2289 lines
106 KiB
Python
2289 lines
106 KiB
Python
# coding=utf-8
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# Copyright 2025 The OpenBMB Team. All rights reserved.
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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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""" PyTorch MiniCPM model."""
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import math
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import warnings
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from typing import Any, List, Optional, Tuple, Union, Dict
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from einops import rearrange, einsum
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import numpy as np
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import torch
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import torch.nn.functional as F
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import torch.utils.checkpoint
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from torch import nn, tensor
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
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from transformers.activations import ACT2FN
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from transformers.cache_utils import Cache,DynamicCache
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from transformers.modeling_attn_mask_utils import (
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AttentionMaskConverter,
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_prepare_4d_attention_mask,
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_prepare_4d_causal_attention_mask,
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_prepare_4d_causal_attention_mask_for_sdpa,
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)
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from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
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from transformers.modeling_utils import PreTrainedModel
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from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS, is_torch_greater_or_equal_than_1_13
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from transformers.utils import (
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add_start_docstrings,
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add_start_docstrings_to_model_forward,
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is_flash_attn_2_available,
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is_flash_attn_greater_or_equal_2_10,
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logging,
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replace_return_docstrings,
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)
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from transformers.utils.import_utils import is_torch_fx_available
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from .configuration_minicpm import MiniCPMConfig
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import re
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try:
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from flash_attn import flash_attn_func, flash_attn_varlen_func
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from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa
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except:
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pass
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#! nsa
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#! debug token
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debug_token=3
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token_now =0
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save_no_cache =False
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save_cache=False
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from functools import lru_cache
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from .compressed_attention import compressed_attention
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from block_sparse_attn import (
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block_sparse_attn_func,block_sparse_attn_kvcache_func
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)
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def prepare_fa2_from_position_ids(query, key, value, position_ids):
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"""
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This function returns necessary arguments to call `flash_attn_varlen_func`.
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All three query, key, value states will be flattened.
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Cumulative lengths of each examples in the batch will be extracted from position_ids.
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NOTE: ideally cumulative lengths should be prepared at the data collator stage
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Arguments:
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query (`torch.Tensor`):
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Query state with padding. Shape: (batch_size, query_length, num_heads, head_dim).
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key (`torch.Tensor`):
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Key state with padding. Shape: (batch_size, kv_seq_len, num_key_value_heads, head_dim).
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value (`torch.Tensor`):
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Value state with padding. Shape: (batch_size, kv_seq_len, num_key_value_heads, head_dim).
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position_ids (`torch.Tensor`):
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Boolean or int tensor of shape (batch_size, sequence_length), 1 means valid and 0 means not valid.
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Return:
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query (`torch.Tensor`):
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Query state without padding. Shape: (total_target_length, num_heads, head_dim).
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key (`torch.Tensor`):
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Key state with padding. Shape: (total_source_length, num_key_value_heads, head_dim).
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value (`torch.Tensor`):
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Value state with padding. Shape: (total_source_length, num_key_value_heads, head_dim).
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indices_q (`torch.Tensor`):
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The indices of non-masked tokens from the flattened input target sequence.
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(cu_seqlens_q, cu_seqlens_k) (`Tuple[int]`):
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The cumulative sequence lengths for the target (query) and source (key, value), used to index into ragged (unpadded) tensors. `cu_seqlens` shape is (batch_size + 1,).
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(max_seqlen_in_batch_q, max_seqlen_in_batch_k) (`Tuple[int]`):
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Maximum sequence length in batch (`max_seqlen_in_batch_q` for the target sequence i.e. query, `max_seqlen_in_batch_k` for the source sequence i.e. key/value).
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"""
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query = query.view(-1, query.size(-2), query.size(-1))
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key = key.contiguous().view(-1, key.size(-2), key.size(-1))
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value = value.contiguous().view(-1, value.size(-2), value.size(-1))
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position_ids = position_ids.flatten()
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indices_q = torch.arange(position_ids.size(0), device=position_ids.device, dtype=torch.int32)
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cu_seq_lens = torch.cat(
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(
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indices_q[position_ids == 0],
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torch.tensor(position_ids.size(), device=position_ids.device, dtype=torch.int32),
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)
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)
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max_length = position_ids.max() + 1
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return (query, key, value, indices_q, (cu_seq_lens, cu_seq_lens), (max_length, max_length))
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def convert_topk_to_base_blockmask(
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topk_idx: torch.Tensor,
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max_seqlen_k: int,
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block_size: int,
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device: str = "cuda"
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) -> torch.Tensor:
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"""
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将topk索引转换为块稀疏注意力掩码，仅处理-1的情况
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Args:
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topk_idx: 形状 [num_heads, total_seqlen, k] 的块索引张量
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cu_seqlens_q: 累积序列长度（用于计算总长度）
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max_seqlen_k: 最大键序列长度（用于计算键块数量）
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block_size: block_size
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device: 输出设备
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Returns:
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mask: 布尔掩码，形状 [num_heads, total_seqlen, k_blocks]
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"""
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# 计算键块数量
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k_blocks = (max_seqlen_k + block_size - 1) // block_size # 向上取整
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num_heads, total_seqlen, k = topk_idx.shape
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# 初始化全False掩码
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mask = torch.zeros(num_heads, total_seqlen, k_blocks,
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dtype=torch.bool, device=device)
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# 过滤掉 -1，确保索引合法
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valid_idx = topk_idx[topk_idx != -1]
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# 生成索引掩码
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batch_idx, seq_idx, _ = torch.where(topk_idx != -1) # 找到非-1索引的 (head, seq) 位置
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mask[batch_idx, seq_idx, valid_idx] = True # 设置对应位置为 True
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return mask
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@lru_cache(maxsize=16)
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def calc_chunks_with_stride(cu_seqlen, moba_chunk_size, kernel_stride):
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"""
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计算需要 MOBA 注意力的 chunks，支持 stride。
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返回:
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- filtered_indices: 用于直接索引 kv 的索引。
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- cu_seqlens_compressed: 压缩后的累积序列长度。
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"""
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# 1. 计算每个序列的长度
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batch_sizes = cu_seqlen[1:] - cu_seqlen[:-1]
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# 2. 计算每个序列的 chunk 起始位置 (考虑 stride)
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max_seq_len = torch.max(batch_sizes)
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max_num_chunks_per_seq = (max_seq_len - moba_chunk_size) // kernel_stride + 1 # 修正公式
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chunk_start_offsets = torch.arange(0, max_num_chunks_per_seq * kernel_stride, kernel_stride, device=cu_seqlen.device)
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seq_starts = cu_seqlen[:-1]
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chunk_start_in_seq = seq_starts[:, None] + chunk_start_offsets[None, :] # [batch_size, max_num_chunks_per_seq]
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# 3. 过滤掉超出序列长度的 chunk 和非完整大小的 chunk
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chunk_end_in_seq = chunk_start_in_seq + moba_chunk_size
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valid_chunk_mask = (chunk_end_in_seq <= (seq_starts[:, None] + batch_sizes[:, None])) # 完整 chunk
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# 4. 根据 valid_chunk_mask 过滤有效的 chunk 起始位置
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valid_chunk_starts = chunk_start_in_seq[valid_chunk_mask] # [num_valid_chunks]
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del chunk_start_in_seq
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# 5. 生成 filtered_indices
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chunk_indices = torch.arange(
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0, moba_chunk_size, device=cu_seqlen.device
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)[None, :] # [1, moba_chunk_size]
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filtered_indices = valid_chunk_starts[:, None] + chunk_indices # [num_valid_chunks, moba_chunk_size]
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filtered_indices = filtered_indices.view(-1) # 展平为一维索引
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# 6. 计算压缩后的累积序列长度
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num_filtered_chunks_per_batch = valid_chunk_mask.sum(dim=1) # 每个 batch 的有效 chunk 数量
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cu_seqlens_compressed = torch.zeros(
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len(cu_seqlen), dtype=torch.int32, device=cu_seqlen.device
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)
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cu_seqlens_compressed[1:] = num_filtered_chunks_per_batch.cumsum(dim=0)
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del num_filtered_chunks_per_batch, chunk_start_offsets, seq_starts, chunk_end_in_seq, valid_chunk_mask, chunk_indices
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return filtered_indices, cu_seqlens_compressed
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class CompressKV(torch.nn.Module):
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def __init__(self, head_num_k, head_dim, kernel_size, compress_func, add_pos_embed=False, kernel_stride=16):
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"""
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压缩KV模块，支持多种压缩方式
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Args:
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head_num_k: KV头的数量
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head_dim: 每个头的维度
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kernel_size: 每个chunk的大小
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compress_func: 压缩方式（如meanpool, mlp, conv1d等）
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add_pos_embed: 是否添加位置编码
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stride: 分块时的步长
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"""
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super().__init__()
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self.kernel_size = kernel_size
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self.compress_func = compress_func
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self.head_num_k = head_num_k
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self.head_dim = head_dim
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self.kernel_stride = kernel_stride # 新增stride参数
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# 定义不同的压缩方式
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if compress_func == 'mlp' or compress_func == 'mlp+residual':
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self.kv_compress = nn.Sequential(
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nn.Linear(kernel_size * 2, kernel_size * 4),
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nn.ReLU(),
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nn.Linear(kernel_size * 4, 2)
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)
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elif compress_func == 'conv1d':
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self.k_conv = nn.Conv1d(in_channels=self.head_dim, out_channels=self.head_dim, kernel_size=kernel_size)
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self.v_conv = nn.Conv1d(in_channels=self.head_dim, out_channels=self.head_dim, kernel_size=kernel_size)
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elif compress_func == 'weighted_sum':
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self.weight_net_v = nn.Linear(self.head_dim, 1)
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self.weight_net_k = nn.Linear(self.head_dim, 1)
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elif compress_func == 'weighted_sum+proj':
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self.weight_net_v = nn.Linear(self.head_dim, 1)
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self.weight_net_k = nn.Linear(self.head_dim, 1)
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self.k_proj = nn.Linear(self.head_dim, self.head_dim)
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self.v_proj = nn.Linear(self.head_dim, self.head_dim)
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if add_pos_embed:
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# 修改位置编码层：为每个头创建独立的位置编码
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self.pos_embed = nn.Embedding(
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kernel_size,
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head_num_k * head_dim # 维度扩展为 [kernel_size, num_heads * head_dim]
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)
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else:
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self.pos_embed = None
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def forward(self, kv: torch.Tensor, cu_seqlens):
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"""
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前向传播，压缩KV
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Args:
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kv: 输入的KV张量
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cu_seqlens: 累积序列长度
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Returns:
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compress_k: 压缩后的K
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compress_v: 压缩后的V
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cu_seqlens_compressed: 压缩后的累积序列长度
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"""
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# 计算chunk相关信息，支持stride
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filtered_kv_indices, cu_seqlens_compressed = calc_chunks_with_stride(
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cu_seqlens, self.kernel_size, self.kernel_stride
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)
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# 提取过滤后的kv
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filtered_kv = kv.index_select(0, filtered_kv_indices.view(-1))
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# 分块
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filtered_kv = filtered_kv.view( filtered_kv.shape[0]// self.kernel_size, self.kernel_size, 2, self.head_num_k, self.head_dim) #[l, block_size,2,h,d]
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if self.pos_embed is not None:
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positions = torch.arange(self.kernel_size, device=kv.device)
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pos_emb = self.pos_embed(positions) # [kernel_size, num_heads * head_dim]
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# 重塑形状以匹配多头结构
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pos_emb = pos_emb.view(
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self.kernel_size,
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self.head_num_k, # 使用实际头数参数（需在__init__中保存）
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self.head_dim
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) # [kernel_size, num_heads, head_dim]
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# 添加维度用于广播
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pos_emb = pos_emb.reshape(1,self.kernel_size,1, self.head_num_k, self.head_dim) # [1, block_size, 1, num_heads, head_dim]
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filtered_kv = filtered_kv + pos_emb
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if self.compress_func == "meanpool":
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compressed_kv = filtered_kv.mean(dim=1)
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compress_k = compressed_kv[:, 0, :, :]#.reshape(-1, self.head_num_k, self.head_dim)
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compress_v = compressed_kv[:, 1, :, :]#.reshape(-1, self.head_num_k, self.head_dim)
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elif self.compress_func == "mlp":
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filtered_kv = filtered_kv.permute(0, 3,4,2, 1).reshape(filtered_kv.shape[0], self.head_num_k, self.head_dim,-1)
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compressed_kv = self.kv_compress(filtered_kv)
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compress_k = compressed_kv[:, :, :, 0]#.reshape(-1, self.head_num_k, self.head_dim)
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compress_v = compressed_kv[:, :, :, 1]#.reshape(-1, self.head_num_k, self.head_dim)
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elif self.compress_func == "mlp+residual":
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mean_kv = filtered_kv.mean(dim=1)
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mlp_kv = self.kv_compress(filtered_kv.permute(0, 3,4,2, 1).reshape(filtered_kv.shape[0], self.head_num_k, self.head_dim,-1)).permute(0, 3,1,2) #[l, h,d,2]->[l,2,h,d]
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compressed_kv = mean_kv + mlp_kv
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compress_k = compressed_kv[:, 0, :, :]
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compress_v = compressed_kv[:, 1, :, :]
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elif self.compress_func == 'conv1d':
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k = filtered_kv[:,: ,0,:, :]
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k = rearrange(k, 'l block_size h d -> (l h) d block_size') #只能3维
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v = filtered_kv[:,: ,1,:, :]
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v = rearrange(v, 'l block_size h d -> (l h) d block_size')
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compress_k = self.k_conv(k).squeeze(-1) # [(l h), d]
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compress_v = self.v_conv(v).squeeze(-1) # [(l h), d]
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compress_k = rearrange(compress_k, '(l h) d -> l h d', h=self.head_num_k)
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compress_v = rearrange(compress_v, '(l h) d -> l h d', h=self.head_num_k)
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elif self.compress_func == 'weighted_sum':
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k = filtered_kv[:,: ,0,:, :]
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k = rearrange(k, 'l block_size h d -> l h block_size d')
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v = filtered_kv[:,: ,1,:, :]
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v = rearrange(v, 'l block_size h d -> l h block_size d')
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weight_k = torch.softmax(self.weight_net_k(k), dim=2) # [l, h, block_size, 1]
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weight_v = torch.softmax(self.weight_net_v(v), dim=2) # [l, h, block_size, 1]
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compress_k = (weight_k * k).sum(dim=2) # [l, h, d]
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compress_v = (weight_v * v).sum(dim=2) # [l, h, d]
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elif self.compress_func == 'weighted_sum+proj':
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k = filtered_kv[:,: ,0,:, :]
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k = rearrange(k, 'l block_size h d -> l h block_size d')
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v = filtered_kv[:,: ,1,:, :]
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v = rearrange(v, 'l block_size h d -> l h block_size d')
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weight_k = torch.softmax(self.weight_net_k(k), dim=2) # [l, h, block_size, 1]
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weight_v = torch.softmax(self.weight_net_v(v), dim=2) # [l, h, block_size, 1]
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compress_k = (weight_k * self.k_proj(k)).sum(dim=2) # [l, h, d]
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compress_v = (weight_v * self.v_proj(v)).sum(dim=2) # [l, h, d]
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else:
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raise ValueError(f"Unsupported compress type: {self.compress_func}")
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del filtered_kv
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if 'compressed_kv' in locals(): del compressed_kv
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return compress_k, compress_v, cu_seqlens_compressed
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class DynamicCacheQKV(DynamicCache):
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"""
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A cache that grows dynamically as more tokens are generated. This is the default for generative models.
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It stores the Key and Value states as a list of tensors, one for each layer. The expected shape for each tensor is
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`[batch_size, num_heads, seq_len, head_dim]`.
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Example:
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```python
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>>> from transformers import AutoTokenizer, AutoModelForCausalLM, DynamicCache
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>>> model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-0.5B-Instruct")
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>>> tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-0.5B-Instruct")
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>>> inputs = tokenizer(text="My name is Qwen2", return_tensors="pt")
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>>> # Prepare a cache class and pass it to model's forward
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>>> past_key_values = DynamicCache()
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>>> outputs = model(**inputs, past_key_values=past_key_values, use_cache=True)
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>>> outputs.past_key_values # access cache filled with key/values from generation
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DynamicCache()
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```
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"""
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def __init__(self, num_hidden_layers: Optional[int] = None) -> None:
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super().__init__()
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if num_hidden_layers is None:
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self.key_cache: List[torch.Tensor] = []
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self.value_cache: List[torch.Tensor] = []
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self.query_cache: List[torch.Tensor] = []
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else:
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self.key_cache: List[torch.Tensor] = [[] for _ in range(num_hidden_layers)]
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self.value_cache: List[torch.Tensor] = [[] for _ in range(num_hidden_layers)]
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self.query_cache: List[torch.Tensor] = [[] for _ in range(num_hidden_layers)]
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self._seen_tokens = 0 # Used in `generate` to keep tally of how many tokens the cache has seen
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def __getitem__(self, layer_idx: int) -> List[Tuple[torch.Tensor]]:
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"""
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Support for backwards-compatible `past_key_value` indexing, e.g. `past_key_value[0][0].shape[2]` to get the
|
||
sequence length.
|
||
"""
|
||
if layer_idx < len(self):
|
||
return (self.key_cache[layer_idx], self.value_cache[layer_idx],self.query_cache[layer_idx])
|
||
else:
|
||
raise KeyError(f"Cache only has {len(self)} layers, attempted to access layer with index {layer_idx}")
|
||
|
||
def __iter__(self):
|
||
"""
|
||
Support for backwards-compatible `past_key_value` iteration, e.g. `for x in past_key_value:` to iterate over
|
||
keys and values
|
||
"""
|
||
for layer_idx in range(len(self)):
|
||
yield (self.key_cache[layer_idx], self.value_cache[layer_idx],self.query_cache[layer_idx])
|
||
|
||
def __len__(self):
|
||
"""
|
||
Support for backwards-compatible `past_key_value` length, e.g. `len(past_key_value)`. This value corresponds
|
||
to the number of layers in the model.
|
||
"""
|
||
return len(self.key_cache)
|
||
|
||
def update(
|
||
self,
|
||
key_states: torch.Tensor,
|
||
value_states: torch.Tensor,
|
||
layer_idx: int,
|
||
cache_kwargs: Optional[Dict[str, Any]] = None,query_states: torch.Tensor=None,
|
||
) -> Tuple[torch.Tensor, torch.Tensor]:
|
||
"""
|
||
Updates the cache with the new `key_states` and `value_states` for the layer `layer_idx`.
|
||
|
||
Parameters:
|
||
key_states (`torch.Tensor`):
|
||
The new key states to cache.
|
||
value_states (`torch.Tensor`):
|
||
The new value states to cache.
|
||
layer_idx (`int`):
|
||
The index of the layer to cache the states for.
|
||
cache_kwargs (`Dict[str, Any]`, `optional`):
|
||
Additional arguments for the cache subclass. No additional arguments are used in `DynamicCache`.
|
||
|
||
Return:
|
||
A tuple containing the updated key and value states.
|
||
"""
|
||
# Update the number of seen tokens
|
||
if layer_idx == 0:
|
||
self._seen_tokens += key_states.shape[-2]
|
||
if query_states is None:
|
||
raise ValueError("query_states must be provided for DynamicCacheQKV")
|
||
|
||
# Update the cache
|
||
if len(self.key_cache) <= layer_idx:
|
||
self.key_cache.append(key_states)
|
||
self.value_cache.append(value_states)
|
||
self.query_cache.append(query_states)
|
||
# content on layer cache can be a tensor and checking not tensor causes errors
|
||
# so we explicitly check for the empty list
|
||
elif self.key_cache[layer_idx] == []:
|
||
self.key_cache[layer_idx] = key_states
|
||
self.value_cache[layer_idx] = value_states
|
||
self.query_cache[layer_idx] = query_states
|
||
else:
|
||
self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=-2)
|
||
self.value_cache[layer_idx] = torch.cat([self.value_cache[layer_idx], value_states], dim=-2)
|
||
self.query_cache[layer_idx] = torch.cat([self.query_cache[layer_idx], query_states], dim=-2)
|
||
|
||
return self.key_cache[layer_idx], self.value_cache[layer_idx], self.query_cache[layer_idx]
|
||
|
||
def get_seq_length(self, layer_idx: Optional[int] = 0) -> int:
|
||
"""Returns the sequence length of the cached states. A layer index can be optionally passed."""
|
||
# TODO: deprecate this function in favor of `cache_position`
|
||
if len(self.key_cache) <= layer_idx or (len(self.key_cache) > layer_idx and self.key_cache[layer_idx] == []):
|
||
return 0
|
||
return self.key_cache[layer_idx].shape[-2]
|
||
|
||
def get_max_length(self) -> Optional[int]:
|
||
"""Returns the maximum sequence length of the cached states. DynamicCache does not have a maximum length."""
|
||
return None
|
||
|
||
def to_legacy_cache(self) -> Tuple[Tuple[torch.Tensor], Tuple[torch.Tensor]]:
|
||
"""Converts the `DynamicCache` instance into the its equivalent in the legacy cache format. Used for
|
||
backward compatibility."""
|
||
legacy_cache = ()
|
||
for layer_idx in range(len(self)):
|
||
legacy_cache += ((self.key_cache[layer_idx], self.value_cache[layer_idx]),)
|
||
return legacy_cache
|
||
|
||
# @classmethod
|
||
# def from_legacy_cache(
|
||
# cls, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, num_hidden_layers: int = None
|
||
# ) -> "DynamicCacheQKV":
|
||
# """Converts a cache in the legacy cache format into an equivalent `DynamicCache`. Used for
|
||
# backward compatibility."""
|
||
# cache = cls(num_hidden_layers)
|
||
# if past_key_values is not None:
|
||
# for layer_idx in range(len(past_key_values)):
|
||
# key_states, value_states, query_status = past_key_values[layer_idx]
|
||
# cache.update(key_states, value_states, query_status,layer_idx)
|
||
# return cache
|
||
|
||
def crop(self, max_length: int):
|
||
"""Crop the past key values up to a new `max_length` in terms of tokens. `max_length` can also be
|
||
negative to remove `max_length` tokens. This is used in assisted decoding and contrastive search."""
|
||
# In case it is negative
|
||
if max_length < 0:
|
||
max_length = self.get_seq_length() - abs(max_length)
|
||
|
||
if self.get_seq_length() <= max_length:
|
||
return
|
||
|
||
self._seen_tokens = max_length
|
||
for idx in range(len(self.key_cache)):
|
||
if self.key_cache[idx] != []:
|
||
self.key_cache[idx] = self.key_cache[idx][..., :max_length, :]
|
||
self.value_cache[idx] = self.value_cache[idx][..., :max_length, :]
|
||
self.query_cache[idx] = self.query_cache[idx][..., :max_length, :]
|
||
|
||
def batch_split(self, full_batch_size: int, split_size: int, num_hidden_layers: int) -> List["DynamicCacheQKV"]:
|
||
"""Split the current instance into a list of `DynamicCache` by the batch size. This will be used by
|
||
`_split_model_inputs()` in `generation.utils`"""
|
||
out = []
|
||
for i in range(0, full_batch_size, split_size):
|
||
current_split = DynamicCacheQKV(num_hidden_layers)
|
||
current_split._seen_tokens = self._seen_tokens
|
||
current_split.key_cache = [tensor[i : i + split_size] for tensor in self.key_cache]
|
||
current_split.value_cache = [tensor[i : i + split_size] for tensor in self.value_cache]
|
||
current_split.query_cache = [tensor[i : i + split_size] for tensor in self.query_cache]
|
||
out.append(current_split)
|
||
return out
|
||
|
||
@classmethod
|
||
def from_batch_splits(cls, splits: List["DynamicCacheQKV"], num_hidden_layers: int) -> "DynamicCacheQKV":
|
||
"""This is the opposite of the above `batch_split()` method. This will be used by `stack_model_outputs` in
|
||
`generation.utils`"""
|
||
cache = cls(num_hidden_layers)
|
||
for idx in range(len(splits[0])):
|
||
key_cache = [current.key_cache[idx] for current in splits if current.key_cache[idx] != []]
|
||
value_cache = [current.key_cache[idx] for current in splits if current.key_cache[idx] != []]
|
||
query_cache = [current.key_cache[idx] for current in splits if current.key_cache[idx] != []]
|
||
if key_cache != []:
|
||
layer_keys = torch.cat(key_cache, dim=0)
|
||
layer_values = torch.cat(value_cache, dim=0)
|
||
layer_query = torch.cat(query_cache, dim=0)
|
||
cache.update(layer_keys, layer_values, idx,query_states=layer_query)
|
||
return cache
|
||
|
||
def batch_repeat_interleave(self, repeats: int):
|
||
"""Repeat the cache `repeats` times in the batch dimension. Used in contrastive search."""
|
||
for layer_idx in range(len(self)):
|
||
self.key_cache[layer_idx] = self.key_cache[layer_idx].repeat_interleave(repeats, dim=0)
|
||
self.value_cache[layer_idx] = self.value_cache[layer_idx].repeat_interleave(repeats, dim=0)
|
||
self.query_cache[layer_idx] = self.query_cache[layer_idx].repeat_interleave(repeats, dim=0)
|
||
|
||
def batch_select_indices(self, indices: torch.Tensor):
|
||
"""Only keep the `indices` in the batch dimension of the cache. Used in contrastive search."""
|
||
for layer_idx in range(len(self)):
|
||
self.key_cache[layer_idx] = self.key_cache[layer_idx][indices, ...]
|
||
self.value_cache[layer_idx] = self.value_cache[layer_idx][indices, ...]
|
||
self.query_cache[layer_idx] = self.query_cache[layer_idx][indices, ...]
|
||
|
||
#! nsa
|
||
# This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph.
|
||
# It means that the function will not be traced through and simply appear as a node in the graph.
|
||
if is_torch_fx_available():
|
||
if not is_torch_greater_or_equal_than_1_13:
|
||
import torch.fx
|
||
|
||
_prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask)
|
||
|
||
|
||
logger = logging.get_logger(__name__)
|
||
|
||
_CONFIG_FOR_DOC = "MiniCPMConfig"
|
||
|
||
|
||
def _get_unpad_data(attention_mask):
|
||
seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
|
||
indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
|
||
max_seqlen_in_batch = seqlens_in_batch.max().item()
|
||
cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
|
||
return (
|
||
indices,
|
||
cu_seqlens,
|
||
max_seqlen_in_batch,
|
||
)
|
||
|
||
|
||
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
|
||
warnings.warn(
|
||
"Calling `transformers.models.minicpm.modeling_minicpm._prepare_4d_attention_mask` is deprecated and will be removed in v4.37. Use `transformers.modeling_attn_mask_utils._prepare_4d_attention_mask"
|
||
)
|
||
return _prepare_4d_attention_mask(mask=mask, dtype=dtype, tgt_len=tgt_len)
|
||
|
||
|
||
def _make_causal_mask(
|
||
input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
|
||
):
|
||
warnings.warn(
|
||
"Calling `transformers.models.minicpm.modeling_minicpm._make_causal_mask` is deprecated and will be removed in v4.37. Use `transformers.models.minicpm.modeling_minicpm.AttentionMaskConverter._make_causal_mask"
|
||
)
|
||
return AttentionMaskConverter._make_causal_mask(
|
||
input_ids_shape=input_ids_shape, dtype=dtype, device=device, past_key_values_length=past_key_values_length
|
||
)
|
||
|
||
# @torch.jit.script # type: ignore
|
||
def rms_layernorm(hidden: torch.Tensor, weight: torch.Tensor, eps: float):
|
||
old_dtype = hidden.dtype
|
||
variance = hidden.to(torch.float32).pow(2).mean(dim=-1, keepdim=True)
|
||
hidden = (hidden * torch.rsqrt(variance + eps)).to(old_dtype)
|
||
return hidden * weight
|
||
|
||
|
||
class MiniCPMRMSNorm(nn.Module):
|
||
def __init__(self, hidden_size, eps=1e-6):
|
||
"""
|
||
MiniCPMRMSNorm is equivalent to T5LayerNorm
|
||
"""
|
||
super().__init__()
|
||
self.weight = nn.Parameter(torch.ones(hidden_size))
|
||
self.variance_epsilon = eps
|
||
|
||
def forward(self, hidden_states):
|
||
return rms_layernorm(hidden_states, self.weight, self.variance_epsilon)
|
||
|
||
|
||
ALL_LAYERNORM_LAYERS.append(MiniCPMRMSNorm)
|
||
|
||
|
||
class MiniCPMRotaryEmbedding(nn.Module):
|
||
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
|
||
super().__init__()
|
||
|
||
self.dim = dim
|
||
self.max_position_embeddings = max_position_embeddings
|
||
self.base = base
|
||
inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
|
||
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
||
|
||
# Build here to make `torch.jit.trace` work.
|
||
self._set_cos_sin_cache(
|
||
# seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
|
||
seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.float32
|
||
)
|
||
|
||
def _set_cos_sin_cache(self, seq_len, device, dtype):
|
||
self.max_seq_len_cached = seq_len
|
||
t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
|
||
freqs = torch.outer(t, self.inv_freq)
|
||
# Different from paper, but it uses a different permutation in order to obtain the same calculation
|
||
emb = torch.cat((freqs, freqs), dim=-1)
|
||
|
||
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
|
||
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
|
||
|
||
def forward(self, x, seq_len=None):
|
||
# x: [bs, num_attention_heads, seq_len, head_size]
|
||
if seq_len > self.max_seq_len_cached:
|
||
self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
|
||
|
||
return (
|
||
self.cos_cached[:seq_len].to(dtype=x.dtype),
|
||
self.sin_cached[:seq_len].to(dtype=x.dtype),
|
||
)
|
||
|
||
|
||
class MiniCPMLongRoPE(MiniCPMRotaryEmbedding):
|
||
"""MiniCPMRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
|
||
|
||
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, short_factor=None, long_factor=None, original_max_position_embeddings=None):
|
||
self.short_factor = short_factor
|
||
self.long_factor = long_factor
|
||
self.original_max_position_embeddings = original_max_position_embeddings
|
||
scale = (max_position_embeddings /
|
||
self.original_max_position_embeddings)
|
||
self.scaling_factor = math.sqrt(
|
||
1 + math.log(scale) /
|
||
math.log(self.original_max_position_embeddings))
|
||
super().__init__(dim, max_position_embeddings, base, device)
|
||
|
||
def _set_cos_sin_cache(self, seq_len, device, dtype):
|
||
self.max_seq_len_cached = seq_len
|
||
t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
|
||
if seq_len > self.original_max_position_embeddings:
|
||
ext_factors = torch.tensor(self.long_factor, dtype=torch.float32, device=device)
|
||
else:
|
||
ext_factors = torch.tensor(self.short_factor, dtype=torch.float32, device=device)
|
||
|
||
freqs = torch.mul(
|
||
torch.outer(t, 1.0 / ext_factors).to(device=device),
|
||
self.inv_freq.to(device=device).to(dtype)
|
||
)
|
||
# Different from paper, but it uses a different permutation in order to obtain the same calculation
|
||
emb = torch.cat((freqs, freqs), dim=-1)
|
||
self.register_buffer("cos_cached", emb.cos().to(dtype) * self.scaling_factor, persistent=False)
|
||
self.register_buffer("sin_cached", emb.sin().to(dtype) * self.scaling_factor, persistent=False)
|
||
|
||
|
||
class MiniCPMLinearScalingRotaryEmbedding(MiniCPMRotaryEmbedding):
|
||
"""MiniCPMRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
|
||
|
||
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
|
||
self.scaling_factor = scaling_factor
|
||
super().__init__(dim, max_position_embeddings, base, device)
|
||
|
||
def _set_cos_sin_cache(self, seq_len, device, dtype):
|
||
self.max_seq_len_cached = seq_len
|
||
t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
|
||
t = t / self.scaling_factor
|
||
|
||
freqs = torch.outer(t, self.inv_freq)
|
||
# Different from paper, but it uses a different permutation in order to obtain the same calculation
|
||
emb = torch.cat((freqs, freqs), dim=-1)
|
||
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
|
||
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
|
||
|
||
|
||
class MiniCPMDynamicNTKScalingRotaryEmbedding(MiniCPMRotaryEmbedding):
|
||
"""MiniCPMRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
|
||
|
||
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
|
||
self.scaling_factor = scaling_factor
|
||
super().__init__(dim, max_position_embeddings, base, device)
|
||
|
||
def _set_cos_sin_cache(self, seq_len, device, dtype):
|
||
self.max_seq_len_cached = seq_len
|
||
|
||
if seq_len > self.max_position_embeddings:
|
||
base = self.base * (
|
||
(self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
|
||
) ** (self.dim / (self.dim - 2))
|
||
inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
|
||
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
||
|
||
t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
|
||
|
||
freqs = torch.outer(t, self.inv_freq)
|
||
# Different from paper, but it uses a different permutation in order to obtain the same calculation
|
||
emb = torch.cat((freqs, freqs), dim=-1)
|
||
|
||
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
|
||
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
|
||
|
||
|
||
def rotate_half(x):
|
||
"""Rotates half the hidden dims of the input."""
|
||
x1 = x[..., : x.shape[-1] // 2]
|
||
x2 = x[..., x.shape[-1] // 2 :]
|
||
return torch.cat((-x2, x1), dim=-1)
|
||
|
||
|
||
def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
|
||
"""Applies Rotary Position Embedding to the query and key tensors.
|
||
|
||
Args:
|
||
q (`torch.Tensor`): The query tensor.
|
||
k (`torch.Tensor`): The key tensor.
|
||
cos (`torch.Tensor`): The cosine part of the rotary embedding.
|
||
sin (`torch.Tensor`): The sine part of the rotary embedding.
|
||
position_ids (`torch.Tensor`):
|
||
The position indices of the tokens corresponding to the query and key tensors. For example, this can be
|
||
used to pass offsetted position ids when working with a KV-cache.
|
||
unsqueeze_dim (`int`, *optional*, defaults to 1):
|
||
The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
|
||
sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
|
||
that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
|
||
k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
|
||
cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
|
||
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
|
||
Returns:
|
||
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
|
||
"""
|
||
# cos = cos[position_ids].unsqueeze(unsqueeze_dim)
|
||
# sin = sin[position_ids].unsqueeze(unsqueeze_dim)
|
||
# q_embed = (q * cos) + (rotate_half(q) * sin)
|
||
# k_embed = (k * cos) + (rotate_half(k) * sin)
|
||
orig_dtype = k.dtype
|
||
cos = cos[position_ids].unsqueeze(unsqueeze_dim) # [bs, 1, seq_len, dim]
|
||
sin = sin[position_ids].unsqueeze(unsqueeze_dim) # [bs, 1, seq_len, dim]
|
||
q_fp32 = q.to(dtype=torch.float32, device=q.device)
|
||
k_fp32 = k.to(dtype=torch.float32, device=k.device)
|
||
q_embed = (q_fp32 * cos) + (rotate_half(q_fp32) * sin)
|
||
k_embed = (k_fp32 * cos) + (rotate_half(k_fp32) * sin)
|
||
return q_embed.to(dtype=orig_dtype), k_embed.to(dtype=orig_dtype)
|
||
|
||
class MiniCPMMLP(nn.Module):
|
||
def __init__(self, config):
|
||
super().__init__()
|
||
self.config = config
|
||
self.hidden_size = config.hidden_size
|
||
self.intermediate_size = config.intermediate_size
|
||
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
|
||
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
|
||
self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
|
||
self.act_fn = ACT2FN[config.hidden_act]
|
||
|
||
def forward(self, x):
|
||
if self.config.pretraining_tp > 1:
|
||
slice = self.intermediate_size // self.config.pretraining_tp
|
||
gate_proj_slices = self.gate_proj.weight.split(slice, dim=0)
|
||
up_proj_slices = self.up_proj.weight.split(slice, dim=0)
|
||
down_proj_slices = self.down_proj.weight.split(slice, dim=1)
|
||
|
||
gate_proj = torch.cat(
|
||
[F.linear(x, gate_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1
|
||
)
|
||
up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1)
|
||
|
||
intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2)
|
||
down_proj = [
|
||
F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.config.pretraining_tp)
|
||
]
|
||
down_proj = sum(down_proj)
|
||
else:
|
||
down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
|
||
|
||
return down_proj
|
||
|
||
|
||
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
|
||
"""
|
||
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
|
||
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
|
||
"""
|
||
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
|
||
if n_rep == 1:
|
||
return hidden_states
|
||
hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
|
||
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
|
||
|
||
|
||
|
||
class MiniCPMAttention(nn.Module):
|
||
"""Multi-headed attention from 'Attention Is All You Need' paper"""
|
||
|
||
def __init__(self, config: MiniCPMConfig, layer_idx: Optional[int] = None):
|
||
super().__init__()
|
||
self.config = config
|
||
self.layer_idx = layer_idx
|
||
if layer_idx is None:
|
||
logger.warning_once(
|
||
f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
|
||
"to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
|
||
"when creating this class."
|
||
)
|
||
|
||
self.attention_dropout = config.attention_dropout
|
||
self.hidden_size = config.hidden_size
|
||
self.num_heads = config.num_attention_heads
|
||
self.head_dim = self.hidden_size // self.num_heads
|
||
self.num_key_value_heads = config.num_key_value_heads
|
||
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
|
||
self.max_position_embeddings = config.max_position_embeddings
|
||
self.rope_theta = config.rope_theta
|
||
self.is_causal = True
|
||
|
||
if (self.head_dim * self.num_heads) != self.hidden_size:
|
||
raise ValueError(
|
||
f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
|
||
f" and `num_heads`: {self.num_heads})."
|
||
)
|
||
|
||
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
|
||
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
|
||
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
|
||
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.attention_bias)
|
||
self._init_rope()
|
||
|
||
def _init_rope(self):
|
||
if self.config.rope_scaling is None:
|
||
self.rotary_emb = MiniCPMRotaryEmbedding(
|
||
self.head_dim,
|
||
max_position_embeddings=self.max_position_embeddings,
|
||
base=self.rope_theta,
|
||
)
|
||
else:
|
||
scaling_type = self.config.rope_scaling["rope_type"]
|
||
scaling_factor = self.config.rope_scaling.get("factor", None)
|
||
if scaling_type == "linear":
|
||
self.rotary_emb = MiniCPMLinearScalingRotaryEmbedding(
|
||
self.head_dim,
|
||
max_position_embeddings=self.max_position_embeddings,
|
||
scaling_factor=scaling_factor,
|
||
base=self.rope_theta,
|
||
)
|
||
elif scaling_type == "dynamic":
|
||
self.rotary_emb = MiniCPMDynamicNTKScalingRotaryEmbedding(
|
||
self.head_dim,
|
||
max_position_embeddings=self.max_position_embeddings,
|
||
scaling_factor=scaling_factor,
|
||
base=self.rope_theta,
|
||
)
|
||
elif scaling_type == "longrope":
|
||
self.rotary_emb = MiniCPMLongRoPE(
|
||
self.head_dim,
|
||
max_position_embeddings=self.max_position_embeddings,
|
||
short_factor = self.config.rope_scaling["short_factor"],
|
||
long_factor = self.config.rope_scaling["long_factor"],
|
||
base=self.rope_theta,
|
||
original_max_position_embeddings=self.config.rope_scaling["original_max_position_embeddings"]
|
||
)
|
||
else:
|
||
raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
|
||
|
||
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
|
||
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
|
||
|
||
def forward(
|
||
self,
|
||
hidden_states: torch.Tensor,
|
||
attention_mask: Optional[torch.Tensor] = None,
|
||
position_ids: Optional[torch.LongTensor] = None,
|
||
past_key_value: Optional[Cache] = None,
|
||
output_attentions: bool = False,
|
||
use_cache: bool = False,
|
||
**kwargs,
|
||
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
||
if "padding_mask" in kwargs:
|
||
warnings.warn(
|
||
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
|
||
)
|
||
|
||
bsz, q_len, _ = hidden_states.size()
|
||
|
||
if self.config.pretraining_tp > 1:
|
||
key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp
|
||
query_slices = self.q_proj.weight.split(
|
||
(self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0
|
||
)
|
||
key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
|
||
value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
|
||
|
||
query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.config.pretraining_tp)]
|
||
query_states = torch.cat(query_states, dim=-1)
|
||
|
||
key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.config.pretraining_tp)]
|
||
key_states = torch.cat(key_states, dim=-1)
|
||
|
||
value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.config.pretraining_tp)]
|
||
value_states = torch.cat(value_states, dim=-1)
|
||
|
||
else:
|
||
query_states = self.q_proj(hidden_states)
|
||
key_states = self.k_proj(hidden_states)
|
||
value_states = self.v_proj(hidden_states)
|
||
|
||
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
||
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
||
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
||
|
||
kv_seq_len = key_states.shape[-2]
|
||
if past_key_value is not None:
|
||
if self.layer_idx is None:
|
||
raise ValueError(
|
||
f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
|
||
"for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
|
||
"with a layer index."
|
||
)
|
||
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
|
||
cos, sin = self.rotary_emb(value_states.to(torch.float32), seq_len=kv_seq_len)
|
||
|
||
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
|
||
|
||
if past_key_value is not None:
|
||
cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
|
||
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
||
|
||
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
||
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
||
|
||
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
|
||
if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
|
||
raise ValueError(
|
||
f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
|
||
f" {attn_weights.size()}"
|
||
)
|
||
|
||
if attention_mask is not None:
|
||
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
|
||
raise ValueError(
|
||
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
|
||
)
|
||
attn_weights = attn_weights + attention_mask
|
||
|
||
# upcast attention to fp32
|
||
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
|
||
attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
|
||
attn_output = torch.matmul(attn_weights, value_states)
|
||
|
||
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
|
||
raise ValueError(
|
||
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
|
||
f" {attn_output.size()}"
|
||
)
|
||
|
||
attn_output = attn_output.transpose(1, 2).contiguous()
|
||
|
||
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
||
|
||
if self.config.pretraining_tp > 1:
|
||
attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)
|
||
o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1)
|
||
attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.config.pretraining_tp)])
|
||
else:
|
||
attn_output = self.o_proj(attn_output)
|
||
|
||
if not output_attentions:
|
||
attn_weights = None
|
||
|
||
return attn_output, attn_weights, past_key_value
|
||
|
||
|
||
class MiniCPMFlashAttention2(MiniCPMAttention):
|
||
"""
|
||
MiniCPM flash attention module. This module inherits from `MiniCPMAttention` as the weights of the module stays
|
||
untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
|
||
flash attention and deal with padding tokens in case the input contains any of them.
|
||
"""
|
||
|
||
def __init__(self, *args, **kwargs):
|
||
super().__init__(*args, **kwargs)
|
||
|
||
# TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
|
||
# flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
|
||
# Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
|
||
self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
|
||
#! -------nsa-------
|
||
self.kernel_size = 32
|
||
self.kernel_stride = 16
|
||
compress_type = 'meanpool'
|
||
self.init_blocks=1
|
||
|
||
self.block_size=64
|
||
|
||
self.window_size=2048
|
||
|
||
self.local_blocks = self.window_size // self.block_size; #local_blocks
|
||
self.topk = 32
|
||
self.compress_kv = CompressKV(self.num_key_value_heads, self.head_dim,compress_func= compress_type, kernel_size=self.kernel_size,kernel_stride=self.kernel_stride, add_pos_embed=False)
|
||
|
||
|
||
def forward(
|
||
self,
|
||
hidden_states: torch.Tensor,
|
||
attention_mask: Optional[torch.LongTensor] = None,
|
||
position_ids: Optional[torch.LongTensor] = None,
|
||
past_key_value: Optional[Cache] = None,
|
||
output_attentions: bool = False,
|
||
use_cache: bool = False,
|
||
**kwargs,
|
||
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
||
# MiniCPMFlashAttention2 attention does not support output_attentions
|
||
if "padding_mask" in kwargs:
|
||
warnings.warn(
|
||
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
|
||
)
|
||
|
||
# overwrite attention_mask with padding_mask
|
||
attention_mask = kwargs.pop("padding_mask")
|
||
|
||
output_attentions = False
|
||
|
||
bsz, q_len, _ = hidden_states.size()
|
||
# assert bsz == 1, '现在只支持batch_size=1'
|
||
|
||
query_states = self.q_proj(hidden_states)
|
||
key_states = self.k_proj(hidden_states)
|
||
value_states = self.v_proj(hidden_states)
|
||
|
||
# Flash attention requires the input to have the shape
|
||
# batch_size x seq_length x head_dim x hidden_dim
|
||
# therefore we just need to keep the original shape
|
||
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
||
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
||
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
||
|
||
kv_seq_len = key_states.shape[-2]
|
||
if past_key_value is not None:
|
||
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
|
||
cos, sin = self.rotary_emb(value_states.to(torch.float32), seq_len=kv_seq_len)
|
||
# if key_states.shape[-2] == 1: #这里是possition ids的问题
|
||
# position_ids = torch.tensor([[kv_seq_len-1]], device=key_states.device, dtype=position_ids.dtype)
|
||
# # breakpoint()
|
||
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
|
||
|
||
if past_key_value is not None:
|
||
|
||
cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
|
||
new_k = key_states
|
||
new_v = value_states
|
||
new_q = query_states
|
||
try:
|
||
|
||
past_k , past_v, past_q = past_key_value.__getitem__(self.layer_idx)
|
||
except Exception as e:
|
||
# If the cache is empty, we need to create a new one
|
||
past_k , past_v, past_q = key_states, value_states, query_states
|
||
new_k, new_v = None, None
|
||
|
||
key_states, value_states ,query_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs,query_states=query_states)
|
||
|
||
# TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
|
||
# to be able to avoid many of these transpose/reshape/view.
|
||
query_states = query_states.transpose(1, 2)
|
||
key_states = key_states.transpose(1, 2)
|
||
value_states = value_states.transpose(1, 2)
|
||
|
||
dropout_rate = self.attention_dropout if self.training else 0.0
|
||
|
||
# In PEFT, usually we cast the layer norms in float32 for training stability reasons
|
||
# therefore the input hidden states gets silently casted in float32. Hence, we need
|
||
# cast them back in the correct dtype just to be sure everything works as expected.
|
||
# This might slowdown training & inference so it is recommended to not cast the LayerNorms
|
||
# in fp32. (MiniCPMRMSNorm handles it correctly)
|
||
|
||
input_dtype = query_states.dtype
|
||
if input_dtype == torch.float32:
|
||
# Handle the case where the model is quantized
|
||
if hasattr(self.config, "_pre_quantization_dtype"):
|
||
target_dtype = self.config._pre_quantization_dtype
|
||
else:
|
||
target_dtype = self.q_proj.weight.dtype
|
||
|
||
logger.warning_once(
|
||
f"The input hidden states seems to be silently casted in float32, this might be related to"
|
||
f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
|
||
f" {target_dtype}."
|
||
)
|
||
|
||
query_states = query_states.to(target_dtype)
|
||
key_states = key_states.to(target_dtype)
|
||
value_states = value_states.to(target_dtype)
|
||
if past_key_value is None or new_k is None:
|
||
attn_output = self._flash_attention_forward(
|
||
query_states, key_states, value_states, attention_mask, position_ids, q_len, dropout=dropout_rate,original_hidden_states=hidden_states,)
|
||
else:
|
||
# breakpoint()
|
||
attn_output = self._flash_attention_forward_with_kv_cache(
|
||
query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate,original_hidden_states=hidden_states,past_k=past_k,past_v=past_v,new_k=new_k,new_v=new_v,new_q=new_q)
|
||
|
||
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
|
||
attn_output = self.o_proj(attn_output)
|
||
|
||
if not output_attentions:
|
||
attn_weights = None
|
||
|
||
return attn_output, attn_weights, past_key_value
|
||
|
||
def _flash_attention_forward(
|
||
self, query_states, key_states, value_states, attention_mask, position_ids, query_length, dropout=0.0, softmax_scale=None,original_hidden_states=None
|
||
):
|
||
"""
|
||
Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
|
||
first unpad the input, then computes the attention scores and pad the final attention scores.
|
||
|
||
Args:
|
||
query_states (`torch.Tensor`):
|
||
Input query states to be passed to Flash Attention API
|
||
key_states (`torch.Tensor`):
|
||
Input key states to be passed to Flash Attention API
|
||
value_states (`torch.Tensor`):
|
||
Input value states to be passed to Flash Attention API
|
||
attention_mask (`torch.Tensor`):
|
||
The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
|
||
position of padding tokens and 1 for the position of non-padding tokens.
|
||
dropout (`int`, *optional*):
|
||
Attention dropout
|
||
softmax_scale (`float`, *optional*):
|
||
The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
|
||
"""
|
||
if not self._flash_attn_uses_top_left_mask:
|
||
causal = self.is_causal
|
||
else:
|
||
# TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in MiniCPMFlashAttention2 __init__.
|
||
causal = self.is_causal and query_length != 1
|
||
# Contains at least one padding token in the sequence
|
||
if attention_mask is not None:
|
||
batch_size = query_states.shape[0]
|
||
query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
|
||
query_states, key_states, value_states, attention_mask, query_length
|
||
)
|
||
original_hidden_states = self._unpad_hidden_states(original_hidden_states, indices_q)
|
||
cu_seqlens_q, cu_seqlens_k = cu_seq_lens
|
||
max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
|
||
attn_output_unpad = self.nsa_forward(
|
||
query_states,
|
||
key_states,
|
||
value_states,
|
||
cu_seqlens_q, cu_seqlens_k ,
|
||
max_seqlen_in_batch_q, max_seqlen_in_batch_k,
|
||
original_hidden_states=original_hidden_states
|
||
)
|
||
|
||
attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
|
||
elif attention_mask is None and position_ids is not None:
|
||
batch_size = query_states.size(0)
|
||
|
||
query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = (
|
||
prepare_fa2_from_position_ids(query_states, key_states, value_states, position_ids)
|
||
)
|
||
cu_seqlens_q, cu_seqlens_k = cu_seq_lens
|
||
max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
|
||
|
||
attn_output_unpad = self.nsa_forward(
|
||
query_states,
|
||
key_states,
|
||
value_states,
|
||
cu_seqlens_q, cu_seqlens_k ,
|
||
max_seqlen_in_batch_q, max_seqlen_in_batch_k,
|
||
)
|
||
attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
|
||
else:
|
||
raise ValueError
|
||
|
||
|
||
return attn_output
|
||
def _flash_attention_forward_with_kv_cache(
|
||
self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None,original_hidden_states=None,past_k=None,past_v=None,new_k=None,new_v=None,new_q=None
|
||
):
|
||
"""
|
||
Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
|
||
first unpad the input, then computes the attention scores and pad the final attention scores.
|
||
|
||
Args:
|
||
query_states (`torch.Tensor`):
|
||
Input query states to be passed to Flash Attention API
|
||
key_states (`torch.Tensor`):
|
||
Input key states to be passed to Flash Attention API
|
||
value_states (`torch.Tensor`):
|
||
Input value states to be passed to Flash Attention API
|
||
attention_mask (`torch.Tensor`):
|
||
The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
|
||
position of padding tokens and 1 for the position of non-padding tokens.
|
||
dropout (`int`, *optional*):
|
||
Attention dropout
|
||
softmax_scale (`float`, *optional*):
|
||
The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
|
||
"""
|
||
if not self._flash_attn_uses_top_left_mask:
|
||
causal = self.is_causal
|
||
else:
|
||
# TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in MiniCPMFlashAttention2 __init__.
|
||
causal = self.is_causal and query_length != 1
|
||
# Contains at least one padding token in the sequence
|
||
if attention_mask is not None:
|
||
query_length = query_states.shape[1]
|
||
batch_size = query_states.shape[0]
|
||
|
||
# query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
|
||
# query_states, key_states, value_states, attention_mask, query_length=query_length
|
||
# )
|
||
|
||
#! 这里的attention_mask是没有包括最后一个,所以不准
|
||
assert batch_size == 1, '现在只支持batch_size=1'
|
||
query_states = query_states.squeeze(0)
|
||
key_states = key_states.squeeze(0)
|
||
value_states = value_states.squeeze(0)
|
||
original_hidden_states = original_hidden_states.squeeze(0)
|
||
cu_seqlens_q=cu_seqlens_k = tensor([0, query_length], device=query_states.device, dtype=torch.int32)
|
||
max_seqlen_in_batch_q=max_seqlen_in_batch_k = query_length
|
||
attn_output = self.nsa_forward_with_kv_cache(
|
||
query_states,
|
||
key_states,
|
||
value_states,
|
||
cu_seqlens_q, cu_seqlens_k ,
|
||
max_seqlen_in_batch_q, max_seqlen_in_batch_k,
|
||
original_hidden_states=original_hidden_states,past_k=past_k,past_v=past_v,new_k=new_k,new_v=new_v,new_q=new_q, batch_size=batch_size )
|
||
# attn_output_unpad = flash_attn_varlen_func(
|
||
# query_states,
|
||
# key_states,
|
||
# value_states,
|
||
# cu_seqlens_q=cu_seqlens_q,
|
||
# cu_seqlens_k=cu_seqlens_k,
|
||
# max_seqlen_q=max_seqlen_in_batch_q,
|
||
# max_seqlen_k=max_seqlen_in_batch_k,
|
||
# dropout_p=dropout,
|
||
# softmax_scale=softmax_scale,
|
||
# causal=causal,
|
||
# )
|
||
|
||
# attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
|
||
else:
|
||
attn_output = flash_attn_func(
|
||
query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
|
||
)
|
||
|
||
return attn_output
|
||
#! -------nsa-------
|
||
def nsa_forward(self,
|
||
query_layer,
|
||
key_layer,
|
||
value_layer,
|
||
cu_seqlens_q, cu_seqlens_k ,
|
||
max_seqlen_in_batch_q, max_seqlen_in_batch_k,
|
||
original_hidden_states=None
|
||
):
|
||
kv = torch.stack((key_layer, value_layer), dim=1)
|
||
compressed_k,compressed_v, compressed_cu_seqlens = self.compress_kv(kv, cu_seqlens_k)
|
||
compressed_seqlens = compressed_cu_seqlens[1:] - \
|
||
compressed_cu_seqlens[:-1]
|
||
compressed_attn_output, topk_idx = compressed_attention(
|
||
query_layer,
|
||
compressed_k,
|
||
compressed_v,
|
||
self.kernel_size,
|
||
self.kernel_stride,
|
||
self.block_size,
|
||
self.topk,
|
||
cu_seqlens_q,
|
||
compressed_cu_seqlens,
|
||
max_seqlen_in_batch_q,
|
||
compressed_seqlens.max().item(),
|
||
None,
|
||
init_blocks=self.init_blocks,
|
||
local_blocks=self.local_blocks,
|
||
)
|
||
|
||
del compressed_k, compressed_v, compressed_cu_seqlens, kv, compressed_seqlens
|
||
nheads_k = key_layer.shape[1]
|
||
head_mask_type = torch.tensor([1] * nheads_k, device=query_layer.device, dtype=torch.int32)
|
||
streaming_info = torch.tensor([0, 0] * nheads_k, device=query_layer.device, dtype=torch.int32)
|
||
exact_streaming =False
|
||
|
||
repeat_times = 1
|
||
if repeat_times > 1:
|
||
query_layer_repeat = query_layer.repeat_interleave(repeat_times, dim=-2)
|
||
else:
|
||
query_layer_repeat = query_layer
|
||
topk_attn_output = block_sparse_attn_func(
|
||
query_layer_repeat,
|
||
key_layer,
|
||
value_layer,
|
||
cu_seqlens_q,
|
||
cu_seqlens_k,
|
||
head_mask_type,
|
||
streaming_info,
|
||
topk_idx,
|
||
max_seqlen_in_batch_q, max_seqlen_in_batch_k,
|
||
self.attention_dropout,
|
||
deterministic=False,
|
||
softmax_scale=None,
|
||
is_causal=True,
|
||
exact_streaming=False,
|
||
return_attn_probs=False,
|
||
block_window_size=self.window_size // self.block_size,
|
||
use_checkpoint=False,
|
||
)
|
||
# import pdb; pdb.set_trace()
|
||
# raise ValueError('debug')
|
||
if repeat_times > 1:
|
||
topk_attn_output = topk_attn_output.view(topk_attn_output.shape[0],topk_attn_output.shape[1]//repeat_times,repeat_times,-1).mean(dim=-2)
|
||
return topk_attn_output
|
||
|
||
#! -------nsa-------
|
||
def nsa_forward_with_kv_cache(self,
|
||
query_layer,
|
||
key_layer,
|
||
value_layer,
|
||
cu_seqlens_q, cu_seqlens_k ,
|
||
max_seqlen_in_batch_q, max_seqlen_in_batch_k,
|
||
original_hidden_states=None,past_k=None,past_v=None,new_k=None,new_v=None,new_q=None, batch_size=None,
|
||
):
|
||
# breakpoint()
|
||
kv = torch.stack((key_layer, value_layer), dim=1)
|
||
compressed_k,compressed_v, compressed_cu_seqlens = self.compress_kv(kv, cu_seqlens_k)
|
||
compressed_seqlens = compressed_cu_seqlens[1:] - \
|
||
compressed_cu_seqlens[:-1]
|
||
compressed_attn_output, topk_idx = compressed_attention(
|
||
query_layer,
|
||
compressed_k,
|
||
compressed_v,
|
||
self.kernel_size,
|
||
self.kernel_stride,
|
||
self.block_size,
|
||
self.topk,
|
||
cu_seqlens_q,
|
||
compressed_cu_seqlens,
|
||
max_seqlen_in_batch_q,
|
||
compressed_seqlens.max().item(),
|
||
None,
|
||
init_blocks=self.init_blocks,
|
||
local_blocks=self.local_blocks,
|
||
)
|
||
compressed_attn_output = compressed_attn_output[-1].unsqueeze(0).unsqueeze(0)
|
||
|
||
del compressed_k, compressed_v, compressed_cu_seqlens, kv, compressed_seqlens
|
||
nheads_k = key_layer.shape[1]
|
||
head_mask_type = torch.tensor([1] * nheads_k, device=query_layer.device, dtype=torch.int32)
|
||
streaming_info = torch.tensor([0, 0] * nheads_k, device=query_layer.device, dtype=torch.int32)
|
||
exact_streaming =False
|
||
|
||
repeat_times = 1
|
||
past_k = past_k.transpose(1, 2).contiguous()
|
||
past_v = past_v.transpose(1, 2).contiguous()
|
||
if new_k is not None:
|
||
|
||
new_k = new_k.transpose(1, 2).contiguous()
|
||
if new_v is not None:
|
||
new_v = new_v.transpose(1, 2).contiguous()
|
||
new_q = new_q.transpose(1, 2).contiguous()
|
||
if repeat_times > 1:
|
||
new_q = new_q.repeat_interleave(repeat_times, dim=-2)
|
||
else:
|
||
new_q = new_q
|
||
#! 暂时
|
||
# assert batch_size == 1, '只支持batch_size =1'
|
||
|
||
|
||
cache_batch_idx = torch.arange(batch_size, device=query_layer.device, dtype=torch.int32)
|
||
current_seqlens_k = cu_seqlens_k[1:] - cu_seqlens_k[:-1]
|
||
new_topk_idx = []
|
||
if new_k is not None:
|
||
for i in range(batch_size):
|
||
new_topk_idx.append(topk_idx[:,current_seqlens_k[i]-1,:].unsqueeze(1))
|
||
topk_idx = torch.stack(new_topk_idx, dim=0)
|
||
else:
|
||
# prefilling
|
||
for i in range(batch_size):
|
||
if i == 0:
|
||
start = 0
|
||
else:
|
||
start = current_seqlens_k[i-1]
|
||
new_topk_idx.append(topk_idx[:,start:current_seqlens_k[i],:])
|
||
topk_idx = torch.stack(new_topk_idx, dim=0)
|
||
seqlen_k=key_layer.shape[0] #! 只考虑单个batch
|
||
seqlens_k = torch.full((batch_size,), seqlen_k - 1, dtype=torch.int32, device=new_q.device)
|
||
|
||
|
||
past_k = torch.cat([past_k, torch.zeros_like(new_k,dtype=new_k.dtype)], dim=1) #填充多一个
|
||
past_v = torch.cat([past_v, torch.zeros_like(new_v,dtype=new_v.dtype)], dim=1) #填充多一个
|
||
topk_attn_output, softmax_lse = block_sparse_attn_kvcache_func(
|
||
q=new_q, # [batch_size, seqlen_q, nheads, d]
|
||
k_cache=past_k, # [batch_size, max_seqlen_k, nheads_k, d]
|
||
v_cache=past_v, # [batch_size, max_seqlen_k, nheads_k, d]
|
||
m_block_dim=16,
|
||
n_block_dim=64,
|
||
head_mask_type=head_mask_type,
|
||
streaming_info=None,#streaming_info,
|
||
topk_idx=topk_idx,
|
||
k=new_k, # [batch_size, 1, nheads_k, d]
|
||
v=new_v, # [batch_size, 1, nheads_k, d]
|
||
seqlens_k= seqlens_k,#current_seqlens_k-1 ,#! 这边要对齐kv cahce的长度, # Current positions in cache
|
||
rotary_cos=None, # No rotary embeddings
|
||
rotary_sin=None, # No rotary embeddings
|
||
cache_batch_idx=cache_batch_idx,
|
||
alibi_slopes=None,
|
||
softmax_scale=None,
|
||
causal=False, # Renaming to match function signature
|
||
exact_streaming=exact_streaming,
|
||
window_size_left=-1, # Using individual parameters instead of tuple
|
||
window_size_right=-1,
|
||
block_window_size=self.window_size // self.block_size,
|
||
rotary_interleaved=False,
|
||
num_splits=16,
|
||
# num_topk=self.topk,
|
||
)
|
||
|
||
if repeat_times > 1:
|
||
topk_attn_output = topk_attn_output.view(topk_attn_output.shape[0],topk_attn_output.shape[1],topk_attn_output.shape[2]//repeat_times,repeat_times,-1).mean(dim=-2)
|
||
return topk_attn_output
|
||
|
||
|
||
def _unpad_hidden_states(self, hidden_states,indices):
|
||
# Unpad the hidden states using the indices
|
||
batch_size, seq_len, hidden_dim = hidden_states.shape
|
||
if seq_len ==1:
|
||
return hidden_states.reshape(batch_size , -1, self.head_dim)
|
||
hidden_states = index_first_axis(
|
||
hidden_states.reshape(batch_size * seq_len, -1, self.head_dim), indices
|
||
)
|
||
return hidden_states
|
||
def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
|
||
indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
|
||
batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
|
||
|
||
key_layer = index_first_axis(
|
||
key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
|
||
)
|
||
value_layer = index_first_axis(
|
||
value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
|
||
)
|
||
if query_length == kv_seq_len:
|
||
query_layer = index_first_axis(
|
||
query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
|
||
)
|
||
cu_seqlens_q = cu_seqlens_k
|
||
max_seqlen_in_batch_q = max_seqlen_in_batch_k
|
||
indices_q = indices_k
|
||
elif query_length == 1:
|
||
max_seqlen_in_batch_q = 1
|
||
cu_seqlens_q = torch.arange(
|
||
batch_size + 1, dtype=torch.int32, device=query_layer.device
|
||
) # There is a memcpy here, that is very bad.
|
||
indices_q = cu_seqlens_q[:-1]
|
||
query_layer = query_layer.squeeze(1)
|
||
else:
|
||
# The -q_len: slice assumes left padding.
|
||
attention_mask = attention_mask[:, -query_length:]
|
||
query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
|
||
|
||
return (
|
||
query_layer,
|
||
key_layer,
|
||
value_layer,
|
||
indices_q,
|
||
(cu_seqlens_q, cu_seqlens_k),
|
||
(max_seqlen_in_batch_q, max_seqlen_in_batch_k),
|
||
)
|
||
|
||
|
||
class MiniCPMSdpaAttention(MiniCPMAttention):
|
||
"""
|
||
MiniCPM attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
|
||
`MiniCPMAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
|
||
SDPA API.
|
||
"""
|
||
|
||
# Adapted from MiniCPMAttention.forward
|
||
def forward(
|
||
self,
|
||
hidden_states: torch.Tensor,
|
||
attention_mask: Optional[torch.Tensor] = None,
|
||
position_ids: Optional[torch.LongTensor] = None,
|
||
past_key_value: Optional[Cache] = None,
|
||
output_attentions: bool = False,
|
||
use_cache: bool = False,
|
||
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
||
if output_attentions:
|
||
# TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
|
||
logger.warning_once(
|
||
"MiniCPMModel is using MiniCPMSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
|
||
'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
|
||
)
|
||
return super().forward(
|
||
hidden_states=hidden_states,
|
||
attention_mask=attention_mask,
|
||
position_ids=position_ids,
|
||
past_key_value=past_key_value,
|
||
output_attentions=output_attentions,
|
||
use_cache=use_cache,
|
||
)
|
||
|
||
bsz, q_len, _ = hidden_states.size()
|
||
|
||
query_states = self.q_proj(hidden_states)
|
||
key_states = self.k_proj(hidden_states)
|
||
value_states = self.v_proj(hidden_states)
|
||
|
||
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
||
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
||
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
||
|
||
kv_seq_len = key_states.shape[-2]
|
||
if past_key_value is not None:
|
||
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
|
||
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
|
||
|
||
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
|
||
|
||
if past_key_value is not None:
|
||
cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
|
||
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
||
|
||
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
||
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
||
|
||
if attention_mask is not None:
|
||
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
|
||
raise ValueError(
|
||
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
|
||
)
|
||
|
||
# SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
|
||
# Reference: https://github.com/pytorch/pytorch/issues/112577.
|
||
if query_states.device.type == "cuda" and attention_mask is not None:
|
||
query_states = query_states.contiguous()
|
||
key_states = key_states.contiguous()
|
||
value_states = value_states.contiguous()
|
||
|
||
attn_output = torch.nn.functional.scaled_dot_product_attention(
|
||
query_states,
|
||
key_states,
|
||
value_states,
|
||
attn_mask=attention_mask,
|
||
dropout_p=self.attention_dropout if self.training else 0.0,
|
||
# The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.
|
||
is_causal=self.is_causal and attention_mask is None and q_len > 1,
|
||
)
|
||
|
||
attn_output = attn_output.transpose(1, 2).contiguous()
|
||
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
||
|
||
attn_output = self.o_proj(attn_output)
|
||
|
||
return attn_output, None, past_key_value
|
||
|
||
|
||
MINICPM_ATTENTION_CLASSES = {
|
||
"eager": MiniCPMAttention,
|
||
"flash_attention_2": MiniCPMFlashAttention2,
|
||
"sdpa": MiniCPMSdpaAttention,
|
||
}
|
||
|
||
|
||
class MiniCPMDecoderLayer(nn.Module):
|
||
def __init__(self, config: MiniCPMConfig, layer_idx: int):
|
||
super().__init__()
|
||
self.hidden_size = config.hidden_size
|
||
self.self_attn = MINICPM_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
|
||
|
||
self.mlp = MiniCPMMLP(config)
|
||
self.input_layernorm = MiniCPMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||
self.post_attention_layernorm = MiniCPMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||
|
||
self.scale_depth = config.scale_depth
|
||
self.num_hidden_layers = config.num_hidden_layers
|
||
|
||
def forward(
|
||
self,
|
||
hidden_states: torch.Tensor,
|
||
attention_mask: Optional[torch.Tensor] = None,
|
||
position_ids: Optional[torch.LongTensor] = None,
|
||
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
||
output_attentions: Optional[bool] = False,
|
||
use_cache: Optional[bool] = False,
|
||
**kwargs,
|
||
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
|
||
"""
|
||
Args:
|
||
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
|
||
attention_mask (`torch.FloatTensor`, *optional*):
|
||
attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
|
||
query_sequence_length, key_sequence_length)` if default attention is used.
|
||
output_attentions (`bool`, *optional*):
|
||
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
|
||
returned tensors for more detail.
|
||
use_cache (`bool`, *optional*):
|
||
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
|
||
(see `past_key_values`).
|
||
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
|
||
"""
|
||
if "padding_mask" in kwargs:
|
||
warnings.warn(
|
||
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
|
||
)
|
||
|
||
residual = hidden_states
|
||
hidden_states = self.input_layernorm(hidden_states)
|
||
# Self Attention
|
||
hidden_states, self_attn_weights, present_key_value = self.self_attn(
|
||
hidden_states=hidden_states,
|
||
attention_mask=attention_mask,
|
||
position_ids=position_ids,
|
||
past_key_value=past_key_value,
|
||
output_attentions=output_attentions,
|
||
use_cache=use_cache,
|
||
**kwargs,
|
||
)
|
||
|
||
hidden_states = residual + hidden_states * (self.scale_depth / math.sqrt(self.num_hidden_layers))
|
||
|
||
# Fully Connected
|
||
residual = hidden_states
|
||
hidden_states = self.post_attention_layernorm(hidden_states)
|
||
|
||
hidden_states = self.mlp(hidden_states)
|
||
hidden_states = residual + hidden_states * (self.scale_depth / math.sqrt(self.num_hidden_layers))
|
||
|
||
outputs = (hidden_states,)
|
||
|
||
if output_attentions:
|
||
outputs += (self_attn_weights,)
|
||
|
||
if use_cache:
|
||
outputs += (present_key_value,)
|
||
|
||
return outputs
|
||
|
||
|
||
MINICPM_START_DOCSTRING = r"""
|
||
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
|
||
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
|
||
etc.)
|
||
|
||
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
|
||
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
|
||
and behavior.
|
||
|
||
Parameters:
|
||
config ([`MiniCPMConfig`]):
|
||
Model configuration class with all the parameters of the model. Initializing with a config file does not
|
||
load the weights associated with the model, only the configuration. Check out the
|
||
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
|
||
"""
|
||
|
||
|
||
@add_start_docstrings(
|
||
"The bare MiniCPM Model outputting raw hidden-states without any specific head on top.",
|
||
MINICPM_START_DOCSTRING,
|
||
)
|
||
class MiniCPMPreTrainedModel(PreTrainedModel):
|
||
config_class = MiniCPMConfig
|
||
base_model_prefix = "model"
|
||
supports_gradient_checkpointing = True
|
||
_no_split_modules = ["MiniCPMDecoderLayer"]
|
||
_skip_keys_device_placement = "past_key_values"
|
||
_supports_flash_attn_2 = True
|
||
_supports_sdpa = True
|
||
_supports_cache_class = True
|
||
|
||
def _init_weights(self, module):
|
||
std = self.config.initializer_range
|
||
if isinstance(module, nn.Linear):
|
||
module.weight.data.normal_(mean=0.0, std=std)
|
||
if module.bias is not None:
|
||
module.bias.data.zero_()
|
||
elif isinstance(module, nn.Embedding):
|
||
module.weight.data.normal_(mean=0.0, std=std)
|
||
if module.padding_idx is not None:
|
||
module.weight.data[module.padding_idx].zero_()
|
||
|
||
|
||
MINICPM_INPUTS_DOCSTRING = r"""
|
||
Args:
|
||
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
|
||
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
|
||
it.
|
||
|
||
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
||
[`PreTrainedTokenizer.__call__`] for details.
|
||
|
||
[What are input IDs?](../glossary#input-ids)
|
||
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
|
||
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
||
|
||
- 1 for tokens that are **not masked**,
|
||
- 0 for tokens that are **masked**.
|
||
|
||
[What are attention masks?](../glossary#attention-mask)
|
||
|
||
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
||
[`PreTrainedTokenizer.__call__`] for details.
|
||
|
||
If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
|
||
`past_key_values`).
|
||
|
||
If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
|
||
and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
|
||
information on the default strategy.
|
||
|
||
- 1 indicates the head is **not masked**,
|
||
- 0 indicates the head is **masked**.
|
||
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
||
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
|
||
config.n_positions - 1]`.
|
||
|
||
[What are position IDs?](../glossary#position-ids)
|
||
past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
|
||
Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
|
||
blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
|
||
returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
|
||
|
||
Two formats are allowed:
|
||
- a [`~cache_utils.Cache`] instance;
|
||
- Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
|
||
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
|
||
cache format.
|
||
|
||
The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
|
||
legacy cache format will be returned.
|
||
|
||
If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
|
||
have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
|
||
of shape `(batch_size, sequence_length)`.
|
||
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
|
||
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
|
||
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
|
||
model's internal embedding lookup matrix.
|
||
use_cache (`bool`, *optional*):
|
||
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
|
||
`past_key_values`).
|
||
output_attentions (`bool`, *optional*):
|
||
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
|
||
tensors for more detail.
|
||
output_hidden_states (`bool`, *optional*):
|
||
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
|
||
more detail.
|
||
return_dict (`bool`, *optional*):
|
||
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
||
"""
|
||
|
||
|
||
@add_start_docstrings(
|
||
"The bare MiniCPM Model outputting raw hidden-states without any specific head on top.",
|
||
MINICPM_START_DOCSTRING,
|
||
)
|
||
class MiniCPMModel(MiniCPMPreTrainedModel):
|
||
"""
|
||
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MiniCPMDecoderLayer`]
|
||
|
||
Args:
|
||
config: MiniCPMConfig
|
||
"""
|
||
|
||
def __init__(self, config: MiniCPMConfig):
|
||
super().__init__(config)
|
||
assert config._attn_implementation == "flash_attention_2", "Only flash_attention_2 is supported for hybrid attention"
|
||
self.padding_idx = config.pad_token_id
|
||
self.vocab_size = config.vocab_size
|
||
|
||
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
|
||
self.layers = nn.ModuleList(
|
||
[MiniCPMDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
|
||
)
|
||
self._use_sdpa = config._attn_implementation == "sdpa"
|
||
self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
|
||
|
||
self.norm = MiniCPMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||
|
||
self.gradient_checkpointing = True
|
||
# Initialize weights and apply final processing
|
||
self.post_init()
|
||
|
||
def get_input_embeddings(self):
|
||
return self.embed_tokens
|
||
|
||
def set_input_embeddings(self, value):
|
||
self.embed_tokens = value
|
||
|
||
@add_start_docstrings_to_model_forward(MINICPM_INPUTS_DOCSTRING)
|
||
def forward(
|
||
self,
|
||
input_ids: torch.LongTensor = None,
|
||
attention_mask: Optional[torch.Tensor] = None,
|
||
position_ids: Optional[torch.LongTensor] = None,
|
||
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
||
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||
use_cache: Optional[bool] = None,
|
||
output_attentions: Optional[bool] = None,
|
||
output_hidden_states: Optional[bool] = None,
|
||
return_dict: Optional[bool] = None,
|
||
) -> Union[Tuple, BaseModelOutputWithPast]:
|
||
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
||
output_hidden_states = (
|
||
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
||
)
|
||
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
||
|
||
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||
|
||
# retrieve input_ids and inputs_embeds
|
||
if input_ids is not None and inputs_embeds is not None:
|
||
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
|
||
elif input_ids is not None:
|
||
batch_size, seq_length = input_ids.shape[:2]
|
||
elif inputs_embeds is not None:
|
||
batch_size, seq_length = inputs_embeds.shape[:2]
|
||
else:
|
||
raise ValueError("You have to specify either input_ids or inputs_embeds")
|
||
|
||
if self.gradient_checkpointing and self.training:
|
||
if use_cache:
|
||
logger.warning_once(
|
||
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
||
)
|
||
use_cache = False
|
||
|
||
past_key_values_length = 0
|
||
globals()['token_now'] +=1
|
||
if use_cache:
|
||
# breakpoint()
|
||
use_legacy_cache = not isinstance(past_key_values, Cache)
|
||
if use_legacy_cache:
|
||
raise ValueError(
|
||
"必须使用新的past_key_values格式, 例如Cache类, 而不是旧的tuple格式."
|
||
)
|
||
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
|
||
# 添加新的query_cache
|
||
past_key_values_length = past_key_values.get_usable_length(seq_length)
|
||
if past_key_values_length == 0:
|
||
past_key_values = DynamicCacheQKV()
|
||
else:
|
||
assert isinstance(past_key_values, DynamicCacheQKV), "past_key_values must be a DynamicCacheQKV instance"
|
||
|
||
if position_ids is None:
|
||
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
||
position_ids = torch.arange(
|
||
past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
|
||
)
|
||
position_ids = position_ids.unsqueeze(0)
|
||
|
||
if inputs_embeds is None:
|
||
inputs_embeds = self.embed_tokens(input_ids) * self.config.scale_emb
|
||
|
||
# if self._use_flash_attention_2:
|
||
# # 2d mask is passed through the layers
|
||
# # attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
|
||
# if attention_mask is None:
|
||
# raise ValueError(
|
||
# f"需要attention_mask for flash attention, but got {attention_mask}."
|
||
# )
|
||
elif self._use_sdpa and not output_attentions:
|
||
# output_attentions=True can not be supported when using SDPA, and we fall back on
|
||
# the manual implementation that requires a 4D causal mask in all cases.
|
||
attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
|
||
attention_mask,
|
||
(batch_size, seq_length),
|
||
inputs_embeds,
|
||
past_key_values_length,
|
||
)
|
||
else:
|
||
# 4d mask is passed through the layers
|
||
attention_mask = _prepare_4d_causal_attention_mask(
|
||
attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
|
||
)
|
||
|
||
# embed positions
|
||
hidden_states = inputs_embeds
|
||
|
||
# decoder layers
|
||
all_hidden_states = () if output_hidden_states else None
|
||
all_self_attns = () if output_attentions else None
|
||
next_decoder_cache = None
|
||
|
||
for decoder_layer in self.layers:
|
||
if output_hidden_states:
|
||
all_hidden_states += (hidden_states,)
|
||
|
||
if self.gradient_checkpointing and self.training:
|
||
layer_outputs = self._gradient_checkpointing_func(
|
||
decoder_layer.__call__,
|
||
hidden_states,
|
||
attention_mask,
|
||
position_ids,
|
||
past_key_values,
|
||
output_attentions,
|
||
use_cache,
|
||
)
|
||
else:
|
||
layer_outputs = decoder_layer(
|
||
hidden_states,
|
||
attention_mask=attention_mask,
|
||
position_ids=position_ids,
|
||
past_key_value=past_key_values,
|
||
output_attentions=output_attentions,
|
||
use_cache=use_cache,
|
||
)
|
||
|
||
hidden_states = layer_outputs[0]
|
||
|
||
if use_cache:
|
||
next_decoder_cache = layer_outputs[2 if output_attentions else 1]
|
||
|
||
if output_attentions:
|
||
all_self_attns += (layer_outputs[1],)
|
||
|
||
hidden_states = self.norm(hidden_states)
|
||
|
||
# add hidden states from the last decoder layer
|
||
if output_hidden_states:
|
||
all_hidden_states += (hidden_states,)
|
||
|
||
next_cache = None
|
||
if use_cache:
|
||
next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
|
||
if not return_dict:
|
||
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
|
||
return BaseModelOutputWithPast(
|
||
last_hidden_state=hidden_states,
|
||
past_key_values=next_cache,
|
||
hidden_states=all_hidden_states,
|
||
attentions=all_self_attns,
|
||
)
|
||
|
||
|
||
class MiniCPMForCausalLM(MiniCPMPreTrainedModel):
|
||
_tied_weights_keys = ["lm_head.weight"]
|
||
|
||
def __init__(self, config):
|
||
super().__init__(config)
|
||
self.model = MiniCPMModel(config)
|
||
self.vocab_size = config.vocab_size
|
||
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
||
|
||
# Initialize weights and apply final processing
|
||
self.post_init()
|
||
|
||
def get_input_embeddings(self):
|
||
return self.model.embed_tokens
|
||
|
||
def set_input_embeddings(self, value):
|
||
self.model.embed_tokens = value
|
||
|
||
def get_output_embeddings(self):
|
||
return self.lm_head
|
||
|
||
def set_output_embeddings(self, new_embeddings):
|
||
self.lm_head = new_embeddings
|
||
|
||
def set_decoder(self, decoder):
|
||
self.model = decoder
|
||
|
||
def get_decoder(self):
|
||
return self.model
|
||
|
||
@add_start_docstrings_to_model_forward(MINICPM_INPUTS_DOCSTRING)
|
||
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
|
||
def forward(
|
||
self,
|
||
input_ids: torch.LongTensor = None,
|
||
attention_mask: Optional[torch.Tensor] = None,
|
||
position_ids: Optional[torch.LongTensor] = None,
|
||
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
||
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||
labels: Optional[torch.LongTensor] = None,
|
||
use_cache: Optional[bool] = None,
|
||
output_attentions: Optional[bool] = None,
|
||
output_hidden_states: Optional[bool] = None,
|
||
return_dict: Optional[bool] = None,
|
||
) -> Union[Tuple, CausalLMOutputWithPast]:
|
||
r"""
|
||
Args:
|
||
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
||
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
|
||
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
|
||
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
|
||
|
||
Returns:
|
||
|
||
Example:
|
||
|
||
```python
|
||
>>> from transformers import AutoTokenizer, MiniCPMForCausalLM
|
||
|
||
>>> model = MiniCPMForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
|
||
>>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
|
||
|
||
>>> prompt = "Hey, are you conscious? Can you talk to me?"
|
||
>>> inputs = tokenizer(prompt, return_tensors="pt")
|
||
|
||
>>> # Generate
|
||
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
|
||
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
||
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
|
||
```"""
|
||
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
||
output_hidden_states = (
|
||
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
||
)
|
||
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||
|
||
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
|
||
outputs = self.model(
|
||
input_ids=input_ids,
|
||
attention_mask=attention_mask,
|
||
position_ids=position_ids,
|
||
past_key_values=past_key_values,
|
||
inputs_embeds=inputs_embeds,
|
||
use_cache=use_cache,
|
||
output_attentions=output_attentions,
|
||
output_hidden_states=output_hidden_states,
|
||
return_dict=return_dict,
|
||
)
|
||
|
||
hidden_states = outputs[0]
|
||
if self.config.pretraining_tp > 1:
|
||
lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
|
||
logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)]
|
||
logits = torch.cat(logits, dim=-1)
|
||
else:
|
||
logits = self.lm_head(hidden_states / (self.config.hidden_size / self.config.dim_model_base))
|
||
logits = logits.float()
|
||
|
||
loss = None
|
||
if labels is not None:
|
||
# Shift so that tokens < n predict n
|
||
shift_logits = logits[..., :-1, :].contiguous()
|
||
shift_labels = labels[..., 1:].contiguous()
|
||
# Flatten the tokens
|
||
loss_fct = CrossEntropyLoss()
|
||
shift_logits = shift_logits.view(-1, self.config.vocab_size)
|
||
shift_labels = shift_labels.view(-1)
|
||
# Enable model parallelism
|
||
shift_labels = shift_labels.to(shift_logits.device)
|
||
loss = loss_fct(shift_logits, shift_labels)
|
||
|
||
if not return_dict:
|
||
output = (logits,) + outputs[1:]
|
||
return (loss,) + output if loss is not None else output
|
||
|
||
return CausalLMOutputWithPast(
|
||
loss=loss,
|
||
logits=logits,
|
||
past_key_values=outputs.past_key_values,
|
||
hidden_states=outputs.hidden_states,
|
||
attentions=outputs.attentions,
|
||
)
|
||
|
||
def prepare_inputs_for_generation(
|
||
self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
|
||
):
|
||
if past_key_values is not None:
|
||
if isinstance(past_key_values, Cache):
|
||
cache_length = past_key_values.get_seq_length()
|
||
past_length = past_key_values.seen_tokens
|
||
max_cache_length = None # past_key_values.get_max_length() #! 换成max, 因为如果不是max的话,会裁剪attentio mask
|
||
else:
|
||
cache_length = past_length = past_key_values[0][0].shape[2]
|
||
max_cache_length = None
|
||
|
||
# Keep only the unprocessed tokens:
|
||
# 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
|
||
# some of the inputs are exclusivelly passed as part of the cache (e.g. when passing input_embeds as
|
||
# input)
|
||
if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
|
||
input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
|
||
# 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
|
||
# input_ids based on the past_length.
|
||
elif past_length < input_ids.shape[1]:
|
||
input_ids = input_ids[:, past_length:]
|
||
# 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
|
||
|
||
# If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
|
||
if (
|
||
max_cache_length is not None
|
||
and attention_mask is not None
|
||
and cache_length + input_ids.shape[1] > max_cache_length
|
||
):
|
||
attention_mask = attention_mask[:, -max_cache_length:]
|
||
|
||
position_ids = kwargs.get("position_ids", None)
|
||
if attention_mask is not None and position_ids is None:
|
||
# create position_ids on the fly for batch generation
|
||
position_ids = attention_mask.long().cumsum(-1) - 1
|
||
position_ids.masked_fill_(attention_mask == 0, 1)
|
||
if past_key_values:
|
||
position_ids = position_ids[:, -input_ids.shape[1] :]
|
||
|
||
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
|
||
if inputs_embeds is not None and past_key_values is None:
|
||
model_inputs = {"inputs_embeds": inputs_embeds}
|
||
else:
|
||
model_inputs = {"input_ids": input_ids}
|
||
|
||
model_inputs.update(
|
||
{
|
||
"position_ids": position_ids,
|
||
"past_key_values": past_key_values,
|
||
"use_cache": kwargs.get("use_cache"),
|
||
"attention_mask": attention_mask,
|
||
}
|
||
)
|
||
return model_inputs
|
||
|
||
@staticmethod
|
||
def _reorder_cache(past_key_values, beam_idx):
|
||
reordered_past = ()
|
||
for layer_past in past_key_values:
|
||
reordered_past += (
|
||
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
|
||
)
|
||
return reordered_past
|
||
|
||
@torch.inference_mode()
|
||
def chat(self, tokenizer, query: str, history: List[Dict] = None, role: str = "user",
|
||
max_length: int = 4096, num_beams=1, do_sample=True, top_p=0.8, temperature=0.3, logits_processor=None,
|
||
**kwargs):
|
||
if history is None:
|
||
history = []
|
||
if logits_processor:
|
||
gen_kwargs = {"max_length": max_length, "num_beams": num_beams, "do_sample": do_sample, "top_p": top_p,
|
||
"temperature": temperature, "logits_processor": logits_processor, **kwargs}
|
||
else:
|
||
gen_kwargs = {"max_length": max_length, "num_beams": num_beams, "do_sample": do_sample, "top_p": top_p,
|
||
"temperature": temperature, "logits_processor": logits_processor, **kwargs}
|
||
|
||
history.append({"role": role, "content": query})
|
||
history_str = tokenizer.apply_chat_template(history, tokenize=False, add_generation_prompt=False)
|
||
inputs = tokenizer(history_str, return_tensors='pt').to(self.device)
|
||
outputs = self.generate(**inputs, **gen_kwargs)
|
||
outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):-1]
|
||
response = tokenizer.decode(outputs)
|
||
pattern = re.compile(r".*?(?=<AI>|<用户>)", re.DOTALL)
|
||
matches = pattern.findall(response)
|
||
if len(matches) > 0:
|
||
response = matches[0]
|
||
history.append({"role": "assistant", "content": response})
|
||
return response, history
|
||
|
||
|
||
@add_start_docstrings(
|
||
"""
|
||
The MiniCPM Model transformer with a sequence classification head on top (linear layer).
|
||
|
||
[`MiniCPMForSequenceClassification`] uses the last token in order to do the classification, as other causal models
|
||
(e.g. GPT-2) do.
|
||
|
||
Since it does classification on the last token, it requires to know the position of the last token. If a
|
||
`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
|
||
no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
|
||
padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
|
||
each row of the batch).
|
||
""",
|
||
MINICPM_START_DOCSTRING,
|
||
)
|
||
class MiniCPMForSequenceClassification(MiniCPMPreTrainedModel):
|
||
def __init__(self, config):
|
||
super().__init__(config)
|
||
self.num_labels = config.num_labels
|
||
self.model = MiniCPMModel(config)
|
||
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
|
||
|
||
# Initialize weights and apply final processing
|
||
self.post_init()
|
||
|
||
def get_input_embeddings(self):
|
||
return self.model.embed_tokens
|
||
|
||
def set_input_embeddings(self, value):
|
||
self.model.embed_tokens = value
|
||
|
||
@add_start_docstrings_to_model_forward(MINICPM_INPUTS_DOCSTRING)
|
||
def forward(
|
||
self,
|
||
input_ids: torch.LongTensor = None,
|
||
attention_mask: Optional[torch.Tensor] = None,
|
||
position_ids: Optional[torch.LongTensor] = None,
|
||
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
||
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||
labels: Optional[torch.LongTensor] = None,
|
||
use_cache: Optional[bool] = None,
|
||
output_attentions: Optional[bool] = None,
|
||
output_hidden_states: Optional[bool] = None,
|
||
return_dict: Optional[bool] = None,
|
||
) -> Union[Tuple, SequenceClassifierOutputWithPast]:
|
||
r"""
|
||
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
||
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
|
||
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
||
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
||
"""
|
||
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||
|
||
transformer_outputs = self.model(
|
||
input_ids,
|
||
attention_mask=attention_mask,
|
||
position_ids=position_ids,
|
||
past_key_values=past_key_values,
|
||
inputs_embeds=inputs_embeds,
|
||
use_cache=use_cache,
|
||
output_attentions=output_attentions,
|
||
output_hidden_states=output_hidden_states,
|
||
return_dict=return_dict,
|
||
)
|
||
hidden_states = transformer_outputs[0]
|
||
logits = self.score(hidden_states)
|
||
|
||
if input_ids is not None:
|
||
batch_size = input_ids.shape[0]
|
||
else:
|
||
batch_size = inputs_embeds.shape[0]
|
||
|
||
if self.config.pad_token_id is None and batch_size != 1:
|
||
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
|
||
if self.config.pad_token_id is None:
|
||
sequence_lengths = -1
|
||
else:
|
||
if input_ids is not None:
|
||
sequence_lengths = (torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1).to(
|
||
logits.device
|
||
)
|
||
else:
|
||
sequence_lengths = -1
|
||
|
||
pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
|
||
|
||
loss = None
|
||
if labels is not None:
|
||
labels = labels.to(logits.device)
|
||
if self.config.problem_type is None:
|
||
if self.num_labels == 1:
|
||
self.config.problem_type = "regression"
|
||
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
|
||
self.config.problem_type = "single_label_classification"
|
||
else:
|
||
self.config.problem_type = "multi_label_classification"
|
||
|
||
if self.config.problem_type == "regression":
|
||
loss_fct = MSELoss()
|
||
if self.num_labels == 1:
|
||
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
|
||
else:
|
||
loss = loss_fct(pooled_logits, labels)
|
||
elif self.config.problem_type == "single_label_classification":
|
||
loss_fct = CrossEntropyLoss()
|
||
loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
|
||
elif self.config.problem_type == "multi_label_classification":
|
||
loss_fct = BCEWithLogitsLoss()
|
||
loss = loss_fct(pooled_logits, labels)
|
||
if not return_dict:
|
||
output = (pooled_logits,) + transformer_outputs[1:]
|
||
return ((loss,) + output) if loss is not None else output
|
||
|
||
return SequenceClassifierOutputWithPast(
|
||
loss=loss,
|
||
logits=pooled_logits,
|
||
past_key_values=transformer_outputs.past_key_values,
|
||
hidden_states=transformer_outputs.hidden_states,
|
||
attentions=transformer_outputs.attentions,
|
||
)
|
||
|
||
|