xc-llm-ascend/vllm_ascend/torchair/models/qwen3_moe.py

# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
# Copyright 2024 The Qwen team.
# Copyright 2023 The vLLM team.
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Adapted from vllm/model_executor/models/qwen3_moe.py
# This file is a part of the vllm-ascend project.
from typing import Any, List, Optional, Union

import torch
from torch import nn
from transformers import PretrainedConfig
from vllm.attention import Attention, AttentionMetadata
from vllm.compilation.decorators import support_torch_compile
from vllm.config import CacheConfig, CompilationMode, VllmConfig
from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
from vllm.distributed.parallel_state import (get_dp_group, get_ep_group,
                                             get_tp_group)
from vllm.forward_context import get_forward_context
from vllm.model_executor.layers.fused_moe.layer import FusedMoE
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import (QKVParallelLinear,
                                               ReplicatedLinear,
                                               RowParallelLinear)
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.rotary_embedding import get_rope
from vllm.model_executor.layers.vocab_parallel_embedding import (
    ParallelLMHead, VocabParallelEmbedding)
from vllm.model_executor.models.interfaces import (MixtureOfExperts,
                                                   SupportsLoRA, SupportsPP)
from vllm.model_executor.models.qwen3_moe import (Qwen3MoeAttention,
                                                  Qwen3MoeDecoderLayer,
                                                  Qwen3MoeForCausalLM,
                                                  Qwen3MoeMLP, Qwen3MoeModel,
                                                  Qwen3MoeSparseMoeBlock)
from vllm.model_executor.models.utils import (
    PPMissingLayer, extract_layer_index,
    make_empty_intermediate_tensors_factory, make_layers, maybe_prefix)
from vllm.sequence import IntermediateTensors

from vllm_ascend.ascend_config import get_ascend_config
from vllm_ascend.attention.attention_v1 import AscendAttentionState
from vllm_ascend.torchair.ops.sequence_parallel import (MetadataForPadding,
                                                        init_metadata_for_sp)
from vllm_ascend.torchair.ops.torchair_fused_moe import TorchairAscendFusedMoE


class CustomSparseMoeBlock(Qwen3MoeSparseMoeBlock):

    def __init__(
        self,
        config: PretrainedConfig,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ):
        nn.Module.__init__(self)
        self.tp_size = get_tensor_model_parallel_world_size()
        if self.tp_size > config.num_experts:
            raise ValueError(
                f"Tensor parallel size {self.tp_size} is greater than "
                f"the number of experts {config.num_experts}.")

        self.gate = ReplicatedLinear(
            config.hidden_size,
            config.num_experts,
            bias=False,
            quant_config=None,
            prefix=f"{prefix}.gate",
        )

        self.experts = TorchairAscendFusedMoE(
            num_experts=config.num_experts,
            top_k=config.num_experts_per_tok,
            hidden_size=config.hidden_size,
            intermediate_size=config.moe_intermediate_size,
            reduce_results=False,
            renormalize=config.norm_topk_prob,
            quant_config=quant_config,
            prefix=f"{prefix}.experts",
        )

        self.top_k = config.num_experts_per_tok

        self.dp_size = get_dp_group().world_size

        self.tp_group = get_tp_group().device_group
        self.tp_rank = get_tp_group().rank_in_group
        self.ep_group = get_ep_group()

        self.params_dtype = torch.get_default_dtype()

    def forward(
        self,
        hidden_states,
        attn_metadata=None,
        _metadata_for_padding: Optional[MetadataForPadding] = None,
    ):
        if attn_metadata is None:
            attn_metadata = get_forward_context().attn_metadata
        # when profile runs, force experts to load balanced tokens
        # to avoid high memory consumption on a single rank.
        enable_force_load_balance = get_forward_context().in_profile_run
        is_prefill = get_forward_context().with_prefill

        # router_logits: (num_tokens, n_experts)
        router_logits, _ = self.gate(hidden_states)

        hidden_states = self.experts(
            hidden_states=hidden_states,
            router_logits=router_logits,
            is_prefill=is_prefill,
            top_k=self.top_k,
            enable_force_load_balance=enable_force_load_balance,
            shared_experts=None,
            _metadata_for_padding=_metadata_for_padding,
        )

        return hidden_states


class CustomQwen3MoeAttention(Qwen3MoeAttention):

    def __init__(
        self,
        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,
        head_dim: Optional[int] = None,
        rms_norm_eps: float = 1e-06,
        qkv_bias: bool = False,
        cache_config: Optional[CacheConfig] = None,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ) -> None:
        nn.Module.__init__(self)
        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 = head_dim or (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.max_position_embeddings = max_position_embeddings

        self.qkv_proj = QKVParallelLinear(hidden_size,
                                          self.head_dim,
                                          self.total_num_heads,
                                          self.total_num_kv_heads,
                                          bias=qkv_bias,
                                          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_embeddings,
            base=rope_theta,
            rope_scaling=rope_scaling,
        )
        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,
                              prefix=f"{prefix}.attn")

        self.q_norm = RMSNorm(self.head_dim, eps=rms_norm_eps)
        self.k_norm = RMSNorm(self.head_dim, eps=rms_norm_eps)
        ascend_config = get_ascend_config()
        self.torchair_graph_enabled = ascend_config.torchair_graph_config.enabled

    @staticmethod
    def normalize_qkv(qkv: torch.Tensor, q_size: int, kv_size: int,
                      head_dim: int, q_norm, k_norm):
        q, k, v = qkv.split([q_size, kv_size, kv_size], dim=-1)

        q_by_head = q.view(*q.shape[:-1], q.shape[-1] // head_dim, head_dim)
        q_by_head = q_norm(q_by_head)
        q = q_by_head.view(q.shape)

        k_by_head = k.view(*k.shape[:-1], k.shape[-1] // head_dim, head_dim)
        k_by_head = k_norm(k_by_head)
        k = k_by_head.view(k.shape)

        return q, k, v

    def forward(
            self,
            positions: torch.Tensor,
            hidden_states: torch.Tensor,
            kv_cache: Optional[torch.Tensor] = None,
            attn_metadata: Optional[AttentionMetadata] = None) -> torch.Tensor:
        qkv, _ = self.qkv_proj(hidden_states)
        q, k, v = self.normalize_qkv(qkv, self.q_size, self.kv_size,
                                     self.head_dim, self.q_norm, self.k_norm)

        if (self.torchair_graph_enabled and attn_metadata is not None and
                attn_metadata.attn_state == AscendAttentionState.DecodeOnly):
            q, k = self.rotary_emb(positions,
                                   q,
                                   k,
                                   is_prefill=False,
                                   is_qwen_torchair=True)
            forward_kwargs = {}
            output_shape = q.shape
            output = torch.empty(output_shape, dtype=q.dtype, device=q.device)
            forward_kwargs['output'] = output

            attn_output = self.attn.impl.forward(self.attn,
                                                 q,
                                                 k,
                                                 v,
                                                 kv_cache=kv_cache,
                                                 attn_metadata=attn_metadata,
                                                 **forward_kwargs)
            output, _ = self.o_proj(attn_output)
            return output
        else:
            q, k = self.rotary_emb(positions, q, k, is_qwen_torchair=True)
            attn_output = self.attn(q, k, v)
            output, _ = self.o_proj(attn_output)
            return output


class CustomQwen3MoeDecoderLayer(Qwen3MoeDecoderLayer):

    def __init__(
        self,
        config: PretrainedConfig,
        cache_config: Optional[CacheConfig] = None,
        quant_config: Optional[QuantizationConfig] = None,
        vllm_config: Optional[VllmConfig] = None,
        prefix: str = "",
    ) -> None:

        nn.Module.__init__(self)
        self.hidden_size = config.hidden_size
        rope_theta = getattr(config, "rope_theta", 10000)
        rope_scaling = getattr(config, "rope_scaling", None)
        max_position_embeddings = getattr(config, "max_position_embeddings",
                                          8192)
        self.self_attn = CustomQwen3MoeAttention(
            hidden_size=self.hidden_size,
            num_heads=config.num_attention_heads,
            num_kv_heads=config.num_key_value_heads,
            rope_theta=rope_theta,
            rope_scaling=rope_scaling,
            max_position_embeddings=max_position_embeddings,
            rms_norm_eps=config.rms_norm_eps,
            qkv_bias=getattr(config, 'attention_bias', False),
            head_dim=getattr(config, 'head_dim', None),
            cache_config=cache_config,
            quant_config=quant_config,
            prefix=f"{prefix}.self_attn",
        )

        # `mlp_only_layers` in the config.
        layer_idx = extract_layer_index(prefix)
        mlp_only_layers = ([] if not hasattr(config, "mlp_only_layers") else
                           config.mlp_only_layers)
        self.use_aclgraph = (vllm_config is not None
                             and vllm_config.compilation_config.mode
                             == CompilationMode.VLLM_COMPILE
                             and not vllm_config.model_config.enforce_eager)
        if (layer_idx not in mlp_only_layers) and (
                config.num_experts > 0 and
            (layer_idx + 1) % config.decoder_sparse_step == 0):
            if not self.use_aclgraph:
                # FIXME: custom sparse moe block doesn't work with aclgraph.
                self.mlp = CustomSparseMoeBlock(config=config,
                                                quant_config=quant_config,
                                                prefix=f"{prefix}.mlp")
            else:
                self.mlp = Qwen3MoeSparseMoeBlock(vllm_config=vllm_config,
                                                  prefix=f"{prefix}.mlp")
        else:
            self.mlp = Qwen3MoeMLP(hidden_size=config.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)

        self.enable_sequence_parallelism = (
            vllm_config.compilation_config.pass_config.
            enable_sequence_parallelism if vllm_config is not None else False)

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        residual: Optional[torch.Tensor],
        kv_cache: Optional[torch.Tensor] = None,
        attn_metadata: Optional[AttentionMetadata] = None,
        _metadata_for_padding: Optional[MetadataForPadding] = None,
    ) -> torch.Tensor:

        # To prevent precision issues during the decoder phase when only prefilling enables SP
        if not self.enable_sequence_parallelism:
            self.self_attn.o_proj.reduce_results = True
        else:
            self.self_attn.o_proj.reduce_results = not _metadata_for_padding.not_dummy_and_is_prefill if _metadata_for_padding is not None else True

        # Self Attention
        if residual is None:
            residual = hidden_states
            if _metadata_for_padding and _metadata_for_padding.not_dummy_and_is_prefill:
                residual = _metadata_for_padding.padding_slice(residual)

            hidden_states = self.input_layernorm(hidden_states)
        else:
            hidden_states, residual = self.input_layernorm(
                hidden_states, residual)

            if _metadata_for_padding and _metadata_for_padding.not_dummy_and_is_prefill:
                hidden_states = _metadata_for_padding.allgather_unpadding_aligned(
                    hidden_states)

        hidden_states = self.self_attn(
            positions=positions,
            hidden_states=hidden_states,
            kv_cache=kv_cache,
            attn_metadata=attn_metadata,
        )

        if _metadata_for_padding and _metadata_for_padding.not_dummy_and_is_prefill:
            hidden_states = _metadata_for_padding.padding_aligned_reduce_scatter(
                hidden_states)

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

        if not self.use_aclgraph:
            hidden_states = self.mlp(
                hidden_states, _metadata_for_padding=_metadata_for_padding)
        else:
            hidden_states = self.mlp(hidden_states)

        return hidden_states, residual


@support_torch_compile
class CustomQwen3MoeModel(Qwen3MoeModel):

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        nn.Module.__init__(self)
        config = vllm_config.model_config.hf_config
        cache_config = vllm_config.cache_config
        quant_config = vllm_config.quant_config

        parallel_config = vllm_config.parallel_config
        eplb_config = parallel_config.eplb_config
        self.num_redundant_experts = eplb_config.num_redundant_experts
        self.padding_idx = config.pad_token_id
        self.vocab_size = config.vocab_size
        self.config = config
        self.embed_tokens = VocabParallelEmbedding(
            config.vocab_size,
            config.hidden_size,
            prefix=f"{prefix}.embed_tokens")
        self.start_layer, self.end_layer, self.layers = make_layers(
            config.num_hidden_layers,
            lambda prefix: CustomQwen3MoeDecoderLayer(
                config=config,
                cache_config=cache_config,
                quant_config=quant_config,
                vllm_config=vllm_config,
                prefix=prefix),
            prefix=f"{prefix}.layers",
        )
        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.make_empty_intermediate_tensors = (
            make_empty_intermediate_tensors_factory(
                ["hidden_states", "residual"], config.hidden_size))

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        kv_caches: Optional[List[torch.Tensor]] = None,
        attn_metadata: Optional[AttentionMetadata] = None,
        intermediate_tensors: Optional[IntermediateTensors] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        _metadata_for_padding: Optional[MetadataForPadding] = 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 i in range(self.start_layer, self.end_layer):
            layer = self.layers[i]
            hidden_states, residual = layer(
                positions,
                hidden_states,
                residual,
                kv_caches[i -
                          self.start_layer] if kv_caches is not None else None,
                attn_metadata,
                _metadata_for_padding=_metadata_for_padding)
        if not get_pp_group().is_last_rank:
            return IntermediateTensors({
                "hidden_states": hidden_states,
                "residual": residual
            })

        hidden_states, _ = self.norm(hidden_states, residual)

        if _metadata_for_padding and _metadata_for_padding.not_dummy_and_is_prefill:
            hidden_states = _metadata_for_padding.allgather_unpadding_aligned(
                hidden_states)

        return hidden_states


class CustomQwen3MoeForCausalLM(Qwen3MoeForCausalLM):
    packed_modules_mapping = {
        "qkv_proj": [
            "q_proj",
            "k_proj",
            "v_proj",
        ],
        "gate_up_proj": [
            "gate_proj",
            "up_proj",
        ],
        "experts":
        ["experts.0.gate_proj", "experts.0.up_proj", "experts.0.down_proj"],
    }

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        nn.Module.__init__(self)
        SupportsPP.__init__(self)
        SupportsLoRA.__init__(self)
        MixtureOfExperts.__init__(self)
        config = vllm_config.model_config.hf_config
        quant_config = vllm_config.quant_config
        self.config = config
        self.quant_config = quant_config
        self.model = CustomQwen3MoeModel(vllm_config=vllm_config,
                                         prefix=maybe_prefix(prefix, "model"))
        self.lm_head = ParallelLMHead(config.vocab_size,
                                      config.hidden_size,
                                      quant_config=quant_config,
                                      prefix=maybe_prefix(prefix, "lm_head"))
        if self.config.tie_word_embeddings:
            self.lm_head.weight = self.model.embed_tokens.weight
        self.logits_processor = LogitsProcessor(config.vocab_size)
        self.make_empty_intermediate_tensors = (
            self.model.make_empty_intermediate_tensors)

        self.enable_sequence_parallelism = vllm_config.compilation_config.pass_config.enable_sequence_parallelism
        # Set MoE hyperparameters
        self.expert_weights: list[torch.Tensor] = []

        self.moe_layers: list[FusedMoE] = []
        example_layer = None
        for layer in self.model.layers:
            if isinstance(layer, PPMissingLayer):
                continue

            assert isinstance(layer, Qwen3MoeDecoderLayer)
            if isinstance(layer.mlp, Qwen3MoeSparseMoeBlock):
                example_layer = layer.mlp
                self.moe_layers.append(layer.mlp.experts)

        if example_layer is None:
            raise RuntimeError("No Qwen3MoE layer found in the model.layers.")

        self.num_moe_layers = len(self.moe_layers)
        self.num_expert_groups = 1
        self.num_shared_experts = 0

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        kv_caches: Optional[List[torch.Tensor]] = None,
        attn_metadata: Optional[AttentionMetadata] = None,
        intermediate_tensors: Optional[IntermediateTensors] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
    ) -> Union[torch.Tensor, IntermediateTensors]:
        _metadata_for_padding = init_metadata_for_sp(
            input_ids, self.enable_sequence_parallelism)
        hidden_states = self.model(input_ids, positions, kv_caches,
                                   attn_metadata, intermediate_tensors,
                                   inputs_embeds, _metadata_for_padding)
        return hidden_states