# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project # Adapted from # https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py # Copyright 2023 The vLLM team. # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. # # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX # and OPT implementations in this library. It has been modified from its # original forms to accommodate minor architectural differences compared # to GPT-NeoX and OPT used by the Meta AI team that trained the model. # # 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. """Inference-only Mixtral model.""" import typing from collections.abc import Callable, Iterable from itertools import islice import torch from torch import nn from transformers import MixtralConfig from vllm.attention.layer import Attention from vllm.compilation.decorators import support_torch_compile from vllm.config import CacheConfig, VllmConfig, get_current_vllm_config from vllm.distributed import ( get_ep_group, get_pp_group, get_tensor_model_parallel_world_size, ) from vllm.model_executor.layers.fused_moe import FusedMoE from vllm.model_executor.layers.layernorm import RMSNorm from vllm.model_executor.layers.linear import ( 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.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, maybe_remap_kv_scale_name, ) from vllm.sequence import IntermediateTensors from .interfaces import MixtureOfExperts, SupportsLoRA, SupportsPP from .utils import ( AutoWeightsLoader, PPMissingLayer, is_pp_missing_parameter, make_empty_intermediate_tensors_factory, make_layers, maybe_prefix, ) class MixtralMoE(nn.Module): """A tensor-parallel MoE implementation for Mixtral that shards each expert across all ranks. Each expert's weights are sharded across all ranks and a fused MoE kernel is used for the forward pass, and finally we reduce the outputs across ranks. """ def __init__( self, num_experts: int, top_k: int, hidden_size: int, intermediate_size: int, params_dtype: torch.dtype | None = None, quant_config: QuantizationConfig | None = None, tp_size: int | None = None, dp_size: int | None = None, prefix: str = "", enable_eplb: bool = False, ): super().__init__() self.hidden_size = hidden_size self.ep_group = get_ep_group().device_group self.ep_rank = get_ep_group().rank_in_group self.ep_size = self.ep_group.size() # Expert Parallelism Load balancing settings. vllm_config = get_current_vllm_config() parallel_config = vllm_config.parallel_config self.enable_eplb = enable_eplb self.n_routed_experts = num_experts self.n_logical_experts = num_experts self.n_redundant_experts = parallel_config.eplb_config.num_redundant_experts self.n_physical_experts = self.n_logical_experts + self.n_redundant_experts self.n_local_physical_experts = self.n_physical_experts // self.ep_size self.physical_expert_start = self.ep_rank * self.n_local_physical_experts self.physical_expert_end = ( self.physical_expert_start + self.n_local_physical_experts ) # Gate always runs at half / full precision for now. self.gate = ReplicatedLinear( hidden_size, num_experts, bias=False, params_dtype=params_dtype, quant_config=None, prefix=f"{prefix}.gate", ) self.experts = FusedMoE( num_experts=num_experts, top_k=top_k, hidden_size=hidden_size, intermediate_size=intermediate_size, params_dtype=params_dtype, reduce_results=True, renormalize=True, quant_config=quant_config, tp_size=tp_size, dp_size=dp_size, prefix=f"{prefix}.experts", enable_eplb=self.enable_eplb, num_redundant_experts=self.n_redundant_experts, ) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: # NOTE: hidden_states can have either 1D or 2D shape. orig_shape = hidden_states.shape hidden_states = hidden_states.view(-1, self.hidden_size) # router_logits: (num_tokens, n_experts) router_logits, _ = self.gate(hidden_states) final_hidden_states = self.experts(hidden_states, router_logits) return final_hidden_states.view(orig_shape) class MixtralAttention(nn.Module): def __init__( self, config: MixtralConfig, hidden_size: int, num_heads: int, num_kv_heads: int, max_position: int = 4096 * 32, 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) # MixtralConfig has an optional head_dim argument self.head_dim = getattr(config, "head_dim", None) if self.head_dim is None: 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.scaling = self.head_dim**-0.5 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, max_position=max_position, rope_parameters=config.rope_parameters, 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 MixtralDecoderLayer(nn.Module): def __init__( self, config: MixtralConfig, cache_config: CacheConfig | None = None, quant_config: QuantizationConfig | None = None, prefix: str = "", enable_eplb: bool = False, ) -> None: super().__init__() self.hidden_size = config.hidden_size self.self_attn = MixtralAttention( config=config, hidden_size=self.hidden_size, num_heads=config.num_attention_heads, max_position=config.max_position_embeddings, num_kv_heads=config.num_key_value_heads, cache_config=cache_config, quant_config=quant_config, prefix=f"{prefix}.self_attn", ) self.block_sparse_moe = MixtralMoE( num_experts=config.num_local_experts, top_k=config.num_experts_per_tok, hidden_size=config.hidden_size, intermediate_size=config.intermediate_size, quant_config=quant_config, prefix=f"{prefix}.block_sparse_moe", enable_eplb=enable_eplb, ) 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, 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.self_attn( positions=positions, hidden_states=hidden_states, ) # Fully Connected hidden_states, residual = self.post_attention_layernorm(hidden_states, residual) hidden_states = self.block_sparse_moe(hidden_states) return hidden_states, residual @support_torch_compile class MixtralModel(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 parallel_config = vllm_config.parallel_config self.config = config self.quant_config = quant_config self.vocab_size = config.vocab_size self.org_vocab_size = config.vocab_size self.embed_tokens = VocabParallelEmbedding( self.vocab_size, config.hidden_size, ) self.enable_eplb = parallel_config.enable_eplb self.num_redundant_experts = parallel_config.eplb_config.num_redundant_experts self.start_layer, self.end_layer, self.layers = make_layers( config.num_hidden_layers, lambda prefix: MixtralDecoderLayer( config, cache_config, quant_config=quant_config, prefix=prefix, enable_eplb=self.enable_eplb, ), prefix=f"{prefix}.layers", ) self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory( ["hidden_states", "residual"], config.hidden_size ) def embed_input_ids(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.embed_input_ids(input_ids) residual = None else: assert intermediate_tensors is not None hidden_states = intermediate_tensors["hidden_states"] residual = intermediate_tensors["residual"] for layer in islice(self.layers, self.start_layer, self.end_layer): hidden_states, residual = layer(positions, hidden_states, residual) if not get_pp_group().is_last_rank: return IntermediateTensors( {"hidden_states": hidden_states, "residual": residual} ) hidden_states, _ = self.norm(hidden_states, residual) return hidden_states def get_expert_mapping(self) -> list[tuple[str, str, int, str]]: # Params for weights, fp8 weight scales, fp8 activation scales # (param_name, weight_name, expert_id, shard_id) return FusedMoE.make_expert_params_mapping( self, ckpt_gate_proj_name="w1", ckpt_down_proj_name="w2", ckpt_up_proj_name="w3", num_experts=self.config.num_local_experts, num_redundant_experts=self.num_redundant_experts, ) 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"), ] params_dict = dict(self.named_parameters()) loaded_params: set[str] = set() expert_params_mapping = self.get_expert_mapping() for name, loaded_weight in weights: if self.quant_config is not None and ( scale_name := self.quant_config.get_cache_scale(name) ): # Loading kv cache quantization scales param = params_dict[scale_name] weight_loader = getattr(param, "weight_loader", default_weight_loader) loaded_weight = ( loaded_weight if loaded_weight.dim() == 0 else loaded_weight[0] ) weight_loader(param, loaded_weight) loaded_params.add(scale_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) # Skip loading extra bias for GPTQ models. if ( name.endswith(".bias") or name.endswith("_bias") ) and name not in params_dict: continue # Skip layers on other devices. if is_pp_missing_parameter(name, self): continue if name.endswith("scale"): # Remapping the name of FP8 kv-scale. name = maybe_remap_kv_scale_name(name, params_dict) if name is None: continue param = params_dict[name] weight_loader = param.weight_loader weight_loader(param, loaded_weight, shard_id) break else: is_expert_weight = False for mapping in expert_params_mapping: param_name, weight_name, expert_id, shard_id = mapping if weight_name not in name: continue is_expert_weight = True name_mapped = name.replace(weight_name, param_name) # Skip layers on other devices. if is_pp_missing_parameter(name_mapped, self): continue if ( name_mapped.endswith(".bias") or name_mapped.endswith("_bias") ) and name_mapped not in params_dict: continue param = params_dict[name_mapped] weight_loader = typing.cast( Callable[..., bool], param.weight_loader ) success = weight_loader( param, loaded_weight, name_mapped, shard_id=shard_id, expert_id=expert_id, return_success=True, ) if success: name = name_mapped break else: if is_expert_weight: continue # Skip loading extra bias for GPTQ models. if ( name.endswith(".bias") or name.endswith("_bias") ) and name not in params_dict: continue # Skip layers on other devices. if is_pp_missing_parameter(name, self): continue # Remapping the name of FP8 kv-scale. name = maybe_remap_kv_scale_name(name, params_dict) if name is None: 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 MixtralForCausalLM(nn.Module, SupportsLoRA, SupportsPP, MixtureOfExperts): fall_back_to_pt_during_load = False packed_modules_mapping = { "qkv_proj": [ "q_proj", "k_proj", "v_proj", ], } # LoRA specific attributes embedding_modules = { "embed_tokens": "input_embeddings", "lm_head": "output_embeddings", } 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 = MixtralModel( 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 ) self.expert_weights = [] self.moe_layers = [] example_moe = None for layer in self.model.layers: if isinstance(layer, PPMissingLayer): continue assert isinstance(layer, MixtralDecoderLayer) if hasattr(layer, "block_sparse_moe") and isinstance( layer.block_sparse_moe, MixtralMoE ): example_moe = layer.block_sparse_moe self.moe_layers.append(layer.block_sparse_moe.experts) self.num_moe_layers = len(self.moe_layers) if example_moe is None: raise RuntimeError("No MixtralMoE layer found in model.layers.") self.num_logical_experts = example_moe.n_logical_experts self.num_physical_experts = example_moe.n_physical_experts self.num_local_physical_experts = example_moe.n_local_physical_experts self.num_routed_experts = example_moe.n_routed_experts self.num_redundant_experts = example_moe.n_redundant_experts self.num_expert_groups = 1 self.num_shared_experts = 0 def update_physical_experts_metadata( self, num_physical_experts: int, num_local_physical_experts: int, ) -> None: assert self.num_local_physical_experts == num_local_physical_experts self.num_physical_experts = num_physical_experts self.num_local_physical_experts = num_local_physical_experts self.num_redundant_experts = num_physical_experts - self.num_logical_experts for layer in self.model.layers: if hasattr(layer, "block_sparse_moe") and isinstance( layer.block_sparse_moe, MixtralMoE ): moe = layer.block_sparse_moe moe.n_local_physical_experts = num_local_physical_experts moe.n_physical_experts = num_physical_experts moe.n_redundant_experts = self.num_redundant_experts moe.experts.update_expert_map() 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, 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) def get_expert_mapping(self) -> list[tuple[str, str, int, str]]: return self.model.get_expert_mapping()