wehub-resource-sync
740 lines
28 KiB
Python
740 lines
28 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# coding=utf-8
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# Copyright 2026 The HY 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 HY 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 Any
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import torch
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from torch import nn
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from transformers import PretrainedConfig
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from vllm.compilation.decorators import support_torch_compile
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from vllm.config import CacheConfig, VllmConfig, get_current_vllm_config
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from vllm.distributed import (
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get_ep_group,
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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.logger import init_logger
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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 (
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FusedMoE,
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GateLinear,
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fused_moe_make_expert_params_mapping,
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)
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from vllm.model_executor.layers.hpc import HpcRopeNorm, QkNormPolicy
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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 (
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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.sequence import IntermediateTensors
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from vllm.transformers_utils.configs.hy_v3 import HYV3Config
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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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logger = init_logger(__name__)
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class HYV3FeedForward(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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intermediate_size: int,
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hidden_act: str,
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quant_config: QuantizationConfig | None = None,
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reduce_results: bool = True,
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expert_gate: torch.nn.Linear | None = None,
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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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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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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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if hidden_act != "silu":
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raise ValueError(
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f"Unsupported activation: {hidden_act}. Only silu is supported for now."
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)
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self.act_fn = SiluAndMul()
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def forward(self, x):
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gate_up, _ = self.gate_up_proj(x)
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out = self.act_fn(gate_up)
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out, _ = self.down_proj(out)
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return out
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class HYV3MoEFused(nn.Module):
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def __init__(
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self,
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config: HYV3Config,
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quant_config: QuantizationConfig | None = None,
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prefix: str = "",
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enable_eplb: bool = False,
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):
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super().__init__()
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self.tp_size = get_tensor_model_parallel_world_size()
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self.ep_group = get_ep_group().device_group
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self.ep_rank = get_ep_group().rank_in_group
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self.ep_size = self.ep_group.size()
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self.n_routed_experts = config.num_experts
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if self.tp_size > config.num_experts:
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raise ValueError(
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f"Tensor parallel size {self.tp_size} is greater than "
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f"the number of experts {config.num_experts}."
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)
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top_k = config.num_experts_per_tok
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intermediate_size = config.expert_hidden_dim
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router_scaling_factor = getattr(config, "router_scaling_factor", 1.0)
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vllm_config = get_current_vllm_config()
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eplb_config = vllm_config.parallel_config.eplb_config
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self.enable_eplb = enable_eplb
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self.n_logical_experts = self.n_routed_experts
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self.n_redundant_experts = eplb_config.num_redundant_experts
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self.n_physical_experts = self.n_logical_experts + self.n_redundant_experts
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self.n_local_physical_experts = self.n_physical_experts // self.ep_size
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self.physical_expert_start = self.ep_rank * self.n_local_physical_experts
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self.physical_expert_end = (
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self.physical_expert_start + self.n_local_physical_experts
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)
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self.gate = GateLinear(
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config.hidden_size,
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config.num_experts,
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bias=False,
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out_dtype=torch.float32,
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params_dtype=torch.float32,
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prefix=f"{prefix}.gate",
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)
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if config.num_shared_experts > 0:
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self.shared_mlp = HYV3FeedForward(
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hidden_size=config.hidden_size,
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intermediate_size=config.expert_hidden_dim * config.num_shared_experts,
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hidden_act=config.hidden_act,
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quant_config=quant_config,
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prefix=f"{prefix}",
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reduce_results=False,
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)
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else:
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self.shared_mlp = None
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self.expert_bias = nn.Parameter(
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torch.empty(config.num_experts, dtype=torch.float32)
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)
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scoring_func = "sigmoid"
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e_score_correction_bias = self.expert_bias
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self.experts = FusedMoE(
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num_experts=self.n_routed_experts,
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top_k=top_k,
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hidden_size=config.hidden_size,
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intermediate_size=intermediate_size,
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renormalize=config.route_norm,
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quant_config=quant_config,
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prefix=f"{prefix}.experts",
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enable_eplb=self.enable_eplb,
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num_redundant_experts=self.n_redundant_experts,
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scoring_func=scoring_func,
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use_grouped_topk=True,
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num_expert_group=1,
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topk_group=1,
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routed_scaling_factor=router_scaling_factor,
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e_score_correction_bias=e_score_correction_bias,
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n_shared_experts=config.num_shared_experts,
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shared_experts=self.shared_mlp,
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)
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def forward(
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self,
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hidden_states: torch.Tensor,
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) -> torch.Tensor:
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orig_shape = hidden_states.shape
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hidden_dim = hidden_states.shape[-1]
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hidden_states = hidden_states.view(-1, hidden_dim)
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# router_logits: (num_tokens, n_experts)
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router_logits, _ = self.gate(hidden_states)
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final_hidden_states = self.experts(
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hidden_states=hidden_states, router_logits=router_logits
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)
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return final_hidden_states.view(orig_shape)
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class HYV3Attention(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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hidden_size: int,
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num_heads: int,
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num_kv_heads: int,
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rope_parameters: dict[str, Any],
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max_position_embeddings: int = 8192,
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head_dim: int | None = None,
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rms_norm_eps: float = 1e-5,
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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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dual_chunk_attention_config: dict[str, Any] | None = None,
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) -> None:
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super().__init__()
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self.dtype = torch.get_default_dtype()
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self.hidden_size = hidden_size
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tp_size = get_tensor_model_parallel_world_size()
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self.total_num_heads = num_heads
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assert self.total_num_heads % tp_size == 0
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self.num_heads = self.total_num_heads // tp_size
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self.total_num_kv_heads = num_kv_heads
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if self.total_num_kv_heads >= tp_size:
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# Number of KV heads is greater than TP size, so we partition
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# the KV heads across multiple tensor parallel GPUs.
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assert self.total_num_kv_heads % tp_size == 0
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else:
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# Number of KV heads is less than TP size, so we replicate
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# the KV heads across multiple tensor parallel GPUs.
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assert tp_size % self.total_num_kv_heads == 0
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self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
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if hasattr(config, "head_dim") and config.head_dim:
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self.head_dim = config.head_dim
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else:
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self.head_dim = head_dim or (hidden_size // self.total_num_heads)
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self.q_size = self.num_heads * self.head_dim
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self.kv_size = self.num_kv_heads * self.head_dim
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self.scaling = self.head_dim**-0.5
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self.use_qk_norm = getattr(config, "qk_norm", False)
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self.max_position_embeddings = max_position_embeddings
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self.qkv_proj = QKVParallelLinear(
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hidden_size,
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self.head_dim,
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self.total_num_heads,
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self.total_num_kv_heads,
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quant_config=quant_config,
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bias=None,
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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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self.total_num_heads * self.head_dim,
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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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# When the HPC fused RoPE+QK-Norm path is enabled, the RoPE cos/sin
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# cache must be float32 to match the HPC kernel's expectations.
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kv_cache_dtype = cache_config.cache_dtype if cache_config else "auto"
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rope_support = HpcRopeNorm.support(
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self.num_heads, self.num_kv_heads, self.head_dim, kv_cache_dtype
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)
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self.rotary_emb = get_rope(
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self.head_dim,
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max_position=max_position_embeddings,
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rope_parameters=rope_parameters,
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is_neox_style=True,
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dtype=torch.float32 if rope_support else torch.get_default_dtype(),
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)
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self.attn = Attention(
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self.num_heads,
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self.head_dim,
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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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if self.use_qk_norm:
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self.q_norm = RMSNorm(self.head_dim, rms_norm_eps)
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self.k_norm = RMSNorm(self.head_dim, rms_norm_eps)
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# HPC fused RoPE + QK-Norm + KV-Cache-Write (+ optional FP8 Q quant).
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# HunYuan V3 applies QK-Norm *before* RoPE, so NORM_THEN_ROPE.
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self.hpc_rope_norm: HpcRopeNorm | None = None
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if rope_support:
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self.hpc_rope_norm = HpcRopeNorm(
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num_heads=self.num_heads,
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num_kv_heads=self.num_kv_heads,
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head_dim=self.head_dim,
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cos_sin_cache=self.rotary_emb.cos_sin_cache,
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use_qk_norm=self.use_qk_norm,
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fallback_qnorm=self.q_norm if self.use_qk_norm else None,
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fallback_knorm=self.k_norm if self.use_qk_norm else None,
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kv_cache_dtype=kv_cache_dtype,
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layer_name=self.attn.layer_name,
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qk_norm_policy=QkNormPolicy.NORM_THEN_ROPE,
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)
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# FP8 Q is produced by HpcRopeNorm, so the attention layer must not
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# re-quantize the query.
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if self.hpc_rope_norm.use_fp8 and hasattr(self.attn, "query_quant"):
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self.attn.query_quant = None
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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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qkv, _ = self.qkv_proj(hidden_states)
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q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
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output_shape = None
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if self.hpc_rope_norm is not None:
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# HPC handles QK-Norm + RoPE + KV-cache write (+ optional FP8 Q
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# quant) internally and returns the processed query. K/V are
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# written into the paged cache by the fused op.
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q = self.hpc_rope_norm(qkv, self.attn.layer_name)
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q = q.view(-1, self.num_heads * self.head_dim)
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attn_output = self.attn(q, k, v, output_shape, self.dtype)
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else:
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if self.use_qk_norm:
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q_by_head = q.view(
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*q.shape[:-1], q.shape[-1] // self.head_dim, self.head_dim
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)
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q_by_head = self.q_norm(q_by_head)
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q = q_by_head.view(q.shape)
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k_by_head = k.view(
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*k.shape[:-1], k.shape[-1] // self.head_dim, self.head_dim
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)
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k_by_head = self.k_norm(k_by_head)
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k = k_by_head.view(k.shape)
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q, k = self.rotary_emb(positions, q, k)
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attn_output = self.attn(q, k, v, output_shape)
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attn_output = attn_output.view(q.shape[0], -1)
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output, _ = self.o_proj(attn_output)
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return output
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class HYV3DecoderLayer(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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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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layer_idx = int(prefix.split(".")[-1])
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max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
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self.self_attn = HYV3Attention(
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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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num_kv_heads=config.num_key_value_heads,
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rope_parameters=config.rope_parameters,
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max_position_embeddings=max_position_embeddings,
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head_dim=config.head_dim,
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rms_norm_eps=config.rms_norm_eps,
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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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)
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self.input_layernorm = RMSNorm(config.hidden_size, config.rms_norm_eps)
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self.post_attention_layernorm = RMSNorm(config.hidden_size, config.rms_norm_eps)
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if not hasattr(config, "first_k_dense_replace"):
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raise ValueError("first_k_dense_replace not exist,please check config")
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if layer_idx < config.first_k_dense_replace:
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self.mlp = HYV3FeedForward(
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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.block_type = "feedforward"
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else:
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self.mlp = HYV3MoEFused(
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config=config, quant_config=quant_config, prefix=f"{prefix}.mlp"
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)
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self.block_type = "moe"
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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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idx: int = -1,
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) -> torch.Tensor:
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if residual is None:
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residual = hidden_states
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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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hidden_states = self.self_attn(
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positions=positions,
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hidden_states=hidden_states,
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)
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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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class HYV3Model(nn.Module, MixtureOfExperts):
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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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cache_config = vllm_config.cache_config
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quant_config = vllm_config.quant_config
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parallel_config = vllm_config.parallel_config
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eplb_config = parallel_config.eplb_config
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self.num_redundant_experts = eplb_config.num_redundant_experts
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self.vocab_size = config.vocab_size
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self.config = config
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self.quant_config = quant_config
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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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)
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self.start_layer, self.end_layer, self.layers = make_layers(
|
|
config.num_hidden_layers,
|
|
lambda prefix: HYV3DecoderLayer(
|
|
config=config,
|
|
cache_config=cache_config,
|
|
quant_config=quant_config,
|
|
prefix=prefix,
|
|
),
|
|
prefix=f"{prefix}.layers",
|
|
)
|
|
self.norm = RMSNorm(config.hidden_size, config.rms_norm_eps)
|
|
self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
|
|
["hidden_states", "residual"], config.hidden_size
|
|
)
|
|
|
|
# Set MoE hyperparameters
|
|
self.num_expert_groups = 1
|
|
self.moe_layers = []
|
|
example_layer = None
|
|
for layer in self.layers:
|
|
if isinstance(layer, PPMissingLayer):
|
|
continue
|
|
|
|
assert isinstance(layer, HYV3DecoderLayer)
|
|
if layer.block_type == "moe":
|
|
example_layer = layer.mlp
|
|
self.moe_layers.append(layer.mlp.experts)
|
|
|
|
if example_layer is None:
|
|
self.num_moe_layers = 0
|
|
raise RuntimeError("No MoE layer found in model.layers.")
|
|
|
|
self.num_moe_layers = len(self.moe_layers)
|
|
self.num_logical_experts = getattr(example_layer, "n_logical_experts", None)
|
|
self.num_physical_experts = getattr(example_layer, "n_physical_experts", None)
|
|
self.num_local_physical_experts = getattr(
|
|
example_layer, "n_local_physical_experts", None
|
|
)
|
|
self.num_routed_experts = getattr(example_layer, "n_routed_experts", None)
|
|
self.num_redundant_experts = getattr(example_layer, "n_redundant_experts", None)
|
|
|
|
def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
|
|
return self.embed_tokens(input_ids)
|
|
|
|
def update_physical_experts_metadata(
|
|
self,
|
|
num_physical_experts: int,
|
|
num_local_physical_experts: int,
|
|
) -> None:
|
|
assert self.num_local_physical_experts == num_local_physical_experts
|
|
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 layer in self.layers:
|
|
if isinstance(layer.mlp, HYV3MoEFused):
|
|
moe = layer.mlp
|
|
moe.n_local_physical_experts = num_local_physical_experts
|
|
moe.n_physical_experts = num_physical_experts
|
|
moe.n_redundant_experts = self.num_redundant_experts
|
|
moe.experts.update_expert_map()
|
|
|
|
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 fused_moe_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 forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
intermediate_tensors: IntermediateTensors | None = 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 idx, layer in enumerate(
|
|
islice(self.layers, self.start_layer, self.end_layer)
|
|
):
|
|
hidden_states, residual = layer(positions, hidden_states, residual, idx=idx)
|
|
if not get_pp_group().is_last_rank:
|
|
return IntermediateTensors(
|
|
{"hidden_states": hidden_states, "residual": residual}
|
|
)
|
|
|
|
hidden_states = hidden_states + residual
|
|
residual = hidden_states
|
|
|
|
hidden_states = self.norm(hidden_states)
|
|
|
|
return hidden_states
|
|
|
|
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
|
|
stacked_params_mapping = [
|
|
# (param_name, shard_name, shard_id)
|
|
(".qkv_proj", ".q_proj", "q"),
|
|
(".qkv_proj", ".k_proj", "k"),
|
|
(".qkv_proj", ".v_proj", "v"),
|
|
(".gate_up_proj", ".gate_proj", 0),
|
|
(".gate_up_proj", ".up_proj", 1),
|
|
]
|
|
params_dict = dict(self.named_parameters())
|
|
expert_params_mapping = self.get_expert_mapping()
|
|
loaded_params: set[str] = set()
|
|
for name, loaded_weight in weights:
|
|
if self.config.tie_word_embeddings and "lm_head.weight" in name:
|
|
continue
|
|
if "scale" in name:
|
|
# Remapping the name of FP8 kv-scale.
|
|
name = maybe_remap_kv_scale_name(name, params_dict)
|
|
if name is None:
|
|
continue
|
|
is_found = False
|
|
for param_name, weight_name, shard_id in stacked_params_mapping:
|
|
if weight_name not in name:
|
|
continue
|
|
if "mlp.experts" in name:
|
|
continue
|
|
name = name.replace(weight_name, param_name)
|
|
# Skip loading extra bias for GPTQ models.
|
|
if name.endswith(".bias") and name not in params_dict:
|
|
continue
|
|
|
|
# Skip layers on other devices.
|
|
if is_pp_missing_parameter(name, self):
|
|
continue
|
|
|
|
param = params_dict[name]
|
|
weight_loader = param.weight_loader
|
|
weight_loader(param, loaded_weight, shard_id)
|
|
loaded_params.add(name)
|
|
is_found = True
|
|
break
|
|
if is_found:
|
|
continue
|
|
|
|
if name.endswith(".bias") and name not in params_dict:
|
|
continue
|
|
is_expert_weight = False
|
|
for mapping in expert_params_mapping:
|
|
param_name, weight_name, expert_id, shard_id = mapping
|
|
if weight_name not in name:
|
|
continue
|
|
is_expert_weight = True
|
|
name_mapped = name.replace(weight_name, param_name)
|
|
# Skip layers on other devices.
|
|
if is_pp_missing_parameter(name_mapped, self):
|
|
continue
|
|
|
|
param = params_dict[name_mapped]
|
|
weight_loader = typing.cast(Callable[..., bool], param.weight_loader)
|
|
success = weight_loader(
|
|
param,
|
|
loaded_weight,
|
|
name_mapped,
|
|
shard_id=shard_id,
|
|
expert_id=expert_id,
|
|
return_success=True,
|
|
)
|
|
if success:
|
|
name = name_mapped
|
|
break
|
|
else:
|
|
if is_expert_weight:
|
|
# We've checked that this is an expert weight
|
|
# However it's not mapped locally to this rank
|
|
# So we simply skip it
|
|
continue
|
|
if name is None:
|
|
continue
|
|
if is_pp_missing_parameter(name, self):
|
|
continue
|
|
if "router.gate." in name:
|
|
name = name.replace("router.", "")
|
|
|
|
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
|
|
|
|
|
|
def get_spec_layer_idx_from_weight_name(
|
|
config: PretrainedConfig, weight_name: str
|
|
) -> int | None:
|
|
# HYV3MTP is enabled only when num_nextn_predict_layers is greater than 1
|
|
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 weight_name.startswith(f"model.layers.{layer_idx + i}."):
|
|
return layer_idx + i
|
|
return None
|
|
|
|
|
|
class HYV3ForCausalLM(nn.Module, SupportsPP, SupportsLoRA):
|
|
packed_modules_mapping = {
|
|
"qkv_proj": ["q_proj", "k_proj", "v_proj"],
|
|
"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
|
|
|
|
parallel_config = vllm_config.parallel_config
|
|
eplb_config = parallel_config.eplb_config
|
|
self.num_redundant_experts = eplb_config.num_redundant_experts
|
|
|
|
self.model = HYV3Model(
|
|
vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
|
|
)
|
|
self.lm_head = ParallelLMHead(
|
|
config.vocab_size,
|
|
config.hidden_size,
|
|
quant_config=quant_config,
|
|
prefix=maybe_prefix(prefix, "lm_head"),
|
|
)
|
|
if self.config.tie_word_embeddings:
|
|
self.lm_head.weight = self.model.embed_tokens.weight
|
|
self.logits_processor = LogitsProcessor(config.vocab_size)
|
|
self.make_empty_intermediate_tensors = (
|
|
self.model.make_empty_intermediate_tensors
|
|
)
|
|
|
|
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:
|
|
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 load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
|
|
def _filter_weights(weights):
|
|
for name, weight in weights:
|
|
spec_layer = get_spec_layer_idx_from_weight_name(self.config, name)
|
|
if spec_layer is not None:
|
|
continue
|
|
yield name, weight
|
|
|
|
loader = AutoWeightsLoader(
|
|
self,
|
|
skip_prefixes=(["lm_head."] if self.config.tie_word_embeddings else None),
|
|
)
|
|
return loader.load_weights(_filter_weights(weights))
|
|
|
|
def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
|
|
return self.model.get_expert_mapping()
|