################################################################################ # Copyright(c)2020-2025 Shanghai Biren Technology Co., Ltd. All rights reserved. # 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. # ################################################################################ from collections.abc import Iterable from typing import Optional import torch import torch.distributed as dist import torch_br from torch import nn from transformers import GptOssConfig import vllm import vllm.model_executor.models.gpt_oss from vllm.attention import Attention, AttentionType 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.model_executor.layers.fused_moe import FusedMoE from vllm.model_executor.layers.linear import (QKVParallelLinear, RowParallelLinear) from vllm.model_executor.layers.quantization import QuantizationConfig from vllm.model_executor.layers.rotary_embedding import get_rope from vllm.model_executor.model_loader.weight_utils import default_weight_loader from vllm.model_executor.models.utils import (extract_layer_index, is_pp_missing_parameter) from vllm.sequence import IntermediateTensors from vllm.utils import cdiv from vllm_br import envs class OAIAttention(nn.Module): def __init__( self, config: GptOssConfig, quant_config: Optional[QuantizationConfig] = None, cache_config: Optional[CacheConfig] = None, prefix: str = "", ): super().__init__() self.layer_idx = extract_layer_index(prefix) self.head_dim = config.head_dim self.num_attention_heads = config.num_attention_heads self.num_key_value_heads = config.num_key_value_heads self.hidden_size = config.hidden_size self.rotary_emb = get_rope( self.head_dim, rotary_dim=self.head_dim, max_position=config.max_position_embeddings, base=config.rope_theta, dtype=torch.float32, rope_scaling={ "rope_type": "yarn", "factor": config.rope_scaling["factor"], "original_max_position_embeddings": config.rope_scaling["original_max_position_embeddings"], "beta_fast": config.rope_scaling["beta_fast"], "beta_slow": config.rope_scaling["beta_slow"], }, is_neox_style=True, ) tp_size = get_tensor_model_parallel_world_size() attention_sink_dtype = torch.float32 self.sinks = torch.nn.Parameter( torch.empty(config.num_attention_heads // tp_size, dtype=attention_sink_dtype, requires_grad=False)) self.q_size = self.num_attention_heads * self.head_dim // tp_size self.kv_size = self.num_key_value_heads * self.head_dim // tp_size self.scaling = self.head_dim**-0.5 self.rope_theta = config.rope_theta self.qkv = QKVParallelLinear( hidden_size=self.hidden_size, head_size=self.head_dim, total_num_heads=self.num_attention_heads, total_num_kv_heads=self.num_key_value_heads, quant_config=quant_config, prefix=f"{prefix}.qkv_proj", ) self.o_proj = RowParallelLinear( input_size=self.num_attention_heads * self.head_dim, output_size=self.hidden_size, quant_config=quant_config, prefix=f"{prefix}.o_proj", ) self.num_local_attention_heads = config.num_attention_heads // tp_size self.num_local_key_value_heads = config.num_key_value_heads // tp_size # Only apply sliding window to every other layer sliding_window = (config.sliding_window if self.layer_idx % 2 == 0 else None) self.attn = Attention( self.num_local_attention_heads, self.head_dim, self.scaling, num_kv_heads=self.num_local_key_value_heads, cache_config=cache_config, quant_config=quant_config, per_layer_sliding_window=sliding_window, attn_type=AttentionType.DECODER, prefix=f"{prefix}.attn", sinks=self.sinks, ) def forward(self, hidden_states: torch.Tensor, positions: torch.Tensor) -> torch.Tensor: qkv, _ = self.qkv(hidden_states) if envs.VLLM_BR_DEVICE_SPC_NUM > 16: q, k, v = torch_br.split_w_sbp_infer( qkv, [self.q_size, self.kv_size, self.kv_size]) else: q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1) q, k = self.rotary_emb(positions, q, k) v = v.contiguous() attn_output = self.attn(q, k, v) output, _ = self.o_proj(attn_output) return output vllm.model_executor.models.gpt_oss.OAIAttention = OAIAttention class MLPBlock(torch.nn.Module): def __init__( self, vllm_config: VllmConfig, layer_idx: int, prefix: str = "", ): super().__init__() self.tp_size = get_tensor_model_parallel_world_size() config = vllm_config.model_config.hf_config quant_config = vllm_config.quant_config parallel_config = vllm_config.parallel_config self.is_sequence_parallel = parallel_config.use_sequence_parallel_moe self.layer_idx = layer_idx self.num_experts = config.num_local_experts self.experts_per_token = config.num_experts_per_tok self.world_size = dist.get_world_size() if dist.is_initialized() else 1 self.router = torch.nn.Linear(config.hidden_size, config.num_local_experts, dtype=torch.bfloat16) assert config.intermediate_size % self.world_size == 0 self.experts = FusedMoE(num_experts=config.num_local_experts, top_k=config.num_experts_per_tok, hidden_size=config.hidden_size, intermediate_size=config.intermediate_size, reduce_results=True, renormalize=True, quant_config=quant_config, prefix=f"{prefix}.experts", apply_router_weight_on_input=False, has_bias=True, activation="swigluoai", is_sequence_parallel=self.is_sequence_parallel) def forward(self, x: torch.Tensor) -> torch.Tensor: final_hidden_states = self.experts(hidden_states=x.squeeze(0), router_logits=self.router.weight) if hasattr(final_hidden_states, 'all_reduced'): # NOTE: this flag indicates that the final_hidden_states has been reduced in fused_moe delattr(final_hidden_states, 'all_reduced') elif self.tp_size > 1: final_hidden_states = tensor_model_parallel_all_reduce( final_hidden_states) return final_hidden_states vllm.model_executor.models.gpt_oss.MLPBlock = MLPBlock def GptOssModel_forward( self, input_ids: torch.Tensor, positions: torch.Tensor, intermediate_tensors: Optional[IntermediateTensors] = None, inputs_embeds: Optional[torch.Tensor] = None, ) -> torch.Tensor: if get_pp_group().is_first_rank: if inputs_embeds is not None: x = inputs_embeds else: x = self.get_input_embeddings(input_ids) residual = None else: assert intermediate_tensors is not None x = intermediate_tensors["hidden_states"] residual = intermediate_tensors["residual"] residual = residual.unsqueeze(0) x = x.unsqueeze(0) aux_hidden_states = [] for i in range(self.start_layer, self.end_layer): layer = self.layers[i] if i in self.aux_hidden_state_layers: aux_hidden_states.append(x if residual is None else x + residual) x, residual = layer(x, positions, residual) if not get_pp_group().is_last_rank: return IntermediateTensors({ "hidden_states": x.squeeze(0), "residual": residual.squeeze(0) if residual is not None else None, }) x, _ = self.norm(x, residual) if len(aux_hidden_states) > 0: return x, aux_hidden_states return x.squeeze(0) vllm.model_executor.models.gpt_oss.GptOssModel.forward = GptOssModel_forward def GptOssModel_load_weights_other( self, ep_rank_end: int, ep_rank_start: int, heads_per_rank: int, head_start: int, weights: Iterable[tuple[str, torch.Tensor]], stacked_params_mapping: list[tuple[str, ...]], ) -> set[str]: params_dict = dict(self.named_parameters()) loaded_params: set[str] = set() use_ep = self.parallel_config.enable_expert_parallel tp_rank = get_tensor_model_parallel_rank() tp_size = get_tensor_model_parallel_world_size() intermediate_size = self.config.intermediate_size per_rank_intermediate_size = cdiv(intermediate_size, tp_size) # Calculate common slicing bounds for current rank tp_rank_start = tp_rank * per_rank_intermediate_size tp_rank_end = min((tp_rank + 1) * per_rank_intermediate_size, intermediate_size) for name, weight in weights: # Skip layers on other devices. if is_pp_missing_parameter(name, self): continue if ".w13_weight" in name: # Handle MLP gate and up projection weights # Extract gate and up projection parts if use_ep: narrow_weight = weight[ep_rank_start:ep_rank_end, ...] else: narrow_weight = weight[:, :, 2 * tp_rank_start:2 * tp_rank_end] narrow_weight = narrow_weight.permute(0, 2, 1).contiguous() param = params_dict[name] param.copy_(narrow_weight) loaded_params.add(name) continue elif ".w2_weight" in name: # Handle MLP down projection weights if use_ep: narrow_weight = weight[ep_rank_start:ep_rank_end, ...] else: narrow_weight = weight[:, tp_rank_start:tp_rank_end, :] narrow_weight = narrow_weight.permute(0, 2, 1).contiguous() param = params_dict[name] param.copy_(narrow_weight) loaded_params.add(name) continue elif ".w13_bias" in name: # Handle MLP gate and up projection biases # Extract gate and up projection bias parts if use_ep: narrow_weight = weight[ep_rank_start:ep_rank_end, ...] else: narrow_weight = weight[:, 2 * tp_rank_start:2 * tp_rank_end] param = params_dict[name] param.copy_(narrow_weight) loaded_params.add(name) continue elif ".w2_bias" in name: # Handle MLP down projection bias if use_ep: weight = weight[ep_rank_start:ep_rank_end, ...] else: # (only load on rank 0 to avoid duplication) if tp_rank != 0: weight.zero_() param = params_dict[name] param.copy_(weight) loaded_params.add(name) continue elif "sinks" in name: # Handle attention sinks (distributed across ranks) param = params_dict[name] narrow_weight = weight.narrow(0, head_start, heads_per_rank) param.data.copy_(narrow_weight) loaded_params.add(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) param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) if weight_loader == default_weight_loader: weight_loader(param, weight) else: weight_loader(param, weight, shard_id) break else: # Handle all other weights with potential renaming if name not in params_dict: continue param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, weight) loaded_params.add(name) return loaded_params vllm.model_executor.models.gpt_oss.GptOssModel._load_weights_other = GptOssModel_load_weights_other