# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project from collections.abc import Iterable import torch import torch.distributed as dist from torch import nn from transformers import GptOssConfig from vllm.compilation.decorators import support_torch_compile from vllm.config import CacheConfig, VllmConfig from vllm.distributed import ( get_dp_group, get_ep_group, get_pcp_group, get_pp_group, get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size, tensor_model_parallel_all_gather, ) from vllm.model_executor.layers.attention import Attention from vllm.model_executor.layers.fused_moe import FusedMoE from vllm.model_executor.layers.fused_moe.config import FusedMoEParallelConfig from vllm.model_executor.layers.layernorm import RMSNorm from vllm.model_executor.layers.linear import 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.utils import rocm_unquantized_gemm 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.models.utils import sequence_parallel_chunk from vllm.platforms import current_platform from vllm.sequence import IntermediateTensors from vllm.utils.math_utils import cdiv from vllm.v1.attention.backend import AttentionType from .interfaces import SupportsEagle3, SupportsLoRA, SupportsPP from .utils import ( AutoWeightsLoader, WeightsMapper, extract_layer_index, is_pp_missing_parameter, make_empty_intermediate_tensors_factory, make_layers, maybe_prefix, ) class OAIAttention(nn.Module): def __init__( self, config: GptOssConfig, quant_config: QuantizationConfig | None = None, cache_config: CacheConfig | None = 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, max_position=config.max_position_embeddings, dtype=torch.float32, rope_parameters={ "rope_theta": config.rope_parameters["rope_theta"], "rope_type": "yarn", "factor": config.rope_parameters["factor"], "original_max_position_embeddings": config.rope_parameters[ "original_max_position_embeddings" ], "beta_fast": config.rope_parameters["beta_fast"], "beta_slow": config.rope_parameters["beta_slow"], "truncate": config.rope_parameters.get("truncate", True), }, is_neox_style=True, ) tp_size = get_tensor_model_parallel_world_size() self.sinks = torch.nn.Parameter( torch.empty(config.num_attention_heads // tp_size, 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.qkv_proj = 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_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) v = v.contiguous() attn_output = self.attn(q, k, v) output, _ = self.o_proj(attn_output) return output class MLPBlock(torch.nn.Module): def __init__( self, vllm_config: VllmConfig, layer_idx: int, prefix: str = "", ): super().__init__() 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.hidden_size = config.hidden_size 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) 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: num_tokens = x.shape[0] if self.is_sequence_parallel: x = sequence_parallel_chunk(x) if current_platform.is_rocm(): g = rocm_unquantized_gemm( self, x[:, : self.hidden_size], self.router.weight, self.router.bias ) else: g = self.router(x) x = self.experts(hidden_states=x, router_logits=g)[:, : self.hidden_size] if self.is_sequence_parallel: x = tensor_model_parallel_all_gather(x.contiguous(), 0) x = x[:num_tokens] return x class TransformerBlock(torch.nn.Module): def __init__( self, vllm_config: VllmConfig, quant_config: QuantizationConfig, prefix: str = "", ): super().__init__() config = vllm_config.model_config.hf_config cache_config = vllm_config.cache_config self.layer_idx = extract_layer_index(prefix) self.attn = OAIAttention( config, prefix=f"{prefix}.attn", quant_config=quant_config, cache_config=cache_config, ) self.mlp = MLPBlock(vllm_config, self.layer_idx, prefix=f"{prefix}.mlp") self.input_layernorm = RMSNorm(config.hidden_size, eps=1e-5) self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=1e-5) def forward( self, hidden_states: torch.Tensor, positions: torch.Tensor, residual: torch.Tensor | None, ) -> 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.attn(hidden_states, positions) # Fully Connected hidden_states, residual = self.post_attention_layernorm(hidden_states, residual) output = self.mlp(hidden_states) return output, residual @support_torch_compile class GptOssModel(nn.Module): def __init__( self, *, vllm_config: VllmConfig, prefix: str = "", ): super().__init__() self.config = vllm_config.model_config.hf_config self.quant_config = vllm_config.quant_config self.parallel_config = vllm_config.parallel_config self.config.hidden_size = self.config.hidden_size self.embedding = VocabParallelEmbedding( self.config.vocab_size, self.config.hidden_size, ) self.start_layer, self.end_layer, self.layers = make_layers( self.config.num_hidden_layers, lambda prefix: TransformerBlock( vllm_config, prefix=prefix, quant_config=self.quant_config, ), prefix=f"{prefix}.layers", ) self.norm = RMSNorm(self.config.hidden_size, eps=1e-5) self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory( ["hidden_states", "residual"], self.config.hidden_size ) self.aux_hidden_state_layers = tuple[int, ...]() def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor: return self.embedding(input_ids) def forward( self, input_ids: torch.Tensor | None, positions: torch.Tensor, intermediate_tensors: IntermediateTensors | None = None, inputs_embeds: torch.Tensor | None = None, ) -> torch.Tensor: if get_pp_group().is_first_rank: if inputs_embeds is not None: x = inputs_embeds else: x = self.embed_input_ids(input_ids) residual = None else: assert intermediate_tensors is not None x = intermediate_tensors["hidden_states"] residual = intermediate_tensors["residual"] 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, "residual": residual}) x, _ = self.norm(x, residual) if len(aux_hidden_states) > 0: return x, aux_hidden_states return x def _load_weights_mxfp4( 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() mxfp4_block = 32 use_ep = self.parallel_config.enable_expert_parallel num_experts = self.config.num_local_experts # In MoE, we need to flatten the tensor parallel size across the data # parallel size when EP is disabled. tp_size, tp_rank = FusedMoEParallelConfig.flatten_tp_across_dp_and_pcp( tp_size=get_tensor_model_parallel_world_size(), dp_size=get_dp_group().world_size, dp_rank=get_dp_group().rank_in_group, pcp_size=get_pcp_group().world_size, pcp_rank=get_pcp_group().rank_in_group, ) intermediate_size = self.config.intermediate_size intermediate_size_block = intermediate_size // mxfp4_block per_rank_intermediate_size_block = cdiv(intermediate_size_block, tp_size) per_rank_intermediate_size = per_rank_intermediate_size_block * mxfp4_block # 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_scale" in name: # Handle MLP gate and up projection weights scale 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] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader( param, narrow_weight, weight_name=name, shard_id=None, expert_id=None, ) loaded_params.add(name) continue elif ".w2_weight_scale" 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 // mxfp4_block : tp_rank_end // mxfp4_block ] param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader( param, narrow_weight, weight_name=name, shard_id=None, expert_id=None, ) loaded_params.add(name) continue elif ".w13_weight" in name: # Handle MLP gate and up projection weights # flat weight from (E, 2 * N, block_size, entry_per_block) # to (E, 2 * N, -1), shouldn't trigger copy for contiguous weight = weight.view( num_experts, 2 * intermediate_size, -1 ).contiguous() # Extract gate and up projection parts # since the weight is shuffled, we can slice directly 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] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader( param, narrow_weight, weight_name=name, shard_id=None, expert_id=None, ) loaded_params.add(name) continue elif ".w2_weight" in name: # Handle MLP down projection weights # same flatten here, but since 2 mx4 value are packed in 1 # uint8, divide by 2 weight = weight.view( num_experts, -1, intermediate_size // 2 ).contiguous() if use_ep: narrow_weight = weight[ep_rank_start:ep_rank_end, ...] else: narrow_weight = weight[..., tp_rank_start // 2 : tp_rank_end // 2] param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader( param, narrow_weight, weight_name=name, shard_id=None, expert_id=None, ) 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] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader( param, narrow_weight, weight_name=name, shard_id=None, expert_id=None, ) loaded_params.add(name) continue elif ".w2_bias" in name: # Handle MLP down projection bias param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) 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_() weight_loader( param, weight, weight_name=name, shard_id=None, expert_id=None ) 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 def _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 # In MoE, we need to flatten the tensor parallel size across the data # parallel size when EP is disabled. tp_size, tp_rank = FusedMoEParallelConfig.flatten_tp_across_dp_and_pcp( tp_size=get_tensor_model_parallel_world_size(), dp_size=get_dp_group().world_size, dp_rank=get_dp_group().rank_in_group, pcp_size=get_pcp_group().world_size, pcp_rank=get_pcp_group().rank_in_group, ) 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 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"), ] tp_rank = get_tensor_model_parallel_rank() tp_size = get_tensor_model_parallel_world_size() # Attention heads per rank heads_per_rank = self.config.num_attention_heads // tp_size head_start = tp_rank * heads_per_rank ep_size = get_ep_group().world_size ep_rank = get_ep_group().rank num_experts = self.config.num_local_experts experts_per_rank = num_experts // ep_size ep_rank_start = ep_rank * experts_per_rank ep_rank_end = (ep_rank + 1) * experts_per_rank quant_method = ( self.config.quantization_config["quant_method"] if hasattr(self.config, "quantization_config") else None ) if quant_method == "mxfp4": return self._load_weights_mxfp4( ep_rank_end, ep_rank_start, heads_per_rank, head_start, weights, stacked_params_mapping, ) else: return self._load_weights_other( ep_rank_end, ep_rank_start, heads_per_rank, head_start, weights, stacked_params_mapping, ) class GptOssForCausalLM(nn.Module, SupportsPP, SupportsEagle3, SupportsLoRA): is_3d_moe_weight: bool = True packed_modules_mapping = {"qkv_proj": ["q_proj", "k_proj", "v_proj"]} hf_to_vllm_mapper = WeightsMapper( orig_to_new_substr={ ".self_attn.": ".attn.", }, orig_to_new_suffix={ ".embed_tokens.weight": ".embedding.weight", # MoE MXFP4 weights ".gate_up_proj_blocks": ".w13_weight", ".down_proj_blocks": ".w2_weight", ".gate_up_proj_scales": ".w13_weight_scale", ".down_proj_scales": ".w2_weight_scale", # MoE other weights ".gate_up_proj": ".w13_weight", ".down_proj": ".w2_weight", # MoE Bias ".gate_up_proj_bias": ".w13_bias", ".down_proj_bias": ".w2_bias", }, ) def __init__( self, vllm_config: VllmConfig, prefix: str = "", ): super().__init__() self.vllm_config = vllm_config self.config = vllm_config.model_config.hf_config self.model = GptOssModel( vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model"), ) self.lm_head = ParallelLMHead( self.config.vocab_size, self.config.hidden_size, prefix=maybe_prefix(prefix, "lm_head"), ) self.logits_processor = LogitsProcessor(self.config.vocab_size) self.make_empty_intermediate_tensors = ( self.model.make_empty_intermediate_tensors ) def set_aux_hidden_state_layers(self, layers: tuple[int, ...]) -> None: self.model.aux_hidden_state_layers = layers def get_eagle3_aux_hidden_state_layers(self) -> tuple[int, ...]: num_layers = len(self.model.layers) return (2, num_layers // 2, num_layers - 3) def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor: return self.model.embed_input_ids(input_ids) def forward( self, input_ids: torch.Tensor | None, positions: torch.Tensor, intermediate_tensors: IntermediateTensors | None = None, inputs_embeds: torch.Tensor | None = None, ) -> torch.Tensor: return self.model(input_ids, positions, intermediate_tensors, inputs_embeds) def compute_logits(self, hidden_states: torch.Tensor) -> torch.Tensor: logits = self.logits_processor(self.lm_head, hidden_states) return logits def get_expert_mapping(self) -> list[tuple[str, str, int, str]]: # Params for weights, weight scales, activation scales # (param_name, weight_name, expert_id, shard_id) return FusedMoE.make_expert_params_mapping( self, ckpt_gate_proj_name="gate_proj", ckpt_down_proj_name="down_proj", ckpt_up_proj_name="up_proj", num_experts=self.config.num_local_experts, num_redundant_experts=0, ) def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]: loader = AutoWeightsLoader( self, skip_prefixes=(["lm_head."] if self.config.tie_word_embeddings else None), ) return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)