mirror of
https://github.com/jingyaogong/minimind.git
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459 lines
22 KiB
Python
Executable File
459 lines
22 KiB
Python
Executable File
# 📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘
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# MiniMind Config
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# 📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘
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from transformers import PretrainedConfig
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class MiniMindConfig(PretrainedConfig):
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model_type = "minimind"
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def __init__(
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self,
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dropout: float = 0.0,
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bos_token_id: int = 1,
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eos_token_id: int = 2,
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hidden_act: str = 'silu',
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hidden_size: int = 512,
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intermediate_size: int = None,
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max_position_embeddings: int = 32768,
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num_attention_heads: int = 8,
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num_hidden_layers: int = 8,
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num_key_value_heads: int = 2,
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vocab_size: int = 6400,
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rms_norm_eps: float = 1e-05,
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rope_theta: int = 1000000.0,
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inference_rope_scaling: bool = False,
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flash_attn: bool = True,
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####################################################
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# Here are the specific configurations of MOE
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# When use_moe is false, the following is invalid
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####################################################
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use_moe: bool = False,
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num_experts_per_tok: int = 2,
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n_routed_experts: int = 4,
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n_shared_experts: int = 1,
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scoring_func: str = 'softmax',
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aux_loss_alpha: float = 0.01,
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seq_aux: bool = True,
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norm_topk_prob: bool = True,
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**kwargs
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):
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super().__init__(**kwargs)
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self.dropout = dropout
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self.bos_token_id = bos_token_id
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self.eos_token_id = eos_token_id
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self.hidden_act = hidden_act
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.max_position_embeddings = max_position_embeddings
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self.num_attention_heads = num_attention_heads
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self.num_hidden_layers = num_hidden_layers
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self.num_key_value_heads = num_key_value_heads
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self.vocab_size = vocab_size
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self.rms_norm_eps = rms_norm_eps
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self.rope_theta = rope_theta
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self.inference_rope_scaling = inference_rope_scaling
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# 外推长度 = factor * original_max_position_embeddings = 32768
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self.rope_scaling = {
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"beta_fast": 32,
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"beta_slow": 1,
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"factor": 16,
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"original_max_position_embeddings": 2048,
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"attention_factor": 1.0,
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"type": "yarn"
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} if self.inference_rope_scaling else None
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self.flash_attn = flash_attn
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####################################################
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# Here are the specific configurations of MOE
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# When use_moe is false, the following is invalid
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####################################################
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self.use_moe = use_moe
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self.num_experts_per_tok = num_experts_per_tok # 每个token选择的专家数量
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self.n_routed_experts = n_routed_experts # 总的专家数量
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self.n_shared_experts = n_shared_experts # 共享专家
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self.scoring_func = scoring_func # 评分函数，默认为'softmax'
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self.aux_loss_alpha = aux_loss_alpha # 辅助损失的alpha参数
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self.seq_aux = seq_aux # 是否在序列级别上计算辅助损失
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self.norm_topk_prob = norm_topk_prob # 是否标准化top-k概率
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# 📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘
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# MiniMind Model
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# 📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘
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import math
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import torch
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import torch.nn.init as init
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import torch.nn.functional as F
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from torch import nn
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from transformers.activations import ACT2FN
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from typing import Optional, Tuple, List, Union
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from transformers import PreTrainedModel, GenerationMixin, PretrainedConfig
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from transformers.modeling_outputs import CausalLMOutputWithPast
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class RMSNorm(torch.nn.Module):
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def __init__(self, dim: int, eps: float = 1e-5):
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super().__init__()
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self.eps = eps
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self.weight = nn.Parameter(torch.ones(dim))
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def _norm(self, x):
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return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
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def forward(self, x):
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return self.weight * self._norm(x.float()).type_as(x)
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def precompute_freqs_cis(dim: int, end: int = int(32 * 1024), rope_base: float = 1e6,
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rope_scaling: Optional[dict] = None):
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freqs, attn_factor = 1.0 / (rope_base ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim)), 1.0
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if rope_scaling is not None:
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orig_max, factor, beta_fast, beta_slow, attn_factor = (
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rope_scaling.get("original_max_position_embeddings", 2048), rope_scaling.get("factor", 16),
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rope_scaling.get("beta_fast", 32.0), rope_scaling.get("beta_slow", 1.0), rope_scaling.get("attention_factor", 1.0)
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)
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if end / orig_max > 1.0:
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# YaRN: f'(i) = f(i)((1-γ) + γ/s), where γ∈[0,1] is linear ramp
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inv_dim = lambda b: (dim * math.log(orig_max / (b * 2 * math.pi))) / (2 * math.log(rope_base))
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low, high = max(math.floor(inv_dim(beta_fast)), 0), min(math.ceil(inv_dim(beta_slow)), dim // 2 - 1)
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ramp = torch.clamp((torch.arange(dim // 2, device=freqs.device).float() - low) / max(high - low, 0.001), 0, 1)
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freqs = freqs * (1 - ramp + ramp / factor)
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t = torch.arange(end, device=freqs.device)
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freqs = torch.outer(t, freqs).float()
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freqs_cos = torch.cat([torch.cos(freqs), torch.cos(freqs)], dim=-1) * attn_factor
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freqs_sin = torch.cat([torch.sin(freqs), torch.sin(freqs)], dim=-1) * attn_factor
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return freqs_cos, freqs_sin
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def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
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def rotate_half(x):
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return torch.cat((-x[..., x.shape[-1] // 2:], x[..., : x.shape[-1] // 2]), dim=-1)
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q_embed = (q * cos.unsqueeze(unsqueeze_dim)) + (rotate_half(q) * sin.unsqueeze(unsqueeze_dim))
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k_embed = (k * cos.unsqueeze(unsqueeze_dim)) + (rotate_half(k) * sin.unsqueeze(unsqueeze_dim))
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return q_embed, k_embed
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def repeat_kv(x: torch.Tensor, n_rep: int) -> torch.Tensor:
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"""torch.repeat_interleave(x, dim=2, repeats=n_rep)"""
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bs, slen, num_key_value_heads, head_dim = x.shape
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if n_rep == 1:
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return x
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return (
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x[:, :, :, None, :].expand(bs, slen, num_key_value_heads, n_rep, head_dim).reshape(bs, slen, num_key_value_heads * n_rep, head_dim)
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)
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class Attention(nn.Module):
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def __init__(self, args: MiniMindConfig):
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super().__init__()
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self.num_key_value_heads = args.num_attention_heads if args.num_key_value_heads is None else args.num_key_value_heads
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assert args.num_attention_heads % self.num_key_value_heads == 0
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self.n_local_heads = args.num_attention_heads
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self.n_local_kv_heads = self.num_key_value_heads
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self.n_rep = self.n_local_heads // self.n_local_kv_heads
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self.head_dim = args.hidden_size // args.num_attention_heads
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self.q_proj = nn.Linear(args.hidden_size, args.num_attention_heads * self.head_dim, bias=False)
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self.k_proj = nn.Linear(args.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
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self.v_proj = nn.Linear(args.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
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self.o_proj = nn.Linear(args.num_attention_heads * self.head_dim, args.hidden_size, bias=False)
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self.attn_dropout = nn.Dropout(args.dropout)
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self.resid_dropout = nn.Dropout(args.dropout)
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self.dropout = args.dropout
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self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention') and args.flash_attn
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# print("WARNING: using slow attention. Flash Attention requires PyTorch >= 2.0")
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def forward(self,
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x: torch.Tensor,
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position_embeddings: Tuple[torch.Tensor, torch.Tensor], # 修改为接收cos和sin
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past_key_value: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
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use_cache=False,
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attention_mask: Optional[torch.Tensor] = None):
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bsz, seq_len, _ = x.shape
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xq, xk, xv = self.q_proj(x), self.k_proj(x), self.v_proj(x)
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xq = xq.view(bsz, seq_len, self.n_local_heads, self.head_dim)
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xk = xk.view(bsz, seq_len, self.n_local_kv_heads, self.head_dim)
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xv = xv.view(bsz, seq_len, self.n_local_kv_heads, self.head_dim)
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cos, sin = position_embeddings
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xq, xk = apply_rotary_pos_emb(xq, xk, cos[:seq_len], sin[:seq_len])
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# kv_cache实现
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if past_key_value is not None:
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xk = torch.cat([past_key_value[0], xk], dim=1)
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xv = torch.cat([past_key_value[1], xv], dim=1)
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past_kv = (xk, xv) if use_cache else None
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xq, xk, xv = (
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xq.transpose(1, 2),
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repeat_kv(xk, self.n_rep).transpose(1, 2),
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repeat_kv(xv, self.n_rep).transpose(1, 2)
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)
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if self.flash and seq_len > 1 and (attention_mask is None or torch.all(attention_mask == 1)):
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output = F.scaled_dot_product_attention(xq, xk, xv, dropout_p=self.dropout if self.training else 0.0, is_causal=True)
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else:
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scores = (xq @ xk.transpose(-2, -1)) / math.sqrt(self.head_dim)
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scores = scores + torch.triu(
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torch.full((seq_len, seq_len), float("-inf"), device=scores.device),
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diagonal=1
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).unsqueeze(0).unsqueeze(0) # scores+mask
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if attention_mask is not None:
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extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
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extended_attention_mask = (1.0 - extended_attention_mask) * -1e9
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scores = scores + extended_attention_mask
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scores = F.softmax(scores.float(), dim=-1).type_as(xq)
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scores = self.attn_dropout(scores)
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output = scores @ xv
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output = output.transpose(1, 2).reshape(bsz, seq_len, -1)
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output = self.resid_dropout(self.o_proj(output))
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return output, past_kv
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class FeedForward(nn.Module):
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def __init__(self, config: MiniMindConfig):
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super().__init__()
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if config.intermediate_size is None:
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intermediate_size = int(config.hidden_size * 8 / 3)
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config.intermediate_size = 64 * ((intermediate_size + 64 - 1) // 64)
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self.gate_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
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self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
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self.up_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
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self.dropout = nn.Dropout(config.dropout)
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self.act_fn = ACT2FN[config.hidden_act]
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def forward(self, x):
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return self.dropout(self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)))
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class MoEGate(nn.Module):
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def __init__(self, config: MiniMindConfig):
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super().__init__()
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self.config = config
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self.top_k = config.num_experts_per_tok
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self.n_routed_experts = config.n_routed_experts
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self.scoring_func = config.scoring_func
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self.alpha = config.aux_loss_alpha
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self.seq_aux = config.seq_aux
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self.norm_topk_prob = config.norm_topk_prob
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self.gating_dim = config.hidden_size
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self.weight = nn.Parameter(torch.empty((self.n_routed_experts, self.gating_dim)))
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self.reset_parameters()
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def reset_parameters(self) -> None:
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init.kaiming_uniform_(self.weight, a=math.sqrt(5))
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def forward(self, hidden_states):
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bsz, seq_len, h = hidden_states.shape
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hidden_states = hidden_states.view(-1, h)
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logits = F.linear(hidden_states, self.weight, None)
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if self.scoring_func == 'softmax':
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scores = logits.softmax(dim=-1)
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else:
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raise NotImplementedError(f'insupportable scoring function for MoE gating: {self.scoring_func}')
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topk_weight, topk_idx = torch.topk(scores, k=self.top_k, dim=-1, sorted=False)
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if self.top_k > 1 and self.norm_topk_prob:
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denominator = topk_weight.sum(dim=-1, keepdim=True) + 1e-20
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topk_weight = topk_weight / denominator
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if self.training and self.alpha > 0.0:
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scores_for_aux = scores
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aux_topk = self.top_k
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topk_idx_for_aux_loss = topk_idx.view(bsz, -1)
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if self.seq_aux:
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scores_for_seq_aux = scores_for_aux.view(bsz, seq_len, -1)
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ce = torch.zeros(bsz, self.n_routed_experts, device=hidden_states.device)
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ce.scatter_add_(1, topk_idx_for_aux_loss,
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torch.ones(bsz, seq_len * aux_topk, device=hidden_states.device)).div_(
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seq_len * aux_topk / self.n_routed_experts)
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aux_loss = (ce * scores_for_seq_aux.mean(dim=1)).sum(dim=1).mean() * self.alpha
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else:
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mask_ce = F.one_hot(topk_idx_for_aux_loss.view(-1), num_classes=self.n_routed_experts)
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ce = mask_ce.float().mean(0)
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Pi = scores_for_aux.mean(0)
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fi = ce * self.n_routed_experts
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aux_loss = (Pi * fi).sum() * self.alpha
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else:
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aux_loss = scores.new_zeros(1).squeeze()
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return topk_idx, topk_weight, aux_loss
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class MOEFeedForward(nn.Module):
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def __init__(self, config: MiniMindConfig):
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super().__init__()
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self.config = config
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self.experts = nn.ModuleList([
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FeedForward(config)
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for _ in range(config.n_routed_experts)
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])
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self.gate = MoEGate(config)
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if config.n_shared_experts > 0:
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self.shared_experts = nn.ModuleList([
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FeedForward(config)
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for _ in range(config.n_shared_experts)
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])
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def forward(self, x):
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identity = x
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orig_shape = x.shape
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bsz, seq_len, _ = x.shape
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# 使用门控机制选择专家
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topk_idx, topk_weight, aux_loss = self.gate(x)
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x = x.view(-1, x.shape[-1])
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flat_topk_idx = topk_idx.view(-1)
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if self.training:
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x = x.repeat_interleave(self.config.num_experts_per_tok, dim=0)
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y = torch.empty_like(x, dtype=x.dtype)
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for i, expert in enumerate(self.experts):
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expert_out = expert(x[flat_topk_idx == i])
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if expert_out.shape[0] > 0: y[flat_topk_idx == i] = expert_out.to(y.dtype)
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else: y[flat_topk_idx == i] = expert_out.to(y.dtype) + 0 * sum(p.sum() for p in expert.parameters())
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y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1)
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y = y.view(*orig_shape)
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else:
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y = self.moe_infer(x, flat_topk_idx, topk_weight.view(-1, 1)).view(*orig_shape)
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if self.config.n_shared_experts > 0:
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for expert in self.shared_experts:
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y = y + expert(identity)
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self.aux_loss = aux_loss
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return y
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@torch.no_grad()
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def moe_infer(self, x, flat_expert_indices, flat_expert_weights):
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expert_cache = torch.zeros_like(x)
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idxs = flat_expert_indices.argsort()
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tokens_per_expert = flat_expert_indices.bincount().cpu().numpy().cumsum(0)
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token_idxs = idxs // self.config.num_experts_per_tok
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# 当tokens_per_expert = [6, 15, 20, 26]，tokens_per_expert.shape[0]即为专家数量（此时为4）
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# 且token_idxs = [3, 7, 19, 21, 24, 25, 4, 5, 6, 10, 11, 12...] 时
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# 意味token_idxs[:6] -> [3, 7, 19, 21, 24, 25]这6个位置属于专家0处理的token（每个token有可能被多个专家处理，这取决于num_experts_per_tok）
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# 接下来9个位置token_idxs[6:15] -> [4, 5, 6, 10, 11, 12...]属于专家1处理的token...依此类推
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for i, end_idx in enumerate(tokens_per_expert):
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start_idx = 0 if i == 0 else tokens_per_expert[i - 1]
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if start_idx == end_idx:
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continue
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expert = self.experts[i]
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exp_token_idx = token_idxs[start_idx:end_idx]
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expert_tokens = x[exp_token_idx]
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expert_out = expert(expert_tokens).to(expert_cache.dtype)
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expert_out.mul_(flat_expert_weights[idxs[start_idx:end_idx]])
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expert_cache.scatter_add_(0, exp_token_idx.view(-1, 1).repeat(1, x.shape[-1]), expert_out)
|
||
|
||
return expert_cache
|
||
|
||
|
||
class MiniMindBlock(nn.Module):
|
||
def __init__(self, layer_id: int, config: MiniMindConfig):
|
||
super().__init__()
|
||
self.num_attention_heads = config.num_attention_heads
|
||
self.hidden_size = config.hidden_size
|
||
self.head_dim = config.hidden_size // config.num_attention_heads
|
||
self.self_attn = Attention(config)
|
||
|
||
self.layer_id = layer_id
|
||
self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||
self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||
self.mlp = FeedForward(config) if not config.use_moe else MOEFeedForward(config)
|
||
|
||
def forward(self, hidden_states, position_embeddings, past_key_value=None, use_cache=False, attention_mask=None):
|
||
residual = hidden_states
|
||
hidden_states, present_key_value = self.self_attn(
|
||
self.input_layernorm(hidden_states), position_embeddings,
|
||
past_key_value, use_cache, attention_mask
|
||
)
|
||
hidden_states += residual
|
||
hidden_states = hidden_states + self.mlp(self.post_attention_layernorm(hidden_states))
|
||
return hidden_states, present_key_value
|
||
|
||
|
||
class MiniMindModel(nn.Module):
|
||
def __init__(self, config: MiniMindConfig):
|
||
super().__init__()
|
||
self.config = config
|
||
self.vocab_size, self.num_hidden_layers = config.vocab_size, config.num_hidden_layers
|
||
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
|
||
self.dropout = nn.Dropout(config.dropout)
|
||
self.layers = nn.ModuleList([MiniMindBlock(l, config) for l in range(self.num_hidden_layers)])
|
||
self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||
|
||
freqs_cos, freqs_sin = precompute_freqs_cis(dim=config.hidden_size // config.num_attention_heads,
|
||
end=config.max_position_embeddings, rope_base=config.rope_theta,
|
||
rope_scaling=config.rope_scaling)
|
||
self.register_buffer("freqs_cos", freqs_cos, persistent=False)
|
||
self.register_buffer("freqs_sin", freqs_sin, persistent=False)
|
||
|
||
def forward(self,
|
||
input_ids: Optional[torch.Tensor] = None,
|
||
attention_mask: Optional[torch.Tensor] = None,
|
||
past_key_values: Optional[List[Tuple[torch.Tensor, torch.Tensor]]] = None,
|
||
use_cache: bool = False,
|
||
**kwargs):
|
||
batch_size, seq_length = input_ids.shape
|
||
if hasattr(past_key_values, 'layers'): past_key_values = None
|
||
past_key_values = past_key_values or [None] * len(self.layers)
|
||
start_pos = past_key_values[0][0].shape[1] if past_key_values[0] is not None else 0
|
||
|
||
hidden_states = self.dropout(self.embed_tokens(input_ids))
|
||
|
||
position_embeddings = (
|
||
self.freqs_cos[start_pos:start_pos + seq_length],
|
||
self.freqs_sin[start_pos:start_pos + seq_length]
|
||
)
|
||
|
||
presents = []
|
||
for layer_idx, (layer, past_key_value) in enumerate(zip(self.layers, past_key_values)):
|
||
hidden_states, present = layer(
|
||
hidden_states,
|
||
position_embeddings,
|
||
past_key_value=past_key_value,
|
||
use_cache=use_cache,
|
||
attention_mask=attention_mask
|
||
)
|
||
presents.append(present)
|
||
|
||
hidden_states = self.norm(hidden_states)
|
||
|
||
aux_loss = sum([l.mlp.aux_loss for l in self.layers if isinstance(l.mlp, MOEFeedForward)], hidden_states.new_zeros(1).squeeze())
|
||
return hidden_states, presents, aux_loss
|
||
|
||
|
||
class MiniMindForCausalLM(PreTrainedModel, GenerationMixin):
|
||
config_class = MiniMindConfig
|
||
|
||
def __init__(self, config: MiniMindConfig = None):
|
||
self.config = config or MiniMindConfig()
|
||
super().__init__(self.config)
|
||
self.model = MiniMindModel(self.config)
|
||
self.lm_head = nn.Linear(self.config.hidden_size, self.config.vocab_size, bias=False)
|
||
self.model.embed_tokens.weight = self.lm_head.weight
|
||
|
||
def forward(self,
|
||
input_ids: Optional[torch.Tensor] = None,
|
||
attention_mask: Optional[torch.Tensor] = None,
|
||
past_key_values: Optional[List[Tuple[torch.Tensor, torch.Tensor]]] = None,
|
||
use_cache: bool = False,
|
||
logits_to_keep: Union[int, torch.Tensor] = 0,
|
||
**args):
|
||
hidden_states, past_key_values, aux_loss = self.model(
|
||
input_ids=input_ids,
|
||
attention_mask=attention_mask,
|
||
past_key_values=past_key_values,
|
||
use_cache=use_cache,
|
||
**args
|
||
)
|
||
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, :])
|
||
output = CausalLMOutputWithPast(logits=logits, past_key_values=past_key_values, hidden_states=hidden_states)
|
||
output.aux_loss = aux_loss
|
||
return output
|