# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project # Adapted from # https://huggingface.co/OrionStarAI/Orion-14B-Base/blob/main/modeling_orion.py # Copyright (c) OrionStar Inc. # LICENSE: https://huggingface.co/OrionStarAI/Orion-14B-Base/blob/main/LICENSE """Inference-only Orion-14B model compatible with HuggingFace weights.""" from collections.abc import Iterable from itertools import islice from typing import Any import torch from torch import nn from transformers import PretrainedConfig from vllm.attention import Attention 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.model_executor.layers.activation import SiluAndMul from vllm.model_executor.layers.linear import ( MergedColumnParallelLinear, QKVParallelLinear, RowParallelLinear, ) 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 ( ParallelLMHead, VocabParallelEmbedding, ) from vllm.model_executor.model_loader.weight_utils import default_weight_loader from vllm.sequence import IntermediateTensors from .interfaces import SupportsPP from .utils import ( AutoWeightsLoader, is_pp_missing_parameter, make_empty_intermediate_tensors_factory, make_layers, maybe_prefix, ) class OrionMLP(nn.Module): def __init__( self, hidden_size: int, intermediate_size: int, hidden_act: str, quant_config: QuantizationConfig | None = 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 OrionAttention(nn.Module): def __init__( self, hidden_size: int, num_heads: int, num_kv_heads: int, rope_theta: float = 10000, rope_scaling: dict[str, Any] | None = None, max_position_embeddings: int = 8192, cache_config: CacheConfig | None = None, quant_config: QuantizationConfig | None = 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) 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 self.qkv_proj = QKVParallelLinear( hidden_size, self.head_dim, self.total_num_heads, self.total_num_kv_heads, bias=False, quant_config=quant_config, ) self.o_proj = RowParallelLinear( self.total_num_heads * self.head_dim, hidden_size, bias=False, 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, cache_config=cache_config, quant_config=quant_config, prefix=f"{prefix}.attn", ) def forward( self, positions: torch.Tensor, hidden_states: torch.Tensor, ) -> 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) output, _ = self.o_proj(attn_output) return output class OrionDecoderLayer(nn.Module): def __init__( self, config: PretrainedConfig, cache_config: CacheConfig | None = None, quant_config: QuantizationConfig | None = 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) max_position_embeddings = getattr(config, "max_position_embeddings", 8192) self.self_attn = OrionAttention( hidden_size=self.hidden_size, num_heads=config.num_attention_heads, num_kv_heads=config.num_key_value_heads, rope_theta=rope_theta, rope_scaling=rope_scaling, max_position_embeddings=max_position_embeddings, cache_config=cache_config, quant_config=quant_config, prefix=f"{prefix}.self_attn", ) self.mlp = OrionMLP( hidden_size=self.hidden_size, intermediate_size=config.intermediate_size, hidden_act=config.hidden_act, quant_config=quant_config, ) self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.rms_norm_eps) self.post_attention_layernorm = nn.LayerNorm( config.hidden_size, eps=config.rms_norm_eps ) def forward( self, positions: torch.Tensor, hidden_states: torch.Tensor, ) -> tuple[torch.Tensor, torch.Tensor]: # Self Attention residual = hidden_states hidden_states = self.input_layernorm(hidden_states) hidden_states = self.self_attn( positions=positions, hidden_states=hidden_states, ) hidden_states = residual + hidden_states # Fully Connected residual = hidden_states hidden_states = self.post_attention_layernorm(hidden_states) hidden_states = self.mlp(hidden_states) hidden_states = residual + hidden_states return hidden_states @support_torch_compile class OrionModel(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 self.config = config self.vocab_size = config.vocab_size self.embed_tokens = VocabParallelEmbedding( config.vocab_size, config.hidden_size, ) self.start_layer, self.end_layer, self.layers = make_layers( config.num_hidden_layers, lambda prefix: OrionDecoderLayer( config, cache_config, quant_config, prefix=prefix ), prefix=f"{prefix}.layers", ) self.norm = nn.LayerNorm(config.hidden_size, eps=config.rms_norm_eps) self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory( [ "hidden_states", ], 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: torch.Tensor, positions: torch.Tensor, intermediate_tensors: IntermediateTensors | None, inputs_embeds: torch.Tensor | None = None, ) -> 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) else: assert intermediate_tensors is not None hidden_states = intermediate_tensors["hidden_states"] for layer in islice(self.layers, self.start_layer, self.end_layer): hidden_states = layer(positions, hidden_states) if not get_pp_group().is_last_rank: return IntermediateTensors( { "hidden_states": hidden_states, } ) hidden_states = self.norm(hidden_states) 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: 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 OrionForCausalLM(nn.Module, SupportsPP): def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): super().__init__() config = vllm_config.model_config.hf_config quant_config = vllm_config.quant_config self.config = config self.quant_config = quant_config self.model = OrionModel( vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model") ) self.lm_head = ParallelLMHead( config.vocab_size, config.hidden_size, quant_config=quant_config, prefix=maybe_prefix(prefix, "lm_head"), ) if self.config.tie_word_embeddings: self.lm_head.weight = self.model.embed_tokens.weight self.logits_processor = LogitsProcessor(config.vocab_size) 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, intermediate_tensors: IntermediateTensors | None = None, inputs_embeds: torch.Tensor | None = None, ) -> torch.Tensor | IntermediateTensors: hidden_states = self.model( input_ids, positions, intermediate_tensors, inputs_embeds ) return hidden_states def compute_logits( self, hidden_states: torch.Tensor, ) -> torch.Tensor | None: logits = self.logits_processor(self.lm_head, hidden_states) return logits def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]: loader = AutoWeightsLoader(self) return loader.load_weights(weights)