# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project """Inference-only Snowflake Arctic model.""" from collections.abc import Iterable from typing import Optional, Union import torch from torch import nn 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_rank, get_tensor_model_parallel_world_size, tensor_model_parallel_all_reduce) from vllm.logger import init_logger from vllm.model_executor.layers.activation import SiluAndMul from vllm.model_executor.layers.fused_moe import fused_experts, fused_topk from vllm.model_executor.layers.layernorm import RMSNorm from vllm.model_executor.layers.linear import (MergedColumnParallelLinear, QKVParallelLinear, ReplicatedLinear, RowParallelLinear) from vllm.model_executor.layers.logits_processor import LogitsProcessor from vllm.model_executor.layers.quantization import QuantizationConfig from vllm.model_executor.layers.quantization.deepspeedfp import ( DeepSpeedFPConfig, DeepSpeedFPParameter) 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.model_executor.sampling_metadata import SamplingMetadata from vllm.model_executor.utils import set_weight_attrs from vllm.platforms import current_platform from vllm.sequence import IntermediateTensors from vllm.transformers_utils.configs.arctic import ArcticConfig from .interfaces import SupportsPP, SupportsQuant from .utils import (extract_layer_index, is_pp_missing_parameter, make_empty_intermediate_tensors_factory, make_layers, maybe_prefix) logger = init_logger(__name__) class ArcticMLP(nn.Module): def __init__(self, config: ArcticConfig, expert_id: int = -1, is_residual_mlp: bool = False, quant_config: Optional[QuantizationConfig] = None, reduce_results: bool = True, prefix: str = ""): super().__init__() self.hidden_size = config.hidden_size self.expert_id = expert_id self.ffn_dim = config.intermediate_size if not is_residual_mlp \ else self.hidden_size self.w13 = MergedColumnParallelLinear(self.hidden_size, [self.ffn_dim] * 2, bias=False, quant_config=quant_config) self.w2 = RowParallelLinear(self.ffn_dim, self.hidden_size, bias=False, reduce_results=reduce_results, quant_config=quant_config) if config.hidden_act != "silu": raise ValueError(f"Unsupported activation: {config.hidden_act}. " "Only silu is supported for now.") self.act_fn = SiluAndMul() def forward(self, hidden_states): gate_up, _ = self.w13(hidden_states) hidden_states = self.act_fn(gate_up) hidden_states, _ = self.w2(hidden_states) return hidden_states class ArcticMoE(nn.Module): """ Model-parallel implementation of Arctic MoE Layer. """ def __init__(self, config: ArcticConfig, tp_size: Optional[int] = None, params_dtype: Optional[torch.dtype] = None, quant_config: Optional[QuantizationConfig] = None, reduce_results: bool = True, prefix: str = ""): super().__init__() layer_id = extract_layer_index(prefix) self.tp_size = tp_size or get_tensor_model_parallel_world_size() self.hidden_size = config.hidden_size self.num_experts = config.num_local_experts self.layer_id = layer_id self.top_k = config.num_experts_per_tok self.intermediate_size = config.intermediate_size // self.tp_size self.is_moe_layer = (layer_id + 1) % config.moe_layer_frequency == 0 self.is_quant = isinstance(quant_config, DeepSpeedFPConfig) self.reduce_results = reduce_results # Some other parameters if params_dtype is None: params_dtype = torch.get_default_dtype() self.params_dtype = params_dtype if not self.is_moe_layer: self.mlp = ArcticMLP(config, quant_config=quant_config, reduce_results=reduce_results, prefix=f"{prefix}.mlp") else: self.gate = ReplicatedLinear(self.hidden_size, self.num_experts, bias=False, params_dtype=self.params_dtype, quant_config=quant_config, prefix=f"{prefix}.gate") if self.is_quant: self.ws = DeepSpeedFPParameter( torch.Size((self.num_experts, 2 * self.intermediate_size, self.hidden_size)), params_dtype=params_dtype, quant_config=quant_config, ) self.w2s = DeepSpeedFPParameter( torch.Size((self.num_experts, self.hidden_size, self.intermediate_size)), params_dtype=params_dtype, quant_config=quant_config, ) else: self.ws = nn.Parameter( torch.empty(self.num_experts, 2 * self.intermediate_size, self.hidden_size, device=current_platform.device_type, dtype=self.params_dtype)) self.w2s = nn.Parameter( torch.empty(self.num_experts, self.hidden_size, self.intermediate_size, device=current_platform.device_type, dtype=self.params_dtype)) set_weight_attrs(self.ws, { "weight_loader": self.weight_loader, }) set_weight_attrs(self.w2s, { "weight_loader": self.weight_loader, }) def weight_loader(self, param: nn.Parameter, loaded_weight: torch.Tensor, weight_name: str, expert_id: int): tp_rank = get_tensor_model_parallel_rank() param_data = param.ds_dequantize() if self.is_quant else param.data shard_size = self.intermediate_size shard = slice(tp_rank * shard_size, (tp_rank + 1) * shard_size) if weight_name.endswith("w1.weight"): param_data[expert_id, 0:shard_size, :] = loaded_weight[shard, :] if weight_name.endswith("w3.weight"): param_data[expert_id, shard_size:2 * shard_size, :] = loaded_weight[shard, :] if weight_name.endswith("w2.weight"): param_data[expert_id, :, :] = loaded_weight[:, shard] if self.is_quant: param.ds_quantize_(param_data) def local_moe_fused(self, hidden_states: torch.Tensor) -> torch.Tensor: num_tokens, hidden_size = hidden_states.shape hidden_states = hidden_states.view(-1, self.hidden_size) # router_logits: (num_tokens, n_experts) router_logits, _ = self.gate(hidden_states) do_normalize = self.top_k > 1 topk_weights, topk_ids, token_expert_indices = fused_topk( hidden_states, router_logits, self.top_k, renormalize=do_normalize) # topk_ids: (num_tokens, k) if self.is_quant: if 2 * num_tokens <= self.num_experts: # If much fewer tokens than experts, use selective dequantize. ws_dequantized = self.ws.ds_selective_dequantize( topk_ids.flatten()) w2s_dequantized = self.w2s.ds_selective_dequantize( topk_ids.flatten()) # We gathered the experts to the tokens so update the mapping. topk_ids = torch.arange( 0, topk_ids.numel(), device=topk_ids.device, ).reshape(topk_ids.shape) else: ws_dequantized = self.ws.ds_dequantize() w2s_dequantized = self.w2s.ds_dequantize() final_hidden_states = fused_experts( hidden_states, ws_dequantized if self.is_quant else self.ws, w2s_dequantized if self.is_quant else self.w2s, topk_weights, topk_ids, inplace=True) if self.reduce_results and self.tp_size > 1: final_hidden_states = tensor_model_parallel_all_reduce( final_hidden_states) return final_hidden_states.view(num_tokens, hidden_size) def forward(self, hidden_states: torch.Tensor): if self.is_moe_layer: final_hidden_states = self.local_moe_fused(hidden_states) else: final_hidden_states = self.mlp(hidden_states) return final_hidden_states class ArcticAttention(nn.Module): def __init__( self, config: ArcticConfig, cache_config: Optional[CacheConfig] = None, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", ): super().__init__() self.config = config self.hidden_size = config.hidden_size tp_size = get_tensor_model_parallel_world_size() self.total_num_heads = config.num_attention_heads assert self.total_num_heads % tp_size == 0 self.num_heads = self.total_num_heads // tp_size self.total_num_kv_heads = config.num_key_value_heads if self.total_num_kv_heads >= tp_size: assert self.total_num_kv_heads % tp_size == 0 else: 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 = 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.max_position_embeddings = config.max_position_embeddings self.rope_theta = config.rope_theta self.scaling = self.head_dim**-0.5 self.qkv_proj = QKVParallelLinear(self.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, self.hidden_size, bias=False, reduce_results=True, quant_config=quant_config, ) self.rotary_emb = get_rope( self.head_dim, rotary_dim=self.head_dim, max_position=self.max_position_embeddings, base=int(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, 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 ArcticDecoderLayer(nn.Module): def __init__( self, config: ArcticConfig, cache_config: Optional[CacheConfig] = None, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", ) -> None: super().__init__() self.hidden_size = config.hidden_size layer_idx = extract_layer_index(prefix) is_moe_layer = (layer_idx + 1) % config.moe_layer_frequency == 0 self.use_residual = config.use_residual and is_moe_layer self.self_attn = ArcticAttention(config, cache_config, quant_config=quant_config, prefix=f"{prefix}.self_attn") self.block_sparse_moe = ArcticMoE( config, quant_config=quant_config, reduce_results=(not self.use_residual), prefix=f"{prefix}.block_sparse_moe", ) 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) if self.use_residual: self.residual_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.residual_mlp = ArcticMLP(config, is_residual_mlp=True, reduce_results=False, prefix=f"{prefix}.residual_mlp") def forward( self, positions: torch.Tensor, hidden_states: torch.Tensor, ) -> torch.Tensor: residual_input = hidden_states hidden_states = self.input_layernorm(hidden_states) hidden_states = self.self_attn( positions=positions, hidden_states=hidden_states, ) hidden_states = residual_input + hidden_states residual_attn = hidden_states if self.use_residual: hidden_states = self.residual_layernorm(hidden_states) hidden_states = self.residual_mlp(hidden_states) residual_mlp = hidden_states hidden_states = self.post_attention_layernorm(residual_input) hidden_states = self.block_sparse_moe(hidden_states) hidden_states = residual_mlp + hidden_states hidden_states = tensor_model_parallel_all_reduce(hidden_states) hidden_states = residual_attn + hidden_states else: hidden_states = self.post_attention_layernorm(hidden_states) hidden_states = self.block_sparse_moe(hidden_states) hidden_states = residual_attn + hidden_states return hidden_states @support_torch_compile class ArcticModel(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.vocab_size = config.vocab_size self.embed_tokens = VocabParallelEmbedding( self.vocab_size, config.hidden_size, org_num_embeddings=self.vocab_size) self.start_layer, self.end_layer, self.layers = make_layers( config.num_hidden_layers, lambda prefix: ArcticDecoderLayer( config, cache_config, quant_config, prefix=prefix), prefix=f"{prefix}.layers") self._attn_implementation = config._attn_implementation self.norm = RMSNorm(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: 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) else: assert intermediate_tensors is not None hidden_states = intermediate_tensors["hidden_states"] for layer in 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 class ArcticForCausalLM(nn.Module, SupportsPP, SupportsQuant): packed_modules_mapping = {"qkv_proj": ["q_proj", "k_proj", "v_proj"]} 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.model = ArcticModel(vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")) self.vocab_size = config.vocab_size self.lm_head = ParallelLMHead( self.vocab_size, config.hidden_size, quant_config=quant_config, ) if self.config.tie_word_embeddings: self.lm_head.weight = self.model.embed_tokens.weight self.num_experts = config.num_local_experts self.num_experts_per_tok = config.num_experts_per_tok self.unpadded_vocab_size = config.vocab_size self.logits_processor = LogitsProcessor(self.unpadded_vocab_size, 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: Optional[IntermediateTensors] = None, inputs_embeds: Optional[torch.Tensor] = None, ) -> Union[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, 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]: stacked_params_mapping = [ # (param_name, shard_name, shard_id) ("qkv_proj", "q_proj", "q"), ("qkv_proj", "k_proj", "k"), ("qkv_proj", "v_proj", "v"), ] mlp_params_mapping: list[tuple[str, str, int]] = [] expert_params_mapping: list[tuple[str, str, int]] = [] num_layers = self.config.num_hidden_layers for layer in range(num_layers): mlp_params_mapping.append( (f"layers.{layer}.residual_mlp.w13.weight", f"layers.{layer}.residual_mlp.w1.weight", 0)) mlp_params_mapping.append( (f"layers.{layer}.residual_mlp.w13.weight", f"layers.{layer}.residual_mlp.w3.weight", 1)) if layer % 2 == 0: # MLP layers mlp_params_mapping.append( (f"layers.{layer}.block_sparse_moe.mlp.w13.weight", f"layers.{layer}.block_sparse_moe.mlp.w1.weight", 0)) mlp_params_mapping.append( (f"layers.{layer}.block_sparse_moe.mlp.w13.weight", f"layers.{layer}.block_sparse_moe.mlp.w3.weight", 1)) else: # MoE layers for expert_id in range(self.config.num_local_experts): expert_params_mapping.append( ("ws", f"experts.{expert_id}.w1.weight", expert_id)) expert_params_mapping.append( ("w2s", f"experts.{expert_id}.w2.weight", expert_id)) expert_params_mapping.append( ("ws", f"experts.{expert_id}.w3.weight", expert_id)) params_dict = dict(self.named_parameters()) loaded_params: set[str] = set() logger.info( "It will take ~10 minutes loading from the 16-bit weights. " "Alternatively, use the prequantized 8-bit weights of arctic " "and set load-format to `sharded_state` will accelerate loading.") 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: for param_name, weight_name, shard_id in mlp_params_mapping: if weight_name not in name: continue name = name.replace(weight_name, param_name) 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: for param_name, weight_name, shard_id \ in expert_params_mapping: if weight_name not in name: continue name = name.replace(weight_name, param_name) if is_pp_missing_parameter(name, self): continue param = params_dict[name] weight_loader = param.weight_loader weight_loader(param, loaded_weight, weight_name, expert_id=shard_id) break else: 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