remove qwen2.py llama.py fix llama output

vllm_kunlun/models/__init__.py

@@ -5,7 +5,6 @@ def register_model():
     # from .demo_model import DemoModel  # noqa: F401
     from .qwen2_vl import Qwen2VLForConditionalGeneration #noqa: F401
     from .qwen2_5_vl import Qwen2_5_VLForConditionalGeneration #noqa: F401
-    from .qwen3 import Qwen3ForCausalLM #noqa: F401
     from .qwen3_moe import Qwen3MoeForCausalLM #noqa: F401
     from .qwen3_vl import Qwen3VLForConditionalGeneration
     from .qwen3_vl_moe import Qwen3VLMoeForConditionalGeneration
@@ -48,11 +47,7 @@ def register_model():
 
     ModelRegistry.register_model(
         "InternLM2ForCausalLM",
-        "vllm_kunlun.models.internlm2:InternLM2ForCausalLM")   
-
-    ModelRegistry.register_model(
-        "Qwen2ForCausalLM",
-        "vllm_kunlun.models.qwen2:Qwen2ForCausalLM")
+        "vllm_kunlun.models.internlm2:InternLM2ForCausalLM")
     
     ModelRegistry.register_model(
         "InternVLChatModel",
@@ -78,10 +73,6 @@ def register_model():
         "SeedOssForCausalLM",
         "vllm_kunlun.models.seed_oss:SeedOssForCausalLM")
 
-    ModelRegistry.register_model(
-        "LlamaForCausalLM",
-        "vllm_kunlun.models.llama:LlamaForCausalLM")
-
     ModelRegistry.register_model(
         "MiMoV2FlashForCausalLM",
         "vllm_kunlun.models.mimo_v2_flash:MiMoV2FlashForCausalLM")

vllm_kunlun/models/llama.py

@@ -1,673 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-# Adapted from
-# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
-# Copyright 2023 The vLLM team.
-# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
-#
-# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
-# and OPT implementations in this library. It has been modified from its
-# original forms to accommodate minor architectural differences compared
-# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
-#
-# 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 itertools import islice
-from typing import Any, Optional, Union
-
-import torch
-from torch import nn
-from transformers import LlamaConfig
-
-from vllm.attention import AttentionType
-from vllm_kunlun.ops.attention.layer import Attention
-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_kunlun.ops.activation import SiluAndMul
-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_kunlun.ops.vocab_parallel_embedding import VocabParallelEmbedding
-
-from vllm.model_executor.layers.vocab_parallel_embedding import (
-    DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead)
-from vllm.model_executor.model_loader.weight_utils import (
-    default_weight_loader, maybe_remap_kv_scale_name)
-from vllm.sequence import IntermediateTensors
-
-from vllm.model_executor.models.interfaces import 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 LlamaMLP(nn.Module):
-
-    def __init__(
-        self,
-        hidden_size: int,
-        intermediate_size: int,
-        hidden_act: str,
-        quant_config: Optional[QuantizationConfig] = None,
-        bias: bool = False,
-        prefix: str = "",
-        reduce_results: bool = True,
-        disable_tp: bool = False,
-    ) -> None:
-        super().__init__()
-        self.gate_up_proj = MergedColumnParallelLinear(
-            input_size=hidden_size,
-            output_sizes=[intermediate_size] * 2,
-            bias=bias,
-            quant_config=quant_config,
-            disable_tp=disable_tp,
-            prefix=f"{prefix}.gate_up_proj",
-        )
-        self.down_proj = RowParallelLinear(
-            input_size=intermediate_size,
-            output_size=hidden_size,
-            bias=bias,
-            quant_config=quant_config,
-            reduce_results=reduce_results,
-            disable_tp=disable_tp,
-            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):
-        x, _ = self.gate_up_proj(x)
-        x = self.act_fn(x)
-        x, _ = self.down_proj(x)
-        return x
-
-
-class LlamaAttention(nn.Module):
-
-    def __init__(
-        self,
-        config: LlamaConfig,
-        hidden_size: int,
-        num_heads: int,
-        num_kv_heads: int,
-        rope_theta: float = 10000,
-        rope_scaling: Optional[dict[str, Any]] = None,
-        max_position_embeddings: int = 8192,
-        quant_config: Optional[QuantizationConfig] = None,
-        bias: bool = False,
-        bias_o_proj: bool = False,
-        cache_config: Optional[CacheConfig] = None,
-        prefix: str = "",
-        attn_type: str = AttentionType.DECODER,
-    ) -> None:
-        super().__init__()
-        layer_idx = extract_layer_index(prefix)
-        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)
-        # MistralConfig has an optional head_dim introduced by Mistral-Nemo
-        head_dim = getattr(config, "head_dim", None)
-        if head_dim is None:
-            head_dim = self.hidden_size // self.total_num_heads
-        self.head_dim = head_dim
-        # Phi models introduced a partial_rotary_factor parameter in the config
-        self.partial_rotary_factor = getattr(config, "partial_rotary_factor",
-                                             1)
-        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.rope_theta = rope_theta
-        self.max_position_embeddings = max_position_embeddings
-
-        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",
-        )
-
-        self._init_rotary_emb(config,
-                              rope_scaling=rope_scaling,
-                              quant_config=quant_config)
-
-        sliding_window = None
-        if layer_types := getattr(config, "layer_types", None):
-            # Fix for Eagle3 compatibility:
-            # for draft models, subtract target layer count
-            # to get draft-relative layer index starting from 0
-            if hasattr(config, 'target_layer_count'):
-                # This is a draft model,
-                # adjust layer_idx to be relative to draft layers
-                effective_layer_idx = layer_idx - config.target_layer_count
-            else:
-                # This is a target model, use layer_idx directly
-                effective_layer_idx = layer_idx
-            assert effective_layer_idx < len(layer_types), \
-                f"effective_layer_idx: {effective_layer_idx} \
-                is out of bounds for layer_types: {layer_types}"
-
-            is_sliding = layer_types[
-                effective_layer_idx] == "sliding_attention"
-            if is_sliding:
-                sliding_window = config.sliding_window
-
-        attn_cls = (EncoderOnlyAttention
-                    if attn_type == AttentionType.ENCODER_ONLY 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,
-            per_layer_sliding_window=sliding_window,
-            attn_type=attn_type,
-            prefix=f"{prefix}.attn",
-        )
-
-    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)
-        q, k = self.rotary_emb(positions, q, k)
-        attn_output = self.attn(q, k, v)
-        output, _ = self.o_proj(attn_output)
-        return output
-
-    def _init_rotary_emb(self, config: LlamaConfig,
-                         rope_scaling: Optional[dict[str, Any]],
-                         quant_config: Optional[QuantizationConfig]) -> None:
-        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=self.max_position_embeddings,
-            base=self.rope_theta,
-            rope_scaling=rope_scaling,
-            is_neox_style=is_neox_style,
-            partial_rotary_factor=self.partial_rotary_factor,
-        )
-
-
-class LlamaDecoderLayer(nn.Module):
-
-    def __init__(self,
-                 vllm_config: VllmConfig,
-                 prefix: str = "",
-                 config: Optional[LlamaConfig] = 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.hidden_size = config.hidden_size
-        rope_theta = getattr(config, "rope_theta", 10000)
-        rope_scaling = getattr(config, "rope_scaling", None)
-        if rope_scaling is not None and getattr(
-                config, "original_max_position_embeddings", None):
-            rope_scaling["original_max_position_embeddings"] = (
-                config.original_max_position_embeddings)
-        max_position_embeddings = getattr(config, "max_position_embeddings",
-                                          8192)
-        # Support abacusai/Smaug-72B-v0.1 with attention_bias
-        # Support internlm/internlm-7b with bias
-        attention_bias = getattr(config, "attention_bias", False) or getattr(
-            config, "bias", False)
-        bias_o_proj = attention_bias
-        # support internlm/internlm3-8b with qkv_bias
-        if hasattr(config, 'qkv_bias'):
-            attention_bias = config.qkv_bias
-
-        # By default, Llama uses causal attention as it is a decoder-only model.
-        # You can override the HF config with `is_causal=False` to enable
-        # bidirectional attention, which is used in some embedding models
-        # (e.g. parasail-ai/GritLM-7B-vllm)
-        if getattr(config, "is_causal", True):
-            attn_type = AttentionType.DECODER
-        else:
-            attn_type = AttentionType.ENCODER_ONLY
-
-        self.self_attn = LlamaAttention(
-            config=config,
-            hidden_size=self.hidden_size,
-            num_heads=config.num_attention_heads,
-            num_kv_heads=getattr(config, "num_key_value_heads",
-                                 config.num_attention_heads),
-            rope_theta=rope_theta,
-            rope_scaling=rope_scaling,
-            max_position_embeddings=max_position_embeddings,
-            quant_config=quant_config,
-            bias=attention_bias,
-            bias_o_proj=bias_o_proj,
-            cache_config=cache_config,
-            prefix=f"{prefix}.self_attn",
-            attn_type=attn_type,
-        )
-        self.mlp = LlamaMLP(
-            hidden_size=self.hidden_size,
-            intermediate_size=config.intermediate_size,
-            hidden_act=config.hidden_act,
-            quant_config=quant_config,
-            bias=getattr(config, "mlp_bias", False),
-            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: Optional[torch.Tensor],
-    ) -> 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.mlp(hidden_states)
-        return hidden_states, residual
-
-
-@support_torch_compile
-class LlamaModel(nn.Module):
-
-    def __init__(self,
-                 *,
-                 vllm_config: VllmConfig,
-                 prefix: str = "",
-                 layer_type: type[nn.Module] = LlamaDecoderLayer):
-        super().__init__()
-
-        config = vllm_config.model_config.hf_config
-        quant_config = vllm_config.quant_config
-        lora_config = vllm_config.lora_config
-
-        self.config = config
-        self.quant_config = quant_config
-        lora_vocab = (lora_config.lora_extra_vocab_size *
-                      (lora_config.max_loras or 1)) if lora_config else 0
-        self.vocab_size = config.vocab_size + lora_vocab
-        self.org_vocab_size = config.vocab_size
-        if get_pp_group().is_first_rank or (config.tie_word_embeddings
-                                            and get_pp_group().is_last_rank):
-            self.embed_tokens = VocabParallelEmbedding(
-                self.vocab_size,
-                config.hidden_size,
-                org_num_embeddings=config.vocab_size,
-                quant_config=quant_config,
-            )
-        else:
-            self.embed_tokens = PPMissingLayer()
-        self.start_layer, self.end_layer, self.layers = make_layers(
-            config.num_hidden_layers,
-            lambda prefix: layer_type(vllm_config=vllm_config, prefix=prefix),
-            prefix=f"{prefix}.layers",
-        )
-        if get_pp_group().is_last_rank:
-            self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        else:
-            self.norm = PPMissingLayer()
-
-        self.aux_hidden_state_layers = tuple[int, ...]()
-
-        self.make_empty_intermediate_tensors = (
-            make_empty_intermediate_tensors_factory(
-                ["hidden_states", "residual"], config.hidden_size))
-
-    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
-        return self.embed_tokens(input_ids)
-
-    def forward(
-        self,
-        input_ids: Optional[torch.Tensor],
-        positions: torch.Tensor,
-        intermediate_tensors: Optional[IntermediateTensors],
-        inputs_embeds: Optional[torch.Tensor] = None,
-    ) -> Union[torch.Tensor, IntermediateTensors, tuple[torch.Tensor,
-                                                        list[torch.Tensor]]]:
-        if get_pp_group().is_first_rank:
-            if inputs_embeds is not None:
-                hidden_states = inputs_embeds
-            else:
-                hidden_states = self.get_input_embeddings(input_ids)
-            residual = None
-        else:
-            assert intermediate_tensors is not None
-            hidden_states = intermediate_tensors["hidden_states"]
-            residual = intermediate_tensors["residual"]
-
-        aux_hidden_states = []
-        for idx, layer in enumerate(
-                islice(self.layers, self.start_layer, self.end_layer)):
-            if idx in self.aux_hidden_state_layers:
-                aux_hidden_states.append(hidden_states + residual)
-            hidden_states, residual = layer(positions, hidden_states, residual)
-
-        if not get_pp_group().is_last_rank:
-            return IntermediateTensors({
-                "hidden_states": hidden_states,
-                "residual": residual
-            })
-
-        hidden_states, _ = self.norm(hidden_states, residual)
-
-        if len(aux_hidden_states) > 0:
-            return hidden_states, aux_hidden_states
-        return hidden_states
-
-    def 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())
-        loaded_params: set[str] = set()
-        for name, loaded_weight in weights:
-            if "rotary_emb.inv_freq" in name:
-                continue
-            if ("rotary_emb.cos_cached" in name
-                    or "rotary_emb.sin_cached" in name):
-                # Models trained using ColossalAI may include these tensors in
-                # the checkpoint. Skip them.
-                continue
-            if (self.quant_config is not None and
-                (scale_name := self.quant_config.get_cache_scale(name))):
-                # 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
-            if "scale" in name:
-                # Remapping the name of FP8 kv-scale.
-                name = maybe_remap_kv_scale_name(name, params_dict)
-                if name is None:
-                    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
-
-                param = params_dict[name]
-                weight_loader = param.weight_loader
-                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
-
-                if is_pp_missing_parameter(name, self):
-                    continue
-
-                param = params_dict[name]
-                weight_loader = getattr(param, "weight_loader",
-                                        default_weight_loader)
-                weight_loader(param, loaded_weight)
-            loaded_params.add(name)
-        return loaded_params
-
-
-class LlamaForCausalLM(nn.Module, SupportsLoRA, SupportsPP, SupportsEagle3):
-    packed_modules_mapping = {
-        "qkv_proj": ["q_proj", "k_proj", "v_proj"],
-        "gate_up_proj": ["gate_proj", "up_proj"]
-    }
-
-    # LoRA specific attributes
-    embedding_modules = {
-        "embed_tokens": "input_embeddings",
-        "lm_head": "output_embeddings"
-    }
-    embedding_padding_modules = ["lm_head"]
-
-    # Mistral/Llama models can also be loaded with --load-format mistral
-    # from consolidated.safetensors checkpoints
-    mistral_mapping = {
-        "layers": "model.layers",
-        "attention": "self_attn",
-        "qscale_act": "input_scale",
-        "qscale_weight": "weight_scale",
-        "kv_fake_quantizer.qscale_act": "kv_scale",
-        "q_fake_quantizer.qscale_act": "attn.q_scale",
-        "k_fake_quantizer.qscale_act": "k_scale",
-        "v_fake_quantizer.qscale_act": "v_scale",
-        "wq": "q_proj",
-        "wk": "k_proj",
-        "wv": "v_proj",
-        "wo": "o_proj",
-        "attention_norm": "input_layernorm",
-        "feed_forward": "mlp",
-        "w1": "gate_proj",
-        "w2": "down_proj",
-        "w3": "up_proj",
-        "ffn_norm": "post_attention_layernorm",
-        "tok_embeddings": "model.embed_tokens",
-        "output": "lm_head",
-        "norm": "model.norm",
-    }
-
-    def __init__(self,
-                 *,
-                 vllm_config: VllmConfig,
-                 prefix: str = "",
-                 layer_type: type[nn.Module] = LlamaDecoderLayer):
-        super().__init__()
-        config = vllm_config.model_config.hf_config
-        quant_config = vllm_config.quant_config
-        lora_config = vllm_config.lora_config
-        self.config = config
-        self.lora_config = lora_config
-
-        self.model = self._init_model(vllm_config=vllm_config,
-                                      prefix=maybe_prefix(prefix, "model"),
-                                      layer_type=layer_type)
-
-        if get_pp_group().is_last_rank:
-            self.unpadded_vocab_size = config.vocab_size
-            if lora_config:
-                self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
-            self.lm_head = ParallelLMHead(
-                self.unpadded_vocab_size,
-                config.hidden_size,
-                org_num_embeddings=config.vocab_size,
-                padding_size=(
-                    DEFAULT_VOCAB_PADDING_SIZE
-                    # We need bigger padding if using lora for kernel
-                    # compatibility
-                    if not lora_config else
-                    lora_config.lora_vocab_padding_size),
-                quant_config=quant_config,
-                prefix=maybe_prefix(prefix, "lm_head"),
-            )
-            if config.tie_word_embeddings:
-                self.lm_head = self.lm_head.tie_weights(
-                    self.model.embed_tokens)
-
-            logit_scale = getattr(config, "logit_scale", 1.0)
-            self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
-                                                    config.vocab_size,
-                                                    logit_scale)
-        else:
-            self.lm_head = PPMissingLayer()
-
-        self.make_empty_intermediate_tensors = (
-            self.model.make_empty_intermediate_tensors)
-
-    def set_aux_hidden_state_layers(self, layers: tuple[int, ...]) -> None:
-        self.model.aux_hidden_state_layers = layers
-
-    def get_eagle3_aux_hidden_state_layers(self) -> tuple[int, ...]:
-        num_layers = len(self.model.layers)
-        return (2, num_layers // 2, num_layers - 3)
-
-    def _init_model(self,
-                    vllm_config: VllmConfig,
-                    prefix: str = "",
-                    layer_type: type[nn.Module] = LlamaDecoderLayer):
-        return LlamaModel(vllm_config=vllm_config,
-                          prefix=prefix,
-                          layer_type=layer_type)
-
-    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
-        return self.model.get_input_embeddings(input_ids)
-
-    def forward(
-        self,
-        input_ids: torch.Tensor,
-        positions: torch.Tensor,
-        intermediate_tensors: Optional[IntermediateTensors] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-    ) -> Union[torch.Tensor, IntermediateTensors]:
-        model_output = self.model(input_ids, positions, intermediate_tensors,
-                                  inputs_embeds)
-        return model_output
-
-    def compute_logits(
-        self,
-        hidden_states: torch.Tensor,
-    ) -> Optional[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(
-            self.maybe_remap_mistral(name, loaded_weight)
-            for name, loaded_weight in weights)
-
-    # This function is used to remap the mistral format as
-    # used by Mistral and Llama <=2
-    def maybe_remap_mistral(
-        self,
-        name: str,
-        loaded_weight: torch.Tensor,
-    ) -> tuple[str, torch.Tensor]:
-
-        def permute(w: torch.Tensor, n_heads: int, attn_out: int):
-            attn_in = self.config.head_dim * n_heads
-
-            return w.view(n_heads, attn_in // n_heads // 2, 2,
-                          attn_out).transpose(1, 2).reshape(attn_in, attn_out)
-
-        mapping = self.mistral_mapping
-        modules = name.split(".")
-
-        # rotary embeds should be sliced
-        # If using quantized model in mistral format,
-        # quantization scales (qscale_weight) also need to be sliced
-        if "wk" in modules and modules[-1] == "weight":
-            loaded_weight = permute(loaded_weight,
-                                    self.config.num_key_value_heads,
-                                    self.config.hidden_size)
-        elif "wk" in modules and modules[
-                -1] == "qscale_weight" and loaded_weight.numel() > 1:
-            loaded_weight = permute(loaded_weight,
-                                    self.config.num_key_value_heads, 1)
-        elif "wq" in modules and modules[-1] == "weight":
-            loaded_weight = permute(loaded_weight,
-                                    self.config.num_attention_heads,
-                                    self.config.hidden_size)
-        elif "wq" in modules and modules[
-                -1] == "qscale_weight" and loaded_weight.numel() > 1:
-            loaded_weight = permute(loaded_weight,
-                                    self.config.num_attention_heads, 1)
-
-        num_modules = len(modules)
-        for i in range(num_modules):
-            item = modules[i]
-            next_item = modules[i + 1] if i < num_modules - 1 else None
-
-            combined_item = (f"{item}.{next_item}"
-                             if next_item is not None else None)
-
-            if combined_item in mapping:
-                name = name.replace(combined_item, mapping[combined_item])
-            elif item in mapping and mapping[item] not in name:
-                name = name.replace(item, mapping[item])
-
-        return name, loaded_weight
-

vllm_kunlun/models/qwen2.py

@@ -1,511 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-# Adapted from
-# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/qwen2/modeling_qwen2.py
-# Copyright 2024 The Qwen team.
-# Copyright 2023 The vLLM team.
-# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
-#
-# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
-# and OPT implementations in this library. It has been modified from its
-# original forms to accommodate minor architectural differences compared
-# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
-#
-# 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 Qwen2 model compatible with HuggingFace weights."""
-import os
-
-from collections.abc import Iterable
-from typing import Any, Optional, Union
-
-import torch
-from torch import nn
-from transformers import Qwen2Config
-
-from vllm.attention import AttentionType
-from vllm_kunlun.ops.attention.layer import Attention
-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_kunlun.ops.activation import SiluAndMul
-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)
-from vllm_kunlun.ops.vocab_parallel_embedding import VocabParallelEmbedding
-from vllm.model_executor.model_loader.weight_utils import (
-    default_weight_loader, maybe_remap_kv_scale_name)
-# from vllm.model_executor.sampling_metadata import SamplingMetadata
-from vllm.sequence import IntermediateTensors
-
-from vllm.model_executor.models.adapters import as_seq_cls_model
-from vllm.model_executor.models.interfaces import 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 Qwen2MLP(nn.Module):
-
-    def __init__(
-        self,
-        hidden_size: int,
-        intermediate_size: int,
-        hidden_act: str,
-        quant_config: Optional[QuantizationConfig] = 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 Qwen2Attention(nn.Module):
-
-    def __init__(
-        self,
-        hidden_size: int,
-        num_heads: int,
-        num_kv_heads: int,
-        max_position: int = 4096 * 32,
-        rope_theta: float = 10000,
-        cache_config: Optional[CacheConfig] = None,
-        quant_config: Optional[QuantizationConfig] = None,
-        rope_scaling: Optional[tuple] = None,
-        prefix: str = "",
-        attn_type: str = AttentionType.DECODER,
-        dual_chunk_attention_config: Optional[dict[str, Any]] = None,
-    ) -> 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.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.rope_theta = rope_theta
-        self.dual_chunk_attention_config = dual_chunk_attention_config
-
-        self.qkv_proj = QKVParallelLinear(
-            hidden_size,
-            self.head_dim,
-            self.total_num_heads,
-            self.total_num_kv_heads,
-            bias=True,
-            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",
-        )
-
-        self.rotary_emb = get_rope(
-            self.head_dim,
-            rotary_dim=self.head_dim,
-            max_position=max_position,
-            base=self.rope_theta,
-            rope_scaling=rope_scaling,
-            dual_chunk_attention_config=dual_chunk_attention_config,
-        )
-        self.attn = Attention(
-            self.num_heads,
-            self.head_dim,
-            self.scaling,
-            num_kv_heads=self.num_kv_heads,
-            cache_config=cache_config,
-            quant_config=quant_config,
-            attn_type=attn_type,
-            prefix=f"{prefix}.attn",
-            **{
-                "layer_idx": extract_layer_index(prefix),
-                "dual_chunk_attention_config": dual_chunk_attention_config,
-            } if dual_chunk_attention_config else {})
-
-    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)
-        # INTERNVL_3暂时使用环境变量来控制是否使用原生rotary_embedding
-        # 若要修改，可尝试参考 qwen3.py
-        if os.getenv('INTERNVL_3') == "1":
-            q, k = self.rotary_emb.forward_native(positions, q, k)
-        else:
-            q, k = self.rotary_emb(positions, q, k)
-        attn_output = self.attn(q, k, v)
-        output, _ = self.o_proj(attn_output)
-        return output
-
-
-class Qwen2DecoderLayer(nn.Module):
-
-    def __init__(
-        self,
-        config: Qwen2Config,
-        cache_config: Optional[CacheConfig] = None,
-        quant_config: Optional[QuantizationConfig] = None,
-        prefix: str = "",
-    ) -> None:
-        super().__init__()
-        self.hidden_size = config.hidden_size
-        # Requires transformers > 4.32.0
-        rope_theta = getattr(config, "rope_theta", 1000000)
-        rope_scaling = getattr(config, "rope_scaling", None)
-        dual_chunk_attention_config = getattr(config,
-                                              "dual_chunk_attention_config",
-                                              None)
-
-        # By default, Qwen2 uses causal attention as it is a decoder-only model.
-        # You can override the HF config with `is_causal=False` to enable
-        # bidirectional attention, which is used in some embedding models
-        # (e.g. Alibaba-NLP/gte-Qwen2-7B-instruct)
-        if getattr(config, "is_causal", True):
-            attn_type = AttentionType.DECODER
-        else:
-            attn_type = AttentionType.ENCODER_ONLY
-
-        self.self_attn = Qwen2Attention(
-            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,
-            rope_theta=rope_theta,
-            cache_config=cache_config,
-            quant_config=quant_config,
-            rope_scaling=rope_scaling,
-            prefix=f"{prefix}.self_attn",
-            attn_type=attn_type,
-            dual_chunk_attention_config=dual_chunk_attention_config,
-        )
-        self.mlp = Qwen2MLP(
-            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: Optional[torch.Tensor],
-    ) -> 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.mlp(hidden_states)
-        return hidden_states, residual
-
-
-@support_torch_compile(
-    dynamic_arg_dims={
-        "input_ids": 0,
-        # positions is of shape (3, seq_len) if mrope is enabled for qwen2-vl,
-        # otherwise (seq_len, ).
-        "positions": -1,
-        "intermediate_tensors": 0,
-        "inputs_embeds": 0,
-    })
-class Qwen2Model(nn.Module):
-
-    def __init__(self,
-                 *,
-                 vllm_config: VllmConfig,
-                 prefix: str = "",
-                 decoder_layer_type: type[nn.Module] = Qwen2DecoderLayer):
-        super().__init__()
-
-        config = vllm_config.model_config.hf_config
-        cache_config = vllm_config.cache_config
-        quant_config = vllm_config.quant_config
-
-        # TODO (@robertgshaw2): see if this can be moved out
-        if (cache_config.sliding_window is not None
-                and hasattr(config, "max_window_layers")):
-            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
-        config = config.get_text_config()
-        self.quant_config = quant_config
-        self.vocab_size = config.vocab_size
-
-        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()
-
-        # Use the provided decoder layer type or default to Qwen2DecoderLayer
-        decoder_layer_type = decoder_layer_type or Qwen2DecoderLayer
-        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 get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
-        return self.embed_tokens(input_ids)
-
-    def forward(
-        self,
-        input_ids: torch.Tensor,
-        positions: torch.Tensor,
-        intermediate_tensors: Optional[IntermediateTensors] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-    ) -> Union[torch.Tensor, IntermediateTensors]:
-        if get_pp_group().is_first_rank:
-            if inputs_embeds is not None:
-                hidden_states = inputs_embeds
-            else:
-                hidden_states = self.get_input_embeddings(input_ids)
-            residual = None
-        else:
-            assert intermediate_tensors is not None
-            hidden_states = intermediate_tensors["hidden_states"]
-            residual = intermediate_tensors["residual"]
-        for layer in self.layers[self.start_layer:self.end_layer]:
-            hidden_states, residual = layer(
-                positions,
-                hidden_states,
-                residual,
-            )
-        if not get_pp_group().is_last_rank:
-            return IntermediateTensors({
-                "hidden_states": hidden_states,
-                "residual": residual
-            })
-        hidden_states, _ = self.norm(hidden_states, residual)
-        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
-            if (self.quant_config is not None and
-                (scale_name := self.quant_config.get_cache_scale(name))):
-                # 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
-                param = params_dict[name]
-                weight_loader = param.weight_loader
-                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
-                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 Qwen2ForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
-    packed_modules_mapping = {
-        "qkv_proj": [
-            "q_proj",
-            "k_proj",
-            "v_proj",
-        ],
-        "gate_up_proj": [
-            "gate_proj",
-            "up_proj",
-        ],
-    }
-
-    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
-        super().__init__()
-        config = vllm_config.model_config.hf_config
-        quant_config = vllm_config.quant_config
-        lora_config = vllm_config.lora_config
-
-        self.config = config
-        self.lora_config = lora_config
-
-        self.quant_config = quant_config
-        self.model = Qwen2Model(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 get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
-        return self.model.get_input_embeddings(input_ids)
-
-    def forward(
-        self,
-        input_ids: torch.Tensor,
-        positions: torch.Tensor,
-        intermediate_tensors: Optional[IntermediateTensors] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-    ) -> Union[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,
-        # sampling_metadata: SamplingMetadata,
-    ) -> Optional[torch.Tensor]:
-        logits = self.logits_processor(self.lm_head, hidden_states,)
-                                    #    sampling_metadata)
-        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)
-
-
-Qwen2ForSequenceClassification = as_seq_cls_model(Qwen2ForCausalLM)

vllm_kunlun/models/qwen3.py

@@ -47,8 +47,8 @@ from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
 from vllm.sequence import IntermediateTensors
 
 from vllm.model_executor.models.interfaces import SupportsEagle3, SupportsLoRA, SupportsPP
-from .qwen2 import Qwen2MLP as Qwen3MLP
-from .qwen2 import Qwen2Model
+from vllm.model_executor.models.qwen2 import Qwen2MLP as Qwen3MLP
+from vllm.model_executor.models.qwen2 import Qwen2Model
 from vllm.model_executor.models.utils import (AutoWeightsLoader, PPMissingLayer, extract_layer_index,
                     maybe_prefix)
 

vllm_kunlun/models/qwen3_vl.py

@@ -80,8 +80,8 @@ from .qwen2_5_vl import (Qwen2_5_VisionAttention,
                          Qwen2_5_VLImagePixelInputs,
                          Qwen2_5_VLVideoEmbeddingInputs, Qwen2_5_VLVideoInputs,
                          Qwen2_5_VLVideoPixelInputs)
-from .qwen2_vl import Qwen2VLProcessingInfo
-from .qwen3 import Qwen3ForCausalLM, Qwen3Model
+from vllm.model_executor.models.qwen2_vl import Qwen2VLProcessingInfo
+from vllm.model_executor.models.qwen3 import Qwen3ForCausalLM, Qwen3Model
 from vllm.model_executor.models.utils import (AutoWeightsLoader, PPMissingLayer, WeightsMapper,
                     maybe_prefix, merge_multimodal_embeddings)
 from vllm.model_executor.models.vision import get_vit_attn_backend, run_dp_sharded_mrope_vision_model

vllm_kunlun/models/qwen3_vl_moe.py

@@ -42,8 +42,8 @@ from vllm.model_executor.model_loader.weight_utils import (
 from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.sequence import IntermediateTensors
 
-from .qwen3_moe import Qwen3MoeForCausalLM, Qwen3MoeModel
-from .qwen3_vl import (Qwen3_VisionTransformer, Qwen3VLDummyInputsBuilder,
+from vllm.model_executor.models.qwen3_moe import Qwen3MoeForCausalLM, Qwen3MoeModel
+from vllm.model_executor.models.qwen3_vl import (Qwen3_VisionTransformer, Qwen3VLDummyInputsBuilder,
                        Qwen3VLForConditionalGeneration,
                        Qwen3VLMultiModalProcessor, Qwen3VLProcessingInfo)
 from vllm.model_executor.models.utils import is_pp_missing_parameter, maybe_prefix

vllm_kunlun/ops/__init__.py

@@ -19,4 +19,5 @@ import vllm_kunlun.ops.rotary_embedding
 import vllm_kunlun.ops.layernorm
 import vllm_kunlun.ops.quantization.awq
 import vllm_kunlun.ops.quantization.gptq
-import vllm_kunlun.ops.vocab_parallel_embedding
+import vllm_kunlun.ops.vocab_parallel_embedding
+import vllm_kunlun.ops.linear

vllm_kunlun/ops/_kunlun_ops.py

@@ -491,7 +491,7 @@ class KunlunOps:
         d = y.shape[-1] // 2
         output_shape = (y.shape[:-1] + (d, ))
         out1 = torch.empty(output_shape, dtype=y.dtype, device=y.device)
-        torch.ops._C.swiglu(y, out1)
+        torch.ops._C.silu_and_mul(out1, y)
 
         out = torch.empty(M,moe_top_k,
                 w2.shape[1],
@@ -570,7 +570,7 @@ class KunlunOps:
                 cur_token = repeat_x[selected_token]
                 up_gate = torch.empty(selected_token.sum(), up_gate_size//2, 
                         dtype=cur_token.dtype, device=cur_token.device)
-                torch.ops._C.swiglu(cur_token@ w13_weight[i].T, up_gate)
+                torch.ops._C.silu_and_mul(up_gate, cur_token@ w13_weight[i].T)
                 out[selected_token] = up_gate @ w2_weight[i].T
         output = (out.view(batch, top_k, hidden_size) * topk_weights.unsqueeze(2)).sum(dim=1).to(x.dtype)
 

vllm_kunlun/ops/activation.py

@@ -98,7 +98,7 @@ class SiluAndMul(CustomOp):
         d = x.shape[-1] // 2
         output_shape = (x.shape[:-1] + (d, ))
         out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
-        torch.ops._C.swiglu(x, out)
+        torch.ops._C.silu_and_mul(out, x)
         return out
 
     def forward_kunlun(self, x: torch.Tensor) -> torch.Tensor:

vllm_kunlun/v1/attention/backends/kunlun_attn.py

@@ -43,7 +43,7 @@ from vllm.v1.kv_cache_interface import AttentionSpec
 from vllm.v1.worker.block_table import BlockTable
 
 from vllm.config import VllmConfig, get_layers_from_vllm_config
-
+import inspect
 
 class KunlunAttentionBackend(AttentionBackend):
     """KunlunAttentionBackend"""
@@ -723,30 +723,45 @@ class KunlunAttentionImpl(AttentionImpl[KunlunMetadata]):
                 tmp_block_tables = decode_meta.block_tables
             else:
                 tmp_block_tables = decode_meta.block_tables * 2 # only test in Qwen3-Next
-                
-            xtorch_ops.speculative_attention(
-                out=output[:num_decode_tokens],
-                # Only MLA support q len > 1 right now         
-                q=decode_query.unsqueeze(0),                
-                k_cache=key_cache,                  
-                v_cache=value_cache,                        
-                context_lens_cpu=decode_meta.seq_lens_tensor_cpu, 
-                context_lens_xpu=decode_meta.seq_lens_tensor,  
-                batch_num=decode_meta.block_tables.shape[0],   
-                # TODO (@xyDong23): Support MTP(q lens >1)                   
-                qlen=1,     
-                # TODO (@xyDong23): Support max_context_len to (262144)                           
-                max_context_len=131072,                  
-                head_num=self.num_heads,                      
-                head_dim=self.head_size,                   
-                scale=0.0,                                 
-                kv_head_num=self.num_kv_heads,                   
-                block_size=key_cache.shape[2],                            
-                max_num_blocks_per_seq=decode_meta.block_tables.shape[1], 
-                max_window_size=self.sliding_window if self.sliding_window is not None else -1, 
-                block_tables=tmp_block_tables,          
-                sink = self.sinks.to(torch.float32) if self.sinks is not None else None          
-            )
+            
+            sig = inspect.signature(xtorch_ops.speculative_attention)
+            if "max_window_size" in sig.parameters:
+                xtorch_ops.speculative_attention(
+                    out=output[:num_decode_tokens],
+                    # Only MLA support q len > 1 right now         
+                    q=decode_query.unsqueeze(0),                
+                    k_cache=key_cache,                  
+                    v_cache=value_cache,                        
+                    context_lens_cpu=decode_meta.seq_lens_tensor_cpu, 
+                    context_lens_xpu=decode_meta.seq_lens_tensor,  
+                    batch_num=decode_meta.block_tables.shape[0],   
+                    # TODO (@xyDong23): Support MTP(q lens >1)                   
+                    qlen=1,     
+                    # TODO (@xyDong23): Support max_context_len to (262144)                           
+                    max_context_len=131072,                  
+                    head_num=self.num_heads,                      
+                    head_dim=self.head_size,                   
+                    scale=0.0,                                 
+                    kv_head_num=self.num_kv_heads,                   
+                    block_size=key_cache.shape[2],                            
+                    max_num_blocks_per_seq=decode_meta.block_tables.shape[1], 
+                    max_window_size=self.sliding_window if self.sliding_window is not None else -1, 
+                    block_tables=tmp_block_tables,          
+                    sink = self.sinks.to(torch.float32) if self.sinks is not None else None          
+                )
+            else:
+                xtorch_ops.paged_attention(
+                    x=decode_query,
+                    k_cache=key_cache,
+                    v_cache=value_cache,
+                    block_tables=tmp_block_tables,
+                    context_lens_cpu=decode_meta.seq_lens_tensor_cpu,
+                    context_lens_xpu=decode_meta.seq_lens_tensor,
+                    is_context=False,
+                    is_causal=True,
+                    out=output[:num_decode_tokens],
+                    vo_head_dim=self.head_size
+                )      
         # Reshape the output tensor.
         return output.view(-1, self.num_heads * self.head_size)
 def use_cascade_attention(

vllm_kunlun/vllm_utils_wrapper.py

@@ -323,27 +323,6 @@ def rms_norm_dynamic_per_token_quant_xpu(
 )->None:
     pass
 
-@custom_op("_C::silu_and_mul", mutates_args=())
-def silu_and_mul(
-    result : torch.Tensor,
-    input: torch.Tensor,
-    residual: torch.Tensor,
-    weight: torch.Tensor,
-    scale: torch.Tensor,
-    epsilon: float
-)->None:
-    pass
-@impl("_C::silu_and_mul", "CUDA")
-def silu_and_mul_xpu(
-    result : torch.Tensor,
-    input: torch.Tensor,
-    residual: torch.Tensor,
-    weight: torch.Tensor,
-    scale: torch.Tensor,
-    epsilon: float
-)->None:
-    pass
-
 @custom_op("_C::silu_and_mul_quant", mutates_args=())
 def silu_and_mul_quant(
     result : torch.Tensor,
@@ -592,39 +571,39 @@ if hasattr(torch.ops.custom_ops, "fc_fusion"):
                         ) -> None:
         pass
 
-@custom_op("_C::swiglu", mutates_args=())
-def swiglu(
+@custom_op("_C::silu_and_mul", mutates_args=())
+def silu_and_mul(
+    out: torch.Tensor,
     x: torch.Tensor,
-    y: torch.Tensor,
     axis: int=-1,
     turn: bool=True
 ) -> None:
     xtorch_ops.swiglu(
-        x,
-        y,
+        x=x,
+        y=out,
     )
 
 
-@impl("_C::swiglu", "CUDA")
-def swiglu_cuda(
+@impl("_C::silu_and_mul", "CUDA")
+def silu_and_mul_cuda(
+    out: torch.Tensor,
     x: torch.Tensor,
-    y: torch.Tensor,
     axis: int=-1,
     turn: bool=True
 ) -> None:
     xtorch_ops.swiglu(
-        x,
-        y,
+        x=x,
+        y=out,
     )
 
-def _fake_swiglu(
+def _fake_silu_and_mul(
+    out: torch.Tensor,
     x: torch.Tensor,
-    y: torch.Tensor,
     axis: int=-1,
     turn: bool=True):
     return None
 
-swiglu.register_fake(_fake_swiglu)
+silu_and_mul.register_fake(_fake_silu_and_mul)