Update bundled plugin: looped-target Eagle3 draft fixes (layer-name offset + target_layer_count stamp)

vllm_plugin/nanbeige_vllm_plugin/__init__.py

@@ -0,0 +1,54 @@
+# Out-of-tree vLLM registration for Nanbeige4.2 (NanbeigeForCausalLM).
+# Source: vllm-project/vllm PR #49433 (Nanbeige/vllm branch nanbeige42),
+# vendored until the PR merges + ships in a release — then DELETE this plugin
+# and drop the pip-install line from the vllm-nanbeige wrapper.
+def _patch_eagle3_draft_layer_naming():
+    # The looped target registers num_loops * num_hidden_layers virtual
+    # attention layers (0..43), but vLLM's Eagle3 draft names its layer at
+    # start_layer_id = physical num_hidden_layers (22), colliding with the
+    # target's loop-2 layer 22 ("Duplicate layer name: model.layers.22...").
+    # Bump the draft's naming offset to the virtual depth. start_layer_id is
+    # ONLY a KV-registry prefix (layer_types indexing uses layer_idx), so this
+    # is a pure rename.
+    try:
+        from vllm.model_executor.models import llama_eagle3
+    except ImportError:
+        return
+    if getattr(llama_eagle3.LlamaModel, "_nanbeige_layer_naming_patch", False):
+        return
+    _orig_init = llama_eagle3.LlamaModel.__init__
+
+    def _patched_init(self, *, vllm_config, start_layer_id=0, prefix=""):
+        tgt_hf = vllm_config.model_config.hf_config
+        num_loops = getattr(tgt_hf, "num_loops", 1) or 1
+        if (
+            getattr(tgt_hf, "model_type", "") == "nanbeige"
+            and num_loops > 1
+            and start_layer_id == tgt_hf.num_hidden_layers
+        ):
+            start_layer_id = tgt_hf.num_hidden_layers * num_loops
+            # vLLM stamped target_layer_count = physical layers (22) on the
+            # draft config; the draft decoder layer indexes its own
+            # layer_types as (prefix_index - target_layer_count), so with the
+            # virtual naming offset the stamp must be virtual too, making the
+            # effective index 0 for the single draft layer.
+            draft_cfg = vllm_config.speculative_config.draft_model_config.hf_config
+            draft_cfg.target_layer_count = start_layer_id
+        _orig_init(
+            self, vllm_config=vllm_config, start_layer_id=start_layer_id, prefix=prefix
+        )
+
+    llama_eagle3.LlamaModel.__init__ = _patched_init
+    llama_eagle3.LlamaModel._nanbeige_layer_naming_patch = True
+
+
+def register():
+    from vllm import ModelRegistry
+
+    _patch_eagle3_draft_layer_naming()
+    if "NanbeigeForCausalLM" in ModelRegistry.get_supported_archs():
+        return  # native support arrived — plugin is a no-op
+    ModelRegistry.register_model(
+        "NanbeigeForCausalLM",
+        "nanbeige_vllm_plugin.nanbeige:NanbeigeForCausalLM",
+    )

vllm_plugin/nanbeige_vllm_plugin/nanbeige.py

@@ -0,0 +1,591 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from collections.abc import Iterable
+from dataclasses import replace
+from itertools import islice
+from typing import Any
+
+import torch
+from torch import nn
+from nanbeige_vllm_plugin.nanbeige_config import NanbeigeConfig
+
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, VllmConfig
+from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
+from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.attention import (
+    Attention,
+    EncoderOnlyAttention,
+)
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (
+    MergedColumnParallelLinear,
+    QKVParallelLinear,
+    RowParallelLinear,
+)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    ParallelLMHead,
+    VocabParallelEmbedding,
+)
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader,
+    maybe_remap_kv_scale_name,
+)
+from vllm.sequence import IntermediateTensors
+from vllm.transformers_utils.config import is_interleaved, set_default_rope_theta
+from vllm.v1.attention.backend import AttentionType
+
+from vllm.model_executor.models.interfaces import (
+    EagleModelMixin,
+    SupportsEagle,
+    SupportsEagle3,
+    SupportsLoRA,
+    SupportsPP,
+)
+from vllm.model_executor.models.utils import (
+    AutoWeightsLoader,
+    PPMissingLayer,
+    extract_layer_index,
+    is_pp_missing_parameter,
+    make_empty_intermediate_tensors_factory,
+    make_layers,
+    maybe_prefix,
+)
+
+
+class NanbeigeMLP(nn.Module):
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.gate_up_proj = MergedColumnParallelLinear(
+            hidden_size,
+            [intermediate_size] * 2,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.gate_up_proj",
+        )
+        self.down_proj = RowParallelLinear(
+            intermediate_size,
+            hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.down_proj",
+        )
+        if hidden_act != "silu":
+            raise ValueError(
+                f"Unsupported activation: {hidden_act}. Only silu is supported for now."
+            )
+        self.act_fn = SiluAndMul()
+
+    def forward(self, x):
+        gate_up, _ = self.gate_up_proj(x)
+        x = self.act_fn(gate_up)
+        x, _ = self.down_proj(x)
+        return x
+
+
+class NanbeigeAttention(nn.Module):
+    def __init__(
+        self,
+        config: NanbeigeConfig,
+        hidden_size: int,
+        num_heads: int,
+        num_kv_heads: int,
+        rope_parameters: dict[str, Any],
+        max_position: int = 4096 * 32,
+        cache_config: CacheConfig | None = None,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+        attn_type: str = AttentionType.DECODER,
+        dual_chunk_attention_config: dict[str, Any] | None = None,
+        qk_norm: bool = False,
+        rms_norm_eps: float = 1e-6,
+    ) -> None:
+        super().__init__()
+        self.hidden_size = hidden_size
+        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 = hidden_size // self.total_num_heads
+        self.head_dim = getattr(config, "head_dim", hidden_size // self.total_num_heads)
+        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.dual_chunk_attention_config = dual_chunk_attention_config
+        self.qk_norm = qk_norm
+
+        self.qkv_proj = QKVParallelLinear(
+            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,
+            hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.o_proj",
+        )
+
+        # QK Normalization support (used in BAGEL and some other models)
+        if self.qk_norm:
+            self.q_norm = RMSNorm(self.head_dim, eps=rms_norm_eps)
+            self.k_norm = RMSNorm(self.head_dim, eps=rms_norm_eps)
+
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            max_position=max_position,
+            rope_parameters=rope_parameters,
+            dual_chunk_attention_config=dual_chunk_attention_config,
+        )
+
+        self.loops_num = getattr(config, "num_loops", 1)
+        total_layers = config.num_hidden_layers
+        self.attn = nn.ModuleList()
+
+        for loop_idx in range(self.loops_num):
+            base_layer_idx = extract_layer_index(prefix)
+            unique_layer_idx = loop_idx * total_layers + base_layer_idx
+            unique_prefix = prefix.replace(
+                f"layers.{base_layer_idx}", f"layers.{unique_layer_idx}"
+            )
+            self.attn.append(
+                Attention(
+                    self.num_heads,
+                    self.head_dim,
+                    self.scaling,
+                    num_kv_heads=self.num_kv_heads,
+                    cache_config=cache_config,
+                    quant_config=quant_config,
+                    attn_type=attn_type,
+                    prefix=f"{unique_prefix}.attn",
+                    **{
+                        "layer_idx": unique_layer_idx,
+                        "dual_chunk_attention_config": dual_chunk_attention_config,
+                    }
+                    if dual_chunk_attention_config and loop_idx == 0
+                    else {},
+                )
+            )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        loop_idx: int,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+
+        # Apply QK normalization if enabled (before RoPE)
+        if self.qk_norm:
+            # Reshape to apply per-head normalization
+            # q shape: (total_tokens, q_size) -> (total_tokens, num_heads, head_dim)
+            total_tokens = q.shape[0]
+            q = q.view(total_tokens, self.num_heads, self.head_dim)
+            k = k.view(total_tokens, self.num_kv_heads, self.head_dim)
+
+            # Apply normalization
+            q = self.q_norm(q)
+            k = self.k_norm(k)
+
+            # Reshape back
+            q = q.view(total_tokens, self.q_size)
+            k = k.view(total_tokens, self.kv_size)
+
+        q, k = self.rotary_emb(positions, q, k)
+        attn_output = self.attn[loop_idx](q, k, v)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class NanbeigeDecoderLayer(nn.Module):
+    def __init__(
+        self,
+        config: NanbeigeConfig,
+        cache_config: CacheConfig | None = None,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        set_default_rope_theta(config, default_theta=1000000)
+        dual_chunk_attention_config = getattr(
+            config, "dual_chunk_attention_config", None
+        )
+
+        if getattr(config, "is_causal", True):
+            attn_type = AttentionType.DECODER
+        else:
+            attn_type = AttentionType.ENCODER_ONLY
+
+        # Check if QK normalization is enabled (used in BAGEL and some other models)
+        qk_norm = getattr(config, "qk_norm", False)
+
+        self.self_attn = NanbeigeAttention(
+            config=config,
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            max_position=config.max_position_embeddings,
+            num_kv_heads=config.num_key_value_heads,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            rope_parameters=config.rope_parameters,
+            prefix=f"{prefix}.self_attn",
+            attn_type=attn_type,
+            dual_chunk_attention_config=dual_chunk_attention_config,
+            qk_norm=qk_norm,
+            rms_norm_eps=config.rms_norm_eps,
+        )
+        self.mlp = NanbeigeMLP(
+            hidden_size=self.hidden_size,
+            intermediate_size=config.intermediate_size,
+            hidden_act=config.hidden_act,
+            quant_config=quant_config,
+            prefix=f"{prefix}.mlp",
+        )
+        self.input_layernorm = RMSNorm(config.hidden_size, eps=config.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,
+        loop_idx: int = 0,
+    ) -> 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,
+            loop_idx=loop_idx,
+        )
+
+        # Fully Connected
+        hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
+        hidden_states = self.mlp(hidden_states)
+        return hidden_states, residual
+
+
+@support_torch_compile(
+    dynamic_arg_dims={
+        "input_ids": {0: "b"},
+        "positions": {-1: "b"},
+        "intermediate_tensors": {0: "b"},
+        "inputs_embeds": {0: "b"},
+    }
+)
+class NanbeigeModel(nn.Module, EagleModelMixin):
+    def __init__(
+        self,
+        *,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+        decoder_layer_type: type[nn.Module] = NanbeigeDecoderLayer,
+    ):
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config.get_text_config()
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+
+        # TODO (@robertgshaw2): see if this can be moved out
+        if is_interleaved(vllm_config.model_config.hf_text_config):
+            assert config.max_window_layers == config.num_hidden_layers, (
+                "Sliding window for some but all layers is not supported. "
+                "This model uses sliding window but `max_window_layers` = {} "
+                "is less than `num_hidden_layers` = {}. Please open an issue "
+                "to discuss this feature.".format(
+                    config.max_window_layers,
+                    config.num_hidden_layers,
+                )
+            )
+
+        self.config = config
+        self.quant_config = quant_config
+        self.vocab_size = config.vocab_size
+        self.loops_num = getattr(config, "num_loops", 1)
+        self.skip_loop_final_norm = getattr(config, "skip_loop_final_norm", False)
+
+        if get_pp_group().is_first_rank or (
+            config.tie_word_embeddings and get_pp_group().is_last_rank
+        ):
+            self.embed_tokens = VocabParallelEmbedding(
+                config.vocab_size,
+                config.hidden_size,
+                quant_config=quant_config,
+                prefix=f"{prefix}.embed_tokens",
+            )
+        else:
+            self.embed_tokens = PPMissingLayer()
+
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: decoder_layer_type(
+                config=config,
+                cache_config=cache_config,
+                quant_config=quant_config,
+                prefix=prefix,
+            ),
+            prefix=f"{prefix}.layers",
+        )
+
+        self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
+            ["hidden_states", "residual"], config.hidden_size
+        )
+        if get_pp_group().is_last_rank:
+            self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        else:
+            self.norm = PPMissingLayer()
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor | None,
+        positions: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+    ) -> torch.Tensor | IntermediateTensors:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.embed_input_ids(input_ids)
+            residual = None
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+            residual = intermediate_tensors["residual"]
+
+        # EAGLE-3 aux-state capture uses VIRTUAL (unrolled) depth, not physical
+        # layer index: 0 = embeddings, 1..22 = loop pass 1, 23..44 = loop pass 2.
+        # The PR's original `idx + 1` keying fired once PER LOOP for each selected
+        # physical layer (EagleModelMixin membership-tests a flat index), so a
+        # 3-layer tap emitted 6 tensors — a silent shape mismatch against any
+        # standard EAGLE-3 head (fc expects 3*hidden). Virtual indexing restores
+        # exactly-once capture AND makes loop-2 features addressable.
+        aux_hidden_states = self._maybe_add_hidden_state([], 0, hidden_states, residual)
+        virtual_idx = 0
+        for loop_idx in range(self.loops_num):
+            for idx, layer in enumerate(
+                islice(self.layers, self.start_layer, self.end_layer)
+            ):
+                hidden_states, residual = layer(positions, hidden_states, residual, loop_idx=loop_idx)
+                virtual_idx += 1
+                self._maybe_add_hidden_state(
+                    aux_hidden_states, virtual_idx, hidden_states, residual
+                )
+
+            if loop_idx < self.loops_num - 1:
+                if residual is not None:
+                    hidden_states = hidden_states + residual
+                    residual = None
+                if not self.skip_loop_final_norm:
+                    hidden_states = self.norm(hidden_states)
+
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors(
+                {"hidden_states": hidden_states, "residual": residual}
+            )
+
+        hidden_states, _ = self.norm(hidden_states, residual)
+
+        if len(aux_hidden_states) > 0:
+            return hidden_states, aux_hidden_states
+
+        return hidden_states
+
+    def 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"),
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+        params_dict = dict(self.named_parameters(remove_duplicate=False))
+        loaded_params: set[str] = set()
+        for name, loaded_weight in weights:
+            if "rotary_emb.inv_freq" in name:
+                continue
+            # LOCAL PATCH: get_cache_scale doesn't exist on CompressedTensorsConfig in
+            # v0.25.1 (fork targets newer main). Our FP8-dynamic checkpoint ships no KV
+            # scales, so skipping the branch when the method is absent is lossless.
+            if self.quant_config is not None and (
+                scale_name := (
+                    self.quant_config.get_cache_scale(name)
+                    if hasattr(self.quant_config, "get_cache_scale")
+                    else None
+                )
+            ):
+                # Loading kv cache quantization scales
+                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
+            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)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                if is_pp_missing_parameter(name, self):
+                    continue
+                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 = 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)
+                break
+            else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                # Remapping the name of FP8 kv-scale.
+                name = maybe_remap_kv_scale_name(name, params_dict)
+                if name is None:
+                    continue
+                if is_pp_missing_parameter(name, self):
+                    continue
+                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
+
+
+class NanbeigeForCausalLM(
+    nn.Module, SupportsLoRA, SupportsPP, SupportsEagle, SupportsEagle3
+):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config.get_text_config()
+        quant_config = vllm_config.quant_config
+
+        self.config = config
+
+        self.quant_config = quant_config
+        self.model = NanbeigeModel(
+            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
+        )
+
+        if get_pp_group().is_last_rank:
+            if config.tie_word_embeddings:
+                self.lm_head = self.model.embed_tokens
+            else:
+                self.lm_head = ParallelLMHead(
+                    config.vocab_size,
+                    config.hidden_size,
+                    quant_config=quant_config,
+                    prefix=maybe_prefix(prefix, "lm_head"),
+                )
+        else:
+            self.lm_head = PPMissingLayer()
+
+        self.logits_processor = LogitsProcessor(config.vocab_size)
+
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors
+        )
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.embed_input_ids(input_ids)
+
+    def get_eagle3_default_aux_hidden_state_layers(self) -> tuple[int, ...]:
+        # Indices are VIRTUAL (unrolled) depth — see NanbeigeModel.forward:
+        # 0 = embeddings, 1..N = loop pass 1, N+1..2N = loop pass 2 (N = 22).
+        # The stock heuristic (2, L//2, L-3) on physical L=22 would pick
+        # loop-1-only features; low/mid/high on the 44-step virtual axis taps
+        # early loop-1, the loop boundary, and late loop-2 — the closest match
+        # to EAGLE-3's feature-diversity intent for a looped model. No prior
+        # art exists for looped-arch taps; triple subject to ablation.
+        n = len(self.model.layers)              # physical layers (22)
+        total = n * self.model.loops_num        # virtual depth (44)
+        return (3, n + 1, total - 3)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor | None,
+        positions: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+    ) -> torch.Tensor | IntermediateTensors:
+        hidden_states = self.model(
+            input_ids, positions, intermediate_tensors, inputs_embeds
+        )
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor | None:
+        logits = self.logits_processor(self.lm_head, hidden_states)
+        return logits
+
+    def 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)
+

vllm_plugin/nanbeige_vllm_plugin/nanbeige_config.py

@@ -0,0 +1,199 @@
+# coding=utf-8
+# 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.
+""" Nanbeige model configuration"""
+
+from transformers.configuration_utils import PretrainedConfig
+
+
+class NanbeigeConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`NanbeigeModel`]. It is used to instantiate an Nanbeige
+    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+    defaults will yield a similar configuration to that of the LLaMA-7B.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 32000):
+            Vocabulary size of the Nanbeige model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`NanbeigeModel`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 11008):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer decoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer decoder.
+        num_key_value_heads (`int`, *optional*):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
+            `num_attention_heads`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 2048):
+            The maximum sequence length that this model might ever be used with. Llama 1 supports up to 2048 tokens,
+            Llama 2 up to 4096, CodeLlama up to 16384.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        pad_token_id (`int`, *optional*):
+            Padding token id.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            Beginning of stream token id.
+        eos_token_id (`int`, *optional*, defaults to 2):
+            End of stream token id.
+        pretraining_tp (`int`, *optional*, defaults to 1):
+            Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
+            document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism) to understand more about it. This value is
+            necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
+            issue](https://github.com/pytorch/pytorch/issues/76232).
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
+            strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
+            `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
+            `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
+            these scaling strategies behave:
+            https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
+            experimental feature, subject to breaking API changes in future versions.
+        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
+            Whether to use a bias in the query, key, value and output projection layers during self-attention.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        num_loops (`int`, *optional*, defaults to 1):
+            The number of loops for the loop model.
+        loop_loss_weights (`List[float]`, *optional*, defaults to `[]`):
+            The weights for the loop loss.
+        skip_loop_final_norm (`bool`, *optional*, defaults to `False`):
+            Whether to skip final norm after each loop (except the last one).
+
+    ```python
+    >>> from configuration_nanbeige import NanbeigeConfig
+    >>> from modeling_nanbeige import NanbeigeModel
+
+    >>> # Initializing a Nanbeige nanbeige-7b style configuration
+    >>> configuration = NanbeigeConfig()
+
+    >>> # Initializing a model from the nanbeige-7b style configuration
+    >>> model = NanbeigeModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "nanbeige"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=166144,
+        hidden_size=3072,
+        intermediate_size=10752,
+        num_hidden_layers=22,
+        num_attention_heads=48,
+        num_key_value_heads=None,
+        head_dim=None,
+        hidden_act="silu",
+        max_position_embeddings=4096,
+        initializer_range=0.02,
+        rms_norm_eps=1e-5,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=1,
+        eos_token_id=2,
+        pretraining_tp=1,
+        tie_word_embeddings=False,
+        rope_theta=50000.0,
+        rope_scaling=None,
+        attention_bias=False,
+        attention_dropout=0.0,
+        num_loops=2,
+        loop_loss_weights=None,
+        skip_loop_final_norm=False,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        
+        # Add head_dim logic
+        if head_dim is not None:
+            self.head_dim = head_dim
+        else:
+            self.head_dim = hidden_size // num_attention_heads
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.pretraining_tp = pretraining_tp
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self._rope_scaling_validation()
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+        
+        self.num_loops = num_loops
+        self.loop_loss_weights = loop_loss_weights if loop_loss_weights is not None else []
+        self.skip_loop_final_norm = skip_loop_final_norm
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+    def _rope_scaling_validation(self):
+        """
+        Validate the `rope_scaling` configuration.
+        """
+        if self.rope_scaling is None:
+            return
+
+        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
+            raise ValueError(
+                "`rope_scaling` must be a dictionary with two fields, `type` and `factor`, " f"got {self.rope_scaling}"
+            )
+        rope_scaling_type = self.rope_scaling.get("type", None)
+        rope_scaling_factor = self.rope_scaling.get("factor", None)
+        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
+            raise ValueError(
+                f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
+            )
+        if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
+            raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")
+