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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,61 @@
+---
+library_name: transformers
+license: other
+base_model: Wannabtl/modeltest
+tags:
+- llama-factory
+- full
+- generated_from_trainer
+model-index:
+- name: ChatTS-14B-timesense
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# ChatTS-14B-timesense
+
+This model is a fine-tuned version of [/xll/models/ChatTS-14B](https://huggingface.co//xll/models/ChatTS-14B) on the generated_timeseries_bench_qa_new2, the generated_timeseries_bench_plus2, the tulu_ift, the model_detect_0823, the model_qa, the new_chatts_sft and the new_chatts_ift datasets.
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 1e-05
+- train_batch_size: 1
+- eval_batch_size: 8
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 8
+- gradient_accumulation_steps: 32
+- total_train_batch_size: 256
+- total_eval_batch_size: 64
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_ratio: 0.02
+- training_steps: 900
+
+### Training results
+
+
+
+### Framework versions
+
+- Transformers 4.52.4
+- Pytorch 2.6.0+cu124
+- Datasets 3.4.1
+- Tokenizers 0.21.4

added_tokens.json

@@ -0,0 +1,26 @@
+{
+  "</tool_call>": 151658,
+  "<tool_call>": 151657,
+  "<ts/>": 151666,
+  "<ts>": 151665,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

all_results.json

@@ -0,0 +1,8 @@
+{
+    "epoch": 0.3520179920307038,
+    "total_flos": 9.027346976391299e+18,
+    "train_loss": 213.8077490248945,
+    "train_runtime": 76099.1347,
+    "train_samples_per_second": 3.028,
+    "train_steps_per_second": 0.012
+}

chat_template.jinja

@@ -0,0 +1,7 @@
+{% set system_message = 'You are a helpful assistant.' %}{% if messages[0]['role'] == 'system' %}{% set system_message = messages[0]['content'] %}{% endif %}{% if system_message is defined %}{{ '<|im_start|>system
+' + system_message + '<|im_end|>
+' }}{% endif %}{% for message in messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<|im_start|>user
+' + content + '<|im_end|>
+<|im_start|>assistant
+' }}{% elif message['role'] == 'assistant' %}{{ content + '<|im_end|>' + '
+' }}{% endif %}{% endfor %}

config.json

@@ -0,0 +1,50 @@
+{
+  "architectures": [
+    "Qwen2TSForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "auto_map": {
+    "AutoConfig": "configuration_qwen2.Qwen2TSConfig",
+    "AutoModel": "modeling_qwen2.Qwen2TSForCausalLM",
+    "AutoModelForCausalLM": "modeling_qwen2.Qwen2TSForCausalLM",
+    "AutoProcessor": "processing_qwen2_ts.Qwen2TSProcessor"
+  },
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 5120,
+  "ignore_index": -100,
+  "initializer_range": 0.02,
+  "intermediate_size": 13824,
+  "max_position_embeddings": 32768,
+  "max_window_layers": 70,
+  "model_type": "qwen2",
+  "num_attention_heads": 40,
+  "num_hidden_layers": 48,
+  "num_key_value_heads": 8,
+  "pad_token_id": 151643,
+  "rms_norm_eps": 1e-06,
+  "rope_theta": 1000000.0,
+  "sliding_window": 131072,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.52.4",
+  "ts": {
+    "embedding_dim": 16,
+    "hidden_size": 5120,
+    "max_length": 32768,
+    "max_sequence_length": 32768,
+    "num_features": 2,
+    "num_layers": 5,
+    "patch_size": 8,
+    "ts_loss_weight": 1.0,
+    "use_position_embedding": true,
+    "use_position_idx": false
+  },
+  "ts_loss_weight": 1.0,
+  "ts_token_end_index": 151666,
+  "ts_token_start_index": 151665,
+  "use_cache": false,
+  "use_sliding_window": false,
+  "vocab_size": 152064
+}

configuration_qwen2.py

@@ -0,0 +1,362 @@
+# 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.
+""" Qwen2 model configuration"""
+
+from transformers import PretrainedConfig
+from transformers.utils import logging
+from typing import *
+
+
+logger = logging.get_logger(__name__)
+
+
+class Qwen2TSConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`Qwen2Model`]. It is used to instantiate a
+    Qwen2 model according to the specified arguments, defining the model architecture. Instantiating a configuration
+    with the defaults will yield a similar configuration to that of
+    Qwen2-7B-beta [Qwen/Qwen2-7B-beta](https://huggingface.co/Qwen/Qwen2-7B-beta).
+
+    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 Qwen2 model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`Qwen2Model`]
+        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`.
+        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.
+        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 Qwen2Model, Qwen2Config
+
+    >>> # Initializing a Qwen2 style configuration
+    >>> configuration = Qwen2Config()
+
+    >>> # Initializing a model from the Qwen2-7B style configuration
+    >>> model = Qwen2Model(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "qwen2"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    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,
+        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,
+        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
+        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.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.attention_dropout = attention_dropout
+
+        super().__init__(
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+TINYTIMEMIXER_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
+
+
+class TinyTimeMixerConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`TinyTimeMixerModel`]. It is used to instantiate a
+    TinyTimeMixer model according to the specified arguments, defining the model architecture. Instantiating a
+    configuration with the defaults will yield a similar configuration to that of the TinyTimeMixer {} architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        context_length (`int`, *optional*, defaults to 64)
+            The context/history length for the input sequence.
+        patch_length (`int`, *optional*, defaults to 8)
+            The patch length for the input sequence.
+        num_input_channels (`int`):
+            Number of input variates. For Univariate, set it to 1.
+        patch_stride (`int`, *optional*, defaults to 8):
+            Amount of points to stride. If its value is same as patch_length, we get non-overlapping patches.
+        d_model (`int`, *optional*, defaults to 16):
+            Hidden feature size of the model.
+        prediction_length (`int`, *optional*, defaults to 16)
+            Number of time steps to forecast for a forecasting task. Also known as the Forecast Horizon.
+        expansion_factor (`int`, *optional*, defaults to 2):
+            Expansion factor to use inside MLP. Recommended range is 2-5. Larger value indicates more complex model.
+        num_layers (`int`, *optional*, defaults to 3):
+            Number of layers to use. Recommended range is 3-15. Larger value indicates more complex model.
+        dropout (`float`, *optional*, defaults to 0.2):
+            The dropout probability the `TinyTimeMixer` backbone. Recommended range is 0.2-0.7
+        mode (`str`, *optional*, defaults to `"common_channel"`):
+            Mixer Mode. Determines how to process the channels. Allowed values: "common_channel", "mix_channel". In
+            "common_channel" mode, we follow Channel-independent modelling with no explicit channel-mixing. Channel
+            mixing happens in an implicit manner via shared weights across channels. (preferred first approach) In
+            "mix_channel" mode, we follow explicit channel-mixing in addition to patch and feature mixer. (preferred
+            approach when channel correlations are very important to model)
+        gated_attn (`bool`, *optional*, defaults to `True`):
+            Enable Gated Attention.
+        norm_mlp (`str`, *optional*, defaults to `"LayerNorm"`):
+            Normalization layer (BatchNorm or LayerNorm).
+        self_attn (`bool`, *optional*, defaults to `False`):
+            Enable Tiny self attention across patches. This can be enabled when the output of Vanilla TinyTimeMixer with
+            gated attention is not satisfactory. Enabling this leads to explicit pair-wise attention and modelling
+            across patches.
+        self_attn_heads (`int`, *optional*, defaults to 1):
+            Number of self-attention heads. Works only when `self_attn` is set to `True`.
+        use_positional_encoding (`bool`, *optional*, defaults to `False`):
+            Enable the use of positional embedding for the tiny self-attention layers. Works only when `self_attn` is
+            set to `True`.
+        positional_encoding_type (`str`, *optional*, defaults to `"sincos"`):
+            Positional encodings. Options `"random"` and `"sincos"` are supported. Works only when
+            `use_positional_encoding` is set to `True`
+        scaling (`string` or `bool`, *optional*, defaults to `"std"`):
+            Whether to scale the input targets via "mean" scaler, "std" scaler or no scaler if `None`. If `True`, the
+            scaler is set to "mean".
+        loss (`string`, *optional*, defaults to `"mse"`):
+            The loss function for the model. Defaults to mean squared error "mse". Allowed values: ["mse", "mae"]
+        init_std (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated normal weight initialization distribution.
+        post_init (`bool`, *optional*, defaults to `False`):
+            Whether to use custom weight initialization from `transformers` library, or the default initialization in
+            `PyTorch`. Setting it to `False` performs `PyTorch` weight initialization.
+        norm_eps (`float`, *optional*, defaults to 1e-05):
+            A value added to the denominator for numerical stability of normalization.
+        adaptive_patching_levels (`int`, *optional*, defaults to 0):
+            If adaptive_patching_levels is i, then we will have i levels with each level having n_layers.
+            Level id starts with 0. num_patches at level i will be multipled by (2^i) and num_features at level i will be divided by (2^i).
+            For Ex. if adaptive_patching_levels is 3 - then we will have 3 levels:
+                level 2: num_features//(2^2), num_patches*(2^2)
+                level 1: num_features//(2^1), num_patches*(2^1)
+                level 0: num_features//(2^0), num_patches*(2^0)
+            adaptive_patching_levels = 1 is same as one level PatchTSMixer. This module gets disabled when adaptive_patching_levels is 0 or neg value. Defaults to 0 (off mode).
+        resolution_prefix_tuning (`bool`, *optional*, defaults to `False`):
+            Enable if your dataloader has time resolution information as defined in `get_freq_mapping` function in `modelling_tinytimemixer`.
+        frequency_token_vocab_size (`int`, *optional*, defaults to 5):
+            Vocab size to use when resolution_prefix_tuning is enabled.
+        head_dropout (`float`, *optional*, defaults to 0.2):
+            The dropout probability the `TinyTimeMixer` head.
+        prediction_channel_indices (`list`, *optional*):
+            List of channel indices to forecast. If None, forecast all channels. Target data is expected to have all
+            channels and we explicitly filter the channels in prediction and target before loss computation. Please provide the indices
+            in sorted ascending order.
+        decoder_num_layers (`int`, *optional*, defaults to 8):
+            Number of layers to use in decoder
+        decoder_d_model(`int`, *optional*, defaults to 16):
+            Defines the hidden feature size of the decoder.
+        decoder_adaptive_patching_levels (`int`, *optional*, defaults to 0):
+            Adaptive Patching levels for decoder. Preferable to set it to 0 for decoder to keep it light weight.
+        decoder_raw_residual (`bool`, *optional*, defaults to `False`):
+            Flag to enable merging of raw embedding with encoder embedding for decoder input. Defaults to False.
+        decoder_mode (`string`, *optional*, defaults to `"common_channel"`):
+            Decoder channel mode. Use `"common_channel" for channel-independent modelling and `"mix_channel"` for channel-mixing modelling
+        use_decoder (`bool`, *optional*, defaults to `True`):
+            Enable to use decoder.
+        prediction_filter_length (`int`,*optional*, defaults to None):
+            Actual length in the prediction output to use for loss calculations.
+
+
+    Example:
+
+    ```python
+    >>> from transformers import TinyTimeMixerConfig, TinyTimeMixerModel
+
+    >>> # Initializing a default TinyTimeMixer configuration
+    >>> configuration = TinyTimeMixerConfig()
+
+    >>> # Randomly initializing a model (with random weights) from the configuration
+    >>> model = TinyTimeMixerModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "tinytimemixer"
+    attribute_map = {
+        "hidden_size": "d_model",
+        "num_hidden_layers": "num_layers",
+    }
+
+    def __init__(
+        self,
+        # Time series specific configuration
+        context_length: int = 64,
+        patch_length: int = 8,
+        num_input_channels: int = 1,
+        prediction_length: int = 16,
+        patch_stride: int = 8,
+        prediction_channel_indices: Optional[list] = None,
+        # General model configuration
+        d_model: int = 16,
+        expansion_factor: int = 2,
+        num_layers: int = 3,
+        dropout: float = 0.2,
+        mode: str = "common_channel",
+        gated_attn: bool = True,
+        norm_mlp: str = "LayerNorm",
+        self_attn: bool = False,
+        self_attn_heads: int = 1,
+        use_positional_encoding: bool = False,
+        positional_encoding_type: str = "sincos",
+        scaling: Optional[Union[str, bool]] = "std",
+        loss: str = "mse",
+        init_std: float = 0.02,
+        post_init: bool = False,
+        norm_eps: float = 1e-5,
+        adaptive_patching_levels: int = 0,
+        resolution_prefix_tuning: bool = False,
+        frequency_token_vocab_size: int = 5,
+        # General head configuration
+        head_dropout: float = 0.2,
+        # decoder parameters
+        decoder_num_layers: int = 8,
+        decoder_d_model: int = 8,
+        decoder_adaptive_patching_levels: int = 0,
+        decoder_raw_residual: bool = False,
+        decoder_mode: str = "common_channel",
+        use_decoder: bool = True,
+        # prediction length filtering
+        prediction_filter_length: Optional[int] = None,
+        **kwargs,
+    ):
+        self.num_input_channels = num_input_channels
+        self.context_length = context_length
+        self.patch_length = patch_length
+        self.expansion_factor = expansion_factor
+        self.num_layers = num_layers
+        self.dropout = dropout
+        self.mode = mode
+        self.gated_attn = gated_attn
+        self.norm_mlp = norm_mlp
+        self.scaling = scaling
+        self.head_dropout = head_dropout
+
+        self.patch_last = True
+        self.use_positional_encoding = use_positional_encoding
+        self.positional_encoding_type = positional_encoding_type
+        self.prediction_length = prediction_length
+        self.prediction_channel_indices = prediction_channel_indices
+        self.self_attn = self_attn
+        self.self_attn_heads = self_attn_heads
+        self.init_std = init_std
+        self.post_init = post_init
+        self.loss = loss
+        self.norm_eps = norm_eps
+
+        self.use_decoder = use_decoder
+
+        self.adaptive_patching_levels = adaptive_patching_levels
+        self.resolution_prefix_tuning = resolution_prefix_tuning
+        self.decoder_num_layers = decoder_num_layers
+        self.decoder_adaptive_patching_levels = decoder_adaptive_patching_levels
+        self.decoder_raw_residual = decoder_raw_residual
+        self.decoder_mode = decoder_mode
+        self.frequency_token_vocab_size = frequency_token_vocab_size
+        self.d_model = d_model
+        self.patch_stride = patch_stride
+        self.decoder_d_model = decoder_d_model
+        self.init_processing = False
+        self.prediction_filter_length = prediction_filter_length
+
+        super().__init__(**kwargs)
+
+    def check_and_init_preprocessing(self):
+        self.init_processing = True
+
+        if not hasattr(self, "num_patches"):
+            self.num_patches = (
+                max(self.context_length, self.patch_length) - self.patch_length
+            ) // self.patch_stride + 1
+
+            if self.resolution_prefix_tuning:
+                self.num_patches += 1
+
+        if self.prediction_filter_length is not None:
+            if self.prediction_filter_length > self.prediction_length or self.prediction_filter_length <= 0:
+                raise ValueError("prediction_filter_length should be positive and less than prediction_length")
+
+        if self.prediction_channel_indices is not None:
+            self.prediction_channel_indices.sort()

generation_config.json

@@ -0,0 +1,14 @@
+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "repetition_penalty": 1.05,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "4.52.4"
+}

modeling_qwen2.py

@@ -0,0 +1,2063 @@
+# coding=utf-8
+# The following code are reused from the QWen project (https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) of Alibaba Cloud.
+# Copyright 2024 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# 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.
+
+# The code is modified by ByteDance and Tsinghua University from the original implementation of Qwen:
+# - Support time series modality for Qwen2 model.
+
+""" PyTorch Qwen2 model."""
+import inspect
+import math
+import copy
+from typing import List, Optional, Tuple, Union, Dict, Any
+from dataclasses import dataclass
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache
+from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask, _prepare_4d_causal_attention_mask_for_sdpa
+from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
+from transformers.modeling_utils import PreTrainedModel
+from transformers import AutoConfig
+from transformers.utils import (
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    is_flash_attn_2_available,
+    is_flash_attn_greater_or_equal_2_10,
+    logging,
+    replace_return_docstrings,
+    ModelOutput
+)
+from .configuration_qwen2 import Qwen2TSConfig, TinyTimeMixerConfig
+
+# from .modeling_tinytimemixer import TinyTimeMixerForPrediction
+# from .configuration_tinytimemixer import TinyTimeMixerConfig
+
+if is_flash_attn_2_available():
+    from flash_attn import flash_attn_func, flash_attn_varlen_func
+    from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input  # noqa
+
+    _flash_supports_window_size = "window_size" in list(inspect.signature(flash_attn_func).parameters)
+
+
+logger = logging.get_logger(__name__)
+
+_CHECKPOINT_FOR_DOC = "Qwen/Qwen2-7B-beta"
+_CONFIG_FOR_DOC = "Qwen2TSConfig"
+_GLOBAL_LOG = None
+_GLOBAL_LOG_w = None
+@dataclass
+class Qwen2TSCausalLMOutputWithPast(ModelOutput):
+    """
+    Base class for Qwen2TS causal language model (or autoregressive) outputs.
+
+    Args:
+        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+            Language modeling loss (for next-token prediction).
+        logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+            `(batch_size, num_heads, sequence_length, embed_size_per_head)`)
+
+            Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
+            `past_key_values` input) to speed up sequential decoding.
+        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
+        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+            sequence_length)`.
+
+            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
+            heads.
+        attention_mask (`torch.FloatTensor`, *optional*):
+            Attentions mask, used to update attention mask and position_ids.
+    """
+
+    loss: Optional[torch.FloatTensor] = None
+    logits: torch.FloatTensor = None
+    past_key_values: Optional[List[torch.FloatTensor]] = None
+    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+    attentions: Optional[Tuple[torch.FloatTensor]] = None
+    attention_mask: Optional[torch.FloatTensor] = None
+    labels: Optional[torch.LongTensor] = None
+    new_token_positions: Optional[torch.LongTensor] = None
+import torch
+import torch.nn as nn
+
+class RelativeSmoothL1Loss(nn.Module):
+    def __init__(self, beta=1.0, eps=1e-6, reduction='mean'):
+        super().__init__()
+        self.beta = beta
+        self.eps = eps
+        self.reduction = reduction
+
+    def forward(self, pred, target):
+        rel_diff = torch.abs(pred - target) / (torch.abs(target) + self.eps)
+        loss = torch.where(
+            rel_diff < self.beta,
+            0.5 * (rel_diff ** 2) / self.beta,
+            rel_diff - 0.5 * self.beta
+        )
+        if self.reduction == 'sum':
+            return loss.sum()
+        elif self.reduction == 'none':
+            return loss
+        else:
+            return loss.mean()
+class RelativeSquaredErrorLoss(nn.Module):
+    def __init__(self, eps=1e-8, reduction='mean'):
+        super().__init__()
+        self.eps = eps
+        self.reduction = reduction
+
+    def forward(self, pred, target):
+        pred = pred.to(target.dtype)
+        
+        numerator = torch.sum((pred - target) ** 2, dim=-1)
+        denominator = torch.sum((target - target.mean(dim=-1, keepdim=True)) ** 2, dim=-1) + self.eps
+        
+        rse = torch.sqrt(numerator / denominator)
+
+        if self.reduction == 'sum':
+            return rse.sum()
+        elif self.reduction == 'none':
+            return rse
+        else:
+            return rse.mean()
+########################Naive TS Decoder2#####################
+class TimeSeriesDecoder(nn.Module):
+    def __init__(self, config):
+        super(TimeSeriesDecoder, self).__init__()
+        self.patch_size = config['patch_size']
+        self.hidden_size = config['hidden_size']
+        self.num_features = 1
+        self.dropout_rate = config.get('dropout_rate', 0.1)
+        
+        layers = []
+        input_size = self.hidden_size
+        
+        for _ in range(config['num_layers'] - 1):
+            layers.append(nn.Linear(input_size, input_size))
+            layers.append(nn.GELU())
+            layers.append(nn.Dropout(self.dropout_rate))
+        layers.append(nn.Linear(input_size, self.patch_size * 1))
+        
+        self.mlp = nn.Sequential(*layers)
+    
+    def forward(self, x: torch.Tensor, patch_cnt: list, original_lengths: torch.Tensor):
+        patches = self.mlp(x)  # (total_patch_cnt, patch_size * num_features)
+        
+        reconstructed = []
+        start_idx = 0
+        for pc in patch_cnt:
+            if pc == 0:
+                reconstructed.append(torch.zeros(0, 1, device=x.device))
+                continue
+                
+            sample_patches = patches[start_idx: start_idx+pc]
+            start_idx += pc
+            sample_ts = sample_patches.reshape(-1, 1).squeeze(-1) 
+            
+            reconstructed.append(sample_ts)
+        if start_idx != patches.shape[0]: 
+            raise ValueError(f'now_idx: {start_idx}\npatches: {patches.shape}\n ')
+        
+        return reconstructed
+
+########################Naive TS Embedding#####################
+class TimeSeriesEmbedding(nn.Module):
+    def __init__(self, config):
+        super(TimeSeriesEmbedding, self).__init__()
+        self.patch_size = config['patch_size']
+        self.num_layers = config['num_layers']
+        self.hidden_size = config['hidden_size']
+        self.num_features = config['num_features']
+        self.max_sequence_length = config['max_sequence_length']  # Maximum time series length
+        self.use_position_embedding = config.get('use_position_embedding', False)
+        self.use_position_idx = config.get('use_position_idx', False)
+        self.embedding_dim = config.get('embedding_dim', 16)  # Embedding dimension
+        
+        if self.use_position_embedding:
+            # Extended vocabulary: [0, max_sequence_length) for real positions, max_sequence_length for padding
+            self.position_embedding = nn.Embedding(self.max_sequence_length + 1, self.embedding_dim)
+            self.padding_idx = self.max_sequence_length  # Special index for padding
+            input_size = 1 * self.patch_size + self.embedding_dim * self.patch_size
+        elif self.use_position_idx:
+            input_size = 2 * self.patch_size
+        else:
+            input_size = 1 * self.patch_size
+        
+        # Build MLP layers
+        layers = []
+        for _ in range(self.num_layers - 1):
+            layers.append(nn.Linear(input_size, self.hidden_size))
+            layers.append(nn.GELU())
+            input_size = self.hidden_size
+        layers.append(nn.Linear(input_size, self.hidden_size))
+
+        self.mlp = nn.Sequential(*layers)
+
+    def forward(self, x: torch.Tensor):
+        batch_size = x.size(0)
+        x = x.reshape(batch_size, -1, self.num_features)
+
+        # Extract mask and calculate valid lengths
+        mask = x[:, :, -1].long()
+        valid_lengths = mask.sum(dim=1).long()
+        patch_cnt = (valid_lengths + self.patch_size - 1) // self.patch_size
+
+        patches_list = []
+        # Collect position indices for batch embedding lookup
+        all_position_indices = []
+        patch_info_list = []  # Store metadata for each patch group
+        
+        for i in range(batch_size):
+            vl = valid_lengths[i].item()
+            pc = patch_cnt[i].item()
+            if pc == 0:
+                continue
+            
+            # Extract time series data (excluding mask)
+            xi = x[i, :vl, :1]  # Time-series data
+            total_padded_length = pc * self.patch_size
+            padding_length = total_padded_length - vl
+            
+            # Create position indices: real positions for actual data, special index for padding
+            position_indices = torch.arange(vl, device=x.device)
+            
+            if padding_length > 0:
+                # Pad with last value
+                last_value = xi[-1:, :]
+                padding = last_value.repeat(padding_length, 1)
+                xi = torch.cat([xi, padding], dim=0)
+                
+                # Use special padding index for padding positions
+                padding_positions = torch.full((padding_length,), self.padding_idx, device=x.device)
+                position_indices = torch.cat([position_indices, padding_positions], dim=0)
+
+            # Reshape to patches
+            xi = xi.reshape(pc, self.patch_size)  # (num_patches, patch_size)
+            position_indices = position_indices.reshape(pc, self.patch_size)  # (num_patches, patch_size)
+
+            if self.use_position_embedding:
+                # Collect position indices instead of calling embedding immediately
+                all_position_indices.append(position_indices)
+                patch_info_list.append({
+                    'xi': xi,
+                    'pc': pc,
+                    'sample_idx': i
+                })
+            elif self.use_position_idx:
+                # Normalize position indices
+                pos_indices = torch.arange(vl, device=x.device).unsqueeze(1)
+                pos_indices = pos_indices / max(1, valid_lengths.max().item() - 1)
+                if padding_length > 0:
+                    # Use -1 for padding positions
+                    padding_indices = torch.full((padding_length, 1), -1, device=x.device)
+                    pos_indices = torch.cat([pos_indices, padding_indices], dim=0)
+                # Combine time series data with position indices
+                xi_combined = torch.cat([xi.reshape(-1, 1), pos_indices], dim=1)
+                patch_input = xi_combined.reshape(pc, self.patch_size * 2)
+                patches_list.append(patch_input)
+            else:
+                # No position embedding, use raw patches
+                patch_input = xi
+                patches_list.append(patch_input)
+
+        # Batch process position embeddings if needed
+        if self.use_position_embedding and all_position_indices:
+            # Concatenate all position indices for batch embedding lookup
+            batch_position_indices = torch.cat(all_position_indices, dim=0)
+            # print(f"{x.shape=}, {x.device=}, {len(all_position_indices)=}, {batch_position_indices=}")
+            batch_pos_emb = self.position_embedding(batch_position_indices)  # Single embedding call
+            
+            # Split embeddings back and create patch inputs
+            emb_start_idx = 0
+            for patch_info in patch_info_list:
+                xi = patch_info['xi']
+                pc = patch_info['pc']
+                
+                # Extract corresponding embeddings
+                pos_emb = batch_pos_emb[emb_start_idx:emb_start_idx + pc]
+                emb_start_idx += pc
+                
+                # Flatten and concatenate
+                xi = xi.unsqueeze(-1)  # (num_patches, patch_size, 1)
+                patch_input = torch.cat([
+                    xi.flatten(1),  # (num_patches, patch_size)
+                    pos_emb.flatten(1)  # (num_patches, patch_size * embedding_dim)
+                ], dim=1)
+                patches_list.append(patch_input)
+
+        # Process all patches through MLP
+        if patches_list:
+            x_patches = torch.cat(patches_list, dim=0).to(dtype=next(self.mlp.parameters()).dtype)
+            x = self.mlp(x_patches)
+        else:
+            # Handle empty case
+            x = torch.empty(0, self.hidden_size, device=x.device)
+
+        return x, patch_cnt
+
+
+########################QWEN2###################################
+# Copied from transformers.models.llama.modeling_llama._get_unpad_data
+def _get_unpad_data(attention_mask):
+    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
+    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
+    max_seqlen_in_batch = seqlens_in_batch.max().item()
+    cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
+    return (
+        indices,
+        cu_seqlens,
+        max_seqlen_in_batch,
+    )
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->Qwen2
+class Qwen2RMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        Qwen2RMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+
+# Copied from transformers.models.mistral.modeling_mistral.MistralRotaryEmbedding with Mistral->Qwen2
+class Qwen2RotaryEmbedding(nn.Module):
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
+        super().__init__()
+
+        self.dim = dim
+        self.max_position_embeddings = max_position_embeddings
+        self.base = base
+        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+        # Build here to make `torch.jit.trace` work.
+        self._set_cos_sin_cache(
+            seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
+        )
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(self.inv_freq)
+
+        freqs = torch.outer(t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+    def forward(self, x, seq_len=None):
+        # x: [bs, num_attention_heads, seq_len, head_size]
+        if seq_len > self.max_seq_len_cached:
+            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
+
+        return (
+            self.cos_cached[:seq_len].to(dtype=x.dtype),
+            self.sin_cached[:seq_len].to(dtype=x.dtype),
+        )
+
+
+# Copied from transformers.models.llama.modeling_llama.rotate_half
+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)
+
+
+# Copied from transformers.models.mistral.modeling_mistral.apply_rotary_pos_emb
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
+    """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`):
+            The position indices of the tokens corresponding to the query and key tensors. For example, this can be
+            used to pass offsetted position ids when working with a KV-cache.
+        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[position_ids].unsqueeze(unsqueeze_dim)
+    sin = sin[position_ids].unsqueeze(unsqueeze_dim)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+# Copied from transformers.models.mistral.modeling_mistral.MistralMLP with Mistral->Qwen2
+class Qwen2MLP(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):
+        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+
+
+# Copied from transformers.models.llama.modeling_llama.repeat_kv
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+class Qwen2Attention(nn.Module):
+    """
+    Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer
+    and "Generating Long Sequences with Sparse Transformers".
+    """
+
+    def __init__(self, config: Qwen2TSConfig, layer_idx: Optional[int] = None):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        if layer_idx is None:
+            logger.warning_once(
+                f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
+                "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
+                "when creating this class."
+            )
+
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.hidden_size // self.num_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.max_position_embeddings = config.max_position_embeddings
+        self.rope_theta = config.rope_theta
+        self.is_causal = True
+        self.attention_dropout = config.attention_dropout
+
+        if (self.head_dim * self.num_heads) != self.hidden_size:
+            raise ValueError(
+                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+                f" and `num_heads`: {self.num_heads})."
+            )
+        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=True)
+        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
+        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
+        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
+
+        self.rotary_emb = Qwen2RotaryEmbedding(
+            self.head_dim,
+            max_position_embeddings=self.max_position_embeddings,
+            base=self.rope_theta,
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            if self.layer_idx is None:
+                raise ValueError(
+                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
+                    "with a layer index."
+                )
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+        if past_key_value is not None:
+            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        # repeat k/v heads if n_kv_heads < n_heads
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+            raise ValueError(
+                f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
+                f" {attn_weights.size()}"
+            )
+
+        if attention_mask is not None:
+            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+                raise ValueError(
+                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+                )
+
+            attn_weights = attn_weights + attention_mask
+
+        # upcast attention to fp32
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+        attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+                f" {attn_output.size()}"
+            )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+class Qwen2FlashAttention2(Qwen2Attention):
+    """
+    Qwen2 flash attention module, following Qwen2 attention module. This module inherits from `Qwen2Attention`
+    as the weights of the module stays untouched. The only required change would be on the forward pass
+    where it needs to correctly call the public API of flash attention and deal with padding tokens
+    in case the input contains any of them. Additionally, for sliding window attention, we apply SWA only to the bottom
+    config.max_window_layers layers.
+    """
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
+        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
+        # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
+        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+    ):
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            if self.layer_idx is None:
+                raise ValueError(
+                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
+                    "with a layer index."
+                )
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+
+        # Because the input can be padded, the absolute sequence length depends on the max position id.
+        rotary_seq_len = max(kv_seq_len, position_ids[:, -1].max().item()) + 1
+        cos, sin = self.rotary_emb(value_states, seq_len=rotary_seq_len)
+
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+        use_sliding_windows = (
+            _flash_supports_window_size
+            and getattr(self.config, "sliding_window", None) is not None
+            and kv_seq_len > self.config.sliding_window
+            and self.config.use_sliding_window
+        )
+
+        if not _flash_supports_window_size:
+            logger.warning_once(
+                "The current flash attention version does not support sliding window attention, for a more memory efficient implementation"
+                " make sure to upgrade flash-attn library."
+            )
+
+        if past_key_value is not None:
+            # Activate slicing cache only if the config has a value `sliding_windows` attribute
+            cache_has_contents = past_key_value.get_seq_length(self.layer_idx) > 0
+            if (
+                getattr(self.config, "sliding_window", None) is not None
+                and kv_seq_len > self.config.sliding_window
+                and cache_has_contents
+            ):
+                slicing_tokens = 1 - self.config.sliding_window
+
+                past_key = past_key_value[self.layer_idx][0]
+                past_value = past_key_value[self.layer_idx][1]
+
+                past_key = past_key[:, :, slicing_tokens:, :].contiguous()
+                past_value = past_value[:, :, slicing_tokens:, :].contiguous()
+
+                if past_key.shape[-2] != self.config.sliding_window - 1:
+                    raise ValueError(
+                        f"past key must have a shape of (`batch_size, num_heads, self.config.sliding_window-1, head_dim`), got"
+                        f" {past_key.shape}"
+                    )
+
+                if attention_mask is not None:
+                    attention_mask = attention_mask[:, slicing_tokens:]
+                    attention_mask = torch.cat([attention_mask, torch.ones_like(attention_mask[:, -1:])], dim=-1)
+
+            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        # repeat k/v heads if n_kv_heads < n_heads
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+        dropout_rate = 0.0 if not self.training else self.attention_dropout
+
+        # In PEFT, usually we cast the layer norms in float32 for training stability reasons
+        # therefore the input hidden states gets silently casted in float32. Hence, we need
+        # cast them back in float16 just to be sure everything works as expected.
+        input_dtype = query_states.dtype
+        if input_dtype == torch.float32:
+            if torch.is_autocast_enabled():
+                target_dtype = torch.get_autocast_gpu_dtype()
+            # Handle the case where the model is quantized
+            elif hasattr(self.config, "_pre_quantization_dtype"):
+                target_dtype = self.config._pre_quantization_dtype
+            else:
+                target_dtype = self.q_proj.weight.dtype
+
+            logger.warning_once(
+                f"The input hidden states seems to be silently casted in float32, this might be related to"
+                f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
+                f" {target_dtype}."
+            )
+
+            query_states = query_states.to(target_dtype)
+            key_states = key_states.to(target_dtype)
+            value_states = value_states.to(target_dtype)
+
+        # Reashape to the expected shape for Flash Attention
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        attn_output = self._flash_attention_forward(
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            q_len,
+            dropout=dropout_rate,
+            use_sliding_windows=use_sliding_windows,
+        )
+
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+    def _flash_attention_forward(
+        self,
+        query_states,
+        key_states,
+        value_states,
+        attention_mask,
+        query_length,
+        dropout=0.0,
+        softmax_scale=None,
+        use_sliding_windows=False,
+    ):
+        """
+        Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
+        first unpad the input, then computes the attention scores and pad the final attention scores.
+
+        Args:
+            query_states (`torch.Tensor`):
+                Input query states to be passed to Flash Attention API
+            key_states (`torch.Tensor`):
+                Input key states to be passed to Flash Attention API
+            value_states (`torch.Tensor`):
+                Input value states to be passed to Flash Attention API
+            attention_mask (`torch.Tensor`):
+                The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
+                position of padding tokens and 1 for the position of non-padding tokens.
+            dropout (`float`):
+                Attention dropout
+            softmax_scale (`float`, *optional*):
+                The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
+            use_sliding_windows (`bool`, *optional*):
+                Whether to activate sliding window attention.
+        """
+        if not self._flash_attn_uses_top_left_mask:
+            causal = self.is_causal
+        else:
+            # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__.
+            causal = self.is_causal and query_length != 1
+
+        # Decide whether to use SWA or not by layer index.
+        if use_sliding_windows and self.layer_idx >= self.config.max_window_layers:
+            use_sliding_windows = False
+
+        # Contains at least one padding token in the sequence
+        if attention_mask is not None:
+            batch_size = query_states.shape[0]
+            query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
+                query_states, key_states, value_states, attention_mask, query_length
+            )
+
+            cu_seqlens_q, cu_seqlens_k = cu_seq_lens
+            max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
+
+            if not use_sliding_windows:
+                attn_output_unpad = flash_attn_varlen_func(
+                    query_states,
+                    key_states,
+                    value_states,
+                    cu_seqlens_q=cu_seqlens_q,
+                    cu_seqlens_k=cu_seqlens_k,
+                    max_seqlen_q=max_seqlen_in_batch_q,
+                    max_seqlen_k=max_seqlen_in_batch_k,
+                    dropout_p=dropout,
+                    softmax_scale=softmax_scale,
+                    causal=causal,
+                )
+            else:
+                attn_output_unpad = flash_attn_varlen_func(
+                    query_states,
+                    key_states,
+                    value_states,
+                    cu_seqlens_q=cu_seqlens_q,
+                    cu_seqlens_k=cu_seqlens_k,
+                    max_seqlen_q=max_seqlen_in_batch_q,
+                    max_seqlen_k=max_seqlen_in_batch_k,
+                    dropout_p=dropout,
+                    softmax_scale=softmax_scale,
+                    causal=causal,
+                    window_size=(self.config.sliding_window, self.config.sliding_window),
+                )
+
+            attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
+        else:
+            if not use_sliding_windows:
+                attn_output = flash_attn_func(
+                    query_states,
+                    key_states,
+                    value_states,
+                    dropout,
+                    softmax_scale=softmax_scale,
+                    causal=causal,
+                )
+            else:
+                attn_output = flash_attn_func(
+                    query_states,
+                    key_states,
+                    value_states,
+                    dropout,
+                    softmax_scale=softmax_scale,
+                    causal=causal,
+                    window_size=(self.config.sliding_window, self.config.sliding_window),
+                )
+
+        return attn_output
+
+    # Copied from transformers.models.mistral.modeling_mistral.MistralFlashAttention2._upad_input
+    def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
+        batch_size, kv_seq_len, num_heads, head_dim = key_layer.shape
+
+        # On the first iteration we need to properly re-create the padding mask
+        # by slicing it on the proper place
+        if kv_seq_len != attention_mask.shape[-1]:
+            attention_mask_num_tokens = attention_mask.shape[-1]
+            attention_mask = attention_mask[:, attention_mask_num_tokens - kv_seq_len :]
+
+        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
+
+        key_layer = index_first_axis(key_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k)
+        value_layer = index_first_axis(value_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k)
+
+        if query_length == kv_seq_len:
+            query_layer = index_first_axis(
+                query_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k
+            )
+            cu_seqlens_q = cu_seqlens_k
+            max_seqlen_in_batch_q = max_seqlen_in_batch_k
+            indices_q = indices_k
+        elif query_length == 1:
+            max_seqlen_in_batch_q = 1
+            cu_seqlens_q = torch.arange(
+                batch_size + 1, dtype=torch.int32, device=query_layer.device
+            )  # There is a memcpy here, that is very bad.
+            indices_q = cu_seqlens_q[:-1]
+            query_layer = query_layer.squeeze(1)
+        else:
+            # The -q_len: slice assumes left padding.
+            attention_mask = attention_mask[:, -query_length:]
+            query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
+
+        return (
+            query_layer,
+            key_layer,
+            value_layer,
+            indices_q,
+            (cu_seqlens_q, cu_seqlens_k),
+            (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
+        )
+
+
+# Copied from transformers.models.mistral.modeling_mistral.MistralSdpaAttention with Mistral->Qwen2
+class Qwen2SdpaAttention(Qwen2Attention):
+    """
+    Qwen2 attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
+    `Qwen2Attention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
+    SDPA API.
+    """
+
+    # Adapted from Qwen2Attention.forward
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        if output_attentions:
+            # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
+            logger.warning_once(
+                "Qwen2Model is using Qwen2SdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
+                'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
+            )
+            return super().forward(
+                hidden_states=hidden_states,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_value=past_key_value,
+                output_attentions=output_attentions,
+                use_cache=use_cache,
+            )
+
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+        if past_key_value is not None:
+            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        if attention_mask is not None:
+            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+                raise ValueError(
+                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+                )
+
+        # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
+        # Reference: https://github.com/pytorch/pytorch/issues/112577.
+        if query_states.device.type == "cuda" and attention_mask is not None:
+            query_states = query_states.contiguous()
+            key_states = key_states.contiguous()
+            value_states = value_states.contiguous()
+
+        attn_output = torch.nn.functional.scaled_dot_product_attention(
+            query_states,
+            key_states,
+            value_states,
+            attn_mask=attention_mask,
+            dropout_p=self.attention_dropout if self.training else 0.0,
+            # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.
+            is_causal=self.is_causal and attention_mask is None and q_len > 1,
+        )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.view(bsz, q_len, self.hidden_size)
+
+        attn_output = self.o_proj(attn_output)
+
+        return attn_output, None, past_key_value
+
+
+QWEN2_ATTENTION_CLASSES = {
+    "eager": Qwen2Attention,
+    "flash_attention_2": Qwen2FlashAttention2,
+    "sdpa": Qwen2SdpaAttention,
+}
+
+
+class Qwen2DecoderLayer(nn.Module):
+    def __init__(self, config: Qwen2TSConfig, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+
+        if config.use_sliding_window and config._attn_implementation != "flash_attention_2":
+            logger.warning_once(
+                f"Sliding Window Attention is enabled but not implemented for `{config._attn_implementation}`; "
+                "unexpected results may be encountered."
+            )
+        self.self_attn = QWEN2_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx)
+
+        self.mlp = Qwen2MLP(config)
+        self.input_layernorm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+            attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
+                `(batch, sequence_length)` where padding elements are indicated by 0.
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            use_cache (`bool`, *optional*):
+                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+                (see `past_key_values`).
+            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+        """
+
+        residual = hidden_states
+
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights, present_key_value = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        return outputs
+
+
+QWEN2_START_DOCSTRING = r"""
+    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+
+    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+    and behavior.
+
+    Parameters:
+        config ([`Qwen2TSConfig`]):
+            Model configuration class with all the parameters of the model. Initializing with a config file does not
+            load the weights associated with the model, only the configuration. Check out the
+            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+
+@add_start_docstrings(
+    "The bare Qwen2 Model outputting raw hidden-states without any specific head on top.",
+    QWEN2_START_DOCSTRING,
+)
+class Qwen2PreTrainedModel(PreTrainedModel):
+    config_class = Qwen2TSConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["Qwen2DecoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+    _supports_cache_class = True
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+
+class TSProjector(nn.Module):
+    def __init__(self, config: Qwen2TSConfig):
+        super().__init__()
+        self.config = config
+        self.linear_1 = nn.Linear(config.ts['d_model'], config.hidden_size, bias=True)
+        self.linear_2 = nn.LayerNorm(config.hidden_size, bias=True)
+        self.linear_3 = nn.Linear(config.hidden_size, config.hidden_size * 4, bias=True)
+        self.linear_4 = nn.LayerNorm(config.hidden_size * 4, bias=True)
+        self.act = nn.GELU()
+
+    def forward(self, ts_features):
+        hidden_states = self.linear_1(ts_features)
+        hidden_states = self.linear_2(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states = self.linear_3(hidden_states)
+        hidden_states = self.linear_4(hidden_states)
+        hidden_states = hidden_states.reshape(hidden_states.size(0), -1, self.config.hidden_size)
+        return hidden_states
+
+
+QWEN2_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+            it.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
+            `past_key_values`).
+
+            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+            information on the default strategy.
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.n_positions - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
+            Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+            blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
+            returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
+
+            Two formats are allowed:
+            - a [`~cache_utils.Cache`] instance;
+            - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
+            shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
+            cache format.
+
+            The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
+            legacy cache format will be returned.
+
+            If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
+            have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
+            of shape `(batch_size, sequence_length)`.
+        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+            model's internal embedding lookup matrix.
+        use_cache (`bool`, *optional*):
+            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+            `past_key_values`).
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+@add_start_docstrings(
+    "The bare Qwen2 Model outputting raw hidden-states without any specific head on top.",
+    QWEN2_START_DOCSTRING,
+)
+class Qwen2Model(Qwen2PreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Qwen2DecoderLayer`]
+
+    Args:
+        config: Qwen2TSConfig
+    """
+
+    def __init__(self, config: Qwen2TSConfig):
+        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(
+            [Qwen2DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self._attn_implementation = config._attn_implementation
+        self.norm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        self.gradient_checkpointing = False
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+
+    @add_start_docstrings_to_model_forward(QWEN2_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # retrieve input_ids and inputs_embeds
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
+        elif input_ids is not None:
+            batch_size, seq_length = input_ids.shape
+        elif inputs_embeds is not None:
+            batch_size, seq_length, _ = inputs_embeds.shape
+        else:
+            raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
+
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                logger.warning_once(
+                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+                )
+                use_cache = False
+
+        past_key_values_length = 0
+
+        if use_cache:
+            use_legacy_cache = not isinstance(past_key_values, Cache)
+            if use_legacy_cache:
+                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+            past_key_values_length = past_key_values.get_usable_length(seq_length)
+
+        if position_ids is None:
+            device = input_ids.device if input_ids is not None else inputs_embeds.device
+            position_ids = torch.arange(
+                past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
+            )
+            position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
+        else:
+            position_ids = position_ids.view(-1, seq_length).long()
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        if attention_mask is not None and self._attn_implementation == "flash_attention_2" and use_cache:
+            is_padding_right = attention_mask[:, -1].sum().item() != batch_size
+            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 Qwen2. Make sure to "
+                    " call `tokenizer.padding_side  = 'left'` before tokenizing the input. "
+                )
+
+        if self._attn_implementation == "flash_attention_2":
+            # 2d mask is passed through the layers
+            attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
+        elif self._attn_implementation == "sdpa" and not output_attentions:
+            # output_attentions=True can not be supported when using SDPA, and we fall back on
+            # the manual implementation that requires a 4D causal mask in all cases.
+            attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
+                attention_mask,
+                (batch_size, seq_length),
+                inputs_embeds,
+                past_key_values_length,
+                sliding_window=self.config.sliding_window,
+            )
+        else:
+            # 4d mask is passed through the layers
+            attention_mask = _prepare_4d_causal_attention_mask(
+                attention_mask,
+                (batch_size, seq_length),
+                inputs_embeds,
+                past_key_values_length,
+                sliding_window=self.config.sliding_window,
+            )
+
+        hidden_states = inputs_embeds
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = None
+
+        for decoder_layer in self.layers:
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    decoder_layer.__call__,
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    past_key_values,
+                    output_attentions,
+                    use_cache,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_values,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                next_decoder_cache = layer_outputs[2 if output_attentions else 1]
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        next_cache = None
+        if use_cache:
+            next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
+
+        if not return_dict:
+            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+
+class Qwen2TSForCausalLM(Qwen2PreTrainedModel):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.config = config
+        
+        self.model = Qwen2Model(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        self.ts_loss_func = RelativeSquaredErrorLoss()
+        # TS embedding
+        self.ts_encoder = TimeSeriesEmbedding(config.ts)
+        self.ts_decoder = TimeSeriesDecoder(config.ts)
+
+        # 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 _merge_input_ids_with_time_series_features(
+    self, time_series_features, inputs_embeds, input_ids, attention_mask, labels, patch_cnt
+):
+        batch_size, sequence_length = input_ids.shape
+        _left_padding = torch.any(attention_mask[:, 0] == 0)
+        _right_padding = torch.any(attention_mask[:, -1] == 0)
+        left_padding = False
+        if batch_size > 1:
+            if _left_padding and not _right_padding:
+                left_padding = True
+            elif not _left_padding and _right_padding:
+                left_padding = False
+            elif not _left_padding and not _right_padding:
+                left_padding = False
+            else:
+                raise ValueError(f"both side of attention_mask has zero, invalid. {attention_mask}")
+        else:
+            if _left_padding and not _right_padding:
+                left_padding = True
+            else:
+                left_padding = False
+
+        # 1. Create a mask to know where special time series tokens are
+        special_ts_token_mask_start = input_ids == self.config.ts_token_start_index
+        special_ts_token_mask_end = input_ids == self.config.ts_token_end_index
+        special_ts_token_mask = special_ts_token_mask_start | special_ts_token_mask_end
+
+        # 2. Calculate patch count
+        num_special_ts_tokens = torch.sum(special_ts_token_mask_start, dim=-1)
+        total_time_steps, embed_dim = time_series_features.shape
+
+        # Correctly calculate the total number of patches per batch
+        patch_index = 0
+        num_total_patches = torch.zeros(batch_size, dtype=patch_cnt.dtype, device=patch_cnt.device)
+        special_ts_token_mask_start_nonzero = special_ts_token_mask_start.nonzero()
+        special_ts_token_mask_start_with_size = special_ts_token_mask_start.clone().long()
+
+        attn_mask_cnt = attention_mask.sum(dim=-1)
+        for i in range(batch_size):
+            num_ts_in_batch = num_special_ts_tokens[i]
+            num_total_patches[i] = patch_cnt[patch_index : patch_index + num_ts_in_batch].sum() - 2 * num_ts_in_batch
+            for idx in range(patch_index, patch_index + num_ts_in_batch):
+                b_idx, pos = special_ts_token_mask_start_nonzero[idx]
+                special_ts_token_mask_start_with_size[b_idx, pos] *= (patch_cnt[idx].item() - 2)
+            patch_index += num_ts_in_batch
+            attn_mask_cnt[i] += num_total_patches[i].item()
+
+        # 3. Embeding length
+        max_embed_dim = sequence_length + num_total_patches.max()
+
+        # 4. Non ts tokens
+        batch_indices, non_ts_indices = torch.where(~special_ts_token_mask)
+        attn_batch_indices, attn_indices = torch.where(attention_mask == 1)
+
+        # 5. Text token in final text positions
+        new_token_positions = torch.cumsum((special_ts_token_mask_start_with_size + 1), dim=-1) - 1
+
+        # nb_ts_pad
+        nb_ts_pad = max_embed_dim - 1 - new_token_positions[:, -1]
+        if left_padding:
+            new_token_positions += nb_ts_pad[:, None]
+
+        text_to_overwrite = new_token_positions[batch_indices, non_ts_indices]
+
+        # 6. Final embedding and attention masks
+        final_embedding = torch.zeros(
+            batch_size, max_embed_dim, embed_dim, dtype=inputs_embeds.dtype, device=inputs_embeds.device
+        )
+
+        final_attention_mask = torch.zeros(batch_size, max_embed_dim, dtype=attention_mask.dtype, device=inputs_embeds.device)
+        for i in range(attention_mask.size(0)):
+            if left_padding:
+                final_attention_mask[i, max_embed_dim - attn_mask_cnt[i] :] = 1
+            else:
+                final_attention_mask[i, : attn_mask_cnt[i]] = 1
+
+        final_labels = None
+        if labels is not None:
+            final_labels = torch.full(
+                (batch_size, max_embed_dim), self.config.ignore_index, dtype=input_ids.dtype, device=input_ids.device
+            )
+
+        target_device = inputs_embeds.device
+        batch_indices, non_ts_indices, text_to_overwrite = (
+            batch_indices.to(target_device),
+            non_ts_indices.to(target_device),
+            text_to_overwrite.to(target_device),
+        )
+
+        # 7. Move embedding and labels to final positions
+        final_embedding[batch_indices, text_to_overwrite] = inputs_embeds[batch_indices, non_ts_indices]
+        if labels is not None:
+            final_labels[batch_indices, text_to_overwrite] = labels[batch_indices, non_ts_indices]
+
+        # 8. Move time series to final positions
+        ts_to_overwrite = torch.full(
+            (batch_size, max_embed_dim), True, dtype=torch.bool, device=inputs_embeds.device
+        )
+        ts_to_overwrite[batch_indices, text_to_overwrite] = False
+
+        reversed_cumsum = ts_to_overwrite.flip(dims=[-1]).cumsum(-1).flip(dims=[-1]) - 1
+        ts_to_overwrite &= reversed_cumsum >= nb_ts_pad[:, None].to(target_device)
+
+        # Check that the number of time series tokens is correct
+        if ts_to_overwrite.sum() != time_series_features.shape[:-1].numel():
+            raise ValueError(
+                f"The input provided to the model are wrong. The number of time series tokens is {torch.sum(special_ts_token_mask_start)} while"
+                f" the number of time series given to the model is {len(patch_cnt)}. This prevents correct indexing and breaks batch generation."
+            )
+        final_embedding[ts_to_overwrite] = time_series_features.contiguous().reshape(-1, embed_dim).to(target_device)
+        position_ids = (final_attention_mask.cumsum(-1) - 1).masked_fill_((final_attention_mask == 0), 1)
+        if position_ids.size(-1) < input_ids.size(-1):
+            position_ids = position_ids[:, -input_ids.size(-1) :]
+
+        # 10. Move attention mask to final positions
+        pad_batch_indices, pad_indices = torch.where(input_ids == self.config.pad_token_id)
+        if len(pad_batch_indices) > 0:
+            indices_to_mask = new_token_positions[pad_batch_indices, pad_indices]
+            final_embedding[pad_batch_indices, indices_to_mask] = 0
+        max_extra_pad = None
+        input_end_pos = None
+        train_start_pos = None
+        if final_labels is not None:
+            # 1. 识别关键位置
+            # 输入内容结束位置（每个样本有效输入的长度）
+            #logger_zr(f'{"-"*10}\n attention_mask {final_attention_mask} \n final_labels {final_labels}\n{"-"*10}')
+            # 训练标注开始位置（每个样本第一个非ignore_index的位置）
+            train_start_pos = torch.argmax((final_labels != self.config.ignore_index).int(), dim=1)
+            use_bos = hasattr(self.config, 'bos_token_id') and self.config.bos_token_id is not None
+            offset = 1  # 在BOS之后进行添加
+            
+            # 计算输入结束位置（训练开始位置 - 偏移量）
+            input_end_pos = train_start_pos - offset
+            # 处理无训练标注的样本
+            has_trainable = (final_labels != self.config.ignore_index).any(dim=1)
+            train_start_pos = torch.where(has_trainable, train_start_pos, input_end_pos)  
+            extra_pad_counts = []
+            patch_idx = 0
+            for i in range(batch_size):
+                num_ts = num_special_ts_tokens[i]
+                total_patch = patch_cnt[patch_idx:patch_idx+num_ts].sum().item()
+                extra_pad_counts.append(total_patch)
+                patch_idx += num_ts
+            extra_pad_counts = torch.tensor(extra_pad_counts, device=labels.device)
+            max_extra_pad = extra_pad_counts.max()
+            new_max_embed_dim = max_embed_dim + max_extra_pad  
+            new_final_embedding = torch.zeros(
+                batch_size, new_max_embed_dim, embed_dim,
+                dtype=inputs_embeds.dtype, device=inputs_embeds.device
+            )
+            new_final_attention_mask = torch.ones(
+                batch_size, new_max_embed_dim,
+                dtype=attention_mask.dtype, device=inputs_embeds.device
+            )
+            new_final_labels = torch.full(
+                (batch_size, new_max_embed_dim), self.config.ignore_index,
+                dtype=labels.dtype, device=labels.device
+            )
+
+            # 4. 填充新张量（核心逻辑：在输入后、标注前插入padding）
+            for i in range(batch_size):
+                pad_count = extra_pad_counts[i]
+                input_end = input_end_pos[i].item()  # 输入内容结束位置
+                train_start = train_start_pos[i].item()  # 训练标注开始位置
+                
+                # 校验位置合理性（输入结束 <= 标注开始）
+                if input_end > train_start:
+                    raise ValueError(f"样本 {i} 输入结束位置({input_end})大于标注开始位置({train_start})，无法插入padding")
+
+                # 复制输入内容（[0, input_end)）
+                new_final_embedding[i, :input_end] = final_embedding[i, :input_end]
+                new_final_attention_mask[i, :input_end] = final_attention_mask[i, :input_end]
+                
+                # 插入额外padding（[input_end, input_end+pad_count)）
+                # embedding保持0，attention_mask设为0（不参与注意力），labels设为ignore_index（不参与loss）
+                
+                # 复制输入与标注之间的非训练区域（[input_end, train_start)）
+                non_train_length = train_start - input_end
+                if non_train_length > 0:
+                    new_final_embedding[i, input_end+pad_count : input_end+pad_count+non_train_length] = \
+                        final_embedding[i, input_end : train_start]
+                    new_final_attention_mask[i, input_end+pad_count : input_end+pad_count+non_train_length] = \
+                        final_attention_mask[i, input_end : train_start]
+                    new_final_labels[i, input_end+pad_count : input_end+pad_count+non_train_length] = \
+                        final_labels[i, input_end : train_start]
+                
+                # 复制训练标注内容（[train_start, ...)）
+                train_content_length = max_embed_dim - train_start
+                new_final_embedding[i, input_end+pad_count+non_train_length : input_end+pad_count+non_train_length+train_content_length] = \
+                    final_embedding[i, train_start : ]
+                new_final_attention_mask[i, input_end+pad_count+non_train_length : input_end+pad_count+non_train_length+train_content_length] = \
+                    final_attention_mask[i, train_start : ]
+                new_final_labels[i, input_end+pad_count+non_train_length : input_end+pad_count+non_train_length+train_content_length] = \
+                    final_labels[i, train_start : ]
+
+            # 5. 更新变量
+            final_embedding = new_final_embedding
+            final_attention_mask = new_final_attention_mask
+            final_labels = new_final_labels
+            max_embed_dim = new_max_embed_dim
+            
+        position_ids = (final_attention_mask.cumsum(-1) - 1).masked_fill_((final_attention_mask == 0), 1)
+        if position_ids.size(-1) < input_ids.size(-1):
+            position_ids = position_ids[:, -input_ids.size(-1) :]
+
+        expanded_new_token_positions = torch.full(
+            (batch_size, max_embed_dim),
+            -1,
+            dtype=new_token_positions.dtype,
+            device=new_token_positions.device
+        )
+
+        expanded_new_token_positions[batch_indices, text_to_overwrite] = \
+            new_token_positions[batch_indices, non_ts_indices]
+        
+        new_token_positions = expanded_new_token_positions.masked_fill(final_attention_mask == 0, -1)
+        return final_embedding, final_attention_mask, position_ids, final_labels, new_token_positions, max_extra_pad, train_start_pos, input_end_pos
+
+    @add_start_docstrings_to_model_forward(QWEN2_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        timeseries: torch.FloatTensor = None,
+        target_timeseries: torch.FloatTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        r"""
+        Args:
+            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]`.
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, Qwen2ForCausalLM
+
+        >>> model = Qwen2ForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
+        >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
+
+        >>> 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."
+        ```"""
+
+        # if input_ids is not None and timeseries is not None:
+        #     # Batch decode the input
+        #     input_text = self.tokenizer.batch_decode(input_ids, skip_special_tokens=False)
+        #     # Print the input text
+        #     print("=================================================================")
+        #     print("Input text:", input_text)
+        #     print("Timeseries shape:", timeseries.shape)
+        #     print("=================================================================\n\n")
+        # else:
+        #     print("Time series is None!!!!")        
+        
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        original_lengths = None
+        if timeseries is not None:
+            original_lengths = timeseries[:, :, -1].long().sum(dim=1)  # (batch_size,)
+        if inputs_embeds is None:
+            inputs_embeds = self.get_input_embeddings()(input_ids)
+
+            if timeseries is not None and timeseries.shape[0] > 0:
+                # use_cache = False
+                # print(f"timeseries shape: {timeseries.shape=}, {input_ids.shape=}")
+                ts_features, patch_cnt = self.ts_encoder(timeseries)
+                inputs_embeds = inputs_embeds.to(ts_features.dtype)
+
+                inputs_embeds, attention_mask, position_ids, labels, new_token_positions, max_extra_pad, _train_start_pos, _input_end_pos = self._merge_input_ids_with_time_series_features(
+                    ts_features, inputs_embeds, input_ids, attention_mask, labels, patch_cnt
+                )
+                # print(f"{inputs_embeds.shape=}, {attention_mask.shape=}, {position_ids.shape=}, {labels.shape=}")
+
+        outputs = self.model(
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        hidden_states = outputs[0]
+        logits = self.lm_head(hidden_states)
+        logits = logits.float()
+        ts_loss = None
+        loss = None
+        if labels is not None:
+            special_ts_token_mask_start = input_ids == self.config.ts_token_start_index
+            num_special_ts_tokens = torch.sum(special_ts_token_mask_start, dim=-1)
+            batch_size = labels.size(0)
+            total_ts_tokens = sum(patch_cnt) if timeseries is not None else 0
+            #logger_zr(f'total_ts_tokens {total_ts_tokens}')
+            if total_ts_tokens > 0:
+                ts_hidden = []
+                current_idx = 0
+                end_pos = torch.argmax((labels != self.config.ignore_index).int(), dim=1)
+                offset = 1
+                for i in range(batch_size):
+                    num_ts_in_batch = num_special_ts_tokens[i]
+                    start_idx = _train_start_pos[i]
+                    # logger_zr(f'check input_end_pos start_idx {start_idx} - {_input_end_pos[i]}')
+                    for k in range(num_ts_in_batch):
+                        pc = patch_cnt[current_idx]
+                        current_idx += 1                        
+                        if pc > 0:
+                            ts_hidden.append(hidden_states[i, start_idx:start_idx+pc]) 
+                            start_idx += pc
+
+                    # logger_zr(f'check input_end_pos end_idx {start_idx} - {end_pos[i]} - {max_extra_pad}')
+
+                ts_hidden = torch.cat(ts_hidden, dim=0) if ts_hidden else None
+                
+                if ts_hidden is not None and original_lengths is not None:
+                    reconstructed = self.ts_decoder(ts_hidden, patch_cnt, original_lengths)
+                    
+                    targets = []
+                    if target_timeseries is None:
+                        # raise ValueError('target_timeseries is None')
+                        ts = timeseries.reshape(timeseries.shape[0], -1, self.config.ts['num_features'])
+                    else:
+                        # change target_timeseries  from 'list' to 'tensor'
+                        ts = [torch.tensor(t, device=ts_hidden.device, dtype=reconstructed[0].dtype) for t in target_timeseries]
+                    if type(ts) == list:
+                        tslen=len(ts)
+                    else :
+                        tslen=ts.size(0)
+                    for i in range(tslen):
+                        #pre_shape = ts[i].shape[0]
+                        if target_timeseries is None:
+                            targets.append(ts[i, :reconstructed[i].shape[0], 0])
+                        else :
+                            targets.append(ts[i][:reconstructed[i].shape[0]])
+                        
+                        reconstructed[i] = reconstructed[i][: ts[i].shape[0]]
+                        
+                        if targets[i].shape != reconstructed[i].shape:
+                            raise ValueError(f'{targets[i].shape}\n---\n{len(reconstructed)} * {reconstructed[i].shape} \n ts {ts.shape} \n---\n patch_cnt {patch_cnt} \n ---\n ts_hidden {ts_hidden.shape}')                    
+                    if len(reconstructed) == len(targets) and len(targets) > 0:
+                        mse_loss = 0
+                        fft_loss = 0 
+                        count = 0
+                        for pred, target in zip(reconstructed, targets):
+                            if pred.size(0) == target.size(0):
+                                mse_loss += self.ts_loss_func(pred, target)
+                                with torch.cuda.amp.autocast(enabled=False):
+                                    pred_fft = torch.fft.rfft(pred.float(), dim=-1)
+                                    target_fft = torch.fft.rfft(target.float(), dim=-1)
+                                    pred_mag = torch.abs(pred_fft)
+                                    target_mag = torch.abs(target_fft)
+                                    fft_loss += self.ts_loss_func(pred_mag, target_mag).to(pred.dtype)
+                           
+                                count += 1
+                                #_w(f'mse_loss={mse_loss} | fft_loss={fft_loss} | {pred.size(0)} | \n pred={pred} \n target={target}')
+                            else :
+                                raise ValueError(f'pred.size {pred.size(0)} target.size {target.size(0)}')
+                        if count > 0:
+                            ts_loss = mse_loss / count # + fft_loss / count
+                    else :
+                        raise ValueError(f'rec {len(reconstructed)} \n targets {len(targets)} \n{tslen=}')
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            loss_fct = CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+        
+        total_loss = None
+        if loss is not None or ts_loss is not None:
+            total_loss = 0
+            if loss is not None:
+                total_loss += loss
+            if ts_loss is not None:
+                total_loss += ts_loss 
+                
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (total_loss,) + output if total_loss is not None else output
+
+        return Qwen2TSCausalLMOutputWithPast(
+            loss=total_loss,
+            labels=labels,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            attention_mask=attention_mask,
+            new_token_positions=new_token_positions
+        )
+
+    def _extract_past_from_model_output(self, outputs: ModelOutput):
+        return "past_key_values", outputs.past_key_values
+
+    def _update_model_kwargs_for_generation(
+        self,
+        outputs: ModelOutput,
+        model_kwargs: Dict[str, Any],
+        is_encoder_decoder: bool = False,
+        num_new_tokens: int = 1,
+    ) -> Dict[str, Any]:
+        # update past_key_values keeping its naming used in model code
+        cache_name, cache = self._extract_past_from_model_output(outputs)
+        model_kwargs[cache_name] = cache
+        if getattr(outputs, "state", None) is not None:
+            model_kwargs["state"] = outputs.state
+
+        # update attention_mask
+        if getattr(outputs, "attention_mask", None) is not None:
+            model_kwargs["attention_mask"] = outputs.attention_mask
+
+        # update token_type_ids with last value
+        if "token_type_ids" in model_kwargs:
+            token_type_ids = model_kwargs["token_type_ids"]
+            model_kwargs["token_type_ids"] = torch.cat([token_type_ids, token_type_ids[:, -1].unsqueeze(-1)], dim=-1)
+
+        if not is_encoder_decoder:
+            # update attention mask
+            if "attention_mask" in model_kwargs:
+                attention_mask = model_kwargs["attention_mask"]
+                model_kwargs["attention_mask"] = torch.cat(
+                    [attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1
+                )
+        else:
+            # update decoder attention mask
+            if "decoder_attention_mask" in model_kwargs:
+                decoder_attention_mask = model_kwargs["decoder_attention_mask"]
+                model_kwargs["decoder_attention_mask"] = torch.cat(
+                    [decoder_attention_mask, decoder_attention_mask.new_ones((decoder_attention_mask.shape[0], 1))],
+                    dim=-1,
+                )
+
+        if model_kwargs.get("use_cache", True):
+            model_kwargs["cache_position"] = model_kwargs["cache_position"][-1:] + num_new_tokens
+        else:
+            past_positions = model_kwargs.pop("cache_position")
+            new_positions = torch.arange(
+                past_positions[-1] + 1, past_positions[-1] + num_new_tokens + 1, dtype=past_positions.dtype
+            ).to(past_positions.device)
+            model_kwargs["cache_position"] = torch.cat((past_positions, new_positions))
+        return model_kwargs
+
+    def prepare_inputs_for_generation(
+        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, timeseries=None, **kwargs
+    ):
+        # Omit tokens covered by past_key_values
+        if past_key_values is not None:
+            if isinstance(past_key_values, Cache):
+                cache_length = past_key_values.get_seq_length()
+                past_length  = past_key_values.seen_tokens
+                max_cache_length = (
+                    past_key_values.get_max_length()
+                    if hasattr(past_key_values, "get_max_length")
+                    else past_key_values.get_max_cache_shape()
+                )
+            else:
+                cache_length = past_length = past_key_values[0][0].shape[2]
+                max_cache_length = None
+
+            has_ts = timeseries is not None and len(timeseries) > 0
+
+            if has_ts and kwargs.get("attention_mask") is not None:
+                attention_mask = kwargs["attention_mask"]
+                attention_mask = torch.cat(
+                    [attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1
+                )
+
+            # Set attention mask and input_ids
+            if has_ts and past_length > 0:
+                # We have only one token added and timeseries are already inferenced
+                input_ids = input_ids[:, -1:]
+                timeseries = None
+            elif attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
+                input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
+            elif past_length < input_ids.shape[1]:
+                input_ids = input_ids[:, past_length:]
+            # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
+
+            # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
+            if (
+                max_cache_length is not None
+                and attention_mask is not None
+                and cache_length + input_ids.size(1) > max_cache_length
+            ):
+                attention_mask = attention_mask[:, -max_cache_length:]
+
+        position_ids = kwargs.get("position_ids", None)
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -input_ids.shape[1] :]
+
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids}
+
+        model_inputs.update(
+            {
+                "position_ids": position_ids,
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+                "attention_mask": attention_mask,
+                "timeseries": timeseries
+            }
+        )
+        return model_inputs
+
+    @staticmethod
+    def _reorder_cache(past_key_values, beam_idx):
+        reordered_past = ()
+        for layer_past in past_key_values:
+            reordered_past += (
+                tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
+            )
+        return reordered_past
+
+
+@add_start_docstrings(
+    """
+    The Qwen2 Model transformer with a sequence classification head on top (linear layer).
+
+    [`Qwen2ForSequenceClassification`] uses the last token in order to do the classification, as other causal models
+    (e.g. GPT-2) do.
+
+    Since it does classification on the last token, it requires to know the position of the last token. If a
+    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
+    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
+    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
+    each row of the batch).
+    """,
+    QWEN2_START_DOCSTRING,
+)
+class Qwen2ForSequenceClassification(Qwen2PreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = Qwen2Model(config)
+        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    @add_start_docstrings_to_model_forward(QWEN2_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        transformer_outputs = self.model(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        hidden_states = transformer_outputs[0]
+        logits = self.score(hidden_states)
+
+        if input_ids is not None:
+            batch_size = input_ids.shape[0]
+        else:
+            batch_size = inputs_embeds.shape[0]
+
+        if self.config.pad_token_id is None and batch_size != 1:
+            raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
+        if self.config.pad_token_id is None:
+            sequence_lengths = -1
+        else:
+            if input_ids is not None:
+                # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
+                sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
+                sequence_lengths = sequence_lengths % input_ids.shape[-1]
+                sequence_lengths = sequence_lengths.to(logits.device)
+            else:
+                sequence_lengths = -1
+
+        pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
+
+        loss = None
+        if labels is not None:
+            labels = labels.to(logits.device)
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(pooled_logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(pooled_logits, labels)
+        if not return_dict:
+            output = (pooled_logits,) + transformer_outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutputWithPast(
+            loss=loss,
+            logits=pooled_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )

processing_qwen2_ts.py

@@ -0,0 +1,224 @@
+# coding=utf-8
+# Copyright 2024 Tsinghua University and ByteDance.
+#
+# Licensed under the MIT License (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://opensource.org/license/mit
+#
+# 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.
+
+import numpy as np
+from typing import List, Union, Tuple, Optional
+import torch
+
+from transformers.feature_extraction_utils import BatchFeature
+from transformers.processing_utils import ProcessorMixin
+from transformers.tokenization_utils_base import PaddingStrategy
+
+def sp_encoding(timeseries: np.ndarray, eots_token: bool = True) -> Tuple[np.ndarray, str, dict]:
+    """
+    Encodes a time series with scalar normalization.
+
+    Args:
+        timeseries (np.ndarray): The raw time series data (1D or 2D).
+
+    Returns:
+        result_timeseries (np.ndarray): The encoded time series, shape [seq_len, 1].
+        prompt (str): The placeholder string with offset and scaling info.
+        metadata (dict): Metadata containing the offset and scaling factor.
+    """
+    timeseries = np.array(timeseries)
+    mean = np.mean(timeseries)
+    scaled_timeseries = timeseries - mean
+    scale_factor = 1.0
+    if np.any(np.abs(scaled_timeseries) >= 3.0):
+        scale_factor = np.max(np.abs(scaled_timeseries)) / 3.0
+        scaled_timeseries /= scale_factor
+
+    prompt = f"[offset={-mean:.4f}|scaling={scale_factor:.4f}|length={len(timeseries)}|max={max(timeseries):.4f}|min={min(timeseries):.4f}|left={timeseries[0]:.4f}|right={timeseries[-1]:.4f}]<ts>"
+    if eots_token:
+        prompt += '<ts/>'
+
+    result_timeseries = np.stack([scaled_timeseries, np.ones_like(scaled_timeseries)], axis=-1).reshape(-1, 1)
+
+    return result_timeseries, prompt, {"offset": float(-mean), "scale_factor": float(scale_factor)}
+
+class Qwen2TSProcessor(ProcessorMixin):
+    """
+    A processor for ChatTS that integrates text prompt processing and time series encoding.
+    """
+
+    attributes = ["tokenizer"]
+    feature_extractor_class = None  # You can add a feature extractor if needed
+    tokenizer_class = "AutoTokenizer"
+
+    def __init__(self, tokenizer=None, chat_template=None, **kwargs):
+        """
+        Args:
+            tokenizer: An optional tokenizer to process text prompts.
+        """
+        if chat_template is None and tokenizer is not None and tokenizer.chat_template is not None:
+            chat_template = tokenizer.chat_template
+        self.chat_template = chat_template
+
+        super().__init__(tokenizer=tokenizer, chat_template=chat_template, **kwargs)
+
+    def __call__(
+        self,
+        text: Union[str, List[str]],
+        timeseries: Optional[List[List[np.ndarray]]] = None,
+        padding: Union[bool, str, PaddingStrategy] = False,
+        padding_side: str = 'left',
+        vllm_flag: bool = False,
+        tokenize: bool = True,
+        **kwargs,
+    ) -> BatchFeature:
+        """
+        Encodes a prompt and its associated time series.
+
+        Args:
+            prompt (List[str]): The input prompt containing <ts><ts/> placeholders.
+            timeseries (List[np.ndarray]): A list of time series matched to placeholders in the prompt.
+            padding (bool or str or PaddingStrategy, optional): Passed to the tokenizer for text padding.
+            return_tensors (str, optional): "pt" to return PyTorch tensors; None to return NumPy arrays.
+            **kwargs: Additional tokenizer parameters.
+
+        Returns:
+            BatchFeature: Contains processed prompt, encoded time series, and tokenizer outputs.
+        """
+        if type(text) == str:
+            text = [text]
+        if timeseries is None:
+            timeseries = []
+
+        reconstructed_prompts = []
+        concatenated_ts = None
+        ts_tokens = []
+
+        if vllm_flag:
+            # All prompt modifications have to be done inside of the vLLM
+            # to work correctly with its caching mechanism.
+            reconstructed_prompts = text
+            
+            # Process timeseries data
+            encoded_ts_arrays = []
+            for ts in timeseries:
+                # Get the normalized data and prompt text
+                encoded_ts, ts_prompt, _ = sp_encoding(ts, eots_token=False)
+                # Tokenize the ts_prompt and add to the tokens list
+                if self.tokenizer is not None:
+                    tokens = self.tokenizer.encode(ts_prompt, add_special_tokens=False)
+                    ts_tokens.append(tokens)
+                encoded_ts_arrays.append(encoded_ts[None, ...])
+        else:
+            encoded_ts_arrays = []
+            total_ts_cnt = 0
+            for idx, prompt in enumerate(text):
+                # Split prompt by <ts><ts/> placeholders
+                last_ts_cnt = total_ts_cnt
+                prompt_segments = prompt.split("<ts><ts/>")
+                total_ts_cnt = total_ts_cnt + len(prompt_segments) - 1
+
+                # Encode each time series and rebuild the prompt
+                reconstructed_prompt = prompt_segments[0]
+
+                for i, ts in enumerate(timeseries[last_ts_cnt:total_ts_cnt]):
+                    encoded_ts, ts_prompt, _ = sp_encoding(ts, eots_token=not vllm_flag)
+                    reconstructed_prompt += ts_prompt + prompt_segments[i + 1]
+                    # Ensure time series shape [1, seq_len, feature_dim] for batch concatenation
+                    encoded_ts_arrays.append(encoded_ts[None, ...])
+
+                reconstructed_prompts.append(reconstructed_prompt)
+
+            if len(timeseries) != len(encoded_ts_arrays):
+                raise ValueError(
+                    f"Mismatch between <ts><ts/> placeholders ({total_ts_cnt}) "
+                    f"and time series ({len(encoded_ts_arrays)})."
+                )
+
+            if len(encoded_ts_arrays) > 0:
+                # Pad time series to the same length
+                max_length = max(ts.shape[1] for ts in encoded_ts_arrays)
+                padded_ts_arrays = [
+                    np.pad(ts, ((0, 0), (0, max_length - ts.shape[1]), (0, 0)), mode="constant", constant_values=0.0)
+                    for ts in encoded_ts_arrays
+                ]
+                concatenated_ts = np.concatenate(padded_ts_arrays, axis=0)  # Shape: [batch_size, max_length, feature_dim]
+                
+                # Convert to torch
+                concatenated_ts = torch.from_numpy(concatenated_ts).half()
+
+        # Tokenize the processed prompt
+        tokenizer_outputs = {}
+        if tokenize and self.tokenizer is not None:
+            tokenizer_outputs = self.tokenizer(reconstructed_prompts, padding=padding, padding_side=padding_side, **kwargs)
+        else:
+            tokenizer_outputs = {"text": reconstructed_prompts}
+
+        # Create the final output
+        outputs = tokenizer_outputs
+        if vllm_flag:
+            outputs["timeseries"] = zip(ts_tokens, encoded_ts_arrays)
+        elif concatenated_ts is not None:
+            outputs["timeseries"] = concatenated_ts
+
+        return BatchFeature(data=outputs)
+
+    def encode_timeseries(
+        self,
+        timeseries: Optional[List[List[np.ndarray]]] = None,
+    ) -> np.ndarray:
+        if timeseries is None:
+            timeseries = []
+
+        concatenated_ts = None
+        encoded_ts_arrays = []
+
+        for i, ts in enumerate(timeseries):
+            encoded_ts, _, _ = sp_encoding(ts)
+            # Ensure time series shape [1, seq_len, feature_dim] for batch concatenation
+            encoded_ts_arrays.append(encoded_ts[None, ...])
+
+        if len(encoded_ts_arrays) > 0:
+            # Pad time series to the same length
+            max_length = max(ts.shape[1] for ts in encoded_ts_arrays)
+            padded_ts_arrays = [
+                np.pad(ts, ((0, 0), (0, max_length - ts.shape[1]), (0, 0)), mode="constant", constant_values=0.0)
+                for ts in encoded_ts_arrays
+            ]
+            concatenated_ts = np.concatenate(padded_ts_arrays, axis=0)  # Shape: [batch_size, max_length, feature_dim]
+            
+            # Convert to torch
+            concatenated_ts = torch.from_numpy(concatenated_ts).half()
+
+        return concatenated_ts
+
+    @property
+    def model_input_names(self):
+        """
+        Define the input names expected by the model.
+        """
+        tokenizer_input_names = []
+        if self.tokenizer and hasattr(self.tokenizer, "model_input_names"):
+            tokenizer_input_names = self.tokenizer.model_input_names
+        return list(dict.fromkeys(["processed_prompt", "time_series"] + tokenizer_input_names))
+
+    def batch_decode(self, *args, **kwargs):
+        """
+        This method forwards all its arguments to Qwen2TokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
+        refer to the docstring of this method for more information.
+        """
+        return self.tokenizer.batch_decode(*args, **kwargs)
+
+    def decode(self, *args, **kwargs):
+        """
+        This method forwards all its arguments to Qwen2TokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
+        the docstring of this method for more information.
+        """
+        return self.tokenizer.decode(*args, **kwargs)

processor_config.json

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+{
+  "auto_map": {
+    "AutoProcessor": "processing_qwen2_ts.Qwen2TSProcessor"
+  },
+  "processor_class": "Qwen2TSProcessor"
+}

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+}

special_tokens_map.json

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+    "<ts>",
+    "<ts/>"
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+    "lstrip": false,
+    "normalized": false,
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+}

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+  },
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train_results.json

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+{
+    "epoch": 0.3520179920307038,
+    "total_flos": 9.027346976391299e+18,
+    "train_loss": 213.8077490248945,
+    "train_runtime": 76099.1347,
+    "train_samples_per_second": 3.028,
+    "train_steps_per_second": 0.012
+}

train_timesense.sh

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+#!/bin/bash
+#source /mnt/home/venv/bin/activate
+trap 'echo "Ctrl+C detected. Exiting..."; exit 1' SIGINT
+set -e
+countdown() {
+  local SECONDS_LEFT=$((0 * 30 * 60))  
+  while [ $SECONDS_LEFT -gt 0 ]; do
+    HOURS=$(($SECONDS_LEFT / 3600))
+    MINUTES=$((($SECONDS_LEFT % 3600) / 60))
+    SECONDS=$(($SECONDS_LEFT % 60))
+    printf "\r%02d:%02d:%02d" $HOURS $MINUTES $SECONDS
+    sleep 1
+    SECONDS_LEFT=$(($SECONDS_LEFT - 1))
+  done
+  echo ""
+}
+countdown
+
+OUTPUT_DIR="/xll/models/ChatTS-14B-timesense"
+MODEL_NAME_OR_PATH="/xll/models/ChatTS-14B"
+#MODEL_NAME_OR_PATH="/mnt/home/xz/zr/LLaMA-Factory-zzr/script/ChatTS/sft_checkpoint/Qwen2.5-14B-Instruct"
+# /mnt/home/zr/models/ChatTS-14B
+APPEND_FILES_DIR="/xll/models/ChatTS-14B/append" 
+echo "Copying files from $APPEND_FILES_DIR to $MODEL_NAME_OR_PATH"
+for file in "$APPEND_FILES_DIR"/*; do
+    cp -f "$file" "$MODEL_NAME_OR_PATH/"
+done
+
+CURRENT_SCRIPT_PATH="${BASH_SOURCE[0]}"
+SCRIPT_FILENAME=$(basename "$CURRENT_SCRIPT_PATH")
+mkdir -p "$OUTPUT_DIR"
+cp "$CURRENT_SCRIPT_PATH" "$OUTPUT_DIR/$SCRIPT_FILENAME"
+
+echo "脚本 '$SCRIPT_FILENAME' 已成功复制到 '$OUTPUT_DIR'"
+echo "Files copied successfully."
+#cd /mnt/home/zr/xz/opsfm-xz
+cd /root/code/chat-ts-training
+
+DEEPSPEED_TIMEOUT=120 deepspeed --num_gpus 8 --master_port=19901 src/train.py \
+    --deepspeed /root/code/chat-ts-training/ds_config/ds_config_2_bf16_offload.json \
+    --stage sft \
+    --model_name_or_path "$MODEL_NAME_OR_PATH"\
+    --dataset "generated_timeseries_bench_qa_new2,generated_timeseries_bench_plus2,tulu_ift,model_detect_0823,model_qa,new_chatts_sft,new_chatts_ift" \
+    --interleave_probs "0.4,0.1,0.1,0.1,0.1,0.1,0.1" \
+    --do_train \
+    --mix_strategy "interleave_over" \
+    --template "chatts"  \
+    --finetuning_type full \
+    --output_dir "$OUTPUT_DIR" \
+    --overwrite_output_dir \
+    --trust_remote_code True \
+    --report_to "none" \
+    --per_device_train_batch_size 1 \
+    --gradient_accumulation_steps 32 \
+    --lr_scheduler_type cosine \
+    --logging_steps 1 \
+    --save_steps 600 \
+    --learning_rate 1e-5 \
+    --warmup_ratio 0.02 \
+    --num_train_epochs 0 \
+    --max_steps 1200 \
+    --plot_loss \
+    --bf16 \
+    --save_only_model \
+    --save_safetensors False \
+    --preprocessing_num_workers 96 \
+    --cutoff_len 9000
+    
+CURRENT_SCRIPT_PATH="${BASH_SOURCE[0]}"
+# 提取脚本文件名
+SCRIPT_FILENAME=$(basename "$CURRENT_SCRIPT_PATH")
+cp "$CURRENT_SCRIPT_PATH" "$OUTPUT_DIR/$SCRIPT_FILENAME"
+echo "Files copied successfully."
+
+
+python /root/code/null.py

training_args.bin

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