[2025.06.10] adjust directory structure
This commit is contained in:
coffee
Executable
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from .configuration_dolly import DollyConfig
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from transformers.modeling_utils import PreTrainedModel, ALL_ATTENTION_FUNCTIONS
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from functools import partial
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from typing import Callable, Optional, Tuple, Union
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from transformers.activations import ACT2FN
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from transformers.modeling_rope_utils import dynamic_rope_update, ROPE_INIT_FUNCTIONS
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import torch
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from torch import nn
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from transformers.utils import can_return_tuple
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from transformers.generation import GenerationMixin
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from transformers.modeling_attn_mask_utils import AttentionMaskConverter
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from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
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from transformers.utils.deprecation import deprecate_kwarg
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from transformers.utils import logging,LossKwargs,add_code_sample_docstrings,add_start_docstrings, add_start_docstrings_to_model_forward
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from transformers.processing_utils import Unpack
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from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast,QuestionAnsweringModelOutput, SequenceClassifierOutputWithPast, TokenClassifierOutput
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from transformers.cache_utils import Cache, DynamicCache, SlidingWindowCache, StaticCache
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logger = logging.get_logger(__name__)
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class DollyRMSNorm(nn.Module):
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'''
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输入的形状为batch_size*heads*seq_len*heads_dim,
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仅在最后一个维度进行归一化，
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参数量为heads_dim
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'''
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def __init__(self, hidden_size, eps=1e-6):
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super().__init__()
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self.weight = nn.Parameter(torch.ones(hidden_size))
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#添加一个非零值，防止除零
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self.variance_epsilon = eps
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def forward(self, hidden_states):
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input_dtype = hidden_states.dtype
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hidden_states = hidden_states.to(torch.float32)
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#x=(x^2)/n
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variance = hidden_states.pow(2).mean(-1, keepdim=True)
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#归一化并缩放
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hidden_states = (hidden_states * torch.rsqrt(variance + self.variance_epsilon)) * self.weight
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return hidden_states.to(input_dtype)
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def extra_repr(self):
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return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
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class DollyMLP(nn.Module):
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'''
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DollyMLP层的实现, code from Qwen3
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可训练参数量为: hidden_size * intermediate_size + hidden_size * intermediate_size + intermediate_size * hidden_size
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'''
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#只传入需要的三个参数
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def __init__(self, hidden_size,intermediate_size,hidden_act):
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super().__init__()
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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#可训练参数量 hidden_size * intermediate_size
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self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
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#可训练参数量 hidden_size * intermediate_size
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self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
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#可训练参数量 intermediate_size * hidden_size
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self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
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#默认激活函数SiLU，=Sigmoid*x,输出范围(0, ∞)
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self.act_fn = ACT2FN[hidden_act]
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def forward(self, x):
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#通过激活函数后的向量[batch_size*seq_len*intermediate_size]和扩展后的向量进行元素乘
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down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
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return down_proj
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class DollyRotaryEmbedding(nn.Module):
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def __init__(self, config: DollyConfig, device=None):
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super().__init__()
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#获取"rope_type"的值，如果没有使用"default"
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self.config = config
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if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
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self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
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else:
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self.rope_type = "default"
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# self.rope_type = getattr(config, "rope_type", "default")
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self.original_max_seq_len = config.max_position_embeddings
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self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
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#得到频率倒数,基础版本公式如下：
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#inv_freq = 1.0 / (rope_theta ** (torch.arange(0, dim, 2) / dim))
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#得到一个长度为attention_dim/2的小数序列，如attention_dim=128,inv_freq=64
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inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
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#注册缓存区
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self.register_buffer("inv_freq", inv_freq, persistent=False)
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@torch.no_grad()
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@dynamic_rope_update
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def forward(self, x, position_ids):
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#将inv_freq进行维度扩展首先扩展为1*64*1，然后扩展为batch_size*64*1
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inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
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#position_ids进行扩展batch_size* 1*seq_len
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position_ids_expanded = position_ids[:, None, :].float()
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device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
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#强制float32，禁止混合精度
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with torch.autocast(device_type=device_type, enabled=False):
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#矩阵乘以后，形状变为batch_size*64*seq_len后，再转为batch_size*seq_len*64
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freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
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#沿64的维度进行拼接batch_size*seq_len*128
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emb = torch.cat((freqs, freqs), dim=-1)
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#emb的cos()部分乘缩放系数:batch_size*seq_len*128
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cos = emb.cos() * self.attention_scaling
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#emb的sin()部分乘缩放系数:batch_size*seq_len*128
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sin = emb.sin() * self.attention_scaling
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return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
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def rotate_half(x):
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#x分为两段，各取batch_size*heads_num*seq_len*64
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x1 = x[..., : x.shape[-1] // 2]
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x2 = x[..., x.shape[-1] // 2 :]
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#下部分*-1,按下+上重新组合x
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return torch.cat((-x2, x1), dim=-1)
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def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1):
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'''
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对q和k分别使用RoPE
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'''
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#由batch_size*seq_len*128增加一个维度变为batch_size*1*seq_len*128
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cos = cos.unsqueeze(unsqueeze_dim)
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sin = sin.unsqueeze(unsqueeze_dim)
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#q:batch_size*heads_num*seq_len*128和sin及con进行元素乘batch_size*1*seq_len*128
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#cos部分直接乘，sin部分进行位置变化后再乘，最后相加完成位置编码的嵌入
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q_embed = (q * cos) + (rotate_half(q) * sin)
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k_embed = (k * cos) + (rotate_half(k) * sin)
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return q_embed, k_embed
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def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
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'''
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在GQA中, k和v的heads数量小于q的heads数量,
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所以在进行attention计算前需要进行repeat_kv操作,
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保持第2维度的数量相等,n=config.num_attention_heads // config.num_key_value_heads
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'''
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#GQA中num_key_value_heads的数量小于attention heads数量
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batch, num_key_value_heads, slen, head_dim = hidden_states.shape
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if n_rep == 1:
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return hidden_states
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#在第3维度[index=2]进行扩充
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hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
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return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
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def eager_attention_forward(
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module: nn.Module,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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attention_mask: Optional[torch.Tensor],
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scaling: float,
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dropout: float = 0.0,
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**kwargs,
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):
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#repeat kv，确保形状和query一致
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key_states = repeat_kv(key, module.num_key_value_groups)
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value_states = repeat_kv(value, module.num_key_value_groups)
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#计算attention score
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attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
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#casual attention
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if attention_mask is not None:
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#causal_mask应当为下三角为0，上三角为负无穷
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causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
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attn_weights = attn_weights + causal_mask
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# sofmax计算
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attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
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# 训练时给attn_weights的随机位置置零
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attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
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#乘value过程
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attn_output = torch.matmul(attn_weights, value_states)
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#最后转置，形状变为batch_size*seq_len*num_heads*head_dim
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attn_output = attn_output.transpose(1, 2).contiguous()
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return attn_output, attn_weights
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class DollyAttention(nn.Module):
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"""
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Attention in transformer
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"""
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def __init__(self, config: DollyConfig, layer_idx: int):
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super().__init__()
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self.config = config
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self.layer_idx = layer_idx
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self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
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#num_key_value_groups即repeat kv的n
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self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
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#attention socre的缩放系数
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self.scaling = self.head_dim**-0.5
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self.attention_dropout = config.attention_dropout
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self.is_causal = True
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#Q权重矩阵
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self.q_proj = nn.Linear(
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config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
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)
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#K权重矩阵
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self.k_proj = nn.Linear(
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config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
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)
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#V权重矩阵
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self.v_proj = nn.Linear(
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config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
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)
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#Attention的最后一步，线性连接层
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self.o_proj = nn.Linear(
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config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias
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)
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#给输入q和k进行归一化操作，使用RMSNom，注意仅在最后一个维度进行归一化，参数量为head_dim
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self.q_norm = DollyRMSNorm(self.head_dim, eps=config.rms_norm_eps)
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self.k_norm = DollyRMSNorm(self.head_dim, eps=config.rms_norm_eps)
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#支持滑动窗口
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self.sliding_window = config.sliding_window
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if not (
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self.config.use_sliding_window
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and getattr(self.config, "sliding_window", None) is not None
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and self.layer_idx >= self.config.max_window_layers
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):
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self.sliding_window = None
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def forward(
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self,
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hidden_states: torch.Tensor,
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position_embeddings: Tuple[torch.Tensor, torch.Tensor],
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attention_mask: Optional[torch.Tensor],
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past_key_value: Optional[Cache] = None,
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cache_position: Optional[torch.LongTensor] = None,
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**kwargs: Unpack[FlashAttentionKwargs],
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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#input_shape: batch_size* seq_len
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input_shape = hidden_states.shape[:-1]
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#hidden_shape: batch_size* seq_len*-1* head_dim
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hidden_shape = (*input_shape, -1, self.head_dim)
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#将输入经过Q矩阵变为query，然后调整形状为batch_size* seq_len*-1* head_dim，然后进行归一化，再调整形状batch_size* -1*seq_len* head_dim
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query_states = self.q_norm(self.q_proj(hidden_states).view(hidden_shape)).transpose(1, 2)
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#将输入经过K矩阵变为key，然后调整形状为batch_size* seq_len*-1* head_dim，然后进行归一化，再调整形状batch_size* -1*seq_len* head_dim
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key_states = self.k_norm(self.k_proj(hidden_states).view(hidden_shape)).transpose(1, 2)
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#将输入经过V矩阵变为value，然后调整形状为batch_size* seq_len*-1* head_dim，再调整形状batch_size* -1*seq_len* head_dim
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value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
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#得到嵌入旋转位置编码信息后的query和key
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cos, sin = position_embeddings
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query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
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if past_key_value is not None:
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# KVCache的cache更新位置信息和kv值
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cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
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key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
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attention_interface: Callable = eager_attention_forward
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if self.config._attn_implementation != "eager":
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if self.config._attn_implementation == "sdpa" and kwargs.get("output_attentions", False):
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logger.warning_once(
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"`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to "
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'eager attention. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
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)
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else:
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attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
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#attention的具体计算，返回结果和中间的attention score
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attn_output, attn_weights = attention_interface(
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self,
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query_states,
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key_states,
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value_states,
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attention_mask,
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dropout=0.0 if not self.training else self.attention_dropout,
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scaling=self.scaling,
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sliding_window=self.sliding_window,
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**kwargs,
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)
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#将batch_size*seq_len*num_heads*head_dim 变为 batch_size*seq_len*hiddent_size
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attn_output = attn_output.reshape(*input_shape, -1).contiguous()
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#attention的线性层
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attn_output = self.o_proj(attn_output)
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return attn_output, attn_weights
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class DollyDecoderLayer(nn.Module):
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def __init__(self, config: DollyConfig, layer_idx: int):
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super().__init__()
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self.hidden_size = config.hidden_size
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self.self_attn = DollyAttention(config=config, layer_idx=layer_idx)
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self.mlp = DollyMLP(config.hidden_size, config.intermediate_size, config.hidden_act)
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self.input_layernorm = DollyRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.post_attention_layernorm = DollyRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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#仅flash_attention_2支持滑动窗口
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if config.sliding_window and config._attn_implementation != "flash_attention_2":
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config.sliding_window = None
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_value: Optional[Cache] = None,
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output_attentions: Optional[bool] = False,
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use_cache: Optional[bool] = False,
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cache_position: Optional[torch.LongTensor] = None,
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position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
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**kwargs: Unpack[FlashAttentionKwargs],
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) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
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#保留最开始的值，后面做残差网络
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residual = hidden_states
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#step1:归一化
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hidden_states = self.input_layernorm(hidden_states)
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#step2: Self Attention
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hidden_states, self_attn_weights = self.self_attn(
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hidden_states=hidden_states,
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attention_mask=attention_mask,
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position_ids=position_ids,
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past_key_value=past_key_value,
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output_attentions=output_attentions,
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use_cache=use_cache,
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cache_position=cache_position,
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position_embeddings=position_embeddings,
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**kwargs,
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)
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#step3:残差
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hidden_states = residual + hidden_states
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residual = hidden_states
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#step4:归一化
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hidden_states = self.post_attention_layernorm(hidden_states)
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#step5:MLP
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hidden_states = self.mlp(hidden_states)
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#step6:残差
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hidden_states = residual + hidden_states
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outputs = (hidden_states,)
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#是否输出attention weights
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if output_attentions:
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outputs += (self_attn_weights,)
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return outputs
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class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ...
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class DollyPreTrainedModel(PreTrainedModel):
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config_class = DollyConfig
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base_model_prefix = "model"
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supports_gradient_checkpointing = True
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_no_split_modules = ["DollyDecoderLayer"]
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_skip_keys_device_placement = ["past_key_values"]
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_supports_flash_attn_2 = True
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_supports_sdpa = True
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_supports_flex_attn = True
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_supports_cache_class = True
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_supports_quantized_cache = True
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_supports_static_cache = True
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_supports_attention_backend = True
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def _init_weights(self, module):
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std = self.config.initializer_range
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if isinstance(module, nn.Linear):
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#初始线性层化权重参数
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module.weight.data.normal_(mean=0.0, std=std)
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if module.bias is not None:
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module.bias.data.zero_()
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elif isinstance(module, nn.Embedding):
|
||||
#初始化embedding层参数
|
||||
module.weight.data.normal_(mean=0.0, std=std)
|
||||
if module.padding_idx is not None:
|
||||
module.weight.data[module.padding_idx].zero_()
|
||||
|
||||
class DollyModel(DollyPreTrainedModel):
|
||||
def __init__(self, config: DollyConfig):
|
||||
super().__init__(config)
|
||||
self.padding_idx = config.pad_token_id
|
||||
self.vocab_size = config.vocab_size
|
||||
#embedding层
|
||||
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
|
||||
#transformer层
|
||||
self.layers = nn.ModuleList(
|
||||
[DollyDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
|
||||
)
|
||||
#RMSNorm层
|
||||
self.norm = DollyRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||||
#RoPE层
|
||||
self.rotary_emb = DollyRotaryEmbedding(config=config)
|
||||
self.gradient_checkpointing = False
|
||||
#进行模型初始化
|
||||
self.post_init()
|
||||
|
||||
def get_input_embeddings(self):
|
||||
return self.embed_tokens
|
||||
|
||||
def set_input_embeddings(self, value):
|
||||
self.embed_tokens = value
|
||||
|
||||
@can_return_tuple
|
||||
def forward(
|
||||
self,
|
||||
input_ids: Optional[torch.LongTensor] = None,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = None,
|
||||
past_key_values: Optional[Cache] = None,
|
||||
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||||
use_cache: Optional[bool] = None,
|
||||
output_attentions: Optional[bool] = None,
|
||||
output_hidden_states: Optional[bool] = None,
|
||||
cache_position: Optional[torch.LongTensor] = None,
|
||||
**flash_attn_kwargs: Unpack[FlashAttentionKwargs],
|
||||
) -> BaseModelOutputWithPast:
|
||||
#是否输出attention的计算结果
|
||||
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
||||
#是否输出隐藏层状态
|
||||
output_hidden_states = (
|
||||
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
||||
)
|
||||
#是否使用cache
|
||||
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
||||
|
||||
if (input_ids is None) ^ (inputs_embeds is not None):
|
||||
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
|
||||
|
||||
if self.gradient_checkpointing and self.training and use_cache:
|
||||
logger.warning_once(
|
||||
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
|
||||
)
|
||||
use_cache = False
|
||||
|
||||
if not isinstance(past_key_values, (type(None), Cache)):
|
||||
raise ValueError("The `past_key_values` should be either a `Cache` object or `None`.")
|
||||
|
||||
if inputs_embeds is None:
|
||||
inputs_embeds = self.embed_tokens(input_ids)
|
||||
|
||||
if use_cache and past_key_values is None:
|
||||
past_key_values = DynamicCache()
|
||||
|
||||
if cache_position is None:
|
||||
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
|
||||
cache_position = torch.arange(
|
||||
past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
|
||||
)
|
||||
|
||||
if position_ids is None:
|
||||
position_ids = cache_position.unsqueeze(0)
|
||||
|
||||
causal_mask = self._update_causal_mask(
|
||||
attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
|
||||
)
|
||||
|
||||
hidden_states = inputs_embeds
|
||||
|
||||
#共享RoPE位置编码
|
||||
position_embeddings = self.rotary_emb(hidden_states, position_ids)
|
||||
|
||||
# 否是输出hidden state
|
||||
all_hidden_states = () if output_hidden_states else None
|
||||
all_self_attns = () if output_attentions else None
|
||||
#transformer的计算：
|
||||
for decoder_layer in self.layers[: self.config.num_hidden_layers]:
|
||||
if output_hidden_states:
|
||||
all_hidden_states += (hidden_states,)
|
||||
#如果启用了梯度检查点，并且处于训练模式下，模型将调用 self._gradient_checkpointing_func，
|
||||
# 该函数使用 partial 函数来计算并重新计算部分前向过程，从而节省内存。
|
||||
if self.gradient_checkpointing and self.training:
|
||||
layer_outputs = self._gradient_checkpointing_func(
|
||||
partial(decoder_layer.__call__, **flash_attn_kwargs),
|
||||
hidden_states,
|
||||
causal_mask,
|
||||
position_ids,
|
||||
past_key_values,
|
||||
output_attentions,
|
||||
use_cache,
|
||||
cache_position,
|
||||
position_embeddings,
|
||||
)
|
||||
else:
|
||||
layer_outputs = decoder_layer(
|
||||
hidden_states,
|
||||
attention_mask=causal_mask,
|
||||
position_ids=position_ids,
|
||||
past_key_value=past_key_values,
|
||||
output_attentions=output_attentions,
|
||||
use_cache=use_cache,
|
||||
cache_position=cache_position,
|
||||
position_embeddings=position_embeddings,
|
||||
**flash_attn_kwargs,
|
||||
)
|
||||
|
||||
hidden_states = layer_outputs[0]
|
||||
|
||||
if output_attentions:
|
||||
all_self_attns += (layer_outputs[1],)
|
||||
#归一化
|
||||
hidden_states = self.norm(hidden_states)
|
||||
|
||||
# 添加最后一层hidden_states
|
||||
if output_hidden_states:
|
||||
all_hidden_states += (hidden_states,)
|
||||
#将结果打包为BaseModelOutputWithPast数据结构
|
||||
return BaseModelOutputWithPast(
|
||||
last_hidden_state=hidden_states,
|
||||
past_key_values=past_key_values if use_cache else None,
|
||||
hidden_states=all_hidden_states,
|
||||
attentions=all_self_attns,
|
||||
)
|
||||
|
||||
def _update_causal_mask(
|
||||
self,
|
||||
attention_mask: torch.Tensor,
|
||||
input_tensor: torch.Tensor,
|
||||
cache_position: torch.Tensor,
|
||||
past_key_values: Cache,
|
||||
output_attentions: bool = False,
|
||||
):
|
||||
#处理batch生成时padding问题
|
||||
if self.config._attn_implementation == "flash_attention_2":
|
||||
if attention_mask is not None and past_key_values is not None:
|
||||
is_padding_right = attention_mask[:, -1].sum().item() != input_tensor.size()[0]
|
||||
if is_padding_right:
|
||||
raise ValueError(
|
||||
"You are attempting to perform batched generation with padding_side='right'"
|
||||
" call `tokenizer.padding_side = 'left'` before tokenizing the input. "
|
||||
)
|
||||
if attention_mask is not None and 0.0 in attention_mask:
|
||||
return attention_mask
|
||||
return None
|
||||
|
||||
# For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
|
||||
# order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
|
||||
# to infer the attention mask.
|
||||
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
|
||||
using_static_cache = isinstance(past_key_values, StaticCache)
|
||||
using_sliding_window_cache = isinstance(past_key_values, SlidingWindowCache)
|
||||
|
||||
# When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
|
||||
if (
|
||||
self.config._attn_implementation == "sdpa"
|
||||
and not (using_static_cache or using_sliding_window_cache)
|
||||
and not output_attentions
|
||||
):
|
||||
if AttentionMaskConverter._ignore_causal_mask_sdpa(
|
||||
attention_mask,
|
||||
inputs_embeds=input_tensor,
|
||||
past_key_values_length=past_seen_tokens,
|
||||
sliding_window=self.config.sliding_window,
|
||||
is_training=self.training,
|
||||
):
|
||||
return None
|
||||
|
||||
dtype, device = input_tensor.dtype, input_tensor.device
|
||||
min_dtype = torch.finfo(dtype).min
|
||||
sequence_length = input_tensor.shape[1]
|
||||
# SlidingWindowCache or StaticCache
|
||||
if using_sliding_window_cache or using_static_cache:
|
||||
target_length = past_key_values.get_max_cache_shape()
|
||||
# DynamicCache or no cache
|
||||
else:
|
||||
target_length = (
|
||||
attention_mask.shape[-1]
|
||||
if isinstance(attention_mask, torch.Tensor)
|
||||
else past_seen_tokens + sequence_length + 1
|
||||
)
|
||||
|
||||
# In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
|
||||
causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
|
||||
attention_mask,
|
||||
sequence_length=sequence_length,
|
||||
target_length=target_length,
|
||||
dtype=dtype,
|
||||
device=device,
|
||||
cache_position=cache_position,
|
||||
batch_size=input_tensor.shape[0],
|
||||
config=self.config,
|
||||
past_key_values=past_key_values,
|
||||
)
|
||||
|
||||
if (
|
||||
self.config._attn_implementation == "sdpa"
|
||||
and attention_mask is not None
|
||||
and attention_mask.device.type in ["cuda", "xpu"]
|
||||
and not output_attentions
|
||||
):
|
||||
causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
|
||||
|
||||
return causal_mask
|
||||
|
||||
@staticmethod
|
||||
def _prepare_4d_causal_attention_mask_with_cache_position(
|
||||
attention_mask: torch.Tensor,
|
||||
sequence_length: int,
|
||||
target_length: int,
|
||||
dtype: torch.dtype,
|
||||
device: torch.device,
|
||||
cache_position: torch.Tensor,
|
||||
batch_size: int,
|
||||
config: DollyConfig,
|
||||
past_key_values: Cache,
|
||||
):
|
||||
if attention_mask is not None and attention_mask.dim() == 4:
|
||||
# In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
|
||||
causal_mask = attention_mask
|
||||
else:
|
||||
min_dtype = torch.finfo(dtype).min
|
||||
causal_mask = torch.full(
|
||||
(sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device
|
||||
)
|
||||
diagonal_attend_mask = torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
|
||||
if config.sliding_window is not None:
|
||||
# if we have sliding window, we should not attend to tokens beyond sliding window length, so we mask them out also
|
||||
# the check is needed to verify is current checkpoint was trained with sliding window or not
|
||||
if not isinstance(past_key_values, SlidingWindowCache) or sequence_length > target_length:
|
||||
sliding_attend_mask = torch.arange(target_length, device=device) <= (
|
||||
cache_position.reshape(-1, 1) - config.sliding_window
|
||||
)
|
||||
diagonal_attend_mask.bitwise_or_(sliding_attend_mask)
|
||||
causal_mask *= diagonal_attend_mask
|
||||
causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
|
||||
if attention_mask is not None:
|
||||
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
|
||||
if attention_mask.shape[-1] > target_length:
|
||||
attention_mask = attention_mask[:, :target_length]
|
||||
mask_length = attention_mask.shape[-1]
|
||||
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
|
||||
causal_mask.device
|
||||
)
|
||||
padding_mask = padding_mask == 0
|
||||
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
|
||||
padding_mask, min_dtype
|
||||
)
|
||||
return causal_mask
|
||||
|
||||
|
||||
class DollyForCausalLM(DollyPreTrainedModel, GenerationMixin):
|
||||
_tied_weights_keys = ["lm_head.weight"]
|
||||
_tp_plan = {"lm_head": "colwise_rep"}
|
||||
_pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
|
||||
|
||||
def __init__(self, config):
|
||||
super().__init__(config)
|
||||
self.model = DollyModel(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
|
||||
|
||||
@can_return_tuple
|
||||
@deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
|
||||
def forward(
|
||||
self,
|
||||
input_ids: Optional[torch.LongTensor] = None,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = None,
|
||||
past_key_values: Optional[Cache] = 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,
|
||||
cache_position: Optional[torch.LongTensor] = None,
|
||||
logits_to_keep: Union[int, torch.Tensor] = 0,
|
||||
**kwargs: Unpack[KwargsForCausalLM],
|
||||
) -> CausalLMOutputWithPast:
|
||||
|
||||
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
|
||||
)
|
||||
|
||||
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
|
||||
outputs: BaseModelOutputWithPast = 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,
|
||||
cache_position=cache_position,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
hidden_states = outputs.last_hidden_state
|
||||
# Only compute necessary logits, and do not upcast them to float if we are not computing the loss
|
||||
slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
|
||||
logits = self.lm_head(hidden_states[:, slice_indices, :])
|
||||
|
||||
loss = None
|
||||
if labels is not None:
|
||||
loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
|
||||
|
||||
return CausalLMOutputWithPast(
|
||||
loss=loss,
|
||||
logits=logits,
|
||||
past_key_values=outputs.past_key_values,
|
||||
hidden_states=outputs.hidden_states,
|
||||
attentions=outputs.attentions,
|
||||
)
|
||||
|
||||
class DollyForSequenceClassification(DollyPreTrainedModel):
|
||||
def __init__(self, config):
|
||||
super().__init__(config)
|
||||
self.num_labels = config.num_labels
|
||||
self.model = DollyModel(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
|
||||
|
||||
@can_return_tuple
|
||||
def forward(
|
||||
self,
|
||||
input_ids: Optional[torch.LongTensor] = None,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = None,
|
||||
past_key_values: Optional[Cache] = 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,
|
||||
) -> SequenceClassifierOutputWithPast:
|
||||
|
||||
transformer_outputs: BaseModelOutputWithPast = 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,
|
||||
)
|
||||
hidden_states = transformer_outputs.last_hidden_state
|
||||
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:
|
||||
last_non_pad_token = -1
|
||||
elif input_ids is not None:
|
||||
# To handle both left- and right- padding, we take the rightmost token that is not equal to pad_token_id
|
||||
non_pad_mask = (input_ids != self.config.pad_token_id).to(logits.device, torch.int32)
|
||||
token_indices = torch.arange(input_ids.shape[-1], device=logits.device, dtype=torch.int32)
|
||||
last_non_pad_token = (token_indices * non_pad_mask).argmax(-1)
|
||||
else:
|
||||
last_non_pad_token = -1
|
||||
logger.warning_once(
|
||||
f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
|
||||
"unexpected if using padding tokens in conjunction with `inputs_embeds.`"
|
||||
)
|
||||
|
||||
pooled_logits = logits[torch.arange(batch_size, device=logits.device), last_non_pad_token]
|
||||
|
||||
loss = None
|
||||
if labels is not None:
|
||||
loss = self.loss_function(logits=logits, labels=labels, pooled_logits=pooled_logits, config=self.config)
|
||||
|
||||
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,
|
||||
)
|
||||
|
||||
|
||||
class DollyForTokenClassification(DollyPreTrainedModel):
|
||||
def __init__(self, config):
|
||||
super().__init__(config)
|
||||
self.num_labels = config.num_labels
|
||||
self.model = DollyModel(config)
|
||||
if getattr(config, "classifier_dropout", None) is not None:
|
||||
classifier_dropout = config.classifier_dropout
|
||||
elif getattr(config, "hidden_dropout", None) is not None:
|
||||
classifier_dropout = config.hidden_dropout
|
||||
else:
|
||||
classifier_dropout = 0.1
|
||||
self.dropout = nn.Dropout(classifier_dropout)
|
||||
self.score = nn.Linear(config.hidden_size, config.num_labels)
|
||||
|
||||
# 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
|
||||
|
||||
@can_return_tuple
|
||||
def forward(
|
||||
self,
|
||||
input_ids: Optional[torch.LongTensor] = None,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = None,
|
||||
past_key_values: Optional[Cache] = 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,
|
||||
) -> TokenClassifierOutput:
|
||||
|
||||
outputs: BaseModelOutputWithPast = 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,
|
||||
)
|
||||
sequence_output = outputs.last_hidden_state
|
||||
sequence_output = self.dropout(sequence_output)
|
||||
logits = self.score(sequence_output)
|
||||
|
||||
loss = None
|
||||
if labels is not None:
|
||||
loss = self.loss_function(logits, labels, self.config)
|
||||
|
||||
return TokenClassifierOutput(
|
||||
loss=loss,
|
||||
logits=logits,
|
||||
hidden_states=outputs.hidden_states,
|
||||
attentions=outputs.attentions,
|
||||
)
|
||||
|
||||
|
||||
class DollyForQuestionAnswering(DollyPreTrainedModel):
|
||||
base_model_prefix = "transformer"
|
||||
|
||||
def __init__(self, config):
|
||||
super().__init__(config)
|
||||
self.transformer = DollyModel(config)
|
||||
self.qa_outputs = nn.Linear(config.hidden_size, 2)
|
||||
|
||||
# Initialize weights and apply final processing
|
||||
self.post_init()
|
||||
|
||||
def get_input_embeddings(self):
|
||||
return self.transformer.embed_tokens
|
||||
|
||||
def set_input_embeddings(self, value):
|
||||
self.transformer.embed_tokens = value
|
||||
|
||||
@can_return_tuple
|
||||
def forward(
|
||||
self,
|
||||
input_ids: Optional[torch.LongTensor] = None,
|
||||
attention_mask: Optional[torch.FloatTensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = None,
|
||||
past_key_values: Optional[Cache] = None,
|
||||
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||||
start_positions: Optional[torch.LongTensor] = None,
|
||||
end_positions: Optional[torch.LongTensor] = None,
|
||||
output_attentions: Optional[bool] = None,
|
||||
output_hidden_states: Optional[bool] = None,
|
||||
**kwargs,
|
||||
) -> QuestionAnsweringModelOutput:
|
||||
|
||||
outputs: BaseModelOutputWithPast = self.transformer(
|
||||
input_ids,
|
||||
attention_mask=attention_mask,
|
||||
position_ids=position_ids,
|
||||
past_key_values=past_key_values,
|
||||
inputs_embeds=inputs_embeds,
|
||||
output_attentions=output_attentions,
|
||||
output_hidden_states=output_hidden_states,
|
||||
)
|
||||
|
||||
sequence_output = outputs.last_hidden_state
|
||||
logits = self.qa_outputs(sequence_output)
|
||||
start_logits, end_logits = logits.split(1, dim=-1)
|
||||
start_logits = start_logits.squeeze(-1).contiguous()
|
||||
end_logits = end_logits.squeeze(-1).contiguous()
|
||||
|
||||
loss = None
|
||||
if start_positions is not None and end_positions is not None:
|
||||
loss = self.loss_function(start_logits, end_logits, start_positions, end_positions, **kwargs)
|
||||
|
||||
return QuestionAnsweringModelOutput(
|
||||
loss=loss,
|
||||
start_logits=start_logits,
|
||||
end_logits=end_logits,
|
||||
hidden_states=outputs.hidden_states,
|
||||
attentions=outputs.attentions,
|
||||
)
|
||||
|
||||
|
||||
__all__ = [
|
||||
"DollyForCausalLM",
|
||||
"DollyForQuestionAnswering",
|
||||
"DollyModel",
|
||||
"DollyPreTrainedModel",
|
||||
"DollyForSequenceClassification",
|
||||
"DollyForTokenClassification",
|
||||
]
|
||||
Reference in New Issue
Block a user