gpt_oss.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import typing
from collections.abc import Callable, Iterable

import torch
import torch.distributed as dist
from torch import nn
from transformers import GptOssConfig

from vllm.compilation.decorators import support_torch_compile
from vllm.config import CacheConfig, VllmConfig
from vllm.distributed import (
    get_dp_group,
    get_ep_group,
    get_pcp_group,
    get_pp_group,
    get_tensor_model_parallel_rank,
    get_tensor_model_parallel_world_size,
    tensor_model_parallel_all_gather,
)
from vllm.model_executor.layers.attention import Attention
from vllm.model_executor.layers.fused_moe import FusedMoE
from vllm.model_executor.layers.fused_moe.config import FusedMoEParallelConfig
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import (
    QKVParallelLinear,
    ReplicatedLinear,
    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.ocp_mx_utils import OCP_MX_BLOCK_SIZE
from vllm.model_executor.layers.rotary_embedding import get_rope
from vllm.model_executor.layers.utils import rocm_unquantized_gemm
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.platforms import current_platform
from vllm.sequence import IntermediateTensors
from vllm.utils.math_utils import cdiv
from vllm.v1.attention.backend import AttentionType

from .interfaces import (
    EagleModelMixin,
    SupportsEagle,
    SupportsEagle3,
    SupportsLoRA,
    SupportsPP,
)
from .utils import (
    AutoWeightsLoader,
    WeightsMapper,
    extract_layer_index,
    is_pp_missing_parameter,
    make_empty_intermediate_tensors_factory,
    make_layers,
    maybe_prefix,
)


class OAIAttention(nn.Module):
    def __init__(
        self,
        config: GptOssConfig,
        quant_config: QuantizationConfig | None = None,
        cache_config: CacheConfig | None = None,
        prefix: str = "",
    ):
        super().__init__()
        self.layer_idx = extract_layer_index(prefix)
        self.head_dim = config.head_dim
        self.num_attention_heads = config.num_attention_heads
        self.num_key_value_heads = config.num_key_value_heads
        self.hidden_size = config.hidden_size

        self.rotary_emb = get_rope(
            self.head_dim,
            max_position=config.max_position_embeddings,
            dtype=torch.float32,
            rope_parameters={
                "rope_theta": config.rope_parameters["rope_theta"],
                "rope_type": "yarn",
                "factor": config.rope_parameters["factor"],
                "original_max_position_embeddings": config.rope_parameters[
                    "original_max_position_embeddings"
                ],
                "beta_fast": config.rope_parameters["beta_fast"],
                "beta_slow": config.rope_parameters["beta_slow"],
                "truncate": config.rope_parameters.get("truncate", True),
            },
            is_neox_style=True,
        )

        tp_size = get_tensor_model_parallel_world_size()

        self.sinks = torch.nn.Parameter(
            torch.empty(config.num_attention_heads // tp_size, requires_grad=False)
        )

        self.q_size = self.num_attention_heads * self.head_dim // tp_size
        self.kv_size = self.num_key_value_heads * self.head_dim // tp_size
        self.scaling = self.head_dim**-0.5

        self.qkv_proj = QKVParallelLinear(
            hidden_size=self.hidden_size,
            head_size=self.head_dim,
            total_num_heads=self.num_attention_heads,
            total_num_kv_heads=self.num_key_value_heads,
            bias=True,
            quant_config=quant_config,
            prefix=f"{prefix}.qkv_proj",
        )

        self.o_proj = RowParallelLinear(
            input_size=self.num_attention_heads * self.head_dim,
            output_size=self.hidden_size,
            bias=True,
            quant_config=quant_config,
            prefix=f"{prefix}.o_proj",
        )

        self.num_local_attention_heads = config.num_attention_heads // tp_size
        self.num_local_key_value_heads = config.num_key_value_heads // tp_size

        # Only apply sliding window to every other layer
        sliding_window = config.sliding_window if self.layer_idx % 2 == 0 else None
        self.attn = Attention(
            self.num_local_attention_heads,
            self.head_dim,
            self.scaling,
            num_kv_heads=self.num_local_key_value_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.sinks,
        )

    def forward(
        self, hidden_states: torch.Tensor, positions: torch.Tensor
    ) -> 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, k = self.rotary_emb(positions, q, k)
        attn_output = self.attn(q, k, v)
        output, _ = self.o_proj(attn_output)
        return output


class MLPBlock(torch.nn.Module):
    def __init__(
        self,
        vllm_config: VllmConfig,
        layer_idx: int,
        prefix: str = "",
    ):
        super().__init__()

        config = vllm_config.model_config.hf_config
        quant_config = vllm_config.quant_config
        parallel_config = vllm_config.parallel_config

        self.is_sequence_parallel = parallel_config.use_sequence_parallel_moe

        self.layer_idx = layer_idx
        self.num_experts = config.num_local_experts
        self.hidden_size = config.hidden_size
        self.experts_per_token = config.num_experts_per_tok
        self.world_size = dist.get_world_size() if dist.is_initialized() else 1
        self.router = ReplicatedLinear(
            config.hidden_size,
            config.num_local_experts,
            bias=True,
            quant_config=None,
            prefix=f"{prefix}.router",
            return_bias=False,
        )
        assert config.intermediate_size % self.world_size == 0
        self.experts = FusedMoE(
            num_experts=config.num_local_experts,
            top_k=config.num_experts_per_tok,
            hidden_size=config.hidden_size,
            intermediate_size=config.intermediate_size,
            reduce_results=True,
            renormalize=True,
            quant_config=quant_config,
            prefix=f"{prefix}.experts",
            apply_router_weight_on_input=False,
            has_bias=True,
            activation="swigluoai",
            is_sequence_parallel=self.is_sequence_parallel,
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        num_tokens = x.shape[0]
        if self.is_sequence_parallel:
            x = sequence_parallel_chunk(x)

        if current_platform.is_rocm():
            g = rocm_unquantized_gemm(
                self, x[:, : self.hidden_size], self.router.weight, self.router.bias
            )
        else:
            g = self.router(x)
        x = self.experts(hidden_states=x, router_logits=g)[:, : self.hidden_size]

        if self.is_sequence_parallel:
            x = tensor_model_parallel_all_gather(x.contiguous(), 0)
            x = x[:num_tokens]
        return x


class TransformerBlock(torch.nn.Module):
    def __init__(
        self,
        vllm_config: VllmConfig,
        quant_config: QuantizationConfig,
        prefix: str = "",
    ):
        super().__init__()

        config = vllm_config.model_config.hf_config
        cache_config = vllm_config.cache_config

        self.layer_idx = extract_layer_index(prefix)
        self.attn = OAIAttention(
            config,
            prefix=f"{prefix}.attn",
            quant_config=quant_config,
            cache_config=cache_config,
        )
        self.mlp = MLPBlock(vllm_config, self.layer_idx, prefix=f"{prefix}.mlp")
        self.input_layernorm = RMSNorm(config.hidden_size, eps=1e-5)
        self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=1e-5)

    def forward(
        self,
        hidden_states: torch.Tensor,
        positions: torch.Tensor,
        residual: torch.Tensor | None,
    ) -> torch.Tensor:
        # Self Attention
        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.attn(hidden_states, positions)

        # Fully Connected
        hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
        output = self.mlp(hidden_states)
        return output, residual


@support_torch_compile
class GptOssModel(nn.Module, EagleModelMixin):
    def __init__(
        self,
        *,
        vllm_config: VllmConfig,
        prefix: str = "",
    ):
        super().__init__()
        self.config = vllm_config.model_config.hf_config
        self.quant_config = vllm_config.quant_config
        self.parallel_config = vllm_config.parallel_config
        self.embedding = VocabParallelEmbedding(
            self.config.vocab_size,
            self.config.hidden_size,
        )
        self.start_layer, self.end_layer, self.layers = make_layers(
            self.config.num_hidden_layers,
            lambda prefix: TransformerBlock(
                vllm_config,
                prefix=prefix,
                quant_config=self.quant_config,
            ),
            prefix=f"{prefix}.layers",
        )
        self.norm = RMSNorm(self.config.hidden_size, eps=1e-5)
        self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
            ["hidden_states", "residual"], self.config.hidden_size
        )

    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
        return self.embedding(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:
        if get_pp_group().is_first_rank:
            if inputs_embeds is not None:
                x = inputs_embeds
            else:
                x = self.embed_input_ids(input_ids)

            residual = None
        else:
            assert intermediate_tensors is not None
            x = intermediate_tensors["hidden_states"]
            residual = intermediate_tensors["residual"]

        aux_hidden_states = self._maybe_add_hidden_state(
            [], self.start_layer, x, residual
        )
        for i in range(self.start_layer, self.end_layer):
            layer = self.layers[i]
            x, residual = layer(x, positions, residual)
            self._maybe_add_hidden_state(aux_hidden_states, i + 1, x, residual)
        if not get_pp_group().is_last_rank:
            return IntermediateTensors({"hidden_states": x, "residual": residual})
        x, _ = self.norm(x, residual)

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

    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
        # Params for weights, weight scales, activation scales
        # (param_name, weight_name, expert_id, shard_id)
        # NOTE: this is only used for quark.
        return FusedMoE.make_expert_params_mapping(
            self,
            ckpt_gate_proj_name="w1",
            ckpt_down_proj_name="w2",
            ckpt_up_proj_name="w3",
            num_experts=self.config.num_local_experts,
            num_redundant_experts=0,
        )

    def _load_weights_mxfp4(
        self,
        ep_rank_end: int,
        ep_rank_start: int,
        heads_per_rank: int,
        head_start: int,
        weights: Iterable[tuple[str, torch.Tensor]],
        stacked_params_mapping: list[tuple[str, ...]],
    ) -> set[str]:
        params_dict = dict(self.named_parameters())
        loaded_params: set[str] = set()

        use_ep = self.parallel_config.enable_expert_parallel
        num_experts = self.config.num_local_experts

        # In MoE, we need to flatten the tensor parallel size across the data
        # parallel size when EP is disabled.
        tp_size, tp_rank = FusedMoEParallelConfig.flatten_tp_across_dp_and_pcp(
            tp_size=get_tensor_model_parallel_world_size(),
            dp_size=get_dp_group().world_size,
            dp_rank=get_dp_group().rank_in_group,
            pcp_size=get_pcp_group().world_size,
            pcp_rank=get_pcp_group().rank_in_group,
        )

        intermediate_size = self.config.intermediate_size
        intermediate_size_block = intermediate_size // OCP_MX_BLOCK_SIZE
        per_rank_intermediate_size_block = cdiv(intermediate_size_block, tp_size)
        per_rank_intermediate_size = (
            per_rank_intermediate_size_block * OCP_MX_BLOCK_SIZE
        )

        # Calculate common slicing bounds for current rank
        tp_rank_start = tp_rank * per_rank_intermediate_size
        tp_rank_end = min((tp_rank + 1) * per_rank_intermediate_size, intermediate_size)

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

            if ".w13_weight_scale" in name:
                # Handle MLP gate and up projection weights scale
                if use_ep:
                    narrow_weight = weight[ep_rank_start:ep_rank_end, ...]
                else:
                    narrow_weight = weight[:, 2 * tp_rank_start : 2 * tp_rank_end, ...]

                param = params_dict[name]
                weight_loader = getattr(param, "weight_loader", default_weight_loader)
                weight_loader(
                    param,
                    narrow_weight,
                    weight_name=name,
                    shard_id=None,
                    expert_id=None,
                )
                loaded_params.add(name)
                continue
            elif ".w2_weight_scale" in name:
                # Handle MLP down projection weights
                if use_ep:
                    narrow_weight = weight[ep_rank_start:ep_rank_end, ...]
                else:
                    narrow_weight = weight[
                        ...,
                        tp_rank_start // OCP_MX_BLOCK_SIZE : tp_rank_end
                        // OCP_MX_BLOCK_SIZE,
                    ]

                param = params_dict[name]
                weight_loader = getattr(param, "weight_loader", default_weight_loader)
                weight_loader(
                    param,
                    narrow_weight,
                    weight_name=name,
                    shard_id=None,
                    expert_id=None,
                )
                loaded_params.add(name)
                continue
            elif ".w13_weight" in name:
                # Handle MLP gate and up projection weights
                # flat weight from (E, 2 * N, block_size, entry_per_block)
                # to (E, 2 * N, -1), shouldn't trigger copy for contiguous
                weight = weight.view(
                    num_experts, 2 * intermediate_size, -1
                ).contiguous()

                # Extract gate and up projection parts
                # since the weight is shuffled, we can slice directly
                if use_ep:
                    narrow_weight = weight[ep_rank_start:ep_rank_end, ...]
                else:
                    narrow_weight = weight[:, 2 * tp_rank_start : 2 * tp_rank_end, ...]

                param = params_dict[name]
                weight_loader = getattr(param, "weight_loader", default_weight_loader)
                weight_loader(
                    param,
                    narrow_weight,
                    weight_name=name,
                    shard_id=None,
                    expert_id=None,
                )
                loaded_params.add(name)
                continue
            elif ".w2_weight" in name:
                # Handle MLP down projection weights
                # same flatten here, but since 2 mx4 value are packed in 1
                # uint8, divide by 2
                weight = weight.view(
                    num_experts, -1, intermediate_size // 2
                ).contiguous()
                if use_ep:
                    narrow_weight = weight[ep_rank_start:ep_rank_end, ...]
                else:
                    narrow_weight = weight[..., tp_rank_start // 2 : tp_rank_end // 2]

                param = params_dict[name]
                weight_loader = getattr(param, "weight_loader", default_weight_loader)
                weight_loader(
                    param,
                    narrow_weight,
                    weight_name=name,
                    shard_id=None,
                    expert_id=None,
                )
                loaded_params.add(name)
                continue
            elif ".w13_bias" in name:
                # Handle MLP gate and up projection biases
                # Extract gate and up projection bias parts
                if use_ep:
                    narrow_weight = weight[ep_rank_start:ep_rank_end, ...]
                else:
                    narrow_weight = weight[:, 2 * tp_rank_start : 2 * tp_rank_end]

                param = params_dict[name]
                weight_loader = getattr(param, "weight_loader", default_weight_loader)
                weight_loader(
                    param,
                    narrow_weight,
                    weight_name=name,
                    shard_id=None,
                    expert_id=None,
                )
                loaded_params.add(name)
                continue
            elif ".w2_bias" in name:
                # Handle MLP down projection bias
                param = params_dict[name]
                weight_loader = getattr(param, "weight_loader", default_weight_loader)
                if use_ep:
                    weight = weight[ep_rank_start:ep_rank_end, ...]
                else:
                    # (only load on rank 0 to avoid duplication)
                    if tp_rank != 0:
                        weight.zero_()
                weight_loader(
                    param, weight, weight_name=name, shard_id=None, expert_id=None
                )
                loaded_params.add(name)
                continue
            elif "sinks" in name:
                # Handle attention sinks (distributed across ranks)
                param = params_dict[name]
                narrow_weight = weight.narrow(0, head_start, heads_per_rank)
                param.data.copy_(narrow_weight)
                loaded_params.add(name)
                continue
            for param_name, weight_name, shard_id in stacked_params_mapping:
                if weight_name not in name:
                    continue
                name = name.replace(weight_name, param_name)
                param = params_dict[name]
                weight_loader = getattr(param, "weight_loader", default_weight_loader)
                if weight_loader == default_weight_loader:
                    weight_loader(param, weight)
                else:
                    weight_loader(param, weight, shard_id)
                break
            else:
                # Handle all other weights with potential renaming
                if name not in params_dict:
                    continue
                param = params_dict[name]
                weight_loader = getattr(param, "weight_loader", default_weight_loader)
                weight_loader(param, weight)
            loaded_params.add(name)
        return loaded_params

    def _load_weights_quark(
        self,
        ep_rank_end: int,
        ep_rank_start: int,
        heads_per_rank: int,
        head_start: int,
        weights: Iterable[tuple[str, torch.Tensor]],
        stacked_params_mapping: list[tuple[str, ...]],
    ) -> set[str]:
        params_dict = dict(self.named_parameters())
        loaded_params: set[str] = set()

        use_ep = self.parallel_config.enable_expert_parallel
        num_experts = self.config.num_local_experts

        if use_ep:
            tp_rank = get_tensor_model_parallel_rank()
            tp_size = get_tensor_model_parallel_world_size()
        else:
            tp_size, tp_rank = FusedMoEParallelConfig.flatten_tp_across_dp_and_pcp(
                tp_size=get_tensor_model_parallel_world_size(),
                dp_size=get_dp_group().world_size,
                dp_rank=get_dp_group().rank_in_group,
                pcp_size=get_pcp_group().world_size,
                pcp_rank=get_pcp_group().rank_in_group,
            )

        def _is_mxfp4(weight_dtype: str | None) -> bool:
            """Return True for any MXFP4 weight-dtype variant.

            Covers "gpt_oss_mxfp4" (GptOssMxfp4MoEMethod) and "mxfp4"
            (QuarkMoEMethod with fp4 weights) and any future variants.
            """
            return weight_dtype is not None and "mxfp4" in weight_dtype

        def _get_moe_weight_dtype(layer_id: int = 0) -> str | None:
            """Helper function to get MoE quantization weight dtype.

            Args:
                layer_id: Layer index to check (default 0, as all layers should
                        have the same quantization method)

            Returns:
                Weight dtype string (e.g., "mxfp4", "fp8") or None if not available
            """
            if hasattr(self.layers[layer_id].mlp.experts.quant_method, "weight_dtype"):
                return self.layers[layer_id].mlp.experts.quant_method.weight_dtype
            return None

        intermediate_size = self.config.intermediate_size

        moe_weight_dtype = _get_moe_weight_dtype(layer_id=0)

        if _is_mxfp4(moe_weight_dtype):
            # MXFP4 requires OCP_MX_BLOCK_SIZE alignment
            intermediate_size_block = intermediate_size // OCP_MX_BLOCK_SIZE
            per_rank_intermediate_size_block = cdiv(intermediate_size_block, tp_size)
            per_rank_intermediate_size = (
                per_rank_intermediate_size_block * OCP_MX_BLOCK_SIZE
            )
        else:
            # FP8 and other formats don't need alignment
            per_rank_intermediate_size = cdiv(intermediate_size, tp_size)

        tp_rank_start = tp_rank * per_rank_intermediate_size
        tp_rank_end = min((tp_rank + 1) * per_rank_intermediate_size, intermediate_size)
        expert_params_mapping = self.get_expert_mapping()
        for name, loaded_weight in weights:
            if is_pp_missing_parameter(name, self):
                continue

            layer_id, expert_id, fused_name = None, None, None
            moe_quant_method = None
            if "experts" in name:
                parts = name.split(".")
                ids = [s for s in parts if s.isdigit()]

                # for amd-quark format that each expert is separated
                # need to extract the parameter name with experts fused.
                # example model: amd/gpt-oss-20b-MoE-Quant-W-MXFP4-A-FP8-KV-FP8
                if len(ids) == 2:
                    layer_id, expert_id = int(ids[0]), int(ids[-1])
                    parts.pop(len(parts) - 1 - parts[::-1].index(str(expert_id)))
                    fused_name = ".".join(parts)

                # for openai mxfp4 format that all experts are combined
                # no need to extract the parameter name with experts fused.
                # models: openai/gpt-oss-20b, openai/gpt-oss-120b
                elif len(ids) == 1:
                    layer_id, expert_id = int(ids[0]), None
                    fused_name = name

                else:
                    raise NameError(
                        f"Layer {name} contains more than 2 numeric indices. This is "
                        "an unexpected condition. Please open an issue if encountered."
                    )

                moe_quant_method = _get_moe_weight_dtype(layer_id=layer_id)

            def kv_cache_scale_loader(
                quant_config: QuantizationConfig,
                name: str,
                params_dict: dict[str, typing.Any],
                weight: torch.Tensor,
                default_weight_loader: Callable[..., None],
                loaded_params: set[str],
            ) -> tuple[bool, set[str]]:
                """
                Load KV cache output scales.
                Returns:
                    Tuple of (bool, set):
                    - bool: True if KV-cache scale was loaded into loaded_params
                    - set: Updated set of loaded_params if True else the original set
                """
                # load explicit cached KV output scale from quant_config
                if quant_config is not None and (
                    scale_name := quant_config.get_cache_scale(name)
                ):
                    param = params_dict[scale_name]
                    weight_loader = getattr(
                        param, "weight_loader", default_weight_loader
                    )
                    if weight.numel() != 1:
                        raise ValueError(
                            f"KV cache scale '{scale_name}' is expected to be a "
                            f"scalar, but got a tensor of shape {weight.shape}."
                        )
                    # Ensure weight is a scalar before passing to loader.
                    weight_loader(param, weight.flatten()[0])
                    loaded_params.add(scale_name)
                    return True, loaded_params

                return False, loaded_params

            load_kv_cache_scale_completed, loaded_params = kv_cache_scale_loader(
                self.quant_config,
                name,
                params_dict,
                loaded_weight,
                default_weight_loader,
                loaded_params,
            )
            if load_kv_cache_scale_completed:
                continue

            if (
                all(key in name for key in ["input_scale", "mlp.experts"])
                and expert_id is not None
            ):
                assert loaded_weight.numel() == 1
                expert_data = params_dict[fused_name].data[expert_id]
                expert_data.copy_(loaded_weight)
                loaded_params.add(fused_name)
                continue

            # Unified handler for mxfp4 weights and scales
            elif _is_mxfp4(moe_quant_method) and any(
                name.endswith(suffix)
                for suffix in [
                    ".w13_weight_scale",
                    ".w2_weight_scale",
                    ".w13_weight",
                    ".w2_weight",
                ]
            ):
                is_w13 = ".w13_" in name
                is_scale = "_scale" in name

                # Reshape weight for mxfp4 if needed (not for scales)
                if not is_scale and expert_id is None:
                    if is_w13:
                        if loaded_weight.dim() < 3:
                            raise ValueError(
                                f"Expected w13_weight to have at least 3 "
                                f"dimensions, got shape "
                                f"{loaded_weight.shape}"
                            )
                        if loaded_weight.shape[0] != num_experts:
                            raise ValueError(
                                f"Expected w13_weight first dimension to be "
                                f"{num_experts}, got "
                                f"{loaded_weight.shape[0]}"
                            )
                        loaded_weight = loaded_weight.view(
                            num_experts, 2 * intermediate_size, -1
                        ).contiguous()
                    else:
                        if loaded_weight.dim() < 3:
                            raise ValueError(
                                f"Expected w2_weight to have at least 3 "
                                f"dimensions, got shape "
                                f"{loaded_weight.shape}"
                            )
                        if loaded_weight.shape[0] != num_experts:
                            raise ValueError(
                                f"Expected w2_weight first dimension to be "
                                f"{num_experts}, got "
                                f"{loaded_weight.shape[0]}"
                            )
                        loaded_weight = loaded_weight.view(
                            num_experts, -1, intermediate_size // 2
                        ).contiguous()

                if use_ep:
                    sliced_weight = loaded_weight[ep_rank_start:ep_rank_end, ...]
                else:
                    if is_w13:
                        if expert_id is None:
                            sliced_weight = loaded_weight[
                                :, 2 * tp_rank_start : 2 * tp_rank_end, ...
                            ]
                        else:
                            sliced_weight = loaded_weight[
                                2 * tp_rank_start : 2 * tp_rank_end, ...
                            ]
                    else:
                        if is_scale:
                            sliced_weight = loaded_weight[
                                ...,
                                tp_rank_start // OCP_MX_BLOCK_SIZE : tp_rank_end
                                // OCP_MX_BLOCK_SIZE,
                            ]
                        else:
                            sliced_weight = loaded_weight[
                                ..., tp_rank_start // 2 : tp_rank_end // 2
                            ]

                # NOTE(rob): because gpt-oss ckpt has "unique" structure with
                # fused gate_up_proj fused on disk, we cannot use the existing
                # weight loaders without added complexity, so just do the
                # direct load here.
                param = params_dict[fused_name]
                expert_data = param.data[expert_id]
                dim1 = sliced_weight.shape[0]
                dim2 = sliced_weight.shape[1]
                expert_data.data[:dim1, :dim2].copy_(sliced_weight)
                loaded_params.add(fused_name)
                continue

            elif name.endswith(".w13_weight") and moe_quant_method == "fp8":
                if use_ep:
                    narrow_weight = loaded_weight[ep_rank_start:ep_rank_end, ...]
                else:
                    if expert_id is None:
                        narrow_weight = loaded_weight[
                            :, 2 * tp_rank_start : 2 * tp_rank_end, :
                        ]
                    else:
                        narrow_weight = loaded_weight[
                            2 * tp_rank_start : 2 * tp_rank_end, :
                        ]

                assert fused_name is not None
                param = params_dict[fused_name]

                if expert_id is None:
                    param.data.copy_(narrow_weight)
                else:
                    param.data[expert_id].copy_(narrow_weight)

                loaded_params.add(fused_name)
                continue

            elif name.endswith(".w13_weight_scale") and moe_quant_method == "fp8":
                assert fused_name is not None
                param = params_dict[fused_name]

                # Check if this is per-channel or per-tensor scale
                if loaded_weight.numel() > 1 and loaded_weight.dim() == 1:
                    if use_ep:
                        narrow_weight = loaded_weight[ep_rank_start:ep_rank_end, ...]
                    else:
                        narrow_weight = loaded_weight[
                            2 * tp_rank_start : 2 * tp_rank_end
                        ]
                else:
                    narrow_weight = loaded_weight

                if expert_id is None:
                    param.data.copy_(narrow_weight)
                else:
                    param.data[expert_id].copy_(narrow_weight)

                loaded_params.add(fused_name)
                continue

            elif name.endswith(".w13_input_scale") and moe_quant_method == "fp8":
                assert fused_name is not None
                param = params_dict[fused_name]

                if expert_id is None:
                    param.data.copy_(loaded_weight)
                else:
                    param.data[expert_id].copy_(loaded_weight)

                loaded_params.add(fused_name)
                continue

            elif name.endswith(".w2_weight") and moe_quant_method == "fp8":
                if use_ep:
                    narrow_weight = loaded_weight[ep_rank_start:ep_rank_end, ...]
                else:
                    if expert_id is None:
                        narrow_weight = loaded_weight[..., tp_rank_start:tp_rank_end]
                    else:
                        narrow_weight = loaded_weight[..., tp_rank_start:tp_rank_end]

                assert fused_name is not None
                param = params_dict[fused_name]

                if expert_id is None:
                    param.data.copy_(narrow_weight)
                else:
                    param.data[expert_id].copy_(narrow_weight)

                loaded_params.add(fused_name)
                continue

            elif name.endswith(".w2_weight_scale") and moe_quant_method == "fp8":
                assert fused_name is not None
                param = params_dict[fused_name]

                if use_ep:
                    narrow_weight = loaded_weight[ep_rank_start:ep_rank_end, ...]
                else:
                    narrow_weight = loaded_weight

                if expert_id is None:
                    param.data.copy_(narrow_weight)
                else:
                    param.data[expert_id].copy_(narrow_weight)

                loaded_params.add(fused_name)
                continue

            # Unified handler for bias loading (w13_bias and w2_bias)
            elif name.endswith(".w13_bias") or name.endswith(".w2_bias"):
                is_w13_bias = name.endswith(".w13_bias")

                if use_ep:
                    sliced_weight = loaded_weight[ep_rank_start:ep_rank_end, ...]
                else:
                    if is_w13_bias:
                        if expert_id is None:
                            sliced_weight = loaded_weight[
                                :, 2 * tp_rank_start : 2 * tp_rank_end
                            ]
                        else:
                            sliced_weight = loaded_weight[
                                2 * tp_rank_start : 2 * tp_rank_end
                            ]
                    else:
                        sliced_weight = loaded_weight
                        if tp_rank != 0:
                            sliced_weight = sliced_weight.zero_()

                # NOTE(rob): because gpt-oss ckpt has "unique" structure with
                # fused gate_up_proj fused on disk, we cannot use the existing
                # weight loaders without added complexity, so just do the
                # direct load here.
                assert fused_name is not None
                param = params_dict[fused_name]
                expert_data = param.data[expert_id]
                dim1 = sliced_weight.shape[0]
                expert_data.data[:dim1].copy_(sliced_weight)
                loaded_params.add(fused_name)
                continue

            elif "sinks" in name:
                # Handle attention sinks (distributed across ranks)
                param = params_dict[name]
                narrow_weight = loaded_weight.narrow(0, head_start, heads_per_rank)
                param.data.copy_(narrow_weight)
                loaded_params.add(name)
                continue

            for param_name, weight_name, shard_id in stacked_params_mapping:
                # Skip non-stacked layers and experts (experts handled below).
                if weight_name not in name:
                    continue
                # We have mlp.experts[0].gate_proj in the checkpoint.
                # Since we handle the experts below in expert_params_mapping,
                # we need to skip here BEFORE we update the name, otherwise
                # name will be updated to mlp.experts[0].gate_up_proj, which
                # will then be updated below in expert_params_mapping
                # for mlp.experts[0].gate_gate_up_proj, which breaks load.
                if ("mlp.experts." in name) and name not in params_dict:
                    continue
                name = name.replace(weight_name, param_name)

                if name.endswith("scale"):
                    # Remapping the name of FP8 kv-scale.
                    name = maybe_remap_kv_scale_name(name, params_dict)
                    if name is None:
                        continue

                param = params_dict[name]
                weight_loader = param.weight_loader

                weight_loader(param, loaded_weight, shard_id)
                loaded_params.add(name)
                break
            else:
                for mapping in expert_params_mapping:
                    # Anyway, this is an expert weight and should not be
                    # attempted to load as other weights later
                    param_name, weight_name, mapping_expert_id, shard_id = mapping
                    weight_name = (
                        weight_name[:-1] if weight_name.endswith(".") else weight_name
                    )

                    if weight_name not in name:
                        continue

                    param = params_dict[fused_name]
                    # We should ask the weight loader to return success or not
                    # here since otherwise we may skip experts with other
                    # available replicas.
                    weight_loader = typing.cast(
                        Callable[..., bool], param.weight_loader
                    )
                    # Use checkpoint's expert_id for quark format (when expert_id
                    # is extracted from weight name), otherwise use mapping's expert_id
                    actual_expert_id = (
                        expert_id if expert_id is not None else mapping_expert_id
                    )
                    success = weight_loader(
                        param,
                        loaded_weight,
                        fused_name,
                        shard_id=shard_id,
                        expert_id=actual_expert_id,
                        return_success=True,
                    )
                    if success:
                        name = fused_name
                        loaded_params.add(name)
                        break
                else:
                    if name not in params_dict:
                        continue
                    param = params_dict[name]
                    weight_loader = getattr(
                        param, "weight_loader", default_weight_loader
                    )
                    weight_loader(param, loaded_weight)

                loaded_params.add(name)
        return loaded_params

    def _load_weights_other(
        self,
        ep_rank_end: int,
        ep_rank_start: int,
        heads_per_rank: int,
        head_start: int,
        weights: Iterable[tuple[str, torch.Tensor]],
        stacked_params_mapping: list[tuple[str, ...]],
    ) -> set[str]:
        params_dict = dict(self.named_parameters())
        loaded_params: set[str] = set()

        use_ep = self.parallel_config.enable_expert_parallel

        # In MoE, we need to flatten the tensor parallel size across the data
        # parallel size when EP is disabled.
        tp_size, tp_rank = FusedMoEParallelConfig.flatten_tp_across_dp_and_pcp(
            tp_size=get_tensor_model_parallel_world_size(),
            dp_size=get_dp_group().world_size,
            dp_rank=get_dp_group().rank_in_group,
            pcp_size=get_pcp_group().world_size,
            pcp_rank=get_pcp_group().rank_in_group,
        )

        intermediate_size = self.config.intermediate_size
        per_rank_intermediate_size = cdiv(intermediate_size, tp_size)
        # Calculate common slicing bounds for current rank
        tp_rank_start = tp_rank * per_rank_intermediate_size
        tp_rank_end = min((tp_rank + 1) * per_rank_intermediate_size, intermediate_size)

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

            if ".w13_weight" in name:
                # Handle MLP gate and up projection weights
                # Extract gate and up projection parts
                if use_ep:
                    narrow_weight = weight[ep_rank_start:ep_rank_end, ...]
                else:
                    narrow_weight = weight[:, :, 2 * tp_rank_start : 2 * tp_rank_end]

                narrow_weight = narrow_weight.permute(0, 2, 1).contiguous()
                param = params_dict[name]

                param.copy_(narrow_weight)
                loaded_params.add(name)
                continue
            elif ".w2_weight" in name:
                # Handle MLP down projection weights
                if use_ep:
                    narrow_weight = weight[ep_rank_start:ep_rank_end, ...]
                else:
                    narrow_weight = weight[:, tp_rank_start:tp_rank_end, :]
                narrow_weight = narrow_weight.permute(0, 2, 1).contiguous()
                param = params_dict[name]

                param.copy_(narrow_weight)
                loaded_params.add(name)
                continue
            elif ".w13_bias" in name:
                # Handle MLP gate and up projection biases
                # Extract gate and up projection bias parts
                if use_ep:
                    narrow_weight = weight[ep_rank_start:ep_rank_end, ...]
                else:
                    narrow_weight = weight[:, 2 * tp_rank_start : 2 * tp_rank_end]

                param = params_dict[name]
                param.copy_(narrow_weight)
                loaded_params.add(name)
                continue
            elif ".w2_bias" in name:
                # Handle MLP down projection bias
                if use_ep:
                    weight = weight[ep_rank_start:ep_rank_end, ...]
                else:
                    # (only load on rank 0 to avoid duplication)
                    if tp_rank != 0:
                        weight.zero_()
                param = params_dict[name]
                param.copy_(weight)
                loaded_params.add(name)
                continue
            elif "sinks" in name:
                # Handle attention sinks (distributed across ranks)
                param = params_dict[name]
                narrow_weight = weight.narrow(0, head_start, heads_per_rank)
                param.data.copy_(narrow_weight)
                loaded_params.add(name)
                continue
            for param_name, weight_name, shard_id in stacked_params_mapping:
                if weight_name not in name:
                    continue
                name = name.replace(weight_name, param_name)
                param = params_dict[name]
                weight_loader = getattr(param, "weight_loader", default_weight_loader)
                if weight_loader == default_weight_loader:
                    weight_loader(param, weight)
                else:
                    weight_loader(param, weight, shard_id)
                break
            else:
                # Handle all other weights with potential renaming
                if name not in params_dict:
                    continue
                param = params_dict[name]
                weight_loader = getattr(param, "weight_loader", default_weight_loader)
                weight_loader(param, weight)
            loaded_params.add(name)
        return loaded_params

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

        tp_rank = get_tensor_model_parallel_rank()
        tp_size = get_tensor_model_parallel_world_size()

        # Attention heads per rank
        heads_per_rank = self.config.num_attention_heads // tp_size
        head_start = tp_rank * heads_per_rank

        ep_size = get_ep_group().world_size
        ep_rank = get_ep_group().rank
        num_experts = self.config.num_local_experts
        experts_per_rank = num_experts // ep_size
        ep_rank_start = ep_rank * experts_per_rank
        ep_rank_end = (ep_rank + 1) * experts_per_rank

        quant_method = (
            self.config.quantization_config["quant_method"]
            if hasattr(self.config, "quantization_config")
            else None
        )
        # Normalize the checkpoint's quant_method to the internal name.
        # Note: there are three places where "mxfp4" -> "gpt_oss_mxfp4"
        # normalization occurs, each serving a different data path:
        #   1. GptOssMxfp4Config.override_quantization_method() — sets
        #      ModelConfig.quantization (used to select the QuantizationConfig
        #      class at model init time), reading from model_arch_config which
        #      is a snapshot taken before verify_and_update_model_config runs.
        #   2. GptOssForCausalLMConfig.verify_and_update_model_config() —
        #      patches hf_config.quantization_config in-place (a separate copy
        #      of the dict from model_arch_config) for later hf_config lookups.
        #   3. Here — reads directly from self.config (the raw HF config) which
        #      may still carry the original "mxfp4" string from the checkpoint.
        if quant_method == "mxfp4":
            quant_method = "gpt_oss_mxfp4"

        if quant_method == "gpt_oss_mxfp4":
            return self._load_weights_mxfp4(
                ep_rank_end,
                ep_rank_start,
                heads_per_rank,
                head_start,
                weights,
                stacked_params_mapping,
            )
        elif quant_method == "quark":
            return self._load_weights_quark(
                ep_rank_end,
                ep_rank_start,
                heads_per_rank,
                head_start,
                weights,
                stacked_params_mapping,
            )
        else:
            return self._load_weights_other(
                ep_rank_end,
                ep_rank_start,
                heads_per_rank,
                head_start,
                weights,
                stacked_params_mapping,
            )


class GptOssForCausalLM(
    nn.Module, SupportsPP, SupportsEagle, SupportsEagle3, SupportsLoRA
):
    is_3d_moe_weight: bool = True
    packed_modules_mapping = {"qkv_proj": ["q_proj", "k_proj", "v_proj"]}

    hf_to_vllm_mapper = WeightsMapper(
        orig_to_new_substr={
            ".self_attn.": ".attn.",
        },
        orig_to_new_suffix={
            ".embed_tokens.weight": ".embedding.weight",
            # MoE MXFP4 weights
            ".gate_up_proj_blocks": ".w13_weight",
            ".down_proj_blocks": ".w2_weight",
            ".gate_up_proj_scales": ".w13_weight_scale",
            ".down_proj_scales": ".w2_weight_scale",
            # MoE other weights
            ".gate_up_proj": ".w13_weight",
            ".down_proj": ".w2_weight",
            # MoE Bias
            ".gate_up_proj_bias": ".w13_bias",
            ".down_proj_bias": ".w2_bias",
            # For quark format
            ".gate_up_proj.weight": ".w13_weight",
            ".gate_up_proj.weight_scale": ".w13_weight_scale",
            ".gate_up_proj.bias": ".w13_bias",
            ".gate_up_proj.input_scale": ".w13_input_scale",
            ".down_proj.weight": ".w2_weight",
            ".down_proj.weight_scale": ".w2_weight_scale",
            ".down_proj.bias": ".w2_bias",
            ".down_proj.input_scale": ".w2_input_scale",
        },
    )

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

        self.model = GptOssModel(
            vllm_config=vllm_config,
            prefix=maybe_prefix(prefix, "model"),
        )
        self.lm_head = ParallelLMHead(
            self.config.vocab_size,
            self.config.hidden_size,
            prefix=maybe_prefix(prefix, "lm_head"),
        )
        self.logits_processor = LogitsProcessor(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 | None,
        positions: torch.Tensor,
        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
    ) -> torch.Tensor:
        return self.model(input_ids, positions, intermediate_tensors, inputs_embeds)

    def compute_logits(self, hidden_states: torch.Tensor) -> torch.Tensor:
        logits = self.logits_processor(self.lm_head, hidden_states)
        return logits

    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
        loader = AutoWeightsLoader(
            self,
            skip_prefixes=(["lm_head."] if self.config.tie_word_embeddings else None),
        )
        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)