# Copyright 2023-2024 SGLang Team # 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. # ============================================================================== # Adapted from # https://github.com/vllm-project/vllm/blob/c7f2cf2b7f67bce5842fedfdba508440fe257375/vllm/model_executor/models/llama.py#L1 """Inference-only LLaMA model compatible with HuggingFace weights.""" import logging from typing import Any, Dict, Iterable, Optional, Tuple import torch from torch import nn from transformers import LlamaConfig from vllm.distributed import get_tensor_model_parallel_world_size from vllm.model_executor.layers.rotary_embedding import get_rope from vllm.model_executor.model_loader.weight_utils import default_weight_loader from sglang.srt.layers.activation import SiluAndMul from sglang.srt.layers.layernorm import RMSNorm from sglang.srt.layers.linear import ( MergedColumnParallelLinear, QKVParallelLinear, RowParallelLinear, ) from sglang.srt.layers.logits_processor import LogitsProcessor, LogitsProcessorOutput from sglang.srt.layers.pooler import Pooler, PoolingType from sglang.srt.layers.quantization.base_config import QuantizationConfig from sglang.srt.layers.radix_attention import RadixAttention from sglang.srt.layers.torchao_utils import apply_torchao_config_ from sglang.srt.layers.vocab_parallel_embedding import ( ParallelLMHead, VocabParallelEmbedding, ) from sglang.srt.managers.schedule_batch import global_server_args_dict from sglang.srt.model_executor.forward_batch_info import ForwardBatch from sglang.srt.utils import make_layers logger = logging.getLogger(__name__) class LlamaMLP(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 LlamaAttention(nn.Module): def __init__( self, config: LlamaConfig, hidden_size: int, num_heads: int, num_kv_heads: int, layer_id: int = 0, rope_theta: float = 10000, rope_scaling: Optional[Dict[str, Any]] = None, rope_is_neox_style: bool = True, max_position_embeddings: int = 8192, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", ) -> 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) # MistralConfig has an optional head_dim introduced by Mistral-Nemo self.head_dim = getattr( config, "head_dim", self.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 self.qkv_proj = QKVParallelLinear( hidden_size, self.head_dim, self.total_num_heads, self.total_num_kv_heads, bias=False, 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_embeddings, base=rope_theta, rope_scaling=rope_scaling, is_neox_style=rope_is_neox_style, ) self.attn = RadixAttention( self.num_heads, self.head_dim, self.scaling, num_kv_heads=self.num_kv_heads, layer_id=layer_id, ) def forward( self, positions: torch.Tensor, hidden_states: torch.Tensor, forward_batch: ForwardBatch, ) -> 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, forward_batch) output, _ = self.o_proj(attn_output) return output class LlamaDecoderLayer(nn.Module): def __init__( self, config: LlamaConfig, layer_id: int = 0, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", ) -> 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 ) rope_is_neox_style = getattr(config, "rope_is_neox_style", True) max_position_embeddings = getattr(config, "max_position_embeddings", 8192) self.self_attn = LlamaAttention( config=config, hidden_size=self.hidden_size, num_heads=config.num_attention_heads, num_kv_heads=config.num_key_value_heads, layer_id=layer_id, rope_theta=rope_theta, rope_scaling=rope_scaling, rope_is_neox_style=rope_is_neox_style, max_position_embeddings=max_position_embeddings, quant_config=quant_config, prefix=f"{prefix}.self_attn", ) self.mlp = LlamaMLP( 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, forward_batch: ForwardBatch, 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, forward_batch=forward_batch, ) # 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, ) -> None: super().__init__() self.config = config self.padding_idx = config.pad_token_id self.vocab_size = config.vocab_size self.embed_tokens = VocabParallelEmbedding( config.vocab_size, config.hidden_size, quant_config=quant_config, ) self.layers = make_layers( config.num_hidden_layers, lambda idx, prefix: LlamaDecoderLayer( config=config, quant_config=quant_config, layer_id=idx, prefix=prefix ), prefix="model.layers", ) self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) def forward( self, input_ids: torch.Tensor, positions: torch.Tensor, forward_batch: ForwardBatch, input_embeds: torch.Tensor = None, ) -> torch.Tensor: if input_embeds is None: hidden_states = self.embed_tokens(input_ids) else: hidden_states = input_embeds residual = None for i in range(len(self.layers)): layer = self.layers[i] hidden_states, residual = layer( positions, hidden_states, forward_batch, residual, ) hidden_states, _ = self.norm(hidden_states, residual) return hidden_states class LlamaForCausalLM(nn.Module): def __init__( self, config: LlamaConfig, quant_config: Optional[QuantizationConfig] = None, cache_config=None, ) -> None: super().__init__() self.config = config self.quant_config = quant_config self.torchao_config = global_server_args_dict["torchao_config"] self.model = LlamaModel(config, quant_config=quant_config) if self.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 ) self.logits_processor = LogitsProcessor(config) self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True) self.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), ] @torch.no_grad() def forward( self, input_ids: torch.Tensor, positions: torch.Tensor, forward_batch: ForwardBatch, input_embeds: torch.Tensor = None, get_embedding: bool = False, ) -> LogitsProcessorOutput: hidden_states = self.model(input_ids, positions, forward_batch, input_embeds) if not get_embedding: return self.logits_processor( input_ids, hidden_states, self.lm_head, forward_batch ) else: return self.pooler(hidden_states, forward_batch) def get_hidden_dim(self, module_name): # return input_dim, output_dim if module_name in ["q_proj", "o_proj", "qkv_proj"]: return self.config.hidden_size, self.config.hidden_size elif module_name in ["kv_proj"]: return self.config.hidden_size, self.config.hidden_size // ( self.config.num_attention_heads // self.config.num_key_value_heads ) elif module_name == "gate_up_proj": return self.config.hidden_size, self.config.intermediate_size elif module_name == "down_proj": return self.config.intermediate_size, self.config.hidden_size else: raise NotImplementedError() def get_module_name(self, name): params_mapping = { "q_proj": "qkv_proj", "k_proj": "qkv_proj", "v_proj": "qkv_proj", "gate_proj": "gate_up_proj", "up_proj": "gate_up_proj", } return params_mapping.get(name, name) def get_module_name_from_weight_name(self, name): for param_name, weight_name, shard_id, num_shard in self.stacked_params_mapping: if weight_name in name: return ( name.replace(weight_name, param_name)[: -len(".weight")], num_shard, ) return name[: -len(".weight")], 1 def get_num_params(self): params_dict = dict(self.named_parameters()) return len(params_dict) 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 or "projector" 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 name.startswith("model.vision_tower") and name not in params_dict: 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 # Skip loading kv_scale from ckpts towards new design. if name.endswith(".kv_scale") 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) apply_torchao_config_(self, params_dict, set(["proj.weight"])) def get_weights_by_name( self, name: str, truncate_size: int = 100, tp_size: int = 1 ) -> Optional[torch.Tensor]: """Get the weights of the parameter by its name. Similar to `get_parameter` in Hugging Face. Only used for unit test with an unoptimized performance. For optimized performance, please use torch.save and torch.load. """ try: mapped_name = name mapped_shard_id = None for param_name, weight_name, shard_id in self.stacked_params_mapping: if weight_name in name: mapped_name = name.replace(weight_name, param_name) mapped_shard_id = shard_id break params_dict = dict(self.named_parameters()) if mapped_name in params_dict: param = params_dict[mapped_name] if mapped_shard_id is not None: if mapped_shard_id in ["q", "k", "v"]: num_heads = self.config.num_attention_heads // tp_size num_kv_heads = self.config.num_key_value_heads // tp_size head_dim = ( self.config.hidden_size // self.config.num_attention_heads ) if mapped_shard_id == "q": offset = 0 size = num_heads * head_dim elif mapped_shard_id == "k": offset = num_heads * head_dim size = num_kv_heads * head_dim elif mapped_shard_id == "v": offset = (num_heads + num_kv_heads) * head_dim size = num_kv_heads * head_dim weight = param.data.narrow(0, offset, size) elif mapped_shard_id in [0, 1]: intermediate_size = self.config.intermediate_size hidden_size = self.config.hidden_size slice_size = intermediate_size // tp_size if mapped_shard_id == 0: # gate_proj offset = 0 size = slice_size elif mapped_shard_id == 1: # up_proj offset = slice_size size = slice_size weight = param.data.narrow(0, offset, size) else: weight = param.data else: weight = param.data if tp_size > 1 and ("o_proj" in name or "down_proj" in name): gathered_weights = [ torch.zeros_like(weight) for _ in range(tp_size) ] torch.distributed.all_gather(gathered_weights, weight) weight = torch.cat(gathered_weights, dim=1) return weight.cpu().to(torch.float32).numpy().tolist()[:truncate_size] else: return None except Exception as e: logger.error( f"Error getting weights by name {name} in LlamaForCausalLM: {e}" ) return None class Phi3ForCausalLM(LlamaForCausalLM): pass EntryClass = [LlamaForCausalLM, Phi3ForCausalLM]