vllm_patch/mimo_v2.py

# primitive-ai: ModelOpt MIXED_PRECISION loading (FP8_PB_WO block scales are named weight_scale, 4-D)
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from collections.abc import Iterable
from itertools import islice

import torch
from torch import nn

from vllm.compilation.decorators import support_torch_compile
from vllm.config import (
    CacheConfig,
    VllmConfig,
    get_current_vllm_config,
    str_dtype_to_torch_dtype,
)
from vllm.distributed import (
    get_ep_group,
    get_pp_group,
    get_tensor_model_parallel_rank,
    get_tensor_model_parallel_world_size,
    tensor_model_parallel_all_gather,
)
from vllm.logger import init_logger
from vllm.model_executor.layers.activation import SiluAndMul
from vllm.model_executor.layers.attention import Attention
from vllm.model_executor.layers.fused_moe import (
    FusedMoEFactory,
    fused_moe_make_expert_params_mapping,
)
from vllm.model_executor.layers.fused_moe.router.gate_linear import GateLinear
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import (
    MergedColumnParallelLinear,
    QKVParallelLinear,
    RowParallelLinear,
)
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.quantization.utils.quant_utils import (
    GroupShape,
    scaled_quantize,
)
from vllm.model_executor.layers.rotary_embedding import get_rope
from vllm.model_executor.layers.vocab_parallel_embedding import (
    ParallelLMHead,
    VocabParallelEmbedding,
)
from vllm.model_executor.model_loader.weight_utils import (
    default_weight_loader,
    maybe_remap_kv_scale_name,
)
from vllm.model_executor.models.utils import sequence_parallel_chunk
from vllm.sequence import IntermediateTensors
from vllm.utils.math_utils import cdiv
from vllm.v1.attention.backend import AttentionType
from vllm.v1.attention.backends.registry import AttentionBackendEnum

from .interfaces import (
    EagleModelMixin,
    MixtureOfExperts,
    SupportsEagle3,
    SupportsPP,
)
from .utils import (
    AutoWeightsLoader,
    PPMissingLayer,
    extract_layer_index,
    is_pp_missing_parameter,
    make_empty_intermediate_tensors_factory,
    make_layers,
    maybe_prefix,
)

logger = init_logger(__name__)


class MiMoV2MLP(nn.Module):
    def __init__(
        self,
        hidden_size: int,
        intermediate_size: int,
        hidden_act: str,
        quant_config: QuantizationConfig | None = None,
        reduce_results: bool = True,
        prefix: str = "",
    ) -> None:
        super().__init__()
        self.gate_up_proj = MergedColumnParallelLinear(
            hidden_size,
            [intermediate_size] * 2,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.gate_up_proj",
        )
        self.down_proj = RowParallelLinear(
            intermediate_size,
            hidden_size,
            bias=False,
            quant_config=quant_config,
            reduce_results=reduce_results,
            prefix=f"{prefix}.down_proj",
        )
        if hidden_act != "silu":
            raise ValueError(
                f"Unsupported activation: {hidden_act}. Only silu is supported for now."
            )
        self.act_fn = SiluAndMul()

    def forward(self, x):
        gate_up, _ = self.gate_up_proj(x)
        x = self.act_fn(gate_up)
        x, _ = self.down_proj(x)
        return x


class MiMoV2MoE(nn.Module):
    def __init__(
        self,
        vllm_config: VllmConfig,
        prefix: str = "",
        is_nextn: bool = False,
    ):
        super().__init__()

        config = vllm_config.model_config.hf_text_config
        parallel_config = vllm_config.parallel_config
        quant_config = vllm_config.quant_config

        self.tp_size = get_tensor_model_parallel_world_size()

        self.ep_group = get_ep_group().device_group
        self.ep_size = self.ep_group.size()
        self.n_routed_experts = config.n_routed_experts

        self.is_sequence_parallel = parallel_config.use_sequence_parallel_moe

        if self.tp_size > config.n_routed_experts:
            raise ValueError(
                f"Tensor parallel size {self.tp_size} is greater than "
                f"the number of experts {config.n_routed_experts}."
            )

        if config.hidden_act != "silu":
            raise ValueError(
                f"Unsupported activation: {config.hidden_act}. "
                "Only silu is supported for now."
            )

        vllm_config = get_current_vllm_config()
        eplb_config = vllm_config.parallel_config.eplb_config
        self.enable_eplb = parallel_config.enable_eplb

        self.n_logical_experts = self.n_routed_experts
        self.n_redundant_experts = eplb_config.num_redundant_experts
        self.n_physical_experts = self.n_logical_experts + self.n_redundant_experts
        self.n_local_physical_experts = self.n_physical_experts // self.ep_size

        dtype = getattr(config, "moe_router_dtype", "float32")
        self.gate_dtype = str_dtype_to_torch_dtype(dtype)
        self.gate = GateLinear(
            config.hidden_size,
            config.n_routed_experts,
            bias=False,
            params_dtype=self.gate_dtype,
            out_dtype=torch.float32,
            prefix=f"{prefix}.gate",
        )
        self.gate.e_score_correction_bias = nn.Parameter(
            torch.empty(config.n_routed_experts, dtype=self.gate.out_dtype)
        )

        self.experts = FusedMoEFactory(
            num_experts=self.n_routed_experts,
            top_k=config.num_experts_per_tok,
            hidden_size=config.hidden_size,
            intermediate_size=config.moe_intermediate_size,
            renormalize=config.norm_topk_prob,
            quant_config=quant_config,
            prefix=f"{prefix}.experts",
            e_score_correction_bias=self.gate.e_score_correction_bias,
            enable_eplb=self.enable_eplb,
            num_redundant_experts=self.n_redundant_experts,
            is_sequence_parallel=self.is_sequence_parallel,
            use_grouped_topk=True,
            num_expert_group=config.n_group,
            topk_group=config.topk_group,
            scoring_func="sigmoid",
            router_logits_dtype=self.gate.out_dtype,
        )

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        assert hidden_states.dim() <= 2, "MiMoV2MoE only supports 1D or 2D inputs"
        is_input_1d = hidden_states.dim() == 1
        num_tokens, hidden_dim = hidden_states.shape
        hidden_states = hidden_states.view(-1, hidden_dim)

        if self.is_sequence_parallel:
            hidden_states = sequence_parallel_chunk(hidden_states)

        if self.gate_dtype is not None:
            gate_input = hidden_states.to(self.gate_dtype)
        else:
            gate_input = hidden_states
        router_logits, _ = self.gate(gate_input)
        final_hidden_states = self.experts(
            hidden_states=hidden_states, router_logits=router_logits
        )

        if self.is_sequence_parallel:
            final_hidden_states = tensor_model_parallel_all_gather(
                final_hidden_states, 0
            )
            final_hidden_states = final_hidden_states[:num_tokens]

        return final_hidden_states.squeeze(0) if is_input_1d else final_hidden_states


class MiMoV2Attention(nn.Module):
    def __init__(
        self,
        hidden_size: int,
        num_heads: int,
        num_kv_heads: int,
        head_dim: int,
        v_head_dim: int | None = None,
        v_scale: float | None = None,
        sliding_window_size: int = -1,
        attention_bias: bool = False,
        add_swa_attention_sink_bias: bool = False,
        layer_id: int = 0,
        rope_theta: float = 1000000,
        max_position_embeddings: int = 32768,
        cache_config: CacheConfig | None = None,
        quant_config: QuantizationConfig | None = None,
        partial_rotary_factor: float = 1.0,
        prefix: str = "",
    ) -> None:
        super().__init__()
        self.hidden_size = hidden_size
        self.layer_id = layer_id
        tp_size = get_tensor_model_parallel_world_size()

        self.total_num_heads = num_heads
        self.num_heads = self.total_num_heads // tp_size

        self.total_num_kv_heads = num_kv_heads
        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)

        self.head_dim = head_dim

        self.v_head_dim = v_head_dim if v_head_dim is not None else head_dim

        self.q_size = self.num_heads * self.head_dim
        self.k_size = self.num_kv_heads * self.head_dim
        self.v_size = self.num_kv_heads * self.v_head_dim

        self.v_scale = v_scale
        self.scaling = self.head_dim**-0.5
        self.rope_theta = rope_theta
        self.max_position_embeddings = max_position_embeddings

        self.qkv_proj = QKVParallelLinear(
            hidden_size,
            self.head_dim,
            self.total_num_heads,
            self.total_num_kv_heads,
            bias=attention_bias,
            quant_config=quant_config,
            prefix=f"{prefix}.qkv_proj",
            v_head_size=self.v_head_dim,
        )

        self.o_proj = RowParallelLinear(
            self.total_num_heads * self.v_head_dim,
            hidden_size,
            bias=False,
            quant_config=quant_config if "mtp.layers" not in prefix else None,
            reduce_results=True,
            prefix=f"{prefix}.o_proj",
        )

        self.rotary_emb = get_rope(
            head_size=self.head_dim,
            max_position=max_position_embeddings,
            rope_parameters={
                "rope_type": "default",
                "rope_theta": rope_theta,
                "partial_rotary_factor": partial_rotary_factor,
            },
        )

        self.attention_sink_bias = (
            torch.nn.Parameter(torch.empty(self.num_heads), requires_grad=False)
            if add_swa_attention_sink_bias
            else None
        )

        sliding_window = sliding_window_size if sliding_window_size > -1 else None

        # Use DiffKV backend when V has a different head dim than K.
        # Auto-pick FA-DiffKV when FA3/4 is usable on this device, else fall
        # back to TRITON_ATTN_DIFFKV.  Users can force a choice via
        # `--attention-backend <FLASH_ATTN_DIFFKV|TRITON_ATTN_DIFFKV>`.
        if self.v_head_dim != self.head_dim:
            requested = get_current_vllm_config().attention_config.backend
            if requested is not None and requested.name.endswith("_DIFFKV"):
                backend_enum = requested
            else:
                fa_backend = AttentionBackendEnum.FLASH_ATTN_DIFFKV.get_class()
                assert hasattr(fa_backend, "is_supported_on_current_device")
                if fa_backend.is_supported_on_current_device(
                    head_size=self.head_dim,
                    head_size_v=self.v_head_dim,
                    has_sinks=self.attention_sink_bias is not None,
                ):
                    backend_enum = AttentionBackendEnum.FLASH_ATTN_DIFFKV
                else:
                    backend_enum = AttentionBackendEnum.TRITON_ATTN_DIFFKV
            attn_backend = backend_enum.get_class()
            assert hasattr(attn_backend, "set_head_size_v")
            attn_backend.set_head_size_v(self.v_head_dim)
            logger.info_once("Using %s for attention.", attn_backend.get_name())
        else:
            attn_backend = None

        self.attn = Attention(
            self.num_heads,
            self.head_dim,
            self.scaling,
            num_kv_heads=self.num_kv_heads,
            cache_config=cache_config,
            quant_config=quant_config,
            per_layer_sliding_window=sliding_window,
            attn_type=AttentionType.DECODER,
            prefix=f"{prefix}.attn",
            sinks=self.attention_sink_bias,
            attn_backend=attn_backend,
            head_size_v=self.v_head_dim,
        )

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
        qkv, _ = self.qkv_proj(hidden_states)
        q, k, v = qkv.split([self.q_size, self.k_size, self.v_size], dim=-1)
        q, k = self.rotary_emb(positions, q, k)

        # Apply v_scale before attention
        if self.v_scale is not None:
            v = v * self.v_scale

        attn_output = self.attn(q, k, v)

        output, _ = self.o_proj(attn_output)
        return output


class MiMoV2FlashDecoderLayer(nn.Module):
    def __init__(self, vllm_config: VllmConfig, prefix: str = "") -> None:
        super().__init__()
        config = vllm_config.model_config.hf_text_config
        quant_config = vllm_config.quant_config
        layer_id = extract_layer_index(prefix)

        self.hidden_size = config.hidden_size
        self.config = config
        self.layer_id = layer_id

        rope_theta = getattr(config, "rope_theta", 1000000)
        max_position_embeddings = getattr(config, "max_position_embeddings", 32768)

        v_scale = getattr(config, "attention_value_scale", None)

        if self.is_compressed_softmax_layer():
            self.self_attn = MiMoV2Attention(
                hidden_size=self.hidden_size,
                num_heads=config.swa_num_attention_heads,
                num_kv_heads=config.swa_num_key_value_heads,
                head_dim=config.swa_head_dim,
                v_head_dim=getattr(config, "swa_v_head_dim", None),
                v_scale=v_scale,
                sliding_window_size=config.sliding_window_size,
                attention_bias=config.attention_bias,
                add_swa_attention_sink_bias=getattr(
                    config, "add_swa_attention_sink_bias", False
                ),
                layer_id=layer_id,
                rope_theta=getattr(config, "swa_rope_theta", rope_theta),
                max_position_embeddings=max_position_embeddings,
                quant_config=quant_config,
                partial_rotary_factor=getattr(config, "partial_rotary_factor", 1.0),
                prefix=f"{prefix}.self_attn",
            )
        else:
            self.self_attn = MiMoV2Attention(
                hidden_size=self.hidden_size,
                num_heads=config.num_attention_heads,
                num_kv_heads=config.num_key_value_heads,
                head_dim=config.head_dim,
                v_head_dim=getattr(config, "v_head_dim", None),
                v_scale=v_scale,
                sliding_window_size=-1,  # normal attention
                attention_bias=config.attention_bias,
                layer_id=layer_id,
                rope_theta=rope_theta,
                max_position_embeddings=max_position_embeddings,
                quant_config=quant_config,
                partial_rotary_factor=getattr(config, "partial_rotary_factor", 1.0),
                prefix=f"{prefix}.self_attn",
            )

        self.is_layer_sparse = self.is_moe_layer(layer_id)
        if self.is_layer_sparse:
            self.mlp = MiMoV2MoE(
                vllm_config=vllm_config,
                prefix=f"{prefix}.mlp",
            )
        else:
            self.mlp = MiMoV2MLP(
                hidden_size=self.hidden_size,
                intermediate_size=config.intermediate_size,
                hidden_act=config.hidden_act,
                quant_config=quant_config,
                prefix=f"{prefix}.mlp",
            )

        self.input_layernorm = RMSNorm(config.hidden_size, eps=config.layernorm_epsilon)
        self.post_attention_layernorm = RMSNorm(
            config.hidden_size, eps=config.layernorm_epsilon
        )

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        residual: torch.Tensor | None,
    ) -> tuple[torch.Tensor, torch.Tensor]:
        if residual is None:
            residual = hidden_states
            hidden_states = self.input_layernorm(hidden_states)
        else:
            hidden_states, residual = self.input_layernorm(hidden_states, residual)

        hidden_states = self.self_attn(
            positions=positions,
            hidden_states=hidden_states,
        )

        hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
        hidden_states = self.mlp(hidden_states)
        return hidden_states, residual

    def is_moe_layer(self, layer_idx: int) -> bool:
        return (
            hasattr(self.config, "moe_layer_freq")
            and layer_idx >= 0
            and not isinstance(self.config.moe_layer_freq, int)
            and self.config.moe_layer_freq[layer_idx]
        )

    def is_compressed_softmax_layer(self) -> bool:
        return self.config.hybrid_layer_pattern[self.layer_id] == 1


def _requantize_fp8(
    grouped: torch.Tensor, rows_rank: int, block: int, dtype: torch.dtype
) -> tuple[torch.Tensor, torch.Tensor]:
    """Block-quantize a rank's ``[Q | K | V]`` rows back to fp8.

    A rank's rows need not end on a block boundary (a single 1856-row slice is
    14.5 blocks) while ``scaled_quantize`` requires both dims to be multiples of
    the block size, so pad the tail with zeros: zero rows cannot raise a block's
    amax, so every scale -- and the number of scale rows -- is unchanged. The
    padding is dropped again here.
    """
    padded = cdiv(rows_rank, block) * block
    if padded != rows_rank:
        grouped = torch.cat(
            [grouped, grouped.new_zeros(padded - rows_rank, grouped.shape[1])], dim=0
        )
    w_rank, s_rank = scaled_quantize(
        grouped, GroupShape(block, block), dtype, compute_dtype=torch.float32
    )
    return w_rank[:rows_rank], s_rank


def _shard_fp8_qkv_proj(
    w_full: torch.Tensor,
    s_full: torch.Tensor,
    num_heads: int,
    num_kv_heads: int,
    head_dim: int,
    v_head_dim: int,
    tp_rank: int,
    tp_size: int,
    ckpt_tp: int,
    block: int = 128,
) -> tuple[torch.Tensor, torch.Tensor]:
    """Shard the fp8 qkv_proj weights for ``tp_rank``.

    The checkpoint stores the fused QKV pre-sharded for ``ckpt_tp`` ranks (the
    model config's ``num_key_value_heads``), each chunk holding that slice's
    Q, K and V rows:

        [Q_0 | K_0 | V_0 | Q_1 | K_1 | V_1 | ... | Q_n | K_n | V_n]   (n = ckpt_tp)

    Per chunk, Q has ``(num_heads / ckpt_tp) * head_dim`` rows, K has
    ``(num_kv_heads / ckpt_tp) * head_dim`` rows, and V has
    ``(num_kv_heads / ckpt_tp) * v_head_dim`` rows, and the fp8 block scales
    are tiled per chunk too (``ceil(rows_per_chunk / block)`` rows each).

    ``ckpt_tp`` is not the layer's KV-head count: a MiMo-V2.5 SWA layer has 8
    KV heads over 4 chunks, so each chunk carries two KV heads (3712 rows = 29
    blocks) while a GA layer has 4 KV heads over 4 chunks (3392 rows each).

    The forward expects each rank's slice de-interleaved:

        [Q_1 | Q_2 | ... | Q_g | K_1 | K_2 | ... | K_g | V_1 | V_2 | ... | V_g]

    When ``tp_size == ckpt_tp`` the checkpoint chunk *is* that layout, so a
    plain chunk of both weight and scale suffices. Otherwise each rank's Q, K
    and V rows are gathered from the chunks that hold them, dequantized with
    the chunk's own scales, reordered, and re-quantized to fp8.
    """
    assert num_heads % tp_size == 0, (
        f"num_heads={num_heads} must be divisible by tp_size={tp_size}."
    )
    if ckpt_tp <= 0 or num_heads % ckpt_tp or num_kv_heads % ckpt_tp:
        raise ValueError(
            f"fused qkv_proj is pre-sharded at {ckpt_tp} chunks, which do not "
            f"divide num_heads={num_heads} / num_kv_heads={num_kv_heads}."
        )
    # When there are fewer KV heads than ranks, vLLM replicates them
    # (`num_kv_head_replicas`) and rank r owns KV head r // replicas, which
    # keeps every Q head grouped with the KV head it attends to.
    if tp_size <= num_kv_heads:
        assert num_kv_heads % tp_size == 0, (
            f"num_kv_heads={num_kv_heads} must be divisible by tp_size={tp_size}."
        )
        kv_head_ids = list(
            range(
                tp_rank * (num_kv_heads // tp_size),
                (tp_rank + 1) * (num_kv_heads // tp_size),
            )
        )
    else:
        assert tp_size % num_kv_heads == 0, (
            f"tp_size={tp_size} must be divisible by num_kv_heads={num_kv_heads}."
        )
        kv_head_ids = [tp_rank // (tp_size // num_kv_heads)]
    q_head_ids = list(
        range(tp_rank * (num_heads // tp_size), (tp_rank + 1) * (num_heads // tp_size))
    )

    rows_per_chunk = w_full.shape[0] // ckpt_tp
    q_per_chunk = (num_heads // ckpt_tp) * head_dim
    k_per_chunk = (num_kv_heads // ckpt_tp) * head_dim
    v_per_chunk = (num_kv_heads // ckpt_tp) * v_head_dim
    if q_per_chunk + k_per_chunk + v_per_chunk != rows_per_chunk:
        raise ValueError(
            f"fused qkv_proj has {w_full.shape[0]} rows, not {ckpt_tp} chunks of "
            f"{q_per_chunk} Q + {k_per_chunk} K + {v_per_chunk} V rows."
        )

    # The scales are tiled per chunk; they collapse to one continuous grid when
    # a chunk is a whole number of blocks (the SWA chunks are: 3712 = 29 * 128).
    chunk_scale_rows = cdiv(rows_per_chunk, block)
    rows = torch.arange(w_full.shape[0])
    per_chunk_scales = s_full.shape[0] == ckpt_tp * chunk_scale_rows
    if per_chunk_scales:
        scale_index = (rows // rows_per_chunk) * chunk_scale_rows + (
            rows % rows_per_chunk
        ) // block
    elif s_full.shape[0] == cdiv(w_full.shape[0], block):
        scale_index = rows // block
    else:
        raise ValueError(
            f"fused qkv_proj scale has {s_full.shape[0]} rows, expected either "
            f"{ckpt_tp * chunk_scale_rows} ({ckpt_tp} chunks of "
            f"{chunk_scale_rows} rows) or {cdiv(w_full.shape[0], block)} "
            f"(one continuous grid)"
        )

    if tp_size == ckpt_tp and per_chunk_scales:
        # One checkpoint chunk per rank: already [Q | K | V] for that rank, and
        # its scale rows line up with ceil(rows_per_chunk / block).
        return (
            w_full.chunk(ckpt_tp, dim=0)[tp_rank],
            s_full.chunk(ckpt_tp, dim=0)[tp_rank],
        )

    # Gather this rank's Q, K and V rows from the chunks that hold them.
    q_heads_per_chunk = num_heads // ckpt_tp
    kv_heads_per_chunk = num_kv_heads // ckpt_tp
    head_rows = torch.arange(head_dim)
    v_head_rows = torch.arange(v_head_dim)
    row_index: list[torch.Tensor] = []
    for head in q_head_ids:
        chunk = head // q_heads_per_chunk
        row_index.append(
            chunk * rows_per_chunk + (head % q_heads_per_chunk) * head_dim + head_rows
        )
    for head in kv_head_ids:
        chunk = head // kv_heads_per_chunk
        row_index.append(
            chunk * rows_per_chunk
            + q_per_chunk
            + (head % kv_heads_per_chunk) * head_dim
            + head_rows
        )
    for head in kv_head_ids:
        chunk = head // kv_heads_per_chunk
        row_index.append(
            chunk * rows_per_chunk
            + q_per_chunk
            + k_per_chunk
            + (head % kv_heads_per_chunk) * v_head_dim
            + v_head_rows
        )
    index = torch.cat(row_index)
    grouped = w_full[index].to(torch.float32) * s_full[
        scale_index[index]
    ].repeat_interleave(block, dim=1)
    return _requantize_fp8(grouped, index.numel(), block, w_full.dtype)


@support_torch_compile
class MiMoV2Model(nn.Module, EagleModelMixin):
    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()

        config = vllm_config.model_config.hf_config.get_text_config()
        quant_config = vllm_config.quant_config
        eplb_config = vllm_config.parallel_config.eplb_config

        self.config = config
        self.quant_config = quant_config
        self.vocab_size = config.vocab_size
        self.num_redundant_experts = eplb_config.num_redundant_experts

        if get_pp_group().is_first_rank or (
            config.tie_word_embeddings and get_pp_group().is_last_rank
        ):
            self.embed_tokens = VocabParallelEmbedding(
                config.vocab_size,
                config.hidden_size,
                quant_config=quant_config,
                prefix=f"{prefix}.embed_tokens",
            )
        else:
            self.embed_tokens = PPMissingLayer()

        self.start_layer, self.end_layer, self.layers = make_layers(
            config.num_hidden_layers,
            lambda prefix: MiMoV2FlashDecoderLayer(
                vllm_config=vllm_config,
                prefix=prefix,
            ),
            prefix=f"{prefix}.layers",
        )

        self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
            ["hidden_states", "residual"], config.hidden_size
        )
        if get_pp_group().is_last_rank:
            self.norm = RMSNorm(config.hidden_size, eps=config.layernorm_epsilon)
        else:
            self.norm = PPMissingLayer()

    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
        return self.embed_tokens(input_ids)

    def forward(
        self,
        input_ids: torch.Tensor | None,
        positions: torch.Tensor,
        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
    ) -> torch.Tensor | IntermediateTensors:
        if get_pp_group().is_first_rank:
            if inputs_embeds is not None:
                hidden_states = inputs_embeds
            else:
                hidden_states = self.embed_input_ids(input_ids)
            residual = None
        else:
            assert intermediate_tensors is not None
            hidden_states = intermediate_tensors["hidden_states"]
            residual = intermediate_tensors["residual"]

        aux_hidden_states = self._maybe_add_hidden_state(
            [], self.start_layer, hidden_states, residual
        )
        for idx, layer in enumerate(
            islice(self.layers, self.start_layer, self.end_layer)
        ):
            hidden_states, residual = layer(positions, hidden_states, residual)
            self._maybe_add_hidden_state(
                aux_hidden_states, idx + 1, hidden_states, residual
            )

        if not get_pp_group().is_last_rank:
            return IntermediateTensors(
                {"hidden_states": hidden_states, "residual": residual}
            )

        hidden_states, _ = self.norm(hidden_states, residual)

        if len(aux_hidden_states) > 0:
            return hidden_states, aux_hidden_states
        return hidden_states

    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
        # Params for weights, fp8 weight scales, fp8 activation scales
        # (param_name, weight_name, expert_id, shard_id)
        return fused_moe_make_expert_params_mapping(
            self,
            ckpt_gate_proj_name="gate_proj",
            ckpt_down_proj_name="down_proj",
            ckpt_up_proj_name="up_proj",
            num_experts=self.config.n_routed_experts,
            num_redundant_experts=self.num_redundant_experts,
        )

    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
        stacked_params_mapping: list[tuple[str, str, str | int]] = [
            # (param_name, shard_name, shard_id)
            ("qkv_proj", "q_proj", "q"),
            ("qkv_proj", "k_proj", "k"),
            ("qkv_proj", "v_proj", "v"),
            ("gate_up_proj", "gate_proj", 0),
            ("gate_up_proj", "up_proj", 1),
        ]

        tp_rank = get_tensor_model_parallel_rank()
        tp_size = get_tensor_model_parallel_world_size()

        params_dict = dict(self.named_parameters(remove_duplicate=False))
        loaded_params: set[str] = set()
        expert_params_mapping = self.get_expert_mapping()
        # Pro-format fused qkv_proj arrives as two tensors (weight and
        # weight_scale_inv). Store them per-layer so that they can be
        # sharded together.
        pending_fp8_qkv_proj: dict[str, dict[str, torch.Tensor]] = {}
        # primitive-ai: ModelOpt FP8_PB_WO dense layers name the block scale weight_scale
        _mopt_ws = not any(k.endswith("weight_scale_inv") and ".experts" not in k for k in params_dict) and any(
            k.endswith(".weight_scale") and ".experts" not in k for k in params_dict)
        for name, loaded_weight in weights:
            if "rotary_emb.inv_freq" in name:
                continue
            if "rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name:
                continue
            if "mtp" in name:
                continue

            expert_matched = False
            for param_name, weight_name, expert_id, shard_id in expert_params_mapping:
                if weight_name not in name:
                    continue

                name_rewritten = name.replace(weight_name, param_name)

                if is_pp_missing_parameter(name_rewritten, self):
                    continue

                if (
                    name_rewritten.endswith(".bias") or name_rewritten.endswith("_bias")
                ) and name_rewritten not in params_dict:
                    continue

                if name_rewritten not in params_dict:
                    continue

                param = params_dict[name_rewritten]
                weight_loader = param.weight_loader

                weight_loader(
                    param,
                    loaded_weight,
                    name_rewritten,
                    shard_id=shard_id,
                    expert_id=expert_id,
                )
                loaded_params.add(name_rewritten)
                expert_matched = True
                break

            if expert_matched:
                continue
            # Support fused qkv_proj checkpoint (Pro format)
            if self._try_load_fp8_qkv_proj(
                name,
                loaded_weight,
                pending_fp8_qkv_proj,
                params_dict,
                loaded_params,
                tp_rank,
                tp_size,
            ):
                continue
            if name.endswith(".weight_scale_inv") and _mopt_ws:
                name = name[: -len("_inv")]
            stacked_matched = False
            for param_name, weight_name, stacked_shard_id in stacked_params_mapping:
                if weight_name not in name:
                    continue
                name_rewritten = name.replace(weight_name, param_name)

                if (
                    name_rewritten.endswith(".bias")
                    and name_rewritten not in params_dict
                ):
                    continue

                if is_pp_missing_parameter(name_rewritten, self):
                    continue

                if name_rewritten not in params_dict:
                    continue

                param = params_dict[name_rewritten]
                weight_loader = getattr(param, "weight_loader", default_weight_loader)
                if param.dim() == 4 and loaded_weight.dim() == 2 and name_rewritten.endswith("weight_scale"):
                    loaded_weight = loaded_weight.reshape(loaded_weight.shape[0], 1, loaded_weight.shape[1], 1)
                weight_loader(param, loaded_weight, stacked_shard_id)
                loaded_params.add(name_rewritten)

                stacked_matched = True
                break

            if stacked_matched:
                continue

            if name.endswith(".bias") and name not in params_dict:
                continue

            orig_name = name
            mapped_name = maybe_remap_kv_scale_name(name, params_dict)
            name = mapped_name if mapped_name is not None else orig_name

            if name not in params_dict:
                continue

            param = params_dict[name]

            if "attention_sink_bias" in name:
                total_heads = loaded_weight.shape[0]
                heads_per_rank = total_heads // tp_size
                head_start = tp_rank * heads_per_rank
                narrow_weight = loaded_weight.narrow(0, head_start, heads_per_rank)

                param.data.copy_(narrow_weight)
                loaded_params.add(name)
            else:
                weight_loader = getattr(param, "weight_loader", default_weight_loader)
                if param.dim() == 4 and loaded_weight.dim() == 2 and name.endswith("weight_scale"):
                    loaded_weight = loaded_weight.reshape(loaded_weight.shape[0], 1, loaded_weight.shape[1], 1)
                weight_loader(param, loaded_weight)
                loaded_params.add(name)

        return loaded_params

    def _try_load_fp8_qkv_proj(
        self,
        name: str,
        tensor: torch.Tensor,
        fp8_qkv_proj_dict: dict[str, dict[str, torch.Tensor]],
        params_dict: dict[str, torch.nn.Parameter],
        loaded_params: set[str],
        tp_rank: int,
        tp_size: int,
    ) -> bool:
        """The fused fp8 QKV projection weights and scale are stored separately.
        Special care must be taken while sharding these tensors across TP ranks.
        See _shard_fp8_qkv_proj for more details.

        Returns:
            True if ``tensor`` was an fp8 qkv_proj weight/scale and was consumed
            (caller should skip it); False otherwise, so the caller falls
            through to its normal loading path.

        """
        is_weight = (
            name.endswith("qkv_proj.weight") and tensor.dtype == torch.float8_e4m3fn
        )
        is_scale = name.endswith("qkv_proj.weight_scale_inv")
        if not is_weight and not is_scale:
            # Weight is not in FP8 format. Ignore.
            return False

        if is_pp_missing_parameter(name, self):
            # This qkv_proj is for a layer not on this PP rank.
            return True

        prefix, qkv_kind = name.rsplit(".", 1)
        entry = fp8_qkv_proj_dict.setdefault(prefix, {})
        entry[qkv_kind] = tensor
        if "weight" not in entry or "weight_scale_inv" not in entry:
            # Still waiting for the other param.
            return True
        del fp8_qkv_proj_dict[prefix]

        # Get self_attn module, which is a parent of qkv_proj.
        attn = self.get_submodule(prefix.rsplit(".", 1)[0])

        # Shard the qkv_proj per-rank.
        w_rank, s_rank = _shard_fp8_qkv_proj(
            entry["weight"],
            entry["weight_scale_inv"],
            num_heads=attn.total_num_heads,
            num_kv_heads=attn.total_num_kv_heads,
            head_dim=attn.head_dim,
            v_head_dim=attn.v_head_dim,
            tp_rank=tp_rank,
            tp_size=tp_size,
            # The fused qkv_proj is pre-sharded for this many ranks.
            ckpt_tp=self.config.num_key_value_heads,
        )
        sharded = {"weight": w_rank, "weight_scale_inv": s_rank}
        for kind, tensor in sharded.items():
            param_name = f"{prefix}.{kind}"
            if param_name not in params_dict and kind == "weight_scale_inv":
                param_name = f"{prefix}.weight_scale"
            param = params_dict[param_name]
            if param.dim() == 4 and tensor.dim() == 2:
                tensor = tensor.reshape(tensor.shape[0], 1, tensor.shape[1], 1)
            if tensor.shape[0] > param.shape[0]:
                tensor = tensor[: param.shape[0]]
            default_weight_loader(param, tensor)
            loaded_params.add(param_name)
        return True


class MiMoV2FlashForCausalLM(nn.Module, SupportsPP, MixtureOfExperts, SupportsEagle3):
    packed_modules_mapping = {
        "qkv_proj": ["q_proj", "k_proj", "v_proj"],
        "gate_up_proj": ["gate_proj", "up_proj"],
    }

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()
        config = vllm_config.model_config.hf_config
        quant_config = vllm_config.quant_config

        self.config = config
        self.quant_config = quant_config
        self.model = MiMoV2Model(
            vllm_config=vllm_config,
            prefix=maybe_prefix(prefix, "model"),
        )

        if get_pp_group().is_last_rank:
            self.lm_head = ParallelLMHead(
                config.vocab_size,
                config.hidden_size,
                quant_config=quant_config,
                prefix=maybe_prefix(prefix, "lm_head"),
            )
        else:
            self.lm_head = PPMissingLayer()

        self.logits_processor = LogitsProcessor(config.vocab_size)

        self.make_empty_intermediate_tensors = (
            self.model.make_empty_intermediate_tensors
        )

    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
        return self.model.embed_input_ids(input_ids)

    def forward(
        self,
        input_ids: torch.Tensor | None,
        positions: torch.Tensor,
        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
    ) -> torch.Tensor | IntermediateTensors:
        hidden_states = self.model(
            input_ids, positions, intermediate_tensors, inputs_embeds
        )
        return hidden_states

    def compute_logits(
        self,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor | None:
        logits = self.logits_processor(self.lm_head, hidden_states)
        return logits

    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
        return self.model.get_expert_mapping()

    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
        loader = AutoWeightsLoader(self)
        return loader.load_weights(weights)


class MiMoV2ForCausalLM(MiMoV2FlashForCausalLM):
    packed_modules_mapping = {
        "qkv_proj": ["qkv_proj"],
        "gate_up_proj": ["gate_proj", "up_proj"],
    }