# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
#
# Copyright 2025 the LLAMA4, Meta Inc., vLLM, and HuggingFace Inc. team.
# All rights reserved.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Inference-only LLaMA model compatible with HuggingFace weights."""

from collections.abc import Iterable
from typing import Any

import torch
from torch import nn
from transformers import Llama4TextConfig

from vllm.attention import Attention
from vllm.attention.layers.chunked_local_attention import ChunkedLocalAttention
from vllm.compilation.decorators import support_torch_compile
from vllm.config import CacheConfig, VllmConfig
from vllm.distributed import (
    get_tensor_model_parallel_world_size,
    tensor_model_parallel_all_gather,
)
from vllm.model_executor.layers.fused_moe import SharedFusedMoE
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import (
    QKVParallelLinear,
    ReplicatedLinear,
    RowParallelLinear,
)
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.rotary_embedding import get_rope
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 .llama import LlamaForCausalLM, LlamaMLP, LlamaModel
from .utils import (
    AutoWeightsLoader,
    extract_layer_index,
    fast_topk,
    is_pp_missing_parameter,
)


class Llama4MoE(nn.Module):
    @staticmethod
    def custom_routing_function(
        hidden_states: torch.Tensor,
        gating_output: torch.Tensor,
        topk: int,
        renormalize: bool,
    ) -> tuple[torch.Tensor, torch.Tensor]:
        router_scores, router_indices = fast_topk(gating_output, topk, dim=-1)
        # pseudo-standard is that the router scores are floats
        router_scores = torch.sigmoid(router_scores.float())
        return (router_scores, router_indices.to(torch.int32))

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

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

        self.tp_size = get_tensor_model_parallel_world_size()
        self.top_k = config.num_experts_per_tok
        self.is_sequence_parallel = parallel_config.use_sequence_parallel_moe

        intermediate_size_moe = config.intermediate_size
        self.router = ReplicatedLinear(
            config.hidden_size,
            config.num_local_experts,
            bias=False,
            quant_config=None,
            prefix=f"{prefix}.router",
        )

        self.shared_expert = LlamaMLP(
            hidden_size=config.hidden_size,
            intermediate_size=intermediate_size_moe,
            hidden_act="silu",
            quant_config=quant_config,
            bias=False,
            prefix=f"{prefix}.shared_expert",
            reduce_results=False,
            disable_tp=self.is_sequence_parallel,
        )

        self.experts = SharedFusedMoE(
            shared_experts=self.shared_expert,
            num_experts=config.num_local_experts,
            top_k=config.num_experts_per_tok,
            hidden_size=config.hidden_size,
            custom_routing_function=Llama4MoE.custom_routing_function,
            intermediate_size=intermediate_size_moe,
            apply_router_weight_on_input=True,
            reduce_results=False,
            renormalize=False,
            quant_config=quant_config,
            prefix=f"{prefix}.experts",
            is_sequence_parallel=self.is_sequence_parallel,
        )

    def forward(self, hidden_states):
        num_tokens = hidden_states.shape[0]
        if self.is_sequence_parallel:
            hidden_states = sequence_parallel_chunk(hidden_states)

        router_logits, _ = self.router(hidden_states)

        shared_out, routed_out = self.experts(
            hidden_states=hidden_states,
            router_logits=router_logits,
        )
        experts_out = routed_out + shared_out

        if self.is_sequence_parallel:
            experts_out = tensor_model_parallel_all_gather(experts_out, 0)
            experts_out = experts_out[:num_tokens]
        elif self.tp_size > 1:
            experts_out = self.experts.maybe_all_reduce_tensor_model_parallel(
                experts_out
            )

        return experts_out


class Llama4Attention(nn.Module):
    def __init__(
        self,
        config: Llama4TextConfig,
        hidden_size: int,
        num_heads: int,
        num_kv_heads: int,
        rope_theta: float = 10000,
        rope_scaling: dict[str, Any] | None = None,
        max_position_embeddings: int = 8192,
        quant_config: QuantizationConfig | None = None,
        bias: bool = False,
        bias_o_proj: bool = False,
        cache_config: CacheConfig | None = None,
        prefix: str = "",
    ) -> None:
        super().__init__()
        self.layer_idx = extract_layer_index(prefix)
        self.hidden_size = hidden_size
        self.no_rope_layers = config.no_rope_layers
        self.nope = self.no_rope_layers[self.layer_idx] == 0
        self.use_qk_norm = config.use_qk_norm and not self.nope
        tp_size = get_tensor_model_parallel_world_size()
        self.total_num_heads = num_heads
        assert self.total_num_heads % tp_size == 0
        self.num_heads = self.total_num_heads // tp_size
        self.total_num_kv_heads = num_kv_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.head_dim
        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.attn_temperature_tuning = self.nope and config.attn_temperature_tuning

        self.floor_scale = getattr(config, "floor_scale", 8192.0)
        self.attn_scale = getattr(config, "attn_scale", 0.1)
        self.rope_theta = rope_theta
        self.max_position_embeddings = max_position_embeddings
        self.n_rep = self.num_heads // self.num_kv_heads
        self.qk_norm = (
            RMSNorm(
                hidden_size=self.head_dim,
                eps=config.rms_norm_eps,
                has_weight=False,
                dtype=torch.float32,
            )
            if self.use_qk_norm
            else None
        )
        self.qkv_proj = QKVParallelLinear(
            hidden_size=hidden_size,
            head_size=self.head_dim,
            total_num_heads=self.total_num_heads,
            total_num_kv_heads=self.total_num_kv_heads,
            bias=bias,
            quant_config=quant_config,
            prefix=f"{prefix}.qkv_proj",
        )

        self.o_proj = RowParallelLinear(
            input_size=self.total_num_heads * self.head_dim,
            output_size=hidden_size,
            bias=bias_o_proj,
            quant_config=quant_config,
            prefix=f"{prefix}.o_proj",
        )
        is_neox_style = True
        is_gguf = quant_config and quant_config.get_name() == "gguf"
        if is_gguf and config.model_type == "llama":
            is_neox_style = False

        self.rotary_emb = (
            get_rope(
                self.head_dim,
                rotary_dim=self.head_dim,
                max_position=max_position_embeddings,
                base=int(rope_theta),
                rope_scaling=rope_scaling if rope_scaling != "default" else None,
                is_neox_style=is_neox_style,
            )
            if not self.nope
            else None
        )

        use_chunked_local_attn = not self.nope and config.attention_chunk_size
        attn_cls = ChunkedLocalAttention if use_chunked_local_attn else Attention
        self.attn = attn_cls(
            self.num_heads,
            self.head_dim,
            self.scaling,
            num_kv_heads=self.num_kv_heads,
            cache_config=cache_config,
            quant_config=quant_config,
            prefix=f"{prefix}.attn",
            **(
                {"attention_chunk_size": config.attention_chunk_size}
                if use_chunked_local_attn
                else {}
            ),
        )

    def _get_attn_scale(self, positions: torch.Tensor) -> torch.Tensor:
        floor = torch.floor((positions + 1.0) / self.floor_scale)
        attn_scale = torch.log(floor + 1.0) * self.attn_scale + 1.0

        return attn_scale.unsqueeze(-1)

    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.kv_size, self.kv_size], dim=-1)

        if self.rotary_emb is not None:
            q, k = self.rotary_emb(positions, q, k)

        if self.qk_norm is not None:
            # Normalization is applied on the head_dim dimension. The rest of
            # the dimensions are collapsed into a single dimension to support
            # custom rms_norm cuda kernel.
            q = q.reshape(-1, self.head_dim)
            q = self.qk_norm(q.float()).reshape(-1, self.q_size).to(q.dtype)
            k = k.reshape(-1, self.head_dim)
            k = self.qk_norm(k.float()).reshape(-1, self.kv_size).to(k.dtype)

        # We are applying temperature tuning (https://arxiv.org/abs/2501.19399)
        # to NoPE layers, where the inference-time temperature tuning function
        # is customized to not affect short context
        # while working at very long context
        # https://arxiv.org/abs/2501.19399
        #
        # We should apply temperature tuning between (after) rotary / QK norm
        # and (before) attention.
        if self.attn_temperature_tuning and self.nope:
            attn_scale = self._get_attn_scale(positions)
            q = (q * attn_scale).to(q.dtype)
        attn_output = self.attn(q, k, v)
        output, _ = self.o_proj(attn_output)
        return output


class Llama4DecoderLayer(nn.Module):
    def __init__(
        self,
        vllm_config: VllmConfig,
        prefix: str = "",
        config: Llama4TextConfig | None = None,
    ) -> None:
        super().__init__()

        config = config or vllm_config.model_config.hf_config
        cache_config = vllm_config.cache_config
        quant_config = vllm_config.quant_config

        self.layer_idx = extract_layer_index(prefix)
        self.global_layer = config.no_rope_layers[self.layer_idx] == 0
        self.hidden_size = config.hidden_size
        rope_theta = config.rope_theta
        rope_scaling = config.rope_scaling
        max_position_embeddings = config.max_position_embeddings

        self.self_attn = Llama4Attention(
            config=config,
            hidden_size=self.hidden_size,
            num_heads=config.num_attention_heads,
            num_kv_heads=config.num_key_value_heads,
            rope_theta=rope_theta,
            rope_scaling=rope_scaling,
            max_position_embeddings=max_position_embeddings,
            quant_config=quant_config,
            bias=False,
            bias_o_proj=False,
            cache_config=cache_config,
            prefix=f"{prefix}.self_attn",
        )
        is_moe_layer = (
            config.interleave_moe_layer_step > 0
            and (self.layer_idx + 1) % config.interleave_moe_layer_step == 0
        )
        if is_moe_layer:
            self.feed_forward = Llama4MoE(
                vllm_config=vllm_config,
                prefix=f"{prefix}.feed_forward",
            )
        else:
            self.feed_forward = LlamaMLP(
                hidden_size=self.hidden_size,
                intermediate_size=config.intermediate_size_mlp,
                hidden_act="silu",
                quant_config=quant_config,
                bias=False,
                prefix=f"{prefix}.feed_forward",
            )
        self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_attention_layernorm = RMSNorm(
            config.hidden_size, eps=config.rms_norm_eps
        )

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        residual: torch.Tensor | None,
    ) -> tuple[torch.Tensor, 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.self_attn(positions=positions, hidden_states=hidden_states)

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


@support_torch_compile
class Llama4Model(LlamaModel):
    def __init__(
        self,
        *,
        vllm_config: VllmConfig,
        prefix: str = "",
        layer_type: type[Llama4DecoderLayer] = Llama4DecoderLayer,
    ):
        self.num_experts = vllm_config.model_config.hf_config.num_local_experts
        super().__init__(vllm_config=vllm_config, prefix=prefix, layer_type=layer_type)

    def load_moe_expert_weights(
        self,
        name: str,
        loaded_weight: torch.Tensor,
        params_dict: dict[str, nn.Parameter],
        loaded_params: set[str],
        expert_params_mapping: list[tuple[str, str, int, str]],
        fused: bool = True,
    ) -> bool:
        """
        Load MoE expert weights.

        Args:
            name: The name of the weight to load.
            loaded_weight: The weight to load.
            params_dict: The dictionary of module parameters.
            loaded_params: The set of already loaded parameters.
            expert_params_mapping: The mapping of expert parameters. Must be
                generated by SharedFusedMoE.make_expert_params_mapping().
            fused: Whether the expert weights are fused into a single weight
                tensor or are separate weight tensors for each expert.
                When fused is True, loaded_weight should have shape of:
                [num_experts, hidden_in, hidden_out] for gate/up/down proj and
                [hidden_out, hidden_in] for the others like router.
                When fused is False, loaded_weight should have shape of:
                [hidden_out, hidden_in].

        Returns:
            True if loaded_weight is one of MoE weights and the MoE expert
            weights are loaded successfully, False otherwise.
        """

        # Whether the MoE expert weights are loaded successfully.
        expert_param_loaded = False

        # If fused is True, the loaded weight is in the layout of:
        # [num_experts, hidden_in, hidden_out], so we must transpose the last
        # two dimensions to match the expected layout of the parameters.
        if fused and loaded_weight.ndim == 3:
            loaded_weight = loaded_weight.transpose(-1, -2)

            # If the gate_proj and up_proj weights are fused into a single
            # weight tensor, we need to split the weight tensor into a tuple
            # of two weight tensors along the hidden_out dimension.
            if "experts.gate_up_proj" in name:
                loaded_weight = loaded_weight.chunk(2, dim=-2)

        # Iterate over all the expert parameters and load the weights if we find
        # a match in weight name.
        for param_name, weight_name, expert_id, shard_id in expert_params_mapping:
            # Get a view of the loaded_weight to avoid modifying the original
            # one across iterations.
            new_loaded_weight = loaded_weight

            # If expert weights are fused into a single weight tensor, remove
            # the expert index from the expected weight name.
            if fused:
                # The string between e_str and proj_str is the expert index.
                e_str, _, proj_str, _ = weight_name.split(".")
                weight_name = f"{e_str}.{proj_str}"
                param_name = f"{param_name}weight"

            # Skip if the current weight is not one of the MoE weights.
            if weight_name not in name:
                continue

            # Replace the weight name with the parameter name.
            full_param_name = name.replace(weight_name, param_name)

            # Skip if the current weight corresponds to a parameter that
            # does not exist on the current PP (pipeline parallel) rank.
            if is_pp_missing_parameter(name, self):
                continue

            # Skip if the current weight is for the bias.
            if (
                name.endswith(".bias") or name.endswith("_bias")
            ) and name not in params_dict:
                continue

            param = params_dict[full_param_name]
            weight_loader = param.weight_loader

            if fused:
                # If the parameter is for w13 together, the corresponding weight
                # will be a tuple, so we must select the correct weight
                # depending on the shard id, which is either "w1" or "w3".
                if "w13" in full_param_name:
                    assert shard_id in ["w1", "w3"]
                    shard_idx = 0 if shard_id == "w1" else 1
                    new_loaded_weight = new_loaded_weight[shard_idx]

                # If EP (expert parallel) is enabled, update expert_id to the
                # starting expert index for the current EP rank and extract the
                # corresponding expert weights.
                layer_idx = extract_layer_index(name)
                expert_map = self.layers[layer_idx].feed_forward.experts.expert_map
                if expert_map is not None:
                    local_expert_indices = (
                        (expert_map != -1)
                        .nonzero()
                        .flatten()
                        .to(new_loaded_weight.device)
                    )
                    new_loaded_weight = new_loaded_weight[local_expert_indices]
                    expert_id = local_expert_indices[0].item()
            else:
                # TODO: add EP support for non fused weights
                pass

            # Load the weight into the module parameter with corresponding
            # shard id and expert id.
            weight_loader(
                param,
                new_loaded_weight,
                full_param_name,
                shard_id=shard_id,
                expert_id=expert_id,
            )

            loaded_params.add(full_param_name)
            expert_param_loaded = True

        return expert_param_loaded

    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
        # Name mapping from the parameter name to the shard name and
        # corresponding shard id.
        stacked_params_mapping = [
            # (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),
        ]
        # Indicate whether the expert weights are fused into a single weight
        # tensor.
        fused_experts_params = False
        # Expert parameter mapping for the case where the expert weights are
        # not fused into a single weight tensor.
        expert_params_mapping = SharedFusedMoE.make_expert_params_mapping(
            ckpt_gate_proj_name="gate_proj",
            ckpt_down_proj_name="down_proj",
            ckpt_up_proj_name="up_proj",
            num_experts=self.num_experts,
        )
        # Expert parameter mapping for the case where the expert weights are
        # fused into a single weight tensor.
        expert_params_mapping_fused = SharedFusedMoE.make_expert_params_mapping(
            ckpt_gate_proj_name="gate_up_proj",
            ckpt_down_proj_name="down_proj",
            ckpt_up_proj_name="gate_up_proj",
            num_experts=1,
        )
        # All the module parameters.
        params_dict = dict(self.named_parameters())
        # The module parameters that have been loaded.
        loaded_params: set[str] = set()

        # Iterate over all the weights and load them into module parameters.
        for name, loaded_weight in weights:
            # If the name contains "experts.gate_up_proj" or "experts.down_proj"
            # without the expert indices, it means the expert weights are fused
            # into a single weight tensor across all experts.
            if "experts.gate_up_proj" in name or "experts.down_proj" in name:
                fused_experts_params = True
                expert_params_mapping = expert_params_mapping_fused

            # If kv cache quantization scales exist and the weight name
            # corresponds to one of the kv cache quantization scales, load
            # them.
            if self.quant_config is not None and (
                scale_name := self.quant_config.get_cache_scale(name)
            ):
                param = params_dict[scale_name]
                weight_loader = getattr(param, "weight_loader", default_weight_loader)
                loaded_weight = (
                    loaded_weight if loaded_weight.dim() == 0 else loaded_weight[0]
                )
                weight_loader(param, loaded_weight)
                loaded_params.add(scale_name)
                continue

            # Iterate over stacked_params_mapping to check if the current weight
            # is one of the stacked parameters. If so, load the weight with the
            # corresponding shard id. Note that MoE weights are handled
            # separately in the else block.
            for param_name, weight_name, shard_id in stacked_params_mapping:
                # Skip if the current weight is not one of the stacked
                # parameters or if the current weight is a MoE weight.
                if weight_name not in name or "experts" in name:
                    continue

                # For ModelOpt checkpoints, we need to rename the self_attn
                # weight/weight_scale names except for kv cache scales.
                if not (
                    name.endswith((".k_scale", ".v_scale")) and "self_attn" in name
                ):
                    name = name.replace(weight_name, param_name)

                # Skip if the current weight corresponds to a parameter that
                # does not exist on the current PP (pipeline parallel) rank.
                if is_pp_missing_parameter(name, self):
                    continue

                # Remap kv cache scale names for ModelOpt checkpoints.
                # TODO: ModelOpt should implement get_cache_scale() such that
                #       kv cache scale name remapping can be done there.
                if name.endswith("scale"):
                    name = maybe_remap_kv_scale_name(name, params_dict)
                    if name is None:
                        continue

                # Load the weight into the module parameter with corresponding
                # shard id and exit the for loop and the else block.
                param = params_dict[name]
                weight_loader = getattr(param, "weight_loader", default_weight_loader)

                if weight_loader == default_weight_loader:
                    weight_loader(param, loaded_weight)
                else:
                    weight_loader(param, loaded_weight, shard_id)

                loaded_params.add(name)
                break

            # Handle normal (non-stacked) weights and MoE weights.
            else:
                # First, try to load MoE weights using load_moe_expert_weights.
                # If successful, move on to next loaded weight.
                if self.load_moe_expert_weights(
                    name,
                    loaded_weight,
                    params_dict,
                    loaded_params,
                    expert_params_mapping,
                    fused=fused_experts_params,
                ):
                    continue

                # Skip if the current weight corresponds to a parameter that
                # does not exist on the current PP (pipeline parallel) rank.
                if is_pp_missing_parameter(name, self):
                    continue

                # Handle flat expert scale parameters that don't match
                # per-expert patterns, i.e. one weight scale tensor for all
                # experts.
                scale_names = [
                    "w13_input_scale",
                    "w13_weight_scale",
                    "w2_input_scale",
                    "w2_weight_scale",
                ]
                if "experts." in name and any(
                    scale_name in name for scale_name in scale_names
                ):
                    param = params_dict[name]
                    weight_loader = getattr(
                        param, "weight_loader", default_weight_loader
                    )

                    # If weight loader supports special moe loading, use it to
                    # avoid expensive runtime reflection
                    if getattr(weight_loader, "supports_moe_loading", False):
                        # Map the weight name to the corresponding shard id.
                        shard_id = "w2" if "w2_" in name else "w1"

                        # Transpose if weight scales are FP8 block scales with
                        # three dimensions:
                        # [num_experts, hidden_in, hidden_out].
                        if (
                            name.endswith("weight_scale")
                            and loaded_weight.dtype == torch.float8_e4m3fn
                            and loaded_weight.ndim == 3
                        ):
                            loaded_weight = loaded_weight.transpose(-1, -2)

                        # Load the weight into the module parameter with
                        # corresponding shard id and expert id.
                        weight_loader(
                            param, loaded_weight, name, shard_id=shard_id, expert_id=0
                        )

                    else:
                        # Regular weight loader (handles both
                        # param.weight_loader and default_weight_loader)
                        weight_loader(param, loaded_weight)

                    loaded_params.add(name)
                    continue

                # Handle normal (non-stacked, non-MoE) weights.
                param = params_dict[name]
                weight_loader = getattr(param, "weight_loader", default_weight_loader)
                weight_loader(param, loaded_weight)
                loaded_params.add(name)

        # Finally, return the set of loaded parameters.
        return loaded_params


class Llama4ForCausalLM(LlamaForCausalLM):
    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 = ""):
        # update temperature tuning config from generation config
        gen_config = vllm_config.model_config.try_get_generation_config()
        gen_config.update(vllm_config.model_config.override_generation_config)
        # enable temperature tuning by default when max_model_len > 32K
        default_attn_temperature_tuning = vllm_config.model_config.max_model_len > 32768
        vllm_config.model_config.hf_config.attn_temperature_tuning = gen_config.get(
            "attn_temperature_tuning", default_attn_temperature_tuning
        )

        super().__init__(
            vllm_config=vllm_config, prefix=prefix, layer_type=Llama4DecoderLayer
        )

    def _init_model(
        self,
        vllm_config: VllmConfig,
        prefix: str = "",
        layer_type: type[Llama4DecoderLayer] = Llama4DecoderLayer,
    ):
        return Llama4Model(
            vllm_config=vllm_config, prefix=prefix, layer_type=layer_type
        )

    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),
        )
        weights = [
            self.permute_qk_weight_for_rotary(name, loaded_weight)
            for name, loaded_weight in weights
        ]
        return loader.load_weights(weights)

    def permute_qk_weight_for_rotary(
        self,
        name: str,
        loaded_weight: torch.Tensor,
    ) -> tuple[str, torch.Tensor]:
        # Helper function to permute the weight's channels
        def permute(w: torch.Tensor, n_heads: int, is_weight_scale: bool):
            # Calculate the expected shape of the weight.
            # Do not rely on w's shape, as it may be in another layout.
            attn_in = self.config.head_dim * n_heads
            attn_out = self.config.hidden_size

            # If the weight is FP4 packed as uint8, we need to divide attn_out
            # by 2.
            if w.dtype == torch.uint8 and w.shape[1] * 2 == attn_out:
                attn_out = attn_out // 2

            # If the weight is a weight scale, we need to divide attn_out by
            # block size, which is currently 16.
            elif (
                w.dtype == torch.float8_e4m3fn
                and is_weight_scale
                and w.shape[1] * 16 == attn_out
            ):
                attn_out = attn_out // 16

            return (
                w.view(n_heads, attn_in // n_heads // 2, 2, attn_out)
                .transpose(1, 2)
                .reshape(attn_in, attn_out)
            )

        modules = name.split(".")

        # Permute Q/K weights and weight block scales for rotary embedding
        is_weight = modules[-1] == "weight"
        is_nvfp4_weight_scale = (
            modules[-1] == "weight_scale" and loaded_weight.dtype == torch.float8_e4m3fn
        )

        if is_weight or is_nvfp4_weight_scale:
            if "wk" in modules or "k_proj" in modules:
                loaded_weight = permute(
                    loaded_weight,
                    self.config.num_key_value_heads,
                    is_nvfp4_weight_scale,
                )
            elif "wq" in modules or "q_proj" in modules:
                loaded_weight = permute(
                    loaded_weight,
                    self.config.num_attention_heads,
                    is_nvfp4_weight_scale,
                )

        return name, loaded_weight
