Preview: Thor-validated CUDA AWQ checkpoint

LICENSE

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+MIT License
+
+Copyright (c) 2026 Baidu
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+---
+license: mit
+base_model: baidu/Unlimited-OCR
+base_model_relation: quantized
+pipeline_tag: image-text-to-text
+library_name: vllm
+tags:
+- ocr
+- vllm
+- cuda
+- awq
+- compressed-tensors
+- preview
+- jetson-thor
+---
+
+# AX-Unlimited-OCR-3B-MoE-CUDA-AWQ-W4A16
+
+> **CUDA PREVIEW — not a full multi-architecture GA release.**
+> NVIDIA Thor `aarch64` passed formal native and immutable OCI acceptance.
+> `x86_64` remains experimental until the exact Production A100 OCI gate passes.
+
+This is one shared AWQ W4A16 checkpoint intended for CUDA/vLLM. The same model
+files may be downloaded on Thor and x86 Linux, but this preview makes a formal
+support claim only for NVIDIA Thor.
+
+## Architecture status
+
+| Target | Status | Release claim |
+|---|---|---|
+| NVIDIA Thor (`aarch64`) | Formally validated | Supported by this preview |
+| A100-SXM4-80GB (`x86_64`, SM80) | Experimental | Native evidence exists; formal Production OCI pending |
+| RTX A6000 (`x86_64`, SM86) | Experimental | No formal compatibility or performance claim |
+
+The available x86 development hosts currently report Thunder
+`mode=prototyping`, Docker `fastvfs`, and `/etc/thunder/libthunder.so`
+injection. Increasing their disks to 200 GB solved storage capacity but did not
+turn them into standard Production NVIDIA runtimes. This is an evidence-host
+limitation, not a claim that the checkpoint itself failed on x86.
+
+`native_x86_64_acceptance` remains recorded as `false`; it was not removed or
+changed to pass. The existing full CUDA publisher still requires both
+architectures and all 31 GA gates. No immutable SemVer GA tag is created for
+this preview.
+
+## Thor evidence
+
+- Formal OCI child: `127.0.0.1:5000/axocr-cuda@sha256:73257ded4e5a0b763ce7c117dca846c6796b786b8933c236ab479e4f58845575`
+- Image config: `sha256:d6418adfcb11c371a13595d5df1af3d76f78542fc5f4050ac2534ae8285a0565`
+- OCI receipt: `7ef7bb054547ad3ec479ace48a2ec22d18ded21fd6738017fadee055d461f6d4`
+- Frozen runtime source: `9b71932f621ee2939b40507a8ab942559851e757c4e54fe59528b498072fce10`
+- Upstream source: `baidu/Unlimited-OCR@ee63731b6461c8afcdcc7b15352e7d2ffecc2ead`
+- Evaluation set: `ax-ocr-accuracy-v3-20260723` (208 samples)
+
+| Thor metric | BF16 | AWQ |
+|---|---:|---:|
+| Aggregate throughput (completion tok/s) | 30.9081 | 31.0139 |
+| p95 latency (s) | 23.2570 | 23.0460 |
+| Mean CER | 0.089845 | 0.088245 |
+| Digit CER | 0.076077 | 0.076705 |
+
+The exact public-safe evidence summary is in `preview_manifest.json`.
+
+## Serving
+
+Use AX-OCR 0.5.0 source matching the frozen runtime identity and public vLLM
+0.25.1 with CUDA 13.0. On Thor the AX-OCR vLLM plugin selects the validated
+eager compatibility path.
+
+```bash
+ax-ocr vllm-serve \
+  --model-path AutomatosX/AX-Unlimited-OCR-3B-MoE-CUDA-AWQ-W4A16 \
+  --host 127.0.0.1 \
+  --port 8000
+```
+
+Requests must preserve the literal `<image>` prompt, set
+`skip_special_tokens=false`, and use the Unlimited-OCR no-repeat/R-SWA request
+settings recorded by AX-OCR.
+
+## Limitations
+
+- This preview is not a Production support statement for x86 CUDA.
+- A6000 and other NVIDIA GPUs may require different kernels or fallbacks.
+- The model is not MLX or GGUF.
+- OCR can hallucinate or omit content; verify high-stakes documents.

config.json

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+{
+  "architectures": [
+    "UnlimitedOCRForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "auto_map": {
+    "AutoConfig": "modeling_unlimitedocr.UnlimitedOCRConfig",
+    "AutoModel": "modeling_unlimitedocr.UnlimitedOCRForCausalLM"
+  },
+  "aux_loss_alpha": 0.001,
+  "bos_token_id": 0,
+  "candidate_resolutions": [
+    [
+      1024,
+      1024
+    ]
+  ],
+  "dtype": "bfloat16",
+  "eos_token_id": 1,
+  "ep_size": 1,
+  "first_k_dense_replace": 1,
+  "global_view_pos": "head",
+  "hidden_act": "silu",
+  "hidden_size": 1280,
+  "initializer_range": 0.02,
+  "intermediate_size": 6848,
+  "kv_lora_rank": null,
+  "language_config": {
+    "architectures": [
+      "DeepseekOCRForCausalLM"
+    ],
+    "auto_map": {
+      "AutoConfig": "configuration_deepseekv2.DeepseekV2Config",
+      "AutoModel": "modeling_deepseek.DeepseekV2Model",
+      "AutoModelForCausalLM": "modeling_deepseek.DeepseekV2ForCausalLM"
+    },
+    "bos_token_id": 0,
+    "eos_token_id": 1,
+    "first_k_dense_replace": 1,
+    "hidden_size": 1280,
+    "intermediate_size": 6848,
+    "kv_lora_rank": null,
+    "lm_head": true,
+    "max_position_embeddings": 32768,
+    "moe_intermediate_size": 896,
+    "n_group": 1,
+    "n_routed_experts": 64,
+    "n_shared_experts": 2,
+    "num_attention_heads": 10,
+    "num_experts_per_tok": 6,
+    "num_hidden_layers": 12,
+    "num_key_value_heads": 10,
+    "q_lora_rank": null,
+    "qk_nope_head_dim": 0,
+    "qk_rope_head_dim": 0,
+    "rm_head": false,
+    "sliding_window_size": 128,
+    "topk_group": 1,
+    "topk_method": "greedy",
+    "torch_dtype": "bfloat16",
+    "use_mla": false,
+    "v_head_dim": 128,
+    "vocab_size": 129280
+  },
+  "lm_head": true,
+  "max_position_embeddings": 32768,
+  "model_type": "unlimited-ocr",
+  "moe_intermediate_size": 896,
+  "moe_layer_freq": 1,
+  "n_group": 1,
+  "n_routed_experts": 64,
+  "n_shared_experts": 2,
+  "norm_topk_prob": false,
+  "num_attention_heads": 10,
+  "num_experts_per_tok": 6,
+  "num_hidden_layers": 12,
+  "num_key_value_heads": 10,
+  "pretraining_tp": 1,
+  "projector_config": {
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+    "model_type": "mlp_projector",
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+    "projector_type": "linear"
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+  "q_lora_rank": null,
+  "qk_nope_head_dim": 0,
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+        "input_activations": null,
+        "output_activations": null,
+        "targets": [
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+          "dynamic": false,
+          "group_size": 128,
+          "num_bits": 4,
+          "observer": "memoryless_minmax",
+          "observer_kwargs": {},
+          "scale_dtype": null,
+          "strategy": "group",
+          "symmetric": true,
+          "type": "int",
+          "zp_dtype": null
+        }
+      }
+    },
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+    ],
+    "kv_cache_scheme": null,
+    "quant_method": "compressed-tensors",
+    "quantization_status": "compressed",
+    "sparsity_config": {},
+    "transform_config": {},
+    "version": "0.14.0.1"
+  },
+  "rm_head": false,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "routed_scaling_factor": 1.0,
+  "scoring_func": "softmax",
+  "seq_aux": true,
+  "sliding_window": 128,
+  "sliding_window_size": 128,
+  "tie_word_embeddings": false,
+  "tile_tag": "2D",
+  "topk_group": 1,
+  "topk_method": "greedy",
+  "transformers_version": "4.57.1",
+  "use_cache": true,
+  "use_mla": false,
+  "v_head_dim": 128,
+  "vision_config": {
+    "image_size": 1024,
+    "mlp_ratio": 3.7362,
+    "model_name": "deeplip_b_l",
+    "model_type": "vision",
+    "width": {
+      "clip-l-14-224": {
+        "heads": 16,
+        "image_size": 224,
+        "layers": 24,
+        "patch_size": 14,
+        "width": 1024
+      },
+      "sam_vit_b": {
+        "downsample_channels": [
+          512,
+          1024
+        ],
+        "global_attn_indexes": [
+          2,
+          5,
+          8,
+          11
+        ],
+        "heads": 12,
+        "layers": 12,
+        "width": 768
+      }
+    }
+  },
+  "vocab_size": 129280
+}

configuration_deepseek_v2.py

@@ -0,0 +1,212 @@
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+DEEPSEEK_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
+class DeepseekV2Config(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`DeepseekV2Model`]. It is used to instantiate an DeepSeek
+    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 DeepSeek-V2 with multi-latent attention.
+
+    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 102400):
+            Vocabulary size of the Deep model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`DeepseekV2Model`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 11008):
+            Dimension of the MLP representations.
+        moe_intermediate_size (`int`, *optional*, defaults to 1407):
+            Dimension of the MoE representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer decoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer decoder.
+        n_shared_experts (`int`, *optional*, defaults to None):
+            Number of shared experts, None means dense model.
+        n_routed_experts (`int`, *optional*, defaults to None):
+            Number of routed experts, None means dense model.
+        routed_scaling_factor (`float`, *optional*, defaults to 1.0):
+            Scaling factor or routed experts.
+        topk_method (`str`, *optional*, defaults to `gready`):
+            Topk method used in routed gate.
+        n_group (`int`, *optional*, defaults to None):
+            Number of groups for routed experts.
+        topk_group (`int`, *optional*, defaults to None):
+            Number of selected groups for each token(for each token, ensuring the selected experts is only within `topk_group` groups).
+        num_experts_per_tok (`int`, *optional*, defaults to None):
+            Number of selected experts, None means dense model.
+        moe_layer_freq (`int`, *optional*, defaults to 1):
+            The frequency of the MoE layer: one expert layer for every `moe_layer_freq - 1` dense layers.
+        first_k_dense_replace (`int`, *optional*, defaults to 0):
+            Number of dense layers in shallow layers(embed->dense->dense->...->dense->moe->moe...->lm_head).
+                                                            \--k dense layers--/
+        norm_topk_prob (`bool`, *optional*, defaults to False):
+            Whether to normalize the weights of the routed experts.
+        scoring_func (`str`, *optional*, defaults to 'softmax'):
+            Method of computing expert weights.
+        aux_loss_alpha (`float`, *optional*, defaults to 0.001):
+            Auxiliary loss weight coefficient.
+        seq_aux = (`bool`, *optional*, defaults to True):
+            Whether to compute the auxiliary loss for each individual sample.
+        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.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        pad_token_id (`int`, *optional*):
+            Padding token id.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            Beginning of stream token id.
+        eos_token_id (`int`, *optional*, defaults to 2):
+            End of stream token id.
+        pretraining_tp (`int`, *optional*, defaults to 1):
+            Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
+            document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
+            necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
+            issue](https://github.com/pytorch/pytorch/issues/76232).
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
+            strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
+            `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
+            `max_position_embeddings` to the expected new maximum.
+        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
+            Whether to use a bias in the query, key, value and output projection layers during self-attention.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        use_mla (`bool`, *optional*, defaults to `True`): Use multi-latent attention or multi-head attention. If True,
+            the model will use multi-latent attention, otherwise, it will use multi-head attention.
+
+    ```python
+    >>> from transformers import DeepseekV2Model, DeepseekV2Config
+
+    >>> # Initializing a Deepseek-V2 style configuration
+    >>> configuration = DeepseekV2Config()
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "deepseek_v2"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=102400,
+        hidden_size=4096,
+        intermediate_size=11008,
+        moe_intermediate_size = 1407,
+        num_hidden_layers=30,
+        num_attention_heads=32,
+        num_key_value_heads=32,
+        n_shared_experts = None,
+        n_routed_experts = None,
+        ep_size = 1,
+        routed_scaling_factor = 1.0,
+        kv_lora_rank = 512,
+        q_lora_rank = 1536,
+        qk_rope_head_dim = 64,
+        v_head_dim = 128,
+        qk_nope_head_dim = 128,
+        topk_method = 'gready',
+        n_group = None,
+        topk_group = None,
+        num_experts_per_tok = None,
+        moe_layer_freq = 1,
+        first_k_dense_replace = 0,
+        norm_topk_prob = False,
+        scoring_func = 'softmax',
+        aux_loss_alpha = 0.001,
+        seq_aux = True,
+        hidden_act="silu",
+        max_position_embeddings=2048,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=100000,
+        eos_token_id=100001,
+        pretraining_tp=1,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        attention_bias=False,
+        attention_dropout=0.0,
+        use_mla=True,
+        sliding_window=None,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.moe_intermediate_size = moe_intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.n_shared_experts = n_shared_experts
+        self.n_routed_experts = n_routed_experts
+        self.ep_size = ep_size
+        self.routed_scaling_factor = routed_scaling_factor
+        self.kv_lora_rank = kv_lora_rank
+        self.q_lora_rank = q_lora_rank
+        self.qk_rope_head_dim = qk_rope_head_dim
+        self.v_head_dim = v_head_dim
+        self.qk_nope_head_dim = qk_nope_head_dim
+        self.topk_method = topk_method
+        self.n_group = n_group
+        self.topk_group = topk_group
+        self.num_experts_per_tok = num_experts_per_tok
+        self.moe_layer_freq = moe_layer_freq
+        self.first_k_dense_replace = first_k_dense_replace
+        self.norm_topk_prob = norm_topk_prob
+        self.scoring_func = scoring_func
+        self.aux_loss_alpha = aux_loss_alpha
+        self.seq_aux = seq_aux
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = float(rms_norm_eps)
+        self.pretraining_tp = pretraining_tp
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+        self.use_mla = use_mla
+        self.sliding_window = sliding_window
+
+        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,
+        )

conversation.py

@@ -0,0 +1,280 @@
+"""
+From https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py
+"""
+
+import dataclasses
+from enum import IntEnum, auto
+from typing import Any, Dict, List
+
+
+class SeparatorStyle(IntEnum):
+    """Separator styles."""
+
+    DeepSeek = auto()
+    DeepSeekV2 = auto()
+    PLAIN = auto()
+    ALIGNMENT = auto()
+
+
+@dataclasses.dataclass
+class Conversation:
+    """A class that manages prompt templates and keeps all conversation history."""
+
+    # The name of this template
+    name: str
+    # The template of the system prompt
+    system_template: str = "{system_message}"
+    # The system message
+    system_message: str = ""
+    # The names of two roles
+    roles: List[str] = (("USER", "ASSISTANT"),)
+    # All messages. Each item is (role, message).
+    messages: List[List[str]] = ()
+    # The number of few shot examples
+    offset: int = 0
+    # The separator style and configurations
+    sep_style: SeparatorStyle = SeparatorStyle.DeepSeek
+    sep: str = "\n"
+    sep2: str = None
+    # Stop criteria (the default one is EOS token)
+    stop_str: str = None
+    # Stops generation if meeting any token in this list
+    stop_token_ids: List[int] = None
+
+    def get_prompt(self) -> str:
+        """Get the prompt for generation."""
+        system_prompt = self.system_template.format(system_message=self.system_message)
+        if self.sep_style == SeparatorStyle.DeepSeek:
+            seps = [self.sep, self.sep2]
+            if system_prompt == "" or system_prompt is None:
+                ret = ""
+            else:
+                ret = system_prompt + seps[0]
+            for i, (role, message) in enumerate(self.messages):
+                if message:
+                    ret += role + ": " + message + seps[i % 2]
+                else:
+                    ret += role + ":"
+            return ret
+        elif self.sep_style == SeparatorStyle.DeepSeekV2:
+            seps = [self.sep, self.sep2]
+            if system_prompt == "" or system_prompt is None:
+                ret = ""
+            else:
+                ret = system_prompt + seps[0]
+            for i, (role, message) in enumerate(self.messages):
+                if message:
+                    if role == "User":
+                        ret += "<｜sft▁begin｜>\n" + message + self.sep #<｜sft▁begin｜>User Input<｜sft▁end｜>\nResponse<｜end▁of▁sentence｜>
+                    else:
+                        ret += message + self.sep2
+                else:
+                    ret = ret
+            return ret
+
+        elif self.sep_style == SeparatorStyle.PLAIN:
+            seps = [self.sep, self.sep2]
+            ret = ""
+            for i, (role, message) in enumerate(self.messages):
+                if message:
+                    if type(message) is tuple:
+                        message, _, _ = message
+                    if i % 2 == 0:
+                        ret += message + seps[i % 2]
+                    else:
+                        ret += message + seps[i % 2]
+                else:
+                    ret += ""
+            return ret
+        elif self.sep_style == SeparatorStyle.ALIGNMENT:
+            seps = [self.sep, self.sep2]
+            ret = ""
+            for i, (role, message) in enumerate(self.messages):
+                if message:
+                    if type(message) is tuple:
+                        message, _, _ = message
+                    if i % 2 == 0:
+                        ret += '<image>\n' + seps[i % 2]
+                    else:
+                        ret += message + seps[i % 2]
+                else:
+                    ret += ""
+            return ret
+        else:
+            raise ValueError(f"Invalid style: {self.sep_style}")
+
+    def set_system_message(self, system_message: str):
+        """Set the system message."""
+        self.system_message = system_message
+
+    def append_message(self, role: str, message: str):
+        """Append a new message."""
+        self.messages.append([role, message])
+
+    def update_last_message(self, message: str):
+        """Update the last output.
+
+        The last message is typically set to be None when constructing the prompt,
+        so we need to update it in-place after getting the response from a model.
+        """
+        self.messages[-1][1] = message
+
+    def reset_message(self):
+        """Reset a new message."""
+        self.messages = []
+
+    def to_gradio_chatbot(self):
+        """Convert the conversation to gradio chatbot format."""
+        ret = []
+        for i, (role, msg) in enumerate(self.messages[self.offset :]):
+            if i % 2 == 0:
+                ret.append([msg, None])
+            else:
+                ret[-1][-1] = msg
+        return ret
+
+    def to_openai_api_messages(self):
+        """Convert the conversation to OpenAI chat completion format."""
+        system_prompt = self.system_template.format(system_message=self.system_message)
+        ret = [{"role": "system", "content": system_prompt}]
+
+        for i, (_, msg) in enumerate(self.messages[self.offset :]):
+            if i % 2 == 0:
+                ret.append({"role": "user", "content": msg})
+            else:
+                if msg is not None:
+                    ret.append({"role": "assistant", "content": msg})
+        return ret
+
+    def copy(self):
+        return Conversation(
+            name=self.name,
+            system_template=self.system_template,
+            system_message=self.system_message,
+            roles=self.roles,
+            messages=[[x, y] for x, y in self.messages],
+            offset=self.offset,
+            sep_style=self.sep_style,
+            sep=self.sep,
+            sep2=self.sep2,
+            stop_str=self.stop_str,
+            stop_token_ids=self.stop_token_ids,
+        )
+
+    def dict(self):
+        return {
+            "template_name": self.name,
+            "system_message": self.system_message,
+            "roles": self.roles,
+            "messages": self.messages,
+            "offset": self.offset,
+        }
+
+
+# A global registry for all conversation templates
+conv_templates: Dict[str, Conversation] = {}
+
+
+def register_conv_template(template: Conversation, override: bool = False):
+    """Register a new conversation template."""
+    if not override:
+        assert template.name not in conv_templates, f"{template.name} has been registered."
+
+    conv_templates[template.name] = template
+
+
+def get_conv_template(name: str) -> Conversation:
+    """Get a conversation template."""
+    return conv_templates[name].copy()
+
+
+register_conv_template(
+    Conversation(
+        name="deepseek",
+        system_template="{system_message}",
+        # system_message="You are a helpful assistant. Please answer truthfully and write out your "
+        # "thinking step by step to be sure you get the right answer.",
+        system_message="",
+        roles=("<|User|>", "<|Assistant|>"),
+        messages=(),
+        offset=0,
+        sep_style=SeparatorStyle.DeepSeek,
+        sep="\n\n",
+        sep2="<｜end▁of▁sentence｜>",
+        stop_token_ids=[100001],
+        stop_str=["User:", "<｜end▁of▁sentence｜>"]
+    )
+)
+register_conv_template(
+    Conversation(
+        name="deepseekv2",
+        system_template="{system_message}",
+        # system_message="You are a helpful assistant. Please answer truthfully and write out your "
+        # "thinking step by step to be sure you get the right answer.",
+        system_message="",
+        roles=("<｜User｜>", "<｜Assistant｜>"),
+        messages=(),
+        offset=0,
+        sep_style=SeparatorStyle.DeepSeek,
+        sep="",
+        sep2="<｜end▁of▁sentence｜>",
+        stop_token_ids=[100001],
+        stop_str=["User:", "<｜end▁of▁sentence｜>"]
+    )
+)
+
+
+register_conv_template(
+    Conversation(
+        name="plain",
+        system_template="",
+        system_message="",
+        roles=("", ""),
+        messages=(),
+        offset=0,
+        sep_style=SeparatorStyle.PLAIN,
+        sep="",
+        sep2="",
+        stop_token_ids=[100001],
+        stop_str=['</s>'],
+    )
+)
+
+
+register_conv_template(
+    Conversation(
+        name="alignment",
+        system_template="",
+        system_message="",
+        roles=("", ""),
+        messages=(),
+        offset=0,
+        sep_style=SeparatorStyle.ALIGNMENT,
+        sep="",
+        sep2="",
+        stop_token_ids=[100001],
+        stop_str=['</s>'],
+    )
+)
+
+
+if __name__ == "__main__":
+    print("deepseek template:")
+    conv = get_conv_template("deepseek")
+    conv.append_message(conv.roles[0], "Hello!")
+    conv.append_message(conv.roles[1], "Hi! This is Tony.")
+    conv.append_message(conv.roles[0], "Who are you?")
+    conv.append_message(conv.roles[1], "I am a helpful assistant.")
+    conv.append_message(conv.roles[0], "How are you?")
+    conv.append_message(conv.roles[1], None)
+    print(conv.get_prompt())
+
+    print("deepseekv2 template:")
+    conv = get_conv_template("deepseekv2")
+    conv.append_message(conv.roles[0], "Hello!")
+    conv.append_message(conv.roles[1], "Hi! This is Tony.")
+    conv.append_message(conv.roles[0], "Who are you?")
+    conv.append_message(conv.roles[1], "I am a helpful assistant.")
+    conv.append_message(conv.roles[0], "How are you?")
+    conv.append_message(conv.roles[1], None)
+    print(conv.get_prompt())

cuda_quantization_manifest.json

@@ -0,0 +1,817 @@
+{
+  "schema_version": 3,
+  "source": {
+    "repo_id": "baidu/Unlimited-OCR",
+    "requested_revision": "ee63731b6461c8afcdcc7b15352e7d2ffecc2ead",
+    "resolved_revision": "ee63731b6461c8afcdcc7b15352e7d2ffecc2ead"
+  },
+  "target_repo": "AutomatosX/AX-Unlimited-OCR-3B-MoE-CUDA-AWQ-W4A16",
+  "quantization": {
+    "algorithm": "AWQ",
+    "scheme": "W4A16",
+    "targets": [
+      "Linear"
+    ],
+    "vision_precision": "bfloat16",
+    "protection_profile": "attention_shared_bf16",
+    "mapping_count": 716,
+    "mapping_identity": {
+      "schema_version": 1,
+      "mapping_count": 716,
+      "balance_target_count": 2147,
+      "router_first_mapping_count": 11,
+      "sha256": "1b993bbc90baa7b6f3c884f4eabc55e4fbae19cf9a821c84edd0b85cc34909ef"
+    },
+    "ignore": [
+      "lm_head",
+      "re:.*embed_tokens$",
+      "re:.*sam_model.*",
+      "re:.*vision_model.*",
+      "re:.*projector.*",
+      "re:.*mlp\\.gate$",
+      "re:.*image_newline.*",
+      "re:.*view_sep[ae]rator.*",
+      "re:^model\\.layers\\.[0-9]+\\.mlp\\.down_proj$",
+      "re:^model\\.layers\\.[0-9]+\\.self_attn\\.(?:q_proj|k_proj|v_proj|o_proj)$",
+      "re:^model\\.layers\\.[0-9]+\\.mlp\\.shared_experts\\.(?:gate_proj|up_proj|down_proj)$"
+    ]
+  },
+  "layout": {
+    "num_hidden_layers": 12,
+    "num_routed_experts": 64,
+    "first_k_dense_replace": 1
+  },
+  "calibration": {
+    "mode": "image_conditioned_teacher_forced",
+    "num_samples": 256,
+    "max_sequence_length": 4096,
+    "sha256": "3f4a0b3ec8395524d68f2ab0332484c6d8402be9c2ff7c7d5a9d8560b85c8623",
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+        "SlidingWindowLlamaAttention"
+      ],
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+      "modules": [
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+      "class": "AWQModifier",
+      "method": "_run_samples",
+      "strategy": "inspect_bound_arguments_positional_replay_v1"
+    },
+    "vllm_nonpersistent_buffers": {
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+        "model.vision_model.embeddings.position_ids"
+      ],
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+    },
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+      "safetensors_present": true,
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+}

deepencoder.py

@@ -0,0 +1,1058 @@
+import torch.nn as nn
+import torch
+import torch.nn.functional as F
+import copy
+
+from contextlib import nullcontext
+import math
+from typing import Optional, Tuple
+# from megatron.model import LayerNorm
+
+from einops import rearrange
+from easydict import EasyDict as adict
+
+
+from typing import Optional, Tuple, Type
+from functools import partial
+
+
+
+class MlpProjector(nn.Module):
+
+    def __init__(self, cfg):
+
+        super().__init__()
+
+        self.cfg = cfg
+
+        if cfg.projector_type == "identity":
+            modules = nn.Identity()
+
+        elif cfg.projector_type == "linear":
+            modules = nn.Linear(cfg.input_dim, cfg.n_embed)
+
+        elif cfg.projector_type == "mlp_gelu":
+            mlp_depth = cfg.get("depth", 1)
+            modules = [nn.Linear(cfg.input_dim, cfg.n_embed)]
+            for _ in range(1, mlp_depth):
+                modules.append(nn.GELU())
+                modules.append(nn.Linear(cfg.n_embed, cfg.n_embed))
+            modules = nn.Sequential(*modules)
+        
+        elif cfg.projector_type == "normlayer_downsample_mlp_gelu":
+            mlp_depth = cfg.get("depth", 1)
+            mlp_ratio = cfg.get("mlp_ratio", 1)
+            modules = [
+                nn.LayerNorm(cfg.input_dim * cfg.downsample_ratio * cfg.downsample_ratio),
+                nn.Linear(cfg.input_dim * cfg.downsample_ratio * cfg.downsample_ratio, cfg.n_embed * mlp_ratio)
+            ]
+            for _ in range(1, mlp_depth - 1):
+                modules.append(nn.GELU())
+                modules.append(nn.Linear(cfg.n_embed * mlp_ratio, cfg.n_embed * mlp_ratio))
+            modules.append(nn.GELU())
+            modules.append(nn.Linear(cfg.n_embed * mlp_ratio, cfg.n_embed))
+            modules = nn.Sequential(*modules)
+        
+        elif cfg.projector_type == "downsample_mlp_gelu":
+            mlp_depth = cfg.get("depth", 1)
+            mlp_ratio = cfg.get("mlp_ratio", 1)
+            modules = [nn.Linear(cfg.input_dim * cfg.downsample_ratio * cfg.downsample_ratio, cfg.n_embed * mlp_ratio)]
+            for _ in range(1, mlp_depth - 1):
+                modules.append(nn.GELU())
+                modules.append(nn.Linear(cfg.n_embed * mlp_ratio, cfg.n_embed * mlp_ratio))
+            modules.append(nn.GELU())
+            modules.append(nn.Linear(cfg.n_embed * mlp_ratio, cfg.n_embed))
+            modules = nn.Sequential(*modules)
+
+        elif cfg.projector_type == "low_high_hybrid_split_mlp_gelu":
+            mlp_depth = cfg.get("depth", 1)
+            self.high_up_proj = nn.Linear(cfg.input_dim, cfg.n_embed // 2)
+            self.low_up_proj = nn.Linear(cfg.input_dim, cfg.n_embed // 2)
+
+            modules = []
+            for _ in range(1, mlp_depth):
+                modules.append(nn.GELU())
+                modules.append(nn.Linear(cfg.n_embed, cfg.n_embed))
+            modules = nn.Sequential(*modules)
+
+        elif cfg.projector_type == "hybrid_split_feature_mlp_gelu":
+            mlp_depth = cfg.get("depth", 1)
+            channel_div = cfg.get("channel_div", 0.5)
+            self.high_up_proj = nn.Linear(cfg.input_dim[0], int(cfg.n_embed * channel_div))
+            self.low_up_proj = nn.Linear(cfg.input_dim[1], cfg.n_embed - int(cfg.n_embed * channel_div))
+
+            modules = []
+            for _ in range(1, mlp_depth):
+                modules.append(nn.GELU())
+                modules.append(nn.Linear(cfg.n_embed, cfg.n_embed))
+            modules = nn.Sequential(*modules)
+
+        elif cfg.projector_type == "low_high_split_mlp_gelu":
+            mlp_depth = cfg.get("depth", 1)
+            modules = []
+            for _ in range(1, mlp_depth):
+                modules.append(nn.GELU())
+                modules.append(nn.Linear(cfg.n_embed // 2, cfg.n_embed // 2))
+            modules = nn.Sequential(*modules)
+            self.high_layers = nn.Sequential(*modules)
+            self.low_layers = copy.deepcopy(modules)
+
+        else:
+            raise ValueError(f"Unknown projector type: {cfg.projector_type}")
+
+        if cfg.get("token_pooling", False):
+            self.token_pooling_layer = nn.Linear(cfg.input_dim * 4, cfg.input_dim)
+
+        if cfg.get("conv_fusion_high_low_features", False):
+            self.fusion_layer = nn.Linear(cfg.input_dim, cfg.input_dim)
+        self.layers = modules
+
+    def forward(self, x):
+        if self.cfg.get("token_pooling", False):
+            batch_size, wxh, channels = x.shape
+            w = h = int(wxh**0.5)
+            x = x.view(batch_size, w, h, channels)
+            x = x.permute(0, 3, 1, 2)
+            # import ipdb; ipdb.set_trace()
+            patches = x.unfold(2, 2, 2).unfold(3, 2, 2)
+            batch_size, channels, h_patches, w_patches, _, _ = patches.size()
+            # 在通道维度上拼接
+            patches = patches.contiguous().view(batch_size, channels, h_patches * w_patches, -1)
+
+            # 通过线性层
+            patches = patches.permute(0, 2, 1, 3).contiguous()
+            patches = patches.view(batch_size, h_patches * w_patches, channels * 4)
+
+            x = self.token_pooling_layer(patches)
+        
+        if self.cfg.get("conv_fusion_high_low_features", False):
+            x = self.fusion_layer(x[:, 0]) + x[:, 1]
+
+        if self.cfg.projector_type == 'low_high_hybrid_split_mlp_gelu':
+            high_x, low_x = x[0], x[1]
+            high_x = self.high_up_proj(high_x)
+            low_x = self.low_up_proj(low_x)
+            x = torch.concat([high_x, low_x], dim=-1)
+        
+        if self.cfg.projector_type == 'hybrid_split_feature_mlp_gelu':
+            high_x = x[...,:self.cfg.input_dim[0]]
+            low_x = x[...,self.cfg.input_dim[0]:]
+            high_x = self.high_up_proj(high_x)
+            low_x = self.low_up_proj(low_x)
+            x = torch.concat([high_x, low_x], dim=-1)
+        
+        if self.cfg.projector_type == 'low_high_split_mlp_gelu':
+            high_x, low_x = x[0], x[1]
+            high_x = self.high_layers(high_x)
+            low_x = self.low_layers(low_x)
+            x = torch.concat([high_x, low_x], dim=-1)
+            return x
+        
+        if self.cfg.projector_type == 'downsample_mlp_gelu' or self.cfg.projector_type == 'normlayer_downsample_mlp_gelu':
+            bs, hw, input_dim = x.shape
+            h = w = int((hw) ** 0.5)
+
+            """compute padding"""
+            if h % self.cfg.downsample_ratio:
+                pad = self.cfg.downsample_ratio - h % self.cfg.downsample_ratio
+            else:
+                pad = 0
+            x = x.reshape(bs, h, w, input_dim)
+            if pad > 0:
+                x = F.pad(x, (0, 0, 0, pad, 0, pad), "constant", 0)
+
+            """4 to 1 concat"""
+            x = x.permute(0, 3, 1, 2)  # B, C, H, W
+            x = F.unfold(x, kernel_size=self.cfg.downsample_ratio, stride=self.cfg.downsample_ratio, padding=0) # B, C*4, HW // 4
+            x = x.permute(0, 2, 1)
+            
+        return self.layers(x)
+
+    @staticmethod
+    def get_flops_per_sample(cfg):
+        if cfg.projector_type == "linear":
+            fwd = 2 * cfg.input_dim * cfg.n_embed
+
+        elif "mlp_gelu" in cfg.projector_type :
+            mlp_depth = cfg.get("depth", 1)
+            downsample_ratio = cfg.get("downsample_ratio", 1)
+            input_dim = sum(cfg.input_dim) if isinstance(cfg.input_dim, list) else cfg.input_dim
+            input_dim = input_dim * downsample_ratio * downsample_ratio
+            fwd = 2 * input_dim * cfg.n_embed + (mlp_depth - 1) * 2 * cfg.n_embed * cfg.n_embed
+        else:
+            fwd = 0
+
+        return fwd * 3
+    
+
+#===================clip============================================================
+
+class LayerNormfp32(torch.nn.LayerNorm):
+    """Subclass torch's LayerNorm to handle fp16."""
+
+    def forward(self, x: torch.Tensor):
+        orig_type = x.dtype
+        ret = super().forward(x.type(torch.float32))
+        return ret.type(orig_type)
+
+
+def get_abs_pos(abs_pos, tgt_size):
+    # abs_pos: L, C
+    # tgt_size: M
+    # return: M, C
+
+    # print(tgt_size)
+    # print(abs_pos.shape)
+    # exit()
+    dim = abs_pos.size(-1)
+    # print(dim)
+    abs_pos_new = abs_pos.squeeze(0)
+    cls_token, old_pos_embed = abs_pos_new[:1], abs_pos_new[1:]
+
+
+
+    src_size = int(math.sqrt(abs_pos_new.shape[0] - 1))
+    tgt_size = int(math.sqrt(tgt_size))
+    dtype = abs_pos.dtype
+
+    if src_size != tgt_size:
+        old_pos_embed = old_pos_embed.view(1, src_size, src_size, dim).permute(0, 3, 1,
+                                                                                    2).contiguous()
+        old_pos_embed = old_pos_embed.to(torch.float32)
+        new_pos_embed = F.interpolate(
+            old_pos_embed,
+            size=(tgt_size, tgt_size),
+            mode='bicubic',
+            antialias=True,
+            align_corners=False,
+        ).to(dtype)
+        new_pos_embed = new_pos_embed.permute(0, 2, 3, 1)
+        new_pos_embed = new_pos_embed.view(tgt_size * tgt_size, dim)
+        vision_pos_embed = torch.cat([cls_token, new_pos_embed], dim=0)
+        vision_pos_embed = vision_pos_embed.view(1, tgt_size * tgt_size + 1, dim)
+        return vision_pos_embed
+    else:
+        return abs_pos
+
+@torch.jit.script
+def quick_gelu(x):
+    return x * torch.sigmoid(1.702 * x)
+
+
+
+class CLIPVisionEmbeddings(nn.Module):
+    def __init__(self, hidden_size=1024, image_size=224, patch_size=14, num_channels=3):
+        super().__init__()
+        self.embed_dim = hidden_size
+        self.image_size = image_size
+        self.patch_size = patch_size
+
+        self.class_embedding = torch.nn.Parameter(torch.randn(self.embed_dim))
+
+        self.patch_embedding = torch.nn.Conv2d(
+            in_channels=num_channels,
+            out_channels=self.embed_dim,
+            kernel_size=self.patch_size,
+            stride=self.patch_size,
+            bias=False,
+        )
+
+        self.num_patches = (self.image_size // self.patch_size) ** 2
+        self.num_positions = self.num_patches + 1
+        self.position_embedding = torch.nn.Embedding(self.num_positions, self.embed_dim)
+        self.register_buffer(
+            "position_ids", torch.arange(self.num_positions).expand((1, -1))
+        )
+
+    def forward(self, pixel_values, patch_embeds):
+        batch_size = pixel_values.shape[0]
+        # patch_embeds = self.patch_embedding(
+        #     pixel_values
+        # )  # shape = [*, width, grid, grid]
+
+
+        if patch_embeds is not None:
+            patch_embeds = patch_embeds
+            # print(patch_embeds.shape)
+        else:
+            patch_embeds = self.patch_embedding(pixel_values)  
+            # print(111111)
+        # shape = [*, width, grid, grid]
+        # patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
+
+        patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
+
+
+        class_embeds = self.class_embedding.expand(batch_size, 1, -1)
+        embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
+
+        # x = torch.cat([cls_token, x], dim=1)
+        embeddings = embeddings + get_abs_pos(self.position_embedding(self.position_ids), embeddings.size(1))
+        # embeddings = embeddings + self.position_embedding(self.position_ids)
+        return embeddings
+
+
+class NoTPFeedForward(nn.Module):
+    def __init__(
+            self,
+            cfg,
+            dim: int,
+            hidden_dim: int,
+    ):
+        super().__init__()
+
+        self.fc1 = torch.nn.Linear(dim, hidden_dim, bias=True)
+        self.fc2 = torch.nn.Linear(hidden_dim, dim, bias=True)
+
+    def forward(self, x):
+        output = self.fc2(quick_gelu(self.fc1(x)))
+        return output
+
+
+
+
+class NoTPAttention(torch.nn.Module):
+    def __init__(self, cfg):
+        super().__init__()
+        self.num_heads = cfg.num_attention_heads
+        self.n_local_heads = cfg.num_attention_heads
+        self.head_dim = cfg.hidden_size // cfg.num_attention_heads
+        self.max_seq_len = cfg.seq_length
+        self.use_flash_attention = cfg.use_flash_attn
+
+        self.qkv_proj = torch.nn.Linear(cfg.hidden_size, cfg.hidden_size * 3, bias=True)
+        self.out_proj = torch.nn.Linear(cfg.hidden_size, cfg.hidden_size, bias=True)
+
+        # self.core_attention = CoreAttention(cfg, AttnType.self_attn)
+
+        self.attn_drop = cfg.attention_dropout
+
+    def forward(
+            self,
+            x: torch.Tensor,
+    ):
+        bsz, seqlen, _ = x.shape
+        xqkv = self.qkv_proj(x)
+        xqkv = xqkv.view(bsz, seqlen, 3, self.num_heads, self.head_dim)
+
+        if self.use_flash_attention:
+
+            xq, xk, xv = torch.split(xqkv, 1, dim=2)
+            xq = xq.squeeze(2)
+            xk = xk.squeeze(2)
+            xv = xv.squeeze(2)
+            # xq, xk, xv = xqkv[:, :, 0, ...], xqkv[:, :, 1, ...], xqkv[:, :, 2, ...]
+
+            # （B, num_head, S, head_size)
+            xq = xq.permute(0, 2, 1, 3)
+            xk = xk.permute(0, 2, 1, 3)
+            xv = xv.permute(0, 2, 1, 3)
+            # with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=False, enable_mem_efficient=False):
+            output = torch.nn.functional.scaled_dot_product_attention(xq, xk, xv, attn_mask=None)
+            output = output.permute(0, 2, 1, 3).reshape(bsz, seqlen, -1)
+                # output = output.permute(0, 2, 1, 3).contiguous().view(bsz, seqlen, -1)
+        else:
+            # print(22222)
+            xq, xk, xv = torch.split(xqkv, 1, dim=2)
+            xq = xq.squeeze(2)
+            xk = xk.squeeze(2)
+            xv = xv.squeeze(2)
+            # xq, xk, xv = xqkv[:, :, 0, ...], xqkv[:, :, 1, ...], xqkv[:, :, 2, ...]
+
+            # （B, num_head, S, head_size)
+            xq = xq.permute(0, 2, 1, 3)
+            xk = xk.permute(0, 2, 1, 3)
+            xv = xv.permute(0, 2, 1, 3)
+            # with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=False, enable_mem_efficient=False):
+            output = torch.nn.functional.scaled_dot_product_attention(xq, xk, xv, attn_mask=None)
+            output = output.permute(0, 2, 1, 3).reshape(bsz, seqlen, -1)
+            # output = output.permute(0, 2, 1, 3).contiguous().view(bsz, seqlen, -1)
+        output = self.out_proj(output)
+        return output
+
+class NoTPTransformerBlock(nn.Module):
+    def __init__(self, cfg, layer_id: int, multiple_of=256):
+        super().__init__()
+
+        self.n_heads = cfg.num_attention_heads
+        self.dim = cfg.hidden_size
+        self.head_dim = cfg.hidden_size // cfg.num_attention_heads
+        self.self_attn = NoTPAttention(cfg)
+        self.mlp = NoTPFeedForward(
+            cfg, dim=cfg.hidden_size, hidden_dim=cfg.ffn_hidden_size
+        )
+        self.layer_id = layer_id
+        self.layer_norm1 = torch.nn.LayerNorm(
+            cfg.hidden_size, eps=cfg.layernorm_epsilon
+        )
+        self.layer_norm2 = torch.nn.LayerNorm(
+            cfg.hidden_size, eps=cfg.layernorm_epsilon
+        )
+
+    def forward(self, x: torch.Tensor):
+        residual = self.self_attn.forward(self.layer_norm1(x))
+        h = x + residual
+        out = h + self.mlp.forward(self.layer_norm2(h))
+        return out
+
+
+class NoTPTransformer(nn.Module):
+    def __init__(self, cfg):
+        super().__init__()
+
+        self.cfg = cfg
+        # self.recompute_list = self.cfg.get("recompute_list", [])
+        self.num_layers = cfg.num_layers  # _get_num_layers(cfg)
+
+        self.layers = torch.nn.ModuleList()
+        for layer_id in range(self.num_layers):
+            self.layers.append(
+                NoTPTransformerBlock(
+                    cfg,
+                    layer_id + 1,
+                )
+            )
+
+    def forward(
+            self,
+            hidden_states,
+    ):
+
+        for lid, layer in enumerate(self.layers):
+            # if lid in self.recompute_list:
+            #     def custom(layer_id):
+            #         def custom_forward(*args, **kwargs):
+            #             x_ = self.layers[layer_id](*args, **kwargs)
+            #             return x_
+
+            #         return custom_forward
+
+            #     assert hidden_states.requires_grad == True, logger.warning(
+            #         "When using recalculation, the input must have grad fn"
+            #     )
+            #     hidden_states = tensor_parallel.checkpoint(
+            #         custom(lid),
+            #         False,
+            #         hidden_states.contiguous()
+            #     )
+            # else:
+            hidden_states = layer(hidden_states)
+
+        return hidden_states
+
+
+# from megatron.core.tensor_parallel.layers import non_tensor_paralleled, local_dp_reduce, local_dp_scatter
+
+class VitModel(nn.Module):
+    def __init__(
+            self,
+            cfg,
+            freeze_embed=False,
+            freeze_pre_norm=False
+    ) -> None:
+        super().__init__()
+
+        self.embeddings = CLIPVisionEmbeddings(hidden_size=cfg.hidden_size, image_size=cfg.image_size, patch_size=cfg.patch_size)
+
+        if freeze_embed:
+            for name, param in self.embeddings.named_parameters():
+                param.requires_grad = False
+
+        self.transformer = NoTPTransformer(cfg=cfg)
+
+        if cfg.get("fp32norm", False):
+            logger.info("Load fp32 layernorm for ViT.")
+            self.pre_layrnorm = LayerNormfp32(
+                cfg.hidden_size,
+                eps=cfg.get("pre_layernorm_epsilon", 1e-5),
+            )
+        else:
+            self.pre_layrnorm = torch.nn.LayerNorm(
+                cfg.hidden_size,
+                eps=cfg.get("pre_layernorm_epsilon", 1e-5),
+            )
+
+        # self.pre_layrnorm = RMSNorm(
+        #     cfg.hidden_size,
+        #     eps=cfg.get("pre_layernorm_epsilon", 1e-5),
+        #     sequence_parallel=False,
+        #     use_fp32=True,
+        #     use_optimus=True,
+        # )
+
+        if freeze_pre_norm:
+            for name, param in self.pre_layrnorm.named_parameters():
+                param.requires_grad = False
+
+        for p in self.parameters():
+            p.micro_dp = True
+
+    def set_input_tensor(self, input_tensor):
+        if not isinstance(input_tensor, list):
+            input_tensor = [input_tensor]
+        self.transformer.set_input_tensor(input_tensor[0])
+
+    def __str__(self) -> str:
+        return "open_clip"
+
+    def forward(
+            self,
+            x,
+            patch_embeds
+    ):
+        x = self.embeddings(x, patch_embeds)
+        hidden_states = self.pre_layrnorm(x)
+
+        # hidden_states, dis = local_dp_scatter(hidden_states)
+        output = self.transformer(hidden_states)
+
+        # output = local_dp_reduce(output, dis)
+
+        return output
+
+
+vit_model_cfg = adict(
+    num_layers=24,
+    hidden_size=1024,
+    num_heads = 16,
+    num_attention_heads=16,
+    ffn_hidden_size=4096,
+    seq_length=256,
+    max_position_embeddings=256,
+    use_flash_attn=False,
+    understand_projector_stride=2,
+    hidden_dropout = 0.0,
+    attention_dropout = 0.0,
+    no_persist_layer_norm = False,
+    layernorm_epsilon = 1e-5,
+    pre_layernorm_epsilon = 1e-5,
+    image_size = 224,
+    patch_size = 14,
+    recompute_list = []
+)
+
+def build_clip_l():
+    return VitModel(
+        cfg=vit_model_cfg,
+        freeze_embed=False,
+        freeze_pre_norm=False,
+    )
+
+
+
+
+
+#=========================Sam-Vary=================================
+
+
+def get_abs_pos_sam(abs_pos, tgt_size):
+
+    dtype = abs_pos.dtype
+
+    src_size = abs_pos.size(1)
+
+    if src_size != tgt_size:
+        old_pos_embed = abs_pos.permute(0, 3, 1, 2)
+        old_pos_embed = old_pos_embed.to(torch.float32)
+        new_pos_embed = F.interpolate(
+            old_pos_embed,
+            size=(tgt_size, tgt_size),
+            mode='bicubic',
+            antialias=True,
+            align_corners=False,
+        ).to(dtype)
+        new_pos_embed = new_pos_embed.permute(0, 2, 3, 1)
+        return new_pos_embed
+    else:
+        return abs_pos
+
+
+
+
+class MLPBlock(nn.Module):
+    def __init__(
+        self,
+        embedding_dim: int,
+        mlp_dim: int,
+        act: Type[nn.Module] = nn.GELU,
+    ) -> None:
+        super().__init__()
+        self.lin1 = nn.Linear(embedding_dim, mlp_dim)
+        self.lin2 = nn.Linear(mlp_dim, embedding_dim)
+        self.act = act()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.lin2(self.act(self.lin1(x)))
+
+
+# From https://github.com/facebookresearch/detectron2/blob/main/detectron2/layers/batch_norm.py # noqa
+# Itself from https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119  # noqa
+class LayerNorm2d(nn.Module):
+    def __init__(self, num_channels: int, eps: float = 1e-6) -> None:
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(num_channels))
+        self.bias = nn.Parameter(torch.zeros(num_channels))
+        self.eps = eps
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        u = x.mean(1, keepdim=True)
+        s = (x - u).pow(2).mean(1, keepdim=True)
+        x = (x - u) / torch.sqrt(s + self.eps)
+        x = self.weight[:, None, None] * x + self.bias[:, None, None]
+        return x
+
+
+# This class and its supporting functions below lightly adapted from the ViTDet backbone available at: https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/backbone/vit.py # noqa
+class ImageEncoderViT(nn.Module):
+    def __init__(
+        self,
+        img_size: int = 1024,
+        patch_size: int = 16,
+        in_chans: int = 3,
+        embed_dim: int = 768,
+        depth: int = 12,
+        num_heads: int = 12,
+        mlp_ratio: float = 4.0,
+        out_chans: int = 256,
+        qkv_bias: bool = True,
+        norm_layer: Type[nn.Module] = nn.LayerNorm,
+        act_layer: Type[nn.Module] = nn.GELU,
+        use_abs_pos: bool = True,
+        use_rel_pos: bool = False,
+        rel_pos_zero_init: bool = True,
+        window_size: int = 0,
+        global_attn_indexes: Tuple[int, ...] = (),
+    ) -> None:
+        """
+        Args:
+            img_size (int): Input image size.
+            patch_size (int): Patch size.
+            in_chans (int): Number of input image channels.
+            embed_dim (int): Patch embedding dimension.
+            depth (int): Depth of ViT.
+            num_heads (int): Number of attention heads in each ViT block.
+            mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+            qkv_bias (bool): If True, add a learnable bias to query, key, value.
+            norm_layer (nn.Module): Normalization layer.
+            act_layer (nn.Module): Activation layer.
+            use_abs_pos (bool): If True, use absolute positional embeddings.
+            use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
+            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
+            window_size (int): Window size for window attention blocks.
+            global_attn_indexes (list): Indexes for blocks using global attention.
+        """
+        super().__init__()
+        self.img_size = img_size
+
+        self.patch_embed = PatchEmbed(
+            kernel_size=(patch_size, patch_size),
+            stride=(patch_size, patch_size),
+            in_chans=in_chans,
+            embed_dim=embed_dim,
+        )
+
+        self.pos_embed: Optional[nn.Parameter] = None
+        if use_abs_pos:
+            # Initialize absolute positional embedding with pretrain image size.
+            self.pos_embed = nn.Parameter(
+                torch.zeros(1, img_size // patch_size, img_size // patch_size, embed_dim)
+            )
+
+        self.blocks = nn.ModuleList()
+        for i in range(depth):
+            block = Block(
+                dim=embed_dim,
+                num_heads=num_heads,
+                mlp_ratio=mlp_ratio,
+                qkv_bias=qkv_bias,
+                norm_layer=norm_layer,
+                act_layer=act_layer,
+                use_rel_pos=use_rel_pos,
+                rel_pos_zero_init=rel_pos_zero_init,
+                window_size=window_size if i not in global_attn_indexes else 0,
+                input_size=(img_size // patch_size, img_size // patch_size),
+            )
+            self.blocks.append(block)
+
+        self.neck = nn.Sequential(
+            nn.Conv2d(
+                embed_dim,
+                out_chans,
+                kernel_size=1,
+                bias=False,
+            ),
+            LayerNorm2d(out_chans),
+            nn.Conv2d(
+                out_chans,
+                out_chans,
+                kernel_size=3,
+                padding=1,
+                bias=False,
+            ),
+            LayerNorm2d(out_chans),
+        )
+
+        self.net_2 = nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1, bias=False)
+        self.net_3 = nn.Conv2d(512, 1024, kernel_size=3, stride=2, padding=1, bias=False)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.patch_embed(x)
+        if self.pos_embed is not None:
+            # x = x + self.pos_embed
+            x = x + get_abs_pos_sam(self.pos_embed, x.size(1))
+
+        for blk in self.blocks:
+            x = blk(x)
+
+        x = self.neck(x.permute(0, 3, 1, 2))
+        x2 = self.net_2(x)
+        x3 = self.net_3(x2.clone())
+
+        return x3
+
+
+class Block(nn.Module):
+    """Transformer blocks with support of window attention and residual propagation blocks"""
+
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int,
+        mlp_ratio: float = 4.0,
+        qkv_bias: bool = True,
+        norm_layer: Type[nn.Module] = nn.LayerNorm,
+        act_layer: Type[nn.Module] = nn.GELU,
+        use_rel_pos: bool = False,
+        rel_pos_zero_init: bool = True,
+        window_size: int = 0,
+        input_size: Optional[Tuple[int, int]] = None,
+    ) -> None:
+        """
+        Args:
+            dim (int): Number of input channels.
+            num_heads (int): Number of attention heads in each ViT block.
+            mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+            qkv_bias (bool): If True, add a learnable bias to query, key, value.
+            norm_layer (nn.Module): Normalization layer.
+            act_layer (nn.Module): Activation layer.
+            use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
+            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
+            window_size (int): Window size for window attention blocks. If it equals 0, then
+                use global attention.
+            input_size (tuple(int, int) or None): Input resolution for calculating the relative
+                positional parameter size.
+        """
+        super().__init__()
+        self.norm1 = norm_layer(dim)
+        self.attn = Attention(
+            dim,
+            num_heads=num_heads,
+            qkv_bias=qkv_bias,
+            use_rel_pos=use_rel_pos,
+            rel_pos_zero_init=rel_pos_zero_init,
+            input_size=input_size if window_size == 0 else (window_size, window_size),
+        )
+
+        self.norm2 = norm_layer(dim)
+        self.mlp = MLPBlock(embedding_dim=dim, mlp_dim=int(dim * mlp_ratio), act=act_layer)
+
+        self.window_size = window_size
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        shortcut = x
+        x = self.norm1(x)
+        # Window partition
+        if self.window_size > 0:
+            H, W = x.shape[1], x.shape[2]
+            x, pad_hw = window_partition(x, self.window_size)
+
+        x = self.attn(x)
+        # Reverse window partition
+        if self.window_size > 0:
+            x = window_unpartition(x, self.window_size, pad_hw, (H, W))
+
+        x = shortcut + x
+        x = x + self.mlp(self.norm2(x))
+
+        return x
+
+
+class Attention(nn.Module):
+    """Multi-head Attention block with relative position embeddings."""
+
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int = 8,
+        qkv_bias: bool = True,
+        use_rel_pos: bool = False,
+        rel_pos_zero_init: bool = True,
+        input_size: Optional[Tuple[int, int]] = None,
+    ) -> None:
+        """
+        Args:
+            dim (int): Number of input channels.
+            num_heads (int): Number of attention heads.
+            qkv_bias (bool):  If True, add a learnable bias to query, key, value.
+            rel_pos (bool): If True, add relative positional embeddings to the attention map.
+            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
+            input_size (tuple(int, int) or None): Input resolution for calculating the relative
+                positional parameter size.
+        """
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = head_dim**-0.5
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.proj = nn.Linear(dim, dim)
+
+        self.use_rel_pos = use_rel_pos
+        if self.use_rel_pos:
+            assert (
+                input_size is not None
+            ), "Input size must be provided if using relative positional encoding."
+            # initialize relative positional embeddings
+            self.rel_pos_h = nn.Parameter(torch.zeros(2 * input_size[0] - 1, head_dim))
+            self.rel_pos_w = nn.Parameter(torch.zeros(2 * input_size[1] - 1, head_dim))
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        B, H, W, _ = x.shape
+        # qkv with shape (3, B, nHead, H * W, C)
+        qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
+        # q, k, v with shape (B * nHead, H * W, C)
+        q, k, v = qkv.reshape(3, B * self.num_heads, H * W, -1).unbind(0)
+
+        rel_h, rel_w = None, None
+        if self.use_rel_pos:
+            rel_h, rel_w = add_decomposed_rel_pos(q, self.rel_pos_h, self.rel_pos_w, (H, W), (H, W))
+
+        q = q.view(B, self.num_heads, H * W, -1)
+        k = k.view(B, self.num_heads, H * W, -1)
+        v = v.view(B, self.num_heads, H * W, -1)
+
+        if self.use_rel_pos:
+            rel_h = rel_h.view(B, self.num_heads, rel_h.size(1), rel_h.size(2), rel_h.size(3))
+            rel_w = rel_w.view(B, self.num_heads, rel_w.size(1), rel_w.size(2), rel_w.size(3))
+            attn_bias = (rel_h + rel_w).view(B, self.num_heads, rel_h.size(2), rel_h.size(3) * rel_w.size(4))
+            x = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_bias)
+            # x = _attention_rel_h_rel_w(q, k, v, rel_h, rel_w)
+        else:
+            x = torch.nn.functional.scaled_dot_product_attention(q, k, v)
+
+        x = x.view(B, self.num_heads, H, W, -1).permute(0, 2, 3, 1, 4).reshape(B, H, W, -1)
+
+        x = self.proj(x)
+
+        return x
+
+
+def window_partition(x: torch.Tensor, window_size: int) -> Tuple[torch.Tensor, Tuple[int, int]]:
+    """
+    Partition into non-overlapping windows with padding if needed.
+    Args:
+        x (tensor): input tokens with [B, H, W, C].
+        window_size (int): window size.
+
+    Returns:
+        windows: windows after partition with [B * num_windows, window_size, window_size, C].
+        (Hp, Wp): padded height and width before partition
+    """
+    B, H, W, C = x.shape
+
+    pad_h = (window_size - H % window_size) % window_size
+    pad_w = (window_size - W % window_size) % window_size
+    if pad_h > 0 or pad_w > 0:
+        x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h))
+    Hp, Wp = H + pad_h, W + pad_w
+
+    x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C)
+    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
+    return windows, (Hp, Wp)
+
+
+def window_unpartition(
+    windows: torch.Tensor, window_size: int, pad_hw: Tuple[int, int], hw: Tuple[int, int]
+) -> torch.Tensor:
+    """
+    Window unpartition into original sequences and removing padding.
+    Args:
+        windows (tensor): input tokens with [B * num_windows, window_size, window_size, C].
+        window_size (int): window size.
+        pad_hw (Tuple): padded height and width (Hp, Wp).
+        hw (Tuple): original height and width (H, W) before padding.
+
+    Returns:
+        x: unpartitioned sequences with [B, H, W, C].
+    """
+    Hp, Wp = pad_hw
+    H, W = hw
+    B = windows.shape[0] // (Hp * Wp // window_size // window_size)
+    x = windows.view(B, Hp // window_size, Wp // window_size, window_size, window_size, -1)
+    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1)
+
+    if Hp > H or Wp > W:
+        x = x[:, :H, :W, :].contiguous()
+    return x
+
+
+def get_rel_pos(q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor:
+    """
+    Get relative positional embeddings according to the relative positions of
+        query and key sizes.
+    Args:
+        q_size (int): size of query q.
+        k_size (int): size of key k.
+        rel_pos (Tensor): relative position embeddings (L, C).
+
+    Returns:
+        Extracted positional embeddings according to relative positions.
+    """
+    max_rel_dist = int(2 * max(q_size, k_size) - 1)
+    # Interpolate rel pos if needed.
+    if rel_pos.shape[0] != max_rel_dist:
+        # Interpolate rel pos.
+        dtype = rel_pos.dtype
+        rel_pos = rel_pos.to(torch.float32)
+        rel_pos_resized = F.interpolate(
+            rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1),
+            size=max_rel_dist,
+            mode="linear",
+        ).to(dtype)
+        rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0)
+    else:
+        rel_pos_resized = rel_pos
+
+    # Scale the coords with short length if shapes for q and k are different.
+    q_coords = torch.arange(q_size, device=rel_pos.device)[:, None] * max(k_size / q_size, 1.0)
+    k_coords = torch.arange(k_size, device=rel_pos.device)[None, :] * max(q_size / k_size, 1.0)
+    relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0)
+
+    return rel_pos_resized[relative_coords.long()]
+
+
+def add_decomposed_rel_pos(
+    q: torch.Tensor,
+    rel_pos_h: torch.Tensor,
+    rel_pos_w: torch.Tensor,
+    q_size: Tuple[int, int],
+    k_size: Tuple[int, int],
+) -> torch.Tensor:
+    """
+    Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`.
+    https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py   # noqa B950
+    Args:
+        q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C).
+        rel_pos_h (Tensor): relative position embeddings (Lh, C) for height axis.
+        rel_pos_w (Tensor): relative position embeddings (Lw, C) for width axis.
+        q_size (Tuple): spatial sequence size of query q with (q_h, q_w).
+        k_size (Tuple): spatial sequence size of key k with (k_h, k_w).
+
+    Returns:
+        attn (Tensor): attention map with added relative positional embeddings.
+    """
+    q_h, q_w = q_size
+    k_h, k_w = k_size
+    Rh = get_rel_pos(q_h, k_h, rel_pos_h)
+    Rw = get_rel_pos(q_w, k_w, rel_pos_w)
+
+    B, _, dim = q.shape
+    r_q = q.reshape(B, q_h, q_w, dim)
+    rel_h = torch.einsum("bhwc,hkc->bhwk", r_q, Rh)
+    rel_w = torch.einsum("bhwc,wkc->bhwk", r_q, Rw)
+    rel_h = rel_h.unsqueeze(-1)
+    rel_w = rel_w.unsqueeze(-2)
+    rel_h = rel_h.reshape(B, q_h * q_w, k_h, 1)
+    rel_w = rel_w.reshape(B, q_h * q_w, 1, k_w)
+
+    return rel_h, rel_w
+
+
+class PatchEmbed(nn.Module):
+    """
+    Image to Patch Embedding.
+    """
+
+    def __init__(
+        self,
+        kernel_size: Tuple[int, int] = (16, 16),
+        stride: Tuple[int, int] = (16, 16),
+        padding: Tuple[int, int] = (0, 0),
+        in_chans: int = 3,
+        embed_dim: int = 768,
+    ) -> None:
+        """
+        Args:
+            kernel_size (Tuple): kernel size of the projection layer.
+            stride (Tuple): stride of the projection layer.
+            padding (Tuple): padding size of the projection layer.
+            in_chans (int): Number of input image channels.
+            embed_dim (int): Patch embedding dimension.
+        """
+        super().__init__()
+
+        self.proj = nn.Conv2d(
+            in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.proj(x)
+        # B C H W -> B H W C
+        x = x.permute(0, 2, 3, 1)
+        return x
+
+
+def build_sam_vit_b(checkpoint=None):
+    return _build_sam(
+        encoder_embed_dim=768,
+        encoder_depth=12,
+        encoder_num_heads=12,
+        encoder_global_attn_indexes=[2, 5, 8, 11],
+        checkpoint=checkpoint,
+    )
+
+def build_sam_fast_vit_b(checkpoint=None, compile_mode='max-autotune', dtype=torch.bfloat16):
+    image_encoder = build_sam_vit_b(checkpoint).eval().to(dtype)
+    # sam = _apply_eval_dtype_sam(sam, dtype)
+    image_encoder = torch.compile(image_encoder, mode=compile_mode)
+    return image_encoder
+
+
+def _build_sam(
+    encoder_embed_dim,
+    encoder_depth,
+    encoder_num_heads,
+    encoder_global_attn_indexes,
+    checkpoint=None,
+):
+    prompt_embed_dim = 256
+    image_size = 1024
+    vit_patch_size = 16
+    image_embedding_size = image_size // vit_patch_size
+    image_encoder=ImageEncoderViT(
+            depth=encoder_depth,
+            embed_dim=encoder_embed_dim,
+            img_size=image_size,
+            mlp_ratio=4,
+            norm_layer=partial(torch.nn.LayerNorm, eps=1e-6),
+            num_heads=encoder_num_heads,
+            patch_size=vit_patch_size,
+            qkv_bias=True,
+            use_rel_pos=True,
+            global_attn_indexes=encoder_global_attn_indexes,
+            window_size=14,
+            out_chans=prompt_embed_dim,
+        )
+    image_encoder.eval()
+    if checkpoint is not None:
+        # with open(checkpoint, "rb") as f:
+        state_dict = torch.load(checkpoint)
+        # print(state_dict.keys())
+        # for key in state_dict:
+        # image_encoder.load_state_dict({k[14:]: v for k, v in state_dict.items() if 'image_encoder' in k}, strict=False)
+        # ocr-anyting
+        # image_encoder.load_state_dict(state_dict, strict=True)
+        # tob
+        image_encoder.load_state_dict({k[30:]: v for k, v in state_dict.items() if 'vision_tower_high' in k}, strict=True)
+        print(checkpoint)
+    return image_encoder

generation_config.json

@@ -0,0 +1,6 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 0,
+  "eos_token_id": 1,
+  "transformers_version": "4.57.1"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:710f73bcfe68f2419bfb7480837936cf0ec625ed4a9c3e91a223f544a515ca3a
+size 3051600712

modeling_deepseekv2.py

@@ -0,0 +1,2141 @@
+# coding=utf-8
+# Copyright 2023 DeepSeek-AI and The HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" PyTorch DeepSeek model and compatible with both DeepSeekV2 and DeepSeekV3"""
+import math
+import warnings
+from typing import List, Optional, Tuple, Union
+import numpy as np
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+import torch.distributed as dist
+from einops import repeat
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache
+from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
+from transformers.models.llama.modeling_llama import (
+    LlamaAttention,
+    apply_rotary_pos_emb as _llama_apply_rotary_pos_emb,
+    repeat_kv as _llama_repeat_kv,
+    # LlamaFlashAttention2
+)
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPast,
+    CausalLMOutputWithPast,
+    SequenceClassifierOutputWithPast,
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.pytorch_utils import (
+    ALL_LAYERNORM_LAYERS,
+    is_torch_greater_or_equal_than_1_13,
+)
+from transformers.utils import (
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    is_flash_attn_2_available,
+    is_flash_attn_greater_or_equal_2_10,
+    logging,
+    replace_return_docstrings,
+)
+from transformers.utils.import_utils import is_torch_fx_available
+
+from .configuration_deepseek_v2 import DeepseekV2Config
+
+if is_flash_attn_2_available():
+    from flash_attn import flash_attn_func, flash_attn_varlen_func
+    from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input  # noqa
+
+# This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph.
+# It means that the function will not be traced through and simply appear as a node in the graph.
+if is_torch_fx_available():
+    if not is_torch_greater_or_equal_than_1_13:
+        import torch.fx
+
+    _prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask)
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "DeepseekV2Config"
+
+
+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,
+    )
+
+
+class DeepseekV2RMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        DeepseekV2RMSNorm 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)
+
+
+ALL_LAYERNORM_LAYERS.append(DeepseekV2RMSNorm)
+
+
+
+
+class DeepseekV2RotaryEmbedding(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(),
+        )
+        self.max_seq_len_cached = None
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(
+            self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype
+        )
+
+        freqs = torch.outer(t, self.inv_freq.to(t.device))
+        # 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 self.max_seq_len_cached is None or seq_len > self.max_seq_len_cached:
+            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
+
+        return (
+            self.cos_cached[:seq_len].to(dtype=x.dtype),
+            self.sin_cached[:seq_len].to(dtype=x.dtype),
+        )
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->DeepseekV2
+class DeepseekV2LinearScalingRotaryEmbedding(DeepseekV2RotaryEmbedding):
+    """DeepseekV2RotaryEmbedding 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, dtype=self.inv_freq.dtype
+        )
+        t = t / self.scaling_factor
+
+        freqs = torch.outer(t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->DeepseekV2
+class DeepseekV2DynamicNTKScalingRotaryEmbedding(DeepseekV2RotaryEmbedding):
+    """DeepseekV2RotaryEmbedding 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, dtype=self.inv_freq.dtype
+        )
+
+        freqs = torch.outer(t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+
+# Inverse dim formula to find dim based on number of rotations
+def yarn_find_correction_dim(
+    num_rotations, dim, base=10000, max_position_embeddings=2048
+):
+    return (dim * math.log(max_position_embeddings / (num_rotations * 2 * math.pi))) / (
+        2 * math.log(base)
+    )
+
+
+# Find dim range bounds based on rotations
+def yarn_find_correction_range(
+    low_rot, high_rot, dim, base=10000, max_position_embeddings=2048
+):
+    low = math.floor(
+        yarn_find_correction_dim(low_rot, dim, base, max_position_embeddings)
+    )
+    high = math.ceil(
+        yarn_find_correction_dim(high_rot, dim, base, max_position_embeddings)
+    )
+    return max(low, 0), min(high, dim - 1)  # Clamp values just in case
+
+
+def yarn_get_mscale(scale=1, mscale=1):
+    if scale <= 1:
+        return 1.0
+    return 0.1 * mscale * math.log(scale) + 1.0
+
+
+def yarn_linear_ramp_mask(min, max, dim):
+    if min == max:
+        max += 0.001  # Prevent singularity
+
+    linear_func = (torch.arange(dim, dtype=torch.float32) - min) / (max - min)
+    ramp_func = torch.clamp(linear_func, 0, 1)
+    return ramp_func
+
+
+class DeepseekV2YarnRotaryEmbedding(DeepseekV2RotaryEmbedding):
+
+    def __init__(
+        self,
+        dim,
+        max_position_embeddings=2048,
+        base=10000,
+        device=None,
+        scaling_factor=1.0,
+        original_max_position_embeddings=4096,
+        beta_fast=32,
+        beta_slow=1,
+        mscale=1,
+        mscale_all_dim=0,
+    ):
+        self.scaling_factor = scaling_factor
+        self.original_max_position_embeddings = original_max_position_embeddings
+        self.beta_fast = beta_fast
+        self.beta_slow = beta_slow
+        self.mscale = mscale
+        self.mscale_all_dim = mscale_all_dim
+        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
+        dim = self.dim
+
+        freq_extra = 1.0 / (
+            self.base
+            ** (torch.arange(0, dim, 2, dtype=torch.float32, device=device) / dim)
+        )
+        freq_inter = 1.0 / (
+            self.scaling_factor
+            * self.base
+            ** (torch.arange(0, dim, 2, dtype=torch.float32, device=device) / dim)
+        )
+
+        low, high = yarn_find_correction_range(
+            self.beta_fast,
+            self.beta_slow,
+            dim,
+            self.base,
+            self.original_max_position_embeddings,
+        )
+        inv_freq_mask = 1.0 - yarn_linear_ramp_mask(low, high, dim // 2).to(
+            device=device, dtype=torch.float32
+        )
+        inv_freq = freq_inter * (1 - inv_freq_mask) + freq_extra * inv_freq_mask
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+        t = torch.arange(seq_len, device=device, dtype=torch.float32)
+
+        freqs = torch.outer(t, inv_freq)
+
+        _mscale = float(
+            yarn_get_mscale(self.scaling_factor, self.mscale)
+            / yarn_get_mscale(self.scaling_factor, self.mscale_all_dim)
+        )
+
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer(
+            "cos_cached", (emb.cos() * _mscale).to(dtype), persistent=False
+        )
+        self.register_buffer(
+            "sin_cached", (emb.sin() * _mscale).to(dtype), persistent=False
+        )
+
+
+# Copied from transformers.models.llama.modeling_llama.rotate_half
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+# Copied from transformers.models.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.
+
+    Args:
+        q (`torch.Tensor`): The query tensor.
+        k (`torch.Tensor`): The key tensor.
+        cos (`torch.Tensor`): The cosine part of the rotary embedding.
+        sin (`torch.Tensor`): The sine part of the rotary embedding.
+        position_ids (`torch.Tensor`):
+            The position indices of the tokens corresponding to the query and key tensors. For example, this can be
+            used to pass offsetted position ids when working with a KV-cache.
+        unsqueeze_dim (`int`, *optional*, defaults to 1):
+            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    cos = cos[position_ids].unsqueeze(unsqueeze_dim)
+    sin = sin[position_ids].unsqueeze(unsqueeze_dim)
+
+
+    # print()
+
+    b, h, s, d = q.shape
+    q = q.view(b, h, s, d // 2, 2).transpose(4, 3).reshape(b, h, s, d)
+
+    b, h, s, d = k.shape
+    k = k.view(b, h, s, d // 2, 2).transpose(4, 3).reshape(b, h, s, d)
+
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+
+
+    return q_embed, k_embed
+
+
+class DeepseekV2MLP(nn.Module):
+    def __init__(self, config, hidden_size=None, intermediate_size=None):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size if hidden_size is None else hidden_size
+        self.intermediate_size = (
+            config.intermediate_size if intermediate_size is None else intermediate_size
+        )
+
+        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, x):
+        down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+        return down_proj
+
+
+class MoEGate(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.top_k = config.num_experts_per_tok
+        self.n_routed_experts = config.n_routed_experts
+        self.routed_scaling_factor = config.routed_scaling_factor
+        self.scoring_func = config.scoring_func
+        self.alpha = config.aux_loss_alpha
+        self.seq_aux = config.seq_aux
+        self.topk_method = config.topk_method
+        self.n_group = config.n_group
+        self.topk_group = config.topk_group
+
+        # topk selection algorithm
+        self.norm_topk_prob = config.norm_topk_prob
+        self.gating_dim = config.hidden_size
+        self.weight = nn.Parameter(
+            torch.empty((self.n_routed_experts, self.gating_dim))
+        )
+        if self.topk_method == "noaux_tc":
+            self.e_score_correction_bias = nn.Parameter(
+                torch.empty((self.n_routed_experts))
+            )
+        self.reset_parameters()
+
+    def reset_parameters(self) -> None:
+        import torch.nn.init as init
+
+        init.kaiming_uniform_(self.weight, a=math.sqrt(5))
+
+    def forward(self, hidden_states):
+        bsz, seq_len, h = hidden_states.shape
+        ### compute gating score
+        hidden_states = hidden_states.view(-1, h)
+        logits = F.linear(
+            hidden_states.type(torch.float32), self.weight.type(torch.float32), None
+        )
+        if self.scoring_func == "softmax":
+            scores = logits.softmax(dim=-1, dtype=torch.float32)
+        elif self.scoring_func == "sigmoid":
+            scores = logits.sigmoid()
+        else:
+            raise NotImplementedError(
+                f"insupportable scoring function for MoE gating: {self.scoring_func}"
+            )
+
+        ### select top-k experts
+        if self.topk_method == "greedy":
+            topk_weight, topk_idx = torch.topk(
+                scores, k=self.top_k, dim=-1, sorted=False
+            )
+        elif self.topk_method == "group_limited_greedy":
+            group_scores = (
+                scores.view(bsz * seq_len, self.n_group, -1).max(dim=-1).values
+            )  # [n, n_group]
+            group_idx = torch.topk(
+                group_scores, k=self.topk_group, dim=-1, sorted=False
+            )[
+                1
+            ]  # [n, top_k_group]
+            group_mask = torch.zeros_like(group_scores)  # [n, n_group]
+            group_mask.scatter_(1, group_idx, 1)  # [n, n_group]
+            score_mask = (
+                group_mask.unsqueeze(-1)
+                .expand(
+                    bsz * seq_len, self.n_group, self.n_routed_experts // self.n_group
+                )
+                .reshape(bsz * seq_len, -1)
+            )  # [n, e]
+            tmp_scores = scores.masked_fill(~score_mask.bool(), 0.0)  # [n, e]
+            topk_weight, topk_idx = torch.topk(
+                tmp_scores, k=self.top_k, dim=-1, sorted=False
+            )
+        elif self.topk_method == "noaux_tc":
+            assert not self.training
+            scores_for_choice = scores.view(bsz * seq_len, -1) + self.e_score_correction_bias.unsqueeze(0)
+            group_scores = (
+                scores_for_choice.view(bsz * seq_len, self.n_group, -1).topk(2, dim=-1)[0].sum(dim = -1)
+            )  # [n, n_group]
+            group_idx = torch.topk(
+                group_scores, k=self.topk_group, dim=-1, sorted=False
+            )[
+                1
+            ]  # [n, top_k_group]
+            group_mask = torch.zeros_like(group_scores)  # [n, n_group]
+            group_mask.scatter_(1, group_idx, 1)  # [n, n_group]
+            score_mask = (
+                group_mask.unsqueeze(-1)
+                .expand(
+                    bsz * seq_len, self.n_group, self.n_routed_experts // self.n_group
+                )
+                .reshape(bsz * seq_len, -1)
+            )  # [n, e]
+            tmp_scores = scores_for_choice.masked_fill(~score_mask.bool(), 0.0)  # [n, e]
+            _, topk_idx = torch.topk(
+                tmp_scores, k=self.top_k, dim=-1, sorted=False
+            )
+            topk_weight = scores.gather(1, topk_idx)
+
+        ### norm gate to sum 1
+        if self.top_k > 1 and self.norm_topk_prob:
+            denominator = topk_weight.sum(dim=-1, keepdim=True) + 1e-20
+            topk_weight = topk_weight / denominator * self.routed_scaling_factor
+        else:
+            topk_weight = topk_weight * self.routed_scaling_factor
+        ### expert-level computation auxiliary loss
+        if self.training and self.alpha > 0.0:
+            scores_for_aux = scores
+            aux_topk = self.top_k
+            # always compute aux loss based on the naive greedy topk method
+            topk_idx_for_aux_loss = topk_idx.view(bsz, -1)
+            if self.seq_aux:
+                scores_for_seq_aux = scores_for_aux.view(bsz, seq_len, -1)
+                ce = torch.zeros(
+                    bsz, self.n_routed_experts, device=hidden_states.device
+                )
+                ce.scatter_add_(
+                    1,
+                    topk_idx_for_aux_loss,
+                    torch.ones(bsz, seq_len * aux_topk, device=hidden_states.device),
+                ).div_(seq_len * aux_topk / self.n_routed_experts)
+                aux_loss = (ce * scores_for_seq_aux.mean(dim=1)).sum(
+                    dim=1
+                ).mean() * self.alpha
+            else:
+                mask_ce = F.one_hot(
+                    topk_idx_for_aux_loss.view(-1), num_classes=self.n_routed_experts
+                )
+                ce = mask_ce.float().mean(0)
+                Pi = scores_for_aux.mean(0)
+                fi = ce * self.n_routed_experts
+                aux_loss = (Pi * fi).sum() * self.alpha
+        else:
+            aux_loss = None
+        return topk_idx, topk_weight, aux_loss
+
+
+class AddAuxiliaryLoss(torch.autograd.Function):
+    """
+    The trick function of adding auxiliary (aux) loss,
+    which includes the gradient of the aux loss during backpropagation.
+    """
+
+    @staticmethod
+    def forward(ctx, x, loss):
+        assert loss.numel() == 1
+        ctx.dtype = loss.dtype
+        ctx.required_aux_loss = loss.requires_grad
+        return x
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        grad_loss = None
+        if ctx.required_aux_loss:
+            grad_loss = torch.ones(1, dtype=ctx.dtype, device=grad_output.device)
+        return grad_output, grad_loss
+
+
+class DeepseekV2MoE(nn.Module):
+    """
+    A mixed expert module containing shared experts.
+    """
+
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.num_experts_per_tok = config.num_experts_per_tok
+
+        if hasattr(config, "ep_size") and config.ep_size > 1:
+            assert config.ep_size == dist.get_world_size()
+            self.ep_size = config.ep_size
+            self.experts_per_rank = config.n_routed_experts // config.ep_size
+            self.ep_rank = dist.get_rank()
+            self.experts = nn.ModuleList(
+                [
+                    (
+                        DeepseekV2MLP(
+                            config, intermediate_size=config.moe_intermediate_size
+                        )
+                        if i >= self.ep_rank * self.experts_per_rank
+                        and i < (self.ep_rank + 1) * self.experts_per_rank
+                        else None
+                    )
+                    for i in range(config.n_routed_experts)
+                ]
+            )
+        else:
+            self.ep_size = 1
+            self.experts_per_rank = config.n_routed_experts
+            self.ep_rank = 0
+            self.experts = nn.ModuleList(
+                [
+                    DeepseekV2MLP(
+                        config, intermediate_size=config.moe_intermediate_size
+                    )
+                    for i in range(config.n_routed_experts)
+                ]
+            )
+        self.gate = MoEGate(config)
+        if config.n_shared_experts is not None:
+            intermediate_size = config.moe_intermediate_size * config.n_shared_experts
+            self.shared_experts = DeepseekV2MLP(
+                config=config, intermediate_size=intermediate_size
+            )
+
+    def forward(self, hidden_states):
+        identity = hidden_states
+        orig_shape = hidden_states.shape
+        topk_idx, topk_weight, aux_loss = self.gate(hidden_states)
+        hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
+        flat_topk_idx = topk_idx.view(-1)
+        if self.training:
+            hidden_states = hidden_states.repeat_interleave(
+                self.num_experts_per_tok, dim=0
+            )
+            y = torch.empty_like(hidden_states)
+            for i, expert in enumerate(self.experts):
+                y[flat_topk_idx == i] = expert(hidden_states[flat_topk_idx == i])
+            y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1)
+            y = y.to(hidden_states.dtype).view(*orig_shape)
+            y = AddAuxiliaryLoss.apply(y, aux_loss)
+        else:
+            y = self.moe_infer(hidden_states, topk_idx, topk_weight).view(*orig_shape)
+        if self.config.n_shared_experts is not None:
+            y = y + self.shared_experts(identity)
+        return y
+
+    @torch.no_grad()
+    def moe_infer(self, x, topk_ids, topk_weight):
+        cnts = topk_ids.new_zeros((topk_ids.shape[0], len(self.experts)))
+        cnts.scatter_(1, topk_ids, 1)
+        tokens_per_expert = cnts.sum(dim=0)
+        idxs = topk_ids.view(-1).argsort()
+        sorted_tokens = x[idxs // topk_ids.shape[1]]
+        sorted_tokens_shape = sorted_tokens.shape
+        if self.ep_size > 1:
+            tokens_per_ep_rank = tokens_per_expert.view(self.ep_size, -1).sum(dim=1)
+            tokens_per_expert_group = tokens_per_expert.new_empty(
+                tokens_per_expert.shape[0]
+            )
+            dist.all_to_all_single(tokens_per_expert_group, tokens_per_expert)
+            output_splits = (
+                tokens_per_expert_group.view(self.ep_size, -1)
+                .sum(1)
+                .cpu()
+                .numpy()
+                .tolist()
+            )
+            gathered_tokens = sorted_tokens.new_empty(
+                tokens_per_expert_group.sum(dim=0).cpu().item(), sorted_tokens.shape[1]
+            )
+            input_split_sizes = tokens_per_ep_rank.cpu().numpy().tolist()
+            dist.all_to_all(
+                list(gathered_tokens.split(output_splits)),
+                list(sorted_tokens.split(input_split_sizes)),
+            )
+            tokens_per_expert_post_gather = tokens_per_expert_group.view(
+                self.ep_size, self.experts_per_rank
+            ).sum(dim=0)
+            gatherd_idxs = np.zeros(shape=(gathered_tokens.shape[0],), dtype=np.int32)
+            s = 0
+            for i, k in enumerate(tokens_per_expert_group.cpu().numpy()):
+                gatherd_idxs[s : s + k] = i % self.experts_per_rank
+                s += k
+            gatherd_idxs = gatherd_idxs.argsort()
+            sorted_tokens = gathered_tokens[gatherd_idxs]
+            tokens_per_expert = tokens_per_expert_post_gather
+        tokens_per_expert = tokens_per_expert.cpu().numpy()
+
+        outputs = []
+        start_idx = 0
+        for i, num_tokens in enumerate(tokens_per_expert):
+            end_idx = start_idx + num_tokens
+            if num_tokens == 0:
+                continue
+            expert = self.experts[i + self.ep_rank * self.experts_per_rank]
+            tokens_for_this_expert = sorted_tokens[start_idx:end_idx]
+            expert_out = expert(tokens_for_this_expert)
+            outputs.append(expert_out)
+            start_idx = end_idx
+
+        outs = torch.cat(outputs, dim=0) if len(outputs) else sorted_tokens.new_empty(0)
+        if self.ep_size > 1:
+            new_x = torch.empty_like(outs)
+            new_x[gatherd_idxs] = outs
+            gathered_tokens = new_x.new_empty(*sorted_tokens_shape)
+            dist.all_to_all(
+                list(gathered_tokens.split(input_split_sizes)),
+                list(new_x.split(output_splits)),
+            )
+            outs = gathered_tokens
+
+        new_x = torch.empty_like(outs)
+        new_x[idxs] = outs
+        final_out = (
+            new_x.view(*topk_ids.shape, -1)
+            .type(topk_weight.dtype)
+            .mul_(topk_weight.unsqueeze(dim=-1))
+            .sum(dim=1)
+            .type(new_x.dtype)
+        )
+        return final_out
+
+
+# Copied from transformers.models.llama.modeling_llama.repeat_kv
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(
+        batch, num_key_value_heads, n_rep, slen, head_dim
+    )
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaAttention with Llama->DeepseekV2
+class DeepseekV2Attention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: DeepseekV2Config, layer_idx: Optional[int] = None):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        if layer_idx is None:
+            logger.warning_once(
+                f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
+                "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
+                "when creating this class."
+            )
+
+        self.attention_dropout = config.attention_dropout
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+
+        self.max_position_embeddings = config.max_position_embeddings
+        self.rope_theta = config.rope_theta
+        self.q_lora_rank = config.q_lora_rank
+        self.qk_rope_head_dim = config.qk_rope_head_dim
+        self.kv_lora_rank = config.kv_lora_rank
+        self.v_head_dim = config.v_head_dim
+        self.qk_nope_head_dim = config.qk_nope_head_dim
+        self.q_head_dim = config.qk_nope_head_dim + config.qk_rope_head_dim
+
+        self.is_causal = True
+
+        if self.q_lora_rank is None:
+            self.q_proj = nn.Linear(
+                self.hidden_size, self.num_heads * self.q_head_dim, bias=False
+            )
+        else:
+            self.q_a_proj = nn.Linear(
+                self.hidden_size, config.q_lora_rank, bias=config.attention_bias
+            )
+            self.q_a_layernorm = DeepseekV2RMSNorm(config.q_lora_rank)
+            self.q_b_proj = nn.Linear(
+                config.q_lora_rank, self.num_heads * self.q_head_dim, bias=False
+            )
+        # config.kv_lora_rank + config.qk_rope_head_dim,
+        self.kv_a_proj_with_mqa = nn.Linear(
+            self.hidden_size,
+            config.kv_lora_rank + config.qk_rope_head_dim,
+            bias=config.attention_bias,
+        )
+        self.kv_a_layernorm = DeepseekV2RMSNorm(config.kv_lora_rank)
+        self.kv_b_proj = nn.Linear(
+            config.kv_lora_rank,
+            self.num_heads
+            * (self.q_head_dim - self.qk_rope_head_dim + self.v_head_dim),
+            bias=False,
+        )
+
+        self.o_proj = nn.Linear(
+            self.num_heads * self.v_head_dim,
+            self.hidden_size,
+            bias=config.attention_bias,
+        )
+        self._init_rope()
+
+        self.softmax_scale = self.q_head_dim ** (-0.5)
+        if self.config.rope_scaling is not None:
+            mscale_all_dim = self.config.rope_scaling.get("mscale_all_dim", 0)
+            scaling_factor = self.config.rope_scaling["factor"]
+            if mscale_all_dim:
+                mscale = yarn_get_mscale(scaling_factor, mscale_all_dim)
+                self.softmax_scale = self.softmax_scale * mscale * mscale
+
+    def _init_rope(self):
+        if self.config.rope_scaling is None:
+            self.rotary_emb = DeepseekV2RotaryEmbedding(
+                self.qk_rope_head_dim,
+                max_position_embeddings=self.max_position_embeddings,
+                base=self.rope_theta,
+            )
+            # self.rotary_emb = DeepseekV2LinearScalingRotaryEmbedding(
+            #     self.qk_rope_head_dim,
+            #     max_position_embeddings=self.max_position_embeddings,
+            #     scaling_factor=scaling_factor,
+            #     base=self.rope_theta,
+            # )
+        else:
+            scaling_type = self.config.rope_scaling["type"]
+            scaling_factor = self.config.rope_scaling["factor"]
+            if scaling_type == "linear":
+                self.rotary_emb = DeepseekV2LinearScalingRotaryEmbedding(
+                    self.qk_rope_head_dim,
+                    max_position_embeddings=self.max_position_embeddings,
+                    scaling_factor=scaling_factor,
+                    base=self.rope_theta,
+                )
+            elif scaling_type == "dynamic":
+                self.rotary_emb = DeepseekV2DynamicNTKScalingRotaryEmbedding(
+                    self.qk_rope_head_dim,
+                    max_position_embeddings=self.max_position_embeddings,
+                    scaling_factor=scaling_factor,
+                    base=self.rope_theta,
+                )
+            elif scaling_type == "yarn":
+                kwargs = {
+                    key: self.config.rope_scaling[key]
+                    for key in [
+                        "original_max_position_embeddings",
+                        "beta_fast",
+                        "beta_slow",
+                        "mscale",
+                        "mscale_all_dim",
+                    ]
+                    if key in self.config.rope_scaling
+                }
+                self.rotary_emb = DeepseekV2YarnRotaryEmbedding(
+                    self.qk_rope_head_dim,
+                    max_position_embeddings=self.max_position_embeddings,
+                    scaling_factor=scaling_factor,
+                    base=self.rope_theta,
+                    **kwargs,
+                )
+            else:
+                raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
+
+    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+        return (
+            tensor.view(bsz, seq_len, self.num_heads, self.v_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[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+            )
+        bsz, q_len, _ = hidden_states.size()
+
+        if self.q_lora_rank is None:
+            q = self.q_proj(hidden_states)
+        else:
+            q = self.q_b_proj(self.q_a_layernorm(self.q_a_proj(hidden_states)))
+        q = q.view(bsz, q_len, self.num_heads, self.q_head_dim).transpose(1, 2)
+
+
+        q_nope, q_pe = torch.split(
+            q, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1
+        )
+
+        compressed_kv = self.kv_a_proj_with_mqa(hidden_states)
+        compressed_kv, k_pe = torch.split(
+            compressed_kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1
+        )
+        compressed_kv = self.kv_a_layernorm(compressed_kv)
+        k_pe = k_pe.view(bsz, q_len, 1, self.qk_rope_head_dim).transpose(1, 2)
+
+        kv_seq_len = k_pe.shape[-2]
+        if past_key_value is not None:
+            if self.layer_idx is None:
+                raise ValueError(
+                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
+                    "with a layer index."
+                )
+            kv_seq_len += past_key_value.get_seq_length(self.layer_idx)
+
+        cos, sin = self.rotary_emb(q_pe, seq_len=kv_seq_len)
+        q_pe, k_pe = apply_rotary_pos_emb(q_pe, k_pe, cos, sin, position_ids)
+
+        if past_key_value is not None:
+            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
+            compressed_kv = compressed_kv.unsqueeze(1)
+            k_pe, compressed_kv = past_key_value.update(k_pe, compressed_kv, self.layer_idx, cache_kwargs)
+            compressed_kv = compressed_kv.squeeze(1)
+
+        kv_b_proj = self.kv_b_proj.weight.view(self.num_heads, -1, self.kv_lora_rank)
+        q_absorb = kv_b_proj[:, :self.qk_nope_head_dim, :]
+        out_absorb = kv_b_proj[:, self.qk_nope_head_dim:, :]
+
+        q_nope = torch.matmul(q_nope, q_absorb)
+        attn_weights = (torch.matmul(q_pe, k_pe.mT) +
+                        torch.matmul(q_nope, compressed_kv.unsqueeze(-3).mT)) * self.softmax_scale
+        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()}"
+            )
+        assert attention_mask is not None
+        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(q_pe.dtype)
+        attn_weights = nn.functional.dropout(
+            attn_weights, p=self.attention_dropout, training=self.training
+        )
+        attn_output = torch.einsum('bhql,blc->bhqc', attn_weights, compressed_kv)
+
+        attn_output = torch.matmul(attn_output, out_absorb.mT)
+
+        if attn_output.size() != (bsz, self.num_heads, q_len, self.v_head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.v_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.num_heads * self.v_head_dim)
+
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2 with Llama->DeepseekV2
+class DeepseekV2FlashAttention2(DeepseekV2Attention):
+    """
+    DeepseekV2 flash attention module. This module inherits from `DeepseekV2Attention` 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 __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
+        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
+        # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
+        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        # DeepseekV2FlashAttention2 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()
+
+        if self.q_lora_rank is None:
+            q = self.q_proj(hidden_states)
+        else:
+            q = self.q_b_proj(self.q_a_layernorm(self.q_a_proj(hidden_states)))
+        q = q.view(bsz, q_len, self.num_heads, self.q_head_dim).transpose(1, 2)
+        q_nope, q_pe = torch.split(
+            q, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1
+        )
+
+        # Flash attention requires the input to have the shape
+        # batch_size x seq_length x head_dim x hidden_dim
+        # therefore we just need to keep the original shape
+        compressed_kv = self.kv_a_proj_with_mqa(hidden_states)
+        compressed_kv, k_pe = torch.split(
+            compressed_kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1
+        )
+        k_pe = k_pe.view(bsz, q_len, 1, self.qk_rope_head_dim).transpose(1, 2)
+        kv = (
+            self.kv_b_proj(self.kv_a_layernorm(compressed_kv))
+            .view(bsz, q_len, self.num_heads, self.qk_nope_head_dim + self.v_head_dim)
+            .transpose(1, 2)
+        )
+
+        k_nope, value_states = torch.split(
+            kv, [self.qk_nope_head_dim, self.v_head_dim], dim=-1
+        )
+        kv_seq_len = value_states.shape[-2]
+
+        kv_seq_len = value_states.shape[-2]
+        if past_key_value is not None:
+            kv_seq_len += past_key_value.get_seq_length(self.layer_idx)
+
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+        q_pe, k_pe = apply_rotary_pos_emb(q_pe, k_pe, cos, sin, position_ids)
+
+        query_states = k_pe.new_empty(bsz, self.num_heads, q_len, self.q_head_dim)
+        query_states[:, :, :, : self.qk_nope_head_dim] = q_nope
+        query_states[:, :, :, self.qk_nope_head_dim :] = q_pe
+
+        key_states = k_pe.new_empty(bsz, self.num_heads, q_len, self.q_head_dim)
+        key_states[:, :, :, : self.qk_nope_head_dim] = k_nope
+        key_states[:, :, :, self.qk_nope_head_dim :] = k_pe
+
+        if self.q_head_dim != self.v_head_dim:
+            value_states = F.pad(value_states, [0, self.q_head_dim - self.v_head_dim])
+
+        # TODO: support compressed_kv for kv_cache (instead of key_states, value_states) in flash_attention version
+        if past_key_value is not None:
+            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
+            key_states, value_states = past_key_value.update(
+                key_states, value_states, self.layer_idx, cache_kwargs
+            )
+
+        # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
+        # to be able to avoid many of these transpose/reshape/view.
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        dropout_rate = self.attention_dropout if self.training else 0.0
+
+        # In PEFT, usually we cast the layer norms in float32 for training stability reasons
+        # therefore the input hidden states gets silently casted in float32. Hence, we need
+        # cast them back in the correct dtype just to be sure everything works as expected.
+        # This might slowdown training & inference so it is recommended to not cast the LayerNorms
+        # in fp32. (DeepseekV2RMSNorm handles it correctly)
+
+        input_dtype = query_states.dtype
+        if input_dtype == torch.float32:
+            # Handle the case where the model is quantized
+            if hasattr(self.config, "_pre_quantization_dtype"):
+                target_dtype = self.config._pre_quantization_dtype
+            elif torch.is_autocast_enabled():
+                target_dtype = torch.get_autocast_gpu_dtype()
+            else:
+                target_dtype = (
+                    self.q_proj.weight.dtype
+                    if self.q_lora_rank is None
+                    else self.q_a_proj.weight.dtype
+                )
+
+            logger.warning_once(
+                f"The input hidden states seems to be silently casted in float32, this might be related to"
+                f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
+                f" {target_dtype}."
+            )
+
+            query_states = query_states.to(target_dtype)
+            key_states = key_states.to(target_dtype)
+            value_states = value_states.to(target_dtype)
+
+        attn_output = self._flash_attention_forward(
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            q_len,
+            dropout=dropout_rate,
+            softmax_scale=self.softmax_scale,
+        )
+        if self.q_head_dim != self.v_head_dim:
+            attn_output = attn_output[:, :, :, : self.v_head_dim]
+
+        attn_output = attn_output.reshape(
+            bsz, q_len, self.num_heads * self.v_head_dim
+        ).contiguous()
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+    def _flash_attention_forward(
+        self,
+        query_states,
+        key_states,
+        value_states,
+        attention_mask,
+        query_length,
+        dropout=0.0,
+        softmax_scale=None,
+    ):
+        """
+        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)
+        """
+        if not self._flash_attn_uses_top_left_mask:
+            causal = self.is_causal
+        else:
+            # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in DeepseekV2FlashAttention2 __init__.
+            causal = self.is_causal and query_length != 1
+
+        # Contains at least one padding token in the sequence
+        if attention_mask is not None:
+            batch_size = query_states.shape[0]
+            (
+                query_states,
+                key_states,
+                value_states,
+                indices_q,
+                cu_seq_lens,
+                max_seq_lens,
+            ) = self._upad_input(
+                query_states, key_states, value_states, attention_mask, query_length
+            )
+
+            cu_seqlens_q, cu_seqlens_k = cu_seq_lens
+            max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
+
+            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 _upad_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,
+            (cu_seqlens_q, cu_seqlens_k),
+            (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
+        )
+
+
+class SlidingWindowLlamaAttention(LlamaAttention):
+    """LlamaAttention with sliding window KV cache using a ring buffer during decode."""
+
+    def __init__(self, config, layer_idx):
+        super().__init__(config, layer_idx)
+        # New transformers moved rotary_emb to model level; create our own
+        if not hasattr(self, 'rotary_emb'):
+            from transformers.models.llama.modeling_llama import LlamaRotaryEmbedding
+            self.rotary_emb = LlamaRotaryEmbedding(config=config)
+        # Save sliding_window separately so we can disable it in config to prevent
+        # DynamicCache from truncating prefill tokens
+        self._sliding_window = getattr(config, 'sliding_window', None)
+
+    def forward(self, *args, **kwargs):
+        import math
+
+        # Compatibility: new DynamicCache uses .layers[i].keys/.values instead of .key_cache[i]/.value_cache[i]
+        def _get_kcache(cache, layer_idx):
+            if hasattr(cache, 'key_cache'):
+                return cache.key_cache[layer_idx]
+            return cache.layers[layer_idx].keys
+
+        def _get_vcache(cache, layer_idx):
+            if hasattr(cache, 'value_cache'):
+                return cache.value_cache[layer_idx]
+            return cache.layers[layer_idx].values
+
+        # Extract args
+        def _get(name, idx, default=None):
+            if name in kwargs:
+                return kwargs[name]
+            if len(args) > idx:
+                return args[idx]
+            return default
+
+        hidden_states = _get('hidden_states', 0)
+        attention_mask = _get('attention_mask', 1)
+        position_ids = _get('position_ids', 2)
+        past_kv = _get('past_key_value', 3)
+        if past_kv is None:
+            past_kv = kwargs.get('past_key_values', None)
+        output_attentions = _get('output_attentions', 4, False)
+
+        # Dimensions from config (new transformers removed self.num_heads)
+        num_heads = self.config.num_attention_heads
+        num_kv_heads = self.config.num_key_value_heads
+        head_dim = self.head_dim
+        num_kv_groups = self.num_key_value_groups
+
+        bsz, q_len, _ = hidden_states.size()
+        W = getattr(self.config, '_ring_window', None)  # Read from config (set before generate)
+
+        # --- Helper: standard QKV attention ---
+        def _attn_forward(use_cache_update=True):
+            query_states = self.q_proj(hidden_states).view(bsz, q_len, num_heads, head_dim).transpose(1, 2)
+            key_states = self.k_proj(hidden_states).view(bsz, q_len, num_kv_heads, head_dim).transpose(1, 2)
+            value_states = self.v_proj(hidden_states).view(bsz, q_len, num_kv_heads, head_dim).transpose(1, 2)
+
+            cos, sin = self.rotary_emb(value_states, position_ids)
+            query_states, key_states = _llama_apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+            if past_kv is not None and use_cache_update:
+                key_states, value_states = past_kv.update(key_states, value_states, self.layer_idx)
+
+            k = _llama_repeat_kv(key_states, num_kv_groups)
+            v = _llama_repeat_kv(value_states, num_kv_groups)
+
+            attn_weights = torch.matmul(query_states, k.transpose(2, 3)) / math.sqrt(head_dim)
+            if attention_mask is not None:
+                causal_mask = attention_mask[:, :, :, :k.shape[-2]]
+                attn_weights = attn_weights + causal_mask
+            attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+            attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
+            attn_output = torch.matmul(attn_weights, v)
+            attn_output = attn_output.transpose(1, 2).contiguous().reshape(bsz, q_len, -1)
+            attn_output = self.o_proj(attn_output)
+            return attn_output, None, past_kv
+
+        # Prefill or no sliding window
+        # True prefill: W disabled, no cache, or first forward (prefill_length not yet recorded)
+        _is_true_prefill = (W is None or past_kv is None or
+                            (q_len > 1 and (not hasattr(past_kv, '_prefill_length') or
+                                            self.layer_idx not in past_kv._prefill_length)))
+        if _is_true_prefill:
+            result = _attn_forward()
+            if W is not None and past_kv is not None and q_len > 1:
+                # Only record prefill_length the FIRST time (don't overwrite on subsequent q_len>1 calls)
+                if not hasattr(past_kv, '_prefill_length'):
+                    past_kv._prefill_length = {}
+                if self.layer_idx not in past_kv._prefill_length:
+                    past_kv._prefill_length[self.layer_idx] = _get_kcache(past_kv, self.layer_idx).shape[-2]
+            return result
+
+        # Decode path: first decode step -> record prefill_length (only once!)
+        if not hasattr(past_kv, '_prefill_length') or self.layer_idx not in past_kv._prefill_length:
+            if not hasattr(past_kv, '_prefill_length'):
+                past_kv._prefill_length = {}
+            past_kv._prefill_length[self.layer_idx] = _get_kcache(past_kv, self.layer_idx).shape[-2]
+
+        prefill_len = past_kv._prefill_length[self.layer_idx]
+        cur_len = _get_kcache(past_kv, self.layer_idx).shape[-2]
+
+        # Warmup: cat-append until ring region is full
+        if cur_len < prefill_len + W:
+            result = _attn_forward()
+            new_len = _get_kcache(past_kv, self.layer_idx).shape[-2]
+            if new_len >= prefill_len + W:
+                if not hasattr(past_kv, '_ring_pos'):
+                    past_kv._ring_pos = {}
+                past_kv._ring_pos[self.layer_idx] = 0
+            return result
+
+        # Steady state: ring in-place overwrite
+        if not hasattr(past_kv, '_ring_pos') or self.layer_idx not in past_kv._ring_pos:
+            past_kv._ring_pos = getattr(past_kv, '_ring_pos', {}) or {}
+            past_kv._ring_pos[self.layer_idx] = 0
+
+        # Ring decode: overwrite ring slots, then attention over full cache
+        ring_pos = past_kv._ring_pos[self.layer_idx]
+        kcache = _get_kcache(past_kv, self.layer_idx)
+        vcache = _get_vcache(past_kv, self.layer_idx)
+
+        # Compute new K, V and apply RoPE, then overwrite ring slots
+        query_states = self.q_proj(hidden_states).view(bsz, q_len, num_heads, head_dim).transpose(1, 2)
+        key_states = self.k_proj(hidden_states).view(bsz, q_len, num_kv_heads, head_dim).transpose(1, 2)
+        value_states = self.v_proj(hidden_states).view(bsz, q_len, num_kv_heads, head_dim).transpose(1, 2)
+        cos, sin = self.rotary_emb(value_states, position_ids)
+        query_states, key_states = _llama_apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+        # Overwrite ring slots in-place
+        for t in range(q_len):
+            slot = prefill_len + ring_pos
+            kcache[:, :, slot:slot + 1, :] = key_states[:, :, t:t + 1, :]
+            vcache[:, :, slot:slot + 1, :] = value_states[:, :, t:t + 1, :]
+            ring_pos = (ring_pos + 1) % W
+        past_kv._ring_pos[self.layer_idx] = ring_pos
+
+        # Attention over full cache (no causal mask needed for decode q_len=1)
+        k = _llama_repeat_kv(kcache, num_kv_groups)
+        v = _llama_repeat_kv(vcache, num_kv_groups)
+        attn_weights = torch.matmul(query_states, k.transpose(2, 3)) / math.sqrt(head_dim)
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+        attn_output = torch.matmul(attn_weights, v)
+        attn_output = attn_output.transpose(1, 2).contiguous().reshape(bsz, q_len, -1)
+        attn_output = self.o_proj(attn_output)
+        return attn_output, None, past_kv
+
+
+ATTENTION_CLASSES = {
+    "eager": DeepseekV2Attention,
+    "flash_attention_2": DeepseekV2FlashAttention2,
+
+    "mla_eager": DeepseekV2Attention,
+    "mla_flash_attention_2": DeepseekV2FlashAttention2,
+
+    "mha_eager": SlidingWindowLlamaAttention,
+    # "mha_flash_attention_2": LlamaFlashAttention2
+}
+
+
+class DeepseekV2DecoderLayer(nn.Module):
+    def __init__(self, config: DeepseekV2Config, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+
+
+        if config.use_mla:
+            attn_implementation = "mla_" + config._attn_implementation
+        else:
+            attn_implementation = "mha_" + config._attn_implementation
+
+        self.self_attn = ATTENTION_CLASSES[attn_implementation](
+            config=config, layer_idx=layer_idx
+        )
+
+        self.mlp = (
+            DeepseekV2MoE(config)
+            if (
+                config.n_routed_experts is not None
+                and layer_idx >= config.first_k_dense_replace
+                and layer_idx % config.moe_layer_freq == 0
+            )
+            else DeepseekV2MLP(config)
+        )
+        self.input_layernorm = DeepseekV2RMSNorm(
+            config.hidden_size, eps=config.rms_norm_eps
+        )
+        self.post_attention_layernorm = DeepseekV2RMSNorm(
+            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.input_layernorm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights, present_key_value = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+            **kwargs,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        return outputs
+
+
+DeepseekV2_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 ([`DeepseekV2Config`]):
+            Model configuration class with all the parameters of the model. Initializing with a config file does not
+            load the weights associated with the model, only the configuration. Check out the
+            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+
+@add_start_docstrings(
+    "The bare DeepseekV2 Model outputting raw hidden-states without any specific head on top.",
+    DeepseekV2_START_DOCSTRING,
+)
+class DeepseekV2PreTrainedModel(PreTrainedModel):
+    config_class = DeepseekV2Config
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["DeepseekV2DecoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
+    _supports_flash_attn_2 = True
+    _supports_cache_class = True
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+
+DeepseekV2_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 (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
+            Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+            blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
+            returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
+
+            Two formats are allowed:
+            - a [`~cache_utils.Cache`] instance;
+            - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
+            shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
+            cache format.
+
+            The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
+            legacy cache format will be returned.
+
+            If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
+            have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
+            of shape `(batch_size, sequence_length)`.
+        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+            model's internal embedding lookup matrix.
+        use_cache (`bool`, *optional*):
+            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+            `past_key_values`).
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+@add_start_docstrings(
+    "The bare DeepseekV2 Model outputting raw hidden-states without any specific head on top.",
+    DeepseekV2_START_DOCSTRING,
+)
+class DeepseekV2Model(DeepseekV2PreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`DeepseekV2DecoderLayer`]
+
+    Args:
+        config: DeepseekV2Config
+    """
+
+    def __init__(self, config: DeepseekV2Config):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = nn.Embedding(
+            config.vocab_size, config.hidden_size, self.padding_idx
+        )
+        self.layers = nn.ModuleList(
+            [
+                DeepseekV2DecoderLayer(config, layer_idx)
+                for layer_idx in range(config.num_hidden_layers)
+            ]
+        )
+        # print(config._attn_implementation)
+        self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
+        self.norm = DeepseekV2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        self.gradient_checkpointing = False
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+
+    @add_start_docstrings_to_model_forward(DeepseekV2_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,
+        cache_position: Optional[torch.LongTensor] = None
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+        output_attentions = (
+            output_attentions
+            if output_attentions is not None
+            else self.config.output_attentions
+        )
+        output_hidden_states = (
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        # retrieve input_ids and inputs_embeds
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError(
+                "You cannot specify both 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")
+
+        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`transformers."
+                )
+                use_cache = False
+
+        past_key_values_length = 0
+        if use_cache:
+            use_legacy_cache = not isinstance(past_key_values, Cache)
+            if use_legacy_cache:
+                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+            past_key_values_length = past_key_values.get_seq_length()
+
+        if position_ids is None:
+            device = input_ids.device if input_ids is not None else inputs_embeds.device
+            position_ids = torch.arange(
+                past_key_values_length,
+                seq_length + past_key_values_length,
+                dtype=torch.long,
+                device=device,
+            )
+            position_ids = position_ids.unsqueeze(0)
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        # Skip 4D causal mask for decode (q_len=1 with KV cache doesn't need it)
+        if seq_length == 1 and past_key_values_length > 0:
+            attention_mask = None
+        elif self._use_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:
+            # 4d mask is passed through the layers
+            attention_mask = _prepare_4d_causal_attention_mask(
+                attention_mask,
+                (batch_size, seq_length),
+                inputs_embeds,
+                past_key_values_length,
+            )
+
+        # embed positions
+        hidden_states = inputs_embeds
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = None
+
+        for decoder_layer in self.layers:
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    decoder_layer.__call__,
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    past_key_values,
+                    output_attentions,
+                    use_cache,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_values,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                next_decoder_cache = layer_outputs[2 if output_attentions else 1]
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        next_cache = None
+        if use_cache:
+            next_cache = next_decoder_cache  # Always return DynamicCache to preserve custom attributes
+        if not return_dict:
+            return tuple(
+                v
+                for v in [hidden_states, next_cache, all_hidden_states, all_self_attns]
+                if v is not None
+            )
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+
+class DeepseekV2ForCausalLM(DeepseekV2PreTrainedModel):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = DeepseekV2Model(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+
+    @add_start_docstrings_to_model_forward(DeepseekV2_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,
+            cache_position: Optional[torch.LongTensor] = 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, transformers.,
+                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, transformers., config.vocab_size]`.
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, DeepseekV2ForCausalLM
+
+        >>> model = DeepseekV2ForCausalLM.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,
+            cache_position=cache_position
+        )
+
+        hidden_states = outputs[0]
+        logits = self.lm_head(hidden_states)
+        logits = logits.float()
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            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
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(
+            self,
+            input_ids,
+            past_key_values=None,
+            attention_mask=None,
+            inputs_embeds=None,
+            **kwargs,
+    ):
+        past_length = 0
+        if past_key_values is not None:
+            if isinstance(past_key_values, Cache):
+                cache_length = past_key_values.get_seq_length()
+                past_length = past_key_values.get_seq_length()
+                max_cache_length = getattr(past_key_values, 'get_max_length', lambda: None)()
+            else:
+                cache_length = past_length = past_key_values[0][0].shape[2]
+                max_cache_length = None
+
+            # Keep only the unprocessed tokens:
+            # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
+            # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
+            # input)
+            if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
+                input_ids = input_ids[:, -(attention_mask.shape[1] - past_length):]
+            # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
+            # input_ids based on the past_length.
+            elif past_length < input_ids.shape[1]:
+                input_ids = input_ids[:, past_length:]
+            # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
+
+            # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
+            if (
+                    max_cache_length is not None
+                    and attention_mask is not None
+                    and cache_length + input_ids.shape[1] > max_cache_length
+            ):
+                attention_mask = attention_mask[:, -max_cache_length:]
+
+        position_ids = kwargs.get("position_ids", None)
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -input_ids.shape[1]:]
+
+        if self.generation_config.cache_implementation == "static":
+            # generation with static cache
+            cache_position = kwargs.get("cache_position", None)
+            if cache_position is None:
+                past_length = 0
+            else:
+                past_length = cache_position[-1] + 1
+            input_ids = input_ids[:, past_length:]
+            position_ids = position_ids[:, past_length:]
+
+        # TODO @gante we should only keep a `cache_position` in generate, and do +=1.
+        # same goes for position ids. Could also help with continued generation.
+        cache_position = torch.arange(past_length, past_length + position_ids.shape[-1], device=position_ids.device)
+
+        # 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:
+            # The `contiguous()` here is necessary to have a static stride during decoding. torchdynamo otherwise
+            # recompiles graphs as the stride of the inputs is a guard. Ref: https://github.com/huggingface/transformers/pull/29114
+            # TODO: use `next_tokens` directly instead.
+            model_inputs = {"input_ids": input_ids.contiguous()}
+
+        model_inputs.update(
+            {
+                "position_ids": position_ids.contiguous(),
+                "cache_position": cache_position,
+                "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
+
+
+@add_start_docstrings(
+    """
+    The DeepseekV2 Model transformer with a sequence classification head on top (linear layer).
+
+    [`DeepseekV2ForSequenceClassification`] 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).
+    """,
+    DeepseekV2_START_DOCSTRING,
+)
+class DeepseekV2ForSequenceClassification(DeepseekV2PreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = DeepseekV2Model(config)
+        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    @add_start_docstrings_to_model_forward(DeepseekV2_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, transformers.,
+            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,
+        )

modeling_unlimitedocr.py

@@ -0,0 +1,1299 @@
+from .modeling_deepseekv2 import DeepseekV2Model, DeepseekV2ForCausalLM
+from .configuration_deepseek_v2 import DeepseekV2Config
+from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
+from typing import List, Optional, Tuple, Union
+from transformers.cache_utils import Cache
+import requests
+from PIL import Image, ImageOps, ImageDraw, ImageFont
+from io import BytesIO
+import torch
+import torch.nn as nn
+from torch.nn import CrossEntropyLoss
+from torchvision import transforms
+from torchvision.transforms.functional import InterpolationMode
+import os
+from .deepencoder import build_sam_vit_b, build_clip_l, MlpProjector
+from addict import Dict
+from transformers import TextStreamer
+from .conversation import get_conv_template
+from abc import ABC
+import math
+import re
+from tqdm import tqdm
+import numpy as np
+import time
+
+
+def load_image(image_path):
+
+    try:
+        image = Image.open(image_path)
+        
+        corrected_image = ImageOps.exif_transpose(image)
+        
+        return corrected_image
+        
+    except Exception as e:
+        print(f"error: {e}")
+        try:
+            return Image.open(image_path)
+        except:
+            return None
+
+
+def re_match(text):
+    ref_pattern = r'(<\|ref\|>(.*?)<\|/ref\|><\|det\|>(.*?)<\|/det\|>)'
+    matches = re.findall(ref_pattern, text, re.DOTALL)
+
+    det_pattern = r'(<\|det\|>\s*([A-Za-z_][\w-]*)\s*(\[[^\]]+\])\s*<\|/det\|>)'
+    for full_match, label, box in re.findall(det_pattern, text, re.DOTALL):
+        matches.append((full_match, label, box))
+
+    mathes_image = []
+    mathes_other = []
+    for a_match in matches:
+        if a_match[1].strip() == 'image' or '<|ref|>image<|/ref|>' in a_match[0]:
+            mathes_image.append(a_match[0])
+        else:
+            mathes_other.append(a_match[0])
+    return matches, mathes_image, mathes_other
+
+
+def extract_coordinates_and_label(ref_text, image_width, image_height):
+
+    try:
+        label_type = ref_text[1]
+        cor_list = eval(ref_text[2])
+        if cor_list and isinstance(cor_list[0], (int, float)):
+            cor_list = [cor_list]
+    except Exception as e:
+        print(e)
+        return None
+
+    return (label_type, cor_list)
+
+
+def draw_bounding_boxes(image, refs, ouput_path, image_prefix=''):
+
+    image_width, image_height = image.size
+    
+    img_draw = image.copy()
+    draw = ImageDraw.Draw(img_draw)
+
+    overlay = Image.new('RGBA', img_draw.size, (0, 0, 0, 0))
+    draw2 = ImageDraw.Draw(overlay)
+    
+    # try:
+    # except IOError:
+    #     try:
+    #         font = ImageFont.truetype("DejaVuSans.ttf", 20) 
+    #     except IOError:
+    font = ImageFont.load_default()
+
+    img_idx = 0
+    
+    for i, ref in enumerate(refs):
+        try:
+            result = extract_coordinates_and_label(ref, image_width, image_height)
+            if result:
+                label_type, points_list = result
+                
+                color = (np.random.randint(0, 200), np.random.randint(0, 200), np.random.randint(0, 255))
+
+                color_a = color + (20, )
+                for points in points_list:
+                    x1, y1, x2, y2 = points
+
+                    x1 = int(x1 / 999 * image_width)
+                    y1 = int(y1 / 999 * image_height)
+
+                    x2 = int(x2 / 999 * image_width)
+                    y2 = int(y2 / 999 * image_height)
+
+                    if label_type == 'image':
+                        try:
+                            cropped = image.crop((x1, y1, x2, y2))
+                            cropped.save(f"{ouput_path}/images/{image_prefix}{img_idx}.jpg")
+                        except Exception as e:
+                            print(e)
+                            pass
+                        img_idx += 1
+                        
+                    try:
+                        if label_type == 'title':
+                            draw.rectangle([x1, y1, x2, y2], outline=color, width=4)
+                            draw2.rectangle([x1, y1, x2, y2], fill=color_a, outline=(0, 0, 0, 0), width=1)
+                        else:
+                            draw.rectangle([x1, y1, x2, y2], outline=color, width=2)
+                            draw2.rectangle([x1, y1, x2, y2], fill=color_a, outline=(0, 0, 0, 0), width=1)
+                        text_x = x1
+                        text_y = max(0, y1 - 15)
+                            
+                        
+                        text_bbox = draw.textbbox((0, 0), label_type, font=font)
+                        text_width = text_bbox[2] - text_bbox[0]
+                        text_height = text_bbox[3] - text_bbox[1]
+                        draw.rectangle([text_x, text_y, text_x + text_width, text_y + text_height], 
+                                    fill=(255, 255, 255, 30))
+                        
+                        draw.text((text_x, text_y), label_type, font=font, fill=color)
+                    except:
+                        pass
+        except:
+            continue
+    img_draw.paste(overlay, (0, 0), overlay)
+    return img_draw
+
+
+def process_image_with_refs(image, ref_texts, output_path, image_prefix=''):
+
+    result_image = draw_bounding_boxes(image, ref_texts, output_path, image_prefix=image_prefix)
+    
+    return result_image
+
+
+
+
+
+def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
+    best_ratio_diff = float('inf')
+    best_ratio = (1, 1)
+    area = width * height
+    for ratio in target_ratios:
+        target_aspect_ratio = ratio[0] / ratio[1]
+        ratio_diff = abs(aspect_ratio - target_aspect_ratio)
+        if ratio_diff < best_ratio_diff:
+            best_ratio_diff = ratio_diff
+            best_ratio = ratio
+        elif ratio_diff == best_ratio_diff:
+            if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
+                best_ratio = ratio
+    # print(f'width: {width}, height: {height}, best_ratio: {best_ratio}')
+    return best_ratio
+
+
+def dynamic_preprocess(image, min_num=2, max_num=32, image_size=640, use_thumbnail=False):
+    orig_width, orig_height = image.size
+    aspect_ratio = orig_width / orig_height
+
+    # calculate the existing image aspect ratio
+    target_ratios = set(
+        (i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
+        i * j <= max_num and i * j >= min_num)
+    # print(target_ratios)
+    target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
+
+    # find the closest aspect ratio to the target
+    target_aspect_ratio = find_closest_aspect_ratio(
+        aspect_ratio, target_ratios, orig_width, orig_height, image_size)
+
+    # print(target_aspect_ratio)
+    # calculate the target width and height
+    target_width = image_size * target_aspect_ratio[0]
+    target_height = image_size * target_aspect_ratio[1]
+    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
+
+    # resize the image
+    resized_img = image.resize((target_width, target_height))
+    processed_images = []
+    for i in range(blocks):
+        box = (
+            (i % (target_width // image_size)) * image_size,
+            (i // (target_width // image_size)) * image_size,
+            ((i % (target_width // image_size)) + 1) * image_size,
+            ((i // (target_width // image_size)) + 1) * image_size
+        )
+        # split the image
+        split_img = resized_img.crop(box)
+        processed_images.append(split_img)
+    assert len(processed_images) == blocks
+    if use_thumbnail and len(processed_images) != 1:
+        thumbnail_img = image.resize((image_size, image_size))
+        processed_images.append(thumbnail_img)
+    return processed_images, target_aspect_ratio
+
+
+
+def normalize_transform(mean, std):
+    if mean is None and std is None:
+        transform = None
+    elif mean is None and std is not None:
+        mean = [0.] * len(std)
+        transform = transforms.Normalize(mean=mean, std=std)
+    elif mean is not None and std is None:
+        std = [1.] * len(mean)
+        transform = transforms.Normalize(mean=mean, std=std)
+    else:
+        transform = transforms.Normalize(mean=mean, std=std)
+
+    return transform
+
+
+
+def format_messages(
+        conversations: List[Dict[str, str]],
+        sft_format: str = "deepseek",
+        system_prompt: str = "",
+):
+    """
+    Applies the SFT template to conversation.
+
+    Args:
+        conversations (List[Dict]): A List of messages.
+        sft_format (str, optional): The format of the SFT template to use. Defaults to "deepseek".
+        system_prompt (str, optional): The system prompt to use in the SFT template. Defaults to "".
+
+    Returns:
+        sft_prompt (str): The formatted text.
+    """
+
+    conv = get_conv_template(sft_format)
+    conv.set_system_message(system_prompt)
+    for message in conversations:
+        conv.append_message(message["role"], message["content"].strip())
+    sft_prompt = conv.get_prompt().strip()
+
+    return sft_prompt
+
+
+def text_encode(tokenizer, text: str, bos: bool = True, eos: bool = False):
+    t = tokenizer.encode(text, add_special_tokens=False)
+    bos_id = 0
+    eos_id = 1
+    if bos:
+        t = [bos_id] + t
+    if eos:
+        t = t + [eos_id]
+
+    return t
+
+def load_pil_images(conversations: List[Dict[str, str]]) -> List[Image.Image]:
+    """
+
+    Args:
+        conversations (List[Dict[str, str]]): the conversations with a list of messages. An example is :
+            [
+                {
+                    "role": "User",
+                    "content": "<image_placeholder>\nExtract all information from this image and convert them into markdown format.",
+                    "images": ["./examples/table_datasets.png"]
+                },
+                {"role": "Assistant", "content": ""},
+            ]
+
+    Returns:
+        pil_images (List[PIL.Image.Image]): the list of PIL images.
+
+    """
+
+    pil_images = []
+
+    for message in conversations:
+        if "images" not in message:
+            continue
+
+        for image_path in message["images"]:
+            # print('----------------')
+            # print(image_path)
+            # print('----------------')
+            # exit()
+            
+            # pil_img = Image.open(image_path)
+            pil_img = load_image(image_path)
+            pil_img = pil_img.convert("RGB")
+            pil_images.append(pil_img)
+
+    return pil_images
+
+
+class BaseTransform(ABC):
+
+    def set_rng(self, *args, **kwargs):
+        pass
+
+    def __call__(self, *args, **kwargs) -> torch.Tensor:
+        pass
+
+    @property
+    def default_shape(self):
+        raise NotImplementedError
+
+
+class BasicImageTransform(BaseTransform):
+    def __init__(
+        self, 
+        mean: Optional[Tuple[float, float, float]] = (0.5, 0.5, 0.5),
+        std: Optional[Tuple[float, float, float]] = (0.5, 0.5, 0.5),
+        normalize: bool = True
+    ):
+        self.mean = mean
+        self.std = std
+    
+        transform_pipelines = [
+            transforms.ToTensor()
+        ]
+
+        normalize = normalize_transform(mean, std) if normalize else nn.Identity()
+        if normalize is not None:
+            transform_pipelines.append(normalize)
+
+        self.transform = transforms.Compose(transform_pipelines)
+    
+    def __call__(self, x):
+        x = self.transform(x)
+        return x
+
+class NoEOSTextStreamer(TextStreamer):
+    def on_finalized_text(self, text: str, stream_end: bool = False):
+
+        eos_text = self.tokenizer.decode([self.tokenizer.eos_token_id], skip_special_tokens=False)
+        text = text.replace(eos_text, "\n")
+        print(text, flush=True, end="")
+
+
+class SlidingWindowNoRepeatNgramProcessor:
+    """Block n-gram repetitions within a sliding window.
+    Aligned with SGLang DeepseekOCRNoRepeatNGramLogitProcessor."""
+    def __init__(self, ngram_size, window, whitelist_token_ids=None):
+        self.ngram_size = ngram_size
+        self.window = window
+        self.whitelist = set(whitelist_token_ids) if whitelist_token_ids else set()
+
+    def __call__(self, input_ids, scores):
+        for batch_idx in range(input_ids.shape[0]):
+            sequence = input_ids[batch_idx].tolist()
+            if len(sequence) < self.ngram_size:
+                continue
+            search_start = max(0, len(sequence) - self.window)
+            search_end = len(sequence) - self.ngram_size + 1
+            if search_end <= search_start:
+                continue
+            if self.ngram_size > 1:
+                current_prefix = tuple(sequence[-(self.ngram_size - 1):])
+            else:
+                current_prefix = tuple()
+            banned = set()
+            for idx in range(search_start, search_end):
+                ngram = sequence[idx:idx + self.ngram_size]
+                if self.ngram_size == 1 or tuple(ngram[:-1]) == current_prefix:
+                    banned.add(ngram[-1])
+            banned.difference_update(self.whitelist)
+            for token_id in banned:
+                scores[batch_idx, token_id] = float('-inf')
+        return scores
+
+
+class TPSTextStreamer(TextStreamer):
+    """Streamer that prints TPS every `interval` tokens. Set interval=0 to disable."""
+    def __init__(self, tokenizer, interval=100, **kwargs):
+        super().__init__(tokenizer, **kwargs)
+        self.interval = interval
+        self.token_count = 0
+        self.start_time = None
+        self.start_token_count = 0
+        self.last_report_count = 0
+        self.last_report_time = None
+
+    def put(self, value):
+        import time
+        if hasattr(value, 'numel'):
+            self.token_count += value.numel()
+        else:
+            self.token_count += 1
+        # 第一次 put 时开始计时（跳过 prefill）
+        if self.start_time is None:
+            self.start_time = time.time()
+            self.last_report_time = self.start_time
+            self.last_report_count = self.token_count
+            self.start_token_count = self.token_count
+            super().put(value)
+            return
+        if self.interval > 0 and self.token_count - self.last_report_count >= self.interval:
+            now = time.time()
+            delta_tokens = self.token_count - self.last_report_count
+            delta_time = now - self.last_report_time
+            recent_tps = delta_tokens / delta_time if delta_time > 0 else 0
+            avg_tps = (self.token_count - self.start_token_count) / (now - self.start_time) if (now - self.start_time) > 0 else 0
+            print(f"\n[TPS] tokens={self.token_count}, recent={recent_tps:.1f} t/s, avg={avg_tps:.1f} t/s", flush=True)
+            self.last_report_count = self.token_count
+            self.last_report_time = now
+        super().put(value)
+
+    def on_finalized_text(self, text: str, stream_end: bool = False):
+        eos_text = self.tokenizer.decode([self.tokenizer.eos_token_id], skip_special_tokens=False)
+        text = text.replace(eos_text, "\n")
+        print(text, flush=True, end="")
+
+
+class UnlimitedOCRConfig(DeepseekV2Config):
+    model_type = "unlimited-ocr"
+
+class UnlimitedOCRModel(DeepseekV2Model):
+    config_class = UnlimitedOCRConfig
+
+    def __init__(self, config: DeepseekV2Config):
+        super(UnlimitedOCRModel, self).__init__(config)
+
+        self.sam_model = build_sam_vit_b()
+        self.vision_model = build_clip_l()
+        # self.conv_2 = nn.Conv2d(in_channels=1024, out_channels=2048, kernel_size=2, stride=2)
+        n_embed = 1280
+        self.projector =  MlpProjector(Dict(projector_type="linear", input_dim=2048, n_embed=n_embed))
+        embed_std = 1 / torch.sqrt(torch.tensor(n_embed, dtype=torch.float32))
+        self.image_newline = nn.Parameter(torch.randn(n_embed) * embed_std)
+        self.view_seperator = nn.Parameter(torch.randn(n_embed) * embed_std)
+
+
+
+    
+    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,
+        images: Optional[torch.FloatTensor] = None,
+        images_seq_mask: Optional[torch.FloatTensor] = None,
+        images_spatial_crop: Optional[torch.FloatTensor] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+
+
+
+
+        if inputs_embeds is None:
+            # inputs_embeds = self.embed_tokens(input_ids)
+            inputs_embeds = self.get_input_embeddings()(input_ids)
+
+
+
+        sam_model = getattr(self, 'sam_model', None)
+        # sam_model = self.sam_model
+        vision_model = getattr(self, 'vision_model', None)
+
+
+
+        if sam_model is not None and images is not None and (input_ids.shape[1] != 1 or self.training) and torch.sum(images[0][1]).item() != 0:
+
+            idx = 0
+            
+            # sam_model = torch.jit.script(sam_model)
+            
+            # start_time = time.time()
+            for image, crop_shape in zip(images, images_spatial_crop):
+                images_in_this_batch = []
+
+                patches = image[0]
+                image_ori = image[1]
+
+                with torch.no_grad():
+                # with torch.inference_mode(): 
+                    
+                    if torch.sum(patches).item() != 0:
+                        # P, C, H, W = patches.shape
+                        crop_flag = 1
+                        local_features_1 = sam_model(patches)
+
+                        local_features_2 = vision_model(patches, local_features_1)  
+                        # vit_time = time.time()
+                        local_features = torch.cat((local_features_2[:, 1:], local_features_1.flatten(2).permute(0, 2, 1)), dim=-1) 
+                        local_features = self.projector(local_features)
+
+
+                        global_features_1 = sam_model(image_ori)
+                        global_features_2 = vision_model(image_ori, global_features_1) 
+                        global_features = torch.cat((global_features_2[:, 1:], global_features_1.flatten(2).permute(0, 2, 1)), dim=-1) 
+                        global_features = self.projector(global_features)
+
+                        # print('=====================')
+                        # print('BASE: ', global_features.shape)
+                        # print('PATCHES: ', local_features.shape)
+                        # print('=====================')
+
+                        _, hw, n_dim = global_features.shape
+                        h = w = int(hw ** 0.5)
+
+                        _2, hw2, n_dim2 = local_features.shape
+                        h2 = w2 = int(hw2 ** 0.5)
+
+                        width_crop_num, height_crop_num = crop_shape[0], crop_shape[1]
+
+                        global_features = global_features.view(h, w, n_dim)
+
+                        global_features = torch.cat(
+                            [global_features, self.image_newline[None, None, :].expand(h, 1, n_dim)], dim=1
+                        )
+
+                        global_features = global_features.view(-1, n_dim)
+
+
+                        local_features = local_features.view(height_crop_num, width_crop_num, h2, w2, n_dim2).permute(0, 2, 1, 3, 4).reshape(height_crop_num*h2, width_crop_num*w2, n_dim2)
+                        local_features = torch.cat(
+                            [local_features, self.image_newline[None, None, :].expand(height_crop_num * h2, 1, n_dim2)], dim=1
+                        )
+                        local_features = local_features.view(-1, n_dim2)
+
+                        global_local_features = torch.cat([local_features, global_features, self.view_seperator[None, :]], dim=0)
+                        images_in_this_batch.append(global_local_features)
+
+                        # end_time = time.time()
+
+                        # print('sam: ', sam_time - start_time)
+                        # print('vit: ', vit_time - sam_time)
+                        # print('all: ', end_time - start_time)
+
+                        # exit()
+                   
+                    else:
+                        # Handle single or multiple images in image_ori
+                        num_imgs = image_ori.shape[0]
+                        for img_idx in range(num_imgs):
+                            single_img = image_ori[img_idx:img_idx+1]  # [1, 3, H, W]
+                            global_features_1 = sam_model(single_img)
+                            global_features_2 = vision_model(single_img, global_features_1) 
+                            global_features = torch.cat((global_features_2[:, 1:], global_features_1.flatten(2).permute(0, 2, 1)), dim=-1) 
+                            global_features = self.projector(global_features)
+
+                            _, hw, n_dim = global_features.shape
+                            h = w = int(hw ** 0.5)
+
+                            global_features = global_features.view(h, w, n_dim)
+
+                            global_features = torch.cat(
+                                [global_features, self.image_newline[None, None, :].expand(h, 1, n_dim)], dim=1
+                            )
+
+                            global_features = global_features.view(-1, n_dim)
+
+                            global_local_features = torch.cat([global_features, self.view_seperator[None, :]], dim=0)
+                            images_in_this_batch.append(global_local_features)
+                
+
+                # print(inputs_embeds.shape)
+
+                if images_in_this_batch:
+                    images_in_this_batch = torch.cat(images_in_this_batch, dim=0)
+                    # exit()
+
+                    inputs_embeds[idx].masked_scatter_(images_seq_mask[idx].unsqueeze(-1).cuda(), images_in_this_batch)
+
+                idx += 1
+            
+
+        return super(UnlimitedOCRModel, self).forward(
+            input_ids=None, attention_mask=attention_mask, past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds, use_cache=use_cache, position_ids = position_ids,
+            output_attentions=output_attentions, output_hidden_states=output_hidden_states,
+            return_dict=return_dict
+        )
+    
+
+class UnlimitedOCRForCausalLM(DeepseekV2ForCausalLM):
+
+    config_class = UnlimitedOCRConfig
+    # supports_gradient_checkpointing = True
+
+    def __init__(self, config):
+        super(DeepseekV2ForCausalLM, self).__init__(config)
+        self.model = UnlimitedOCRModel(config)
+
+        self.vocab_size = config.vocab_size
+
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_model(self):
+        return self.model
+
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        images: Optional[torch.FloatTensor] = None,
+        images_seq_mask: Optional[torch.FloatTensor] = None,
+        images_spatial_crop: Optional[torch.FloatTensor] = None,
+        return_dict: Optional[bool] = None,
+        
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+
+
+        outputs  = self.model(
+            input_ids=input_ids,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            images=images,
+            images_seq_mask = images_seq_mask,
+            images_spatial_crop = images_spatial_crop,
+            return_dict=return_dict
+            
+        )
+
+
+        
+        # print(transformer_outputs)
+
+        hidden_states = outputs[0]
+        logits = self.lm_head(hidden_states)
+        logits = logits.float()
+
+        # logits
+
+        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
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+    def prepare_inputs_for_generation(
+        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
+    ):
+        # Omit tokens covered by past_key_values
+        past_length = 0
+        if past_key_values is not None:
+            if isinstance(past_key_values, Cache):
+                cache_length = past_key_values.get_seq_length()
+                past_length = past_key_values.get_seq_length()
+                max_cache_length = getattr(past_key_values, 'get_max_length', lambda: None)()
+            else:
+                cache_length = past_length = past_key_values[0][0].shape[2]
+                max_cache_length = None
+
+            # Ring buffer: cache size is fixed, but we've processed more tokens.
+            # Always just take the last token for decode.
+            if hasattr(past_key_values, '_prefill_length') and past_length > 0:
+                input_ids = input_ids[:, -1:]
+            # Keep only the unprocessed tokens:
+            # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
+            # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
+            # input)
+            elif attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
+                input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
+            # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
+            # input_ids based on the past_length.
+            elif past_length < input_ids.shape[1]:
+                input_ids = input_ids[:, past_length:]
+            # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
+
+            # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
+            if (
+                max_cache_length is not None
+                and attention_mask is not None
+                and cache_length + input_ids.shape[1] > max_cache_length
+            ):
+                attention_mask = attention_mask[:, -max_cache_length:]
+
+        position_ids = kwargs.get("position_ids", None)
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -input_ids.shape[1] :]
+
+        # if self.generation_config.cache_implementation == "static":
+        #     # generation with static cache
+        #     cache_position = kwargs.get("cache_position", None)
+        #     if cache_position is None:
+        #         past_length = 0
+        #     else:
+        #         past_length = cache_position[-1] + 1
+        #     input_ids = input_ids[:, past_length:]
+        #     position_ids = position_ids[:, past_length:]
+
+        # TODO @gante we should only keep a `cache_position` in generate, and do +=1.
+        # same goes for position ids. Could also help with continued generation.
+        cache_position = torch.arange(past_length, past_length + position_ids.shape[-1], device=position_ids.device)
+
+        # 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}
+
+        # Pass images only on prefill (cache empty or None)
+        _is_prefill = (past_key_values is None or
+                       (isinstance(past_key_values, Cache) and past_key_values.get_seq_length() == 0))
+        model_inputs.update(
+            {
+                "position_ids": position_ids,
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+                "attention_mask": attention_mask,
+                "images": kwargs.get("images", None) if _is_prefill else None,
+                "images_seq_mask": kwargs.get("images_seq_mask", None) if _is_prefill else None,
+                "images_spatial_crop": kwargs.get("images_spatial_crop", None) if _is_prefill else None,
+            }
+        )
+        return model_inputs
+    
+
+    def disable_torch_init(self):
+        """
+        Disable the redundant torch default initialization to accelerate model creation.
+        """
+        import torch
+        setattr(torch.nn.Linear, "reset_parameters", lambda self: None)
+        setattr(torch.nn.LayerNorm, "reset_parameters", lambda self: None)
+
+
+
+    def infer(self, tokenizer, prompt='', image_file='', output_path = '', base_size=1024, image_size=640, crop_mode=True, test_compress=False, save_results=False, eval_mode=False, max_length=32768, tps_interval=0, no_repeat_ngram_size=0, ngram_window=0, temperature=0.0):
+        self.disable_torch_init()
+
+        os.makedirs(output_path, exist_ok=True)
+        os.makedirs(f'{output_path}/images', exist_ok=True)
+
+        if prompt and image_file:
+            conversation = [
+                {
+                    "role": "<|User|>",
+                    # "content": "<image>\n<|grounding|>Given the layout of the image. ",
+                    "content": f'{prompt}',
+                    # "content": "君不见黄河之水天上来的下一句是什么？",
+                    # "content": "<image>\nFree OCR. ",
+                    # "content": "<image>\nParse the figure. ",
+                    # "content": "<image>\nExtract the text in the image. ",
+                    "images": [f'{image_file}'],
+                },
+                {"role": "<|Assistant|>", "content": ""},
+            ]
+        
+        elif prompt:
+            conversation = [
+                {
+                    "role": "<|User|>",
+                    # "content": "<image>\n<|grounding|>Given the layout of the image. ",
+                    "content": f'{prompt}',
+                    # "content": "君不见黄河之水天上来的下一句是什么？",
+                    # "content": "<image>\nFree OCR. ",
+                    # "content": "<image>\nParse the figure. ",
+                    # "content": "<image>\nExtract the text in the image. ",
+                    # "images": [f'{image_file}'],
+                },
+                {"role": "<|Assistant|>", "content": ""},
+            ]
+        else:
+            assert False, f'prompt is none!'
+        
+        prompt = format_messages(conversations=conversation, sft_format='plain', system_prompt='')
+
+        patch_size = 16
+        downsample_ratio = 4
+        images = load_pil_images(conversation)
+
+        valid_img_tokens = 0
+        ratio = 1
+
+        image_draw = images[0].copy()
+
+        w,h = image_draw.size
+        # print(w, h)
+        ratio = 1 - ((max(w, h) - min(w, h)) / (max(w, h)))
+    
+
+        image_transform=BasicImageTransform(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), normalize=True)
+        images_seq_mask = []
+
+        image_token = '<image>'
+        image_token_id = 128815
+        text_splits = prompt.split(image_token)
+
+        images_list, images_crop_list, images_seq_mask = [], [], []
+        tokenized_str = []
+        images_spatial_crop = []
+        for text_sep, image in zip(text_splits, images):
+
+            tokenized_sep = text_encode(tokenizer, text_sep, bos=False, eos=False)
+            tokenized_str += tokenized_sep
+            images_seq_mask += [False] * len(tokenized_sep)
+
+            if crop_mode:
+
+                if image.size[0] <= 640 and image.size[1] <= 640:
+                    crop_ratio = [1, 1]
+
+                else:
+                    if crop_mode:
+                        # best_width, best_height = select_best_resolution(image.size, self.candidate_resolutions)
+                        images_crop_raw, crop_ratio = dynamic_preprocess(image)
+                    else:
+                        # best_width, best_height = self.image_size, self.image_size
+                        crop_ratio = [1, 1]
+                
+                """process the global view"""
+                # image = image.resize((base_size, base_size))
+                global_view = ImageOps.pad(image, (base_size, base_size),
+                                        color=tuple(int(x * 255) for x in image_transform.mean))
+                
+                if base_size == 1024:
+                    valid_img_tokens += int(256 * ratio)
+                elif base_size == 1280:
+                    valid_img_tokens += int(400 * ratio)
+                # elif base_size == 640:
+                #     valid_img_tokens += int(100 * ratio)
+                
+
+
+
+                
+                images_list.append(image_transform(global_view).to(torch.bfloat16))
+
+                # global_view_tensor = image_transform(global_view).to(torch.bfloat16)
+
+                width_crop_num, height_crop_num = crop_ratio
+
+                images_spatial_crop.append([width_crop_num, height_crop_num])
+                
+                
+                if width_crop_num > 1 or height_crop_num > 1:
+                    """process the local views"""
+                    
+                    for i in range(len(images_crop_raw)):
+                        images_crop_list.append(image_transform(images_crop_raw[i]).to(torch.bfloat16))
+                
+                if image_size == 640:
+                    valid_img_tokens += len(images_crop_list) * 100
+
+                num_queries = math.ceil((image_size // patch_size) / downsample_ratio)
+                num_queries_base = math.ceil((base_size // patch_size) / downsample_ratio)
+
+
+
+                """add image tokens"""
+
+                
+
+                tokenized_image = ([image_token_id] * num_queries_base + [image_token_id]) * num_queries_base
+                tokenized_image += [image_token_id]
+                if width_crop_num > 1 or height_crop_num > 1:
+                    tokenized_image += ([image_token_id] * (num_queries * width_crop_num) + [image_token_id]) * (
+                                num_queries * height_crop_num)
+                tokenized_str += tokenized_image
+                images_seq_mask += [True] * len(tokenized_image)
+                # num_image_tokens.append(len(tokenized_image))
+
+            else:
+                # best_width, best_height = self.image_size, self.image_size
+                # print(image.size, (best_width, best_height)) # check the select_best_resolutions func
+
+                """process the global view"""
+                if image_size <= 640:
+                    print('directly resize')
+                    image = image.resize((image_size, image_size))
+                # else:
+                global_view = ImageOps.pad(image, (image_size, image_size),
+                                        color=tuple(int(x * 255) for x in image_transform.mean))
+                images_list.append(image_transform(global_view).to(torch.bfloat16))
+
+                if base_size == 1024:
+                    valid_img_tokens += int(256 * ratio)
+                elif base_size == 1280:
+                    valid_img_tokens += int(400 * ratio)
+                elif base_size == 640:
+                    valid_img_tokens += int(100 * 1)
+                elif base_size == 512:
+                    valid_img_tokens += int(64 * 1)
+
+                width_crop_num, height_crop_num = 1, 1
+
+                images_spatial_crop.append([width_crop_num, height_crop_num])
+
+
+                """add image tokens"""
+                num_queries = math.ceil((image_size // patch_size) / downsample_ratio)
+
+                tokenized_image = ([image_token_id] * num_queries + [image_token_id]) * num_queries
+                tokenized_image += [image_token_id]
+                # tokenized_image += ([self.image_token_id] * (num_queries * width_crop_num) + [self.image_token_id]) * (
+                #             num_queries * height_crop_num)
+                tokenized_str += tokenized_image
+                images_seq_mask += [True] * len(tokenized_image)
+                # num_image_tokens.append(len(tokenized_image))
+        
+
+        """process the last text split"""
+        tokenized_sep = text_encode(tokenizer, text_splits[-1], bos=False, eos=False)
+        tokenized_str += tokenized_sep
+        images_seq_mask += [False] * len(tokenized_sep)
+
+        """add the bos tokens"""
+        bos_id = 0
+        tokenized_str = [bos_id] + tokenized_str 
+        images_seq_mask = [False] + images_seq_mask
+
+
+
+        input_ids = torch.LongTensor(tokenized_str)
+
+
+        
+
+        images_seq_mask = torch.tensor(images_seq_mask, dtype=torch.bool)
+
+
+        if len(images_list) == 0:
+            images_ori = torch.zeros((1, 3, image_size, image_size))
+            images_spatial_crop = torch.zeros((1, 2), dtype=torch.long)
+            images_crop = torch.zeros((1, 3, base_size, base_size))
+
+        else:
+            images_ori = torch.stack(images_list, dim=0)
+            images_spatial_crop = torch.tensor(images_spatial_crop, dtype=torch.long)
+            if images_crop_list:
+                images_crop = torch.stack(images_crop_list, dim=0)
+            else:
+                images_crop = torch.zeros((1, 3, base_size, base_size))
+
+
+
+        if not eval_mode:
+            streamer = TPSTextStreamer(tokenizer, interval=tps_interval, skip_prompt=True, skip_special_tokens=False)
+            _orig_sw = getattr(self.config, 'sliding_window_size', None) or getattr(self.config, 'sliding_window', None)
+            self.config._ring_window = _orig_sw
+            self.config.sliding_window = None
+            # Build logits processors for ngram
+            gen_kwargs = dict(
+                input_ids=input_ids.unsqueeze(0).cuda(),
+                images=[(images_crop.cuda(), images_ori.cuda())],
+                images_seq_mask=images_seq_mask.unsqueeze(0).cuda(),
+                images_spatial_crop=images_spatial_crop,
+                do_sample=temperature > 0,
+                temperature=temperature if temperature > 0 else None,
+                eos_token_id=tokenizer.eos_token_id,
+                streamer=streamer,
+                max_length=max_length,
+                use_cache=True
+            )
+            if no_repeat_ngram_size > 0 and ngram_window > 0:
+                gen_kwargs['logits_processor'] = [SlidingWindowNoRepeatNgramProcessor(no_repeat_ngram_size, ngram_window)]
+            elif no_repeat_ngram_size > 0:
+                gen_kwargs['no_repeat_ngram_size'] = no_repeat_ngram_size
+            with torch.autocast("cuda", dtype=torch.bfloat16):
+                with torch.no_grad():
+                    output_ids = self.generate(**gen_kwargs)
+            self.config.sliding_window = _orig_sw
+
+        else:
+            _orig_sw = getattr(self.config, 'sliding_window_size', None) or getattr(self.config, 'sliding_window', None)
+            self.config._ring_window = _orig_sw
+            self.config.sliding_window = None
+            gen_kwargs = dict(
+                input_ids=input_ids.unsqueeze(0).cuda(),
+                images=[(images_crop.cuda(), images_ori.cuda())],
+                images_seq_mask=images_seq_mask.unsqueeze(0).cuda(),
+                images_spatial_crop=images_spatial_crop,
+                do_sample=temperature > 0,
+                temperature=temperature if temperature > 0 else None,
+                eos_token_id=tokenizer.eos_token_id,
+                max_length=max_length,
+                use_cache=True
+            )
+            if no_repeat_ngram_size > 0 and ngram_window > 0:
+                gen_kwargs['logits_processor'] = [SlidingWindowNoRepeatNgramProcessor(no_repeat_ngram_size, ngram_window)]
+            elif no_repeat_ngram_size > 0:
+                gen_kwargs['no_repeat_ngram_size'] = no_repeat_ngram_size
+            with torch.autocast("cuda", dtype=torch.bfloat16):
+                with torch.no_grad():
+                    output_ids = self.generate(**gen_kwargs)
+            self.config.sliding_window = _orig_sw
+                
+
+        if '<image>' in conversation[0]['content'] and eval_mode:
+                outputs = tokenizer.decode(output_ids[0, input_ids.unsqueeze(0).cuda().shape[1]:])
+                stop_str = '<｜end▁of▁sentence｜>'
+                if outputs.endswith(stop_str):
+                    outputs = outputs[:-len(stop_str)]
+                # re_match
+                outputs = outputs.strip()
+
+                return outputs
+        
+        if '<image>' in conversation[0]['content'] and test_compress:
+            outputs = tokenizer.decode(output_ids[0, input_ids.unsqueeze(0).cuda().shape[1]:])
+            pure_texts_outputs_token_length = len(text_encode(tokenizer, outputs, bos=False, eos=False))
+            print('='*50)
+            print('image size: ', (w, h))
+            print('valid image tokens: ', int(valid_img_tokens))
+            print('output texts tokens (valid): ', pure_texts_outputs_token_length)
+            print('compression ratio: ', round(pure_texts_outputs_token_length/valid_img_tokens, 2))
+            print('='*50)
+
+
+        if '<image>' in conversation[0]['content'] and save_results:
+            outputs = tokenizer.decode(output_ids[0, input_ids.unsqueeze(0).cuda().shape[1]:])
+            stop_str = '<｜end▁of▁sentence｜>'
+
+            print('='*15 + 'save results:' + '='*15)
+            
+            # # # # conv.messages[-1][-1] = outputs
+            if outputs.endswith(stop_str):
+                outputs = outputs[:-len(stop_str)]
+            outputs = outputs.strip()
+
+            matches_ref, matches_images, mathes_other = re_match(outputs)
+            # print(matches_ref)
+            result = process_image_with_refs(image_draw, matches_ref, output_path)
+
+
+            for idx, a_match_image in enumerate(tqdm(matches_images, desc="image")):
+                outputs = outputs.replace(a_match_image, '![](images/' + str(idx) + '.jpg)\n')
+            
+            for idx, a_match_other in enumerate(tqdm(mathes_other, desc="other")):
+                outputs = outputs.replace(a_match_other, '').replace('\\coloneqq', ':=').replace('\\eqqcolon', '=:')
+
+
+            # if 'structural formula' in conversation[0]['content']:
+            #     outputs = '<smiles>' + outputs + '</smiles>'
+            with open(f'{output_path}/result.md', 'w', encoding = 'utf-8') as afile:
+                afile.write(outputs)
+
+            if 'line_type' in outputs:
+                import matplotlib.pyplot as plt
+                lines = eval(outputs)['Line']['line']
+
+                line_type = eval(outputs)['Line']['line_type']
+                # print(lines)
+
+                endpoints = eval(outputs)['Line']['line_endpoint']
+
+                fig, ax = plt.subplots(figsize=(3,3), dpi=200)
+                ax.set_xlim(-15, 15)
+                ax.set_ylim(-15, 15)
+
+                for idx, line in enumerate(lines):
+                    try:
+                        p0 = eval(line.split(' -- ')[0])
+                        p1 = eval(line.split(' -- ')[-1])
+
+                        if line_type[idx] == '--':
+                            ax.plot([p0[0], p1[0]], [p0[1], p1[1]], linewidth=0.8, color='k')
+                        else:
+                            ax.plot([p0[0], p1[0]], [p0[1], p1[1]], linewidth = 0.8, color = 'k')
+
+                        ax.scatter(p0[0], p0[1], s=5, color = 'k')
+                        ax.scatter(p1[0], p1[1], s=5, color = 'k')
+                    except:
+                        pass
+
+                for endpoint in endpoints:
+
+                    label = endpoint.split(': ')[0]
+                    (x, y) = eval(endpoint.split(': ')[1])
+                    ax.annotate(label, (x, y), xytext=(1, 1), textcoords='offset points', 
+                                fontsize=5, fontweight='light')
+                
+
+                plt.savefig(f'{output_path}/geo.jpg')
+                plt.close()
+
+            result.save(f"{output_path}/result_with_boxes.jpg")
+
+
+    def infer_multi(self, tokenizer, prompt='', image_files=None, output_path='', image_size=640, save_results=False, max_length=32768, tps_interval=0, no_repeat_ngram_size=0, ngram_window=0, temperature=0.0):
+        """
+        Multi-image inference. Does NOT support crop mode.
+        Prompt uses a single <image> token (e.g. "<image>Multi page parsing.").
+        All images' token sequences are concatenated at that single <image> position,
+        separated by a single image_token_id between each image (same as crop mode separator).
+        
+        Args:
+            prompt: text prompt with one <image> token, e.g. "<image>Multi page parsing."
+            image_files: list of image file paths
+            image_size: size to resize each image to
+            save_results: whether to save output to file
+        """
+        self.disable_torch_init()
+        
+        if image_files is None or len(image_files) == 0:
+            assert False, 'image_files must be a non-empty list for multi-image inference!'
+        
+        os.makedirs(output_path, exist_ok=True)
+        os.makedirs(f'{output_path}/images', exist_ok=True)
+
+        # Prompt contains a single <image>, all image files go into "images" list
+        conversation = [
+            {
+                "role": "<|User|>",
+                "content": f'{prompt}',
+                "images": image_files,
+            },
+            {"role": "<|Assistant|>", "content": ""},
+        ]
+
+        formatted_prompt = format_messages(conversations=conversation, sft_format='plain', system_prompt='')
+
+        patch_size = 16
+        downsample_ratio = 4
+
+        # Load all images
+        images = load_pil_images(conversation)
+
+        image_transform = BasicImageTransform(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), normalize=True)
+
+        image_token = '<image>'
+        image_token_id = 128815
+
+        # Split on the single <image> token -> 2 parts: before and after
+        text_splits = formatted_prompt.split(image_token)
+
+        images_list, images_seq_mask = [], []
+        tokenized_str = []
+        images_spatial_crop = []
+
+        num_queries = math.ceil((image_size // patch_size) / downsample_ratio)
+
+        # Tokenize text before <image>
+        tokenized_sep = text_encode(tokenizer, text_splits[0], bos=False, eos=False)
+        tokenized_str += tokenized_sep
+        images_seq_mask += [False] * len(tokenized_sep)
+
+        # Process all images at the single <image> position
+        for idx, image in enumerate(images):
+            # Match single-image logic: if image_size <= 640, resize all images
+            if image_size <= 640:
+                image = image.resize((image_size, image_size))
+            global_view = ImageOps.pad(image, (image_size, image_size),
+                                      color=tuple(int(x * 255) for x in image_transform.mean))
+
+            images_list.append(image_transform(global_view).to(torch.bfloat16))
+            images_spatial_crop.append([1, 1])
+
+            # Image tokens for this image (same structure as single-image non-crop mode)
+            tokenized_image = ([image_token_id] * num_queries + [image_token_id]) * num_queries
+            tokenized_image += [image_token_id]  # separator token between images
+            tokenized_str += tokenized_image
+            images_seq_mask += [True] * len(tokenized_image)
+
+        # Tokenize text after <image>
+        tokenized_sep = text_encode(tokenizer, text_splits[1], bos=False, eos=False)
+        tokenized_str += tokenized_sep
+        images_seq_mask += [False] * len(tokenized_sep)
+
+        # Add bos token
+        bos_id = 0
+        tokenized_str = [bos_id] + tokenized_str
+        images_seq_mask = [False] + images_seq_mask
+
+        input_ids = torch.LongTensor(tokenized_str)
+        images_seq_mask = torch.tensor(images_seq_mask, dtype=torch.bool)
+
+        # Stack all images as image_ori; dummy_crop is zeros (triggers no-crop branch)
+        images_ori = torch.stack(images_list, dim=0)  # [N, 3, H, W]
+        images_spatial_crop = torch.tensor(images_spatial_crop, dtype=torch.long)
+        dummy_crop = torch.zeros((1, 3, image_size, image_size))
+
+        streamer = TPSTextStreamer(tokenizer, interval=tps_interval, skip_prompt=True, skip_special_tokens=False)
+        # Disable config.sliding_window to prevent DynamicCache from truncating prefill tokens.
+        # The ring buffer in SlidingWindowLlamaAttention handles sliding window manually.
+        _orig_sw = getattr(self.config, 'sliding_window_size', None) or getattr(self.config, 'sliding_window', None)
+        self.config._ring_window = _orig_sw  # Save for ring buffer to read
+        self.config.sliding_window = None
+        with torch.autocast("cuda", dtype=torch.bfloat16):
+            with torch.no_grad():
+                gen_kwargs = dict(
+                    input_ids=input_ids.unsqueeze(0).cuda(),
+                    images=[(dummy_crop.cuda(), images_ori.cuda())],
+                    images_seq_mask=images_seq_mask.unsqueeze(0).cuda(),
+                    images_spatial_crop=images_spatial_crop,
+                    do_sample=temperature > 0,
+                    temperature=temperature if temperature > 0 else None,
+                    eos_token_id=tokenizer.eos_token_id,
+                    streamer=streamer,
+                    max_length=max_length,
+                    use_cache=True
+                )
+                if no_repeat_ngram_size > 0 and ngram_window > 0:
+                    gen_kwargs['logits_processor'] = [SlidingWindowNoRepeatNgramProcessor(no_repeat_ngram_size, ngram_window)]
+                elif no_repeat_ngram_size > 0:
+                    gen_kwargs['no_repeat_ngram_size'] = no_repeat_ngram_size
+                output_ids = self.generate(**gen_kwargs)
+        self.config.sliding_window = _orig_sw  # Restore
+
+        outputs = tokenizer.decode(output_ids[0, input_ids.unsqueeze(0).cuda().shape[1]:])
+        stop_str = '<｜end▁of▁sentence｜>'
+        if outputs.endswith(stop_str):
+            outputs = outputs[:-len(stop_str)]
+        outputs = outputs.strip()
+
+        output_tokens = len(text_encode(tokenizer, outputs, bos=False, eos=False))
+
+        if save_results:
+            print('=' * 15 + 'save results:' + '=' * 15)
+            pages = outputs.split('<PAGE>')[1:]
+            processed_pages = []
+            for page_idx, page_output in enumerate(pages):
+                page_output = page_output.strip()
+                if page_idx >= len(images):
+                    processed_pages.append(page_output)
+                    continue
+
+                matches_ref, matches_images, mathes_other = re_match(page_output)
+                image_prefix = f'page_{page_idx}_'
+                result = process_image_with_refs(
+                    images[page_idx].copy(),
+                    matches_ref,
+                    output_path,
+                    image_prefix=image_prefix,
+                )
+                result.save(f"{output_path}/result_with_boxes_{page_idx}.jpg")
+
+                for idx, a_match_image in enumerate(tqdm(matches_images, desc=f"image_page_{page_idx}")):
+                    page_output = page_output.replace(a_match_image, f'![](images/{image_prefix}{idx}.jpg)\n')
+
+                for idx, a_match_other in enumerate(tqdm(mathes_other, desc=f"other_page_{page_idx}")):
+                    page_output = page_output.replace(a_match_other, '').replace('\\coloneqq', ':=').replace('\\eqqcolon', '=:')
+
+                processed_pages.append(page_output)
+
+            outputs = '<PAGE>\n' + '\n<PAGE>\n'.join(processed_pages)
+            with open(f'{output_path}/result.md', 'w', encoding='utf-8') as afile:
+                afile.write(outputs)
+
+        return outputs, output_tokens

processor_config.json

@@ -0,0 +1,28 @@
+{
+  "add_special_token": false,
+  "candidate_resolutions": [
+    [
+      1024,
+      1024
+    ]
+  ],
+  "downsample_ratio": 4,
+  "ignore_id": -100,
+  "image_mean": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "image_std": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "image_token": "<image>",
+  "mask_prompt": false,
+  "normalize": true,
+  "pad_token": "<\uff5c\u2581pad\u2581\uff5c>",
+  "patch_size": 16,
+  "processor_class": "UnlimitedOCRHFProcessor",
+  "sft_format": "unlimitedocr"
+}

special_tokens_map.json

@@ -0,0 +1,39 @@
+{
+  "additional_special_tokens": [
+    {
+      "content": "<|User|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<|Assistant|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    }
+  ],
+  "bos_token": {
+    "content": "<｜begin▁of▁sentence｜>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<｜end▁of▁sentence｜>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<｜▁pad▁｜>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}