# coding=utf-8 # Adapted from # https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py # Copyright 2024 The ModelBest team. # Copyright 2023 The PygmalionAI 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 MiniCPM3 model compatible with HuggingFace weights.""" from typing import Any, Dict, Optional import torch from torch import nn from aphrodite.attention import Attention, AttentionMetadata from aphrodite.common.config import CacheConfig from aphrodite.distributed import get_tensor_model_parallel_world_size from aphrodite.modeling.layers.layernorm import RMSNorm from aphrodite.modeling.layers.linear import (ColumnParallelLinear, ReplicatedLinear, RowParallelLinear) from aphrodite.modeling.layers.rotary_embedding import get_rope from aphrodite.modeling.models.minicpm import (MiniCPMDecoderLayer, MiniCPMForCausalLM, MiniCPMModel) from aphrodite.quantization.base_config import QuantizationConfig class MiniCPM3Attention(nn.Module): def __init__( self, config, hidden_size: int, num_heads: int, qk_nope_head_dim: int, qk_rope_head_dim: int, v_head_dim: int, q_lora_rank: int, kv_lora_rank: 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 self.qk_nope_head_dim = qk_nope_head_dim self.qk_rope_head_dim = qk_rope_head_dim self.qk_head_dim = qk_nope_head_dim + qk_rope_head_dim self.v_head_dim = v_head_dim self.q_lora_rank = q_lora_rank self.kv_lora_rank = kv_lora_rank self.num_heads = num_heads tp_size = get_tensor_model_parallel_world_size() assert self.num_heads % tp_size == 0 self.num_local_heads = num_heads // tp_size self.scaling = self.qk_head_dim**-0.5 self.rope_theta = rope_theta self.max_position_embeddings = max_position_embeddings self.q_a_proj = ReplicatedLinear(self.hidden_size, self.q_lora_rank, bias=False, quant_config=quant_config) self.q_a_layernorm = RMSNorm(self.q_lora_rank, eps=config.rms_norm_eps) self.q_b_proj = ColumnParallelLinear(q_lora_rank, self.num_heads * self.qk_head_dim, bias=False, quant_config=quant_config) self.kv_a_proj_with_mqa = ReplicatedLinear(self.hidden_size, self.kv_lora_rank + self.qk_rope_head_dim, bias=False, quant_config=quant_config) self.kv_a_layernorm = RMSNorm(self.kv_lora_rank, eps=config.rms_norm_eps) self.kv_b_proj = ColumnParallelLinear( self.kv_lora_rank, self.num_heads * (self.qk_nope_head_dim + self.v_head_dim), bias=False, quant_config=quant_config) # O projection. self.o_proj = RowParallelLinear(self.num_heads * self.v_head_dim, self.hidden_size, bias=False, quant_config=quant_config) self.rotary_emb = get_rope( self.qk_rope_head_dim, rotary_dim=self.qk_rope_head_dim, max_position=max_position_embeddings, base=rope_theta, rope_scaling=rope_scaling, ) self.attn = Attention(self.num_local_heads, self.qk_head_dim, self.scaling, num_kv_heads=self.num_local_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: q, _ = self.q_a_proj(hidden_states) q = self.q_a_layernorm(q) q, _ = self.q_b_proj(q) q = q.view(-1, self.num_local_heads, self.qk_head_dim) _, q_pe = q.split([self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1) latent_cache, _ = self.kv_a_proj_with_mqa(hidden_states) kv_a, _ = latent_cache.split( [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1) latent_cache = latent_cache.unsqueeze(1) kv_a = self.kv_a_layernorm(kv_a.contiguous()) kv, _ = self.kv_b_proj(kv_a) kv = kv.view(-1, self.num_local_heads, self.qk_nope_head_dim + self.v_head_dim) k_nope, v = kv.split([self.qk_nope_head_dim, self.v_head_dim], dim=-1) k_pe = latent_cache[:, :, self.kv_lora_rank:] q_pe, k_pe = self.rotary_emb( positions, q_pe.reshape(-1, self.num_local_heads * self.qk_rope_head_dim), k_pe.reshape(-1, self.qk_rope_head_dim)) q_pe = q_pe.view(-1, self.num_local_heads, self.qk_rope_head_dim) k_pe = k_pe.view(-1, 1, self.qk_rope_head_dim) q[..., self.qk_nope_head_dim:] = q_pe k = torch.empty_like(q) k[..., :self.qk_nope_head_dim] = k_nope k[..., self.qk_nope_head_dim:] = k_pe q = q.reshape(-1, self.num_local_heads * self.qk_head_dim) k = k.view(-1, self.num_local_heads * self.qk_head_dim) v = torch.nn.functional.pad( v, [0, self.qk_head_dim - self.v_head_dim], value=0).view(-1, self.num_local_heads * self.qk_head_dim) attn_output = self.attn(q, k, v, kv_cache, attn_metadata) attn_output = attn_output.view( -1, self.num_local_heads, self.qk_head_dim)[..., :self.v_head_dim].reshape( -1, self.num_local_heads * self.v_head_dim) output, _ = self.o_proj(attn_output) return output class MiniCPM3DecoderLayer(MiniCPMDecoderLayer): def _init_attn_block(self): self.input_layernorm = RMSNorm(self.config.hidden_size, eps=self.config.rms_norm_eps) self.self_attn = MiniCPM3Attention( config=self.config, hidden_size=self.hidden_size, num_heads=self.config.num_attention_heads, qk_nope_head_dim=self.config.qk_nope_head_dim, qk_rope_head_dim=self.config.qk_rope_head_dim, v_head_dim=self.config.v_head_dim, q_lora_rank=self.config.q_lora_rank, kv_lora_rank=self.config.kv_lora_rank, rope_theta=self.rope_theta, rope_scaling=self.rope_scaling, max_position_embeddings=self.max_position_embeddings, cache_config=self.cache_config, quant_config=self.quant_config, ) class MiniCPM3Model(MiniCPMModel): def _init_layers(self): self.layers = nn.ModuleList([ MiniCPM3DecoderLayer(self.config, self.cache_config, self.quant_config) for _ in range(self.config.num_hidden_layers) ]) class MiniCPM3ForCausalLM(MiniCPMForCausalLM): def _init_model(self): self.model = MiniCPM3Model(config=self.config, cache_config=self.cache_config, quant_config=self.quant_config, lora_config=self.lora_config)