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README.md

@@ -1,3 +1,214 @@
----
-license: mit
----
+---
+license: mit
+library_name: transformers
+pipeline_tag: image-text-to-text
+language:
+- en
+tags:
+- multimodal
+- vision
+- video
+- long-video
+- token-selection
+- compression
+- qwen2.5-vl
+- qtsplus
+---
+
+[![arXiv](https://img.shields.io/badge/arXiv-2511.11910-grey?labelColor=B31B1B&logo=arxiv&logoColor=white)](https://arxiv.org/abs/2511.11910)
+[![Website](https://img.shields.io/badge/Website-QTSplus-grey?labelColor=3776AB&logo=GoogleChrome&logoColor=white)](https://qtsplus.github.io/)
+[![Github](https://img.shields.io/badge/Github-QTSplus-grey?labelColor=000&logo=github)](https://github.com/Siyou-Li/QTSplus)
+
+## Model Description
+![](./assets/qtsplus.svg)
+
+QTSplus-InternVL2.5-8B is a Qwen2.5-VL–based multimodal LLM finetuned with Query‑Aware Token Selector (QTSplus), a lightweight visual token selection module that acts as an information gate between the vision encoder and the LLM.
+
+- Query‑aware selection: scores vision tokens via cross‑attention against the input text query.
+- Adaptive retention: predicts an instance‑specific budget and keeps only the most relevant tokens.
+- Temporal reasoning: a small re‑encoder preserves temporal order with absolute time cues.
+- Efficient long‑video understanding: up to 89% vision token compression and 28% end‑to‑end latency reduction on long videos (see paper for details).
+
+## Intended Uses & Limitations
+
+Intended uses
+- Long‑video question answering and captioning
+- Multi‑image reasoning and story understanding
+- Efficient multimodal chat with reduced latency on long inputs
+
+Limitations
+- May miss fine details if the predicted retention budget is too small.
+- Inherits biases and failure modes from the base Qwen2.5‑VL model and training data.
+- Not a safety‑aligned system; outputs may be inaccurate or unsafe without human oversight.
+
+## Quick Start
+
+The repository is designed around a conda‑based Python 3.11 environment with a CUDA‑enabled GPU.
+
+1. **Create and activate the conda environment**
+
+```bash
+conda create -n qtsplus python=3.11 -y
+conda activate qtsplus
+```
+
+2. **Install toolchain and CUDA toolkit**
+
+```bash
+conda install conda-forge::gcc=11 conda-forge::gxx=11 -y
+conda install nvidia/label/cuda-12.8.1::cuda-toolkit -y
+conda install av -c conda-forge -y
+```
+
+3. **Install PyTorch with CUDA 12.8 support**
+
+```bash
+pip3 install torch==2.9.0 torchvision --index-url https://download.pytorch.org/whl/cu128
+```
+
+4. **Install core Python libraries**
+
+```bash
+pip install transformers==4.57.1
+DS_BUILD_CUTLASS_OPS=0 DS_BUILD_RAGGED_DEVICE_OPS=0 DS_BUILD_EVOFORMER_ATTN=0 pip install deepspeed
+pip install accelerate pandas wandb matplotlib scikit-learn datasets evaluate ftfy sentencepiece bitsandbytes
+```
+
+5. **Install FlashAttention (prebuilt wheel)**
+
+```bash
+pip install https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.4.22/flash_attn-2.8.1+cu128torch2.9-cp311-cp311-linux_x86_64.whl
+```
+
+This wheel is specific to Linux x86_64, CUDA 12.8, PyTorch 2.9.0 and Python 3.11; if you deviate from this configuration, you will need to install a compatible FlashAttention build instead.
+
+6. **Verify installation**
+
+After installation, you should be able to run:
+
+```bash
+python -c "import torch, transformers, deepspeed, accelerate; print(torch.cuda.is_available())"
+```
+
+which should print `True` on a correctly configured GPU machine.
+
+Video example
+```python
+from __future__ import annotations
+
+import argparse
+
+import torch
+from transformers import AutoModelForCausalLM, AutoProcessor
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description="QTSplus-InternVL2.5-8B video QA demo")
+    parser.add_argument(
+        "--model",
+        type=str,
+        default="AlpachinoNLP/QTSplus-InternVL2.5-8B",
+        help="Model ID or path",
+    )
+    parser.add_argument(
+        "--video",
+        type=str,
+        default="your/path/to/video.mp4",
+        help="Path to a video file",
+    )
+    parser.add_argument(
+        "--question",
+        type=str,
+        default="play the video and describe what is happening?",
+        help="Question about the video (if omitted, you will be prompted)",
+    )
+    parser.add_argument("--num_frames", type=int, default=20, help="Number of frames uniformly sampled from the video")
+    parser.add_argument("--max_new_tokens", type=int, default=512, help="Max new tokens to generate")
+    args = parser.parse_args()
+
+    question = args.question
+    if not question:
+        question = input("Question: ").strip()
+    if not question:
+        raise SystemExit("Empty question.")
+
+    processor = AutoProcessor.from_pretrained(args.model, trust_remote_code=True)
+    dtype = torch.bfloat16 if torch.cuda.is_available() else torch.float32
+    try:
+        model = AutoModelForCausalLM.from_pretrained(
+            args.model,
+            trust_remote_code=True,
+            dtype=dtype,
+            device_map="auto" if torch.cuda.is_available() else None,
+            low_cpu_mem_usage=True,
+        ).eval()
+    except TypeError:
+        model = AutoModelForCausalLM.from_pretrained(
+            args.model,
+            trust_remote_code=True,
+            torch_dtype=dtype,
+            device_map="auto" if torch.cuda.is_available() else None,
+            low_cpu_mem_usage=True,
+        ).eval()
+
+    inputs = processor(text=question, videos=args.video, num_frames=args.num_frames, return_tensors="pt")
+    for k, v in list(inputs.items()):
+        if isinstance(v, torch.Tensor):
+            inputs[k] = v.to(model.device)
+
+    with torch.inference_mode():
+        output_ids = model.generate(**inputs, max_new_tokens=args.max_new_tokens, do_sample=False)
+
+    input_len = int(inputs["input_ids"].shape[1])
+    gen_ids = output_ids[0, input_len:]
+    answer = processor.tokenizer.decode(gen_ids, skip_special_tokens=True).strip()
+
+    print("\nAnswer:\n" + answer)
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
+
+```
+
+Multiple images (treated as a video sequence)
+```python
+
+```
+
+Notes
+- The chat template is applied via `processor.apply_chat_template` and expects the messages schema shown above.
+- QTSplus expects the vision payload under the `vision_input` keyword argument during generation.
+- For fully offline use, pass `local_files_only=True` to `from_pretrained` calls once the files are cached locally.
+
+## Efficiency & Controls
+
+The following QTSplus hyperparameters in `config.json` control compression and selection behavior:
+- `qts_plus_rho_min` / `qts_plus_rho_max`: min/max retention ratio bounds.(default: 0.05 / 0.5)
+- `qts_plus_tau_s`: scoring temperature for cross‑attention.(default: 0.5)
+- `qts_plus_nmax`: hard cap on selected tokens per sample. （default: 25600)
+These trade off detail vs. speed/memory. See the paper for guidance, ablations, and latency/throughput measurements.
+
+
+## Safety, Bias, and Limitations
+
+- Outputs may be factually incorrect, biased, or unsafe. Do not use without human oversight.
+- QTSplus compresses the vision stream; extremely small budgets may drop rare but important details.
+- Inherits safety/bias characteristics from the underlying Qwen2.5‑VL model and training data.
+
+## Citation
+
+If you find this work helpful, please cite:
+
+```bibtex
+@misc{li2025seeingforesttreesqueryaware,
+  title        = {Seeing the Forest and the Trees: Query-Aware Tokenizer for Long-Video Multimodal Language Models},
+  author       = {Siyou Li and Huanan Wu and Juexi Shao and Yinghao Ma and Yujian Gan and Yihao Luo and Yuwei Wang and Dong Nie and Lu Wang and Wengqing Wu and Le Zhang and Massimo Poesio and Juntao Yu},
+  year         = {2025},
+  eprint       = {2511.11910},
+  archivePrefix= {arXiv},
+  primaryClass = {cs.CV},
+  url          = {https://arxiv.org/abs/2511.11910}
+}
+```

added_tokens.json

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+{
+  "</box>": 92552,
+  "</img>": 92545,
+  "</quad>": 92548,
+  "</ref>": 92550,
+  "<IMG_CONTEXT>": 92546,
+  "<box>": 92551,
+  "<img>": 92544,
+  "<quad>": 92547,
+  "<ref>": 92549
+}

chat_template.jinja

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+{{ bos_token }}{% for message in messages %}{{'<|im_start|>' + message['role'] + '
+' + message['content'] + '<|im_end|>' + '
+'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
+' }}{% endif %}

config.json

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+{
+  "architectures": [
+    "QTSplusInternLM2_ForCausalLM"
+  ],
+  "attn_implementation": "flash_attention_2",
+  "auto_map": {
+    "AutoConfig": "qts_plus_internlm2_lm.QTSplusInternLM2_CausalLM_Config",
+    "AutoModelForCausalLM": "qts_plus_internlm2_lm.QTSplusInternLM2_ForCausalLM",
+    "AutoProcessor": "processing_qts_plus_internvl2_5.QTSplusInternVL2_5_Processor"
+  },
+  "bias": false,
+  "bos_token_id": 1,
+  "downsample_ratio": 0.5,
+  "dtype": "bfloat16",
+  "enable_qts_plus": true,
+  "eos_token_id": 2,
+  "force_image_size": 448,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "image_token_id": 92546,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "lambda_m": 0,
+  "lambda_s": 0,
+  "lambda_t": 0,
+  "llm_hidden_size": 4096,
+  "max_position_embeddings": 32768,
+  "model_type": "qts_plus_internlm2_causal_lm",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "out_hidden_size": 4096,
+  "pad_token_id": 2,
+  "pretraining_tp": 1,
+  "project_text_if_needed": false,
+  "ps_version": "v2",
+  "qts_plus_block_dropout": 0.0,
+  "qts_plus_nmax": 2560,
+  "qts_plus_reencode": false,
+  "qts_plus_reencode_layers": 0,
+  "qts_plus_rho_max": 0.5,
+  "qts_plus_rho_min": 0.05,
+  "qts_plus_scoring_layers": 4,
+  "qts_plus_tau_s": 0.1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 2.0,
+    "type": "dynamic"
+  },
+  "rope_theta": 1000000,
+  "select_layer": -1,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.57.6",
+  "use_bfloat16": true,
+  "use_cache": true,
+  "vision_config": {
+    "architectures": [
+      "InternVisionModel"
+    ],
+    "attention_dropout": 0.0,
+    "drop_path_rate": 0.0,
+    "dropout": 0.0,
+    "dtype": "bfloat16",
+    "hidden_act": "gelu",
+    "hidden_size": 1024,
+    "image_size": 448,
+    "initializer_factor": 1.0,
+    "initializer_range": 0.02,
+    "intermediate_size": 4096,
+    "layer_norm_eps": 1e-06,
+    "model_type": "intern_vit_6b",
+    "norm_type": "layer_norm",
+    "num_attention_heads": 16,
+    "num_channels": 3,
+    "num_hidden_layers": 24,
+    "patch_size": 14,
+    "qk_normalization": false,
+    "qkv_bias": true,
+    "use_bfloat16": true,
+    "use_flash_attn": true
+  },
+  "vision_embed_size": 4096,
+  "vision_tower": "internvl2_5_vision",
+  "vocab_size": 92553
+}

configuration_intern_vit.py

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+# --------------------------------------------------------
+# InternVL
+# Copyright (c) 2024 OpenGVLab
+# Licensed under The MIT License [see LICENSE for details]
+# --------------------------------------------------------
+
+import os
+from typing import Union
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+
+class InternVisionConfig(PretrainedConfig):
+    r"""
+    Configuration class for InternVL's vision encoder (`InternVisionModel`).
+    """
+
+    model_type = "intern_vit_6b"
+
+    def __init__(
+        self,
+        num_channels=3,
+        patch_size=14,
+        image_size=224,
+        qkv_bias=False,
+        hidden_size=3200,
+        num_attention_heads=25,
+        intermediate_size=12800,
+        qk_normalization=True,
+        num_hidden_layers=48,
+        use_flash_attn=True,
+        hidden_act="gelu",
+        norm_type="rms_norm",
+        layer_norm_eps=1e-6,
+        dropout=0.0,
+        drop_path_rate=0.0,
+        attention_dropout=0.0,
+        initializer_range=0.02,
+        initializer_factor=0.1,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.dropout = dropout
+        self.drop_path_rate = drop_path_rate
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_channels = num_channels
+        self.patch_size = patch_size
+        self.image_size = image_size
+        self.initializer_range = initializer_range
+        self.initializer_factor = initializer_factor
+        self.attention_dropout = attention_dropout
+        self.layer_norm_eps = layer_norm_eps
+        self.hidden_act = hidden_act
+        self.norm_type = norm_type
+        self.qkv_bias = qkv_bias
+        self.qk_normalization = qk_normalization
+        self.use_flash_attn = use_flash_attn
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
+        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
+
+        # InternVL chat checkpoints store this config under `vision_config`.
+        if "vision_config" in config_dict:
+            config_dict = config_dict["vision_config"]
+
+        if (
+            "model_type" in config_dict
+            and hasattr(cls, "model_type")
+            and config_dict["model_type"] != cls.model_type
+        ):
+            logger.warning(
+                f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
+                f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
+            )
+
+        return cls.from_dict(config_dict, **kwargs)
+

configuration_internlm2.py

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+# Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on transformers/src/transformers/models/llama/configuration_llama.py
+#
+# 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.
+""" InternLM2 model configuration"""
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+INTERNLM2_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
+
+
+# Modified from transformers.model.llama.configuration_llama.LlamaConfig
+class InternLM2Config(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`InternLM2Model`]. It is used to instantiate
+    an InternLM2 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 InternLM2-7B.
+
+    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 32000):
+            Vocabulary size of the InternLM2 model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`InternLM2Model`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 11008):
+            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*):
+            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
+            `num_attention_heads`.
+        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 2048):
+            The maximum sequence length that this model might ever be used with. Typically set this to something large
+            just in case (e.g., 512 or 1024 or 2048).
+        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-12):
+            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 to tie weight embeddings
+        Example:
+
+    """
+    model_type = 'internlm2'
+    _auto_class = 'AutoConfig'
+
+    def __init__(  # pylint: disable=W0102
+        self,
+        vocab_size=103168,
+        hidden_size=4096,
+        intermediate_size=11008,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        hidden_act='silu',
+        max_position_embeddings=2048,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=0,
+        bos_token_id=1,
+        eos_token_id=2,
+        tie_word_embeddings=False,
+        bias=True,
+        rope_theta=10000,
+        rope_scaling=None,
+        attn_implementation='eager',
+        **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.bias = bias
+
+        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.rope_scaling = rope_scaling
+        self._rope_scaling_validation()
+
+        self.attn_implementation = attn_implementation
+        if self.attn_implementation is None:
+            self.attn_implementation = 'eager'
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+    def _rope_scaling_validation(self):
+        """
+        Validate the `rope_scaling` configuration.
+        """
+        if self.rope_scaling is None:
+            return
+
+        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
+            raise ValueError(
+                '`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, '
+                f'got {self.rope_scaling}'
+            )
+        rope_scaling_type = self.rope_scaling.get('type', None)
+        rope_scaling_factor = self.rope_scaling.get('factor', None)
+        if rope_scaling_type is None or rope_scaling_type not in ['linear', 'dynamic']:
+            raise ValueError(
+                f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
+            )
+        if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor < 1.0:
+            raise ValueError(f"`rope_scaling`'s factor field must be a float >= 1, got {rope_scaling_factor}")

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": [
+    2
+  ],
+  "pad_token_id": 2,
+  "transformers_version": "4.57.6"
+}

latest

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+global_step2000

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b7110f58be7dc116b07aaf7eca3ecc1d15db3162e14375ce785f71aa046e2bec
+size 17564508786

modeling_intern_vit.py

@@ -0,0 +1,391 @@
+# --------------------------------------------------------
+# InternVL
+# Copyright (c) 2024 OpenGVLab
+# Licensed under The MIT License [see LICENSE for details]
+# --------------------------------------------------------
+
+from typing import Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from einops import rearrange
+from torch import nn
+from transformers.activations import ACT2FN
+from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import logging
+
+from .configuration_intern_vit import InternVisionConfig
+
+logger = logging.get_logger(__name__)
+
+
+try:  # Optional dependency
+    from timm.models.layers import DropPath as _DropPath  # type: ignore
+
+    DropPath = _DropPath
+except Exception:  # pragma: no cover
+    class DropPath(nn.Module):
+        """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
+
+        def __init__(self, drop_prob: float = 0.0) -> None:
+            super().__init__()
+            self.drop_prob = float(drop_prob)
+
+        def forward(self, x: torch.Tensor) -> torch.Tensor:
+            if self.drop_prob == 0.0 or not self.training:
+                return x
+            keep_prob = 1.0 - self.drop_prob
+            shape = (x.shape[0],) + (1,) * (x.ndim - 1)
+            random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
+            random_tensor = random_tensor.floor()
+            return x.div(keep_prob) * random_tensor
+
+
+try:
+    from flash_attn.bert_padding import pad_input, unpad_input  # type: ignore
+    from flash_attn.flash_attn_interface import flash_attn_varlen_qkvpacked_func  # type: ignore
+
+    has_flash_attn = True
+except Exception:  # pragma: no cover
+    pad_input, unpad_input, flash_attn_varlen_qkvpacked_func = None, None, None
+    has_flash_attn = False
+
+
+class FlashAttention(nn.Module):
+    """Scaled dot-product attention implemented with FlashAttention2."""
+
+    def __init__(self, softmax_scale=None, attention_dropout=0.0, device=None, dtype=None):
+        super().__init__()
+        self.softmax_scale = softmax_scale
+        self.dropout_p = attention_dropout
+
+    def forward(
+        self,
+        qkv,
+        key_padding_mask=None,
+        causal=False,
+        cu_seqlens=None,
+        max_s=None,
+        need_weights=False,
+    ):
+        assert not need_weights
+        assert qkv.dtype in [torch.float16, torch.bfloat16]
+        assert qkv.is_cuda
+
+        if cu_seqlens is None:
+            batch_size = qkv.shape[0]
+            seqlen = qkv.shape[1]
+            if key_padding_mask is None:
+                qkv = rearrange(qkv, "b s ... -> (b s) ...")
+                max_s = seqlen
+                cu_seqlens = torch.arange(
+                    0,
+                    (batch_size + 1) * seqlen,
+                    step=seqlen,
+                    dtype=torch.int32,
+                    device=qkv.device,
+                )
+                output = flash_attn_varlen_qkvpacked_func(
+                    qkv,
+                    cu_seqlens,
+                    max_s,
+                    self.dropout_p if self.training else 0.0,
+                    softmax_scale=self.softmax_scale,
+                    causal=causal,
+                )
+                output = rearrange(output, "(b s) ... -> b s ...", b=batch_size)
+            else:
+                nheads = qkv.shape[-2]
+                x = rearrange(qkv, "b s three h d -> b s (three h d)")
+                x_unpad, indices, cu_seqlens, max_s = unpad_input(x, key_padding_mask)
+                x_unpad = rearrange(x_unpad, "nnz (three h d) -> nnz three h d", three=3, h=nheads)
+                output_unpad = flash_attn_varlen_qkvpacked_func(
+                    x_unpad,
+                    cu_seqlens,
+                    max_s,
+                    self.dropout_p if self.training else 0.0,
+                    softmax_scale=self.softmax_scale,
+                    causal=causal,
+                )
+                output = rearrange(
+                    pad_input(rearrange(output_unpad, "nnz h d -> nnz (h d)"), indices, batch_size, seqlen),
+                    "b s (h d) -> b s h d",
+                    h=nheads,
+                )
+        else:
+            assert max_s is not None
+            output = flash_attn_varlen_qkvpacked_func(
+                qkv,
+                cu_seqlens,
+                max_s,
+                self.dropout_p if self.training else 0.0,
+                softmax_scale=self.softmax_scale,
+                causal=causal,
+            )
+
+        return output, None
+
+
+class InternRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        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)
+
+
+try:
+    from apex.normalization import FusedRMSNorm  # type: ignore
+
+    InternRMSNorm = FusedRMSNorm  # noqa
+    logger.info("Discovered apex.normalization.FusedRMSNorm - using it instead of InternRMSNorm")
+except Exception:  # pragma: no cover
+    pass
+
+
+NORM2FN = {
+    "rms_norm": InternRMSNorm,
+    "layer_norm": nn.LayerNorm,
+}
+
+
+class InternVisionEmbeddings(nn.Module):
+    def __init__(self, config: InternVisionConfig):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.hidden_size
+        self.image_size = config.image_size
+        self.patch_size = config.patch_size
+
+        self.class_embedding = nn.Parameter(torch.randn(1, 1, self.embed_dim))
+
+        self.patch_embedding = nn.Conv2d(
+            in_channels=3,
+            out_channels=self.embed_dim,
+            kernel_size=self.patch_size,
+            stride=self.patch_size,
+        )
+
+        self.num_patches = (self.image_size // self.patch_size) ** 2
+        self.num_positions = self.num_patches + 1
+
+        self.position_embedding = nn.Parameter(torch.randn(1, self.num_positions, self.embed_dim))
+
+    def _get_pos_embed(self, pos_embed, H, W):
+        target_dtype = pos_embed.dtype
+        pos_embed = (
+            pos_embed.float()
+            .reshape(1, self.image_size // self.patch_size, self.image_size // self.patch_size, -1)
+            .permute(0, 3, 1, 2)
+        )
+        pos_embed = (
+            F.interpolate(pos_embed, size=(H, W), mode="bicubic", align_corners=False)
+            .reshape(1, -1, H * W)
+            .permute(0, 2, 1)
+            .to(target_dtype)
+        )
+        return pos_embed
+
+    def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
+        batch_size = pixel_values.shape[0]
+        patch_embeds = self.patch_embedding(pixel_values)  # shape = [*, width, grid, grid]
+        H = patch_embeds.shape[-2]
+        W = patch_embeds.shape[-1]
+        patch_embeds = patch_embeds.flatten(2).transpose(1, 2)  # shape = [*, grid ** 2, width]
+
+        class_embeds = self.class_embedding.expand(batch_size, -1, -1)
+        embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
+
+        pos_embeds = self.position_embedding
+        if H != self.image_size // self.patch_size or W != self.image_size // self.patch_size:
+            pos_embeds = torch.cat(
+                [pos_embeds[:, :1, :], self._get_pos_embed(pos_embeds[:, 1:, :], H, W)],
+                dim=1,
+            )
+
+        embeddings = embeddings + pos_embeds
+        return embeddings
+
+
+class InternSelfAttention(nn.Module):
+    def __init__(self, config: InternVisionConfig):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.embed_dim // self.num_heads
+        self.scale = self.head_dim**-0.5
+        self.qkv_bias = config.qkv_bias
+
+        self.qkv = nn.Linear(self.embed_dim, self.embed_dim * 3, bias=self.qkv_bias)
+        self.proj = nn.Linear(self.embed_dim, self.embed_dim)
+
+        self.attn_drop = nn.Dropout(config.attention_dropout)
+        self.proj_drop = nn.Dropout(config.dropout)
+
+        self.qk_normalization = config.qk_normalization
+        if self.qk_normalization:
+            self.q_norm = InternRMSNorm(self.head_dim)
+            self.k_norm = InternRMSNorm(self.head_dim)
+
+        if config.use_flash_attn and has_flash_attn:
+            self.inner_attn = FlashAttention(softmax_scale=None, attention_dropout=config.attention_dropout)
+        else:
+            self.inner_attn = None
+
+    def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None):
+        B, N, C = x.shape
+        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, self.head_dim)
+
+        if self.qk_normalization:
+            q, k, v = qkv.unbind(dim=2)
+            q = self.q_norm(q)
+            k = self.k_norm(k)
+            qkv = torch.stack([q, k, v], dim=2)
+
+        if self.inner_attn is not None and x.is_cuda:
+            attn_output, _ = self.inner_attn(qkv=qkv, key_padding_mask=attn_mask, need_weights=False)
+            attn_output = rearrange(attn_output, "b s h d -> b s (h d)")
+        else:
+            qkv = qkv.permute(2, 0, 3, 1, 4)
+            q, k, v = qkv[0], qkv[1], qkv[2]
+            attn = (q @ k.transpose(-2, -1)) * self.scale
+            if attn_mask is not None:
+                attn = attn.masked_fill(attn_mask.unsqueeze(1).unsqueeze(2).to(dtype=torch.bool), float("-inf"))
+            attn = attn.softmax(dim=-1)
+            attn = self.attn_drop(attn)
+            attn_output = (attn @ v).transpose(1, 2).reshape(B, N, C)
+
+        x = self.proj(attn_output)
+        x = self.proj_drop(x)
+        return x
+
+
+class InternMLP(nn.Module):
+    def __init__(self, config: InternVisionConfig):
+        super().__init__()
+        self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
+        self.act = ACT2FN[config.hidden_act]
+        self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
+        self.drop = nn.Dropout(config.dropout)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+
+
+class InternVisionEncoderLayer(nn.Module):
+    def __init__(self, config: InternVisionConfig, drop_path_rate: float):
+        super().__init__()
+        self.norm1 = NORM2FN[config.norm_type](config.hidden_size, eps=config.layer_norm_eps)
+        self.attn = InternSelfAttention(config)
+        self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0.0 else nn.Identity()
+        self.norm2 = NORM2FN[config.norm_type](config.hidden_size, eps=config.layer_norm_eps)
+        self.mlp = InternMLP(config)
+
+    def forward(self, hidden_states: torch.Tensor, attn_mask: Optional[torch.Tensor] = None):
+        hidden_states = hidden_states + self.drop_path(self.attn(self.norm1(hidden_states), attn_mask=attn_mask))
+        hidden_states = hidden_states + self.drop_path(self.mlp(self.norm2(hidden_states)))
+        return hidden_states
+
+
+class InternVisionEncoder(nn.Module):
+    def __init__(self, config: InternVisionConfig):
+        super().__init__()
+        dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, config.num_hidden_layers)]
+        self.layers = nn.ModuleList(
+            [InternVisionEncoderLayer(config, drop_path_rate=dpr[i]) for i in range(config.num_hidden_layers)]
+        )
+
+    def forward(
+        self,
+        inputs_embeds: torch.Tensor,
+        attn_mask: Optional[torch.Tensor] = None,
+        output_hidden_states: bool = False,
+        return_dict: bool = True,
+    ) -> Union[Tuple, BaseModelOutput]:
+        hidden_states = inputs_embeds
+        all_hidden_states = () if output_hidden_states else None
+        for layer in self.layers:
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_states,)
+            if self.training:
+                hidden_states = torch.utils.checkpoint.checkpoint(layer, hidden_states, attn_mask)
+            else:
+                hidden_states = layer(hidden_states, attn_mask=attn_mask)
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states,)
+
+        if not return_dict:
+            return tuple(v for v in [hidden_states, all_hidden_states] if v is not None)
+
+        return BaseModelOutput(last_hidden_state=hidden_states, hidden_states=all_hidden_states)
+
+
+class InternVisionModel(PreTrainedModel):
+    config_class = InternVisionConfig
+    main_input_name = "pixel_values"
+    _no_split_modules = ["InternVisionEncoderLayer"]
+
+    def __init__(self, config: InternVisionConfig):
+        super().__init__(config)
+        self.embeddings = InternVisionEmbeddings(config)
+        self.encoder = InternVisionEncoder(config)
+        self.post_layernorm = NORM2FN[config.norm_type](config.hidden_size, eps=config.layer_norm_eps)
+        self.pooler = nn.Linear(config.hidden_size, config.hidden_size)
+
+        self.post_init()
+
+    def forward(
+        self,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPooling]:
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+
+        if pixel_values is None:
+            raise ValueError("You have to specify pixel_values")
+
+        embeddings = self.embeddings(pixel_values)
+        encoder_outputs = self.encoder(
+            inputs_embeds=embeddings,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        last_hidden_state = encoder_outputs[0]
+        last_hidden_state = self.post_layernorm(last_hidden_state)
+
+        pooled_output = last_hidden_state[:, 0, :]
+        pooled_output = self.pooler(pooled_output)
+
+        if not return_dict:
+            return (last_hidden_state, pooled_output) + encoder_outputs[1:]
+
+        return BaseModelOutputWithPooling(
+            last_hidden_state=last_hidden_state,
+            pooler_output=pooled_output,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=None,
+        )
+
+
+__all__ = [
+    "InternVisionConfig",
+    "InternVisionModel",
+    "has_flash_attn",
+]
+

modeling_internlm2.py

@@ -0,0 +1,1453 @@
+# Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on transformers/src/transformers/models/llama/modeling_llama.py
+#
+# 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.
+""" PyTorch InternLM2 model."""
+import math
+import queue
+import threading
+import warnings
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from einops import rearrange
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+from transformers.activations import ACT2FN
+from transformers.modeling_outputs import (BaseModelOutputWithPast,
+                                           CausalLMOutputWithPast,
+                                           SequenceClassifierOutputWithPast)
+from transformers.modeling_utils import PreTrainedModel
+# `generate()` lives on `GenerationMixin` in recent Transformers. Some versions
+# are deprecating `PreTrainedModel` inheriting from it, so include it explicitly
+# to keep `generate()` available without changing the installed transformers.
+try:  # transformers>=4.27
+    from transformers.generation.utils import GenerationMixin  # type: ignore
+except Exception:  # pragma: no cover
+    try:  # transformers<4.27
+        from transformers.generation_utils import GenerationMixin  # type: ignore
+    except Exception:  # pragma: no cover
+        GenerationMixin = object  # type: ignore[misc,assignment]
+from transformers.utils import (add_start_docstrings,
+                                add_start_docstrings_to_model_forward, logging,
+                                replace_return_docstrings)
+
+try:
+    from transformers.generation.streamers import BaseStreamer
+except:  # noqa # pylint: disable=bare-except
+    BaseStreamer = None
+
+from .configuration_internlm2 import InternLM2Config
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = 'InternLM2Config'
+
+flash_attn_func, flash_attn_varlen_func = None, None
+pad_input, index_first_axis, unpad_input = None, None, None
+try:
+    from flash_attn import flash_attn_func as _flash_attn_func
+    from flash_attn import flash_attn_varlen_func as _flash_attn_varlen_func
+    from flash_attn.bert_padding import index_first_axis as _index_first_axis
+    from flash_attn.bert_padding import pad_input as _pad_input
+    from flash_attn.bert_padding import unpad_input as _unpad_input
+
+    flash_attn_func, flash_attn_varlen_func = _flash_attn_func, _flash_attn_varlen_func
+    pad_input, index_first_axis, unpad_input = _pad_input, _index_first_axis, _unpad_input
+    has_flash_attn = True
+except:
+    has_flash_attn = False
+
+
+def _import_flash_attn():
+    global flash_attn_func, flash_attn_varlen_func
+    global pad_input, index_first_axis, unpad_input
+    try:
+        from flash_attn import flash_attn_func as _flash_attn_func
+        from flash_attn import \
+            flash_attn_varlen_func as _flash_attn_varlen_func
+        from flash_attn.bert_padding import \
+            index_first_axis as _index_first_axis
+        from flash_attn.bert_padding import pad_input as _pad_input
+        from flash_attn.bert_padding import unpad_input as _unpad_input
+        flash_attn_func, flash_attn_varlen_func = _flash_attn_func, _flash_attn_varlen_func
+        pad_input, index_first_axis, unpad_input = _pad_input, _index_first_axis, _unpad_input
+    except ImportError:
+        raise ImportError('flash_attn is not installed.')
+
+
+# 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.torch.int32), (1, 0))
+    return (
+        indices,
+        cu_seqlens,
+        max_seqlen_in_batch,
+    )
+
+
+# Copied from transformers.models.bart.modeling_bart._make_causal_mask
+def _make_causal_mask(
+    input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
+):
+    """
+    Make causal mask used for bi-directional self-attention.
+    """
+    bsz, tgt_len = input_ids_shape
+    mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min, device=device), device=device)
+    mask_cond = torch.arange(mask.size(-1), device=device)
+    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
+    mask = mask.to(dtype)
+
+    if past_key_values_length > 0:
+        mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
+    return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
+
+
+# Copied from transformers.models.bart.modeling_bart._expand_mask
+def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
+    """
+    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
+    """
+    bsz, src_len = mask.size()
+    tgt_len = tgt_len if tgt_len is not None else src_len
+
+    expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
+
+    inverted_mask = 1.0 - expanded_mask
+
+    return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->InternLM2
+class InternLM2RMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        InternLM2RMSNorm 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)
+
+
+try:
+    from functools import partial
+
+    from apex.normalization import FusedRMSNorm
+    InternLM2RMSNorm = partial(FusedRMSNorm, eps=1e-6)   # noqa
+    print('Discovered apex.normalization.FusedRMSNorm - will use it instead of InternLM2RMSNorm')
+except ImportError:
+    # using the normal LlamaRMSNorm
+    pass
+except Exception:
+    print('discovered apex but it failed to load, falling back to InternLM2RMSNorm')
+    pass
+
+
+# Copied from transformers.model.llama.modeling_llama.LlamaRotaryEmbedding with Llama->InternLM2
+class InternLM2RotaryEmbedding(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).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).to(dtype=self.inv_freq.dtype)
+
+        freqs = torch.einsum('i,j->ij', 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=torch.float32)
+
+        return (
+            self.cos_cached[:seq_len].to(dtype=x.dtype),
+            self.sin_cached[:seq_len].to(dtype=x.dtype),
+        )
+
+
+# Copied from transformers.model.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->InternLM2
+class InternLM2LinearScalingRotaryEmbedding(InternLM2RotaryEmbedding):
+    """InternLM2RotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
+
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
+        self.scaling_factor = scaling_factor
+        super().__init__(dim, max_position_embeddings, base, device)
+
+    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).to(dtype=self.inv_freq.dtype)
+        t = t / self.scaling_factor
+
+        freqs = torch.einsum('i,j->ij', 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)
+
+
+# Copied from transformers.model.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->InternLM2
+class InternLM2DynamicNTKScalingRotaryEmbedding(InternLM2RotaryEmbedding):
+    """InternLM2RotaryEmbedding extended with Dynamic NTK scaling.
+    Credits to the Reddit users /u/bloc97 and /u/emozilla.
+    """
+
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
+        self.scaling_factor = scaling_factor
+        super().__init__(dim, max_position_embeddings, base, device)
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+
+        if seq_len > self.max_position_embeddings:
+            base = self.base * (
+                (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
+            ) ** (self.dim / (self.dim - 2))
+            inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+            self.register_buffer('inv_freq', inv_freq, persistent=False)
+
+        t = torch.arange(self.max_seq_len_cached, device=device).to(dtype=self.inv_freq.dtype)
+
+        freqs = torch.einsum('i,j->ij', 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)
+
+
+# Copied from transformers.model.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.model.llama.modeling_llama.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."""
+    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
+
+
+class InternLM2MLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.w1 = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.w3 = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.w2 = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, x):
+        down_proj = self.w2(self.act_fn(self.w1(x)) * self.w3(x))
+
+        return down_proj
+
+
+# Copied from transformers.model.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)
+
+
+# Modified from transformers.model.llama.modeling_llama.LlamaAttention
+class InternLM2Attention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: InternLM2Config):
+        super().__init__()
+        self.config = config
+        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.is_causal = True
+
+        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.wqkv = nn.Linear(
+            self.hidden_size,
+            (self.num_heads + 2 * self.num_key_value_heads) * self.head_dim,
+            bias=config.bias,
+        )
+
+        self.wo = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.bias)
+        self._init_rope()
+
+    def _init_rope(self):
+        if self.config.rope_scaling is None:
+            self.rotary_emb = InternLM2RotaryEmbedding(
+                self.head_dim,
+                max_position_embeddings=self.max_position_embeddings,
+                base=self.config.rope_theta,
+            )
+        else:
+            scaling_type = self.config.rope_scaling['type']
+            scaling_factor = self.config.rope_scaling['factor']
+            if scaling_type == 'dynamic':
+                self.rotary_emb = InternLM2DynamicNTKScalingRotaryEmbedding(
+                    self.head_dim,
+                    max_position_embeddings=self.max_position_embeddings,
+                    base=self.config.rope_theta,
+                    scaling_factor=scaling_factor,
+                )
+            elif scaling_type == 'linear':
+                self.rotary_emb = InternLM2LinearScalingRotaryEmbedding(
+                    self.head_dim,
+                    max_position_embeddings=self.max_position_embeddings,
+                    base=self.config.rope_theta,
+                    scaling_factor=scaling_factor,
+                )
+            else:
+                raise ValueError("Currently we only support rotary embedding's type being 'dynamic' or 'linear'.")
+        return self.rotary_emb
+
+    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+
+    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: bool = False,
+        use_cache: bool = False,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        if 'padding_mask' in kwargs:
+            warnings.warn(
+                'Passing `padding_mask` is deprecated and will be removed in v4.37. '
+                'Please make sure use `attention_mask` instead.`'
+            )
+
+        bsz, q_len, _ = hidden_states.size()
+
+        qkv_states = self.wqkv(hidden_states)
+
+        qkv_states = rearrange(
+            qkv_states,
+            'b q (h gs d) -> b q h gs d',
+            gs=2 + self.num_key_value_groups,
+            d=self.head_dim,
+        )
+
+        query_states = qkv_states[..., : self.num_key_value_groups, :]
+        query_states = rearrange(query_states, 'b q h gs d -> b q (h gs) d')
+        key_states = qkv_states[..., -2, :]
+        value_states = qkv_states[..., -1, :]
+
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            kv_seq_len += past_key_value[0].shape[-2]
+        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:
+            # reuse k, v, self_attention
+            key_states = torch.cat([past_key_value[0], key_states], dim=2)
+            value_states = torch.cat([past_key_value[1], value_states], dim=2)
+
+        past_key_value = (key_states, value_states) if use_cache else None
+
+        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_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.wo(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+# Modified from transformers.model.llama.modeling_llama.InternLM2FlashAttention2
+class InternLM2FlashAttention2(InternLM2Attention):
+    """
+    InternLM2 flash attention module. This module inherits from `InternLM2Attention` 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.
+    """
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        # InternLM2FlashAttention2 attention does not support output_attentions
+        if 'padding_mask' in kwargs:
+            warnings.warn(
+                'Passing `padding_mask` is deprecated and will be removed in v4.37. '
+                'Please make sure use `attention_mask` instead.`'
+            )
+
+            # overwrite attention_mask with padding_mask
+            attention_mask = kwargs.pop('padding_mask')
+        
+        output_attentions = False
+
+        bsz, q_len, _ = hidden_states.size()
+
+        qkv_states = self.wqkv(hidden_states)
+
+        qkv_states = rearrange(
+            qkv_states,
+            'b q (h gs d) -> b q h gs d',
+            gs=2 + self.num_key_value_groups,
+            d=self.head_dim,
+        )
+
+        query_states = qkv_states[..., : self.num_key_value_groups, :]
+        query_states = rearrange(query_states, 'b q h gs d -> b q (h gs) d')
+        key_states = qkv_states[..., -2, :]
+        value_states = qkv_states[..., -1, :]
+
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            kv_seq_len += past_key_value[0].shape[-2]
+
+        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:
+            # reuse k, v, self_attention
+            key_states = torch.cat([past_key_value[0], key_states], dim=2)
+            value_states = torch.cat([past_key_value[1], value_states], dim=2)
+
+        past_key_value = (key_states, value_states) if use_cache else None
+
+        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
+        )
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
+        attn_output = self.wo(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
+    ):
+        """
+        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 (`int`, *optional*):
+                Attention dropout
+            softmax_scale (`float`, *optional*):
+                The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
+        """
+        # Contains at least one padding token in the sequence
+        causal = self.is_causal and query_length != 1
+        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._unpad_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
+
+            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,
+            )
+
+            attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
+        else:
+            attn_output = flash_attn_func(
+                query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
+            )
+
+        return attn_output
+
+    def _unpad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
+        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
+        batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
+
+        key_layer = index_first_axis(
+            key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
+        )
+        value_layer = index_first_axis(
+            value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
+        )
+
+        if query_length == kv_seq_len:
+            query_layer = index_first_axis(
+                query_layer.reshape(batch_size * kv_seq_len, self.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.to(torch.int64),
+            (cu_seqlens_q, cu_seqlens_k),
+            (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
+        )
+
+
+INTERNLM2_ATTENTION_CLASSES = {
+    'eager': InternLM2Attention,
+    'flash_attention_2': InternLM2FlashAttention2,
+}
+
+
+# Modified from transformers.model.llama.modeling_llama.LlamaDecoderLayer
+class InternLM2DecoderLayer(nn.Module):
+    def __init__(self, config: InternLM2Config):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+
+        self.attention = INTERNLM2_ATTENTION_CLASSES[config.attn_implementation](config=config)
+
+        self.feed_forward = InternLM2MLP(config)
+        self.attention_norm = InternLM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.ffn_norm = InternLM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        **kwargs,
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+            attention_mask (`torch.FloatTensor`, *optional*):
+                attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
+                query_sequence_length, key_sequence_length)` if default attention is used.
+            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
+        """
+        if 'padding_mask' in kwargs:
+            warnings.warn(
+                'Passing `padding_mask` is deprecated and will be removed in v4.37. '
+                'Please make sure use `attention_mask` instead.`'
+            )
+
+        residual = hidden_states
+
+        hidden_states = self.attention_norm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights, present_key_value = self.attention(
+            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,
+            **kwargs,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.ffn_norm(hidden_states)
+        hidden_states = self.feed_forward(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
+
+
+InternLM2_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 ([`InternLM2Config`]):
+            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.
+"""
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaPreTrainedModel with Llama->InternLM2
+@add_start_docstrings(
+    'The bare InternLM2 Model outputting raw hidden-states without any specific head on top.',
+    InternLM2_START_DOCSTRING,
+)
+class InternLM2PreTrainedModel(PreTrainedModel):
+    config_class = InternLM2Config
+    base_model_prefix = 'model'
+    supports_gradient_checkpointing = True
+    _no_split_modules = ['InternLM2DecoderLayer']
+    _skip_keys_device_placement = 'past_key_values'
+    _supports_flash_attn_2 = 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_()
+
+
+InternLM2_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 `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 (`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)`) and 2 additional tensors of shape
+            `(batch_size, num_heads, decoder_sequence_length, embed_size_per_head)`.
+
+            Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+            blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
+
+            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.
+"""
+
+
+# Modified from transformers.model.llama.modeling_llama.LlamaModel
+@add_start_docstrings(
+    'The bare InternLM2 Model outputting raw hidden-states without any specific head on top.',
+    InternLM2_START_DOCSTRING,
+)
+class InternLM2Model(InternLM2PreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`InternLM2DecoderLayer`]
+
+    Args:
+        config: InternLM2Config
+    """
+
+    _auto_class = 'AutoModel'
+
+    def __init__(self, config: InternLM2Config):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+        self.config = config
+        # import pdb; pdb.set_trace()
+        if not has_flash_attn:
+            self.config.attn_implementation = 'eager'
+            print('Warning: Flash attention is not available, using eager attention instead.')
+
+        self.tok_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+
+        self.layers = nn.ModuleList([InternLM2DecoderLayer(config) for _ in range(config.num_hidden_layers)])
+        self.norm = InternLM2RMSNorm(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.tok_embeddings
+
+    def set_input_embeddings(self, value):
+        self.tok_embeddings = value
+
+    def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
+        # create causal mask
+        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+        combined_attention_mask = None
+        if input_shape[-1] > 1:
+            combined_attention_mask = _make_causal_mask(
+                input_shape,
+                inputs_embeds.dtype,
+                device=inputs_embeds.device,
+                past_key_values_length=past_key_values_length,
+            )
+
+        if attention_mask is not None:
+            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+            expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
+                inputs_embeds.device
+            )
+            combined_attention_mask = (
+                expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
+            )
+
+        return combined_attention_mask
+
+    @add_start_docstrings_to_model_forward(InternLM2_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
+
+        if self.config.attn_implementation == 'flash_attention_2':
+            _import_flash_attn()
+
+        # retrieve input_ids and inputs_embeds
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError('You cannot specify both input_ids and inputs_embeds at the same time')
+        elif input_ids is not None:
+            batch_size, seq_length = input_ids.shape[:2]
+        elif inputs_embeds is not None:
+            batch_size, seq_length = inputs_embeds.shape[:2]
+        else:
+            raise ValueError('You have to specify either input_ids or inputs_embeds')
+
+        seq_length_with_past = seq_length
+        past_key_values_length = 0
+        if past_key_values is not None:
+            past_key_values_length = past_key_values[0][0].shape[2]
+            seq_length_with_past = seq_length_with_past + past_key_values_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)
+
+        if inputs_embeds is None:
+            inputs_embeds = self.tok_embeddings(input_ids)
+
+        if self.config.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
+        else:
+            if attention_mask is None:
+                attention_mask = torch.ones(
+                    (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
+                )
+            attention_mask = self._prepare_decoder_attention_mask(
+                attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
+            )
+
+        # embed positions
+        hidden_states = 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
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = () if use_cache else None
+
+        for idx, decoder_layer in enumerate(self.layers):
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            past_key_value = past_key_values[idx] if past_key_values is not None else None
+
+            if self.gradient_checkpointing and self.training:
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        # None for past_key_value
+                        return module(*inputs, output_attentions, None)
+
+                    return custom_forward
+
+                layer_outputs = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(decoder_layer),
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    None,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    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 = 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 = next_decoder_cache if use_cache else None
+        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,
+        )
+
+
+# Modified from transformers.model.llama.modeling_llama.LlamaForCausalLM
+class InternLM2ForCausalLM(InternLM2PreTrainedModel, GenerationMixin):
+    _auto_class = 'AutoModelForCausalLM'
+
+    _tied_weights_keys = ['output.weight']
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = InternLM2Model(config)
+        self.vocab_size = config.vocab_size
+        self.output = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.tok_embeddings
+
+    def set_input_embeddings(self, value):
+        self.model.tok_embeddings = value
+
+    def get_output_embeddings(self):
+        return self.output
+
+    def set_output_embeddings(self, new_embeddings):
+        self.output = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+
+    @add_start_docstrings_to_model_forward(InternLM2_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        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, InternLM2ForCausalLM
+
+        >>> model = InternLM2ForCausalLM.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."
+        ```"""
+
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        hidden_states = outputs[0]
+        logits = self.output(hidden_states)
+        logits = logits.float()
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            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)
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        device = input_ids.device if input_ids is not None else inputs_embeds.device
+        output = CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+        output['logits'] = output['logits'].to(device)
+        return output
+
+    def prepare_inputs_for_generation(
+            self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
+    ):
+        # HF>=4.57 may initialize `past_key_values` as a Cache object (e.g. `DynamicCache`).
+        # InternLM2 currently implements the legacy tuple-of-tuples cache format, so we convert
+        # non-empty Cache instances back to the legacy format and treat empty caches as None.
+        if past_key_values is not None and hasattr(past_key_values, "to_legacy_cache"):
+            try:
+                cache_len = past_key_values.get_seq_length() if hasattr(past_key_values, "get_seq_length") else 0
+            except Exception:
+                cache_len = 0
+            if cache_len == 0:
+                past_key_values = None
+            else:
+                past_key_values = past_key_values.to_legacy_cache()
+
+        if past_key_values is not None:
+            first_key = past_key_values[0][0] if past_key_values[0] is not None else None
+            past_length = first_key.shape[2] if first_key is not None else 0
+
+            # Some generation methods already pass only the last input ID
+            if input_ids.shape[1] > past_length:
+                remove_prefix_length = past_length
+            else:
+                # Default to old behavior: keep only final ID
+                remove_prefix_length = input_ids.shape[1] - 1
+
+            input_ids = input_ids[:, remove_prefix_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,
+            }
+        )
+        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
+
+    def build_inputs(self, tokenizer, query: str, history: List[Tuple[str, str]] = [], meta_instruction=''):
+        if tokenizer.add_bos_token:
+            prompt = ''
+        else:
+            prompt = tokenizer.bos_token
+        if meta_instruction:
+            prompt += f"""<|im_start|>system\n{meta_instruction}<|im_end|>\n"""
+        for record in history:
+            prompt += f"""<|im_start|>user\n{record[0]}<|im_end|>\n<|im_start|>assistant\n{record[1]}<|im_end|>\n"""
+        prompt += f"""<|im_start|>user\n{query}<|im_end|>\n<|im_start|>assistant\n"""
+        return tokenizer([prompt], return_tensors='pt')
+
+    @torch.no_grad()
+    def chat(
+        self,
+        tokenizer,
+        query: str,
+        history: List[Tuple[str, str]] = [],
+        streamer: Optional[BaseStreamer] = None,
+        max_new_tokens: int = 1024,
+        do_sample: bool = True,
+        temperature: float = 0.8,
+        top_p: float = 0.8,
+        meta_instruction: str = 'You are an AI assistant whose name is InternLM (书生·浦语).\n'
+                                '- InternLM (书生·浦语) is a conversational language model that is developed by Shanghai AI Laboratory (上海人工智能实验室). It is designed to be helpful, honest, and harmless.\n'
+                                '- InternLM (书生·浦语) can understand and communicate fluently in the language chosen by the user such as English and 中文.',
+        **kwargs,
+    ):
+        inputs = self.build_inputs(tokenizer, query, history, meta_instruction)
+        inputs = {k: v.to(self.device) for k, v in inputs.items() if torch.is_tensor(v)}
+        # also add end-of-assistant token in eos token id to avoid unnecessary generation
+        eos_token_id = [tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids(['<|im_end|>'])[0]]
+        outputs = self.generate(
+            **inputs,
+            streamer=streamer,
+            max_new_tokens=max_new_tokens,
+            do_sample=do_sample,
+            temperature=temperature,
+            top_p=top_p,
+            eos_token_id=eos_token_id,
+            **kwargs,
+        )
+        outputs = outputs[0].cpu().tolist()[len(inputs['input_ids'][0]):]
+        response = tokenizer.decode(outputs, skip_special_tokens=True)
+        response = response.split('<|im_end|>')[0]
+        history = history + [(query, response)]
+        return response, history
+
+    @torch.no_grad()
+    def stream_chat(
+        self,
+        tokenizer,
+        query: str,
+        history: List[Tuple[str, str]] = [],
+        max_new_tokens: int = 1024,
+        do_sample: bool = True,
+        temperature: float = 0.8,
+        top_p: float = 0.8,
+        **kwargs,
+    ):
+        """
+        Return a generator in format: (response, history)
+        Eg.
+        ('你好，有什么可以帮助您的吗', [('你好', '你好，有什么可以帮助您的吗')])
+        ('你好，有什么���以帮助您的吗？', [('你好', '你好，有什么可以帮助您的吗？')])
+        """
+        if BaseStreamer is None:
+            raise ModuleNotFoundError(
+                'The version of `transformers` is too low. Please make sure '
+                'that you have installed `transformers>=4.28.0`.'
+            )
+
+        response_queue = queue.Queue(maxsize=20)
+
+        class ChatStreamer(BaseStreamer):
+            def __init__(self, tokenizer) -> None:
+                super().__init__()
+                self.tokenizer = tokenizer
+                self.queue = response_queue
+                self.query = query
+                self.history = history
+                self.response = ''
+                self.cache = []
+                self.received_inputs = False
+                self.queue.put((self.response, history + [(self.query, self.response)]))
+
+            def put(self, value):
+                if len(value.shape) > 1 and value.shape[0] > 1:
+                    raise ValueError('ChatStreamer only supports batch size 1')
+                elif len(value.shape) > 1:
+                    value = value[0]
+
+                if not self.received_inputs:
+                    # The first received value is input_ids, ignore here
+                    self.received_inputs = True
+                    return
+
+                self.cache.extend(value.tolist())
+                token = self.tokenizer.decode(self.cache, skip_special_tokens=True)
+                if token.strip() != '<|im_end|>':
+                    self.response = self.response + token
+                    history = self.history + [(self.query, self.response)]
+                    self.queue.put((self.response, history))
+                    self.cache = []
+                else:
+                    self.end()
+
+            def end(self):
+                self.queue.put(None)
+
+        def stream_producer():
+            return self.chat(
+                tokenizer=tokenizer,
+                query=query,
+                streamer=ChatStreamer(tokenizer=tokenizer),
+                history=history,
+                max_new_tokens=max_new_tokens,
+                do_sample=do_sample,
+                temperature=temperature,
+                top_p=top_p,
+                **kwargs,
+            )
+
+        def consumer():
+            producer = threading.Thread(target=stream_producer)
+            producer.start()
+            while True:
+                res = response_queue.get()
+                if res is None:
+                    return
+                yield res
+
+        return consumer()
+
+
+# Copied from transformers.model.llama.modeling_llama.LlamaForSequenceClassification with Llama->InternLM2
+@add_start_docstrings(
+    """
+    The InternLM2 Model transformer with a sequence classification head on top (linear layer).
+
+    [`InternLM2ForSequenceClassification`] 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).
+    """,
+    InternLM2_START_DOCSTRING,
+)
+class InternLM2ForSequenceClassification(InternLM2PreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = InternLM2Model(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.tok_embeddings
+
+    def set_input_embeddings(self, value):
+        self.model.tok_embeddings = value
+
+    @add_start_docstrings_to_model_forward(InternLM2_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:
+                sequence_lengths = (torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1).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,
+        )

preprocessor_config.json

@@ -0,0 +1,27 @@
+{
+  "crop_size": {
+    "height": 448,
+    "width": 448
+  },
+  "do_center_crop": true,
+  "do_convert_rgb": true,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.485,
+    0.456,
+    0.406
+  ],
+  "image_processor_type": "CLIPImageProcessor",
+  "image_std": [
+    0.229,
+    0.224,
+    0.225
+  ],
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "shortest_edge": 448
+  }
+}

processing_qts_plus_internvl2_5.py

@@ -0,0 +1,164 @@
+"""
+Self-contained processor shim for trust_remote_code.
+
+This processor:
+- loads frames from a video path (OpenCV)
+- applies InternVL2.5 `<img>...<IMG_CONTEXT>*N...</img>` token template
+- returns `vision_input`, `input_ids`, `attention_mask`, and `question_input_ids`
+  compatible with `QTSplusInternLM2_ForCausalLM.generate`.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import List, Optional, Union
+
+import numpy as np
+from PIL import Image
+
+from transformers.feature_extraction_utils import BatchFeature
+from transformers.processing_utils import ProcessorMixin
+from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
+
+
+def _uniform_indices(num_frames: int, vlen: int) -> List[int]:
+    num_frames = max(int(num_frames), 1)
+    if vlen <= 0:
+        return []
+    if num_frames == 1:
+        return [max(0, (vlen - 1) // 2)]
+    last = vlen - 1
+    return [int(round(i * last / (num_frames - 1))) for i in range(num_frames)]
+
+
+def _load_video_frames_cv2(path: str, num_frames: int = 8) -> List[Image.Image]:
+    import cv2
+
+    cap = cv2.VideoCapture(path)
+    if not cap.isOpened():
+        raise FileNotFoundError(f"Failed to open video: {path}")
+
+    vlen = int(cap.get(cv2.CAP_PROP_FRAME_COUNT) or 0)
+    if vlen <= 0:
+        # Fallback: decode sequentially and take the first `num_frames`.
+        frames: List[Image.Image] = []
+        while len(frames) < num_frames:
+            ok, frame = cap.read()
+            if not ok:
+                break
+            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+            frames.append(Image.fromarray(frame))
+        cap.release()
+        return frames
+
+    indices = _uniform_indices(num_frames, vlen)
+    frames = []
+    for idx in indices:
+        cap.set(cv2.CAP_PROP_POS_FRAMES, int(idx))
+        ok, frame = cap.read()
+        if not ok:
+            continue
+        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        frames.append(Image.fromarray(frame))
+    cap.release()
+    return frames
+
+
+@dataclass
+class QTSplusInternVL2_5_ProcessorKwargs:
+    num_frames: int = 8
+    system_prompt: str = "You are a helpful assistant."
+
+
+class QTSplusInternVL2_5_Processor(ProcessorMixin):
+    attributes = ["image_processor", "tokenizer"]
+    image_processor_class = "AutoImageProcessor"
+    tokenizer_class = "AutoTokenizer"
+
+    def __init__(self, image_processor=None, tokenizer=None, **kwargs):
+        super().__init__(image_processor=image_processor, tokenizer=tokenizer)
+        self.img_start_token = "<img>"
+        self.img_end_token = "</img>"
+        self.img_context_token = "<IMG_CONTEXT>"
+
+        # InternVL2.5 default: (448/14)^2 * (0.5^2) = 256 tokens per image.
+        self.num_image_token = int(kwargs.pop("num_image_token", 256))
+        self.system_prompt = str(kwargs.pop("system_prompt", "You are a helpful assistant."))
+
+    def _build_image_tokens(self, num_images: int) -> str:
+        num_images = max(int(num_images), 1)
+        one = self.img_start_token + (self.img_context_token * int(self.num_image_token)) + self.img_end_token
+        return one * num_images
+
+    def __call__(
+        self,
+        text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
+        images=None,
+        videos: Optional[Union[str, List[Image.Image]]] = None,
+        return_tensors: Optional[str] = "pt",
+        num_frames: int = 8,
+        system_prompt: Optional[str] = None,
+        **kwargs,
+    ) -> BatchFeature:
+        if text is None:
+            raise ValueError("`text` is required")
+        if isinstance(text, list):
+            if len(text) != 1:
+                raise ValueError("Only single-example processing is supported for now")
+            text = text[0]
+
+        if videos is not None and images is not None:
+            raise ValueError("Pass only one of `videos` or `images`")
+
+        if videos is not None:
+            if isinstance(videos, str):
+                frames = _load_video_frames_cv2(videos, num_frames=num_frames)
+            elif isinstance(videos, list):
+                frames = videos
+            else:
+                raise ValueError(f"Unsupported `videos` type: {type(videos)}")
+            images = frames
+
+        if images is None:
+            raise ValueError("Either `videos` or `images` must be provided")
+        if isinstance(images, Image.Image):
+            images = [images]
+
+        if not isinstance(images, list) or not images:
+            raise ValueError("No frames/images loaded")
+
+        img_tokens = self._build_image_tokens(num_images=len(images))
+        user_content = f"{img_tokens}\n{text}"
+        messages = [
+            {"role": "system", "content": system_prompt or self.system_prompt},
+            {"role": "user", "content": user_content},
+        ]
+
+        if not hasattr(self.tokenizer, "apply_chat_template"):
+            raise ValueError("Tokenizer does not support apply_chat_template; missing chat_template.jinja?")
+
+        prompt = self.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+
+        text_inputs = self.tokenizer(
+            prompt,
+            add_special_tokens=False,
+            return_tensors=return_tensors,
+        )
+        question_inputs = self.tokenizer(
+            str(text),
+            add_special_tokens=False,
+            return_tensors=return_tensors,
+        )
+
+        vision_inputs = self.image_processor(images=images, return_tensors=return_tensors)
+        pixel_values = vision_inputs["pixel_values"]
+
+        return BatchFeature(
+            data={
+                "input_ids": text_inputs["input_ids"],
+                "attention_mask": text_inputs.get("attention_mask"),
+                "question_input_ids": question_inputs["input_ids"],
+                "vision_input": pixel_values,
+            },
+            tensor_type=return_tensors,
+        )

processor_config.json

@@ -0,0 +1,8 @@
+{
+  "auto_map": {
+    "AutoProcessor": "processing_qts_plus_internvl2_5.QTSplusInternVL2_5_Processor"
+  },
+  "image_processor_type": "CLIPImageProcessor",
+  "processor_class": "QTSplusInternVL2_5_Processor",
+  "tokenizer_class": "AutoTokenizer"
+}

qts_plus.py

@@ -0,0 +1,791 @@
+# -*- encoding: utf-8 -*-
+# @File        :   qts_plus.py
+# @Time        :   2025/08/27 03:12:40
+# @Author      :   Siyou
+# @Description :
+
+from __future__ import annotations
+import math
+from dataclasses import dataclass
+from typing import Optional, Tuple, Dict, Any
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+
+# Small utilities
+class RMSNorm(nn.Module):
+    def __init__(self, d: int, eps: float = 1e-6):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(d))
+        self.eps = eps
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        # x: [B, T, D]
+        norm = x.pow(2).mean(dim=-1, keepdim=True)
+        x = x * torch.rsqrt(norm + self.eps)
+        return self.weight * x
+
+
+class RMSNormFp32(nn.Module):
+    """RMSNorm that computes statistics in fp32 for stability (common in modern LMs)."""
+
+    def __init__(self, d: int, eps: float = 1e-6):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(d))
+        self.eps = eps
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        dtype = x.dtype
+        x_fp32 = x.to(torch.float32)
+        var = x_fp32.pow(2).mean(dim=-1, keepdim=True)
+        x_norm = x_fp32 * torch.rsqrt(var + self.eps)
+        return (self.weight * x_norm).to(dtype)
+
+
+class FeedForward(nn.Module):
+    def __init__(self, d_model: int, d_ff: int, dropout: float = 0.0):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Linear(d_model, d_ff),
+            nn.GELU(),
+            nn.Linear(d_ff, d_model),
+            nn.Dropout(dropout),
+        )
+
+    def forward(self, x):  # [B, T, D]
+        return self.net(x)
+
+
+class TinyTransformerBlock(nn.Module):
+    """
+    Lightweight re-encoding block used after pruning.
+    Single block with RMSNorms, MHA, FFN.
+    """
+    def __init__(self, d_model: int, n_heads: int = 8, d_ff: Optional[int] = None, dropout: float = 0.0):
+        super().__init__()
+        self.norm1 = RMSNorm(d_model)
+        self.mha = nn.MultiheadAttention(d_model, n_heads, dropout=dropout, batch_first=True)
+        self.norm2 = RMSNorm(d_model)
+        self.ffn = FeedForward(d_model, d_ff or (4 * d_model), dropout=dropout)
+
+    def forward(self, x: torch.Tensor, key_padding_mask: Optional[torch.Tensor] = None) -> torch.Tensor:
+        # x: [B, T, D]
+        h = self.norm1(x)
+        # self-attention on pruned tokens; support key_padding_mask for padded positions
+        attn_out, _ = self.mha(h, h, h, key_padding_mask=key_padding_mask, need_weights=False)
+        x = x + attn_out
+        h = self.norm2(x)
+        x = x + self.ffn(h)
+        return x
+
+class ScoringCrossAttentionLayer(nn.Module):
+    """
+    Cross-attention block: pre-norm Q and KV, MHA(Q, K=V), then FFN on Q path.
+    Returns updated Q and optional attention weights.
+    """
+    def __init__(self, d_model: int, n_heads: int, dropout: float = 0.0, d_ff: Optional[int] = None):
+        super().__init__()
+        self.q_norm = RMSNorm(d_model)
+        self.kv_norm = RMSNorm(d_model)
+        self.mha = nn.MultiheadAttention(d_model, n_heads, dropout=dropout, batch_first=True)
+        self.ffn_norm = RMSNorm(d_model)
+        self.ffn = FeedForward(d_model, d_ff or (4 * d_model), dropout=dropout)
+
+    def forward(
+        self,
+        q: torch.Tensor,                # [B, L, D]
+        kv: torch.Tensor,               # [B, M, D]
+        kv_key_padding_mask: Optional[torch.Tensor] = None,  # [B, M]
+        need_weights: bool = False,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
+        hq = self.q_norm(q)
+        hkv = self.kv_norm(kv)
+        out, attn = self.mha(
+            hq, hkv, hkv, 
+            key_padding_mask=kv_key_padding_mask, 
+            need_weights=need_weights, 
+            average_attn_weights=False
+            )
+        q = q + out
+        h = self.ffn_norm(q)
+        q = q + self.ffn(h)
+        return q, attn
+
+class LMScoringCrossAttentionLayer(nn.Module):
+    """
+    Cross-attention block that can be initialized from a downstream decoder LM layer.
+    - Separate q/k/v projections with optional GQA/MQA (num_key_value_heads).
+    - Rotary embeddings are intentionally NOT applied (kv come from vision features).
+    - Pre/post RMSNorm + simple FFN on the query path.
+
+    Notes:
+    - If num_key_value_heads < num_heads, k/v heads are repeated to match num_heads.
+    """
+    def __init__(
+        self,
+        d_model: int,
+        num_heads: int,
+        num_key_value_heads: Optional[int] = None,
+        dropout: float = 0.0,
+        d_ff: Optional[int] = None,
+        rms_norm_eps: float = 1e-6,
+    ):
+        super().__init__()
+        assert d_model % num_heads == 0, "hidden size must be divisible by num_heads"
+        self.hidden_size = d_model
+        self.num_heads = num_heads
+        self.head_dim = d_model // num_heads
+        self.num_key_value_heads = num_key_value_heads or num_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.attention_dropout = dropout
+
+        # Norms (fp32 stats for stability; common across modern decoder LMs)
+        self.q_norm = RMSNormFp32(d_model, eps=rms_norm_eps)
+        self.kv_norm = RMSNormFp32(d_model, eps=rms_norm_eps)
+        self.ffn_norm = RMSNormFp32(d_model, eps=rms_norm_eps)
+
+        # LM-style projections (q/k/v may be GQA/MQA with fewer kv 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)
+
+        # FFN on query path
+        self.ffn = FeedForward(d_model, d_ff or (4 * d_model), dropout=dropout)
+
+    @staticmethod
+    def _repeat_kv(x: torch.Tensor, n_rep: int) -> torch.Tensor:
+        # x: [B, H_kv, T, Dh] -> [B, H, T, Dh]
+        b, h_kv, t, dh = x.shape
+        if n_rep == 1:
+            return x
+        x = x[:, :, None, :, :].expand(b, h_kv, n_rep, t, dh)
+        return x.reshape(b, h_kv * n_rep, t, dh)
+
+    def forward(
+        self,
+        q: torch.Tensor,                # [B, L, D]
+        kv: torch.Tensor,               # [B, M, D]
+        kv_key_padding_mask: Optional[torch.Tensor] = None,  # [B, M]
+        need_weights: bool = False,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
+        B, L, _ = q.shape
+        _, M, _ = kv.shape
+
+        # Pre norms
+        qn = self.q_norm(q)
+        kvn = self.kv_norm(kv)
+
+        # Projections
+        q_states = self.q_proj(qn)      # [B, L, H*Dh]
+        k_states = self.k_proj(kvn)     # [B, M, Hkv*Dh]
+        v_states = self.v_proj(kvn)     # [B, M, Hkv*Dh]
+
+        # Reshape to heads
+        q_states = q_states.view(B, L, self.num_heads, self.head_dim).transpose(1, 2)   # [B, H, L, Dh]
+        k_states = k_states.view(B, M, self.num_key_value_heads, self.head_dim).transpose(1, 2)  # [B, Hkv, M, Dh]
+        v_states = v_states.view(B, M, self.num_key_value_heads, self.head_dim).transpose(1, 2)  # [B, Hkv, M, Dh]
+
+        # Repeat kv if necessary
+        if self.num_key_value_groups > 1:
+            k_states = self._repeat_kv(k_states, self.num_key_value_groups)
+            v_states = self._repeat_kv(v_states, self.num_key_value_groups)
+
+        # Attention weights [B, H, L, M]
+        attn_weights = torch.matmul(q_states, k_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+        if kv_key_padding_mask is not None:
+            # Convert mask [B, M] -> broadcast [B, 1, 1, M]
+            mask = kv_key_padding_mask[:, None, None, :].to(dtype=attn_weights.dtype)
+            attn_weights = attn_weights.masked_fill(mask > 0.5, float('-inf'))
+
+        # Softmax in float32 for stability
+        attn_dtype = attn_weights.dtype
+        attn_weights = F.softmax(attn_weights, dim=-1, dtype=torch.float32).to(attn_dtype)
+        attn_weights = F.dropout(attn_weights, p=self.attention_dropout, training=self.training)
+
+        # Aggregate values -> [B, H, L, Dh]
+        attn_output = torch.matmul(attn_weights, v_states)
+        attn_output = attn_output.transpose(1, 2).contiguous().view(B, L, self.num_heads * self.head_dim)
+
+        # Final projection and residual + FFN
+        out = self.o_proj(attn_output)
+        q = q + out
+        q = q + self.ffn(self.ffn_norm(q))
+
+        return q, (attn_weights if need_weights else None)
+
+    def init_from_lm_attn(
+        self,
+        lm_attn: nn.Module,
+        lm_input_norm: Optional[nn.Module] = None,
+        lm_post_attn_norm: Optional[nn.Module] = None,
+    ) -> None:
+        """Best-effort init from a downstream LM attention module + norms.
+
+        Supported projection patterns:
+        - Separate projections: `q_proj`, `k_proj`, `v_proj`, and (`o_proj` or `out_proj`).
+        - Fused QKV: (`query_key_value` or `c_attn`) where weights/biases are split into q/k/v.
+        """
+
+        def _copy_param(dst: torch.nn.Parameter, src: torch.Tensor, name: str) -> bool:
+            if dst.shape != src.shape:
+                print(f"[QTS+] Skip init for {name}: shape mismatch {tuple(src.shape)} -> {tuple(dst.shape)}")
+                return False
+            dst.copy_(src)
+            return True
+
+        def _maybe_copy_linear(dst: nn.Linear, src_w: torch.Tensor, src_b: Optional[torch.Tensor], prefix: str) -> None:
+            _copy_param(dst.weight, src_w, f"{prefix}.weight")
+            if dst.bias is None:
+                return
+            if src_b is None:
+                dst.bias.zero_()
+                return
+            _copy_param(dst.bias, src_b, f"{prefix}.bias")
+
+        with torch.no_grad():
+            # 1) Projections
+            if all(hasattr(lm_attn, n) for n in ("q_proj", "k_proj", "v_proj")):
+                q_src = lm_attn.q_proj
+                k_src = lm_attn.k_proj
+                v_src = lm_attn.v_proj
+                o_src = getattr(lm_attn, "o_proj", None) or getattr(lm_attn, "out_proj", None)
+                if o_src is None:
+                    o_src = getattr(lm_attn, "c_proj", None)
+                if q_src is not None:
+                    _maybe_copy_linear(self.q_proj, q_src.weight, getattr(q_src, "bias", None), "q_proj")
+                if k_src is not None:
+                    _maybe_copy_linear(self.k_proj, k_src.weight, getattr(k_src, "bias", None), "k_proj")
+                if v_src is not None:
+                    _maybe_copy_linear(self.v_proj, v_src.weight, getattr(v_src, "bias", None), "v_proj")
+                if o_src is not None and hasattr(o_src, "weight"):
+                    _copy_param(self.o_proj.weight, o_src.weight, "o_proj.weight")
+            else:
+                # Fused QKV weights common in some HF models (e.g., GPT-NeoX, GPT-2 style)
+                fused = getattr(lm_attn, "query_key_value", None) or getattr(lm_attn, "c_attn", None)
+                out = getattr(lm_attn, "o_proj", None) or getattr(lm_attn, "out_proj", None) or getattr(lm_attn, "c_proj", None)
+                if fused is not None and hasattr(fused, "weight"):
+                    w = fused.weight
+                    b = getattr(fused, "bias", None)
+                    # Handle both (3D, D) and (D, 3D) conventions
+                    if w.shape[0] == 3 * self.hidden_size and w.shape[1] == self.hidden_size:
+                        qw, kw, vw = w.split(self.hidden_size, dim=0)
+                        qb, kb, vb = (b.split(self.hidden_size, dim=0) if b is not None and b.numel() == 3 * self.hidden_size else (None, None, None))
+                    elif w.shape[0] == self.hidden_size and w.shape[1] == 3 * self.hidden_size:
+                        qw, kw, vw = w.split(self.hidden_size, dim=1)
+                        qw, kw, vw = qw.t(), kw.t(), vw.t()
+                        qb, kb, vb = (b.split(self.hidden_size, dim=0) if b is not None and b.numel() == 3 * self.hidden_size else (None, None, None))
+                    else:
+                        qw = kw = vw = qb = kb = vb = None
+
+                    if qw is not None:
+                        _maybe_copy_linear(self.q_proj, qw, qb, "q_proj")
+                        _maybe_copy_linear(self.k_proj, kw, kb, "k_proj")
+                        _maybe_copy_linear(self.v_proj, vw, vb, "v_proj")
+                    if out is not None and hasattr(out, "weight"):
+                        _copy_param(self.o_proj.weight, out.weight, "o_proj.weight")
+
+            # 2) Norms
+            if lm_input_norm is not None and hasattr(lm_input_norm, "weight"):
+                _copy_param(self.q_norm.weight, lm_input_norm.weight, "q_norm.weight")
+                _copy_param(self.kv_norm.weight, lm_input_norm.weight, "kv_norm.weight")
+            if lm_post_attn_norm is not None and hasattr(lm_post_attn_norm, "weight"):
+                _copy_param(self.ffn_norm.weight, lm_post_attn_norm.weight, "ffn_norm.weight")
+            elif lm_input_norm is not None and hasattr(lm_input_norm, "weight"):
+                _copy_param(self.ffn_norm.weight, lm_input_norm.weight, "ffn_norm.weight")
+
+        print("[QTS+] Scoring layer initialized from downstream LM weights (best-effort).")
+
+class LMSelfReencodeLayer(nn.Module):
+    """
+    Thin wrapper that reuses LMScoringCrossAttentionLayer as a self-attention
+    re-encoding block (q == kv).
+    """
+    def __init__(
+        self,
+        d_model: int,
+        num_heads: int,
+        num_key_value_heads: Optional[int] = None,
+        dropout: float = 0.0,
+        d_ff: Optional[int] = None,
+        rms_norm_eps: float = 1e-6,
+    ):
+        super().__init__()
+        self.core = LMScoringCrossAttentionLayer(
+            d_model=d_model,
+            num_heads=num_heads,
+            num_key_value_heads=num_key_value_heads or num_heads,
+            dropout=dropout,
+            d_ff=d_ff,
+            rms_norm_eps=rms_norm_eps,
+        )
+
+    def forward(self, x: torch.Tensor, key_padding_mask: Optional[torch.Tensor] = None) -> torch.Tensor:
+        y, _ = self.core(x, x, kv_key_padding_mask=key_padding_mask, need_weights=False)
+        return y
+
+    def init_from_lm_attn(
+        self,
+        lm_attn: nn.Module,
+        lm_input_norm: Optional[nn.Module] = None,
+        lm_post_attn_norm: Optional[nn.Module] = None,
+    ) -> None:
+        self.core.init_from_lm_attn(lm_attn, lm_input_norm=lm_input_norm, lm_post_attn_norm=lm_post_attn_norm)
+
+# QTS+ 
+class BudgetHead(nn.Module):
+    """
+    ρ = ρ_min + (ρ_max - ρ_min) * σ( MLP([sq, log M, max r, H(p)]) )
+    where sq is the mean query embedding.
+    """
+    def __init__(self, d_model: int, hidden: int = 256, rho_min: float = 0.05, rho_max: float = 0.5):
+        super().__init__()
+        self.rho_min = rho_min
+        self.rho_max = rho_max
+        self.mlp = nn.Sequential(
+            nn.Linear(d_model + 3, hidden),
+            nn.GELU(),
+            nn.Linear(hidden, 1)
+        )
+
+    def forward(self, sq: torch.Tensor, logM: torch.Tensor, r_max: torch.Tensor, H: torch.Tensor) -> torch.Tensor:
+        """
+        sq: [B, D] (mean of query embeddings)
+        logM, r_max, H: [B]
+        returns ρ in [rho_min, rho_max], shape [B]
+        """
+        B, D = sq.shape
+        x = torch.cat([sq, logM.view(B, 1), r_max.view(B, 1), H.view(B, 1)], dim=1)
+        logits = self.mlp(x).squeeze(1)
+        rho = self.rho_min + (self.rho_max - self.rho_min) * torch.sigmoid(logits)
+        return rho
+
+class QTSplus(nn.Module):
+    """
+    Query-Aware Token Selector with Adaptive Budget.
+    - Cross-attention scoring: r in [0,1]^M via max over text & heads.
+    - Predict ρ from query & video stats.
+    - Train mode: differentiable threshold gate with bisection.
+    - Infer mode: hard Top-n selection.
+    - Then one tiny re-encoding transformer block.
+    """
+    def __init__(
+        self,
+        d_model: int,
+        n_heads: int = 8,
+        n_kv_heads: Optional[int] = None,
+        tau_s: float = 0.1,
+        nmax: int = 2560,
+        rho_min: float = 0.05,
+        rho_max: float = 0.5,
+        block_dropout: float = 0.0,
+        use_reencode: bool = True,
+        n_scoring_layers: int = 1,
+        n_reencode_layers: int = 1,
+    ):
+        super().__init__()
+        assert d_model % n_heads == 0
+        self.d_model = d_model
+        self.n_heads = n_heads
+        self.d_head = d_model // n_heads
+        self.tau_s = tau_s
+        self.nmax = nmax
+        self.use_reencode = use_reencode
+        self.n_scoring_layers = max(int(n_scoring_layers), 1)
+        self.n_reencode_layers = max(int(n_reencode_layers), 1)
+
+        # linear projections for cross-attn scoring
+        # self.Wk = nn.Linear(d_model, d_model, bias=False)
+        # self.Wq = nn.Linear(d_model, d_model, bias=False)
+
+        # scoring layers: initialized from downstream LM when available
+        n_heads_eff = self.n_heads
+        n_kv_heads_eff = int(n_kv_heads) if (n_kv_heads is not None and int(n_kv_heads) > 0) else self.n_heads
+        self.scoring_layers = nn.ModuleList([
+            LMScoringCrossAttentionLayer(
+                d_model,
+                num_heads=n_heads_eff,
+                num_key_value_heads=n_kv_heads_eff,
+                dropout=0.0,
+                rms_norm_eps=1e-6,
+            ) for _ in range(self.n_scoring_layers)
+        ])
+        
+        self.budget = BudgetHead(d_model, rho_min=rho_min, rho_max=rho_max)
+
+        # re-encode layers: self-attention blocks that can be initialized from downstream LM
+        if use_reencode:
+            self.reencode_layers = nn.ModuleList([
+                LMSelfReencodeLayer(
+                    d_model,
+                    num_heads=self.n_heads,
+                    num_key_value_heads=n_kv_heads_eff,
+                    dropout=block_dropout,
+                    rms_norm_eps=1e-6,
+                ) for _ in range(self.n_reencode_layers)
+            ])
+        else:
+            self.reencode_layers = None
+
+    @staticmethod
+    def _entropy_from_r(r: torch.Tensor, eps: float = 1e-8) -> torch.Tensor:
+        # r: [B, M] relevance in [0,1]; form normalized p then H(p)
+        p = r / (r.sum(dim=1, keepdim=True) + eps)  # [B, M]
+        H = -(p * (p + eps).log()).sum(dim=1)       # [B]
+        return H.clamp_min(0.0), p
+
+    def _find_threshold(self, r: torch.Tensor, rho: torch.Tensor, tau_s: float, iters: int = 10) -> torch.Tensor:
+        """
+        Bisection per-batch-element for t s.t. sum σ((r - t)/τ) = ρ M
+        r: [B, M], rho: [B]
+        returns t: [B]
+        """
+        B, M = r.shape
+        t_low = r.min(dim=1).values - 6.0 * tau_s
+        t_high = r.max(dim=1).values + 6.0 * tau_s
+        for _ in range(iters):
+            t = 0.5 * (t_low + t_high)
+            s = torch.sigmoid((r - t.unsqueeze(1)) / tau_s).sum(dim=1) - (rho * M)
+            go_low = s > 0   # if too many kept, increase threshold lower bound
+            t_low = torch.where(go_low, t, t_low)
+            t_high = torch.where(go_low, t_high, t)
+        return 0.5 * (t_low + t_high)
+    
+    def _find_threshold_differentiable(self, r, rho, tau_s, iters=6, eps=1e-6):
+        # r: [B, M], rho: [B]
+        t = r.median(dim=1, keepdim=True).values  # good starting point
+        M = r.size(1)
+        for _ in range(iters):
+            s = torch.sigmoid((r - t) / tau_s)             # [B, M]
+            g = s.sum(dim=1, keepdim=True) - (rho*M).view(-1,1)
+            gp = -(s * (1 - s) / tau_s).sum(dim=1, keepdim=True)  # d/dt
+            t = t - g / (gp + eps)
+        return t.squeeze(1)
+
+    def _cross_attention_scores(self, Xv: torch.Tensor, Qt: torch.Tensor) -> torch.Tensor:
+        """
+        Xv: [B, M, D] visual tokens (after codebook, with abs pos encoding kept upstream)
+        Qt: [B, L, D] text tokens
+        returns r: [B, M] in [0,1]
+        """
+        q = Qt
+        attn_weights: Optional[torch.Tensor] = None
+        for i, layer in enumerate(self.scoring_layers):
+            need_w = (i == len(self.scoring_layers) - 1)
+            q, w = layer(q, Xv, kv_key_padding_mask=None, need_weights=need_w)
+            if need_w:
+                attn_weights = w  # [B, h, L, M]
+        # r via max over text (L) and heads (h)
+        assert attn_weights is not None
+        r = attn_weights.amax(dim=2).amax(dim=1)  # [B, M]
+        return r
+
+        # B, M, D = Xv.shape
+        # _, L, _ = Qt.shape
+        # # Fallback: manual scaled dot-product using explicit Wq/Wk
+        # K = self.Wk(Xv)  # [B, M, D]
+        # U = self.Wq(Qt)  # [B, L, D]
+
+        # # reshape to heads
+        # K = K.view(B, M, self.n_heads, self.d_head).transpose(1, 2)  # [B, h, M, dh]
+        # U = U.view(B, L, self.n_heads, self.d_head).transpose(1, 2)  # [B, h, L, dh]
+
+        # # attention: softmax over visual positions (M)
+        # # scores: [B, h, L, M]
+        # scores = torch.matmul(U, K.transpose(-2, -1)) / math.sqrt(self.d_head)
+        # A = F.softmax(scores, dim=-1)
+
+        # # max-pool over text (L) and heads (h): r in [0,1]^M
+        # r = A.amax(dim=2).amax(dim=1)  # [B, M]
+        # return r
+
+    def forward(
+        self,
+        Xv: torch.Tensor,  # [B, M, D]
+        Qt: torch.Tensor,  # [B, L, D]
+        mode: str = "train",
+    ) -> Dict[str, Any]:
+        B, M, D = Xv.shape
+        assert D == self.d_model
+
+        # 1) Cross-attention scoring
+        r = self._cross_attention_scores(Xv, Qt)                  # [B, M] in [0,1]
+
+        # 2) Adaptive budget prediction
+        H, p = self._entropy_from_r(r)
+        sq = Qt.mean(dim=1)                                       # [B, D]
+        logM = torch.full((B,), float(math.log(max(M, 1))), device=Xv.device, dtype=Xv.dtype)
+        r_max = r.max(dim=1).values
+        rho = self.budget(sq, logM, r_max, H)                     # [B], clamp in head
+
+        # fixed rho for debugging
+        # rho = torch.full_like(rho, 0.5)
+
+        n_pred = torch.clamp((rho * M).ceil().long(), min=1)      # at least 1
+        n = torch.minimum(n_pred, torch.full_like(n_pred, self.nmax))
+
+        # 3) Train-time differentiable gate / Inference hard Top-n
+        if mode == "train":
+            # Differentiable threshold with Newton-style refinement (keeps budget expectation)
+            t = self._find_threshold_differentiable(r, rho, self.tau_s, iters=10)  # [B]
+
+            # Replace TopK + manual straight-through with Gumbel-Softmax (binary keep/drop)
+            # logits_keep ~ (r - t); logits_drop ~ 0; temperature = tau_s
+            logits = torch.stack([r - t.unsqueeze(1), torch.zeros_like(r)], dim=-1)  # [B, M, 2]
+            y = F.gumbel_softmax(logits, tau=self.tau_s, hard=True, dim=-1)         # one-hot along 2
+            s_keep = y[..., 0]                                                      # [B, M] in {0,1}, grad via GS
+
+            # Ensure at least one token per sample (rare edge if GS picks all drop)
+            with torch.no_grad():
+                none_kept = (s_keep.sum(dim=1) < 0.5)
+                if none_kept.any():
+                    for b in torch.nonzero(none_kept, as_tuple=False).view(-1):
+                        j = torch.argmax(r[b])
+                        s_keep[b].zero_()
+                        s_keep[b, j] = 1.0
+
+            Z = s_keep.unsqueeze(-1) * Xv  # [B, M, D]
+
+            # Gather kept tokens per sample in original order
+            kept_list = []
+            kept_idx_list = []
+            for b in range(B):
+                kb = (s_keep[b] > 0.5).nonzero(as_tuple=False).squeeze(1)
+                kb, _ = torch.sort(kb)
+                kept_list.append(Z[b, kb])
+                kept_idx_list.append(kb)
+
+            if self.use_reencode:
+                # Pad/tile to max kept for batched re-encoding
+                max_keep = int(max([len(x) for x in kept_list]))
+                Zb = []
+                for b in range(B):
+                    x = kept_list[b]
+                    if x.size(0) < max_keep:
+                        # Repeat last kept token to pad; guaranteed at least one by fallback above
+                        pad = x[-1:].repeat(max_keep - x.size(0), 1)
+                        x = torch.cat([x, pad], dim=0)
+                    Zb.append(x.unsqueeze(0))
+                Zb = torch.cat(Zb, dim=0)  # [B, max_keep, D]
+
+                # Debug: skip previous step and re-encode all visual tokens
+                # Zb = Xv
+
+                # Apply each re-encode block sequentially
+                for layer in self.reencode_layers:
+                    Zb = layer(Zb)
+                # Slice back to each sample's true kept count
+                Z_out = []
+                for b in range(B):
+                    Z_out.append(Zb[b, : kept_idx_list[b].numel()])
+            else:
+                # Skip re-encoding; directly return kept features
+                Z_out = kept_list
+            # ragged output, collate as list for flexibility
+            return {
+                "Z": Z_out,               # list of [n[b], D]
+                "indices": kept_idx_list, # list of [n[b]]
+                "rho": rho,               # [B]
+                "r": r,                   # [B, M]
+                "p": p,                   # [B, M]            
+                "n": n,                   # [B]
+            }
+        else:
+            # inference: hard Top-n, but preserve original temporal order
+            kept_idx_list = []
+            Z_out = []
+            for b in range(B):
+                kb = torch.topk(r[b], k=int(n[b].item()), dim=0).indices
+                kb, _ = torch.sort(kb)  # keep ascending to preserve original positions
+                kept_idx_list.append(kb)
+                Z_out.append(Xv[b, kb])
+            if self.use_reencode:
+                # optional single re-encoding applied per batch via padding
+                max_keep = int(max([z.size(0) for z in Z_out]))
+                Zb = []
+                for z in Z_out:
+                    if z.size(0) < max_keep:
+                        pad = z[-1:].repeat(max_keep - z.size(0), 1)
+                        z = torch.cat([z, pad], dim=0)
+                    Zb.append(z.unsqueeze(0))
+                Zb = torch.cat(Zb, dim=0)  # [B, max_keep, D]
+                # apply each re-encode block sequentially
+                for layer in self.reencode_layers:
+                    Zb = layer(Zb)
+                Z_final = []
+                for b in range(B):
+                    Z_final.append(Zb[b, : kept_idx_list[b].numel()])
+            else:
+                # Skip re-encoding; return selected tokens directly
+                Z_final = Z_out
+            return {
+                "Z": Z_final,
+                "indices": kept_idx_list,
+                "rho": rho,
+                "r": r,
+                "n": n,
+            }
+
+    # --- Utilities to initialize scoring/re-encode layers from a downstream LM ---
+    @staticmethod
+    def _collect_lm_decoder_layers(lm_model: nn.Module) -> list[nn.Module]:
+        """Best-effort extraction of decoder layers across common HF model layouts."""
+        candidates = [
+            ("layers",),
+            ("model", "layers"),
+            ("model", "model", "layers"),
+            ("transformer", "h"),
+            ("gpt_neox", "layers"),
+            ("decoder", "layers"),
+            ("model", "decoder", "layers"),
+        ]
+        for chain in candidates:
+            cur = lm_model
+            ok = True
+            for attr in chain:
+                if not hasattr(cur, attr):
+                    ok = False
+                    break
+                cur = getattr(cur, attr)
+            if ok and isinstance(cur, (nn.ModuleList, list, tuple)):
+                return list(cur)
+
+        # Fallback: scan for the first ModuleList whose *final* component is `layers` or `h`.
+        for name, mod in lm_model.named_modules():
+            if not isinstance(mod, nn.ModuleList):
+                continue
+            last = name.split(".")[-1]
+            if last in {"layers", "h"}:
+                return list(mod)
+        return []
+
+    @staticmethod
+    def _extract_lm_layer_components(
+        lm_layer: nn.Module,
+    ) -> tuple[Optional[nn.Module], Optional[nn.Module], Optional[nn.Module]]:
+        """Return (attn, input_norm, post_attn_norm) for a decoder layer, best-effort."""
+        attn = None
+        for n in ("self_attn", "attn", "attention"):
+            if hasattr(lm_layer, n):
+                attn = getattr(lm_layer, n)
+                break
+
+        input_norm = None
+        for n in ("input_layernorm", "ln_1", "layernorm1", "norm1", "pre_attention_layernorm"):
+            if hasattr(lm_layer, n):
+                input_norm = getattr(lm_layer, n)
+                break
+
+        post_attn_norm = None
+        for n in ("post_attention_layernorm", "ln_2", "layernorm2", "norm2", "post_attention_norm"):
+            if hasattr(lm_layer, n):
+                post_attn_norm = getattr(lm_layer, n)
+                break
+
+        return attn, input_norm, post_attn_norm
+
+    def init_scoring_from_lm_model(self, lm_model: nn.Module, layer_indices: list, rms_norm_eps: Optional[float] = None):
+        """Initialize scoring layers from the provided downstream LM layers (best-effort)."""
+        text_cfg = getattr(lm_model, 'config', None)
+        hidden_size = getattr(text_cfg, 'hidden_size', self.d_model)
+        num_heads = getattr(text_cfg, 'num_attention_heads', self.n_heads)
+        num_kv_heads = getattr(text_cfg, 'num_key_value_heads', num_heads)
+        if rms_norm_eps is None:
+            rms_norm_eps = getattr(text_cfg, 'rms_norm_eps', 1e-6)
+
+        # Rebuild if d_model differs or head counts differ and are compatible
+        want_heads = int(num_heads)
+        can_use_lm_heads = (self.d_model % want_heads) == 0
+        cur_kv_heads = None
+        if hasattr(self, 'scoring_layers') and len(self.scoring_layers) > 0:
+            cur_kv_heads = getattr(self.scoring_layers[0], 'num_key_value_heads', None)
+        rebuild = (
+            (hidden_size != self.d_model)
+            or ((want_heads != self.n_heads) and can_use_lm_heads)
+            or (cur_kv_heads is None or int(cur_kv_heads) != int(num_kv_heads))
+        )
+        if rebuild:
+            # Only adopt LM head count if compatible with our d_model; else keep current heads
+            self.n_heads = want_heads if can_use_lm_heads else self.n_heads
+            self.d_head = self.d_model // self.n_heads
+            self.scoring_layers = nn.ModuleList([
+                LMScoringCrossAttentionLayer(
+                    self.d_model,
+                    num_heads=self.n_heads,
+                    num_key_value_heads=int(num_kv_heads),
+                    dropout=0.0,
+                    rms_norm_eps=rms_norm_eps,
+                ) for _ in range(self.n_scoring_layers)
+            ])
+
+        # Collect LM layers and copy
+        lm_layers = self._collect_lm_decoder_layers(lm_model)
+        if not lm_layers:
+            return  # can't proceed
+
+        for i, layer in enumerate(self.scoring_layers):
+            idx = int(layer_indices[i]) if i < len(layer_indices) else int(layer_indices[-1])
+            idx = max(0, min(idx, len(lm_layers) - 1))
+            q_layer = lm_layers[idx]
+            attn, in_norm, post_norm = self._extract_lm_layer_components(q_layer)
+            if attn is None:
+                continue
+            layer.init_from_lm_attn(attn, lm_input_norm=in_norm, lm_post_attn_norm=post_norm)
+        print("[QTS+] Scoring layers initialized from downstream LM model (where shapes matched).")
+
+    def init_reencode_from_lm_model(self, lm_model: nn.Module, layer_indices: list, rms_norm_eps: Optional[float] = None):
+        """Initialize re-encoding self-attention layers from downstream LM layers (best-effort)."""
+        if not self.use_reencode:
+            return
+
+        text_cfg = getattr(lm_model, 'config', None)
+        hidden_size = getattr(text_cfg, 'hidden_size', self.d_model)
+        num_heads = getattr(text_cfg, 'num_attention_heads', self.n_heads)
+        num_kv_heads = getattr(text_cfg, 'num_key_value_heads', num_heads)
+        if rms_norm_eps is None:
+            rms_norm_eps = getattr(text_cfg, 'rms_norm_eps', 1e-6)
+
+        # Rebuild re-encode layers if head/kv-head config differs
+        can_use_lm_heads = (self.d_model % int(num_heads)) == 0
+        # Detect current kv heads from the first reencode layer (wrapper -> core)
+        cur_kv_heads = None
+        if hasattr(self, 'reencode_layers') and self.reencode_layers is not None and len(self.reencode_layers) > 0:
+            core0 = getattr(self.reencode_layers[0], 'core', self.reencode_layers[0])
+            cur_kv_heads = getattr(core0, 'num_key_value_heads', None)
+        rebuild = (
+            (hidden_size != self.d_model)
+            or ((int(num_heads) != self.n_heads) and can_use_lm_heads)
+            or (cur_kv_heads is None or int(cur_kv_heads) != int(num_kv_heads))
+        )
+        if rebuild:
+            if can_use_lm_heads:
+                self.n_heads = int(num_heads)
+                self.d_head = self.d_model // self.n_heads
+            self.reencode_layers = nn.ModuleList([
+                LMSelfReencodeLayer(
+                    self.d_model,
+                    num_heads=self.n_heads,
+                    num_key_value_heads=int(num_kv_heads),
+                    dropout=0.0,
+                    rms_norm_eps=rms_norm_eps,
+                ) for _ in range(self.n_reencode_layers)
+            ])
+
+        # Collect LM layers and copy
+        lm_layers = self._collect_lm_decoder_layers(lm_model)
+        if not lm_layers:
+            return
+
+        for i, layer in enumerate(self.reencode_layers):
+            idx = int(layer_indices[i]) if i < len(layer_indices) else int(layer_indices[-1])
+            idx = max(0, min(idx, len(lm_layers) - 1))
+            q_layer = lm_layers[idx]
+            attn, in_norm, post_norm = self._extract_lm_layer_components(q_layer)
+            if attn is None:
+                continue
+            layer.init_from_lm_attn(attn, lm_input_norm=in_norm, lm_post_attn_norm=post_norm)
+        print("[QTS+] Re-encode layers initialized from downstream LM model (where shapes matched).")

qts_plus_arch.py

@@ -0,0 +1,449 @@
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+from typing import Any, Optional, Tuple
+
+import torch
+import torch.nn as nn
+from transformers.configuration_utils import PretrainedConfig
+from transformers.modeling_utils import PreTrainedModel
+
+from .configuration_intern_vit import InternVisionConfig
+from .modeling_intern_vit import InternVisionModel
+from .qts_plus_tokenizer import QTSplusTokenizer, QTSplusTokenizerConfig
+
+
+def qts_integrate_embeddings(
+    vision_features: torch.Tensor,
+    input_ids: torch.Tensor,
+    attention_mask: torch.Tensor,
+    labels: Optional[torch.Tensor] = None,
+    image_token_id: Optional[int] = None,
+    video_token_id: Optional[int] = None,
+    text_model_embed_layer: Optional[nn.Embedding] = None,
+    kept_indices: Optional[torch.Tensor] = None,
+) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
+    """Replace multimodal placeholder token embeddings with vision features.
+
+    Supports two prompt formats:
+    - multiple placeholders (e.g. InternVL `<IMG_CONTEXT>` repeated per vision token)
+    - a single placeholder token (expanded into N vision tokens)
+    """
+
+    if text_model_embed_layer is None:
+        raise ValueError("text_model_embed_layer is required")
+    if input_ids.dtype is not torch.long:
+        input_ids = input_ids.long()
+
+    placeholder_token_id = video_token_id if video_token_id is not None else image_token_id
+    if placeholder_token_id is None:
+        raise ValueError("Either `image_token_id` or `video_token_id` must be provided")
+
+    inputs_embeds = text_model_embed_layer(input_ids)
+    if vision_features.ndim != 2:
+        raise ValueError(f"vision_features must be [N, D], got {tuple(vision_features.shape)}")
+
+    if input_ids.ndim != 2 or input_ids.shape[0] != 1:
+        raise ValueError("Only batch_size==1 is currently supported")
+
+    pos = (input_ids[0] == int(placeholder_token_id)).nonzero(as_tuple=False).flatten()
+    if pos.numel() == 0:
+        raise ValueError("No multimodal placeholder tokens found in input_ids")
+
+    n_feats = int(vision_features.shape[0])
+    if n_feats <= 0:
+        raise ValueError("vision_features must contain at least one vector")
+
+    # Single placeholder: expand into N tokens.
+    if pos.numel() == 1 and n_feats >= 1:
+        insert_at = int(pos.item())
+        vision_features = vision_features.to(inputs_embeds.device, inputs_embeds.dtype)
+
+        pre = inputs_embeds[:, :insert_at, :]
+        post = inputs_embeds[:, insert_at + 1 :, :]
+        inputs_embeds = torch.cat([pre, vision_features.unsqueeze(0), post], dim=1)
+
+        pre_mask = attention_mask[:, :insert_at]
+        post_mask = attention_mask[:, insert_at + 1 :]
+        feats_mask = torch.ones((1, n_feats), device=attention_mask.device, dtype=attention_mask.dtype)
+        attention_mask = torch.cat([pre_mask, feats_mask, post_mask], dim=1)
+
+        if labels is not None:
+            pre_lab = labels[:, :insert_at]
+            post_lab = labels[:, insert_at + 1 :]
+            feats_lab = torch.full((1, n_feats), -100, device=labels.device, dtype=labels.dtype)
+            labels = torch.cat([pre_lab, feats_lab, post_lab], dim=1)
+
+        return inputs_embeds, attention_mask.to(inputs_embeds.device), labels
+
+    # Multi-placeholder: drop unselected placeholders, then replace remaining.
+    vision_features = vision_features.to(inputs_embeds.device, inputs_embeds.dtype)
+    m_placeholders = int(pos.numel())
+    if n_feats > m_placeholders:
+        raise ValueError(
+            f"Number of vision features ({n_feats}) exceeds placeholder tokens ({m_placeholders}). "
+            "Ensure the prompt inserts enough <IMG_CONTEXT> tokens."
+        )
+
+    if n_feats < m_placeholders:
+        if kept_indices is not None:
+            keep_idx = kept_indices.flatten().to(device=pos.device, dtype=torch.long)
+            keep_idx = keep_idx[(keep_idx >= 0) & (keep_idx < m_placeholders)]
+            if keep_idx.numel() != n_feats:
+                keep_idx = torch.arange(n_feats, device=pos.device, dtype=torch.long)
+            order = torch.argsort(keep_idx)
+            keep_idx = keep_idx[order]
+            vision_features = vision_features[order.to(device=vision_features.device)]
+
+            keep_mask = torch.zeros((m_placeholders,), device=pos.device, dtype=torch.bool)
+            keep_mask[keep_idx] = True
+            drop_pos = pos[~keep_mask]
+        else:
+            drop_pos = pos[n_feats:]
+
+        if drop_pos.numel() > 0:
+            keep_seq = torch.ones((input_ids.shape[1],), device=input_ids.device, dtype=torch.bool)
+            keep_seq[drop_pos] = False
+            input_ids = input_ids[:, keep_seq]
+            attention_mask = attention_mask[:, keep_seq]
+            inputs_embeds = inputs_embeds[:, keep_seq, :]
+            if labels is not None:
+                labels = labels[:, keep_seq]
+
+            pos = (input_ids[0] == int(placeholder_token_id)).nonzero(as_tuple=False).flatten()
+
+    # Replace placeholder embeddings.
+    if int(pos.numel()) != n_feats:
+        raise ValueError(f"Placeholder tokens ({int(pos.numel())}) != vision features ({n_feats}) after trimming")
+
+    for i in range(n_feats):
+        inputs_embeds[0, int(pos[i].item()), :] = vision_features[i, :]
+
+    if labels is not None and n_feats > 0:
+        labels = labels.clone()
+        labels[0, pos[:n_feats]] = -100
+
+    return inputs_embeds, attention_mask.to(inputs_embeds.device), labels
+
+
+class InternVL2_5VisionConfig(PretrainedConfig):
+    model_type = "internvl2_5_vision"
+    is_composition = True
+
+    def __init__(
+        self,
+        vision_config: Optional[dict[str, Any]] = None,
+        llm_hidden_size: Optional[int] = None,
+        select_layer: int = -1,
+        force_image_size: Optional[int] = None,
+        downsample_ratio: float = 0.5,
+        ps_version: str = "v2",
+        **kwargs: Any,
+    ) -> None:
+        super().__init__(**kwargs)
+
+        if vision_config is None:
+            vision_config = {"architectures": ["InternVisionModel"]}
+        self.vision_config = InternVisionConfig(**vision_config)
+
+        self.select_layer = int(select_layer)
+        self.force_image_size = int(force_image_size) if force_image_size is not None else None
+        self.downsample_ratio = float(downsample_ratio)
+        self.ps_version = str(ps_version)
+
+        self.hidden_size = int(self.vision_config.hidden_size)
+        self.out_hidden_size = int(llm_hidden_size) if llm_hidden_size is not None else int(self.hidden_size)
+        self.llm_hidden_size = int(self.out_hidden_size)
+
+        self.architectures = ["InternVL2_5VisionTower"]
+
+    def to_dict(self) -> dict[str, Any]:
+        out = dict(self.__dict__)
+        out["vision_config"] = self.vision_config.to_dict()
+        out["model_type"] = self.__class__.model_type
+        return out
+
+
+class InternVL2_5VisionTower(PreTrainedModel):
+    config_class = InternVL2_5VisionConfig
+    main_input_name = "pixel_values"
+
+    def __init__(self, config: InternVL2_5VisionConfig):
+        super().__init__(config)
+
+        vision_cfg = config.vision_config
+        if config.force_image_size is not None:
+            vision_cfg = InternVisionConfig(**vision_cfg.to_dict())
+            vision_cfg.image_size = int(config.force_image_size)
+
+        self.vision_model = InternVisionModel(vision_cfg)
+        self.select_layer = int(config.select_layer)
+        self.downsample_ratio = float(config.downsample_ratio)
+        self.ps_version = str(config.ps_version)
+
+        vit_hidden_size = int(vision_cfg.hidden_size)
+        llm_hidden_size = int(config.out_hidden_size)
+        mlp_in = vit_hidden_size * int(1 / self.downsample_ratio) ** 2
+        self.mlp1 = nn.Sequential(
+            nn.LayerNorm(mlp_in),
+            nn.Linear(mlp_in, llm_hidden_size),
+            nn.GELU(),
+            nn.Linear(llm_hidden_size, llm_hidden_size),
+        )
+
+        self.post_init()
+
+    def pixel_shuffle(self, x: torch.Tensor, scale_factor: float = 0.5) -> torch.Tensor:
+        n, w, h, c = x.size()
+        x = x.view(n, w, int(h * scale_factor), int(c / scale_factor))
+        x = x.permute(0, 2, 1, 3).contiguous()
+        x = x.view(
+            n,
+            int(h * scale_factor),
+            int(w * scale_factor),
+            int(c / (scale_factor * scale_factor)),
+        )
+        if self.ps_version != "v1":
+            x = x.permute(0, 2, 1, 3).contiguous()
+        return x
+
+    def extract_feature(self, pixel_values: torch.Tensor) -> torch.Tensor:
+        if self.select_layer == -1:
+            vit_out = self.vision_model(
+                pixel_values=pixel_values,
+                output_hidden_states=False,
+                return_dict=True,
+            ).last_hidden_state
+        else:
+            vit_out = self.vision_model(
+                pixel_values=pixel_values,
+                output_hidden_states=True,
+                return_dict=True,
+            ).hidden_states[self.select_layer]
+
+        vit_out = vit_out[:, 1:, :]  # drop CLS
+        h = w = int(vit_out.shape[1] ** 0.5)
+        vit_out = vit_out.reshape(vit_out.shape[0], h, w, -1)
+        vit_out = self.pixel_shuffle(vit_out, scale_factor=self.downsample_ratio)
+        vit_out = vit_out.reshape(vit_out.shape[0], -1, vit_out.shape[-1])
+        vit_out = self.mlp1(vit_out)
+        return vit_out
+
+    def get_image_features(self, pixel_values: torch.Tensor) -> torch.Tensor:
+        return self.extract_feature(pixel_values)
+
+    def forward(self, pixel_values: torch.Tensor, **_: Any) -> torch.Tensor:
+        return self.get_image_features(pixel_values)
+
+
+def build_vision_tower(config: PretrainedConfig) -> InternVL2_5VisionTower:
+    vision_cfg = getattr(config, "vision_config", None)
+    if not isinstance(vision_cfg, dict):
+        raise ValueError("Missing `vision_config` in model config for InternVL2.5 vision tower")
+
+    llm_hidden = getattr(config, "hidden_size", None)
+    if not isinstance(llm_hidden, int) or llm_hidden <= 0:
+        llm_hidden = getattr(config, "llm_hidden_size", None)
+    if not isinstance(llm_hidden, int) or llm_hidden <= 0:
+        raise ValueError("Missing `hidden_size` / `llm_hidden_size` in config")
+
+    vt_cfg = InternVL2_5VisionConfig(
+        vision_config=vision_cfg,
+        llm_hidden_size=int(llm_hidden),
+        select_layer=int(getattr(config, "select_layer", -1)),
+        force_image_size=getattr(config, "force_image_size", None),
+        downsample_ratio=float(getattr(config, "downsample_ratio", 0.5)),
+        ps_version=str(getattr(config, "ps_version", "v2")),
+    )
+    return InternVL2_5VisionTower(vt_cfg)
+
+
+def build_qts_plus_tower(config: PretrainedConfig) -> QTSplusTokenizer:
+    vision_dim = getattr(config, "vision_embed_size", None)
+    if not isinstance(vision_dim, int) or vision_dim <= 0:
+        vision_dim = getattr(config, "hidden_size", None)
+    if not isinstance(vision_dim, int) or vision_dim <= 0:
+        raise ValueError("Missing `vision_embed_size` / `hidden_size` in config")
+
+    lm_heads = getattr(config, "num_attention_heads", None)
+    if not isinstance(lm_heads, int) or lm_heads <= 0:
+        raise ValueError("Missing `num_attention_heads` in config")
+    if vision_dim % lm_heads != 0:
+        raise ValueError(f"vision_embed_size ({vision_dim}) must be divisible by num_attention_heads ({lm_heads})")
+
+    kv_heads = getattr(config, "num_key_value_heads", None)
+
+    cfg = QTSplusTokenizerConfig(
+        embedding_dim=int(vision_dim),
+        n_heads=int(lm_heads),
+        num_kv_heads=int(kv_heads) if isinstance(kv_heads, int) and kv_heads > 0 else None,
+        tau_s=float(getattr(config, "qts_plus_tau_s", 0.1)),
+        nmax=int(getattr(config, "qts_plus_nmax", 2560)),
+        rho_min=float(getattr(config, "qts_plus_rho_min", 0.05)),
+        rho_max=float(getattr(config, "qts_plus_rho_max", 0.5)),
+        block_dropout=float(getattr(config, "qts_plus_block_dropout", 0.0)),
+        reencode=bool(getattr(config, "qts_plus_reencode", False)),
+        scoring_layers=int(getattr(config, "qts_plus_scoring_layers", 1)),
+        reencode_layers=int(getattr(config, "qts_plus_reencode_layers", 0)),
+        lambda_t=float(getattr(config, "lambda_t", 1.0)),
+        lambda_m=float(getattr(config, "lambda_m", 1.7)),
+        lambda_s=float(getattr(config, "lambda_s", 0.05)),
+        project_text_if_needed=bool(getattr(config, "project_text_if_needed", False)),
+    )
+    return QTSplusTokenizer(cfg)
+
+
+class QTSplusMetaModel:
+    def __init__(self, config: PretrainedConfig):
+        super().__init__(config)
+        self.config = config
+
+        self.vision_tower = None
+        if getattr(config, "vision_tower", None) in {"internvl2_5_vision", "internvl_vision"}:
+            self.vision_tower = build_vision_tower(config)
+
+        self.qts_plus = None
+        if getattr(config, "enable_qts_plus", False):
+            self.qts_plus = build_qts_plus_tower(config)
+
+    def get_qts_plus_tower(self):
+        return getattr(self, "qts_plus", None)
+
+    def get_vision_tower(self):
+        return getattr(self, "vision_tower", None)
+
+
+class QTSplusMetaForCausalLM(ABC):
+    @abstractmethod
+    def get_model(self):  # pragma: no cover
+        raise NotImplementedError
+
+    def get_qts_plus_tower(self):
+        return self.get_model().get_qts_plus_tower()
+
+    def get_vision_tower(self):
+        return self.get_model().get_vision_tower()
+
+    def prepare_inputs_for_multimodal(
+        self,
+        vision_input: Optional[torch.FloatTensor] = None,
+        input_ids: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        past_key_values: Optional[list[torch.FloatTensor]] = None,
+        labels: Optional[torch.LongTensor] = None,
+        question_input_ids: Optional[torch.LongTensor] = None,
+        image_token_id: Optional[int] = None,
+        video_token_id: Optional[int] = None,
+        mode: str = "train",
+    ):
+        if attention_mask is None and input_ids is not None:
+            attention_mask = torch.ones_like(input_ids, dtype=torch.long, device=input_ids.device)
+
+        # Default: no multimodal inputs -> no-op.
+        if vision_input is None:
+            z = torch.tensor(0.0, device=input_ids.device if input_ids is not None else None)
+            return vision_input, position_ids, attention_mask, past_key_values, None, labels, z, z, z
+
+        if question_input_ids is None:
+            raise ValueError("`question_input_ids` is required for QTSplus InternVL2.5 inference/training.")
+        if question_input_ids.dtype is not torch.long:
+            question_input_ids = question_input_ids.long()
+        if question_input_ids.ndim == 1:
+            question_input_ids = question_input_ids.unsqueeze(0)
+
+        vision_tower = self.get_vision_tower()
+        qts_plus_tower = self.get_qts_plus_tower()
+        text_embed_layer = self.get_model().get_input_embeddings()
+
+        if vision_tower is None or qts_plus_tower is None:
+            raise ValueError("Both `vision_tower` and `qts_plus` must be initialized for multimodal inference.")
+
+        # Normalize `vision_input` into a pixel_values tensor.
+        if isinstance(vision_input, list):
+            if len(vision_input) == 0:
+                z = torch.tensor(0.0, device=input_ids.device)
+                return None, position_ids, attention_mask, past_key_values, None, labels, z, z, z
+            vision_input = vision_input[0]
+
+        pixel_values = vision_input.get("pixel_values") if isinstance(vision_input, dict) else vision_input
+        if not isinstance(pixel_values, torch.Tensor):
+            raise ValueError(f"vision_input must be a torch.Tensor or dict with pixel_values, got {type(vision_input)}")
+
+        if pixel_values.ndim == 3:  # [3, H, W]
+            pixel_values = pixel_values.unsqueeze(0).unsqueeze(0)  # [1, 1, 3, H, W]
+        elif pixel_values.ndim == 4:  # [B, 3, H, W] or [T, 3, H, W]
+            b_txt = int(question_input_ids.shape[0])
+            if pixel_values.shape[0] == b_txt:
+                pixel_values = pixel_values.unsqueeze(1)  # [B, 1, 3, H, W]
+            else:
+                pixel_values = pixel_values.unsqueeze(0)  # [1, T, 3, H, W]
+        elif pixel_values.ndim != 5:
+            raise ValueError(f"Unsupported InternVL pixel_values shape: {tuple(pixel_values.shape)}")
+
+        b, t, c, h, w = pixel_values.shape
+        pixel_values_flat = pixel_values.reshape(b * t, c, h, w)
+
+        try:
+            vt_param = next(vision_tower.parameters())
+            vt_device = vt_param.device
+            vt_dtype = vt_param.dtype
+        except StopIteration:
+            vt_device = pixel_values_flat.device
+            vt_dtype = pixel_values_flat.dtype
+
+        vision_features = vision_tower.get_image_features(pixel_values_flat.to(device=vt_device, dtype=vt_dtype))
+        if not (isinstance(vision_features, torch.Tensor) and vision_features.ndim == 3):
+            raise ValueError(f"vision_tower must return [B, N, D], got {type(vision_features)} {vision_features.shape}")
+        vision_features = vision_features.reshape(b, t * vision_features.shape[1], vision_features.shape[2])
+
+        text_embeddings = text_embed_layer(question_input_ids.to(text_embed_layer.weight.device))
+        vision_features = vision_features.to(device=text_embeddings.device, dtype=text_embeddings.dtype)
+        try:
+            qts_plus_tower.to(device=text_embeddings.device, dtype=text_embeddings.dtype)
+        except Exception:
+            qts_plus_tower.to(device=text_embeddings.device)
+
+        qts_plus_out = qts_plus_tower(vision_features, text_embeddings, mode=mode)
+        z_list = qts_plus_out["Z"]
+        if not (isinstance(z_list, list) and len(z_list) == 1 and isinstance(z_list[0], torch.Tensor)):
+            raise ValueError("Expected QTSplusTokenizer to return a list of 1 tensor for batch_size==1")
+
+        kept = None
+        try:
+            kept_list = qts_plus_out.get("indices")
+            kept = kept_list[0] if isinstance(kept_list, list) and len(kept_list) == 1 else None
+        except Exception:
+            kept = None
+
+        if image_token_id is None:
+            image_token_id = getattr(self.config, "image_token_id", 92546)
+
+        inputs_embeds, attention_mask, labels = qts_integrate_embeddings(
+            vision_features=z_list[0],
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            labels=labels,
+            image_token_id=image_token_id,
+            video_token_id=video_token_id,
+            text_model_embed_layer=text_embed_layer,
+            kept_indices=kept,
+        )
+
+        add_loss = qts_plus_out.get("add_loss") or {}
+        flops_loss = add_loss.get("flops", 0.0)
+        kv_loss = add_loss.get("kv", 0.0)
+        smooth_loss = add_loss.get("smooth", 0.0)
+
+        # Return `inputs_embeds` so the LM consumes the integrated embeddings.
+        return (
+            vision_input,
+            position_ids,
+            attention_mask,
+            past_key_values,
+            inputs_embeds,
+            labels,
+            flops_loss,
+            kv_loss,
+            smooth_loss,
+        )

qts_plus_internlm2_lm.py

@@ -0,0 +1,226 @@
+# -*- encoding: utf-8 -*-
+# QTSplus wrapper for InternLM2 Causal LM (used by InternVL2.5-8B)
+
+from __future__ import annotations
+
+import os
+from typing import Any, List, Optional, Union
+
+import torch
+import torch.nn as nn
+
+from transformers import AutoConfig, AutoModelForCausalLM
+from transformers.modeling_flash_attention_utils import is_flash_attn_available
+
+from .configuration_internlm2 import InternLM2Config
+from .modeling_internlm2 import InternLM2ForCausalLM, InternLM2Model
+from .qts_plus_arch import QTSplusMetaForCausalLM, QTSplusMetaModel
+
+# Ensure nested trust_remote_code dependencies are captured by Transformers'
+# dynamic module snapshotting (it only bundles files imported from this module).
+from .configuration_intern_vit import InternVisionConfig as _InternVisionConfig  # noqa: F401
+from .modeling_intern_vit import InternVisionModel as _InternVisionModel  # noqa: F401
+from .qts_plus import QTSplus as _QTSplus  # noqa: F401
+from .qts_plus_tokenizer import QTSplusTokenizer as _QTSplusTokenizer  # noqa: F401
+
+def _hf_generate_fallback(model: "QTSplusInternLM2_ForCausalLM", **kwargs):
+    """Call into HF generation even when `super().generate` is unavailable."""
+    try:
+        return super(QTSplusInternLM2_ForCausalLM, model).generate(**kwargs)
+    except AttributeError as e:
+        msg = str(e)
+        if "generate" not in msg:
+            raise
+        try:
+            from transformers.generation.utils import GenerationMixin  # type: ignore
+        except Exception:  # pragma: no cover
+            from transformers.generation_utils import GenerationMixin  # type: ignore
+        return GenerationMixin.generate(model, **kwargs)
+
+
+class QTSplusInternLM2_CausalLM_Config(InternLM2Config):
+    model_type = "qts_plus_internlm2_causal_lm"
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs):
+        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
+        if str(config_dict.get("model_type") or "").lower() == "internlm2":
+            config_dict["model_type"] = cls.model_type
+        return cls.from_dict(config_dict, **kwargs)
+
+
+class QTSplusInternLM2_Model(QTSplusMetaModel, InternLM2Model):
+    config_class = QTSplusInternLM2_CausalLM_Config
+
+    def __init__(self, config: InternLM2Config):
+        super().__init__(config)
+
+
+class QTSplusInternLM2_ForCausalLM(QTSplusMetaForCausalLM, InternLM2ForCausalLM):
+    config_class = QTSplusInternLM2_CausalLM_Config
+    _tied_weights_keys = ["output.weight"]
+
+    def __init__(self, config: InternLM2Config):
+        # Configure attention backend before modules are built.
+        try:
+            cfg_attn = getattr(config, "attn_implementation", None)
+            if (cfg_attn is None or str(cfg_attn) == "auto") and is_flash_attn_available():
+                setattr(config, "attn_implementation", "flash_attention_2")
+        except Exception:
+            pass
+
+        # Skip InternLM2ForCausalLM.__init__ to avoid constructing a second backbone.
+        super(InternLM2ForCausalLM, self).__init__(config)
+        self.model = QTSplusInternLM2_Model(config)
+        self.vocab_size = config.vocab_size
+        self.output = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        self.post_init()
+
+    def get_model(self):
+        return self.model
+
+    def forward(
+        self,
+        vision_input: Optional[torch.FloatTensor] = None,
+        input_ids: Optional[torch.LongTensor] = None,
+        labels: Optional[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,
+        cache_position: Optional[torch.LongTensor] = None,
+        question_input_ids: Optional[torch.LongTensor] = None,
+        image_token_id: Optional[int] = None,
+        video_token_id: Optional[int] = None,
+        **kwargs: Any,
+    ):
+        # InternLM2ForCausalLM doesn't accept cache_position; ignore for compatibility with newer HF.
+        _ = cache_position
+        # HF Trainer (>=4.56) may pass this for loss normalization; InternLM2 forward doesn't accept it.
+        kwargs.pop("num_items_in_batch", None)
+
+        if inputs_embeds is not None:
+            input_ids = None
+
+        if inputs_embeds is None:
+            (
+                vision_input,
+                position_ids,
+                attention_mask,
+                past_key_values,
+                inputs_embeds,
+                labels,
+                flops_loss,
+                kv_loss,
+                smooth_loss,
+            ) = self.prepare_inputs_for_multimodal(
+                vision_input,
+                input_ids,
+                position_ids,
+                attention_mask,
+                past_key_values,
+                labels,
+                question_input_ids,
+                video_token_id=video_token_id,
+                image_token_id=image_token_id,
+                mode="train" if self.training else "infer",
+            )
+            if inputs_embeds is None and input_ids is not None:
+                inputs_embeds = self.get_model().get_input_embeddings()(input_ids)
+
+        input_ids = None
+
+        outputs = super().forward(
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            labels=labels,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            **kwargs,
+        )
+
+        add_loss = {
+            "flops_loss": flops_loss if vision_input is not None else 0.0,
+            "kv_loss": kv_loss if vision_input is not None else 0.0,
+            "smooth_loss": smooth_loss if vision_input is not None else 0.0,
+        }
+
+        if labels is None and not self.training:
+            return outputs
+
+        return (outputs, add_loss)
+
+    @torch.no_grad()
+    def generate(
+        self,
+        vision_input: Optional[torch.Tensor] = None,
+        input_ids: Optional[torch.Tensor] = None,
+        question_input_ids: Optional[torch.Tensor] = None,
+        image_token_id: Optional[int] = None,
+        video_token_id: Optional[int] = None,
+        **kwargs,
+    ):
+        # `generate()` should run in eval mode to avoid returning the training-only
+        # tuple `(outputs, add_loss)` from `forward()` when `self.training == True`.
+        was_training = self.training
+        if was_training:
+            self.eval()
+        position_ids = kwargs.pop("position_ids", None)
+        attention_mask = kwargs.pop("attention_mask", None)
+        if attention_mask is None and input_ids is not None:
+            attention_mask = torch.ones_like(input_ids, dtype=torch.long, device=input_ids.device)
+        if "inputs_embeds" in kwargs:
+            raise NotImplementedError("`inputs_embeds` is not supported")
+
+        if vision_input is not None:
+            (
+                vision_input,
+                position_ids,
+                attention_mask,
+                _,
+                inputs_embeds,
+                _,
+                *_unused_losses,
+            ) = self.prepare_inputs_for_multimodal(
+                vision_input,
+                input_ids,
+                position_ids,
+                attention_mask,
+                None,
+                None,
+                question_input_ids,
+                video_token_id=video_token_id,
+                image_token_id=image_token_id,
+                mode="infer",
+            )
+        else:
+            inputs_embeds = self.get_model().get_input_embeddings()(input_ids)
+
+        kwargs["attention_mask"] = attention_mask
+        if position_ids is not None:
+            kwargs["position_ids"] = position_ids
+        kwargs.pop("input_ids", None)
+
+        if "use_cache" not in kwargs:
+            kwargs["use_cache"] = True
+        try:
+            output_ids = _hf_generate_fallback(self, inputs_embeds=inputs_embeds, **kwargs)
+        finally:
+            if was_training:
+                self.train()
+        if input_ids is not None:
+            input_ids = input_ids.to(output_ids.device)
+            output_ids = torch.cat([input_ids, output_ids], dim=1)
+        return output_ids
+
+
+AutoConfig.register("qts_plus_internlm2_causal_lm", QTSplusInternLM2_CausalLM_Config)
+AutoModelForCausalLM.register(QTSplusInternLM2_CausalLM_Config, QTSplusInternLM2_ForCausalLM)

qts_plus_tokenizer.py

@@ -0,0 +1,143 @@
+# -*- encoding: utf-8 -*-
+# @File        :   tokenizer.py
+# @Time        :   2025/03/16 20:45:07
+# @Author      :   Siyou
+# @Description :
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Optional, Dict, Any, List, Tuple
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .qts_plus import QTSplus
+
+
+@dataclass
+class QTSplusTokenizerConfig:
+
+    embedding_dim: int
+
+    # QTS+
+    n_heads: int = 8
+    num_kv_heads: Optional[int] = None
+    tau_s: float = 0.1
+    nmax: int = 2560
+    rho_min: float = 0.05
+    rho_max: float = 0.5
+    block_dropout: float = 0.0
+    reencode: bool = True
+    scoring_layers: int = 1
+    reencode_layers: int = 1
+
+    lambda_t: float = 1.0
+    lambda_m: float = 1.7
+    lambda_s: float = 0.05
+
+    # Misc
+    project_text_if_needed: bool = False
+
+    # Scoring/re-encode layers can be initialized from downstream LM weights.
+
+
+class QTSplusTokenizer(nn.Module):
+    """
+    End-to-end *QTSplusTok* tokenizer.
+
+    Pipeline:
+      X_v --(QTS+)--> X′
+    """
+    def __init__(self, cfg: QTSplusTokenizerConfig):
+        super().__init__()
+        self.cfg = cfg
+
+        self.selector = QTSplus(
+            d_model=cfg.embedding_dim,
+            n_heads=cfg.n_heads,
+            n_kv_heads=cfg.num_kv_heads or cfg.n_heads,
+            tau_s=cfg.tau_s,
+            nmax=cfg.nmax,
+            rho_min=cfg.rho_min,
+            rho_max=cfg.rho_max,
+            block_dropout=cfg.block_dropout,
+            use_reencode=cfg.reencode,
+            n_scoring_layers=cfg.scoring_layers,
+            n_reencode_layers=cfg.reencode_layers,
+        )
+
+        # If text embeddings come in a different dimensionality, learn a light projection.
+        self.text_proj: Optional[nn.Linear] = None
+
+        self.rho_sum = 0
+        self.rho_count = 0
+
+    def forward(
+        self,
+        X_v: torch.Tensor,        # [B, M, D]
+        Q_t: torch.Tensor,      # [B, L, D_txt]
+        mode: str = "train",      # 'train' | 'infer'
+    ) -> Dict[str, Any]:
+        assert mode in ("train", "infer")
+        B, M, D = X_v.shape
+        D_txt = Q_t.shape[-1]
+
+        # --- Project text if needed ---
+        if D_txt != D:
+            if self.cfg.project_text_if_needed:
+                if self.text_proj is None:
+                    self.text_proj = nn.Linear(D_txt, D, bias=False)
+                    # Ensure the projection layer uses the same dtype as input
+                    self.text_proj = self.text_proj.to(device=Q_t.device, dtype=Q_t.dtype)
+                Q_proj = self.text_proj(Q_t)
+            else:
+                raise ValueError(f"QTS+ expects text dim {D}, got {D_txt}. Set project_text_if_needed=True.")
+        else:
+            Q_proj = Q_t
+
+        sel = self.selector(X_v, Q_proj, mode=mode)  # returns dict per qts_plus.py
+        Z_list: List[torch.Tensor] = sel["Z"]          # list of [T_b, D] tensors per sample
+        n_vec: torch.Tensor = sel["n"]                 # [B]
+        rho: torch.Tensor = sel["rho"]                 # [B]
+        r: torch.Tensor = sel["r"]                     # [B, M]
+
+        # Compute Eq. (1) compute proxies (per-batch averages for convenience)
+        # flops ~ (ρM)^2 / n_max^2 ; kv ~ (ρM) / n_max
+        M_tensor = torch.tensor(float(M), device=X_v.device)
+        flops_proxy = ((rho * M_tensor) ** 2) / float(self.cfg.nmax ** 2)
+        kv_proxy = (rho * M_tensor) / float(self.cfg.nmax)
+        self.rho_sum += rho.sum().item()
+        self.rho_count += B
+        rho_loss = (rho - self.rho_sum / self.rho_count) ** 2
+
+        return {
+            "indices": sel["indices"],  # kept indices per sample (list[LongTensor])
+            "Z": Z_list,
+            "rho": rho,
+            "r": r,
+            "n": n_vec,
+            "add_loss": {
+                "flops": flops_proxy.mean() * self.cfg.lambda_t,
+                "kv": kv_proxy.mean() * self.cfg.lambda_m,
+                "smooth": rho_loss.mean() * self.cfg.lambda_s,
+            },
+        }
+
+if __name__ == "__main__":
+    cfg = QTSplusTokenizerConfig(
+        embedding_dim=1024, n_heads=8, tau_s=0.1, nmax=512, rho_min=0.05, rho_max=0.5
+    )
+
+    qts = QTSplusTokenizer(cfg)
+
+    # X_v: [B, M, D] vision latents (abs. pos kept upstream)
+    X_v = torch.randn(1, 4096, 1024)
+    # Q_t: [B, L, D] text/query embeddings (will be projected if D differs)
+    Q_t = torch.randn(1, 77, 1024)
+    out = qts(X_v, Q_t, mode='train')
+
+    for k, v in out.items():
+        if k != "indices":
+            print(f"{k}: {v}")
+        else:
+            print(f"indices: {[x.shape for x in v]}")

special_tokens_map.json

@@ -0,0 +1,47 @@
+{
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|action_start|>",
+    "<|action_end|>",
+    "<|interpreter|>",
+    "<|plugin|>",
+    "<img>",
+    "</img>",
+    "<IMG_CONTEXT>",
+    "<quad>",
+    "</quad>",
+    "<ref>",
+    "</ref>",
+    "<box>",
+    "</box>"
+  ],
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenization_internlm2.py

@@ -0,0 +1,235 @@
+# Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on transformers/src/transformers/models/llama/tokenization_llama.py
+#
+# 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.
+
+"""Tokenization classes for InternLM."""
+import os
+from shutil import copyfile
+from typing import Any, Dict, List, Optional, Tuple
+
+import sentencepiece as spm
+from transformers.tokenization_utils import PreTrainedTokenizer
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {'vocab_file': './tokenizer.model'}
+
+PRETRAINED_VOCAB_FILES_MAP = {}
+
+
+# Modified from transformers.model.llama.tokenization_llama.LlamaTokenizer
+class InternLM2Tokenizer(PreTrainedTokenizer):
+    """
+    Construct a InternLM2 tokenizer. Based on byte-level Byte-Pair-Encoding.
+
+    Args:
+        vocab_file (`str`):
+            Path to the vocabulary file.
+    """
+
+    vocab_files_names = VOCAB_FILES_NAMES
+    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
+    model_input_names = ['input_ids', 'attention_mask']
+    _auto_class = 'AutoTokenizer'
+
+    def __init__(
+        self,
+        vocab_file,
+        unk_token='<unk>',
+        bos_token='<s>',
+        eos_token='</s>',
+        pad_token='</s>',
+        sp_model_kwargs: Optional[Dict[str, Any]] = None,
+        add_bos_token=True,
+        add_eos_token=False,
+        decode_with_prefix_space=False,
+        clean_up_tokenization_spaces=False,
+        **kwargs,
+    ):
+        self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
+        self.vocab_file = vocab_file
+        self.add_bos_token = add_bos_token
+        self.add_eos_token = add_eos_token
+        self.decode_with_prefix_space = decode_with_prefix_space
+        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
+        self.sp_model.Load(vocab_file)
+        self._no_prefix_space_tokens = None
+        super().__init__(
+            bos_token=bos_token,
+            eos_token=eos_token,
+            unk_token=unk_token,
+            pad_token=pad_token,
+            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+            **kwargs,
+        )
+
+    @property
+    def no_prefix_space_tokens(self):
+        if self._no_prefix_space_tokens is None:
+            vocab = self.convert_ids_to_tokens(list(range(self.vocab_size)))
+            self._no_prefix_space_tokens = {i for i, tok in enumerate(vocab) if not tok.startswith('▁')}
+        return self._no_prefix_space_tokens
+
+    @property
+    def vocab_size(self):
+        """Returns vocab size"""
+        return self.sp_model.get_piece_size()
+
+    @property
+    def bos_token_id(self) -> Optional[int]:
+        return self.sp_model.bos_id()
+
+    @property
+    def eos_token_id(self) -> Optional[int]:
+        return self.sp_model.eos_id()
+
+    def get_vocab(self):
+        """Returns vocab as a dict"""
+        vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
+        vocab.update(self.added_tokens_encoder)
+        return vocab
+
+    def _tokenize(self, text):
+        """Returns a tokenized string."""
+        return self.sp_model.encode(text, out_type=str)
+
+    def _convert_token_to_id(self, token):
+        """Converts a token (str) in an id using the vocab."""
+        return self.sp_model.piece_to_id(token)
+
+    def _convert_id_to_token(self, index):
+        """Converts an index (integer) in a token (str) using the vocab."""
+        token = self.sp_model.IdToPiece(index)
+        return token
+
+    def _maybe_add_prefix_space(self, tokens, decoded):
+        if tokens and tokens[0] not in self.no_prefix_space_tokens:
+            return ' ' + decoded
+        else:
+            return decoded
+
+    def convert_tokens_to_string(self, tokens):
+        """Converts a sequence of tokens (string) in a single string."""
+        current_sub_tokens = []
+        out_string = ''
+        prev_is_special = False
+        for token in tokens:
+            # make sure that special tokens are not decoded using sentencepiece model
+            if token in self.all_special_tokens:
+                if not prev_is_special:
+                    out_string += ' '
+                out_string += self.sp_model.decode(current_sub_tokens) + token
+                prev_is_special = True
+                current_sub_tokens = []
+            else:
+                current_sub_tokens.append(token)
+                prev_is_special = False
+        out_string += self.sp_model.decode(current_sub_tokens)
+        out_string = self.clean_up_tokenization(out_string)
+        out_string = self._maybe_add_prefix_space(tokens=tokens, decoded=out_string)
+        return out_string[1:]
+
+    def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:
+        """
+        Save the vocabulary and special tokens file to a directory.
+
+        Args:
+            save_directory (`str`):
+                The directory in which to save the vocabulary.
+
+        Returns:
+            `Tuple(str)`: Paths to the files saved.
+        """
+        if not os.path.isdir(save_directory):
+            logger.error(f'Vocabulary path ({save_directory}) should be a directory')
+            return
+        out_vocab_file = os.path.join(
+            save_directory, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']
+        )
+
+        if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
+            copyfile(self.vocab_file, out_vocab_file)
+        elif not os.path.isfile(self.vocab_file):
+            with open(out_vocab_file, 'wb') as fi:
+                content_spiece_model = self.sp_model.serialized_model_proto()
+                fi.write(content_spiece_model)
+
+        return (out_vocab_file,)
+
+    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
+        if self.add_bos_token:
+            bos_token_ids = [self.bos_token_id]
+        else:
+            bos_token_ids = []
+
+        output = bos_token_ids + token_ids_0
+
+        if token_ids_1 is not None:
+            output = output + token_ids_1
+
+        if self.add_eos_token:
+            output = output + [self.eos_token_id]
+
+        return output
+
+    def get_special_tokens_mask(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
+    ) -> List[int]:
+        """
+        Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
+        special tokens using the tokenizer `prepare_for_model` method.
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
+                Whether or not the token list is already formatted with special tokens for the model.
+
+        Returns:
+            `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+        """
+        if already_has_special_tokens:
+            return super().get_special_tokens_mask(
+                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
+            )
+
+        if token_ids_1 is None:
+            return [1] + ([0] * len(token_ids_0)) + [1]
+        return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
+
+    def create_token_type_ids_from_sequences(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        """
+        Create a mask from the two sequences passed to be used in a sequence-pair classification task. T5 does not make
+        use of token type ids, therefore a list of zeros is returned.
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+
+        Returns:
+            `List[int]`: List of zeros.
+        """
+        eos = [self.eos_token_id]
+
+        if token_ids_1 is None:
+            return len(token_ids_0 + eos) * [0]
+        return len(token_ids_0 + eos + token_ids_1 + eos) * [0]

tokenizer.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f868398fc4e05ee1e8aeba95ddf18ddcc45b8bce55d5093bead5bbf80429b48b
+size 1477754

tokenizer_config.json

@@ -0,0 +1,179 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "92538": {
+      "content": "<|plugin|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "92539": {
+      "content": "<|interpreter|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "92540": {
+      "content": "<|action_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "92541": {
+      "content": "<|action_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "92542": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "92543": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "92544": {
+      "content": "<img>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "92545": {
+      "content": "</img>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "92546": {
+      "content": "<IMG_CONTEXT>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "92547": {
+      "content": "<quad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "92548": {
+      "content": "</quad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "92549": {
+      "content": "<ref>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "92550": {
+      "content": "</ref>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "92551": {
+      "content": "<box>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "92552": {
+      "content": "</box>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|action_start|>",
+    "<|action_end|>",
+    "<|interpreter|>",
+    "<|plugin|>",
+    "<img>",
+    "</img>",
+    "<IMG_CONTEXT>",
+    "<quad>",
+    "</quad>",
+    "<ref>",
+    "</ref>",
+    "<box>",
+    "</box>"
+  ],
+  "auto_map": {
+    "AutoTokenizer": [
+      "tokenization_internlm2.InternLM2Tokenizer",
+      null
+    ]
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "extra_special_tokens": {},
+  "model_max_length": 16384,
+  "pad_token": "</s>",
+  "tokenizer_class": "InternLM2Tokenizer",
+  "unk_token": "<unk>"
+}

zero_to_fp32.py

@@ -0,0 +1,760 @@
+#!/usr/bin/env python
+
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+# This script extracts fp32 consolidated weights from a zero 1, 2 and 3 DeepSpeed checkpoints. It gets
+# copied into the top level checkpoint dir, so the user can easily do the conversion at any point in
+# the future. Once extracted, the weights don't require DeepSpeed and can be used in any
+# application.
+#
+# example:
+#   python zero_to_fp32.py . output_dir/
+#   or
+#   python zero_to_fp32.py . output_dir/ --safe_serialization
+
+import argparse
+import torch
+import glob
+import math
+import os
+import re
+import gc
+import json
+import numpy as np
+from tqdm import tqdm
+from collections import OrderedDict
+from dataclasses import dataclass
+
+# while this script doesn't use deepspeed to recover data, since the checkpoints are pickled with
+# DeepSpeed data structures it has to be available in the current python environment.
+from deepspeed.utils import logger
+from deepspeed.checkpoint.constants import (DS_VERSION, OPTIMIZER_STATE_DICT, SINGLE_PARTITION_OF_FP32_GROUPS,
+                                            FP32_FLAT_GROUPS, ZERO_STAGE, PARTITION_COUNT, PARAM_SHAPES, BUFFER_NAMES,
+                                            FROZEN_PARAM_SHAPES, FROZEN_PARAM_FRAGMENTS)
+
+
+@dataclass
+class zero_model_state:
+    buffers: dict()
+    param_shapes: dict()
+    shared_params: list
+    ds_version: int
+    frozen_param_shapes: dict()
+    frozen_param_fragments: dict()
+
+
+debug = 0
+
+# load to cpu
+device = torch.device('cpu')
+
+
+def atoi(text):
+    return int(text) if text.isdigit() else text
+
+
+def natural_keys(text):
+    '''
+    alist.sort(key=natural_keys) sorts in human order
+    http://nedbatchelder.com/blog/200712/human_sorting.html
+    (See Toothy's implementation in the comments)
+    '''
+    return [atoi(c) for c in re.split(r'(\d+)', text)]
+
+
+def get_model_state_file(checkpoint_dir, zero_stage):
+    if not os.path.isdir(checkpoint_dir):
+        raise FileNotFoundError(f"Directory '{checkpoint_dir}' doesn't exist")
+
+    # there should be only one file
+    if zero_stage <= 2:
+        file = os.path.join(checkpoint_dir, "mp_rank_00_model_states.pt")
+    elif zero_stage == 3:
+        file = os.path.join(checkpoint_dir, "zero_pp_rank_0_mp_rank_00_model_states.pt")
+
+    if not os.path.exists(file):
+        raise FileNotFoundError(f"can't find model states file at '{file}'")
+
+    return file
+
+
+def get_checkpoint_files(checkpoint_dir, glob_pattern):
+    # XXX: need to test that this simple glob rule works for multi-node setup too
+    ckpt_files = sorted(glob.glob(os.path.join(checkpoint_dir, glob_pattern)), key=natural_keys)
+
+    if len(ckpt_files) == 0:
+        raise FileNotFoundError(f"can't find {glob_pattern} files in directory '{checkpoint_dir}'")
+
+    return ckpt_files
+
+
+def get_optim_files(checkpoint_dir):
+    return get_checkpoint_files(checkpoint_dir, "*_optim_states.pt")
+
+
+def get_model_state_files(checkpoint_dir):
+    return get_checkpoint_files(checkpoint_dir, "*_model_states.pt")
+
+
+def parse_model_states(files):
+    zero_model_states = []
+    for file in files:
+        state_dict = torch.load(file, map_location=device, weights_only=False)
+
+        if BUFFER_NAMES not in state_dict:
+            raise ValueError(f"{file} is not a model state checkpoint")
+        buffer_names = state_dict[BUFFER_NAMES]
+        if debug:
+            print("Found buffers:", buffer_names)
+
+        # recover just the buffers while restoring them to fp32 if they were saved in fp16
+        buffers = {k: v.float() for k, v in state_dict["module"].items() if k in buffer_names}
+        param_shapes = state_dict[PARAM_SHAPES]
+
+        # collect parameters that are included in param_shapes
+        param_names = []
+        for s in param_shapes:
+            for name in s.keys():
+                param_names.append(name)
+
+        # update with frozen parameters
+        frozen_param_shapes = state_dict.get(FROZEN_PARAM_SHAPES, None)
+        if frozen_param_shapes is not None:
+            if debug:
+                print(f"Found frozen_param_shapes: {frozen_param_shapes}")
+            param_names += list(frozen_param_shapes.keys())
+
+        # handle shared params
+        shared_params = [[k, v] for k, v in state_dict["shared_params"].items()]
+
+        ds_version = state_dict.get(DS_VERSION, None)
+
+        frozen_param_fragments = state_dict.get(FROZEN_PARAM_FRAGMENTS, None)
+
+        z_model_state = zero_model_state(buffers=buffers,
+                                         param_shapes=param_shapes,
+                                         shared_params=shared_params,
+                                         ds_version=ds_version,
+                                         frozen_param_shapes=frozen_param_shapes,
+                                         frozen_param_fragments=frozen_param_fragments)
+        zero_model_states.append(z_model_state)
+
+    return zero_model_states
+
+
+def parse_optim_states(files, ds_checkpoint_dir):
+    total_files = len(files)
+    state_dicts = []
+    for f in tqdm(files, desc='Loading checkpoint shards'):
+        state_dict = torch.load(f, map_location=device, mmap=True, weights_only=False)
+        # immediately discard the potentially huge 2 optimizer states as we only care for fp32 master weights
+        # and also handle the case where it was already removed by another helper script
+        state_dict["optimizer_state_dict"].pop("optimizer_state_dict", None)
+        state_dicts.append(state_dict)
+
+    if ZERO_STAGE not in state_dicts[0][OPTIMIZER_STATE_DICT]:
+        raise ValueError(f"{files[0]} is not a zero checkpoint")
+    zero_stage = state_dicts[0][OPTIMIZER_STATE_DICT][ZERO_STAGE]
+    world_size = state_dicts[0][OPTIMIZER_STATE_DICT][PARTITION_COUNT]
+
+    # For ZeRO-2 each param group can have different partition_count as data parallelism for expert
+    # parameters can be different from data parallelism for non-expert parameters. So we can just
+    # use the max of the partition_count to get the dp world_size.
+
+    if type(world_size) is list:
+        world_size = max(world_size)
+
+    if world_size != total_files:
+        raise ValueError(
+            f"Expected {world_size} of '*_optim_states.pt' under '{ds_checkpoint_dir}' but found {total_files} files. "
+            "Possibly due to an overwrite of an old checkpoint, or a checkpoint didn't get saved by one or more processes."
+        )
+
+    # the groups are named differently in each stage
+    if zero_stage <= 2:
+        fp32_groups_key = SINGLE_PARTITION_OF_FP32_GROUPS
+    elif zero_stage == 3:
+        fp32_groups_key = FP32_FLAT_GROUPS
+    else:
+        raise ValueError(f"unknown zero stage {zero_stage}")
+
+    fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] for i in range(len(state_dicts))]
+    return zero_stage, world_size, fp32_flat_groups
+
+
+def _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters):
+    """
+    Returns fp32 state_dict reconstructed from ds checkpoint
+
+    Args:
+        - ``ds_checkpoint_dir``: path to the deepspeed checkpoint folder (where the optimizer files are)
+
+    """
+    print(f"Processing zero checkpoint '{ds_checkpoint_dir}'")
+
+    optim_files = get_optim_files(ds_checkpoint_dir)
+    zero_stage, world_size, fp32_flat_groups = parse_optim_states(optim_files, ds_checkpoint_dir)
+    print(f"Detected checkpoint of type zero stage {zero_stage}, world_size: {world_size}")
+
+    model_files = get_model_state_files(ds_checkpoint_dir)
+
+    zero_model_states = parse_model_states(model_files)
+    print(f'Parsing checkpoint created by deepspeed=={zero_model_states[0].ds_version}')
+
+    if zero_stage <= 2:
+        return _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states,
+                                                          exclude_frozen_parameters)
+    elif zero_stage == 3:
+        return _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states,
+                                                          exclude_frozen_parameters)
+
+
+def _zero2_merge_frozen_params(state_dict, zero_model_states):
+    if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
+        return
+
+    frozen_param_shapes = zero_model_states[0].frozen_param_shapes
+    frozen_param_fragments = zero_model_states[0].frozen_param_fragments
+
+    if debug:
+        num_elem = sum(s.numel() for s in frozen_param_shapes.values())
+        print(f'rank 0: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
+
+        wanted_params = len(frozen_param_shapes)
+        wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
+        avail_numel = sum([p.numel() for p in frozen_param_fragments.values()])
+        print(f'Frozen params: Have {avail_numel} numels to process.')
+        print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
+
+    total_params = 0
+    total_numel = 0
+    for name, shape in frozen_param_shapes.items():
+        total_params += 1
+        unpartitioned_numel = shape.numel()
+        total_numel += unpartitioned_numel
+
+        state_dict[name] = frozen_param_fragments[name]
+
+        if debug:
+            print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
+
+    print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
+
+
+def _has_callable(obj, fn):
+    attr = getattr(obj, fn, None)
+    return callable(attr)
+
+
+def _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
+    param_shapes = zero_model_states[0].param_shapes
+
+    # Reconstruction protocol:
+    #
+    # XXX: document this
+
+    if debug:
+        for i in range(world_size):
+            for j in range(len(fp32_flat_groups[0])):
+                print(f"{FP32_FLAT_GROUPS}[{i}][{j}].shape={fp32_flat_groups[i][j].shape}")
+
+    # XXX: memory usage doubles here (zero2)
+    num_param_groups = len(fp32_flat_groups[0])
+    merged_single_partition_of_fp32_groups = []
+    for i in range(num_param_groups):
+        merged_partitions = [sd[i] for sd in fp32_flat_groups]
+        full_single_fp32_vector = torch.cat(merged_partitions, 0)
+        merged_single_partition_of_fp32_groups.append(full_single_fp32_vector)
+    avail_numel = sum(
+        [full_single_fp32_vector.numel() for full_single_fp32_vector in merged_single_partition_of_fp32_groups])
+
+    if debug:
+        wanted_params = sum([len(shapes) for shapes in param_shapes])
+        wanted_numel = sum([sum(shape.numel() for shape in shapes.values()) for shapes in param_shapes])
+        # not asserting if there is a mismatch due to possible padding
+        print(f"Have {avail_numel} numels to process.")
+        print(f"Need {wanted_numel} numels in {wanted_params} params.")
+
+    # params
+    # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
+    # out-of-core computing solution
+    total_numel = 0
+    total_params = 0
+    for shapes, full_single_fp32_vector in zip(param_shapes, merged_single_partition_of_fp32_groups):
+        offset = 0
+        avail_numel = full_single_fp32_vector.numel()
+        for name, shape in shapes.items():
+
+            unpartitioned_numel = shape.numel() if _has_callable(shape, 'numel') else math.prod(shape)
+            total_numel += unpartitioned_numel
+            total_params += 1
+
+            if debug:
+                print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
+            state_dict[name] = full_single_fp32_vector.narrow(0, offset, unpartitioned_numel).view(shape)
+            offset += unpartitioned_numel
+
+        # Z2 started to align to 2*world_size to improve nccl performance. Therefore both offset and
+        # avail_numel can differ by anywhere between 0..2*world_size. Due to two unrelated complex
+        # paddings performed in the code it's almost impossible to predict the exact numbers w/o the
+        # live optimizer object, so we are checking that the numbers are within the right range
+        align_to = 2 * world_size
+
+        def zero2_align(x):
+            return align_to * math.ceil(x / align_to)
+
+        if debug:
+            print(f"original offset={offset}, avail_numel={avail_numel}")
+
+        offset = zero2_align(offset)
+        avail_numel = zero2_align(avail_numel)
+
+        if debug:
+            print(f"aligned  offset={offset}, avail_numel={avail_numel}")
+
+        # Sanity check
+        if offset != avail_numel:
+            raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")
+
+    print(f"Reconstructed fp32 state dict with {total_params} params {total_numel} elements")
+
+
+def _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states,
+                                               exclude_frozen_parameters):
+    state_dict = OrderedDict()
+
+    # buffers
+    buffers = zero_model_states[0].buffers
+    state_dict.update(buffers)
+    if debug:
+        print(f"added {len(buffers)} buffers")
+
+    if not exclude_frozen_parameters:
+        _zero2_merge_frozen_params(state_dict, zero_model_states)
+
+    _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
+
+    # recover shared parameters
+    for pair in zero_model_states[0].shared_params:
+        if pair[1] in state_dict:
+            state_dict[pair[0]] = state_dict[pair[1]]
+
+    return state_dict
+
+
+def zero3_partitioned_param_info(unpartitioned_numel, world_size):
+    remainder = unpartitioned_numel % world_size
+    padding_numel = (world_size - remainder) if remainder else 0
+    partitioned_numel = math.ceil(unpartitioned_numel / world_size)
+    return partitioned_numel, padding_numel
+
+
+def _zero3_merge_frozen_params(state_dict, world_size, zero_model_states):
+    if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
+        return
+
+    if debug:
+        for i in range(world_size):
+            num_elem = sum(s.numel() for s in zero_model_states[i].frozen_param_fragments.values())
+            print(f'rank {i}: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
+
+        frozen_param_shapes = zero_model_states[0].frozen_param_shapes
+        wanted_params = len(frozen_param_shapes)
+        wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
+        avail_numel = sum([p.numel() for p in zero_model_states[0].frozen_param_fragments.values()]) * world_size
+        print(f'Frozen params: Have {avail_numel} numels to process.')
+        print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
+
+    total_params = 0
+    total_numel = 0
+    for name, shape in zero_model_states[0].frozen_param_shapes.items():
+        total_params += 1
+        unpartitioned_numel = shape.numel()
+        total_numel += unpartitioned_numel
+
+        param_frags = tuple(model_state.frozen_param_fragments[name] for model_state in zero_model_states)
+        state_dict[name] = torch.cat(param_frags, 0).narrow(0, 0, unpartitioned_numel).view(shape)
+
+        partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
+
+        if debug:
+            print(
+                f"Frozen params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
+            )
+
+    print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
+
+
+class GatheredTensor:
+    """
+    A pseudo tensor that collects partitioned weights.
+    It is more memory efficient when there are multiple groups.
+    """
+
+    def __init__(self, flat_groups, flat_groups_offset, offset, partitioned_numel, shape):
+        self.flat_groups = flat_groups
+        self.flat_groups_offset = flat_groups_offset
+        self.offset = offset
+        self.partitioned_numel = partitioned_numel
+        self.shape = shape
+        self.dtype = self.flat_groups[0][0].dtype
+
+    def contiguous(self):
+        """
+        Merge partitioned weights from flat_groups into a single tensor.
+        """
+        end_idx = self.offset + self.partitioned_numel
+        world_size = len(self.flat_groups)
+        pad_flat_param_chunks = []
+
+        for rank_i in range(world_size):
+            # for each rank, we need to collect weights from related group/groups
+            flat_groups_at_rank_i = self.flat_groups[rank_i]
+            start_group_id = None
+            end_group_id = None
+            for group_id in range(len(self.flat_groups_offset)):
+                if self.flat_groups_offset[group_id] <= self.offset < self.flat_groups_offset[group_id + 1]:
+                    start_group_id = group_id
+                if self.flat_groups_offset[group_id] < end_idx <= self.flat_groups_offset[group_id + 1]:
+                    end_group_id = group_id
+                    break
+            # collect weights from related group/groups
+            for group_id in range(start_group_id, end_group_id + 1):
+                flat_tensor = flat_groups_at_rank_i[group_id]
+                start_offset = self.offset - self.flat_groups_offset[group_id]
+                end_offset = min(end_idx, self.flat_groups_offset[group_id + 1]) - self.flat_groups_offset[group_id]
+                pad_flat_param_chunks.append(flat_tensor[start_offset:end_offset])
+
+        # collect weights from all ranks
+        pad_flat_param = torch.cat(pad_flat_param_chunks, dim=0)
+        param = pad_flat_param[:self.shape.numel()].view(self.shape).contiguous()
+        return param
+
+
+def _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
+    param_shapes = zero_model_states[0].param_shapes
+    avail_numel = sum([flat_group.numel() for flat_group in fp32_flat_groups[0]]) * world_size
+
+    # Reconstruction protocol: For zero3 we need to zip the partitions together at boundary of each
+    # param, re-consolidating each param, while dealing with padding if any
+
+    # merge list of dicts, preserving order
+    param_shapes = {k: v for d in param_shapes for k, v in d.items()}
+
+    if debug:
+        for i in range(world_size):
+            print(f"{FP32_FLAT_GROUPS}[{i}].shape={fp32_flat_groups[i].shape}")
+
+        wanted_params = len(param_shapes)
+        wanted_numel = sum(shape.numel() for shape in param_shapes.values())
+        # not asserting if there is a mismatch due to possible padding
+        avail_numel = fp32_flat_groups[0].numel() * world_size
+        print(f"Trainable params: Have {avail_numel} numels to process.")
+        print(f"Trainable params: Need {wanted_numel} numels in {wanted_params} params.")
+
+    # params
+    # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
+    # out-of-core computing solution
+    offset = 0
+    total_numel = 0
+    total_params = 0
+    flat_groups_offset = [0] + list(np.cumsum([flat_tensor.numel() for flat_tensor in fp32_flat_groups[0]]))
+    for name, shape in tqdm(param_shapes.items(), desc='Gathering sharded weights'):
+        unpartitioned_numel = shape.numel()
+        total_numel += unpartitioned_numel
+        total_params += 1
+        partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
+
+        if debug:
+            print(
+                f"Trainable params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
+            )
+
+        # memory efficient tensor
+        tensor = GatheredTensor(fp32_flat_groups, flat_groups_offset, offset, partitioned_numel, shape)
+        state_dict[name] = tensor
+        offset += partitioned_numel
+
+    offset *= world_size
+
+    # Sanity check
+    if offset != avail_numel:
+        raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")
+
+    print(f"Reconstructed Trainable fp32 state dict with {total_params} params {total_numel} elements")
+
+
+def _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states,
+                                               exclude_frozen_parameters):
+    state_dict = OrderedDict()
+
+    # buffers
+    buffers = zero_model_states[0].buffers
+    state_dict.update(buffers)
+    if debug:
+        print(f"added {len(buffers)} buffers")
+
+    if not exclude_frozen_parameters:
+        _zero3_merge_frozen_params(state_dict, world_size, zero_model_states)
+
+    _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
+
+    # recover shared parameters
+    for pair in zero_model_states[0].shared_params:
+        if pair[1] in state_dict:
+            state_dict[pair[0]] = state_dict[pair[1]]
+
+    return state_dict
+
+
+def to_torch_tensor(state_dict, return_empty_tensor=False):
+    """
+    Convert state_dict of GatheredTensor to torch tensor
+    """
+    torch_state_dict = {}
+    converted_tensors = {}
+    for name, tensor in state_dict.items():
+        tensor_id = id(tensor)
+        if tensor_id in converted_tensors:  # shared tensors
+            shared_tensor = torch_state_dict[converted_tensors[tensor_id]]
+            torch_state_dict[name] = shared_tensor
+        else:
+            converted_tensors[tensor_id] = name
+            if return_empty_tensor:
+                torch_state_dict[name] = torch.empty(tensor.shape, dtype=tensor.dtype)
+            else:
+                torch_state_dict[name] = tensor.contiguous()
+    return torch_state_dict
+
+
+def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir,
+                                             tag=None,
+                                             exclude_frozen_parameters=False,
+                                             lazy_mode=False):
+    """
+    Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated state_dict that can be loaded with
+    ``load_state_dict()`` and used for training without DeepSpeed or shared with others, for example
+    via a model hub.
+
+    Args:
+        - ``checkpoint_dir``: path to the desired checkpoint folder
+        - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in 'latest' file. e.g., ``global_step14``
+        - ``exclude_frozen_parameters``: exclude frozen parameters
+        - ``lazy_mode``: get state_dict in lazy mode. It returns a dict of pesduo tensor instead of torch tensor, which is more memory efficient.
+          Convert the pesduo tensor to torch tensor by ``.contiguous()``
+
+    Returns:
+        - pytorch ``state_dict``
+
+    A typical usage might be ::
+
+        from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
+        # do the training and checkpoint saving
+        state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) # already on cpu
+        model = model.cpu() # move to cpu
+        model.load_state_dict(state_dict)
+        # submit to model hub or save the model to share with others
+
+    In this example the ``model`` will no longer be usable in the deepspeed context of the same
+    application. i.e. you will need to re-initialize the deepspeed engine, since
+    ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
+
+    If you want it all done for you, use ``load_state_dict_from_zero_checkpoint`` instead.
+
+    Note: the above usage may not work if your application doesn't have sufficient free CPU memory.
+    You may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with
+    the checkpoint. Or you can load state_dict in lazy mode ::
+
+        from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
+        state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, lazy_mode=True) # not on cpu
+        for name, lazy_tensor in state_dict.item():
+            tensor = lazy_tensor.contiguous()  # to cpu
+            print(name, tensor)
+            # del tensor to release memory if it no longer in use
+    """
+    if tag is None:
+        latest_path = os.path.join(checkpoint_dir, 'latest')
+        if os.path.isfile(latest_path):
+            with open(latest_path, 'r') as fd:
+                tag = fd.read().strip()
+        else:
+            raise ValueError(f"Unable to find 'latest' file at {latest_path}")
+
+    ds_checkpoint_dir = os.path.join(checkpoint_dir, tag)
+
+    if not os.path.isdir(ds_checkpoint_dir):
+        raise FileNotFoundError(f"Directory '{ds_checkpoint_dir}' doesn't exist")
+
+    state_dict = _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters)
+    if lazy_mode:
+        return state_dict
+    else:
+        return to_torch_tensor(state_dict)
+
+
+def convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir,
+                                               output_dir,
+                                               max_shard_size="5GB",
+                                               safe_serialization=False,
+                                               tag=None,
+                                               exclude_frozen_parameters=False):
+    """
+    Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` file that can be
+    loaded with ``torch.load(file)`` + ``load_state_dict()`` and used for training without DeepSpeed.
+
+    Args:
+        - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
+        - ``output_dir``: directory to the pytorch fp32 state_dict output files
+        - ``max_shard_size``: the maximum size for a checkpoint before being sharded, default value is 5GB
+        - ``safe_serialization``:  whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).
+        - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
+        - ``exclude_frozen_parameters``: exclude frozen parameters
+    """
+
+    # Dependency pre-check
+    if safe_serialization:
+        try:
+            from safetensors.torch import save_file
+        except ImportError:
+            print('If you want to use `safe_serialization`, please `pip install safetensors`')
+            raise
+    if max_shard_size is not None:
+        try:
+            from huggingface_hub import split_torch_state_dict_into_shards
+        except ImportError:
+            print('If you want to use `max_shard_size`, please `pip install huggingface_hub`')
+            raise
+
+    # Convert zero checkpoint to state_dict
+    state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir,
+                                                          tag,
+                                                          exclude_frozen_parameters,
+                                                          lazy_mode=True)
+
+    # Shard the model if it is too big.
+    weights_name = "model.safetensors" if safe_serialization else "pytorch_model.bin"
+    if max_shard_size is not None:
+        filename_pattern = weights_name.replace(".bin", "{suffix}.bin").replace(".safetensors", "{suffix}.safetensors")
+        # an memory-efficient approach for sharding
+        empty_state_dict = to_torch_tensor(state_dict, return_empty_tensor=True)
+        state_dict_split = split_torch_state_dict_into_shards(empty_state_dict,
+                                                              filename_pattern=filename_pattern,
+                                                              max_shard_size=max_shard_size)
+    else:
+        from collections import namedtuple
+        StateDictSplit = namedtuple("StateDictSplit", ["is_sharded", "filename_to_tensors"])
+        state_dict_split = StateDictSplit(is_sharded=False,
+                                          filename_to_tensors={weights_name: list(state_dict.keys())})
+
+    # Save the model by shard
+    os.makedirs(output_dir, exist_ok=True)
+    filename_to_tensors = state_dict_split.filename_to_tensors.items()
+    for shard_file, tensors in tqdm(filename_to_tensors, desc="Saving checkpoint shards"):
+        shard_state_dict = {tensor_name: state_dict[tensor_name] for tensor_name in tensors}
+        shard_state_dict = to_torch_tensor(shard_state_dict)
+        output_path = os.path.join(output_dir, shard_file)
+        if safe_serialization:
+            save_file(shard_state_dict, output_path, metadata={"format": "pt"})
+        else:
+            torch.save(shard_state_dict, output_path)
+        # release the memory of current shard
+        for tensor_name in list(shard_state_dict.keys()):
+            del state_dict[tensor_name]
+            del shard_state_dict[tensor_name]
+        del shard_state_dict
+        gc.collect()
+
+    # Save index if sharded
+    if state_dict_split.is_sharded:
+        index = {
+            "metadata": state_dict_split.metadata,
+            "weight_map": state_dict_split.tensor_to_filename,
+        }
+        save_index_file = "model.safetensors.index.json" if safe_serialization else "pytorch_model.bin.index.json"
+        save_index_file = os.path.join(output_dir, save_index_file)
+        with open(save_index_file, "w", encoding="utf-8") as f:
+            content = json.dumps(index, indent=2, sort_keys=True) + "\n"
+            f.write(content)
+
+
+def load_state_dict_from_zero_checkpoint(model, checkpoint_dir, tag=None):
+    """
+    1. Put the provided model to cpu
+    2. Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict``
+    3. Load it into the provided model
+
+    Args:
+        - ``model``: the model object to update
+        - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
+        - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
+
+    Returns:
+        - ``model`: modified model
+
+    Make sure you have plenty of CPU memory available before you call this function. If you don't
+    have enough use the ``zero_to_fp32.py`` utility to do the conversion. You will find it
+    conveniently placed for you in the checkpoint folder.
+
+    A typical usage might be ::
+
+        from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint
+        model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir)
+        # submit to model hub or save the model to share with others
+
+    Note, that once this was run, the ``model`` will no longer be usable in the deepspeed context
+    of the same application. i.e. you will need to re-initialize the deepspeed engine, since
+    ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
+
+    """
+    logger.info("Extracting fp32 weights")
+    state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
+
+    logger.info("Overwriting model with fp32 weights")
+    model = model.cpu()
+    model.load_state_dict(state_dict, strict=False)
+
+    return model
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("checkpoint_dir",
+                        type=str,
+                        help="path to the desired checkpoint folder, e.g., path/checkpoint-12")
+    parser.add_argument("output_dir",
+                        type=str,
+                        help="directory to the pytorch fp32 state_dict output files"
+                        "(e.g. path/checkpoint-12-output/)")
+    parser.add_argument(
+        "--max_shard_size",
+        type=str,
+        default="5GB",
+        help="The maximum size for a checkpoint before being sharded. Checkpoints shard will then be each of size"
+        "lower than this size. If expressed as a string, needs to be digits followed by a unit (like `5MB`"
+        "We default it to 5GB in order for models to be able to run easily on free-tier google colab instances"
+        "without CPU OOM issues.")
+    parser.add_argument(
+        "--safe_serialization",
+        default=False,
+        action='store_true',
+        help="Whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).")
+    parser.add_argument("-t",
+                        "--tag",
+                        type=str,
+                        default=None,
+                        help="checkpoint tag used as a unique identifier for checkpoint. e.g., global_step1")
+    parser.add_argument("--exclude_frozen_parameters", action='store_true', help="exclude frozen parameters")
+    parser.add_argument("-d", "--debug", action='store_true', help="enable debug")
+    args = parser.parse_args()
+
+    debug = args.debug
+
+    convert_zero_checkpoint_to_fp32_state_dict(args.checkpoint_dir,
+                                               args.output_dir,
+                                               max_shard_size=args.max_shard_size,
+                                               safe_serialization=args.safe_serialization,
+                                               tag=args.tag,
+                                               exclude_frozen_parameters=args.exclude_frozen_parameters)