Russell Bryant
e489ad7a21
- **Add SPDX license headers to python source files** - **Check for SPDX headers using pre-commit** commit 9d7ef44c3cfb72ca4c32e1c677d99259d10d4745 Author: Russell Bryant <rbryant@redhat.com> Date: Fri Jan 31 14:18:24 2025 -0500 Add SPDX license headers to python source files This commit adds SPDX license headers to python source files as recommended to the project by the Linux Foundation. These headers provide a concise way that is both human and machine readable for communicating license information for each source file. It helps avoid any ambiguity about the license of the code and can also be easily used by tools to help manage license compliance. The Linux Foundation runs license scans against the codebase to help ensure we are in compliance with the licenses of the code we use, including dependencies. Having these headers in place helps that tool do its job. More information can be found on the SPDX site: - https://spdx.dev/learn/handling-license-info/ Signed-off-by: Russell Bryant <rbryant@redhat.com> commit 5a1cf1cb3b80759131c73f6a9dddebccac039dea Author: Russell Bryant <rbryant@redhat.com> Date: Fri Jan 31 14:36:32 2025 -0500 Check for SPDX headers using pre-commit Signed-off-by: Russell Bryant <rbryant@redhat.com> --------- Signed-off-by: Russell Bryant <rbryant@redhat.com>
248 lines
9.6 KiB
Python
248 lines
9.6 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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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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from typing import Any, Dict, Optional
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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, AttentionMetadata
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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 (ColumnParallelLinear,
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ReplicatedLinear,
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RowParallelLinear)
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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 (MiniCPMDecoderLayer,
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MiniCPMForCausalLM,
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MiniCPMModel)
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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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rope_theta: float = 10000,
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rope_scaling: Optional[Dict[str, Any]] = None,
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max_position_embeddings: int = 8192,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = 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.rope_theta = rope_theta
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self.max_position_embeddings = max_position_embeddings
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self.q_a_proj = ReplicatedLinear(self.hidden_size,
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self.q_lora_rank,
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bias=False,
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quant_config=quant_config)
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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(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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self.kv_a_proj_with_mqa = ReplicatedLinear(self.hidden_size,
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self.kv_lora_rank +
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self.qk_rope_head_dim,
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bias=False,
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quant_config=quant_config)
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self.kv_a_layernorm = RMSNorm(self.kv_lora_rank,
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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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# O projection.
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self.o_proj = RowParallelLinear(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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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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base=rope_theta,
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rope_scaling=rope_scaling,
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)
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self.attn = Attention(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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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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kv_cache: torch.Tensor,
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attn_metadata: AttentionMetadata,
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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],
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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(
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[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,
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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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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],
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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)
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attn_output = attn_output.view(
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-1, self.num_local_heads,
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self.qk_head_dim)[..., :self.v_head_dim].reshape(
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-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(self.config.hidden_size,
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eps=self.config.rms_norm_eps)
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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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rope_theta=self.rope_theta,
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rope_scaling=self.rope_scaling,
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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: Optional[CacheConfig],
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quant_config: Optional[QuantizationConfig],
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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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prefix=f"{prefix}.layers")
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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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# LoRA specific attributes
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supported_lora_modules = [
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"kv_a_proj_with_mqa",
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"q_a_proj",
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"q_b_proj",
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"kv_b_proj",
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"o_proj",
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"gate_up_proj",
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"down_proj",
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"embed_tokens",
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"lm_head",
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]
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# `embedding_modules` and `embedding_padding_modules`
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# are inherited from MiniCPMForCausalLM
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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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