# Adapted from # https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/qwen2/modeling_qwen2.py # Copyright 2024 The Qwen team. # 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 Qwen2 model compatible with HuggingFace weights.""" from typing import Iterable, List, Optional, Set, Tuple, Union import torch from torch import nn from transformers import Qwen2Config from vllm.attention import Attention, AttentionMetadata, AttentionType from vllm.compilation.decorators import support_torch_compile from vllm.config import CacheConfig, VllmConfig from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size from vllm.logger import init_logger from vllm.model_executor.layers.activation import SiluAndMul from vllm.model_executor.layers.layernorm import RMSNorm from vllm.model_executor.layers.linear import (MergedColumnParallelLinear, QKVParallelLinear, RowParallelLinear) from vllm.model_executor.layers.logits_processor import LogitsProcessor from vllm.model_executor.layers.pooler import Pooler, PoolingType from vllm.model_executor.layers.quantization import QuantizationConfig from vllm.model_executor.layers.rotary_embedding import get_rope from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler from vllm.model_executor.layers.vocab_parallel_embedding import ( ParallelLMHead, VocabParallelEmbedding) from vllm.model_executor.model_loader.weight_utils import ( default_weight_loader, maybe_remap_kv_scale_name) from vllm.model_executor.pooling_metadata import PoolingMetadata from vllm.model_executor.sampling_metadata import SamplingMetadata from vllm.sequence import IntermediateTensors, PoolerOutput from .interfaces import SupportsLoRA, SupportsPP from .utils import (AutoWeightsLoader, PPMissingLayer, WeightsMapper, is_pp_missing_parameter, make_empty_intermediate_tensors_factory, make_layers, maybe_prefix) logger = init_logger(__name__) class Qwen2MLP(nn.Module): def __init__( self, hidden_size: int, intermediate_size: int, hidden_act: str, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", ) -> None: super().__init__() self.gate_up_proj = MergedColumnParallelLinear( hidden_size, [intermediate_size] * 2, bias=False, quant_config=quant_config, prefix=f"{prefix}.gate_up_proj", ) self.down_proj = RowParallelLinear( intermediate_size, hidden_size, bias=False, quant_config=quant_config, prefix=f"{prefix}.down_proj", ) 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 Qwen2Attention(nn.Module): def __init__(self, hidden_size: int, num_heads: int, num_kv_heads: int, max_position: int = 4096 * 32, rope_theta: float = 10000, cache_config: Optional[CacheConfig] = None, quant_config: Optional[QuantizationConfig] = None, rope_scaling: Optional[Tuple] = None, prefix: str = "", attn_type: str = AttentionType.DECODER) -> 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.qkv_proj = QKVParallelLinear( hidden_size, self.head_dim, self.total_num_heads, self.total_num_kv_heads, bias=True, quant_config=quant_config, prefix=f"{prefix}.qkv_proj", ) self.o_proj = RowParallelLinear( self.total_num_heads * self.head_dim, hidden_size, bias=False, quant_config=quant_config, prefix=f"{prefix}.o_proj", ) self.rotary_emb = get_rope( self.head_dim, rotary_dim=self.head_dim, max_position=max_position, base=self.rope_theta, rope_scaling=rope_scaling, ) self.attn = Attention(self.num_heads, self.head_dim, self.scaling, num_kv_heads=self.num_kv_heads, cache_config=cache_config, quant_config=quant_config, prefix=f"{prefix}.attn", attn_type=attn_type) 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) output, _ = self.o_proj(attn_output) return output class Qwen2DecoderLayer(nn.Module): def __init__( self, config: Qwen2Config, cache_config: Optional[CacheConfig] = None, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", ) -> None: super().__init__() self.hidden_size = config.hidden_size # Requires transformers > 4.32.0 rope_theta = getattr(config, "rope_theta", 1000000) rope_scaling = getattr(config, "rope_scaling", None) # By default, Qwen2 uses causal attention as it is a decoder-only model. # You can override the HF config with `is_causal=False` to enable # bidirectional attention, which is used in some embedding models # (e.g. Alibaba-NLP/gte-Qwen2-7B-instruct) if getattr(config, "is_causal", True): attn_type = AttentionType.DECODER else: attn_type = AttentionType.ENCODER_ONLY self.self_attn = Qwen2Attention( hidden_size=self.hidden_size, num_heads=config.num_attention_heads, max_position=config.max_position_embeddings, num_kv_heads=config.num_key_value_heads, rope_theta=rope_theta, cache_config=cache_config, quant_config=quant_config, rope_scaling=rope_scaling, prefix=f"{prefix}.self_attn", attn_type=attn_type, ) self.mlp = Qwen2MLP( hidden_size=self.hidden_size, intermediate_size=config.intermediate_size, hidden_act=config.hidden_act, quant_config=quant_config, prefix=f"{prefix}.mlp", ) 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 @support_torch_compile class Qwen2Model(nn.Module): def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): super().__init__() config = vllm_config.model_config.hf_config cache_config = vllm_config.cache_config quant_config = vllm_config.quant_config # TODO (@robertgshaw2): see if this can be moved out if (cache_config.sliding_window is not None and hasattr(config, "max_window_layers")): raise ValueError("Sliding window for some but all layers is not " "supported. This model uses sliding window " "but `max_window_layers` = {} is less than " "`num_hidden_layers` = {}. Please open an issue " "to discuss this feature.".format( config.max_window_layers, config.num_hidden_layers, )) self.config = config self.padding_idx = config.pad_token_id self.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( config.vocab_size, config.hidden_size, quant_config=quant_config, prefix=f"{prefix}.embed_tokens", ) else: self.embed_tokens = PPMissingLayer() self.start_layer, self.end_layer, self.layers = make_layers( config.num_hidden_layers, lambda prefix: Qwen2DecoderLayer(config=config, cache_config=cache_config, quant_config=quant_config, prefix=prefix), prefix=f"{prefix}.layers", ) self.make_empty_intermediate_tensors = ( make_empty_intermediate_tensors_factory( ["hidden_states", "residual"], config.hidden_size)) if get_pp_group().is_last_rank: self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) else: self.norm = PPMissingLayer() def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: return self.embed_tokens(input_ids) def forward( self, input_ids: torch.Tensor, positions: torch.Tensor, kv_caches: List[torch.Tensor], attn_metadata: AttentionMetadata, intermediate_tensors: Optional[IntermediateTensors] = None, 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"] for i in range(self.start_layer, self.end_layer): layer = self.layers[i] hidden_states, residual = layer( positions, hidden_states, kv_caches[i - self.start_layer], attn_metadata, 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(remove_duplicate=False)) loaded_params: Set[str] = set() for name, loaded_weight in weights: 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 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 # Remapping the name of FP8 kv-scale. name = maybe_remap_kv_scale_name(name, params_dict) if name is None: 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 Qwen2ForCausalLM(nn.Module, SupportsLoRA, SupportsPP): 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", ] embedding_modules = {} embedding_padding_modules = [] # BitandBytes specific attributes bitsandbytes_stacked_params_mapping = { # shard_name, weight_name, index "q_proj": ("qkv_proj", 0), "k_proj": ("qkv_proj", 1), "v_proj": ("qkv_proj", 2), "gate_proj": ("gate_up_proj", 0), "up_proj": ("gate_up_proj", 1), } 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.quant_config = quant_config self.model = Qwen2Model(vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")) if get_pp_group().is_last_rank: if 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=maybe_prefix( prefix, "lm_head")) else: self.lm_head = PPMissingLayer() self.logits_processor = LogitsProcessor(config.vocab_size) self.sampler = get_sampler() self.make_empty_intermediate_tensors = ( self.model.make_empty_intermediate_tensors) 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, kv_caches: List[torch.Tensor], attn_metadata: AttentionMetadata, intermediate_tensors: Optional[IntermediateTensors] = None, inputs_embeds: Optional[torch.Tensor] = None, ) -> Union[torch.Tensor, IntermediateTensors]: hidden_states = self.model(input_ids, positions, kv_caches, attn_metadata, intermediate_tensors, inputs_embeds) return hidden_states 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 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]]) -> Set[str]: loader = AutoWeightsLoader( self, skip_prefixes=(["lm_head."] if self.config.tie_word_embeddings else None), ) return loader.load_weights(weights) class Qwen2EmbeddingModel(nn.Module, SupportsLoRA, SupportsPP): 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", ] embedding_modules = {} embedding_padding_modules = [] hf_to_vllm_mapper = WeightsMapper(orig_to_new_prefix={"model.": ""}) 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 pooler_config = vllm_config.model_config.pooler_config self.config = config self.lora_config = lora_config self.quant_config = quant_config self.model = Qwen2Model(vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")) # TODO: Replace this model class with as_embedding_model( # Qwen2ForCausalLM) after changing the default pooling method if pooler_config.pooling_type is None: logger.warning( "This embedding model will default to last-token pooling in " "an upcoming version. To avoid breaking changes, you should " "pass `--override-pooler-config '{\"pooling_type\": \"MEAN\"}'`" " explicitly.") self._pooler = Pooler.from_config_with_defaults( pooler_config, pooling_type=PoolingType.MEAN, normalize=True, softmax=False) def forward( self, input_ids: torch.Tensor, positions: torch.Tensor, kv_caches: List[torch.Tensor], attn_metadata: AttentionMetadata, intermediate_tensors: Optional[IntermediateTensors] = None, ) -> torch.Tensor: return self.model(input_ids, positions, kv_caches, attn_metadata, intermediate_tensors) def pooler( self, hidden_states: torch.Tensor, pooling_metadata: PoolingMetadata, ) -> Optional[PoolerOutput]: return self._pooler(hidden_states, pooling_metadata) def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]): weights = self.hf_to_vllm_mapper.apply(weights) weights = ((name, data) for name, data in weights if not name.startswith("lm_head.")) self.model.load_weights(weights)