# coding=utf-8 # Copyright 2023 The vLLM team. # Copyright (c) Google Inc. # # 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 Gemma model compatible with HuggingFace weights.""" from typing import List, Optional, Tuple import torch from torch import nn from transformers import GemmaConfig from vllm.attention import Attention, AttentionMetadata from vllm.config import LoRAConfig from vllm.model_executor.layers.activation import GeluAndMul from vllm.model_executor.layers.layernorm import RMSNorm from vllm.model_executor.layers.linear import (LinearMethodBase, MergedColumnParallelLinear, QKVParallelLinear, RowParallelLinear) from vllm.model_executor.layers.logits_processor import LogitsProcessor from vllm.model_executor.layers.rotary_embedding import get_rope from vllm.model_executor.layers.sampler import Sampler from vllm.model_executor.layers.vocab_parallel_embedding import ( VocabParallelEmbedding) from vllm.model_executor.parallel_utils.parallel_state import ( get_tensor_model_parallel_world_size) from vllm.model_executor.sampling_metadata import SamplingMetadata from vllm.model_executor.weight_utils import (default_weight_loader, hf_model_weights_iterator) from vllm.sequence import SamplerOutput class GemmaMLP(nn.Module): def __init__( self, hidden_size: int, intermediate_size: int, linear_method: Optional[LinearMethodBase] = None, ) -> 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) self.act_fn = GeluAndMul() 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 GemmaAttention(nn.Module): def __init__(self, hidden_size: int, num_heads: int, num_kv_heads: int, head_dim: int, max_position_embeddings: int = 8192, rope_theta: float = 10000, linear_method: Optional[LinearMethodBase] = 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 = head_dim 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=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.rotary_emb = get_rope( self.head_dim, rotary_dim=self.head_dim, max_position=max_position_embeddings, base=self.rope_theta, is_neox_style=True, ) self.attn = Attention(self.num_heads, self.head_dim, self.scaling, num_kv_heads=self.num_kv_heads) 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 GemmaDecoderLayer(nn.Module): def __init__( self, config: GemmaConfig, linear_method: Optional[LinearMethodBase] = None, ) -> None: super().__init__() self.hidden_size = config.hidden_size self.self_attn = GemmaAttention( hidden_size=self.hidden_size, num_heads=config.num_attention_heads, num_kv_heads=config.num_key_value_heads, head_dim=config.head_dim, max_position_embeddings=config.max_position_embeddings, rope_theta=config.rope_theta, linear_method=linear_method, ) self.mlp = GemmaMLP( hidden_size=self.hidden_size, intermediate_size=config.intermediate_size, linear_method=linear_method, ) 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 GemmaModel(nn.Module): def __init__( self, config: GemmaConfig, linear_method: Optional[LinearMethodBase] = None, ) -> None: super().__init__() self.config = config self.embed_tokens = VocabParallelEmbedding( config.vocab_size, config.hidden_size, ) self.layers = nn.ModuleList([ GemmaDecoderLayer(config, linear_method) 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[torch.Tensor], attn_metadata: AttentionMetadata, ) -> torch.Tensor: hidden_states = self.embed_tokens(input_ids) # Normalize the embedding by sqrt(hidden_size) hidden_states *= self.config.hidden_size**0.5 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 GemmaForCausalLM(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", ] # Gemma does not apply LoRA to the embedding layer. embedding_modules = {} embedding_padding_modules = [] def __init__( self, config: GemmaConfig, linear_method: Optional[LinearMethodBase] = None, lora_config: Optional[LoRAConfig] = None, ) -> None: del lora_config # Unused. super().__init__() self.config = config self.linear_method = linear_method self.model = GemmaModel(config, linear_method) self.logits_processor = LogitsProcessor(config.vocab_size) self.sampler = Sampler() @torch.no_grad() 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.model.embed_tokens.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, 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()) loaded_params = set() for name, loaded_weight in hf_model_weights_iterator( model_name_or_path, cache_dir, load_format, revision): for (param_name, shard_name, shard_id) in stacked_params_mapping: if shard_name not in name: continue name = name.replace(shard_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: # lm_head is not used in vllm as it is tied with embed_token. # To prevent errors, skip loading lm_head.weight. if "lm_head.weight" in name: continue # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue # GemmaRMSNorm is different from Llama's in that it multiplies # (1 + weight) to the output, instead of just weight. if "norm.weight" in name: loaded_weight += 1.0 param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, loaded_weight) loaded_params.add(name) unloaded_params = params_dict.keys() - loaded_params if unloaded_params: raise RuntimeError( "Some weights are not initialized from checkpoints: " f"{unloaded_params}")