368 lines
24 KiB
Python
Executable File
368 lines
24 KiB
Python
Executable File
import math, torch, 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 transformers import PreTrainedModel, GenerationMixin, PretrainedConfig
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from transformers.modeling_outputs import MoeCausalLMOutputWithPast
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# 🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏
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# MiniMind Config
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# 🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏
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class MiniMindConfig(PretrainedConfig):
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model_type = "minimind"
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def __init__(self, hidden_size=768, num_hidden_layers=8, use_moe=False, **kwargs):
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super().__init__(**kwargs)
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.use_moe = use_moe
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self.dropout = kwargs.get("dropout", 0.0)
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self.vocab_size = kwargs.get("vocab_size", 6400)
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self.bos_token_id = kwargs.get("bos_token_id", 1)
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self.eos_token_id = kwargs.get("eos_token_id", 2)
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self.flash_attn = kwargs.get("flash_attn", True)
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self.num_attention_heads = kwargs.get("num_attention_heads", 8)
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self.attention_type = kwargs.get("attention_type", "gqa")
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num_key_value_heads = kwargs.get("num_key_value_heads", 4)
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if self.attention_type == "mha":
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num_key_value_heads = self.num_attention_heads
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elif self.attention_type == "mqa":
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num_key_value_heads = 1
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self.num_key_value_heads = num_key_value_heads
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if self.num_attention_heads % self.num_key_value_heads != 0:
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raise ValueError("num_attention_heads must be divisible by num_key_value_heads")
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self.head_dim = kwargs.get("head_dim", self.hidden_size // self.num_attention_heads)
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self.hidden_act = kwargs.get("hidden_act", 'silu')
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self.intermediate_size = kwargs.get("intermediate_size", math.ceil(hidden_size * math.pi / 64) * 64)
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self.max_position_embeddings = kwargs.get("max_position_embeddings", 32768)
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self.rms_norm_eps = kwargs.get("rms_norm_eps", 1e-6)
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self.rope_theta = kwargs.get("rope_theta", 1e6)
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self.tie_word_embeddings = kwargs.get("tie_word_embeddings", True)
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self.inference_rope_scaling = kwargs.get("inference_rope_scaling", False)
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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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### MoE specific configs (ignored if use_moe = False)
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self.num_experts = kwargs.get("num_experts", 4)
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self.num_experts_per_tok = kwargs.get("num_experts_per_tok", 1)
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self.moe_intermediate_size = kwargs.get("moe_intermediate_size", self.intermediate_size)
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self.norm_topk_prob = kwargs.get("norm_topk_prob", True)
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self.router_aux_loss_coef = kwargs.get("router_aux_loss_coef", 5e-4)
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# 🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏
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# MiniMind Model
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# 🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏
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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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#rsqrt是sqrt的倒数，keepdim是保持维度不变，这里是把求平均的维度位置为1
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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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#self.weight是一个可学习的参数
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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, rope_scaling: 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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"""
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把原本只能支持较短上下文的RoPE，平滑地扩展到更长上下文
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普通rope的频率是f，位置m的旋转角度是mf，如果上下文长度很长，m很大，角度会转的太快，模型泛化不好
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YaRN的做法是对一部分频率进行缩放，让他们转的慢一点
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高频部分频率保持原样，保留短上下文能力，
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中频部分频率平滑过渡
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低频部分缩小，用于支持长上下文
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"""
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if rope_scaling is not None: # YaRN: f'(i) = f(i)((1-γ) + γ/s), where γ∈[0,1] is linear ramp，y=1是表示低频部分缩小，y=0表示高频部分不变
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"""
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orig_max是原模型训练时支持的最大位置长度
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factor是上下文扩展倍数
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beta_fast和beat_slow用来决定哪些维度的频率保持不变
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attn_factir是乘在cos/sin上的attention的缩放因子
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"""
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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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#只有当你实际预计算的长度end超过原始最大长度时，才需要做rope scaling
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if end / orig_max > 1.0:
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#inv_dim推理公式，用beta，也就是这里的b控制周期和原始上下文长度之间的关系
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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是两个分界点
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#0到low的区间，ramp=0，频率不变，low到high的区间，ramp从0平滑加到1，high到dim//2的区间，ramp=1，频率除以factor
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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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#torch.clamp是裁剪或限制张量中元素的数值范围，torch.clamp(input,min,max),不满足范围内的用最大最小值代替
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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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#[end,dim//2]
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freqs = torch.outer(t, freqs).float()
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#[end,dim]
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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, unsqueeze_dim=1):
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# rope作用在qk上，传统的正弦位置编码一般加在token embedding上，不用作用在v上，因为注意力分数计算只用qk
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#常见的qk shape是[baych,num_heads,seq_len,head_dim]要对头和seq_len做一个维度交换以适应硬件要求
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#...表示省略前面所有的维度，前面的维度都按原样保留
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def rotate_half(x): 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))).to(q.dtype)
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k_embed = ((k * cos.unsqueeze(unsqueeze_dim)) + (rotate_half(k) * sin.unsqueeze(unsqueeze_dim))).to(k.dtype)
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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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"""
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用于GQA和MQA attention 把k/v heads复制成和query_heads一样多，方便后面做attention计算
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"""
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bs, slen, num_key_value_heads, head_dim = x.shape
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if n_rep == 1: return x
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#None是在这里插入一个新维度，expand通常不真的复制维度，只是创建一个广播视图，所以更省内存
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return (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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class Attention(nn.Module):
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def __init__(self, config: MiniMindConfig):
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super().__init__()
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#qk用几个头
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self.num_key_value_heads = config.num_attention_heads if config.num_key_value_heads is None else config.num_key_value_heads
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self.n_local_heads = config.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 = config.head_dim
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self.is_causal = True
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self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=False)
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self.k_proj = nn.Linear(config.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
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self.v_proj = nn.Linear(config.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
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self.o_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=False)
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self.q_norm = RMSNorm(self.head_dim, eps=config.rms_norm_eps)
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self.k_norm = RMSNorm(self.head_dim, eps=config.rms_norm_eps)
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self.attn_dropout = nn.Dropout(config.dropout)
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self.resid_dropout = nn.Dropout(config.dropout)
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self.dropout = config.dropout
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#self.flash是布尔值
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self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention') and config.flash_attn
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def forward(self, x, position_embeddings, past_key_value=None, use_cache=False, attention_mask=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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xq, xk = self.q_norm(xq), self.k_norm(xk)
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cos, sin = position_embeddings
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xq, xk = apply_rotary_pos_emb(xq, xk, cos, sin)
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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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#这段是在判断，当前情况能不能走Pytorch中内置的高效attention实现，scaled_dot_product_attention
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xq, xk, xv = (xq.transpose(1, 2), repeat_kv(xk, self.n_rep).transpose(1, 2), repeat_kv(xv, self.n_rep).transpose(1, 2))
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if self.flash and (seq_len > 1) and (not self.is_causal or past_key_value is None) 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=self.is_causal)
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else:
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#@是矩阵乘法，*是逐元素相乘，两个张量形状必须相同
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scores = (xq @ xk.transpose(-2, -1)) / math.sqrt(self.head_dim)
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#torch.full是生成一个形状指定，且所有元素都填充为同一个固定值的张量
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#triu是pytorch中用于提取矩阵上三角的函数，diagonal参数=0时，保留主对角线及以上的元素，>0时保留主对角线上方的第N条对角线及以上的元素，<0时保留主对角线下方及以下的元素,其余用0替代
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#triu只对张量的后两个维度进行操作
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if self.is_causal: scores[:, :, :, -seq_len:] += torch.full((seq_len, seq_len), float("-inf"), device=scores.device).triu(1)
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#casual mask和attention_mask管的是两件事
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#casual_mask是阻止当前位置看到未来token，attention_mask是告诉模型哪些是有效的，哪些token不应该看见
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#attention_mask是在padding时忽略padding部分的注意力分数
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if attention_mask is not None: scores += (1.0 - attention_mask.unsqueeze(1).unsqueeze(2)) * -1e9
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#对注意力分数做一个正则化
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output = self.attn_dropout(F.softmax(scores.float(), dim=-1).type_as(xq)) @ xv
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output = output.transpose(1, 2).reshape(bsz, seq_len, -1)
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#还要对输出结果做一个正则化
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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, intermediate_size: int = None):
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super().__init__()
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intermediate_size = intermediate_size or config.intermediate_size
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self.gate_proj = nn.Linear(config.hidden_size, intermediate_size, bias=False)
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self.down_proj = nn.Linear(intermediate_size, config.hidden_size, bias=False)
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self.up_proj = nn.Linear(config.hidden_size, intermediate_size, bias=False)
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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.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
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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.gate = nn.Linear(config.hidden_size, config.num_experts, bias=False)
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self.experts = nn.ModuleList([FeedForward(config, intermediate_size=config.moe_intermediate_size) for _ in range(config.num_experts)])
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self.act_fn = ACT2FN[config.hidden_act]
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def forward(self, x):
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batch_size, seq_len, hidden_dim = x.shape
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#[bs*seq_len,hidden_dim]
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x_flat = x.view(-1, hidden_dim)
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#[bs*seq_len,num_experts]
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scores = F.softmax(self.gate(x_flat), dim=-1)
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#[bs*seq_len,num_experts_per_tok]
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topk_weight, topk_idx = torch.topk(scores, k=self.config.num_experts_per_tok, dim=-1, sorted=False)
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if self.config.norm_topk_prob: topk_weight = topk_weight / (topk_weight.sum(dim=-1, keepdim=True) + 1e-20)
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#[bs*seq_len,hidden_dim]
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y = torch.zeros_like(x_flat)
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for i, expert in enumerate(self.experts):
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#[bs*seq_len,num_experts_per_tok]
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mask = (topk_idx == i)
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#any()用来判断是否至少有一个元素为真
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if mask.any():
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#nonzero用来返回非零元素的索引，flatten将结果展成一维向量
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#[num_selected_tokens]
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token_idx = mask.any(dim=-1).nonzero().flatten()
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#[num_selected_tokens,1]，这里mask是一个和top_weight同维度的布尔矩阵，会把top_weight中和mask矩阵位置相同且为True的部分提取出来
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weight = topk_weight[mask].view(-1, 1)
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#x_flat[token_idx]->[num_selected_tokens,hidden_dim]
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#y[token_idx] += expert_output，index_add_(dim,index,source)
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y.index_add_(0, token_idx, (expert(x_flat[token_idx]) * weight).to(y.dtype))
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elif self.training:
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#某个专家没参与计算，也把它挂到计算图上
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y[0, 0] += 0 * sum(p.sum() for p in expert.parameters())
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if self.training and self.config.router_aux_loss_coef > 0:
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#F.one_hot(topk_idx, self.config.num_experts)->[bs*seq_len,num_experts_per_tok,num_experts]，会把原来的每一个数字变成一组one-hot向量
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#做mean(0)后，会在第一个维度做平均，[num_experts_per_tok,num_experts]
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load = F.one_hot(topk_idx, self.config.num_experts).float().mean(0)
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self.aux_loss = (load * scores.mean(0)).sum() * self.config.num_experts * self.config.router_aux_loss_coef
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else:
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self.aux_loss = scores.new_zeros(1).squeeze()
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return y.view(batch_size, seq_len, hidden_dim)
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class MiniMindBlock(nn.Module):
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def __init__(self, layer_id: int, config: MiniMindConfig):
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super().__init__()
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self.self_attn = Attention(config)
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self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.mlp = FeedForward(config) if not config.use_moe else MOEFeedForward(config)
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def forward(self, hidden_states, position_embeddings, past_key_value=None, use_cache=False, attention_mask=None):
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residual = hidden_states
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hidden_states, present_key_value = self.self_attn(
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self.input_layernorm(hidden_states), position_embeddings,
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past_key_value, use_cache, attention_mask
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)
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hidden_states += residual
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hidden_states = hidden_states + self.mlp(self.post_attention_layernorm(hidden_states))
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return hidden_states, present_key_value
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class MiniMindModel(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.vocab_size, self.num_hidden_layers = config.vocab_size, config.num_hidden_layers
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self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
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self.dropout = nn.Dropout(config.dropout)
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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)
|
||
#[max_position_embeddings,head_dim]
|
||
freqs_cos, freqs_sin = precompute_freqs_cis(dim=config.head_dim, 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, attention_mask=None, past_key_values=None, use_cache=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))
|
||
# Recompute RoPE buffers lost during meta-device init (transformers>=5.x)
|
||
if self.freqs_cos[0, 0] == 0:
|
||
freqs_cos, freqs_sin = precompute_freqs_cis(dim=self.config.head_dim, end=self.config.max_position_embeddings, rope_base=self.config.rope_theta, rope_scaling=self.config.rope_scaling)
|
||
self.freqs_cos, self.freqs_sin = freqs_cos.to(hidden_states.device), freqs_sin.to(hidden_states.device)
|
||
position_embeddings = (self.freqs_cos[start_pos:start_pos + seq_length], self.freqs_sin[start_pos:start_pos + seq_length])
|
||
presents = []
|
||
for layer, past_key_value in 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)
|
||
#sum方法中第二个参数为初始值，new_zeros创建与当前张量同类型，同设备的新零张量。里面的参数是形状，设置一个初值0
|
||
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
|
||
_tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"}
|
||
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)
|
||
#nn.Embedding的权重矩阵存储是(input_dim,output_dim),nn.Linear的权重矩阵存储是转秩过后的
|
||
if self.config.tie_word_embeddings: self.model.embed_tokens.weight = self.lm_head.weight
|
||
#在模型所有子模块创建完成后，执行Huggingface规定的后处理初始化逻辑
|
||
self.post_init()
|
||
|
||
def forward(self, input_ids, attention_mask=None, past_key_values=None, use_cache=False, logits_to_keep=0, labels=None, **kwargs):
|
||
hidden_states, past_key_values, aux_loss = self.model(input_ids, attention_mask, past_key_values, use_cache, **kwargs)
|
||
#创建一个切片对象 x[3:5]和x[slice(3,5)]等价，这里等价于[-logits_to_keep:]
|
||
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:
|
||
#训练时输入和输出的长度相同，由 12345预测 23456 所以要计算2345的loss
|
||
x, y = logits[..., :-1, :].contiguous(), labels[..., 1:].contiguous()
|
||
loss = F.cross_entropy(x.view(-1, x.size(-1)), y.view(-1), ignore_index=-100)
|
||
#把loss，aux_loss，logits等封装成一个标准输出对象，方便训练，推理,以后可以用字典的方式访问
|
||
return MoeCausalLMOutputWithPast(loss=loss, aux_loss=aux_loss, logits=logits, past_key_values=past_key_values, hidden_states=hidden_states)
|
||
|
||
# https://github.com/jingyaogong/minimind/discussions/611
|
||
#表示这个函数在推理模式下运行，不计算梯度
|
||
@torch.inference_mode()
|
||
def generate(self, inputs=None, attention_mask=None, max_new_tokens=8192, temperature=0.85, top_p=0.85, top_k=50, eos_token_id=2, streamer=None, use_cache=True, num_return_sequences=1, do_sample=True, repetition_penalty=1.0, **kwargs):
|
||
#这里的repeat是向量复制，这里是在第0维复制num_return_sequences次，第1维复制1次，也就是不变，repeat（a,b,c)是第一维复制a次，第二维复制b次，第三维复制c次，以此类推
|
||
input_ids = kwargs.pop("input_ids", inputs).repeat(num_return_sequences, 1)
|
||
attention_mask = attention_mask.repeat(num_return_sequences, 1) if attention_mask is not None else None
|
||
past_key_values = kwargs.pop("past_key_values", None)
|
||
finished = torch.zeros(input_ids.shape[0], dtype=torch.bool, device=input_ids.device)
|
||
if streamer: streamer.put(input_ids.cpu())
|
||
for _ in range(max_new_tokens):
|
||
past_len = past_key_values[0][0].shape[1] if past_key_values else 0
|
||
outputs = self.forward(input_ids[:, past_len:], attention_mask, past_key_values, use_cache=use_cache, **kwargs)
|
||
attention_mask = torch.cat([attention_mask, attention_mask.new_ones(attention_mask.shape[0], 1)], -1) if attention_mask is not None else None
|
||
logits = outputs.logits[:, -1, :] / temperature
|
||
#重复惩罚，如果某个token已经在当前生成序列里出现过，就降低它下一次再次被选中的概率
|
||
if repetition_penalty != 1.0:
|
||
#torch.unique是去重，给张量
|
||
for i in range(input_ids.shape[0]):
|
||
seen = torch.unique(input_ids[i]); score = logits[i, seen]; logits[i, seen] = torch.where(score > 0, score / repetition_penalty, score * repetition_penalty)
|
||
if top_k > 0:
|
||
logits[logits < torch.topk(logits, top_k)[0][..., -1, None]] = -float('inf')
|
||
#按概率从高到低，只保留累计概率达到top_P之前的token
|
||
if top_p < 1.0:
|
||
sorted_logits, sorted_indices = torch.sort(logits, descending=True)
|
||
mask = torch.cumsum(torch.softmax(sorted_logits, dim=-1), dim=-1) > top_p
|
||
mask[..., 1:], mask[..., 0] = mask[..., :-1].clone(), 0
|
||
logits[mask.scatter(1, sorted_indices, mask)] = -float('inf')
|
||
next_token = torch.multinomial(torch.softmax(logits, dim=-1),num_samples=1) if do_sample else torch.argmax(logits, dim=-1, keepdim=True)
|
||
#torch.where是pytorch的条件选择函数，类似三元运算符torch.where(condition,x,y)
|
||
if eos_token_id is not None: next_token = torch.where(finished.unsqueeze(-1), next_token.new_full((next_token.shape[0], 1), eos_token_id), next_token)
|
||
input_ids = torch.cat([input_ids, next_token], dim=-1)
|
||
past_key_values = outputs.past_key_values if use_cache else None
|
||
if streamer: streamer.put(next_token.cpu())
|
||
if eos_token_id is not None:
|
||
finished |= next_token.squeeze(-1).eq(eos_token_id)
|
||
if finished.all(): break
|
||
if streamer: streamer.end()
|
||
if kwargs.get("return_kv"): return {'generated_ids': input_ids, 'past_kv': past_key_values}
|
||
return input_ids
|