Sync from v0.13

2026-01-19 10:38:50 +08:00
parent b2ef04d792
commit 5aef6c175a
3714 changed files with 854317 additions and 89342 deletions
--- a/vllm/model_executor/models/step3_text.py
+++ b/vllm/model_executor/models/step3_text.py
@@ -0,0 +1,554 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Inference-only Jurassic model."""
+
+from collections.abc import Iterable
+from itertools import islice
+from typing import Any
+
+import torch
+from torch import nn
+
+from vllm.attention.layer import Attention
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, ModelConfig, VllmConfig
+from vllm.distributed import (
+    get_pp_group,
+    get_tensor_model_parallel_world_size,
+    tensor_model_parallel_all_reduce,
+)
+from vllm.logger import init_logger
+from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (
+    ColumnParallelLinear,
+    MergedColumnParallelLinear,
+    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.model_loader.weight_utils import default_weight_loader
+from vllm.sequence import IntermediateTensors
+from vllm.transformers_utils.configs.step3_vl import Step3TextConfig
+
+from .interfaces import SupportsPP
+from .utils import (
+    PPMissingLayer,
+    is_pp_missing_parameter,
+    make_empty_intermediate_tensors_factory,
+    make_layers,
+    maybe_prefix,
+)
+
+logger = init_logger(__name__)
+
+
+class FusedMoEBlock(nn.Module):
+    def __init__(
+        self,
+        config: ModelConfig,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.tp_size = get_tensor_model_parallel_world_size()
+
+        if self.tp_size > config.moe_num_experts:
+            raise ValueError(
+                f"Tensor parallel size {self.tp_size} is greater than "
+                f"the number of experts {config.moe_num_experts}."
+            )
+
+        self.experts = FusedMoE(
+            num_experts=config.moe_num_experts,
+            top_k=config.moe_top_k,
+            hidden_size=config.hidden_size,
+            intermediate_size=config.moe_intermediate_size,
+            reduce_results=False,
+            renormalize=config.norm_expert_weight,
+            quant_config=quant_config,
+            prefix=f"{prefix}.experts",
+        )
+        self.gate = ReplicatedLinear(
+            config.hidden_size,
+            config.moe_num_experts,
+            bias=False,
+            quant_config=None,
+            prefix=f"{prefix}.gate",
+        )
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        orig_shape = hidden_states.shape
+        hidden_dim = hidden_states.shape[-1]
+        hidden_states = hidden_states.view(-1, hidden_dim)
+
+        router_logits, _ = self.gate(hidden_states)
+
+        final_hidden_states = self.experts(
+            hidden_states=hidden_states, router_logits=router_logits
+        )
+        if self.tp_size > 1:
+            final_hidden_states = tensor_model_parallel_all_reduce(final_hidden_states)
+
+        return final_hidden_states.view(orig_shape)
+
+
+class Step3TextMLP(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()
+        self.hidden_size = hidden_size
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        gate_up, _ = self.gate_up_proj(hidden_states)
+        intermediate_act = self.act_fn(gate_up)
+        output, _ = self.down_proj(intermediate_act)
+        return output
+
+
+class Step3TextAttention(nn.Module):
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        num_kv_heads: int,
+        norm_eps: float,
+        rope_parameters: dict[str, Any],
+        share_q_dim: int | None = None,
+        max_position_embedding: int = 8192,
+        head_dim: int = 256,
+        cache_config: CacheConfig | None = None,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+    ):
+        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
+
+        if num_kv_heads != 1:
+            raise ValueError(
+                f"Step3TextAttention num_kv_heads must be 1, but got {num_kv_heads}."
+            )
+        self.num_kv_heads = num_kv_heads
+
+        self.head_dim = head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.q_size = share_q_dim if share_q_dim else self.head_dim
+
+        self.qkv_proj = ReplicatedLinear(
+            hidden_size,
+            self.q_size + self.kv_size * 2,
+            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",
+        )
+        self.inter_norm = RMSNorm(self.q_size, eps=norm_eps)
+        self.wq = ColumnParallelLinear(
+            self.q_size,
+            self.head_dim * self.total_num_heads,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.wq",
+        )
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            max_position=max_position_embedding,
+            rope_parameters=rope_parameters,
+        )
+        scaling = self.head_dim**-0.5
+        self.attn = Attention(
+            self.num_heads,
+            self.head_dim,
+            scaling,
+            self.num_kv_heads,
+            cache_config=cache_config,
+            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 = self.inter_norm(q)
+        q = self.wq(q)[0]
+        q, k = self.rotary_emb(positions, q, k)
+        attn_output = self.attn(q, k, v)
+        residual, _ = self.o_proj(attn_output)
+        return residual
+
+
+class Step3TextDecoderLayer(nn.Module):
+    def __init__(
+        self,
+        config: Step3TextConfig,
+        cache_config: CacheConfig | None = None,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+
+        self.self_attn = Step3TextAttention(
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            num_kv_heads=1,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            norm_eps=config.rms_norm_eps,
+            max_position_embedding=config.max_position_embedding,
+            head_dim=config.head_dim,
+            share_q_dim=config.share_q_dim,
+            rope_parameters=config.rope_parameters,
+            prefix=f"{prefix}.self_attn",
+        )
+
+        layer_idx = int(prefix.split("layers.")[1].split(".")[0])
+        moe_layers_enum = getattr(config, "moe_layers_enum", None)
+        if moe_layers_enum is not None:
+            moe_layers_idx = [int(i) for i in moe_layers_enum.strip().split(",")]
+        else:
+            # Default to 1dense.
+            moe_layers_idx = [i for i in range(1, config.num_hidden_layers)]
+
+        if layer_idx in moe_layers_idx:
+            self.moe = FusedMoEBlock(
+                config=config, quant_config=quant_config, prefix=f"{prefix}.moe"
+            )
+            self.share_expert = Step3TextMLP(
+                hidden_size=self.hidden_size,
+                intermediate_size=config.share_expert_dim,
+                hidden_act="silu",
+                quant_config=quant_config,
+                prefix=f"{prefix}.share_expert",
+            )
+            self.use_moe = True
+        else:
+            self.mlp = Step3TextMLP(
+                hidden_size=config.hidden_size,
+                intermediate_size=config.intermediate_size,
+                hidden_act="silu",
+                quant_config=quant_config,
+                prefix=f"{prefix}.mlp",
+            )
+            self.use_moe = False
+        self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(
+            config.hidden_size, eps=config.rms_norm_eps
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        residual: torch.Tensor | None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        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,
+        )
+
+        hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
+
+        if self.use_moe:
+            share_output = self.share_expert(hidden_states)
+            moe_output = self.moe(hidden_states)
+            hidden_states = share_output + moe_output
+        else:
+            hidden_states = self.mlp(hidden_states)
+
+        return hidden_states, residual
+
+
+@support_torch_compile
+class Step3TextModel(nn.Module):
+    def __init__(self, vllm_config: VllmConfig, prefix: str = "") -> None:
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+        self.vocab_size = config.vocab_size
+        self.config = config
+
+        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,
+            )
+        else:
+            self.embed_tokens = PPMissingLayer()
+
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: Step3TextDecoderLayer(
+                config=config,
+                cache_config=cache_config,
+                quant_config=quant_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.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
+            ["hidden_states"], config.hidden_size
+        )
+
+    def embed_input_ids(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: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        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"]
+
+        for layer in islice(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
+
+
+class Step3TextForCausalLM(nn.Module, SupportsPP):
+    def __init__(
+        self,
+        *,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+    ):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+
+        self.config = config
+        self.vllm_config = vllm_config
+
+        self.model = Step3TextModel(vllm_config=vllm_config, prefix=prefix)
+
+        if get_pp_group().is_last_rank:
+            self.lm_head = ParallelLMHead(
+                config.vocab_size,
+                config.hidden_size,
+                prefix=maybe_prefix(prefix, "lm_head"),
+            )
+            self.logits_processor = LogitsProcessor(config.vocab_size)
+        else:
+            self.lm_head = PPMissingLayer()
+
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors
+        )
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.embed_input_ids(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+    ):
+        hidden_states = self.model(
+            input_ids, positions, intermediate_tensors, inputs_embeds
+        )
+        return hidden_states
+
+    def compute_logits(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        logits = self.logits_processor(self.lm_head, hidden_states)
+        return logits
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        qkv_params_mapping = [
+            # (param_name, shard_name, relative_start_idx, relative_end_idx)
+            (
+                ".qkv_proj",
+                ".q_proj",
+                0,
+                self.config.share_q_dim
+                / (self.config.share_q_dim + self.config.head_dim * 2),
+            ),
+            (
+                ".qkv_proj",
+                ".k_proj",
+                self.config.share_q_dim
+                / (self.config.share_q_dim + self.config.head_dim * 2),
+                (self.config.share_q_dim + self.config.head_dim)
+                / (self.config.share_q_dim + self.config.head_dim * 2),
+            ),
+            (
+                ".qkv_proj",
+                ".v_proj",
+                (self.config.share_q_dim + self.config.head_dim)
+                / (self.config.share_q_dim + self.config.head_dim * 2),
+                (self.config.share_q_dim + self.config.head_dim * 2)
+                / (self.config.share_q_dim + self.config.head_dim * 2),
+            ),
+        ]
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            (".gate_up_proj", ".gate_proj", 0),
+            (".gate_up_proj", ".up_proj", 1),
+        ]
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+
+        expert_params_mapping = [
+            (".moe.experts.w13_weight", ".moe.gate_proj.weight", "w1"),
+            (".moe.experts.w13_weight", ".moe.up_proj.weight", "w3"),
+            (".moe.experts.w2_weight", ".moe.down_proj.weight", "w2"),
+        ]
+
+        disable_moe_stacked_params = [data[1] for data in expert_params_mapping]
+
+        for name, loaded_weight in weights:
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                if any(
+                    disable_moe_stacked_param in name
+                    for disable_moe_stacked_param in disable_moe_stacked_params
+                ):
+                    continue
+                name = name.replace(weight_name, param_name)
+                if is_pp_missing_parameter(name, self):
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                loaded_params.add(name)
+                break
+            else:
+                for mapping in expert_params_mapping:
+                    param_name, weight_name, shard_id = mapping
+                    if weight_name not in name:
+                        continue
+                    name = name.replace(weight_name, param_name)
+                    # Skip layers on other devices.
+                    if is_pp_missing_parameter(name, self):
+                        continue
+                    # Skip loading extra bias for GPTQ models.
+                    if (
+                        name.endswith(".bias") or name.endswith("_bias")
+                    ) and name not in params_dict:
+                        continue
+                    param = params_dict[name]
+                    weight_loader = param.weight_loader
+                    for expert_id in range(loaded_weight.shape[0]):
+                        loaded_weight_expert = loaded_weight[expert_id]
+                        weight_loader(
+                            param,
+                            loaded_weight_expert,
+                            name,
+                            shard_id=shard_id,
+                            expert_id=expert_id,
+                        )
+                    loaded_params.add(name)
+                    break
+                else:
+                    for (
+                        param_name,
+                        weight_name,
+                        start_idx,
+                        end_idx,
+                    ) in qkv_params_mapping:
+                        if weight_name not in name:
+                            continue
+                        name = name.replace(weight_name, param_name)
+                        if is_pp_missing_parameter(name, self):
+                            continue
+                        param = params_dict[name]
+                        dim = param.shape[param.output_dim]
+                        begin_idx = int(start_idx * dim)
+                        end_idx = int(end_idx * dim)
+                        param_slice = param.narrow(
+                            param.output_dim, begin_idx, end_idx - begin_idx
+                        )
+                        param_slice.copy_(loaded_weight)
+                        loaded_params.add(name)
+                        break
+                    else:
+                        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