Upload modeling_ouro.py.bak with huggingface_hub

modeling_ouro.py.bak

@@ -0,0 +1,843 @@
+import logging
+from typing import Any, Callable, Optional, Union
+
+import torch
+from torch import nn
+
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache
+from transformers.generation import GenerationMixin
+from transformers.integrations import use_kernel_forward_from_hub
+from transformers.masking_utils import (
+    create_causal_mask,
+    create_sliding_window_causal_mask,
+)
+from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
+from transformers.modeling_layers import (
+    GenericForQuestionAnswering,
+    GenericForSequenceClassification,
+    GenericForTokenClassification,
+    GradientCheckpointingLayer,
+)
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPast,
+    CausalLMOutputWithPast,
+)
+from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
+from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from transformers.processing_utils import Unpack
+from transformers.utils import TransformersKwargs, auto_docstring, can_return_tuple
+from transformers.utils.generic import check_model_inputs
+from .configuration_ouro import OuroConfig
+
+
+logger = logging.getLogger(__name__)
+
+
+def needs_universal_cache(
+    cache: Optional[Cache], max_cache_size: Optional[int]
+) -> bool:
+    if cache is None:
+        return True
+    if isinstance(cache, UniversalTransformerCache):
+        return False
+    if not isinstance(cache, Cache):
+        return False
+    can_grow = getattr(cache, "layer_class_to_replicate", None) is not None
+    if can_grow:
+        # Dynamic caches can extend to any index, so let them be
+        return False
+    cache_layers = getattr(cache, "layers", [])
+    if max_cache_size is not None and len(cache_layers) < max_cache_size:
+        try:
+            cached_tokens = cache.get_seq_length()
+        except Exception:
+            cached_tokens = 0
+        if cached_tokens > 0:
+            raise ValueError(
+                "The provided cache cannot store all Universal Transformer iterations. Please "
+                "instantiate Ouro.modeling_ouro.UniversalTransformerCache and pass it as past_key_values."
+            )
+        return True
+    return False
+
+
+class OuroMLP(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):
+        down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+        return down_proj
+
+
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    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, cos, sin, position_ids=None, unsqueeze_dim=1):
+    """Applies Rotary Position Embedding to the query and key tensors.
+
+    Args:
+        q (`torch.Tensor`): The query tensor.
+        k (`torch.Tensor`): The key tensor.
+        cos (`torch.Tensor`): The cosine part of the rotary embedding.
+        sin (`torch.Tensor`): The sine part of the rotary embedding.
+        position_ids (`torch.Tensor`, *optional*):
+            Deprecated and unused.
+        unsqueeze_dim (`int`, *optional*, defaults to 1):
+            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+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 UniversalTransformerCache(Cache):
+    """Cache implementation that supports Ouro's multi-step Universal Transformer loops."""
+
+    def __init__(self, max_cache_size: Optional[int] = None):
+        # We intentionally don't call super().__init__ because the parent assumes static cache sizes.
+        self.key_cache: list[Optional[torch.Tensor]] = []
+        self.value_cache: list[Optional[torch.Tensor]] = []
+        self.layers: list[Any] = []  # attribute expected by HF Cache utilities
+        self._seen_tokens = 0
+        self.max_cache_size = max_cache_size
+
+    def update(
+        self,
+        key_states: torch.Tensor,
+        value_states: torch.Tensor,
+        layer_idx: int,
+        cache_kwargs: Optional[dict] = None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        if layer_idx < 0:
+            raise ValueError(f"layer_idx must be non-negative, got {layer_idx}")
+
+        if self.max_cache_size is not None and layer_idx >= self.max_cache_size:
+            raise IndexError(
+                f"Cache index {layer_idx} exceeds configured max_cache_size={self.max_cache_size}. "
+                "Check total_ut_steps and num_hidden_layers."
+            )
+
+        # Expand cache storage so the requested index is available.
+        while len(self.key_cache) <= layer_idx:
+            self.key_cache.append(None)
+            self.value_cache.append(None)
+
+        cached_key = self.key_cache[layer_idx]
+        cached_value = self.value_cache[layer_idx]
+
+        if cached_key is None:
+            self.key_cache[layer_idx] = key_states
+            self.value_cache[layer_idx] = value_states
+        else:
+            if (
+                key_states.shape[0] != cached_key.shape[0]
+                or key_states.shape[1] != cached_key.shape[1]
+                or key_states.shape[3] != cached_key.shape[3]
+            ):
+                raise ValueError(
+                    "Cached and incoming key/value tensors must match on batch, head, and head_dim dimensions."
+                )
+            assert cached_value is not None
+            self.key_cache[layer_idx] = torch.cat([cached_key, key_states], dim=2)
+            self.value_cache[layer_idx] = torch.cat([cached_value, value_states], dim=2)
+
+        result_key = self.key_cache[layer_idx]
+        result_value = self.value_cache[layer_idx]
+        assert result_key is not None and result_value is not None
+
+        # Track sequence length using the first populated cache entry.
+        self._seen_tokens = result_key.shape[2]
+        return result_key, result_value
+
+    def get_seq_length(self, layer_idx: Optional[int] = 0) -> int:
+        if layer_idx is None:
+            layer_idx = 0
+        if layer_idx < 0 or len(self.key_cache) <= layer_idx:
+            return 0
+        cached = self.key_cache[layer_idx]
+        if cached is None:
+            return 0
+        return cached.shape[2]
+
+    def get_max_length(self) -> Optional[int]:
+        return None
+
+    def get_usable_length(
+        self, new_seq_length: int, layer_idx: Optional[int] = 0
+    ) -> int:
+        return self.get_seq_length(layer_idx)
+
+    def reorder_cache(self, beam_idx: torch.LongTensor) -> None:
+        for idx, (key_entry, value_entry) in enumerate(
+            zip(self.key_cache, self.value_cache)
+        ):
+            if key_entry is None:
+                continue
+            assert value_entry is not None
+            device = key_entry.device
+            self.key_cache[idx] = key_entry.index_select(0, beam_idx.to(device))
+            self.value_cache[idx] = value_entry.index_select(0, beam_idx.to(device))
+
+    @property
+    def is_compileable(self) -> bool:
+        return False
+
+    def clear(self) -> None:
+        logger.debug("Clearing UniversalTransformerCache")
+        self.key_cache = []
+        self.value_cache = []
+        self._seen_tokens = 0
+
+
+def eager_attention_forward(
+    module: nn.Module,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attention_mask: Optional[torch.Tensor],
+    scaling: float,
+    dropout: float = 0.0,
+    **kwargs: Unpack[TransformersKwargs],
+):
+    key_states = repeat_kv(key, module.num_key_value_groups)
+    value_states = repeat_kv(value, module.num_key_value_groups)
+
+    attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
+    if attention_mask is not None:
+        causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+        attn_weights = attn_weights + causal_mask
+
+    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(
+        query.dtype
+    )
+    attn_weights = nn.functional.dropout(
+        attn_weights, p=dropout, training=module.training
+    )
+    attn_output = torch.matmul(attn_weights, value_states)
+    attn_output = attn_output.transpose(1, 2).contiguous()
+
+    return attn_output, attn_weights
+
+
+class OuroAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: OuroConfig, layer_idx: int):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        self.head_dim = getattr(
+            config, "head_dim", config.hidden_size // config.num_attention_heads
+        )
+        self.num_key_value_groups = (
+            config.num_attention_heads // config.num_key_value_heads
+        )
+        self.scaling = self.head_dim**-0.5
+        self.attention_dropout = config.attention_dropout
+        self.is_causal = True
+        self.q_proj = nn.Linear(
+            config.hidden_size, config.num_attention_heads * self.head_dim, bias=False
+        )
+        self.k_proj = nn.Linear(
+            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=False
+        )
+        self.v_proj = nn.Linear(
+            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=False
+        )
+        self.o_proj = nn.Linear(
+            config.num_attention_heads * self.head_dim, config.hidden_size, bias=False
+        )
+        self.sliding_window = (
+            config.sliding_window
+            if config.layer_types[layer_idx] == "sliding_attention"
+            else None
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
+        attention_mask: Optional[torch.Tensor],
+        past_key_value: Optional[Cache] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        current_ut: int = 0,
+        **kwargs: Unpack[FlashAttentionKwargs],
+    ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
+        input_shape = hidden_states.shape[:-1]
+        hidden_shape = (*input_shape, -1, self.head_dim)
+
+        query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+        key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+        value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+
+        cos, sin = position_embeddings
+        query_states, key_states = apply_rotary_pos_emb(
+            query_states, key_states, cos, sin
+        )
+
+        if past_key_value is not None:
+            # sin and cos are specific to RoPE models; cache_position needed for the static cache
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+            key_states, value_states = past_key_value.update(
+                key_states,
+                value_states,
+                current_ut * self.config.num_hidden_layers + self.layer_idx,
+                cache_kwargs,
+            )
+
+        attention_interface: Callable = eager_attention_forward
+        if self.config._attn_implementation != "eager":
+            attention_interface = ALL_ATTENTION_FUNCTIONS[
+                self.config._attn_implementation
+            ]
+
+        attn_output, attn_weights = attention_interface(
+            self,
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            dropout=0.0 if not self.training else self.attention_dropout,
+            scaling=self.scaling,
+            sliding_window=self.sliding_window,  # main diff with Llama
+            **kwargs,
+        )
+
+        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+        attn_output = self.o_proj(attn_output)
+        return attn_output, attn_weights
+
+
+@use_kernel_forward_from_hub("RMSNorm")
+class OuroRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        OuroRMSNorm is equivalent to T5LayerNorm
+        """
+        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)
+
+    def extra_repr(self):
+        return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
+
+
+class OuroDecoderLayer(GradientCheckpointingLayer):
+    def __init__(self, config: OuroConfig, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+
+        self.self_attn = OuroAttention(config=config, layer_idx=layer_idx)
+
+        self.mlp = OuroMLP(config)
+        self.input_layernorm = OuroRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.input_layernorm_2 = OuroRMSNorm(
+            config.hidden_size, eps=config.rms_norm_eps
+        )
+        self.post_attention_layernorm = OuroRMSNorm(
+            config.hidden_size, eps=config.rms_norm_eps
+        )
+        self.post_attention_layernorm_2 = OuroRMSNorm(
+            config.hidden_size, eps=config.rms_norm_eps
+        )
+        self.attention_type = config.layer_types[layer_idx]
+
+    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,
+        use_cache: Optional[bool] = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        position_embeddings: Optional[
+            tuple[torch.Tensor, torch.Tensor]
+        ] = None,  # necessary, but kept here for BC
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple[torch.Tensor]:
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        # Self Attention
+        hidden_states, _ = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            use_cache=use_cache,
+            cache_position=cache_position,
+            position_embeddings=position_embeddings,
+            **kwargs,
+        )
+        hidden_states = self.input_layernorm_2(hidden_states)
+        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 = self.post_attention_layernorm_2(hidden_states)
+        hidden_states = residual + hidden_states
+        return hidden_states
+
+
+@auto_docstring
+class OuroPreTrainedModel(PreTrainedModel):
+    config: OuroConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["OuroDecoderLayer"]
+    _skip_keys_device_placement = ["past_key_values"]
+    _supports_flash_attn = True
+    _supports_sdpa = True
+    _supports_flex_attn = True
+
+    _can_compile_fullgraph = True
+    _supports_attention_backend = True
+    _can_record_outputs = {
+        "hidden_states": OuroDecoderLayer,
+        "attentions": OuroAttention,
+    }
+
+
+class OuroRotaryEmbedding(nn.Module):
+    def __init__(self, config: OuroConfig, device=None):
+        super().__init__()
+        # BC: "rope_type" was originally "type"
+        if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict):
+            self.rope_type = config.rope_scaling.get(
+                "rope_type", config.rope_scaling.get("type")
+            )
+        else:
+            self.rope_type = "default"
+        self.max_seq_len_cached = config.max_position_embeddings
+        self.original_max_seq_len = config.max_position_embeddings
+
+        self.config = config
+        self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+
+        inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self.original_inv_freq = self.inv_freq
+
+    @torch.no_grad()
+    @dynamic_rope_update  # power user: used with advanced RoPE types (e.g. dynamic rope)
+    def forward(self, x, position_ids):
+        inv_freq_expanded = (
+            self.inv_freq[None, :, None]
+            .float()
+            .expand(position_ids.shape[0], -1, 1)
+            .to(x.device)
+        )
+        position_ids_expanded = position_ids[:, None, :].float()
+
+        device_type = (
+            x.device.type
+            if isinstance(x.device.type, str) and x.device.type != "mps"
+            else "cpu"
+        )
+        with torch.autocast(device_type=device_type, enabled=False):  # Force float32
+            freqs = (
+                inv_freq_expanded.float() @ position_ids_expanded.float()
+            ).transpose(1, 2)
+            emb = torch.cat((freqs, freqs), dim=-1)
+            cos = emb.cos() * self.attention_scaling
+            sin = emb.sin() * self.attention_scaling
+
+        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+@auto_docstring
+class OuroModel(OuroPreTrainedModel):
+    def __init__(self, config: OuroConfig):
+        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(
+            [
+                OuroDecoderLayer(config, layer_idx)
+                for layer_idx in range(config.num_hidden_layers)
+            ]
+        )
+        self.norm = OuroRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.rotary_emb = OuroRotaryEmbedding(config=config)
+        self.gradient_checkpointing = False
+        self.has_sliding_layers = "sliding_attention" in self.config.layer_types
+        self.total_ut_steps = getattr(self.config, "total_ut_steps", 4)
+        self.early_exit_gate = nn.Linear(config.hidden_size, 1)
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @check_model_inputs
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Cache] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> BaseModelOutputWithPast:
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError(
+                "You must specify exactly one of input_ids or inputs_embeds"
+            )
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        if use_cache is None:
+            use_cache = self.config.use_cache
+
+        max_cache_size: Optional[int] = None
+        if use_cache:
+            total_ut_steps = getattr(self.config, "total_ut_steps", 1) or 1
+            total_layers = getattr(self.config, "num_hidden_layers", None)
+            if total_layers is not None:
+                max_cache_size = total_layers * total_ut_steps
+
+            if needs_universal_cache(past_key_values, max_cache_size):
+                past_key_values = UniversalTransformerCache(max_cache_size)
+
+        if cache_position is None:
+            past_seen_tokens = (
+                past_key_values.get_seq_length() if past_key_values is not None else 0
+            )
+            cache_position = torch.arange(
+                past_seen_tokens,
+                past_seen_tokens + inputs_embeds.shape[1],
+                device=inputs_embeds.device,
+            )
+
+        if position_ids is None:
+            position_ids = cache_position.unsqueeze(0)
+
+        # It may already have been prepared by e.g. `generate`
+        if not isinstance(causal_mask_mapping := attention_mask, dict):
+            # Prepare mask arguments
+            mask_kwargs = {
+                "config": self.config,
+                "input_embeds": inputs_embeds,
+                "attention_mask": attention_mask,
+                "cache_position": cache_position,
+                "past_key_values": past_key_values,
+                "position_ids": position_ids,
+            }
+            # Create the masks
+            causal_mask_mapping = {
+                "full_attention": create_causal_mask(**mask_kwargs),
+            }
+            # The sliding window alternating layers are not always activated depending on the config
+            if self.has_sliding_layers:
+                causal_mask_mapping["sliding_attention"] = (
+                    create_sliding_window_causal_mask(**mask_kwargs)
+                )
+
+        hidden_states = inputs_embeds
+
+        # create position embeddings to be shared across the decoder layers
+        position_embeddings = self.rotary_emb(hidden_states, position_ids)
+        hidden_states_list = []
+        gate_list = []
+
+        for current_ut in range(self.total_ut_steps):
+            for decoder_layer in self.layers[: self.config.num_hidden_layers]:
+                hidden_states = decoder_layer(
+                    hidden_states,
+                    attention_mask=causal_mask_mapping[decoder_layer.attention_type],
+                    position_ids=position_ids,
+                    past_key_value=past_key_values,
+                    use_cache=use_cache,
+                    cache_position=cache_position,
+                    position_embeddings=position_embeddings,
+                    current_ut=current_ut,
+                    **kwargs,
+                )
+
+            hidden_states = self.norm(hidden_states)
+            hidden_states_list.append(hidden_states)
+            gate_list.append(self.early_exit_gate(hidden_states))
+
+        return (
+            BaseModelOutputWithPast(
+                last_hidden_state=hidden_states,
+                past_key_values=past_key_values if use_cache else None,
+            ),
+            hidden_states_list,
+            gate_list,
+        )
+
+
+@auto_docstring
+class OuroForCausalLM(OuroPreTrainedModel, GenerationMixin):
+    _tied_weights_keys = ["lm_head.weight"]
+    _tp_plan = {"lm_head": "colwise_rep"}
+    _pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = OuroModel(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # 分块大小配置
+        self.chunk_size = getattr(config, "chunk_size", 2)  # 默认分块大小为2
+        self.early_exit_step = getattr(config, "early_exit_step", None)
+        self.early_exit_threshold = getattr(config, "early_exit_threshold", None)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Cache] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        logits_to_keep: Union[int, torch.Tensor] = 0,
+        use_weighted_exit: Optional[bool] = False,  # 控制是否使用加权 early exit
+        exit_at_step: Optional[int] = None,
+        exit_threshold: Optional[float] = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> CausalLMOutputWithPast:
+        r"""
+        Args:
+            use_weighted_exit (`bool`, *optional*, defaults to `False`):
+                Whether to use weighted early exit. If `True`, the logits from all UT steps will be
+                averaged according to the exit probability distribution.
+            exit_at_step (`int`, *optional*):
+                Specifies which UT step to exit at. If set, the model will directly use the hidden states
+                from this step to generate logits, ignoring other exit strategies.
+            exit_threshold (`float`, *optional*):
+                The cumulative probability threshold for early exit. When the cumulative exit probability
+                reaches this threshold, the model will exit at that step.
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, OuroForCausalLM
+
+        >>> prompt = "Hey, are you conscious? Can you talk to me?"
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+        ```"""
+        exit_at_step = (
+            exit_at_step if exit_at_step is not None else self.early_exit_step
+        )
+        exit_threshold = (
+            exit_threshold if exit_threshold is not None else self.early_exit_threshold
+        )
+
+        outputs, hidden_states_list, gate_list = 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,
+            cache_position=cache_position,
+            **kwargs,
+        )
+        slice_indices = (
+            slice(-logits_to_keep, None)
+            if isinstance(logits_to_keep, int)
+            else logits_to_keep
+        )
+
+        def _select_token_positions(tensor: torch.Tensor) -> torch.Tensor:
+            if isinstance(slice_indices, slice):
+                return tensor[:, slice_indices, ...]
+            if isinstance(slice_indices, torch.Tensor):
+                return tensor.index_select(1, slice_indices.to(tensor.device))
+            raise TypeError(
+                f"Unsupported index type for logits_to_keep: {type(slice_indices)}"
+            )
+
+        stacked_exit_pdf = None
+        if gate_list:
+            pdf_list = []
+            remaining_prob = torch.ones_like(gate_list[0].squeeze(-1))
+            for idx, gate_tensor in enumerate(gate_list):
+                lambda_i = torch.sigmoid(gate_tensor.squeeze(-1))
+                if idx < len(gate_list) - 1:
+                    p_i = lambda_i * remaining_prob
+                    remaining_prob = remaining_prob * (1.0 - lambda_i)
+                else:
+                    p_i = remaining_prob
+                pdf_list.append(p_i)
+            stacked_exit_pdf = torch.stack(pdf_list, dim=2)
+
+        expected_logits_cache: Optional[torch.Tensor] = None
+
+        def compute_expected_logits() -> Optional[torch.Tensor]:
+            nonlocal expected_logits_cache
+            if expected_logits_cache is not None:
+                return expected_logits_cache
+            if stacked_exit_pdf is None or not hidden_states_list:
+                return None
+            token_exit_pdf = _select_token_positions(stacked_exit_pdf)
+            expected_logits = None
+            for step_idx, hidden in enumerate(hidden_states_list):
+                step_hidden = _select_token_positions(hidden)
+                step_logits = self.lm_head(step_hidden)
+                weight = (
+                    token_exit_pdf[..., step_idx].unsqueeze(-1).to(step_logits.dtype)
+                )
+                expected_logits = (
+                    step_logits * weight
+                    if expected_logits is None
+                    else expected_logits + step_logits * weight
+                )
+            expected_logits_cache = expected_logits
+            return expected_logits_cache
+
+        logits: Optional[torch.Tensor] = None
+        loss: Optional[torch.Tensor] = None
+
+        if labels is not None:
+            logits = compute_expected_logits()
+            if logits is None:
+                hidden_states = outputs.last_hidden_state
+                logits = self.lm_head(_select_token_positions(hidden_states))
+            loss = self.loss_function(
+                logits=logits,
+                labels=labels,
+                vocab_size=self.config.vocab_size,
+                **kwargs,
+            )
+        else:
+            if stacked_exit_pdf is not None and hidden_states_list:
+                if exit_at_step is not None and 0 <= exit_at_step < len(
+                    hidden_states_list
+                ):
+                    selected_hidden = hidden_states_list[exit_at_step]
+                    logits = self.lm_head(_select_token_positions(selected_hidden))
+                elif exit_threshold is not None:
+                    cumulative_probs = torch.cumsum(stacked_exit_pdf, dim=2)
+                    threshold_value = exit_threshold
+                    if isinstance(threshold_value, torch.Tensor):
+                        threshold_value = threshold_value.to(cumulative_probs.device)
+                    threshold_mask = cumulative_probs >= threshold_value
+                    exit_steps = torch.argmax(threshold_mask.float(), dim=2)
+                    last_step_idx = stacked_exit_pdf.shape[2] - 1
+                    if last_step_idx >= 0:
+                        never_exceeded = ~threshold_mask.any(dim=2)
+                        exit_steps[never_exceeded] = last_step_idx
+                    stacked_hidden = torch.stack(hidden_states_list, dim=2)
+                    gather_index = (
+                        exit_steps.unsqueeze(-1)
+                        .unsqueeze(-1)
+                        .expand(-1, -1, 1, stacked_hidden.size(-1))
+                    )
+                    final_hidden_states = torch.gather(
+                        stacked_hidden, 2, gather_index
+                    ).squeeze(2)
+                    logits = self.lm_head(_select_token_positions(final_hidden_states))
+                elif use_weighted_exit:
+                    logits = compute_expected_logits()
+
+            if logits is None:
+                hidden_states = outputs.last_hidden_state
+                logits = self.lm_head(_select_token_positions(hidden_states))
+
+        result = CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+        return result
+
+
+class OuroForSequenceClassification(
+    GenericForSequenceClassification, OuroPreTrainedModel
+):
+    pass
+
+
+class OuroForTokenClassification(GenericForTokenClassification, OuroPreTrainedModel):
+    pass
+
+
+class OuroForQuestionAnswering(GenericForQuestionAnswering, OuroPreTrainedModel):
+    base_model_prefix = (
+        "transformer"  # For BC, where `transformer` was used instead of `model`
+    )
+
+
+__all__ = [
+    "OuroPreTrainedModel",
+    "OuroModel",
+    "OuroForCausalLM",
+    "OuroForSequenceClassification",
+    "OuroForTokenClassification",
+    "OuroForQuestionAnswering",
+    "UniversalTransformerCache",
+]