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config.json

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+{
+  "architectures": [
+    "TinyllmForCausalLM"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_tinyllm.TinyllmConfig",
+    "AutoModelForCausalLM": "modeling_tinyllm.TinyllmForCausalLM"
+  },
+  "attention_dropout": 0.0,
+  "hidden_act": "silu",
+  "hidden_size": 512,
+  "initializer_range": 0.02,
+  "intermediate_size": 1408,
+  "max_position_embeddings": 1024,
+  "model_type": "tinyllm",
+  "num_attention_heads": 8,
+  "num_hidden_layers": 8,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-06,
+  "rope_theta": 10000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.38.2",
+  "use_cache": true,
+  "vocab_size": 64798
+}

configuration_tinyllm.py

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+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__) 
+
+
+class TinyllmConfig(PretrainedConfig):
+    """ TinyLLM 配置文件
+    """
+    
+    model_type = "tinyllm"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self, 
+        vocab_size=64797,
+        hidden_size=4096,
+        intermediate_size=11008,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        hidden_act="silu",
+        max_position_embeddings=2048,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=None,
+        eos_token_id=None,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        attention_dropout=0.0,
+        **kwargs
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+            
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.attention_dropout = attention_dropout   
+            
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs
+        )

generation_config.json

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+{
+  "bos_token_id": 1,
+  "do_sample": true,
+  "eos_token_id": 2,
+  "pad_token_id": 0,
+  "top_k":5,
+  "top_p":0.8,
+  "temperature":0.8,
+  "repetition_penalty": 1.1, 
+  "_from_model_config": true,
+  "transformers_version": "4.38.2"
+}

modeling_tinyllm.py

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+""" 
+Tiny LLM 模型架构
+
+到处抄，整体还是Llama2的模型架构
+"""
+
+import math
+import warnings
+from threading import Thread
+from typing import List, Optional, Tuple, Union
+    
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache
+from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask, _prepare_4d_causal_attention_mask_for_sdpa
+from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import logging
+from transformers.generation.utils import GenerationConfig
+
+from .configuration_tinyllm import TinyllmConfig
+
+logger = logging.get_logger(__name__)
+
+def debug(key, value):
+    """
+    """
+    try:
+        res = {"var": torch.var(value).item(), "mean": torch.mean(value).item(), 
+            "max":torch.max(value).item(), "size": value.size(), "dtype": value.dtype}
+    except:
+        res = value
+    print("debug", key, res, sep="\t")
+
+
+def report_memory(name):
+    """Simple GPU memory report."""
+    mega_bytes = 1024.0 * 1024.0
+    string = name + ' memory (MB)'
+    # 变量分配显存
+    string += ' | allocated: {}'.format(
+        torch.cuda.memory_allocated() / mega_bytes)
+    string += ' | max allocated: {}'.format(
+        torch.cuda.max_memory_allocated() / mega_bytes)
+    # 缓存和变量分配显存，实际显存还需要+pytorch context
+    string += ' | reserved: {}'.format(
+        torch.cuda.memory_reserved() / mega_bytes)
+    string += ' | max reserved: {}'.format(
+        torch.cuda.max_memory_reserved() / mega_bytes)
+    try:
+        if torch.distributed.get_rank() == 0:
+            print("[Rank {}] {}".format(torch.distributed.get_rank(), string),
+                flush=True)
+            pass
+    except:
+        pass
+    
+class TinyllmRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """ TinyllmRMSNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+class TinyllmRotaryEmbedding(nn.Module):
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
+        """ 旋转位置编码
+            - dim (int): 旋转嵌入的维度大小。
+            - max_position_embeddings (int): 预计算的最大位置嵌入数，默认为2048。
+            - base (int): 用于计算逆频率的基本频率，默认为10000。
+        """
+        super().__init__()
+
+        self.dim = dim
+        self.max_position_embeddings = max_position_embeddings
+        self.base = base
+        # 计算逆频率值，并将其注册为模型的缓冲区
+        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+        # 为了支持`torch.jit.trace`功能，立即计算预存储的余弦和正弦缓存
+        self._set_cos_sin_cache(
+            seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
+        )
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        """ 预计算的余弦和正弦缓存
+        """
+        self.max_seq_len_cached = seq_len
+        # 创建一个从0到最大序列长度-1的整数张量，与 inv_freq 具有相同的设备和数据类型
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(self.inv_freq)
+
+        # 计算每个位置与每个维度的频率，形成频谱矩阵
+        freqs = torch.outer(t, self.inv_freq)
+        
+        # 不同于论文中的实现，这里采用了不同的排列方式以获得相同的计算结果
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+    def forward(self, x, seq_len=None):
+        # x: [bs, num_attention_heads, seq_len, head_size]
+        if seq_len > self.max_seq_len_cached:
+            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
+
+        return (
+            self.cos_cached[:seq_len].to(dtype=x.dtype),
+            self.sin_cached[:seq_len].to(dtype=x.dtype),
+        )
+
+def rotate_half(x):
+    """ 旋转输入一半的 hidden dim
+    """
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+# Copied from transformers.models.mistral.modeling_mistral.apply_rotary_pos_emb
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
+    """ 在 qk 应用旋转位置编码
+
+    Args:
+        q (`torch.Tensor`): q
+        k (`torch.Tensor`): k
+        cos (`torch.Tensor`): 旋转位置嵌入的余弦部分
+        sin (`torch.Tensor`): 旋转位置嵌入的正弦部分
+        position_ids (`torch.Tensor`): 与q和k对应位置的标记索引。例如，在处理KV缓存时，可以使用偏移过的位置ID。
+        unsqueeze_dim (`int`, *optional*, defaults to 1): 'unsqueeze_dim' 参数指定了沿哪个维度对 cos[position_ids] 
+            和 sin[position_ids] 进行扩展，以便它们能够适当地广播到 q 和 k 的维度上。
+            例如，注意 cos[position_ids] 和 sin[position_ids] 具有形状 [batch_size, seq_len, head_dim]。
+            那么，如果 q 和 k 的形状分别为 [batch_size, heads, seq_len, head_dim]，
+            则设置 unsqueeze_dim=1 可使 cos[position_ids] 和 sin[position_ids] 可以广播到 q 和 k 的形状上。
+            同样地，如果 q 和 k 的形状为 [batch_size, seq_len, heads, head_dim]，则应将 unsqueeze_dim 设置为 2
+    Returns:
+        包含使用旋转位置嵌入变换后的q和k张量的 `tuple(torch.Tensor)`。
+    """
+    # print("ori cos: ", cos.shape)
+    cos = cos[position_ids].unsqueeze(unsqueeze_dim)
+    sin = sin[position_ids].unsqueeze(unsqueeze_dim)
+
+    # print("q: ", q.shape)
+    # print("cos: ", cos.shape)
+    # print("sin: ", sin.shape)
+    # print("rotate_half: ", rotate_half(q).shape)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+class TinyllmMLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, x):
+        intermediate = self.act_fn(self.gate_proj(x)) * self.up_proj(x)
+        down_proj = self.down_proj(intermediate) 
+        return down_proj
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+class TinyllmAttention(nn.Module):
+    """ 多头注意力
+    """
+
+    def __init__(self, config: TinyllmConfig, layer_idx: Optional[int] = None):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        if layer_idx is None:
+            logger.warning_once(
+                f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
+                "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
+                "when creating this class."
+            )
+
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.hidden_size // self.num_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.max_position_embeddings = config.max_position_embeddings
+        self.rope_theta = config.rope_theta
+        # 因果自回归模式
+        self.is_causal = True
+        self.attention_dropout = config.attention_dropout
+
+        if (self.head_dim * self.num_heads) != self.hidden_size:
+            raise ValueError(
+                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+                f" and `num_heads`: {self.num_heads})."
+            )
+        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=True)
+        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
+        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
+        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
+
+        self.rotary_emb = TinyllmRotaryEmbedding(
+            self.head_dim,
+            max_position_embeddings=self.max_position_embeddings,
+            base=self.rope_theta,
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+            )
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        # 重新投影，变成多头注意力结构
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            if self.layer_idx is None:
+                raise ValueError(
+                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
+                    "with a layer index."
+                )
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+        # 应用旋转位置编码到 qk 向量
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+        # 如果存在缓存，则更新 kv 
+        if past_key_value is not None:
+            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        # repeat k/v heads if n_kv_heads < n_heads
+        # 如果 num_key_value_heads 小于 num_heads，则重复key和value向量以匹配头数量
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        # 计算注意力权重
+        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+            raise ValueError(
+                f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
+                f" {attn_weights.size()}"
+            )
+
+        if attention_mask is not None:
+            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+                raise ValueError(
+                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+                )
+
+            attn_weights = attn_weights + attention_mask
+
+        # softmax归一化注意力权重，并转换至float32类型以防止数值溢出
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+        attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
+        # 注意力输出
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+                f" {attn_output.size()}"
+            )
+        
+        # 还原注意力输出的形状以与后续层对接
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+        # 通过o_proj层进一步处理注意力输出
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+    
+class TinyllmSdpaAttention(TinyllmAttention):
+    """ 使用 torch.nn.functional.scaled_dot_product_attention 实现的注意力模块。
+        该模块继承自 `TinyllmAttention`，因为模块的权重保持不变。唯一的变化在于前向传播过程中适应 SDPA API。
+        Scaled Dot Product Attention (SDPA) 
+    """
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        # 当设置output_attentions=True时，由于torch.nn.functional.scaled_dot_product_attention不支持直接返回注意力权重
+        # 因此暂时降级回用父类的手动实现方式，并发出警告提示用户未来版本的更改要求
+        if output_attentions:
+            # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
+            logger.warning_once(
+                "Model is using SdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
+                'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
+            )
+            return super().forward(
+                hidden_states=hidden_states,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_value=past_key_value,
+                output_attentions=output_attentions,
+                use_cache=use_cache,
+            )
+        # 获取输入维度信息
+        bsz, q_len, _ = hidden_states.size()
+
+        # 对输入进行线性映射得到query、key、value向量
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        # 将映射后的向量调整为多头注意力所需格式
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        # 计算有效的 kv 序列长度（考虑缓存的情况）
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+        
+        # 应用旋转位置嵌入（RoPE）
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+        # 如果有缓存，更新key和value状态
+        if past_key_value is not None:
+            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        if attention_mask is not None:
+            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+                raise ValueError(
+                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+                )
+
+        # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
+        # Reference: https://github.com/pytorch/pytorch/issues/112577.
+        if query_states.device.type == "cuda" and attention_mask is not None:
+            query_states = query_states.contiguous()
+            key_states = key_states.contiguous()
+            value_states = value_states.contiguous()
+
+        # 使用scaled_dot_product_attention进行计算
+        attn_output = torch.nn.functional.scaled_dot_product_attention(
+            query_states,
+            key_states,
+            value_states,
+            attn_mask=attention_mask,
+            dropout_p=self.attention_dropout if self.training else 0.0,
+            # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.
+            is_causal=self.is_causal and attention_mask is None and q_len > 1,
+        )
+
+        # 还原注意力输出的形状
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.view(bsz, q_len, self.hidden_size)
+
+        # 将注意力输出通过最终的线性层（o_proj层）
+        attn_output = self.o_proj(attn_output)
+
+        return attn_output, None, past_key_value    
+
+TINYLLM_ATTENTION_CLASSES = {
+    "eager": TinyllmAttention,
+    "sdpa": TinyllmSdpaAttention,
+}
+
+class TinyllmDecoderLayer(nn.Module):
+    def __init__(self, config: TinyllmConfig, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+
+        self.self_attn = TINYLLM_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx)
+        self.mlp = TinyllmMLP(config)
+        self.input_layernorm = TinyllmRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = TinyllmRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        **kwargs,
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`): 输入形状 `(batch, seq_len, embed_dim)`
+            attention_mask (`torch.FloatTensor`, *optional*): attention mask 形状`(batch, sequence_length)`，
+                填充使用0表示
+            output_attentions (`bool`, *optional*): 是否返回所有注意力层的注意力张量。
+            use_cache (`bool`, *optional*): 如果设置为 `True`，则返回 `past_key_values` 关键值状态，可用于加速解码
+            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): 缓存的之前kv状态
+        """
+
+        residual = hidden_states
+
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights, present_key_value = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        return outputs
+
+
+class TinyllmPreTrainedModel(PreTrainedModel):
+    config_class = TinyllmConfig
+    # 定义了模型内部子模块命名的基础前缀，当加载或保存模型时，这个前缀将用于识别模型主体部分。
+    base_model_prefix = "model"
+    # 表明该模型支持梯度检查点技术，这是一种内存优化策略，可减少模型训练时所需的显存
+    supports_gradient_checkpointing = True
+    # 指定了在序列化过程中不应被拆分的模块列表，即在模型保存与加载时保持这些模块作为一个整体。
+    _no_split_modules = ["TinyllmDecoderLayer"]
+    # 在跨设备数据移动时，指示哪些关键字（key）对应的数据应该跳过设备放置步骤。
+    _skip_keys_device_placement = "past_key_values"
+    # Scaled Dot Product Attention (SDPA) 
+    _supports_sdpa = True
+    # 表示模型支持缓存机制，这在自回归模型（如Transformer解码器）中很常见，
+    # 用于存储先前计算的结果以加快后续时间步长的计算速度。
+    _supports_cache_class = True
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+class TinyllmModel(TinyllmPreTrainedModel):
+    """ 根据配置文件堆叠 TinyllmDecoderLayer 
+    Args:
+        config: TinyllmConfig
+    """
+
+    def __init__(self, config: TinyllmConfig):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+        self.layers = nn.ModuleList(
+            [TinyllmDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self._attn_implementation = config._attn_implementation
+        self.norm = TinyllmRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        self.gradient_checkpointing = False
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,    # 每个输入序列词元在位置嵌入中的位置索引
+        past_key_values: Optional[List[torch.FloatTensor]] = None,  # 可用于加速序列解码预先计算的隐藏状态（自注意力块和交叉注意力块中的键和值）
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # retrieve input_ids and inputs_embeds
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
+        elif input_ids is not None:
+            batch_size, seq_length = input_ids.shape
+        elif inputs_embeds is not None:
+            batch_size, seq_length, _ = inputs_embeds.shape
+        else:
+            raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
+
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                logger.warning_once(
+                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+                )
+                use_cache = False
+
+        past_key_values_length = 0
+
+        if use_cache:
+            use_legacy_cache = not isinstance(past_key_values, Cache)
+            if use_legacy_cache:
+                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+            past_key_values_length = past_key_values.get_usable_length(seq_length)
+
+        if position_ids is None:
+            device = input_ids.device if input_ids is not None else inputs_embeds.device
+            # 生成一个从past_key_values_length到seq_length + past_key_values_length的整数序列
+            position_ids = torch.arange(
+                past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
+            )
+            # 将生成的序列重塑为形状为(1, seq_length)的张量，然后展平为形状为(-1, seq_length)的张量
+            position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
+        else:
+            position_ids = position_ids.view(-1, seq_length).long()
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        # 适应不同注意力机制对注意力掩码的不同要求而设计的
+        if self._attn_implementation == "sdpa" and not output_attentions:
+            # output_attentions=True can not be supported when using SDPA, and we fall back on
+            # the manual implementation that requires a 4D causal mask in all cases.
+            attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
+                attention_mask,
+                (batch_size, seq_length),
+                inputs_embeds,
+                past_key_values_length,
+            )
+        else:
+            # 4d mask is passed through the layers
+            attention_mask = _prepare_4d_causal_attention_mask(
+                attention_mask,
+                (batch_size, seq_length),
+                inputs_embeds,
+                past_key_values_length,
+            )
+
+        hidden_states = inputs_embeds
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = None
+
+        for decoder_layer in self.layers:
+            # 1.隐藏状态保存
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+            # 2.梯度检查，方便在反向传播时只激活部分层，节省内存资源
+            # 3.解码层：
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    decoder_layer.__call__,
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    past_key_values,
+                    output_attentions,
+                    use_cache,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_values,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                )
+            # 4.更新隐藏状态
+            hidden_states = layer_outputs[0]
+            # 5.更新缓存
+            if use_cache:
+                next_decoder_cache = layer_outputs[2 if output_attentions else 1]
+            # 6.注意力输出保存
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        next_cache = None
+        if use_cache:
+            next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
+
+        if not return_dict:
+            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+        
+class TinyllmForCausalLM(TinyllmPreTrainedModel):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = TinyllmModel(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
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        hidden_states = outputs[0]
+        logits = self.lm_head(hidden_states)
+        logits = logits.float()
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            # 对于自回归模型（如GPT系列），我们需要将模型输出的logits向前移动一位，
+            # 这样使得模型预测的是当前时刻 t 的下一个词，而非当前词本身
+            shift_logits = logits[..., :-1, :].contiguous()
+            # 同时，也需要将真实标签（labels）向前移动一位以与调整后的logits对齐
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss(ignore_index=-100)
+
+            # 将移位后的 logits 和 labels 扁平化，即将它们展平为一维张量
+            # 其中shift_logits变成 (batch_size * sequence_length, vocab_size) 的形式
+            # shift_labels变为 (batch_size * sequence_length) 的形式
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            
+            # Enable model parallelism
+            # 确保模型并行计算时，labels的数据存储位置与logits一致
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(
+        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
+    ):
+        """ 准备模型的输入参数
+            包括处理input_ids、past_key_values（历史隐藏状态缓存）、attention_mask以及可选的inputs_embeds。
+        """
+        # Omit tokens covered by past_key_values
+        if past_key_values is not None:
+            if isinstance(past_key_values, Cache):
+                cache_length = past_key_values.get_seq_length()
+                past_length = past_key_values.seen_tokens
+                max_cache_length = past_key_values.get_max_length()
+            else:
+                cache_length = past_length = past_key_values[0][0].shape[2]
+                max_cache_length = None
+
+            # 根据缓存情况裁剪input_ids，只保留未处理的token：
+            # # 1. 如果 attention_mask 比 input_ids 更长，说明部分输入已通过缓存传递（如仅传入inputs_embeds）
+            if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
+                # 取最后未处理的部分
+                input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
+            # 2. 若已处理的 token 数小于input_ids中的总数，表明input_ids包含全部输入，从中去掉已处理的部分
+            elif past_length < input_ids.shape[1]:
+                input_ids = input_ids[:, past_length:]
+            # 3. 否则，认为input_ids中只有待处理的新token
+
+            # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
+            if (
+                max_cache_length is not None
+                and attention_mask is not None
+                and cache_length + input_ids.shape[1] > max_cache_length
+            ):
+                attention_mask = attention_mask[:, -max_cache_length:]
+
+        # 初始化或处理position_ids
+        position_ids = kwargs.get("position_ids", None)
+        # 如果attention_mask存在但position_ids不存在，则基于attention_mask动态创建position_ids
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -input_ids.shape[1] :]
+
+        # 根据inputs_embeds和past_key_values的存在与否来决定模型输入
+        # 如果提供了inputs_embeds且没有past_key_values（首次生成步骤），则直接使用inputs_embeds作为模型输入
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids}
+
+        model_inputs.update(
+            {
+                "position_ids": position_ids,
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+                "attention_mask": attention_mask,
+            }
+        )
+        return model_inputs
+
+    @staticmethod
+    def _reorder_cache(past_key_values, beam_idx):
+        """ 用于重新排序缓存中的历史隐藏状态，以适应束搜索（beam search）算法
+        """
+        reordered_past = ()
+        # 遍历每一层的隐藏状态
+        for layer_past in past_key_values:
+            # 对于每一层的每个隐藏状态向量，执行索引选择操作
+            reordered_past += (
+                tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
+            )
+        return reordered_past
+    
+    def chat(self, tokenizer, messages: List[dict], stream=False, generation_config: Optional[GenerationConfig]=None):
+        pass
+
+    @torch.no_grad()
+    def generate(self, input_ids, generation_config):
+        eos_token_id = generation_config.eos_token_id
+        max_new_tokens = generation_config.max_new_tokens
+        temperature = generation_config.temperature
+        top_k = generation_config.top_k
+
+        past_key_values = None
+        history = input_ids
+
+        for _ in range(max_new_tokens):
+            outputs = self(input_ids, past_key_values=past_key_values, use_cache=True)
+            logits = outputs["logits"][:, -1, :]
+            past_key_values = outputs["past_key_values"]
+
+            if temperature == 0.0:
+                _, next_token = torch.topk(logits, k=1, dim=-1)
+            else:
+                logits /= temperature
+                if top_k:
+                    top_vals, _ = torch.topk(logits, min(top_k, logits.size(-1)))
+                    logits[logits < top_vals[:, [-1]]] = -float('Inf')
+                probs = F.softmax(logits, dim=-1)
+                next_token = torch.multinomial(probs, num_samples=1)
+
+            history = torch.cat((history, next_token), dim=1)
+            input_ids = next_token
+            if next_token == eos_token_id:
+                break
+        return history
+    
+class TinyllmForSequenceClassification(TinyllmPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = TinyllmModel(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
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        transformer_outputs = self.model(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        hidden_states = transformer_outputs[0]
+        logits = self.score(hidden_states)
+
+        if input_ids is not None:
+            batch_size = input_ids.shape[0]
+        else:
+            batch_size = inputs_embeds.shape[0]
+
+        if self.config.pad_token_id is None and batch_size != 1:
+            raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
+        # 确定输入序列的有效长度，即从起始到第一个填充符出现之前的所有非填充字符的数量
+        if self.config.pad_token_id is None:
+            # 无法计算有效长度
+            sequence_lengths = -1
+        else:
+            if input_ids is not None:
+                # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
+                # 对于给定的输入IDs（input_ids），查找其中等于填充符ID的位置
+                # argmax(-1)作用在最后一个维度上，找到每个序列中填充符首次出现的最大索引位置
+                # 因为索引是从0开始的，减去1可得到每个序列的有效字符数（不含填充符）
+                sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
+                # 为了保证与ONNX兼容以及防止越界，当序列尾部被完全填充时，采用模运算来保持有效长度
+                # 即使索引超过了输入序列的实际长度，也会自动对应回到有效的范围之内
+                sequence_lengths = sequence_lengths % input_ids.shape[-1]
+                # 确保计算出的序列长度在与logits相同的设备上，便于后续操作
+                sequence_lengths = sequence_lengths.to(logits.device)
+            else:
+                sequence_lengths = -1
+
+        # 提取实际标签对应的logits
+        # 使用arange函数生成一个从0到batch_size-1的索引，并与sequence_lengths结合，
+        # 选取每个样本的有效logit
+        pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
+
+        loss = None
+        if labels is not None:
+            labels = labels.to(logits.device)
+            # 若模型配置没有明确指定 problem_type ，则根据num_labels和labels的数据类型推断 problem_type 
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                # 使用均方误差损失函数
+                loss_fct = MSELoss()
+                # 如果num_labels为1，则直接计算单输出的损失；否则，按列计算所有输出的损失
+                if self.num_labels == 1:
+                    loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(pooled_logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                # 单标签分类任务，使用交叉熵损失函数
+                loss_fct = CrossEntropyLoss()
+                # 将pooled_logits展平为(batch_size * num_labels)的形式，与同样展平后的labels进行比较
+                loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                # 多标签分类任务，使用带Sigmoid激活的二元交叉熵损失函数
+                loss_fct = BCEWithLogitsLoss()
+                # 直接计算sigmoid之前的logits与标签之间的损失
+                loss = loss_fct(pooled_logits, labels)
+
+        if not return_dict:
+            output = (pooled_logits,) + transformer_outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutputWithPast(
+            loss=loss,
+            logits=pooled_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )
+     
+def print_model_parameters(model):
+    """ 打印模型各个层参数
+    """
+    param_sum = 0
+    for name, param in model.named_parameters():
+        if param.requires_grad:
+            param_sum += param.numel()
+            print(f"Layer: {name}, Parameters: {param.numel()}")
+    print(f"Total of parameters: {param_sum}") 
+    
+if __name__ == "__main__":
+    # vocav size https://github.com/THUDM/ChatGLM3/issues/634
+    args_1480m = TinyllmConfig(
+        hidden_size=2048,
+        num_hidden_layers=24,
+        num_attention_heads=16,
+        intermediate_size=5504,
+        rope_theta=10000.0,
+        max_position_embeddings=1024,
+        vocab_size=64798,
+    ) 
+    
+    args_440m = TinyllmConfig(
+        hidden_size=1024,
+        num_hidden_layers=24,
+        num_attention_heads=16,
+        intermediate_size=2816,
+        rope_theta=10000.0,
+        max_position_embeddings=1024,
+        vocab_size=64798,
+    ) 
+    
+    args_210m = TinyllmConfig(
+        hidden_size=768,
+        num_hidden_layers=16,
+        num_attention_heads=12,
+        intermediate_size=2048,
+        rope_theta=10000.0,
+        max_position_embeddings=1024,
+        vocab_size=64798,
+    ) 
+    
+    args_92m = TinyllmConfig(
+        hidden_size=512,
+        num_hidden_layers=8,
+        num_attention_heads=8,
+        intermediate_size=1408,
+        rope_theta=10000.0,
+        max_position_embeddings=1024,
+        vocab_size=64798,
+    )
+    
+    args_42m = TinyllmConfig(
+        hidden_size=288,
+        num_hidden_layers=6,
+        num_attention_heads=6,
+        intermediate_size=768,
+        rope_theta=10000.0,
+        max_position_embeddings=512,
+        vocab_size=64798,
+    )  
+    
+    args_16m = TinyllmConfig(
+        hidden_size=120,
+        num_hidden_layers=6,
+        num_attention_heads=6,
+        intermediate_size=384,
+        rope_theta=10000.0,
+        max_position_embeddings=512,
+        vocab_size=64798,
+    )      
+    
+    model = TinyllmForCausalLM(args_210m)
+    
+    inputs_ids = torch.tensor([[1,2,4],[4,3,2]])
+    labels = torch.tensor([[1,4,3],[2,3,1]])
+    print(inputs_ids.shape)
+    outputs = model(input_ids=inputs_ids, labels=labels)
+    print(outputs.logits)
+    print(outputs.loss)
+    
+    # print_model_parameters(model)
+        

tokenization_chatglm.py

@@ -0,0 +1,390 @@
+import json
+import os
+import re
+from typing import List, Optional, Union, Dict
+from sentencepiece import SentencePieceProcessor
+from transformers import PreTrainedTokenizer
+from transformers.utils import logging, PaddingStrategy
+from transformers.tokenization_utils_base import EncodedInput, BatchEncoding
+
+
+logger = logging.get_logger(__name__)
+
+
+class SPTokenizer:
+    def __init__(self, model_path: str):
+        # reload tokenizer
+        assert os.path.isfile(model_path), model_path
+        self.sp_model = SentencePieceProcessor(model_file=model_path)
+
+        # BOS / EOS token IDs
+        self.n_words: int = self.sp_model.vocab_size()
+        self.bos_id: int = self.sp_model.bos_id()
+        self.eos_id: int = self.sp_model.eos_id()
+        self.pad_id: int = self.sp_model.unk_id()
+        # 确保vocab_size与piece数量一致
+        assert self.sp_model.vocab_size() == self.sp_model.get_piece_size()
+
+        # 定义聊天角色相关的特殊token
+        role_special_tokens = ["<|system|>", "<|user|>", "<|assistant|>", "<|observation|>"]
+        # 添加额外的通用特殊token
+        special_tokens = ["[MASK]", "[gMASK]", "[sMASK]", "sop", "eop"] + role_special_tokens
+        # 创建特殊token与ID之间的映射关系
+        self.special_tokens = {}
+        self.index_special_tokens = {}
+        for token in special_tokens:
+            # 分配新的词汇表ID给特殊token
+            self.special_tokens[token] = self.n_words
+            self.index_special_tokens[self.n_words] = token
+            self.n_words += 1
+        # 生成正则表达式，用于在apply_chat_template方法中查找特殊token
+        self.role_special_token_expression = "|".join([re.escape(token) for token in special_tokens]) # for apply_chat_template
+
+    def tokenize(self, s: str, encode_special_tokens=False):
+        """ 对输入字符串进行分词操作，可选择是否编码特殊token
+        """
+        if encode_special_tokens:
+            # 对特殊字符进行处理
+            last_index = 0
+            t = []
+            for match in re.finditer(self.role_special_token_expression, s):
+                # 查找并保留非特殊token部分的分词结果
+                if last_index < match.start():
+                    t.extend(self.sp_model.EncodeAsPieces(s[last_index:match.start()]))
+                # 直接添加特殊token
+                t.append(s[match.start():match.end()])
+                last_index = match.end()
+            # 处理剩余非特殊token部分
+            if last_index < len(s):
+                t.extend(self.sp_model.EncodeAsPieces(s[last_index:]))
+            return t
+        else:
+            # 当encode_special_tokens为False时，直接调用SentencePiece模型进行分词
+            return self.sp_model.EncodeAsPieces(s)
+
+    def encode(self, s: str, bos: bool = False, eos: bool = False) -> List[int]:
+        """ 
+        将字符串转化为ID列表，可选择是否添加BOS/EOS token。对于特殊字符，将会被分开
+        """
+        assert type(s) is str
+        t = self.sp_model.encode(s)
+        if bos:
+            t = [self.bos_id] + t
+        if eos:
+            t = t + [self.eos_id]
+        return t
+
+    def decode(self, t: List[int]) -> str:
+        """ 将ID列表解码为字符串
+        """
+        text, buffer = "", []
+        for token in t:
+            # 处理特殊tokenID转字符串
+            if token in self.index_special_tokens:
+                if buffer:
+                    text += self.sp_model.decode(buffer)
+                    buffer = []
+                text += self.index_special_tokens[token]
+            else:
+                buffer.append(token)
+        # 解码剩余普通tokenID
+        if buffer:
+            text += self.sp_model.decode(buffer)
+        return text
+
+    def decode_tokens(self, tokens: List[str]) -> str:
+        """ 将分词结果（List[str]）解码为字符串
+        """
+        text = self.sp_model.DecodePieces(tokens)
+        return text
+
+    def convert_token_to_id(self, token):
+        """ 将给定的token字符串转化为对应的ID
+        """
+        if token in self.special_tokens:
+            return self.special_tokens[token]
+        return self.sp_model.PieceToId(token)
+
+    def convert_id_to_token(self, index):
+        """ 将给定的ID转化为对应的token字符串
+        """
+        # 处理特殊tokenID
+        if index in self.index_special_tokens:
+            return self.index_special_tokens[index]
+        # 处理边界情况和其他特殊ID
+        if index in [self.eos_id, self.bos_id, self.pad_id] or index < 0 or index > self.sp_model.vocab_size():
+            return ""
+        # 将普通ID转换为token
+        return self.sp_model.IdToPiece(index)
+
+
+class ChatGLMTokenizer(PreTrainedTokenizer):
+    # 预训练模型所需的文件名配置，这里指向tokenizer的model文件
+    vocab_files_names = {"vocab_file": "tokenizer.model"}
+    # 模型输入的特征名称列表
+    model_input_names = ["input_ids", "attention_mask", "position_ids"]
+
+    def __init__(
+        self,
+        vocab_file,
+        padding_side="left",
+        clean_up_tokenization_spaces=False,
+        encode_special_tokens=False,
+        **kwargs
+    ):
+        # 设置tokenizer的名称
+        self.name = "GLMTokenizer"
+        # 存储vocab文件路径
+        self.vocab_file = vocab_file
+        # 使用SPTokenizer作为基础分词器
+        self.tokenizer = SPTokenizer(vocab_file)
+        # 定义特殊token及其对应的ID
+        self.special_tokens = {
+            "<bos>": self.tokenizer.bos_id,
+            "<eos>": self.tokenizer.eos_id,
+            "<unk>": self.tokenizer.pad_id,
+            "<pad>": self.tokenizer.pad_id
+        }
+        self.encode_special_tokens = encode_special_tokens
+
+        super().__init__(
+            padding_side=padding_side,
+            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+            **kwargs
+        )
+
+    def get_command(self, token):
+        """ 获取指定特殊 token 对应的 id
+        """
+        if token in self.special_tokens:
+            return self.special_tokens[token]
+        # 如果不在自定义特殊 token 中，则从基础SPTokenizer的特殊 token 中查找
+        assert token in self.tokenizer.special_tokens, f"{token} is not a special token for {self.name}"
+        return self.tokenizer.special_tokens[token]
+
+    @property
+    def unk_token(self) -> str:
+        """ 通过ID获取未登录词、填充符和结束符的字符串形式
+        """
+        return self.tokenizer.sp_model.IdToPiece(self.get_command("<unk>"))
+
+    @property
+    def pad_token(self) -> str:
+        return self.tokenizer.sp_model.IdToPiece(self.get_command("<pad>"))
+
+    @property
+    def eos_token(self) -> str:
+        return self.tokenizer.sp_model.IdToPiece(self.get_command("<eos>"))
+
+    @property
+    def unk_token_id(self) -> int:
+        """ 获取未登录词、填充符和结束符的ID形式
+        """
+        return self.get_command("<unk>")
+
+    @property
+    def pad_token_id(self) -> int:
+        return self.get_command("<pad>")
+
+    @property
+    def eos_token_id(self):
+        return self.get_command("<eos>")
+
+    @unk_token.setter
+    def unk_token(self, value):
+        """ 不支持设置未登录词、填充符和结束符，输出警告信息
+        """
+        logger.warning("Setting unk_token is not supported, use the default one.")
+
+    @pad_token.setter
+    def pad_token(self, value):
+        logger.warning("Setting pad_token is not supported, use the default one.")
+
+    @eos_token.setter
+    def eos_token(self, value):
+        logger.warning("Setting eos_token is not supported, use the default one.")
+
+    @property
+    def vocab_size(self):
+        """ 返回整个词汇表的大小
+        """
+        return self.tokenizer.n_words
+
+    def get_vocab(self):
+        """ 获取词汇表字典，其中键是token，值是其对应的ID
+        """
+        vocab = {self._convert_id_to_token(i): i for i in range(self.vocab_size)}
+        vocab.update(self.added_tokens_encoder)
+        return vocab
+
+    def _tokenize(self, text, **kwargs):
+        """ 实现分词功能，利用SPTokenizer进行分词操作
+        """
+        return self.tokenizer.tokenize(text, encode_special_tokens=self.encode_special_tokens)
+
+    def _convert_token_to_id(self, token):
+        """ 将token字符串转化为ID
+        """
+        return self.tokenizer.convert_token_to_id(token)
+
+    def _convert_id_to_token(self, index):
+        """ 将ID转化为token字符串
+        """
+        return self.tokenizer.convert_id_to_token(index)
+
+    def convert_tokens_to_string(self, tokens: List[str]) -> str:
+        """ 将分词结果的tokens列表还原为字符串
+        """
+        return self.tokenizer.decode_tokens(tokens)
+
+    def save_vocabulary(self, save_directory, filename_prefix=None):
+        """ 将词汇表和特殊令牌token保存到指定目录。
+
+        Args:
+            save_directory (`str`): 将词汇表和特殊令牌文件保存到指定目录。
+            filename_prefix (`str`, *optional*): 可选添加到保存文件名前的前缀。
+
+        Returns:
+            `Tuple(str)`: 保存文件的路径
+        """
+        if os.path.isdir(save_directory):
+            vocab_file = os.path.join(
+                save_directory, self.vocab_files_names["vocab_file"]
+            )
+        else:
+            vocab_file = save_directory
+
+        with open(self.vocab_file, 'rb') as fin:
+            proto_str = fin.read()
+
+        with open(vocab_file, "wb") as writer:
+            writer.write(proto_str)
+
+        return (vocab_file,)
+
+    def get_prefix_tokens(self):
+        """ 获取用于模型输入的前缀 token 
+        """
+        prefix_tokens = [self.get_command("[gMASK]"), self.get_command("sop")]
+        return prefix_tokens
+
+    def build_single_message(self, role, metadata, message):
+        """ 构建单条消息的 token 序列
+        """
+        assert role in ["system", "user", "assistant", "observation"], role
+        # 构建角色标识Token序列
+        role_tokens = [self.get_command(f"<|{role}|>")] + self.tokenizer.encode(f"{metadata}\n")
+        # 构建消息正文Token序列
+        message_tokens = self.tokenizer.encode(message)
+        # 合并角色标识Token与消息正文Token
+        tokens = role_tokens + message_tokens
+        return tokens
+
+    def build_chat_input(self, query, history=None, role="user"):
+        """ 根据对话历史及当前query构建模型输入
+        """
+        if history is None:
+            history = []
+        input_ids = []
+        # 遍历对话历史
+        for item in history:
+            # 获取内容
+            content = item["content"]
+            # 若为系统消息且包含工具信息，将其加入内容
+            if item["role"] == "system" and "tools" in item:
+                content = content + "\n" + json.dumps(item["tools"], indent=4, ensure_ascii=False)
+            # 构建单条历史消息的Token序列并加入到模型输入ID列表
+            input_ids.extend(self.build_single_message(item["role"], item.get("metadata", ""), content))
+        # 构建当前query的Token序列并加入到模型输入ID列表
+        input_ids.extend(self.build_single_message(role, "", query))
+        # 添加表示回复的assistant标记
+        input_ids.extend([self.get_command("<|assistant|>")])
+        # 调用tokenizer批量编码方法，返回PyTorch张量形式的模型输入
+        return self.batch_encode_plus([input_ids], return_tensors="pt", is_split_into_words=True)
+
+    def build_inputs_with_special_tokens(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        """ 通过拼接和添加特殊标记，从一个或两个序列构建用于序列分类任务的模型输入。
+        
+        BERT序列格式如下：
+        - 单一序列：`[CLS] X [SEP]`
+        - 序列对：`[CLS] A [SEP] B [SEP]`
+
+        Args:
+            token_ids_0 (`List[int]`): 将添加特殊token的IDs列表
+            token_ids_1 (`List[int]`, *optional*): 可选的第二个序列的IDs列表，用于序列对。
+
+        Returns:
+            `List[int]`: 包含适当特殊标记的[输入IDs](../glossary#input-ids)列表。
+        """
+        # 获取前缀标记
+        prefix_tokens = self.get_prefix_tokens()
+        # 在token_ids_0前添加前缀标记
+        token_ids_0 = prefix_tokens + token_ids_0
+        # 若存在token_ids_1，将token_ids_0、token_ids_1连接，并添加结束标记，然后返回
+        if token_ids_1 is not None:
+            token_ids_0 = token_ids_0 + token_ids_1 + [self.get_command("<eos>")]
+        return token_ids_0
+
+    def _pad(
+        self,
+        encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding],
+        max_length: Optional[int] = None,
+        padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
+        pad_to_multiple_of: Optional[int] = None,
+        return_attention_mask: Optional[bool] = None,
+    ) -> dict:
+        """ 此方法用于对编码后的输入进行填充（左右两侧填充，直至达到预设长度或批次中的最大长度）
+
+        Args:
+            encoded_inputs: 字典形式的编码后输入，键为特征名称，值为整数列表（例如，`List[int]`），或者一批编码后的输入（例如，`List[List[int]]`）。
+            max_length: 返回列表的最大长度，也可作为填充长度
+            padding_strategy: 填充策略，有以下选项：
+                - PaddingStrategy.LONGEST : 根据批次中最长序列进行填充
+                - PaddingStrategy.MAX_LENGTH: 默认策略，填充至最大长度
+                - PaddingStrategy.DO_NOT_PAD: 不进行填充
+                本tokenizer的填充方向由self.padding_side属性决定：
+                    - 'left': 在序列左侧填充
+                    - 'right': 在序列右侧填充
+            pad_to_multiple_of: （可选）若设置，则将序列填充至给定值的倍数。这对于在NVIDIA硬件上启用具有计算能力`>= 7.5`（Volta及以上）的Tensor Core非常有用。
+            return_attention_mask:（可选）若设置为False，则避免返回注意力掩码（默认：根据模型特性设置
+        """
+        # 从模型默认设置中加载填充侧信息
+        assert self.padding_side == "left"
+
+        # 获取必要的输入特征，这里假设第一个特征为主要输入特征
+        required_input = encoded_inputs[self.model_input_names[0]]
+        seq_length = len(required_input)
+
+        # 如果填充策略为最长序列，则将最大长度设置为当前序列长度
+        if padding_strategy == PaddingStrategy.LONGEST:
+            max_length = len(required_input)
+
+        # 计算实际最大长度，确保满足pad_to_multiple_of的要求
+        if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
+            max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
+
+        # 判断是否需要填充
+        needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length
+
+        # 若不存在注意力掩码，则初始化
+        if "attention_mask" not in encoded_inputs:
+            encoded_inputs["attention_mask"] = [1] * seq_length
+
+        if "position_ids" not in encoded_inputs:
+            encoded_inputs["position_ids"] = list(range(seq_length))
+
+        # 若需要填充，则执行填充操作
+        if needs_to_be_padded:
+            difference = max_length - len(required_input)
+            # 对注意力掩码进行填充
+            if "attention_mask" in encoded_inputs:
+                encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"]
+            # 对位置标识进行填充
+            if "position_ids" in encoded_inputs:
+                encoded_inputs["position_ids"] = [0] * difference + encoded_inputs["position_ids"]
+            # 对主要输入特征进行填充
+            encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input
+
+        return encoded_inputs

tokenizer.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e7dc4c393423b76e4373e5157ddc34803a0189ba96b21ddbb40269d31468a6f2
+size 1018370

tokenizer_config.json

@@ -0,0 +1,60 @@
+{
+  "added_tokens_decoder": {
+    "64790": {
+      "content": "[gMASK]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "64792": {
+      "content": "sop",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "64794": {
+      "content": "<|system|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "64795": {
+      "content": "<|user|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "64796": {
+      "content": "<|assistant|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "auto_map": {
+    "AutoTokenizer": [
+      "tokenization_chatglm.ChatGLMTokenizer",
+      null
+    ]
+  },
+  "chat_template": "{% for message in messages %}{% if loop.first %}[gMASK]sop<|{{ message['role'] }}|>\n {{ message['content'] }}{% else %}<|{{ message['role'] }}|>\n {{ message['content'] }}{% endif %}{% endfor %}{% if add_generation_prompt %}<|assistant|>{% endif %}",
+  "clean_up_tokenization_spaces": false,
+  "do_lower_case": false,
+  "eos_token": "</s>",
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<unk>",
+  "padding_side": "left",
+  "remove_space": false,
+  "tokenizer_class": "ChatGLMTokenizer",
+  "unk_token": "<unk>"
+}