# 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 List, Optional, Tuple import torch from torch import nn from transformers import LlamaConfig from vllm.model_executor.input_metadata import InputMetadata from vllm.model_executor.layers.activation import SiluAndMul from vllm.model_executor.layers.layernorm import RMSNorm from vllm.model_executor.layers.attention import PagedAttentionWithRoPE from vllm.model_executor.layers.sampler import Sampler from vllm.model_executor.weight_utils import ( load_tensor_parallel_weights, load_padded_tensor_parallel_vocab, hf_model_weights_iterator) from vllm.model_executor.parallel_utils.parallel_state import ( get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size) from vllm.model_executor.parallel_utils.tensor_parallel import ( VocabParallelEmbedding, ColumnParallelLinear, RowParallelLinear) from vllm.sequence import SamplerOutput KVCache = Tuple[torch.Tensor, torch.Tensor] class LlamaMLP(nn.Module): def __init__( self, hidden_size: int, intermediate_size: int, hidden_act: str, ): super().__init__() self.gate_up_proj = ColumnParallelLinear(hidden_size, 2 * intermediate_size, bias=False, gather_output=False, perform_initialization=False) self.down_proj = RowParallelLinear(intermediate_size, hidden_size, bias=False, input_is_parallel=True, perform_initialization=False) 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 LlamaAttention(nn.Module): def __init__( self, hidden_size: int, num_heads: int, num_kv_heads: int, rope_theta: float = 10000, ): 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.qkv_proj = ColumnParallelLinear( hidden_size, (self.total_num_heads + 2 * self.total_num_kv_heads) * self.head_dim, bias=False, gather_output=False, perform_initialization=False, ) self.o_proj = RowParallelLinear( self.total_num_heads * self.head_dim, hidden_size, bias=False, input_is_parallel=True, perform_initialization=False, ) self.attn = PagedAttentionWithRoPE(self.num_heads, self.head_dim, self.scaling, base=self.rope_theta, rotary_dim=self.head_dim, num_kv_heads=self.num_kv_heads) 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 LlamaDecoderLayer(nn.Module): def __init__(self, config: LlamaConfig): super().__init__() self.hidden_size = config.hidden_size # Requires transformers > 4.32.0 rope_theta = getattr(config, "rope_theta", 10000) self.self_attn = LlamaAttention( hidden_size=self.hidden_size, num_heads=config.num_attention_heads, num_kv_heads=config.num_key_value_heads, rope_theta=rope_theta, ) self.mlp = LlamaMLP( hidden_size=self.hidden_size, intermediate_size=config.intermediate_size, hidden_act=config.hidden_act, ) 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, 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 LlamaModel(nn.Module): def __init__(self, config: LlamaConfig): super().__init__() self.config = config self.padding_idx = config.pad_token_id self.vocab_size = config.vocab_size vocab_size = ((config.vocab_size + 63) // 64) * 64 self.embed_tokens = VocabParallelEmbedding( vocab_size, config.hidden_size, perform_initialization=False) self.layers = nn.ModuleList([ LlamaDecoderLayer(config) for _ 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, 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)): if cache_events is None: cache_event = None else: cache_event = 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 LlamaForCausalLM(nn.Module): def __init__(self, config): super().__init__() self.config = config self.model = LlamaModel(config) vocab_size = ((config.vocab_size + 63) // 64) * 64 self.lm_head = ColumnParallelLinear(config.hidden_size, vocab_size, bias=False, gather_output=False, perform_initialization=False) 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 _column_parallel_weights = [ "qkv_proj.weight", "gate_proj.weight", "up_proj.weight" ] _row_parallel_weights = ["o_proj.weight", "down_proj.weight"] def load_weights(self, model_name_or_path: str, cache_dir: Optional[str] = None, load_format: str = "auto", revision: Optional[str] = None): tp_size = get_tensor_model_parallel_world_size() tensor_model_parallel_rank = get_tensor_model_parallel_rank() q_proj_shard_size = (self.config.hidden_size // tp_size) kv_proj_shard_size = (self.config.hidden_size // self.config.num_attention_heads * self.config.num_key_value_heads // tp_size) attention_weight_specs = [ # (weight_name, shard_size, offset) ("q_proj", q_proj_shard_size, 0), ("k_proj", kv_proj_shard_size, q_proj_shard_size), ("v_proj", kv_proj_shard_size, q_proj_shard_size + kv_proj_shard_size), ] state_dict = self.state_dict() 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 is_attention_weight = False for weight_name, shard_size, offset in attention_weight_specs: if weight_name not in name: continue param = state_dict[name.replace(weight_name, "qkv_proj")] loaded_weight = loaded_weight[ shard_size * tensor_model_parallel_rank:shard_size * (tensor_model_parallel_rank + 1)] param_slice = param.data[offset:offset + shard_size] assert param_slice.shape == loaded_weight.shape param_slice.copy_(loaded_weight) is_attention_weight = True break if is_attention_weight: continue is_gate_up_weight = False for stride_id, weight_name in enumerate(["gate_proj", "up_proj"]): if weight_name not in name: continue param = state_dict[name.replace(weight_name, "gate_up_proj")] shard_size = param.shape[0] // 2 loaded_weight = loaded_weight[ shard_size * tensor_model_parallel_rank:shard_size * (tensor_model_parallel_rank + 1)] param_slice = param.data[shard_size * stride_id:shard_size * (stride_id + 1)] assert param_slice.shape == loaded_weight.shape param_slice.copy_(loaded_weight) is_gate_up_weight = True break if is_gate_up_weight: continue param = state_dict[name] if "embed_tokens" in name or "lm_head" in name: load_padded_tensor_parallel_vocab(param, loaded_weight, tensor_model_parallel_rank) continue load_tensor_parallel_weights(param, loaded_weight, name, self._column_parallel_weights, self._row_parallel_weights, tensor_model_parallel_rank)