# 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.""" from typing import Any, Dict, Iterable, List, Optional, Tuple import torch from torch import nn from transformers import LlamaConfig from aphrodite.attention import Attention, AttentionMetadata from aphrodite.common.config import LoRAConfig from aphrodite.common.sequence import SamplerOutput from aphrodite.common.utils import is_hip from aphrodite.distributed import (get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size) from aphrodite.modeling.layers.activation import SiluAndMul from aphrodite.modeling.layers.layernorm import RMSNorm from aphrodite.modeling.layers.linear import (MergedColumnParallelLinear, QKVParallelLinear, RowParallelLinear) from aphrodite.modeling.layers.logits_processor import LogitsProcessor from aphrodite.modeling.layers.rotary_embedding import get_rope from aphrodite.modeling.layers.sampler import Sampler from aphrodite.modeling.layers.vocab_parallel_embedding import ( DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding) from aphrodite.modeling.model_loader.weight_utils import ( default_weight_loader, kv_cache_scales_loader) from aphrodite.modeling.sampling_metadata import SamplingMetadata from aphrodite.quantization.base_config import QuantizationConfig class LlamaMLP(nn.Module): def __init__( self, hidden_size: int, intermediate_size: int, hidden_act: str, quant_config: Optional[QKVParallelLinear] = None, ) -> None: super().__init__() self.gate_up_proj = MergedColumnParallelLinear( hidden_size, [intermediate_size] * 2, bias=False, quant_config=quant_config) self.down_proj = RowParallelLinear(intermediate_size, hidden_size, bias=False, quant_config=quant_config) 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, rope_scaling: Optional[Dict[str, Any]] = None, max_position_embeddings: int = 8192, quant_config: Optional[QuantizationConfig] = None, bias: bool = False, sliding_window: Optional[int] = None, ) -> 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 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) 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 # This will be overwritten by model initialization if we are using it. # N.B. currently we only support per tensor scalar scaling factors # & only applicable to ROCm (AMD GPU). # The scaling factor convention we are assuming is # quantized_value * scaling_factor ~= true_value # which is consistent with the practice of setting # scaling_factor = tensor_amax / FPtype_max self.kv_scale = 1.0 self.qkv_proj = QKVParallelLinear( hidden_size, self.head_dim, self.total_num_heads, self.total_num_kv_heads, bias=bias, quant_config=quant_config, ) self.o_proj = RowParallelLinear( self.total_num_heads * self.head_dim, hidden_size, bias=bias, quant_config=quant_config, ) self.rotary_emb = get_rope( self.head_dim, rotary_dim=self.head_dim, max_position=max_position_embeddings, base=rope_theta, rope_scaling=rope_scaling, ) self.attn = Attention(self.num_heads, self.head_dim, self.scaling, num_kv_heads=self.num_kv_heads, sliding_window=sliding_window) def forward( self, positions: torch.Tensor, hidden_states: torch.Tensor, kv_cache: torch.Tensor, attn_metadata: AttentionMetadata, ) -> torch.Tensor: qkv, _ = self.qkv_proj(hidden_states) q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1) q, k = self.rotary_emb(positions, q, k) attn_output = self.attn(q, k, v, kv_cache, attn_metadata, self.kv_scale) output, _ = self.o_proj(attn_output) return output class LlamaDecoderLayer(nn.Module): def __init__( self, config: LlamaConfig, quant_config: Optional[QuantizationConfig] = None, ) -> None: super().__init__() self.hidden_size = config.hidden_size rope_theta = getattr(config, "rope_theta", 10000) rope_scaling = getattr(config, "rope_scaling", None) if rope_scaling is not None and getattr( config, "original_max_position_embeddings", None): rope_scaling["original_max_position_embeddings"] = ( config.original_max_position_embeddings) max_position_embeddings = getattr(config, "max_position_embeddings", 8192) sliding_window = getattr(config, "sliding_window", None) # Support abacusai/Smaug-72B-v0.1 with attention_bias # Support internlm/internlm-7b with bias attention_bias = getattr(config, "attention_bias", False) or getattr( config, "bias", False) self.self_attn = LlamaAttention( hidden_size=self.hidden_size, num_heads=config.num_attention_heads, num_kv_heads=getattr(config, "num_key_value_heads", config.num_attention_heads), rope_theta=rope_theta, rope_scaling=rope_scaling, max_position_embeddings=max_position_embeddings, quant_config=quant_config, bias=attention_bias, sliding_window=sliding_window, ) self.mlp = LlamaMLP( hidden_size=self.hidden_size, intermediate_size=config.intermediate_size, hidden_act=config.hidden_act, quant_config=quant_config, ) 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: torch.Tensor, attn_metadata: AttentionMetadata, 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, kv_cache=kv_cache, attn_metadata=attn_metadata, ) # Fully Connected hidden_states, residual = self.post_attention_layernorm( hidden_states, residual) hidden_states = self.mlp(hidden_states) return hidden_states, residual class LlamaModel(nn.Module): def __init__( self, config: LlamaConfig, quant_config: Optional[QuantizationConfig] = None, lora_config: Optional[LoRAConfig] = None, ) -> None: super().__init__() self.config = config self.padding_idx = config.pad_token_id lora_vocab = (lora_config.lora_extra_vocab_size * (lora_config.max_loras or 1)) if lora_config else 0 self.vocab_size = config.vocab_size + lora_vocab self.org_vocab_size = config.vocab_size self.embed_tokens = VocabParallelEmbedding( self.vocab_size, config.hidden_size, org_num_embeddings=config.vocab_size, ) self.layers = nn.ModuleList([ LlamaDecoderLayer(config, quant_config) for _ in range(config.num_hidden_layers) ]) self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: return self.embed_tokens(input_ids) def forward( self, input_ids: Optional[torch.Tensor], positions: torch.Tensor, kv_caches: List[torch.Tensor], attn_metadata: AttentionMetadata, inputs_embeds: Optional[torch.Tensor] = None, ) -> torch.Tensor: if inputs_embeds is not None: hidden_states = inputs_embeds else: hidden_states = self.get_input_embeddings(input_ids) residual = None for i in range(len(self.layers)): layer = self.layers[i] hidden_states, residual = layer( positions, hidden_states, kv_caches[i], attn_metadata, residual, ) hidden_states, _ = self.norm(hidden_states, residual) return hidden_states class LlamaForCausalLM(nn.Module): 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"] def __init__( self, config: LlamaConfig, quant_config: Optional[QuantizationConfig] = None, lora_config: Optional[LoRAConfig] = None, ) -> None: super().__init__() self.config = config self.model = LlamaModel(config, quant_config, lora_config=lora_config) 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, ) logit_scale = getattr(config, "logit_scale", 1.0) self.logits_processor = LogitsProcessor(self.unpadded_vocab_size, config.vocab_size, logit_scale) self.sampler = Sampler() def forward( self, input_ids: torch.Tensor, positions: torch.Tensor, kv_caches: List[torch.Tensor], attn_metadata: AttentionMetadata, ) -> torch.Tensor: hidden_states = self.model(input_ids, positions, kv_caches, attn_metadata) return hidden_states def compute_logits(self, hidden_states: torch.Tensor, sampling_metadata: SamplingMetadata) -> torch.Tensor: logits = self.logits_processor(self.lm_head.weight, 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 load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]): 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 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 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) # 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): layer_self_attn = self.model.layers[layer_idx].self_attn if is_hip(): # The scaling factor convention we are assuming is # quantized_value * scaling_factor ~= true_value # which is consistent with the practice of setting # scaling_factor = tensor_amax / FPtype_max scaling_factor *= 2 if hasattr(layer_self_attn, "kv_scale"): layer_self_attn.kv_scale = scaling_factor else: raise RuntimeError("Self attention has no KV cache scaling " "factor attribute!")