644 lines
24 KiB
Python
644 lines
24 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# Copyright 2025 The ZhipuAI Team.
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# Copyright 2023 The vLLM team.
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# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
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#
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# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
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# and OPT implementations in this library. It has been modified from its
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# original forms to accommodate minor architectural differences compared
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# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
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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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"""Inference-only GLM-4.7-Flash model compatible with HuggingFace weights."""
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import typing
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from collections.abc import Callable, Iterable
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from itertools import islice
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from typing import TYPE_CHECKING
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import torch
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from torch import nn
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if TYPE_CHECKING:
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from transformers.models.glm4_moe_lite import Glm4MoeLiteConfig
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from vllm._aiter_ops import rocm_aiter_ops
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from vllm.compilation.decorators import support_torch_compile
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from vllm.config import VllmConfig
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from vllm.distributed import (
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get_pp_group,
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)
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from vllm.logger import init_logger
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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.logits_processor import LogitsProcessor
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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 (
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default_weight_loader,
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maybe_remap_kv_scale_name,
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)
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from vllm.model_executor.models.deepseek_v2 import (
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DeepseekV2Attention,
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DeepseekV2MLAAttention,
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)
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from vllm.model_executor.models.glm4_moe import (
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Glm4MixtureOfExperts,
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Glm4MoE,
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Glm4MoeMLP,
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)
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from vllm.platforms import current_platform
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from vllm.sequence import IntermediateTensors
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from .interfaces import 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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logger = init_logger(__name__)
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class Glm4MoeLiteMLP(Glm4MoeMLP):
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pass
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class Glm4MoeLite(Glm4MoE):
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pass
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class Glm4LiteMixtureOfExperts(Glm4MixtureOfExperts):
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pass
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class Glm4MoeLiteAttention(DeepseekV2Attention):
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pass
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class Glm4MoeLiteMLAAttention(DeepseekV2MLAAttention):
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pass
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class Glm4MoeLiteDecoderLayer(nn.Module):
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def __init__(
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self,
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vllm_config: VllmConfig,
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prefix: str,
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config: "Glm4MoeLiteConfig | None" = None,
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topk_indices_buffer: torch.Tensor | None = None,
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) -> None:
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super().__init__()
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if config is None:
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config = vllm_config.model_config.hf_config
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model_config = vllm_config.model_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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self.hidden_size = config.hidden_size
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max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
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moe_layer_freq = getattr(config, "moe_layer_freq", 1)
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# DecoderLayers are created with `make_layers` which passes the prefix
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# with the layer's index.
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layer_idx = int(prefix.split(sep=".")[-1])
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self.layer_idx = layer_idx
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# verify MLA attention specific fields
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qk_nope_head_dim = getattr(config, "qk_nope_head_dim", 0)
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qk_rope_head_dim = getattr(config, "qk_rope_head_dim", 0)
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v_head_dim = getattr(config, "v_head_dim", 0)
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kv_lora_rank = getattr(config, "kv_lora_rank", 0)
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if model_config.use_mla:
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attn_cls = Glm4MoeLiteMLAAttention
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else:
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attn_cls = Glm4MoeLiteAttention
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self.self_attn = attn_cls(
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vllm_config=vllm_config,
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config=config,
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hidden_size=self.hidden_size,
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num_heads=config.num_attention_heads,
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qk_nope_head_dim=qk_nope_head_dim,
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qk_rope_head_dim=qk_rope_head_dim,
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v_head_dim=v_head_dim,
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q_lora_rank=config.q_lora_rank if hasattr(config, "q_lora_rank") else None,
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kv_lora_rank=kv_lora_rank,
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max_position_embeddings=max_position_embeddings,
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=f"{prefix}.self_attn",
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topk_indices_buffer=topk_indices_buffer,
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)
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if (
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config.n_routed_experts is not None
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and layer_idx >= config.first_k_dense_replace
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and layer_idx % moe_layer_freq == 0
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):
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self.mlp = Glm4MoeLite(
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config=config,
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quant_config=quant_config,
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prefix=f"{prefix}.mlp",
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)
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else:
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self.mlp = Glm4MoeLiteMLP(
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hidden_size=config.hidden_size,
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intermediate_size=config.intermediate_size,
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hidden_act=config.hidden_act,
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quant_config=quant_config,
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prefix=f"{prefix}.mlp",
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)
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self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.post_attention_layernorm = RMSNorm(
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config.hidden_size, eps=config.rms_norm_eps
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)
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self.routed_scaling_factor = getattr(config, "routed_scaling_factor", 1.0)
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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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residual: torch.Tensor | None,
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llama_4_scaling: torch.Tensor | None = None,
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) -> torch.Tensor:
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# Self Attention
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if residual is None:
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residual = hidden_states.clone()
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hidden_states = self.input_layernorm(hidden_states)
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else:
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hidden_states, residual = self.input_layernorm(hidden_states, residual)
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attn_kwargs = {
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"positions": positions,
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"hidden_states": hidden_states,
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}
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attn_kwargs["llama_4_scaling"] = llama_4_scaling
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hidden_states = self.self_attn(**attn_kwargs)
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hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
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hidden_states = self.mlp(hidden_states)
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return hidden_states, residual
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@support_torch_compile(
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dynamic_arg_dims={
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"input_ids": 0,
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"positions": -1,
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"intermediate_tensors": 0,
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"inputs_embeds": 0,
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}
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)
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class Glm4MoeLiteModel(nn.Module):
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__()
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config = vllm_config.model_config.hf_config
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quant_config = vllm_config.quant_config
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self.config = config
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self.device = current_platform.device_type
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self.vocab_size = config.vocab_size
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self.is_v32 = hasattr(config, "index_topk")
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if self.is_v32:
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topk_tokens = config.index_topk
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topk_indices_buffer = torch.empty(
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vllm_config.scheduler_config.max_num_batched_tokens,
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topk_tokens,
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dtype=torch.int32,
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device=self.device,
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)
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else:
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topk_indices_buffer = None
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if get_pp_group().is_first_rank:
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self.embed_tokens = VocabParallelEmbedding(
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config.vocab_size,
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config.hidden_size,
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quant_config=quant_config,
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prefix=f"{prefix}.embed_tokens",
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)
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else:
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self.embed_tokens = PPMissingLayer()
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self.start_layer, self.end_layer, self.layers = make_layers(
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config.num_hidden_layers,
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lambda prefix: Glm4MoeLiteDecoderLayer(
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vllm_config=vllm_config,
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config=config,
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prefix=prefix,
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topk_indices_buffer=topk_indices_buffer,
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),
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prefix=f"{prefix}.layers",
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)
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if get_pp_group().is_last_rank:
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self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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else:
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self.norm = PPMissingLayer()
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self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
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["hidden_states", "residual"], config.hidden_size
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)
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def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.embed_tokens(input_ids)
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def forward(
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self,
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input_ids: torch.Tensor | None,
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positions: torch.Tensor,
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intermediate_tensors: IntermediateTensors | None = None,
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inputs_embeds: torch.Tensor | None = None,
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) -> torch.Tensor | IntermediateTensors:
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if get_pp_group().is_first_rank:
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if inputs_embeds is not None:
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hidden_states = inputs_embeds
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else:
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hidden_states = self.embed_input_ids(input_ids)
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residual = None
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else:
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assert intermediate_tensors is not None
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hidden_states = intermediate_tensors["hidden_states"]
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residual = intermediate_tensors["residual"]
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for layer in islice(self.layers, self.start_layer, self.end_layer):
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hidden_states, residual = layer(positions, hidden_states, residual)
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if not get_pp_group().is_last_rank:
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return IntermediateTensors(
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{"hidden_states": hidden_states, "residual": residual}
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)
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hidden_states, _ = self.norm(hidden_states, residual)
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return hidden_states
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def make_empty_intermediate_tensors(
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self, batch_size: int, dtype: torch.dtype, device: torch.device
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) -> IntermediateTensors:
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return IntermediateTensors(
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{
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"hidden_states": torch.zeros(
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(batch_size, self.config.hidden_size), dtype=dtype, device=device
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),
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"residual": torch.zeros(
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(batch_size, self.config.hidden_size), dtype=dtype, device=device
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),
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}
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)
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def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
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# Params for weights, fp8 weight scales, fp8 activation scales
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# (param_name, weight_name, expert_id, shard_id)
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return SharedFusedMoE.make_expert_params_mapping(
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self,
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ckpt_gate_proj_name="gate_proj",
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ckpt_down_proj_name="down_proj",
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ckpt_up_proj_name="up_proj",
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num_experts=self.config.n_routed_experts,
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)
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def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
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rocm_aiter_moe_shared_expert_enabled = (
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rocm_aiter_ops.is_fusion_moe_shared_experts_enabled()
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)
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stacked_params_mapping = [
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# (param_name, shard_name, shard_id)
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("gate_up_proj", "gate_proj", 0),
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("gate_up_proj", "up_proj", 1),
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]
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mla_params_mapping = [
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("fused_qkv_a_proj", "q_a_proj", 0),
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("fused_qkv_a_proj", "kv_a_proj_with_mqa", 1),
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]
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stacked_params_mapping.extend(mla_params_mapping)
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# Params for weights, fp8 weight scales, fp8 activation scales
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# (param_name, weight_name, expert_id, shard_id)
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expert_params_mapping = SharedFusedMoE.make_expert_params_mapping(
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self,
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ckpt_gate_proj_name="gate_proj",
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ckpt_down_proj_name="down_proj",
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ckpt_up_proj_name="up_proj",
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num_experts=self.config.n_routed_experts
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+ (
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self.config.n_shared_experts
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if rocm_aiter_moe_shared_expert_enabled
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else 0
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),
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)
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params_dict = dict(self.named_parameters())
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loaded_params: set[str] = set()
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for name, loaded_weight in weights:
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if "rotary_emb.inv_freq" in name:
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continue
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spec_layer = get_spec_layer_idx_from_weight_name(self.config, name)
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if spec_layer is not None:
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continue # skip spec decode layers for main model
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is_fusion_moe_shared_experts_layer = (
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rocm_aiter_moe_shared_expert_enabled and ("mlp.shared_experts" in name)
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)
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for param_name, weight_name, shard_id in stacked_params_mapping:
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# Skip non-stacked layers and experts (experts handled below).
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if weight_name not in name:
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continue
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# We have mlp.experts[0].gate_proj in the checkpoint.
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# Since we handle the experts below in expert_params_mapping,
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# we need to skip here BEFORE we update the name, otherwise
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# name will be updated to mlp.experts[0].gate_up_proj, which
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# will then be updated below in expert_params_mapping
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# for mlp.experts[0].gate_gate_up_proj, which breaks load.
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if ("mlp.experts." in name) and name not in params_dict:
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continue
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if is_fusion_moe_shared_experts_layer:
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continue
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name_mapped = name.replace(weight_name, param_name)
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# QKV fusion is optional, fall back to normal
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# weight loading if it's not enabled
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# if go with fusion option, then update name
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if (
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param_name == "fused_qkv_a_proj"
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) and name_mapped not in params_dict:
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continue
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else:
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name = name_mapped
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# Skip loading extra bias for GPTQ models.
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if name.endswith(".bias") and name not in params_dict:
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continue
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if is_pp_missing_parameter(name, self):
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continue
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param = params_dict[name]
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weight_loader = param.weight_loader
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weight_loader(param, loaded_weight, shard_id)
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break
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else:
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is_expert_weight = False
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# Special handling: when AITER fusion_shared_experts is enabled,
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# checkpoints may provide a single widened shared_experts tensor
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# without explicit expert indices
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# (e.g. ...mlp.shared_experts.gate_proj.weight).
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# For models with multiple shared experts, split that tensor
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# evenly into per-shared-expert slices and load them into
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# appended expert slots mlp.experts.{n_routed_experts + j}.*
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# accordingly.
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num_chunks = 1
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if is_fusion_moe_shared_experts_layer:
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num_chunks = getattr(self.config, "n_shared_experts", 1) or 1
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# Determine split axis based on op type
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# gate/up: ColumnParallel → split along dim 0
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# down: RowParallel → split along dim 1
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split_dim = 1 if "down_proj.weight" in name else 0
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total = loaded_weight.shape[split_dim]
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assert total % num_chunks == 0, (
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f"Shared expert weight dim {total} "
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f"not divisible by num_chunks {num_chunks}"
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)
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chunk_size = total // num_chunks
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for j in range(num_chunks):
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chunk_name = name
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weight_to_load = loaded_weight
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if is_fusion_moe_shared_experts_layer:
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if split_dim == 0:
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weight_to_load = loaded_weight[
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j * chunk_size : (j + 1) * chunk_size, :
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]
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else:
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weight_to_load = loaded_weight[
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:, j * chunk_size : (j + 1) * chunk_size
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]
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# Synthesize an expert-style name so expert mapping
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# can route it
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chunk_name = name.replace(
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"mlp.shared_experts",
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f"mlp.experts.{self.config.n_routed_experts + j}",
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)
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# Use expert_params_mapping to locate the destination
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# param and delegate to its expert-aware weight_loader
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# with expert_id.
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for mapping in expert_params_mapping:
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param_name, weight_name, expert_id, shard_id = mapping
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if weight_name not in chunk_name:
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continue
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# Anyway, this is an expert weight and should not be
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# attempted to load as other weights later
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is_expert_weight = True
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# Do not modify `name` since the loop may continue here
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# Instead, create a new variable
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name_mapped = chunk_name.replace(weight_name, param_name)
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if is_pp_missing_parameter(name_mapped, self):
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continue
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param = params_dict[name_mapped]
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# We should ask the weight loader to return success or
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# not here since otherwise we may skip experts with
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# other available replicas.
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weight_loader = typing.cast(
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Callable[..., bool], param.weight_loader
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)
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success = weight_loader(
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param,
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weight_to_load,
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name_mapped,
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shard_id=shard_id,
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expert_id=expert_id,
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return_success=True,
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)
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if success:
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if not is_fusion_moe_shared_experts_layer:
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name = name_mapped
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else:
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loaded_params.add(name_mapped)
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break
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else:
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if is_expert_weight:
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# We've checked that this is an expert weight
|
|
# However it's not mapped locally to this rank
|
|
# So we simply skip it
|
|
continue
|
|
|
|
# Skip loading extra bias for GPTQ models.
|
|
if name.endswith(".bias") and name not in params_dict:
|
|
continue
|
|
|
|
# Remapping the name of FP8 kv-scale.
|
|
name = maybe_remap_kv_scale_name(name, params_dict)
|
|
if name is None:
|
|
continue
|
|
|
|
if is_pp_missing_parameter(name, self):
|
|
continue
|
|
|
|
param = params_dict[name]
|
|
weight_loader = getattr(
|
|
param, "weight_loader", default_weight_loader
|
|
)
|
|
weight_loader(param, loaded_weight)
|
|
if not is_fusion_moe_shared_experts_layer:
|
|
loaded_params.add(name)
|
|
|
|
return loaded_params
|
|
|
|
|
|
class Glm4MoeLiteForCausalLM(
|
|
nn.Module, SupportsPP, SupportsLoRA, Glm4LiteMixtureOfExperts
|
|
):
|
|
packed_modules_mapping = {
|
|
"gate_up_proj": ["gate_proj", "up_proj"],
|
|
}
|
|
|
|
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
|
super().__init__()
|
|
config = vllm_config.model_config.hf_config
|
|
quant_config = vllm_config.quant_config
|
|
self.config = config
|
|
self.quant_config = quant_config
|
|
|
|
qk_nope_head_dim = getattr(config, "qk_nope_head_dim", 0)
|
|
qk_rope_head_dim = getattr(config, "qk_rope_head_dim", 0)
|
|
self.use_mha = config.model_type == "deepseek" or all(
|
|
dim == 0 for dim in (qk_nope_head_dim, qk_rope_head_dim)
|
|
)
|
|
|
|
if self.use_mha:
|
|
self.packed_modules_mapping["qkv_proj"] = ["q_proj", "k_proj", "v_proj"]
|
|
|
|
# `packed_modules_mapping` needs to be modified before
|
|
# initializing DeepseekV2Model, as it is passed inplace to
|
|
# quantization config init and may be used to select the
|
|
# quant_method for relevant layers during initialization.
|
|
self.fuse_qkv_a_proj = (
|
|
hasattr(config, "q_lora_rank") and config.q_lora_rank is not None
|
|
)
|
|
if self.fuse_qkv_a_proj:
|
|
self.packed_modules_mapping["fused_qkv_a_proj"] = [
|
|
"q_a_proj",
|
|
"kv_a_proj_with_mqa",
|
|
]
|
|
|
|
self.model = Glm4MoeLiteModel(
|
|
vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
|
|
)
|
|
if get_pp_group().is_last_rank:
|
|
self.lm_head = ParallelLMHead(
|
|
config.vocab_size,
|
|
config.hidden_size,
|
|
quant_config=quant_config,
|
|
prefix=maybe_prefix(prefix, "lm_head"),
|
|
)
|
|
else:
|
|
self.lm_head = PPMissingLayer()
|
|
self.logits_processor = LogitsProcessor(config.vocab_size)
|
|
self.make_empty_intermediate_tensors = (
|
|
self.model.make_empty_intermediate_tensors
|
|
)
|
|
# Set MoE hyperparameters
|
|
self.num_moe_layers = (
|
|
self.config.num_hidden_layers - self.config.first_k_dense_replace
|
|
)
|
|
self.set_moe_parameters()
|
|
|
|
def set_moe_parameters(self):
|
|
self.expert_weights = []
|
|
|
|
self.num_expert_groups = getattr(self.config, "n_group", 1)
|
|
|
|
self.moe_layers = []
|
|
self.moe_mlp_layers = []
|
|
example_moe = None
|
|
for layer in self.model.layers:
|
|
if isinstance(layer, PPMissingLayer):
|
|
continue
|
|
|
|
assert isinstance(layer, Glm4MoeLiteDecoderLayer)
|
|
if isinstance(layer.mlp, Glm4MoeLite):
|
|
# Pick last one layer since the first ones may be dense layers.
|
|
example_moe = layer.mlp
|
|
self.moe_mlp_layers.append(layer.mlp)
|
|
self.moe_layers.append(layer.mlp.experts)
|
|
|
|
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 | None,
|
|
positions: torch.Tensor,
|
|
intermediate_tensors: IntermediateTensors | None = None,
|
|
inputs_embeds: torch.Tensor | None = None,
|
|
) -> torch.Tensor | IntermediateTensors:
|
|
hidden_states = self.model(
|
|
input_ids, positions, intermediate_tensors, inputs_embeds
|
|
)
|
|
return hidden_states
|
|
|
|
def compute_logits(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
) -> torch.Tensor | None:
|
|
logits = self.logits_processor(self.lm_head, hidden_states)
|
|
return logits
|
|
|
|
def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
|
|
# Params for weights, fp8 weight scales, fp8 activation scales
|
|
# (param_name, weight_name, expert_id, shard_id)
|
|
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.n_routed_experts,
|
|
num_redundant_experts=0,
|
|
)
|
|
|
|
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
|
|
loader = AutoWeightsLoader(self)
|
|
return loader.load_weights(weights)
|
|
|
|
|
|
def get_spec_layer_idx_from_weight_name(
|
|
config: "Glm4MoeLiteConfig", weight_name: str
|
|
) -> int | None:
|
|
if hasattr(config, "num_nextn_predict_layers") and (
|
|
config.num_nextn_predict_layers > 0
|
|
):
|
|
layer_idx = config.num_hidden_layers
|
|
for i in range(config.num_nextn_predict_layers):
|
|
if f"layers.{layer_idx + i}." in weight_name:
|
|
return layer_idx + i
|
|
return None
|