Adding support to Sarvam's MoE models (#33942)
Signed-off-by: rahul-sarvam <140298821+rahul-sarvam@users.noreply.github.com>
This commit is contained in:
rahul-sarvam
@@ -469,6 +469,8 @@ th {
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| `Qwen3MoeForCausalLM` | Qwen3MoE | `Qwen/Qwen3-30B-A3B`, etc. | ✅︎ | ✅︎ |
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| `Qwen3NextForCausalLM` | Qwen3NextMoE | `Qwen/Qwen3-Next-80B-A3B-Instruct`, etc. | ✅︎ | ✅︎ |
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| `RWForCausalLM` | Falcon RW | `tiiuae/falcon-40b`, etc. | | ✅︎ |
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| `SarvamMoEForCausalLM` | Sarvam 2 | `sarvamai/sarvam2-30b-a3b`, etc. | ✅︎ | ✅︎ |
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| `SarvamMLAForCausalLM` | Sarvam 2 | `sarvamai/sarvam2-105b-a9b`, etc. | | ✅︎ |
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| `SeedOssForCausalLM` | SeedOss | `ByteDance-Seed/Seed-OSS-36B-Instruct`, etc. | ✅︎ | ✅︎ |
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| `SolarForCausalLM` | Solar Pro | `upstage/solar-pro-preview-instruct`, etc. | ✅︎ | ✅︎ |
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| `StableLmForCausalLM` | StableLM | `stabilityai/stablelm-3b-4e1t`, `stabilityai/stablelm-base-alpha-7b-v2`, etc. | | |
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@@ -480,6 +480,18 @@ _TEXT_GENERATION_EXAMPLE_MODELS = {
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min_transformers_version="4.56.3",
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),
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"RWForCausalLM": _HfExamplesInfo("tiiuae/falcon-40b"),
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"SarvamMoEForCausalLM": _HfExamplesInfo(
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"sarvamai/sarvam-30b",
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trust_remote_code=True,
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max_model_len=4096,
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is_available_online=True,
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),
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"SarvamMLAForCausalLM": _HfExamplesInfo(
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"sarvamai/sarvam-105b",
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trust_remote_code=True,
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max_model_len=4096,
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is_available_online=True,
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),
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"SeedOssForCausalLM": _HfExamplesInfo(
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"ByteDance-Seed/Seed-OSS-36B-Instruct",
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trust_remote_code=True,
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@@ -191,6 +191,8 @@ _TEXT_GENERATION_MODELS = {
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"Qwen3ForCausalLM": ("qwen3", "Qwen3ForCausalLM"),
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"Qwen3MoeForCausalLM": ("qwen3_moe", "Qwen3MoeForCausalLM"),
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"RWForCausalLM": ("falcon", "FalconForCausalLM"),
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"SarvamMoEForCausalLM": ("sarvam", "SarvamMoEForCausalLM"),
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"SarvamMLAForCausalLM": ("sarvam", "SarvamMLAForCausalLM"),
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"SeedOssForCausalLM": ("seed_oss", "SeedOssForCausalLM"),
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"Step1ForCausalLM": ("step1", "Step1ForCausalLM"),
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"Step3TextForCausalLM": ("step3_text", "Step3TextForCausalLM"),
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786
vllm/model_executor/models/sarvam.py
Normal file
786
vllm/model_executor/models/sarvam.py
Normal file
@@ -0,0 +1,786 @@
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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 Sarvam AI team. All rights reserved.
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#
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# This code is based on Llama, Deepseek, and Bailing MoE implementations
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# in this library. It has been modified from its original forms to
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# accommodate Sarvam's MoE architectures.
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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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from __future__ import annotations
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import math
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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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from torch import nn
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from vllm.config import CacheConfig, ParallelConfig, VllmConfig
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from vllm.distributed import (
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get_pp_group,
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get_tensor_model_parallel_rank,
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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.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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ColumnParallelLinear,
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MergedColumnParallelLinear,
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ReplicatedLinear,
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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.mla import MLAModules, MultiHeadLatentAttentionWrapper
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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 .bailing_moe import BailingMoeForCausalLM
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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 yarn_get_mscale(scale: float = 1, mscale: float = 1) -> float:
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if scale <= 1:
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return 1.0
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return 0.1 * mscale * math.log(scale) + 1.0
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def _is_gate_expert_bias_name(name: str) -> bool:
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return name.endswith(".mlp.gate.e_score_correction_bias") or name.endswith(
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".gate.e_score_correction_bias"
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)
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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 _normalized_weights(
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weights: Iterable[tuple[str, torch.Tensor]],
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) -> Iterator[tuple[str, torch.Tensor]]:
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for name, w in weights:
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if _is_gate_expert_bias_name(name):
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yield name, _zero_mean_tensor(w)
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else:
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yield name, w
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class SarvamMLAAttention(nn.Module):
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def __init__(
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self,
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vllm_config: VllmConfig,
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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.config = config
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self.hidden_size = config.hidden_size
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self.qk_nope_head_dim = config.qk_nope_head_dim
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self.qk_rope_head_dim = config.qk_rope_head_dim
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self.qk_head_dim = self.qk_nope_head_dim + self.qk_rope_head_dim
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self.v_head_dim = config.v_head_dim
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self.q_lora_rank = getattr(config, "q_lora_rank", None)
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self.kv_lora_rank = config.kv_lora_rank
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self.total_num_heads = config.num_attention_heads
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tp_size = get_tensor_model_parallel_world_size()
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assert self.total_num_heads % tp_size == 0
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self.num_local_heads = self.total_num_heads // tp_size
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self.scaling = self.qk_head_dim**-0.5
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self.max_position_embeddings = config.max_position_embeddings
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if self.q_lora_rank is not None:
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self.q_a_proj = ReplicatedLinear(
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self.hidden_size,
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self.q_lora_rank,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.q_a_proj",
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)
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self.q_a_layernorm = RMSNorm(self.q_lora_rank, eps=config.rms_norm_eps)
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self.q_b_proj = ColumnParallelLinear(
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self.q_lora_rank,
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self.total_num_heads * self.qk_head_dim,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.q_b_proj",
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)
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self.q_proj = None # type: ignore
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else:
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self.q_proj = ColumnParallelLinear(
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self.hidden_size,
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self.total_num_heads * self.qk_head_dim,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.q_proj",
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)
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self.q_a_proj = None # type: ignore
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self.q_a_layernorm = None # type: ignore
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self.q_b_proj = None # type: ignore
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# KV latent (MQA-style) A-proj
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self.kv_a_proj_with_mqa = ReplicatedLinear(
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self.hidden_size,
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self.kv_lora_rank + self.qk_rope_head_dim,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.kv_a_proj_with_mqa",
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)
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self.kv_a_layernorm = RMSNorm(self.kv_lora_rank, eps=config.rms_norm_eps)
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# KV B-proj produces per-head K_nope and V
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self.kv_b_proj = ColumnParallelLinear(
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self.kv_lora_rank,
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self.total_num_heads * (self.qk_nope_head_dim + self.v_head_dim),
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.kv_b_proj",
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)
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self.o_proj = RowParallelLinear(
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self.total_num_heads * self.v_head_dim,
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self.hidden_size,
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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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self.rotary_emb = get_rope(
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self.qk_rope_head_dim,
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# rotary_dim=self.qk_rope_head_dim,
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max_position=config.max_position_embeddings,
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rope_parameters=config.rope_parameters,
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is_neox_style=False,
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)
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if config.rope_parameters.get("rope_type", None) == "deepseek_yarn":
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mscale_all_dim = config.rope_parameters.get("mscale_all_dim", False)
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scaling_factor = config.rope_parameters["factor"]
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mscale = yarn_get_mscale(scaling_factor, float(mscale_all_dim))
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self.scaling = self.scaling * mscale * mscale
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mla_modules = MLAModules(
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kv_a_layernorm=self.kv_a_layernorm,
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kv_b_proj=self.kv_b_proj,
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rotary_emb=self.rotary_emb,
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o_proj=self.o_proj,
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fused_qkv_a_proj=None,
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kv_a_proj_with_mqa=self.kv_a_proj_with_mqa,
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q_a_layernorm=self.q_a_layernorm if self.q_lora_rank is not None else None,
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q_b_proj=self.q_b_proj if self.q_lora_rank is not None else None,
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q_proj=self.q_proj if self.q_lora_rank is None else None,
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indexer=None,
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indexer_rotary_emb=None,
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is_sparse=False,
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topk_indices_buffer=None,
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)
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self.mla_attn = MultiHeadLatentAttentionWrapper(
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self.hidden_size,
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self.num_local_heads,
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self.scaling,
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self.qk_nope_head_dim,
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self.qk_rope_head_dim,
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self.v_head_dim,
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self.q_lora_rank,
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self.kv_lora_rank,
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mla_modules,
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=prefix,
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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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return self.mla_attn(positions, hidden_states, llama_4_scaling=None)
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class SarvamMLAMLP(nn.Module):
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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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config.hidden_size,
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[intermediate_size] * 2,
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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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intermediate_size,
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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 SarvamMLAMoE(nn.Module):
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def __init__(
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self,
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config,
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parallel_config: ParallelConfig,
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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.tp_rank = get_tensor_model_parallel_rank()
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self.hidden_size = config.hidden_size
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self.num_experts = config.num_experts
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self.top_k = config.num_experts_per_tok
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self.routed_scaling_factor = getattr(config, "routed_scaling_factor", 2.5)
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self.n_group = getattr(config, "n_group", None)
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self.topk_group = getattr(config, "topk_group", None)
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self.use_grouped_topk = self.n_group is not None and self.topk_group is not None
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self.norm_expert_prob = getattr(config, "norm_topk_prob", True)
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router_dtype_cfg = getattr(config, "router_dtype", "fp32")
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if router_dtype_cfg is None:
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self.router_dtype = None
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elif router_dtype_cfg == "fp32":
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self.router_dtype = torch.float32
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else:
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self.router_dtype = torch.bfloat16
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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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dtype=self.router_dtype,
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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.empty(
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(self.num_experts,),
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dtype=torch.float32,
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)
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)
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else:
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self.gate.e_score_correction_bias = None
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self.score_function = getattr(config, "score_function", "sigmoid")
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self.num_shared_experts = getattr(config, "num_shared_experts", 1)
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if self.num_shared_experts > 0:
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if hasattr(config, "moe_shared_expert_intermediate_size"):
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shared_int = config.moe_shared_expert_intermediate_size
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else:
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shared_int = config.moe_intermediate_size
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shared_int *= self.num_shared_experts
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self.shared_experts = SarvamMLAMLP(
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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
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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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|
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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_logits = self.gate(
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hidden_states.to(self.router_dtype)
|
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if self.router_dtype is not None
|
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else hidden_states
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)
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router_logits = router_logits.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
|
||||
else:
|
||||
shared_output, expert_output = None, final_hidden
|
||||
|
||||
if shared_output is not None:
|
||||
expert_output = expert_output + shared_output
|
||||
|
||||
if self.tp_size > 1:
|
||||
expert_output = self.experts.maybe_all_reduce_tensor_model_parallel(
|
||||
expert_output
|
||||
)
|
||||
|
||||
return expert_output.view(num_tokens, hidden_dim)
|
||||
|
||||
|
||||
class SarvamMLABlock(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
|
||||
parallel_config = vllm_config.parallel_config
|
||||
layer_idx = int(prefix.split(".")[-1])
|
||||
hidden_size = config.hidden_size
|
||||
dense_intermediate = getattr(config, "intermediate_size", 16384)
|
||||
|
||||
self.input_layernorm = RMSNorm(hidden_size, eps=config.rms_norm_eps)
|
||||
self.self_attn = SarvamMLAAttention(
|
||||
vllm_config=vllm_config,
|
||||
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)
|
||||
use_moe = hasattr(config, "num_experts") and config.num_experts is not None
|
||||
first_k_dense = getattr(config, "first_k_dense_replace", 1)
|
||||
moe_layer_freq = getattr(config, "moe_layer_freq", 1)
|
||||
if use_moe:
|
||||
is_moe_layer = layer_idx >= first_k_dense and (
|
||||
(layer_idx - first_k_dense) % moe_layer_freq == 0
|
||||
)
|
||||
else:
|
||||
is_moe_layer = False
|
||||
|
||||
if is_moe_layer:
|
||||
self.mlp = SarvamMLAMoE(
|
||||
config=config,
|
||||
parallel_config=parallel_config,
|
||||
quant_config=quant_config,
|
||||
prefix=f"{prefix}.mlp",
|
||||
)
|
||||
else:
|
||||
self.mlp = SarvamMLAMLP(
|
||||
intermediate_size=dense_intermediate,
|
||||
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]:
|
||||
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)
|
||||
hidden_states = self.mlp(hidden_states)
|
||||
return hidden_states, residual
|
||||
|
||||
|
||||
class SarvamMLAModel(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 = getattr(config, "tie_word_embeddings", False)
|
||||
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.embedding_dropout = torch.nn.Dropout(
|
||||
getattr(config, "embedding_dropout", 0.0)
|
||||
)
|
||||
self.start_layer, self.end_layer, self.layers = make_layers(
|
||||
config.num_hidden_layers,
|
||||
lambda prefix: SarvamMLABlock(
|
||||
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)
|
||||
hidden_states = self.embedding_dropout(hidden_states)
|
||||
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 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,
|
||||
)
|
||||
|
||||
def load_weights(
|
||||
self,
|
||||
weights: Iterable[tuple[str, torch.Tensor]],
|
||||
) -> set[str]:
|
||||
"""Load weights with stacked gate+up and MoE expert remapping."""
|
||||
weights = _normalized_weights(weights)
|
||||
stacked_params_mapping = [
|
||||
("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:
|
||||
for param_name, weight_name, shard_id in stacked_params_mapping:
|
||||
if weight_name not in name:
|
||||
continue
|
||||
if "mlp.experts" in name:
|
||||
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)
|
||||
break
|
||||
else:
|
||||
mapped = 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)
|
||||
mapped = True
|
||||
break
|
||||
|
||||
if mapped:
|
||||
continue
|
||||
|
||||
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)
|
||||
weight_loader(param, loaded_weight)
|
||||
loaded_params.add(name)
|
||||
|
||||
return loaded_params
|
||||
|
||||
|
||||
class SarvamMixtureOfExperts(MixtureOfExperts):
|
||||
def extract_moe_parameters(self, example_moe: SarvamMLAMoE | None) -> None:
|
||||
if example_moe is None:
|
||||
raise RuntimeError("No SarvamMLAMoE layer found in model.layers.")
|
||||
|
||||
self.num_logical_experts = example_moe.num_experts
|
||||
self.num_routed_experts = example_moe.num_experts # routed pool size
|
||||
self.num_shared_experts = getattr(example_moe.config, "num_shared_experts", 1)
|
||||
|
||||
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, eplb_state) -> None:
|
||||
self.eplb_state = eplb_state
|
||||
for moe in self.moe_layers:
|
||||
if hasattr(moe, "set_eplb_state"):
|
||||
moe.set_eplb_state(eplb_state)
|
||||
|
||||
|
||||
class SarvamMLAForCausalLM(nn.Module, SupportsPP, SupportsLoRA, SarvamMixtureOfExperts):
|
||||
packed_modules_mapping = {
|
||||
"q_proj": ["q_proj"],
|
||||
"q_a_proj": ["q_a_proj"],
|
||||
"q_b_proj": ["q_b_proj"],
|
||||
"kv_a_proj_with_mqa": ["kv_a_proj_with_mqa"],
|
||||
"kv_b_proj": ["kv_b_proj"],
|
||||
"gate_up_proj": ["gate_proj", "up_proj"],
|
||||
}
|
||||
|
||||
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 = SarvamMLAModel(
|
||||
vllm_config=vllm_config,
|
||||
prefix=maybe_prefix(prefix, "model"),
|
||||
)
|
||||
|
||||
self.tie_word_embeddings = 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
|
||||
|
||||
self.make_empty_intermediate_tensors = (
|
||||
self.model.make_empty_intermediate_tensors
|
||||
)
|
||||
|
||||
self.expert_weights = []
|
||||
self.num_moe_layers = 0
|
||||
|
||||
self.moe_layers = []
|
||||
self.moe_mlp_layers = []
|
||||
|
||||
example_moe = None
|
||||
for layer in self.model.layers:
|
||||
if isinstance(layer, PPMissingLayer):
|
||||
continue
|
||||
if isinstance(layer.mlp, SarvamMLAMoE):
|
||||
example_moe = layer.mlp
|
||||
self.moe_mlp_layers.append(layer.mlp)
|
||||
self.moe_layers.append(layer.mlp.experts)
|
||||
self.num_moe_layers += 1
|
||||
|
||||
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
|
||||
logits = self.logits_processor(self.lm_head, hidden_states)
|
||||
return logits
|
||||
|
||||
def load_weights(
|
||||
self,
|
||||
weights: Iterable[tuple[str, torch.Tensor]],
|
||||
) -> set[str]:
|
||||
loader = AutoWeightsLoader(
|
||||
self,
|
||||
skip_prefixes=(["lm_head."] if self.tie_word_embeddings else None),
|
||||
)
|
||||
return loader.load_weights(weights)
|
||||
|
||||
def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
|
||||
return self.model.get_expert_mapping()
|
||||
|
||||
|
||||
class SarvamMoEForCausalLM(BailingMoeForCausalLM):
|
||||
"""Same as BailingMoeForCausalLM, but normalizes gate expert_bias pre-load."""
|
||||
|
||||
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
|
||||
return super().load_weights(_normalized_weights(weights))
|
||||
Reference in New Issue
Block a user