# coding=utf-8 # Adapted from # https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py # Copyright 2024 The Qwen team. # 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 Qwen2MoE model compatible with HuggingFace weights.""" from typing import Any, Dict, Iterable, List, Optional, Tuple import torch import torch.nn.functional as F from torch import nn from transformers import PretrainedConfig from aphrodite.attention import Attention, AttentionMetadata from aphrodite.common.config import CacheConfig from aphrodite.common.sequence import IntermediateTensors, SamplerOutput from aphrodite.common.utils import print_warning_once from aphrodite.distributed import (get_pp_group, get_tensor_model_parallel_world_size, tensor_model_parallel_all_reduce) from aphrodite.modeling.layers.activation import SiluAndMul from aphrodite.modeling.layers.fused_moe import FusedMoE from aphrodite.modeling.layers.layernorm import RMSNorm from aphrodite.modeling.layers.linear import (MergedColumnParallelLinear, QKVParallelLinear, ReplicatedLinear, RowParallelLinear) from aphrodite.modeling.layers.logits_processor import LogitsProcessor from aphrodite.modeling.layers.rotary_embedding import get_rope from aphrodite.modeling.layers.sampler import Sampler from aphrodite.modeling.layers.vocab_parallel_embedding import ( ParallelLMHead, VocabParallelEmbedding) from aphrodite.modeling.model_loader.weight_utils import default_weight_loader from aphrodite.modeling.sampling_metadata import SamplingMetadata from aphrodite.quantization.base_config import QuantizationConfig from .utils import is_pp_missing_parameter, make_layers class Qwen2MoeMLP(nn.Module): def __init__( self, hidden_size: int, intermediate_size: int, hidden_act: str, quant_config: Optional[QuantizationConfig] = None, reduce_results: bool = True, ) -> None: super().__init__() self.gate_up_proj = MergedColumnParallelLinear( hidden_size, [intermediate_size] * 2, bias=False, quant_config=quant_config) self.down_proj = RowParallelLinear(intermediate_size, hidden_size, bias=False, quant_config=quant_config, reduce_results=reduce_results) if hidden_act != "silu": raise ValueError(f"Unsupported activation: {hidden_act}. " "Only silu is supported for now.") self.act_fn = SiluAndMul() def forward(self, x): gate_up, _ = self.gate_up_proj(x) x = self.act_fn(gate_up) x, _ = self.down_proj(x) return x class Qwen2MoeSparseMoeBlock(nn.Module): def __init__( self, config: PretrainedConfig, quant_config: Optional[QuantizationConfig] = None, ): super().__init__() self.tp_size = get_tensor_model_parallel_world_size() if self.tp_size > config.num_experts: raise ValueError( f"Tensor parallel size {self.tp_size} is greater than " f"the number of experts {config.num_experts}.") self.experts = FusedMoE(num_experts=config.num_experts, top_k=config.num_experts_per_tok, hidden_size=config.hidden_size, intermediate_size=config.moe_intermediate_size, reduce_results=False, renormalize=config.norm_topk_prob, quant_config=quant_config) self.gate = ReplicatedLinear(config.hidden_size, config.num_experts, bias=False, quant_config=None) if config.shared_expert_intermediate_size > 0: self.shared_expert = Qwen2MoeMLP( hidden_size=config.hidden_size, intermediate_size=config.shared_expert_intermediate_size, hidden_act=config.hidden_act, quant_config=quant_config, reduce_results=False, ) else: self.shared_expert = None self.shared_expert_gate = torch.nn.Linear(config.hidden_size, 1, bias=False) 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_dim = hidden_states.shape[-1] hidden_states = hidden_states.view(-1, hidden_dim) shared_output = None if self.shared_expert is not None: shared_output = self.shared_expert(hidden_states) if self.shared_expert_gate is not None: shared_output = F.sigmoid( self.shared_expert_gate(hidden_states)) * shared_output # router_logits: (num_tokens, n_experts) router_logits, _ = self.gate(hidden_states) final_hidden_states = self.experts(hidden_states=hidden_states, router_logits=router_logits) if shared_output is not None: final_hidden_states = final_hidden_states + shared_output if self.tp_size > 1: final_hidden_states = tensor_model_parallel_all_reduce( final_hidden_states) return final_hidden_states.view(orig_shape) class Qwen2MoeAttention(nn.Module): def __init__( self, hidden_size: int, num_heads: int, num_kv_heads: int, rope_theta: float = 10000, rope_scaling: Optional[Dict[str, Any]] = None, max_position_embeddings: int = 8192, cache_config: Optional[CacheConfig] = None, quant_config: Optional[QuantizationConfig] = None, ) -> 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) self.head_dim = 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.max_position_embeddings = max_position_embeddings self.qkv_proj = QKVParallelLinear( hidden_size, self.head_dim, self.total_num_heads, self.total_num_kv_heads, bias=True, quant_config=quant_config, ) self.o_proj = RowParallelLinear( self.total_num_heads * self.head_dim, hidden_size, bias=False, quant_config=quant_config, ) self.rotary_emb = get_rope( self.head_dim, rotary_dim=self.head_dim, max_position=max_position_embeddings, base=rope_theta, rope_scaling=rope_scaling, ) 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) def forward( self, positions: torch.Tensor, hidden_states: torch.Tensor, kv_cache: torch.Tensor, attn_metadata: AttentionMetadata, ) -> 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, kv_cache, attn_metadata) output, _ = self.o_proj(attn_output) return output class Qwen2MoeDecoderLayer(nn.Module): def __init__( self, config: PretrainedConfig, layer_idx: int, cache_config: Optional[CacheConfig] = None, quant_config: Optional[QuantizationConfig] = None, ) -> None: super().__init__() self.hidden_size = config.hidden_size rope_theta = getattr(config, "rope_theta", 10000) rope_scaling = getattr(config, "rope_scaling", None) max_position_embeddings = getattr(config, "max_position_embeddings", 8192) self.self_attn = Qwen2MoeAttention( hidden_size=self.hidden_size, num_heads=config.num_attention_heads, num_kv_heads=config.num_key_value_heads, rope_theta=rope_theta, rope_scaling=rope_scaling, max_position_embeddings=max_position_embeddings, cache_config=cache_config, quant_config=quant_config, ) # Note: Qwen/Qwen2-57B-A14B-Instruct does not have # `mlp_only_layers` in the config. mlp_only_layers = ([] if not hasattr(config, "mlp_only_layers") else config.mlp_only_layers) if (layer_idx not in mlp_only_layers) and ( config.num_experts > 0 and (layer_idx + 1) % config.decoder_sparse_step == 0): self.mlp = Qwen2MoeSparseMoeBlock(config=config, quant_config=quant_config) else: self.mlp = Qwen2MoeMLP( hidden_size=config.hidden_size, intermediate_size=config.intermediate_size, hidden_act=config.hidden_act, quant_config=quant_config, ) 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, kv_cache: torch.Tensor, attn_metadata: AttentionMetadata, 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, kv_cache=kv_cache, attn_metadata=attn_metadata, ) # Fully Connected hidden_states, residual = self.post_attention_layernorm( hidden_states, residual) hidden_states = self.mlp(hidden_states) return hidden_states, residual class Qwen2MoeModel(nn.Module): def __init__( self, config: PretrainedConfig, cache_config: Optional[CacheConfig] = None, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", ) -> None: super().__init__() self.padding_idx = config.pad_token_id self.vocab_size = config.vocab_size self.embed_tokens = VocabParallelEmbedding( config.vocab_size, config.hidden_size, ) self.start_layer, self.end_layer, self.layers = make_layers( config.num_hidden_layers, lambda prefix: Qwen2MoeDecoderLayer(config=config, layer_idx=int( prefix.split(".")[-1]), cache_config=cache_config, quant_config=quant_config), prefix=f"{prefix}.layers", ) self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) def forward( self, input_ids: torch.Tensor, positions: torch.Tensor, kv_caches: List[torch.Tensor], attn_metadata: AttentionMetadata, intermediate_tensors: Optional[IntermediateTensors] = None, ) -> torch.Tensor: if get_pp_group().is_first_rank: hidden_states = self.embed_tokens(input_ids) residual = None else: assert intermediate_tensors is not None hidden_states = intermediate_tensors["hidden_states"] residual = intermediate_tensors["residual"] for i in range(self.start_layer, self.end_layer): layer = self.layers[i] hidden_states, residual = layer(positions, hidden_states, kv_caches[i - self.start_layer], attn_metadata, 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 class Qwen2MoeForCausalLM(nn.Module): fall_back_to_pt_during_load = False def __init__( self, config: PretrainedConfig, cache_config: Optional[CacheConfig] = None, quant_config: Optional[QuantizationConfig] = None, ) -> None: super().__init__() self.config = config self.quant_config = quant_config self.model = Qwen2MoeModel(config, cache_config, quant_config) self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size, quant_config=quant_config) self.logits_processor = LogitsProcessor(config.vocab_size) self.sampler = Sampler() def forward( self, input_ids: torch.Tensor, positions: torch.Tensor, kv_caches: List[torch.Tensor], attn_metadata: AttentionMetadata, intermediate_tensors: Optional[IntermediateTensors] = None, ) -> torch.Tensor: hidden_states = self.model(input_ids, positions, kv_caches, attn_metadata, intermediate_tensors) return hidden_states def compute_logits(self, hidden_states: torch.Tensor, sampling_metadata: SamplingMetadata) -> torch.Tensor: logits = self.logits_processor(self.lm_head, hidden_states, sampling_metadata) return logits def make_empty_intermediate_tensors( self, batch_size: int, dtype: torch.dtype, device: torch.device) -> IntermediateTensors: return IntermediateTensors({ "hidden_states": torch.zeros((batch_size, self.config.hidden_size), dtype=dtype, device=device), "residual": torch.zeros((batch_size, self.config.hidden_size), dtype=dtype, device=device), }) def sample( self, logits: Optional[torch.Tensor], sampling_metadata: SamplingMetadata, ) -> Optional[SamplerOutput]: next_tokens = self.sampler(logits, sampling_metadata) return next_tokens def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]): stacked_params_mapping = [ # (param_name, shard_name, shard_id) ("qkv_proj", "q_proj", "q"), ("qkv_proj", "k_proj", "k"), ("qkv_proj", "v_proj", "v"), ("gate_up_proj", "gate_proj", 0), ("gate_up_proj", "up_proj", 1), ] # 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="gate_proj", ckpt_down_proj_name="down_proj", ckpt_up_proj_name="up_proj", num_experts=self.config.num_experts) params_dict = dict(self.named_parameters()) for name, loaded_weight in weights: if "rotary_emb.inv_freq" in name: continue for (param_name, weight_name, shard_id) in stacked_params_mapping: # Skip non-stacked layers and experts (experts handled below). if weight_name not in name: continue # We have mlp.experts[0].gate_proj in the checkpoint. # Since we handle the experts below in expert_params_mapping, # we need to skip here BEFORE we update the name, otherwise # name will be updated to mlp.experts[0].gate_up_proj, which # will then be updated below in expert_params_mapping # for mlp.experts[0].gate_gate_up_proj, which breaks load. if "mlp.experts" 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 # Skip layers on other devices. if is_pp_missing_parameter(name, self): continue if name not in params_dict: 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 param = params_dict[name] weight_loader = param.weight_loader weight_loader(param, loaded_weight, weight_name, shard_id=shard_id, expert_id=expert_id) break else: # Skip loading extra bias for GPTQ models. if 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. if name.endswith("kv_scale"): remapped_kv_scale_name = name.replace( ".kv_scale", ".attn.kv_scale") if remapped_kv_scale_name not in params_dict: print_warning_once( "Found kv scale in the checkpoint " f"(e.g. {name}), but not found the expected " f"name in the model " f"(e.g. {remapped_kv_scale_name}). " "kv-scale is not loaded.") continue else: name = remapped_kv_scale_name param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, loaded_weight)