / reduce-scatter for
+        # data parallelism and layernorm all-reduce for sequence parallelism).
+        config.finalize_model_grads_func(
+            [model], total_num_tokens if config.calculate_per_token_loss else None
+        )
+
+    if config.timers is not None:
+        config.timers('forward-backward').stop()
+
+    return forward_data_store
+
+
+def forward_backward_pipelining_with_interleaving(
+    *,
+    forward_step_func,
+    data_iterator: Union[Iterator, List[Iterator]],
+    model: Union[torch.nn.Module, List[torch.nn.Module]],
+    num_microbatches: int,
+    seq_length: int,
+    micro_batch_size: int,
+    decoder_seq_length: int = None,
+    forward_only: bool = False,
+    collect_non_loss_data: bool = False,
+    first_val_step: bool = None,
+):
+    """Run interleaved 1F1B schedule (model split into model chunks), with
+    communication between pipeline stages as needed.
+
+    Returns dictionary with losses if the last stage, empty dict otherwise."""
+    assert isinstance(model, list), "interleaved pipeline parallelism expected model chunking"
+    assert all(isinstance(chunk, torch.nn.Module) for chunk in model), "invalid model chunking"
+    assert isinstance(
+        data_iterator, list
+    ), "interleaved pipeline parallelism expected each model chunk to have a data iterator"
+
+    config = get_model_config(model[0])
+    if config.overlap_p2p_comm and config.batch_p2p_comm:
+        raise ValueError("Can not use both overlap_p2p_comm and batch_p2p_comm")
+
+    if config.timers is not None:
+        config.timers('forward-backward', log_level=1).start(barrier=config.barrier_with_L1_time)
+
+    # Disable async grad reductions
+    no_sync_func = config.no_sync_func
+    if isinstance(no_sync_func, list):
+
+        def multi_no_sync():
+            stack = contextlib.ExitStack()
+            for model_chunk_no_sync_func in config.no_sync_func:
+                stack.enter_context(model_chunk_no_sync_func())
+            return stack
+
+        no_sync_func = multi_no_sync
+    if no_sync_func is None:
+        no_sync_func = contextlib.nullcontext
+    no_sync_context = None
+
+    if config.grad_sync_func is not None and not isinstance(config.grad_sync_func, list):
+        config.grad_sync_func = [config.grad_sync_func for _ in model]
+
+    if config.param_sync_func is not None and not isinstance(config.param_sync_func, list):
+        config.param_sync_func = [config.param_sync_func for _ in model]
+
+    def disable_grad_sync():
+        """Disable asynchronous grad reductions"""
+        nonlocal no_sync_context
+        if no_sync_context is None:
+            no_sync_context = no_sync_func()
+            no_sync_context.__enter__()
+
+    def enable_grad_sync():
+        """Enable asynchronous grad reductions"""
+        nonlocal no_sync_context
+        if no_sync_context is not None:
+            no_sync_context.__exit__(None, None, None)
+            no_sync_context = None
+
+    disable_grad_sync()
+
+    # Model chunk IDs with synchronized grads
+    synchronized_model_chunks = set()
+
+    input_tensors = [[] for _ in range(len(model))]
+    output_tensors = [[] for _ in range(len(model))]
+    total_num_tokens = torch.tensor(0, dtype=torch.int).cuda()
+
+    forward_data_store = []
+    if not forward_only:
+        output_tensor_grads = [[] for _ in range(len(model))]
+
+    pipeline_parallel_size = parallel_state.get_pipeline_model_parallel_world_size()
+    pipeline_parallel_rank = parallel_state.get_pipeline_model_parallel_rank()
+
+    if num_microbatches % pipeline_parallel_size != 0:
+        msg = f'number of microbatches ({num_microbatches}) is not divisible by '
+        msg += f'pipeline-model-parallel-size ({pipeline_parallel_size}) '
+        msg += 'when using interleaved schedule'
+        raise RuntimeError(msg)
+
+    model_type = get_model_type(model[0])
+    if model_type == ModelType.encoder_and_decoder:
+        raise RuntimeError("Interleaving is not supported with an encoder and decoder model.")
+
+    if decoder_seq_length is not None and decoder_seq_length != seq_length:
+        raise RuntimeError(
+            "Interleaving is not supported with a different decoder sequence length."
+        )
+
+    tensor_shape = [seq_length, micro_batch_size, config.hidden_size]
+    tensor_shape[0] = tensor_shape[0] // parallel_state.get_context_parallel_world_size()
+    if config.sequence_parallel:
+        tensor_shape[0] = tensor_shape[0] // parallel_state.get_tensor_model_parallel_world_size()
+
+    # Compute number of warmup and remaining microbatches.
+    num_model_chunks = len(model)
+    total_num_microbatches = num_microbatches * num_model_chunks
+    all_warmup_microbatches = False
+    if forward_only:
+        num_warmup_microbatches = total_num_microbatches
+    else:
+        # Run all forward passes and then all backward passes if number of
+        # microbatches is just the number of pipeline stages.
+        # Otherwise, perform (num_model_chunks-1)*pipeline_parallel_size on
+        # all workers, followed by more microbatches after depending on
+        # stage ID (more forward passes for earlier stages, later stages can
+        # immediately start with 1F1B).
+        if num_microbatches == pipeline_parallel_size:
+            num_warmup_microbatches = total_num_microbatches
+            all_warmup_microbatches = True
+        else:
+            num_warmup_microbatches = (pipeline_parallel_size - pipeline_parallel_rank - 1) * 2
+            num_warmup_microbatches += (num_model_chunks - 1) * pipeline_parallel_size
+            num_warmup_microbatches = min(num_warmup_microbatches, total_num_microbatches)
+    num_microbatches_remaining = total_num_microbatches - num_warmup_microbatches
+
+    # Checkpoint the activations of partial Transformer layers in a number of micro-batches
+    # within the maximum outstanding micro-batch backpropagations.
+    # Micro-batches with the ids less than 'num_microbatches_with_partial_activation_checkpoints'
+    # checkpoint partial Transformer layers (or skip checkpointing) and
+    # the rest of micro-batches within a window of micro-batches checkpoint
+    # all Transformer layers. The window of micro-batches is set by the maximum
+    # outstanding backpropagations and becomes smaller at later pipeline stages.
+    # Please refer the appendix C in https://arxiv.org/pdf/2205.05198.pdf
+    max_outstanding_backprops = None
+    if config.num_microbatches_with_partial_activation_checkpoints is not None:
+        max_outstanding_backprops = num_warmup_microbatches + 1
+
+    # Synchronize params for first two model chunks
+    if config.param_sync_func is not None:
+        config.param_sync_func[0](model[0].parameters())
+        config.param_sync_func[1](model[1].parameters())
+
+    def get_model_chunk_id(microbatch_id, forward):
+        """Helper method to get the model chunk ID given the iteration number."""
+        microbatch_id_in_group = microbatch_id % (pipeline_parallel_size * num_model_chunks)
+        model_chunk_id = microbatch_id_in_group // pipeline_parallel_size
+        if not forward:
+            model_chunk_id = num_model_chunks - model_chunk_id - 1
+        return model_chunk_id
+
+    def get_microbatch_id_in_model_chunk(iteration_id, forward):
+        """Helper method to get the microbatch_id within model chunk given the iteration number."""
+        assert forward
+        iteration_group_id = iteration_id // (pipeline_parallel_size * num_model_chunks)
+        microbatch_id_in_model_chunk = (iteration_group_id * pipeline_parallel_size) + (
+            iteration_id % pipeline_parallel_size
+        )
+        return microbatch_id_in_model_chunk
+
+    def is_first_microbatch_for_model_chunk(microbatch_id: int) -> bool:
+        """Check if an iteration is the first for a model chunk."""
+        microbatch_group_size = pipeline_parallel_size * num_model_chunks
+        num_microbatch_groups = total_num_microbatches // microbatch_group_size
+        microbatch_group_id = microbatch_id // microbatch_group_size
+        microbatch_id_in_group = microbatch_id % microbatch_group_size
+        if microbatch_group_id == 0:
+            return microbatch_id_in_group % pipeline_parallel_size == 0
+        else:
+            return False
+
+    def is_last_microbatch_for_model_chunk(microbatch_id: int) -> bool:
+        """Check if an iteration is the last for a model chunk."""
+        microbatch_group_size = pipeline_parallel_size * num_model_chunks
+        num_microbatch_groups = total_num_microbatches // microbatch_group_size
+        microbatch_group_id = microbatch_id // microbatch_group_size
+        microbatch_id_in_group = microbatch_id % microbatch_group_size
+        if microbatch_group_id == num_microbatch_groups - 1:
+            return microbatch_id_in_group % pipeline_parallel_size == pipeline_parallel_size - 1
+        else:
+            return False
+
+    def forward_step_helper(microbatch_id, current_microbatch, checkpoint_activations_microbatch):
+        """Helper method to run forward step with model split into chunks
+        (run set_virtual_pipeline_model_parallel_rank() before calling
+        forward_step())."""
+        model_chunk_id = get_model_chunk_id(microbatch_id, forward=True)
+        parallel_state.set_virtual_pipeline_model_parallel_rank(model_chunk_id)
+
+        # launch param synchronization for next model chunk
+        # Note: Asynchronous communication tends to slow down compute.
+        # To reduce idling from mismatched microbatch times, we launch
+        # asynchronous communication at the same time across the
+        # pipeline-parallel group.
+        if config.param_sync_func is not None:
+            param_sync_microbatch_id = microbatch_id + pipeline_parallel_rank
+            if (
+                param_sync_microbatch_id < total_num_microbatches
+                and is_first_microbatch_for_model_chunk(param_sync_microbatch_id)
+            ):
+                param_sync_chunk_id = get_model_chunk_id(param_sync_microbatch_id, forward=True) + 1
+                if 1 < param_sync_chunk_id < num_model_chunks:
+                    config.param_sync_func[param_sync_chunk_id](
+                        model[param_sync_chunk_id].parameters()
+                    )
+
+        # forward step
+        if parallel_state.is_pipeline_first_stage():
+            if len(input_tensors[model_chunk_id]) == len(output_tensors[model_chunk_id]):
+                input_tensors[model_chunk_id].append(None)
+        input_tensor = input_tensors[model_chunk_id][-1]
+
+        output_tensor, num_tokens = forward_step(
+            forward_step_func,
+            data_iterator[model_chunk_id],
+            model[model_chunk_id],
+            num_microbatches,
+            input_tensor,
+            forward_data_store,
+            config,
+            collect_non_loss_data,
+            checkpoint_activations_microbatch,
+            check_first_val_step(
+                first_val_step, forward_only, is_first_microbatch_for_model_chunk(microbatch_id),
+            ),
+            current_microbatch=current_microbatch,
+        )
+        output_tensors[model_chunk_id].append(output_tensor)
+
+        nonlocal total_num_tokens
+        total_num_tokens += num_tokens.item()
+
+        # if forward-only, no need to save tensors for a backward pass
+        if forward_only:
+            input_tensors[model_chunk_id].pop()
+            output_tensors[model_chunk_id].pop()
+
+        return output_tensor
+
+    def backward_step_helper(microbatch_id):
+        """Helper method to run backward step with model split into chunks
+        (run set_virtual_pipeline_model_parallel_rank() before calling
+        backward_step())."""
+        model_chunk_id = get_model_chunk_id(microbatch_id, forward=False)
+        parallel_state.set_virtual_pipeline_model_parallel_rank(model_chunk_id)
+
+        # launch grad synchronization (default)
+        if config.grad_sync_func is None and is_last_microbatch_for_model_chunk(microbatch_id):
+            enable_grad_sync()
+            synchronized_model_chunks.add(model_chunk_id)
+
+        if parallel_state.is_pipeline_last_stage():
+            if len(output_tensor_grads[model_chunk_id]) == 0:
+                output_tensor_grads[model_chunk_id].append(None)
+        input_tensor = input_tensors[model_chunk_id].pop(0)
+        output_tensor = output_tensors[model_chunk_id].pop(0)
+        output_tensor_grad = output_tensor_grads[model_chunk_id].pop(0)
+        input_tensor_grad = backward_step(
+            input_tensor, output_tensor, output_tensor_grad, model_type, config
+        )
+
+        # launch grad synchronization (custom grad sync)
+        # Note: Asynchronous communication tends to slow down compute.
+        # To reduce idling from mismatched microbatch times, we launch
+        # asynchronous communication at the same time across the
+        # pipeline-parallel group.
+        if config.grad_sync_func is not None:
+            grad_sync_microbatch_id = microbatch_id - pipeline_parallel_rank
+            if grad_sync_microbatch_id >= 0 and is_last_microbatch_for_model_chunk(
+                grad_sync_microbatch_id
+            ):
+                grad_sync_chunk_id = get_model_chunk_id(grad_sync_microbatch_id, forward=False)
+                enable_grad_sync()
+                config.grad_sync_func[grad_sync_chunk_id](model[grad_sync_chunk_id].parameters())
+                synchronized_model_chunks.add(grad_sync_chunk_id)
+        disable_grad_sync()
+
+        return input_tensor_grad
+
+    # Run warmup forward passes.
+    parallel_state.set_virtual_pipeline_model_parallel_rank(0)
+    input_tensors[0].append(p2p_communication.recv_forward(tensor_shape, config))
+
+    fwd_wait_handles = None
+    bwd_wait_handles = None
+
+    for k in range(num_warmup_microbatches):
+
+        if fwd_wait_handles is not None:
+            for req in fwd_wait_handles:
+                req.wait()
+
+        cur_model_chunk_id = get_model_chunk_id(k, forward=True)
+        # Decide to checkpoint all layers' activations of the current micro-batch
+        if max_outstanding_backprops is not None:
+            checkpoint_activations_microbatch = (
+                k % max_outstanding_backprops
+                >= config.num_microbatches_with_partial_activation_checkpoints
+            )
+        else:
+            checkpoint_activations_microbatch = None
+
+        current_microbatch = get_microbatch_id_in_model_chunk(k, forward=True)
+        output_tensor = forward_step_helper(
+            k, current_microbatch, checkpoint_activations_microbatch
+        )
+
+        # Determine if tensor should be received from previous stage.
+        next_forward_model_chunk_id = get_model_chunk_id(k + 1, forward=True)
+        recv_prev = True
+        if parallel_state.is_pipeline_first_stage(ignore_virtual=True):
+            if next_forward_model_chunk_id == 0:
+                recv_prev = False
+        if k == (total_num_microbatches - 1):
+            recv_prev = False
+
+        # Don't send tensor downstream if on last stage.
+        if parallel_state.is_pipeline_last_stage():
+            output_tensor = None
+
+        # Send and receive tensors as appropriate (send tensors computed
+        # in this iteration; receive tensors for next iteration).
+        if not config.overlap_p2p_comm:
+            if (
+                k == (num_warmup_microbatches - 1)
+                and not forward_only
+                and not all_warmup_microbatches
+            ):
+                input_tensor_grad = None
+                recv_next = True
+                if parallel_state.is_pipeline_last_stage(ignore_virtual=True):
+                    recv_next = False
+                (
+                    input_tensor,
+                    output_tensor_grad,
+                ) = p2p_communication.send_forward_backward_recv_forward_backward(
+                    output_tensor,
+                    input_tensor_grad,
+                    recv_prev=recv_prev,
+                    recv_next=recv_next,
+                    tensor_shape=tensor_shape,
+                    config=config,
+                )
+                output_tensor_grads[num_model_chunks - 1].append(output_tensor_grad)
+            else:
+                input_tensor = p2p_communication.send_forward_recv_forward(
+                    output_tensor, recv_prev=recv_prev, tensor_shape=tensor_shape, config=config
+                )
+            input_tensors[next_forward_model_chunk_id].append(input_tensor)
+        else:
+            input_tensor, fwd_wait_handles = p2p_communication.send_forward_recv_forward(
+                output_tensor,
+                recv_prev=recv_prev,
+                tensor_shape=tensor_shape,
+                config=config,
+                overlap_p2p_comm=True,
+            )
+
+            if (
+                k == (num_warmup_microbatches - 1)
+                and not forward_only
+                and not all_warmup_microbatches
+            ):
+                input_tensor_grad = None
+                recv_next = True
+                if parallel_state.is_pipeline_last_stage(ignore_virtual=True):
+                    recv_next = False
+
+                (
+                    output_tensor_grad,
+                    bwd_wait_handles,
+                ) = p2p_communication.send_backward_recv_backward(
+                    input_tensor_grad,
+                    recv_next=recv_next,
+                    tensor_shape=tensor_shape,
+                    config=config,
+                    overlap_p2p_comm=True,
+                )
+
+                output_tensor_grads[num_model_chunks - 1].append(output_tensor_grad)
+            input_tensors[next_forward_model_chunk_id].append(input_tensor)
+
+        deallocate_output_tensor(output_tensor, config.deallocate_pipeline_outputs)
+
+    # Run 1F1B in steady state.
+    for k in range(num_microbatches_remaining):
+        # Forward pass.
+        forward_k = k + num_warmup_microbatches
+
+        # Decide to checkpoint all layers' activations of the current micro-batch
+        if max_outstanding_backprops is not None:
+            checkpoint_activations_microbatch = (
+                forward_k % max_outstanding_backprops
+                >= config.num_microbatches_with_partial_activation_checkpoints
+            )
+        else:
+            checkpoint_activations_microbatch = None
+
+        cur_model_chunk_id = get_model_chunk_id(forward_k, forward=True)
+        current_microbatch = get_microbatch_id_in_model_chunk(forward_k, forward=True)
+        if config.overlap_p2p_comm:
+            if fwd_wait_handles is not None:
+                for req in fwd_wait_handles:
+                    req.wait()
+
+            deallocate_output_tensor(output_tensor, config.deallocate_pipeline_outputs)
+
+            output_tensor = forward_step_helper(
+                forward_k, current_microbatch, checkpoint_activations_microbatch
+            )
+
+            # Determine if current stage has anything to send in either direction,
+            # otherwise set tensor to None.
+            forward_model_chunk_id = get_model_chunk_id(forward_k, forward=True)
+            parallel_state.set_virtual_pipeline_model_parallel_rank(forward_model_chunk_id)
+
+            # Last virtual stage no activation tensor to send
+            if parallel_state.is_pipeline_last_stage():
+                output_tensor = None
+
+            # Determine if peers are sending, and where in data structure to put
+            # received tensors.
+            recv_prev = True
+            if parallel_state.is_pipeline_first_stage(ignore_virtual=True):
+                # First stage is ahead of last stage by (pipeline_parallel_size - 1).
+                next_forward_model_chunk_id = get_model_chunk_id(
+                    forward_k - (pipeline_parallel_size - 1), forward=True
+                )
+                if next_forward_model_chunk_id == (num_model_chunks - 1):
+                    recv_prev = False
+                next_forward_model_chunk_id += 1
+            else:
+                next_forward_model_chunk_id = get_model_chunk_id(forward_k + 1, forward=True)
+
+            # If last iteration, don't receive; we already received one extra
+            # before the start of the for loop.
+            if k == (num_microbatches_remaining - 1):
+                recv_prev = False
+
+            # Send activation tensor to the next stage and receive activation tensor from the
+            # previous stage
+            input_tensor, fwd_wait_handles = p2p_communication.send_forward_recv_forward(
+                output_tensor,
+                recv_prev=recv_prev,
+                tensor_shape=tensor_shape,
+                config=config,
+                overlap_p2p_comm=True,
+            )
+            # assert fwd_wait_handles is not None
+
+            if bwd_wait_handles is not None:
+                for req in bwd_wait_handles:
+                    req.wait()
+
+            # Backward pass.
+            backward_k = k
+            input_tensor_grad = backward_step_helper(backward_k)
+
+            backward_model_chunk_id = get_model_chunk_id(backward_k, forward=False)
+            parallel_state.set_virtual_pipeline_model_parallel_rank(backward_model_chunk_id)
+
+            # First virtual stage no activation gradient tensor to send
+            if parallel_state.is_pipeline_first_stage():
+                input_tensor_grad = None
+
+            # Determine if the current virtual stage has an activation gradient tensor to receive
+            recv_next = True
+            if parallel_state.is_pipeline_last_stage(ignore_virtual=True):
+                # Last stage is ahead of first stage by (pipeline_parallel_size - 1).
+                next_backward_model_chunk_id = get_model_chunk_id(
+                    backward_k - (pipeline_parallel_size - 1), forward=False
+                )
+                if next_backward_model_chunk_id == 0:
+                    recv_next = False
+                next_backward_model_chunk_id -= 1
+            else:
+                next_backward_model_chunk_id = get_model_chunk_id(backward_k + 1, forward=False)
+
+            output_tensor_grad, bwd_wait_handles = p2p_communication.send_backward_recv_backward(
+                input_tensor_grad,
+                recv_next=recv_next,
+                tensor_shape=tensor_shape,
+                config=config,
+                overlap_p2p_comm=True,
+            )
+
+        else:  # no p2p overlap
+            output_tensor = forward_step_helper(
+                forward_k, current_microbatch, checkpoint_activations_microbatch
+            )
+
+            # Backward pass.
+            backward_k = k
+            input_tensor_grad = backward_step_helper(backward_k)
+
+            # Send output_tensor and input_tensor_grad, receive input_tensor
+            # and output_tensor_grad.
+
+            # Determine if current stage has anything to send in either direction,
+            # otherwise set tensor to None.
+            forward_model_chunk_id = get_model_chunk_id(forward_k, forward=True)
+            parallel_state.set_virtual_pipeline_model_parallel_rank(forward_model_chunk_id)
+            if parallel_state.is_pipeline_last_stage():
+                output_tensor = None
+
+            backward_model_chunk_id = get_model_chunk_id(backward_k, forward=False)
+            parallel_state.set_virtual_pipeline_model_parallel_rank(backward_model_chunk_id)
+            if parallel_state.is_pipeline_first_stage():
+                input_tensor_grad = None
+
+            # Determine if peers are sending, and where in data structure to put
+            # received tensors.
+            recv_prev = True
+            if parallel_state.is_pipeline_first_stage(ignore_virtual=True):
+                # First stage is ahead of last stage by (pipeline_parallel_size - 1).
+                next_forward_model_chunk_id = get_model_chunk_id(
+                    forward_k - (pipeline_parallel_size - 1), forward=True
+                )
+                if next_forward_model_chunk_id == (num_model_chunks - 1):
+                    recv_prev = False
+                next_forward_model_chunk_id += 1
+            else:
+                next_forward_model_chunk_id = get_model_chunk_id(forward_k + 1, forward=True)
+
+            recv_next = True
+            if parallel_state.is_pipeline_last_stage(ignore_virtual=True):
+                # Last stage is ahead of first stage by (pipeline_parallel_size - 1).
+                next_backward_model_chunk_id = get_model_chunk_id(
+                    backward_k - (pipeline_parallel_size - 1), forward=False
+                )
+                if next_backward_model_chunk_id == 0:
+                    recv_next = False
+                next_backward_model_chunk_id -= 1
+            else:
+                next_backward_model_chunk_id = get_model_chunk_id(backward_k + 1, forward=False)
+
+            # If last iteration, don't receive; we already received one extra
+            # before the start of the for loop.
+            if k == (num_microbatches_remaining - 1):
+                recv_prev = False
+
+            # Communicate tensors.
+            (
+                input_tensor,
+                output_tensor_grad,
+            ) = p2p_communication.send_forward_backward_recv_forward_backward(
+                output_tensor,
+                input_tensor_grad,
+                recv_prev=recv_prev,
+                recv_next=recv_next,
+                tensor_shape=tensor_shape,
+                config=config,
+            )
+            deallocate_output_tensor(output_tensor, config.deallocate_pipeline_outputs)
+
+        # Put input_tensor and output_tensor_grad in data structures in the
+        # right location.
+        if recv_prev:
+            input_tensors[next_forward_model_chunk_id].append(input_tensor)
+        if recv_next:
+            output_tensor_grads[next_backward_model_chunk_id].append(output_tensor_grad)
+
+    deallocate_output_tensor(output_tensor, config.deallocate_pipeline_outputs)
+
+    # Run cooldown backward passes (flush out pipeline).
+    if not forward_only:
+        if config.overlap_p2p_comm and bwd_wait_handles is not None:
+            for wait_handle in bwd_wait_handles:
+                wait_handle.wait()
+
+        if all_warmup_microbatches:
+            output_tensor_grads[num_model_chunks - 1].append(
+                p2p_communication.recv_backward(tensor_shape, config=config)
+            )
+        for k in range(num_microbatches_remaining, total_num_microbatches):
+            input_tensor_grad = backward_step_helper(k)
+            next_backward_model_chunk_id = get_model_chunk_id(k + 1, forward=False)
+            recv_next = True
+            if parallel_state.is_pipeline_last_stage(ignore_virtual=True):
+                if next_backward_model_chunk_id == (num_model_chunks - 1):
+                    recv_next = False
+            if k == (total_num_microbatches - 1):
+                recv_next = False
+            output_tensor_grads[next_backward_model_chunk_id].append(
+                p2p_communication.send_backward_recv_backward(
+                    input_tensor_grad, recv_next=recv_next, tensor_shape=tensor_shape, config=config
+                )
+            )
+
+        # Launch any remaining grad reductions.
+        enable_grad_sync()
+        if config.grad_sync_func is not None:
+            for model_chunk_id in range(num_model_chunks):
+                if model_chunk_id not in synchronized_model_chunks:
+                    config.grad_sync_func[model_chunk_id](model[model_chunk_id].parameters())
+                    synchronized_model_chunks.add(model_chunk_id)
+
+    if config.finalize_model_grads_func is not None and not forward_only:
+        # Finalize model grads (perform full grad all-reduce / reduce-scatter for
+        # data parallelism, layernorm all-reduce for sequence parallelism, and
+        # embedding all-reduce for pipeline parallelism).
+        config.finalize_model_grads_func(
+            model, total_num_tokens if config.calculate_per_token_loss else None
+        )
+
+    if config.timers is not None:
+        config.timers('forward-backward').stop()
+
+    return forward_data_store
+
+
+def get_tensor_shapes(
+    *,
+    rank: int,
+    model_type: ModelType,
+    seq_length: int,
+    micro_batch_size: int,
+    decoder_seq_length: int,
+    config,
+):
+    # Determine right tensor sizes (based on position of rank with respect to split
+    # rank) and model size.
+    # Send two tensors if model is T5 and rank is in decoder stage:
+    #     first tensor is decoder (pre-transpose),
+    #     second tensor is encoder (post-transpose).
+    # If model is T5 and rank is at the boundary:
+    #     send one tensor (post-transpose from encoder).
+    # Otherwise, send one tensor (pre-transpose).
+    tensor_shapes = []
+
+    seq_length = seq_length // parallel_state.get_context_parallel_world_size()
+    if model_type == ModelType.encoder_and_decoder:
+        decoder_seq_length = decoder_seq_length // parallel_state.get_context_parallel_world_size()
+
+    if config.sequence_parallel:
+        seq_length = seq_length // parallel_state.get_tensor_model_parallel_world_size()
+        if model_type == ModelType.encoder_and_decoder:
+            decoder_seq_length = (
+                decoder_seq_length // parallel_state.get_tensor_model_parallel_world_size()
+            )
+
+    if model_type == ModelType.encoder_and_decoder:
+        if parallel_state.is_pipeline_stage_before_split(rank):
+            tensor_shapes.append((seq_length, micro_batch_size, config.hidden_size))
+        else:
+            tensor_shapes.append((decoder_seq_length, micro_batch_size, config.hidden_size))
+            tensor_shapes.append((seq_length, micro_batch_size, config.hidden_size))
+    else:
+        tensor_shapes.append((seq_length, micro_batch_size, config.hidden_size))
+    return tensor_shapes
+
+
+def recv_forward(tensor_shapes, config):
+    input_tensors = []
+    for tensor_shape in tensor_shapes:
+        if tensor_shape is None:
+            input_tensors.append(None)
+        else:
+            input_tensors.append(p2p_communication.recv_forward(tensor_shape, config))
+    return input_tensors
+
+
+def recv_backward(tensor_shapes, config):
+    output_tensor_grads = []
+    for tensor_shape in tensor_shapes:
+        if tensor_shape is None:
+            output_tensor_grads.append(None)
+        else:
+            output_tensor_grads.append(p2p_communication.recv_backward(tensor_shape, config))
+    return output_tensor_grads
+
+
+def send_forward(output_tensors, tensor_shapes, config):
+    if not isinstance(output_tensors, list):
+        output_tensors = [output_tensors]
+    for (output_tensor, tensor_shape) in zip(output_tensors, tensor_shapes):
+        if tensor_shape is None:
+            continue
+        p2p_communication.send_forward(output_tensor, config)
+
+
+def send_backward(input_tensor_grads, tensor_shapes, config):
+    if not isinstance(input_tensor_grads, list):
+        input_tensor_grads = [input_tensor_grads]
+    for (input_tensor_grad, tensor_shape) in zip(input_tensor_grads, tensor_shapes):
+        if tensor_shape is None:
+            continue
+        p2p_communication.send_backward(input_tensor_grad, config)
+
+
+def send_forward_recv_backward(output_tensors, tensor_shapes, config):
+    if not isinstance(output_tensors, list):
+        output_tensors = [output_tensors]
+    output_tensor_grads = []
+    for (output_tensor, tensor_shape) in zip(output_tensors, tensor_shapes):
+        if tensor_shape is None:
+            output_tensor_grads.append(None)
+            continue
+        output_tensor_grad = p2p_communication.send_forward_recv_backward(
+            output_tensor, tensor_shape, config
+        )
+        output_tensor_grads.append(output_tensor_grad)
+    return output_tensor_grads
+
+
+def send_backward_recv_forward(input_tensor_grads, tensor_shapes, config):
+    if not isinstance(input_tensor_grads, list):
+        input_tensor_grads = [input_tensor_grads]
+    input_tensors = []
+    for (input_tensor_grad, tensor_shape) in zip(input_tensor_grads, tensor_shapes):
+        if tensor_shape is None:
+            input_tensors.append(None)
+            continue
+        input_tensor = p2p_communication.send_backward_recv_forward(
+            input_tensor_grad, tensor_shape, config
+        )
+        input_tensors.append(input_tensor)
+    return input_tensors
+
+
+def forward_backward_pipelining_without_interleaving(
+    *,
+    forward_step_func,
+    data_iterator: Union[Iterator, List[Iterator]],
+    model: Union[torch.nn.Module, List[torch.nn.Module]],
+    num_microbatches: int,
+    seq_length: int,
+    micro_batch_size: int,
+    decoder_seq_length: int = None,
+    forward_only: bool = False,
+    collect_non_loss_data: bool = False,
+    first_val_step: bool = None,
+):
+    """Run non-interleaved 1F1B schedule, with communication between pipeline
+    stages.
+
+    Returns dictionary with losses if the last stage, empty dict otherwise."""
+
+    if isinstance(model, list):
+        assert (
+            len(model) == 1
+        ), "non-interleaved pipeline parallelism does not support model chunking"
+        model = model[0]
+    if isinstance(data_iterator, list):
+        assert (
+            len(data_iterator) == 1
+        ), "non-pipeline-parallel schedule does not support model chunking"
+        data_iterator = data_iterator[0]
+
+    config = get_model_config(model)
+    if config.overlap_p2p_comm:
+        raise ValueError(
+            "Non-interleaved pipeline parallelism does not support overlapping p2p communication"
+        )
+
+    if config.timers is not None:
+        config.timers('forward-backward', log_level=1).start(barrier=config.barrier_with_L1_time)
+
+    # Disable async grad reductions
+    no_sync_func = config.no_sync_func
+    if no_sync_func is None:
+        no_sync_func = contextlib.nullcontext
+    no_sync_context = None
+
+    def disable_grad_sync():
+        """Disable asynchronous grad reductions"""
+        nonlocal no_sync_context
+        if no_sync_context is None:
+            no_sync_context = no_sync_func()
+            no_sync_context.__enter__()
+
+    def enable_grad_sync():
+        """Enable asynchronous grad reductions"""
+        nonlocal no_sync_context
+        if no_sync_context is not None:
+            no_sync_context.__exit__(None, None, None)
+            no_sync_context = None
+
+    disable_grad_sync()
+
+    # Compute number of warmup microbatches.
+    num_warmup_microbatches = (
+        parallel_state.get_pipeline_model_parallel_world_size()
+        - parallel_state.get_pipeline_model_parallel_rank()
+        - 1
+    )
+    num_warmup_microbatches = min(num_warmup_microbatches, num_microbatches)
+    num_microbatches_remaining = num_microbatches - num_warmup_microbatches
+
+    # Checkpoint the activations of partial Transformer layers in a number of micro-batches
+    # within the maximum outstanding micro-batch backpropagations.
+    # Micro-batches with the ids less than 'num_microbatches_with_partial_activation_checkpoints'
+    # checkpoint partial Transformer layers (or skip checkpointing) and
+    # the rest of micro-batches within a window of micro-batches checkpoint
+    # all Transformer layers. The window of micro-batches is set by the maximum
+    # outstanding backpropagations and becomes smaller at later pipeline stages.
+    # Please refer the appendix C in https://arxiv.org/pdf/2205.05198.pdf
+    max_outstanding_backprops = None
+    if config.num_microbatches_with_partial_activation_checkpoints is not None:
+        max_outstanding_backprops = num_warmup_microbatches + 1
+
+    model_type = get_model_type(model)
+
+    rank = parallel_state.get_pipeline_model_parallel_rank()
+    recv_tensor_shapes = get_tensor_shapes(
+        rank=rank - 1,
+        model_type=model_type,
+        seq_length=seq_length,
+        micro_batch_size=micro_batch_size,
+        decoder_seq_length=decoder_seq_length,
+        config=config,
+    )
+    send_tensor_shapes = get_tensor_shapes(
+        rank=rank,
+        model_type=model_type,
+        seq_length=seq_length,
+        micro_batch_size=micro_batch_size,
+        decoder_seq_length=decoder_seq_length,
+        config=config,
+    )
+
+    # Input, output tensors only need to be saved when doing backward passes
+    input_tensors = None
+    output_tensors = None
+    total_num_tokens = torch.tensor(0, dtype=torch.int).cuda()
+
+    if not forward_only:
+        input_tensors = []
+        output_tensors = []
+    forward_data_store = []
+
+    # Run warmup forward passes.
+    for i in range(num_warmup_microbatches):
+        # Decide to checkpoint all layers' activations of the current micro-batch
+        if max_outstanding_backprops is not None:
+            checkpoint_activations_microbatch = (
+                i % max_outstanding_backprops
+                >= config.num_microbatches_with_partial_activation_checkpoints
+            )
+        else:
+            checkpoint_activations_microbatch = None
+
+        input_tensor = recv_forward(recv_tensor_shapes, config)
+        output_tensor, num_tokens = forward_step(
+            forward_step_func,
+            data_iterator,
+            model,
+            num_microbatches,
+            input_tensor,
+            forward_data_store,
+            config,
+            collect_non_loss_data,
+            checkpoint_activations_microbatch,
+            check_first_val_step(first_val_step, forward_only, i == 0),
+            current_microbatch=i,
+        )
+        send_forward(output_tensor, send_tensor_shapes, config)
+        total_num_tokens += num_tokens.item()
+
+        if not forward_only:
+            input_tensors.append(input_tensor)
+            output_tensors.append(output_tensor)
+            deallocate_output_tensor(output_tensor[0], config.deallocate_pipeline_outputs)
+
+    # Before running 1F1B, need to receive first forward tensor.
+    # If all microbatches are run in warmup / cooldown phase, then no need to
+    # receive this tensor here.
+    if num_microbatches_remaining > 0:
+        input_tensor = recv_forward(recv_tensor_shapes, config)
+
+    # Run 1F1B in steady state.
+    for i in range(num_microbatches_remaining):
+        last_iteration = i == (num_microbatches_remaining - 1)
+
+        # Decide to checkpoint all layers' activations of the current micro-batch
+        if max_outstanding_backprops is not None:
+            checkpoint_activations_microbatch = (
+                (i + num_warmup_microbatches) % max_outstanding_backprops
+            ) >= config.num_microbatches_with_partial_activation_checkpoints
+        else:
+            checkpoint_activations_microbatch = None
+
+        output_tensor, num_tokens = forward_step(
+            forward_step_func,
+            data_iterator,
+            model,
+            num_microbatches,
+            input_tensor,
+            forward_data_store,
+            config,
+            collect_non_loss_data,
+            checkpoint_activations_microbatch,
+            check_first_val_step(
+                first_val_step, forward_only, (i == 0) and (num_warmup_microbatches == 0)
+            ),
+            current_microbatch=i + num_warmup_microbatches,
+        )
+        total_num_tokens += num_tokens.item()
+
+        if forward_only:
+            send_forward(output_tensor, send_tensor_shapes, config)
+
+            if not last_iteration:
+                input_tensor = recv_forward(recv_tensor_shapes, config)
+
+        else:
+            output_tensor_grad = send_forward_recv_backward(
+                output_tensor, send_tensor_shapes, config
+            )
+
+            # Add input_tensor and output_tensor to end of list.
+            input_tensors.append(input_tensor)
+            output_tensors.append(output_tensor)
+            deallocate_output_tensor(output_tensor[0], config.deallocate_pipeline_outputs)
+
+            # Pop input_tensor and output_tensor from the start of the list for
+            # the backward pass.
+            input_tensor = input_tensors.pop(0)
+            output_tensor = output_tensors.pop(0)
+
+            # Enable grad sync for the last microbatch in the batch if the full
+            # backward pass completes in the 1F1B stage.
+            if num_warmup_microbatches == 0 and last_iteration:
+                if config.grad_sync_func is None or rank == 0:
+                    enable_grad_sync()
+
+            input_tensor_grad = backward_step(
+                input_tensor, output_tensor, output_tensor_grad, model_type, config
+            )
+
+            if last_iteration:
+                input_tensor = None
+                send_backward(input_tensor_grad, recv_tensor_shapes, config)
+            else:
+                input_tensor = send_backward_recv_forward(
+                    input_tensor_grad, recv_tensor_shapes, config
+                )
+
+    # Run cooldown backward passes.
+    if not forward_only:
+        for i in range(num_warmup_microbatches):
+
+            # Enable async grad reduction in the last backward pass
+            # Note: If grad sync function is provided, only enable
+            # async grad reduction in first pipeline stage. Other
+            # pipeline stages do grad reduction during pipeline
+            # bubble.
+            if i == num_warmup_microbatches - 1:
+                if config.grad_sync_func is None or rank == 0:
+                    enable_grad_sync()
+
+            input_tensor = input_tensors.pop(0)
+            output_tensor = output_tensors.pop(0)
+
+            output_tensor_grad = recv_backward(send_tensor_shapes, config)
+
+            input_tensor_grad = backward_step(
+                input_tensor, output_tensor, output_tensor_grad, model_type, config
+            )
+
+            send_backward(input_tensor_grad, recv_tensor_shapes, config)
+
+        # Launch any remaining grad reductions.
+        if no_sync_context is not None:
+            enable_grad_sync()
+            if config.grad_sync_func is not None:
+                config.grad_sync_func(model.parameters())
+
+    if config.finalize_model_grads_func is not None and not forward_only:
+        # Finalize model grads (perform full grad all-reduce / reduce-scatter for
+        # data parallelism, layernorm all-reduce for sequence parallelism, and
+        # embedding all-reduce for pipeline parallelism).
+        config.finalize_model_grads_func(
+            [model], total_num_tokens if config.calculate_per_token_loss else None
+        )
+
+    if config.timers is not None:
+        config.timers('forward-backward').stop()
+
+    return forward_data_store

megatron/core/requirements.txt

+torch
\ No newline at end of file

megatron/core/ssm/mamba_block.py

+# Copyright (c) 2024, Tri Dao, Albert Gu.
+# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+
+# Some of this code was adopted from https://github.com/state-spaces/mamba/
+# This source code is licensed under the Apache license found in the
+# LICENSE file in the root directory of this source tree.
+
+import math
+from dataclasses import dataclass
+from functools import partial
+from typing import Union
+
+from torch import Tensor, nn
+
+from megatron.core import parallel_state
+from megatron.core.ssm.mamba_hybrid_layer_allocation import Symbols as LayerSymbols
+from megatron.core.ssm.mamba_hybrid_layer_allocation import allocate_layers
+from megatron.core.tensor_parallel import get_cuda_rng_tracker
+from megatron.core.transformer.custom_layers.transformer_engine import TENorm
+from megatron.core.transformer.identity_op import IdentityOp
+from megatron.core.transformer.module import MegatronModule
+from megatron.core.transformer.spec_utils import ModuleSpec, build_module
+from megatron.core.transformer.transformer_config import TransformerConfig
+from megatron.core.utils import make_viewless_tensor
+
+
+def create_mamba_block(
+    config, mamba_layer_spec, residual_in_fp32=False, layer_idx=None,
+):
+    block = build_module(
+        mamba_layer_spec, config, residual_in_fp32=residual_in_fp32, layer_idx=layer_idx,
+    )
+    block.layer_idx = layer_idx
+    return block
+
+
+# https://github.com/huggingface/transformers/blob/c28d04e9e252a1a099944e325685f14d242ecdcd/src/transformers/models/gpt2/modeling_gpt2.py#L454
+def _init_weights(
+    module,
+    n_layer,
+    initializer_range=0.02,  # Now only used for embedding layer.
+    rescale_prenorm_residual=True,
+    n_residuals_per_layer=1,  # Change to 2 if we have MLP
+):
+    with get_cuda_rng_tracker().fork():
+        if isinstance(module, nn.Linear):
+            if not getattr(module.weight, "_no_reinit", False):
+                nn.init.normal_(module.weight, std=initializer_range)
+            if module.bias is not None:
+                if not getattr(module.bias, "_no_reinit", False):
+                    nn.init.zeros_(module.bias)
+        elif isinstance(module, nn.Embedding):
+            nn.init.normal_(module.weight, std=initializer_range)
+
+        for name, p in module.named_parameters():
+            if name in ["in_proj.weight", "x_proj.weight", "conv1d.weight", "out_proj.weight"]:
+                nn.init.kaiming_uniform(p, a=math.sqrt(5))
+
+        if rescale_prenorm_residual:
+            # Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme:
+            #   > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale
+            #   > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers.
+            #   >   -- GPT-2 :: https://openai.com/blog/better-language-models/
+            #
+            # Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py
+            for name, p in module.named_parameters():
+                if name in ["out_proj.weight", "fc2.weight"]:
+                    # Special Scaled Initialization
+                    nn.init.normal_(
+                        p,
+                        mean=0.0,
+                        std=initializer_range / math.sqrt(n_residuals_per_layer * n_layer),
+                    )
+
+
+@dataclass
+class MambaStackSubmodules:
+    mamba_layer: Union[ModuleSpec, type] = IdentityOp
+    attention_layer: Union[ModuleSpec, type] = IdentityOp
+    mlp_layer: Union[ModuleSpec, type] = IdentityOp
+
+
+class MambaStack(MegatronModule):
+    def __init__(
+        self,
+        config: TransformerConfig,
+        submodules: MambaStackSubmodules,
+        residual_in_fp32=False,
+        pre_process: bool = True,
+        hybrid_attention_ratio: float = 0.0,
+        hybrid_mlp_ratio: float = 0.0,
+        hybrid_override_pattern: str = None,
+        post_layer_norm: bool = True,
+        post_process: bool = True,
+        device=None,
+        dtype=None,
+    ) -> None:
+        super().__init__(config=config)
+        self.residual_in_fp32 = residual_in_fp32
+        self.pre_process = pre_process
+        self.post_layer_norm = post_layer_norm
+        self.post_process = post_process
+
+        # Required for pipeline parallel schedules
+        self.input_tensor = None
+
+        self.hybrid_attention_ratio = hybrid_attention_ratio
+        self.hybrid_mlp_ratio = hybrid_mlp_ratio
+        self.hybrid_override_pattern = hybrid_override_pattern
+
+        layer_type_list = allocate_layers(
+            self.config.num_layers,
+            self.hybrid_attention_ratio,
+            self.hybrid_mlp_ratio,
+            self.hybrid_override_pattern,
+        )
+
+        pp_layer_offset = 0
+        if parallel_state.get_pipeline_model_parallel_world_size() > 1:
+            pp_layer_offset, layer_type_list = self._select_layers_for_pipeline_parallel(
+                layer_type_list
+            )
+
+        self.layers = nn.ModuleList()
+        for i, layer_type in enumerate(layer_type_list):
+            if layer_type == LayerSymbols.MAMBA:
+                layer_idx = i + pp_layer_offset
+                block = create_mamba_block(
+                    self.config,
+                    submodules.mamba_layer,
+                    residual_in_fp32=residual_in_fp32,
+                    layer_idx=layer_idx,
+                )
+            elif layer_type == LayerSymbols.ATTENTION:
+                # Wondering if layer_number should be i+1. See TransformerBlock
+                # and TransformerLayer::sharded_state_dict
+                # Also, transformer layers apply their own pp_layer_offset
+                block = build_module(submodules.attention_layer, config=self.config, layer_number=i)
+            elif layer_type == LayerSymbols.MLP:
+                # Wondering if layer_number should be i+1. See TransformerBlock
+                # and TransformerLayer::sharded_state_dict
+                # Also, transformer layers apply their own pp_layer_offset
+                block = build_module(submodules.mlp_layer, config=self.config, layer_number=i)
+            else:
+                assert True, "unexpected layer_type"
+            self.layers.append(block)
+
+        # Required for activation recomputation
+        self.num_layers_per_pipeline_rank = len(self.layers)
+
+        if self.post_process and self.post_layer_norm:
+            # Final layer norm before output.
+            self.final_norm = TENorm(
+                config=self.config,
+                hidden_size=self.config.hidden_size,
+                eps=self.config.layernorm_epsilon,
+            )
+
+        self.apply(partial(_init_weights, n_layer=self.config.num_layers,))
+
+    def _select_layers_for_pipeline_parallel(self, layer_type_list):
+        pipeline_rank = parallel_state.get_pipeline_model_parallel_rank()
+        num_layers_per_pipeline_rank = (
+            self.config.num_layers // parallel_state.get_pipeline_model_parallel_world_size()
+        )
+
+        assert parallel_state.get_virtual_pipeline_model_parallel_world_size() is None, (
+            "The Mamba hybrid model does not currently support "
+            "virtual/interleaved pipeline parallelism"
+        )
+
+        offset = pipeline_rank * num_layers_per_pipeline_rank
+        selected_list = layer_type_list[offset : offset + num_layers_per_pipeline_rank]
+
+        return offset, selected_list
+
+    def allocate_inference_cache(self, batch_size, max_seqlen, dtype=None):
+        return {
+            i: layer.allocate_inference_cache(batch_size, max_seqlen, dtype=dtype)
+            for i, layer in enumerate(self.layers)
+        }
+
+    def set_input_tensor(self, input_tensor: Tensor):
+        """Set input tensor to be used instead of forward()'s input.
+
+        When doing pipeline parallelism the input from the previous
+        stage comes from communication, not from the input, so the
+        model's forward_step_func won't have it. This function is thus
+        used by internal code to bypass the input provided by the
+        forward_step_func"""
+        self.input_tensor = input_tensor
+
+    def forward(
+        self,
+        hidden_states: Tensor,
+        attention_mask: Tensor,
+        inference_params=None,
+        rotary_pos_emb: Tensor = None,
+    ):
+        if not self.pre_process:
+            # See set_input_tensor()
+            hidden_states = self.input_tensor
+
+        if inference_params:
+            # NOTE(bnorick): match InferenceParams attributes for mamba_ssm.utils.generation.InferenceParams,
+            # this hack supports eval
+            inference_params.max_seqlen = inference_params.max_sequence_length
+            inference_params.seqlen_offset = inference_params.sequence_len_offset
+
+        for layer in self.layers:
+            hidden_states = layer(
+                hidden_states,
+                attention_mask,
+                inference_params=inference_params,
+                rotary_pos_emb=rotary_pos_emb,
+            )
+
+            # The attention layer (currently a simplified transformer layer)
+            # outputs a tuple of (hidden_states, context). Context is intended
+            # for cross-attention, and is not needed in our model.
+            if isinstance(hidden_states, tuple):
+                hidden_states = hidden_states[0]
+
+        # Final layer norm.
+        if self.post_process and self.post_layer_norm:
+            hidden_states = self.final_norm(hidden_states)
+
+        # Ensure that the tensor passed between pipeline parallel stages is
+        # viewless. See related notes in TransformerBlock and TransformerLayer
+        output = make_viewless_tensor(
+            inp=hidden_states, requires_grad=hidden_states.requires_grad, keep_graph=True
+        )
+
+        return hidden_states

megatron/core/ssm/mamba_hybrid_layer_allocation.py

+# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+
+import logging
+
+if __name__ != "__main__":
+    from megatron.core.utils import log_single_rank
+else:
+    from typing import Any
+
+    def log_single_rank(logger: logging.Logger, *args: Any, rank: int = 0, **kwargs: Any):
+        print(*args[1:], **kwargs)
+
+
+logger = logging.getLogger(__name__)
+
+
+class Symbols:
+    MAMBA = 'M'
+    ATTENTION = '*'
+    MLP = '-'
+    VALID = {MAMBA, ATTENTION, MLP}
+
+
+def _allocate_auto(
+    total_layers_count: int, target_attention_ratio: float, target_mlp_ratio: float
+) -> list:
+    # First, allocate attention (evenly spaced, starting and ending with mamba)
+    attention_layers_count: int = round(total_layers_count * target_attention_ratio)
+    mamba_layers_count: int = total_layers_count - attention_layers_count
+    mamba_sections_count: int = attention_layers_count + 1
+    mamba_section_length: float = mamba_layers_count / mamba_sections_count
+
+    layer_type_list = [Symbols.MAMBA] * total_layers_count
+    x: float = mamba_section_length
+    for l in range(total_layers_count):
+        if x < 0.5:
+            layer_type_list[l] = Symbols.ATTENTION
+            x += mamba_section_length
+        else:
+            x -= 1
+
+    # Next, allocate mlp
+    # (evenly distributed, but right-justified, not replacing attention)
+    mlp_layers_count: int = round(total_layers_count * target_mlp_ratio)
+    if mlp_layers_count > 0:
+        mamba_layers_count -= mlp_layers_count
+        mamba_to_mlp_ratio: float = mamba_layers_count / mlp_layers_count
+
+        x: float = mamba_to_mlp_ratio
+        for l in range(total_layers_count):
+            if layer_type_list[l] == Symbols.MAMBA:
+                if x < 0.5:
+                    layer_type_list[l] = Symbols.MLP
+                    x += mamba_to_mlp_ratio
+                else:
+                    x -= 1
+
+    return layer_type_list
+
+
+def _allocate_override(total_layers_count: int, override_pattern: str) -> list:
+    layer_type_list = list(override_pattern)
+    override_pattern_length = len(layer_type_list)
+    if override_pattern_length != total_layers_count:
+        raise ValueError(
+            "The hybrid override pattern is the wrong "
+            f"length: got {override_pattern_length}, expected "
+            f"{total_layers_count}"
+        )
+    for l in layer_type_list:
+        if l not in Symbols.VALID:
+            raise ValueError(f"In hybrid override pattern, '{l}' is not " f"one of {Symbols.VALID}")
+
+    return layer_type_list
+
+
+def _layer_counts_match(a: list, b: list) -> bool:
+    for s in Symbols.VALID:
+        if a.count(s) != b.count(s):
+            return False
+    return True
+
+
+def allocate_layers(
+    total_layers_count: int,
+    target_attention_ratio: float,
+    target_mlp_ratio: float,
+    override_pattern: str = None,
+) -> list:
+    assert total_layers_count > 0
+    assert target_attention_ratio >= 0.0 and target_attention_ratio <= 1.0
+    assert target_mlp_ratio >= 0.0 and target_mlp_ratio <= 1.0
+    assert target_attention_ratio + target_mlp_ratio <= 1.0
+    # Note: target_mamba_ratio = 1.0 - target_attention_ratio - target_mlp_ratio
+
+    layer_type_list = _allocate_auto(total_layers_count, target_attention_ratio, target_mlp_ratio)
+
+    if override_pattern is not None:
+        layer_type_list_override = _allocate_override(total_layers_count, override_pattern)
+        log_single_rank(logger, logging.INFO, "Using hybrid override pattern")
+        if (target_attention_ratio > 0.0 or target_mlp_ratio > 0.0) and not _layer_counts_match(
+            layer_type_list_override, layer_type_list
+        ):
+            raise ValueError(
+                "The number of each type of layer in the override "
+                "pattern must match the number in the overridden "
+                "pattern."
+            )
+        if layer_type_list_override == layer_type_list:
+            log_single_rank(
+                logger, logging.INFO, "The override pattern matches the overridden pattern"
+            )
+        else:
+            log_single_rank(logger, logging.INFO, "Warning: overriding pattern A with pattern B")
+            log_single_rank(logger, logging.INFO, f"A: {''.join(layer_type_list)}")
+            log_single_rank(logger, logging.INFO, f"B: {''.join(layer_type_list_override)}")
+        layer_type_list = layer_type_list_override
+
+    if target_attention_ratio > 0.0 or target_mlp_ratio > 0.0 or override_pattern is not None:
+        actual_attention_layers_count = layer_type_list.count(Symbols.ATTENTION)
+        actual_attention_ratio = actual_attention_layers_count / total_layers_count
+        actual_mlp_layers_count = layer_type_list.count(Symbols.MLP)
+        actual_mlp_ratio = actual_mlp_layers_count / total_layers_count
+        allocation_string = ''.join(layer_type_list)
+        log_single_rank(
+            logger,
+            logging.INFO,
+            f"Hybrid allocation ({Symbols.MAMBA} is mamba, "
+            f"{Symbols.ATTENTION} is attention, "
+            f"{Symbols.MLP} is mlp):",
+        )
+        log_single_rank(logger, logging.INFO, allocation_string)
+        log_single_rank(
+            logger,
+            logging.INFO,
+            f"{actual_attention_layers_count} attention layers in "
+            f"{total_layers_count} total layers.",
+        )
+        log_single_rank(
+            logger,
+            logging.INFO,
+            f"Target attention ratio: {target_attention_ratio:.2f}. "
+            f"Actual attention ratio: {actual_attention_ratio:.2f}.",
+        )
+        log_single_rank(
+            logger,
+            logging.INFO,
+            f"{actual_mlp_layers_count} mlp layers in " f"{total_layers_count} total layers.",
+        )
+        log_single_rank(
+            logger,
+            logging.INFO,
+            f"Target mlp ratio: {target_mlp_ratio:.2f}. "
+            f"Actual mlp ratio: {actual_mlp_ratio:.2f}.",
+        )
+    return layer_type_list
+
+
+if __name__ == "__main__":
+    test_cases = [
+        # (10, 0.2, 0.0),
+        # (48, 0.0, 0.0), # will not print anything
+        # (48, 0.1, 0.0),
+        # 48, 0.3, 0.0),
+        # (48, 0.5, 0.0),
+        # (48, 0.6, 0.0),
+        # (48, 0.7, 0.0),
+        # (10, 0.0, 0.1),
+        # (10, 0.0, 0.3),
+        # (10, 0.0, 0.5),
+        # (10, 0.1, 0.1),
+        # (10, 0.2, 0.2),
+        # (10, 0.3, 0.3),
+        # (10, 0.5, 0.5),
+        # (48, 0.2, 0.3),
+        # (48, 0.5, 0.2),
+        # (48, 0.5, 0.2, "MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-"),
+        # (48, 0.25, 0.25, "MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-"),
+        # (48, 0.25, 0.25, "MM-*MM-*MM*-MM*-MM*-MM*-M*M-M*M-M*M-M*M-*MM-*MM-"),
+        # (48, 0.0, 0.2, "MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-"),
+        # (48, 0.2, 0.0, "MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-"),
+        # (48, 0.0, 0.0, "MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-"),
+        # (48, 0.5, 0.5),
+        # (10, 0.3, 0.2, "MMM*-*M*M-"),
+        # (10, 0.3, 0.2, "MM*M-*M*M-"),
+        (9, 0.0, 0.0, "M*-M*-M*-"),
+        (9, 0.0, 0.0, "MMMMMMMMM"),
+    ]
+    for t in test_cases:
+        print("")
+        allocate_layers(*t)

megatron/core/ssm/mamba_layer.py

+# Copyright (c) 2024, Tri Dao, Albert Gu.
+# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+
+# Some of this code was adopted from https://github.com/state-spaces/mamba/
+# This source code is licensed under the Apache license found in the
+# LICENSE file in the root directory of this source tree.
+
+from dataclasses import dataclass
+from typing import Union
+
+import torch
+from torch import Tensor
+
+from megatron.core.transformer.identity_op import IdentityOp
+from megatron.core.transformer.module import MegatronModule
+from megatron.core.transformer.spec_utils import ModuleSpec, build_module
+from megatron.core.transformer.transformer_config import TransformerConfig
+
+
+@dataclass
+class MambaLayerSubmodules:
+    norm: Union[ModuleSpec, type] = IdentityOp
+    mixer: Union[ModuleSpec, type] = IdentityOp
+
+
+class MambaLayer(MegatronModule):
+    def __init__(
+        self,
+        config: TransformerConfig,
+        submodules: MambaLayerSubmodules,
+        layer_idx=None,
+        residual_in_fp32=False,
+    ):
+        """
+        Top level Mamba Layer
+        """
+        super().__init__(config)
+        self.config = config
+        self.residual_in_fp32 = residual_in_fp32
+        self.mixer = build_module(
+            submodules.mixer, self.config, self.config.hidden_size, layer_idx=layer_idx,
+        )
+        self.norm = build_module(submodules.norm, self.config, self.config.hidden_size)
+
+    def forward(
+        self,
+        hidden_states: Tensor,
+        attention_mask: Tensor,  # Not used in MambaLayer
+        inference_params=None,
+        rotary_pos_emb: Tensor = None,  # Not used in MambaLayer
+    ):
+
+        residual = hidden_states
+        hidden_states = self.norm(residual.to(dtype=self.norm.weight.dtype))
+        if self.residual_in_fp32:
+            residual = residual.to(torch.float32)
+
+        hidden_states = self.mixer(hidden_states, inference_params=inference_params)
+        return hidden_states + residual
+
+    def allocate_inference_cache(self, batch_size, max_seqlen, dtype=None):
+        return self.mixer.allocate_inference_cache(batch_size, max_seqlen, dtype=dtype)

megatron/core/ssm/mamba_mixer.py

+# Copyright (c) 2024, Tri Dao, Albert Gu.
+# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+
+# Some of this code was adopted from https://github.com/state-spaces/mamba/
+# This source code is licensed under the Apache license found in the
+# LICENSE file in the root directory of this source tree.
+
+import math
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from megatron.core.parallel_state import get_tensor_model_parallel_world_size
+from megatron.core.tensor_parallel import (
+    ColumnParallelLinear,
+    RowParallelLinear,
+    copy_to_tensor_model_parallel_region,
+    gather_from_sequence_parallel_region,
+    get_cuda_rng_tracker,
+    reduce_from_tensor_model_parallel_region,
+    reduce_scatter_to_sequence_parallel_region,
+)
+from megatron.core.transformer.module import MegatronModule
+from megatron.core.transformer.transformer_config import TransformerConfig
+
+try:
+    from mamba_ssm.ops.triton.selective_state_update import selective_state_update
+except ImportError:
+    selective_state_update = None
+
+try:
+    from causal_conv1d import causal_conv1d_fn, causal_conv1d_update
+except ImportError:
+    causal_conv1d_fn = None
+    causal_conv1d_update = None
+
+try:
+    from mamba_ssm.ops.triton.layernorm_gated import RMSNorm as RMSNormGated
+    from mamba_ssm.ops.triton.ssd_combined import mamba_chunk_scan_combined
+except ImportError:
+    raise ImportError("mamba-ssm is required by the Mamba model but cannot be imported")
+
+try:
+    from einops import rearrange, repeat
+except ImportError:
+    raise ImportError("einops is required by the Mamba model but cannot be imported")
+
+
+class Mamba(MegatronModule):
+    def __init__(
+        self,
+        config: TransformerConfig,
+        d_model,
+        d_state=128,
+        d_conv=4,
+        conv_init=None,
+        expand=2,
+        headdim=64,
+        ngroups=8,
+        A_init_range=(1, 16),
+        D_has_hdim=False,
+        rmsnorm=True,
+        norm_before_gate=False,
+        dt_min=0.001,
+        dt_max=0.1,
+        dt_init="random",
+        dt_scale=1.0,
+        dt_init_floor=1e-4,
+        bias=False,
+        conv_bias=True,
+        # Fused kernel and sharding options
+        chunk_size=128,
+        use_fast_path=True,
+        layer_idx=None,
+    ):
+        super().__init__(config)
+        self.config = config
+        self.d_model = d_model
+        self.d_state = d_state
+        self.d_conv = d_conv
+        self.conv_init = conv_init
+        self.expand = expand
+        self.d_inner = int(self.expand * self.d_model)
+        self.headdim = headdim
+        self.ngroups = ngroups
+        assert self.d_inner % self.headdim == 0
+        self.nheads = self.d_inner // self.headdim
+        self.D_has_hdim = D_has_hdim
+        self.rmsnorm = rmsnorm
+        self.norm_before_gate = norm_before_gate
+        self.chunk_size = chunk_size
+        self.use_fast_path = use_fast_path
+        self.layer_idx = layer_idx
+
+        self.tensor_model_parallel_size = get_tensor_model_parallel_world_size()
+        assert self.d_inner % self.tensor_model_parallel_size == 0
+        assert self.ngroups % self.tensor_model_parallel_size == 0
+        assert self.nheads % self.tensor_model_parallel_size == 0
+        assert not bias
+
+        self.d_inner_local = self.d_inner // self.tensor_model_parallel_size
+        self.ngroups_local = self.ngroups // self.tensor_model_parallel_size
+        self.nheads_local = self.nheads // self.tensor_model_parallel_size
+
+        assert self.d_inner_local % self.ngroups_local == 0
+
+        # Assume sequence parallelism: input is already partitioned along the
+        # sequence dimension
+        self.in_proj = ColumnParallelLinear(
+            self.d_model,
+            self.d_inner * 2 + 2 * self.ngroups * self.d_state + self.nheads,
+            config=self.config,
+            init_method=self.config.init_method,
+            gather_output=False,
+            bias=bias,
+        )
+
+        conv_dim = self.d_inner_local + 2 * self.ngroups_local * self.d_state
+        with get_cuda_rng_tracker().fork():
+            self.conv1d = nn.Conv1d(
+                in_channels=conv_dim,
+                out_channels=conv_dim,
+                bias=conv_bias,
+                kernel_size=d_conv,
+                groups=conv_dim,
+                padding=d_conv - 1,
+                device=torch.cuda.current_device(),
+                dtype=config.params_dtype,
+            )
+            setattr(self.conv1d.weight, 'tensor_model_parallel', True)
+            setattr(self.conv1d.bias, 'tensor_model_parallel', True)
+
+            if self.conv_init is not None:
+                nn.init.uniform_(self.conv1d.weight, -self.conv_init, self.conv_init)
+
+        self.activation = "silu"
+        self.act = nn.SiLU()
+
+        with get_cuda_rng_tracker().fork():
+            # Initialize dt bias so that F.softplus(dt_bias) is between dt_min and dt_max
+            dt = torch.exp(
+                torch.rand(
+                    self.nheads_local, device=torch.cuda.current_device(), dtype=config.params_dtype
+                )
+                * (math.log(dt_max) - math.log(dt_min))
+                + math.log(dt_min)
+            ).clamp(min=dt_init_floor)
+            # Inverse of softplus: https://github.com/pytorch/pytorch/issues/72759
+            inv_dt = dt + torch.log(-torch.expm1(-dt))
+            with torch.no_grad():
+                self.dt_bias = nn.Parameter(inv_dt)
+            # Our initialization would set all Linear.bias to zero, need to mark this one as _no_reinit
+            self.dt_bias._no_reinit = True
+            # Just to be explicit. Without this we already don't put wd on dt_bias because of the check
+            # name.endswith("bias") in param_grouping.py
+            self.dt_bias._no_weight_decay = True
+
+            assert A_init_range[0] > 0 and A_init_range[1] >= A_init_range[0]
+            A = torch.empty(
+                self.nheads_local, dtype=torch.float32, device=torch.cuda.current_device()
+            ).uniform_(*A_init_range)
+            A_log = torch.log(A)  # Keep A_log in fp32
+            self.A_log = nn.Parameter(A_log)
+            self.A_log._no_weight_decay = True
+            setattr(self.A_log, 'tensor_model_parallel', True)
+
+        # D "skip" parameter
+        self.D = nn.Parameter(
+            torch.ones(
+                self.d_inner_local if self.D_has_hdim else self.nheads_local,
+                device=torch.cuda.current_device(),
+            )
+        )  # Keep in fp32
+        self.D._no_weight_decay = True
+        setattr(self.D, 'tensor_model_parallel', True)
+
+        if self.rmsnorm:
+            assert RMSNormGated is not None
+            self.norm = RMSNormGated(
+                self.d_inner_local,
+                eps=1e-5,
+                group_size=self.d_inner_local // self.ngroups_local,
+                norm_before_gate=False,
+                device=torch.cuda.current_device(),
+                dtype=config.params_dtype,
+            )
+
+        # Assume sequence parallelism: input is partitioned along d_inner and
+        # output is partitioned along the sequence dimension
+        self.out_proj = RowParallelLinear(
+            self.d_inner,
+            self.d_model,
+            config=self.config,
+            init_method=self.config.output_layer_init_method,
+            bias=bias,
+            input_is_parallel=True,
+            skip_bias_add=False,
+        )
+
+    def forward(self, hidden_states, inference_params=None):
+        """
+        hidden_states: (nL, B, D) / (L B D)
+        Returns: same shape as hidden_states
+        """
+        _, batch, dim = hidden_states.shape
+
+        conv_state, ssm_state = None, None
+        if inference_params is not None:
+            assert not self.config.sequence_parallel
+            conv_state, ssm_state = self._get_states_from_cache(inference_params, batch)
+            if inference_params.seqlen_offset > 0:
+                # The states are updated inplace
+                out, _, _ = self.step(hidden_states, conv_state, ssm_state)
+                return out
+
+        # (nheads_local)
+        A = -torch.exp(self.A_log.float())
+
+        # pl b d ->  l b p(2d)
+        # TODO move transpose to GEMM
+        if self.config.sequence_parallel:
+            # gather data along sequenece dimension
+            hidden_states = gather_from_sequence_parallel_region(hidden_states)
+        else:
+            hidden_states = copy_to_tensor_model_parallel_region(hidden_states)
+        xz = hidden_states @ self.in_proj.weight.t()
+
+        z, xBC, dt = torch.split(
+            xz,
+            [
+                self.d_inner_local,
+                self.d_inner_local + 2 * self.ngroups_local * self.d_state,
+                self.nheads_local,
+            ],
+            dim=-1,
+        )
+
+        # transpose: l b pd --> b pd l
+        xBC = rearrange(xBC, "l b d -> b d l")
+        xBC = xBC.contiguous()
+
+        # Compute short convolution
+        if conv_state is not None:
+            # If we just take x[:, :, -self.d_conv :], it will error if seqlen < self.d_conv
+            # Instead F.pad will pad with zeros if seqlen < self.d_conv, and truncate otherwise.
+            conv_state.copy_(F.pad(xBC, (self.d_conv - xBC.shape[-1], 0)))  # Update state (B D W)
+
+        seqlen = xBC.size(2)
+        if causal_conv1d_fn is None:
+            xBC = self.act(self.conv1d(xBC)[..., :seqlen])
+        else:
+            assert self.activation in ["silu", "swish"]
+            xBC = causal_conv1d_fn(
+                x=xBC,
+                weight=rearrange(self.conv1d.weight, "d 1 w -> d w"),
+                bias=self.conv1d.bias,
+                activation=self.activation,
+            )
+
+        # transpose b pd l --> l b pd
+        xBC = rearrange(xBC, "b d l ->  l b d")
+        xBC = xBC.contiguous()
+
+        x, B, C = torch.split(
+            xBC,
+            [
+                self.d_inner_local,
+                self.ngroups_local * self.d_state,
+                self.ngroups_local * self.d_state,
+            ],
+            dim=-1,
+        )
+
+        # TODO Vijay: fuse most of the transposes with the GEMMS
+        x = rearrange(x, "l b (h p) -> b l h p", p=self.headdim).contiguous()
+        dt = rearrange(dt, "l b d -> b l d").contiguous()
+        B = rearrange(B, "l b (g n) -> b l g n", n=self.d_state).contiguous()
+        C = rearrange(C, "l b (g n) -> b l g n", n=self.d_state).contiguous()
+        z = rearrange(z, "l b (h p) -> b l h p", p=self.headdim).contiguous()
+        y = mamba_chunk_scan_combined(
+            x,
+            dt,
+            A,
+            B,
+            C,
+            self.chunk_size,
+            D=rearrange(self.D.float(), "(h p) -> h p", p=self.headdim)
+            if self.D_has_hdim
+            else self.D,
+            z=z if not self.rmsnorm else None,
+            dt_bias=self.dt_bias.float(),
+            dt_softplus=True,
+            return_final_states=ssm_state is not None,
+        )
+
+        if ssm_state is not None:
+            y, last_state = y
+            ssm_state.copy_(last_state)
+
+        if self.rmsnorm:
+            y = rearrange(y, "b l h p -> b l (h p)").contiguous()
+            z = rearrange(z, "b l h p -> b l (h p)").contiguous()
+            y = self.norm(y, z)
+            y = rearrange(y, "b l d -> l b d").contiguous()
+        else:
+            y = rearrange(y, "b l h p -> l b (h p)").contiguous()
+
+        #  l b pd --> pl b d
+        out_full = y @ self.out_proj.weight.t()
+        if self.config.sequence_parallel:
+            out = reduce_scatter_to_sequence_parallel_region(out_full)
+        else:
+            out = reduce_from_tensor_model_parallel_region(out_full)
+        return out
+
+    def step(self, hidden_states, conv_state, ssm_state):
+        # assert self.ngroups_local == 1, "Only support ngroups=1 for inference for now"
+        dtype = hidden_states.dtype
+        assert hidden_states.shape[0] == 1, "Only support decoding with 1 token at a time for now"
+
+        # l b d --> b d
+        hidden_states = hidden_states.squeeze(0)
+
+        #  b d_model --> b p(2d)
+        xz = hidden_states @ self.in_proj.weight.t()
+
+        z, xBC, dt = torch.split(
+            xz,
+            [
+                self.d_inner_local,
+                self.d_inner_local + 2 * self.ngroups_local * self.d_state,
+                self.nheads_local,
+            ],
+            dim=-1,
+        )
+
+        # Conv step
+        if causal_conv1d_update is None:
+            conv_state.copy_(torch.roll(conv_state, shifts=-1, dims=-1))  # Update state (B D W)
+            conv_state[:, :, -1] = xBC
+            xBC = torch.sum(
+                conv_state * rearrange(self.conv1d.weight, "d 1 w -> d w"), dim=-1
+            )  # (B D)
+            if self.conv1d.bias is not None:
+                xBC = xBC + self.conv1d.bias
+            xBC = self.act(xBC).to(dtype=dtype)
+        else:
+            xBC = causal_conv1d_update(
+                xBC,
+                conv_state,
+                rearrange(self.conv1d.weight, "d 1 w -> d w"),
+                self.conv1d.bias,
+                self.activation,
+            )
+
+        x, B, C = torch.split(
+            xBC,
+            [
+                self.d_inner_local,
+                self.ngroups_local * self.d_state,
+                self.ngroups_local * self.d_state,
+            ],
+            dim=-1,
+        )
+        A = -torch.exp(self.A_log.float())
+
+        # SSM step
+        if selective_state_update is None:
+            if self.ngroups_local > 1:
+                B = rearrange(B, "b (g n) -> b g n", n=self.d_state)
+                C = rearrange(C, "b (g n) -> b g n", n=self.d_state)
+                B = repeat(B, "b g n -> b (g h) n", h=self.d_inner_local // self.ngroups_local)
+                C = repeat(C, "b g n -> b (g h) n", h=self.d_inner_local // self.ngroups_local)
+
+                dt = repeat(dt, "b h -> b (h p)", p=self.headdim)
+                dt_bias = repeat(self.dt_bias, "h -> (h p)", p=self.headdim)
+                A = repeat(A, "h -> (h p) n", p=self.headdim, n=self.d_state)
+                D = repeat(self.D, "h -> (h p)", p=self.headdim)
+
+                dt = F.softplus(dt + dt_bias.to(dtype=dt.dtype))
+                dA = torch.exp(torch.einsum("bd,dn->bdn", dt, A))
+
+                dB_x = torch.einsum('bd,bdn,bd->bdn', dt, B, x)
+                ssm_state.copy_(
+                    ssm_state * rearrange(dA, "b (h p) n -> b h p n", p=self.headdim)
+                    + rearrange(dB_x, "b (h p) n -> b h p n", p=self.headdim)
+                )
+
+                y = torch.einsum(
+                    "bdn,bdn->bd",
+                    rearrange(ssm_state.to(dtype), "b h p n -> b (h p) n", p=self.headdim),
+                    C,
+                )
+                y = y + D.to(dtype) * x
+                if not self.rmsnorm:
+                    y = y * self.act(z)  # (B D)
+            else:
+                # Discretize A and B (b (g n))
+                dt = F.softplus(dt + self.dt_bias.to(dtype=dt.dtype))  # (batch, nheads)
+                dA = torch.exp(dt * A)
+                x = rearrange(x, "b (h p) -> b h p", p=self.headdim)
+                dBx = torch.einsum("bh,bn,bhp->bhpn", dt, B, x)
+                ssm_state.copy_(ssm_state * rearrange(dA, "b h -> b h 1 1") + dBx)
+                y = torch.einsum("bhpn,bn->bhp", ssm_state.to(dtype), C)
+                y = y + rearrange(self.D.to(dtype), "h -> h 1") * x
+                y = rearrange(y, "b h p -> b (h p)")
+                if not self.rmsnorm:
+                    y = y * self.act(z)  # (B D)
+        else:
+            A = repeat(A, "h -> h p n", p=self.headdim, n=self.d_state).to(dtype=torch.float32)
+            dt = repeat(dt, "b h -> b h p", p=self.headdim)
+            dt_bias = repeat(self.dt_bias, "h -> h p", p=self.headdim)
+            D = repeat(self.D, "h -> h p", p=self.headdim)
+            B = rearrange(B, "b (g n) -> b g n", g=self.ngroups_local)
+            C = rearrange(C, "b (g n) -> b g n", g=self.ngroups_local)
+            x_reshaped = rearrange(x, "b (h p) -> b h p", p=self.headdim)
+            if not self.rmsnorm:
+                z = rearrange(z, "b (h p) -> b h p", p=self.headdim)
+            y = selective_state_update(
+                ssm_state,
+                x_reshaped,
+                dt,
+                A,
+                B,
+                C,
+                D,
+                z=z if not self.rmsnorm else None,
+                dt_bias=dt_bias,
+                dt_softplus=True,
+            )
+            y = rearrange(y, "b h p -> b (h p)")
+
+        if self.rmsnorm:
+            y = self.norm(y, z)
+
+        # b pd --> b d
+        out = y @ self.out_proj.weight.t()
+        out = reduce_from_tensor_model_parallel_region(out)
+        return out.unsqueeze(0), conv_state, ssm_state
+
+    def allocate_inference_cache(self, batch_size, max_seqlen, dtype=None):
+        device = self.out_proj.weight.device
+        conv_dtype = self.conv1d.weight.dtype if dtype is None else dtype
+        conv_state = torch.zeros(
+            batch_size, self.conv1d.weight.shape[0], self.d_conv, device=device, dtype=conv_dtype
+        )
+        ssm_dtype = self.in_proj.weight.dtype if dtype is None else dtype
+        # ssm_dtype = torch.float32
+        ssm_state = torch.zeros(
+            batch_size,
+            self.nheads_local,
+            self.headdim,
+            self.d_state,
+            device=device,
+            dtype=ssm_dtype,
+        )
+        return conv_state, ssm_state
+
+    def _get_states_from_cache(self, inference_params, batch_size, initialize_states=False):
+        assert self.layer_idx is not None
+        if self.layer_idx not in inference_params.key_value_memory_dict:
+            conv_state = torch.zeros(
+                batch_size,
+                self.conv1d.weight.shape[0],
+                self.d_conv,
+                device=self.conv1d.weight.device,
+                dtype=self.conv1d.weight.dtype,
+            )
+            ssm_state = torch.zeros(
+                batch_size,
+                self.nheads_local,
+                self.headdim,
+                self.d_state,
+                device=self.in_proj.weight.device,
+                dtype=self.in_proj.weight.dtype,
+            )
+            inference_params.key_value_memory_dict[self.layer_idx] = (conv_state, ssm_state)
+        else:
+            conv_state, ssm_state = inference_params.key_value_memory_dict[self.layer_idx]
+            # TODO: What if batch size changes between generation, and we reuse the same states?
+            if initialize_states:
+                conv_state.zero_()
+                ssm_state.zero_()
+        return conv_state, ssm_state

megatron/core/ssm/triton_cache_manager.py

+# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+
+import os
+import socket
+from pathlib import Path
+
+import torch
+
+try:
+    from triton.runtime.cache import FileCacheManager
+except ImportError:
+    raise ImportError("triton is required by the Mamba model but cannot be imported")
+
+
+def get_rank():
+    return torch.distributed.get_rank()
+
+
+def default_cache_dir():
+    return os.path.join(Path.home(), ".triton", "cache")
+
+
+class ParallelFileCacheManager(FileCacheManager):
+
+    # See https://github.com/triton-lang/triton/blob/main/python/triton/runtime/cache.py
+
+    # When running Triton with multiple ranks, they each create their own cache manager. Their input
+    # keys to that class are mostly (but not entirely) the same across ranks, which leads many ranks
+    # to write to the same 'key' directories in the cache dir at the same time during compilation,
+    # leading to conflicts.  This works around that by making each cache dir be rank specific by
+    # adding "rank_<host>_<pid>" to the cache directory.
+
+    def __init__(self, key):
+        self.key = key
+        self.lock_path = None
+        # create cache directory if it doesn't exist
+        self.cache_dir = os.environ.get('TRITON_CACHE_DIR', default_cache_dir())
+        self.cache_dir = os.path.join(
+            self.cache_dir, "rank_{}_{}".format(socket.gethostname(), os.getpid())
+        )
+        if self.cache_dir:
+            self.cache_dir = os.path.join(self.cache_dir, self.key)
+            self.lock_path = os.path.join(self.cache_dir, "lock")
+            os.makedirs(self.cache_dir, exist_ok=True)

megatron/core/tensor_parallel/__init__.py

+from .cross_entropy import vocab_parallel_cross_entropy
+from .data import broadcast_data
+from .layers import (
+    ColumnParallelLinear,
+    RowParallelLinear,
+    VocabParallelEmbedding,
+    copy_tensor_model_parallel_attributes,
+    linear_with_grad_accumulation_and_async_allreduce,
+    param_is_not_tensor_parallel_duplicate,
+    set_defaults_if_not_set_tensor_model_parallel_attributes,
+    set_tensor_model_parallel_attributes,
+)
+from .mappings import (
+    all_gather_last_dim_from_tensor_parallel_region,
+    all_to_all,
+    all_to_all_hp2sp,
+    all_to_all_sp2hp,
+    copy_to_tensor_model_parallel_region,
+    gather_from_sequence_parallel_region,
+    gather_from_sequence_parallel_region_to_moe,
+    gather_from_tensor_model_parallel_region,
+    reduce_from_tensor_model_parallel_region,
+    reduce_scatter_last_dim_to_tensor_parallel_region,
+    reduce_scatter_to_sequence_parallel_region,
+    reduce_scatter_to_sequence_parallel_region_from_moe,
+    scatter_to_sequence_parallel_region,
+    scatter_to_tensor_model_parallel_region,
+)
+from .random import (
+    checkpoint,
+    get_cuda_rng_tracker,
+    get_data_parallel_rng_tracker_name,
+    model_parallel_cuda_manual_seed,
+)
+from .utils import (
+    gather_split_1d_tensor,
+    split_tensor_along_last_dim,
+    split_tensor_into_1d_equal_chunks,
+)
+
+__all__ = [
+    # cross_entropy.py
+    "vocab_parallel_cross_entropy",
+    # data.py
+    "broadcast_data",
+    # layers.py
+    "ColumnParallelLinear",
+    "RowParallelLinear",
+    "VocabParallelEmbedding",
+    "set_tensor_model_parallel_attributes",
+    "set_defaults_if_not_set_tensor_model_parallel_attributes",
+    "copy_tensor_model_parallel_attributes",
+    "param_is_not_tensor_parallel_duplicate",
+    "linear_with_grad_accumulation_and_async_allreduce",
+    # mappings.py
+    "copy_to_tensor_model_parallel_region",
+    "gather_from_tensor_model_parallel_region",
+    "gather_from_sequence_parallel_region",
+    "reduce_from_tensor_model_parallel_region",
+    "reduce_scatter_to_sequence_parallel_region",
+    "scatter_to_tensor_model_parallel_region",
+    "scatter_to_sequence_parallel_region",
+    # random.py
+    "checkpoint",
+    "get_cuda_rng_tracker",
+    "model_parallel_cuda_manual_seed",
+    # utils.py
+    "split_tensor_along_last_dim",
+    "split_tensor_into_1d_equal_chunks",
+    "gather_split_1d_tensor",
+    "gather_from_sequence_parallel_region_to_moe",
+    "reduce_scatter_to_sequence_parallel_region_from_moe",
+]