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megatron/core/models/gpt/moe_module_specs.py

+# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
+
+import warnings
+from typing import Optional
+
+from megatron.core.tensor_parallel.layers import ColumnParallelLinear, RowParallelLinear
+from megatron.core.transformer.mlp import MLPSubmodules
+from megatron.core.transformer.moe.experts import GroupedMLP, SequentialMLP, TEGroupedMLP
+from megatron.core.transformer.moe.moe_layer import MoELayer, MoESubmodules
+from megatron.core.transformer.moe.shared_experts import SharedExpertMLP
+from megatron.core.transformer.spec_utils import ModuleSpec
+from megatron.core.utils import get_te_version, is_te_min_version
+
+try:
+    from megatron.core.extensions.transformer_engine import (
+        TEColumnParallelGroupedLinear,
+        TEColumnParallelLinear,
+        TERowParallelGroupedLinear,
+        TERowParallelLinear,
+    )
+
+    HAVE_TE = True
+except ImportError:
+    HAVE_TE = False
+
+
+def get_moe_module_spec(
+    use_te: Optional[bool] = True,
+    num_experts: Optional[int] = None,
+    moe_grouped_gemm: Optional[bool] = False,
+    moe_use_legacy_grouped_gemm: Optional[bool] = False,
+) -> ModuleSpec:
+    """Helper function to get module spec for MoE"""
+    assert num_experts is not None
+
+    mlp = MLPSubmodules(
+        linear_fc1=TEColumnParallelLinear if use_te else ColumnParallelLinear,
+        linear_fc2=TERowParallelLinear if use_te else RowParallelLinear,
+    )
+
+    # experts spec
+    if moe_grouped_gemm:
+        ## use GroupedMLP
+        if use_te and TEColumnParallelGroupedLinear is not None and not moe_use_legacy_grouped_gemm:
+            ## use TEGroupedLinear
+            expert_module = TEGroupedMLP
+            expert_submodule = MLPSubmodules(
+                linear_fc1=TEColumnParallelGroupedLinear, linear_fc2=TERowParallelGroupedLinear
+            )
+        else:
+            ## use legacy GroupedMLP
+            expert_module = GroupedMLP
+            expert_submodule = None
+            warnings.warn(
+                'The legacy GroupedMLP will be deprecated in Megatron-Core v0.12.0. '
+                'Please update the TransformerEngine to version>=1.7.0 and use TEGroupedMLP.'
+            )
+    else:
+        ## use SequentialMLP
+        expert_module = SequentialMLP
+        if use_te and not is_te_min_version("1.7.0.dev0"):
+            warnings.warn(
+                "Only transformer-engine>=1.7.0 supports MoE experts, "
+                f"but your version is {get_te_version()}. Use local linear implementation instead."
+            )
+            expert_submodule = MLPSubmodules(
+                linear_fc1=ColumnParallelLinear, linear_fc2=RowParallelLinear
+            )
+        else:
+            expert_submodule = mlp
+
+    experts = ModuleSpec(module=expert_module, submodules=expert_submodule)
+
+    # shared experts spec
+    shared_experts = ModuleSpec(module=SharedExpertMLP, params={"gate": False}, submodules=mlp)
+
+    # MoE module spec
+    moe_module_spec = ModuleSpec(
+        module=MoELayer, submodules=MoESubmodules(experts=experts, shared_experts=shared_experts)
+    )
+    return moe_module_spec

megatron/core/models/mamba/__init__.py

+# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+from .mamba_model import MambaModel

megatron/core/models/mamba/mamba_layer_specs.py

+# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
+
+from megatron.core.extensions.transformer_engine import (
+    TEDotProductAttention,
+    TELayerNormColumnParallelLinear,
+    TERowParallelLinear,
+)
+from megatron.core.fusions.fused_bias_dropout import get_bias_dropout_add
+from megatron.core.ssm.mamba_block import MambaStack, MambaStackSubmodules
+from megatron.core.ssm.mamba_layer import MambaLayer, MambaLayerSubmodules
+from megatron.core.ssm.mamba_mixer import MambaMixer, MambaMixerSubmodules
+from megatron.core.transformer.attention import SelfAttention, SelfAttentionSubmodules
+from megatron.core.transformer.enums import AttnMaskType
+from megatron.core.transformer.mlp import MLP, MLPSubmodules
+from megatron.core.transformer.spec_utils import ModuleSpec
+from megatron.core.transformer.transformer_layer import TransformerLayer, TransformerLayerSubmodules
+
+mamba_stack_spec = ModuleSpec(
+    module=MambaStack,
+    submodules=MambaStackSubmodules(
+        mamba_layer=ModuleSpec(
+            module=MambaLayer,
+            submodules=MambaLayerSubmodules(
+                mixer=ModuleSpec(
+                    module=MambaMixer,
+                    submodules=MambaMixerSubmodules(
+                        in_proj=TELayerNormColumnParallelLinear, out_proj=TERowParallelLinear
+                    ),
+                ),
+                mamba_bda=get_bias_dropout_add,
+            ),
+        ),
+        # Started with spec from gpt_layer_specs.py (with MLP removed)
+        # Using the TE spec because we had problems getting the non-TE spec
+        # working
+        attention_layer=ModuleSpec(
+            module=TransformerLayer,
+            submodules=TransformerLayerSubmodules(
+                self_attention=ModuleSpec(
+                    module=SelfAttention,
+                    params={"attn_mask_type": AttnMaskType.causal},
+                    submodules=SelfAttentionSubmodules(
+                        linear_qkv=TELayerNormColumnParallelLinear,
+                        core_attention=TEDotProductAttention,
+                        linear_proj=TERowParallelLinear,
+                    ),
+                ),
+                self_attn_bda=get_bias_dropout_add,
+            ),
+        ),
+        # Started with spec from gpt_layer_specs.py
+        # Using the TE spec because we had problems getting the non-TE spec
+        # working
+        mlp_layer=ModuleSpec(
+            module=TransformerLayer,
+            submodules=TransformerLayerSubmodules(
+                mlp=ModuleSpec(
+                    module=MLP,
+                    submodules=MLPSubmodules(
+                        linear_fc1=TELayerNormColumnParallelLinear, linear_fc2=TERowParallelLinear
+                    ),
+                ),
+                mlp_bda=get_bias_dropout_add,
+            ),
+        ),
+    ),
+)

megatron/core/models/mamba/mamba_model.py

+# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
+
+from typing import Literal, Optional
+
+from torch import Tensor
+
+from megatron.core import InferenceParams, tensor_parallel
+from megatron.core.config_logger import has_config_logger_enabled, log_config_to_disk
+from megatron.core.models.common.embeddings.language_model_embedding import LanguageModelEmbedding
+from megatron.core.models.common.embeddings.rotary_pos_embedding import RotaryEmbedding
+from megatron.core.models.common.language_module.language_module import LanguageModule
+from megatron.core.transformer.enums import ModelType
+from megatron.core.transformer.spec_utils import ModuleSpec, build_module
+from megatron.core.transformer.transformer_config import TransformerConfig
+
+
+class MambaModel(LanguageModule):
+    """Mamba language model.
+
+    Args:
+        config (TransformerConfig): Transformer config
+        mamba_stack_spec (ModuleSpec): Specifies the modules to use for the various layer types
+        vocab_size (int): Vocabulary size
+        max_sequence_length (int): maximum size of sequence.
+            This is used for positional embedding
+        pre_process (bool, optional): Include embedding layer
+            (used with pipeline parallelism). Defaults to True.
+        mamba_ssm_ngroups (int, optional): Specifies the number of groups to use.
+            The default value is 8, as in the NVIDIA Mamba2 (pure and hybrid) 8b.
+            However, in the original Mamba2 paper, the checkpoints use a setting of 1.
+            Defaults to 8.
+        hybrid_attention_ratio (float, optional): The target ratio of attention
+            layers to total layers
+        hybrid_mlp_ratio (float, optional): The target ratio of mlp layers to total layers
+        hybrid_override_pattern (str, optional): The hybrid layer pattern to override with
+        post_process (bool, optional): Include an output layer (used with pipeline parallelism).
+            Defaults to True.
+        fp16_lm_cross_entropy (bool, optional): Defaults to False.
+        parallel_output (bool, optional): Do not gather the outputs, keep them split across tensor
+            parallel ranks. Defaults to True.
+        share_embeddings_and_output_weights (bool, optional): When True, input embeddings and
+            output logit weights are shared. Defaults to False.
+        position_embedding_type (Literal[learned_absolute,rope,none], optional):  Position
+            embedding type. Defaults to 'none'.
+        rotary_percent (float, optional): Percent of rotary dimension to use for rotary position
+            embeddings. Ignored unless position_embedding_type is 'rope'. Defaults to 1.0.
+        rotary_base (int, optional): Base period for rotary position embeddings. Ignored unless
+            position_embedding_type is 'rope'. Defaults to 10000.
+        seq_len_interpolation_factor (Optional[float], optional): scale of linearly
+            interpolating RoPE for longer sequences. The value must be a float larger than 1.0.
+             Defaults to None.
+    """
+
+    def __init__(
+        self,
+        config: TransformerConfig,
+        mamba_stack_spec: ModuleSpec,
+        vocab_size: int,
+        max_sequence_length: int,
+        mamba_ssm_ngroups: int = 8,
+        pre_process: bool = True,
+        hybrid_attention_ratio: float = 0.0,
+        hybrid_mlp_ratio: float = 0.0,
+        hybrid_override_pattern: str = None,
+        post_process: bool = True,
+        fp16_lm_cross_entropy: bool = False,
+        parallel_output: bool = True,
+        share_embeddings_and_output_weights: bool = False,
+        # Mamba with no attention has no need for position embeddings, so none is default
+        position_embedding_type: Literal['learned_absolute', 'rope', 'none'] = 'none',
+        rotary_percent: float = 1.0,
+        rotary_base: int = 10000,
+        seq_len_interpolation_factor: Optional[float] = None,
+    ) -> None:
+        super().__init__(config=config)
+
+        if has_config_logger_enabled(config):
+            log_config_to_disk(config, locals(), prefix=type(self).__name__)
+
+        self.mamba_stack_spec: ModuleSpec = mamba_stack_spec
+        self.vocab_size = vocab_size
+        self.max_sequence_length = max_sequence_length
+        self.mamba_ssm_ngroups = mamba_ssm_ngroups
+        self.pre_process = pre_process
+        self.hybrid_attention_ratio = hybrid_attention_ratio
+        self.hybrid_mlp_ratio = hybrid_mlp_ratio
+        self.hybrid_override_pattern = hybrid_override_pattern
+        self.post_process = post_process
+        self.fp16_lm_cross_entropy = fp16_lm_cross_entropy
+        self.parallel_output = parallel_output
+        self.share_embeddings_and_output_weights = share_embeddings_and_output_weights
+        self.position_embedding_type = position_embedding_type
+
+        # megatron core pipelining currently depends on model type
+        # TODO: remove this dependency ?
+        self.model_type = ModelType.encoder_or_decoder
+
+        if self.pre_process:
+            self.embedding = LanguageModelEmbedding(
+                config=self.config,
+                vocab_size=self.vocab_size,
+                max_sequence_length=self.max_sequence_length,
+                position_embedding_type=position_embedding_type,
+            )
+
+        if self.position_embedding_type == 'rope':
+            self.rotary_pos_emb = RotaryEmbedding(
+                kv_channels=self.config.kv_channels,
+                rotary_percent=rotary_percent,
+                seq_len_interpolation_factor=seq_len_interpolation_factor,
+                rotary_base=rotary_base,
+                use_cpu_initialization=self.config.use_cpu_initialization,
+            )
+
+        self.decoder = build_module(
+            mamba_stack_spec,
+            self.config,
+            mamba_ssm_ngroups=self.mamba_ssm_ngroups,
+            pre_process=self.pre_process,
+            hybrid_attention_ratio=self.hybrid_attention_ratio,
+            hybrid_mlp_ratio=self.hybrid_mlp_ratio,
+            hybrid_override_pattern=self.hybrid_override_pattern,
+            post_process=self.post_process,
+            dtype=config.params_dtype,
+        )
+
+        # Output
+        if post_process:
+            self.output_layer = tensor_parallel.ColumnParallelLinear(
+                config.hidden_size,
+                self.vocab_size,
+                config=config,
+                init_method=config.init_method,
+                bias=False,
+                skip_bias_add=False,
+                gather_output=not self.parallel_output,
+                skip_weight_param_allocation=self.pre_process
+                and self.share_embeddings_and_output_weights,
+            )
+
+        if self.pre_process or self.post_process:
+            self.setup_embeddings_and_output_layer()
+
+    def set_input_tensor(self, input_tensor: Tensor) -> None:
+        """Sets input tensor to the model.
+
+        See megatron.model.transformer.set_input_tensor()
+
+        Args:
+            input_tensor (Tensor): Sets the input tensor for the model.
+        """
+        # This is usually handled in schedules.py but some inference code still
+        # gives us non-lists or None
+        if not isinstance(input_tensor, list):
+            input_tensor = [input_tensor]
+
+        assert len(input_tensor) == 1, 'input_tensor should only be length 1 for gpt/bert'
+        self.decoder.set_input_tensor(input_tensor[0])
+
+    def forward(
+        self,
+        input_ids: Tensor,
+        position_ids: Tensor,
+        attention_mask: Tensor,
+        decoder_input: Tensor = None,
+        labels: Tensor = None,
+        inference_params: InferenceParams = None,
+    ) -> Tensor:
+        """Forward function of the Mamba model. This function passes the input tensors
+        through the embedding layer, and then the decoder and finally into the post
+        processing layer (optional).
+
+        It either returns the Loss values if labels are given or the final hidden units
+        """
+        # If decoder_input is provided (not None), then input_ids and position_ids are ignored.
+        # Otherwise, apply embedding layer on input_ids and position_ids to get decoder_input.
+
+        # Decoder embedding.
+        if decoder_input is not None:
+            pass
+        elif self.pre_process:
+            decoder_input = self.embedding(input_ids=input_ids, position_ids=position_ids)
+        else:
+            # intermediate stage of pipeline
+            # decoder will get hidden_states from encoder.input_tensor
+            decoder_input = None
+
+        rotary_pos_emb = None
+        if self.position_embedding_type == 'rope':
+            rotary_seq_len = self.rotary_pos_emb.get_rotary_seq_len(
+                inference_params, self.decoder, decoder_input, self.config
+            )
+            rotary_pos_emb = self.rotary_pos_emb(rotary_seq_len)
+
+        # The following assert will currently fail when running inference.
+        # Commented out for now.
+        # TODO (duncan/rwaleffe): (1) confirm that the externally-generated
+        #   attention mask is not needed and is ignored by the model in
+        #   inference mode, (2) reduce the size of the externally-generated
+        #   attention mask to prevent CPU OOM (as we did for training), (3)
+        #   force the attention mask passed to the model in inference mode to
+        #   be None, so this assert will succeed.
+        # assert attention_mask is None, "The attention mask is ignored and should be set to None"
+
+        # Run decoder.
+        hidden_states = self.decoder(
+            hidden_states=decoder_input,
+            attention_mask=attention_mask,
+            inference_params=inference_params,
+            rotary_pos_emb=rotary_pos_emb,
+        )
+
+        if not self.post_process:
+            return hidden_states
+
+        # logits and loss
+        output_weight = None
+        if self.share_embeddings_and_output_weights:
+            output_weight = self.shared_embedding_or_output_weight()
+        logits, _ = self.output_layer(hidden_states, weight=output_weight)
+
+        if labels is None:
+            # [s b h] => [b s h]
+            return logits.transpose(0, 1).contiguous()
+
+        loss = self.compute_language_model_loss(labels, logits)
+
+        return loss

megatron/core/models/multimodal/__init__.py

+# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.

megatron/core/models/multimodal/llava_model.py

+# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+import logging
+from collections import namedtuple
+from functools import partial
+from typing import List, Optional
+
+import torch
+
+from megatron.core import InferenceParams, tensor_parallel
+from megatron.core.config_logger import has_config_logger_enabled, log_config_to_disk
+from megatron.core.models.gpt import GPTModel
+from megatron.core.models.vision.clip_vit_model import CLIPViTModel, get_num_image_embeddings
+from megatron.core.models.vision.multimodal_projector import MultimodalProjector
+from megatron.core.packed_seq_params import PackedSeqParams
+from megatron.core.parallel_state import get_context_parallel_group, get_context_parallel_world_size
+from megatron.core.transformer import MegatronModule
+from megatron.core.transformer.spec_utils import ModuleSpec
+from megatron.core.transformer.transformer_config import TransformerConfig
+from megatron.core.utils import log_single_rank
+
+try:
+    import transformer_engine  # pylint: disable=unused-import
+    from transformer_engine.pytorch.distributed import gather_along_first_dim
+
+    from megatron.core.extensions.transformer_engine import TEDotProductAttention
+    from megatron.core.utils import is_te_min_version
+
+    HAVE_TE = True
+except:
+    HAVE_TE = False
+    if get_context_parallel_world_size() > 1:
+        raise RuntimeError("ContextParallelism requires TransformerEngine support, but not found.")
+
+
+IGNORE_INDEX = -100  # ID for labels that should be ignored.
+# Image token index can be tokenizer dependent so the default value does not work in all cases.
+DEFAULT_IMAGE_TOKEN_INDEX = -200
+IMAGE_TOKEN = "<image>"
+VIDEO_TOKEN = "<video>"
+
+
+# Note: This is under development and may be missing features.
+class LLaVAModel(MegatronModule):
+    """LLaVA multi-modal model.
+
+    Args:
+        language_transformer_config (TransformerConfig): Transformer config for the language model.
+        language_transformer_layer_spec (ModuleSpec): Language model spec.
+        language_vocab_size (int): Language model vocabulary size.
+        language_max_sequence_length (int): Language model maximum sequence length.
+        vision_transformer_config (TransformerConfig): Transformer config for the vision model.
+        vision_transformer_layer_spec (ModuleSpec): Vision model spec.
+        drop_vision_class_token (bool): Drop vision class token(s) before the language model.
+        vision_projection_config (TransformerConfig): Vision projection config.
+        vision_projection_layer_spec (ModuleSpec): Vision projection spec.
+        vision_projection_type (str): Type of the vision projection. Default: 2-layer MLP.
+        allow_missing_vision_projection_checkpoint (bool): Allow vision projection weights to be
+            missing when loading a checkpoint. Default False.
+        parallel_output (bool): Keep outputs split across tensor parallel ranks.
+            This is typically True for training and False for inference.
+        language_position_embedding_type (str): Language model position embedding type.
+        language_rotary_percent (float): RoPE percent. Defaults to 1.0.
+        pre_process (bool): Include embedding layer in the decoder (used with pipeline parallel).
+        post_process (bool): Include output layer in the decoder (used with pipeline parallel).
+        add_encoder (bool): Construct the encoder (used with pipeline parallel).
+            When we use pipelining, the encoder will live on only the first stage
+        add_decoder (bool): Construct the decoder (used with pipeline parallel).
+            When we use pipelining, the decoder will live on every stage after the first one.
+        img_h (int): Input image height.
+        img_w (int): Input image width.
+        patch_dim (int): The size of each image patch side.
+        language_rotary_base (int): RoPE base.
+        language_rope_scaling (bool): Toggle RoPE scaling.
+        image_token_index (int): Token ID for image token such as <image>.
+        pixel_shuffle (bool): Enable pixel shuffle.
+        tile_tags (list): Optional tile tags.
+    """
+
+    def __init__(
+        self,
+        language_transformer_config: TransformerConfig,
+        language_transformer_layer_spec: ModuleSpec,
+        language_vocab_size: int,
+        language_max_sequence_length: int,
+        vision_transformer_config: TransformerConfig,
+        vision_transformer_layer_spec: ModuleSpec,
+        drop_vision_class_token: bool,
+        vision_projection_config: TransformerConfig,
+        vision_projection_layer_spec: ModuleSpec,
+        vision_projection_type: str = "mlp",
+        allow_missing_vision_projection_checkpoint: bool = False,
+        parallel_output: bool = True,
+        language_position_embedding_type: str = 'learned_absolute',
+        language_rotary_percent: float = 1.0,
+        pre_process: bool = True,
+        post_process: bool = True,
+        add_encoder: bool = True,
+        add_decoder: bool = True,
+        img_h: int = 336,
+        img_w: int = 336,
+        patch_dim: int = 14,
+        language_rotary_base: int = 10000,
+        language_rope_scaling: bool = False,
+        image_token_index: int = DEFAULT_IMAGE_TOKEN_INDEX,
+        pixel_shuffle: bool = False,
+        tile_tags: Optional[list] = None,
+    ) -> None:
+        super().__init__(config=language_transformer_config)
+
+        if has_config_logger_enabled(language_transformer_config):
+            log_config_to_disk(language_transformer_config, locals(), prefix=type(self).__name__)
+
+        log_single_rank(
+            logging.getLogger(__name__),
+            logging.WARNING,
+            "LLaVA is work in progress. Features are missing and methods can change.",
+        )
+
+        self.pre_process = pre_process
+        self.post_process = post_process
+        self.add_encoder = add_encoder
+        self.add_decoder = add_decoder
+
+        self.encoder_hidden_state = None
+        self.vision_model = None
+        self.vision_projection = None
+        self.language_model = None
+
+        self.sequence_parallel_lm = language_transformer_config.sequence_parallel
+        self.tp_comm_overlap_lm = language_transformer_config.tp_comm_overlap
+        self.context_parallel_lm = language_transformer_config.context_parallel_size
+        if self.sequence_parallel_lm or self.context_parallel_lm > 1:
+            assert (
+                language_transformer_layer_spec.submodules.self_attention.submodules.core_attention
+                == TEDotProductAttention
+                and HAVE_TE
+            ), "Sequence/Context Parallelism is supported only with TE DotProductAttention."
+            if self.context_parallel_lm > 1:
+                assert is_te_min_version(
+                    "1.10.0"
+                ), "Context Parallelism in LLaVA requires TE v1.10 or higher"
+        self.tensor_model_parallel_size_lm = language_transformer_config.tensor_model_parallel_size
+
+        # This attribute is needed to check if an all-reduce is required
+        # on the word embeddings inside `finalize_model_grads._allreduce_word_embedding_grads`.
+        self.share_embeddings_and_output_weights = False
+        if self.add_decoder:
+            self.language_model = GPTModel(
+                config=language_transformer_config,
+                transformer_layer_spec=language_transformer_layer_spec,
+                vocab_size=language_vocab_size,
+                max_sequence_length=language_max_sequence_length,
+                parallel_output=parallel_output,
+                position_embedding_type=language_position_embedding_type,
+                rotary_percent=language_rotary_percent,
+                pre_process=self.pre_process,
+                post_process=self.post_process,
+                rotary_base=language_rotary_base,
+                rope_scaling=language_rope_scaling,
+                scatter_embedding_sequence_parallel=False,
+            )
+            self.share_embeddings_and_output_weights = (
+                self.language_model.share_embeddings_and_output_weights
+            )
+            self._language_max_sequence_length = language_max_sequence_length
+            self._language_is_pipeline_parallel = (
+                language_transformer_config.pipeline_model_parallel_size > 1
+            )
+
+        class_token_len = 1
+        if self.add_encoder:
+            self._drop_vision_class_token = drop_vision_class_token
+            add_class_token = True
+            if vision_transformer_config.vision_model_type == "siglip":
+                class_token_len = 0
+                add_class_token = False
+                error_msg = (
+                    "Siglip does not support vision class token, "
+                    "set disable-vision-class-token to False."
+                )
+                assert not self._drop_vision_class_token, error_msg
+            self.vision_model = CLIPViTModel(
+                vision_transformer_config,
+                vision_transformer_layer_spec,
+                img_h=img_h,
+                img_w=img_w,
+                class_token_len=class_token_len,
+                patch_dim=patch_dim,
+                model_subtype=vision_transformer_config.vision_model_type,
+                add_class_token=add_class_token,
+            )
+
+            vision_projection_input_size = vision_transformer_config.hidden_size
+            vision_projection_input_size *= 4 if pixel_shuffle else 1
+
+            # Map (intermediate) vision model outputs to the language model input dimension.
+            self.vision_projection = MultimodalProjector(
+                vision_projection_config,
+                vision_projection_layer_spec,
+                vision_projection_type,
+                vision_projection_input_size,
+            )
+            # Ignore missing weights for the vision projection during checkpoint loading.
+            # This should be disabled by default but can be enabled if your checkpoint contains
+            # pretrained vision and language models but not the projection from vision model
+            # outputs to language model inputs.
+            if allow_missing_vision_projection_checkpoint:
+                vision_projection_param_names = [
+                    f"vision_projection.{name}"
+                    for name in self.vision_projection.state_dict().keys()
+                ]
+                self.vision_projection.register_load_state_dict_post_hook(
+                    partial(_load_state_dict_hook_ignore_param_names, vision_projection_param_names)
+                )
+
+        self._img_seq_len = get_num_image_embeddings(
+            img_h,
+            img_w,
+            patch_dim,
+            vision_transformer_config.vision_model_type,
+            drop_vision_class_token,
+            class_token_len,
+            pixel_shuffle,
+            tile_tags is not None,  # Tile tags enabled/disabled.
+        )
+
+        self.image_token_index = image_token_index
+        self._pixel_shuffle = pixel_shuffle
+        self._tile_tags = tile_tags
+
+    def shared_embedding_or_output_weight(self):
+        """This is a convenience method to surface the language model's word embeddings, which is
+        necessary for `finalize_model_grads._allreduce_word_embedding_grads`."""
+        if self.add_decoder:
+            return self.language_model.shared_embedding_or_output_weight()
+        return None
+
+    def set_input_tensor(self, input_tensor) -> None:
+        """Set model chunk input tensor."""
+        # This is usually handled in schedules.py but some inference code still
+        # gives us non-lists or None
+        if not isinstance(input_tensor, list):
+            input_tensor = [input_tensor]
+        assert len(input_tensor) == 1, 'input_tensor should only be length 1 for llava'
+
+        if self.add_encoder and self.add_decoder:
+            self.vision_model.set_input_tensor(input_tensor[0])
+        elif self.add_encoder:
+            self.vision_model.set_input_tensor(input_tensor[0])
+        elif self.pre_process:
+            self.encoder_hidden_state = input_tensor[0]
+        else:
+            self.language_model.set_input_tensor(input_tensor[0])
+
+    def freeze(
+        self, freeze_language_model: bool, freeze_vision_model: bool, freeze_vision_projection: bool
+    ):
+        """Freeze model modules.
+
+        Make specific modules non-trainable by setting requires_grad to False.
+
+        Args:
+            freeze_language_model (bool): Freeze the language model module.
+            freeze_vision_model (bool): Freeze the vision model module.
+            freeze_vision_projection (bool): Freeze the vision projection module.
+        """
+        modules = []
+        if freeze_language_model and self.language_model is not None:
+            modules.append(self.language_model)
+        if freeze_vision_model and self.vision_model is not None:
+            modules.append(self.vision_model)
+        if freeze_vision_projection and self.vision_projection is not None:
+            modules.append(self.vision_projection)
+
+        for module in modules:
+            for param in module.parameters():
+                param.requires_grad = False
+
+    def _preprocess_data(
+        self,
+        image_embeddings,
+        language_embeddings,
+        input_ids,
+        loss_mask,
+        labels,
+        use_inference_kv_cache,
+        inference_params,
+        image_token_index,
+        num_image_tiles,
+        image_token_mask=None,
+    ):
+        """Preprocess input data before input to language model.
+
+        This function is adopted from
+        https://github.com/huggingface/transformers/blob/85817d98fb60977c97e3014196a462b732d2ed1a/src/transformers/models/llava_next/modeling_llava_next.py#L409
+        for our input data conventions.
+
+        image_token_index = -200 indicates the image position in the input_ids = [0, 1, -200, 2, 3]
+        and labels = [1, -200, 2, 3, 4], for example.
+        We want to replace the image position (-200) with image_embeddings and return the following:
+        - final_embeddings = [0, 1, image_embeddings, 2, 3],
+        - final_labels = [1, -100, 2, 3, 4]
+        - final_loss_mask = [1, 0, 0, 1, 1]
+
+        This function handles samples without images (text-only sample). It also handles samples
+        with images that are split into multiples tiles.
+
+        If pipeline parallelism is not used, then self.pre_process and self.post_process
+        are both True and we update both input embeddings, labels and loss masks (if available).
+
+        If pipeline parallelism is used, then we do the following
+        - the first language model chunk has self.pre_process = True and
+          self.post_process = False. We update input embeddings.
+        - the middle language model chunk(s) has self.pre_process = False and
+          self.post_process = False. We don't need to update anything.
+        - the last language model chunk has self.pre_process = False and
+          self.post_process = True. We update labels and loss mask.
+
+        TODO: This function should adjust the attention mask too.
+        Currently, we assume the language model uses a causal mask.
+
+        Returns:
+            final_embedding (torch.Tensor): image and text embeddings [combined_seq_len, b, h].
+            final_labels (torch.Tensor): labels for image and text positions [b, combined_seq_len].
+            final_loss_mask (torch.Tensor): loss mask [b, combined_seq_len].
+        """
+        assert self.add_decoder, "input text preprocessing is only needed for the language model"
+
+        # No pre- or postprocessing needed.
+        # With pipeline parallel > 2, this means a chunk in the middle of the model.
+        if not self.pre_process and not self.post_process:
+            return None, None, None
+
+        # If using the inference KV cache, the image tokens are already computed.
+        if use_inference_kv_cache:
+            return language_embeddings, loss_mask, labels
+
+        img_seq_len = self._img_seq_len
+        batch_size, text_seq_len = input_ids.shape
+        # input_ids seq len is expected to be sharded by CP size
+        if self.context_parallel_lm:
+            text_seq_len *= self.context_parallel_lm
+
+        has_labels = labels is not None
+        if has_labels:
+            assert (
+                labels.shape == loss_mask.shape
+            ), f"mismatching labels shape {labels.shape} and loss mask shape {loss_mask.shape}"
+
+        # Create indices for new text and label positions.
+        with torch.no_grad():
+            if image_token_mask is None:
+                assert (
+                    self.context_parallel_lm <= 1
+                ), "image_token_mask cannot be inferred from input_ids if using \
+                        Context Parallelism. Please provide in forward_step"
+                image_token_mask = input_ids == image_token_index
+            num_images_per_sample = torch.sum(image_token_mask, dim=-1)
+
+            # Number of tiles per sample.
+            num_image_tiles_batch = num_image_tiles.split(num_images_per_sample.tolist(), dim=0)
+            num_image_tiles_batch = torch.tensor(
+                [x.sum() for x in num_image_tiles_batch], device=input_ids.device
+            )
+
+            # Sequence length for each sample is the image sequence length multiplied by
+            # the number of tiles for that image, minus image token indices,
+            # plus text sequence length.
+            seq_lens = num_image_tiles_batch * img_seq_len - num_images_per_sample + text_seq_len
+            max_seq_len = seq_lens.max()
+            # Pipeline parallel expects fixed input size. Check if we need to pad.
+            if (
+                self._language_is_pipeline_parallel
+                and max_seq_len < self._language_max_sequence_length
+                and inference_params is None
+            ):
+                max_seq_len = self._language_max_sequence_length
+
+            batch_indices, non_image_indices = torch.where(image_token_mask != True)
+
+            # New position ids for the text tokens, shifted by the image sequence length.
+            # E.g. for input_ids = [-200, 1, 2, 3] and img_seq_len = 576, we get
+            # new_position_ids = [576, 577, 578, 579]. text_position_ids are then [577, 578, 579].
+            image_token_mask_lens = image_token_mask.int().clone()
+            # -1 is for the removed image token index.
+            image_token_mask_lens[image_token_mask] = num_image_tiles * img_seq_len - 1
+            # +1 is needed here for the cumulative sum. -1 is adjusting for zero-based indexing.
+            new_position_ids = torch.cumsum((image_token_mask_lens + 1), dim=-1) - 1
+            text_position_ids = new_position_ids[batch_indices, non_image_indices]
+
+            # Labels are shifted to left by one.
+            # So, shift text position ids and non-image indices to left by one.
+            if has_labels:
+                label_text_position_ids = text_position_ids - 1
+                valid_label_text_position_ids = label_text_position_ids >= 0
+                label_text_position_ids = label_text_position_ids[valid_label_text_position_ids]
+
+                label_batch_indices = batch_indices[valid_label_text_position_ids]
+
+                label_non_image_indices = non_image_indices - 1
+                valid_label_non_image_indices = label_non_image_indices >= 0
+                label_non_image_indices = label_non_image_indices[valid_label_non_image_indices]
+
+            # Create a mask for the image embedding positions.
+            images_mask = torch.full(
+                (batch_size, max_seq_len), True, dtype=torch.bool, device=input_ids.device
+            )
+            # No images in the text positions.
+            images_mask[batch_indices, text_position_ids] = False
+            # Samples can have different amount of images tokens.
+            # new_position_ids[:, -1] gives the last text position id for each sample.
+            # Padding is needed when the number of image tokens differs.
+            first_padding_idx = new_position_ids[:, -1] + 1
+            images_mask[
+                torch.arange(max_seq_len, device=first_padding_idx.device).repeat(batch_size, 1)
+                >= first_padding_idx.unsqueeze(1)
+            ] = False
+
+        # Create the final input embedding (if this is the first language model stage).
+        final_embedding = None
+        if self.pre_process:
+            embed_dim = language_embeddings.shape[-1]
+            final_embedding = torch.zeros(
+                batch_size,
+                max_seq_len,
+                embed_dim,
+                dtype=language_embeddings.dtype,
+                device=language_embeddings.device,
+            )
+
+            # Put text embeddings to the text positions in the result tensor.
+            final_embedding[batch_indices, text_position_ids] = language_embeddings[
+                batch_indices, non_image_indices
+            ]
+
+            # Put image embeddings to image positions.
+            final_embedding[images_mask] = (
+                image_embeddings.permute(1, 0, 2).reshape(-1, embed_dim).contiguous()
+            )
+
+        # Create the final labels and loss mask (if this is the last language model stage).
+        final_labels, final_loss_mask = None, None
+        if self.post_process and has_labels:
+            final_labels = torch.full(
+                (batch_size, max_seq_len), IGNORE_INDEX, dtype=labels.dtype, device=labels.device
+            )
+            final_loss_mask = torch.full(
+                (batch_size, max_seq_len), 0, dtype=loss_mask.dtype, device=loss_mask.device
+            )
+
+            # Put text labels and loss mask to the text positions.
+            final_labels[label_batch_indices, label_text_position_ids] = labels[
+                label_batch_indices, label_non_image_indices
+            ]
+
+            final_loss_mask[batch_indices, text_position_ids] = loss_mask[
+                batch_indices, non_image_indices
+            ]
+
+            # For labels, pick the last label index that got dropped by the shift to left.
+            label_extra_text_position_ids = seq_lens - 1
+            batch_range = torch.arange(len(label_extra_text_position_ids))
+            final_labels[batch_range, label_extra_text_position_ids] = labels[batch_range, -1]
+
+            # Loss mask the image positions.
+            final_loss_mask[images_mask] = 0
+
+            # Loss mask last text position just before an image
+            # so that text token does not need to predict the first image token.
+            batch_image_indices, image_indices = torch.where(image_token_mask)
+            # Indices just before image tokens. If it's -1, skip it.
+            before_image_indices = image_indices - 1
+            valid = before_image_indices >= 0
+            valid_batch_image_indices = batch_image_indices[valid]
+            valid_before_image_indices = before_image_indices[valid]
+            # Map those indices those position ids.
+            valid_before_image_indices = new_position_ids[
+                valid_batch_image_indices, valid_before_image_indices
+            ]
+
+            final_loss_mask[valid_batch_image_indices, valid_before_image_indices] = 0
+
+        if final_embedding is not None and final_labels is not None:
+            assert (
+                final_embedding.shape[:2] == final_labels.shape == final_loss_mask.shape
+            ), "unexpected shapes after data preprocessing"
+
+        if final_embedding is not None:
+            # Truncate if exceeding the language model's max sequence length.
+            if final_embedding.shape[1] > self._language_max_sequence_length:
+                final_embedding = final_embedding[:, : self._language_max_sequence_length]
+            # Transpose to [s,b,h] if not using CP because CP Sharding expects seq in dim=1
+            if self.context_parallel_lm == 1:
+                final_embedding = final_embedding.transpose(1, 0).contiguous()
+
+        truncate_labels = (
+            final_labels is not None and final_labels.shape[1] > self._language_max_sequence_length
+        )
+        if truncate_labels:
+            final_labels = final_labels[:, : self._language_max_sequence_length]
+            final_loss_mask = final_loss_mask[:, : self._language_max_sequence_length]
+
+        return final_embedding, final_labels, final_loss_mask
+
+    def _process_embedding_token_parallel(
+        self, combined_embeddings, new_labels, new_loss_mask, packed_seq_params
+    ):
+        """Processes the input data for model parallelism support.
+
+        When using sequence parallelism (SP) or context parallelism (CP), the sequence is sharded
+        across different GPUs. This function helps ensure that the sharding is done correctly by
+        1. Calculates `padding_factor` which determines based on how many chunks we expect to shard
+           the sequence
+        2. Calculates and pads the inputs to necessary length to ensure equal sized chunks
+        3. Creates/Modifies PackedSeqParams which helps mask padded tokens during calculations
+        4. Performs any layout changes if necessary
+        5. Distributes the sequence across GPUs for SP and CP
+
+        Context Parallelism is a feature that helps improve memory efficiency for
+        long sequence training by distributing sequence across CP ranks.
+        It requires token length to be divisible by (CP size *2) to ensure proper load balance.
+        Please refer to `get_batch_on_this_cp_rank` function for more details.
+
+        Sequence Parallelism is a feature that helps improve memory efficiency for
+        long sequence training by distributing sequence across TP ranks.
+        It requires token length to be divisible by TP size.
+
+        Returns:
+            combined_embeddings (torch.Tensor): image and text embeddings combined and distributed.
+            new_labels (torch.Tensor): Distributed labels for image and text positions.
+            new_loss_mask (torch.Tensor): Distributed loss mask.
+            packed_seq_params (PackedSeqParams): Dict with padded token information.
+
+        """
+        # combined_embeddings - `s,b,h` if not using CP, `b,s,h` if using CP
+        batch_size = (
+            combined_embeddings.shape[0]
+            if self.context_parallel_lm > 1
+            else combined_embeddings.shape[1]
+        )
+        seq_dim = 1 if self.context_parallel_lm > 1 else 0
+
+        padding_mask_type = 'padding' in str(
+            self.language_model.transformer_layer_spec.submodules.self_attention.params.get(
+                'attn_mask_type', ''
+            )
+        )
+        if self.sequence_parallel_lm and self.tp_comm_overlap_lm:
+            assert (
+                combined_embeddings.shape[seq_dim] == self._language_max_sequence_length
+            ) or padding_mask_type, f"TP Comm overlap either requires Vision+Text token length \
+             == language_max_sequence_length or mask type to be set to padding/padding_causal"
+
+        if padding_mask_type:
+            # Calculate the padded sequence length needed to support SP and CP
+            # SP and CP are used to distributed the sequence across GPUs to improve
+            # memory efficiency and enable very long context training.
+            # To distribute workload equally, we need to ensure that the sequence is
+            # divisible by the appropriate padding factor calculated below.
+            padding_factor = None
+            padded_seq_len = None
+            mp_padding_needed = 0
+            if self.context_parallel_lm > 1 and self.sequence_parallel_lm:
+                padding_factor = self.tensor_model_parallel_size_lm * self.context_parallel_lm * 2
+            elif self.context_parallel_lm > 1:
+                padding_factor = self.context_parallel_lm * 2
+            elif self.sequence_parallel_lm:
+                padding_factor = self.tensor_model_parallel_size_lm
+
+            padded_seq_len = int(
+                (combined_embeddings.shape[seq_dim] + (padding_factor - 1))
+                // padding_factor
+                * padding_factor
+            )
+
+            assert (
+                padded_seq_len <= self._language_max_sequence_length
+            ), f"Sequence length after padding {padded_seq_len} for SP/CP has exceeded \
+                language_max_sequence_length. Ensure language_max_sequence_length is \
+                divisible by SP/CP factor: {padding_factor}"
+
+            if self.sequence_parallel_lm and self.tp_comm_overlap_lm:
+                # TP Comm overlap initializes the user buffer shape used for communication
+                # at the beginning of training run and the same shape is expected to be
+                # used throughout the training.
+                # Pad to language_max_sequence_length to use TP Comm overlap.
+                assert (
+                    self._language_max_sequence_length % padding_factor == 0
+                ), f"TP Comm overlap uses language_max_sequence_length \
+                    which needs to be divisible by SP/CP factor {padding_factor}"
+                padded_seq_len = self._language_max_sequence_length
+
+            assert (
+                packed_seq_params is not None
+            ), "Please provide PackedSeqParams dict when using SP or CP with padding"
+            valid_seqlens = packed_seq_params.cu_seqlens_q[1:] - packed_seq_params.cu_seqlens_q[:-1]
+            valid_seq_len = max(valid_seqlens)
+            assert (
+                padded_seq_len >= valid_seq_len
+            ), f"Padded Seq Len calculated for model parallelism: {padded_seq_len} \
+                    is shorter than expected valid token len {valid_seq_len} provided."
+
+            mp_padding_needed = padded_seq_len - combined_embeddings.shape[seq_dim]
+            if mp_padding_needed > 0:
+                new_labels = torch.nn.functional.pad(
+                    new_labels, (0, mp_padding_needed), value=IGNORE_INDEX
+                )
+                new_loss_mask = torch.nn.functional.pad(new_loss_mask, (0, mp_padding_needed))
+                if self.context_parallel_lm > 1:
+                    combined_embeddings = torch.nn.functional.pad(
+                        combined_embeddings, (0, 0, 0, mp_padding_needed)
+                    )
+                else:
+                    combined_embeddings = torch.nn.functional.pad(
+                        combined_embeddings, (0, 0, 0, 0, 0, mp_padding_needed)
+                    )
+
+                # Update PackedSeqParams if padding needed beyond user provided PackedSeqParams
+                packed_seq_params.max_seqlen_q = padded_seq_len
+                packed_seq_params.max_seqlen_kv = padded_seq_len
+                cu_seqlens_padded = None
+                # We need cu_seqlens_q_padded/cu_seqlens_kv_padded when doing
+                # CP+Padding to support accurate Attention with THD format.
+                if self.context_parallel_lm > 1:
+                    cu_seqlens_padded = torch.arange(
+                        0,
+                        (batch_size + 1) * (padded_seq_len),
+                        step=(padded_seq_len),
+                        dtype=torch.int32,
+                        device=combined_embeddings.device,
+                    )
+                    packed_seq_params.cu_seqlens_q_padded = cu_seqlens_padded
+                    packed_seq_params.cu_seqlens_kv_padded = cu_seqlens_padded
+                    packed_seq_params.qkv_format = 'thd'
+                else:
+                    packed_seq_params.qkv_format = 'sbhd'
+
+        if self.context_parallel_lm > 1:
+            # Distribute sequence across CP ranks
+            from megatron.training.utils import get_batch_on_this_cp_rank
+
+            batch = get_batch_on_this_cp_rank(
+                {
+                    "combined_embeddings": combined_embeddings,
+                    "new_labels": new_labels,
+                    "new_loss_mask": new_loss_mask,
+                }
+            )
+
+            combined_embeddings = batch["combined_embeddings"]  # [B, S/CP, H]
+            new_labels = batch["new_labels"]
+            new_loss_mask = batch["new_loss_mask"]
+
+            if getattr(packed_seq_params, 'qkv_format', None) == 'thd':
+                # If PackedSeqParams requires THD format,
+                # reshape embedding from [B,S,H] to [T,1,H] where T=B*S
+                combined_embeddings = (
+                    combined_embeddings.contiguous()
+                    .view(combined_embeddings.shape[0] * combined_embeddings.shape[1], -1)
+                    .unsqueeze(1)
+                )
+                new_labels = new_labels.view(new_labels.shape[0] * new_labels.shape[1]).unsqueeze(0)
+                new_loss_mask = new_loss_mask.view(
+                    new_loss_mask.shape[0] * new_loss_mask.shape[1]
+                ).unsqueeze(0)
+            else:
+                combined_embeddings = combined_embeddings.transpose(
+                    1, 0
+                ).contiguous()  # [B,S/CP,H] -> [S/CP,B,H]
+
+        if self.sequence_parallel_lm:
+            combined_embeddings = tensor_parallel.scatter_to_sequence_parallel_region(
+                combined_embeddings
+            )  # [S/(CP*TP),B,H]
+
+        return combined_embeddings, new_labels, new_loss_mask, packed_seq_params
+
+    def _apply_tile_tagging(self, image_embeddings, num_image_tiles):
+        """Apply tile tagging.
+
+        The image embeddings of multiple tiles are prepended with tile tags such as <tile_1>.
+        This implements the method used in NVLM https://arxiv.org/pdf/2409.11402.
+
+        Args:
+            image_embeddings (torch.Tensor): [img_seq_len, num_tiles, h_language].
+            num_image_tiles (torch.Tensor): Number of tiles for each input image [num_images].
+
+        Returns:
+            torch.Tensor: Tile tags prepended to image embeddings.
+                [tile_seq_len (=5) + img_seq_len, num_tiles, h_language]
+        """
+        assert (
+            num_image_tiles.shape[0] == 1 and len(num_image_tiles) == 1
+        ), "multiple input images are not supported yet."
+
+        num_tiles = num_image_tiles[0].item()
+        tile_tags = self._tile_tags[: num_tiles - 1] + [self._tile_tags[-1]]
+
+        # [num_tiles, tile_seq_len (=5)]
+        tile_tag_input_ids = torch.tensor(
+            tile_tags, dtype=torch.int64, device=num_image_tiles.device
+        )
+
+        # [tile_seq_len, num_tiles, h_language]
+        tile_tag_embeds = self.language_model.embedding(tile_tag_input_ids, position_ids=None)
+
+        # [num_tiles, dim] should be the same same
+        assert tile_tag_embeds.shape[1:] == image_embeddings.shape[1:]
+
+        image_embeddings = torch.cat([tile_tag_embeds, image_embeddings])
+
+        return image_embeddings  # [tile_seq_len + img_seq_len, num_tiles, h_language]
+
+    def forward(
+        self,
+        images: torch.Tensor,
+        input_ids: torch.Tensor,
+        position_ids: torch.Tensor,
+        attention_mask: torch.Tensor,
+        labels: Optional[torch.Tensor] = None,
+        loss_mask: Optional[torch.Tensor] = None,
+        inference_params: Optional[InferenceParams] = None,
+        num_image_tiles: Optional[List[int]] = None,
+        image_token_index: Optional[int] = None,
+        runtime_gather_output: Optional[bool] = None,
+        image_token_mask: Optional[torch.Tensor] = None,
+        packed_seq_params: Optional[PackedSeqParams] = None,
+    ) -> torch.Tensor:
+        """Forward function of the LLaVA model.
+
+        Args:
+            images (torch.Tensor): input images of shape [num_tiles, img_h, img_w].
+                num_tiles means the number of image tiles in this batch.
+                num_tiles = 0 if the batch doesn't contain images.
+            input_ids (torch.Tensor): input text ids [batch, text_seq_len].
+            position_ids (torch.Tensor): input text position ids [batch, text_seq_len].
+            attention_mask (torch.Tensor): Language model attention mask
+                [batch, 1, 1, combined_seq_len]. NOTE: attention_mask is typically None and
+                attn_mask_type in layer specs determines the attention mask used.
+            labels (torch.Tensor): Optional target text labels [batch, combined_seq_len].
+            loss_mask (torch.Tensor): Text loss mask [batch, text_seq_len].
+            inference_params (InferenceParams): Inference-time parameters including KV cache.
+            num_image_tiles (list of int): Number of tiles per image. Default 1 tile per image.
+            image_token_index (int): ID for input images. Default None means `image_token_index`
+                arg in the constructor will be used.
+            runtime_gather_output (bool): Gather output at runtime. Default None means
+                `parallel_output` arg in the constructor will be used.
+            image_token_mask (torch.Tensor): Tensor indicating the location of
+                image token index in input_ids.
+            packed_seq_params (PackedSeqParams): 1) If using sequence packing, must contain
+                subsample length information. 2) If using SP/CP with padding mask type,
+                must contain padded token information.
+
+        Returns:
+            output (torch.Tensor): Loss of shape [b, s] if labels are provided,
+                otherwise logits of shape [b, s, vocab_size].
+            loss_mask (torch.Tensor): Loss mask expanded to combined sequence length. Shape [b, s].
+        """
+        use_inference_kv_cache = (
+            inference_params is not None
+            and "image_tokens_count" in inference_params.key_value_memory_dict
+        )
+        has_images = images is not None and images.shape[0] > 0
+
+        # If running inference, we can skip image token computation
+        # if they were computed already earlier for this sample.
+        if use_inference_kv_cache:
+            image_embeddings = None
+        elif self.add_encoder and not has_images:
+            # If no images provided, use an empty image embeddings tensor.
+            image_embeddings = torch.tensor([], dtype=images.dtype, device=images.device).reshape(
+                0, 0, 0
+            )
+        elif self.add_encoder and has_images:
+            image_embeddings = self.vision_model(images)  # [num_tiles, img_seq_len, h_vision]
+            if self._drop_vision_class_token:
+                image_embeddings = image_embeddings[:, self.vision_model.class_token_len :, :]
+
+            if self._pixel_shuffle:
+                image_embeddings = pixel_shuffle(
+                    image_embeddings
+                )  # [num_tiles, img_seq_len_shuffled, h_vision_shuffled]
+
+            # contiguous() required as `permute` can sparsify the tensor and this breaks pipelining
+            image_embeddings = image_embeddings.permute(
+                1, 0, 2
+            ).contiguous()  # [img_seq_len, num_tiles, h_vision]
+
+            # map vision model output size to language model input size.
+            image_embeddings = self.vision_projection(
+                image_embeddings
+            )  # [img_seq_len, num_tiles, h_language]
+
+            # Apply tile tagging if enabled and an image token is present.
+            if self._tile_tags is not None and torch.any(input_ids == self.image_token_index):
+                image_embeddings = self._apply_tile_tagging(image_embeddings, num_image_tiles)
+
+            # TODO: Support batched inference.
+            # In inference, the language model KV cache will be updated for image token positions.
+            # Store the image tokens sequence length to be used as an offset to the KV cache later.
+            if inference_params is not None:
+                inference_params.key_value_memory_dict["image_tokens_count"] = (
+                    image_embeddings.shape[0] * image_embeddings.shape[1]
+                )
+        else:
+            image_embeddings = self.encoder_hidden_state
+
+        if not self.add_decoder:
+            return image_embeddings, loss_mask
+
+        language_embeddings = None
+        if self.pre_process:
+            input_ids_text = input_ids.clone()
+            input_ids_text[input_ids_text == self.image_token_index] = 0
+            # Note: This adds absolute position embedding but not RoPE.
+            # Each image is counted as one position.
+            # RoPE is added in language_model forward. Each image embedding is one position.
+            language_embeddings = self.language_model.embedding(
+                input_ids=input_ids_text, position_ids=position_ids
+            )  # [text_seq_len, b, h_language]
+            # Gather the language embeddings back. We need the full embedding to insert
+            # image embeddings and then scatter again to avoid load imbalance.
+            if self.context_parallel_lm > 1:
+                cp_group = get_context_parallel_group()
+                language_embeddings, _ = gather_along_first_dim(language_embeddings, cp_group)
+
+            language_embeddings = language_embeddings.transpose(
+                1, 0
+            ).contiguous()  # [b, text_seq_len, h_language]
+
+        # Assume 1 tile per image if the number of tiles is not provided.
+        if num_image_tiles is None and images is not None:
+            num_image_tiles = torch.ones(images.shape[0], dtype=torch.int, device=input_ids.device)
+
+        combined_embeddings, new_labels, new_loss_mask = self._preprocess_data(
+            image_embeddings,
+            language_embeddings,
+            input_ids,
+            loss_mask,
+            labels,
+            use_inference_kv_cache,
+            inference_params,
+            image_token_index if image_token_index is not None else self.image_token_index,
+            num_image_tiles,
+            image_token_mask,
+        )  # [combined_seq_len, b, h_language], [b, combined_seq_len], [b, combined_seq_len]
+
+        if self.context_parallel_lm > 1 or self.sequence_parallel_lm:
+            combined_embeddings, new_labels, new_loss_mask, packed_seq_params = (
+                self._process_embedding_token_parallel(
+                    combined_embeddings, new_labels, new_loss_mask, packed_seq_params
+                )
+            )
+
+        output = self.language_model(
+            input_ids=None,
+            position_ids=None,
+            attention_mask=attention_mask,
+            decoder_input=combined_embeddings,
+            labels=new_labels,
+            inference_params=inference_params,
+            runtime_gather_output=runtime_gather_output,
+            packed_seq_params=packed_seq_params,
+        )
+
+        return output, new_loss_mask
+
+
+def _load_state_dict_hook_ignore_param_names(
+    param_names: List[str], module: torch.nn.Module, incompatible_keys: namedtuple
+):
+    """Hook to ignore missing keys during checkpoint loading.
+
+    By default, this should not be used to avoid accidentally missing weights in checkpoint loading.
+
+    Example use case: Use this if you want to load a checkpoint that contains vision and language
+    model weights but not the vision projection weights.
+
+    Args:
+        param_names (list str): Parameter names allowed to be missing when calling load_state_dict.
+        module (torch.nn.Module): The torch module this hook applies to. Required by the torch API.
+        incompatible_keys (namedtuple): Namedtuple with fields missing_keys and unexpected_keys,
+            which collect the missing and unexpected keys, respectively.
+    """
+    for param_name in param_names:
+        if param_name in incompatible_keys.missing_keys:
+            logging.getLogger(__name__).warning(
+                f"{param_name} being removed from incompatible_keys.missing_keys in LlavaModel"
+            )
+            incompatible_keys.missing_keys.remove(param_name)
+
+
+# pylint: disable-next=line-too-long
+# Based on https://github.com/OpenGVLab/InternVL/blob/c7c5af1a8930b4862afe8ed14672307082ef61fa/internvl_chat/internvl/model/internvl_chat/modeling_internvl_chat.py#L218
+# Copyright (c) 2023 OpenGVLab.
+def pixel_shuffle(x, scale_factor=0.5, version=2):
+    """Pixel shuffle based on InternVL but adapted for our use case.
+
+    Args:
+        x (torch.Tensor): Vision model outputs [num_tiles, img_seq_len, h_vision]
+        version (int): Implementation version.
+
+    Returns:
+        Shuffled vision model outputs [num_tiles, (sq ** 2) * (scale ** 2), h_vision / (scale ** 2)]
+    """
+    h = w = int(x.shape[1] ** 0.5)  # sq
+    x = x.reshape(x.shape[0], h, w, -1)  # [num_tiles, sq, sq, h_vision]
+
+    n, w, h, c = x.size()
+    # N, W, H, C --> N, W, H * scale, C // scale
+    x = x.view(n, w, int(h * scale_factor), int(c / scale_factor))
+    # N, W, H * scale, C // scale --> N, H * scale, W, C // scale
+    x = x.permute(0, 2, 1, 3).contiguous()
+    # N, H * scale, W, C // scale --> N, H * scale, W * scale, C // (scale ** 2)
+    x = x.view(
+        n, int(h * scale_factor), int(w * scale_factor), int(c / (scale_factor * scale_factor))
+    )
+
+    if version == 2:
+        x = x.permute(0, 2, 1, 3).contiguous()
+
+    x = x.reshape(x.shape[0], -1, x.shape[-1])
+
+    return x

megatron/core/models/multimodal/llava_spec.py

+# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+from megatron.core.extensions.transformer_engine import (
+    TEDotProductAttention,
+    TELayerNormColumnParallelLinear,
+    TENorm,
+    TERowParallelLinear,
+)
+from megatron.core.fusions.fused_bias_dropout import get_bias_dropout_add
+from megatron.core.models.gpt.gpt_layer_specs import _get_mlp_module_spec
+from megatron.core.tensor_parallel.layers import ColumnParallelLinear, RowParallelLinear
+from megatron.core.transformer.attention import SelfAttention, SelfAttentionSubmodules
+from megatron.core.transformer.dot_product_attention import DotProductAttention
+from megatron.core.transformer.enums import AttnMaskType
+from megatron.core.transformer.identity_op import IdentityOp
+from megatron.core.transformer.spec_utils import ModuleSpec
+from megatron.core.transformer.transformer_layer import TransformerLayer, TransformerLayerSubmodules
+
+try:
+    import apex  # pylint: disable=unused-import
+
+    from megatron.core.fusions.fused_layer_norm import FusedLayerNorm
+
+    HAVE_APEX = True
+    LNImpl = FusedLayerNorm
+except ImportError:
+    import warnings
+
+    from megatron.core.transformer.torch_norm import WrappedTorchNorm
+
+    warnings.warn(f'Apex is not installed. Falling back to Torch Norm')
+    LNImpl = WrappedTorchNorm
+
+
+def decoder_model_with_transformer_engine_default_spec(
+    num_experts: int = None, moe_grouped_gemm: bool = False, qk_layernorm: bool = False
+) -> ModuleSpec:
+    """LLava decoder TE spec (uses Transformer Engine components)."""
+    mlp = _get_mlp_module_spec(
+        use_te=True, num_experts=num_experts, moe_grouped_gemm=moe_grouped_gemm
+    )
+    return ModuleSpec(
+        module=TransformerLayer,
+        submodules=TransformerLayerSubmodules(
+            self_attention=ModuleSpec(
+                module=SelfAttention,
+                params={"attn_mask_type": AttnMaskType.causal},
+                submodules=SelfAttentionSubmodules(
+                    linear_qkv=TELayerNormColumnParallelLinear,
+                    core_attention=TEDotProductAttention,
+                    linear_proj=TERowParallelLinear,
+                    q_layernorm=TENorm if qk_layernorm else IdentityOp,
+                    k_layernorm=TENorm if qk_layernorm else IdentityOp,
+                ),
+            ),
+            self_attn_bda=get_bias_dropout_add,
+            mlp=mlp,
+            mlp_bda=get_bias_dropout_add,
+        ),
+    )
+
+
+def decoder_model_with_local_default_spec(
+    num_experts: int = None, moe_grouped_gemm: bool = False, qk_layernorm: bool = False
+) -> ModuleSpec:
+    """LLava decoder local spec."""
+    mlp = _get_mlp_module_spec(
+        use_te=False, num_experts=num_experts, moe_grouped_gemm=moe_grouped_gemm
+    )
+    return ModuleSpec(
+        module=TransformerLayer,
+        submodules=TransformerLayerSubmodules(
+            input_layernorm=LNImpl,
+            self_attention=ModuleSpec(
+                module=SelfAttention,
+                params={"attn_mask_type": AttnMaskType.causal},
+                submodules=SelfAttentionSubmodules(
+                    linear_qkv=ColumnParallelLinear,
+                    core_attention=DotProductAttention,
+                    linear_proj=RowParallelLinear,
+                ),
+            ),
+            self_attn_bda=get_bias_dropout_add,
+            pre_mlp_layernorm=LNImpl,
+            mlp=mlp,
+            mlp_bda=get_bias_dropout_add,
+        ),
+    )

megatron/core/models/retro/__init__.py

+# Copyright (c) 2024, NVIDIA CORPORATION.  All rights reserved.
+
+"""
+Exports:
+
+  - RetroConfig: configuration dataclass for RetroModel.
+  - RetroModel: The Retro model.
+  - get_retro_decoder_block_spec: Get spec for Retro decoder transformer block.
+"""
+
+from .config import RetroConfig
+from .decoder_spec import get_retro_decoder_block_spec
+from .model import RetroModel

megatron/core/models/retro/base_attention.py

+# Copyright (c) 2024, NVIDIA CORPORATION.  All rights reserved.
+
+"""Base class for decoder and encoder attention modules."""
+
+from megatron.core.models.retro.config import RetroConfig
+from megatron.core.transformer.attention import CrossAttention, CrossAttentionSubmodules
+from megatron.core.transformer.enums import AttnMaskType
+from megatron.core.transformer.module import MegatronModule
+
+
+class BaseRetroCrossAttention(MegatronModule):
+    """Base class for Retro cross attention, for both encoder & decoder layers.
+
+    This class collects the retro arguments below (i.e., num neighbors, chunk
+    length, and retrieve length) for use in Retro's custom cross attention
+    operators.
+
+    Args:
+        config (RetroConfig): Retro config.
+        submodules (CrossAttentionSubmodules): Cross attention submodules.
+        layer_number (int): Layer number within transformer block.
+        attn_mask_type (AttnMaskType): Mask type ('causal' or 'padding').
+    """
+
+    def __init__(
+        self,
+        config: RetroConfig,
+        submodules: CrossAttentionSubmodules,
+        layer_number: int = 1,
+        attn_mask_type: AttnMaskType = AttnMaskType.padding,
+    ):
+        super().__init__(config=config)
+
+        self.attn = CrossAttention(
+            config=config,
+            submodules=submodules,
+            layer_number=layer_number,
+            attn_mask_type=attn_mask_type,
+        )
+
+        self.retro_num_neighbors = config.retro_num_neighbors
+        self.retro_chunk_length = config.retro_chunk_length
+        self.retro_retrieved_length = config.retro_retrieved_length