704 lines
29 KiB
Python
Executable File
704 lines
29 KiB
Python
Executable File
# coding=utf-8
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# Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved.
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#
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# Licensed under the TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT (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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# https://github.com/Tencent/Tencent-Hunyuan-Large/blob/main/License.docx
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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 HunYuan model compatible with HuggingFace weights."""
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from typing import Any, Dict, Iterable, List, Optional, Tuple, Union
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import re
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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.attention import Attention, AttentionMetadata
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from vllm.config import CacheConfig, LoRAConfig, VllmConfig
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from vllm.distributed import (get_pp_group, get_tensor_model_parallel_rank,
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get_tensor_model_parallel_world_size,
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tensor_model_parallel_all_reduce)
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from vllm.model_executor.layers.activation import SiluAndMul
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from vllm.model_executor.layers.fused_moe import FusedMoE
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
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QKVParallelLinear,
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ReplicatedLinear,
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ColumnParallelLinear,
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RowParallelLinear)
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.quantization.base_config import (
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QuantizationConfig)
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from vllm.model_executor.layers.quantization.compressed_tensors.utils import (
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get_compressed_tensors_cache_scale)
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from vllm.model_executor.layers.rotary_embedding import get_rope
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from vllm.model_executor.layers.sampler import Sampler, SamplerOutput
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
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from vllm.model_executor.model_loader.weight_utils import (
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default_weight_loader, kv_cache_scales_loader,
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maybe_remap_kv_scale_name)
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.sequence import IntermediateTensors
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from .interfaces import SupportsLoRA
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from .utils import (PPMissingLayer, is_pp_missing_parameter, make_layers,
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maybe_prefix)
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class HunYuanMLP(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: Optional[QuantizationConfig] = None,
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bias: bool = False,
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prefix: str = "",
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reduce_results: bool = True,
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) -> None:
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super().__init__()
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self.gate_up_proj = MergedColumnParallelLinear(
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input_size=hidden_size,
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output_sizes=[intermediate_size] * 2,
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bias=bias,
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quant_config=quant_config,
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prefix=f"{prefix}.gate_up_proj")
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self.down_proj = RowParallelLinear(input_size=intermediate_size,
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output_size=hidden_size,
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bias=bias,
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quant_config=quant_config,
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prefix=f"{prefix}.down_proj",
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reduce_results=reduce_results)
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if hidden_act != "silu":
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raise ValueError(f"Unsupported activation: {hidden_act}. "
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"Only silu is supported for now.")
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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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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 HunYuanSparseMoeBlock(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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quant_config: Optional[QuantizationConfig] = None,
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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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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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self.experts = FusedMoE(num_experts=config.num_experts,
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top_k=config.moe_topk,
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hidden_size=config.hidden_size,
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intermediate_size=config.intermediate_size,
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reduce_results=False,
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renormalize=True if config.moe_topk>1 else False,
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quant_config=quant_config)
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self.gate = ReplicatedLinear(config.hidden_size,
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config.num_experts,
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bias=False,
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quant_config=None)
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if config.use_mixed_mlp_moe > 0:
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self.shared_mlp = HunYuanMLP(
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hidden_size=config.hidden_size,
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intermediate_size=config.intermediate_size * config.num_shared_expert,
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hidden_act=config.hidden_act,
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quant_config=quant_config,
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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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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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# NOTE: hidden_states can have either 1D or 2D shape.
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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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shared_output = None
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if self.shared_mlp is not None:
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shared_output = self.shared_mlp(hidden_states)
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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(hidden_states=hidden_states,
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router_logits=router_logits)
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if shared_output is not None:
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final_hidden_states = final_hidden_states + shared_output
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if self.tp_size > 1:
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final_hidden_states = tensor_model_parallel_all_reduce(
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final_hidden_states)
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return final_hidden_states.view(orig_shape)
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class HunYuanAttention(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_theta: float = 10000,
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rope_scaling: Optional[Dict[str, Any]] = None,
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max_position_embeddings: int = 8192,
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quant_config: Optional[QuantizationConfig] = None,
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bias: bool = False,
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cache_config: Optional[CacheConfig] = None,
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prefix: str = "",
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attention_type: str = "self",
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) -> None:
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super().__init__()
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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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# MistralConfig has an optional head_dim introduced by Mistral-Nemo
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self.head_dim = getattr(config, "head_dim",
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self.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.rope_theta = rope_theta
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self.max_position_embeddings = max_position_embeddings
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self.use_qk_norm = config.use_qk_norm
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self.attention_type = attention_type
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if attention_type == "self":
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self.qkv_proj = QKVParallelLinear(
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hidden_size=hidden_size,
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head_size=self.head_dim,
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total_num_heads=self.total_num_heads,
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total_num_kv_heads=self.total_num_kv_heads,
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bias=bias,
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quant_config=quant_config,
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prefix=f"{prefix}.qkv_proj",
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)
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elif attention_type == "cross":
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self.q_proj = ColumnParallelLinear(
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hidden_size,
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hidden_size,
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bias=bias,
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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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else:
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raise RuntimeError("Not support attnention type")
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self.o_proj = RowParallelLinear(
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input_size=self.total_num_heads * self.head_dim,
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output_size=hidden_size,
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bias=bias,
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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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is_neox_style = True
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if quant_config is not None and quant_config.get_name() == "gguf":
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is_neox_style = False
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self.rotary_emb = get_rope(
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self.head_dim,
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rotary_dim=self.head_dim,
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max_position=max_position_embeddings,
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base=rope_theta,
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rope_scaling=rope_scaling,
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is_neox_style=is_neox_style,
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)
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self.attn = Attention(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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if self.use_qk_norm:
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self.query_layernorm = RMSNorm(self.head_dim,
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eps=config.rms_norm_eps)
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self.key_layernorm = RMSNorm(self.head_dim,
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eps=config.rms_norm_eps)
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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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kv_cache: torch.Tensor,
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attn_metadata: AttentionMetadata,
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kv_states: Optional[Tuple[torch.Tensor]] = None,
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) -> torch.Tensor:
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if self.attention_type == "self":
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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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q, k = self.rotary_emb(positions, q, k)
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ori_k = k
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if self.use_qk_norm:
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q = self.query_layernorm(q.view(-1, self.num_heads, self.head_dim).contiguous())
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k = self.key_layernorm(k.view(-1, self.num_kv_heads, self.head_dim).contiguous())
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elif self.attention_type == "cross":
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assert kv_states is not None
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ori_k, v = kv_states # use last layer kv,
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k = ori_k
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q, _ = self.q_proj(hidden_states)
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k_tmp = torch.empty_like(k) # Todo: reduant rotary embedding
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q, _ = self.rotary_emb(positions, q, k_tmp)
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if self.use_qk_norm:
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q = self.query_layernorm(q.view(-1, self.num_heads, self.head_dim).contiguous())
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k = self.key_layernorm(k.view(-1, self.num_kv_heads, self.head_dim).contiguous())
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else:
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raise RuntimeError("Not support attnention type")
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attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
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output, _ = self.o_proj(attn_output)
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return output, (ori_k, v)
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class HunYuanDecoderLayer(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: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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layer_id: int = -1,
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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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rope_theta = getattr(config, "rope_theta", 10000)
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rope_scaling = getattr(config, "rope_scaling", None)
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if rope_scaling is not None and getattr(
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config, "original_max_position_embeddings", None):
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rope_scaling["original_max_position_embeddings"] = (
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config.original_max_position_embeddings)
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max_position_embeddings = getattr(config, "max_position_embeddings",
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8192)
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# Support abacusai/Smaug-72B-v0.1 with attention_bias
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# Support internlm/internlm-7b with bias
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attention_bias = getattr(config, "attention_bias", False) or getattr(
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config, "bias", False)
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cla_factor = getattr(config, "cla_share_factor", 1)
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attention_type = "cross" \
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if layer_id >= 0 and layer_id % cla_factor != 0 else "self"
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self.self_attn = HunYuanAttention(
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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=getattr(config, "num_key_value_heads",
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config.num_attention_heads),
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rope_theta=rope_theta,
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rope_scaling=rope_scaling,
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max_position_embeddings=max_position_embeddings,
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quant_config=quant_config,
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bias=attention_bias,
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cache_config=cache_config,
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prefix=f"{prefix}.self_attn",
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attention_type=attention_type,
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)
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if getattr(config, "num_experts", None):
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self.mlp = HunYuanSparseMoeBlock(config=config,
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quant_config=quant_config)
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else:
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self.mlp = HunYuanMLP(
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hidden_size=self.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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bias=getattr(config, "mlp_bias", False),
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prefix=f"{prefix}.mlp",
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)
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self.input_layernorm = RMSNorm(config.hidden_size,
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eps=config.rms_norm_eps)
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self.post_attention_layernorm = RMSNorm(config.hidden_size,
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eps=config.rms_norm_eps)
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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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kv_cache: torch.Tensor,
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attn_metadata: AttentionMetadata,
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residual: Optional[torch.Tensor],
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kv_states: Optional[Tuple[torch.Tensor]] = None,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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# Self Attention
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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(
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hidden_states, residual)
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hidden_states, ori_kv_states = self.self_attn(
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positions=positions,
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hidden_states=hidden_states,
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kv_cache=kv_cache,
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attn_metadata=attn_metadata,
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kv_states=kv_states,
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)
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# Fully Connected
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hidden_states, residual = self.post_attention_layernorm(
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hidden_states, residual)
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hidden_states = self.mlp(hidden_states)
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return hidden_states, residual, ori_kv_states
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class HunYuanModel(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: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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lora_config: Optional[LoRAConfig] = 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.padding_idx = config.pad_token_id
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lora_vocab = (lora_config.lora_extra_vocab_size *
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(lora_config.max_loras or 1)) if lora_config else 0
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self.vocab_size = config.vocab_size + lora_vocab
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self.org_vocab_size = config.vocab_size
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if get_pp_group().is_first_rank or (config.tie_word_embeddings
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and get_pp_group().is_last_rank):
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self.embed_tokens = VocabParallelEmbedding(
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self.vocab_size,
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config.hidden_size,
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org_num_embeddings=config.vocab_size,
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quant_config=quant_config,
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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: HunYuanDecoderLayer(config=config,
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layer_id=int(
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prefix.split(".")[-1]),
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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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prefix=f"{prefix}.layers")
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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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def get_input_embeddings(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: Optional[torch.Tensor],
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positions: torch.Tensor,
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kv_caches: List[torch.Tensor],
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attn_metadata: AttentionMetadata,
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intermediate_tensors: Optional[IntermediateTensors],
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inputs_embeds: Optional[torch.Tensor] = None,
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) -> Union[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.get_input_embeddings(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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cla_factor = getattr(self.config, "cla_share_factor", 1)
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prev_kv_states = None
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for i in range(self.start_layer, self.end_layer):
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layer = self.layers[i]
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hidden_states, residual, kv_states = layer(
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positions,
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hidden_states,
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kv_caches[i - self.start_layer],
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# kv_caches[(i - self.start_layer) // cla_factor],
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attn_metadata,
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residual,
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prev_kv_states,
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)
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if (i - self.start_layer) % cla_factor == 0:
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prev_kv_states = kv_states
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else:
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prev_kv_states = None
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if not get_pp_group().is_last_rank:
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return IntermediateTensors({
|
|
"hidden_states": hidden_states,
|
|
"residual": residual
|
|
})
|
|
hidden_states, _ = self.norm(hidden_states, residual)
|
|
return hidden_states
|
|
|
|
|
|
class HunYuanForCausalLM(nn.Module, SupportsLoRA):
|
|
packed_modules_mapping = {
|
|
"qkv_proj": [
|
|
"q_proj",
|
|
"k_proj",
|
|
"v_proj",
|
|
],
|
|
"gate_up_proj": [
|
|
"gate_proj",
|
|
"up_proj",
|
|
],
|
|
}
|
|
|
|
# LoRA specific attributes
|
|
supported_lora_modules = [
|
|
"qkv_proj", "o_proj", "gate_up_proj", "down_proj", "embed_tokens",
|
|
"lm_head"
|
|
]
|
|
embedding_modules = {
|
|
"embed_tokens": "input_embeddings",
|
|
"lm_head": "output_embeddings",
|
|
}
|
|
embedding_padding_modules = ["lm_head"]
|
|
bitsandbytes_stacked_params_mapping = {
|
|
# shard_name, weight_name, index
|
|
"q_proj": ("qkv_proj", 0),
|
|
"k_proj": ("qkv_proj", 1),
|
|
"v_proj": ("qkv_proj", 2),
|
|
"gate_proj": ("gate_up_proj", 0),
|
|
"up_proj": ("gate_up_proj", 1),
|
|
}
|
|
|
|
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
|
|
lora_config = vllm_config.lora_config
|
|
self.config = config
|
|
self.lora_config = lora_config
|
|
|
|
self.model = HunYuanModel(config,
|
|
cache_config,
|
|
quant_config,
|
|
lora_config=lora_config,
|
|
prefix=maybe_prefix(prefix, "model"))
|
|
if get_pp_group().is_last_rank:
|
|
self.unpadded_vocab_size = config.vocab_size
|
|
if lora_config:
|
|
self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
|
|
self.lm_head = ParallelLMHead(
|
|
self.unpadded_vocab_size,
|
|
config.hidden_size,
|
|
org_num_embeddings=config.vocab_size,
|
|
padding_size=DEFAULT_VOCAB_PADDING_SIZE
|
|
# We need bigger padding if using lora for kernel
|
|
# compatibility
|
|
if not lora_config else lora_config.lora_vocab_padding_size,
|
|
quant_config=quant_config,
|
|
)
|
|
if config.tie_word_embeddings:
|
|
self.lm_head.weight = self.model.embed_tokens.weight
|
|
|
|
logit_scale = getattr(config, "logit_scale", 1.0)
|
|
self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
|
|
config.vocab_size,
|
|
logit_scale)
|
|
self.sampler = Sampler()
|
|
else:
|
|
self.lm_head = PPMissingLayer()
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
kv_caches: List[torch.Tensor],
|
|
attn_metadata: AttentionMetadata,
|
|
intermediate_tensors: Optional[IntermediateTensors] = None,
|
|
) -> Union[torch.Tensor, IntermediateTensors]:
|
|
model_output = self.model(input_ids, positions, kv_caches,
|
|
attn_metadata, intermediate_tensors)
|
|
return model_output
|
|
|
|
def compute_logits(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
sampling_metadata: SamplingMetadata,
|
|
) -> Optional[torch.Tensor]:
|
|
logits = self.logits_processor(self.lm_head, hidden_states,
|
|
sampling_metadata)
|
|
return logits
|
|
|
|
def sample(
|
|
self,
|
|
logits: torch.Tensor,
|
|
sampling_metadata: SamplingMetadata,
|
|
) -> Optional[SamplerOutput]:
|
|
next_tokens = self.sampler(logits, sampling_metadata)
|
|
return next_tokens
|
|
|
|
def make_empty_intermediate_tensors(
|
|
self, batch_size: int, dtype: torch.dtype,
|
|
device: torch.device) -> IntermediateTensors:
|
|
return IntermediateTensors({
|
|
"hidden_states":
|
|
torch.zeros((batch_size, self.config.hidden_size),
|
|
dtype=dtype,
|
|
device=device),
|
|
"residual":
|
|
torch.zeros((batch_size, self.config.hidden_size),
|
|
dtype=dtype,
|
|
device=device),
|
|
})
|
|
|
|
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
|
|
cla_factor = getattr(self.config, "cla_share_factor", 1)
|
|
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),
|
|
]
|
|
|
|
if getattr(self.config, "num_experts", None):
|
|
# Params for weights, fp8 weight scales, fp8 activation scales
|
|
# (param_name, weight_name, expert_id, shard_id)
|
|
expert_params_mapping = FusedMoE.make_expert_params_mapping(
|
|
ckpt_gate_proj_name="gate_proj",
|
|
ckpt_down_proj_name="down_proj",
|
|
ckpt_up_proj_name="up_proj",
|
|
num_experts=self.config.num_experts)
|
|
else:
|
|
expert_params_mapping = {}
|
|
|
|
params_dict = dict(self.named_parameters())
|
|
for name, loaded_weight in weights:
|
|
if "rotary_emb.inv_freq" in name:
|
|
continue
|
|
if ("rotary_emb.cos_cached" in name
|
|
or "rotary_emb.sin_cached" in name):
|
|
# Models trained using ColossalAI may include these tensors in
|
|
# the checkpoint. Skip them.
|
|
continue
|
|
# With tie_word_embeddings, we can skip lm_head.weight
|
|
# The weight might appear unnecessarily in the files if the model is
|
|
# processed with quantization, LoRA, fine-tuning, etc.
|
|
if self.config.tie_word_embeddings and "lm_head.weight" in name:
|
|
continue
|
|
if scale_name := get_compressed_tensors_cache_scale(name):
|
|
# Loading kv cache scales for compressed-tensors quantization
|
|
param = params_dict[scale_name]
|
|
weight_loader = getattr(param, "weight_loader",
|
|
default_weight_loader)
|
|
loaded_weight = loaded_weight[0]
|
|
weight_loader(param, loaded_weight)
|
|
continue
|
|
for (param_name, weight_name, shard_id) in stacked_params_mapping:
|
|
if weight_name not in name:
|
|
continue
|
|
if "mlp.experts" in name:
|
|
continue
|
|
# cross layer only have q_proj, skip qkv pack
|
|
if weight_name == ".q_proj":
|
|
match = re.search(r'layers\.\d+', name)
|
|
if match:
|
|
layer_id = int(match.group(0).split('.')[-1])
|
|
if cla_factor > 1 and layer_id % cla_factor != 0:
|
|
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
|
|
|
|
if is_pp_missing_parameter(name, self):
|
|
continue
|
|
|
|
param = params_dict[name]
|
|
weight_loader = param.weight_loader
|
|
weight_loader(param, loaded_weight, shard_id)
|
|
break
|
|
else:
|
|
# Skip loading extra bias for GPTQ models.
|
|
if name.endswith(".bias") and name not in params_dict:
|
|
continue
|
|
for mapping in expert_params_mapping:
|
|
param_name, weight_name, expert_id, shard_id = mapping
|
|
if weight_name not in name:
|
|
continue
|
|
name = name.replace(weight_name, param_name)
|
|
# Skip layers on other devices.
|
|
if is_pp_missing_parameter(name, self):
|
|
continue
|
|
param = params_dict[name]
|
|
weight_loader = param.weight_loader
|
|
weight_loader(param,
|
|
loaded_weight,
|
|
name,
|
|
shard_id=shard_id,
|
|
expert_id=expert_id)
|
|
break
|
|
else:
|
|
# 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
|
|
|
|
if "mlp.gate.wg." in name:
|
|
name = name.replace("wg.", "")
|
|
|
|
param = params_dict[name]
|
|
weight_loader = getattr(param, "weight_loader",
|
|
default_weight_loader)
|
|
weight_loader(param, loaded_weight)
|
|
|
|
# If this function is called, it should always initialize KV cache scale
|
|
# factors (or else raise an exception). Thus, handled exceptions should
|
|
# make sure to leave KV cache scale factors in a known good (dummy) state
|
|
def load_kv_cache_scales(self, quantization_param_path: str) -> None:
|
|
tp_size = get_tensor_model_parallel_world_size()
|
|
tp_rank = get_tensor_model_parallel_rank()
|
|
for layer_idx, scaling_factor in kv_cache_scales_loader(
|
|
quantization_param_path, tp_rank, tp_size,
|
|
self.config.num_hidden_layers,
|
|
self.config.__class__.model_type):
|
|
if not isinstance(self.model.layers[layer_idx], nn.Identity):
|
|
layer_self_attn = self.model.layers[layer_idx].self_attn
|
|
|
|
if hasattr(layer_self_attn, "kv_scale"):
|
|
layer_self_attn.attn._kv_scale = scaling_factor
|
|
else:
|
|
raise RuntimeError("Self attention has no KV cache scaling "
|
|
"factor attribute!")
|