Upload TextGenerationPipeline

zzjrabbit3.py

@@ -0,0 +1,299 @@
+from typing import Optional, Union
+
+import torch
+import torch.nn as nn
+from tokenizers import Tokenizer, decoders, pre_tokenizers
+from tokenizers.models import BPE
+from transformers import (
+    GenerationMixin,
+    PreTrainedConfig,
+    PreTrainedModel,
+    TokenizersBackend,
+)
+from transformers.modeling_outputs import BaseModelOutput, CausalLMOutput
+
+
+class ZZJRabbit3Config(PreTrainedConfig):
+    model_type = "zzjrabbit3"
+
+    def __init__(
+        self,
+        vocab_size: int = 100000,
+        hidden_size: int = 1024,
+        num_hidden_layers: int = 12,
+        num_attention_heads: int = 8,
+        attention_dropout: float | int = 0.0,
+        pad_token_id: int | None = None,
+        eos_token_id: int | list[int] | None = None,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.attention_dropout = attention_dropout
+        self.pad_token_id = pad_token_id
+        self.eos_token_id = eos_token_id
+        super().__init__(**kwargs)
+
+
+class ZZJRabbit3RotaryEmbedding(nn.Module):
+    def __init__(self, dim, max_position_embeddings=2048, base=10000):
+        """
+        Rotary Embedding 模块
+
+        Args:
+            dim: 每个 token embedding 的维度
+            max_position_embeddings: 最大位置数
+            base: rotary embedding 的频率基底
+        """
+        super().__init__()
+        self.dim = dim
+        self.base = base
+
+        # 生成频率向量
+        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim))
+        self.register_buffer("inv_freq", inv_freq)
+
+        # 可选：预先计算 cos/sin
+        t = torch.arange(max_position_embeddings, dtype=torch.float32)
+        freqs = torch.einsum("i,j->ij", t, inv_freq)
+        self.register_buffer("cos_cached", freqs.cos())
+        self.register_buffer("sin_cached", freqs.sin())
+
+    def forward(self, position_ids):
+        """
+        position_ids: (batch_size, seq_len)
+        返回：
+            cos: (batch_size, seq_len, dim)
+            sin: (batch_size, seq_len, dim)
+        """
+        # 从缓存中选取对应位置
+        cos = self.cos_cached[position_ids]  # shape (batch, seq_len, dim/2)
+        sin = self.sin_cached[position_ids]
+
+        # 将维度对齐为 (dim)
+        # cos/sin 当前 shape 为 (..., dim/2)，重复到 dim
+        cos = torch.stack([cos, cos], dim=-1).flatten(-2)
+        sin = torch.stack([sin, sin], dim=-1).flatten(-2)
+        return cos, sin
+
+
+def rotate_half(x):
+    """[-x2, x1]"""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, sin, cos):
+    cos = cos.unsqueeze(1)
+    sin = sin.unsqueeze(1)
+
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+
+    return q_embed, k_embed
+
+
+class ZZJRabbit3Attention(nn.Module):
+    def __init__(self, config: ZZJRabbit3Config):
+        super().__init__()
+        self.config = config
+        self.head_dim = config.hidden_size // config.num_attention_heads
+        self.q_proj = nn.Linear(config.hidden_size, config.hidden_size)
+        self.k_proj = nn.Linear(config.hidden_size, config.hidden_size)
+        self.v_proj = nn.Linear(config.hidden_size, config.hidden_size)
+        self.out_proj = nn.Linear(config.hidden_size, config.hidden_size)
+        self.dropout = nn.Dropout(0.1)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
+        key_padding_mask: Optional[torch.BoolTensor] = None,
+        attn_mask: Optional[torch.BoolTensor] = None,
+    ) -> torch.Tensor:
+        batch_size = x.size(0)
+        Q = (
+            self.q_proj(x)
+            .view(batch_size, -1, self.config.num_attention_heads, self.head_dim)
+            .transpose(1, 2)
+        )
+        K = (
+            self.k_proj(x)
+            .view(batch_size, -1, self.config.num_attention_heads, self.head_dim)
+            .transpose(1, 2)
+        )
+        V = (
+            self.v_proj(x)
+            .view(batch_size, -1, self.config.num_attention_heads, self.head_dim)
+            .transpose(1, 2)
+        )
+        cos, sin = position_embeddings
+        Q, K = apply_rotary_pos_emb(Q, K, sin.to(Q.dtype), cos.to(Q.dtype))
+        scores = torch.matmul(Q, K.transpose(-2, -1)) * (self.head_dim**-0.5)
+        if key_padding_mask is not None:
+            scores = scores.masked_fill(
+                key_padding_mask.view(batch_size, 1, 1, -1), float("-inf")
+            )
+        if attn_mask is not None:
+            scores = scores.masked_fill(attn_mask, float("-inf"))
+        attn_weights = nn.functional.softmax(scores, dim=-1)
+        attn_weights = self.dropout(attn_weights)
+        context = torch.matmul(attn_weights, V)
+        context = context.transpose(1, 2).contiguous()
+        context = context.view(batch_size, -1, self.config.hidden_size)
+        return self.out_proj(context)
+
+
+class ZZJRabbit3Layer(nn.Module):
+    def __init__(self, config: ZZJRabbit3Config):
+        super().__init__()
+        self.attn = ZZJRabbit3Attention(config)
+        self.l1 = nn.Linear(config.hidden_size, config.hidden_size)
+        self.l2 = nn.Linear(config.hidden_size, config.hidden_size)
+        self.activate = nn.ReLU()
+        self.norm = nn.RMSNorm(config.hidden_size)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        postition_embeddings: tuple[torch.Tensor, torch.Tensor],
+        attention_mask: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        key_padding_mask = None
+        attn_mask = torch.gt(
+            torch.triu(torch.ones(x.size(-2), x.size(-2), device=x.device), 1), 0
+        )
+        if attention_mask is not None:
+            key_padding_mask = torch.lt(attention_mask, 1)
+        attn = self.attn(
+            x,
+            postition_embeddings,
+            key_padding_mask=key_padding_mask,
+            attn_mask=attn_mask,
+        )
+        x = self.norm(x + attn)
+        o = self.l1(x)
+        o = self.activate(o)
+        o = self.l2(o)
+        return self.norm(x + o)
+
+
+class ZZJRabbit3Model(PreTrainedModel):
+    config_class = ZZJRabbit3Config
+
+    def __init__(self, config: ZZJRabbit3Config, **kwargs):
+        super().__init__(config, **kwargs)
+        self.config = config
+        self.embedding = nn.Embedding(config.vocab_size, config.hidden_size)
+        self.rotary_emb = ZZJRabbit3RotaryEmbedding(
+            config.hidden_size // config.num_attention_heads
+        )
+        self.layers = nn.ModuleList(
+            [ZZJRabbit3Layer(config) for _ in range(config.num_hidden_layers)]
+        )
+        self.post_init()
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        return_dict: Optional[bool] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        **kwargs,
+    ) -> tuple | BaseModelOutput:
+        res = self.embedding(input_ids)
+        batch_size, seq_len = input_ids.shape
+        position_ids = (
+            torch.arange(seq_len, device=input_ids.device)
+            .unsqueeze(0)
+            .expand(batch_size, -1)
+        )
+        position_embeddings = self.rotary_emb(position_ids)
+        for layer in self.layers:
+            res = layer(res, position_embeddings, attention_mask)
+        if not return_dict:
+            return (res,)
+        else:
+            return BaseModelOutput(res)
+
+
+class ZZJRabbit3ForCausalLM(PreTrainedModel, GenerationMixin):
+    config_class = ZZJRabbit3Config
+
+    def __init__(self, config: ZZJRabbit3Config, **kwargs):
+        super().__init__(config, **kwargs)
+        self.model = ZZJRabbit3Model(config, **kwargs)
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size)
+        self.post_init()
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        return_dict: Optional[bool] = None,
+        labels: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        logits_to_keep: Union[int, torch.Tensor] = 0,
+        **kwargs,
+    ) -> tuple | CausalLMOutput:
+        hidden = self.model(input_ids=input_ids, attention_mask=attention_mask)[0]
+        logits = self.lm_head(
+            hidden[
+                :,
+                slice(-logits_to_keep, None)
+                if isinstance(logits_to_keep, int)
+                else logits_to_keep,
+                :,
+            ]
+        )
+        if labels is not None:
+            loss = self.loss_function(
+                logits=logits,
+                labels=labels,
+                vocab_size=self.config.vocab_size,
+                **kwargs,
+            )
+        if not return_dict:
+            return (loss, logits) if labels is not None else (logits,)
+        else:
+            return (
+                CausalLMOutput(logits=logits, loss=loss)
+                if labels is not None
+                else CausalLMOutput(logits=logits)
+            )
+
+    @classmethod
+    def can_generate(cls):
+        return True
+
+    def prepare_inputs_for_generation(self, input_ids, **kwargs):
+        return {"input_ids": input_ids}
+
+
+class ZZJRabbit3Tokenizer(TokenizersBackend):
+    model = BPE
+
+    def __init__(
+        self,
+        vocab=None,
+        merges=None,
+        unk_token="<eos>",
+        eos_token="<eos>",
+        pad_token="<eos>",
+        **kwargs,
+    ):
+        self._vocab = vocab or {
+            "<eos>": 0,
+        }
+        self._merges = merges or []
+
+        self._tokenizer = Tokenizer(
+            BPE(vocab=self._vocab, merges=self._merges, fuse_unk=True)
+        )
+        self._tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=False)
+        self._tokenizer.decoder = decoders.ByteLevel()
+
+        super().__init__(
+            unk_token=unk_token, eos_token=eos_token, pad_token=pad_token, **kwargs
+        )