# coding=utf-8 # Adapted from # https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py # Copyright 2023 The vLLM team. # 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 LLaMA model compatible with HuggingFace weights. The input of the model is flattened to a 1D tensor of tokens. The model uses InputMetadata to extract the original 2D shape of the input. """ from typing import Any, Dict, List, Optional, Tuple import torch from torch import nn from vllm.model_executor.input_metadata import InputMetadata from vllm.model_executor.layers.activation import SiluAndMul from vllm.model_executor.layers.attention import PagedAttentionWithRoPE from vllm.model_executor.layers.linear import (LinearMethodBase, MergedColumnParallelLinear, QKVParallelLinear, RowParallelLinear) from vllm.model_executor.layers.sampler import Sampler from vllm.model_executor.layers.vocab_parallel_embedding import ( VocabParallelEmbedding, ParallelLMHead) from vllm.model_executor.parallel_utils.parallel_state import ( get_tensor_model_parallel_world_size) from vllm.model_executor.weight_utils import (default_weight_loader, hf_model_weights_iterator) from vllm.sequence import SamplerOutput from vllm.transformers_utils.configs.aquila import AquilaConfig KVCache = Tuple[torch.Tensor, torch.Tensor] class AquilaMLP(nn.Module): def __init__( self, hidden_size: int, intermediate_size: int, hidden_act: str, linear_method: Optional[LinearMethodBase] = None, ): super().__init__() self.gate_up_proj = MergedColumnParallelLinear( hidden_size, [intermediate_size] * 2, bias=False, linear_method=linear_method) self.down_proj = RowParallelLinear(intermediate_size, hidden_size, bias=False, linear_method=linear_method) 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 AquilaRMSNorm(nn.Module): def __init__(self, hidden_size, eps=1e-6): """ AquilaRMSNorm is equivalent to T5LayerNorm """ super().__init__() self.weight = nn.Parameter(torch.ones(hidden_size)) self.variance_epsilon = eps def forward(self, hidden_states): input_dtype = hidden_states.dtype variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True) hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) return (self.weight * hidden_states).to(input_dtype) class AquilaAttention(nn.Module): def __init__( self, hidden_size: int, num_heads: int, num_kv_heads: int, rope_theta: float = 10000, max_position_embeddings: int = 8192, rope_scaling: Optional[Dict[str, Any]] = None, linear_method: Optional[LinearMethodBase] = None, ): super().__init__() self.hidden_size = hidden_size tp_size = get_tensor_model_parallel_world_size() self.total_num_heads = num_heads assert self.total_num_heads % tp_size == 0 self.num_heads = self.total_num_heads // tp_size self.total_num_kv_heads = num_kv_heads assert self.total_num_kv_heads % tp_size == 0 self.num_kv_heads = self.total_num_kv_heads // tp_size self.head_dim = hidden_size // self.total_num_heads self.q_size = self.num_heads * self.head_dim self.kv_size = self.num_kv_heads * self.head_dim self.scaling = self.head_dim**-0.5 self.rope_theta = rope_theta self.max_position_embeddings = max_position_embeddings self.qkv_proj = QKVParallelLinear( hidden_size, self.head_dim, self.total_num_heads, self.total_num_kv_heads, bias=False, linear_method=linear_method, ) self.o_proj = RowParallelLinear( self.total_num_heads * self.head_dim, hidden_size, bias=False, linear_method=linear_method, ) self.attn = PagedAttentionWithRoPE( self.num_heads, self.head_dim, self.scaling, base=self.rope_theta, max_position=self.max_position_embeddings, rotary_dim=self.head_dim, num_kv_heads=self.num_kv_heads, rope_scaling=rope_scaling) def forward( self, positions: torch.Tensor, hidden_states: torch.Tensor, kv_cache: KVCache, input_metadata: InputMetadata, cache_event: Optional[torch.cuda.Event], ) -> torch.Tensor: qkv, _ = self.qkv_proj(hidden_states) q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1) k_cache, v_cache = kv_cache attn_output = self.attn(positions, q, k, v, k_cache, v_cache, input_metadata, cache_event) output, _ = self.o_proj(attn_output) return output class AquilaDecoderLayer(nn.Module): def __init__( self, config: AquilaConfig, linear_method: Optional[LinearMethodBase] = None, ): super().__init__() self.hidden_size = config.hidden_size rope_theta = getattr(config, "rope_theta", 10000) rope_scaling = getattr(config, "rope_scaling", None) max_position_embeddings = getattr(config, "max_position_embeddings", 8192) self.self_attn = AquilaAttention( hidden_size=self.hidden_size, num_heads=config.num_attention_heads, num_kv_heads=config.num_key_value_heads, rope_theta=rope_theta, max_position_embeddings=max_position_embeddings, rope_scaling=rope_scaling, linear_method=linear_method, ) self.mlp = AquilaMLP( hidden_size=self.hidden_size, intermediate_size=config.intermediate_size, hidden_act=config.hidden_act, linear_method=linear_method, ) self.input_layernorm = AquilaRMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.post_attention_layernorm = AquilaRMSNorm(config.hidden_size, eps=config.rms_norm_eps) def forward( self, positions: torch.Tensor, hidden_states: torch.Tensor, kv_cache: KVCache, input_metadata: InputMetadata, cache_event: Optional[torch.cuda.Event], ) -> torch.Tensor: # Self Attention residual = hidden_states hidden_states = self.input_layernorm(hidden_states) hidden_states = self.self_attn( positions=positions, hidden_states=hidden_states, kv_cache=kv_cache, input_metadata=input_metadata, cache_event=cache_event, ) hidden_states = residual + hidden_states # Fully Connected residual = hidden_states hidden_states = self.post_attention_layernorm(hidden_states) hidden_states = self.mlp(hidden_states) hidden_states = residual + hidden_states return hidden_states class AquilaModel(nn.Module): def __init__( self, config: AquilaConfig, linear_method: Optional[LinearMethodBase] = None, ): 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([ AquilaDecoderLayer(config, linear_method) for _ in range(config.num_hidden_layers) ]) self.norm = AquilaRMSNorm(config.hidden_size, eps=config.rms_norm_eps) def forward( self, input_ids: torch.Tensor, positions: torch.Tensor, kv_caches: List[KVCache], input_metadata: InputMetadata, cache_events: Optional[List[torch.cuda.Event]], ) -> torch.Tensor: hidden_states = self.embed_tokens(input_ids) for i in range(len(self.layers)): cache_event = None if cache_events is None else cache_events[i] layer = self.layers[i] hidden_states = layer( positions, hidden_states, kv_caches[i], input_metadata, cache_event, ) hidden_states = self.norm(hidden_states) return hidden_states class AquilaForCausalLM(nn.Module): def __init__( self, config, linear_method: Optional[LinearMethodBase] = None, ): super().__init__() self.config = config self.linear_method = linear_method self.model = AquilaModel(config, linear_method) self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size) self.sampler = Sampler(config.vocab_size) def forward( self, input_ids: torch.Tensor, positions: torch.Tensor, kv_caches: List[KVCache], input_metadata: InputMetadata, cache_events: Optional[List[torch.cuda.Event]], ) -> SamplerOutput: hidden_states = self.model(input_ids, positions, kv_caches, input_metadata, cache_events) next_tokens = self.sampler(self.lm_head.weight, hidden_states, input_metadata) return next_tokens def load_weights(self, model_name_or_path: str, cache_dir: Optional[str] = None, load_format: str = "auto", revision: Optional[str] = None): 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()) for name, loaded_weight in hf_model_weights_iterator( model_name_or_path, cache_dir, load_format, revision): if "rotary_emb.inv_freq" in name: continue for (param_name, weight_name, shard_id) in stacked_params_mapping: if weight_name not in name: continue param = params_dict[name.replace(weight_name, param_name)] weight_loader = param.weight_loader weight_loader(param, loaded_weight, shard_id) break else: param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, loaded_weight)