# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project # 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 deci model compatible with HuggingFace weights.""" from collections.abc import Iterable from typing import Any, Optional, Union import torch from torch import nn from transformers import LlamaConfig from vllm.attention import AttentionType from vllm.compilation.decorators import support_torch_compile from vllm.config import CacheConfig, VllmConfig from vllm.distributed import get_pp_group from vllm.model_executor.layers.layernorm import RMSNorm from vllm.model_executor.layers.logits_processor import LogitsProcessor from vllm.model_executor.layers.quantization import QuantizationConfig from vllm.model_executor.layers.rotary_embedding import get_rope from vllm.model_executor.layers.vocab_parallel_embedding import ( DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding) from vllm.model_executor.model_loader.weight_utils import ( default_weight_loader, maybe_remap_kv_scale_name) from vllm.model_executor.models.llama import LlamaAttention, LlamaMLP from vllm.model_executor.sampling_metadata import SamplingMetadata from vllm.sequence import IntermediateTensors from .interfaces import HasNoOps, SupportsLoRA, SupportsPP from .utils import (AutoWeightsLoader, PPMissingLayer, is_pp_missing_parameter, make_empty_intermediate_tensors_factory, make_layers, maybe_prefix) def _ffn_mult_to_intermediate_size(ffn_mult: float, n_embd: int) -> int: # DeciLM-specific code intermediate_size = int(2 * ffn_mult * n_embd / 3) return _find_multiple(intermediate_size, 256) def _find_multiple(n: int, k: int) -> int: # DeciLM-specific code if n % k == 0: return n return n + k - (n % k) class DeciLMAttention(LlamaAttention): def __init__( self, config: LlamaConfig, 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, bias_o_proj: bool = False, cache_config: Optional[CacheConfig] = None, prefix: str = "", attn_type: str = AttentionType.DECODER, ) -> None: super().__init__(config, hidden_size, num_heads, num_kv_heads, rope_theta, rope_scaling, max_position_embeddings, quant_config, bias, bias_o_proj, cache_config, prefix, attn_type) def _init_rotary_emb(self, config, rope_scaling: Optional[dict[str, Any]], quant_config: Optional[QuantizationConfig]) -> None: # Enables YARN for Mistral and LLaMA4 derivatives. is_neox_style = True if hasattr(config, "position_embedding_type"): is_neox_style = config.position_embedding_type not in [ "mistral_yarn", "rope_llama4" ] self.rotary_emb = get_rope( self.head_dim, rotary_dim=self.head_dim, max_position=self.max_position_embeddings, base=self.rope_theta, rope_scaling=rope_scaling, is_neox_style=is_neox_style, partial_rotary_factor=self.partial_rotary_factor) class DeciLMDecoderLayer(nn.Module): def __init__( self, config: LlamaConfig, layer_idx: int, cache_config: Optional[CacheConfig] = None, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", ) -> None: super().__init__() block_config = config.block_configs[layer_idx] self._is_no_op_attention = block_config.attention.no_op self._is_no_op_ffn = block_config.ffn.no_op 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) # 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) bias_o_proj = attention_bias # support internlm/internlm3-8b with qkv_bias if hasattr(config, "qkv_bias"): attention_bias = config.qkv_bias if not self._is_no_op_attention: num_kv_heads = (config.num_attention_heads // block_config.attention.n_heads_in_group) self.self_attn = DeciLMAttention( config=config, hidden_size=self.hidden_size, num_heads=config.num_attention_heads, num_kv_heads=num_kv_heads, rope_theta=rope_theta, rope_scaling=rope_scaling, max_position_embeddings=max_position_embeddings, quant_config=quant_config, bias=attention_bias, bias_o_proj=bias_o_proj, cache_config=cache_config, prefix=f"{prefix}.self_attn", ) self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) if not self._is_no_op_ffn: ffn_mult = block_config.ffn.ffn_mult intermediate_size = _ffn_mult_to_intermediate_size( ffn_mult, config.hidden_size) self.mlp = LlamaMLP( hidden_size=self.hidden_size, intermediate_size=intermediate_size, hidden_act=config.hidden_act, quant_config=quant_config, bias=getattr(config, "mlp_bias", False), prefix=f"{prefix}.mlp", ) self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) def forward( self, positions: torch.Tensor, hidden_states: torch.Tensor, residual: Optional[torch.Tensor], ) -> tuple[torch.Tensor, torch.Tensor]: # Self Attention if self._is_no_op_attention: pass else: 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, ) # Fully Connected if not self._is_no_op_ffn: hidden_states, residual = self.post_attention_layernorm( hidden_states, residual) hidden_states = self.mlp(hidden_states) return hidden_states, residual @support_torch_compile class DeciModel(nn.Module): def __init__( self, *, vllm_config: VllmConfig, prefix: str = "", layer_type: type[DeciLMDecoderLayer] = DeciLMDecoderLayer, ): 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.quant_config = quant_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 if get_pp_group().is_first_rank or (config.tie_word_embeddings and get_pp_group().is_last_rank): self.embed_tokens = VocabParallelEmbedding( self.vocab_size, config.hidden_size, org_num_embeddings=config.vocab_size, quant_config=quant_config, ) else: self.embed_tokens = PPMissingLayer() def get_layer(prefix: str): layer_idx = int(prefix.rsplit(".", 1)[1]) return layer_type( config, layer_idx, cache_config, quant_config=quant_config, prefix=prefix, ) self.start_layer, self.end_layer, self.layers = make_layers( config.num_hidden_layers, get_layer, prefix=f"{prefix}.layers", ) if get_pp_group().is_last_rank: self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) else: self.norm = PPMissingLayer() self.make_empty_intermediate_tensors = ( make_empty_intermediate_tensors_factory( ["hidden_states", "residual"], config.hidden_size)) 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, intermediate_tensors: Optional[IntermediateTensors], inputs_embeds: Optional[torch.Tensor] = None, ) -> Union[torch.Tensor, IntermediateTensors]: if get_pp_group().is_first_rank: if inputs_embeds is not None: hidden_states = inputs_embeds else: hidden_states = self.get_input_embeddings(input_ids) residual = None else: assert intermediate_tensors is not None hidden_states = intermediate_tensors["hidden_states"] residual = intermediate_tensors["residual"] kv_cache_index = 0 for i in range(self.start_layer, self.end_layer): layer = self.layers[i] if not layer._is_no_op_attention: hidden_states, residual = layer(positions, hidden_states, residual) kv_cache_index += 1 else: hidden_states, residual = layer(positions, hidden_states, residual) if not get_pp_group().is_last_rank: return IntermediateTensors({ "hidden_states": hidden_states, "residual": residual }) hidden_states, _ = self.norm(hidden_states, residual) return hidden_states def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]: 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()) loaded_params: set[str] = set() 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 if self.quant_config is not None and ( scale_name := self.quant_config.get_cache_scale(name)): # Loading kv cache quantization scales param = params_dict[scale_name] weight_loader = getattr(param, "weight_loader", default_weight_loader) loaded_weight = (loaded_weight if loaded_weight.dim() == 0 else loaded_weight[0]) weight_loader(param, loaded_weight) loaded_params.add(scale_name) continue if "scale" in name: # Remapping the name of FP8 kv-scale. name = maybe_remap_kv_scale_name(name, params_dict) if name is None: 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 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 if is_pp_missing_parameter(name, self): continue param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, loaded_weight) loaded_params.add(name) return loaded_params class DeciLMForCausalLM(nn.Module, SupportsLoRA, SupportsPP, HasNoOps): packed_modules_mapping = { "qkv_proj": ["q_proj", "k_proj", "v_proj"], "gate_up_proj": ["gate_proj", "up_proj"], } # LoRA specific attributes embedding_modules = { "embed_tokens": "input_embeddings", "lm_head": "output_embeddings", } embedding_padding_modules = ["lm_head"] # Mistral/Llama models can also be loaded with --load-format mistral # from consolidated.safetensors checkpoints mistral_mapping = { "layers": "model.layers", "attention": "self_attn", "wq": "q_proj", "wk": "k_proj", "wv": "v_proj", "wo": "o_proj", "attention_norm": "input_layernorm", "feed_forward": "mlp", "w1": "gate_proj", "w2": "down_proj", "w3": "up_proj", "ffn_norm": "post_attention_layernorm", "tok_embeddings": "model.embed_tokens", "output": "lm_head", "norm": "model.norm", } def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): super().__init__() config = vllm_config.model_config.hf_config quant_config = vllm_config.quant_config lora_config = vllm_config.lora_config self.config = config self.lora_config = lora_config self.model = self._init_model(vllm_config=vllm_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, prefix=maybe_prefix(prefix, "lm_head"), ) if config.tie_word_embeddings: self.lm_head = self.lm_head.tie_weights( self.model.embed_tokens) logit_scale = getattr(config, "logit_scale", 1.0) self.logits_processor = LogitsProcessor(self.unpadded_vocab_size, config.vocab_size, logit_scale) else: self.lm_head = PPMissingLayer() self.make_empty_intermediate_tensors = ( self.model.make_empty_intermediate_tensors) def _init_model(self, vllm_config: VllmConfig, prefix: str = ""): return DeciModel(vllm_config=vllm_config, prefix=prefix) def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: return self.model.get_input_embeddings(input_ids) def forward( self, input_ids: torch.Tensor, positions: torch.Tensor, intermediate_tensors: Optional[IntermediateTensors] = None, inputs_embeds: Optional[torch.Tensor] = None, ) -> Union[torch.Tensor, IntermediateTensors]: model_output = self.model(input_ids, positions, intermediate_tensors, inputs_embeds) 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 load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]: loader = AutoWeightsLoader( self, skip_prefixes=(["lm_head."] if self.config.tie_word_embeddings else None), ) return loader.load_weights(weights)