Initial commit

megatron/core/models/common/language_module/language_module.py

+import logging
+from typing import Optional, Tuple
+
+import torch
+from torch import Tensor
+
+from megatron.core import parallel_state, tensor_parallel
+from megatron.core.dist_checkpointing.mapping import ShardedStateDict
+from megatron.core.fusions.fused_cross_entropy import fused_vocab_parallel_cross_entropy
+from megatron.core.transformer.module import MegatronModule
+from megatron.core.transformer.transformer_config import TransformerConfig
+from megatron.core.utils import make_tp_sharded_tensor_for_checkpoint
+
+
+class LanguageModule(MegatronModule):
+    """Base language module that has common helper functions used across GPT, BERT etc.
+
+    Args:
+        config (TransformerConfig): Input transformer config for the model
+    """
+
+    def __init__(self, config: TransformerConfig) -> None:
+        super().__init__(config=config)
+
+    def compute_language_model_loss(self, labels: Tensor, logits: Tensor) -> Tensor:
+        """Computes the language model loss (Cross entropy across vocabulary)
+
+        Args:
+            labels (Tensor): The labels of dimension [batch size, seq length]
+            logits (Tensor): The final logits returned by the output layer of the transformer model
+
+        Returns:
+            Tensor: Loss tensor of dimensions [batch size, sequence_length]
+        """
+        # [b s] => [s b]
+        labels = labels.transpose(0, 1).contiguous()
+        if self.config.cross_entropy_loss_fusion:
+            loss = fused_vocab_parallel_cross_entropy(logits, labels)
+        else:
+            loss = tensor_parallel.vocab_parallel_cross_entropy(logits, labels)
+
+        # [s b] => [b, s]
+        loss = loss.transpose(0, 1).contiguous()
+        return loss
+
+    def setup_embeddings_and_output_layer(self) -> None:
+        """Sets up embedding layer in first stage and output layer in last stage.
+
+        This function initalizes word embeddings in the final stage when we are
+        using pipeline parallelism and sharing word embeddings, and sets up param
+        attributes on the embedding and output layers.
+        """
+
+        # Set `is_embedding_or_output_parameter` attribute.
+        if self.pre_process:
+            self.embedding.word_embeddings.weight.is_embedding_or_output_parameter = True
+        if self.post_process and self.output_layer.weight is not None:
+            self.output_layer.weight.is_embedding_or_output_parameter = True
+
+        if not self.share_embeddings_and_output_weights:
+            return
+
+        if self.pre_process and self.post_process:
+            # Zero out wgrad if sharing embeddings between two layers on same
+            # pipeline stage to make sure grad accumulation into main_grad is
+            # correct and does not include garbage values (e.g., from torch.empty).
+            self.shared_embedding_or_output_weight().zero_out_wgrad = True
+            return
+
+        if self.pre_process and not self.post_process:
+            assert parallel_state.is_pipeline_first_stage()
+            self.shared_embedding_or_output_weight().shared_embedding = True
+
+        if self.post_process and not self.pre_process:
+            assert not parallel_state.is_pipeline_first_stage()
+            # set word_embeddings weights to 0 here, then copy first
+            # stage's weights using all_reduce below.
+            self.output_layer.weight.data.fill_(0)
+            self.output_layer.weight.shared = True
+            self.output_layer.weight.shared_embedding = True
+
+        # Parameters are shared between the word embeddings layers, and the
+        # heads at the end of the model. In a pipelined setup with more than
+        # one stage, the initial embedding layer and the head are on different
+        # workers, so we do the following:
+        # 1. Create a second copy of word_embeddings on the last stage, with
+        #    initial parameters of 0.0.
+        # 2. Do an all-reduce between the first and last stage to ensure that
+        #    the two copies of word_embeddings start off with the same
+        #    parameter values.
+        # 3. In the training loop, before an all-reduce between the grads of
+        #    the two word_embeddings layers to ensure that every applied weight
+        #    update is the same on both stages.
+
+        # Ensure that first and last stages have the same initial parameter
+        # values.
+        if torch.distributed.is_initialized():
+            if parallel_state.is_rank_in_embedding_group():
+                weight = self.shared_embedding_or_output_weight()
+                weight.data = weight.data.cuda()
+                torch.distributed.all_reduce(
+                    weight.data, group=parallel_state.get_embedding_group()
+                )
+
+        elif not getattr(LanguageModule, "embedding_warning_printed", False):
+            logging.getLogger(__name__).warning(
+                "Distributed processes aren't initialized, so the output layer "
+                "is not initialized with weights from the word embeddings. "
+                "If you are just manipulating a model this is fine, but "
+                "this needs to be handled manually. If you are training "
+                "something is definitely wrong."
+            )
+            LanguageModule.embedding_warning_printed = True
+
+    def shared_embedding_or_output_weight(self) -> Tensor:
+        """Gets the emedding weight or output logit weights when share embedding and output weights set to True.
+
+        Returns:
+            Tensor: During pre processing it returns the input embeddings weight while during post processing it returns the final output layers weight
+        """
+        if self.pre_process:
+            return self.embedding.word_embeddings.weight
+        elif self.post_process:
+            return self.output_layer.weight
+        return None
+
+    def sharded_state_dict(
+        self,
+        prefix: str = '',
+        sharded_offsets: Tuple[Tuple[int, int, int]] = (),
+        metadata: Optional[dict] = None,
+    ) -> ShardedStateDict:
+        """ Sharded state dict implementation that handles the output layer weights tying.
+
+        Args:
+            prefix (str): Module name prefix.
+            sharded_offsets (tuple): PP related offsets, expected to be empty at this module level.
+            metadata (Optional[Dict]): metadata controlling sharded state dict creation.
+
+        Returns:
+            ShardedStateDict: sharded state dict for the LanguageModel
+        """
+        assert not sharded_offsets, "Unexpected sharded offsets"
+        sharded_state_dict = super().sharded_state_dict(prefix, sharded_offsets, metadata)
+
+        first_stage_word_emb_key = f'{prefix}embedding.word_embeddings.weight'
+        output_layer_weight_key = f'{prefix}output_layer.weight'
+        output_layer_bias_key = f'{prefix}output_layer.bias'
+
+        if self.share_embeddings_and_output_weights:
+            self.tie_embeddings_and_output_weights_state_dict(
+                sharded_state_dict, output_layer_weight_key, first_stage_word_emb_key
+            )
+        elif self.post_process:
+            # Make sure the output layer follows the embeddings padding logic
+            sharded_state_dict[output_layer_weight_key].allow_shape_mismatch = True
+
+        # Regardless of sharing the output weights with embeddings, we must handle the bias padding
+        if self.post_process and output_layer_bias_key in sharded_state_dict:
+            sharded_state_dict[output_layer_bias_key].allow_shape_mismatch = True
+
+        return sharded_state_dict
+
+    def tie_embeddings_and_output_weights_state_dict(
+        self,
+        sharded_state_dict: ShardedStateDict,
+        output_layer_weight_key: str,
+        first_stage_word_emb_key: str,
+    ) -> None:
+        """Ties the embedding and output weights in a given sharded state dict.
+
+        Args:
+            sharded_state_dict (ShardedStateDict): state dict with the weight to tie
+            output_layer_weight_key (str): key of the output layer weight in the state dict.
+                This entry will be replaced with a tied version
+            first_stage_word_emb_key (str): this must be the same as the
+                ShardedTensor.key of the first stage word embeddings.
+
+        Returns: None, acts in-place
+        """
+        if not self.post_process:
+            # No output layer
+            assert output_layer_weight_key not in sharded_state_dict, sharded_state_dict.keys()
+            return
+
+        if self.pre_process:
+            # Output layer is equivalent to the embedding already
+            return
+
+        # Replace the default output layer with a one sharing the weights with the embedding
+        del sharded_state_dict[output_layer_weight_key]
+        tensor = self.shared_embedding_or_output_weight()
+        last_stage_word_emb_replica_id = (
+            1,  # copy of first stage embedding
+            0,
+            parallel_state.get_data_parallel_rank(with_context_parallel=True),
+        )
+
+        sharded_state_dict[output_layer_weight_key] = make_tp_sharded_tensor_for_checkpoint(
+            tensor=tensor,
+            key=first_stage_word_emb_key,
+            replica_id=last_stage_word_emb_replica_id,
+            allow_shape_mismatch=True,
+        )

megatron/core/models/common/vision_module/vision_module.py

+# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+"""Megatron Vision Module."""
+
+from megatron.core.transformer.module import MegatronModule
+from megatron.core.transformer.transformer_config import TransformerConfig
+
+
+# Note: This is only a stub at the moment. This will be expanded in follow-up changes.
+class VisionModule(MegatronModule):
+    """Base vision module that has common helper functions used across CLIP, ViT, etc.
+
+    Args:
+        config (TransformerConfig): Input transformer config for the model
+    """
+
+    def __init__(self, config: TransformerConfig) -> None:
+        super().__init__(config=config)

megatron/core/models/gpt/__init__.py

+from .gpt_model import GPTModel

megatron/core/models/gpt/gpt_layer_specs.py

+# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
+
+from megatron.core.fusions.fused_bias_dropout import get_bias_dropout_add
+from megatron.core.fusions.fused_layer_norm import FusedLayerNorm
+from megatron.core.tensor_parallel.layers import ColumnParallelLinear, RowParallelLinear
+from megatron.core.transformer.attention import SelfAttention, SelfAttentionSubmodules
+try:
+   from megatron.core.transformer.custom_layers.transformer_engine import (
+       TEDotProductAttention,
+       TELayerNormColumnParallelLinear,
+       TENorm,
+       TERowParallelLinear,
+   )
+except ImportError:
+   TEDotProductAttention = None
+   TELayerNormColumnParallelLinear = None
+   TENorm = None
+   TERowParallelLinear = None
+   #print("Do not support transformer_engine")
+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.mlp import MLP, MLPSubmodules
+from megatron.core.transformer.moe.moe_layer import MoELayer
+from megatron.core.transformer.spec_utils import ModuleSpec
+from megatron.core.transformer.transformer_block import TransformerBlockSubmodules
+from megatron.core.transformer.transformer_layer import TransformerLayer, TransformerLayerSubmodules
+
+
+# Use this spec to use lower level Transformer Engine modules (required for fp8 training)
+def get_gpt_layer_with_transformer_engine_spec(
+    num_experts: int = None, moe_grouped_gemm: bool = False, qk_layernorm: bool = False
+) -> ModuleSpec:
+    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,
+            pre_mlp_layernorm=TENorm if num_experts else IdentityOp,
+            mlp=mlp,
+            mlp_bda=get_bias_dropout_add,
+        ),
+    )
+
+
+# Use this spec for an implementation using only modules in megatron core
+def get_gpt_layer_local_spec(
+    num_experts: int = None, moe_grouped_gemm: bool = False, qk_layernorm: bool = False
+) -> ModuleSpec:
+    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=FusedLayerNorm,
+            self_attention=ModuleSpec(
+                module=SelfAttention,
+                params={"attn_mask_type": AttnMaskType.causal},
+                submodules=SelfAttentionSubmodules(
+                    linear_qkv=ColumnParallelLinear,
+                    core_attention=DotProductAttention,
+                    linear_proj=RowParallelLinear,
+                    q_layernorm=FusedLayerNorm if qk_layernorm else IdentityOp,
+                    k_layernorm=FusedLayerNorm if qk_layernorm else IdentityOp,
+                ),
+            ),
+            self_attn_bda=get_bias_dropout_add,
+            pre_mlp_layernorm=FusedLayerNorm,
+            mlp=mlp,
+            mlp_bda=get_bias_dropout_add,
+            sharded_state_dict_keys_map={
+                'input_layernorm.': 'self_attention.linear_qkv.layer_norm_',
+                'pre_mlp_layernorm.': 'mlp.linear_fc1.layer_norm_',
+            },
+        ),
+    )
+
+
+# Helper function to get module spec for MLP/MoE
+def _get_mlp_module_spec(
+    use_te: bool = True, num_experts: int = None, moe_grouped_gemm: bool = False
+) -> ModuleSpec:
+    if num_experts is None:
+        # Dense MLP w/ or w/o TE modules.
+        return ModuleSpec(
+            module=MLP,
+            submodules=MLPSubmodules(
+                linear_fc1=TELayerNormColumnParallelLinear if use_te else ColumnParallelLinear,
+                linear_fc2=TERowParallelLinear if use_te else RowParallelLinear,
+            ),
+        )
+    else:
+        # Mixture of experts with modules in megatron core.
+        return ModuleSpec(
+            module=MoELayer,
+            submodules=MLPSubmodules(linear_fc1=ColumnParallelLinear, linear_fc2=RowParallelLinear,)
+            if not moe_grouped_gemm
+            else None,
+        )

megatron/core/models/gpt/gpt_model.py

+# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
+
+import logging
+from typing import Dict, Literal, Optional, Tuple, Union
+
+import torch
+from torch import Tensor
+
+from megatron.core import InferenceParams, parallel_state, tensor_parallel
+from megatron.core.dist_checkpointing.mapping import ShardedStateDict
+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.packed_seq_params import PackedSeqParams
+from megatron.core.transformer.enums import AttnMaskType, ModelType
+from megatron.core.transformer.spec_utils import ModuleSpec
+from megatron.core.transformer.transformer_block import TransformerBlock
+from megatron.core.transformer.transformer_config import TransformerConfig
+from megatron.core.utils import make_tp_sharded_tensor_for_checkpoint
+
+
+class GPTModel(LanguageModule):
+    """GPT Transformer language model.
+
+    Args:
+        config (TransformerConfig): Transformer config
+        transformer_layer_spec (ModuleSpec): Specifies module to use for transformer layers
+        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.
+        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], optional):  Position embedding type.. Defaults to 'learned_absolute'.
+        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,
+        transformer_layer_spec: ModuleSpec,
+        vocab_size: int,
+        max_sequence_length: int,
+        pre_process: bool = True,
+        post_process: bool = True,
+        fp16_lm_cross_entropy: bool = False,
+        parallel_output: bool = True,
+        share_embeddings_and_output_weights: bool = False,
+        position_embedding_type: Literal['learned_absolute', 'rope', 'none'] = 'learned_absolute',
+        rotary_percent: float = 1.0,
+        rotary_base: int = 10000,
+        seq_len_interpolation_factor: Optional[float] = None,
+    ) -> None:
+        super().__init__(config=config)
+
+        self.transformer_layer_spec: ModuleSpec = transformer_layer_spec
+        self.vocab_size = vocab_size
+        self.max_sequence_length = max_sequence_length
+        self.pre_process = pre_process
+        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
+
+        # These 2 attributes are needed for TensorRT-LLM export.
+        self.max_position_embeddings = max_sequence_length
+        self.rotary_percent = rotary_percent
+
+        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,
+                rotary_interleaved=self.config.rotary_interleaved,
+                seq_len_interpolation_factor=seq_len_interpolation_factor,
+                rotary_base=rotary_base,
+            )
+
+        # Transformer.
+        self.decoder = TransformerBlock(
+            config=self.config,
+            spec=transformer_layer_spec,
+            pre_process=self.pre_process,
+            post_process=self.post_process,
+        )
+
+        # Output
+        if post_process:
+            if self.config.defer_embedding_wgrad_compute:
+                # The embedding activation buffer preserves a reference to the input activations
+                # of the final embedding projection layer GEMM. It will hold the activations for
+                # all the micro-batches of a global batch for the last pipeline stage. Once we are
+                # done with all the back props for all the microbatches for the last pipeline stage,
+                # it will be in the pipeline flush stage. During this pipeline flush we use the
+                # input activations stored in embedding activation buffer and gradient outputs stored
+                # in gradient buffer to calculate the weight gradients for the embedding final linear layer.
+                self.embedding_activation_buffer = []
+                self.grad_output_buffer = []
+            else:
+                self.embedding_activation_buffer = None
+                self.grad_output_buffer = None
+
+            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,
+                embedding_activation_buffer=self.embedding_activation_buffer,
+                grad_output_buffer=self.grad_output_buffer,
+            )
+
+        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,
+        packed_seq_params: PackedSeqParams = None,
+        extra_block_kwargs: dict = None,
+    ) -> Tensor:
+        """Forward function of the GPT Model This function passes the input tensors
+        through the embedding layer, and then the decoeder 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 positional embeddings (embedding is None for PP intermediate devices)
+        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)
+
+        # Run decoder.
+        hidden_states = self.decoder(
+            hidden_states=decoder_input,
+            attention_mask=attention_mask,
+            inference_params=inference_params,
+            rotary_pos_emb=rotary_pos_emb,
+            packed_seq_params=packed_seq_params,
+            **(extra_block_kwargs or {}),
+        )
+
+        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
+
+    def sharded_state_dict(
+        self, prefix: str = '', sharded_offsets: tuple = (), metadata: Optional[Dict] = None
+    ) -> ShardedStateDict:
+        """ Sharded state dict implementation for GPTModel backward-compatibility (removing extra state).
+
+        Args:
+            prefix (str): Module name prefix.
+            sharded_offsets (tuple): PP related offsets, expected to be empty at this module level.
+            metadata (Optional[Dict]): metadata controlling sharded state dict creation.
+
+        Returns:
+            ShardedStateDict: sharded state dict for the GPTModel
+        """
+        sharded_state_dict = super().sharded_state_dict(prefix, sharded_offsets, metadata)
+        output_layer_extra_state_key = f'{prefix}output_layer._extra_state'
+
+        # Old GPT checkpoints only stored the output layer weight key. So we remove the _extra_state key
+        # but check that it doesn't contain any data anyway
+        output_extra_state = sharded_state_dict.pop(output_layer_extra_state_key, None)
+        assert not (
+            output_extra_state and output_extra_state.data
+        ), f'Expected output layer extra state to be empty, got: {output_extra_state}'
+
+        return sharded_state_dict

megatron/core/models/mamba/__init__.py

+from .mamba_model import MambaModel

megatron/core/models/mamba/mamba_layer_specs.py

+# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
+
+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 Mamba
+from megatron.core.transformer.attention import SelfAttention, SelfAttentionSubmodules
+from megatron.core.transformer.custom_layers.transformer_engine import (
+    TEDotProductAttention,
+    TELayerNormColumnParallelLinear,
+    TENorm,
+    TERowParallelLinear,
+)
+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(norm=TENorm, mixer=Mamba,),
+        ),
+        # 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.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.
+        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,
+        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)
+
+        self.mamba_stack_spec: ModuleSpec = mamba_stack_spec
+        self.vocab_size = vocab_size
+        self.max_sequence_length = max_sequence_length
+        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,
+            )
+
+        self.decoder = build_module(
+            mamba_stack_spec,
+            self.config,
+            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/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
+
+import torch
+
+from megatron.core import InferenceParams, parallel_state
+from megatron.core.models.gpt import GPTModel
+from megatron.core.models.vision.clip_vit_model import CLIPViTModel
+from megatron.core.models.vision.multimodal_projector import MultimodalProjector
+from megatron.core.transformer import MegatronModule
+from megatron.core.transformer.spec_utils import ModuleSpec
+from megatron.core.transformer.transformer_config import TransformerConfig
+
+
+# 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): Specifies module to use for transformer layers of the language model.
+        language_vocab_size (int): Language model vocabulary size.
+        language_max_sequence_length (int): Language model maximum sequence length. This is used for positional embedding.
+        vision_transformer_config (TransformerConfig): Transformer config for the vision model.
+        vision_transformer_layer_spec (ModuleSpec): Specifies module to use for transformer layers of the vision model.
+        drop_vision_class_token (bool): Drop vision class token(s) before input to the language model.
+        vision_projection_config (TransformerConfig): Config for the projection from vision model outputs to language model inputs.
+        vision_projection_layer_spec (ModuleSpec): Specifies the module to use for the vision projection.
+        vision_projection_type (str): Type of the vision projection to use. Default is a 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): Do not gather the outputs, keep them split across tensor parallel ranks. This is typically True for training and False for inference.
+        language_position_embedding_type (str): Position embedding type to use in the language model. Default learned absolute.
+        language_rotary_percent (float): Percent of rotary dimension to use for rotary position embeddings in the language model. Defaults to 1.0.
+    """
+
+    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,
+    ) -> None:
+        super().__init__(config=language_transformer_config)
+
+        logging.getLogger(__name__).warning(
+            "LLaVA model is under development and may be missing features."
+        )
+
+        if parallel_state.get_pipeline_model_parallel_world_size() > 1:
+            raise NotImplementedError("pipeline parallelism is not supported in this model yet.")
+
+        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,
+        )
+
+        self.vision_model = CLIPViTModel(vision_transformer_config, vision_transformer_layer_spec)
+        self._drop_vision_class_token = drop_vision_class_token
+
+        # 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_transformer_config.hidden_size,  # input size to the projection.
+        )
+
+        # This allows ignoring 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)
+            )
+
+    def set_input_tensor(self, input_tensor: torch.Tensor) -> None:
+        """Sets input tensor to the model.
+
+        NOTE: Pipeline parallelism is not supported in this model yet. This is just a placeholder implementation.
+
+        Args:
+            input_tensor (Tensor): Sets the input tensor for the model.
+        """
+        self.vision_model.set_input_tensor(input_tensor)
+
+    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 for the module's parameters.
+
+        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:
+            modules.append(self.language_model)
+        if freeze_vision_model:
+            modules.append(self.vision_model)
+        if freeze_vision_projection:
+            modules.append(self.vision_projection)
+
+        for module in modules:
+            for param in module.parameters():
+                param.requires_grad = False
+
+    def forward(
+        self,
+        images: torch.Tensor,
+        input_ids: torch.Tensor,
+        position_ids: torch.Tensor,
+        attention_mask: torch.Tensor,
+        labels: torch.Tensor = None,
+        inference_params: InferenceParams = None,
+    ) -> torch.Tensor:
+        """Forward function of the LLaVA model.
+
+        Args:
+            images (torch.Tensor): input image of shape [batch, img_h, img_w].
+            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): attention mask for the language model [batch, 1, combined_seq_len, combined_seq_len].
+            labels (torch.Tensor): Optional target text labels [batch, combined_seq_len].
+            inference_params (InferenceParams): Inference-time parameters including KV cache.
+
+        Returns:
+            output (torch.Tensor): Loss of shape [b, s] if labels are provided, otherwise logits of shape [b, s, vocab_size].
+        """
+        language_embeddings = self.language_model.embedding(
+            input_ids=input_ids, position_ids=position_ids
+        )  # [text_seq_len, b, h_language]
+
+        # If running inference, we can skip image token computation if they were computed already earlier for this sample.
+        if (
+            inference_params is not None
+            and "image_tokens_count" in inference_params.key_value_memory_dict
+        ):
+            combined_embeddings = language_embeddings
+        else:
+            image_embeddings = self.vision_model(images)  # [b, img_seq_len, h_vision]
+
+            if self._drop_vision_class_token:
+                image_embeddings = image_embeddings[:, self.vision_model.class_token_len :, :]
+
+            image_embeddings = image_embeddings.permute(1, 0, 2)  # [img_seq_len, b, h_vision]
+
+            # map vision model output size to language model input size.
+            image_embeddings = self.vision_projection(
+                image_embeddings
+            )  # [img_seq_len, b, h_vision]
+
+            # If running inference, the language model KV cache will be updated for image token positions.
+            # Here we store the image tokens sequence length, which can 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]
+
+            combined_embeddings = torch.cat(
+                [image_embeddings, language_embeddings], dim=0
+            )  # [combined_seq_len, b, h_language]
+
+        # Embedding is computed above so we can discard input and position ids.
+        input_ids = None
+        position_ids = None
+
+        # Note: This returns loss if labels are provided, otherwise logits.
+        output = self.language_model(
+            input_ids,
+            position_ids,
+            attention_mask,
+            decoder_input=combined_embeddings,
+            labels=labels,
+            inference_params=inference_params,
+        )
+
+        return output
+
+
+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 for the vision projection if you want to load a checkpoint that contains vision and language model weights
+    but not the vision projection weights.
+
+    Args:
+        param_names (list of str): Parameter names allowed to be missing when calling load_state_dict.
+        module (torch.nn.Module): The torch module this hook applies to. Unused here but required by the torch API.
+        incompatible_keys (namedtuple): Namedtuple with fields missing_keys and unexpected_keys, which collect the missing and unexpected
+            keys when calling load_state_dict on this torch module, respectively.
+    """
+    for param_name in param_names:
+        incompatible_keys.missing_keys.remove(param_name)

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