plamo2.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Inference-only PLaMo2 model."""

from collections.abc import Iterable
from itertools import islice

import torch
from torch import nn
from transformers import PretrainedConfig

from vllm.attention.layer import Attention
from vllm.compilation.decorators import support_torch_compile
from vllm.config import VllmConfig, get_current_vllm_config
from vllm.distributed import divide, get_tensor_model_parallel_world_size
from vllm.distributed.parallel_state import get_pp_group
from vllm.forward_context import ForwardContext, get_forward_context
from vllm.model_executor.custom_op import CustomOp
from vllm.model_executor.layers.activation import SiluAndMul
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import (
    ColumnParallelLinear,
    MergedColumnParallelLinear,
    QKVParallelLinear,
    RowParallelLinear,
)
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.mamba.abstract import MambaBase
from vllm.model_executor.layers.mamba.mamba_utils import (
    MambaStateDtypeCalculator,
    MambaStateShapeCalculator,
)
from vllm.model_executor.layers.mamba.ops.causal_conv1d import (
    causal_conv1d_fn,
    causal_conv1d_update,
)
from vllm.model_executor.layers.mamba.ops.mamba_ssm import selective_state_update
from vllm.model_executor.layers.mamba.ops.ssd_combined import (
    mamba_chunk_scan_combined_varlen,
)
from vllm.model_executor.layers.quantization import QuantizationConfig
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 (
    composed_weight_loader,
    default_weight_loader,
    sharded_weight_loader,
)
from vllm.model_executor.models.interfaces import (
    HasInnerState,
    IsHybrid,
    SupportsLoRA,
    SupportsPP,
)
from vllm.model_executor.models.utils import (
    AutoWeightsLoader,
    is_pp_missing_parameter,
    make_empty_intermediate_tensors_factory,
    make_layers,
    maybe_prefix,
)
from vllm.model_executor.utils import set_weight_attrs
from vllm.platforms import current_platform
from vllm.sequence import IntermediateTensors
from vllm.utils.torch_utils import direct_register_custom_op
from vllm.v1.attention.backend import AttentionMetadata
from vllm.v1.attention.backends.mamba2_attn import Mamba2AttentionMetadata


# Only used for type hinting.
class Plamo2Config(PretrainedConfig):  # type: ignore
    model_type: str = "plamo2"

    hidden_size: int
    num_hidden_layers: int
    rms_norm_eps: float
    # Attention
    num_attention_heads: int
    hidden_size_per_head: int
    num_key_value_heads: int
    # Mamba
    mamba_d_state: int
    mamba_d_conv: int
    mamba_num_heads: int
    mamba_step: int
    # MLP
    intermediate_size: int
    # Tokenizer
    vocab_size: int


def is_mamba(config: Plamo2Config, i: int) -> bool:
    assert config.mamba_step > 1

    if config.num_hidden_layers <= (config.mamba_step // 2):
        # use attention in last layer
        return i != config.num_hidden_layers - 1
    return (i % config.mamba_step) != (config.mamba_step // 2)


# Adapted from:
# vllm.model_executor.layers.mamba.mamba_mixer2.MambaMixer2
# transformers.models.mamba.modeling_mamba.MambaMixer
# --8<-- [start:plamo2_mamba_mixer]
@CustomOp.register("plamo2_mamba_mixer")
class Plamo2MambaMixer(MambaBase, CustomOp):
    # --8<-- [end:plamo2_mamba_mixer]

    def __init__(self, vllm_config: VllmConfig, *, prefix: str = "", **kwargs) -> None:
        super().__init__()
        self.config = vllm_config.model_config.hf_config
        self.cache_config = vllm_config.cache_config
        self.model_config = vllm_config.model_config
        self.quant_config = vllm_config.quant_config
        self.is_lora_enabled = bool(vllm_config.lora_config)
        self.hidden_size = self.config.hidden_size
        self.ssm_state_size = self.config.mamba_d_state
        self.conv_kernel_size = self.config.mamba_d_conv
        self.intermediate_size = (
            self.config.mamba_num_heads * self.config.hidden_size_per_head
        )
        self.tp_size = get_tensor_model_parallel_world_size()
        self.head_dim = self.config.hidden_size_per_head
        self.num_heads = self.config.mamba_num_heads
        self.time_step_rank = max(64, self.hidden_size // 16)
        self.conv1d = ColumnParallelLinear(
            input_size=self.conv_kernel_size,
            output_size=self.intermediate_size,
            bias=False,
            prefix=f"{prefix}.conv1d",
            return_bias=False,
        )
        # unsqueeze to fit conv1d weights shape into the linear weights shape.
        # Can't do this in `weight_loader` since it already exists in
        # `ColumnParallelLinear` and `set_weight_attrs`
        # doesn't allow to override it
        self.conv1d.weight.data = self.conv1d.weight.data.unsqueeze(1)

        self.in_proj = MergedColumnParallelLinear(
            self.hidden_size,
            [self.intermediate_size] * 2,
            bias=False,
            quant_config=self.quant_config,
            prefix=f"{prefix}.in_proj",
            return_bias=False,
        )
        # selective projection used to make dt, B and C input dependent
        self.bcdt_proj = RowParallelLinear(
            self.intermediate_size,
            self.time_step_rank + self.ssm_state_size * 2,
            bias=False,
            quant_config=self.quant_config,
            prefix=f"{prefix}.bcdt_proj",
            return_bias=False,
        )
        # time step projection (discretization) -
        # In the forward we need to apply dt_proj without the bias,
        # as the bias is added in the selective scan kernel.
        self.dt_proj = ColumnParallelLinear(
            self.time_step_rank,
            self.num_heads,
            bias=False,
            quant_config=self.quant_config,
            prefix=f"{prefix}.dt_proj",
            return_bias=False,
        )

        self.A = nn.Parameter(
            torch.empty(
                divide(self.num_heads, self.tp_size),
                dtype=torch.float32,
            )
        )
        self.D = nn.Parameter(torch.ones(divide(self.num_heads, self.tp_size)))
        self.dt_bias = nn.Parameter(torch.ones(divide(self.num_heads, self.tp_size)))

        set_weight_attrs(self.D, {"weight_loader": sharded_weight_loader(0)})
        a_weight_loader = composed_weight_loader(
            sharded_weight_loader(0), lambda x: -torch.exp(x.float())
        )
        set_weight_attrs(self.A, {"weight_loader": a_weight_loader})
        set_weight_attrs(self.dt_bias, {"weight_loader": sharded_weight_loader(0)})

        self.out_proj = RowParallelLinear(
            self.intermediate_size,
            self.hidden_size,
            bias=False,
            input_is_parallel=True,
            quant_config=self.quant_config,
            prefix=f"{prefix}.out_proj",
            return_bias=False,
        )
        # The activation function is fixed to SiLU.
        self.activation = "silu"

        self.dt_norm = RMSNorm(self.time_step_rank, eps=self.config.rms_norm_eps)
        self.B_norm = RMSNorm(self.ssm_state_size, eps=self.config.rms_norm_eps)
        self.C_norm = RMSNorm(self.ssm_state_size, eps=self.config.rms_norm_eps)

        self.chunk_size = self.config.mamba_chunk_size

        compilation_config = get_current_vllm_config().compilation_config
        if prefix in compilation_config.static_forward_context:
            raise ValueError(f"Duplicate layer name: {prefix}")
        compilation_config.static_forward_context[prefix] = self
        # The tuple is (conv_state, ssm_state)
        self.kv_cache = (torch.tensor([]), torch.tensor([]))
        assert self.chunk_size != -1, "chunk_size must be set for v1"

        self.prefix = prefix

    def _project_ssm_parameters(self, hidden_states):
        if self.is_lora_enabled:
            #  Lora kernel requires contiguous tensor.
            ssm_parameters = self.bcdt_proj(hidden_states.contiguous())
        else:
            ssm_parameters = self.bcdt_proj(hidden_states)
        B, C, time_step = torch.split(
            ssm_parameters,
            [self.ssm_state_size, self.ssm_state_size, self.time_step_rank],
            dim=-1,
        )

        # vllm._custom_ops.rms_norm requires contiguous input tensors.
        time_step = self.dt_norm(time_step.contiguous())
        B = self.B_norm(B.contiguous())
        C = self.C_norm(C.contiguous())
        dt = self.dt_proj(time_step)
        return B, C, dt

    def forward_native(
        self,
        hidden_states: torch.Tensor,
        output: torch.Tensor,
        **kwargs,
    ):
        pass

    def forward(
        self,
        hidden_states: torch.Tensor,
        output: torch.Tensor,
        **kwargs,
    ):
        torch.ops.vllm.plamo2_mamba_mixer(
            hidden_states,
            output,
            self.prefix,
        )

    def forward_cuda(
        self,
        hidden_states: torch.Tensor,
        output: torch.Tensor,
        **kwargs,
    ):
        forward_context = get_forward_context()
        # attn_metadata contains metadata necessary for the mamba2 triton
        # kernels to operate in continuous batching and in chunked prefill
        # modes; they are computed at top-level model forward since they
        # stay the same and reused for all mamba layers in the same iteration
        attn_metadata: AttentionMetadata = forward_context.attn_metadata

        if attn_metadata is not None:
            assert isinstance(attn_metadata, dict)
            attn_metadata = attn_metadata[self.prefix]
            assert isinstance(attn_metadata, Mamba2AttentionMetadata)
            self_kv_cache = self.kv_cache[forward_context.virtual_engine]
            # conv_state = (..., dim, width-1) yet contiguous along 'dim'
            conv_state = self_kv_cache[0].transpose(-1, -2)
            ssm_state = self_kv_cache[1]
            state_indices_tensor = attn_metadata.state_indices_tensor
            has_initial_states_p = attn_metadata.has_initial_states_p
            prep_initial_states = attn_metadata.prep_initial_states
            chunk_size = attn_metadata.chunk_size
            seq_idx_p = attn_metadata.seq_idx_p
            query_start_loc_p = attn_metadata.query_start_loc_p
            cu_chunk_seqlen_p = attn_metadata.cu_chunk_seqlen_p
            last_chunk_indices_p = attn_metadata.last_chunk_indices_p

        # 1. Gated MLP's linear projection
        projected_states = self.in_proj(hidden_states)
        gate, hidden_states = projected_states.chunk(2, dim=-1)

        # 2. Convolution sequence transformation
        conv_weights = self.conv1d.weight.view(
            self.conv1d.weight.size(0), self.conv1d.weight.size(2)
        )

        if attn_metadata is None:
            # profile run
            hidden_states = (
                hidden_states.transpose(0, 1).clone().transpose(0, 1)
            ).contiguous()
            output[:] = self.out_proj(hidden_states)
            return

        num_prefills = attn_metadata.num_prefills  # request count
        num_decodes = attn_metadata.num_decode_tokens  # token count (=request)
        num_prefill_tokens = attn_metadata.num_prefill_tokens  # token count
        has_prefill = num_prefills > 0
        has_decode = num_decodes > 0
        num_actual_tokens = num_prefill_tokens + num_decodes

        # Separate prefill and decode by splitting varlen input
        # Split along token dimension
        hidden_states_d, hidden_states_p = torch.split(
            hidden_states[:num_actual_tokens],
            [num_decodes, num_prefill_tokens],
            dim=0,
        )
        gate_d, gate_p = torch.split(
            gate[:num_actual_tokens], [num_decodes, num_prefill_tokens], dim=0
        )
        # Split along batch dimension
        state_indices_tensor_d, state_indices_tensor_p = torch.split(
            state_indices_tensor,
            [num_decodes, num_prefills],
            dim=0,
        )

        # Preallocate output tensor to avoid memcpy cost for merging prefill
        # and decode outputs
        preallocated_ssm_out = torch.empty(
            [
                num_prefill_tokens + num_decodes,
                (self.num_heads // self.tp_size) * self.head_dim,
            ],
            dtype=hidden_states.dtype,
            device=hidden_states.device,
        )
        preallocated_ssm_out_d, preallocated_ssm_out_p = torch.split(
            preallocated_ssm_out,
            [num_decodes, num_prefill_tokens],
            dim=0,
        )

        # Process prefill requests
        if has_prefill:
            # 2. Convolution sequence transformation
            # - "cache_indices" updates the conv_state cache in positions
            #   pointed to by "state_indices_tensor"
            x = hidden_states_p.transpose(0, 1)  # this is the form that causal-conv see
            hidden_states_p = causal_conv1d_fn(
                x,
                conv_weights,
                self.conv1d.bias,
                activation=self.activation,
                conv_states=conv_state,
                has_initial_state=has_initial_states_p,
                cache_indices=state_indices_tensor_p,
                metadata=attn_metadata,
                query_start_loc=query_start_loc_p,
            )
            hidden_states_p = hidden_states_p.transpose(0, 1)
            hidden_states_p = hidden_states_p[:num_prefill_tokens]
            # In some instances, the following `bcdt_proj` op
            # requires contiguous inputs
            # (e.g. if the Marlin kernel is used).
            hidden_states_p = hidden_states_p.contiguous()

            B, C, dt = self._project_ssm_parameters(hidden_states_p)

            # 3. State Space Model sequence transformation
            initial_states = None
            if has_initial_states_p is not None and prep_initial_states:
                # making a copy of the states
                initial_states = torch.where(
                    has_initial_states_p[:, None, None, None],
                    ssm_state[state_indices_tensor_p],
                    0,
                )

            varlen_state = mamba_chunk_scan_combined_varlen(
                hidden_states_p.view(
                    num_prefill_tokens, self.num_heads // self.tp_size, self.head_dim
                ),
                dt,
                self.A,
                B.view(num_prefill_tokens, 1, -1),
                C.view(num_prefill_tokens, 1, -1),
                chunk_size=chunk_size,
                D=self.D,
                z=gate_p.view(
                    num_prefill_tokens, self.num_heads // self.tp_size, self.head_dim
                ),
                dt_bias=self.dt_bias,
                seq_idx=seq_idx_p,
                cu_seqlens=query_start_loc_p,
                cu_chunk_seqlens=cu_chunk_seqlen_p,
                last_chunk_indices=last_chunk_indices_p,
                initial_states=initial_states,
                dt_softplus=True,
                dt_limit=(0.0, float("inf")),
                out=preallocated_ssm_out_p.view(num_prefill_tokens, -1, self.head_dim),
                state_dtype=ssm_state.dtype,
            )

            # update ssm states
            # - varlen state is a (batch, nheads, headdim, dstate) tensor
            ssm_state[state_indices_tensor_p] = varlen_state

        # Process decode requests
        if has_decode:
            # 2. Convolution sequence transformation
            hidden_states_d = causal_conv1d_update(
                hidden_states_d,
                conv_state,
                conv_weights,
                self.conv1d.bias,
                self.activation,
                conv_state_indices=state_indices_tensor_d,
            )

            # ROCm: Ensure contiguous tensor for bcdt_proj linear layer.
            # causal_conv1d_update returns a non-contiguous view (stride 8192
            # instead of 4096 for shape [batch, 4096]), causing incorrect GEMM
            # results when batch > 1 on ROCm.
            if current_platform.is_rocm():
                hidden_states_d = hidden_states_d.contiguous()

            B, C, dt = self._project_ssm_parameters(hidden_states_d)

            # 3. State Space Model sequence transformation
            A = self.A[:, None, ...][:, :, None].expand(
                -1, self.head_dim, self.config.mamba_d_state
            )
            dt = dt[:, :, None].expand(-1, -1, self.head_dim)
            dt_bias = self.dt_bias[:, None, ...].expand(-1, self.head_dim)
            D = self.D[:, None, ...].expand(-1, self.head_dim)
            B = B.unsqueeze(1)
            C = C.unsqueeze(1)
            hidden_states_d = hidden_states_d.view(
                -1, self.num_heads // self.tp_size, self.head_dim
            )

            # - the hidden is reshaped into (bs, num_heads, head_dim)
            # - ssm_state's slots will be selected
            #   using state_indices_tensor_d

            # NOTE: final output is an in-place update of out tensor
            selective_state_update(
                ssm_state,
                hidden_states_d,
                dt,
                A,
                B,
                C,
                D,
                z=gate_d.reshape(num_decodes, -1, self.head_dim),
                dt_bias=dt_bias,
                dt_softplus=True,
                state_batch_indices=state_indices_tensor_d,
                out=preallocated_ssm_out_d.view(num_decodes, -1, self.head_dim),
            )

        # 4. Final linear projection
        output[:num_actual_tokens] = self.out_proj(preallocated_ssm_out)

    def get_state_dtype(self) -> tuple[torch.dtype, torch.dtype]:
        assert self.model_config is not None
        assert self.cache_config is not None
        return MambaStateDtypeCalculator.mamba2_state_dtype(
            self.model_config.dtype,
            self.cache_config.mamba_cache_dtype,
            self.cache_config.mamba_ssm_cache_dtype,
        )

    def get_state_shape(self) -> tuple[tuple[int, ...], tuple[int, ...]]:
        return MambaStateShapeCalculator.mamba2_state_shape(
            intermediate_size=self.intermediate_size,
            tp_world_size=get_tensor_model_parallel_world_size(),
            n_groups=0,
            num_heads=self.num_heads,
            head_dim=self.head_dim,
            state_size=self.ssm_state_size,
            conv_kernel=self.conv_kernel_size,
        )

    @property
    def mamba_type(self) -> str:
        return "mamba2"


def plamo2_mamba_mixer(
    hidden_states: torch.Tensor,
    output: torch.Tensor,
    layer_name: str,
) -> None:
    forward_context: ForwardContext = get_forward_context()
    self = forward_context.no_compile_layers[layer_name]
    self.forward_cuda(hidden_states=hidden_states, output=output)


def plamo2_mamba_mixer_fake(
    hidden_states: torch.Tensor,
    output: torch.Tensor,
    layer_name: str,
) -> None:
    return


direct_register_custom_op(
    op_name="plamo2_mamba_mixer",
    op_func=plamo2_mamba_mixer,
    mutates_args=["output"],
    fake_impl=plamo2_mamba_mixer_fake,
)


class DenseMLP(nn.Module):
    def __init__(
        self,
        config: Plamo2Config,
        quant_config: QuantizationConfig | None = None,
        prefix: str = "",
    ) -> None:
        super().__init__()
        self.hidden_size = config.hidden_size
        self.intermediate_size = config.intermediate_size
        self.gate_up_proj = MergedColumnParallelLinear(
            self.hidden_size,
            [self.intermediate_size] * 2,
            bias=False,
            prefix=f"{prefix}.gate_up_proj",
            quant_config=quant_config,
            return_bias=False,
        )
        self.act = SiluAndMul()
        self.down_proj = RowParallelLinear(
            self.intermediate_size,
            self.hidden_size,
            bias=False,
            prefix=f"{prefix}.down_proj",
            quant_config=quant_config,
            return_bias=False,
        )

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        h = self.gate_up_proj(hidden_states)
        h = self.act(h)
        return self.down_proj(h)


class Plamo2AttentionMixer(nn.Module):
    def __init__(self, *, vllm_config: VllmConfig, prefix: str = "", **kwargs) -> None:
        super().__init__()
        config = vllm_config.model_config.hf_config
        cache_config = vllm_config.cache_config
        quant_config = vllm_config.quant_config
        self.hidden_size = config.hidden_size
        tp_size = get_tensor_model_parallel_world_size()
        self.total_num_heads = config.num_attention_heads
        assert self.total_num_heads % tp_size == 0
        self.num_heads = self.total_num_heads // tp_size
        self.total_num_kv_heads = config.num_key_value_heads
        if self.total_num_kv_heads >= tp_size:
            # Number of KV heads is greater than TP size, so we partition
            # the KV heads across multiple tensor parallel GPUs.
            assert self.total_num_kv_heads % tp_size == 0
        else:
            # Number of KV heads is less than TP size, so we replicate
            # the KV heads across multiple tensor parallel GPUs.
            assert tp_size % self.total_num_kv_heads == 0
        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
        self.head_dim = config.hidden_size_per_head
        self.q_size = self.num_heads * self.head_dim
        self.kv_size = self.num_kv_heads * self.head_dim
        self.scaling = self.head_dim**-0.5

        self.qkv_proj = QKVParallelLinear(
            config.hidden_size,
            self.head_dim,
            self.total_num_heads,
            self.total_num_kv_heads,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.qkv_proj",
        )
        self.o_proj = RowParallelLinear(
            self.total_num_heads * self.head_dim,
            config.hidden_size,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.o_proj",
        )

        max_position = config.max_position_embeddings
        if hasattr(vllm_config.model_config, "max_model_len") and isinstance(
            vllm_config.model_config.max_model_len, int
        ):
            max_position = min(max_position, vllm_config.model_config.max_model_len)

        self.rotary_emb = get_rope(
            self.head_dim,
            max_position=max_position,
            rope_parameters=config.rope_parameters,
        )
        self.q_norm = RMSNorm(config.hidden_size_per_head, eps=config.rms_norm_eps)
        self.q_norm.weight = torch.nn.Parameter(
            torch.ones((self.num_heads, config.hidden_size_per_head))
        )
        set_weight_attrs(
            self.q_norm.weight, {"weight_loader": sharded_weight_loader(0)}
        )
        self.k_norm = RMSNorm(config.hidden_size_per_head, eps=config.rms_norm_eps)
        self.k_norm.weight = torch.nn.Parameter(
            torch.ones((self.num_kv_heads, config.hidden_size_per_head))
        )
        # Tensor-parallelism shards the K norm weights to the tp ranks
        # in a head-wise manner. This approach does not work if there is only
        # a single KV head, as is the case for PLaMo 2-1B.
        if self.total_num_kv_heads != 1:
            set_weight_attrs(
                self.k_norm.weight, {"weight_loader": sharded_weight_loader(0)}
            )

        self.attn = Attention(
            self.num_heads,
            self.head_dim,
            self.scaling,
            num_kv_heads=self.num_kv_heads,
            cache_config=cache_config,
            prefix=f"{prefix}.attn",
        )

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

        q_shape = q.shape
        q = q.reshape(q_shape[:-1] + self.q_norm.weight.shape)
        q = self.q_norm.forward_native(q).reshape(q_shape)
        k_shape = k.shape
        k = k.reshape(k_shape[:-1] + self.k_norm.weight.shape)
        k = self.k_norm.forward_native(k).reshape(k_shape)

        q, k = self.rotary_emb(positions, q, k)
        attn_output = self.attn(q, k, v)
        output, _ = self.o_proj(attn_output)
        return output


class Plamo2DecoderLayer(nn.Module):
    def __init__(
        self, vllm_config: VllmConfig, layer_idx: int, prefix: str = "", **kwargs
    ) -> None:
        super().__init__()
        config = vllm_config.model_config.hf_config
        quant_config = vllm_config.quant_config

        self.is_mamba = is_mamba(config, layer_idx)
        if self.is_mamba:
            self.mixer = Plamo2MambaMixer(
                vllm_config=vllm_config, prefix=f"{prefix}.mixer"
            )
        else:
            self.mixer = Plamo2AttentionMixer(
                vllm_config=vllm_config, prefix=f"{prefix}.mixer"
            )

        self.mlp = DenseMLP(
            config=config, quant_config=quant_config, prefix=f"{prefix}.mlp"
        )
        self.pre_mixer_norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_mixer_norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.pre_mlp_norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_mlp_norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        residual: torch.Tensor | None,
        **kwargs,
    ):
        if residual is None:
            residual = hidden_states
            hidden_states = self.pre_mixer_norm(hidden_states)
        else:
            hidden_states, residual = self.pre_mixer_norm(hidden_states, residual)

        if self.is_mamba:
            # Plamo2MambaMixer writes output to this tensor
            output = torch.empty_like(hidden_states)
            mixer_kwargs = {
                "output": output,
            }
        else:
            mixer_kwargs = {
                "positions": positions,
            }
        hidden_states = self.mixer(
            hidden_states=hidden_states,
            **mixer_kwargs,
        )
        if self.is_mamba:
            hidden_states = output
        hidden_states = self.post_mixer_norm(hidden_states)
        # Fully Connected
        hidden_states, residual = self.pre_mlp_norm(hidden_states, residual)
        hidden_states = self.mlp(hidden_states)
        hidden_states = self.post_mlp_norm(hidden_states)
        return hidden_states, residual


class Plamo2Decoder(torch.nn.Module):
    def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
        super().__init__()
        config = vllm_config.model_config.hf_config
        extra_kwargs = {"is_lora_enabled": bool(vllm_config.lora_config)}

        def get_layer(prefix: str):
            layer_idx = int(prefix.rsplit(".", 1)[1])
            return Plamo2DecoderLayer(
                vllm_config=vllm_config,
                layer_idx=layer_idx,
                prefix=prefix,
                **extra_kwargs,
            )

        self.start_layer, self.end_layer, self.layers = make_layers(
            config.num_hidden_layers, get_layer, prefix=f"{prefix}.layers"
        )

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        residual: torch.Tensor | None,
    ) -> torch.Tensor:
        for layer in islice(self.layers, self.start_layer, self.end_layer):
            hidden_states, residual = layer(
                positions=positions,
                hidden_states=hidden_states,
                residual=residual,
            )
        return hidden_states, residual


@support_torch_compile
class Plamo2Model(torch.nn.Module):
    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()

        config = vllm_config.model_config.hf_config

        self.config = config
        self.padding_idx = config.pad_token_id
        self.vocab_size = config.vocab_size

        self.embed_tokens = VocabParallelEmbedding(
            self.vocab_size,
            config.hidden_size,
            prefix=f"{prefix}.embed_tokens",
        )
        self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
            ["hidden_states", "residual"], config.hidden_size
        )
        self.layers = Plamo2Decoder(vllm_config=vllm_config, prefix=f"{prefix}.layers")
        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)

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

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

        hidden_states, residual = self.layers(
            positions=positions,
            hidden_states=hidden_states,
            residual=residual,
        )
        if not get_pp_group().is_last_rank:
            return IntermediateTensors(
                {"hidden_states": hidden_states, "residual": residual}
            )
        hidden_states, _ = self.norm(hidden_states, residual)
        return hidden_states


class Plamo2ForCausalLM(
    torch.nn.Module, HasInnerState, SupportsLoRA, SupportsPP, IsHybrid
):
    packed_modules_mapping = {
        "qkv_proj": ["qkv_proj"],
        "gate_up_proj": ["gate_up_proj"],
        "in_proj": ["in_proj"],
    }

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
        super().__init__()
        config = vllm_config.model_config.hf_config
        scheduler_config = vllm_config.scheduler_config

        self.config = config
        self.vllm_config = vllm_config
        self.model_config = vllm_config.model_config
        self.scheduler_config = scheduler_config

        # ModelConfig.get_head_size assumes head_dim is set or calculated as
        # hidden_size // num_attention_heads. However, this is not always
        # the case for PLaMo2, as indicated by the FIXME comment.
        self.config.head_dim = self.config.hidden_size_per_head

        self.model = Plamo2Model(
            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
        )
        self.vocab_size = self.config.vocab_size
        self.lm_head = ParallelLMHead(
            self.vocab_size,
            self.config.hidden_size,
            prefix=f"{prefix}.lm_head",
        )
        if self.config.tie_word_embeddings:
            self.lm_head = self.lm_head.tie_weights(self.model.embed_tokens)

        self.logits_processor = LogitsProcessor(
            config.vocab_size, self.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,
        positions: torch.Tensor,
        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
        **kwargs,
    ):
        hidden_states = self.model(
            input_ids, positions, intermediate_tensors, inputs_embeds
        )
        return hidden_states

    @classmethod
    def get_mamba_state_dtype_from_config(
        cls,
        vllm_config: "VllmConfig",
    ) -> tuple[torch.dtype, torch.dtype]:
        return MambaStateDtypeCalculator.mamba2_state_dtype(
            vllm_config.model_config.dtype,
            vllm_config.cache_config.mamba_cache_dtype,
            vllm_config.cache_config.mamba_ssm_cache_dtype,
        )

    @classmethod
    def get_mamba_state_shape_from_config(
        cls,
        vllm_config: "VllmConfig",
    ) -> tuple[tuple[int, int], tuple[int, int, int]]:
        """Calculate shapes for Mamba's convolutional and state caches.
        Args:
            vllm_config: vLLM config
        Returns:
            Tuple containing:
            - conv_state_shape: Shape for convolutional state cache
            - temporal_state_shape: Shape for state space model cache
        """
        parallel_config = vllm_config.parallel_config
        hf_config = vllm_config.model_config.hf_config
        intermediate_size = hf_config.mamba_num_heads * hf_config.hidden_size_per_head

        return MambaStateShapeCalculator.mamba2_state_shape(
            intermediate_size=intermediate_size,
            tp_world_size=parallel_config.tensor_parallel_size,
            n_groups=0,
            num_heads=hf_config.mamba_num_heads,
            head_dim=hf_config.hidden_size_per_head,
            state_size=hf_config.mamba_d_state,
            conv_kernel=hf_config.mamba_d_conv,
        )

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

    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
        params_dict = dict(self.named_parameters())
        for name, loaded_weight in weights:
            # Both tie_word_embeddings=True and lm_head.weight in the safetensor
            # at the same time causes dict key access error.
            if name == "lm_head.weight" and self.config.tie_word_embeddings:
                assert "lm_head.weight" not in params_dict
                continue
            # Same workaround as AutoWeightsLoader for GPTQModel
            if any(
                substr in name
                for substr in AutoWeightsLoader.ROTARY_EMBEDS_UNUSED_WEIGHTS
            ):
                continue

            # Update the weight names to be compatible with the vllm version
            # of the model.
            # Do not change the order of the replacements.
            replacements = {
                # Rename incompatible weight names.
                ".A_log": ".A",
                ".B_norm_weight": ".B_norm.weight",
                ".C_norm_weight": ".C_norm.weight",
                ".dt_norm_weight": ".dt_norm.weight",
                ".q_weight": ".q_norm.weight",
                ".k_weight": ".k_norm.weight",
            }
            # Apply replacements based on the defined mappings
            for old, new in replacements.items():
                if old in name:
                    name = name.replace(old, new)

            # Reshape the in_proj weights to match the shape expected
            # by MergedColumnParallelLinear.
            # This works both for unquantized weights and
            # for quantized weights.
            # In the quantized case, the weights are already transposed.
            # Also, in addition to the quantized weights,
            # the zero points and scales have to be reshaped as well.
            # Packing should not be affected by this.
            if (
                ".mixer.in_proj.weight" in name
                or "mixer.in_proj.qweight" in name
                or "mixer.in_proj.scales" in name
                or "mixer.in_proj.qzeros" in name
            ):
                if "mixer.in_proj.weight" in name:
                    loaded_weight = loaded_weight.transpose(0, 1)
                # for weight:
                # loaded_weight.shape[0] == self.config.hidden_size
                # for qweight:
                # loaded_weight.shape[0] == self.config.hidden_size // param.pack_factor  # noqa
                # for scales and qzeros:
                # loaded_weight.shape[0] == self.config.hidden_size // self.vllm_config.quant_config.group_size  # noqa
                loaded_weight = loaded_weight.reshape(
                    loaded_weight.shape[0], self.config.mamba_num_heads, -1
                )
                gate_weight, hidden_states_weight = loaded_weight.chunk(2, dim=-1)
                gate_weight = gate_weight.reshape(loaded_weight.shape[0], -1)
                hidden_states_weight = hidden_states_weight.reshape(
                    loaded_weight.shape[0], -1
                )
                loaded_weight = torch.cat([gate_weight, hidden_states_weight], dim=-1)
                if "mixer.in_proj.weight" in name:
                    loaded_weight = loaded_weight.transpose(0, 1)

            # Offset parameter with vllm's RMSNorm haven't been supported yet.
            if ".pre_mixer_norm" in name:
                loaded_weight += 1.0
            elif ".post_mixer_norm" in name:
                loaded_weight += 1.0 / 5
            elif ".pre_mlp_norm" in name:
                loaded_weight += 1.0
            elif ".post_mlp_norm" in name:
                loaded_weight += 1.0 / (5**1.5)
            elif "model.norm.weight" in name:
                loaded_weight += 1.0

            # Skip layers on other devices.
            if is_pp_missing_parameter(name, self):
                continue

            param = params_dict[name]
            weight_loader = getattr(param, "weight_loader", default_weight_loader)
            weight_loader(param, loaded_weight)