[Model] Add support for BharatGen's Param2MoE model (#38000)
Signed-off-by: bhargav-patel-29 <bhargav.patel@tihiitb.org> Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
This commit is contained in:
bhargav-patel-29
900
vllm/model_executor/models/param2moe.py
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vllm/model_executor/models/param2moe.py
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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#
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# Copyright 2026 BharatGen AI team. All rights reserved.
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#
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# This code has been modified to accommodate Param2MoE's GQA-based MoE architecture.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# limitations under the License.
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from __future__ import annotations
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from collections.abc import Iterable, Iterator
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from itertools import islice
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import torch
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import torch.nn.functional as F
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from torch import nn
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from vllm.config import CacheConfig, VllmConfig
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from vllm.distributed import (
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get_pp_group,
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get_tensor_model_parallel_world_size,
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)
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from vllm.model_executor.layers.activation import SiluAndMul
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from vllm.model_executor.layers.attention import Attention
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from vllm.model_executor.layers.fused_moe import SharedFusedMoE
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.linear import (
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MergedColumnParallelLinear,
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QKVParallelLinear,
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RowParallelLinear,
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)
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.layers.rotary_embedding import get_rope
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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ParallelLMHead,
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VocabParallelEmbedding,
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)
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from vllm.sequence import IntermediateTensors
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from .interfaces import MixtureOfExperts, SupportsLoRA, SupportsPP
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from .utils import (
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AutoWeightsLoader,
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PPMissingLayer,
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is_pp_missing_parameter,
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make_empty_intermediate_tensors_factory,
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make_layers,
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maybe_prefix,
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)
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def _is_expert_bias_name(name: str) -> bool:
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"""True when the weight is the MoE router's per-expert score bias."""
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return name.endswith(".mlp.gate.expert_bias")
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def _zero_mean_tensor(t: torch.Tensor) -> torch.Tensor:
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if t.numel() == 0:
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return t
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return t - t.mean()
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def _rename_and_normalize_weights(
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weights: Iterable[tuple[str, torch.Tensor]],
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) -> Iterator[tuple[str, torch.Tensor]]:
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"""
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Translate HuggingFace Param2MoE weight names to vLLM internal names
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and zero-mean the expert-bias tensor so the router stays balanced.
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Mapping table (HF → vLLM):
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model.word_embeddings.* → model.embed_tokens.*
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*.attention.query_key_value.* → *.self_attn.qkv_proj.*
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*.attention.dense.* → *.self_attn.o_proj.*
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*.attention.query_layernorm.* → *.self_attn.q_layernorm.*
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*.attention.key_layernorm.* → *.self_attn.k_layernorm.*
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*.mlp.gate.expert_bias → *.mlp.gate.e_score_correction_bias
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(also zero-meant for load balance)
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"""
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for name, w in weights:
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# Embedding table
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name = name.replace("model.word_embeddings.", "model.embed_tokens.")
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# Fused QKV projection (HF: query_key_value → vLLM: qkv_proj)
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name = name.replace(".attention.query_key_value.", ".self_attn.qkv_proj.")
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# Output projection (HF: dense → vLLM: o_proj)
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name = name.replace(".attention.dense.", ".self_attn.o_proj.")
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# Per-head query norm
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name = name.replace(".attention.query_layernorm.", ".self_attn.q_layernorm.")
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# Per-head key norm
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name = name.replace(".attention.key_layernorm.", ".self_attn.k_layernorm.")
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# Catch any remaining .attention. → .self_attn. prefixes
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# (e.g. future bias params on the projection layers)
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name = name.replace(".attention.", ".self_attn.")
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# Expert-score bias: rename + zero-mean
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if name.endswith(".mlp.gate.expert_bias"):
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name = name.replace(
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".mlp.gate.expert_bias",
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".mlp.gate.e_score_correction_bias",
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)
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w = _zero_mean_tensor(w)
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yield name, w
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class Param2MoEAttention(nn.Module):
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"""
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Grouped-Query Attention (GQA) for Param2MoE.
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Notable differences from a vanilla GQA layer:
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* The checkpoint fuses Q, K, V into a single ``query_key_value`` weight.
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vLLM receives it already renamed to ``qkv_proj`` by the weight-name
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translator and splits it during ``load_weights``.
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* Optional per-head RMS norms on Q and K (``use_qk_norm=True``).
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"""
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def __init__(
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self,
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config,
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cache_config: CacheConfig | None = None,
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quant_config: QuantizationConfig | None = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.hidden_size = config.hidden_size
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self.num_heads = config.num_attention_heads
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self.num_kv_heads = config.num_key_value_heads
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self.head_dim = config.head_dim or (self.hidden_size // self.num_heads)
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self.use_qk_norm: bool = getattr(config, "use_qk_norm", False)
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tp_size = get_tensor_model_parallel_world_size()
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assert self.num_heads % tp_size == 0, (
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f"num_attention_heads ({self.num_heads}) must be divisible "
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f"by tensor-parallel world size ({tp_size})."
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)
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assert self.num_kv_heads % tp_size == 0, (
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f"num_key_value_heads ({self.num_kv_heads}) must be divisible "
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f"by tensor-parallel world size ({tp_size})."
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)
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self.num_local_heads = self.num_heads // tp_size
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self.num_local_kv_heads = self.num_kv_heads // tp_size
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# Sizes after TP split (used in forward to split qkv output)
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self.q_size_local = self.num_local_heads * self.head_dim
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self.kv_size_local = self.num_local_kv_heads * self.head_dim
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self.scaling = self.head_dim**-0.5
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self.qkv_proj = QKVParallelLinear(
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hidden_size=self.hidden_size,
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head_size=self.head_dim,
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total_num_heads=self.num_heads,
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total_num_kv_heads=self.num_kv_heads,
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bias=getattr(config, "use_qkv_bias", False),
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quant_config=quant_config,
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prefix=f"{prefix}.qkv_proj",
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)
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self.o_proj = RowParallelLinear(
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input_size=self.num_heads * self.head_dim,
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output_size=self.hidden_size,
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bias=getattr(config, "use_bias", False),
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quant_config=quant_config,
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prefix=f"{prefix}.o_proj",
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)
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if self.use_qk_norm:
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self.q_layernorm = RMSNorm(self.head_dim, eps=config.rms_norm_eps)
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self.k_layernorm = RMSNorm(self.head_dim, eps=config.rms_norm_eps)
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# `partial_rotary_factor` defaults to 1.0 (full RoPE) if not in config
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partial_rotary_factor: float = getattr(config, "partial_rotary_factor", 1.0)
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rope_dim = int(self.head_dim * partial_rotary_factor)
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rope_parameters: dict = {
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"rope_type": "default",
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"base": config.rope_theta,
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}
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if config.rope_scaling is not None:
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rope_parameters.update(config.rope_scaling)
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# Normalise key: some checkpoints use "type", vLLM wants "rope_type"
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if "type" in rope_parameters and "rope_type" not in rope_parameters:
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rope_parameters["rope_type"] = rope_parameters.pop("type")
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self.rotary_emb = get_rope(
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rope_dim,
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max_position=config.max_position_embeddings,
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rope_parameters=rope_parameters,
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is_neox_style=True,
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)
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self.attn = Attention(
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num_heads=self.num_heads,
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head_size=self.head_dim,
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scale=self.scaling,
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num_kv_heads=self.num_kv_heads,
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=f"{prefix}.attn",
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)
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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) -> torch.Tensor:
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# 1. Fused QKV projection → split into local Q / K / V
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qkv, _ = self.qkv_proj(hidden_states)
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q, k, v = qkv.split(
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[self.q_size_local, self.kv_size_local, self.kv_size_local],
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dim=-1,
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)
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# 2. Optional per-head QK norms
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# Reshape to (T, num_local_heads, head_dim), norm, reshape back.
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if self.use_qk_norm:
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T = q.shape[0]
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q = self.q_layernorm(q.view(T, self.num_local_heads, self.head_dim)).view(
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T, self.q_size_local
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)
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k = self.k_layernorm(
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k.view(T, self.num_local_kv_heads, self.head_dim)
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).view(T, self.kv_size_local)
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# 3. Rotary position embeddings
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q, k = self.rotary_emb(positions, q, k)
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# 4. Paged attention
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attn_output = self.attn(q, k, v)
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# 5. Output projection
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output, _ = self.o_proj(attn_output)
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return output
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class Param2MoEMLP(nn.Module):
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"""SwiGLU feed-forward block used for dense layers."""
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def __init__(
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self,
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intermediate_size: int,
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config,
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quant_config: QuantizationConfig | None = None,
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reduce_results: bool = True,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.gate_up_proj = MergedColumnParallelLinear(
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input_size=config.hidden_size,
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output_sizes=[intermediate_size, intermediate_size],
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.gate_up_proj",
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)
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self.down_proj = RowParallelLinear(
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input_size=intermediate_size,
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output_size=config.hidden_size,
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bias=False,
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quant_config=quant_config,
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reduce_results=reduce_results,
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prefix=f"{prefix}.down_proj",
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)
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self.act_fn = SiluAndMul()
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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gate_up, _ = self.gate_up_proj(x)
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x = self.act_fn(gate_up)
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x, _ = self.down_proj(x)
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return x
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class Param2MoEMoEBlock(nn.Module):
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"""
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Mixture-of-Experts block for Param2MoE.
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Routing:
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* Sigmoid scoring (config.score_function = "sigmoid")
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* Grouped top-k (n_group, topk_group)
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* Per-expert bias (gate.expert_bias → e_score_correction_bias)
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* routed_scaling_factor normalisation
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One set of shared (always-active) experts is added on top.
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"""
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def __init__(
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self,
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config,
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quant_config: QuantizationConfig | None = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.config = config
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self.tp_size = get_tensor_model_parallel_world_size()
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self.hidden_size = config.hidden_size
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self.num_experts: int = config.num_experts
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self.top_k: int = config.num_experts_per_tok
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self.routed_scaling_factor: float = getattr(
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config, "routed_scaling_factor", 1.0
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)
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self.n_group: int | None = getattr(config, "n_group", None)
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self.topk_group: int | None = getattr(config, "topk_group", None)
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self.use_grouped_topk: bool = (
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self.n_group is not None and self.topk_group is not None
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)
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self.norm_expert_prob: bool = getattr(config, "norm_topk_prob", True)
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self.score_function: str = getattr(config, "score_function", "sigmoid")
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self.gate = nn.Linear(
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self.hidden_size,
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self.num_experts,
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bias=False,
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)
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if getattr(config, "moe_router_enable_expert_bias", True):
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self.gate.e_score_correction_bias = nn.Parameter(
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torch.zeros(self.num_experts, dtype=torch.float32)
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)
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else:
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self.gate.e_score_correction_bias = None # type: ignore[assignment]
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self.num_shared_experts: int = getattr(config, "num_shared_experts", 1)
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if self.num_shared_experts > 0:
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# If moe_shared_expert_intermediate_size is present in the config
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# it already encodes the TOTAL intermediate size across all shared
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# experts (i.e. it equals moe_intermediate_size * num_shared_experts).
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# Do NOT multiply again. Fall back to computing the product only
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# when the dedicated field is absent.
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if (
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hasattr(config, "moe_shared_expert_intermediate_size")
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and config.moe_shared_expert_intermediate_size is not None
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):
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shared_int: int = config.moe_shared_expert_intermediate_size
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else:
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shared_int = config.moe_intermediate_size * self.num_shared_experts
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self.shared_experts = Param2MoEMLP(
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intermediate_size=shared_int,
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config=config,
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quant_config=quant_config,
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reduce_results=False,
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prefix=f"{prefix}.shared_experts",
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)
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else:
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self.shared_experts = None # type: ignore[assignment]
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self.experts = SharedFusedMoE(
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shared_experts=self.shared_experts,
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num_experts=self.num_experts,
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top_k=self.top_k,
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hidden_size=self.hidden_size,
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intermediate_size=config.moe_intermediate_size,
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reduce_results=False,
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renormalize=self.norm_expert_prob,
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quant_config=quant_config,
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prefix=f"{prefix}.experts",
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scoring_func=self.score_function,
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e_score_correction_bias=self.gate.e_score_correction_bias,
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num_expert_group=self.n_group,
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topk_group=self.topk_group,
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use_grouped_topk=self.use_grouped_topk,
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routed_scaling_factor=self.routed_scaling_factor,
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)
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def maybe_get_fused_moe(self) -> SharedFusedMoE:
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return self.experts
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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num_tokens, hidden_dim = hidden_states.shape
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hidden_states = hidden_states.view(-1, hidden_dim)
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# Router: both input and weight must be float32 for numerical
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# stability (mirrors the original Param2MoEGate behaviour).
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# The gate nn.Linear weight lives in the model dtype (bfloat16),
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# so we must cast both explicitly via F.linear instead of calling
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# self.gate() which would hit a dtype mismatch.
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router_logits = F.linear(
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hidden_states.float(),
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self.gate.weight.float(),
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).to(hidden_states.dtype)
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final_hidden = self.experts(
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hidden_states=hidden_states,
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router_logits=router_logits,
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)
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if self.shared_experts is not None:
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shared_output, expert_output = final_hidden
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else:
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shared_output, expert_output = None, final_hidden
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if shared_output is not None:
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expert_output = expert_output + shared_output
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if self.tp_size > 1:
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expert_output = self.experts.maybe_all_reduce_tensor_model_parallel(
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expert_output
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)
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return expert_output.view(num_tokens, hidden_dim)
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class Param2MoEDecoderLayer(nn.Module):
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"""
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Single transformer decoder block.
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Dense for the first ``first_k_dense_replace`` layers; MoE thereafter.
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"""
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def __init__(
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self,
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vllm_config: VllmConfig,
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||||
prefix: str = "",
|
||||
) -> None:
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super().__init__()
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config = vllm_config.model_config.hf_config
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cache_config = vllm_config.cache_config
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quant_config = vllm_config.quant_config
|
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hidden_size = config.hidden_size
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||||
# Derive the layer index from the prefix (e.g. "model.layers.3")
|
||||
layer_idx = int(prefix.split(".")[-1])
|
||||
|
||||
self.input_layernorm = RMSNorm(hidden_size, eps=config.rms_norm_eps)
|
||||
self.self_attn = Param2MoEAttention(
|
||||
config=config,
|
||||
cache_config=cache_config,
|
||||
quant_config=quant_config,
|
||||
prefix=f"{prefix}.self_attn",
|
||||
)
|
||||
self.post_attention_layernorm = RMSNorm(hidden_size, eps=config.rms_norm_eps)
|
||||
|
||||
first_k_dense: int = getattr(config, "first_k_dense_replace", 1)
|
||||
is_moe_layer = config.num_experts is not None and layer_idx >= first_k_dense
|
||||
|
||||
if is_moe_layer:
|
||||
self.mlp = Param2MoEMoEBlock(
|
||||
config=config,
|
||||
quant_config=quant_config,
|
||||
prefix=f"{prefix}.mlp",
|
||||
)
|
||||
else:
|
||||
self.mlp = Param2MoEMLP( # type: ignore[assignment]
|
||||
intermediate_size=config.intermediate_size,
|
||||
config=config,
|
||||
quant_config=quant_config,
|
||||
reduce_results=True,
|
||||
prefix=f"{prefix}.mlp",
|
||||
)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
residual: torch.Tensor | None,
|
||||
) -> tuple[torch.Tensor, torch.Tensor]:
|
||||
# Pre-norm + 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,
|
||||
)
|
||||
|
||||
# Pre-norm + MLP
|
||||
hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
|
||||
hidden_states = self.mlp(hidden_states)
|
||||
return hidden_states, residual
|
||||
|
||||
|
||||
class Param2MoEModel(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
vllm_config: VllmConfig,
|
||||
prefix: str = "",
|
||||
) -> None:
|
||||
super().__init__()
|
||||
|
||||
config = vllm_config.model_config.hf_config
|
||||
quant_config = vllm_config.quant_config
|
||||
|
||||
self.config = config
|
||||
self.vocab_size = config.vocab_size
|
||||
self.embed_dim = config.hidden_size
|
||||
self.tie_word_embeddings: bool = getattr(config, "tie_word_embeddings", False)
|
||||
|
||||
# Embedding (HF name: word_embeddings → vLLM name: embed_tokens)
|
||||
if get_pp_group().is_first_rank or (
|
||||
self.tie_word_embeddings and get_pp_group().is_last_rank
|
||||
):
|
||||
self.embed_tokens = VocabParallelEmbedding(
|
||||
self.vocab_size,
|
||||
self.embed_dim,
|
||||
quant_config=quant_config,
|
||||
prefix=f"{prefix}.embed_tokens",
|
||||
)
|
||||
else:
|
||||
self.embed_tokens = PPMissingLayer()
|
||||
|
||||
self.start_layer, self.end_layer, self.layers = make_layers(
|
||||
config.num_hidden_layers,
|
||||
lambda prefix: Param2MoEDecoderLayer(
|
||||
vllm_config=vllm_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(self.embed_dim, eps=config.rms_norm_eps)
|
||||
else:
|
||||
self.norm = PPMissingLayer()
|
||||
|
||||
def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
|
||||
return self.embed_tokens(input_ids)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
intermediate_tensors: IntermediateTensors | None,
|
||||
inputs_embeds: torch.Tensor | None = None,
|
||||
) -> torch.Tensor | IntermediateTensors:
|
||||
if get_pp_group().is_first_rank:
|
||||
if inputs_embeds is not None:
|
||||
hidden_states = inputs_embeds
|
||||
else:
|
||||
hidden_states = self.embed_input_ids(input_ids)
|
||||
residual = None
|
||||
else:
|
||||
assert intermediate_tensors is not None
|
||||
hidden_states = intermediate_tensors["hidden_states"]
|
||||
residual = intermediate_tensors["residual"]
|
||||
|
||||
for layer in islice(self.layers, self.start_layer, self.end_layer):
|
||||
hidden_states, residual = layer(hidden_states, positions, residual)
|
||||
|
||||
if not get_pp_group().is_last_rank:
|
||||
return IntermediateTensors(
|
||||
{"hidden_states": hidden_states, "residual": residual}
|
||||
)
|
||||
|
||||
if residual is None:
|
||||
hidden_states = self.norm(hidden_states)
|
||||
else:
|
||||
hidden_states, _ = self.norm(hidden_states, residual)
|
||||
return hidden_states
|
||||
|
||||
def load_weights(
|
||||
self,
|
||||
weights: Iterable[tuple[str, torch.Tensor]],
|
||||
) -> set[str]:
|
||||
"""
|
||||
Custom weight loader for the inner Param2MoEModel.
|
||||
|
||||
Receives weights that have already been renamed/normalised by the
|
||||
outer model and whose ``model.`` prefix has been stripped by
|
||||
``AutoWeightsLoader``. Handles:
|
||||
1. Fused QKV split (query_key_value → qkv_proj q/k/v shards).
|
||||
2. gate_proj + up_proj → gate_up_proj stacking (dense + shared-exp).
|
||||
3. Routed-expert weights via the fused-MoE mapping.
|
||||
4. All remaining weights via their default loader.
|
||||
"""
|
||||
config = self.config
|
||||
num_heads: int = config.num_attention_heads
|
||||
num_kv_heads: int = config.num_key_value_heads
|
||||
head_dim: int = config.head_dim or (config.hidden_size // num_heads)
|
||||
q_split = num_heads * head_dim
|
||||
kv_split = num_kv_heads * head_dim
|
||||
|
||||
stacked_params_mapping = [
|
||||
# (vllm_param_name, ckpt_weight_name, shard_id)
|
||||
("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()
|
||||
expert_params_mapping = self.get_expert_mapping()
|
||||
|
||||
for name, loaded_weight in weights:
|
||||
# ------------------------------------------------------------------
|
||||
# 1. Fused QKV: split into q / k / v shards for QKVParallelLinear
|
||||
# ------------------------------------------------------------------
|
||||
if name.endswith(".self_attn.qkv_proj.weight"):
|
||||
if 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)
|
||||
q_w = loaded_weight[:q_split, :]
|
||||
k_w = loaded_weight[q_split : q_split + kv_split, :]
|
||||
v_w = loaded_weight[q_split + kv_split :, :]
|
||||
weight_loader(param, q_w, "q")
|
||||
weight_loader(param, k_w, "k")
|
||||
weight_loader(param, v_w, "v")
|
||||
loaded_params.add(name)
|
||||
continue
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# 2. gate_proj / up_proj → gate_up_proj (dense MLP + shared-exp.)
|
||||
# ------------------------------------------------------------------
|
||||
matched_stacked = False
|
||||
for param_name, weight_name, shard_id in stacked_params_mapping:
|
||||
if weight_name not in name:
|
||||
continue
|
||||
if "mlp.experts" in name: # routed experts handled below
|
||||
continue
|
||||
new_name = name.replace(weight_name, param_name)
|
||||
if new_name.endswith(".bias") and new_name not in params_dict:
|
||||
continue
|
||||
if new_name not in params_dict:
|
||||
continue
|
||||
if is_pp_missing_parameter(new_name, self):
|
||||
continue
|
||||
|
||||
param = params_dict[new_name]
|
||||
weight_loader = getattr(param, "weight_loader", default_weight_loader)
|
||||
weight_loader(param, loaded_weight, shard_id)
|
||||
loaded_params.add(new_name)
|
||||
matched_stacked = True
|
||||
break
|
||||
|
||||
if matched_stacked:
|
||||
continue
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# 3. Routed expert weights → fused-MoE kernel layout
|
||||
# ------------------------------------------------------------------
|
||||
matched_expert = False
|
||||
for (
|
||||
param_name,
|
||||
weight_name,
|
||||
expert_id,
|
||||
shard_id,
|
||||
) in expert_params_mapping:
|
||||
if weight_name not in name:
|
||||
continue
|
||||
new_name = name.replace(weight_name, param_name)
|
||||
if is_pp_missing_parameter(new_name, self):
|
||||
continue
|
||||
if new_name not in params_dict:
|
||||
continue
|
||||
|
||||
param = params_dict[new_name]
|
||||
weight_loader = getattr(param, "weight_loader", default_weight_loader)
|
||||
weight_loader(
|
||||
param,
|
||||
loaded_weight,
|
||||
name,
|
||||
shard_id=shard_id,
|
||||
expert_id=expert_id,
|
||||
)
|
||||
loaded_params.add(new_name)
|
||||
matched_expert = True
|
||||
break
|
||||
|
||||
if matched_expert:
|
||||
continue
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# 4. All other weights: direct load (layernorms, embed_tokens, …)
|
||||
# ------------------------------------------------------------------
|
||||
if name.endswith(".bias") and name not in params_dict:
|
||||
continue
|
||||
if 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)
|
||||
try:
|
||||
weight_loader(param, loaded_weight)
|
||||
except Exception as e:
|
||||
raise RuntimeError(
|
||||
f"[param2moe] Failed to load weight '{name}' "
|
||||
f"with shape {tuple(loaded_weight.shape)} "
|
||||
f"into param type {type(param).__name__}: {e}"
|
||||
) from e
|
||||
loaded_params.add(name)
|
||||
|
||||
return loaded_params
|
||||
|
||||
def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
|
||||
return SharedFusedMoE.make_expert_params_mapping(
|
||||
self,
|
||||
ckpt_gate_proj_name="gate_proj",
|
||||
ckpt_down_proj_name="down_proj",
|
||||
ckpt_up_proj_name="up_proj",
|
||||
num_experts=self.config.num_experts,
|
||||
)
|
||||
|
||||
|
||||
class Param2MoEMixtureOfExperts(MixtureOfExperts):
|
||||
"""Implements the vLLM MixtureOfExperts protocol for Param2MoE."""
|
||||
|
||||
expert_weights: list[torch.Tensor]
|
||||
|
||||
def extract_moe_parameters(self, example_moe: Param2MoEMoEBlock | None) -> None:
|
||||
if example_moe is None:
|
||||
raise RuntimeError(
|
||||
"No Param2MoEMoEBlock found in model.layers. "
|
||||
"Check first_k_dense_replace and num_experts in config."
|
||||
)
|
||||
self.num_logical_experts = example_moe.num_experts
|
||||
self.num_routed_experts = example_moe.num_experts
|
||||
self.num_shared_experts = example_moe.num_shared_experts
|
||||
|
||||
self.num_physical_experts = self.num_logical_experts
|
||||
self.num_local_physical_experts = self.num_logical_experts
|
||||
self.num_redundant_experts = 0
|
||||
|
||||
def update_physical_experts_metadata(
|
||||
self,
|
||||
num_physical_experts: int,
|
||||
num_local_physical_experts: int,
|
||||
) -> None:
|
||||
self.num_physical_experts = num_physical_experts
|
||||
self.num_local_physical_experts = num_local_physical_experts
|
||||
self.num_redundant_experts = num_physical_experts - self.num_logical_experts
|
||||
|
||||
for moe in self.moe_mlp_layers:
|
||||
moe.n_physical_experts = num_physical_experts
|
||||
moe.n_local_physical_experts = num_local_physical_experts
|
||||
moe.n_redundant_experts = self.num_redundant_experts
|
||||
|
||||
fused = moe.experts
|
||||
if hasattr(fused, "n_local_physical_experts"):
|
||||
fused.n_local_physical_experts = num_local_physical_experts
|
||||
if hasattr(fused, "n_physical_experts"):
|
||||
fused.n_physical_experts = num_physical_experts
|
||||
if hasattr(fused, "n_redundant_experts"):
|
||||
fused.n_redundant_experts = self.num_redundant_experts
|
||||
if hasattr(fused, "update_expert_map"):
|
||||
fused.update_expert_map()
|
||||
|
||||
def set_eplb_state(
|
||||
self,
|
||||
expert_load_view: torch.Tensor,
|
||||
logical_to_physical_map: torch.Tensor,
|
||||
logical_replica_count: torch.Tensor,
|
||||
) -> None:
|
||||
self.expert_weights.clear()
|
||||
for layer_idx, layer in enumerate(self.moe_layers):
|
||||
if hasattr(layer, "get_expert_weights"):
|
||||
self.expert_weights.append(layer.get_expert_weights())
|
||||
if hasattr(layer, "set_eplb_state"):
|
||||
layer.set_eplb_state(
|
||||
moe_layer_idx=layer_idx,
|
||||
expert_load_view=expert_load_view,
|
||||
logical_to_physical_map=logical_to_physical_map,
|
||||
logical_replica_count=logical_replica_count,
|
||||
)
|
||||
|
||||
|
||||
class Param2MoEForCausalLM(
|
||||
nn.Module, SupportsPP, SupportsLoRA, Param2MoEMixtureOfExperts
|
||||
):
|
||||
"""
|
||||
vLLM-native Param2MoE CausalLM.
|
||||
|
||||
Uses Grouped-Query Attention (GQA) with a Sigmoid-scored,
|
||||
grouped-topk Mixture-of-Experts MLP.
|
||||
"""
|
||||
|
||||
# LoRA packed-module mapping. The fused gate_up_proj handles
|
||||
# gate_proj and up_proj from the checkpoint.
|
||||
packed_modules_mapping = {
|
||||
"qkv_proj": ["query_key_value"],
|
||||
"gate_up_proj": ["gate_proj", "up_proj"],
|
||||
}
|
||||
|
||||
# Modules eligible for LoRA adaptation.
|
||||
supported_lora_modules = [
|
||||
"qkv_proj",
|
||||
"o_proj",
|
||||
"gate_up_proj",
|
||||
"down_proj",
|
||||
]
|
||||
|
||||
# Embedding layers and their weight-tying counterparts.
|
||||
embedding_modules = {
|
||||
"embed_tokens": "input_embeddings",
|
||||
"lm_head": "output_embeddings",
|
||||
}
|
||||
|
||||
# Modules that need vocab-size padding for LoRA.
|
||||
embedding_padding_modules = ["lm_head"]
|
||||
|
||||
def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
|
||||
super().__init__()
|
||||
|
||||
config = vllm_config.model_config.hf_config
|
||||
quant_config = vllm_config.quant_config
|
||||
|
||||
self.config = config
|
||||
self.quant_config = quant_config
|
||||
|
||||
self.model = Param2MoEModel(
|
||||
vllm_config=vllm_config,
|
||||
prefix=maybe_prefix(prefix, "model"),
|
||||
)
|
||||
|
||||
self.tie_word_embeddings: bool = getattr(config, "tie_word_embeddings", False)
|
||||
if get_pp_group().is_last_rank:
|
||||
if self.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"),
|
||||
)
|
||||
self.logits_processor = LogitsProcessor(config.vocab_size)
|
||||
else:
|
||||
self.lm_head = PPMissingLayer()
|
||||
self.logits_processor = None # type: ignore[assignment]
|
||||
|
||||
self.make_empty_intermediate_tensors = (
|
||||
self.model.make_empty_intermediate_tensors
|
||||
)
|
||||
|
||||
self.expert_weights: list[torch.Tensor] = []
|
||||
self.num_moe_layers: int = 0
|
||||
self.moe_layers: list = []
|
||||
self.moe_mlp_layers: list = []
|
||||
|
||||
example_moe: Param2MoEMoEBlock | None = None
|
||||
for layer in self.model.layers:
|
||||
if isinstance(layer, PPMissingLayer):
|
||||
continue
|
||||
if isinstance(layer.mlp, Param2MoEMoEBlock):
|
||||
example_moe = layer.mlp
|
||||
self.moe_mlp_layers.append(layer.mlp)
|
||||
self.moe_layers.append(layer.mlp.experts)
|
||||
self.num_moe_layers += 1
|
||||
|
||||
if self.config.num_experts is not None:
|
||||
self.extract_moe_parameters(example_moe)
|
||||
|
||||
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,
|
||||
) -> torch.Tensor | IntermediateTensors:
|
||||
return self.model(
|
||||
input_ids=input_ids,
|
||||
positions=positions,
|
||||
intermediate_tensors=intermediate_tensors,
|
||||
inputs_embeds=inputs_embeds,
|
||||
)
|
||||
|
||||
def compute_logits(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
) -> torch.Tensor | None:
|
||||
if not get_pp_group().is_last_rank:
|
||||
return None
|
||||
return self.logits_processor(self.lm_head, hidden_states)
|
||||
|
||||
def load_weights(
|
||||
self,
|
||||
weights: Iterable[tuple[str, torch.Tensor]],
|
||||
) -> set[str]:
|
||||
loader = AutoWeightsLoader(self)
|
||||
return loader.load_weights(_rename_and_normalize_weights(weights))
|
||||
@@ -182,6 +182,7 @@ _TEXT_GENERATION_MODELS = {
|
||||
"PanguEmbeddedForCausalLM": ("openpangu", "PanguEmbeddedForCausalLM"),
|
||||
"PanguProMoEV2ForCausalLM": ("openpangu", "PanguProMoEV2ForCausalLM"),
|
||||
"PanguUltraMoEForCausalLM": ("openpangu", "PanguUltraMoEForCausalLM"),
|
||||
"Param2MoEForCausalLM": ("param2moe", "Param2MoEForCausalLM"),
|
||||
"PersimmonForCausalLM": ("persimmon", "PersimmonForCausalLM"),
|
||||
"PhiForCausalLM": ("phi", "PhiForCausalLM"),
|
||||
"Phi3ForCausalLM": ("phi3", "Phi3ForCausalLM"),
|
||||
|
||||
Reference in New Issue
Block a user