Delete models/RL/composition/SDAR-100M-AR-0.999zoo_op2-20+0.001teacher_op2-process-dlm

models/RL/composition/SDAR-100M-AR-0.999zoo_op2-20+0.001teacher_op2-process-dlm/chat_template.jinja

@@ -1 +0,0 @@
-{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% endif %}{% if system_message is defined %}{{ system_message }}{% endif %}{% for message in loop_messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ content }}{% elif message['role'] == 'assistant' %}{{ content }}{% endif %}{% endfor %}

models/RL/composition/SDAR-100M-AR-0.999zoo_op2-20+0.001teacher_op2-process-dlm/config.json

@@ -1,43 +0,0 @@
-{
-  "architectures": [
-    "SDARForCausalLM"
-  ],
-  "attention_bias": false,
-  "attention_dropout": 0.0,
-  "auto_map": {
-    "AutoConfig": "configuration_sdar.SDARConfig",
-    "AutoModel": "modeling_sdar.SDARForCausalLM",
-    "AutoModelForCausalLM": "modeling_sdar.SDARForCausalLM"
-  },
-  "block_size": 4,
-  "bos_token_id": 2,
-  "debug": false,
-  "dtype": "bfloat16",
-  "eos_token_id": 3,
-  "ep_size": 1,
-  "fuse_cross_entropy": true,
-  "head_dim": 128,
-  "hidden_act": "silu",
-  "hidden_size": 768,
-  "initializer_range": 0.02,
-  "intermediate_size": 3072,
-  "mask_token_id": 2196,
-  "max_position_embeddings": 2048,
-  "max_window_layers": 24,
-  "micro_forward": false,
-  "model_type": "sdar",
-  "num_attention_heads": 12,
-  "num_hidden_layers": 12,
-  "num_key_value_heads": 2,
-  "rms_norm_eps": 1e-06,
-  "rope_scaling": null,
-  "rope_theta": 1000000,
-  "skip_checkpoint": false,
-  "sliding_window": 2048,
-  "tie_word_embeddings": true,
-  "transformers_version": "4.57.6",
-  "use_cache": false,
-  "use_deepep": false,
-  "use_sliding_window": false,
-  "vocab_size": 2200
-}

models/RL/composition/SDAR-100M-AR-0.999zoo_op2-20+0.001teacher_op2-process-dlm/configuration_sdar.py

@@ -1,212 +0,0 @@
-# coding=utf-8
-# Copyright 2024 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""SDAR model configuration"""
-
-from transformers.configuration_utils import PretrainedConfig
-from transformers.modeling_rope_utils import rope_config_validation
-from transformers.utils import logging
-
-
-logger = logging.get_logger(__name__)
-
-
-class SDARConfig(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a [`SDARModel`]. It is used to instantiate a
-    SDAR model according to the specified arguments, defining the model architecture. Instantiating a configuration
-    with the defaults will yield a similar configuration to that of
-    SDAR-1.7B [DiffuOpen/SDAR-1.7B-Chat](https://huggingface.co/DiffuOpen/SDAR-1.7B-Chat/).
-
-    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
-    documentation from [`PretrainedConfig`] for more information.
-
-
-    Args:
-        vocab_size (`int`, *optional*, defaults to 151936):
-            Vocabulary size of the SDAR model. Defines the number of different tokens that can be represented by the
-            `inputs_ids` passed when calling [`SDARModel`]
-        hidden_size (`int`, *optional*, defaults to 4096):
-            Dimension of the hidden representations.
-        intermediate_size (`int`, *optional*, defaults to 22016):
-            Dimension of the MLP representations.
-        num_hidden_layers (`int`, *optional*, defaults to 32):
-            Number of hidden layers in the Transformer encoder.
-        num_attention_heads (`int`, *optional*, defaults to 32):
-            Number of attention heads for each attention layer in the Transformer encoder.
-        num_key_value_heads (`int`, *optional*, defaults to 32):
-            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
-            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
-            `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
-            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
-            by meanpooling all the original heads within that group. For more details checkout [this
-            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `32`.
-        head_dim (`int`, *optional*, defaults to 128):
-            The attention head dimension.
-        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
-            The non-linear activation function (function or string) in the decoder.
-        max_position_embeddings (`int`, *optional*, defaults to 32768):
-            The maximum sequence length that this model might ever be used with.
-        initializer_range (`float`, *optional*, defaults to 0.02):
-            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
-            The epsilon used by the rms normalization layers.
-        use_cache (`bool`, *optional*, defaults to `True`):
-            Whether or not the model should return the last key/values attentions (not used by all models). Only
-            relevant if `config.is_decoder=True`.
-        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
-            Whether the model's input and output word embeddings should be tied.
-        rope_theta (`float`, *optional*, defaults to 10000.0):
-            The base period of the RoPE embeddings.
-        rope_scaling (`Dict`, *optional*):
-            Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
-            and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
-            accordingly.
-            Expected contents:
-                `rope_type` (`str`):
-                    The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
-                    'llama3'], with 'default' being the original RoPE implementation.
-                `factor` (`float`, *optional*):
-                    Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
-                    most scaling types, a `factor` of x will enable the model to handle sequences of length x *
-                    original maximum pre-trained length.
-                `original_max_position_embeddings` (`int`, *optional*):
-                    Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
-                    pretraining.
-                `attention_factor` (`float`, *optional*):
-                    Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
-                    computation. If unspecified, it defaults to value recommended by the implementation, using the
-                    `factor` field to infer the suggested value.
-                `beta_fast` (`float`, *optional*):
-                    Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
-                    ramp function. If unspecified, it defaults to 32.
-                `beta_slow` (`float`, *optional*):
-                    Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
-                    ramp function. If unspecified, it defaults to 1.
-                `short_factor` (`List[float]`, *optional*):
-                    Only used with 'longrope'. The scaling factor to be applied to short contexts (<
-                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
-                    size divided by the number of attention heads divided by 2
-                `long_factor` (`List[float]`, *optional*):
-                    Only used with 'longrope'. The scaling factor to be applied to long contexts (<
-                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
-                    size divided by the number of attention heads divided by 2
-                `low_freq_factor` (`float`, *optional*):
-                    Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
-                `high_freq_factor` (`float`, *optional*):
-                    Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
-        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
-            Whether to use a bias in the query, key, value and output projection layers during self-attention.
-        use_sliding_window (`bool`, *optional*, defaults to `False`):
-            Whether to use sliding window attention.
-        sliding_window (`int`, *optional*, defaults to 4096):
-            Sliding window attention (SWA) window size. If not specified, will default to `4096`.
-        max_window_layers (`int`, *optional*, defaults to 28):
-            The number of layers that use SWA (Sliding Window Attention). The bottom layers use SWA while the top use full attention.
-        attention_dropout (`float`, *optional*, defaults to 0.0):
-            The dropout ratio for the attention probabilities.
-
-    ```python
-    >>> from transformers import SDARModel, SDARConfig
-
-    >>> # Initializing a SDAR style configuration
-    >>> configuration = SDARConfig()
-
-    >>> # Initializing a model from the SDAR-8B style configuration
-    >>> model = SDARModel(configuration)
-
-    >>> # Accessing the model configuration
-    >>> configuration = model.config
-    ```"""
-
-    model_type = "sdar"
-    keys_to_ignore_at_inference = ["past_key_values"]
-
-    # Default tensor parallel plan for base model `SDAR`
-    base_model_tp_plan = {
-        "layers.*.self_attn.q_proj": "colwise",
-        "layers.*.self_attn.k_proj": "colwise",
-        "layers.*.self_attn.v_proj": "colwise",
-        "layers.*.self_attn.o_proj": "rowwise",
-        "layers.*.mlp.gate_proj": "colwise",
-        "layers.*.mlp.up_proj": "colwise",
-        "layers.*.mlp.down_proj": "rowwise",
-    }
-    base_model_pp_plan = {
-        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
-        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
-        "norm": (["hidden_states"], ["hidden_states"]),
-    }
-
-    def __init__(
-        self,
-        vocab_size=151936,
-        hidden_size=4096,
-        intermediate_size=22016,
-        num_hidden_layers=32,
-        num_attention_heads=32,
-        num_key_value_heads=32,
-        head_dim=128,
-        hidden_act="silu",
-        max_position_embeddings=32768,
-        initializer_range=0.02,
-        rms_norm_eps=1e-6,
-        use_cache=True,
-        tie_word_embeddings=False,
-        rope_theta=10000.0,
-        rope_scaling=None,
-        attention_bias=False,
-        use_sliding_window=False,
-        sliding_window=4096,
-        max_window_layers=28,
-        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
-        self.use_sliding_window = use_sliding_window
-        self.sliding_window = sliding_window  # we check `use_sliding_window` in the modeling code
-        self.max_window_layers = max_window_layers
-
-        # 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.head_dim = head_dim
-        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.rope_scaling = rope_scaling
-        self.attention_bias = attention_bias
-        self.attention_dropout = attention_dropout
-        # Validate the correctness of rotary position embeddings parameters
-        # BC: if there is a 'type' field, move it to 'rope_type'.
-        if self.rope_scaling is not None and "type" in self.rope_scaling:
-            self.rope_scaling["rope_type"] = self.rope_scaling["type"]
-        rope_config_validation(self)
-
-        super().__init__(
-            tie_word_embeddings=tie_word_embeddings,
-            **kwargs,
-        )
-
-
-__all__ = ["SDARConfig"]

models/RL/composition/SDAR-100M-AR-0.999zoo_op2-20+0.001teacher_op2-process-dlm/fused_linear_diffusion_cross_entropy.py

@@ -1,682 +0,0 @@
-# -*- coding: utf-8 -*-
-
-# Code adapted from
-# https://github.com/fla-org/flash-linear-attention/blob/main/fla/modules/fused_linear_cross_entropy.py
-# Implementation of element-wise division of cross entropy loss
-
-
-# Code adapted from
-# https://github.com/linkedin/Liger-Kernel/blob/main/src/liger_kernel/ops/fused_linear_cross_entropy.py
-
-from functools import partial
-from typing import Optional, Tuple
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import triton
-import triton.language as tl
-from torch.distributed import DeviceMesh
-from torch.distributed.tensor import DTensor, Replicate, Shard, distribute_module
-from torch.distributed.tensor.parallel import ParallelStyle
-
-# The hard limit of TRITON_MAX_TENSOR_NUMEL is 1048576
-# https://github.com/triton-lang/triton/blob/ba42a5c68fd0505f8c42f4202d53be0f8d9a5fe0/python/triton/language/core.py#L19
-# However, setting limit as 65536 as in LayerNorm tutorial is faster because of less register spilling
-# The optimal maximum block size depends on your hardware, your kernel, and your dtype
-MAX_FUSED_SIZE = 65536 // 2
-
-
-@triton.heuristics({
-    'HAS_SCALE': lambda args: args['scale'] is not None
-})
-@triton.autotune(
-    configs=[
-        triton.Config({}, num_warps=num_warps)
-        for num_warps in [1, 2, 4, 8, 16, 32]
-    ],
-    key=['D']
-)
-@triton.jit
-def logsumexp_fwd_kernel(
-    x,
-    z,
-    scale,
-    D: tl.constexpr,
-    B: tl.constexpr,
-    HAS_SCALE: tl.constexpr
-):
-    i_n, i_d = tl.program_id(0).to(tl.int64), tl.program_id(1).to(tl.int64)
-    o_d = i_d * B + tl.arange(0, B)
-    m_d = o_d < D
-
-    b_x = tl.load(x + i_n * D + o_d, mask=m_d, other=-float('inf'))
-    if HAS_SCALE:
-        b_x = b_x * scale
-    b_m = tl.max(b_x, 0)
-    b_z = tl.log(tl.sum(tl.exp(b_x - b_m), 0)) + b_m
-    tl.store(z + i_n * tl.cdiv(D, B) + i_d, b_z)
-
-
-def logsumexp_fwd(
-    x,
-    scale: Optional[float] = None,
-    dtype: Optional[torch.dtype] = None
-):
-    r"""
-    Compute the logsumexp of the input tensor over the last dimension.
-
-    Args:
-        x (Tensor):
-            The input tensor of any shape.
-        scale (Optional[float]):
-            The scale applied to the input tensor. Default: `None`.
-        dtype (Optional[torch.dtype]):
-            The data type of the output tensor. Default: `None`.
-    Returns:
-        Tensor: The logsumexp of the input tensor.
-    """
-
-    shape = x.shape
-    x = x.view(-1, shape[-1])
-    N, D = x.shape
-    B = min(triton.next_power_of_2(D), 64 * 1024)
-    ND = triton.cdiv(D, B)
-
-    z = x.new_empty(N, ND, dtype=torch.float)
-    logsumexp_fwd_kernel[(N, ND)](
-        x=x,
-        z=z,
-        scale=scale,
-        D=D,
-        B=B
-    )
-    z = z.logsumexp(-1).view(*shape[:-1])
-    if dtype is not None and dtype != torch.float:
-        z = z.to(dtype)
-    return z
-
-@triton.jit
-def cross_entropy_kernel(
-    logits,
-    lse,
-    target,
-    p_mask,
-    loss,
-    total,
-    ignore_index,
-    label_smoothing: tl.constexpr,
-    logit_scale: tl.constexpr,
-    reduction: tl.constexpr,
-    V: tl.constexpr,
-    BV: tl.constexpr
-):
-    """
-    This kernel computes both cross entropy loss and the gradient of the input.
-    We only consider hard label + mean reduction for now.
-    Please refer to https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html for the math.
-
-    Args:
-        logits:
-            Pointer to logits tensor.
-        lse:
-            Pointer to logsumexp tensor.
-        target: Pointer to target tensor.
-        loss:
-            Pointer to tensor to store the loss.
-        V (int):
-            The number of columns in the input tensor.
-        total (int):
-            The number of non-ignored classes.
-        ignore_index (int):
-            The index to ignore in the target.
-        label_smoothing (float):
-            The amount of smoothing when computing the loss, where 0.0 means no smoothing.
-        reduction (str):
-            The string for the reduction to apply
-        BV (int):
-            The block size for vocab.
-    """
-
-    # https://github.com/triton-lang/triton/issues/1058
-    # If B*T*V is too large, i_n * stride will overflow out of int32, so we convert to int64
-    i_n = tl.program_id(0).to(tl.int64)
-    NV = tl.cdiv(V, BV)
-
-    # 1. Load target first because if the target is ignore_index, we can return right away
-    b_y = tl.load(target + i_n)
-    # load p_mask
-    b_p_mask = tl.load(p_mask + i_n)
-
-    # 2. locate the start index
-    logits += i_n * V
-
-    if b_y == ignore_index:
-        # set all x as 0
-        for i in range(0, V, BV):
-            o_v = i + tl.arange(0, BV)
-            tl.store(logits + o_v, 0.0, mask=o_v < V)
-        return
-
-    # Online softmax: 2 loads + 1 store (compared with 3 loads + 1 store for the safe softmax)
-    # Refer to Algorithm 3 in the paper: https://arxiv.org/pdf/1805.02867
-
-    # 3. [Online softmax] first pass: compute logsumexp
-    # we did this in anouter kernel
-    b_l = tl.load(logits + b_y) * logit_scale
-    b_lse = tl.load(lse + i_n)
-
-    # 4. Calculate the loss
-    # loss = lse - logits_l
-    # celoss = -log(q_y) = -log(softmax(x_y))
-    b_loss = (b_lse - b_l) / b_p_mask  # Diffusion Scaled '1/t'
-
-    # Label smoothing is a general case of normal cross entropy
-    # See the full derivation at https://github.com/linkedin/Liger-Kernel/pull/198#issue-2503665310
-    b_z = 0.0
-    eps = label_smoothing / V
-
-    # We need tl.debug_barrier() as mentioned in
-    # https://github.com/triton-lang/triton/blob/ba42a5c68fd0505f8c42f4202d53be0f8d9a5fe0/python/triton/ops/cross_entropy.py#L34
-    tl.debug_barrier()
-
-    # 5. [Online Softmax] Second pass: compute gradients
-    # For 'mean' reduction, gradients are normalized by number of non-ignored elements
-    # dx_y = (softmax(x_y) - 1) / N
-    # dx_i = softmax(x_i) / N, i != y
-    # For label smoothing:
-    # dx_i = (softmax(x_y) - label_smoothing / V) / N, i != y
-    # dx_y = (softmax(x_y) - label_smoothing / V - (1 - label_smoothing)) / N
-    #      = dx_i - (1 - label_smoothing) / N
-    for iv in range(0, NV):
-        o_v = iv * BV + tl.arange(0, BV)
-        b_logits = tl.load(logits + o_v, mask=o_v < V, other=float('-inf')) * logit_scale
-        if label_smoothing > 0:
-            # scale X beforehand to avoid overflow
-            b_z += tl.sum(tl.where(o_v < V, -eps * b_logits, 0.0))
-        b_p = (tl.exp(b_logits - b_lse) - eps) * logit_scale
-        b_p /= b_p_mask # 修改
-        if reduction == "mean":
-            b_p = b_p / total
-        tl.store(logits + o_v, b_p, mask=o_v < V)
-
-        tl.debug_barrier()
-
-    # Orginal loss = H(q, p),  with label smoothing regularization = H(q', p) and (label_smoothing / V) = eps
-    # H(q', p) = (1 - label_smoothing) * H(q, p) + label_smoothing * H(u, p)
-    #          = (1 - label_smoothing) * H(q, p) + eps * sum(logsoftmax(x_i))
-    # By using m (global max of xi) and d (sum of e^(xi-m)), we can simplify as:
-    #          = (1 - label_smoothing) * H(q, p) + (-sum(x_i * eps) + label_smoothing * (m + logd))
-    # Refer to H(q', p) in section 7 of the paper:
-    # https://arxiv.org/pdf/1512.00567
-    # pytorch:
-    # https://github.com/pytorch/pytorch/blob/2981534f54d49fa3a9755c9b0855e7929c2527f0/aten/src/ATen/native/LossNLL.cpp#L516
-    # See full derivation at https://github.com/linkedin/Liger-Kernel/pull/198#issuecomment-2333753087
-    if label_smoothing > 0:
-        b_loss = b_loss * (1 - label_smoothing) + (b_z + label_smoothing * b_lse)
-
-    # 6. Specially handle the i==y case where `dx_y = (softmax(x_y) - (1 - label_smoothing) / N`
-    b_l = tl.load(logits + b_y)
-
-    # Normalize the loss by the number of non-ignored elements if reduction is "mean"
-    if reduction == 'mean':
-        b_loss = b_loss / total
-        # b_l += (label_smoothing - 1) / total * logit_scale
-        # b_l has already been divided by b_p_mask and total
-        b_l += (label_smoothing - 1) / b_p_mask / total * logit_scale
-    else:
-        # b_l += (label_smoothing - 1) * logit_scale
-        b_l += (label_smoothing - 1) / b_p_mask * logit_scale
-
-    tl.store(loss + i_n, b_loss)
-    tl.store(logits + b_y, b_l)
-
-
-@triton.jit
-def elementwise_mul_kernel(
-    x,
-    g,
-    N: tl.constexpr,
-    B: tl.constexpr
-):
-    """
-    This function multiplies each element of the tensor pointed by x with the value pointed by g.
-    The multiplication is performed in-place on the tensor pointed by x.
-
-    Parameters:
-    x:
-        Pointer to the input tensor.
-    g:
-        Pointer to the gradient output value.
-    N (int):
-        The number of columns in the input tensor.
-    B (int):
-        The block size for Triton operations.
-    """
-
-    # Get the program ID and convert it to int64 to avoid overflow
-    i_x = tl.program_id(0).to(tl.int64)
-    o_x = i_x * B + tl.arange(0, B)
-
-    # Load the gradient output value
-    b_g = tl.load(g)
-    b_x = tl.load(x + o_x, mask=o_x < N)
-    tl.store(x + o_x, b_x * b_g, mask=o_x < N)
-
-
-def fused_linear_cross_entropy_forward(
-    x: torch.Tensor,
-    target: torch.LongTensor,
-    weight: torch.Tensor,
-    bias: torch.Tensor = None,
-    p_mask: torch.Tensor = None,
-    ignore_index: int = -100,
-    label_smoothing: float = 0.0,
-    logit_scale: float = 1.0,
-    num_chunks: int = 8,
-    reduction: str = "mean"
-):
-    device = x.device
-    # inputs have shape: [N, H]
-    # materialized activations will have shape: [N, V]
-    # the increase in memory = [N, V]
-    # reduction can be achieved by partitioning the number of tokens N into smaller chunks.
-
-    # ideally, we would like to achieve the same memory consumption as [N, H],
-    # so the expected chunk size should be:
-    # NC = ceil(V / H)
-    # C = ceil(N / NC)
-    # for ex: N = 4096*4, V = 32000, H = 4096 ==> NC = 8, C = ceil(N / NC) = 2048
-    N, H, V = *x.shape, weight.shape[0]
-    BV = min(MAX_FUSED_SIZE, triton.next_power_of_2(V))
-    # TODO: in real cases, we may need to limit the number of chunks NC to
-    # ensure the precisions of accumulated gradients
-    NC = min(num_chunks, triton.cdiv(V, H))
-    C = triton.next_power_of_2(triton.cdiv(N, NC))
-    NC = triton.cdiv(N, C)
-
-    # [N, H]
-    dx = torch.zeros_like(x, device=device)
-    # [V, H]
-    dw = torch.zeros_like(weight, device=device, dtype=torch.float) if weight is not None else None
-    # [V]
-    db = torch.zeros_like(bias, device=device, dtype=torch.float) if bias is not None else None
-    # [N]
-    loss = torch.zeros(N, device=device, dtype=torch.float)
-
-    total = target.ne(ignore_index).sum().item()
-
-    for ic in range(NC):
-        start, end = ic * C, min((ic + 1) * C, N)
-        # [C, N]
-        c_x = x[start:end]
-        # when doing matmul, use the original precision
-        # [C, V]
-        c_logits = F.linear(c_x, weight, bias)
-        c_target = target[start:end]
-        c_p_mask = p_mask[start:end]
-        # [C]
-        # keep lse in fp32 to maintain precision
-        c_lse = logsumexp_fwd(c_logits, scale=logit_scale, dtype=torch.float)
-
-        # unreduced loss
-        c_loss = loss[start:end]
-
-        # Here we calculate the gradient of c_logits in place so we can save memory.
-        cross_entropy_kernel[(c_logits.shape[0],)](
-            logits=c_logits,
-            lse=c_lse,
-            target=c_target,
-            p_mask=c_p_mask,
-            loss=c_loss,
-            total=total,
-            ignore_index=ignore_index,
-            label_smoothing=label_smoothing,
-            logit_scale=logit_scale,
-            reduction=reduction,
-            V=V,
-            BV=BV,
-            num_warps=32
-        )
-
-        # gradient of logits is computed in-place by the above triton kernel and is of shape: C x V
-        # thus dx should be of shape: C x H
-        dx[start:end] = torch.mm(c_logits, weight)
-
-        # keep dw in fp32 to maintain precision
-        if weight is not None:
-            dw += c_logits.t() @ c_x
-
-        if bias is not None:
-            torch.add(input=db, other=c_logits.sum(0), out=db)
-
-    loss = loss.sum()
-    if dw is not None:
-        dw = dw.to(weight)
-    if db is not None:
-        db = db.to(bias)
-    return loss, dx, dw, db
-
-
-def fused_linear_cross_entropy_backward(
-    do: torch.Tensor,
-    dx: torch.Tensor,
-    dw: torch.Tensor,
-    db: torch.Tensor
-):
-    # If cross entropy is the last layer, do is 1.0. Skip the mul to save time
-    if torch.ne(do, torch.tensor(1.0, device=do.device)):
-        # We use a Triton kernel instead of a PyTorch operation because modifying inputs in-place
-        # for gradient storage and backward multiple times causes anomalies with PyTorch but not with Triton.
-        N, H = dx.shape
-        B = min(MAX_FUSED_SIZE, triton.next_power_of_2(H))
-
-        elementwise_mul_kernel[(triton.cdiv(N * H, B),)](
-            x=dx,
-            g=do,
-            N=N*H,
-            B=B,
-            num_warps=32,
-        )
-
-        # handle dw
-        if dw is not None:
-            V, H = dw.shape
-            elementwise_mul_kernel[(triton.cdiv(V * H, B),)](
-                x=dw,
-                g=do,
-                N=V*H,
-                B=B,
-                num_warps=32,
-            )
-
-        if db is not None:
-            V = db.shape[0]
-            elementwise_mul_kernel[(triton.cdiv(V, B),)](
-                x=db,
-                g=do,
-                N=V,
-                B=B,
-                num_warps=32,
-            )
-    return dx, dw, db
-
-
-class FusedLinearCrossEntropyFunction(torch.autograd.Function):
-
-    @staticmethod
-    def forward(
-        ctx,
-        x: torch.Tensor,
-        target: torch.LongTensor,
-        weight: torch.Tensor,
-        bias: torch.Tensor = None,
-        p_mask: torch.Tensor = None,
-        ignore_index: int = -100,
-        label_smoothing: float = 0.0,
-        logit_scale: float = 1.0,
-        num_chunks: int = 8,
-        reduction: str = "mean"
-    ):
-        """
-        Fusing the last linear layer with cross-entropy loss
-            Reference: https://github.com/mgmalek/efficient_cross_entropy
-
-        Handle the forward and backward pass of the final linear layer via cross-entropy loss by avoiding
-        the materialization of the large logits tensor. Since Cross Entropy Loss is the last layer, we can
-        compute the gradient at the forward pass. By doing so, we don't have to store the x and target
-        for the backward pass.
-
-        x (torch.Tensor): [batch_size * seq_len, hidden_size]
-        target (torch.LongTensor): [batch_size * seq_len]
-            where each value is in [0, vocab_size).
-        weight (torch.Tensor): [vocab_size, hidden_size]
-            where `vocab_size` is the number of classes.
-        bias (Optional[torch.Tensor]): [vocab_size]
-            where `vocab_size` is the number of classes.
-        p_mask(torch.Tensor): [batch_size * seq_len]
-            Its shape should be same as target. 
-        ignore_index:
-            the index to ignore in the target.
-        label_smoothing:
-            the amount of smoothing when computing the loss, where 0.0 means no smoothing.
-        logit_scale: float = 1.0,
-            A scaling factor applied to the logits. Default: 1.0
-        num_chunks: int
-            The number of chunks to split the input tensor into for processing.
-            This can help optimize memory usage and computation speed.
-            Default: 8
-        reduction:
-            Specifies the reduction to apply to the output: 'mean' | 'sum'.
-            'mean': the weighted mean of the output is taken,
-            'sum': the output will be summed.
-            Default: 'mean'.
-        """
-        loss, dx, dw, db = fused_linear_cross_entropy_forward(
-            x,
-            target,
-            weight,
-            bias,
-            p_mask,
-            ignore_index,
-            label_smoothing,
-            logit_scale,
-            num_chunks,
-            reduction
-        )
-        # downcast to dtype and store for backward
-        ctx.save_for_backward(
-            dx.detach(),
-            dw.detach() if weight is not None else None,
-            db.detach() if bias is not None else None,
-        )
-        return loss
-
-    @staticmethod
-    def backward(ctx, do):
-        dx, dw, db = ctx.saved_tensors
-        dx, dw, db = fused_linear_cross_entropy_backward(do, dx, dw, db)
-        # 10 gradients should be returned, with `p_mask` having no grads
-        # Check the number of arguments in the `forward` method
-        return dx, None, dw, db, None, None, None, None, None, None
-
-
-def fused_linear_cross_entropy_loss(
-    x: torch.Tensor,
-    target: torch.LongTensor,
-    weight: torch.Tensor,
-    bias: torch.Tensor = None,
-    p_mask: torch.Tensor = None,
-    ignore_index: int = -100,
-    label_smoothing: float = 0.0,
-    logit_scale: float = 1.0,
-    num_chunks: int = 8,
-    reduction: str = "mean"
-) -> Tuple[torch.Tensor, torch.Tensor]:
-    """
-    Args:
-        x (torch.Tensor): [batch_size * seq_len, hidden_size]
-        target (torch.LongTensor): [batch_size * seq_len]
-            where each value is in [0, vocab_size).
-        weight (torch.Tensor): [vocab_size, hidden_size]
-            where `vocab_size` is the number of classes.
-        bias (Optional[torch.Tensor]): [vocab_size]
-            where `vocab_size` is the number of classes.
-        p_mask(torch.Tensor): [batch_size * seq_len]
-            Its shape should be same as target. 
-        ignore_index: int.
-            If target == ignore_index, the loss is set to 0.0.
-        label_smoothing: float
-        logit_scale: float
-            A scaling factor applied to the logits. Default: 1.0
-        num_chunks: int
-            The number of chunks to split the input tensor into for processing.
-            This can help optimize memory usage and computation speed.
-            Default: 8
-        reduction:
-            Specifies the reduction to apply to the output: 'mean' | 'sum'.
-            'mean': the weighted mean of the output is taken,
-            'sum': the output will be summed.
-            Default: 'mean'.
-    Returns:
-        losses: [batch,], float
-    """
-    return FusedLinearCrossEntropyFunction.apply(
-        x,
-        target,
-        weight,
-        bias,
-        p_mask,
-        ignore_index,
-        label_smoothing,
-        logit_scale,
-        num_chunks,
-        reduction
-    )
-
-
-class FusedLinearDiffusionCrossEntropyLoss(nn.Module):
-
-    def __init__(
-        self,
-        ignore_index: int = -100,
-        label_smoothing: float = 0.0,
-        logit_scale: float = 1.0,
-        num_chunks: int = 8,
-        reduction: str = "mean"
-    ):
-        """
-        Args:
-            ignore_index: int.
-                If target == ignore_index, the loss is set to 0.0.
-            label_smoothing: float
-            logit_scale: float
-                A scaling factor applied to the logits. Default: 1.0
-            num_chunks: int
-                The number of chunks to split the input tensor into for processing.
-                This can help optimize memory usage and computation speed.
-                Default: 8
-            reduction:
-                Specifies the reduction to apply to the output: 'mean' | 'sum'.
-                'mean': the weighted mean of the output is taken,
-                'sum': the output will be summed.
-                Default: 'mean'.
-        """
-        super().__init__()
-
-        assert reduction in ["mean", "sum"], f"reduction: {reduction} is not supported"
-
-        self.ignore_index = ignore_index
-        self.label_smoothing = label_smoothing
-        self.logit_scale = logit_scale
-        self.num_chunks = num_chunks
-        self.reduction = reduction
-
-    @torch.compiler.disable
-    def forward(
-        self,
-        x: torch.Tensor,
-        target: torch.LongTensor,
-        weight: torch.Tensor,
-        bias: Optional[torch.Tensor] = None,
-        p_mask: torch.Tensor = None
-    ):
-        """
-        Args:
-            x (torch.Tensor): [batch_size, seq_len, hidden_size]
-            target (torch.LongTensor): [batch_size, seq_len]
-                where each value is in [0, V).
-            weight (torch.Tensor): [vocab_size, hidden_size]
-                where `vocab_size` is the number of classes.
-            bias (Optional[torch.Tensor]): [vocab_size]
-                where `vocab_size` is the number of classes.
-            p_mask(torch.Tensor): [batch_size, seq_len]
-                Its shape is same as target. 
-                Shape: (1, packed_length) when varlen attn is used.
-        Returns:
-            loss
-            
-        TODO:
-            follow https://github.com/ML-GSAI/LLaDA/blob/main/GUIDELINES.md#pre-training
-            ```py
-            unreduced_loss /= p_mask
-            ```
-            Scale the values of `unreduced_loss at different positions
-        """
-        if p_mask is None:
-            p_mask = torch.ones_like(target, dtype=torch.float, device=x.device)
-        
-        x = x.contiguous().view(-1, x.shape[-1])
-        target = target.contiguous().view(-1)
-        weight = weight.contiguous()
-        bias = bias.contiguous() if bias else None
-        p_mask = p_mask.contiguous().view(-1)
-        l, d = x.shape
-        assert l == target.shape[0] == p_mask.shape[0], f"{x.shape=}, {target.shape=}, {p_mask.shape=}"
-            
-        loss = fused_linear_cross_entropy_loss(
-            x,
-            target,
-            weight=weight,
-            bias=bias,
-            p_mask=p_mask,
-            ignore_index=self.ignore_index,
-            label_smoothing=self.label_smoothing,
-            logit_scale=self.logit_scale,
-            num_chunks=self.num_chunks,
-            reduction=self.reduction
-        )
-        return loss
-
-
-class LinearLossParallel(ParallelStyle):
-    def __init__(
-        self,
-        *,
-        sequence_dim: int = 1,
-        use_local_output: bool = False,
-    ):
-        super().__init__()
-
-        self.sequence_sharding = (Shard(sequence_dim),)
-        self.use_local_output = use_local_output
-
-    @staticmethod
-    def _prepare_input_fn(sequence_sharding, mod, inputs, device_mesh):
-        x, target, weight, bias = inputs
-
-        if not isinstance(x, DTensor):
-            # assume the input passed in already sharded on the sequence dim and create the DTensor
-            x = DTensor.from_local(x, device_mesh, sequence_sharding)
-        if x.placements != sequence_sharding:
-            x = x.redistribute(placements=sequence_sharding, async_op=True)
-        if not isinstance(target, DTensor):
-            target = DTensor.from_local(target, device_mesh, [Replicate()])
-        if target.placements != sequence_sharding:
-            target = target.redistribute(placements=sequence_sharding, async_op=True)
-
-        if not isinstance(weight, DTensor):
-            weight = DTensor.from_local(weight, device_mesh, [Replicate()])
-        if weight.placements != [Replicate()]:
-            # we replicate the weight/bias in FLCE
-            weight = weight.redistribute(placements=[Replicate()], async_op=True)
-
-        if bias is not None and not isinstance(bias, DTensor):
-            bias = DTensor.from_local(bias, device_mesh, [Replicate()])
-        if bias is not None and bias.placements != [Replicate()]:
-            bias = bias.redistribute(placements=[Replicate()], async_op=True)
-
-        return x.to_local(), target.to_local(), weight.to_local(), bias.to_local() if bias is not None else bias
-
-    @staticmethod
-    def _prepare_output_fn(use_local_output, mod, outputs, device_mesh):
-        return outputs.to_local() if use_local_output else outputs
-
-    def _apply(self, module: nn.Module, device_mesh: DeviceMesh) -> nn.Module:
-        return distribute_module(
-            module,
-            device_mesh,
-            partition_fn=None,
-            input_fn=partial(self._prepare_input_fn, self.sequence_sharding),
-            output_fn=partial(self._prepare_output_fn, self.use_local_output)
-        )

models/RL/composition/SDAR-100M-AR-0.999zoo_op2-20+0.001teacher_op2-process-dlm/generation_config.json

@@ -1,7 +0,0 @@
-{
-  "_from_model_config": true,
-  "bos_token_id": 2,
-  "eos_token_id": 3,
-  "transformers_version": "4.57.6",
-  "use_cache": false
-}

models/RL/composition/SDAR-100M-AR-0.999zoo_op2-20+0.001teacher_op2-process-dlm/model.safetensors

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:685fba7e9a01abef339cc83a80ba6e7cc3f26b6a0dd115af154d2c0743acbc16
-size 239368216

models/RL/composition/SDAR-100M-AR-0.999zoo_op2-20+0.001teacher_op2-process-dlm/modeling_sdar.py

@@ -1,1256 +0,0 @@
-# This file is modified based on https://github.com/huggingface/transformers/blob/v4.52.4/src/transformers/models/qwen3/modeling_qwen3.py.
-#
-#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
-#           This file was automatically generated from src/transformers/models/qwen3/modular_qwen3.py.
-#               Do NOT edit this file manually as any edits will be overwritten by the generation of
-#             the file from the modular. If any change should be done, please apply the change to the
-#                          modular_qwen3.py file directly. One of our CI enforces this.
-#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
-# coding=utf-8
-# Copyright 2025 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from typing import Callable, Optional, Tuple, Union, List
-
-import torch
-from torch import nn
-from einops import rearrange
-
-from transformers.activations import ACT2FN
-from transformers.cache_utils import Cache, DynamicCache, SlidingWindowCache, StaticCache
-from transformers.generation import GenerationMixin
-from transformers.integrations import use_kernel_forward_from_hub
-from transformers.modeling_attn_mask_utils import AttentionMaskConverter
-from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
-from transformers.modeling_layers import GradientCheckpointingLayer
-from transformers.modeling_outputs import (
-    BaseModelOutputWithPast,
-    CausalLMOutputWithPast,
-    QuestionAnsweringModelOutput,
-    SequenceClassifierOutputWithPast,
-    TokenClassifierOutput,
-)
-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
-try:
-    from transformers.utils import LossKwargs
-except ImportError:
-    from transformers.utils import TransformersKwargs as LossKwargs
-from transformers.utils import auto_docstring, can_return_tuple, is_torch_flex_attn_available, logging
-from .configuration_sdar import SDARConfig
-from .fused_linear_diffusion_cross_entropy import FusedLinearDiffusionCrossEntropyLoss
-
-from flash_attn.ops.triton.layer_norm import rms_norm_fn as flash_rms_norm
-
-import torch.nn.functional as F
-try:
-    from flash_attn import flash_attn_func, flash_attn_varlen_func
-    from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input
-except:
-    pass
-
-try:
-    from liger_kernel.ops.swiglu import LigerSiLUMulFunction  # noqa: F401
-    liger_kernel_is_available = True
-except ImportError:
-    liger_kernel_is_available = False
-
-
-if is_torch_flex_attn_available():
-    from torch.nn.attention.flex_attention import BlockMask, create_block_mask, flex_attention
-    from transformers.integrations.flex_attention import make_flex_block_causal_mask
-
-
-logger = logging.get_logger(__name__)
-
-
-def modify_padded_position_ids_2d(position_ids: torch.LongTensor) -> torch.LongTensor:
-    """
-    使用完全向量化的 PyTorch 操作修改一个 batch 的 packed position_ids。
-    这个函数假设输入是一个 2D Tensor，形状为 (batch_size, sequence_length)。
-    它会独立地处理 batch 中的每一行。
-
-    Args:
-        position_ids: 二维 PyTorch Tensor, shape (batch_size, sequence_length).
-
-    Returns:
-        修改后的 position_ids Tensor, shape (batch_size, sequence_length).
-    """
-    if position_ids.dim() != 2:
-        raise ValueError(f"Input tensor must be 2D, but got {position_ids.dim()} dimensions.")
-        
-    batch_size, seq_len = position_ids.shape
-    device = position_ids.device
-
-    col_indices = torch.arange(seq_len, device=device, dtype=position_ids.dtype).expand(batch_size, -1)
-    mask = (position_ids != 0)
-
-    masked_indices = col_indices * mask
-    last_nonzero_idx = torch.max(masked_indices, dim=1).values
-    has_nonzero = torch.any(mask, dim=1)
-    pad_start_idx = torch.where(has_nonzero, last_nonzero_idx + 1, torch.tensor(0, device=device, dtype=position_ids.dtype))
-
-    padding_mask = col_indices >= pad_start_idx.unsqueeze(1)
-    new_pad_values = col_indices - pad_start_idx.unsqueeze(1)
-    position_ids = torch.where(padding_mask, new_pad_values, position_ids)
-
-    return position_ids
-
-
-def calculate_token_nums(position_ids: torch.Tensor):
-    """
-    使用 PyTorch 高效计算一个批次中每个打包序列的长度。
-
-    Args:
-        position_ids (torch.Tensor): 一个 2D Tensor，形状为 (batch_size, sequence_length)。
-                                     例如：tensor([[0,1,2,3,4,0,1,2,3,4,5,0,1,2,3,0,0,0]])
-    Returns:
-        list[list[int]]: 一个嵌套列表，包含每个批次项中各个序列的长度。
-                         例如：[[5, 6, 4, 1, 1, 1]]
-    """
-    # 检查输入是否为 2D Tensor
-    if position_ids.dim() != 2:
-        raise ValueError(f"输入必须是 2D Tensor，但得到了 {position_ids.dim()}D")
-
-    all_lengths = []
-    
-    # 我们按批次逐行处理。因为每行的序列长度数量不同（ragged），
-    # 所以 Python 循环在批次维度上是最高效且最清晰的写法。
-    # 循环内部的操作是完全向量化的。
-    for pids_row in position_ids:
-        # 获取当前行的总长度
-        seq_len = pids_row.shape[0]
-        
-        # 1. 找到所有值为 0 的元素的索引
-        # pids_row == 0 会返回一个布尔 Tensor: [True, False, ..., True, ...]
-        # torch.nonzero 会返回这些 True 值的索引
-        # .flatten() 将其从 (N, 1) 形状的 Tensor 变为 (N,) 形状
-        zero_indices = torch.nonzero(pids_row == 0).flatten()
-        
-        # 2. 将序列的总长度作为一个额外的切分点添加到末尾
-        # 这对于计算最后一个序列的长度至关重要
-        # 注意：要确保新创建的 tensor 和原始 tensor 在同一个设备上 (cpu/cuda)
-        split_points = torch.cat([
-            zero_indices,
-            torch.tensor([seq_len], device=pids_row.device, dtype=zero_indices.dtype)
-        ])
-        
-        # 3. 计算相邻切分点之间的差值，这就是我们想要的长度
-        # torch.diff([a, b, c, d]) 会返回 [b-a, c-b, d-c]
-        lengths = torch.diff(split_points)
-
-        all_lengths.append(lengths)
-
-    return all_lengths
-
-
-def forward_add_noise_packed(
-    inputs_ids: torch.Tensor,
-    num_tokens_list: List[torch.Tensor],
-    prompt_mask: torch.Tensor,
-    mask_id: int,
-    eps: float = 1e-3,
-    max_tries: int = 10,
-) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-    """
-    为一批打包（packed）序列的 token ID 添加噪声。
-
-    此函数保留了为每个逻辑样本（在每个批次项内拼接）生成独立随机噪声率的逻辑。
-    它会随机将一部分 token 的 ID 替换为 mask_id。
-    这个过程会避开被 prompt_mask 标记的位置。
-
-    Args:
-        inputs_ids (torch.Tensor): 
-            输入的 token ID 张量，形状为 (bsz, total_tokens)。
-        num_tokens_list (List[torch.Tensor]): 
-            一个张量列表，长度为 bsz。列表中的每个张量记录了对应批次项中
-            每个逻辑样本的长度。例如: [tensor([len1, len2]), tensor([len3, len4, len5])].
-        prompt_mask (torch.Tensor): 
-            布尔型张量，形状为 (bsz, total_tokens)，值为 True 的位置表示是 prompt，
-            不应添加噪声。
-        mask_id (int): 
-            用于替换的 mask token 的 ID。
-        eps (float): 
-            微小值，用于防止噪声率 t 恰好为 0，确保 p_mask > 0。
-        max_tries (int): 
-            为确保至少一个非 prompt token 被 mask，对每个批次项尝试的最大次数。
-
-    Returns:
-        Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-        - noisy_input_ids (torch.Tensor): 
-            添加噪声后的 token ID 张量，形状为 (bsz, total_tokens)。
-        - final_masked_indices (torch.Tensor): 
-            布尔型张量，标记了哪些位置被实际 mask 了，形状为 (bsz, total_tokens)。
-        - p_masks (torch.Tensor): 
-            一个一维张量，包含了被 mask 的 token 对应的实际噪声率。
-    """
-    # 1. 验证和获取形状
-    bsz, total_tokens = inputs_ids.shape
-    device = inputs_ids.device
-
-    # 检查输入的一致性
-    assert len(num_tokens_list) == bsz, f"num_tokens_list 的长度 ({len(num_tokens_list)}) 必须等于 bsz ({bsz})"
-    assert prompt_mask.shape == (bsz, total_tokens), f"prompt_mask 形状不匹配, 期望 {(bsz, total_tokens)}, 得到 {prompt_mask.shape}"
-
-    # 准备结果容器
-    noisy_ids_list = []
-    final_masked_indices_list = []
-    p_masks_per_token_list = []
-
-    # 2. 在批次维度上迭代
-    # 这是处理不同打包结构最直接有效的方法
-    for i in range(bsz):
-        # 提取当前批次项的数据
-        current_ids = inputs_ids[i:i+1] # shape: (1, total_tokens)
-        current_num_tokens = num_tokens_list[i]
-        current_prompt_mask = prompt_mask[i:i+1] # shape: (1, total_tokens)
-        
-        num_samples_in_item = len(current_num_tokens)
-        # 验证当前批次项的 token 总数���否匹配
-        assert total_tokens == torch.sum(current_num_tokens), \
-            f"批次项 {i} 的 num_tokens 之和 ({torch.sum(current_num_tokens)}) 与 total_tokens ({total_tokens}) 不匹配"
-
-        eligible_for_masking = ~current_prompt_mask
-
-        # 如果没有任何 token 可以被 mask，直接使用原始输入，并设置 p_mask 为 eps
-        if not eligible_for_masking.any():
-            noisy_ids_list.append(current_ids)
-            final_masked_indices_list.append(torch.zeros_like(current_prompt_mask, dtype=torch.bool))
-            # p_mask_per_token 的形状应为 (1, total_tokens) 以便后续拼接
-            p_masks_per_token_list.append(torch.full((1, total_tokens), eps, device=device, dtype=torch.float))
-            continue
-
-        # --- 尝试生成 mask，确保至少 mask 一个 token ---
-        final_masked_indices_item = torch.zeros_like(current_prompt_mask, dtype=torch.bool)
-        p_mask_per_token = None
-        
-        for _ in range(max_tries):
-            # 为每个逻辑样本生成一个独立的噪声率 t
-            t = torch.rand(num_samples_in_item, device=device)
-            p_mask_per_sample = (1 - eps) * t + eps
-
-            # 将每个样本的噪声率扩展到其所有 token 上
-            p_mask_per_token_1d = torch.repeat_interleave(p_mask_per_sample, current_num_tokens)
-            p_mask_per_token = p_mask_per_token_1d.unsqueeze(0) # shape: (1, total_tokens)
-
-            # 根据噪声率生成随机 mask
-            masked_indices = torch.rand_like(p_mask_per_token) < p_mask_per_token
-            # 应用 prompt mask，确保 prompt 不被 mask
-            final_masked_indices_item = masked_indices & eligible_for_masking
-
-            # 如果成功 mask 了至少一个 token，则跳出尝试循环
-            if final_masked_indices_item.any():
-                break
-        
-        # 如果 max_tries 之后仍然没有 mask 任何 token (极小概率)，就强制 mask 一个可 mask 的 token
-        if not final_masked_indices_item.any():
-            eligible_indices = torch.nonzero(eligible_for_masking.squeeze(0), as_tuple=True)[0]
-            if len(eligible_indices) > 0:
-                # 随机选择一个可 mask 的位置
-                random_choice = torch.randint(0, len(eligible_indices), (1,)).item()
-                force_mask_idx = eligible_indices[random_choice]
-                final_masked_indices_item[0, force_mask_idx] = True
-
-
-        # --- 根据最终的 mask 生成带噪声的 IDs ---
-        noisy_ids_item = torch.where(
-            final_masked_indices_item,
-            mask_id,
-            current_ids
-        )
-        
-        # 保存这个批次项的结果
-        noisy_ids_list.append(noisy_ids_item)
-        final_masked_indices_list.append(final_masked_indices_item)
-        p_masks_per_token_list.append(p_mask_per_token)
-
-    # 3. 将列表中的结果堆叠成最终的批处理张量
-    noisy_input_ids = torch.cat(noisy_ids_list, dim=0)
-    final_masked_indices = torch.cat(final_masked_indices_list, dim=0)
-    p_mask_full = torch.cat(p_masks_per_token_list, dim=0)
-    
-    # 4. 提取被 mask 位置对应的噪声率
-    p_masks = p_mask_full[final_masked_indices]
-    
-    return noisy_input_ids, final_masked_indices, p_masks
-
-
-def block_diff_mask(b, h, q_idx, kv_idx, block_size=None, n=None):
-    """
-    Constructs the specialized block diffusion attention mask for training
-    composed of three masks:
-    - **Block Diagonal Mask (M_BD)**: Self-attention within noised blocks
-    - **Offset Block Causal Mask (M_OBC)**: Cross-attention for conditional context
-    - **Block Causal Mask (M_BC)**: Attention to update x0
-
-    Args:
-        b, h: Batch and head indices (ignored for mask logic).
-        q_idx, kv_idx: Query and Key indices.
-        seq_len: Total sequence length.
-        block_size: Defines the block structure.
-
-    Returns:
-        A boolean attention mask.
-    """
-
-    # Indicate whether token belongs to xt or x0
-    x0_flag_q = q_idx >= n
-    x0_flag_kv = kv_idx >= n
-
-    # Compute block indices
-    block_q = torch.where(
-        x0_flag_q == 1, (q_idx - n) // block_size, q_idx // block_size
-    )
-    block_kv = torch.where(
-        x0_flag_kv == 1, (kv_idx - n) // block_size, kv_idx // block_size
-    )
-
-    # **1. Block Diagonal Mask (M_BD) **
-    block_diagonal = (block_q == block_kv) & (x0_flag_q == x0_flag_kv)
-
-    # **2. Offset Block-Causal Mask (M_OBC) **
-    offset_block_causal = (block_q > block_kv) & (
-        x0_flag_kv == 1) & (x0_flag_q == 0)
-
-    # **3. Block-Causal Mask (M_BC) **
-    block_causal = (block_q >= block_kv) & (x0_flag_kv == 1) & (x0_flag_q == 1)
-
-    # **4. Combine Masks **
-    return block_diagonal | offset_block_causal | block_causal
-
-
-def block_attn_mask(num_tokens, block_size, device):
-    masks = []
-    for i in range(len(num_tokens)):
-        cur_masks = []
-        for num in num_tokens[i]:
-            # 全部返回 n*n 而非 2n*2n
-            single_mask = block_diff_mask(
-                b=None,
-                h=None,
-                q_idx=torch.arange(num * 2, device=device)[:, None],
-                kv_idx=torch.arange(num * 2, device=device)[None, :],
-                block_size=block_size,
-                n=num,
-            )
-            cur_masks.append(single_mask)
-        masks.append(torch.block_diag(*cur_masks))
-    masks = torch.stack(masks, dim=0)
-    return masks
-
-
-@torch.compile(fullgraph=True, mode="max-autotune-no-cudagraphs")
-def _fused_flex_attention(query, key, value, attention_mask, **kwargs):
-    return flex_attention(query, key, value, block_mask=attention_mask, **kwargs)
-
-
-def fused_flex_attention(query, key, value, attention_mask, **kwargs):
-    # Cast outside torch.compile: q/k RMSNorm often returns fp32 under bf16 while v stays bf16.
-    dtype = value.dtype
-    return _fused_flex_attention(
-        query.to(dtype), key.to(dtype), value, attention_mask, **kwargs
-    )
-
-
-@use_kernel_forward_from_hub("RMSNorm")
-class SDARRMSNorm(nn.Module):
-    def __init__(self, hidden_size, eps=1e-6):
-        """
-        SDARRMSNorm is equivalent to T5LayerNorm
-        """
-        super().__init__()
-        self.weight = nn.Parameter(torch.ones(hidden_size))
-        self.variance_epsilon = eps
-
-    def forward(self, hidden_states):
-        # flash_rms_norm may return fp32; cast back so Q/K match V under bf16 training.
-        input_dtype = hidden_states.dtype
-        return flash_rms_norm(
-            hidden_states, weight=self.weight, bias=None, eps=self.variance_epsilon
-        ).to(input_dtype)
-        '''
-        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 SDARMLP(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):
-        if liger_kernel_is_available:
-            return self.down_proj(LigerSiLUMulFunction.apply(self.gate_proj(x), self.up_proj(x)))
-        else:
-            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)
-
-
-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,
-):
-    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 SDARAttention(nn.Module):
-    """Multi-headed attention from 'Attention Is All You Need' paper"""
-
-    def __init__(self, config: SDARConfig, 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.hidden_size = config.hidden_size
-        self.num_attention_heads = config.num_attention_heads
-        self.num_key_value_heads = config.num_key_value_heads
-
-        self.q_proj = nn.Linear(
-            config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
-        )
-        self.k_proj = nn.Linear(
-            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
-        )
-        self.v_proj = nn.Linear(
-            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
-        )
-        self.o_proj = nn.Linear(
-            config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias
-        )
-        # unlike olmo, only on the head dim!
-        self.q_norm = SDARRMSNorm(self.head_dim, eps=config.rms_norm_eps)
-        # thus post q_norm does not need reshape
-        self.k_norm = SDARRMSNorm(self.head_dim, eps=config.rms_norm_eps)
-        self.sliding_window = config.sliding_window
-        if not (
-            self.config.use_sliding_window
-            and getattr(self.config, "sliding_window", None) is not None
-            and self.layer_idx >= self.config.max_window_layers
-        ):
-            self.sliding_window = 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,
-        **kwargs: Unpack[FlashAttentionKwargs],
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        input_shape = hidden_states.shape[:-1]
-        bsz, q_len = input_shape
-        hidden_shape = (*input_shape, -1, self.head_dim)
-
-        query_states = self.q_norm(self.q_proj(
-            hidden_states).view(hidden_shape)).transpose(1, 2)
-        key_states = self.k_norm(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 and kwargs.get("store_kv", False):
-            # sin and cos are specific to RoPE models; cache_position needed for the static cache
-            key_states, value_states = past_key_value.update(
-                key_states, value_states, self.layer_idx)
-        elif past_key_value is not None and not kwargs.get("store_kv", False) and len(past_key_value) > self.layer_idx:
-            # only retrive, do not store kv
-            past_key_states, past_value_states = past_key_value[self.layer_idx]
-            key_states = torch.cat(
-                [past_key_states, key_states], dim=-2)
-            value_states = torch.cat(
-                [past_value_states, value_states], dim=-2)
-
-        if self.training:
-            attn_output, attn_weights = fused_flex_attention(
-                query=query_states,
-                key=key_states,
-                value=value_states,
-                attention_mask=attention_mask,
-                enable_gqa=True,
-                scale=self.scaling,
-                return_lse=True
-            )
-            attn_weights = attn_weights.to(
-                value_states.dtype) if attn_weights is not None else None
-            attn_output = rearrange(attn_output, 'b h l d -> b l (h d)')
-        else:
-            attention_mask = attention_mask.bool() if attention_mask is not None else None
-            attn_weights = None
-            if torch.all(attention_mask):  # decoding
-                query_states = query_states.transpose(1, 2)
-                key_states = key_states.transpose(1, 2)
-                value_states = value_states.transpose(1, 2)
-                attn_output = flash_attn_func(
-                    query_states,
-                    key_states,
-                    value_states,
-                    causal=False,
-                    softmax_scale=self.scaling
-                )
-                attn_output = rearrange(attn_output, 'b l h d -> b l (h d)')
-            else:  # prefilling
-                attn_output = F.scaled_dot_product_attention(
-                    query=query_states,
-                    key=key_states,
-                    value=value_states,
-                    attn_mask=attention_mask,
-                    is_causal=False,
-                    scale=self.scaling,
-                    enable_gqa=True
-                )
-                attn_output = rearrange(attn_output, 'b h l d -> b l (h d)')
-        attn_output = self.o_proj(attn_output)
-        return attn_output, attn_weights  # , attn_weights
-
-
-class SDARDecoderLayer(GradientCheckpointingLayer):
-    def __init__(self, config: SDARConfig, layer_idx: int):
-        super().__init__()
-        self.hidden_size = config.hidden_size
-        self.self_attn = SDARAttention(config=config, layer_idx=layer_idx)
-        self.mlp = SDARMLP(config)
-        self.input_layernorm = SDARRMSNorm(
-            config.hidden_size, eps=config.rms_norm_eps)
-        self.post_attention_layernorm = SDARRMSNorm(
-            config.hidden_size, eps=config.rms_norm_eps)
-        if (
-            config.sliding_window and config._attn_implementation != "flash_attention_2"
-        ):  # diff with Llama is this warning
-            logger.warning_once(
-                f"Sliding Window Attention is enabled but not implemented for `{config._attn_implementation}`; "
-                "unexpected results may be encountered."
-            )
-
-    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: Optional[bool] = False,
-        use_cache: Optional[bool] = False,
-        store_kv: Optional[bool] = False,
-        cache_position: Optional[torch.LongTensor] = None,
-        # necessary, but kept here for BC
-        position_embeddings: Optional[Tuple[torch.Tensor,
-                                            torch.Tensor]] = None,
-        **kwargs: Unpack[FlashAttentionKwargs],
-    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
-        residual = hidden_states
-        hidden_states = self.input_layernorm(hidden_states)
-
-        # Self Attention
-        hidden_states, self_attn_weights = 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,
-            store_kv=store_kv,
-            cache_position=cache_position,
-            position_embeddings=position_embeddings,
-            **kwargs,
-        )
-        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,)
-
-        return outputs
-
-
-@auto_docstring
-class SDARPreTrainedModel(PreTrainedModel):
-    config_class = SDARConfig
-    base_model_prefix = "model"
-    supports_gradient_checkpointing = True
-    _no_split_modules = ["SDARDecoderLayer"]
-    _skip_keys_device_placement = ["past_key_values"]
-    _supports_flash_attn_2 = True
-    _supports_sdpa = True
-    _supports_flex_attn = True
-    _supports_cache_class = True
-    _supports_quantized_cache = True
-    _supports_static_cache = True
-    _supports_attention_backend = 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_()
-        elif isinstance(module, SDARRMSNorm):
-            module.weight.data.fill_(1.0)
-
-
-class SDARRotaryEmbedding(nn.Module):
-    def __init__(self, config: SDARConfig, device=None):
-        super().__init__()
-        # BC: "rope_type" was originally "type"
-        if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
-            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()
-    # power user: used with advanced RoPE types (e.g. dynamic rope)
-    @dynamic_rope_update
-    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 SDARModel(SDARPreTrainedModel):
-    def __init__(self, config: SDARConfig):
-        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(
-            [SDARDecoderLayer(config, layer_idx)
-             for layer_idx in range(config.num_hidden_layers)]
-        )
-        self.norm = SDARRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.rotary_emb = SDARRotaryEmbedding(config=config)
-        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
-
-    @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,
-        use_cache: Optional[bool] = None,
-        store_kv: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        cache_position: Optional[torch.LongTensor] = None,
-        **flash_attn_kwargs: Unpack[FlashAttentionKwargs],
-    ) -> 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
-
-        if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError(
-                "You must specify exactly one of input_ids or inputs_embeds")
-
-        if self.gradient_checkpointing and self.training and use_cache:
-            logger.warning_once(
-                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
-            )
-            use_cache = False
-
-        # TODO (joao): remove this exception in v4.56 -- it exists for users that try to pass a legacy cache
-        if not isinstance(past_key_values, (type(None), Cache)):
-            raise ValueError(
-                "The `past_key_values` should be either a `Cache` object or `None`.")
-
-        if inputs_embeds is None:
-            inputs_embeds = self.embed_tokens(input_ids)
-
-        if use_cache and past_key_values is None:
-            past_key_values = DynamicCache()
-
-        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)
-
-        # causal_mask = self._update_causal_mask(
-        #     attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
-        # )
-
-        hidden_states = inputs_embeds
-
-        # create position embeddings to be shared across the decoder layers
-        position_embeddings = self.rotary_emb(hidden_states, position_ids)
-
-        # decoder layers
-        all_hidden_states = () if output_hidden_states else None
-        all_self_attns = () if output_attentions else None
-
-        for decoder_layer in self.layers[: self.config.num_hidden_layers]:
-            if output_hidden_states:
-                all_hidden_states += (hidden_states,)
-
-            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,
-                store_kv=store_kv,
-                cache_position=cache_position,
-                position_embeddings=position_embeddings,
-                **flash_attn_kwargs,
-            )
-
-            hidden_states = layer_outputs[0]
-
-            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,)
-
-        return BaseModelOutputWithPast(
-            last_hidden_state=hidden_states,
-            past_key_values=past_key_values if use_cache else None,
-            hidden_states=all_hidden_states,
-            attentions=all_self_attns,
-        )
-
-    def _update_causal_mask(
-        self,
-        attention_mask: Union[torch.Tensor, "BlockMask"],
-        input_tensor: torch.Tensor,
-        cache_position: torch.Tensor,
-        past_key_values: Cache,
-        output_attentions: bool = False,
-    ):
-        if self.config._attn_implementation == "flash_attention_2":
-            if attention_mask is not None and past_key_values is not None:
-                is_padding_right = attention_mask[:, -
-                                                  1].sum().item() != input_tensor.size()[0]
-                if is_padding_right:
-                    raise ValueError(
-                        "You are attempting to perform batched generation with padding_side='right'"
-                        " this may lead to unexpected behaviour for Flash Attention version of Qwen3. Make sure to "
-                        " call `tokenizer.padding_side  = 'left'` before tokenizing the input. "
-                    )
-            if attention_mask is not None and 0.0 in attention_mask:
-                return attention_mask
-            return None
-        if self.config._attn_implementation == "flex_attention":
-            if isinstance(attention_mask, torch.Tensor):
-                seq_len_q, seq_len_kv = attention_mask.shape
-                assert seq_len_q == seq_len_kv, f"got {attention_mask.shape=}"
-                attention_mask = create_block_mask(
-                    # 2d bool tensor, shape: [2*seqlen, 2*seqlen]
-                    lambda b, h, q_idx, kv_idx: attention_mask[q_idx, kv_idx],
-                    B=None, H=None, Q_LEN=seq_len_q, KV_LEN=seq_len_kv,
-                )
-            else:
-                # Here we pass in flex mask computed externally
-                assert isinstance(attention_mask, BlockMask)
-            return attention_mask
-
-        # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
-        # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
-        # to infer the attention mask.
-        past_seen_tokens = past_key_values.get_seq_length(
-        ) if past_key_values is not None else 0
-        using_static_cache = isinstance(past_key_values, StaticCache)
-        using_sliding_window_cache = isinstance(
-            past_key_values, SlidingWindowCache)
-
-        # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
-        if (
-            self.config._attn_implementation == "sdpa"
-            and not (using_static_cache or using_sliding_window_cache)
-            and not output_attentions
-        ):
-            if AttentionMaskConverter._ignore_causal_mask_sdpa(
-                attention_mask,
-                inputs_embeds=input_tensor,
-                past_key_values_length=past_seen_tokens,
-                sliding_window=self.config.sliding_window,
-                is_training=self.training,
-            ):
-                return None
-
-        dtype = input_tensor.dtype
-        min_dtype = torch.finfo(dtype).min
-        sequence_length = input_tensor.shape[1]
-        # SlidingWindowCache or StaticCache
-        if using_sliding_window_cache or using_static_cache:
-            target_length = past_key_values.get_max_cache_shape()
-        # DynamicCache or no cache
-        else:
-            target_length = (
-                attention_mask.shape[-1]
-                if isinstance(attention_mask, torch.Tensor)
-                else past_seen_tokens + sequence_length + 1
-            )
-
-        # In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
-        causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
-            attention_mask,
-            sequence_length=sequence_length,
-            target_length=target_length,
-            dtype=dtype,
-            cache_position=cache_position,
-            batch_size=input_tensor.shape[0],
-            config=self.config,
-            past_key_values=past_key_values,
-        )
-
-        if (
-            self.config._attn_implementation == "sdpa"
-            and attention_mask is not None
-            and attention_mask.device.type in ["cuda", "xpu", "npu"]
-            and not output_attentions
-        ):
-            # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
-            # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
-            # Details: https://github.com/pytorch/pytorch/issues/110213
-            causal_mask = AttentionMaskConverter._unmask_unattended(
-                causal_mask, min_dtype)
-
-        return causal_mask
-
-    @staticmethod
-    def _prepare_4d_causal_attention_mask_with_cache_position(
-        attention_mask: torch.Tensor,
-        sequence_length: int,
-        target_length: int,
-        dtype: torch.dtype,
-        cache_position: torch.Tensor,
-        batch_size: int,
-        config: SDARConfig,
-        past_key_values: Cache,
-    ):
-        """
-        Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
-        `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
-
-        Args:
-            attention_mask (`torch.Tensor`):
-                A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`.
-            sequence_length (`int`):
-                The sequence length being processed.
-            target_length (`int`):
-                The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet.
-            dtype (`torch.dtype`):
-                The dtype to use for the 4D attention mask.
-            cache_position (`torch.Tensor`):
-                Indices depicting the position of the input sequence tokens in the sequence.
-            batch_size (`torch.Tensor`):
-                Batch size.
-            config (`SDARConfig`):
-                The model's configuration class
-            past_key_values (`Cache`):
-                The cache class that is being used currently to generate
-        """
-        if attention_mask is not None and attention_mask.dim() == 4:
-            # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
-            causal_mask = attention_mask
-        else:
-            min_dtype = torch.finfo(dtype).min
-            causal_mask = torch.full(
-                (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=cache_position.device
-            )
-            diagonal_attend_mask = torch.arange(target_length, device=cache_position.device) > cache_position.reshape(
-                -1, 1
-            )
-            text_config = config.get_text_config()
-            if getattr(text_config, "use_sliding_window", True) and text_config.sliding_window is not None:
-                # if we have sliding window, we should not attend to tokens beyond sliding window length, so we mask them out also
-                # the check is needed to verify is current checkpoint was trained with sliding window or not
-                if not isinstance(past_key_values, SlidingWindowCache) or sequence_length > target_length:
-                    sliding_attend_mask = torch.arange(target_length, device=cache_position.device) <= (
-                        cache_position.reshape(-1, 1) -
-                        text_config.sliding_window
-                    )
-                    diagonal_attend_mask.bitwise_or_(sliding_attend_mask)
-            causal_mask *= diagonal_attend_mask
-            causal_mask = causal_mask[None, None,
-                                      :, :].expand(batch_size, 1, -1, -1)
-            if attention_mask is not None:
-                causal_mask = causal_mask.clone()  # copy to contiguous memory for in-place edit
-                if attention_mask.shape[-1] > target_length:
-                    attention_mask = attention_mask[:, :target_length]
-                mask_length = attention_mask.shape[-1]
-                padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
-                    causal_mask.device
-                )
-                padding_mask = padding_mask == 0
-                causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
-                    padding_mask, min_dtype
-                )
-        return causal_mask
-
-
-class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs):
-    ...
-
-
-@auto_docstring
-class SDARForCausalLM(SDARPreTrainedModel, 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 = SDARModel(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 prepare_for_bd_training(self, inputs_ids, position_ids, prompt_mask):
-        bsz, seq_len = inputs_ids.shape
-        num_tokens = calculate_token_nums(position_ids) # List[torch.Tensor]
-        noisy_inputs_ids, logits_to_keep_half, p_mask = forward_add_noise_packed(
-            inputs_ids=inputs_ids,
-            num_tokens_list=num_tokens,
-            prompt_mask=prompt_mask,
-            mask_id=self.config.mask_token_id,
-        )
-        router_noisy_part_list = []
-        for i in range(bsz):
-            cur_router_noisy_part = (torch.arange(num_tokens[i].shape[0] *2) % 2 == 0).to(inputs_ids.device)
-            cur_router_noisy_part = cur_router_noisy_part.repeat_interleave(num_tokens[i].repeat_interleave(2))
-            router_noisy_part_list.append(cur_router_noisy_part)
-        router_noisy_part = torch.stack(router_noisy_part_list, dim=0)
-
-        # concated inputs_ids: (bzs, seq_len x 2)
-        concat_inputs_ids = inputs_ids.repeat(1, 2)
-        # concated logits_to_keep: (bsz, seq_len x 2)
-        logits_to_keep = torch.zeros(
-                    bsz, 2 * seq_len, dtype=torch.bool, device=inputs_ids.device)
-        # concated position_ids: (bsz, seq_len x 2)
-        concat_position_ids = torch.zeros(
-                    bsz, 2 * seq_len, dtype=position_ids.dtype, device=position_ids.device)
-        for i in range(bsz):
-            concat_inputs_ids[i][router_noisy_part[i]] = noisy_inputs_ids[i]
-            concat_inputs_ids[i][~router_noisy_part[i]] = inputs_ids[i]
-
-            logits_to_keep[i][router_noisy_part[i]] = logits_to_keep_half[i]
-
-            concat_position_ids[i][router_noisy_part[i]] = position_ids[i]
-            concat_position_ids[i][~router_noisy_part[i]] = position_ids[i]
-
-        # create flex_attention mask
-        attention_mask = block_attn_mask(num_tokens, self.config.block_size, inputs_ids.device)
-        flex_attention_mask_3d = create_block_mask(
-                            lambda b, h, q_idx, kv_idx: attention_mask[b, q_idx, kv_idx],
-                            B=attention_mask.size(0), H=None,
-                            Q_LEN=attention_mask.size(1), KV_LEN=attention_mask.size(2),
-                            device=inputs_ids.device,
-        )
-
-        return concat_inputs_ids, concat_position_ids, flex_attention_mask_3d, logits_to_keep_half, logits_to_keep, p_mask
-
-    @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,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        cache_position: Optional[torch.LongTensor] = None,
-        logits_to_keep: Union[int, torch.Tensor] = 0,
-        **kwargs: Unpack[KwargsForCausalLM],
-    ) -> CausalLMOutputWithPast:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
-            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
-            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
-
-        Example:
-
-        ```python
-        >>> from transformers import AutoTokenizer, SDARForCausalLM
-
-        >>> model = SDARForCausalLM.from_pretrained("DiffuOpen/SDAR-1.7B-Chat")
-        >>> tokenizer = AutoTokenizer.from_pretrained("DiffuOpen/SDAR-1.7B-Chat")
-
-        >>> 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."
-        ```"""
-        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
-        )
-        if self.training:
-            assert inputs_embeds is None, "only support input_ids during training"
-            prompt_mask = (labels == -100) if labels is not None else None
-            # PT packing / some collators omit position_ids; SDAR BD mask needs them.
-            if position_ids is None:
-                position_ids = torch.arange(
-                    input_ids.shape[-1], device=input_ids.device, dtype=torch.long
-                ).unsqueeze(0).repeat(input_ids.shape[0], 1)
-            position_ids = modify_padded_position_ids_2d(position_ids)
-            concat_inputs_ids, concat_position_ids, flex_attention_mask_3d, logits_to_keep_half, logits_to_keep, p_mask = self.prepare_for_bd_training(input_ids, position_ids, prompt_mask)
-            # Do not let trainer/collator attention_mask overwrite the BlockMask.
-            kwargs.pop("attention_mask", None)
-            outputs = self.model(
-                input_ids=concat_inputs_ids,
-                attention_mask=flex_attention_mask_3d,
-                position_ids=concat_position_ids,
-                output_attentions=output_attentions,
-                output_hidden_states=output_hidden_states,
-                return_dict=True,
-                cache_position=cache_position,
-                **kwargs,
-            )
-            hidden_states = outputs.last_hidden_state
-            hidden_states = hidden_states[logits_to_keep].contiguous()
-            assert labels is not None, "Labels must be provided for training."
-            answer_len = (labels != -100).sum()
-            loss_fct = FusedLinearDiffusionCrossEntropyLoss(reduction='sum')
-            loss = loss_fct(  # it will return (sum_loss, unreduced_loss)
-                    # conduct `view(-1, V)` inside the function
-                    x=hidden_states,
-                    target=labels[logits_to_keep_half].contiguous(),
-                    weight=self.lm_head.weight,
-                    bias=self.lm_head.bias,
-                    p_mask=p_mask,
-                )
-            loss = loss / answer_len
-            logits = None
-        else:
-            # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
-            outputs: BaseModelOutputWithPast = 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,
-                cache_position=cache_position,
-                **kwargs,
-            )
-
-            hidden_states = outputs.last_hidden_state
-            # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
-            slice_indices = slice(-logits_to_keep,
-                                None) if isinstance(logits_to_keep, int) else logits_to_keep
-            hidden_states = hidden_states[:, slice_indices, :].contiguous()
-            fuse_linear_and_cross_entropy = self.config.fuse_cross_entropy and self.training
-            if fuse_linear_and_cross_entropy:
-                # When using fused_linear_ce_loss, we do not compute the whole logits on HBM
-                logits = None
-            else:
-                logits = self.lm_head(hidden_states)
-
-            loss = None
-            if labels is not None:
-                # FusedLinearCrossEntropyLoss will be implemented by monkey patch when training
-                # We don't use it when inferencing
-                loss_fct = nn.CrossEntropyLoss()  # nn.CE
-                loss = loss_fct(
-                    logits.view(-1, self.config.vocab_size), labels.view(-1))
-
-        return CausalLMOutputWithPast(
-            loss=loss,
-            logits=logits,
-            past_key_values=outputs.past_key_values,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-
-__all__ = [
-    "SDARForCausalLM",
-    "SDARModel",
-    "SDARPreTrainedModel",
-]

models/RL/composition/SDAR-100M-AR-0.999zoo_op2-20+0.001teacher_op2-process-dlm/special_tokens_map.json

@@ -1,46 +0,0 @@
-{
-  "additional_special_tokens": [
-    "<question>",
-    "</question>",
-    "<solution>",
-    "</solution>",
-    "<answer>",
-    "</answer>",
-    "[MASK]"
-  ],
-  "bos_token": {
-    "content": "[BOS]",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  },
-  "eos_token": {
-    "content": "[EOS]",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  },
-  "mask_token": {
-    "content": "[MASK]",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  },
-  "pad_token": {
-    "content": "[PAD]",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  },
-  "unk_token": {
-    "content": "[UNK]",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  }
-}

models/RL/composition/SDAR-100M-AR-0.999zoo_op2-20+0.001teacher_op2-process-dlm/tokenizer_config.json

@@ -1,145 +0,0 @@
-{
-  "add_prefix_space": false,
-  "added_tokens_decoder": {
-    "0": {
-      "content": "[UNK]",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "1": {
-      "content": "[PAD]",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "2": {
-      "content": "[BOS]",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "3": {
-      "content": "[EOS]",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "4": {
-      "content": "<question>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "5": {
-      "content": "</question>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "6": {
-      "content": "<solution>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "7": {
-      "content": "</solution>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "8": {
-      "content": "<answer>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "9": {
-      "content": "</answer>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "2196": {
-      "content": "[MASK]",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "2197": {
-      "content": "<special_token_1>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "2198": {
-      "content": "<special_token_2>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "2199": {
-      "content": "<special_token_3>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "2200": {
-      "content": "<special_token_4>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    }
-  },
-  "additional_special_tokens": [
-    "<question>",
-    "</question>",
-    "<solution>",
-    "</solution>",
-    "<answer>",
-    "</answer>",
-    "[MASK]"
-  ],
-  "bos_token": "[BOS]",
-  "clean_up_tokenization_spaces": false,
-  "eos_token": "[EOS]",
-  "extra_special_tokens": {},
-  "mask_token": "[MASK]",
-  "model_max_length": 1000000000000000019884624838656,
-  "pad_token": "[PAD]",
-  "padding_side": "right",
-  "split_special_tokens": false,
-  "tokenizer_class": "PreTrainedTokenizerFast",
-  "unk_token": "[UNK]"
-}