# 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 os import re from typing import Iterable, Optional, Union from collections.abc import Iterable from typing import Optional, Union import torch from torch import nn from transformers import MixtralConfig 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.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 ( DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding) from vllm.model_executor.model_loader.weight_utils import ( default_weight_loader, maybe_remap_kv_scale_name) from vllm.model_executor.sampling_metadata import SamplingMetadata from vllm.sequence import IntermediateTensors from vllm import _custom_ops as ops from vllm.model_executor.utils import pad_weight, gemm_bank_conf from .interfaces import SupportsLoRA, SupportsPP from .utils import (AutoWeightsLoader, 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: Optional[torch.dtype] = None, quant_config: Optional[QuantizationConfig] = None, tp_size: Optional[int] = None, dp_size: Optional[int] = None, prefix: str = ""): super().__init__() self.hidden_size = hidden_size # 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") 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, rope_theta: float = 10000, cache_config: Optional[CacheConfig] = None, quant_config: Optional[QuantizationConfig] = 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.rope_theta = rope_theta 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, rotary_dim=self.head_dim, max_position=max_position, 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 MixtralDecoderLayer(nn.Module): def __init__( self, config: MixtralConfig, cache_config: Optional[CacheConfig] = None, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", ) -> None: super().__init__() self.hidden_size = config.hidden_size # Requires transformers > 4.32.0 rope_theta = getattr(config, "rope_theta", 10000) 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, rope_theta=rope_theta, 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") 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: Optional[torch.Tensor], ) -> 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 lora_config = vllm_config.lora_config self.config = config self.quant_config = quant_config lora_vocab = (lora_config.lora_extra_vocab_size * (lora_config.max_loras or 1)) if lora_config else 0 self.vocab_size = config.vocab_size + lora_vocab self.org_vocab_size = config.vocab_size self.embed_tokens = VocabParallelEmbedding( self.vocab_size, config.hidden_size, org_num_embeddings=config.vocab_size, ) 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 ), 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)) self.quant_method = None if quant_config is not None: self.quant_method=quant_config.get_name() self.use_llama_nn = os.environ.get('LLAMA_NN') == '1' 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) residual = None else: assert intermediate_tensors is not None hidden_states = intermediate_tensors["hidden_states"] residual = intermediate_tensors["residual"] for layer in 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 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 for weights, fp8 weight scales, fp8 activation scales # (param_name, weight_name, expert_id, shard_id) expert_params_mapping = FusedMoE.make_expert_params_mapping( ckpt_gate_proj_name="w1", ckpt_down_proj_name="w2", ckpt_up_proj_name="w3", num_experts=self.config.num_local_experts) params_dict = dict(self.named_parameters()) loaded_params: set[str] = set() 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: for mapping in expert_params_mapping: param_name, weight_name, expert_id, shard_id = mapping if weight_name not in name: continue name = name.replace(weight_name, param_name) # Skip layers on other devices. if is_pp_missing_parameter(name, self): continue if ((name.endswith(".bias") or name.endswith("_bias")) and name not in params_dict): continue param = params_dict[name] weight_loader = param.weight_loader weight_loader(param, loaded_weight, name, shard_id=shard_id, expert_id=expert_id) break else: # 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) if self.use_llama_nn and self.quant_method is None: lay_key_words = [ "block_sparse_moe.gate.weight", "self_attn.qkv_proj.weight", "self_attn.o_proj.weight", "lm_head.weight", ] combined_words = "|".join(lay_key_words) for layername in loaded_params: weight = params_dict[layername] os.environ['LM_NN'] = '0' matches = re.findall(combined_words, layername) if matches: _weight = torch.zeros_like(weight.data) ori_shape =_weight.shape ops.trans_w16_gemm(_weight, weight.data, _weight.shape[0], _weight.shape[1]) weight.data.copy_(_weight) weight.data=weight.data.reshape(ori_shape[1],-1) return loaded_params class MixtralForCausalLM(nn.Module, SupportsLoRA, SupportsPP): 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", } embedding_padding_modules = ["lm_head"] def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): super().__init__() config = vllm_config.model_config.hf_config quant_config = vllm_config.quant_config lora_config = vllm_config.lora_config self.config = config self.lora_config = lora_config self.quant_config = quant_config self.model = MixtralModel(vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")) self.unpadded_vocab_size = config.vocab_size if lora_config: self.unpadded_vocab_size += lora_config.lora_extra_vocab_size self.lm_head = ParallelLMHead( self.unpadded_vocab_size, config.hidden_size, org_num_embeddings=config.vocab_size, padding_size=DEFAULT_VOCAB_PADDING_SIZE # We need bigger padding if using lora for kernel # compatibility if not lora_config else lora_config.lora_vocab_padding_size, quant_config=quant_config, ) if self.config.tie_word_embeddings: self.lm_head.weight = self.model.embed_tokens.weight 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]: loader = AutoWeightsLoader(self) return loader.load_weights(weights)