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235 lines
8.4 KiB
Python
235 lines
8.4 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# Adapted from
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# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
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# Copyright 2024 The ModelBest team.
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# Copyright 2023 The vLLM team.
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# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
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#
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# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
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# and OPT implementations in this library. It has been modified from its
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# original forms to accommodate minor architectural differences compared
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# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Inference-only MiniCPM3 model compatible with HuggingFace weights."""
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import torch
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from torch import nn
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from transformers import PretrainedConfig
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from vllm.attention import Attention
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from vllm.config import CacheConfig, VllmConfig
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from vllm.distributed import get_tensor_model_parallel_world_size
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.linear import (
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ColumnParallelLinear,
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ReplicatedLinear,
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RowParallelLinear,
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)
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.layers.rotary_embedding import get_rope
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from vllm.model_executor.models.minicpm import (
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MiniCPMDecoderLayer,
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MiniCPMForCausalLM,
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MiniCPMModel,
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)
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from .utils import make_layers
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class MiniCPM3Attention(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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hidden_size: int,
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num_heads: int,
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qk_nope_head_dim: int,
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qk_rope_head_dim: int,
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v_head_dim: int,
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q_lora_rank: int,
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kv_lora_rank: int,
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max_position_embeddings: int = 8192,
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cache_config: CacheConfig | None = None,
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quant_config: QuantizationConfig | None = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.hidden_size = hidden_size
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self.qk_nope_head_dim = qk_nope_head_dim
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self.qk_rope_head_dim = qk_rope_head_dim
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self.qk_head_dim = qk_nope_head_dim + qk_rope_head_dim
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self.v_head_dim = v_head_dim
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self.q_lora_rank = q_lora_rank
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self.kv_lora_rank = kv_lora_rank
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self.num_heads = num_heads
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tp_size = get_tensor_model_parallel_world_size()
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assert self.num_heads % tp_size == 0
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self.num_local_heads = num_heads // tp_size
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self.scaling = self.qk_head_dim**-0.5
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self.max_position_embeddings = max_position_embeddings
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self.q_a_proj = ReplicatedLinear(
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self.hidden_size, self.q_lora_rank, bias=False, quant_config=quant_config
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)
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self.q_a_layernorm = RMSNorm(self.q_lora_rank, eps=config.rms_norm_eps)
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self.q_b_proj = ColumnParallelLinear(
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q_lora_rank,
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self.num_heads * self.qk_head_dim,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.q_b_proj",
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)
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self.kv_a_proj_with_mqa = ReplicatedLinear(
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self.hidden_size,
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self.kv_lora_rank + self.qk_rope_head_dim,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.kv_a_proj_with_mqa",
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)
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self.kv_a_layernorm = RMSNorm(self.kv_lora_rank, eps=config.rms_norm_eps)
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self.kv_b_proj = ColumnParallelLinear(
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self.kv_lora_rank,
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self.num_heads * (self.qk_nope_head_dim + self.v_head_dim),
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.kv_b_proj",
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)
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# O projection.
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self.o_proj = RowParallelLinear(
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self.num_heads * self.v_head_dim,
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self.hidden_size,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.o_proj",
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)
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self.rotary_emb = get_rope(
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self.qk_rope_head_dim,
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rotary_dim=self.qk_rope_head_dim,
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max_position=max_position_embeddings,
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rope_parameters=config.rope_parameters,
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)
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self.attn = Attention(
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self.num_local_heads,
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self.qk_head_dim,
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self.scaling,
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num_kv_heads=self.num_local_heads,
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=f"{prefix}.attn",
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)
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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) -> torch.Tensor:
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q, _ = self.q_a_proj(hidden_states)
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q = self.q_a_layernorm(q)
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q, _ = self.q_b_proj(q)
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q = q.view(-1, self.num_local_heads, self.qk_head_dim)
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_, q_pe = q.split([self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)
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latent_cache, _ = self.kv_a_proj_with_mqa(hidden_states)
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kv_a, _ = latent_cache.split([self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
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latent_cache = latent_cache.unsqueeze(1)
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kv_a = self.kv_a_layernorm(kv_a.contiguous())
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kv, _ = self.kv_b_proj(kv_a)
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kv = kv.view(-1, self.num_local_heads, self.qk_nope_head_dim + self.v_head_dim)
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k_nope, v = kv.split([self.qk_nope_head_dim, self.v_head_dim], dim=-1)
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k_pe = latent_cache[:, :, self.kv_lora_rank :]
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q_pe, k_pe = self.rotary_emb(
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positions,
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q_pe.reshape(-1, self.num_local_heads * self.qk_rope_head_dim),
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k_pe.reshape(-1, self.qk_rope_head_dim),
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)
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q_pe = q_pe.view(-1, self.num_local_heads, self.qk_rope_head_dim)
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k_pe = k_pe.view(-1, 1, self.qk_rope_head_dim)
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q[..., self.qk_nope_head_dim :] = q_pe
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k = torch.empty_like(q)
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k[..., : self.qk_nope_head_dim] = k_nope
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k[..., self.qk_nope_head_dim :] = k_pe
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q = q.reshape(-1, self.num_local_heads * self.qk_head_dim)
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k = k.view(-1, self.num_local_heads * self.qk_head_dim)
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v = torch.nn.functional.pad(
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v, [0, self.qk_head_dim - self.v_head_dim], value=0
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).view(-1, self.num_local_heads * self.qk_head_dim)
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attn_output = self.attn(q, k, v)
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attn_output = attn_output.view(-1, self.num_local_heads, self.qk_head_dim)[
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..., : self.v_head_dim
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].reshape(-1, self.num_local_heads * self.v_head_dim)
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output, _ = self.o_proj(attn_output)
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return output
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class MiniCPM3DecoderLayer(MiniCPMDecoderLayer):
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def _init_attn_block(self):
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self.input_layernorm = RMSNorm(
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self.config.hidden_size, eps=self.config.rms_norm_eps
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)
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self.self_attn = MiniCPM3Attention(
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config=self.config,
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hidden_size=self.hidden_size,
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num_heads=self.config.num_attention_heads,
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qk_nope_head_dim=self.config.qk_nope_head_dim,
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qk_rope_head_dim=self.config.qk_rope_head_dim,
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v_head_dim=self.config.v_head_dim,
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q_lora_rank=self.config.q_lora_rank,
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kv_lora_rank=self.config.kv_lora_rank,
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max_position_embeddings=self.max_position_embeddings,
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cache_config=self.cache_config,
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quant_config=self.quant_config,
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prefix=f"{self.prefix}.self_attn",
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)
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class MiniCPM3Model(MiniCPMModel):
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def _init_layers(
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self,
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prefix: str,
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config: PretrainedConfig,
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cache_config: CacheConfig | None,
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quant_config: QuantizationConfig | None,
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):
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self.start_layer, self.end_layer, self.layers = make_layers(
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config.num_hidden_layers,
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lambda prefix: MiniCPM3DecoderLayer(
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config, cache_config, quant_config, prefix=prefix
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),
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prefix=f"{prefix}.layers",
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)
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class MiniCPM3ForCausalLM(MiniCPMForCausalLM):
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packed_modules_mapping = {
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"gate_up_proj": [
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"gate_proj",
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"up_proj",
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],
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}
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def _init_model(self, *, vllm_config: VllmConfig, prefix: str = ""):
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return MiniCPM3Model(vllm_config=vllm_config, prefix=prefix)
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