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model: add support for MiniCPM-3 (#1044)
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,216 @@ | ||
| # 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 | ||
|
|
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|
|
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| 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) | ||
|
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| k_pe = latent_cache[:, :, self.kv_lora_rank:] | ||
|
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| 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) | ||
|
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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 | ||
| k[..., self.qk_nope_head_dim:] = k_pe | ||
|
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| 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) | ||
|
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| 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) | ||
|
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| output, _ = self.o_proj(attn_output) | ||
| return output | ||
|
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|
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| class MiniCPM3DecoderLayer(MiniCPMDecoderLayer): | ||
|
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| 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, | ||
| ) | ||
|
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| class MiniCPM3Model(MiniCPMModel): | ||
|
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| 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) | ||
| ]) | ||
|
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|
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| class MiniCPM3ForCausalLM(MiniCPMForCausalLM): | ||
|
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| 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) |