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megatron/core/models/common/embeddings/language_model_embedding.py

+# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
+
+from typing import Literal
+
+import torch
+from torch import Tensor
+
+from megatron.core import tensor_parallel
+from megatron.core.transformer.module import MegatronModule
+from megatron.core.transformer.transformer_config import TransformerConfig
+
+
+class LanguageModelEmbedding(MegatronModule):
+    """Language model embeddings.
+
+    Args:
+        config (TransformerConfig): config object with all necessary configs for TransformerBlock
+        vocab_size (int): vocabulary size
+        max_sequence_length (int): maximum size of sequence. This
+                             is used for positional embedding
+        add_position_embedding (bool): Add a position embedding.
+        embedding_dropout_prob (float): dropout probability for embeddings
+        num_tokentypes (int): Set to 0 without binary head, and 2 with a binary head. Defaults to 0.
+        scatter_to_sequence_parallel (bool): Set to False to disable scatter of embedding
+            across sequence parallel region. Defaults to True.
+    """
+
+    def __init__(
+        self,
+        config: TransformerConfig,
+        vocab_size: int,
+        max_sequence_length: int,
+        position_embedding_type: Literal['learned_absolute', 'rope', 'none'] = 'learned_absolute',
+        num_tokentypes: int = 0,
+        scatter_to_sequence_parallel: bool = True,
+    ):
+        super().__init__(config=config)
+
+        self.config: TransformerConfig = config
+        self.vocab_size: int = vocab_size
+        self.max_sequence_length: int = max_sequence_length
+        self.add_position_embedding: bool = position_embedding_type == 'learned_absolute'
+        self.num_tokentypes = num_tokentypes
+        self.scatter_to_sequence_parallel = scatter_to_sequence_parallel
+        self.reduce_scatter_embeddings = (
+            (not self.add_position_embedding)
+            and self.num_tokentypes <= 0
+            and self.config.sequence_parallel
+            and self.scatter_to_sequence_parallel
+        )
+
+        # Word embeddings (parallel).
+        self.word_embeddings = tensor_parallel.VocabParallelEmbedding(
+            num_embeddings=self.vocab_size,
+            embedding_dim=self.config.hidden_size,
+            init_method=self.config.init_method,
+            reduce_scatter_embeddings=self.reduce_scatter_embeddings,
+            config=self.config,
+        )
+
+        # Position embedding (serial).
+        if self.add_position_embedding:
+            self.position_embeddings = torch.nn.Embedding(
+                self.max_sequence_length, self.config.hidden_size
+            )
+
+            # Initialize the position embeddings.
+            if self.config.perform_initialization:
+                self.config.init_method(self.position_embeddings.weight)
+
+        if self.num_tokentypes > 0:
+            self.tokentype_embeddings = torch.nn.Embedding(
+                self.num_tokentypes, self.config.hidden_size
+            )
+            # Initialize the token-type embeddings.
+            if self.config.perform_initialization:
+                self.config.init_method(self.tokentype_embeddings.weight)
+        else:
+            self.tokentype_embeddings = None
+
+        # Embeddings dropout
+        self.embedding_dropout = torch.nn.Dropout(self.config.hidden_dropout)
+
+    def zero_parameters(self):
+        """Zero out all parameters in embedding."""
+        self.word_embeddings.weight.data.fill_(0)
+        self.word_embeddings.weight.shared = True
+        self.position_embeddings.weight.data.fill_(0)
+        self.position_embeddings.weight.shared = True
+        if self.num_tokentypes > 0:
+            self.tokentype_embeddings.weight.data.fill_(0)
+            self.tokentype_embeddings.weight.shared = True
+
+    def forward(self, input_ids: Tensor, position_ids: Tensor, tokentype_ids: int = None) -> Tensor:
+        """Forward pass of the embedding module.
+
+        Args:
+            input_ids (Tensor): The input tokens
+            position_ids (Tensor): The position id's used to calculate position embeddings
+            tokentype_ids (int): The token type ids. Used when args.bert_binary_head is
+                set to True. Defaults to None
+
+        Returns:
+            Tensor: The output embeddings
+        """
+        word_embeddings = self.word_embeddings(input_ids)
+        if self.add_position_embedding:
+            position_embeddings = self.position_embeddings(position_ids)
+            embeddings = word_embeddings + position_embeddings
+        else:
+            embeddings = word_embeddings
+
+        if not self.reduce_scatter_embeddings:
+            # Data format change to avoid explicit tranposes : [b s h] --> [s b h].
+            embeddings = embeddings.transpose(0, 1).contiguous()
+
+        if tokentype_ids is not None:
+            assert self.tokentype_embeddings is not None
+            # [b s h] -> [s b h] (So that it can be added with embeddings)
+            tokentype_embedding = self.tokentype_embeddings(tokentype_ids).permute(1, 0, 2)
+            embeddings = embeddings + tokentype_embedding
+        else:
+            assert self.tokentype_embeddings is None
+
+        # If the input flag for fp32 residual connection is set, convert for float.
+        if self.config.fp32_residual_connection:
+            embeddings = embeddings.float()
+
+        # Dropout.
+        if self.config.sequence_parallel:
+            if not self.reduce_scatter_embeddings and self.scatter_to_sequence_parallel:
+                embeddings = tensor_parallel.scatter_to_sequence_parallel_region(embeddings)
+            # `scatter_to_sequence_parallel_region` returns a view, which prevents
+            # the original tensor from being garbage collected. Clone to facilitate GC.
+            # Has a small runtime cost (~0.5%).
+            if self.config.clone_scatter_output_in_embedding and self.scatter_to_sequence_parallel:
+                embeddings = embeddings.clone()
+            with tensor_parallel.get_cuda_rng_tracker().fork():
+                embeddings = self.embedding_dropout(embeddings)
+        else:
+            embeddings = self.embedding_dropout(embeddings)
+
+        return embeddings

megatron/core/models/common/embeddings/rope_utils.py

+# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Optional
+
+if TYPE_CHECKING:
+    from megatron.core.transformer.transformer_config import TransformerConfig
+
+import logging
+
+import torch
+from torch import Tensor
+
+from megatron.core import parallel_state
+from megatron.core.utils import is_te_min_version
+
+logger = logging.getLogger(__name__)
+
+# Prefer fused RoPE from Apex as we need the `transpose_output_memory` argument for the bshd trick.
+# See https://gitlab-master.nvidia.com/ADLR/megatron-lm/-/merge_requests/2469.
+try:
+    from apex.transformer.functional import fused_apply_rotary_pos_emb
+except ImportError:
+    try:
+        from megatron.core.extensions.transformer_engine import fused_apply_rotary_pos_emb
+    except:
+        fused_apply_rotary_pos_emb = None
+
+
+try:
+    from megatron.core.extensions.transformer_engine import fused_apply_rotary_pos_emb_thd
+except ImportError:
+    try:
+        from apex.transformer.functional import fused_apply_rotary_pos_emb_thd
+    except ImportError:
+        fused_apply_rotary_pos_emb_thd = None
+
+
+try:
+    from flash_attn.layers.rotary import apply_rotary_emb as apply_rotary_emb_flash
+except ImportError:
+    apply_rotary_emb_flash = None
+
+
+__all__ = ['apply_rotary_emb_flash']
+
+
+def get_pos_emb_on_this_cp_rank(pos_emb: Tensor, seq_dim: int) -> Tensor:
+    """Get the position embedding on the current context parallel rank.
+
+    Args:
+        pos_emb (Tensor): Positional embedding tensor
+        seq_dim (int): Sequence dimension
+    """
+    cp_size = parallel_state.get_context_parallel_world_size()
+    cp_rank = parallel_state.get_context_parallel_rank()
+    cp_idx = torch.tensor(
+        [cp_rank, (2 * cp_size - cp_rank - 1)], device="cpu", pin_memory=True
+    ).cuda(non_blocking=True)
+    pos_emb = pos_emb.view(
+        *pos_emb.shape[:seq_dim], 2 * cp_size, -1, *pos_emb.shape[(seq_dim + 1) :]
+    )
+    pos_emb = pos_emb.index_select(seq_dim, cp_idx)
+    pos_emb = pos_emb.view(*pos_emb.shape[:seq_dim], -1, *pos_emb.shape[(seq_dim + 2) :])
+    return pos_emb
+
+
+def _rotate_half(x: Tensor, rotary_interleaved: bool) -> Tensor:
+    """Change sign so the last dimension becomes [-odd, +even]
+
+    Args:
+        x (Tensor): Input tensor
+
+    Returns:
+        Tensor: Tensor rotated half
+    """
+    if not rotary_interleaved:
+        x1, x2 = torch.chunk(x, 2, dim=-1)
+        return torch.cat((-x2, x1), dim=-1)
+    else:
+        x1 = x[:, :, :, ::2]
+        x2 = x[:, :, :, 1::2]
+        x_new = torch.stack((-x2, x1), dim=-1)
+        return x_new.view(x_new.shape[0], x_new.shape[1], x_new.shape[2], -1)
+
+
+def _apply_rotary_pos_emb_bshd(
+    t: Tensor,
+    freqs: Tensor,
+    rotary_interleaved: bool = False,
+    multi_latent_attention: bool = False,
+    mscale: float = 1.0,
+) -> Tensor:
+    """Apply rotary positional embedding to input tensor T.
+
+    check https://kexue.fm/archives/8265 for detailed formulas
+
+    Args:
+        t (Tensor): Input tensor T is of shape [seq_length, ... , dim]
+        freqs (Tensor): Rotary Positional embedding tensor freq is of shape [seq_length, ..., dim]
+
+    Returns:
+        Tensor: The input tensor after applying RoPE
+    """
+    rot_dim = freqs.shape[-1]
+
+    # ideally t_pass is empty so rotary pos embedding is applied to all tensor t
+    t, t_pass = t[..., :rot_dim], t[..., rot_dim:]
+
+    if multi_latent_attention:
+        x1 = t[..., 0::2]
+        x2 = t[..., 1::2]
+        t = torch.cat((x1, x2), dim=-1)
+
+    # first part is cosine component
+    # second part is sine component, need to change signs with _rotate_half method
+    cos_ = (torch.cos(freqs) * mscale).to(t.dtype)
+    sin_ = (torch.sin(freqs) * mscale).to(t.dtype)
+
+    t = (t * cos_) + (_rotate_half(t, rotary_interleaved) * sin_)
+    return torch.cat((t, t_pass), dim=-1)
+
+
+def _get_thd_freqs_on_this_cp_rank(cp_rank: int, cp_size: int, x: Tensor, freqs: Tensor) -> Tensor:
+    if cp_size > 1:
+        cp_seg = x.size(0) // 2
+        full_seqlen = cp_size * x.size(0)
+        return torch.cat(
+            [
+                freqs[cp_rank * cp_seg : (cp_rank + 1) * cp_seg],
+                freqs[full_seqlen - (cp_rank + 1) * cp_seg : full_seqlen - cp_rank * cp_seg],
+            ]
+        )
+    else:
+        return freqs[: x.size(0)]
+
+
+def _apply_rotary_pos_emb_thd(
+    t: Tensor,
+    cu_seqlens: Tensor,
+    freqs: Tensor,
+    rotary_interleaved: bool = False,
+    multi_latent_attention: bool = False,
+    mscale: float = 1.0,
+) -> Tensor:
+    """A baseline implementation of applying RoPE for `thd` format.
+
+    Args:
+        t (Tensor): Input tensor T is of shape [t, h, d]
+        cu_seqlens(Tensor):  Cumulative sum of sequence lengths in a batch for `t`,
+        with shape [b + 1] and dtype torch.int32.
+        freqs (Tensor): Rotary Positional embedding tensor freq is of shape [max_s, 1, 1, d]
+
+    Returns:
+        Tensor: Shape [t, h, d]. The input tensor after applying RoPE.
+    """
+
+    cp_size = parallel_state.get_context_parallel_world_size()
+    cp_rank = parallel_state.get_context_parallel_rank()
+    cu_seqlens = cu_seqlens // cp_size
+    seqlens = (cu_seqlens[1:] - cu_seqlens[:-1]).tolist()
+
+    return torch.cat(
+        [
+            _apply_rotary_pos_emb_bshd(
+                x.unsqueeze(1),
+                _get_thd_freqs_on_this_cp_rank(cp_rank, cp_size, x, freqs),
+                rotary_interleaved=rotary_interleaved,
+                multi_latent_attention=multi_latent_attention,
+                mscale=mscale,
+            )
+            for x in torch.split(t, seqlens)
+        ]
+    ).squeeze(1)
+
+
+def apply_rotary_pos_emb(
+    t: Tensor,
+    freqs: Tensor,
+    config: TransformerConfig,
+    cu_seqlens: Optional[Tensor] = None,
+    mscale: float = 1.0,
+):
+    """
+    Reroute to the appropriate apply_rotary_pos_emb function depending on
+    fused/unfused kernels, or bshd (conventional) / thd (packed seq) format
+    """
+
+    if config.apply_rope_fusion:
+        if cu_seqlens is None:
+            assert fused_apply_rotary_pos_emb is not None, "apply_rope_fusion is not available."
+            return fused_apply_rotary_pos_emb(t, freqs, transpose_output_memory=True)
+        else:
+            assert fused_apply_rotary_pos_emb_thd is not None, "apply_rope_fusion is not available."
+            cp_size = parallel_state.get_context_parallel_world_size()
+            if cp_size > 1:
+                if not is_te_min_version("1.11.0", check_equality=False):
+                    raise ValueError("Only TE >= 1.12 supports RoPE fusion for THD format with CP.")
+                return fused_apply_rotary_pos_emb_thd(
+                    t,
+                    cu_seqlens,
+                    freqs,
+                    cp_size=cp_size,
+                    cp_rank=parallel_state.get_context_parallel_rank(),
+                )
+            else:
+                return fused_apply_rotary_pos_emb_thd(t, cu_seqlens, freqs)
+    else:
+        if cu_seqlens is None:
+            return _apply_rotary_pos_emb_bshd(
+                t,
+                freqs,
+                rotary_interleaved=config.rotary_interleaved,
+                multi_latent_attention=config.multi_latent_attention,
+                mscale=mscale,
+            )
+        else:
+            return _apply_rotary_pos_emb_thd(
+                t,
+                cu_seqlens,
+                freqs,
+                rotary_interleaved=config.rotary_interleaved,
+                multi_latent_attention=config.multi_latent_attention,
+                mscale=mscale,
+            )
+
+
+def apply_rotary_pos_emb_with_cos_sin(
+    t: Tensor, cos: Tensor, sin: Tensor, rotary_interleaved: bool = False
+) -> Tensor:
+    """
+    This function applies rotary positional embedding to the target tensor t
+    using precomputed cos and sin of size (seq_len, d_rot / 2)
+    """
+    cos = cos.to(t.dtype)
+    sin = sin.to(t.dtype)
+
+    if apply_rotary_emb_flash is None:
+        # Combine cos and sin into freqs
+        freqs = torch.stack([cos, sin], dim=-1).flatten(start_dim=-2)
+
+        # Expand freqs to match t's shape
+        while freqs.dim() < t.dim():
+            freqs = freqs.unsqueeze(1)
+        freqs = freqs.expand(t.shape[:-1] + (-1,))
+
+        y = _apply_rotary_pos_emb_bshd(
+            t,
+            freqs,
+            rotary_interleaved=rotary_interleaved,
+            multi_latent_attention=False,
+            mscale=1.0,
+        )
+    else:
+        # Use Flash Attention's optimized kernel for rotary embedding
+        t = t.permute(1, 0, 2, 3)
+        y = apply_rotary_emb_flash(t, cos, sin, rotary_interleaved)
+        y = y.permute(1, 0, 2, 3)
+
+    return y

megatron/core/models/common/embeddings/rotary_pos_embedding.py

+# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from megatron.core.transformer.transformer_config import TransformerConfig
+    from megatron.core.transformer.transformer_block import TransformerBlock
+    from megatron.core.inference_params import InferenceParams
+    from megatron.core.packed_seq_params import PackedSeqParams
+
+import logging
+import math
+from functools import lru_cache
+
+import torch
+from torch import Tensor, nn
+
+from megatron.core import parallel_state
+from megatron.core.models.common.embeddings.rope_utils import (  # for backward compatibility; pylint: disable=unused-import
+    _apply_rotary_pos_emb_bshd,
+    _apply_rotary_pos_emb_thd,
+    _rotate_half,
+    apply_rotary_pos_emb,
+    get_pos_emb_on_this_cp_rank,
+)
+
+logger = logging.getLogger(__name__)
+
+
+__all__ = ['RotaryEmbedding']
+
+
+class RotaryEmbedding(nn.Module):
+    """Rotary Embedding for language model.
+
+    Args:
+        kv_channels (int): Projection weights dimension in multi-head attention. Obtained
+            from transformer config
+        rotary_percent (float): Percent of rotary dimension to use for rotary position
+            embeddings.
+        rotary_interleaved (bool, optional): If True, interleaved rotary position embeddings.
+            Defaults to False.
+        seq_len_interpolation_factor (float, optional): scale of linearly interpolating RoPE
+            for longer sequences. The value must be a float larger than 1.0. Defaults to None
+        rotary_base (int, optional): Base period for rotary position embeddings. Defaults to
+            10000.
+        rope_scaling (bool, optional): Apply rope scaling as used in llama 3.1
+        use_cpu_initialization (bool, optional): If False, initialize the inv_freq directly
+            on the GPU. Defaults to False
+    """
+
+    def __init__(
+        self,
+        kv_channels: int,
+        rotary_percent: float,
+        rotary_interleaved: bool = False,
+        seq_len_interpolation_factor: float = None,
+        rotary_base: int = 10000,
+        rope_scaling: bool = False,
+        use_cpu_initialization: bool = False,
+    ) -> None:
+        super().__init__()
+
+        dim = kv_channels
+        if rotary_percent < 1.0:
+            dim = int(dim * rotary_percent)
+        self.rotary_interleaved = rotary_interleaved
+
+        self.seq_len_interpolation_factor = seq_len_interpolation_factor
+        device = 'cpu' if use_cpu_initialization else torch.cuda.current_device()
+        self.inv_freq = 1.0 / (
+            rotary_base ** (torch.arange(0, dim, 2, dtype=torch.float32, device=device) / dim)
+        )
+
+        if rope_scaling:
+            self.inv_freq = self._apply_scaling(self.inv_freq)
+
+    def _apply_scaling(
+        self,
+        freqs,
+        factor=8,
+        low_freq_factor=1,
+        high_freq_factor=4,
+        original_max_position_embeddings=8192,
+    ):
+        # This implementation is adapted from:
+        # https://github.com/huggingface/transformers/blob/2a5a6ad18aa22e98429bb5ecb880660328030ea0/src/transformers/modeling_rope_utils.py#L303-L343
+
+        factor = factor  # `8` in the original implementation
+        low_freq_factor = low_freq_factor  # `1` in the original implementation
+        high_freq_factor = high_freq_factor  # `4` in the original implementation
+        old_context_len = original_max_position_embeddings  # `8192` in the original implementation
+
+        low_freq_wavelen = old_context_len / low_freq_factor
+        high_freq_wavelen = old_context_len / high_freq_factor
+
+        wavelen = 2 * math.pi / freqs
+        # wavelen < high_freq_wavelen: do nothing
+        # wavelen > low_freq_wavelen: divide by factor
+        inv_freq_llama = torch.where(wavelen > low_freq_wavelen, freqs / factor, freqs)
+        # otherwise: interpolate between the two, using a smooth factor
+        smooth_factor = (old_context_len / wavelen - low_freq_factor) / (
+            high_freq_factor - low_freq_factor
+        )
+        smoothed_inv_freq = (
+            1 - smooth_factor
+        ) * inv_freq_llama / factor + smooth_factor * inv_freq_llama
+        is_medium_freq = ~(wavelen < high_freq_wavelen) * ~(wavelen > low_freq_wavelen)
+        inv_freq_llama = torch.where(is_medium_freq, smoothed_inv_freq, inv_freq_llama)
+
+        return inv_freq_llama
+
+    def get_freqs_non_repeated(self, max_seq_len: int, offset: int = 0) -> Tensor:
+        """Generates matrix of frequencies based on positions in the sequence,
+        used to create positional encodings"""
+        seq = (
+            torch.arange(max_seq_len, device=self.inv_freq.device, dtype=self.inv_freq.dtype)
+            + offset
+        )
+
+        if self.seq_len_interpolation_factor is not None:
+            seq *= 1 / self.seq_len_interpolation_factor
+
+        freqs = torch.outer(seq, self.inv_freq)  # [seq len, dim]
+
+        return freqs
+
+    def get_cos_sin(self, max_seq_len: int, offset: int = 0) -> (Tensor, Tensor):
+        """Cosine and sine values for RoPE are precomputed for all positions up to the maximum
+        sequence length"""
+        freqs = self.get_freqs_non_repeated(max_seq_len, offset)
+        cos = torch.cos(freqs)
+        sin = torch.sin(freqs)
+        return cos, sin
+
+    @lru_cache(maxsize=32)
+    def forward(self, max_seq_len: int, offset: int = 0, packed_seq: bool = False) -> Tensor:
+        """Forward pass of RoPE embedding.
+
+        Args:
+            max_seq_len (int): Maximum size of sequence
+            offset (int, optional): RoPE offset. Defaults to 0.
+            packed_seq (bool, optional): Whether to use packed sequence. Defaults to False.
+
+        Returns:
+            Tensor: Embeddings after applying RoPE.
+        """
+        if self.inv_freq.device.type == 'cpu':
+            # move `inv_freq` to GPU once at the first micro-batch forward pass
+            self.inv_freq = self.inv_freq.to(device=torch.cuda.current_device())
+
+        freqs = self.get_freqs_non_repeated(max_seq_len, offset)
+        # first part even vector components, second part odd vector components,
+        #  2 * dim in dimension size
+        if not self.rotary_interleaved:
+            emb = torch.cat((freqs, freqs), dim=-1)
+        else:
+            emb = torch.stack((freqs.view(-1, 1), freqs.view(-1, 1)), dim=-1).view(
+                freqs.shape[0], -1
+            )
+        # emb [seq_length, .., dim]
+        emb = emb[:, None, None, :]
+        if parallel_state.get_context_parallel_world_size() > 1 and not packed_seq:
+            # slice rotary_pos_emb along sequence dimension and select the parition of the current
+            # CP rank
+            emb = get_pos_emb_on_this_cp_rank(emb, 0)
+        return emb
+
+    def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs):
+        state_dict.pop(f'{prefix}inv_freq', None)
+        return super()._load_from_state_dict(state_dict, prefix, *args, **kwargs)
+
+    def get_rotary_seq_len(
+        self,
+        inference_params: InferenceParams,
+        transformer: TransformerBlock,
+        transformer_input: Tensor,
+        transformer_config: TransformerConfig,
+        packed_seq_params: PackedSeqParams,
+    ) -> float:
+        """Function to get the rotary sequence length.
+
+        Args:
+            inference_params : Used during Inference time
+            transformer (TransformerBlock): The transformer block (decoder/encoder) used
+                by the model
+            transformer_input (Tensor): Input tensor to the transformer
+            transformer_config (TransformerConfig): Transformer config used by the model
+            packed_seq_params (PackedSeqParams): Packed sequence params
+
+        Returns:
+            float: The rotary sequence length
+        """
+        if packed_seq_params is not None:
+            # max_seqlen are the max sequence length in the packed sequence before being divived
+            # by the tp and cp size.
+            return max(packed_seq_params.max_seqlen_q, packed_seq_params.max_seqlen_kv)
+        elif inference_params is not None:
+            rotary_seq_len = inference_params.max_sequence_length
+        else:
+            if transformer.input_tensor is not None:
+                rotary_seq_len = transformer.input_tensor.size(0)
+            else:
+                rotary_seq_len = transformer_input.size(0)
+
+            if transformer_config.sequence_parallel:
+                rotary_seq_len *= transformer_config.tensor_model_parallel_size
+
+        rotary_seq_len *= transformer_config.context_parallel_size
+
+        return rotary_seq_len

megatron/core/models/common/embeddings/yarn_rotary_pos_embedding.py

+# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+
+from __future__ import annotations
+
+import logging
+import math
+from functools import lru_cache
+
+import torch
+from torch import Tensor
+
+from megatron.core import parallel_state
+from megatron.core.models.common.embeddings.rope_utils import get_pos_emb_on_this_cp_rank
+from megatron.core.models.common.embeddings.rotary_pos_embedding import RotaryEmbedding
+
+logger = logging.getLogger(__name__)
+
+
+class YarnRotaryEmbedding(RotaryEmbedding):
+    """Yarn Rotary Embedding for language model.
+
+    Args:
+        kv_channels (int): Projection weights dimension in multi-head attention. Obtained from
+            transformer config
+        rotary_percent (float): Percent of rotary dimension to use for rotary position embeddings.
+        rotary_interleaved (bool, optional): If True, interleaved rotary position embeddings.
+            Defaults to False.
+        seq_len_interpolation_factor (float, optional): scale of linearly interpolating RoPE for
+            longer sequences. The value must be a float larger than 1.0. Defaults to None
+        rotary_base (float, optional): Base period for rotary position embeddings. Defaults to
+            10000.
+        use_cpu_initialization (bool, optional): If False, initialize the inv_freq directly on
+            the GPU. Defaults to False
+        scaling_factor (float, optional): Scaling factor for Yarn RoPE. Defaults to 1.0.
+        original_max_position_embeddings (int, optional): Original maximum position embeddings
+            length. Defaults to 4096.
+        beta_fast (float, optional): Fast beta value for Yarn RoPE. Defaults to 32.
+        beta_slow (float, optional): Slow beta value for Yarn RoPE. Defaults to 1.
+        mscale (float, optional): Mscale value for Yarn RoPE. Defaults to 1.
+        mscale_all_dim (float, optional): Mscale all dim value for Yarn RoPE. Defaults to 0.
+    """
+
+    def __init__(
+        self,
+        kv_channels: int,
+        rotary_percent: float = 1.0,
+        rotary_interleaved: bool = False,
+        seq_len_interpolation_factor: float = None,
+        rotary_base: float = 10000.0,
+        use_cpu_initialization: bool = False,
+        scaling_factor: float = 1.0,
+        original_max_position_embeddings: int = 4096,
+        beta_fast: float = 32.0,
+        beta_slow: float = 1.0,
+        mscale: float = 1.0,
+        mscale_all_dim: float = 0.0,
+    ):
+        self.dim = kv_channels
+        self.rotary_base = rotary_base
+        self.scaling_factor = scaling_factor
+        self.original_max_position_embeddings = original_max_position_embeddings
+        self.beta_fast = beta_fast
+        self.beta_slow = beta_slow
+        self.mscale = mscale
+        self.mscale_all_dim = mscale_all_dim
+
+        device = 'cpu' if use_cpu_initialization else torch.cuda.current_device()
+        self.inv_freq_extra = 1.0 / (
+            self.rotary_base
+            ** (torch.arange(0, self.dim, 2, dtype=torch.float32, device=device) / self.dim)
+        )
+        self.inv_freq_inter = 1.0 / (
+            self.scaling_factor
+            * self.rotary_base
+            ** (torch.arange(0, self.dim, 2, dtype=torch.float32, device=device) / self.dim)
+        )
+        super().__init__(
+            kv_channels,
+            rotary_percent,
+            rotary_interleaved,
+            seq_len_interpolation_factor,
+            rotary_base,
+            use_cpu_initialization,
+        )
+
+    @lru_cache(maxsize=32)
+    def forward(self, max_seq_len: int, offset: int = 0) -> Tensor:
+        """Forward pass of Yarn Rotary Embedding.
+
+        Args:
+            max_seq_len (int): Maximum size of sequence
+            offset (int, optional): RoPE offset. Defaults to 0.
+
+        Returns:
+            Tensor: Embeddings after applying Yarn RoPE.
+        """
+        assert (
+            not self.rotary_interleaved
+        ), "Yarn RoPE does not support interleaved rotary embeddings"
+
+        if self.inv_freq_extra.device.type == 'cpu':
+            # move `inv_freq_extra` to GPU once at the first micro-batch forward pass
+            self.inv_freq_extra = self.inv_freq_extra.to(device=torch.cuda.current_device())
+
+        if self.inv_freq_inter.device.type == 'cpu':
+            # move `inv_freq_inter` to GPU once at the first micro-batch forward pass
+            self.inv_freq_inter = self.inv_freq_inter.to(device=torch.cuda.current_device())
+
+        low, high = _yarn_find_correction_range(
+            self.beta_fast,
+            self.beta_slow,
+            self.dim,
+            self.rotary_base,
+            self.original_max_position_embeddings,
+        )
+        inv_freq_mask = 1.0 - _yarn_linear_ramp_mask(low, high, self.dim // 2).to(
+            device=self.inv_freq_extra.device, dtype=torch.float32
+        )
+        inv_freq = self.inv_freq_inter * (1 - inv_freq_mask) + self.inv_freq_extra * inv_freq_mask
+
+        seq = (
+            torch.arange(
+                max_seq_len, device=self.inv_freq_extra.device, dtype=self.inv_freq_extra.dtype
+            )
+            + offset
+        )
+
+        freqs = torch.outer(seq, inv_freq)
+
+        _mscale = float(
+            _yarn_get_mscale(self.scaling_factor, self.mscale)
+            / _yarn_get_mscale(self.scaling_factor, self.mscale_all_dim)
+        )
+
+        emb = torch.cat((freqs, freqs), dim=-1)
+        # emb [seq_length, .., dim]
+        emb = emb[:, None, None, :]
+        if parallel_state.get_context_parallel_world_size() > 1:
+            # slice rotary_pos_emb along sequence dimension
+            # and select the parition of the current CP rank
+            emb = get_pos_emb_on_this_cp_rank(emb, 0)
+        return emb, _mscale
+
+
+# Inverse dim formula to find dim based on number of rotations
+def _yarn_find_correction_dim(
+    num_rotations: float, dim: int, rotary_base: float = 10000, max_position_embeddings: int = 2048
+) -> float:
+    return (dim * math.log(max_position_embeddings / (num_rotations * 2 * math.pi))) / (
+        2 * math.log(rotary_base)
+    )
+
+
+# Find dim range bounds based on rotations
+def _yarn_find_correction_range(
+    low_rot: float,
+    high_rot: float,
+    dim: int,
+    rotary_base: float = 10000,
+    max_position_embeddings: int = 2048,
+) -> tuple[int, int]:
+    low = math.floor(_yarn_find_correction_dim(low_rot, dim, rotary_base, max_position_embeddings))
+    high = math.ceil(_yarn_find_correction_dim(high_rot, dim, rotary_base, max_position_embeddings))
+    return max(low, 0), min(high, dim - 1)  # Clamp values just in case
+
+
+def _yarn_linear_ramp_mask(min: float, max: float, dim: int) -> Tensor:
+    if min == max:
+        max += 0.001  # Prevent singularity
+
+    linear_func = (torch.arange(dim, dtype=torch.float32) - min) / (max - min)
+    ramp_func = torch.clamp(linear_func, 0, 1)
+    return ramp_func
+
+
+def _yarn_get_mscale(scale: float = 1, mscale: float = 1) -> float:
+    if scale <= 1:
+        return 1.0
+    return 0.1 * mscale * math.log(scale) + 1.0

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

+# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+import logging
+import os
+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.enums import AttnBackend
+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)
+        self._set_attention_backend()
+
+    # pylint: disable=line-too-long
+    def _set_attention_backend(self):
+        """Set attention backend
+
+        Transformer engine works based on optout. By default all three attention backend flags are set to 1. So if the user choses a particular attention backend we set the other two to 0. If the user choses local, we set all 3 TE env variables to 0.
+        """
+
+        def check_and_set_env_variable(
+            env_variable_name: str, expected_value: int, attn_type: AttnBackend
+        ) -> None:
+            current_value = os.getenv(env_variable_name)
+            assert current_value is None or current_value == str(
+                expected_value
+            ), f'{env_variable_name} set to {current_value}, but expected {expected_value} for attention backend type {attn_type.name}. unset NVTE_FLASH_ATTN, NVTE_FUSED_ATTN and NVTE_UNFUSED_ATTN. Use the --attention-backend argument if you want to choose between (flash/fused/unfused/auto/local). Default is auto.'
+            os.environ[env_variable_name] = str(expected_value)
+
+        if self.config.attention_backend == AttnBackend.local:
+            check_and_set_env_variable("NVTE_FLASH_ATTN", 0, AttnBackend.flash)
+            check_and_set_env_variable("NVTE_FUSED_ATTN", 0, AttnBackend.flash)
+            check_and_set_env_variable("NVTE_UNFUSED_ATTN", 0, AttnBackend.flash)
+        elif self.config.attention_backend == AttnBackend.flash:
+            check_and_set_env_variable("NVTE_FLASH_ATTN", 1, AttnBackend.flash)
+            check_and_set_env_variable("NVTE_FUSED_ATTN", 0, AttnBackend.flash)
+            check_and_set_env_variable("NVTE_UNFUSED_ATTN", 0, AttnBackend.flash)
+        elif self.config.attention_backend == AttnBackend.fused:
+            check_and_set_env_variable("NVTE_FLASH_ATTN", 0, AttnBackend.fused)
+            check_and_set_env_variable("NVTE_FUSED_ATTN", 1, AttnBackend.fused)
+            check_and_set_env_variable("NVTE_UNFUSED_ATTN", 0, AttnBackend.fused)
+        elif self.config.attention_backend == AttnBackend.unfused:
+            check_and_set_env_variable("NVTE_FLASH_ATTN", 0, AttnBackend.unfused)
+            check_and_set_env_variable("NVTE_FUSED_ATTN", 0, AttnBackend.unfused)
+            check_and_set_env_variable("NVTE_UNFUSED_ATTN", 1, AttnBackend.unfused)
+        elif self.config.attention_backend == AttnBackend.auto:
+            check_and_set_env_variable("NVTE_FLASH_ATTN", 1, AttnBackend.auto)
+            check_and_set_env_variable("NVTE_FUSED_ATTN", 1, AttnBackend.auto)
+            check_and_set_env_variable("NVTE_UNFUSED_ATTN", 1, AttnBackend.auto)
+
+    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 parallel_state.get_pipeline_model_parallel_world_size() == 1:
+            # 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 parallel_state.is_pipeline_first_stage() and self.pre_process and not self.post_process:
+            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

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

megatron/core/models/gpt/gpt_layer_specs.py

+# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
+
+import warnings
+from typing import Optional
+
+from megatron.core.fusions.fused_bias_dropout import get_bias_dropout_add
+from megatron.core.models.gpt.moe_module_specs import get_moe_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.mlp import MLP, MLPSubmodules
+from megatron.core.transformer.multi_latent_attention import (
+    MLASelfAttention,
+    MLASelfAttentionSubmodules,
+)
+from megatron.core.transformer.spec_utils import ModuleSpec
+from megatron.core.transformer.transformer_block import (
+    TransformerBlockSubmodules,
+    get_num_layers_to_build,
+)
+from megatron.core.transformer.transformer_config import TransformerConfig
+from megatron.core.transformer.transformer_layer import TransformerLayer, TransformerLayerSubmodules
+from megatron.core.utils import is_te_min_version
+
+try:
+    from megatron.core.extensions.transformer_engine import (
+        TEColumnParallelLinear,
+        TEDotProductAttention,
+        TELayerNormColumnParallelLinear,
+        TENorm,
+        TERowParallelLinear,
+    )
+
+    HAVE_TE = True
+except ImportError:
+    HAVE_TE = False
+
+try:
+    import apex  # pylint: disable=unused-import
+
+    from megatron.core.fusions.fused_layer_norm import FusedLayerNorm
+
+    HAVE_APEX = True
+    LNImpl = FusedLayerNorm
+except ImportError:
+    from megatron.core.transformer.torch_norm import WrappedTorchNorm
+
+    warnings.warn('Apex is not installed. Falling back to Torch Norm')
+    LNImpl = WrappedTorchNorm
+
+
+def get_gpt_layer_with_transformer_engine_spec(
+    num_experts: Optional[int] = None,
+    moe_grouped_gemm: Optional[bool] = False,
+    qk_layernorm: Optional[bool] = False,
+    multi_latent_attention: Optional[bool] = False,
+    fp8: Optional[str] = None,  # pylint: disable=unused-arguments
+    moe_use_legacy_grouped_gemm: Optional[bool] = False,
+) -> ModuleSpec:
+    """Use this spec to use lower-level Transformer Engine modules (required for fp8 training).
+
+
+    Args:
+        num_experts (int, optional): Number of experts. Defaults to None.
+        moe_grouped_gemm (bool, optional): To use Grouped GEMM. Defaults to False.
+        qk_layernorm (bool, optional): To use layernorm for queries/keys. Defaults to False.
+        fp8 (str, optional): Deprecated. For temporary Nemo compatibility.
+        moe_use_legacy_grouped_gemm (bool, optional): Force use the legacy GroupedMLP.
+                                                      Defaults to False.
+
+    Returns:
+        ModuleSpec: Module specification with TE modules
+    """
+    if fp8 is not None:
+        warnings.warn(
+            'The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated'
+            ' and will be removed soon. Please update your code accordingly.'
+        )
+
+    mlp = _get_mlp_module_spec(
+        use_te=True,
+        num_experts=num_experts,
+        moe_grouped_gemm=moe_grouped_gemm,
+        moe_use_legacy_grouped_gemm=moe_use_legacy_grouped_gemm,
+    )
+
+    if multi_latent_attention:
+        return ModuleSpec(
+            module=TransformerLayer,
+            submodules=TransformerLayerSubmodules(
+                input_layernorm=TENorm,
+                self_attention=ModuleSpec(
+                    module=MLASelfAttention,
+                    params={"attn_mask_type": AttnMaskType.causal},
+                    submodules=MLASelfAttentionSubmodules(
+                        linear_q_proj=TEColumnParallelLinear,
+                        linear_q_down_proj=TEColumnParallelLinear,
+                        linear_q_up_proj=TEColumnParallelLinear,
+                        linear_kv_down_proj=TEColumnParallelLinear,
+                        linear_kv_up_proj=TEColumnParallelLinear,
+                        core_attention=TEDotProductAttention,
+                        linear_proj=TERowParallelLinear,
+                        q_layernorm=TENorm if qk_layernorm else IdentityOp,
+                        kv_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,
+            ),
+        )
+    else:
+
+        # TENorm significantly harms convergence when used
+        # for QKLayerNorm if TE Version < 1.9;
+        # we instead use the Apex implementation.
+        qk_norm = TENorm if is_te_min_version("1.9.0") else FusedLayerNorm
+
+        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=qk_norm if qk_layernorm else IdentityOp,
+                        k_layernorm=qk_norm 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,
+            ),
+        )
+
+
+def get_gpt_layer_local_spec(
+    num_experts: Optional[int] = None,
+    moe_grouped_gemm: Optional[bool] = False,
+    qk_layernorm: Optional[bool] = False,
+    multi_latent_attention: Optional[bool] = False,
+    fp8: Optional[str] = None,  # pylint: disable=unused-arguments
+    moe_use_legacy_grouped_gemm: Optional[bool] = False,
+) -> ModuleSpec:
+    """Use this spec for an implementation using only modules in Megatron-Core.
+
+
+    Args:
+        num_experts (int, optional): Number of experts. Defaults to None.
+        moe_grouped_gemm (bool, optional): To use Grouped GEMM. Defaults to False.
+        qk_layernorm (bool, optional): To use layernorm for queries/keys. Defaults to False.
+        fp8 (str, optional): Deprecated. For temporary Nemo compatibility.
+        moe_use_legacy_grouped_gemm (bool, optional): Force use the legacy GroupedMLP.
+                                                      Defaults to False.
+
+    Returns:
+        ModuleSpec: Module specification with Megatron-Core modules
+    """
+    if fp8 is not None:
+        warnings.warn(
+            'The fp8 argument in "get_gpt_layer_local_spec" has been deprecated'
+            ' and will be removed soon. Please update your code accordingly.'
+        )
+
+    mlp = _get_mlp_module_spec(
+        use_te=False,
+        num_experts=num_experts,
+        moe_grouped_gemm=moe_grouped_gemm,
+        moe_use_legacy_grouped_gemm=moe_use_legacy_grouped_gemm,
+    )
+
+    if multi_latent_attention:
+        return ModuleSpec(
+            module=TransformerLayer,
+            submodules=TransformerLayerSubmodules(
+                input_layernorm=LNImpl,
+                self_attention=ModuleSpec(
+                    module=MLASelfAttention,
+                    params={"attn_mask_type": AttnMaskType.causal},
+                    submodules=MLASelfAttentionSubmodules(
+                        linear_q_proj=ColumnParallelLinear,
+                        linear_q_down_proj=ColumnParallelLinear,
+                        linear_q_up_proj=ColumnParallelLinear,
+                        linear_kv_down_proj=ColumnParallelLinear,
+                        linear_kv_up_proj=ColumnParallelLinear,
+                        core_attention=DotProductAttention,
+                        linear_proj=RowParallelLinear,
+                        q_layernorm=LNImpl if qk_layernorm else IdentityOp,
+                        kv_layernorm=LNImpl if qk_layernorm else IdentityOp,
+                    ),
+                ),
+                self_attn_bda=get_bias_dropout_add,
+                pre_mlp_layernorm=LNImpl,
+                mlp=mlp,
+                mlp_bda=get_bias_dropout_add,
+            ),
+        )
+    else:
+        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,
+                        q_layernorm=LNImpl if qk_layernorm else IdentityOp,
+                        k_layernorm=LNImpl if qk_layernorm else IdentityOp,
+                    ),
+                ),
+                self_attn_bda=get_bias_dropout_add,
+                pre_mlp_layernorm=LNImpl,
+                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_',
+                },
+            ),
+        )
+
+
+def _get_mlp_module_spec(
+    use_te: Optional[bool] = True,
+    num_experts: Optional[int] = None,
+    moe_grouped_gemm: Optional[bool] = False,
+    fp8: Optional[str] = None,  # pylint: disable=unused-arguments
+    moe_use_legacy_grouped_gemm: Optional[bool] = False,
+) -> ModuleSpec:
+    """Helper function to get module spec for MLP/MoE"""
+    if fp8 is not None:
+        warnings.warn(
+            'The fp8 argument in "_get_mlp_module_spec" has been deprecated'
+            ' and will be removed soon. Please update your code accordingly.'
+        )
+
+    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 get_moe_module_spec(
+            use_te=use_te,
+            num_experts=num_experts,
+            moe_grouped_gemm=moe_grouped_gemm,
+            moe_use_legacy_grouped_gemm=moe_use_legacy_grouped_gemm,
+        )
+
+
+def get_gpt_decoder_block_spec(
+    config: TransformerConfig, use_transformer_engine: bool
+) -> TransformerBlockSubmodules:
+    """GPT block spec."""
+    if use_transformer_engine:
+        layer_norm_impl = TENorm
+    else:
+        layer_norm_impl = LNImpl
+
+    # Layer specs.
+    dense_layer_spec = (
+        get_gpt_layer_with_transformer_engine_spec(
+            num_experts=None,
+            moe_grouped_gemm=False,
+            qk_layernorm=config.qk_layernorm,
+            multi_latent_attention=config.multi_latent_attention,
+            moe_use_legacy_grouped_gemm=config.moe_use_legacy_grouped_gemm,
+        )
+        if use_transformer_engine
+        else get_gpt_layer_local_spec(
+            num_experts=None,
+            moe_grouped_gemm=False,
+            qk_layernorm=config.qk_layernorm,
+            multi_latent_attention=config.multi_latent_attention,
+            moe_use_legacy_grouped_gemm=config.moe_use_legacy_grouped_gemm,
+        )
+    )
+    moe_layer_spec = (
+        get_gpt_layer_with_transformer_engine_spec(
+            num_experts=config.num_moe_experts,
+            moe_grouped_gemm=config.moe_grouped_gemm,
+            qk_layernorm=config.qk_layernorm,
+            multi_latent_attention=config.multi_latent_attention,
+            moe_use_legacy_grouped_gemm=config.moe_use_legacy_grouped_gemm,
+        )
+        if use_transformer_engine
+        else get_gpt_layer_local_spec(
+            num_experts=config.num_moe_experts,
+            moe_grouped_gemm=config.moe_grouped_gemm,
+            qk_layernorm=config.qk_layernorm,
+            multi_latent_attention=config.multi_latent_attention,
+            moe_use_legacy_grouped_gemm=config.moe_use_legacy_grouped_gemm,
+        )
+    )
+
+    # Parse config.moe_layer_freq to determine the pattern of expert/dense layers.
+    # 0 stands for dense layers, 1 stands for expert layers.
+    # For integer N: Creates a pattern with one expert layer every N layers.
+    # For string pattern: Evaluates the str directly (e.g. "[1,0,1]" for alternating expert/dense).
+    if isinstance(config.moe_layer_freq, int):
+        moe_layer_pattern = [
+            1 if (i % config.moe_layer_freq == 0) else 0 for i in range(config.num_layers)
+        ]
+    elif isinstance(config.moe_layer_freq, list):
+        moe_layer_pattern = config.moe_layer_freq
+        assert len(moe_layer_pattern) == config.num_layers, (
+            f"Invalid length of moe_layer_pattern: {len(moe_layer_pattern)}, "
+            f"expected {config.num_layers}, "
+            f"current moe layer pattern: {config.moe_layer_freq}"
+        )
+    else:
+        raise ValueError(
+            f"Invalid moe_layer_freq: {type(config.moe_layer_freq)}, {config.moe_layer_freq}"
+        )
+
+    # Create the layer specs for the model.
+    layer_specs = []
+    for layer_number in range(config.num_layers):
+        if moe_layer_pattern[layer_number] == 1:
+            layer_specs.append(moe_layer_spec)
+        elif moe_layer_pattern[layer_number] == 0:
+            layer_specs.append(dense_layer_spec)
+        else:
+            raise ValueError(f"Invalid layer pattern: {moe_layer_pattern}")
+
+    # Slice the layer specs to only include the layers that are built in this pipeline stage.
+    # Note: MCore layer_number starts at 1
+    offset = TransformerLayer._get_layer_offset(config)
+    num_layers_to_build = get_num_layers_to_build(config)
+    layer_specs = layer_specs[offset : offset + num_layers_to_build]
+
+    # Block spec.
+    block_spec = TransformerBlockSubmodules(layer_specs=layer_specs, layer_norm=layer_norm_impl)
+
+    return block_spec

megatron/core/models/gpt/gpt_model.py

+# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
+
+from collections import OrderedDict
+from typing import Dict, 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.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 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
+
+
+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.
+        scatter_embedding_sequence_parallel (bool, optional):
+            Whether embeddings should be scattered across sequence parallel
+            region or not. Defaults to True.
+        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,
+        rope_scaling: bool = False,
+        scatter_embedding_sequence_parallel: bool = True,
+        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.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 4 attributes are needed for TensorRT-LLM export.
+        self.max_position_embeddings = max_sequence_length
+        self.rotary_percent = rotary_percent
+        self.rotary_base = rotary_base
+        self.rotary_scaling = rope_scaling
+
+        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,
+                scatter_to_sequence_parallel=scatter_embedding_sequence_parallel,
+            )
+
+        if self.position_embedding_type == 'rope' and not self.config.multi_latent_attention:
+            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,
+                rope_scaling=rope_scaling,
+                use_cpu_initialization=self.config.use_cpu_initialization,
+            )
+
+        # 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()
+
+        if has_config_logger_enabled(self.config):
+            log_config_to_disk(
+                self.config, self.state_dict(), prefix=f'{type(self).__name__}_init_ckpt'
+            )
+
+    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,
+        runtime_gather_output: Optional[bool] = 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
+
+        Args:
+            runtime_gather_output (bool): Gather output at runtime. Default None means
+                `parallel_output` arg in the constructor will be used.
+        """
+        # 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
+        rotary_pos_cos = None
+        rotary_pos_sin = None
+        if self.position_embedding_type == 'rope' and not self.config.multi_latent_attention:
+            if not self.training and self.config.flash_decode:
+                # Flash decoding uses precomputed cos and sin for RoPE
+                rotary_pos_cos, rotary_pos_sin = self.rotary_pos_emb.get_cos_sin(
+                    inference_params.max_sequence_length
+                )
+            else:
+                rotary_seq_len = self.rotary_pos_emb.get_rotary_seq_len(
+                    inference_params, self.decoder, decoder_input, self.config, packed_seq_params
+                )
+                rotary_pos_emb = self.rotary_pos_emb(
+                    rotary_seq_len,
+                    packed_seq=packed_seq_params is not None
+                    and packed_seq_params.qkv_format == 'thd',
+                )
+
+        # Run decoder.
+        hidden_states = self.decoder(
+            hidden_states=decoder_input,
+            attention_mask=attention_mask,
+            inference_params=inference_params,
+            rotary_pos_emb=rotary_pos_emb,
+            rotary_pos_cos=rotary_pos_cos,
+            rotary_pos_sin=rotary_pos_sin,
+            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, runtime_gather_output=runtime_gather_output
+        )
+
+        if has_config_logger_enabled(self.config):
+            payload = OrderedDict(
+                {
+                    'input_ids': input_ids,
+                    'position_ids': position_ids,
+                    'attention_mask': attention_mask,
+                    'decoder_input': decoder_input,
+                    'logits': logits,
+                }
+            )
+            log_config_to_disk(self.config, payload, prefix='input_and_logits')
+
+        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