# coding=utf-8 # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. # # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX # and OPT implementations in this library. It has been modified from its # original forms to accommodate minor architectural differences compared # to GPT-NeoX and OPT used by the Meta AI team that trained the model. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Inference-only BaiChuan model compatible with HuggingFace weights.""" import math from typing import Iterable, Optional, Tuple import torch from torch import nn from transformers import PretrainedConfig from sglang.srt.distributed import ( get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size, ) from sglang.srt.layers.activation import SiluAndMul from sglang.srt.layers.layernorm import RMSNorm from sglang.srt.layers.linear import ( MergedColumnParallelLinear, QKVParallelLinear, RowParallelLinear, ) from sglang.srt.layers.logits_processor import LogitsProcessor from sglang.srt.layers.quantization.base_config import QuantizationConfig from sglang.srt.layers.radix_attention import RadixAttention from sglang.srt.layers.rotary_embedding import get_rope from sglang.srt.layers.vocab_parallel_embedding import ( ParallelLMHead, VocabParallelEmbedding, ) from sglang.srt.model_executor.forward_batch_info import ForwardBatch from sglang.srt.model_loader.weight_utils import default_weight_loader from sglang.srt.utils import add_prefix def _get_alibi_slopes(total_num_heads: int) -> torch.Tensor: closest_power_of_2 = 2 ** math.floor(math.log2(total_num_heads)) base = torch.tensor( 2 ** (-(2 ** -(math.log2(closest_power_of_2) - 3))), dtype=torch.float32, ) powers = torch.arange(1, 1 + closest_power_of_2, dtype=torch.int32) slopes = torch.pow(base, powers) if closest_power_of_2 != total_num_heads: extra_base = torch.tensor( 2 ** (-(2 ** -(math.log2(2 * closest_power_of_2) - 3))), dtype=torch.float32, ) num_remaining_heads = min( closest_power_of_2, total_num_heads - closest_power_of_2 ) extra_powers = torch.arange( start=1, end=1 + 2 * num_remaining_heads, step=2, dtype=torch.int32 ) slopes = torch.cat([slopes, torch.pow(extra_base, extra_powers)], dim=0) return slopes class BaiChuanMLP(nn.Module): def __init__( self, hidden_size: int, intermediate_size: int, hidden_act: str, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", ): super().__init__() self.gate_up_proj = MergedColumnParallelLinear( hidden_size, [intermediate_size] * 2, bias=False, quant_config=quant_config, prefix=add_prefix("gate_up_proj", prefix), ) self.down_proj = RowParallelLinear( intermediate_size, hidden_size, bias=False, quant_config=quant_config, prefix=add_prefix("down_proj", prefix), ) if hidden_act != "silu": raise ValueError( f"Unsupported activation: {hidden_act}. " "Only silu is supported for now." ) self.act_fn = SiluAndMul() def forward(self, x): gate_up, _ = self.gate_up_proj(x) x = self.act_fn(gate_up) x, _ = self.down_proj(x) return x class BaiChuanAttention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__( self, hidden_size: int, num_heads: int, position_embedding: str, rope_theta: float = 10000, max_position_embeddings: int = 8192, quant_config: Optional[QuantizationConfig] = None, layer_id: int = 0, prefix: str = "", ): super().__init__() self.hidden_size = hidden_size tensor_model_parallel_world_size = get_tensor_model_parallel_world_size() tp_size = get_tensor_model_parallel_world_size() self.total_num_heads = num_heads assert self.total_num_heads % tensor_model_parallel_world_size == 0 self.num_heads = self.total_num_heads // tensor_model_parallel_world_size self.head_dim = hidden_size // self.total_num_heads self.postion_embedding = position_embedding self.rope_theta = rope_theta self.max_position_embeddings = max_position_embeddings self.total_num_kv_heads = self.num_heads if self.total_num_kv_heads >= tp_size: # Number of KV heads is greater than TP size, so we partition # the KV heads across multiple tensor parallel GPUs. assert self.total_num_kv_heads % tp_size == 0 else: # Number of KV heads is less than TP size, so we replicate # the KV heads across multiple tensor parallel GPUs. assert tp_size % self.total_num_kv_heads == 0 self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size) # pylint: disable=invalid-name self.W_pack = QKVParallelLinear( hidden_size, self.head_dim, self.total_num_heads, self.total_num_heads, bias=False, quant_config=quant_config, ) self.o_proj = RowParallelLinear( self.total_num_heads * self.head_dim, hidden_size, bias=False, quant_config=quant_config, ) # Create the alibi slopes and slice them. if self.postion_embedding == "ALIBI": tp_rank = get_tensor_model_parallel_rank() head_start = tp_rank * self.num_heads head_end = (tp_rank + 1) * self.num_heads alibi_slopes = _get_alibi_slopes(self.total_num_heads) alibi_slopes = alibi_slopes[head_start:head_end].tolist() scaling = self.head_dim**-0.5 self.attn = RadixAttention( self.num_heads, self.head_dim, scaling, num_kv_heads=self.num_kv_heads, layer_id=layer_id, prefix=add_prefix("attn", prefix), ) else: self.rotary_emb = get_rope( self.head_dim, rotary_dim=self.head_dim, max_position=self.max_position_embeddings, base=self.rope_theta, ) self.scaling = self.head_dim**-0.5 self.attn = RadixAttention( self.num_heads, self.head_dim, self.scaling, num_kv_heads=self.num_kv_heads, layer_id=layer_id, prefix=add_prefix("attn", prefix), ) def forward( self, positions: torch.Tensor, hidden_states: torch.Tensor, forward_batch: ForwardBatch, ) -> torch.Tensor: qkv, _ = self.W_pack(hidden_states) q, k, v = qkv.chunk(chunks=3, dim=-1) if self.postion_embedding != "ALIBI": q, k = self.rotary_emb(positions, q, k) attn_output = self.attn(q, k, v, forward_batch) output, _ = self.o_proj(attn_output) return output class BaiChuanDecoderLayer(nn.Module): def __init__( self, config: PretrainedConfig, position_embedding: str, layer_id: int = 0, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", ): super().__init__() self.hidden_size = config.hidden_size rope_theta = getattr(config, "rope_theta", 10000) max_position_embeddings = getattr(config, "max_position_embeddings", 8192) self.self_attn = BaiChuanAttention( hidden_size=self.hidden_size, num_heads=config.num_attention_heads, position_embedding=position_embedding, rope_theta=rope_theta, layer_id=layer_id, max_position_embeddings=max_position_embeddings, quant_config=quant_config, prefix=add_prefix("self_attn", prefix), ) self.mlp = BaiChuanMLP( hidden_size=self.hidden_size, intermediate_size=config.intermediate_size, hidden_act=config.hidden_act, quant_config=quant_config, prefix=add_prefix("mlp", prefix), ) self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.post_attention_layernorm = RMSNorm( config.hidden_size, eps=config.rms_norm_eps ) def forward( self, positions: torch.Tensor, hidden_states: torch.Tensor, forward_batch: ForwardBatch, residual: Optional[torch.Tensor], ) -> Tuple[torch.Tensor, torch.Tensor]: # Self Attention if residual is None: residual = hidden_states hidden_states = self.input_layernorm(hidden_states) else: hidden_states, residual = self.input_layernorm(hidden_states, residual) hidden_states = self.self_attn( positions=positions, hidden_states=hidden_states, forward_batch=forward_batch, ) # Fully Connected hidden_states, residual = self.post_attention_layernorm(hidden_states, residual) hidden_states = self.mlp(hidden_states) return hidden_states, residual class BaiChuanModel(nn.Module): def __init__( self, config: PretrainedConfig, position_embedding: str, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", ): super().__init__() self.config = config self.padding_idx = config.pad_token_id self.vocab_size = config.vocab_size self.embed_tokens = VocabParallelEmbedding( config.vocab_size, config.hidden_size, ) self.layers = nn.ModuleList( [ BaiChuanDecoderLayer( config, layer_id=i, position_embedding=position_embedding, quant_config=quant_config, prefix=add_prefix(f"layers.{i}", prefix), ) for i in range(config.num_hidden_layers) ] ) self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) def forward( self, input_ids: torch.Tensor, positions: torch.Tensor, forward_batch: ForwardBatch, ) -> torch.Tensor: hidden_states = self.embed_tokens(input_ids) residual = None for i in range(len(self.layers)): layer = self.layers[i] hidden_states, residual = layer( positions, hidden_states, forward_batch, residual, ) hidden_states, _ = self.norm(hidden_states, residual) return hidden_states class BaiChuanBaseForCausalLM(nn.Module): packed_modules_mapping = { "W_pack": ["W_pack"], "gate_up_proj": [ "gate_proj", "up_proj", ], } # LoRA specific attributes supported_lora_modules = [ "W_pack", "o_proj", "gate_up_proj", "down_proj", ] embedding_modules = {} embedding_padding_modules = [] def __init__( self, config: PretrainedConfig, position_embedding: str, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", ): super().__init__() self.config = config self.quant_config = quant_config self.model = BaiChuanModel( config, position_embedding, quant_config, prefix=add_prefix("model", prefix) ) if self.config.tie_word_embeddings: self.lm_head = self.model.embed_tokens else: self.lm_head = ParallelLMHead( config.vocab_size, config.hidden_size, quant_config=quant_config, prefix=add_prefix("lm_head", prefix), ) self.logits_processor = LogitsProcessor(config) def forward( self, input_ids: torch.Tensor, positions: torch.Tensor, forward_batch: ForwardBatch, ) -> torch.Tensor: hidden_states = self.model(input_ids, positions, forward_batch) return self.logits_processor( input_ids, hidden_states, self.lm_head, forward_batch ) def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]): stacked_params_mapping = [ # (param_name, shard_name, shard_id) ("gate_up_proj", "gate_proj", 0), ("gate_up_proj", "up_proj", 1), ] params_dict = dict(self.named_parameters()) for name, loaded_weight in weights: if "rotary_emb.inv_freq" in name: continue if name == "lm_head.weight": # Unlike Baichuan, Baichuan2 normalizes the head weights. # Refer to: # https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat/blob/84603cde5ebffb6084e476cfaeceaf0b8b91fe54/modeling_baichuan.py#L508 # Distinguish between Baichuan and Baichuan2 by checking the # vocab size. This is suggested by # https://github.com/vllm-project/vllm/pull/1022#discussion_r1325652704 is_baichuan2 = self.config.vocab_size == 125696 if is_baichuan2: loaded_weight = torch.nn.functional.normalize(loaded_weight) for param_name, weight_name, shard_id in stacked_params_mapping: if weight_name not 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 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 param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, loaded_weight) class BaichuanForCausalLM(BaiChuanBaseForCausalLM): """Baichuan 13B and Baichuan2 7B/13B.""" def __init__( self, config, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", ): if config.hidden_size == 4096: # baichuan2 7b super().__init__(config, "ROPE", quant_config, prefix=prefix) else: # baichuan 13b, baichuan2 13b super().__init__(config, "ALIBI", quant_config, prefix=prefix) EntryClass = [BaichuanForCausalLM]