Upload folder using huggingface_hub

.gitattributes

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

added_tokens.json

@@ -0,0 +1,140 @@
+{
+  "</box>": 151673,
+  "</img>": 151666,
+  "</obj>": 151679,
+  "</p>": 151675,
+  "</quad>": 151669,
+  "</ref>": 151671,
+  "</tool_call>": 151658,
+  "<IMG_CONTEXT>": 151667,
+  "<OBJ_CONTEXT>": 151680,
+  "<box>": 151672,
+  "<img>": 151665,
+  "<obj>": 151678,
+  "<p>": 151674,
+  "<quad>": 151668,
+  "<ref>": 151670,
+  "<tool_call>": 151657,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652,
+  "[BG_CLS]": 151677,
+  "[CLS]": 151676,
+  "[SEG000]": 151681,
+  "[SEG001]": 151682,
+  "[SEG002]": 151683,
+  "[SEG003]": 151684,
+  "[SEG004]": 151685,
+  "[SEG005]": 151686,
+  "[SEG006]": 151687,
+  "[SEG007]": 151688,
+  "[SEG008]": 151689,
+  "[SEG009]": 151690,
+  "[SEG010]": 151691,
+  "[SEG011]": 151692,
+  "[SEG012]": 151693,
+  "[SEG013]": 151694,
+  "[SEG014]": 151695,
+  "[SEG015]": 151696,
+  "[SEG016]": 151697,
+  "[SEG017]": 151698,
+  "[SEG018]": 151699,
+  "[SEG019]": 151700,
+  "[SEG020]": 151701,
+  "[SEG021]": 151702,
+  "[SEG022]": 151703,
+  "[SEG023]": 151704,
+  "[SEG024]": 151705,
+  "[SEG025]": 151706,
+  "[SEG026]": 151707,
+  "[SEG027]": 151708,
+  "[SEG028]": 151709,
+  "[SEG029]": 151710,
+  "[SEG030]": 151711,
+  "[SEG031]": 151712,
+  "[SEG032]": 151713,
+  "[SEG033]": 151714,
+  "[SEG034]": 151715,
+  "[SEG035]": 151716,
+  "[SEG036]": 151717,
+  "[SEG037]": 151718,
+  "[SEG038]": 151719,
+  "[SEG039]": 151720,
+  "[SEG040]": 151721,
+  "[SEG041]": 151722,
+  "[SEG042]": 151723,
+  "[SEG043]": 151724,
+  "[SEG044]": 151725,
+  "[SEG045]": 151726,
+  "[SEG046]": 151727,
+  "[SEG047]": 151728,
+  "[SEG048]": 151729,
+  "[SEG049]": 151730,
+  "[SEG050]": 151731,
+  "[SEG051]": 151732,
+  "[SEG052]": 151733,
+  "[SEG053]": 151734,
+  "[SEG054]": 151735,
+  "[SEG055]": 151736,
+  "[SEG056]": 151737,
+  "[SEG057]": 151738,
+  "[SEG058]": 151739,
+  "[SEG059]": 151740,
+  "[SEG060]": 151741,
+  "[SEG061]": 151742,
+  "[SEG062]": 151743,
+  "[SEG063]": 151744,
+  "[SEG064]": 151745,
+  "[SEG065]": 151746,
+  "[SEG066]": 151747,
+  "[SEG067]": 151748,
+  "[SEG068]": 151749,
+  "[SEG069]": 151750,
+  "[SEG070]": 151751,
+  "[SEG071]": 151752,
+  "[SEG072]": 151753,
+  "[SEG073]": 151754,
+  "[SEG074]": 151755,
+  "[SEG075]": 151756,
+  "[SEG076]": 151757,
+  "[SEG077]": 151758,
+  "[SEG078]": 151759,
+  "[SEG079]": 151760,
+  "[SEG080]": 151761,
+  "[SEG081]": 151762,
+  "[SEG082]": 151763,
+  "[SEG083]": 151764,
+  "[SEG084]": 151765,
+  "[SEG085]": 151766,
+  "[SEG086]": 151767,
+  "[SEG087]": 151768,
+  "[SEG088]": 151769,
+  "[SEG089]": 151770,
+  "[SEG090]": 151771,
+  "[SEG091]": 151772,
+  "[SEG092]": 151773,
+  "[SEG093]": 151774,
+  "[SEG094]": 151775,
+  "[SEG095]": 151776,
+  "[SEG096]": 151777,
+  "[SEG097]": 151778,
+  "[SEG098]": 151779,
+  "[SEG099]": 151780
+}

chat_with_sa2va.py

@@ -0,0 +1,140 @@
+import torch
+from PIL import Image
+import os
+import numpy as np
+
+from transformers import AutoModel, AutoTokenizer, AutoImageProcessor
+
+from types import MethodType
+from detectron2.data import MetadataCatalog
+from detectron2.utils.visualizer import ColorMode, Visualizer
+
+from transformers import AutoModel, AutoTokenizer, AutoImageProcessor
+from detectron2.data.detection_utils import read_image
+from detectron2.utils.visualizer import GenericMask
+import matplotlib.colors as mplc
+def draw_instance_predictions_cache(self, labels, np_masks, jittering: bool = True):
+    """
+    Draw instance-level prediction results on an image.
+
+    Args:
+        predictions (Instances): the output of an instance detection/segmentation
+            model. Following fields will be used to draw:
+            "pred_boxes", "pred_classes", "scores", "pred_masks" (or "pred_masks_rle").
+        jittering: if True, in color mode SEGMENTATION, randomly jitter the colors per class
+            to distinguish instances from the same class
+
+    Returns:
+        output (VisImage): image object with visualizations.
+    """
+    boxes = None
+    scores = None
+    classes = None
+    keypoints = None
+
+    masks = [GenericMask(x, self.output.height, self.output.width) for x in np_masks]
+
+
+    if self._instance_mode == ColorMode.SEGMENTATION and self.metadata.get("thing_colors"):
+        colors = (
+            [self._jitter([x / 255 for x in self.metadata.thing_colors[c]]) for c in classes]
+            if jittering
+            else [
+                tuple(mplc.to_rgb([x / 255 for x in self.metadata.thing_colors[c]]))
+                for c in classes
+            ]
+        )
+
+        alpha = 0.8
+    else:
+        colors = None
+        alpha = 0.5
+
+    self.overlay_instances(
+        masks=masks,
+        boxes=boxes,
+        labels=labels,
+        keypoints=keypoints,
+        assigned_colors=colors,
+        alpha=alpha,
+    )
+    return self.output
+
+
+def visualize(image_path, cat_masks, out_path, tags):
+    if tags is None:
+        left_tags = [f'{i}' for i in range(len(cat_masks))]
+    else:
+        left_tags = tags
+
+    unique_tags = list(set(left_tags))
+    text_prompt = ','.join(unique_tags)
+    metadata = MetadataCatalog.get("__unused_ape_" + text_prompt)
+    metadata.thing_classes = unique_tags
+    metadata.stuff_classes = unique_tags
+
+    result_masks = cat_masks
+    input_image = read_image(image_path, format="BGR")
+    visualizer = Visualizer(input_image[:, :, ::-1], metadata, instance_mode=ColorMode.IMAGE)
+    visualizer.draw_instance_predictions = MethodType(draw_instance_predictions_cache, visualizer)
+    vis_output = visualizer.draw_instance_predictions(labels=left_tags, np_masks=result_masks)
+    output_image = vis_output.get_image()
+    output_image = Image.fromarray(output_image)
+
+    output_image.save(out_path)
+
+path = "./work_dirs/hf_pano_vlm"
+model = AutoModel.from_pretrained(
+    path,
+    torch_dtype=torch.bfloat16,
+    low_cpu_mem_usage=True,
+    use_flash_attn=True,
+    trust_remote_code=True).eval().cuda()
+
+tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True, use_fast=False)
+
+image_path = "./FRAME02_ORI.jpg"
+image = Image.open(image_path)
+width, height = image.size
+
+from projects.llava_sam2.datasets.coco_category import COCO_CATEGORIES
+coco_category_names = ""
+for item in COCO_CATEGORIES:
+    class_name = item['name']
+    coco_category_names += f"<p>{class_name}</p> [CLS], "
+coco_category_names = coco_category_names[:-2]
+# question = f"<image>\nSegment from the class prompt: {coco_category_names}."
+question = f"<image>\nSegment from the class prompt: <p>person</p> [CLS], <p>car</p> [CLS], <p>road</p> [CLS], <p>tree</p> [CLS], <p>building</p> [CLS], <p>ground</p> [CLS]."
+
+m2f_processor = AutoImageProcessor.from_pretrained("./facebook/mask2former-swin-large-coco-panoptic", trust_remote_code=True,)
+
+chat_outputs = model.predict_forward(text=question, image=image, tokenizer=tokenizer, m2f_processor=m2f_processor)
+answer = chat_outputs['prediction']
+masks = chat_outputs['prediction_masks']
+
+m2f_outputs = chat_outputs['m2f_outputs']
+
+label_id_to_text = m2f_outputs['label_id_to_text']
+
+post_m2f_outputs = model.post_process_panoptic_segmentation(
+    m2f_outputs['class_queries_logits'],
+    m2f_outputs['masks_queries_logits'],
+    target_sizes=[(height, width)],
+)
+
+print(f"user: {question}")
+print(f"assistant: {answer}")
+
+segmentation = post_m2f_outputs[0]['segmentation']
+segments_info = post_m2f_outputs[0]['segments_info']
+pano_masks, pano_tags = [], []
+for item in segments_info:
+    mask = segmentation == item['id']
+    pano_masks.append(mask.unsqueeze(0).cpu().numpy())
+    pano_tags.append(label_id_to_text[item['label_id']])
+
+pano_masks = np.concatenate(pano_masks, axis=0)
+
+visualize(image_path, pano_masks, "./visualize_test_4.jpg", pano_tags)
+    
+

config.json

@@ -0,0 +1,2677 @@
+{
+  "_commit_hash": null,
+  "_name_or_path": "./OpenGVLab/InternVL2_5-4B",
+  "architectures": [
+    "Sa2VAChatModel"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_sa2va_chat.Sa2VAChatConfig",
+    "AutoModel": "modeling_sa2va_chat.Sa2VAChatModel",
+    "AutoModelForCausalLM": "modeling_sa2va_chat.Sa2VAChatModel"
+  },
+  "downsample_ratio": 0.5,
+  "dynamic_image_size": true,
+  "force_image_size": 448,
+  "hidden_size": 2048,
+  "llm_config": {
+    "_attn_implementation_autoset": false,
+    "_name_or_path": "Qwen/Qwen2.5-3B-Instruct",
+    "add_cross_attention": false,
+    "architectures": [
+      "Qwen2ForCausalLM"
+    ],
+    "attention_dropout": 0.0,
+    "bad_words_ids": null,
+    "begin_suppress_tokens": null,
+    "bos_token_id": 151643,
+    "chunk_size_feed_forward": 0,
+    "cross_attention_hidden_size": null,
+    "decoder_start_token_id": null,
+    "diversity_penalty": 0.0,
+    "do_sample": false,
+    "early_stopping": false,
+    "encoder_no_repeat_ngram_size": 0,
+    "eos_token_id": 151645,
+    "exponential_decay_length_penalty": null,
+    "finetuning_task": null,
+    "forced_bos_token_id": null,
+    "forced_eos_token_id": null,
+    "hidden_act": "silu",
+    "hidden_size": 2048,
+    "id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1"
+    },
+    "initializer_range": 0.02,
+    "intermediate_size": 11008,
+    "is_decoder": false,
+    "is_encoder_decoder": false,
+    "label2id": {
+      "LABEL_0": 0,
+      "LABEL_1": 1
+    },
+    "length_penalty": 1.0,
+    "max_length": 20,
+    "max_position_embeddings": 32768,
+    "max_window_layers": 70,
+    "min_length": 0,
+    "model_type": "qwen2",
+    "no_repeat_ngram_size": 0,
+    "num_attention_heads": 16,
+    "num_beam_groups": 1,
+    "num_beams": 1,
+    "num_hidden_layers": 36,
+    "num_key_value_heads": 2,
+    "num_return_sequences": 1,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "output_scores": false,
+    "pad_token_id": null,
+    "prefix": null,
+    "problem_type": null,
+    "pruned_heads": {},
+    "remove_invalid_values": false,
+    "repetition_penalty": 1.0,
+    "return_dict": true,
+    "return_dict_in_generate": false,
+    "rms_norm_eps": 1e-06,
+    "rope_scaling": null,
+    "rope_theta": 1000000.0,
+    "sep_token_id": null,
+    "sliding_window": null,
+    "suppress_tokens": null,
+    "task_specific_params": null,
+    "temperature": 1.0,
+    "tf_legacy_loss": false,
+    "tie_encoder_decoder": false,
+    "tie_word_embeddings": false,
+    "tokenizer_class": null,
+    "top_k": 50,
+    "top_p": 1.0,
+    "torch_dtype": "bfloat16",
+    "torchscript": false,
+    "transformers_version": "4.47.0",
+    "typical_p": 1.0,
+    "use_bfloat16": true,
+    "use_cache": true,
+    "use_sliding_window": false,
+    "vocab_size": 151781
+  },
+  "m2f_config": {
+    "_attn_implementation_autoset": true,
+    "_name_or_path": "",
+    "activation_function": "relu",
+    "add_cross_attention": false,
+    "architectures": [
+      "Mask2FormerForUniversalSegmentation"
+    ],
+    "backbone": null,
+    "backbone_config": {
+      "_attn_implementation_autoset": false,
+      "_name_or_path": "",
+      "add_cross_attention": false,
+      "architectures": [
+        "SwinForImageClassification"
+      ],
+      "attention_probs_dropout_prob": 0.0,
+      "bad_words_ids": null,
+      "begin_suppress_tokens": null,
+      "bos_token_id": null,
+      "chunk_size_feed_forward": 0,
+      "cross_attention_hidden_size": null,
+      "decoder_start_token_id": null,
+      "depths": [
+        2,
+        2,
+        18,
+        2
+      ],
+      "diversity_penalty": 0.0,
+      "do_sample": false,
+      "drop_path_rate": 0.3,
+      "early_stopping": false,
+      "embed_dim": 192,
+      "encoder_no_repeat_ngram_size": 0,
+      "encoder_stride": 32,
+      "eos_token_id": null,
+      "exponential_decay_length_penalty": null,
+      "finetuning_task": null,
+      "forced_bos_token_id": null,
+      "forced_eos_token_id": null,
+      "hidden_act": "gelu",
+      "hidden_dropout_prob": 0.0,
+      "hidden_size": 1536,
+      "id2label": {
+        "0": "tench, Tinca tinca",
+        "1": "goldfish, Carassius auratus",
+        "2": "great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias",
+        "3": "tiger shark, Galeocerdo cuvieri",
+        "4": "hammerhead, hammerhead shark",
+        "5": "electric ray, crampfish, numbfish, torpedo",
+        "6": "stingray",
+        "7": "cock",
+        "8": "hen",
+        "9": "ostrich, Struthio camelus",
+        "10": "brambling, Fringilla montifringilla",
+        "11": "goldfinch, Carduelis carduelis",
+        "12": "house finch, linnet, Carpodacus mexicanus",
+        "13": "junco, snowbird",
+        "14": "indigo bunting, indigo finch, indigo bird, Passerina cyanea",
+        "15": "robin, American robin, Turdus migratorius",
+        "16": "bulbul",
+        "17": "jay",
+        "18": "magpie",
+        "19": "chickadee",
+        "20": "water ouzel, dipper",
+        "21": "kite",
+        "22": "bald eagle, American eagle, Haliaeetus leucocephalus",
+        "23": "vulture",
+        "24": "great grey owl, great gray owl, Strix nebulosa",
+        "25": "European fire salamander, Salamandra salamandra",
+        "26": "common newt, Triturus vulgaris",
+        "27": "eft",
+        "28": "spotted salamander, Ambystoma maculatum",
+        "29": "axolotl, mud puppy, Ambystoma mexicanum",
+        "30": "bullfrog, Rana catesbeiana",
+        "31": "tree frog, tree-frog",
+        "32": "tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui",
+        "33": "loggerhead, loggerhead turtle, Caretta caretta",
+        "34": "leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea",
+        "35": "mud turtle",
+        "36": "terrapin",
+        "37": "box turtle, box tortoise",
+        "38": "banded gecko",
+        "39": "common iguana, iguana, Iguana iguana",
+        "40": "American chameleon, anole, Anolis carolinensis",
+        "41": "whiptail, whiptail lizard",
+        "42": "agama",
+        "43": "frilled lizard, Chlamydosaurus kingi",
+        "44": "alligator lizard",
+        "45": "Gila monster, Heloderma suspectum",
+        "46": "green lizard, Lacerta viridis",
+        "47": "African chameleon, Chamaeleo chamaeleon",
+        "48": "Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis",
+        "49": "African crocodile, Nile crocodile, Crocodylus niloticus",
+        "50": "American alligator, Alligator mississipiensis",
+        "51": "triceratops",
+        "52": "thunder snake, worm snake, Carphophis amoenus",
+        "53": "ringneck snake, ring-necked snake, ring snake",
+        "54": "hognose snake, puff adder, sand viper",
+        "55": "green snake, grass snake",
+        "56": "king snake, kingsnake",
+        "57": "garter snake, grass snake",
+        "58": "water snake",
+        "59": "vine snake",
+        "60": "night snake, Hypsiglena torquata",
+        "61": "boa constrictor, Constrictor constrictor",
+        "62": "rock python, rock snake, Python sebae",
+        "63": "Indian cobra, Naja naja",
+        "64": "green mamba",
+        "65": "sea snake",
+        "66": "horned viper, cerastes, sand viper, horned asp, Cerastes cornutus",
+        "67": "diamondback, diamondback rattlesnake, Crotalus adamanteus",
+        "68": "sidewinder, horned rattlesnake, Crotalus cerastes",
+        "69": "trilobite",
+        "70": "harvestman, daddy longlegs, Phalangium opilio",
+        "71": "scorpion",
+        "72": "black and gold garden spider, Argiope aurantia",
+        "73": "barn spider, Araneus cavaticus",
+        "74": "garden spider, Aranea diademata",
+        "75": "black widow, Latrodectus mactans",
+        "76": "tarantula",
+        "77": "wolf spider, hunting spider",
+        "78": "tick",
+        "79": "centipede",
+        "80": "black grouse",
+        "81": "ptarmigan",
+        "82": "ruffed grouse, partridge, Bonasa umbellus",
+        "83": "prairie chicken, prairie grouse, prairie fowl",
+        "84": "peacock",
+        "85": "quail",
+        "86": "partridge",
+        "87": "African grey, African gray, Psittacus erithacus",
+        "88": "macaw",
+        "89": "sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita",
+        "90": "lorikeet",
+        "91": "coucal",
+        "92": "bee eater",
+        "93": "hornbill",
+        "94": "hummingbird",
+        "95": "jacamar",
+        "96": "toucan",
+        "97": "drake",
+        "98": "red-breasted merganser, Mergus serrator",
+        "99": "goose",
+        "100": "black swan, Cygnus atratus",
+        "101": "tusker",
+        "102": "echidna, spiny anteater, anteater",
+        "103": "platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus",
+        "104": "wallaby, brush kangaroo",
+        "105": "koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus",
+        "106": "wombat",
+        "107": "jellyfish",
+        "108": "sea anemone, anemone",
+        "109": "brain coral",
+        "110": "flatworm, platyhelminth",
+        "111": "nematode, nematode worm, roundworm",
+        "112": "conch",
+        "113": "snail",
+        "114": "slug",
+        "115": "sea slug, nudibranch",
+        "116": "chiton, coat-of-mail shell, sea cradle, polyplacophore",
+        "117": "chambered nautilus, pearly nautilus, nautilus",
+        "118": "Dungeness crab, Cancer magister",
+        "119": "rock crab, Cancer irroratus",
+        "120": "fiddler crab",
+        "121": "king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica",
+        "122": "American lobster, Northern lobster, Maine lobster, Homarus americanus",
+        "123": "spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish",
+        "124": "crayfish, crawfish, crawdad, crawdaddy",
+        "125": "hermit crab",
+        "126": "isopod",
+        "127": "white stork, Ciconia ciconia",
+        "128": "black stork, Ciconia nigra",
+        "129": "spoonbill",
+        "130": "flamingo",
+        "131": "little blue heron, Egretta caerulea",
+        "132": "American egret, great white heron, Egretta albus",
+        "133": "bittern",
+        "134": "crane",
+        "135": "limpkin, Aramus pictus",
+        "136": "European gallinule, Porphyrio porphyrio",
+        "137": "American coot, marsh hen, mud hen, water hen, Fulica americana",
+        "138": "bustard",
+        "139": "ruddy turnstone, Arenaria interpres",
+        "140": "red-backed sandpiper, dunlin, Erolia alpina",
+        "141": "redshank, Tringa totanus",
+        "142": "dowitcher",
+        "143": "oystercatcher, oyster catcher",
+        "144": "pelican",
+        "145": "king penguin, Aptenodytes patagonica",
+        "146": "albatross, mollymawk",
+        "147": "grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus",
+        "148": "killer whale, killer, orca, grampus, sea wolf, Orcinus orca",
+        "149": "dugong, Dugong dugon",
+        "150": "sea lion",
+        "151": "Chihuahua",
+        "152": "Japanese spaniel",
+        "153": "Maltese dog, Maltese terrier, Maltese",
+        "154": "Pekinese, Pekingese, Peke",
+        "155": "Shih-Tzu",
+        "156": "Blenheim spaniel",
+        "157": "papillon",
+        "158": "toy terrier",
+        "159": "Rhodesian ridgeback",
+        "160": "Afghan hound, Afghan",
+        "161": "basset, basset hound",
+        "162": "beagle",
+        "163": "bloodhound, sleuthhound",
+        "164": "bluetick",
+        "165": "black-and-tan coonhound",
+        "166": "Walker hound, Walker foxhound",
+        "167": "English foxhound",
+        "168": "redbone",
+        "169": "borzoi, Russian wolfhound",
+        "170": "Irish wolfhound",
+        "171": "Italian greyhound",
+        "172": "whippet",
+        "173": "Ibizan hound, Ibizan Podenco",
+        "174": "Norwegian elkhound, elkhound",
+        "175": "otterhound, otter hound",
+        "176": "Saluki, gazelle hound",
+        "177": "Scottish deerhound, deerhound",
+        "178": "Weimaraner",
+        "179": "Staffordshire bullterrier, Staffordshire bull terrier",
+        "180": "American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier",
+        "181": "Bedlington terrier",
+        "182": "Border terrier",
+        "183": "Kerry blue terrier",
+        "184": "Irish terrier",
+        "185": "Norfolk terrier",
+        "186": "Norwich terrier",
+        "187": "Yorkshire terrier",
+        "188": "wire-haired fox terrier",
+        "189": "Lakeland terrier",
+        "190": "Sealyham terrier, Sealyham",
+        "191": "Airedale, Airedale terrier",
+        "192": "cairn, cairn terrier",
+        "193": "Australian terrier",
+        "194": "Dandie Dinmont, Dandie Dinmont terrier",
+        "195": "Boston bull, Boston terrier",
+        "196": "miniature schnauzer",
+        "197": "giant schnauzer",
+        "198": "standard schnauzer",
+        "199": "Scotch terrier, Scottish terrier, Scottie",
+        "200": "Tibetan terrier, chrysanthemum dog",
+        "201": "silky terrier, Sydney silky",
+        "202": "soft-coated wheaten terrier",
+        "203": "West Highland white terrier",
+        "204": "Lhasa, Lhasa apso",
+        "205": "flat-coated retriever",
+        "206": "curly-coated retriever",
+        "207": "golden retriever",
+        "208": "Labrador retriever",
+        "209": "Chesapeake Bay retriever",
+        "210": "German short-haired pointer",
+        "211": "vizsla, Hungarian pointer",
+        "212": "English setter",
+        "213": "Irish setter, red setter",
+        "214": "Gordon setter",
+        "215": "Brittany spaniel",
+        "216": "clumber, clumber spaniel",
+        "217": "English springer, English springer spaniel",
+        "218": "Welsh springer spaniel",
+        "219": "cocker spaniel, English cocker spaniel, cocker",
+        "220": "Sussex spaniel",
+        "221": "Irish water spaniel",
+        "222": "kuvasz",
+        "223": "schipperke",
+        "224": "groenendael",
+        "225": "malinois",
+        "226": "briard",
+        "227": "kelpie",
+        "228": "komondor",
+        "229": "Old English sheepdog, bobtail",
+        "230": "Shetland sheepdog, Shetland sheep dog, Shetland",
+        "231": "collie",
+        "232": "Border collie",
+        "233": "Bouvier des Flandres, Bouviers des Flandres",
+        "234": "Rottweiler",
+        "235": "German shepherd, German shepherd dog, German police dog, alsatian",
+        "236": "Doberman, Doberman pinscher",
+        "237": "miniature pinscher",
+        "238": "Greater Swiss Mountain dog",
+        "239": "Bernese mountain dog",
+        "240": "Appenzeller",
+        "241": "EntleBucher",
+        "242": "boxer",
+        "243": "bull mastiff",
+        "244": "Tibetan mastiff",
+        "245": "French bulldog",
+        "246": "Great Dane",
+        "247": "Saint Bernard, St Bernard",
+        "248": "Eskimo dog, husky",
+        "249": "malamute, malemute, Alaskan malamute",
+        "250": "Siberian husky",
+        "251": "dalmatian, coach dog, carriage dog",
+        "252": "affenpinscher, monkey pinscher, monkey dog",
+        "253": "basenji",
+        "254": "pug, pug-dog",
+        "255": "Leonberg",
+        "256": "Newfoundland, Newfoundland dog",
+        "257": "Great Pyrenees",
+        "258": "Samoyed, Samoyede",
+        "259": "Pomeranian",
+        "260": "chow, chow chow",
+        "261": "keeshond",
+        "262": "Brabancon griffon",
+        "263": "Pembroke, Pembroke Welsh corgi",
+        "264": "Cardigan, Cardigan Welsh corgi",
+        "265": "toy poodle",
+        "266": "miniature poodle",
+        "267": "standard poodle",
+        "268": "Mexican hairless",
+        "269": "timber wolf, grey wolf, gray wolf, Canis lupus",
+        "270": "white wolf, Arctic wolf, Canis lupus tundrarum",
+        "271": "red wolf, maned wolf, Canis rufus, Canis niger",
+        "272": "coyote, prairie wolf, brush wolf, Canis latrans",
+        "273": "dingo, warrigal, warragal, Canis dingo",
+        "274": "dhole, Cuon alpinus",
+        "275": "African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus",
+        "276": "hyena, hyaena",
+        "277": "red fox, Vulpes vulpes",
+        "278": "kit fox, Vulpes macrotis",
+        "279": "Arctic fox, white fox, Alopex lagopus",
+        "280": "grey fox, gray fox, Urocyon cinereoargenteus",
+        "281": "tabby, tabby cat",
+        "282": "tiger cat",
+        "283": "Persian cat",
+        "284": "Siamese cat, Siamese",
+        "285": "Egyptian cat",
+        "286": "cougar, puma, catamount, mountain lion, painter, panther, Felis concolor",
+        "287": "lynx, catamount",
+        "288": "leopard, Panthera pardus",
+        "289": "snow leopard, ounce, Panthera uncia",
+        "290": "jaguar, panther, Panthera onca, Felis onca",
+        "291": "lion, king of beasts, Panthera leo",
+        "292": "tiger, Panthera tigris",
+        "293": "cheetah, chetah, Acinonyx jubatus",
+        "294": "brown bear, bruin, Ursus arctos",
+        "295": "American black bear, black bear, Ursus americanus, Euarctos americanus",
+        "296": "ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus",
+        "297": "sloth bear, Melursus ursinus, Ursus ursinus",
+        "298": "mongoose",
+        "299": "meerkat, mierkat",
+        "300": "tiger beetle",
+        "301": "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle",
+        "302": "ground beetle, carabid beetle",
+        "303": "long-horned beetle, longicorn, longicorn beetle",
+        "304": "leaf beetle, chrysomelid",
+        "305": "dung beetle",
+        "306": "rhinoceros beetle",
+        "307": "weevil",
+        "308": "fly",
+        "309": "bee",
+        "310": "ant, emmet, pismire",
+        "311": "grasshopper, hopper",
+        "312": "cricket",
+        "313": "walking stick, walkingstick, stick insect",
+        "314": "cockroach, roach",
+        "315": "mantis, mantid",
+        "316": "cicada, cicala",
+        "317": "leafhopper",
+        "318": "lacewing, lacewing fly",
+        "319": "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk",
+        "320": "damselfly",
+        "321": "admiral",
+        "322": "ringlet, ringlet butterfly",
+        "323": "monarch, monarch butterfly, milkweed butterfly, Danaus plexippus",
+        "324": "cabbage butterfly",
+        "325": "sulphur butterfly, sulfur butterfly",
+        "326": "lycaenid, lycaenid butterfly",
+        "327": "starfish, sea star",
+        "328": "sea urchin",
+        "329": "sea cucumber, holothurian",
+        "330": "wood rabbit, cottontail, cottontail rabbit",
+        "331": "hare",
+        "332": "Angora, Angora rabbit",
+        "333": "hamster",
+        "334": "porcupine, hedgehog",
+        "335": "fox squirrel, eastern fox squirrel, Sciurus niger",
+        "336": "marmot",
+        "337": "beaver",
+        "338": "guinea pig, Cavia cobaya",
+        "339": "sorrel",
+        "340": "zebra",
+        "341": "hog, pig, grunter, squealer, Sus scrofa",
+        "342": "wild boar, boar, Sus scrofa",
+        "343": "warthog",
+        "344": "hippopotamus, hippo, river horse, Hippopotamus amphibius",
+        "345": "ox",
+        "346": "water buffalo, water ox, Asiatic buffalo, Bubalus bubalis",
+        "347": "bison",
+        "348": "ram, tup",
+        "349": "bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis",
+        "350": "ibex, Capra ibex",
+        "351": "hartebeest",
+        "352": "impala, Aepyceros melampus",
+        "353": "gazelle",
+        "354": "Arabian camel, dromedary, Camelus dromedarius",
+        "355": "llama",
+        "356": "weasel",
+        "357": "mink",
+        "358": "polecat, fitch, foulmart, foumart, Mustela putorius",
+        "359": "black-footed ferret, ferret, Mustela nigripes",
+        "360": "otter",
+        "361": "skunk, polecat, wood pussy",
+        "362": "badger",
+        "363": "armadillo",
+        "364": "three-toed sloth, ai, Bradypus tridactylus",
+        "365": "orangutan, orang, orangutang, Pongo pygmaeus",
+        "366": "gorilla, Gorilla gorilla",
+        "367": "chimpanzee, chimp, Pan troglodytes",
+        "368": "gibbon, Hylobates lar",
+        "369": "siamang, Hylobates syndactylus, Symphalangus syndactylus",
+        "370": "guenon, guenon monkey",
+        "371": "patas, hussar monkey, Erythrocebus patas",
+        "372": "baboon",
+        "373": "macaque",
+        "374": "langur",
+        "375": "colobus, colobus monkey",
+        "376": "proboscis monkey, Nasalis larvatus",
+        "377": "marmoset",
+        "378": "capuchin, ringtail, Cebus capucinus",
+        "379": "howler monkey, howler",
+        "380": "titi, titi monkey",
+        "381": "spider monkey, Ateles geoffroyi",
+        "382": "squirrel monkey, Saimiri sciureus",
+        "383": "Madagascar cat, ring-tailed lemur, Lemur catta",
+        "384": "indri, indris, Indri indri, Indri brevicaudatus",
+        "385": "Indian elephant, Elephas maximus",
+        "386": "African elephant, Loxodonta africana",
+        "387": "lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens",
+        "388": "giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca",
+        "389": "barracouta, snoek",
+        "390": "eel",
+        "391": "coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch",
+        "392": "rock beauty, Holocanthus tricolor",
+        "393": "anemone fish",
+        "394": "sturgeon",
+        "395": "gar, garfish, garpike, billfish, Lepisosteus osseus",
+        "396": "lionfish",
+        "397": "puffer, pufferfish, blowfish, globefish",
+        "398": "abacus",
+        "399": "abaya",
+        "400": "academic gown, academic robe, judge's robe",
+        "401": "accordion, piano accordion, squeeze box",
+        "402": "acoustic guitar",
+        "403": "aircraft carrier, carrier, flattop, attack aircraft carrier",
+        "404": "airliner",
+        "405": "airship, dirigible",
+        "406": "altar",
+        "407": "ambulance",
+        "408": "amphibian, amphibious vehicle",
+        "409": "analog clock",
+        "410": "apiary, bee house",
+        "411": "apron",
+        "412": "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin",
+        "413": "assault rifle, assault gun",
+        "414": "backpack, back pack, knapsack, packsack, rucksack, haversack",
+        "415": "bakery, bakeshop, bakehouse",
+        "416": "balance beam, beam",
+        "417": "balloon",
+        "418": "ballpoint, ballpoint pen, ballpen, Biro",
+        "419": "Band Aid",
+        "420": "banjo",
+        "421": "bannister, banister, balustrade, balusters, handrail",
+        "422": "barbell",
+        "423": "barber chair",
+        "424": "barbershop",
+        "425": "barn",
+        "426": "barometer",
+        "427": "barrel, cask",
+        "428": "barrow, garden cart, lawn cart, wheelbarrow",
+        "429": "baseball",
+        "430": "basketball",
+        "431": "bassinet",
+        "432": "bassoon",
+        "433": "bathing cap, swimming cap",
+        "434": "bath towel",
+        "435": "bathtub, bathing tub, bath, tub",
+        "436": "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon",
+        "437": "beacon, lighthouse, beacon light, pharos",
+        "438": "beaker",
+        "439": "bearskin, busby, shako",
+        "440": "beer bottle",
+        "441": "beer glass",
+        "442": "bell cote, bell cot",
+        "443": "bib",
+        "444": "bicycle-built-for-two, tandem bicycle, tandem",
+        "445": "bikini, two-piece",
+        "446": "binder, ring-binder",
+        "447": "binoculars, field glasses, opera glasses",
+        "448": "birdhouse",
+        "449": "boathouse",
+        "450": "bobsled, bobsleigh, bob",
+        "451": "bolo tie, bolo, bola tie, bola",
+        "452": "bonnet, poke bonnet",
+        "453": "bookcase",
+        "454": "bookshop, bookstore, bookstall",
+        "455": "bottlecap",
+        "456": "bow",
+        "457": "bow tie, bow-tie, bowtie",
+        "458": "brass, memorial tablet, plaque",
+        "459": "brassiere, bra, bandeau",
+        "460": "breakwater, groin, groyne, mole, bulwark, seawall, jetty",
+        "461": "breastplate, aegis, egis",
+        "462": "broom",
+        "463": "bucket, pail",
+        "464": "buckle",
+        "465": "bulletproof vest",
+        "466": "bullet train, bullet",
+        "467": "butcher shop, meat market",
+        "468": "cab, hack, taxi, taxicab",
+        "469": "caldron, cauldron",
+        "470": "candle, taper, wax light",
+        "471": "cannon",
+        "472": "canoe",
+        "473": "can opener, tin opener",
+        "474": "cardigan",
+        "475": "car mirror",
+        "476": "carousel, carrousel, merry-go-round, roundabout, whirligig",
+        "477": "carpenter's kit, tool kit",
+        "478": "carton",
+        "479": "car wheel",
+        "480": "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM",
+        "481": "cassette",
+        "482": "cassette player",
+        "483": "castle",
+        "484": "catamaran",
+        "485": "CD player",
+        "486": "cello, violoncello",
+        "487": "cellular telephone, cellular phone, cellphone, cell, mobile phone",
+        "488": "chain",
+        "489": "chainlink fence",
+        "490": "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour",
+        "491": "chain saw, chainsaw",
+        "492": "chest",
+        "493": "chiffonier, commode",
+        "494": "chime, bell, gong",
+        "495": "china cabinet, china closet",
+        "496": "Christmas stocking",
+        "497": "church, church building",
+        "498": "cinema, movie theater, movie theatre, movie house, picture palace",
+        "499": "cleaver, meat cleaver, chopper",
+        "500": "cliff dwelling",
+        "501": "cloak",
+        "502": "clog, geta, patten, sabot",
+        "503": "cocktail shaker",
+        "504": "coffee mug",
+        "505": "coffeepot",
+        "506": "coil, spiral, volute, whorl, helix",
+        "507": "combination lock",
+        "508": "computer keyboard, keypad",
+        "509": "confectionery, confectionary, candy store",
+        "510": "container ship, containership, container vessel",
+        "511": "convertible",
+        "512": "corkscrew, bottle screw",
+        "513": "cornet, horn, trumpet, trump",
+        "514": "cowboy boot",
+        "515": "cowboy hat, ten-gallon hat",
+        "516": "cradle",
+        "517": "crane",
+        "518": "crash helmet",
+        "519": "crate",
+        "520": "crib, cot",
+        "521": "Crock Pot",
+        "522": "croquet ball",
+        "523": "crutch",
+        "524": "cuirass",
+        "525": "dam, dike, dyke",
+        "526": "desk",
+        "527": "desktop computer",
+        "528": "dial telephone, dial phone",
+        "529": "diaper, nappy, napkin",
+        "530": "digital clock",
+        "531": "digital watch",
+        "532": "dining table, board",
+        "533": "dishrag, dishcloth",
+        "534": "dishwasher, dish washer, dishwashing machine",
+        "535": "disk brake, disc brake",
+        "536": "dock, dockage, docking facility",
+        "537": "dogsled, dog sled, dog sleigh",
+        "538": "dome",
+        "539": "doormat, welcome mat",
+        "540": "drilling platform, offshore rig",
+        "541": "drum, membranophone, tympan",
+        "542": "drumstick",
+        "543": "dumbbell",
+        "544": "Dutch oven",
+        "545": "electric fan, blower",
+        "546": "electric guitar",
+        "547": "electric locomotive",
+        "548": "entertainment center",
+        "549": "envelope",
+        "550": "espresso maker",
+        "551": "face powder",
+        "552": "feather boa, boa",
+        "553": "file, file cabinet, filing cabinet",
+        "554": "fireboat",
+        "555": "fire engine, fire truck",
+        "556": "fire screen, fireguard",
+        "557": "flagpole, flagstaff",
+        "558": "flute, transverse flute",
+        "559": "folding chair",
+        "560": "football helmet",
+        "561": "forklift",
+        "562": "fountain",
+        "563": "fountain pen",
+        "564": "four-poster",
+        "565": "freight car",
+        "566": "French horn, horn",
+        "567": "frying pan, frypan, skillet",
+        "568": "fur coat",
+        "569": "garbage truck, dustcart",
+        "570": "gasmask, respirator, gas helmet",
+        "571": "gas pump, gasoline pump, petrol pump, island dispenser",
+        "572": "goblet",
+        "573": "go-kart",
+        "574": "golf ball",
+        "575": "golfcart, golf cart",
+        "576": "gondola",
+        "577": "gong, tam-tam",
+        "578": "gown",
+        "579": "grand piano, grand",
+        "580": "greenhouse, nursery, glasshouse",
+        "581": "grille, radiator grille",
+        "582": "grocery store, grocery, food market, market",
+        "583": "guillotine",
+        "584": "hair slide",
+        "585": "hair spray",
+        "586": "half track",
+        "587": "hammer",
+        "588": "hamper",
+        "589": "hand blower, blow dryer, blow drier, hair dryer, hair drier",
+        "590": "hand-held computer, hand-held microcomputer",
+        "591": "handkerchief, hankie, hanky, hankey",
+        "592": "hard disc, hard disk, fixed disk",
+        "593": "harmonica, mouth organ, harp, mouth harp",
+        "594": "harp",
+        "595": "harvester, reaper",
+        "596": "hatchet",
+        "597": "holster",
+        "598": "home theater, home theatre",
+        "599": "honeycomb",
+        "600": "hook, claw",
+        "601": "hoopskirt, crinoline",
+        "602": "horizontal bar, high bar",
+        "603": "horse cart, horse-cart",
+        "604": "hourglass",
+        "605": "iPod",
+        "606": "iron, smoothing iron",
+        "607": "jack-o'-lantern",
+        "608": "jean, blue jean, denim",
+        "609": "jeep, landrover",
+        "610": "jersey, T-shirt, tee shirt",
+        "611": "jigsaw puzzle",
+        "612": "jinrikisha, ricksha, rickshaw",
+        "613": "joystick",
+        "614": "kimono",
+        "615": "knee pad",
+        "616": "knot",
+        "617": "lab coat, laboratory coat",
+        "618": "ladle",
+        "619": "lampshade, lamp shade",
+        "620": "laptop, laptop computer",
+        "621": "lawn mower, mower",
+        "622": "lens cap, lens cover",
+        "623": "letter opener, paper knife, paperknife",
+        "624": "library",
+        "625": "lifeboat",
+        "626": "lighter, light, igniter, ignitor",
+        "627": "limousine, limo",
+        "628": "liner, ocean liner",
+        "629": "lipstick, lip rouge",
+        "630": "Loafer",
+        "631": "lotion",
+        "632": "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system",
+        "633": "loupe, jeweler's loupe",
+        "634": "lumbermill, sawmill",
+        "635": "magnetic compass",
+        "636": "mailbag, postbag",
+        "637": "mailbox, letter box",
+        "638": "maillot",
+        "639": "maillot, tank suit",
+        "640": "manhole cover",
+        "641": "maraca",
+        "642": "marimba, xylophone",
+        "643": "mask",
+        "644": "matchstick",
+        "645": "maypole",
+        "646": "maze, labyrinth",
+        "647": "measuring cup",
+        "648": "medicine chest, medicine cabinet",
+        "649": "megalith, megalithic structure",
+        "650": "microphone, mike",
+        "651": "microwave, microwave oven",
+        "652": "military uniform",
+        "653": "milk can",
+        "654": "minibus",
+        "655": "miniskirt, mini",
+        "656": "minivan",
+        "657": "missile",
+        "658": "mitten",
+        "659": "mixing bowl",
+        "660": "mobile home, manufactured home",
+        "661": "Model T",
+        "662": "modem",
+        "663": "monastery",
+        "664": "monitor",
+        "665": "moped",
+        "666": "mortar",
+        "667": "mortarboard",
+        "668": "mosque",
+        "669": "mosquito net",
+        "670": "motor scooter, scooter",
+        "671": "mountain bike, all-terrain bike, off-roader",
+        "672": "mountain tent",
+        "673": "mouse, computer mouse",
+        "674": "mousetrap",
+        "675": "moving van",
+        "676": "muzzle",
+        "677": "nail",
+        "678": "neck brace",
+        "679": "necklace",
+        "680": "nipple",
+        "681": "notebook, notebook computer",
+        "682": "obelisk",
+        "683": "oboe, hautboy, hautbois",
+        "684": "ocarina, sweet potato",
+        "685": "odometer, hodometer, mileometer, milometer",
+        "686": "oil filter",
+        "687": "organ, pipe organ",
+        "688": "oscilloscope, scope, cathode-ray oscilloscope, CRO",
+        "689": "overskirt",
+        "690": "oxcart",
+        "691": "oxygen mask",
+        "692": "packet",
+        "693": "paddle, boat paddle",
+        "694": "paddlewheel, paddle wheel",
+        "695": "padlock",
+        "696": "paintbrush",
+        "697": "pajama, pyjama, pj's, jammies",
+        "698": "palace",
+        "699": "panpipe, pandean pipe, syrinx",
+        "700": "paper towel",
+        "701": "parachute, chute",
+        "702": "parallel bars, bars",
+        "703": "park bench",
+        "704": "parking meter",
+        "705": "passenger car, coach, carriage",
+        "706": "patio, terrace",
+        "707": "pay-phone, pay-station",
+        "708": "pedestal, plinth, footstall",
+        "709": "pencil box, pencil case",
+        "710": "pencil sharpener",
+        "711": "perfume, essence",
+        "712": "Petri dish",
+        "713": "photocopier",
+        "714": "pick, plectrum, plectron",
+        "715": "pickelhaube",
+        "716": "picket fence, paling",
+        "717": "pickup, pickup truck",
+        "718": "pier",
+        "719": "piggy bank, penny bank",
+        "720": "pill bottle",
+        "721": "pillow",
+        "722": "ping-pong ball",
+        "723": "pinwheel",
+        "724": "pirate, pirate ship",
+        "725": "pitcher, ewer",
+        "726": "plane, carpenter's plane, woodworking plane",
+        "727": "planetarium",
+        "728": "plastic bag",
+        "729": "plate rack",
+        "730": "plow, plough",
+        "731": "plunger, plumber's helper",
+        "732": "Polaroid camera, Polaroid Land camera",
+        "733": "pole",
+        "734": "police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria",
+        "735": "poncho",
+        "736": "pool table, billiard table, snooker table",
+        "737": "pop bottle, soda bottle",
+        "738": "pot, flowerpot",
+        "739": "potter's wheel",
+        "740": "power drill",
+        "741": "prayer rug, prayer mat",
+        "742": "printer",
+        "743": "prison, prison house",
+        "744": "projectile, missile",
+        "745": "projector",
+        "746": "puck, hockey puck",
+        "747": "punching bag, punch bag, punching ball, punchball",
+        "748": "purse",
+        "749": "quill, quill pen",
+        "750": "quilt, comforter, comfort, puff",
+        "751": "racer, race car, racing car",
+        "752": "racket, racquet",
+        "753": "radiator",
+        "754": "radio, wireless",
+        "755": "radio telescope, radio reflector",
+        "756": "rain barrel",
+        "757": "recreational vehicle, RV, R.V.",
+        "758": "reel",
+        "759": "reflex camera",
+        "760": "refrigerator, icebox",
+        "761": "remote control, remote",
+        "762": "restaurant, eating house, eating place, eatery",
+        "763": "revolver, six-gun, six-shooter",
+        "764": "rifle",
+        "765": "rocking chair, rocker",
+        "766": "rotisserie",
+        "767": "rubber eraser, rubber, pencil eraser",
+        "768": "rugby ball",
+        "769": "rule, ruler",
+        "770": "running shoe",
+        "771": "safe",
+        "772": "safety pin",
+        "773": "saltshaker, salt shaker",
+        "774": "sandal",
+        "775": "sarong",
+        "776": "sax, saxophone",
+        "777": "scabbard",
+        "778": "scale, weighing machine",
+        "779": "school bus",
+        "780": "schooner",
+        "781": "scoreboard",
+        "782": "screen, CRT screen",
+        "783": "screw",
+        "784": "screwdriver",
+        "785": "seat belt, seatbelt",
+        "786": "sewing machine",
+        "787": "shield, buckler",
+        "788": "shoe shop, shoe-shop, shoe store",
+        "789": "shoji",
+        "790": "shopping basket",
+        "791": "shopping cart",
+        "792": "shovel",
+        "793": "shower cap",
+        "794": "shower curtain",
+        "795": "ski",
+        "796": "ski mask",
+        "797": "sleeping bag",
+        "798": "slide rule, slipstick",
+        "799": "sliding door",
+        "800": "slot, one-armed bandit",
+        "801": "snorkel",
+        "802": "snowmobile",
+        "803": "snowplow, snowplough",
+        "804": "soap dispenser",
+        "805": "soccer ball",
+        "806": "sock",
+        "807": "solar dish, solar collector, solar furnace",
+        "808": "sombrero",
+        "809": "soup bowl",
+        "810": "space bar",
+        "811": "space heater",
+        "812": "space shuttle",
+        "813": "spatula",
+        "814": "speedboat",
+        "815": "spider web, spider's web",
+        "816": "spindle",
+        "817": "sports car, sport car",
+        "818": "spotlight, spot",
+        "819": "stage",
+        "820": "steam locomotive",
+        "821": "steel arch bridge",
+        "822": "steel drum",
+        "823": "stethoscope",
+        "824": "stole",
+        "825": "stone wall",
+        "826": "stopwatch, stop watch",
+        "827": "stove",
+        "828": "strainer",
+        "829": "streetcar, tram, tramcar, trolley, trolley car",
+        "830": "stretcher",
+        "831": "studio couch, day bed",
+        "832": "stupa, tope",
+        "833": "submarine, pigboat, sub, U-boat",
+        "834": "suit, suit of clothes",
+        "835": "sundial",
+        "836": "sunglass",
+        "837": "sunglasses, dark glasses, shades",
+        "838": "sunscreen, sunblock, sun blocker",
+        "839": "suspension bridge",
+        "840": "swab, swob, mop",
+        "841": "sweatshirt",
+        "842": "swimming trunks, bathing trunks",
+        "843": "swing",
+        "844": "switch, electric switch, electrical switch",
+        "845": "syringe",
+        "846": "table lamp",
+        "847": "tank, army tank, armored combat vehicle, armoured combat vehicle",
+        "848": "tape player",
+        "849": "teapot",
+        "850": "teddy, teddy bear",
+        "851": "television, television system",
+        "852": "tennis ball",
+        "853": "thatch, thatched roof",
+        "854": "theater curtain, theatre curtain",
+        "855": "thimble",
+        "856": "thresher, thrasher, threshing machine",
+        "857": "throne",
+        "858": "tile roof",
+        "859": "toaster",
+        "860": "tobacco shop, tobacconist shop, tobacconist",
+        "861": "toilet seat",
+        "862": "torch",
+        "863": "totem pole",
+        "864": "tow truck, tow car, wrecker",
+        "865": "toyshop",
+        "866": "tractor",
+        "867": "trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi",
+        "868": "tray",
+        "869": "trench coat",
+        "870": "tricycle, trike, velocipede",
+        "871": "trimaran",
+        "872": "tripod",
+        "873": "triumphal arch",
+        "874": "trolleybus, trolley coach, trackless trolley",
+        "875": "trombone",
+        "876": "tub, vat",
+        "877": "turnstile",
+        "878": "typewriter keyboard",
+        "879": "umbrella",
+        "880": "unicycle, monocycle",
+        "881": "upright, upright piano",
+        "882": "vacuum, vacuum cleaner",
+        "883": "vase",
+        "884": "vault",
+        "885": "velvet",
+        "886": "vending machine",
+        "887": "vestment",
+        "888": "viaduct",
+        "889": "violin, fiddle",
+        "890": "volleyball",
+        "891": "waffle iron",
+        "892": "wall clock",
+        "893": "wallet, billfold, notecase, pocketbook",
+        "894": "wardrobe, closet, press",
+        "895": "warplane, military plane",
+        "896": "washbasin, handbasin, washbowl, lavabo, wash-hand basin",
+        "897": "washer, automatic washer, washing machine",
+        "898": "water bottle",
+        "899": "water jug",
+        "900": "water tower",
+        "901": "whiskey jug",
+        "902": "whistle",
+        "903": "wig",
+        "904": "window screen",
+        "905": "window shade",
+        "906": "Windsor tie",
+        "907": "wine bottle",
+        "908": "wing",
+        "909": "wok",
+        "910": "wooden spoon",
+        "911": "wool, woolen, woollen",
+        "912": "worm fence, snake fence, snake-rail fence, Virginia fence",
+        "913": "wreck",
+        "914": "yawl",
+        "915": "yurt",
+        "916": "web site, website, internet site, site",
+        "917": "comic book",
+        "918": "crossword puzzle, crossword",
+        "919": "street sign",
+        "920": "traffic light, traffic signal, stoplight",
+        "921": "book jacket, dust cover, dust jacket, dust wrapper",
+        "922": "menu",
+        "923": "plate",
+        "924": "guacamole",
+        "925": "consomme",
+        "926": "hot pot, hotpot",
+        "927": "trifle",
+        "928": "ice cream, icecream",
+        "929": "ice lolly, lolly, lollipop, popsicle",
+        "930": "French loaf",
+        "931": "bagel, beigel",
+        "932": "pretzel",
+        "933": "cheeseburger",
+        "934": "hotdog, hot dog, red hot",
+        "935": "mashed potato",
+        "936": "head cabbage",
+        "937": "broccoli",
+        "938": "cauliflower",
+        "939": "zucchini, courgette",
+        "940": "spaghetti squash",
+        "941": "acorn squash",
+        "942": "butternut squash",
+        "943": "cucumber, cuke",
+        "944": "artichoke, globe artichoke",
+        "945": "bell pepper",
+        "946": "cardoon",
+        "947": "mushroom",
+        "948": "Granny Smith",
+        "949": "strawberry",
+        "950": "orange",
+        "951": "lemon",
+        "952": "fig",
+        "953": "pineapple, ananas",
+        "954": "banana",
+        "955": "jackfruit, jak, jack",
+        "956": "custard apple",
+        "957": "pomegranate",
+        "958": "hay",
+        "959": "carbonara",
+        "960": "chocolate sauce, chocolate syrup",
+        "961": "dough",
+        "962": "meat loaf, meatloaf",
+        "963": "pizza, pizza pie",
+        "964": "potpie",
+        "965": "burrito",
+        "966": "red wine",
+        "967": "espresso",
+        "968": "cup",
+        "969": "eggnog",
+        "970": "alp",
+        "971": "bubble",
+        "972": "cliff, drop, drop-off",
+        "973": "coral reef",
+        "974": "geyser",
+        "975": "lakeside, lakeshore",
+        "976": "promontory, headland, head, foreland",
+        "977": "sandbar, sand bar",
+        "978": "seashore, coast, seacoast, sea-coast",
+        "979": "valley, vale",
+        "980": "volcano",
+        "981": "ballplayer, baseball player",
+        "982": "groom, bridegroom",
+        "983": "scuba diver",
+        "984": "rapeseed",
+        "985": "daisy",
+        "986": "yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum",
+        "987": "corn",
+        "988": "acorn",
+        "989": "hip, rose hip, rosehip",
+        "990": "buckeye, horse chestnut, conker",
+        "991": "coral fungus",
+        "992": "agaric",
+        "993": "gyromitra",
+        "994": "stinkhorn, carrion fungus",
+        "995": "earthstar",
+        "996": "hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa",
+        "997": "bolete",
+        "998": "ear, spike, capitulum",
+        "999": "toilet tissue, toilet paper, bathroom tissue"
+      },
+      "image_size": 384,
+      "initializer_range": 0.02,
+      "is_decoder": false,
+      "is_encoder_decoder": false,
+      "label2id": {
+        "Afghan hound, Afghan": 160,
+        "African chameleon, Chamaeleo chamaeleon": 47,
+        "African crocodile, Nile crocodile, Crocodylus niloticus": 49,
+        "African elephant, Loxodonta africana": 386,
+        "African grey, African gray, Psittacus erithacus": 87,
+        "African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus": 275,
+        "Airedale, Airedale terrier": 191,
+        "American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier": 180,
+        "American alligator, Alligator mississipiensis": 50,
+        "American black bear, black bear, Ursus americanus, Euarctos americanus": 295,
+        "American chameleon, anole, Anolis carolinensis": 40,
+        "American coot, marsh hen, mud hen, water hen, Fulica americana": 137,
+        "American egret, great white heron, Egretta albus": 132,
+        "American lobster, Northern lobster, Maine lobster, Homarus americanus": 122,
+        "Angora, Angora rabbit": 332,
+        "Appenzeller": 240,
+        "Arabian camel, dromedary, Camelus dromedarius": 354,
+        "Arctic fox, white fox, Alopex lagopus": 279,
+        "Australian terrier": 193,
+        "Band Aid": 419,
+        "Bedlington terrier": 181,
+        "Bernese mountain dog": 239,
+        "Blenheim spaniel": 156,
+        "Border collie": 232,
+        "Border terrier": 182,
+        "Boston bull, Boston terrier": 195,
+        "Bouvier des Flandres, Bouviers des Flandres": 233,
+        "Brabancon griffon": 262,
+        "Brittany spaniel": 215,
+        "CD player": 485,
+        "Cardigan, Cardigan Welsh corgi": 264,
+        "Chesapeake Bay retriever": 209,
+        "Chihuahua": 151,
+        "Christmas stocking": 496,
+        "Crock Pot": 521,
+        "Dandie Dinmont, Dandie Dinmont terrier": 194,
+        "Doberman, Doberman pinscher": 236,
+        "Dungeness crab, Cancer magister": 118,
+        "Dutch oven": 544,
+        "Egyptian cat": 285,
+        "English foxhound": 167,
+        "English setter": 212,
+        "English springer, English springer spaniel": 217,
+        "EntleBucher": 241,
+        "Eskimo dog, husky": 248,
+        "European fire salamander, Salamandra salamandra": 25,
+        "European gallinule, Porphyrio porphyrio": 136,
+        "French bulldog": 245,
+        "French horn, horn": 566,
+        "French loaf": 930,
+        "German shepherd, German shepherd dog, German police dog, alsatian": 235,
+        "German short-haired pointer": 210,
+        "Gila monster, Heloderma suspectum": 45,
+        "Gordon setter": 214,
+        "Granny Smith": 948,
+        "Great Dane": 246,
+        "Great Pyrenees": 257,
+        "Greater Swiss Mountain dog": 238,
+        "Ibizan hound, Ibizan Podenco": 173,
+        "Indian cobra, Naja naja": 63,
+        "Indian elephant, Elephas maximus": 385,
+        "Irish setter, red setter": 213,
+        "Irish terrier": 184,
+        "Irish water spaniel": 221,
+        "Irish wolfhound": 170,
+        "Italian greyhound": 171,
+        "Japanese spaniel": 152,
+        "Kerry blue terrier": 183,
+        "Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis": 48,
+        "Labrador retriever": 208,
+        "Lakeland terrier": 189,
+        "Leonberg": 255,
+        "Lhasa, Lhasa apso": 204,
+        "Loafer": 630,
+        "Madagascar cat, ring-tailed lemur, Lemur catta": 383,
+        "Maltese dog, Maltese terrier, Maltese": 153,
+        "Mexican hairless": 268,
+        "Model T": 661,
+        "Newfoundland, Newfoundland dog": 256,
+        "Norfolk terrier": 185,
+        "Norwegian elkhound, elkhound": 174,
+        "Norwich terrier": 186,
+        "Old English sheepdog, bobtail": 229,
+        "Pekinese, Pekingese, Peke": 154,
+        "Pembroke, Pembroke Welsh corgi": 263,
+        "Persian cat": 283,
+        "Petri dish": 712,
+        "Polaroid camera, Polaroid Land camera": 732,
+        "Pomeranian": 259,
+        "Rhodesian ridgeback": 159,
+        "Rottweiler": 234,
+        "Saint Bernard, St Bernard": 247,
+        "Saluki, gazelle hound": 176,
+        "Samoyed, Samoyede": 258,
+        "Scotch terrier, Scottish terrier, Scottie": 199,
+        "Scottish deerhound, deerhound": 177,
+        "Sealyham terrier, Sealyham": 190,
+        "Shetland sheepdog, Shetland sheep dog, Shetland": 230,
+        "Shih-Tzu": 155,
+        "Siamese cat, Siamese": 284,
+        "Siberian husky": 250,
+        "Staffordshire bullterrier, Staffordshire bull terrier": 179,
+        "Sussex spaniel": 220,
+        "Tibetan mastiff": 244,
+        "Tibetan terrier, chrysanthemum dog": 200,
+        "Walker hound, Walker foxhound": 166,
+        "Weimaraner": 178,
+        "Welsh springer spaniel": 218,
+        "West Highland white terrier": 203,
+        "Windsor tie": 906,
+        "Yorkshire terrier": 187,
+        "abacus": 398,
+        "abaya": 399,
+        "academic gown, academic robe, judge's robe": 400,
+        "accordion, piano accordion, squeeze box": 401,
+        "acorn": 988,
+        "acorn squash": 941,
+        "acoustic guitar": 402,
+        "admiral": 321,
+        "affenpinscher, monkey pinscher, monkey dog": 252,
+        "agama": 42,
+        "agaric": 992,
+        "aircraft carrier, carrier, flattop, attack aircraft carrier": 403,
+        "airliner": 404,
+        "airship, dirigible": 405,
+        "albatross, mollymawk": 146,
+        "alligator lizard": 44,
+        "alp": 970,
+        "altar": 406,
+        "ambulance": 407,
+        "amphibian, amphibious vehicle": 408,
+        "analog clock": 409,
+        "anemone fish": 393,
+        "ant, emmet, pismire": 310,
+        "apiary, bee house": 410,
+        "apron": 411,
+        "armadillo": 363,
+        "artichoke, globe artichoke": 944,
+        "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin": 412,
+        "assault rifle, assault gun": 413,
+        "axolotl, mud puppy, Ambystoma mexicanum": 29,
+        "baboon": 372,
+        "backpack, back pack, knapsack, packsack, rucksack, haversack": 414,
+        "badger": 362,
+        "bagel, beigel": 931,
+        "bakery, bakeshop, bakehouse": 415,
+        "balance beam, beam": 416,
+        "bald eagle, American eagle, Haliaeetus leucocephalus": 22,
+        "balloon": 417,
+        "ballplayer, baseball player": 981,
+        "ballpoint, ballpoint pen, ballpen, Biro": 418,
+        "banana": 954,
+        "banded gecko": 38,
+        "banjo": 420,
+        "bannister, banister, balustrade, balusters, handrail": 421,
+        "barbell": 422,
+        "barber chair": 423,
+        "barbershop": 424,
+        "barn": 425,
+        "barn spider, Araneus cavaticus": 73,
+        "barometer": 426,
+        "barracouta, snoek": 389,
+        "barrel, cask": 427,
+        "barrow, garden cart, lawn cart, wheelbarrow": 428,
+        "baseball": 429,
+        "basenji": 253,
+        "basketball": 430,
+        "basset, basset hound": 161,
+        "bassinet": 431,
+        "bassoon": 432,
+        "bath towel": 434,
+        "bathing cap, swimming cap": 433,
+        "bathtub, bathing tub, bath, tub": 435,
+        "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon": 436,
+        "beacon, lighthouse, beacon light, pharos": 437,
+        "beagle": 162,
+        "beaker": 438,
+        "bearskin, busby, shako": 439,
+        "beaver": 337,
+        "bee": 309,
+        "bee eater": 92,
+        "beer bottle": 440,
+        "beer glass": 441,
+        "bell cote, bell cot": 442,
+        "bell pepper": 945,
+        "bib": 443,
+        "bicycle-built-for-two, tandem bicycle, tandem": 444,
+        "bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis": 349,
+        "bikini, two-piece": 445,
+        "binder, ring-binder": 446,
+        "binoculars, field glasses, opera glasses": 447,
+        "birdhouse": 448,
+        "bison": 347,
+        "bittern": 133,
+        "black and gold garden spider, Argiope aurantia": 72,
+        "black grouse": 80,
+        "black stork, Ciconia nigra": 128,
+        "black swan, Cygnus atratus": 100,
+        "black widow, Latrodectus mactans": 75,
+        "black-and-tan coonhound": 165,
+        "black-footed ferret, ferret, Mustela nigripes": 359,
+        "bloodhound, sleuthhound": 163,
+        "bluetick": 164,
+        "boa constrictor, Constrictor constrictor": 61,
+        "boathouse": 449,
+        "bobsled, bobsleigh, bob": 450,
+        "bolete": 997,
+        "bolo tie, bolo, bola tie, bola": 451,
+        "bonnet, poke bonnet": 452,
+        "book jacket, dust cover, dust jacket, dust wrapper": 921,
+        "bookcase": 453,
+        "bookshop, bookstore, bookstall": 454,
+        "borzoi, Russian wolfhound": 169,
+        "bottlecap": 455,
+        "bow": 456,
+        "bow tie, bow-tie, bowtie": 457,
+        "box turtle, box tortoise": 37,
+        "boxer": 242,
+        "brain coral": 109,
+        "brambling, Fringilla montifringilla": 10,
+        "brass, memorial tablet, plaque": 458,
+        "brassiere, bra, bandeau": 459,
+        "breakwater, groin, groyne, mole, bulwark, seawall, jetty": 460,
+        "breastplate, aegis, egis": 461,
+        "briard": 226,
+        "broccoli": 937,
+        "broom": 462,
+        "brown bear, bruin, Ursus arctos": 294,
+        "bubble": 971,
+        "bucket, pail": 463,
+        "buckeye, horse chestnut, conker": 990,
+        "buckle": 464,
+        "bulbul": 16,
+        "bull mastiff": 243,
+        "bullet train, bullet": 466,
+        "bulletproof vest": 465,
+        "bullfrog, Rana catesbeiana": 30,
+        "burrito": 965,
+        "bustard": 138,
+        "butcher shop, meat market": 467,
+        "butternut squash": 942,
+        "cab, hack, taxi, taxicab": 468,
+        "cabbage butterfly": 324,
+        "cairn, cairn terrier": 192,
+        "caldron, cauldron": 469,
+        "can opener, tin opener": 473,
+        "candle, taper, wax light": 470,
+        "cannon": 471,
+        "canoe": 472,
+        "capuchin, ringtail, Cebus capucinus": 378,
+        "car mirror": 475,
+        "car wheel": 479,
+        "carbonara": 959,
+        "cardigan": 474,
+        "cardoon": 946,
+        "carousel, carrousel, merry-go-round, roundabout, whirligig": 476,
+        "carpenter's kit, tool kit": 477,
+        "carton": 478,
+        "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM": 480,
+        "cassette": 481,
+        "cassette player": 482,
+        "castle": 483,
+        "catamaran": 484,
+        "cauliflower": 938,
+        "cello, violoncello": 486,
+        "cellular telephone, cellular phone, cellphone, cell, mobile phone": 487,
+        "centipede": 79,
+        "chain": 488,
+        "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour": 490,
+        "chain saw, chainsaw": 491,
+        "chainlink fence": 489,
+        "chambered nautilus, pearly nautilus, nautilus": 117,
+        "cheeseburger": 933,
+        "cheetah, chetah, Acinonyx jubatus": 293,
+        "chest": 492,
+        "chickadee": 19,
+        "chiffonier, commode": 493,
+        "chime, bell, gong": 494,
+        "chimpanzee, chimp, Pan troglodytes": 367,
+        "china cabinet, china closet": 495,
+        "chiton, coat-of-mail shell, sea cradle, polyplacophore": 116,
+        "chocolate sauce, chocolate syrup": 960,
+        "chow, chow chow": 260,
+        "church, church building": 497,
+        "cicada, cicala": 316,
+        "cinema, movie theater, movie theatre, movie house, picture palace": 498,
+        "cleaver, meat cleaver, chopper": 499,
+        "cliff dwelling": 500,
+        "cliff, drop, drop-off": 972,
+        "cloak": 501,
+        "clog, geta, patten, sabot": 502,
+        "clumber, clumber spaniel": 216,
+        "cock": 7,
+        "cocker spaniel, English cocker spaniel, cocker": 219,
+        "cockroach, roach": 314,
+        "cocktail shaker": 503,
+        "coffee mug": 504,
+        "coffeepot": 505,
+        "coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch": 391,
+        "coil, spiral, volute, whorl, helix": 506,
+        "collie": 231,
+        "colobus, colobus monkey": 375,
+        "combination lock": 507,
+        "comic book": 917,
+        "common iguana, iguana, Iguana iguana": 39,
+        "common newt, Triturus vulgaris": 26,
+        "computer keyboard, keypad": 508,
+        "conch": 112,
+        "confectionery, confectionary, candy store": 509,
+        "consomme": 925,
+        "container ship, containership, container vessel": 510,
+        "convertible": 511,
+        "coral fungus": 991,
+        "coral reef": 973,
+        "corkscrew, bottle screw": 512,
+        "corn": 987,
+        "cornet, horn, trumpet, trump": 513,
+        "coucal": 91,
+        "cougar, puma, catamount, mountain lion, painter, panther, Felis concolor": 286,
+        "cowboy boot": 514,
+        "cowboy hat, ten-gallon hat": 515,
+        "coyote, prairie wolf, brush wolf, Canis latrans": 272,
+        "cradle": 516,
+        "crane": 517,
+        "crash helmet": 518,
+        "crate": 519,
+        "crayfish, crawfish, crawdad, crawdaddy": 124,
+        "crib, cot": 520,
+        "cricket": 312,
+        "croquet ball": 522,
+        "crossword puzzle, crossword": 918,
+        "crutch": 523,
+        "cucumber, cuke": 943,
+        "cuirass": 524,
+        "cup": 968,
+        "curly-coated retriever": 206,
+        "custard apple": 956,
+        "daisy": 985,
+        "dalmatian, coach dog, carriage dog": 251,
+        "dam, dike, dyke": 525,
+        "damselfly": 320,
+        "desk": 526,
+        "desktop computer": 527,
+        "dhole, Cuon alpinus": 274,
+        "dial telephone, dial phone": 528,
+        "diamondback, diamondback rattlesnake, Crotalus adamanteus": 67,
+        "diaper, nappy, napkin": 529,
+        "digital clock": 530,
+        "digital watch": 531,
+        "dingo, warrigal, warragal, Canis dingo": 273,
+        "dining table, board": 532,
+        "dishrag, dishcloth": 533,
+        "dishwasher, dish washer, dishwashing machine": 534,
+        "disk brake, disc brake": 535,
+        "dock, dockage, docking facility": 536,
+        "dogsled, dog sled, dog sleigh": 537,
+        "dome": 538,
+        "doormat, welcome mat": 539,
+        "dough": 961,
+        "dowitcher": 142,
+        "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk": 319,
+        "drake": 97,
+        "drilling platform, offshore rig": 540,
+        "drum, membranophone, tympan": 541,
+        "drumstick": 542,
+        "dugong, Dugong dugon": 149,
+        "dumbbell": 543,
+        "dung beetle": 305,
+        "ear, spike, capitulum": 998,
+        "earthstar": 995,
+        "echidna, spiny anteater, anteater": 102,
+        "eel": 390,
+        "eft": 27,
+        "eggnog": 969,
+        "electric fan, blower": 545,
+        "electric guitar": 546,
+        "electric locomotive": 547,
+        "electric ray, crampfish, numbfish, torpedo": 5,
+        "entertainment center": 548,
+        "envelope": 549,
+        "espresso": 967,
+        "espresso maker": 550,
+        "face powder": 551,
+        "feather boa, boa": 552,
+        "fiddler crab": 120,
+        "fig": 952,
+        "file, file cabinet, filing cabinet": 553,
+        "fire engine, fire truck": 555,
+        "fire screen, fireguard": 556,
+        "fireboat": 554,
+        "flagpole, flagstaff": 557,
+        "flamingo": 130,
+        "flat-coated retriever": 205,
+        "flatworm, platyhelminth": 110,
+        "flute, transverse flute": 558,
+        "fly": 308,
+        "folding chair": 559,
+        "football helmet": 560,
+        "forklift": 561,
+        "fountain": 562,
+        "fountain pen": 563,
+        "four-poster": 564,
+        "fox squirrel, eastern fox squirrel, Sciurus niger": 335,
+        "freight car": 565,
+        "frilled lizard, Chlamydosaurus kingi": 43,
+        "frying pan, frypan, skillet": 567,
+        "fur coat": 568,
+        "gar, garfish, garpike, billfish, Lepisosteus osseus": 395,
+        "garbage truck, dustcart": 569,
+        "garden spider, Aranea diademata": 74,
+        "garter snake, grass snake": 57,
+        "gas pump, gasoline pump, petrol pump, island dispenser": 571,
+        "gasmask, respirator, gas helmet": 570,
+        "gazelle": 353,
+        "geyser": 974,
+        "giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca": 388,
+        "giant schnauzer": 197,
+        "gibbon, Hylobates lar": 368,
+        "go-kart": 573,
+        "goblet": 572,
+        "golden retriever": 207,
+        "goldfinch, Carduelis carduelis": 11,
+        "goldfish, Carassius auratus": 1,
+        "golf ball": 574,
+        "golfcart, golf cart": 575,
+        "gondola": 576,
+        "gong, tam-tam": 577,
+        "goose": 99,
+        "gorilla, Gorilla gorilla": 366,
+        "gown": 578,
+        "grand piano, grand": 579,
+        "grasshopper, hopper": 311,
+        "great grey owl, great gray owl, Strix nebulosa": 24,
+        "great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias": 2,
+        "green lizard, Lacerta viridis": 46,
+        "green mamba": 64,
+        "green snake, grass snake": 55,
+        "greenhouse, nursery, glasshouse": 580,
+        "grey fox, gray fox, Urocyon cinereoargenteus": 280,
+        "grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus": 147,
+        "grille, radiator grille": 581,
+        "grocery store, grocery, food market, market": 582,
+        "groenendael": 224,
+        "groom, bridegroom": 982,
+        "ground beetle, carabid beetle": 302,
+        "guacamole": 924,
+        "guenon, guenon monkey": 370,
+        "guillotine": 583,
+        "guinea pig, Cavia cobaya": 338,
+        "gyromitra": 993,
+        "hair slide": 584,
+        "hair spray": 585,
+        "half track": 586,
+        "hammer": 587,
+        "hammerhead, hammerhead shark": 4,
+        "hamper": 588,
+        "hamster": 333,
+        "hand blower, blow dryer, blow drier, hair dryer, hair drier": 589,
+        "hand-held computer, hand-held microcomputer": 590,
+        "handkerchief, hankie, hanky, hankey": 591,
+        "hard disc, hard disk, fixed disk": 592,
+        "hare": 331,
+        "harmonica, mouth organ, harp, mouth harp": 593,
+        "harp": 594,
+        "hartebeest": 351,
+        "harvester, reaper": 595,
+        "harvestman, daddy longlegs, Phalangium opilio": 70,
+        "hatchet": 596,
+        "hay": 958,
+        "head cabbage": 936,
+        "hen": 8,
+        "hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa": 996,
+        "hermit crab": 125,
+        "hip, rose hip, rosehip": 989,
+        "hippopotamus, hippo, river horse, Hippopotamus amphibius": 344,
+        "hog, pig, grunter, squealer, Sus scrofa": 341,
+        "hognose snake, puff adder, sand viper": 54,
+        "holster": 597,
+        "home theater, home theatre": 598,
+        "honeycomb": 599,
+        "hook, claw": 600,
+        "hoopskirt, crinoline": 601,
+        "horizontal bar, high bar": 602,
+        "hornbill": 93,
+        "horned viper, cerastes, sand viper, horned asp, Cerastes cornutus": 66,
+        "horse cart, horse-cart": 603,
+        "hot pot, hotpot": 926,
+        "hotdog, hot dog, red hot": 934,
+        "hourglass": 604,
+        "house finch, linnet, Carpodacus mexicanus": 12,
+        "howler monkey, howler": 379,
+        "hummingbird": 94,
+        "hyena, hyaena": 276,
+        "iPod": 605,
+        "ibex, Capra ibex": 350,
+        "ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus": 296,
+        "ice cream, icecream": 928,
+        "ice lolly, lolly, lollipop, popsicle": 929,
+        "impala, Aepyceros melampus": 352,
+        "indigo bunting, indigo finch, indigo bird, Passerina cyanea": 14,
+        "indri, indris, Indri indri, Indri brevicaudatus": 384,
+        "iron, smoothing iron": 606,
+        "isopod": 126,
+        "jacamar": 95,
+        "jack-o'-lantern": 607,
+        "jackfruit, jak, jack": 955,
+        "jaguar, panther, Panthera onca, Felis onca": 290,
+        "jay": 17,
+        "jean, blue jean, denim": 608,
+        "jeep, landrover": 609,
+        "jellyfish": 107,
+        "jersey, T-shirt, tee shirt": 610,
+        "jigsaw puzzle": 611,
+        "jinrikisha, ricksha, rickshaw": 612,
+        "joystick": 613,
+        "junco, snowbird": 13,
+        "keeshond": 261,
+        "kelpie": 227,
+        "killer whale, killer, orca, grampus, sea wolf, Orcinus orca": 148,
+        "kimono": 614,
+        "king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica": 121,
+        "king penguin, Aptenodytes patagonica": 145,
+        "king snake, kingsnake": 56,
+        "kit fox, Vulpes macrotis": 278,
+        "kite": 21,
+        "knee pad": 615,
+        "knot": 616,
+        "koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus": 105,
+        "komondor": 228,
+        "kuvasz": 222,
+        "lab coat, laboratory coat": 617,
+        "lacewing, lacewing fly": 318,
+        "ladle": 618,
+        "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle": 301,
+        "lakeside, lakeshore": 975,
+        "lampshade, lamp shade": 619,
+        "langur": 374,
+        "laptop, laptop computer": 620,
+        "lawn mower, mower": 621,
+        "leaf beetle, chrysomelid": 304,
+        "leafhopper": 317,
+        "leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea": 34,
+        "lemon": 951,
+        "lens cap, lens cover": 622,
+        "leopard, Panthera pardus": 288,
+        "lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens": 387,
+        "letter opener, paper knife, paperknife": 623,
+        "library": 624,
+        "lifeboat": 625,
+        "lighter, light, igniter, ignitor": 626,
+        "limousine, limo": 627,
+        "limpkin, Aramus pictus": 135,
+        "liner, ocean liner": 628,
+        "lion, king of beasts, Panthera leo": 291,
+        "lionfish": 396,
+        "lipstick, lip rouge": 629,
+        "little blue heron, Egretta caerulea": 131,
+        "llama": 355,
+        "loggerhead, loggerhead turtle, Caretta caretta": 33,
+        "long-horned beetle, longicorn, longicorn beetle": 303,
+        "lorikeet": 90,
+        "lotion": 631,
+        "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system": 632,
+        "loupe, jeweler's loupe": 633,
+        "lumbermill, sawmill": 634,
+        "lycaenid, lycaenid butterfly": 326,
+        "lynx, catamount": 287,
+        "macaque": 373,
+        "macaw": 88,
+        "magnetic compass": 635,
+        "magpie": 18,
+        "mailbag, postbag": 636,
+        "mailbox, letter box": 637,
+        "maillot": 638,
+        "maillot, tank suit": 639,
+        "malamute, malemute, Alaskan malamute": 249,
+        "malinois": 225,
+        "manhole cover": 640,
+        "mantis, mantid": 315,
+        "maraca": 641,
+        "marimba, xylophone": 642,
+        "marmoset": 377,
+        "marmot": 336,
+        "mashed potato": 935,
+        "mask": 643,
+        "matchstick": 644,
+        "maypole": 645,
+        "maze, labyrinth": 646,
+        "measuring cup": 647,
+        "meat loaf, meatloaf": 962,
+        "medicine chest, medicine cabinet": 648,
+        "meerkat, mierkat": 299,
+        "megalith, megalithic structure": 649,
+        "menu": 922,
+        "microphone, mike": 650,
+        "microwave, microwave oven": 651,
+        "military uniform": 652,
+        "milk can": 653,
+        "miniature pinscher": 237,
+        "miniature poodle": 266,
+        "miniature schnauzer": 196,
+        "minibus": 654,
+        "miniskirt, mini": 655,
+        "minivan": 656,
+        "mink": 357,
+        "missile": 657,
+        "mitten": 658,
+        "mixing bowl": 659,
+        "mobile home, manufactured home": 660,
+        "modem": 662,
+        "monarch, monarch butterfly, milkweed butterfly, Danaus plexippus": 323,
+        "monastery": 663,
+        "mongoose": 298,
+        "monitor": 664,
+        "moped": 665,
+        "mortar": 666,
+        "mortarboard": 667,
+        "mosque": 668,
+        "mosquito net": 669,
+        "motor scooter, scooter": 670,
+        "mountain bike, all-terrain bike, off-roader": 671,
+        "mountain tent": 672,
+        "mouse, computer mouse": 673,
+        "mousetrap": 674,
+        "moving van": 675,
+        "mud turtle": 35,
+        "mushroom": 947,
+        "muzzle": 676,
+        "nail": 677,
+        "neck brace": 678,
+        "necklace": 679,
+        "nematode, nematode worm, roundworm": 111,
+        "night snake, Hypsiglena torquata": 60,
+        "nipple": 680,
+        "notebook, notebook computer": 681,
+        "obelisk": 682,
+        "oboe, hautboy, hautbois": 683,
+        "ocarina, sweet potato": 684,
+        "odometer, hodometer, mileometer, milometer": 685,
+        "oil filter": 686,
+        "orange": 950,
+        "orangutan, orang, orangutang, Pongo pygmaeus": 365,
+        "organ, pipe organ": 687,
+        "oscilloscope, scope, cathode-ray oscilloscope, CRO": 688,
+        "ostrich, Struthio camelus": 9,
+        "otter": 360,
+        "otterhound, otter hound": 175,
+        "overskirt": 689,
+        "ox": 345,
+        "oxcart": 690,
+        "oxygen mask": 691,
+        "oystercatcher, oyster catcher": 143,
+        "packet": 692,
+        "paddle, boat paddle": 693,
+        "paddlewheel, paddle wheel": 694,
+        "padlock": 695,
+        "paintbrush": 696,
+        "pajama, pyjama, pj's, jammies": 697,
+        "palace": 698,
+        "panpipe, pandean pipe, syrinx": 699,
+        "paper towel": 700,
+        "papillon": 157,
+        "parachute, chute": 701,
+        "parallel bars, bars": 702,
+        "park bench": 703,
+        "parking meter": 704,
+        "partridge": 86,
+        "passenger car, coach, carriage": 705,
+        "patas, hussar monkey, Erythrocebus patas": 371,
+        "patio, terrace": 706,
+        "pay-phone, pay-station": 707,
+        "peacock": 84,
+        "pedestal, plinth, footstall": 708,
+        "pelican": 144,
+        "pencil box, pencil case": 709,
+        "pencil sharpener": 710,
+        "perfume, essence": 711,
+        "photocopier": 713,
+        "pick, plectrum, plectron": 714,
+        "pickelhaube": 715,
+        "picket fence, paling": 716,
+        "pickup, pickup truck": 717,
+        "pier": 718,
+        "piggy bank, penny bank": 719,
+        "pill bottle": 720,
+        "pillow": 721,
+        "pineapple, ananas": 953,
+        "ping-pong ball": 722,
+        "pinwheel": 723,
+        "pirate, pirate ship": 724,
+        "pitcher, ewer": 725,
+        "pizza, pizza pie": 963,
+        "plane, carpenter's plane, woodworking plane": 726,
+        "planetarium": 727,
+        "plastic bag": 728,
+        "plate": 923,
+        "plate rack": 729,
+        "platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus": 103,
+        "plow, plough": 730,
+        "plunger, plumber's helper": 731,
+        "pole": 733,
+        "polecat, fitch, foulmart, foumart, Mustela putorius": 358,
+        "police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria": 734,
+        "pomegranate": 957,
+        "poncho": 735,
+        "pool table, billiard table, snooker table": 736,
+        "pop bottle, soda bottle": 737,
+        "porcupine, hedgehog": 334,
+        "pot, flowerpot": 738,
+        "potpie": 964,
+        "potter's wheel": 739,
+        "power drill": 740,
+        "prairie chicken, prairie grouse, prairie fowl": 83,
+        "prayer rug, prayer mat": 741,
+        "pretzel": 932,
+        "printer": 742,
+        "prison, prison house": 743,
+        "proboscis monkey, Nasalis larvatus": 376,
+        "projectile, missile": 744,
+        "projector": 745,
+        "promontory, headland, head, foreland": 976,
+        "ptarmigan": 81,
+        "puck, hockey puck": 746,
+        "puffer, pufferfish, blowfish, globefish": 397,
+        "pug, pug-dog": 254,
+        "punching bag, punch bag, punching ball, punchball": 747,
+        "purse": 748,
+        "quail": 85,
+        "quill, quill pen": 749,
+        "quilt, comforter, comfort, puff": 750,
+        "racer, race car, racing car": 751,
+        "racket, racquet": 752,
+        "radiator": 753,
+        "radio telescope, radio reflector": 755,
+        "radio, wireless": 754,
+        "rain barrel": 756,
+        "ram, tup": 348,
+        "rapeseed": 984,
+        "recreational vehicle, RV, R.V.": 757,
+        "red fox, Vulpes vulpes": 277,
+        "red wine": 966,
+        "red wolf, maned wolf, Canis rufus, Canis niger": 271,
+        "red-backed sandpiper, dunlin, Erolia alpina": 140,
+        "red-breasted merganser, Mergus serrator": 98,
+        "redbone": 168,
+        "redshank, Tringa totanus": 141,
+        "reel": 758,
+        "reflex camera": 759,
+        "refrigerator, icebox": 760,
+        "remote control, remote": 761,
+        "restaurant, eating house, eating place, eatery": 762,
+        "revolver, six-gun, six-shooter": 763,
+        "rhinoceros beetle": 306,
+        "rifle": 764,
+        "ringlet, ringlet butterfly": 322,
+        "ringneck snake, ring-necked snake, ring snake": 53,
+        "robin, American robin, Turdus migratorius": 15,
+        "rock beauty, Holocanthus tricolor": 392,
+        "rock crab, Cancer irroratus": 119,
+        "rock python, rock snake, Python sebae": 62,
+        "rocking chair, rocker": 765,
+        "rotisserie": 766,
+        "rubber eraser, rubber, pencil eraser": 767,
+        "ruddy turnstone, Arenaria interpres": 139,
+        "ruffed grouse, partridge, Bonasa umbellus": 82,
+        "rugby ball": 768,
+        "rule, ruler": 769,
+        "running shoe": 770,
+        "safe": 771,
+        "safety pin": 772,
+        "saltshaker, salt shaker": 773,
+        "sandal": 774,
+        "sandbar, sand bar": 977,
+        "sarong": 775,
+        "sax, saxophone": 776,
+        "scabbard": 777,
+        "scale, weighing machine": 778,
+        "schipperke": 223,
+        "school bus": 779,
+        "schooner": 780,
+        "scoreboard": 781,
+        "scorpion": 71,
+        "screen, CRT screen": 782,
+        "screw": 783,
+        "screwdriver": 784,
+        "scuba diver": 983,
+        "sea anemone, anemone": 108,
+        "sea cucumber, holothurian": 329,
+        "sea lion": 150,
+        "sea slug, nudibranch": 115,
+        "sea snake": 65,
+        "sea urchin": 328,
+        "seashore, coast, seacoast, sea-coast": 978,
+        "seat belt, seatbelt": 785,
+        "sewing machine": 786,
+        "shield, buckler": 787,
+        "shoe shop, shoe-shop, shoe store": 788,
+        "shoji": 789,
+        "shopping basket": 790,
+        "shopping cart": 791,
+        "shovel": 792,
+        "shower cap": 793,
+        "shower curtain": 794,
+        "siamang, Hylobates syndactylus, Symphalangus syndactylus": 369,
+        "sidewinder, horned rattlesnake, Crotalus cerastes": 68,
+        "silky terrier, Sydney silky": 201,
+        "ski": 795,
+        "ski mask": 796,
+        "skunk, polecat, wood pussy": 361,
+        "sleeping bag": 797,
+        "slide rule, slipstick": 798,
+        "sliding door": 799,
+        "slot, one-armed bandit": 800,
+        "sloth bear, Melursus ursinus, Ursus ursinus": 297,
+        "slug": 114,
+        "snail": 113,
+        "snorkel": 801,
+        "snow leopard, ounce, Panthera uncia": 289,
+        "snowmobile": 802,
+        "snowplow, snowplough": 803,
+        "soap dispenser": 804,
+        "soccer ball": 805,
+        "sock": 806,
+        "soft-coated wheaten terrier": 202,
+        "solar dish, solar collector, solar furnace": 807,
+        "sombrero": 808,
+        "sorrel": 339,
+        "soup bowl": 809,
+        "space bar": 810,
+        "space heater": 811,
+        "space shuttle": 812,
+        "spaghetti squash": 940,
+        "spatula": 813,
+        "speedboat": 814,
+        "spider monkey, Ateles geoffroyi": 381,
+        "spider web, spider's web": 815,
+        "spindle": 816,
+        "spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish": 123,
+        "spoonbill": 129,
+        "sports car, sport car": 817,
+        "spotlight, spot": 818,
+        "spotted salamander, Ambystoma maculatum": 28,
+        "squirrel monkey, Saimiri sciureus": 382,
+        "stage": 819,
+        "standard poodle": 267,
+        "standard schnauzer": 198,
+        "starfish, sea star": 327,
+        "steam locomotive": 820,
+        "steel arch bridge": 821,
+        "steel drum": 822,
+        "stethoscope": 823,
+        "stingray": 6,
+        "stinkhorn, carrion fungus": 994,
+        "stole": 824,
+        "stone wall": 825,
+        "stopwatch, stop watch": 826,
+        "stove": 827,
+        "strainer": 828,
+        "strawberry": 949,
+        "street sign": 919,
+        "streetcar, tram, tramcar, trolley, trolley car": 829,
+        "stretcher": 830,
+        "studio couch, day bed": 831,
+        "stupa, tope": 832,
+        "sturgeon": 394,
+        "submarine, pigboat, sub, U-boat": 833,
+        "suit, suit of clothes": 834,
+        "sulphur butterfly, sulfur butterfly": 325,
+        "sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita": 89,
+        "sundial": 835,
+        "sunglass": 836,
+        "sunglasses, dark glasses, shades": 837,
+        "sunscreen, sunblock, sun blocker": 838,
+        "suspension bridge": 839,
+        "swab, swob, mop": 840,
+        "sweatshirt": 841,
+        "swimming trunks, bathing trunks": 842,
+        "swing": 843,
+        "switch, electric switch, electrical switch": 844,
+        "syringe": 845,
+        "tabby, tabby cat": 281,
+        "table lamp": 846,
+        "tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui": 32,
+        "tank, army tank, armored combat vehicle, armoured combat vehicle": 847,
+        "tape player": 848,
+        "tarantula": 76,
+        "teapot": 849,
+        "teddy, teddy bear": 850,
+        "television, television system": 851,
+        "tench, Tinca tinca": 0,
+        "tennis ball": 852,
+        "terrapin": 36,
+        "thatch, thatched roof": 853,
+        "theater curtain, theatre curtain": 854,
+        "thimble": 855,
+        "three-toed sloth, ai, Bradypus tridactylus": 364,
+        "thresher, thrasher, threshing machine": 856,
+        "throne": 857,
+        "thunder snake, worm snake, Carphophis amoenus": 52,
+        "tick": 78,
+        "tiger beetle": 300,
+        "tiger cat": 282,
+        "tiger shark, Galeocerdo cuvieri": 3,
+        "tiger, Panthera tigris": 292,
+        "tile roof": 858,
+        "timber wolf, grey wolf, gray wolf, Canis lupus": 269,
+        "titi, titi monkey": 380,
+        "toaster": 859,
+        "tobacco shop, tobacconist shop, tobacconist": 860,
+        "toilet seat": 861,
+        "toilet tissue, toilet paper, bathroom tissue": 999,
+        "torch": 862,
+        "totem pole": 863,
+        "toucan": 96,
+        "tow truck, tow car, wrecker": 864,
+        "toy poodle": 265,
+        "toy terrier": 158,
+        "toyshop": 865,
+        "tractor": 866,
+        "traffic light, traffic signal, stoplight": 920,
+        "trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi": 867,
+        "tray": 868,
+        "tree frog, tree-frog": 31,
+        "trench coat": 869,
+        "triceratops": 51,
+        "tricycle, trike, velocipede": 870,
+        "trifle": 927,
+        "trilobite": 69,
+        "trimaran": 871,
+        "tripod": 872,
+        "triumphal arch": 873,
+        "trolleybus, trolley coach, trackless trolley": 874,
+        "trombone": 875,
+        "tub, vat": 876,
+        "turnstile": 877,
+        "tusker": 101,
+        "typewriter keyboard": 878,
+        "umbrella": 879,
+        "unicycle, monocycle": 880,
+        "upright, upright piano": 881,
+        "vacuum, vacuum cleaner": 882,
+        "valley, vale": 979,
+        "vase": 883,
+        "vault": 884,
+        "velvet": 885,
+        "vending machine": 886,
+        "vestment": 887,
+        "viaduct": 888,
+        "vine snake": 59,
+        "violin, fiddle": 889,
+        "vizsla, Hungarian pointer": 211,
+        "volcano": 980,
+        "volleyball": 890,
+        "vulture": 23,
+        "waffle iron": 891,
+        "walking stick, walkingstick, stick insect": 313,
+        "wall clock": 892,
+        "wallaby, brush kangaroo": 104,
+        "wallet, billfold, notecase, pocketbook": 893,
+        "wardrobe, closet, press": 894,
+        "warplane, military plane": 895,
+        "warthog": 343,
+        "washbasin, handbasin, washbowl, lavabo, wash-hand basin": 896,
+        "washer, automatic washer, washing machine": 897,
+        "water bottle": 898,
+        "water buffalo, water ox, Asiatic buffalo, Bubalus bubalis": 346,
+        "water jug": 899,
+        "water ouzel, dipper": 20,
+        "water snake": 58,
+        "water tower": 900,
+        "weasel": 356,
+        "web site, website, internet site, site": 916,
+        "weevil": 307,
+        "whippet": 172,
+        "whiptail, whiptail lizard": 41,
+        "whiskey jug": 901,
+        "whistle": 902,
+        "white stork, Ciconia ciconia": 127,
+        "white wolf, Arctic wolf, Canis lupus tundrarum": 270,
+        "wig": 903,
+        "wild boar, boar, Sus scrofa": 342,
+        "window screen": 904,
+        "window shade": 905,
+        "wine bottle": 907,
+        "wing": 908,
+        "wire-haired fox terrier": 188,
+        "wok": 909,
+        "wolf spider, hunting spider": 77,
+        "wombat": 106,
+        "wood rabbit, cottontail, cottontail rabbit": 330,
+        "wooden spoon": 910,
+        "wool, woolen, woollen": 911,
+        "worm fence, snake fence, snake-rail fence, Virginia fence": 912,
+        "wreck": 913,
+        "yawl": 914,
+        "yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum": 986,
+        "yurt": 915,
+        "zebra": 340,
+        "zucchini, courgette": 939
+      },
+      "layer_norm_eps": 1e-05,
+      "length_penalty": 1.0,
+      "max_length": 20,
+      "min_length": 0,
+      "mlp_ratio": 4.0,
+      "model_type": "swin",
+      "no_repeat_ngram_size": 0,
+      "num_beam_groups": 1,
+      "num_beams": 1,
+      "num_channels": 3,
+      "num_heads": [
+        6,
+        12,
+        24,
+        48
+      ],
+      "num_layers": 4,
+      "num_return_sequences": 1,
+      "out_features": [
+        "stage1",
+        "stage2",
+        "stage3",
+        "stage4"
+      ],
+      "out_indices": [
+        1,
+        2,
+        3,
+        4
+      ],
+      "output_attentions": false,
+      "output_hidden_states": false,
+      "output_scores": false,
+      "pad_token_id": null,
+      "patch_size": 4,
+      "path_norm": true,
+      "prefix": null,
+      "problem_type": null,
+      "pruned_heads": {},
+      "qkv_bias": true,
+      "remove_invalid_values": false,
+      "repetition_penalty": 1.0,
+      "return_dict": true,
+      "return_dict_in_generate": false,
+      "sep_token_id": null,
+      "stage_names": [
+        "stem",
+        "stage1",
+        "stage2",
+        "stage3",
+        "stage4"
+      ],
+      "suppress_tokens": null,
+      "task_specific_params": null,
+      "temperature": 1.0,
+      "tf_legacy_loss": false,
+      "tie_encoder_decoder": false,
+      "tie_word_embeddings": true,
+      "tokenizer_class": null,
+      "top_k": 50,
+      "top_p": 1.0,
+      "torch_dtype": "float32",
+      "torchscript": false,
+      "typical_p": 1.0,
+      "use_absolute_embeddings": false,
+      "use_bfloat16": false,
+      "window_size": 12
+    },
+    "backbone_kwargs": null,
+    "bad_words_ids": null,
+    "begin_suppress_tokens": null,
+    "bos_token_id": null,
+    "chunk_size_feed_forward": 0,
+    "class_weight": 2.0,
+    "common_stride": 4,
+    "cross_attention_hidden_size": null,
+    "decoder_layers": 10,
+    "decoder_start_token_id": null,
+    "dice_weight": 5.0,
+    "dim_feedforward": 2048,
+    "diversity_penalty": 0.0,
+    "do_sample": false,
+    "dropout": 0.0,
+    "early_stopping": false,
+    "encoder_feedforward_dim": 1024,
+    "encoder_layers": 6,
+    "encoder_no_repeat_ngram_size": 0,
+    "enforce_input_proj": false,
+    "enforce_input_projection": false,
+    "eos_token_id": null,
+    "exponential_decay_length_penalty": null,
+    "feature_size": 256,
+    "feature_strides": [
+      4,
+      8,
+      16,
+      32
+    ],
+    "finetuning_task": null,
+    "forced_bos_token_id": null,
+    "forced_eos_token_id": null,
+    "hidden_dim": 256,
+    "id2label": {
+      "0": "person",
+      "1": "bicycle",
+      "2": "car",
+      "3": "motorcycle",
+      "4": "airplane",
+      "5": "bus",
+      "6": "train",
+      "7": "truck",
+      "8": "boat",
+      "9": "traffic light",
+      "10": "fire hydrant",
+      "11": "stop sign",
+      "12": "parking meter",
+      "13": "bench",
+      "14": "bird",
+      "15": "cat",
+      "16": "dog",
+      "17": "horse",
+      "18": "sheep",
+      "19": "cow",
+      "20": "elephant",
+      "21": "bear",
+      "22": "zebra",
+      "23": "giraffe",
+      "24": "backpack",
+      "25": "umbrella",
+      "26": "handbag",
+      "27": "tie",
+      "28": "suitcase",
+      "29": "frisbee",
+      "30": "skis",
+      "31": "snowboard",
+      "32": "sports ball",
+      "33": "kite",
+      "34": "baseball bat",
+      "35": "baseball glove",
+      "36": "skateboard",
+      "37": "surfboard",
+      "38": "tennis racket",
+      "39": "bottle",
+      "40": "wine glass",
+      "41": "cup",
+      "42": "fork",
+      "43": "knife",
+      "44": "spoon",
+      "45": "bowl",
+      "46": "banana",
+      "47": "apple",
+      "48": "sandwich",
+      "49": "orange",
+      "50": "broccoli",
+      "51": "carrot",
+      "52": "hot dog",
+      "53": "pizza",
+      "54": "donut",
+      "55": "cake",
+      "56": "chair",
+      "57": "couch",
+      "58": "potted plant",
+      "59": "bed",
+      "60": "dining table",
+      "61": "toilet",
+      "62": "tv",
+      "63": "laptop",
+      "64": "mouse",
+      "65": "remote",
+      "66": "keyboard",
+      "67": "cell phone",
+      "68": "microwave",
+      "69": "oven",
+      "70": "toaster",
+      "71": "sink",
+      "72": "refrigerator",
+      "73": "book",
+      "74": "clock",
+      "75": "vase",
+      "76": "scissors",
+      "77": "teddy bear",
+      "78": "hair drier",
+      "79": "toothbrush",
+      "80": "banner",
+      "81": "blanket",
+      "82": "bridge",
+      "83": "cardboard",
+      "84": "counter",
+      "85": "curtain",
+      "86": "door-stuff",
+      "87": "floor-wood",
+      "88": "flower",
+      "89": "fruit",
+      "90": "gravel",
+      "91": "house",
+      "92": "light",
+      "93": "mirror-stuff",
+      "94": "net",
+      "95": "pillow",
+      "96": "platform",
+      "97": "playingfield",
+      "98": "railroad",
+      "99": "river",
+      "100": "road",
+      "101": "roof",
+      "102": "sand",
+      "103": "sea",
+      "104": "shelf",
+      "105": "snow",
+      "106": "stairs",
+      "107": "tent",
+      "108": "towel",
+      "109": "wall-brick",
+      "110": "wall-stone",
+      "111": "wall-tile",
+      "112": "wall-wood",
+      "113": "water-other",
+      "114": "window-blind",
+      "115": "window-other",
+      "116": "tree-merged",
+      "117": "fence-merged",
+      "118": "ceiling-merged",
+      "119": "sky-other-merged",
+      "120": "cabinet-merged",
+      "121": "table-merged",
+      "122": "floor-other-merged",
+      "123": "pavement-merged",
+      "124": "mountain-merged",
+      "125": "grass-merged",
+      "126": "dirt-merged",
+      "127": "paper-merged",
+      "128": "food-other-merged",
+      "129": "building-other-merged",
+      "130": "rock-merged",
+      "131": "wall-other-merged",
+      "132": "rug-merged"
+    },
+    "ignore_value": 255,
+    "importance_sample_ratio": 0.75,
+    "init_std": 0.02,
+    "init_xavier_std": 1.0,
+    "is_decoder": false,
+    "is_encoder_decoder": false,
+    "label2id": {
+      "airplane": 4,
+      "apple": 47,
+      "backpack": 24,
+      "banana": 46,
+      "banner": 80,
+      "baseball bat": 34,
+      "baseball glove": 35,
+      "bear": 21,
+      "bed": 59,
+      "bench": 13,
+      "bicycle": 1,
+      "bird": 14,
+      "blanket": 81,
+      "boat": 8,
+      "book": 73,
+      "bottle": 39,
+      "bowl": 45,
+      "bridge": 82,
+      "broccoli": 50,
+      "building-other-merged": 129,
+      "bus": 5,
+      "cabinet-merged": 120,
+      "cake": 55,
+      "car": 2,
+      "cardboard": 83,
+      "carrot": 51,
+      "cat": 15,
+      "ceiling-merged": 118,
+      "cell phone": 67,
+      "chair": 56,
+      "clock": 74,
+      "couch": 57,
+      "counter": 84,
+      "cow": 19,
+      "cup": 41,
+      "curtain": 85,
+      "dining table": 60,
+      "dirt-merged": 126,
+      "dog": 16,
+      "donut": 54,
+      "door-stuff": 86,
+      "elephant": 20,
+      "fence-merged": 117,
+      "fire hydrant": 10,
+      "floor-other-merged": 122,
+      "floor-wood": 87,
+      "flower": 88,
+      "food-other-merged": 128,
+      "fork": 42,
+      "frisbee": 29,
+      "fruit": 89,
+      "giraffe": 23,
+      "grass-merged": 125,
+      "gravel": 90,
+      "hair drier": 78,
+      "handbag": 26,
+      "horse": 17,
+      "hot dog": 52,
+      "house": 91,
+      "keyboard": 66,
+      "kite": 33,
+      "knife": 43,
+      "laptop": 63,
+      "light": 92,
+      "microwave": 68,
+      "mirror-stuff": 93,
+      "motorcycle": 3,
+      "mountain-merged": 124,
+      "mouse": 64,
+      "net": 94,
+      "orange": 49,
+      "oven": 69,
+      "paper-merged": 127,
+      "parking meter": 12,
+      "pavement-merged": 123,
+      "person": 0,
+      "pillow": 95,
+      "pizza": 53,
+      "platform": 96,
+      "playingfield": 97,
+      "potted plant": 58,
+      "railroad": 98,
+      "refrigerator": 72,
+      "remote": 65,
+      "river": 99,
+      "road": 100,
+      "rock-merged": 130,
+      "roof": 101,
+      "rug-merged": 132,
+      "sand": 102,
+      "sandwich": 48,
+      "scissors": 76,
+      "sea": 103,
+      "sheep": 18,
+      "shelf": 104,
+      "sink": 71,
+      "skateboard": 36,
+      "skis": 30,
+      "sky-other-merged": 119,
+      "snow": 105,
+      "snowboard": 31,
+      "spoon": 44,
+      "sports ball": 32,
+      "stairs": 106,
+      "stop sign": 11,
+      "suitcase": 28,
+      "surfboard": 37,
+      "table-merged": 121,
+      "teddy bear": 77,
+      "tennis racket": 38,
+      "tent": 107,
+      "tie": 27,
+      "toaster": 70,
+      "toilet": 61,
+      "toothbrush": 79,
+      "towel": 108,
+      "traffic light": 9,
+      "train": 6,
+      "tree-merged": 116,
+      "truck": 7,
+      "tv": 62,
+      "umbrella": 25,
+      "vase": 75,
+      "wall-brick": 109,
+      "wall-other-merged": 131,
+      "wall-stone": 110,
+      "wall-tile": 111,
+      "wall-wood": 112,
+      "water-other": 113,
+      "window-blind": 114,
+      "window-other": 115,
+      "wine glass": 40,
+      "zebra": 22
+    },
+    "length_penalty": 1.0,
+    "mask_feature_size": 256,
+    "mask_weight": 5.0,
+    "max_length": 20,
+    "min_length": 0,
+    "model_type": "mask2former",
+    "no_object_weight": 0.1,
+    "no_repeat_ngram_size": 0,
+    "num_attention_heads": 8,
+    "num_beam_groups": 1,
+    "num_beams": 1,
+    "num_hidden_layers": 10,
+    "num_queries": 200,
+    "num_return_sequences": 1,
+    "output_attentions": false,
+    "output_auxiliary_logits": null,
+    "output_hidden_states": false,
+    "output_scores": false,
+    "oversample_ratio": 3.0,
+    "pad_token_id": null,
+    "pre_norm": false,
+    "prefix": null,
+    "problem_type": null,
+    "pruned_heads": {},
+    "remove_invalid_values": false,
+    "repetition_penalty": 1.0,
+    "return_dict": true,
+    "return_dict_in_generate": false,
+    "sep_token_id": null,
+    "suppress_tokens": null,
+    "task_specific_params": null,
+    "temperature": 1.0,
+    "tf_legacy_loss": false,
+    "tie_encoder_decoder": false,
+    "tie_word_embeddings": true,
+    "tokenizer_class": null,
+    "top_k": 50,
+    "top_p": 1.0,
+    "torch_dtype": "float32",
+    "torchscript": false,
+    "train_num_points": 12544,
+    "transformers_version": "4.47.0",
+    "typical_p": 1.0,
+    "use_auxiliary_loss": true,
+    "use_bfloat16": false,
+    "use_pretrained_backbone": false,
+    "use_timm_backbone": false
+  },
+  "max_dynamic_patch": 12,
+  "min_dynamic_patch": 1,
+  "model_type": "sa2va_chat",
+  "num_m2f_proposals": 100,
+  "num_m2f_queries": 200,
+  "pad2square": false,
+  "ps_version": "v2",
+  "select_layer": -1,
+  "template": "internlm2_chat",
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": null,
+  "use_backbone_lora": 0,
+  "use_llm_lora": 0,
+  "use_thumbnail": true,
+  "vision_config": {
+    "_attn_implementation_autoset": false,
+    "_name_or_path": "",
+    "add_cross_attention": false,
+    "architectures": [
+      "InternVisionModel"
+    ],
+    "attention_dropout": 0.0,
+    "bad_words_ids": null,
+    "begin_suppress_tokens": null,
+    "bos_token_id": null,
+    "chunk_size_feed_forward": 0,
+    "cross_attention_hidden_size": null,
+    "decoder_start_token_id": null,
+    "diversity_penalty": 0.0,
+    "do_sample": false,
+    "drop_path_rate": 0.0,
+    "dropout": 0.0,
+    "early_stopping": false,
+    "encoder_no_repeat_ngram_size": 0,
+    "eos_token_id": null,
+    "exponential_decay_length_penalty": null,
+    "finetuning_task": null,
+    "forced_bos_token_id": null,
+    "forced_eos_token_id": null,
+    "hidden_act": "gelu",
+    "hidden_size": 1024,
+    "id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1"
+    },
+    "image_size": 448,
+    "initializer_factor": 1.0,
+    "initializer_range": 0.02,
+    "intermediate_size": 4096,
+    "is_decoder": false,
+    "is_encoder_decoder": false,
+    "label2id": {
+      "LABEL_0": 0,
+      "LABEL_1": 1
+    },
+    "layer_norm_eps": 1e-06,
+    "length_penalty": 1.0,
+    "max_length": 20,
+    "min_length": 0,
+    "model_type": "intern_vit_6b",
+    "no_repeat_ngram_size": 0,
+    "norm_type": "layer_norm",
+    "num_attention_heads": 16,
+    "num_beam_groups": 1,
+    "num_beams": 1,
+    "num_channels": 3,
+    "num_hidden_layers": 24,
+    "num_return_sequences": 1,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "output_scores": false,
+    "pad_token_id": null,
+    "patch_size": 14,
+    "prefix": null,
+    "problem_type": null,
+    "pruned_heads": {},
+    "qk_normalization": false,
+    "qkv_bias": true,
+    "remove_invalid_values": false,
+    "repetition_penalty": 1.0,
+    "return_dict": true,
+    "return_dict_in_generate": false,
+    "sep_token_id": null,
+    "suppress_tokens": null,
+    "task_specific_params": null,
+    "temperature": 1.0,
+    "tf_legacy_loss": false,
+    "tie_encoder_decoder": false,
+    "tie_word_embeddings": true,
+    "tokenizer_class": null,
+    "top_k": 50,
+    "top_p": 1.0,
+    "torch_dtype": "bfloat16",
+    "torchscript": false,
+    "transformers_version": "4.47.0",
+    "typical_p": 1.0,
+    "use_bfloat16": true,
+    "use_flash_attn": true
+  }
+}

configuration_intern_vit.py

@@ -0,0 +1,120 @@
+# --------------------------------------------------------
+# InternVL
+# Copyright (c) 2024 OpenGVLab
+# Licensed under The MIT License [see LICENSE for details]
+# --------------------------------------------------------
+
+import os
+from typing import Union
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+
+class InternVisionConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`InternVisionModel`]. It is used to
+    instantiate a vision encoder according to the specified arguments, defining the model architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        num_channels (`int`, *optional*, defaults to 3):
+            Number of color channels in the input images (e.g., 3 for RGB).
+        patch_size (`int`, *optional*, defaults to 14):
+            The size (resolution) of each patch.
+        image_size (`int`, *optional*, defaults to 224):
+            The size (resolution) of each image.
+        qkv_bias (`bool`, *optional*, defaults to `False`):
+            Whether to add a bias to the queries and values in the self-attention layers.
+        hidden_size (`int`, *optional*, defaults to 3200):
+            Dimensionality of the encoder layers and the pooler layer.
+        num_attention_heads (`int`, *optional*, defaults to 25):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        intermediate_size (`int`, *optional*, defaults to 12800):
+            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
+        qk_normalization (`bool`, *optional*, defaults to `True`):
+            Whether to normalize the queries and keys in the self-attention layers.
+        num_hidden_layers (`int`, *optional*, defaults to 48):
+            Number of hidden layers in the Transformer encoder.
+        use_flash_attn (`bool`, *optional*, defaults to `True`):
+            Whether to use flash attention mechanism.
+        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
+            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+            `"relu"`, `"selu"` and `"gelu_new"` ``"gelu"` are supported.
+        layer_norm_eps (`float`, *optional*, defaults to 1e-6):
+            The epsilon used by the layer normalization layers.
+        dropout (`float`, *optional*, defaults to 0.0):
+            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+        drop_path_rate (`float`, *optional*, defaults to 0.0):
+            Dropout rate for stochastic depth.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        initializer_factor (`float`, *optional*, defaults to 0.1):
+            A factor for layer scale.
+    """
+
+    model_type = 'intern_vit_6b'
+
+    def __init__(
+            self,
+            num_channels=3,
+            patch_size=14,
+            image_size=224,
+            qkv_bias=False,
+            hidden_size=3200,
+            num_attention_heads=25,
+            intermediate_size=12800,
+            qk_normalization=True,
+            num_hidden_layers=48,
+            use_flash_attn=True,
+            hidden_act='gelu',
+            norm_type='rms_norm',
+            layer_norm_eps=1e-6,
+            dropout=0.0,
+            drop_path_rate=0.0,
+            attention_dropout=0.0,
+            initializer_range=0.02,
+            initializer_factor=0.1,
+            **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.dropout = dropout
+        self.drop_path_rate = drop_path_rate
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_channels = num_channels
+        self.patch_size = patch_size
+        self.image_size = image_size
+        self.initializer_range = initializer_range
+        self.initializer_factor = initializer_factor
+        self.attention_dropout = attention_dropout
+        self.layer_norm_eps = layer_norm_eps
+        self.hidden_act = hidden_act
+        self.norm_type = norm_type
+        self.qkv_bias = qkv_bias
+        self.qk_normalization = qk_normalization
+        self.use_flash_attn = use_flash_attn
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> 'PretrainedConfig':
+        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
+
+        if 'vision_config' in config_dict:
+            config_dict = config_dict['vision_config']
+
+        if 'model_type' in config_dict and hasattr(cls, 'model_type') and config_dict['model_type'] != cls.model_type:
+            logger.warning(
+                f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
+                f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'
+            )
+
+        return cls.from_dict(config_dict, **kwargs)

configuration_internlm2.py

@@ -0,0 +1,150 @@
+# Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on transformers/src/transformers/models/llama/configuration_llama.py
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" InternLM2 model configuration"""
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+INTERNLM2_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
+
+
+# Modified from transformers.model.llama.configuration_llama.LlamaConfig
+class InternLM2Config(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`InternLM2Model`]. It is used to instantiate
+    an InternLM2 model according to the specified arguments, defining the model architecture. Instantiating a
+    configuration with the defaults will yield a similar configuration to that of the InternLM2-7B.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 32000):
+            Vocabulary size of the InternLM2 model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`InternLM2Model`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 11008):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        num_key_value_heads (`int`, *optional*):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
+            `num_attention_heads`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 2048):
+            The maximum sequence length that this model might ever be used with. Typically set this to something large
+            just in case (e.g., 512 or 1024 or 2048).
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-12):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        tie_word_embeddings(`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+        Example:
+
+    """
+    model_type = 'internlm2'
+    _auto_class = 'AutoConfig'
+
+    def __init__(  # pylint: disable=W0102
+        self,
+        vocab_size=103168,
+        hidden_size=4096,
+        intermediate_size=11008,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        hidden_act='silu',
+        max_position_embeddings=2048,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=0,
+        bos_token_id=1,
+        eos_token_id=2,
+        tie_word_embeddings=False,
+        bias=True,
+        rope_theta=10000,
+        rope_scaling=None,
+        attn_implementation='eager',
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.bias = bias
+
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads
+
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self._rope_scaling_validation()
+
+        self.attn_implementation = attn_implementation
+        if self.attn_implementation is None:
+            self.attn_implementation = 'eager'
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+    def _rope_scaling_validation(self):
+        """
+        Validate the `rope_scaling` configuration.
+        """
+        if self.rope_scaling is None:
+            return
+
+        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
+            raise ValueError(
+                '`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, '
+                f'got {self.rope_scaling}'
+            )
+        rope_scaling_type = self.rope_scaling.get('type', None)
+        rope_scaling_factor = self.rope_scaling.get('factor', None)
+        if rope_scaling_type is None or rope_scaling_type not in ['linear', 'dynamic']:
+            raise ValueError(
+                f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
+            )
+        if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor < 1.0:
+            raise ValueError(f"`rope_scaling`'s factor field must be a float >= 1, got {rope_scaling_factor}")

configuration_phi3.py

@@ -0,0 +1,211 @@
+# Copyright 2024 Microsoft and the HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License atd
+#
+#     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.
+
+""" Phi-3 model configuration"""
+
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+PHI3_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    'microsoft/Phi-3-mini-4k-instruct': 'https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/resolve/main/config.json',
+    'microsoft/Phi-3-mini-128k-instruct': 'https://huggingface.co/microsoft/Phi-3-mini-128k-instruct/resolve/main/config.json',
+}
+
+
+class Phi3Config(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`Phi3Model`]. It is used to instantiate a Phi-3
+    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
+    [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct).
+
+    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 32064):
+            Vocabulary size of the Phi-3 model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`Phi3Model`].
+        hidden_size (`int`, *optional*, defaults to 3072):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 8192):
+            Dimension of the MLP 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.
+        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`.
+        resid_pdrop (`float`, *optional*, defaults to 0.0):
+            Dropout probability for mlp outputs.
+        embd_pdrop (`int`, *optional*, defaults to 0.0):
+            The dropout ratio for the embeddings.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio after computing the attention scores.
+        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 4096):
+            The maximum sequence length that this model might ever be used with.
+        original_max_position_embeddings (`int`, *optional*, defaults to 4096):
+            The maximum sequence length that this model was trained with. This is used to determine the size of the
+            original RoPE embeddings when using long scaling.
+        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-05):
+            The epsilon value used for the RMSNorm.
+        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`. Whether to tie weight embeddings or not.
+        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*):
+            The scaling strategy for the RoPE embeddings. If `None`, no scaling is applied. If a dictionary, it must
+            contain the following keys: `type`, `short_factor` and `long_factor`. The `type` must be either `su` or `yarn` and
+            the `short_factor` and `long_factor` must be lists of numbers with the same length as the hidden size
+            divided by the number of attention heads divided by 2.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            The id of the "beginning-of-sequence" token.
+        eos_token_id (`int`, *optional*, defaults to 32000):
+            The id of the "end-of-sequence" token.
+        pad_token_id (`int`, *optional*, defaults to 32000):
+            The id of the padding token.
+        sliding_window (`int`, *optional*):
+            Sliding window attention window size. If `None`, no sliding window is applied.
+
+    Example:
+
+    ```python
+    >>> from transformers import Phi3Model, Phi3Config
+
+    >>> # Initializing a Phi-3 style configuration
+    >>> configuration = Phi3Config.from_pretrained("microsoft/Phi-3-mini-4k-instruct")
+
+    >>> # Initializing a model from the configuration
+    >>> model = Phi3Model(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = 'phi3'
+    keys_to_ignore_at_inference = ['past_key_values']
+
+    def __init__(
+        self,
+        vocab_size=32064,
+        hidden_size=3072,
+        intermediate_size=8192,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        resid_pdrop=0.0,
+        embd_pdrop=0.0,
+        attention_dropout=0.0,
+        hidden_act='silu',
+        max_position_embeddings=4096,
+        original_max_position_embeddings=4096,
+        initializer_range=0.02,
+        rms_norm_eps=1e-5,
+        use_cache=True,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        bos_token_id=1,
+        eos_token_id=32000,
+        pad_token_id=32000,
+        sliding_window=None,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.resid_pdrop = resid_pdrop
+        self.embd_pdrop = embd_pdrop
+        self.attention_dropout = attention_dropout
+        self.hidden_act = hidden_act
+        self.max_position_embeddings = max_position_embeddings
+        self.original_max_position_embeddings = original_max_position_embeddings
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self._rope_scaling_validation()
+        self.sliding_window = sliding_window
+
+        super().__init__(
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            pad_token_id=pad_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+    def _rope_scaling_validation(self):
+        """
+        Validate the `rope_scaling` configuration.
+        """
+        if self.rope_scaling is None:
+            return
+
+        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 3:
+            raise ValueError(
+                '`rope_scaling` must be a dictionary with three fields, `type`, `short_factor` and `long_factor`, '
+                f'got {self.rope_scaling}'
+            )
+        rope_scaling_type = self.rope_scaling.get('type', None)
+        rope_scaling_short_factor = self.rope_scaling.get('short_factor', None)
+        rope_scaling_long_factor = self.rope_scaling.get('long_factor', None)
+        if rope_scaling_type is None or rope_scaling_type not in ['su', 'yarn']:
+            raise ValueError(f"`rope_scaling`'s type field must be one of ['su', 'yarn'], got {rope_scaling_type}")
+        if not (
+            isinstance(rope_scaling_short_factor, list)
+            and all(isinstance(x, (int, float)) for x in rope_scaling_short_factor)
+        ):
+            raise ValueError(
+                f"`rope_scaling`'s short_factor field must be a list of numbers, got {rope_scaling_short_factor}"
+            )
+        if not len(rope_scaling_short_factor) == self.hidden_size // self.num_attention_heads // 2:
+            raise ValueError(
+                f"`rope_scaling`'s short_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_short_factor)}"
+            )
+        if not (
+            isinstance(rope_scaling_long_factor, list)
+            and all(isinstance(x, (int, float)) for x in rope_scaling_long_factor)
+        ):
+            raise ValueError(
+                f"`rope_scaling`'s long_factor field must be a list of numbers, got {rope_scaling_long_factor}"
+            )
+        if not len(rope_scaling_long_factor) == self.hidden_size // self.num_attention_heads // 2:
+            raise ValueError(
+                f"`rope_scaling`'s long_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_long_factor)}"
+            )

configuration_sa2va_chat.py

@@ -0,0 +1,122 @@
+# --------------------------------------------------------
+# InternVL
+# Copyright (c) 2024 OpenGVLab
+# Licensed under The MIT License [see LICENSE for details]
+# --------------------------------------------------------
+
+import copy
+
+from .configuration_internlm2 import InternLM2Config
+from .configuration_phi3 import Phi3Config
+from transformers import AutoConfig, LlamaConfig, Qwen2Config, Mask2FormerConfig
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+from .configuration_intern_vit import InternVisionConfig
+
+logger = logging.get_logger(__name__)
+
+
+class Sa2VAChatConfig(PretrainedConfig):
+    model_type = 'sa2va_chat'
+    is_composition = True
+
+    def __init__(
+            self,
+            vision_config=None,
+            llm_config=None,
+            m2f_config=None,
+            use_backbone_lora=0,
+            use_llm_lora=0,
+            pad2square=False,
+            select_layer=-1,
+            force_image_size=None,
+            downsample_ratio=0.5,
+            template=None,
+            dynamic_image_size=False,
+            use_thumbnail=False,
+            ps_version='v1',
+            min_dynamic_patch=1,
+            max_dynamic_patch=6,
+            # mask2former
+            num_m2f_queries=300,
+            num_m2f_proposals=100,
+            **kwargs):
+        super().__init__(**kwargs)
+        if vision_config is None:
+            vision_config = {"architectures": ["InternVisionModel"]}
+            logger.info('vision_config is None. Initializing the InternVisionConfig with default values.')
+
+        if llm_config is None:
+            llm_config = {'architectures': ['Qwen2ForCausalLM']}
+            logger.info('llm_config is None. Initializing the LlamaConfig config with default values (`LlamaConfig`).')
+        
+        if m2f_config is None:
+            m2f_config = {"architectures": ["SwinForImageClassification"]}
+            logger.info('m2f_config is None. Initializing the Mask2FormerConfig config with default values.')
+
+        self.vision_config = InternVisionConfig(**vision_config)
+        self.m2f_config = Mask2FormerConfig(**m2f_config)
+
+        if llm_config['architectures'][0] == 'LlamaForCausalLM':
+            self.llm_config = LlamaConfig(**llm_config)
+        elif llm_config['architectures'][0] == 'InternLM2ForCausalLM':
+            self.llm_config = InternLM2Config(**llm_config)
+        elif llm_config['architectures'][0] == 'Phi3ForCausalLM':
+            self.llm_config = Phi3Config(**llm_config)
+        elif llm_config['architectures'][0] == 'Qwen2ForCausalLM':
+            self.llm_config = Qwen2Config(**llm_config)
+        else:
+            raise ValueError('Unsupported architecture: {}'.format(llm_config['architectures'][0]))
+        self.use_backbone_lora = use_backbone_lora
+        self.use_llm_lora = use_llm_lora
+        self.pad2square = pad2square
+        self.select_layer = select_layer
+        self.force_image_size = force_image_size
+        self.downsample_ratio = downsample_ratio
+        self.template = template
+        self.dynamic_image_size = dynamic_image_size
+        self.use_thumbnail = use_thumbnail
+        self.ps_version = ps_version  # pixel shuffle version
+        self.min_dynamic_patch = min_dynamic_patch
+        self.max_dynamic_patch = max_dynamic_patch
+        # mask2former
+        self.num_m2f_queries=num_m2f_queries
+        self.num_m2f_proposals=num_m2f_proposals
+
+        self.hidden_size = self.llm_config.hidden_size
+        self.tie_word_embeddings = False
+
+        logger.info(f'vision_select_layer: {self.select_layer}')
+        logger.info(f'ps_version: {self.ps_version}')
+        logger.info(f'min_dynamic_patch: {self.min_dynamic_patch}')
+        logger.info(f'max_dynamic_patch: {self.max_dynamic_patch}')
+
+    def to_dict(self):
+        """
+        Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
+
+        Returns:
+            `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
+        """
+        output = copy.deepcopy(self.__dict__)
+        output['vision_config'] = self.vision_config.to_dict()
+        output['llm_config'] = self.llm_config.to_dict()
+        output['m2f_config'] = self.m2f_config.to_dict()
+        output['model_type'] = self.__class__.model_type
+        output['use_backbone_lora'] = self.use_backbone_lora
+        output['use_llm_lora'] = self.use_llm_lora
+        output['pad2square'] = self.pad2square
+        output['select_layer'] = self.select_layer
+        output['force_image_size'] = self.force_image_size
+        output['downsample_ratio'] = self.downsample_ratio
+        output['template'] = self.template
+        output['dynamic_image_size'] = self.dynamic_image_size
+        output['use_thumbnail'] = self.use_thumbnail
+        output['ps_version'] = self.ps_version
+        output['min_dynamic_patch'] = self.min_dynamic_patch
+        output['max_dynamic_patch'] = self.max_dynamic_patch
+        output['num_m2f_queries'] = self.num_m2f_queries
+        output['num_m2f_proposals'] = self.num_m2f_proposals
+
+        return output

constants.py

@@ -0,0 +1,13 @@
+IMG_CONTEXT_TOKEN = '<IMG_CONTEXT>'
+IMG_START_TOKEN = '<img>'
+IMG_END_TOKEN = '</img>'
+PHRASE_START_TOKEN = '<p>'
+PHRASE_END_TOKEN = '</p>'
+SEG_TOKEN = '[SEG{id}]'
+CLS_TOKEN = '[CLS]'
+BG_CLS_TOKEN = '[BG_CLS]'
+# PROPOSAL_TOKENS = [f'[SEG{str(i).zfill(3)}]' for i in range(300)]
+OBJ_START_TOKEN = '<obj>'
+OBJ_END_TOKEN = '</obj>'
+OBJ_CONTEXT_TOKEN = '<OBJ_CONTEXT>'
+DEFAULT_OBJ_TOKEN = '<obj_tokens>'

flash_attention.py

@@ -0,0 +1,76 @@
+# https://github.com/Dao-AILab/flash-attention/blob/v0.2.8/flash_attn/flash_attention.py
+import torch
+import torch.nn as nn
+from einops import rearrange
+
+try:  # v1
+    from flash_attn.flash_attn_interface import \
+        flash_attn_unpadded_qkvpacked_func
+except:  # v2
+    from flash_attn.flash_attn_interface import flash_attn_varlen_qkvpacked_func as flash_attn_unpadded_qkvpacked_func
+
+from flash_attn.bert_padding import pad_input, unpad_input
+
+
+class FlashAttention(nn.Module):
+    """Implement the scaled dot product attention with softmax.
+    Arguments
+    ---------
+        softmax_scale: The temperature to use for the softmax attention.
+                      (default: 1/sqrt(d_keys) where d_keys is computed at
+                      runtime)
+        attention_dropout: The dropout rate to apply to the attention
+                           (default: 0.0)
+    """
+
+    def __init__(self, softmax_scale=None, attention_dropout=0.0, device=None, dtype=None):
+        super().__init__()
+        self.softmax_scale = softmax_scale
+        self.dropout_p = attention_dropout
+
+    def forward(self, qkv, key_padding_mask=None, causal=False, cu_seqlens=None,
+                max_s=None, need_weights=False):
+        """Implements the multihead softmax attention.
+        Arguments
+        ---------
+            qkv: The tensor containing the query, key, and value. (B, S, 3, H, D) if key_padding_mask is None
+                if unpadded: (nnz, 3, h, d)
+            key_padding_mask: a bool tensor of shape (B, S)
+        """
+        assert not need_weights
+        assert qkv.dtype in [torch.float16, torch.bfloat16]
+        assert qkv.is_cuda
+
+        if cu_seqlens is None:
+            batch_size = qkv.shape[0]
+            seqlen = qkv.shape[1]
+            if key_padding_mask is None:
+                qkv = rearrange(qkv, 'b s ... -> (b s) ...')
+                max_s = seqlen
+                cu_seqlens = torch.arange(0, (batch_size + 1) * seqlen, step=seqlen, dtype=torch.int32,
+                                          device=qkv.device)
+                output = flash_attn_unpadded_qkvpacked_func(
+                    qkv, cu_seqlens, max_s, self.dropout_p if self.training else 0.0,
+                    softmax_scale=self.softmax_scale, causal=causal
+                )
+                output = rearrange(output, '(b s) ... -> b s ...', b=batch_size)
+            else:
+                nheads = qkv.shape[-2]
+                x = rearrange(qkv, 'b s three h d -> b s (three h d)')
+                x_unpad, indices, cu_seqlens, max_s = unpad_input(x, key_padding_mask)
+                x_unpad = rearrange(x_unpad, 'nnz (three h d) -> nnz three h d', three=3, h=nheads)
+                output_unpad = flash_attn_unpadded_qkvpacked_func(
+                    x_unpad, cu_seqlens, max_s, self.dropout_p if self.training else 0.0,
+                    softmax_scale=self.softmax_scale, causal=causal
+                )
+                output = rearrange(pad_input(rearrange(output_unpad, 'nnz h d -> nnz (h d)'),
+                                             indices, batch_size, seqlen),
+                                   'b s (h d) -> b s h d', h=nheads)
+        else:
+            assert max_s is not None
+            output = flash_attn_unpadded_qkvpacked_func(
+                qkv, cu_seqlens, max_s, self.dropout_p if self.training else 0.0,
+                softmax_scale=self.softmax_scale, causal=causal
+            )
+
+        return output, None

generation_config.json

@@ -0,0 +1,4 @@
+{
+  "_from_model_config": true,
+  "transformers_version": "4.47.0"
+}

mask2former.py

@@ -0,0 +1,834 @@
+import torch
+import torch.nn as nn
+import numpy as np
+from typing import Dict, List, Optional, Tuple, Union
+from transformers.models.mask2former.modeling_mask2former import (
+     Mask2FormerMaskedAttentionDecoderOutput, Mask2FormerModelOutput,
+     Mask2FormerForUniversalSegmentationOutput, Mask2FormerMLPPredictionHead,
+     sample_point, pair_wise_sigmoid_cross_entropy_loss, pair_wise_dice_loss,
+     sigmoid_cross_entropy_loss, dice_loss)
+from torch import Tensor
+import torch.nn.functional as F
+
+from transformers.file_utils import is_scipy_available
+
+if is_scipy_available():
+    from scipy.optimize import linear_sum_assignment
+
+
+def get_classification_logits(x, text_classifier, logit_scale):
+    # x in shape of [B, *, C]
+    # text_classifier in shape of [num_classes, C]
+    # logit_scale is a learnable scalar https://github.com/mlfoundations/open_clip/blob/main/src/open_clip/model.py#L201
+    # return: [B, *, num_classes]
+    x = F.normalize(x, dim=-1)
+    text_classifier = F.normalize(text_classifier, dim=-1)
+    logit_scale = torch.clamp(logit_scale.exp(), max=100)
+    pred_logits = logit_scale * x @ text_classifier.T # B, *, N + 1
+    return pred_logits
+
+
+def _post_init(self):
+    self.class_embed = Mask2FormerMLPPredictionHead(self.config.hidden_dim, self.config.hidden_dim, self.config.hidden_dim, 3)
+    self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
+
+
+def ov_class_predictor(self, x, text_classifier):
+    x = self.class_embed(x)
+    all_pred_logits = []
+    for per_x, per_text_classifier in zip(x, text_classifier):
+        per_pred_logits = get_classification_logits(per_x.unsqueeze(0), per_text_classifier, self.logit_scale)
+        all_pred_logits.append(per_pred_logits.squeeze(0))
+
+    return all_pred_logits
+
+
+
+def Mask2FormerLoss_loss_labels(
+        self, class_queries_logits: Tensor, class_labels: List[Tensor], indices: Tuple[np.array]
+    ) -> Dict[str, Tensor]:
+        batch_size = len(class_queries_logits)
+        num_queries = class_queries_logits[0].shape[0]
+        all_ce_loss = []
+        for i in range(batch_size):
+            num_labels_plus1 = class_queries_logits[i].shape[-1]
+            empty_weight = torch.ones(num_labels_plus1)
+            empty_weight[-1] = self.eos_coef
+            empty_weight = empty_weight.to(class_queries_logits[i].device).to(class_queries_logits[i].dtype)
+            criterion = nn.CrossEntropyLoss(weight=empty_weight, reduction='none')
+            target_classes_o = class_labels[i][indices[i][1]]
+            target_classes = torch.full(
+                 (num_queries, ), fill_value=num_labels_plus1-1, dtype=torch.int64, device=class_queries_logits[i].device)
+            target_classes[indices[i][0]] = target_classes_o.to(class_queries_logits[i].device)
+            target_classes = target_classes.unsqueeze(0)
+            pred_logits = class_queries_logits[i].unsqueeze(0).transpose(1, 2)
+            loss_ce = criterion(pred_logits, target_classes)
+            all_ce_loss.append(loss_ce)
+        losses = {"loss_cross_entropy": torch.cat(all_ce_loss, dim=-1).mean()}
+        return losses
+
+def Mask2FormerLoss_loss_masks(
+        self, 
+        masks_queries_logits: torch.Tensor,
+        mask_labels: List[torch.Tensor],
+        indices: Tuple[np.array],
+        num_masks: int
+    ) -> Dict[str, torch.Tensor]:
+        src_idx = self._get_predictions_permutation_indices(indices)
+        tgt_idx = self._get_targets_permutation_indices(indices)
+        # shape (batch_size * num_queries, height, width)
+        pred_masks = masks_queries_logits[src_idx]
+        # shape (batch_size, num_queries, height, width)
+        # pad all and stack the targets to the num_labels dimension
+        target_masks, _ = self._pad_images_to_max_in_batch(mask_labels)
+        target_masks = target_masks[tgt_idx]
+
+        # No need to upsample predictions as we are using normalized coordinates
+        pred_masks = pred_masks[:, None]
+        target_masks = target_masks[:, None]
+
+        # Sample point coordinates
+        with torch.no_grad():
+            point_coordinates = self.sample_points_using_uncertainty(
+                pred_masks,
+                lambda logits: self.calculate_uncertainty(logits),
+                self.num_points,
+                self.oversample_ratio,
+                self.importance_sample_ratio,
+            )
+            point_labels = sample_point(target_masks.to(torch.bfloat16), point_coordinates.to(torch.bfloat16), align_corners=False).squeeze(1)
+        
+        point_logits = sample_point(pred_masks, point_coordinates.to(pred_masks.dtype), align_corners=False).squeeze(1)
+
+        losses = {
+            "loss_mask": sigmoid_cross_entropy_loss(point_logits, point_labels, num_masks),
+            "loss_dice": dice_loss(point_logits, point_labels, num_masks),
+        }
+
+        del pred_masks
+        del target_masks
+        return losses
+
+def Mask2FormerLoss_sample_points_using_uncertainty(
+        self,
+        logits: torch.Tensor,
+        uncertainty_function,
+        num_points: int,
+        oversample_ratio: int,
+        importance_sample_ratio: float,
+    ) -> torch.Tensor:
+        
+        num_boxes = logits.shape[0]
+        num_points_sampled = int(num_points * oversample_ratio)
+
+        # Get random point coordinates
+        point_coordinates = torch.rand(num_boxes, num_points_sampled, 2, device=logits.device)
+        # Get sampled prediction value for the point coordinates
+        point_logits = sample_point(logits, point_coordinates.to(logits.dtype), align_corners=False)
+        # Calculate the uncertainties based on the sampled prediction values of the points
+        point_uncertainties = uncertainty_function(point_logits)
+
+        num_uncertain_points = int(importance_sample_ratio * num_points)
+        num_random_points = num_points - num_uncertain_points
+
+        idx = torch.topk(point_uncertainties[:, 0, :], k=num_uncertain_points, dim=1)[1]
+        shift = num_points_sampled * torch.arange(num_boxes, dtype=torch.long, device=logits.device)
+        idx += shift[:, None]
+        point_coordinates = point_coordinates.view(-1, 2)[idx.view(-1), :].view(num_boxes, num_uncertain_points, 2)
+
+        if num_random_points > 0:
+            point_coordinates = torch.cat(
+                [point_coordinates, torch.rand(num_boxes, num_random_points, 2, device=logits.device)],
+                dim=1,
+            )
+        return point_coordinates
+
+
+
+@torch.no_grad()
+def Mask2FormerHungarianMatcher_forward(
+        self,
+        masks_queries_logits: torch.Tensor,
+        class_queries_logits: torch.Tensor,
+        mask_labels: torch.Tensor,
+        class_labels: torch.Tensor,
+    ) -> List[Tuple[Tensor]]:
+        indices: List[Tuple[np.array]] = []
+
+        # iterate through batch size
+        batch_size = masks_queries_logits.shape[0]
+        for i in range(batch_size):
+            pred_probs = class_queries_logits[i].softmax(-1)
+            pred_mask = masks_queries_logits[i]
+
+            # Compute the classification cost. Contrary to the loss, we don't use the NLL, but approximate it in 1 - proba[target class]. The 1 is a constant that doesn't change the matching, it can be ommitted.
+            cost_class = -pred_probs[:, class_labels[i]]
+            target_mask = mask_labels[i].to(pred_mask)
+            target_mask = target_mask[:, None]
+            pred_mask = pred_mask[:, None]
+
+            # Sample ground truth and predicted masks
+            point_coordinates = torch.rand(1, self.num_points, 2, device=pred_mask.device)
+
+            target_coordinates = point_coordinates.repeat(target_mask.shape[0], 1, 1).to(target_mask.dtype)
+            target_mask = sample_point(target_mask, target_coordinates, align_corners=False).squeeze(1)
+
+            pred_coordinates = point_coordinates.repeat(pred_mask.shape[0], 1, 1).to(pred_mask.dtype)
+            pred_mask = sample_point(pred_mask, pred_coordinates, align_corners=False).squeeze(1)
+
+            # compute the cross entropy loss between each mask pairs -> shape (num_queries, num_labels)
+            cost_mask = pair_wise_sigmoid_cross_entropy_loss(pred_mask, target_mask)
+            # Compute the dice loss betwen each mask pairs -> shape (num_queries, num_labels)
+            cost_dice = pair_wise_dice_loss(pred_mask, target_mask)
+            # final cost matrix
+            cost_matrix = self.cost_mask * cost_mask + self.cost_class * cost_class + self.cost_dice * cost_dice
+            # eliminate infinite values in cost_matrix to avoid the error ``ValueError: cost matrix is infeasible``
+            cost_matrix = torch.minimum(cost_matrix, torch.tensor(1e10))
+            cost_matrix = torch.maximum(cost_matrix, torch.tensor(-1e10))
+            cost_matrix = torch.nan_to_num(cost_matrix, 0)
+            # do the assigmented using the hungarian algorithm in scipy
+            assigned_indices: Tuple[np.array] = linear_sum_assignment(cost_matrix.to(torch.float32).cpu())
+            indices.append(assigned_indices)
+
+        # It could be stacked in one tensor
+        matched_indices = [
+            (torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) for i, j in indices
+        ]
+        return matched_indices
+
+
+
+
+def Mask2FormerMaskedAttentionDecoder_forward_first3layers(
+        self,
+        inputs_embeds: torch.Tensor = None,
+        multi_stage_positional_embeddings: torch.Tensor = None,
+        pixel_embeddings: torch.Tensor = None,
+        encoder_hidden_states: torch.Tensor = None,
+        query_position_embeddings: torch.Tensor = None,
+        feature_size_list: List = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ):
+        r"""
+        Args:
+            inputs_embeds (`torch.FloatTensor` of shape `(num_queries, batch_size, hidden_size)`):
+                The query embeddings that are passed into the decoder.
+            multi_stage_positional_embeddings (`torch.FloatTensor` of shape `(height*width, batch_size, num_channels)`):
+                Position embeddings that are added to the keys in each cross(masked)-attention layer.
+            pixel_embeddings (`torch.FloatTensor`):
+                Tensor of shape `(batch_size, num_channels, height, width)`, 1/4 scale features from the last Pixel
+                Decoder.
+            query_position_embeddings (`torch.FloatTensor` of shape `(num_queries, batch_size, hidden_size)`):
+                , *optional*): Position embeddings that are added to the queries and keys in each self-attention layer.
+            encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`):
+                Sequence of hidden-states at the output of the last layer of the encoder. Used in the
+                cross(masked)-attention of the decoder.
+            feature_size_list (`List[torch.Size]`):
+                This is a list containing shapes (height & width) of multi-scale features from the Pixel Decoder.
+            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.
+        """
+        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
+
+        if inputs_embeds is not None:
+            hidden_states = inputs_embeds
+
+        # intermediate hidden states with layernorm applied - required for predicting class logits
+        intermediate = ()
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        attentions = () if output_attentions else None
+
+        # intermediate mask predictions from transformer decoder layers
+        intermediate_mask_predictions = ()
+
+        intermediate_hidden_states = self.layernorm(inputs_embeds)
+        intermediate += (intermediate_hidden_states,)
+
+        predicted_mask, attention_mask = self.mask_predictor(
+            intermediate_hidden_states, pixel_embeddings, feature_size_list[0]
+        )
+        intermediate_mask_predictions += (predicted_mask,)
+
+        for idx, decoder_layer in enumerate(self.layers[:3]):
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            dropout_probability = torch.rand([])
+
+            if self.training and (dropout_probability < self.layerdrop):
+                continue
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    decoder_layer.__call__,
+                    hidden_states,
+                    attention_mask,
+                    encoder_hidden_states,
+                    None,
+                    None,
+                    output_attentions,
+                )
+
+            else:
+                level_index = idx % self.num_feature_levels
+
+                where = (attention_mask.sum(-1) != attention_mask.shape[-1]).to(attention_mask.dtype)
+                # Multiply the attention mask instead of indexing to avoid issue in torch.export.
+                attention_mask = attention_mask * where.unsqueeze(-1)
+
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    level_index=level_index,
+                    position_embeddings=multi_stage_positional_embeddings,
+                    query_position_embeddings=query_position_embeddings,
+                    encoder_hidden_states=encoder_hidden_states,
+                    encoder_attention_mask=attention_mask,
+                    output_attentions=output_attentions,
+                )
+
+                intermediate_hidden_states = self.layernorm(layer_outputs[0])
+
+                predicted_mask, attention_mask = self.mask_predictor(
+                    intermediate_hidden_states,
+                    pixel_embeddings,
+                    feature_size_list[(idx + 1) % self.num_feature_levels],
+                )
+
+                intermediate_mask_predictions += (predicted_mask,)
+
+                # add intermediate hidden states with layer norm applied which will be used for predicting class logits
+                intermediate += (intermediate_hidden_states,)
+
+            hidden_states = layer_outputs[0]
+
+            if output_attentions:
+                attentions += (layer_outputs[1],)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        hidden_states = hidden_states.transpose(1, 0)
+        if not return_dict:
+            outputs = [hidden_states, all_hidden_states, attentions, intermediate, intermediate_mask_predictions]
+            return tuple(v for v in outputs if v is not None)
+
+        return Mask2FormerMaskedAttentionDecoderOutput(
+            last_hidden_state=hidden_states,
+            hidden_states=all_hidden_states,
+            attentions=attentions,
+            intermediate_hidden_states=intermediate,
+            masks_queries_logits=intermediate_mask_predictions,
+        )
+
+
+def Mask2FormerMaskedAttentionDecoder_forward_last3layers(
+        self,
+        inputs_embeds: torch.Tensor = None,
+        multi_stage_positional_embeddings: torch.Tensor = None,
+        pixel_embeddings: torch.Tensor = None,
+        encoder_hidden_states: torch.Tensor = None,
+        query_position_embeddings: torch.Tensor = None,
+        feature_size_list: List = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ):
+        r"""
+        Args:
+            inputs_embeds (`torch.FloatTensor` of shape `(num_queries, batch_size, hidden_size)`):
+                The query embeddings that are passed into the decoder.
+            multi_stage_positional_embeddings (`torch.FloatTensor` of shape `(height*width, batch_size, num_channels)`):
+                Position embeddings that are added to the keys in each cross(masked)-attention layer.
+            pixel_embeddings (`torch.FloatTensor`):
+                Tensor of shape `(batch_size, num_channels, height, width)`, 1/4 scale features from the last Pixel
+                Decoder.
+            query_position_embeddings (`torch.FloatTensor` of shape `(num_queries, batch_size, hidden_size)`):
+                , *optional*): Position embeddings that are added to the queries and keys in each self-attention layer.
+            encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`):
+                Sequence of hidden-states at the output of the last layer of the encoder. Used in the
+                cross(masked)-attention of the decoder.
+            feature_size_list (`List[torch.Size]`):
+                This is a list containing shapes (height & width) of multi-scale features from the Pixel Decoder.
+            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.
+        """
+        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
+
+        if inputs_embeds is not None:
+            hidden_states = inputs_embeds
+
+        # intermediate hidden states with layernorm applied - required for predicting class logits
+        intermediate = ()
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        attentions = () if output_attentions else None
+
+        # intermediate mask predictions from transformer decoder layers
+        intermediate_mask_predictions = ()
+
+        intermediate_hidden_states = self.layernorm(inputs_embeds)
+        intermediate += (intermediate_hidden_states,)
+
+        predicted_mask, attention_mask = self.mask_predictor(
+            intermediate_hidden_states, pixel_embeddings, feature_size_list[0]
+        )
+        intermediate_mask_predictions += (predicted_mask,)
+
+        for _idx, decoder_layer in enumerate(self.layers[3:]):
+            idx = _idx + 3
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            dropout_probability = torch.rand([])
+
+            if self.training and (dropout_probability < self.layerdrop):
+                continue
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    decoder_layer.__call__,
+                    hidden_states,
+                    attention_mask,
+                    encoder_hidden_states,
+                    None,
+                    None,
+                    output_attentions,
+                )
+
+            else:
+                level_index = idx % self.num_feature_levels
+
+                where = (attention_mask.sum(-1) != attention_mask.shape[-1]).to(attention_mask.dtype)
+                # Multiply the attention mask instead of indexing to avoid issue in torch.export.
+                attention_mask = attention_mask * where.unsqueeze(-1)
+
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    level_index=level_index,
+                    position_embeddings=multi_stage_positional_embeddings,
+                    query_position_embeddings=query_position_embeddings,
+                    encoder_hidden_states=encoder_hidden_states,
+                    encoder_attention_mask=attention_mask,
+                    output_attentions=output_attentions,
+                )
+
+                intermediate_hidden_states = self.layernorm(layer_outputs[0])
+
+                predicted_mask, attention_mask = self.mask_predictor(
+                    intermediate_hidden_states,
+                    pixel_embeddings,
+                    feature_size_list[(idx + 1) % self.num_feature_levels],
+                )
+
+                intermediate_mask_predictions += (predicted_mask,)
+
+                # add intermediate hidden states with layer norm applied which will be used for predicting class logits
+                intermediate += (intermediate_hidden_states,)
+
+            hidden_states = layer_outputs[0]
+
+            if output_attentions:
+                attentions += (layer_outputs[1],)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        hidden_states = hidden_states.transpose(1, 0)
+        if not return_dict:
+            outputs = [hidden_states, all_hidden_states, attentions, intermediate, intermediate_mask_predictions]
+            return tuple(v for v in outputs if v is not None)
+
+        return Mask2FormerMaskedAttentionDecoderOutput(
+            last_hidden_state=hidden_states,
+            hidden_states=all_hidden_states,
+            attentions=attentions,
+            intermediate_hidden_states=intermediate,
+            masks_queries_logits=intermediate_mask_predictions,
+        )
+
+
+def Mask2FormerTransformerModule_forward_first_part(
+        self,
+        multi_scale_features: List[Tensor],
+        mask_features: Tensor,
+        output_hidden_states: bool = False,
+        output_attentions: bool = False,
+    ) -> Mask2FormerMaskedAttentionDecoderOutput:
+        multi_stage_features = []
+        multi_stage_positional_embeddings = []
+        size_list = []
+
+        for i in range(self.num_feature_levels):
+            size_list.append(multi_scale_features[i].shape[-2:])
+            multi_stage_positional_embeddings.append(self.position_embedder(multi_scale_features[i], None).flatten(2))
+            multi_stage_features.append(
+                self.input_projections[i](multi_scale_features[i]).flatten(2)
+                + self.level_embed.weight[i][None, :, None]
+            )
+
+            # Flatten (batch_size, num_channels, height, width) -> (height*width, batch_size, num_channels)
+            multi_stage_positional_embeddings[-1] = multi_stage_positional_embeddings[-1].permute(2, 0, 1)
+            multi_stage_features[-1] = multi_stage_features[-1].permute(2, 0, 1)
+
+        _, batch_size, _ = multi_stage_features[0].shape
+
+        # [num_queries, batch_size, num_channels]
+        query_embeddings = self.queries_embedder.weight.unsqueeze(1).repeat(1, batch_size, 1)
+        query_features = self.queries_features.weight.unsqueeze(1).repeat(1, batch_size, 1)
+
+        decoder_output = self.decoder.Mask2FormerMaskedAttentionDecoder_forward_first3layers(
+            inputs_embeds=query_features,
+            multi_stage_positional_embeddings=multi_stage_positional_embeddings,
+            pixel_embeddings=mask_features,
+            encoder_hidden_states=multi_stage_features,
+            query_position_embeddings=query_embeddings,
+            feature_size_list=size_list,
+            output_hidden_states=output_hidden_states,
+            output_attentions=output_attentions,
+            return_dict=True,
+        )
+
+        return decoder_output
+
+
+def Mask2FormerTransformerModule_forward_second_part(
+        self,
+        query_features: Tensor,
+        query_embeddings: Tensor,
+        multi_scale_features: List[Tensor],
+        mask_features: Tensor,
+        output_hidden_states: bool = False,
+        output_attentions: bool = False,
+    ) -> Mask2FormerMaskedAttentionDecoderOutput:
+        multi_stage_features = []
+        multi_stage_positional_embeddings = []
+        size_list = []
+
+        for i in range(self.num_feature_levels):
+            size_list.append(multi_scale_features[i].shape[-2:])
+            multi_stage_positional_embeddings.append(self.position_embedder(multi_scale_features[i], None).flatten(2))
+            multi_stage_features.append(
+                self.input_projections[i](multi_scale_features[i]).flatten(2)
+                + self.level_embed.weight[i][None, :, None]
+            )
+
+            # Flatten (batch_size, num_channels, height, width) -> (height*width, batch_size, num_channels)
+            multi_stage_positional_embeddings[-1] = multi_stage_positional_embeddings[-1].permute(2, 0, 1)
+            multi_stage_features[-1] = multi_stage_features[-1].permute(2, 0, 1)
+
+        _, batch_size, _ = multi_stage_features[0].shape
+
+        # [num_queries, batch_size, num_channels]
+        # query_embeddings = self.queries_embedder.weight.unsqueeze(1).repeat(1, batch_size, 1)
+        # query_features = self.queries_features.weight.unsqueeze(1).repeat(1, batch_size, 1)
+
+        decoder_output = self.decoder.Mask2FormerMaskedAttentionDecoder_forward_last3layers(
+            inputs_embeds=query_features,
+            multi_stage_positional_embeddings=multi_stage_positional_embeddings,
+            pixel_embeddings=mask_features,
+            encoder_hidden_states=multi_stage_features,
+            query_position_embeddings=query_embeddings,
+            feature_size_list=size_list,
+            output_hidden_states=output_hidden_states,
+            output_attentions=output_attentions,
+            return_dict=True,
+        )
+
+        return decoder_output
+
+
+def Mask2FormerModel_forward_first_part(
+        self,
+        pixel_values: Tensor,
+        pixel_mask: Optional[Tensor] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Mask2FormerModelOutput:
+        r"""
+        Returns:
+            `Mask2FormerModelOutput`
+
+        Examples:
+        ```python
+        >>> import torch
+        >>> from PIL import Image
+        >>> import requests
+        >>> from transformers import AutoImageProcessor, Mask2FormerModel
+
+        >>> # load image
+        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+        >>> image = Image.open(requests.get(url, stream=True).raw)
+
+        >>> # load image preprocessor and Mask2FormerModel trained on COCO instance segmentation dataset
+        >>> image_processor = AutoImageProcessor.from_pretrained("facebook/mask2former-swin-small-coco-instance")
+        >>> model = Mask2FormerModel.from_pretrained("facebook/mask2former-swin-small-coco-instance")
+        >>> inputs = image_processor(image, return_tensors="pt")
+
+        >>> # forward pass
+        >>> with torch.no_grad():
+        ...     outputs = model(**inputs)
+
+        >>> # model outputs last hidden states of shape (batch_size, num_queries, hidden_size)
+        >>> print(outputs.transformer_decoder_last_hidden_state.shape)
+        torch.Size([1, 100, 256])
+        ```
+        """
+        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
+
+        batch_size, _, height, width = pixel_values.shape
+
+        if pixel_mask is None:
+            pixel_mask = torch.ones((batch_size, height, width), device=pixel_values.device)
+
+        pixel_level_module_output = self.pixel_level_module(
+            pixel_values=pixel_values, output_hidden_states=output_hidden_states
+        )
+
+        transformer_module_output = self.transformer_module.Mask2FormerTransformerModule_forward_first_part(
+            multi_scale_features=pixel_level_module_output.decoder_hidden_states,
+            mask_features=pixel_level_module_output.decoder_last_hidden_state,
+            output_hidden_states=True,
+            output_attentions=output_attentions,
+        )
+
+        query_features = transformer_module_output.last_hidden_state
+        return query_features, pixel_level_module_output
+
+
+def Mask2FormerModel_forward_second_part(
+        self,
+        query_features: Tensor,
+        query_embeddings: Tensor,
+        pixel_level_module_output,
+        pixel_values: Tensor,
+        pixel_mask: Optional[Tensor] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Mask2FormerModelOutput:
+        r"""
+        Returns:
+            `Mask2FormerModelOutput`
+
+        Examples:
+        ```python
+        >>> import torch
+        >>> from PIL import Image
+        >>> import requests
+        >>> from transformers import AutoImageProcessor, Mask2FormerModel
+
+        >>> # load image
+        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+        >>> image = Image.open(requests.get(url, stream=True).raw)
+
+        >>> # load image preprocessor and Mask2FormerModel trained on COCO instance segmentation dataset
+        >>> image_processor = AutoImageProcessor.from_pretrained("facebook/mask2former-swin-small-coco-instance")
+        >>> model = Mask2FormerModel.from_pretrained("facebook/mask2former-swin-small-coco-instance")
+        >>> inputs = image_processor(image, return_tensors="pt")
+
+        >>> # forward pass
+        >>> with torch.no_grad():
+        ...     outputs = model(**inputs)
+
+        >>> # model outputs last hidden states of shape (batch_size, num_queries, hidden_size)
+        >>> print(outputs.transformer_decoder_last_hidden_state.shape)
+        torch.Size([1, 100, 256])
+        ```
+        """
+        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
+
+        batch_size, _, height, width = pixel_values.shape
+
+        if pixel_mask is None:
+            pixel_mask = torch.ones((batch_size, height, width), device=pixel_values.device)
+
+        transformer_module_output = self.transformer_module.Mask2FormerTransformerModule_forward_second_part(
+            query_features=query_features,
+            query_embeddings=query_embeddings,
+            multi_scale_features=pixel_level_module_output.decoder_hidden_states,
+            mask_features=pixel_level_module_output.decoder_last_hidden_state,
+            output_hidden_states=True,
+            output_attentions=output_attentions,
+        )
+
+        encoder_hidden_states = None
+        pixel_decoder_hidden_states = None
+        transformer_decoder_hidden_states = None
+        transformer_decoder_intermediate_states = None
+
+        if output_hidden_states:
+            encoder_hidden_states = pixel_level_module_output.encoder_hidden_states
+            pixel_decoder_hidden_states = pixel_level_module_output.decoder_hidden_states
+            transformer_decoder_hidden_states = transformer_module_output.hidden_states
+            transformer_decoder_intermediate_states = transformer_module_output.intermediate_hidden_states
+
+        output = Mask2FormerModelOutput(
+            encoder_last_hidden_state=pixel_level_module_output.encoder_last_hidden_state,
+            pixel_decoder_last_hidden_state=pixel_level_module_output.decoder_last_hidden_state,
+            transformer_decoder_last_hidden_state=transformer_module_output.last_hidden_state,
+            encoder_hidden_states=encoder_hidden_states,
+            pixel_decoder_hidden_states=pixel_decoder_hidden_states,
+            transformer_decoder_hidden_states=transformer_decoder_hidden_states,
+            transformer_decoder_intermediate_states=transformer_decoder_intermediate_states,
+            attentions=transformer_module_output.attentions,
+            masks_queries_logits=transformer_module_output.masks_queries_logits,
+        )
+
+        if not return_dict:
+            output = tuple(v for v in output.values() if v is not None)
+
+        return output
+
+
+def Mask2FormerForUniversalSegmentation_forward_first_part(
+        self,
+        pixel_values: Tensor,
+        mask_labels: Optional[List[Tensor]] = None,
+        class_labels: Optional[List[Tensor]] = None,
+        pixel_mask: Optional[Tensor] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_auxiliary_logits: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Mask2FormerForUniversalSegmentationOutput:
+        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
+
+        query_features, pixel_level_module_output = self.model.Mask2FormerModel_forward_first_part(
+            pixel_values=pixel_values,
+            pixel_mask=pixel_mask,
+            output_hidden_states=output_hidden_states or self.config.use_auxiliary_loss,
+            output_attentions=output_attentions,
+            return_dict=True,
+        )
+
+        return query_features, pixel_level_module_output
+
+
+def Mask2FormerForUniversalSegmentation_forward_second_part(
+        self,
+        query_features,
+        query_embeddings,
+        pixel_level_module_output,
+        text_classifier,
+        pixel_values: Tensor,
+        mask_labels: Optional[List[Tensor]] = None,
+        class_labels: Optional[List[Tensor]] = None,
+        pixel_mask: Optional[Tensor] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_auxiliary_logits: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Mask2FormerForUniversalSegmentationOutput:
+        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.Mask2FormerModel_forward_second_part(
+            query_features=query_features,
+            query_embeddings=query_embeddings,
+            pixel_level_module_output=pixel_level_module_output,
+            pixel_values=pixel_values,
+            pixel_mask=pixel_mask,
+            output_hidden_states=output_hidden_states or self.config.use_auxiliary_loss,
+            output_attentions=output_attentions,
+            return_dict=True,
+        )
+
+        loss, loss_dict, auxiliary_logits = None, None, None
+        class_queries_logits = ()
+
+        for decoder_output in outputs.transformer_decoder_intermediate_states:
+            class_prediction = self.ov_class_predictor(decoder_output.transpose(0, 1), text_classifier)
+            # class_prediction = self.class_predictor(decoder_output.transpose(0, 1))
+            class_queries_logits += (class_prediction,)
+
+        masks_queries_logits = outputs.masks_queries_logits
+
+        auxiliary_logits = self.get_auxiliary_logits(class_queries_logits, masks_queries_logits)
+
+        if mask_labels is not None and class_labels is not None:
+            loss_dict = self.get_loss_dict(
+                masks_queries_logits=masks_queries_logits[-1],
+                class_queries_logits=class_queries_logits[-1],
+                mask_labels=mask_labels,
+                class_labels=class_labels,
+                auxiliary_predictions=auxiliary_logits,
+            )
+            loss = self.get_loss(loss_dict)
+
+        encoder_hidden_states = None
+        pixel_decoder_hidden_states = None
+        transformer_decoder_hidden_states = None
+
+        if output_hidden_states:
+            encoder_hidden_states = outputs.encoder_hidden_states
+            pixel_decoder_hidden_states = outputs.pixel_decoder_hidden_states
+            transformer_decoder_hidden_states = outputs.transformer_decoder_hidden_states
+
+        output_auxiliary_logits = (
+            self.config.output_auxiliary_logits if output_auxiliary_logits is None else output_auxiliary_logits
+        )
+        if not output_auxiliary_logits:
+            auxiliary_logits = None
+
+        output = Mask2FormerForUniversalSegmentationOutput(
+            loss=loss,
+            class_queries_logits=class_queries_logits[-1],
+            masks_queries_logits=masks_queries_logits[-1],
+            auxiliary_logits=auxiliary_logits,
+            encoder_last_hidden_state=outputs.encoder_last_hidden_state,
+            pixel_decoder_last_hidden_state=outputs.pixel_decoder_last_hidden_state,
+            transformer_decoder_last_hidden_state=outputs.transformer_decoder_last_hidden_state,
+            encoder_hidden_states=encoder_hidden_states,
+            pixel_decoder_hidden_states=pixel_decoder_hidden_states,
+            transformer_decoder_hidden_states=transformer_decoder_hidden_states,
+            attentions=outputs.attentions,
+        )
+
+        if not return_dict:
+            output = tuple(v for v in output.values() if v is not None)
+            if loss is not None:
+                output = (loss) + output
+        return output

modeling_intern_vit.py

@@ -0,0 +1,364 @@
+# --------------------------------------------------------
+# InternVL
+# Copyright (c) 2024 OpenGVLab
+# Licensed under The MIT License [see LICENSE for details]
+# --------------------------------------------------------
+
+from typing import Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from einops import rearrange
+from timm.models.layers import DropPath
+from torch import nn
+from transformers.activations import ACT2FN
+from transformers.modeling_outputs import (BaseModelOutput,
+                                           BaseModelOutputWithPooling)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import logging
+
+from .configuration_intern_vit import InternVisionConfig
+
+try:
+    from .flash_attention import FlashAttention
+    has_flash_attn = True
+except:
+    print('FlashAttention is not installed.')
+    has_flash_attn = False
+
+logger = logging.get_logger(__name__)
+
+
+class InternRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+
+try:
+    from apex.normalization import FusedRMSNorm
+
+    InternRMSNorm = FusedRMSNorm  # noqa
+
+    logger.info('Discovered apex.normalization.FusedRMSNorm - will use it instead of InternRMSNorm')
+except ImportError:
+    # using the normal InternRMSNorm
+    pass
+except Exception:
+    logger.warning('discovered apex but it failed to load, falling back to InternRMSNorm')
+    pass
+
+
+NORM2FN = {
+    'rms_norm': InternRMSNorm,
+    'layer_norm': nn.LayerNorm,
+}
+
+
+class InternVisionEmbeddings(nn.Module):
+    def __init__(self, config: InternVisionConfig):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.hidden_size
+        self.image_size = config.image_size
+        self.patch_size = config.patch_size
+
+        self.class_embedding = nn.Parameter(
+            torch.randn(1, 1, self.embed_dim),
+        )
+
+        self.patch_embedding = nn.Conv2d(
+            in_channels=3, out_channels=self.embed_dim, kernel_size=self.patch_size, stride=self.patch_size
+        )
+
+        self.num_patches = (self.image_size // self.patch_size) ** 2
+        self.num_positions = self.num_patches + 1
+
+        self.position_embedding = nn.Parameter(torch.randn(1, self.num_positions, self.embed_dim))
+
+    def _get_pos_embed(self, pos_embed, H, W):
+        target_dtype = pos_embed.dtype
+        pos_embed = pos_embed.float().reshape(
+            1, self.image_size // self.patch_size, self.image_size // self.patch_size, -1).permute(0, 3, 1, 2)
+        pos_embed = F.interpolate(pos_embed, size=(H, W), mode='bicubic', align_corners=False). \
+            reshape(1, -1, H * W).permute(0, 2, 1).to(target_dtype)
+        return pos_embed
+
+    def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
+        target_dtype = self.patch_embedding.weight.dtype
+        patch_embeds = self.patch_embedding(pixel_values)  # shape = [*, channel, width, height]
+        batch_size, _, height, width = patch_embeds.shape
+        patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
+        class_embeds = self.class_embedding.expand(batch_size, 1, -1).to(target_dtype)
+        embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
+        position_embedding = torch.cat([
+            self.position_embedding[:, :1, :],
+            self._get_pos_embed(self.position_embedding[:, 1:, :], height, width)
+        ], dim=1)
+        embeddings = embeddings + position_embedding.to(target_dtype)
+        return embeddings
+
+
+class InternAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: InternVisionConfig):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.use_flash_attn = config.use_flash_attn and has_flash_attn
+        if config.use_flash_attn and not has_flash_attn:
+            print('Warning: Flash Attention is not available, use_flash_attn is set to False.')
+        self.head_dim = self.embed_dim // self.num_heads
+        if self.head_dim * self.num_heads != self.embed_dim:
+            raise ValueError(
+                f'embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:'
+                f' {self.num_heads}).'
+            )
+
+        self.scale = self.head_dim ** -0.5
+        self.qkv = nn.Linear(self.embed_dim, 3 * self.embed_dim, bias=config.qkv_bias)
+        self.attn_drop = nn.Dropout(config.attention_dropout)
+        self.proj_drop = nn.Dropout(config.dropout)
+
+        self.qk_normalization = config.qk_normalization
+
+        if self.qk_normalization:
+            self.q_norm = InternRMSNorm(self.embed_dim, eps=config.layer_norm_eps)
+            self.k_norm = InternRMSNorm(self.embed_dim, eps=config.layer_norm_eps)
+
+        if self.use_flash_attn:
+            self.inner_attn = FlashAttention(attention_dropout=config.attention_dropout)
+        self.proj = nn.Linear(self.embed_dim, self.embed_dim)
+
+    def _naive_attn(self, x):
+        B, N, C = x.shape
+        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv.unbind(0)  # make torchscript happy (cannot use tensor as tuple)
+
+        if self.qk_normalization:
+            B_, H_, N_, D_ = q.shape
+            q = self.q_norm(q.transpose(1, 2).flatten(-2, -1)).view(B_, N_, H_, D_).transpose(1, 2)
+            k = self.k_norm(k.transpose(1, 2).flatten(-2, -1)).view(B_, N_, H_, D_).transpose(1, 2)
+
+        attn = ((q * self.scale) @ k.transpose(-2, -1))
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+    def _flash_attn(self, x, key_padding_mask=None, need_weights=False):
+        qkv = self.qkv(x)
+        qkv = rearrange(qkv, 'b s (three h d) -> b s three h d', three=3, h=self.num_heads)
+
+        if self.qk_normalization:
+            q, k, v = qkv.unbind(2)
+            q = self.q_norm(q.flatten(-2, -1)).view(q.shape)
+            k = self.k_norm(k.flatten(-2, -1)).view(k.shape)
+            qkv = torch.stack([q, k, v], dim=2)
+
+        context, _ = self.inner_attn(
+            qkv, key_padding_mask=key_padding_mask, need_weights=need_weights, causal=False
+        )
+        outs = self.proj(rearrange(context, 'b s h d -> b s (h d)'))
+        outs = self.proj_drop(outs)
+        return outs
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        x = self._naive_attn(hidden_states) if not self.use_flash_attn else self._flash_attn(hidden_states)
+        return x
+
+
+class InternMLP(nn.Module):
+    def __init__(self, config: InternVisionConfig):
+        super().__init__()
+        self.config = config
+        self.act = ACT2FN[config.hidden_act]
+        self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
+        self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.fc1(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states = self.fc2(hidden_states)
+        return hidden_states
+
+
+class InternVisionEncoderLayer(nn.Module):
+    def __init__(self, config: InternVisionConfig, drop_path_rate: float):
+        super().__init__()
+        self.embed_dim = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.norm_type = config.norm_type
+
+        self.attn = InternAttention(config)
+        self.mlp = InternMLP(config)
+        self.norm1 = NORM2FN[self.norm_type](self.embed_dim, eps=config.layer_norm_eps)
+        self.norm2 = NORM2FN[self.norm_type](self.embed_dim, eps=config.layer_norm_eps)
+
+        self.ls1 = nn.Parameter(config.initializer_factor * torch.ones(self.embed_dim))
+        self.ls2 = nn.Parameter(config.initializer_factor * torch.ones(self.embed_dim))
+        self.drop_path1 = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()
+        self.drop_path2 = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()
+
+    def forward(
+            self,
+            hidden_states: torch.Tensor,
+    ) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor], Optional[Tuple[torch.FloatTensor]]]:
+        """
+        Args:
+            hidden_states (`Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]`): input to the layer of shape `(batch, seq_len, embed_dim)`
+        """
+        hidden_states = hidden_states + self.drop_path1(self.attn(self.norm1(hidden_states)) * self.ls1)
+
+        hidden_states = hidden_states + self.drop_path2(self.mlp(self.norm2(hidden_states)) * self.ls2)
+
+        return hidden_states
+
+
+class InternVisionEncoder(nn.Module):
+    """
+    Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
+    [`InternEncoderLayer`].
+
+    Args:
+        config (`InternConfig`):
+            The corresponding vision configuration for the `InternEncoder`.
+    """
+
+    def __init__(self, config: InternVisionConfig):
+        super().__init__()
+        self.config = config
+        # stochastic depth decay rule
+        dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, config.num_hidden_layers)]
+        self.layers = nn.ModuleList([
+            InternVisionEncoderLayer(config, dpr[idx]) for idx in range(config.num_hidden_layers)])
+        self.gradient_checkpointing = True
+
+    def forward(
+            self,
+            inputs_embeds,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutput]:
+        r"""
+        Args:
+            inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+                Embedded representation of the inputs. Should be float, not int tokens.
+            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.
+        """
+        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
+
+        encoder_states = () if output_hidden_states else None
+        hidden_states = inputs_embeds
+
+        for idx, encoder_layer in enumerate(self.layers):
+            if output_hidden_states:
+                encoder_states = encoder_states + (hidden_states,)
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = torch.utils.checkpoint.checkpoint(
+                    encoder_layer,
+                    hidden_states)
+            else:
+                layer_outputs = encoder_layer(
+                    hidden_states,
+                )
+            hidden_states = layer_outputs
+
+        if output_hidden_states:
+            encoder_states = encoder_states + (hidden_states,)
+
+        if not return_dict:
+            return tuple(v for v in [hidden_states, encoder_states] if v is not None)
+        return BaseModelOutput(
+            last_hidden_state=hidden_states, hidden_states=encoder_states
+        )
+
+
+class InternVisionModel(PreTrainedModel):
+    main_input_name = 'pixel_values'
+    _supports_flash_attn_2 = True
+    config_class = InternVisionConfig
+    _no_split_modules = ['InternVisionEncoderLayer']
+
+    def __init__(self, config: InternVisionConfig):
+        super().__init__(config)
+        self.config = config
+
+        self.embeddings = InternVisionEmbeddings(config)
+        self.encoder = InternVisionEncoder(config)
+
+    def resize_pos_embeddings(self, old_size, new_size, patch_size):
+        pos_emb = self.embeddings.position_embedding
+        _, num_positions, embed_dim = pos_emb.shape
+        cls_emb = pos_emb[:, :1, :]
+        pos_emb = pos_emb[:, 1:, :].reshape(1, old_size // patch_size, old_size // patch_size, -1).permute(0, 3, 1, 2)
+        pos_emb = F.interpolate(pos_emb.float(), size=new_size // patch_size, mode='bicubic', align_corners=False)
+        pos_emb = pos_emb.to(cls_emb.dtype).reshape(1, embed_dim, -1).permute(0, 2, 1)
+        pos_emb = torch.cat([cls_emb, pos_emb], dim=1)
+        self.embeddings.position_embedding = nn.Parameter(pos_emb)
+        self.embeddings.image_size = new_size
+        logger.info('Resized position embeddings from {} to {}'.format(old_size, new_size))
+
+    def get_input_embeddings(self):
+        return self.embeddings
+
+    def forward(
+            self,
+            pixel_values: Optional[torch.FloatTensor] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+            pixel_embeds: Optional[torch.FloatTensor] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPooling]:
+        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
+
+        if pixel_values is None and pixel_embeds is None:
+            raise ValueError('You have to specify pixel_values or pixel_embeds')
+
+        if pixel_embeds is not None:
+            hidden_states = pixel_embeds
+        else:
+            if len(pixel_values.shape) == 4:
+                hidden_states = self.embeddings(pixel_values)
+            else:
+                raise ValueError(f'wrong pixel_values size: {pixel_values.shape}')
+        encoder_outputs = self.encoder(
+            inputs_embeds=hidden_states,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        last_hidden_state = encoder_outputs.last_hidden_state
+        pooled_output = last_hidden_state[:, 0, :]
+
+        if not return_dict:
+            return (last_hidden_state, pooled_output) + encoder_outputs[1:]
+
+        return BaseModelOutputWithPooling(
+            last_hidden_state=last_hidden_state,
+            pooler_output=pooled_output,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+        )

modeling_internlm2.py

@@ -0,0 +1,1429 @@
+# Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on transformers/src/transformers/models/llama/modeling_llama.py
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" PyTorch InternLM2 model."""
+import math
+import queue
+import threading
+import warnings
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from einops import rearrange
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+from transformers.activations import ACT2FN
+from transformers.modeling_outputs import (BaseModelOutputWithPast,
+                                           CausalLMOutputWithPast,
+                                           SequenceClassifierOutputWithPast)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import (add_start_docstrings,
+                                add_start_docstrings_to_model_forward, logging,
+                                replace_return_docstrings)
+
+try:
+    from transformers.generation.streamers import BaseStreamer
+except:  # noqa # pylint: disable=bare-except
+    BaseStreamer = None
+
+from .configuration_internlm2 import InternLM2Config
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = 'InternLM2Config'
+
+flash_attn_func, flash_attn_varlen_func = None, None
+pad_input, index_first_axis, unpad_input = None, None, None
+try:
+    from flash_attn import flash_attn_func as _flash_attn_func
+    from flash_attn import flash_attn_varlen_func as _flash_attn_varlen_func
+    from flash_attn.bert_padding import index_first_axis as _index_first_axis
+    from flash_attn.bert_padding import pad_input as _pad_input
+    from flash_attn.bert_padding import unpad_input as _unpad_input
+
+    flash_attn_func, flash_attn_varlen_func = _flash_attn_func, _flash_attn_varlen_func
+    pad_input, index_first_axis, unpad_input = _pad_input, _index_first_axis, _unpad_input
+    has_flash_attn = True
+except:
+    has_flash_attn = False
+
+
+def _import_flash_attn():
+    global flash_attn_func, flash_attn_varlen_func
+    global pad_input, index_first_axis, unpad_input
+    try:
+        from flash_attn import flash_attn_func as _flash_attn_func
+        from flash_attn import \
+            flash_attn_varlen_func as _flash_attn_varlen_func
+        from flash_attn.bert_padding import \
+            index_first_axis as _index_first_axis
+        from flash_attn.bert_padding import pad_input as _pad_input
+        from flash_attn.bert_padding import unpad_input as _unpad_input
+        flash_attn_func, flash_attn_varlen_func = _flash_attn_func, _flash_attn_varlen_func
+        pad_input, index_first_axis, unpad_input = _pad_input, _index_first_axis, _unpad_input
+    except ImportError:
+        raise ImportError('flash_attn is not installed.')
+
+
+# Copied from transformers.models.llama.modeling_llama._get_unpad_data
+def _get_unpad_data(attention_mask):
+    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
+    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
+    max_seqlen_in_batch = seqlens_in_batch.max().item()
+    cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
+    return (
+        indices,
+        cu_seqlens,
+        max_seqlen_in_batch,
+    )
+
+
+# Copied from transformers.models.bart.modeling_bart._make_causal_mask
+def _make_causal_mask(
+    input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
+):
+    """
+    Make causal mask used for bi-directional self-attention.
+    """
+    bsz, tgt_len = input_ids_shape
+    mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min, device=device), device=device)
+    mask_cond = torch.arange(mask.size(-1), device=device)
+    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
+    mask = mask.to(dtype)
+
+    if past_key_values_length > 0:
+        mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
+    return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
+
+
+# Copied from transformers.models.bart.modeling_bart._expand_mask
+def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
+    """
+    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
+    """
+    bsz, src_len = mask.size()
+    tgt_len = tgt_len if tgt_len is not None else src_len
+
+    expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
+
+    inverted_mask = 1.0 - expanded_mask
+
+    return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->InternLM2
+class InternLM2RMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        InternLM2RMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+
+try:
+    from functools import partial
+
+    from apex.normalization import FusedRMSNorm
+    InternLM2RMSNorm = partial(FusedRMSNorm, eps=1e-6)   # noqa
+    print('Discovered apex.normalization.FusedRMSNorm - will use it instead of InternLM2RMSNorm')
+except ImportError:
+    # using the normal LlamaRMSNorm
+    pass
+except Exception:
+    print('discovered apex but it failed to load, falling back to InternLM2RMSNorm')
+    pass
+
+
+# Copied from transformers.model.llama.modeling_llama.LlamaRotaryEmbedding with Llama->InternLM2
+class InternLM2RotaryEmbedding(nn.Module):
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
+        super().__init__()
+
+        self.dim = dim
+        self.max_position_embeddings = max_position_embeddings
+        self.base = base
+        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+        self.register_buffer('inv_freq', inv_freq, persistent=False)
+
+        # Build here to make `torch.jit.trace` work.
+        self._set_cos_sin_cache(
+            seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
+        )
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(self.max_seq_len_cached, device=device).to(dtype=self.inv_freq.dtype)
+
+        freqs = torch.einsum('i,j->ij', t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer('cos_cached', emb.cos().to(dtype), persistent=False)
+        self.register_buffer('sin_cached', emb.sin().to(dtype), persistent=False)
+
+    def forward(self, x, seq_len=None):
+        # x: [bs, num_attention_heads, seq_len, head_size]
+        if seq_len > self.max_seq_len_cached:
+            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=torch.float32)
+
+        return (
+            self.cos_cached[:seq_len].to(dtype=x.dtype),
+            self.sin_cached[:seq_len].to(dtype=x.dtype),
+        )
+
+
+# Copied from transformers.model.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->InternLM2
+class InternLM2LinearScalingRotaryEmbedding(InternLM2RotaryEmbedding):
+    """InternLM2RotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
+
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
+        self.scaling_factor = scaling_factor
+        super().__init__(dim, max_position_embeddings, base, device)
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(self.max_seq_len_cached, device=device).to(dtype=self.inv_freq.dtype)
+        t = t / self.scaling_factor
+
+        freqs = torch.einsum('i,j->ij', t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer('cos_cached', emb.cos().to(dtype), persistent=False)
+        self.register_buffer('sin_cached', emb.sin().to(dtype), persistent=False)
+
+
+# Copied from transformers.model.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->InternLM2
+class InternLM2DynamicNTKScalingRotaryEmbedding(InternLM2RotaryEmbedding):
+    """InternLM2RotaryEmbedding extended with Dynamic NTK scaling.
+    Credits to the Reddit users /u/bloc97 and /u/emozilla.
+    """
+
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
+        self.scaling_factor = scaling_factor
+        super().__init__(dim, max_position_embeddings, base, device)
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+
+        if seq_len > self.max_position_embeddings:
+            base = self.base * (
+                (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
+            ) ** (self.dim / (self.dim - 2))
+            inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+            self.register_buffer('inv_freq', inv_freq, persistent=False)
+
+        t = torch.arange(self.max_seq_len_cached, device=device).to(dtype=self.inv_freq.dtype)
+
+        freqs = torch.einsum('i,j->ij', t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer('cos_cached', emb.cos().to(dtype), persistent=False)
+        self.register_buffer('sin_cached', emb.sin().to(dtype), persistent=False)
+
+
+# Copied from transformers.model.llama.modeling_llama.rotate_half
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2:]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+# Copied from transformers.model.llama.modeling_llama.apply_rotary_pos_emb
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
+    """Applies Rotary Position Embedding to the query and key tensors."""
+    cos = cos[position_ids].unsqueeze(unsqueeze_dim)
+    sin = sin[position_ids].unsqueeze(unsqueeze_dim)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+class InternLM2MLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.w1 = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.w3 = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.w2 = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, x):
+        down_proj = self.w2(self.act_fn(self.w1(x)) * self.w3(x))
+
+        return down_proj
+
+
+# Copied from transformers.model.llama.modeling_llama.repeat_kv
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+# Modified from transformers.model.llama.modeling_llama.LlamaAttention
+class InternLM2Attention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: InternLM2Config):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.hidden_size // self.num_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.max_position_embeddings = config.max_position_embeddings
+        self.is_causal = True
+
+        if (self.head_dim * self.num_heads) != self.hidden_size:
+            raise ValueError(
+                f'hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}'
+                f' and `num_heads`: {self.num_heads}).'
+            )
+
+        self.wqkv = nn.Linear(
+            self.hidden_size,
+            (self.num_heads + 2 * self.num_key_value_heads) * self.head_dim,
+            bias=config.bias,
+        )
+
+        self.wo = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.bias)
+        self._init_rope()
+
+    def _init_rope(self):
+        if self.config.rope_scaling is None:
+            self.rotary_emb = InternLM2RotaryEmbedding(
+                self.head_dim,
+                max_position_embeddings=self.max_position_embeddings,
+                base=self.config.rope_theta,
+            )
+        else:
+            scaling_type = self.config.rope_scaling['type']
+            scaling_factor = self.config.rope_scaling['factor']
+            if scaling_type == 'dynamic':
+                self.rotary_emb = InternLM2DynamicNTKScalingRotaryEmbedding(
+                    self.head_dim,
+                    max_position_embeddings=self.max_position_embeddings,
+                    base=self.config.rope_theta,
+                    scaling_factor=scaling_factor,
+                )
+            elif scaling_type == 'linear':
+                self.rotary_emb = InternLM2LinearScalingRotaryEmbedding(
+                    self.head_dim,
+                    max_position_embeddings=self.max_position_embeddings,
+                    base=self.config.rope_theta,
+                    scaling_factor=scaling_factor,
+                )
+            else:
+                raise ValueError("Currently we only support rotary embedding's type being 'dynamic' or 'linear'.")
+        return self.rotary_emb
+
+    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        if 'padding_mask' in kwargs:
+            warnings.warn(
+                'Passing `padding_mask` is deprecated and will be removed in v4.37. '
+                'Please make sure use `attention_mask` instead.`'
+            )
+
+        bsz, q_len, _ = hidden_states.size()
+
+        qkv_states = self.wqkv(hidden_states)
+
+        qkv_states = rearrange(
+            qkv_states,
+            'b q (h gs d) -> b q h gs d',
+            gs=2 + self.num_key_value_groups,
+            d=self.head_dim,
+        )
+
+        query_states = qkv_states[..., : self.num_key_value_groups, :]
+        query_states = rearrange(query_states, 'b q h gs d -> b q (h gs) d')
+        key_states = qkv_states[..., -2, :]
+        value_states = qkv_states[..., -1, :]
+
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            kv_seq_len += past_key_value[0].shape[-2]
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+        if past_key_value is not None:
+            # reuse k, v, self_attention
+            key_states = torch.cat([past_key_value[0], key_states], dim=2)
+            value_states = torch.cat([past_key_value[1], value_states], dim=2)
+
+        past_key_value = (key_states, value_states) if use_cache else None
+
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+            raise ValueError(
+                f'Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is'
+                f' {attn_weights.size()}'
+            )
+
+        if attention_mask is not None:
+            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+                raise ValueError(
+                    f'Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}'
+                )
+            attn_weights = attn_weights + attention_mask
+
+        # upcast attention to fp32
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+            raise ValueError(
+                f'`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is'
+                f' {attn_output.size()}'
+            )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+        attn_output = self.wo(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+# Modified from transformers.model.llama.modeling_llama.InternLM2FlashAttention2
+class InternLM2FlashAttention2(InternLM2Attention):
+    """
+    InternLM2 flash attention module. This module inherits from `InternLM2Attention` as the weights of the module stays
+    untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
+    flash attention and deal with padding tokens in case the input contains any of them.
+    """
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        # InternLM2FlashAttention2 attention does not support output_attentions
+        if 'padding_mask' in kwargs:
+            warnings.warn(
+                'Passing `padding_mask` is deprecated and will be removed in v4.37. '
+                'Please make sure use `attention_mask` instead.`'
+            )
+
+            # overwrite attention_mask with padding_mask
+            attention_mask = kwargs.pop('padding_mask')
+
+        output_attentions = False
+
+        bsz, q_len, _ = hidden_states.size()
+
+        qkv_states = self.wqkv(hidden_states)
+
+        qkv_states = rearrange(
+            qkv_states,
+            'b q (h gs d) -> b q h gs d',
+            gs=2 + self.num_key_value_groups,
+            d=self.head_dim,
+        )
+
+        query_states = qkv_states[..., : self.num_key_value_groups, :]
+        query_states = rearrange(query_states, 'b q h gs d -> b q (h gs) d')
+        key_states = qkv_states[..., -2, :]
+        value_states = qkv_states[..., -1, :]
+
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            kv_seq_len += past_key_value[0].shape[-2]
+
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+        if past_key_value is not None:
+            # reuse k, v, self_attention
+            key_states = torch.cat([past_key_value[0], key_states], dim=2)
+            value_states = torch.cat([past_key_value[1], value_states], dim=2)
+
+        past_key_value = (key_states, value_states) if use_cache else None
+
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        attn_output = self._flash_attention_forward(
+            query_states, key_states, value_states, attention_mask, q_len
+        )
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
+        attn_output = self.wo(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+    def _flash_attention_forward(
+        self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
+    ):
+        """
+        Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
+        first unpad the input, then computes the attention scores and pad the final attention scores.
+
+        Args:
+            query_states (`torch.Tensor`):
+                Input query states to be passed to Flash Attention API
+            key_states (`torch.Tensor`):
+                Input key states to be passed to Flash Attention API
+            value_states (`torch.Tensor`):
+                Input value states to be passed to Flash Attention API
+            attention_mask (`torch.Tensor`):
+                The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
+                position of padding tokens and 1 for the position of non-padding tokens.
+            dropout (`int`, *optional*):
+                Attention dropout
+            softmax_scale (`float`, *optional*):
+                The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
+        """
+        # Contains at least one padding token in the sequence
+        causal = self.is_causal and query_length != 1
+        if attention_mask is not None:
+            batch_size = query_states.shape[0]
+            query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._unpad_input(
+                query_states, key_states, value_states, attention_mask, query_length
+            )
+
+            cu_seqlens_q, cu_seqlens_k = cu_seq_lens
+            max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
+
+            attn_output_unpad = flash_attn_varlen_func(
+                query_states,
+                key_states,
+                value_states,
+                cu_seqlens_q=cu_seqlens_q,
+                cu_seqlens_k=cu_seqlens_k,
+                max_seqlen_q=max_seqlen_in_batch_q,
+                max_seqlen_k=max_seqlen_in_batch_k,
+                dropout_p=dropout,
+                softmax_scale=softmax_scale,
+                causal=causal,
+            )
+
+            attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
+        else:
+            attn_output = flash_attn_func(
+                query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
+            )
+
+        return attn_output
+
+    def _unpad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
+        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
+        batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
+
+        key_layer = index_first_axis(
+            key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
+        )
+        value_layer = index_first_axis(
+            value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
+        )
+
+        if query_length == kv_seq_len:
+            query_layer = index_first_axis(
+                query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
+            )
+            cu_seqlens_q = cu_seqlens_k
+            max_seqlen_in_batch_q = max_seqlen_in_batch_k
+            indices_q = indices_k
+        elif query_length == 1:
+            max_seqlen_in_batch_q = 1
+            cu_seqlens_q = torch.arange(
+                batch_size + 1, dtype=torch.int32, device=query_layer.device
+            )  # There is a memcpy here, that is very bad.
+            indices_q = cu_seqlens_q[:-1]
+            query_layer = query_layer.squeeze(1)
+        else:
+            # The -q_len: slice assumes left padding.
+            attention_mask = attention_mask[:, -query_length:]
+            query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
+
+        return (
+            query_layer,
+            key_layer,
+            value_layer,
+            indices_q.to(torch.int64),
+            (cu_seqlens_q, cu_seqlens_k),
+            (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
+        )
+
+
+INTERNLM2_ATTENTION_CLASSES = {
+    'eager': InternLM2Attention,
+    'flash_attention_2': InternLM2FlashAttention2,
+}
+
+
+# Modified from transformers.model.llama.modeling_llama.LlamaDecoderLayer
+class InternLM2DecoderLayer(nn.Module):
+    def __init__(self, config: InternLM2Config):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+
+        self.attention = INTERNLM2_ATTENTION_CLASSES[config.attn_implementation](config=config)
+
+        self.feed_forward = InternLM2MLP(config)
+        self.attention_norm = InternLM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.ffn_norm = InternLM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        **kwargs,
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+            attention_mask (`torch.FloatTensor`, *optional*):
+                attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
+                query_sequence_length, key_sequence_length)` if default attention is used.
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            use_cache (`bool`, *optional*):
+                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+                (see `past_key_values`).
+            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+        """
+        if 'padding_mask' in kwargs:
+            warnings.warn(
+                'Passing `padding_mask` is deprecated and will be removed in v4.37. '
+                'Please make sure use `attention_mask` instead.`'
+            )
+
+        residual = hidden_states
+
+        hidden_states = self.attention_norm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights, present_key_value = self.attention(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+            **kwargs,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.ffn_norm(hidden_states)
+        hidden_states = self.feed_forward(hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        return outputs
+
+
+InternLM2_START_DOCSTRING = r"""
+    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+
+    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+    and behavior.
+
+    Parameters:
+        config ([`InternLM2Config`]):
+            Model configuration class with all the parameters of the model. Initializing with a config file does not
+            load the weights associated with the model, only the configuration. Check out the
+            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaPreTrainedModel with Llama->InternLM2
+@add_start_docstrings(
+    'The bare InternLM2 Model outputting raw hidden-states without any specific head on top.',
+    InternLM2_START_DOCSTRING,
+)
+class InternLM2PreTrainedModel(PreTrainedModel):
+    config_class = InternLM2Config
+    base_model_prefix = 'model'
+    supports_gradient_checkpointing = True
+    _no_split_modules = ['InternLM2DecoderLayer']
+    _skip_keys_device_placement = 'past_key_values'
+    _supports_flash_attn_2 = True
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+
+InternLM2_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+            it.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
+            `past_key_values`).
+
+            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+            information on the default strategy.
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.n_positions - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or
+            when `config.use_cache=True`):
+            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+            `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
+            `(batch_size, num_heads, decoder_sequence_length, embed_size_per_head)`.
+
+            Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+            blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
+
+            If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
+            have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
+            of shape `(batch_size, sequence_length)`.
+        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+            model's internal embedding lookup matrix.
+        use_cache (`bool`, *optional*):
+            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+            `past_key_values`).
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+# Modified from transformers.model.llama.modeling_llama.LlamaModel
+@add_start_docstrings(
+    'The bare InternLM2 Model outputting raw hidden-states without any specific head on top.',
+    InternLM2_START_DOCSTRING,
+)
+class InternLM2Model(InternLM2PreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`InternLM2DecoderLayer`]
+
+    Args:
+        config: InternLM2Config
+    """
+
+    _auto_class = 'AutoModel'
+
+    def __init__(self, config: InternLM2Config):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+        self.config = config
+        if not has_flash_attn:
+            self.config.attn_implementation = 'eager'
+            print('Warning: Flash attention is not available, using eager attention instead.')
+
+        self.tok_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+
+        self.layers = nn.ModuleList([InternLM2DecoderLayer(config) for _ in range(config.num_hidden_layers)])
+        self.norm = InternLM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        self.gradient_checkpointing = False
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.tok_embeddings
+
+    def set_input_embeddings(self, value):
+        self.tok_embeddings = value
+
+    def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
+        # create causal mask
+        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+        combined_attention_mask = None
+        if input_shape[-1] > 1:
+            combined_attention_mask = _make_causal_mask(
+                input_shape,
+                inputs_embeds.dtype,
+                device=inputs_embeds.device,
+                past_key_values_length=past_key_values_length,
+            )
+
+        if attention_mask is not None:
+            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+            expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
+                inputs_embeds.device
+            )
+            combined_attention_mask = (
+                expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
+            )
+
+        return combined_attention_mask
+
+    @add_start_docstrings_to_model_forward(InternLM2_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if self.config.attn_implementation == 'flash_attention_2':
+            _import_flash_attn()
+
+        # retrieve input_ids and inputs_embeds
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError('You cannot specify both input_ids and inputs_embeds at the same time')
+        elif input_ids is not None:
+            batch_size, seq_length = input_ids.shape[:2]
+        elif inputs_embeds is not None:
+            batch_size, seq_length = inputs_embeds.shape[:2]
+        else:
+            raise ValueError('You have to specify either input_ids or inputs_embeds')
+
+        seq_length_with_past = seq_length
+        past_key_values_length = 0
+        if past_key_values is not None:
+            past_key_values_length = past_key_values[0][0].shape[2]
+            seq_length_with_past = seq_length_with_past + past_key_values_length
+
+        if position_ids is None:
+            device = input_ids.device if input_ids is not None else inputs_embeds.device
+            position_ids = torch.arange(
+                past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
+            )
+            position_ids = position_ids.unsqueeze(0)
+
+        if inputs_embeds is None:
+            inputs_embeds = self.tok_embeddings(input_ids)
+
+        if self.config.attn_implementation == 'flash_attention_2':
+            # 2d mask is passed through the layers
+            attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
+        else:
+            if attention_mask is None:
+                attention_mask = torch.ones(
+                    (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
+                )
+            attention_mask = self._prepare_decoder_attention_mask(
+                attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
+            )
+
+        # embed positions
+        hidden_states = inputs_embeds
+
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                logger.warning_once(
+                    '`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...'
+                )
+                use_cache = False
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = () if use_cache else None
+
+        for idx, decoder_layer in enumerate(self.layers):
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            past_key_value = past_key_values[idx] if past_key_values is not None else None
+
+            if self.gradient_checkpointing and self.training:
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        # None for past_key_value
+                        return module(*inputs, output_attentions, None)
+
+                    return custom_forward
+
+                layer_outputs = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(decoder_layer),
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    None,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_value,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        next_cache = next_decoder_cache if use_cache else None
+        if not return_dict:
+            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+
+# Modified from transformers.model.llama.modeling_llama.LlamaForCausalLM
+class InternLM2ForCausalLM(InternLM2PreTrainedModel):
+    _auto_class = 'AutoModelForCausalLM'
+
+    _tied_weights_keys = ['output.weight']
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = InternLM2Model(config)
+        self.vocab_size = config.vocab_size
+        self.output = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.tok_embeddings
+
+    def set_input_embeddings(self, value):
+        self.model.tok_embeddings = value
+
+    def get_output_embeddings(self):
+        return self.output
+
+    def set_output_embeddings(self, new_embeddings):
+        self.output = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+
+    @add_start_docstrings_to_model_forward(InternLM2_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        r"""
+        Args:
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, InternLM2ForCausalLM
+
+        >>> model = InternLM2ForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
+        >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
+
+        >>> prompt = "Hey, are you conscious? Can you talk to me?"
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+        ```"""
+
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        hidden_states = outputs[0]
+        logits = self.output(hidden_states)
+        logits = logits.float()
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        device = input_ids.device if input_ids is not None else inputs_embeds.device
+        output = CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+        output['logits'] = output['logits'].to(device)
+        return output
+
+    def prepare_inputs_for_generation(
+            self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
+    ):
+        if past_key_values is not None:
+            past_length = past_key_values[0][0].shape[2]
+
+            # Some generation methods already pass only the last input ID
+            if input_ids.shape[1] > past_length:
+                remove_prefix_length = past_length
+            else:
+                # Default to old behavior: keep only final ID
+                remove_prefix_length = input_ids.shape[1] - 1
+
+            input_ids = input_ids[:, remove_prefix_length:]
+
+        position_ids = kwargs.get('position_ids', None)
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -input_ids.shape[1]:]
+
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {'inputs_embeds': inputs_embeds}
+        else:
+            model_inputs = {'input_ids': input_ids}
+
+        model_inputs.update(
+            {
+                'position_ids': position_ids,
+                'past_key_values': past_key_values,
+                'use_cache': kwargs.get('use_cache'),
+                'attention_mask': attention_mask,
+            }
+        )
+        return model_inputs
+
+    @staticmethod
+    def _reorder_cache(past_key_values, beam_idx):
+        reordered_past = ()
+        for layer_past in past_key_values:
+            reordered_past += (
+                tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
+            )
+        return reordered_past
+
+    def build_inputs(self, tokenizer, query: str, history: List[Tuple[str, str]] = [], meta_instruction=''):
+        if tokenizer.add_bos_token:
+            prompt = ''
+        else:
+            prompt = tokenizer.bos_token
+        if meta_instruction:
+            prompt += f"""<|im_start|>system\n{meta_instruction}<|im_end|>\n"""
+        for record in history:
+            prompt += f"""<|im_start|>user\n{record[0]}<|im_end|>\n<|im_start|>assistant\n{record[1]}<|im_end|>\n"""
+        prompt += f"""<|im_start|>user\n{query}<|im_end|>\n<|im_start|>assistant\n"""
+        return tokenizer([prompt], return_tensors='pt')
+
+    @torch.no_grad()
+    def chat(
+        self,
+        tokenizer,
+        query: str,
+        history: List[Tuple[str, str]] = [],
+        streamer: Optional[BaseStreamer] = None,
+        max_new_tokens: int = 1024,
+        do_sample: bool = True,
+        temperature: float = 0.8,
+        top_p: float = 0.8,
+        meta_instruction: str = 'You are an AI assistant whose name is InternLM (书生·浦语).\n'
+                                '- InternLM (书生·浦语) is a conversational language model that is developed by Shanghai AI Laboratory (上海人工智能实验室). It is designed to be helpful, honest, and harmless.\n'
+                                '- InternLM (书生·浦语) can understand and communicate fluently in the language chosen by the user such as English and 中文.',
+        **kwargs,
+    ):
+        inputs = self.build_inputs(tokenizer, query, history, meta_instruction)
+        inputs = {k: v.to(self.device) for k, v in inputs.items() if torch.is_tensor(v)}
+        # also add end-of-assistant token in eos token id to avoid unnecessary generation
+        eos_token_id = [tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids(['<|im_end|>'])[0]]
+        outputs = self.generate(
+            **inputs,
+            streamer=streamer,
+            max_new_tokens=max_new_tokens,
+            do_sample=do_sample,
+            temperature=temperature,
+            top_p=top_p,
+            eos_token_id=eos_token_id,
+            **kwargs,
+        )
+        outputs = outputs[0].cpu().tolist()[len(inputs['input_ids'][0]):]
+        response = tokenizer.decode(outputs, skip_special_tokens=True)
+        response = response.split('<|im_end|>')[0]
+        history = history + [(query, response)]
+        return response, history
+
+    @torch.no_grad()
+    def stream_chat(
+        self,
+        tokenizer,
+        query: str,
+        history: List[Tuple[str, str]] = [],
+        max_new_tokens: int = 1024,
+        do_sample: bool = True,
+        temperature: float = 0.8,
+        top_p: float = 0.8,
+        **kwargs,
+    ):
+        """
+        Return a generator in format: (response, history)
+        Eg.
+        ('你好，有什么可以帮助您的吗', [('你好', '你好，有什么可以帮助您的吗')])
+        ('你好，有什么可以帮助您的吗？', [('你好', '你好，有什么可以帮助您的吗？')])
+        """
+        if BaseStreamer is None:
+            raise ModuleNotFoundError(
+                'The version of `transformers` is too low. Please make sure '
+                'that you have installed `transformers>=4.28.0`.'
+            )
+
+        response_queue = queue.Queue(maxsize=20)
+
+        class ChatStreamer(BaseStreamer):
+            def __init__(self, tokenizer) -> None:
+                super().__init__()
+                self.tokenizer = tokenizer
+                self.queue = response_queue
+                self.query = query
+                self.history = history
+                self.response = ''
+                self.cache = []
+                self.received_inputs = False
+                self.queue.put((self.response, history + [(self.query, self.response)]))
+
+            def put(self, value):
+                if len(value.shape) > 1 and value.shape[0] > 1:
+                    raise ValueError('ChatStreamer only supports batch size 1')
+                elif len(value.shape) > 1:
+                    value = value[0]
+
+                if not self.received_inputs:
+                    # The first received value is input_ids, ignore here
+                    self.received_inputs = True
+                    return
+
+                self.cache.extend(value.tolist())
+                token = self.tokenizer.decode(self.cache, skip_special_tokens=True)
+                if token.strip() != '<|im_end|>':
+                    self.response = self.response + token
+                    history = self.history + [(self.query, self.response)]
+                    self.queue.put((self.response, history))
+                    self.cache = []
+                else:
+                    self.end()
+
+            def end(self):
+                self.queue.put(None)
+
+        def stream_producer():
+            return self.chat(
+                tokenizer=tokenizer,
+                query=query,
+                streamer=ChatStreamer(tokenizer=tokenizer),
+                history=history,
+                max_new_tokens=max_new_tokens,
+                do_sample=do_sample,
+                temperature=temperature,
+                top_p=top_p,
+                **kwargs,
+            )
+
+        def consumer():
+            producer = threading.Thread(target=stream_producer)
+            producer.start()
+            while True:
+                res = response_queue.get()
+                if res is None:
+                    return
+                yield res
+
+        return consumer()
+
+
+# Copied from transformers.model.llama.modeling_llama.LlamaForSequenceClassification with Llama->InternLM2
+@add_start_docstrings(
+    """
+    The InternLM2 Model transformer with a sequence classification head on top (linear layer).
+
+    [`InternLM2ForSequenceClassification`] uses the last token in order to do the classification,
+    as other causal models (e.g. GPT-2) do.
+
+    Since it does classification on the last token, it requires to know the position of the last token. If a
+    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
+    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
+    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
+    each row of the batch).
+    """,
+    InternLM2_START_DOCSTRING,
+)
+class InternLM2ForSequenceClassification(InternLM2PreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = InternLM2Model(config)
+        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.tok_embeddings
+
+    def set_input_embeddings(self, value):
+        self.model.tok_embeddings = value
+
+    @add_start_docstrings_to_model_forward(InternLM2_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        transformer_outputs = self.model(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        hidden_states = transformer_outputs[0]
+        logits = self.score(hidden_states)
+
+        if input_ids is not None:
+            batch_size = input_ids.shape[0]
+        else:
+            batch_size = inputs_embeds.shape[0]
+
+        if self.config.pad_token_id is None and batch_size != 1:
+            raise ValueError('Cannot handle batch sizes > 1 if no padding token is defined.')
+        if self.config.pad_token_id is None:
+            sequence_lengths = -1
+        else:
+            if input_ids is not None:
+                sequence_lengths = (torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1).to(
+                    logits.device
+                )
+            else:
+                sequence_lengths = -1
+
+        pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
+
+        loss = None
+        if labels is not None:
+            labels = labels.to(logits.device)
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = 'regression'
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = 'single_label_classification'
+                else:
+                    self.config.problem_type = 'multi_label_classification'
+
+            if self.config.problem_type == 'regression':
+                loss_fct = MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(pooled_logits, labels)
+            elif self.config.problem_type == 'single_label_classification':
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == 'multi_label_classification':
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(pooled_logits, labels)
+        if not return_dict:
+            output = (pooled_logits,) + transformer_outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutputWithPast(
+            loss=loss,
+            logits=pooled_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )

modeling_phi3.py

@@ -0,0 +1,1610 @@
+# Copyright 2024 Microsoft and the HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+""" PyTorch Phi-3 model."""
+
+import inspect
+import math
+import warnings
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache
+from transformers.modeling_attn_mask_utils import \
+    _prepare_4d_causal_attention_mask
+from transformers.modeling_outputs import (BaseModelOutputWithPast,
+                                           CausalLMOutputWithPast,
+                                           SequenceClassifierOutputWithPast,
+                                           TokenClassifierOutput)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import (add_code_sample_docstrings,
+                                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 .configuration_phi3 import Phi3Config
+
+logger = logging.get_logger(__name__)
+
+# Transformers scans dependencies in the modeling file, causing issues on conditional loading. The regex only ignores try/catch blocks, but not if statements
+# if is_flash_attn_2_available():
+_flash_supports_window_size = False
+try:
+    from flash_attn import flash_attn_func, flash_attn_varlen_func
+    from flash_attn.bert_padding import (index_first_axis, pad_input,  # noqa
+                                         unpad_input)
+
+    _flash_supports_window_size = 'window_size' in list(inspect.signature(flash_attn_func).parameters)
+    has_flash_attn = True
+except ImportError as error:
+    logger.warning(
+        f'`flash-attention` package not found, consider installing for better performance: {error}.'
+    )
+    if not _flash_supports_window_size:
+        logger.warning(
+            "Current `flash-attenton` does not support `window_size`. Either upgrade or use `attn_implementation='eager'`."
+        )
+    has_flash_attn = False
+
+_CHECKPOINT_FOR_DOC = 'microsoft/Phi-3-mini-4k-instruct'
+_CONFIG_FOR_DOC = 'Phi3Config'
+
+PHI3_PRETRAINED_MODEL_ARCHIVE_LIST = [
+    'microsoft/Phi-3-mini-4k-instruct',
+    'microsoft/Phi-3-mini-128k-instruct',
+    # See all Phi-3 models at https://huggingface.co/models?filter=Phi-3
+]
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->Phi3
+class Phi3RMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        Phi3RMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+
+# Copied from transformers.models.llama.modeling_llama._get_unpad_data
+def _get_unpad_data(attention_mask):
+    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
+    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
+    max_seqlen_in_batch = seqlens_in_batch.max().item()
+    cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
+    return (
+        indices,
+        cu_seqlens,
+        max_seqlen_in_batch,
+    )
+
+
+# Copied from transformers.models.gemma.modeling_gemma.GemmaRotaryEmbedding with gemma->phi3, Gemma->Phi3
+class Phi3RotaryEmbedding(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
+        self.register_buffer('inv_freq', None, persistent=False)
+
+    @torch.no_grad()
+    def forward(self, x, position_ids, seq_len=None):
+        # x: [bs, num_attention_heads, seq_len, head_size]
+        if self.inv_freq is None:
+            self.inv_freq = 1.0 / (
+                self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64, device=x.device).float() / self.dim)
+            )
+        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
+        position_ids_expanded = position_ids[:, None, :].float()
+        # Force float32 since bfloat16 loses precision on long contexts
+        # See https://github.com/huggingface/transformers/pull/29285
+        device_type = x.device.type
+        device_type = device_type if isinstance(device_type, str) and device_type != 'mps' else 'cpu'
+        with torch.autocast(device_type=device_type, enabled=False):
+            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            emb = torch.cat((freqs, freqs), dim=-1)
+            cos = emb.cos()
+            sin = emb.sin()
+        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+class Phi3SuScaledRotaryEmbedding(Phi3RotaryEmbedding):
+    def __init__(self, dim, config, device=None):
+        super().__init__(dim, config.max_position_embeddings, config.rope_theta, device)
+
+        self.short_factor = config.rope_scaling['short_factor']
+        self.long_factor = config.rope_scaling['long_factor']
+        self.original_max_position_embeddings = config.original_max_position_embeddings
+
+    @torch.no_grad()
+    def forward(self, x, position_ids, seq_len=None):
+        seq_len = torch.max(position_ids) + 1
+        if seq_len > self.original_max_position_embeddings:
+            ext_factors = torch.tensor(self.long_factor, dtype=torch.float32, device=x.device)
+        else:
+            ext_factors = torch.tensor(self.short_factor, dtype=torch.float32, device=x.device)
+
+        inv_freq_shape = torch.arange(0, self.dim, 2, dtype=torch.int64, device=x.device).float() / self.dim
+        self.inv_freq = 1.0 / (ext_factors * self.base**inv_freq_shape)
+
+        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
+        position_ids_expanded = position_ids[:, None, :].float()
+
+        # Force float32 since bfloat16 loses precision on long contexts
+        # See https://github.com/huggingface/transformers/pull/29285
+        device_type = x.device.type
+        device_type = device_type if isinstance(device_type, str) and device_type != 'mps' else 'cpu'
+        with torch.autocast(device_type=device_type, enabled=False):
+            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            emb = torch.cat((freqs, freqs), dim=-1)
+
+            scale = self.max_position_embeddings / self.original_max_position_embeddings
+            if scale <= 1.0:
+                scaling_factor = 1.0
+            else:
+                scaling_factor = math.sqrt(1 + math.log(scale) / math.log(self.original_max_position_embeddings))
+
+            cos = emb.cos() * scaling_factor
+            sin = emb.sin() * scaling_factor
+        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+class Phi3YarnScaledRotaryEmbedding(Phi3RotaryEmbedding):
+    def __init__(self, dim, config, device=None):
+        super().__init__(dim, config.max_position_embeddings, config.rope_theta, device)
+
+        self.short_factor = config.rope_scaling['short_factor']
+        self.long_factor = config.rope_scaling['long_factor']
+        self.original_max_position_embeddings = config.original_max_position_embeddings
+
+    @torch.no_grad()
+    def forward(self, x, position_ids, seq_len=None):
+        seq_len = torch.max(position_ids) + 1
+        if seq_len > self.original_max_position_embeddings:
+            ext_factors = torch.tensor(self.long_factor, dtype=torch.float32, device=x.device)
+        else:
+            ext_factors = torch.tensor(self.short_factor, dtype=torch.float32, device=x.device)
+
+        inv_freq_shape = torch.arange(0, self.dim, 2, dtype=torch.int64, device=x.device).float() / self.dim
+        self.inv_freq = 1.0 / (ext_factors * self.base**inv_freq_shape)
+
+        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
+        position_ids_expanded = position_ids[:, None, :].float()
+
+        # Force float32 since bfloat16 loses precision on long contexts
+        # See https://github.com/huggingface/transformers/pull/29285
+        device_type = x.device.type
+        device_type = device_type if isinstance(device_type, str) and device_type != 'mps' else 'cpu'
+        with torch.autocast(device_type=device_type, enabled=False):
+            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            emb = torch.cat((freqs, freqs), dim=-1)
+
+            scale = self.max_position_embeddings / self.original_max_position_embeddings
+            if scale <= 1.0:
+                scaling_factor = 1.0
+            else:
+                scaling_factor = 0.1 * math.log(scale) + 1.0
+
+            cos = emb.cos() * scaling_factor
+            sin = emb.sin() * scaling_factor
+        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+# Copied from transformers.models.llama.modeling_llama.rotate_half
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
+    """Applies Rotary Position Embedding to the query and key tensors.
+
+    Args:
+        q (`torch.Tensor`): The query tensor.
+        k (`torch.Tensor`): The key tensor.
+        cos (`torch.Tensor`): The cosine part of the rotary embedding.
+        sin (`torch.Tensor`): The sine part of the rotary embedding.
+        position_ids (`torch.Tensor`, *optional*):
+            Deprecated and unused.
+        unsqueeze_dim (`int`, *optional*, defaults to 1):
+            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+class Phi3MLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+
+        self.config = config
+        self.gate_up_proj = nn.Linear(config.hidden_size, 2 * config.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
+
+        self.activation_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
+        up_states = self.gate_up_proj(hidden_states)
+
+        gate, up_states = up_states.chunk(2, dim=-1)
+        up_states = up_states * self.activation_fn(gate)
+
+        return self.down_proj(up_states)
+
+
+# Copied from transformers.models.llama.modeling_llama.repeat_kv with llama->phi
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+class Phi3Attention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: Phi3Config, 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 a `layer_idx` is not recommended and will '
+                'lead 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.head_dim = self.hidden_size // self.num_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.max_position_embeddings = config.max_position_embeddings
+        self.original_max_position_embeddings = config.original_max_position_embeddings
+        self.rope_theta = config.rope_theta
+        self.rope_scaling = config.rope_scaling
+        self.is_causal = True
+
+        if (self.head_dim * self.num_heads) != self.hidden_size:
+            raise ValueError(
+                f'hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}'
+                f' and `num_heads`: {self.num_heads}).'
+            )
+
+        op_size = self.num_heads * self.head_dim + 2 * (self.num_key_value_heads * self.head_dim)
+        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
+        self.qkv_proj = nn.Linear(self.hidden_size, op_size, bias=False)
+        self._init_rope()
+
+    def _init_rope(self):
+        if self.rope_scaling is None:
+            self.rotary_emb = Phi3RotaryEmbedding(
+                self.head_dim,
+                max_position_embeddings=self.max_position_embeddings,
+                base=self.rope_theta,
+            )
+        else:
+            scaling_type = self.config.rope_scaling['type']
+            if scaling_type == 'su':
+                self.rotary_emb = Phi3SuScaledRotaryEmbedding(self.head_dim, self.config)
+            elif scaling_type == 'yarn':
+                self.rotary_emb = Phi3YarnScaledRotaryEmbedding(self.head_dim, self.config)
+            else:
+                raise ValueError(f'Unknown RoPE scaling type {scaling_type}')
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        logger.warning_once('You are not running the flash-attention implementation, expect numerical differences.')
+
+        bsz, q_len, _ = hidden_states.size()
+
+        qkv = self.qkv_proj(hidden_states)
+        query_pos = self.num_heads * self.head_dim
+        query_states = qkv[..., :query_pos]
+        key_states = qkv[..., query_pos : query_pos + self.num_key_value_heads * self.head_dim]
+        value_states = qkv[..., query_pos + self.num_key_value_heads * self.head_dim :]
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            if self.layer_idx is None:
+                raise ValueError(
+                    f'The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} '
+                    'for auto-regressive decoding with k/v caching, please make sure to initialize the attention class '
+                    'with a layer index.'
+                )
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+        cos, sin = self.rotary_emb(value_states, position_ids, seq_len=kv_seq_len)
+
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+        if past_key_value is not None:
+            cache_kwargs = {'sin': sin, 'cos': cos}  # Specific to RoPE models
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        # repeat k/v heads if n_kv_heads < n_heads
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+            raise ValueError(
+                f'Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is'
+                f' {attn_weights.size()}'
+            )
+
+        if attention_mask is not None:
+            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+                raise ValueError(
+                    f'Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}'
+                )
+            attn_weights = attn_weights + attention_mask
+
+        # upcast attention to fp32
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(value_states.dtype)
+        attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
+
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+            raise ValueError(
+                f'`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is'
+                f' {attn_output.size()}'
+            )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+class Phi3FlashAttention2(Phi3Attention):
+    """
+    Phi-3 flash attention module. This module inherits from `Phi3Attention` 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.
+    """
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
+        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
+        # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
+        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.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]]]:
+        # Phi3FlashAttention2 attention does not support output_attentions
+
+        if not _flash_supports_window_size:
+            logger.warning_once(
+                "The current flash attention version does not support sliding window attention. Please use `attn_implementation='eager'` or upgrade flash-attn library."
+            )
+            raise ValueError('The current flash attention version does not support sliding window attention.')
+
+        output_attentions = False
+
+        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')
+
+        bsz, q_len, _ = hidden_states.size()
+
+        qkv = self.qkv_proj(hidden_states)
+        query_pos = self.num_heads * self.head_dim
+        query_states = qkv[..., :query_pos]
+        key_states = qkv[..., query_pos : query_pos + self.num_key_value_heads * self.head_dim]
+        value_states = qkv[..., query_pos + self.num_key_value_heads * self.head_dim :]
+
+        # 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
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            if self.layer_idx is None:
+                raise ValueError(
+                    f'The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} '
+                    'for auto-regressive decoding with k/v caching, please make sure to initialize the attention class '
+                    'with a layer index.'
+                )
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+
+        # Because the input can be padded, the absolute sequence length depends on the max position id.
+        rotary_seq_len = max(kv_seq_len, position_ids[:, -1].max().item()) + 1
+        cos, sin = self.rotary_emb(value_states, position_ids, seq_len=rotary_seq_len)
+
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+        use_sliding_windows = (
+            _flash_supports_window_size
+            and getattr(self.config, 'sliding_window', None) is not None
+            and kv_seq_len > self.config.sliding_window
+        )
+
+        if past_key_value is not None:
+            # Activate slicing cache only if the config has a value `sliding_windows` attribute
+            cache_has_contents = past_key_value.get_seq_length(self.layer_idx) > 0
+            if (
+                getattr(self.config, 'sliding_window', None) is not None
+                and kv_seq_len > self.config.sliding_window
+                and cache_has_contents
+            ):
+                slicing_tokens = 1 - self.config.sliding_window
+
+                past_key = past_key_value[self.layer_idx][0]
+                past_value = past_key_value[self.layer_idx][1]
+
+                past_key = past_key[:, :, slicing_tokens:, :].contiguous()
+                past_value = past_value[:, :, slicing_tokens:, :].contiguous()
+
+                if past_key.shape[-2] != self.config.sliding_window - 1:
+                    raise ValueError(
+                        f'past key must have a shape of (`batch_size, num_heads, self.config.sliding_window-1, head_dim`), got'
+                        f' {past_key.shape}'
+                    )
+
+                if attention_mask is not None:
+                    attention_mask = attention_mask[:, slicing_tokens:]
+                    attention_mask = torch.cat([attention_mask, torch.ones_like(attention_mask[:, -1:])], dim=-1)
+
+            cache_kwargs = {'sin': sin, 'cos': cos}  # Specific to RoPE models
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        # repeat k/v heads if n_kv_heads < n_heads
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        attn_dropout = 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.
+
+        if query_states.dtype == torch.float32:
+            if torch.is_autocast_enabled():
+                target_dtype = torch.get_autocast_gpu_dtype()
+            # Handle the case where the model is quantized
+            elif hasattr(self.config, '_pre_quantization_dtype'):
+                target_dtype = self.config._pre_quantization_dtype
+            else:
+                target_dtype = self.qkv_proj.weight.dtype
+
+            logger.warning_once(
+                f'The input hidden states seems to be silently casted in float32, this might be related to'
+                f' the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in'
+                f' {target_dtype}.'
+            )
+
+            query_states = query_states.to(target_dtype)
+            key_states = key_states.to(target_dtype)
+            value_states = value_states.to(target_dtype)
+
+        # Reashape to the expected shape for Flash Attention
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        attn_output = self._flash_attention_forward(
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            q_len,
+            dropout=attn_dropout,
+            use_sliding_windows=use_sliding_windows,
+        )
+
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+    # Copied from transformers.models.mistral.modeling_mistral.MistralFlashAttention2._flash_attention_forward
+    def _flash_attention_forward(
+        self,
+        query_states,
+        key_states,
+        value_states,
+        attention_mask,
+        query_length,
+        dropout=0.0,
+        softmax_scale=None,
+        use_sliding_windows=False,
+    ):
+        """
+        Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
+        first unpad the input, then computes the attention scores and pad the final attention scores.
+
+        Args:
+            query_states (`torch.Tensor`):
+                Input query states to be passed to Flash Attention API
+            key_states (`torch.Tensor`):
+                Input key states to be passed to Flash Attention API
+            value_states (`torch.Tensor`):
+                Input value states to be passed to Flash Attention API
+            attention_mask (`torch.Tensor`):
+                The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
+                position of padding tokens and 1 for the position of non-padding tokens.
+            dropout (`float`):
+                Attention dropout
+            softmax_scale (`float`, *optional*):
+                The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
+            use_sliding_windows (`bool`, *optional*):
+                Whether to activate sliding window attention.
+        """
+        if not self._flash_attn_uses_top_left_mask:
+            causal = self.is_causal
+        else:
+            # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__.
+            causal = self.is_causal and query_length != 1
+
+        # Contains at least one padding token in the sequence
+        if attention_mask is not None:
+            batch_size = query_states.shape[0]
+            query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
+                query_states, key_states, value_states, attention_mask, query_length
+            )
+
+            cu_seqlens_q, cu_seqlens_k = cu_seq_lens
+            max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
+
+            if not use_sliding_windows:
+                attn_output_unpad = flash_attn_varlen_func(
+                    query_states,
+                    key_states,
+                    value_states,
+                    cu_seqlens_q=cu_seqlens_q,
+                    cu_seqlens_k=cu_seqlens_k,
+                    max_seqlen_q=max_seqlen_in_batch_q,
+                    max_seqlen_k=max_seqlen_in_batch_k,
+                    dropout_p=dropout,
+                    softmax_scale=softmax_scale,
+                    causal=causal,
+                )
+            else:
+                attn_output_unpad = flash_attn_varlen_func(
+                    query_states,
+                    key_states,
+                    value_states,
+                    cu_seqlens_q=cu_seqlens_q,
+                    cu_seqlens_k=cu_seqlens_k,
+                    max_seqlen_q=max_seqlen_in_batch_q,
+                    max_seqlen_k=max_seqlen_in_batch_k,
+                    dropout_p=dropout,
+                    softmax_scale=softmax_scale,
+                    causal=causal,
+                    window_size=(self.config.sliding_window, self.config.sliding_window),
+                )
+
+            attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
+        else:
+            if not use_sliding_windows:
+                attn_output = flash_attn_func(
+                    query_states,
+                    key_states,
+                    value_states,
+                    dropout,
+                    softmax_scale=softmax_scale,
+                    causal=causal,
+                )
+            else:
+                attn_output = flash_attn_func(
+                    query_states,
+                    key_states,
+                    value_states,
+                    dropout,
+                    softmax_scale=softmax_scale,
+                    causal=causal,
+                    window_size=(self.config.sliding_window, self.config.sliding_window),
+                )
+
+        return attn_output
+
+    # Copied from transformers.models.mistral.modeling_mistral.MistralFlashAttention2._upad_input
+    def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
+        batch_size, kv_seq_len, num_heads, head_dim = key_layer.shape
+
+        # On the first iteration we need to properly re-create the padding mask
+        # by slicing it on the proper place
+        if kv_seq_len != attention_mask.shape[-1]:
+            attention_mask_num_tokens = attention_mask.shape[-1]
+            attention_mask = attention_mask[:, attention_mask_num_tokens - kv_seq_len :]
+
+        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
+
+        key_layer = index_first_axis(key_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k)
+        value_layer = index_first_axis(value_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k)
+
+        if query_length == kv_seq_len:
+            query_layer = index_first_axis(
+                query_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k
+            )
+            cu_seqlens_q = cu_seqlens_k
+            max_seqlen_in_batch_q = max_seqlen_in_batch_k
+            indices_q = indices_k
+        elif query_length == 1:
+            max_seqlen_in_batch_q = 1
+            cu_seqlens_q = torch.arange(
+                batch_size + 1, dtype=torch.int32, device=query_layer.device
+            )  # There is a memcpy here, that is very bad.
+            indices_q = cu_seqlens_q[:-1]
+            query_layer = query_layer.squeeze(1)
+        else:
+            # The -q_len: slice assumes left padding.
+            attention_mask = attention_mask[:, -query_length:]
+            query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
+
+        return (
+            query_layer,
+            key_layer,
+            value_layer,
+            indices_q,
+            (cu_seqlens_q, cu_seqlens_k),
+            (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
+        )
+
+
+# copied from transformers.models.llama.modeling_llama.LlamaSdpaAttention with Llama->Phi3
+# TODO @Arthur no longer copied from LLama after static cache
+class Phi3SdpaAttention(Phi3Attention):
+    """
+    Phi3 attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
+    `Phi3Attention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
+    SDPA API.
+    """
+
+    # Adapted from Phi3Attention.forward
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        if output_attentions:
+            # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
+            logger.warning_once(
+                'Phi3Model is using Phi3SdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, '
+                'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
+            )
+            return super().forward(
+                hidden_states=hidden_states,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_value=past_key_value,
+                output_attentions=output_attentions,
+                use_cache=use_cache,
+            )
+
+        bsz, q_len, _ = hidden_states.size()
+
+        qkv = self.qkv_proj(hidden_states)
+        query_pos = self.num_heads * self.head_dim
+        query_states = qkv[..., :query_pos]
+        key_states = qkv[..., query_pos : query_pos + self.num_key_value_heads * self.head_dim]
+        value_states = qkv[..., query_pos + self.num_key_value_heads * self.head_dim :]
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+        cos, sin = self.rotary_emb(value_states, position_ids, seq_len=kv_seq_len)
+
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+        if past_key_value is not None:
+            cache_kwargs = {'sin': sin, 'cos': cos}  # Specific to RoPE models
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        if attention_mask is not None:
+            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+                raise ValueError(
+                    f'Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}'
+                )
+
+        # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
+        # Reference: https://github.com/pytorch/pytorch/issues/112577.
+        if query_states.device.type == 'cuda' and attention_mask is not None:
+            query_states = query_states.contiguous()
+            key_states = key_states.contiguous()
+            value_states = value_states.contiguous()
+
+        attn_output = torch.nn.functional.scaled_dot_product_attention(
+            query_states,
+            key_states,
+            value_states,
+            attn_mask=attention_mask,
+            dropout_p=self.attention_dropout if self.training else 0.0,
+            # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.
+            is_causal=self.is_causal and attention_mask is None and q_len > 1,
+        )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.view(bsz, q_len, self.hidden_size)
+
+        attn_output = self.o_proj(attn_output)
+
+        return attn_output, None, past_key_value
+
+
+PHI3_ATTENTION_CLASSES = {
+    'eager': Phi3Attention,
+    'flash_attention_2': Phi3FlashAttention2,
+    'sdpa': Phi3SdpaAttention,
+}
+
+
+class Phi3DecoderLayer(nn.Module):
+    def __init__(self, config: Phi3Config, layer_idx: int):
+        super().__init__()
+
+        self.config = config
+        self.self_attn = PHI3_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx=layer_idx)
+
+        self.mlp = Phi3MLP(config)
+        self.input_layernorm = Phi3RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        self.resid_attn_dropout = nn.Dropout(config.resid_pdrop)
+        self.resid_mlp_dropout = nn.Dropout(config.resid_pdrop)
+        self.post_attention_layernorm = Phi3RMSNorm(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]]]:
+        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.`'
+            )
+        """
+        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, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
+            position_ids (`torch.LongTensor` of shape `({0})`, *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)
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            use_cache (`bool`, *optional*):
+                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+                (see `past_key_values`).
+            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+        """
+
+        residual = hidden_states
+
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Self Attention
+        attn_outputs, self_attn_weights, present_key_value = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+        )
+
+        hidden_states = residual + self.resid_attn_dropout(attn_outputs)
+
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + self.resid_mlp_dropout(hidden_states)
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        return outputs
+
+
+PHI3_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 ([`Phi3Config`]):
+            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 Phi-3 model outputting raw hidden-states without any specific head on top.',
+    PHI3_START_DOCSTRING,
+)
+class Phi3PreTrainedModel(PreTrainedModel):
+    config_class = Phi3Config
+    base_model_prefix = 'model'
+    supports_gradient_checkpointing = True
+    _no_split_modules = ['Phi3DecoderLayer']
+    _skip_keys_device_placement = 'past_key_values'
+    _supports_flash_attn_2 = True
+    _supports_sdpa = False
+    _supports_cache_class = True
+
+    _version = '0.0.5'
+
+    def __init__(self, config: Phi3Config):
+        if not has_flash_attn:
+            config._attn_implementation = 'eager'
+            print('Warning: Flash attention is not available, using eager attention instead.')
+        super().__init__(config)
+
+    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_()
+
+
+PHI3_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 Phi-3 model outputting raw hidden-states without any specific head on top.',
+    PHI3_START_DOCSTRING,
+)
+class Phi3Model(Phi3PreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Phi3DecoderLayer`]
+
+    Args:
+        config: Phi3Config
+    """
+
+    def __init__(self, config: Phi3Config):
+        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.embed_dropout = nn.Dropout(config.embd_pdrop)
+        self.layers = nn.ModuleList(
+            [Phi3DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self._attn_implementation = config._attn_implementation
+
+        self.norm = Phi3RMSNorm(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(PHI3_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # retrieve input_ids and inputs_embeds
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError('You cannot specify both 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')
+
+        past_key_values_length = 0
+
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                logger.warning_once(
+                    '`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...'
+                )
+                use_cache = False
+
+        if use_cache:
+            use_legacy_cache = not isinstance(past_key_values, Cache)
+            if use_legacy_cache:
+                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+            past_key_values_length = past_key_values.get_usable_length(seq_length)
+
+        if position_ids is None:
+            device = input_ids.device if input_ids is not None else inputs_embeds.device
+            position_ids = torch.arange(
+                past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
+            )
+            position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
+        else:
+            position_ids = position_ids.view(-1, seq_length).long()
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        if attention_mask is not None and self._attn_implementation == 'flash_attention_2' and use_cache:
+            is_padding_right = attention_mask[:, -1].sum().item() != batch_size
+            if is_padding_right:
+                raise ValueError(
+                    "You are attempting to perform batched generation with padding_side='right'"
+                    ' this may lead to unexpected behaviour for Flash Attention version of Phi3. Make sure to '
+                    " call `tokenizer.padding_side  = 'left'` before tokenizing the input. "
+                )
+
+        if self._attn_implementation == 'flash_attention_2':
+            # 2d mask is passed through the layers
+            attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
+        else:
+            # 4d mask is passed through the layers
+            attention_mask = _prepare_4d_causal_attention_mask(
+                attention_mask,
+                (batch_size, seq_length),
+                inputs_embeds,
+                past_key_values_length,
+                sliding_window=self.config.sliding_window,
+            )
+
+        hidden_states = inputs_embeds
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = None
+
+        for decoder_layer in self.layers:
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    decoder_layer.__call__,
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    past_key_values,
+                    output_attentions,
+                    use_cache,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_values,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                next_decoder_cache = layer_outputs[2 if output_attentions else 1]
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        next_cache = None
+        if use_cache:
+            next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
+        if not return_dict:
+            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+
+class Phi3ForCausalLM(Phi3PreTrainedModel):
+    _tied_weights_keys = ['lm_head.weight']
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.__init__ with Llama->Phi3
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = Phi3Model(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()
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_input_embeddings
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_input_embeddings
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_output_embeddings
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_output_embeddings
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_decoder
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_decoder
+    def get_decoder(self):
+        return self.model
+
+    # Ignore copy
+    @add_start_docstrings_to_model_forward(PHI3_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        r"""
+        Args:
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, Phi3ForCausalLM
+
+        >>> model = Phi3ForCausalLM.from_pretrained("microsoft/phi-3-mini-4k-instruct")
+        >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-3-mini-4k-instruct")
+
+        >>> prompt = "This is an example script ."
+        >>> 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]
+        'This is an example script .\n Certainly! Below is a sample script that demonstrates a simple task, such as calculating the sum'
+        ```"""
+
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        hidden_states = outputs[0]
+        logits = self.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,
+        )
+
+    # Copied from transformers.models.persimmon.modeling_persimmon.PersimmonForCausalLM.prepare_inputs_for_generation
+    def prepare_inputs_for_generation(
+        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
+    ):
+        if past_key_values is not None:
+            if isinstance(past_key_values, Cache):
+                cache_length = past_key_values.get_seq_length()
+                past_length = past_key_values.seen_tokens
+                max_cache_length = past_key_values.get_max_length()
+            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 `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) or (inputs_embeds is not None and len(past_key_values) == 0):
+            model_inputs = {'inputs_embeds': inputs_embeds}
+        else:
+            model_inputs = {'input_ids': input_ids}
+
+        model_inputs.update(
+            {
+                'position_ids': position_ids,
+                'past_key_values': past_key_values,
+                'use_cache': kwargs.get('use_cache'),
+                'attention_mask': attention_mask,
+            }
+        )
+        return model_inputs
+
+    @staticmethod
+    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM._reorder_cache
+    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 [`Phi3Model`] with a sequence classification head on top (linear layer).
+
+    [`Phi3ForSequenceClassification`] 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).
+    """,
+    PHI3_START_DOCSTRING,
+)
+# Copied from transformers.models.llama.modeling_llama.LlamaForSequenceClassification with Llama->Phi3, LLAMA->PHI3, self.transformer->self.model, transformer_outputs->model_outputs
+class Phi3ForSequenceClassification(Phi3PreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = Phi3Model(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(PHI3_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        model_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 = model_outputs[0]
+        logits = self.score(hidden_states)
+
+        if input_ids is not None:
+            batch_size = input_ids.shape[0]
+        else:
+            batch_size = inputs_embeds.shape[0]
+
+        if self.config.pad_token_id is None and batch_size != 1:
+            raise ValueError('Cannot handle batch sizes > 1 if no padding token is defined.')
+        if self.config.pad_token_id is None:
+            sequence_lengths = -1
+        else:
+            if input_ids is not None:
+                # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
+                sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
+                sequence_lengths = sequence_lengths % input_ids.shape[-1]
+                sequence_lengths = sequence_lengths.to(logits.device)
+            else:
+                sequence_lengths = -1
+
+        pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
+
+        loss = None
+        if labels is not None:
+            labels = labels.to(logits.device)
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = 'regression'
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = 'single_label_classification'
+                else:
+                    self.config.problem_type = 'multi_label_classification'
+
+            if self.config.problem_type == 'regression':
+                loss_fct = MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(pooled_logits, labels)
+            elif self.config.problem_type == 'single_label_classification':
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == 'multi_label_classification':
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(pooled_logits, labels)
+        if not return_dict:
+            output = (pooled_logits,) + model_outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutputWithPast(
+            loss=loss,
+            logits=pooled_logits,
+            past_key_values=model_outputs.past_key_values,
+            hidden_states=model_outputs.hidden_states,
+            attentions=model_outputs.attentions,
+        )
+
+
+@add_start_docstrings(
+    """
+    [`Phi3Model`] with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
+    Named-Entity-Recognition (NER) tasks.
+    """,
+    PHI3_START_DOCSTRING,
+)
+# Copied from transformers.models.mpt.modeling_mpt.MptForTokenClassification with Mpt->Phi3,MPT->PHI3,self.transformer->self.model,transformer_outputs->model_outputs
+class Phi3ForTokenClassification(Phi3PreTrainedModel):
+    def __init__(self, config: Phi3Config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+
+        self.model = Phi3Model(config)
+        if hasattr(config, 'classifier_dropout') and config.classifier_dropout is not None:
+            classifier_dropout = config.classifier_dropout
+        elif hasattr(config, 'hidden_dropout') and config.hidden_dropout is not None:
+            classifier_dropout = config.hidden_dropout
+        else:
+            classifier_dropout = 0.1
+        self.dropout = nn.Dropout(classifier_dropout)
+        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @add_start_docstrings_to_model_forward(PHI3_INPUTS_DOCSTRING)
+    @add_code_sample_docstrings(
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=TokenClassifierOutput,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        labels: Optional[torch.Tensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        **deprecated_arguments,
+    ) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        model_outputs = self.model(
+            input_ids,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        hidden_states = model_outputs[0]
+        hidden_states = self.dropout(hidden_states)
+        logits = self.classifier(hidden_states)
+
+        loss = None
+        if labels is not None:
+            # move labels to correct device to enable model parallelism
+            labels = labels.to(logits.device)
+            batch_size, seq_length = labels.shape
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(
+                logits.view(batch_size * seq_length, self.num_labels), labels.view(batch_size * seq_length)
+            )
+
+        if not return_dict:
+            output = (logits,) + model_outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return TokenClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=model_outputs.hidden_states,
+            attentions=model_outputs.attentions,
+        )

modeling_sa2va_chat.py

@@ -0,0 +1,1100 @@
+# --------------------------------------------------------
+# InternVL
+# Copyright (c) 2024 OpenGVLab
+# Licensed under The MIT License [see LICENSE for details]
+# --------------------------------------------------------
+
+import warnings
+from typing import Any, List, Optional, Tuple, Union, Dict, Set
+from PIL import Image
+import re
+
+import torchvision.transforms as T
+from torchvision.transforms.functional import InterpolationMode
+
+import torch.utils.checkpoint
+import transformers
+
+from .modeling_internlm2 import InternLM2ForCausalLM
+from .modeling_phi3 import Phi3ForCausalLM
+from peft import LoraConfig, get_peft_model
+from torch import nn
+from torch.nn import CrossEntropyLoss
+from transformers import (AutoModel, GenerationConfig, LlamaForCausalLM,
+                          LlamaTokenizer, Qwen2ForCausalLM)
+from transformers.modeling_outputs import CausalLMOutputWithPast
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import ModelOutput, logging, TensorType
+from transformers import StoppingCriteriaList, StoppingCriteria
+from transformers.models.mask2former.image_processing_mask2former import (
+    remove_low_and_no_objects, check_segment_validity)
+
+from .configuration_sa2va_chat import Sa2VAChatConfig
+from .modeling_intern_vit import InternVisionModel, has_flash_attn
+
+from .templates import PROMPT_TEMPLATE
+
+import numpy as np
+from torchvision.transforms.functional import resize, to_pil_image
+
+from types import MethodType
+import torch.nn.functional as F
+
+from transformers import Mask2FormerForUniversalSegmentation
+
+from .mask2former import (
+    Mask2FormerMaskedAttentionDecoder_forward_first3layers,
+    Mask2FormerMaskedAttentionDecoder_forward_last3layers,
+    Mask2FormerTransformerModule_forward_first_part,
+    Mask2FormerTransformerModule_forward_second_part,
+    Mask2FormerModel_forward_first_part,
+    Mask2FormerModel_forward_second_part,
+    Mask2FormerForUniversalSegmentation_forward_first_part,
+    Mask2FormerForUniversalSegmentation_forward_second_part,
+    _post_init,
+    ov_class_predictor,
+    Mask2FormerLoss_loss_labels,
+    Mask2FormerLoss_loss_masks,
+    Mask2FormerLoss_sample_points_using_uncertainty,
+    Mask2FormerHungarianMatcher_forward,
+)
+
+from .constants import (
+    IMG_CONTEXT_TOKEN, OBJ_CONTEXT_TOKEN, SEG_TOKEN, CLS_TOKEN, BG_CLS_TOKEN, OBJ_START_TOKEN, OBJ_END_TOKEN)
+
+
+
+try:
+    from .flash_attention import FlashAttention
+    has_flash_attn = True
+except:
+    print('FlashAttention is not installed.')
+    has_flash_attn = False
+
+logger = logging.get_logger(__name__)
+
+def version_cmp(v1, v2, op='eq'):
+    import operator
+
+    from packaging import version
+    op_func = getattr(operator, op)
+    return op_func(version.parse(v1), version.parse(v2))
+
+class StopWordStoppingCriteria(StoppingCriteria):
+    """StopWord stopping criteria."""
+
+    def __init__(self, tokenizer, stop_word):
+        self.tokenizer = tokenizer
+        self.stop_word = stop_word
+        self.length = len(self.stop_word)
+
+    def __call__(self, input_ids, *args, **kwargs) -> bool:
+        cur_text = self.tokenizer.decode(input_ids[0])
+        cur_text = cur_text.replace('\r', '').replace('\n', '')
+        return cur_text[-self.length:] == self.stop_word
+
+def get_stop_criteria(
+    tokenizer,
+    stop_words=[],
+):
+    stop_criteria = StoppingCriteriaList()
+    for word in stop_words:
+        stop_criteria.append(StopWordStoppingCriteria(tokenizer, word))
+    return stop_criteria
+
+class DirectResize:
+    def __init__(self, target_length: int) -> None:
+        self.target_length = target_length
+
+    def apply_image(self, image: np.ndarray) -> np.ndarray:
+        """
+        Expects a numpy array with shape HxWxC in uint8 format.
+        """
+        img = to_pil_image(image, mode='RGB')
+        return np.array(img.resize((self.target_length, self.target_length)))
+
+class Sa2VAChatModel(PreTrainedModel):
+    config_class = Sa2VAChatConfig
+    main_input_name = 'pixel_values'
+    base_model_prefix = 'language_model'
+    _no_split_modules = ['InternVisionModel', 'LlamaDecoderLayer', 'InternLM2DecoderLayer',
+                         'Phi3DecoderLayer', 'Qwen2DecoderLayer', 'Mask2FormerForUniversalSegmentation']
+    _supports_flash_attn_2 = True
+    supports_gradient_checkpointing = True
+
+    def __init__(self, config: Sa2VAChatConfig, vision_model=None, language_model=None, mask2former=None, use_flash_attn=True):
+        super().__init__(config)
+
+        assert version_cmp(transformers.__version__, '4.37.0', 'ge')
+        image_size = config.force_image_size or config.vision_config.image_size
+        patch_size = config.vision_config.patch_size
+        self.patch_size = patch_size
+        self.select_layer = config.select_layer
+        self.template = config.template
+        self.template = self.template.replace('-', '_')
+        self.num_image_token = int((image_size // patch_size) ** 2 * (config.downsample_ratio ** 2))
+        self.downsample_ratio = config.downsample_ratio
+        self.ps_version = config.ps_version
+        self.llm_arch_name = config.llm_config.architectures[0]
+
+        use_flash_attn = use_flash_attn if has_flash_attn else False
+        config.vision_config.use_flash_attn = True if use_flash_attn else False
+        config.llm_config._attn_implementation = 'flash_attention_2' if use_flash_attn else 'eager'
+
+        logger.info(f'num_image_token: {self.num_image_token}')
+        logger.info(f'ps_version: {self.ps_version}')
+        if vision_model is not None:
+            self.vision_model = vision_model
+        else:
+            self.vision_model = InternVisionModel(config.vision_config)
+        if language_model is not None:
+            self.language_model = language_model
+        else:
+            if config.llm_config.architectures[0] == 'LlamaForCausalLM':
+                self.language_model = LlamaForCausalLM(config.llm_config)
+            elif config.llm_config.architectures[0] == 'InternLM2ForCausalLM':
+                self.language_model = InternLM2ForCausalLM(config.llm_config)
+            elif config.llm_config.architectures[0] == 'Phi3ForCausalLM':
+                self.language_model = Phi3ForCausalLM(config.llm_config)
+            elif config.llm_config.architectures[0] == 'Qwen2ForCausalLM':
+                self.language_model = Qwen2ForCausalLM(config.llm_config)
+            else:
+                raise NotImplementedError(f'{config.llm_config.architectures[0]} is not implemented.')
+
+        vit_hidden_size = config.vision_config.hidden_size
+        llm_hidden_size = config.llm_config.hidden_size
+
+        self.mlp1 = nn.Sequential(
+            nn.LayerNorm(vit_hidden_size * int(1 / self.downsample_ratio) ** 2),
+            nn.Linear(vit_hidden_size * int(1 / self.downsample_ratio) ** 2, llm_hidden_size),
+            nn.GELU(),
+            nn.Linear(llm_hidden_size, llm_hidden_size)
+        )
+
+        self.img_context_token_id = None
+        self.conv_template = PROMPT_TEMPLATE[self.template]
+        self.template = self.conv_template
+        if hasattr(config, 'system_message'):
+            self.system_message = config.system_message
+        self.num_samples = 0
+
+        if config.use_backbone_lora:
+            self.wrap_backbone_lora(r=config.use_backbone_lora, lora_alpha=2 * config.use_backbone_lora)
+
+        if config.use_llm_lora:
+            self.wrap_llm_lora(r=config.use_llm_lora, lora_alpha=2 * config.use_llm_lora)
+        
+        # mask2former
+        if mask2former is None:
+            self.mask2former = Mask2FormerForUniversalSegmentation(config.m2f_config)
+        else:
+            self.mask2former = mask2former
+        assert self.mask2former.config.num_queries == config.num_m2f_queries
+        self.num_m2f_queries =config. num_m2f_queries
+        self.num_m2f_proposals = config.num_m2f_proposals
+        self.m2f_input_size = 1024
+
+        # register functions
+        self.mask2former._post_init = MethodType(_post_init, self.mask2former)
+        self.mask2former.ov_class_predictor = MethodType(ov_class_predictor, self.mask2former)
+        self.mask2former.criterion.loss_labels = MethodType(Mask2FormerLoss_loss_labels, self.mask2former.criterion)
+        self.mask2former.criterion.loss_masks = MethodType(Mask2FormerLoss_loss_masks, self.mask2former.criterion)
+        self.mask2former.criterion.sample_points_using_uncertainty = MethodType(
+            Mask2FormerLoss_sample_points_using_uncertainty, self.mask2former.criterion)
+        self.mask2former.forward_first_part = MethodType(Mask2FormerForUniversalSegmentation_forward_first_part, self.mask2former)
+        self.mask2former.forward_second_part = MethodType(Mask2FormerForUniversalSegmentation_forward_second_part, self.mask2former)
+        self.mask2former.model.Mask2FormerModel_forward_first_part = MethodType(
+            Mask2FormerModel_forward_first_part, self.mask2former.model)
+        self.mask2former.model.Mask2FormerModel_forward_second_part = MethodType(
+            Mask2FormerModel_forward_second_part, self.mask2former.model)
+        self.mask2former.model.transformer_module.Mask2FormerTransformerModule_forward_first_part = MethodType(
+            Mask2FormerTransformerModule_forward_first_part, self.mask2former.model.transformer_module
+        )
+        self.mask2former.model.transformer_module.Mask2FormerTransformerModule_forward_second_part = MethodType(
+            Mask2FormerTransformerModule_forward_second_part, self.mask2former.model.transformer_module
+        )
+        self.mask2former.model.transformer_module.decoder.Mask2FormerMaskedAttentionDecoder_forward_first3layers = MethodType(
+            Mask2FormerMaskedAttentionDecoder_forward_first3layers, self.mask2former.model.transformer_module.decoder
+        )
+        self.mask2former.model.transformer_module.decoder.Mask2FormerMaskedAttentionDecoder_forward_last3layers = MethodType(
+            Mask2FormerMaskedAttentionDecoder_forward_last3layers, self.mask2former.model.transformer_module.decoder
+        )
+        self.mask2former.criterion.matcher.forward = MethodType(Mask2FormerHungarianMatcher_forward, self.mask2former.criterion.matcher)
+        
+        # post_init of mask2former
+        self.mask2former._post_init()
+
+        out_dim = config.m2f_config.hidden_dim
+        in_dim = config.llm_config.hidden_size
+
+        self.m2f_to_llm = nn.Sequential(
+            nn.LayerNorm(out_dim,),
+            nn.Linear(out_dim, in_dim),
+            nn.GELU(),
+            nn.Linear(in_dim, in_dim)
+        )
+
+        self.llm_to_m2f = nn.Sequential(
+            nn.LayerNorm(in_dim),
+            nn.Linear(in_dim, out_dim * 2),
+            nn.GELU(),
+            nn.Linear(out_dim * 2, out_dim * 2)
+        )
+
+        self.llm_to_cls = nn.Sequential(
+            nn.LayerNorm(in_dim),
+            nn.Linear(in_dim, out_dim),
+            nn.GELU(),
+            nn.Linear(out_dim, out_dim)
+        )
+
+        self.init_prediction_config = False
+
+    def wrap_backbone_lora(self, r=128, lora_alpha=256, lora_dropout=0.05):
+        lora_config = LoraConfig(
+            r=r,
+            target_modules=['attn.qkv', 'attn.proj', 'mlp.fc1', 'mlp.fc2'],
+            lora_alpha=lora_alpha,
+            lora_dropout=lora_dropout,
+        )
+        self.vision_model = get_peft_model(self.vision_model, lora_config)
+        self.vision_model.print_trainable_parameters()
+
+    def wrap_llm_lora(self, r=128, lora_alpha=256, lora_dropout=0.05):
+        # Determine the target modules based on the architecture of the language model
+        if self.llm_arch_name == 'InternLM2ForCausalLM':
+            target_modules = ['attention.wqkv', 'attention.wo', 'feed_forward.w1', 'feed_forward.w2', 'feed_forward.w3']
+        elif self.llm_arch_name == 'Phi3ForCausalLM':
+            target_modules = ['mlp.down_proj', 'mlp.gate_up_proj', 'self_attn.o_proj', 'self_attn.qkv_proj']
+        elif self.llm_arch_name in ['Qwen2ForCausalLM', 'LlamaForCausalLM']:
+            target_modules = ['self_attn.q_proj', 'self_attn.k_proj', 'self_attn.v_proj', 'self_attn.o_proj',
+                              'mlp.gate_proj', 'mlp.down_proj', 'mlp.up_proj']
+        else:
+            raise NotImplemented
+        lora_config = LoraConfig(
+            r=r,
+            target_modules=target_modules,
+            lora_alpha=lora_alpha,
+            lora_dropout=lora_dropout,
+            task_type='CAUSAL_LM'
+        )
+        self.language_model = get_peft_model(self.language_model, lora_config)
+        self.language_model.enable_input_require_grads()
+        self.language_model.print_trainable_parameters()
+
+    def pixel_shuffle(self, x, scale_factor=0.5):
+        n, w, h, c = x.size()
+        # N, W, H, C --> N, W, H * scale, C // scale
+        x = x.view(n, w, int(h * scale_factor), int(c / scale_factor))
+        # N, W, H * scale, C // scale --> N, H * scale, W, C // scale
+        x = x.permute(0, 2, 1, 3).contiguous()
+        # N, H * scale, W, C // scale --> N, H * scale, W * scale, C // (scale ** 2)
+        x = x.view(n, int(h * scale_factor), int(w * scale_factor),
+                   int(c / (scale_factor * scale_factor)))
+        if self.ps_version == 'v1':
+            warnings.warn("In ps_version 'v1', the height and width have not been swapped back, "
+                          'which results in a transposed image.')
+        else:
+            x = x.permute(0, 2, 1, 3).contiguous()
+        return x
+
+    def extract_feature(self, pixel_values):
+        if self.select_layer == -1:
+            vit_embeds = self.vision_model(
+                pixel_values=pixel_values,
+                output_hidden_states=False,
+                return_dict=True).last_hidden_state
+        else:
+            vit_embeds = self.vision_model(
+                pixel_values=pixel_values,
+                output_hidden_states=True,
+                return_dict=True).hidden_states[self.select_layer]
+        vit_embeds = vit_embeds[:, 1:, :]
+
+        h = w = int(vit_embeds.shape[1] ** 0.5)
+        vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], h, w, -1)
+        vit_embeds = self.pixel_shuffle(vit_embeds, scale_factor=self.downsample_ratio)
+        vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], -1, vit_embeds.shape[-1])
+        vit_embeds = self.mlp1(vit_embeds)
+        return vit_embeds
+
+    @property
+    def lm_head(self):
+        return self.language_model.get_output_embeddings()
+
+    def get_input_embeddings(self):
+        return self.language_model.get_input_embeddings()
+
+    def get_output_embeddings(self):
+        return self.language_model.get_output_embeddings()
+
+    def forward(self, data, data_samples=None, mode='loss'):
+        pixel_values = data['pixel_values']
+
+        if type(pixel_values) is list or pixel_values.ndim == 5:
+            if type(pixel_values) is list:
+                pixel_values = [
+                    x.unsqueeze(0) if x.ndim == 3 else x for x in pixel_values
+                ]
+            # b*n, c, h, w
+            concat_images = torch.cat(
+                [image.to(self.vision_model.dtype) for image in pixel_values], dim=0)
+        else:
+            raise NotImplementedError()
+
+        input_ids = data['input_ids']
+        position_ids = data['position_ids']
+        attention_mask = data['attention_mask']
+        # sum is 0 are text
+        image_flags = torch.sum(concat_images, dim=(1, 2, 3)) != 0
+        image_flags = image_flags.long()
+
+        labels = data['labels']
+        use_cache = False
+
+        if 'vp_overall_mask' not in data.keys():
+            vp_overall_mask = None
+        else:
+            vp_overall_mask = data['vp_overall_mask']
+
+        if 'prompt_masks' in data.keys():
+            prompt_masks = data['prompt_masks']
+        else:
+            prompt_masks = None
+
+        outputs = self._llm_forward(
+            input_ids=input_ids,
+            position_ids=position_ids,
+            attention_mask=attention_mask,
+            image_flags=image_flags,
+            pixel_values=concat_images,
+            labels=labels,
+            use_cache=use_cache,
+            output_hidden_states=True,
+            vp_overall_mask=vp_overall_mask,
+            prompt_masks=prompt_masks,
+        )
+
+        return outputs
+
+    def _llm_forward(
+            self,
+            pixel_values: torch.FloatTensor,
+            input_ids: torch.LongTensor = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            image_flags: Optional[torch.LongTensor] = None,
+            past_key_values: Optional[List[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,
+            vp_overall_mask=None,
+            prompt_masks=None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        return_dict = return_dict if return_dict is not None \
+            else self.config.use_return_dict
+
+        image_flags = image_flags.squeeze(-1)
+        # We only added the clone code here to avoid the error.
+        input_embeds = self.language_model.get_input_embeddings()(
+            input_ids).clone()
+
+        vit_embeds = self.extract_feature(pixel_values)
+        vit_embeds = vit_embeds.to(input_embeds.dtype)  # FIXME: why vit_embeds is float16?
+        fast_vit_embeds = None
+
+        vit_embeds = vit_embeds[image_flags == 1]
+        vit_batch_size = pixel_values.shape[0]
+
+        B, N, C = input_embeds.shape
+        input_embeds = input_embeds.reshape(B * N, C)
+
+        self._count += 1
+
+        if vp_overall_mask is not None and prompt_masks is not None:
+            vp_embeds = []
+            vp_overall_mask = vp_overall_mask.to(vit_embeds.device).bool()
+            prompt_masks = [item.to(vit_embeds.device).bool() for item in prompt_masks]
+
+            vp_overall_mask = vp_overall_mask[image_flags == 1]
+            overall_tile_vit_embeds = vit_embeds[vp_overall_mask]  # (n_img, hw, c)
+
+            i_vp_img = 0
+            for i_img in range(len(vit_embeds)):
+                vp_embeds.append(vit_embeds[i_img].reshape(-1, C))
+                if vp_overall_mask[i_img]:
+                    tile_vit_embeds = overall_tile_vit_embeds[i_vp_img].reshape(-1, C)  # (hw, C)
+                    objects_prompt_masks = prompt_masks[i_vp_img]
+                    n_obj = len(objects_prompt_masks)
+                    tile_vit_embeds = tile_vit_embeds.unsqueeze(0).repeat(n_obj, 1, 1)
+                    objects_prompt_masks = objects_prompt_masks.reshape(n_obj, -1)
+                    vp_embeds.append(tile_vit_embeds[objects_prompt_masks])
+                    i_vp_img += 1
+            vp_embeds = torch.cat(vp_embeds, dim=0)
+        else:
+            vp_embeds = None
+
+        input_ids = input_ids.reshape(B * N)
+        selected = (input_ids == self.img_context_token_id)
+
+        if vp_embeds is None:
+            try:
+                input_embeds[selected] = vit_embeds.reshape(-1, C)
+            except Exception as e:
+                vit_embeds = vit_embeds.reshape(-1, C)
+                print(f'warning: {e}, input_embeds[selected].shape='
+                      f'{input_embeds[selected].shape}, '
+                      f'vit_embeds.shape={vit_embeds.shape}')
+                n_token = selected.sum()
+                if n_token > len(vit_embeds):
+                    print(f"Wrong !!! {n_token} image tokens in text but only {len(vit_embeds)} vit embeds !!!")
+                    expand_ratio = n_token // len(vit_embeds) + 1
+                    vit_embeds = torch.cat([vit_embeds] * expand_ratio, dim=0)
+
+                input_embeds[selected] = vit_embeds[:n_token]
+        else:
+            try:
+                input_embeds[selected] = vp_embeds.reshape(-1, C)
+            except Exception as e:
+                vp_embeds = vp_embeds.reshape(-1, C)
+                print(f'warning: {e}, input_embeds[selected].shape='
+                      f'{input_embeds[selected].shape}, '
+                      f'vp_embeds.shape={vp_embeds.shape}')
+                n_token = selected.sum()
+                if n_token > len(vp_embeds):
+                    print(f"Wrong !!! {n_token} image tokens in text but only {len(vp_embeds)} vit embeds !!!")
+                    expand_ratio = n_token // len(vp_embeds) + 1
+                    vp_embeds = torch.cat([vp_embeds] * expand_ratio, dim=0)
+
+                input_embeds[selected] = vp_embeds[:n_token]
+
+        input_embeds = input_embeds.reshape(B, N, C)
+
+        outputs = self.language_model(
+            inputs_embeds=input_embeds,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        logits = outputs.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.language_model.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,
+        )
+
+    @torch.no_grad()
+    def generate(
+            self,
+            pixel_values: Optional[torch.FloatTensor] = None,
+            input_ids: Optional[torch.FloatTensor] = None,
+            attention_mask: Optional[torch.LongTensor] = None,
+            visual_features: Optional[torch.FloatTensor] = None,
+            generation_config: Optional[GenerationConfig] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+            prompt_masks=None,
+            vp_overall_mask=None,
+            query_embeds=None,
+            **generate_kwargs,
+    ) -> torch.LongTensor:
+        device = self.device
+        assert self.img_context_token_id is not None
+
+        if pixel_values is not None:
+            if visual_features is not None:
+                vit_embeds = visual_features
+            else:
+                if type(pixel_values) is list or pixel_values.ndim == 5:
+                    if type(pixel_values) is list:
+                        pixel_values = [
+                            x.unsqueeze(0) if x.ndim == 3 else x for x in pixel_values
+                        ]
+                    # b*n, c, h, w
+                    pixel_values = torch.cat(
+                        [image.to(self.vision_model.dtype) for image in pixel_values], dim=0)
+
+                vit_embeds = self.extract_feature(pixel_values.to(device))
+            image_flags = torch.sum(pixel_values, dim=(1, 2, 3)) != 0
+            image_flags = image_flags.long()
+            vit_embeds = vit_embeds[image_flags == 1]
+
+            input_embeds = self.language_model.get_input_embeddings()(input_ids.to(device))
+            B, N, C = input_embeds.shape
+            input_embeds = input_embeds.reshape(B * N, C)
+
+            input_ids = input_ids.reshape(B * N)
+            selected = (input_ids == self.img_context_token_id)
+            assert selected.sum() != 0
+            input_embeds[selected] = vit_embeds.reshape(-1, C).to(input_embeds.device)
+
+            # object queries
+            query_embeds = query_embeds.to(input_embeds.dtype)
+            selected = (input_ids == self.obj_context_token_id)
+            input_embeds[selected] = query_embeds.reshape(-1, C)
+            
+            input_embeds = input_embeds.reshape(B, N, C)
+        else:
+            input_embeds = self.language_model.get_input_embeddings()(input_ids)
+
+        outputs = self.language_model.generate(
+            inputs_embeds=input_embeds,
+            attention_mask=attention_mask.to(device),
+            generation_config=generation_config,
+            output_hidden_states=output_hidden_states,
+            # return_dict=return_dict,
+            use_cache=True,
+            **generate_kwargs,
+        )
+
+        return outputs
+
+    def preparing_for_generation(self, tokenizer, max_new_tokens=2048, torch_dtype=torch.bfloat16):
+        # set stop criteria and generation configs for model
+        if not hasattr(self, 'tokenizer'):
+            self.tokenizer = tokenizer
+        self.bot_name = 'BOT'
+        stop_words = []
+        stop_words += self.template.get('STOP_WORDS', [])
+        stop_criteria = get_stop_criteria(
+            tokenizer=self.tokenizer, stop_words=stop_words)
+        self.stop_criteria = stop_criteria
+
+        default_generation_kwargs = dict(
+            max_new_tokens=max_new_tokens,
+            do_sample=False,
+            eos_token_id=self.tokenizer.eos_token_id,
+            pad_token_id=(
+                self.tokenizer.pad_token_id
+                if self.tokenizer.pad_token_id is not None
+                else self.tokenizer.eos_token_id
+            ),
+        )
+
+        self.gen_config = GenerationConfig(**default_generation_kwargs)
+        self.init_prediction_config = True
+        self.torch_dtype = torch_dtype
+        self.to(torch_dtype)
+        self.extra_image_processor = DirectResize(target_length=1024, )
+        # for multi image process
+        self.min_dynamic_patch = 1
+        self.max_dynamic_patch = 12
+        self.downsample_ratio = 0.5
+        self.image_size = 448
+        self.use_thumbnail = True
+        patch_size = 14
+        self.patch_size = patch_size
+
+        self.patch_token = int((self.image_size // patch_size) ** 2 * (self.downsample_ratio ** 2))
+        self.IMAGENET_MEAN = (0.485, 0.456, 0.406)
+        self.IMAGENET_STD = (0.229, 0.224, 0.225)
+        self.IMG_CONTEXT_TOKEN = '<IMG_CONTEXT>'
+        self.IMG_START_TOKEN = '<img>'
+        self.IMG_END_TOKEN = '</img>'
+
+        self.transformer = T.Compose([
+            T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
+            T.Resize((self.image_size, self.image_size), interpolation=InterpolationMode.BICUBIC),
+            T.ToTensor(),
+            T.Normalize(mean=self.IMAGENET_MEAN, std=self.IMAGENET_STD)
+        ])
+
+        # change phi3 prepare for generation fuction
+        if self.config.llm_config.architectures[0] == 'Phi3ForCausalLM':
+            self.language_model.prepare_inputs_for_generation = MethodType(prepare_inputs_for_generation_phi3, self.language_model)
+        
+        img_context_token_id = tokenizer.convert_tokens_to_ids(IMG_CONTEXT_TOKEN)
+        self.img_context_token_id = img_context_token_id
+        obj_context_token_id = tokenizer.convert_tokens_to_ids(OBJ_CONTEXT_TOKEN)
+        self.obj_context_token_id = obj_context_token_id
+        
+        self.PROPOSAL_TOKENS = [SEG_TOKEN.format(id=str(i).zfill(3)) for i in range(self.num_m2f_proposals)]
+        self.the_first_seg_token_idx = self.tokenizer(self.PROPOSAL_TOKENS[0], add_special_tokens=False).input_ids[0]
+        self.the_last_seg_token_idx = self.tokenizer(self.PROPOSAL_TOKENS[-1], add_special_tokens=False).input_ids[0]
+        self.cls_token_idx = self.tokenizer(CLS_TOKEN, add_special_tokens=False).input_ids[0]
+        self.bg_cls_token_idx = self.tokenizer(BG_CLS_TOKEN, add_special_tokens=False).input_ids[0]
+
+        return
+
+    def predict_forward(
+            self,
+            image=None,
+            video=None,
+            text=None,
+            past_text='',
+            mask_prompts=None,
+            tokenizer=None,
+            m2f_processor=None,
+    ):
+        if not self.init_prediction_config:
+            assert tokenizer
+            self.preparing_for_generation(tokenizer=tokenizer)
+
+        if image is None and video is None and '<image>' not in past_text:
+            text = text.replace('<image>', "")
+            input_text = ''
+            input_text += self.template['INSTRUCTION'].format(
+                input=text, round=1, bot_name=self.bot_name)
+            input_text = past_text + input_text
+            ids = self.tokenizer.encode(input_text)
+            ids = torch.tensor(ids).cuda().unsqueeze(0)
+
+            attention_mask = torch.ones_like(ids, dtype=torch.bool)
+
+            mm_inputs = {
+                'pixel_values': None,
+                'input_ids': ids,
+                'attention_mask': attention_mask,
+                'position_ids': None,
+                'past_key_values': None,
+                'labels': None,
+                'prompt_masks': None,
+                'vp_overall_mask': None,
+                'm2f_inputs': None,
+            }
+        else:
+            input_dict = {}
+            if video is not None:
+                pixel_values = []
+                ori_image_size = video[0].size
+                for frame_idx, frame_image in enumerate(video):
+                    assert ori_image_size == frame_image.size
+                    img = self.transformer(frame_image)
+                    pixel_values.append(img)
+
+                pixel_values = torch.stack(pixel_values, dim=0).to(self.torch_dtype)  # (n_f, 3, h, w)
+                num_image_tokens = self.patch_token
+                num_frames = len(pixel_values)
+
+                # prepapre mask2former inputs
+                m2f_pixel_values, m2f_pixel_masks = [], []
+                for frame_idx, frame_image in enumerate(video):
+                    assert ori_image_size == frame_image.size
+                    w, h = frame_image.size
+                    if w > h:
+                        target_size = (self.m2f_input_size, int(h/w*self.m2f_input_size))
+                    else:
+                        target_size = (int(w/h*self.m2f_input_size), self.m2f_input_size)
+                    
+                    resized_frame_image = frame_image.resize(target_size)
+                    cur_w, cur_h = resized_frame_image.size
+                    padded_frame_image = np.ones(shape=(self.m2f_input_size, self.m2f_input_size, 3), dtype=np.uint8) * 255
+                    padded_frame_image[:cur_h, :cur_w, :] = np.array(resized_frame_image)
+                    m2f_inputs_i = m2f_processor(images=Image.fromarray(padded_frame_image), return_tensors="pt", do_resize=False)
+                    m2f_pixel_values.append(m2f_inputs_i['pixel_values'])
+                    m2f_pixel_masks.append(m2f_inputs_i['pixel_mask'])
+                m2f_inputs = {
+                    'pixel_values': torch.cat(m2f_pixel_values, dim=0),
+                    'pixel_mask': torch.cat(m2f_pixel_masks, dim=0)}
+            else:
+                ori_image_size = image.size
+
+                images = dynamic_preprocess(image, self.min_dynamic_patch,
+                                            self.max_dynamic_patch,
+                                            self.image_size, self.use_thumbnail)
+
+                pixel_values = [self.transformer(patch) for patch in images]
+                pixel_values = torch.stack(pixel_values).to(self.torch_dtype)
+                num_image_tokens = pixel_values.shape[0] * self.patch_token
+                num_frames = 1
+
+                w, h = image.size
+                if w > h:
+                    target_size = (self.m2f_input_size, int(h/w*self.m2f_input_size))
+                else:
+                    target_size = (int(w/h*self.m2f_input_size), self.m2f_input_size)
+                
+                resized_image = image.resize(target_size)
+                cur_w, cur_h = resized_image.size
+                padded_image = np.ones(shape=(self.m2f_input_size, self.m2f_input_size, 3), dtype=np.uint8) * 255
+                padded_image[:cur_h, :cur_w, :] = np.array(resized_image)
+                m2f_inputs = m2f_processor(images=Image.fromarray(padded_image), return_tensors="pt", do_resize=False)
+
+            input_dict['pixel_values'] = pixel_values
+            
+            #TODO add a frame tag to indicate the order
+            image_token_str = f'{self.IMG_START_TOKEN}' \
+                              f'{self.IMG_CONTEXT_TOKEN * num_image_tokens}' \
+                              f'{self.IMG_END_TOKEN}'
+            object_token_str = f"{OBJ_START_TOKEN}"\
+                               f"{OBJ_CONTEXT_TOKEN * self.num_m2f_queries}"\
+                               f"{OBJ_END_TOKEN}"
+            image_token_str = image_token_str + '\n' + object_token_str + '\n'
+            image_token_str = image_token_str * num_frames
+            image_token_str = image_token_str.strip()
+
+            if '<image>' in text or mask_prompts is not None:
+                assert past_text is None or len(past_text) == 0
+            text = text.replace('<image>', image_token_str)
+            input_text = ''
+            input_text += self.template['INSTRUCTION'].format(
+                input=text, round=1, bot_name=self.bot_name)
+            input_text = past_text + input_text
+            ids = self.tokenizer.encode(input_text)
+            ids = torch.tensor(ids).cuda().unsqueeze(0)
+
+            attention_mask = torch.ones_like(ids, dtype=torch.bool)
+
+            # encode multi-scale visual features into 100~300 queries
+            m2f_inputs['pixel_values'] = m2f_inputs['pixel_values'].to(self.mask2former.dtype).to(self.mask2former.device)
+            m2f_inputs['pixel_mask'] = m2f_inputs['pixel_mask'].to(self.mask2former.dtype).to(self.mask2former.device)
+            query_features, pixel_level_module_output = \
+                self.mask2former.forward_first_part(**m2f_inputs)
+            query_embeds = self.m2f_to_llm(query_features)  # BS, m2f_NQ, 2048
+
+
+            mm_inputs = {
+                'pixel_values': input_dict['pixel_values'],
+                'input_ids': ids,
+                'attention_mask': attention_mask,
+                'position_ids': None,
+                'past_key_values': None,
+                'labels': None,
+                'query_embeds': query_embeds,
+                # 'prompt_masks': mask_prompts,
+                # 'vp_overall_mask': input_dict['vp_overall_mask'],
+            }
+
+        generate_output = self.generate(
+            **mm_inputs,
+            generation_config=self.gen_config,
+            streamer=None,
+            bos_token_id=self.tokenizer.bos_token_id,
+            stopping_criteria=self.stop_criteria,
+            output_hidden_states=True,
+            return_dict_in_generate=True
+        )
+        predict = self.tokenizer.decode(
+            generate_output.sequences[0], skip_special_tokens=False).strip()
+        
+        ret_masks = []
+        if image is None and video is None and '<image>' not in past_text:
+            return {'prediction': predict, 'prediction_masks': ret_masks, 'm2f_outputs': None}
+
+        # if have seg result, find the seg hidden states
+        hidden_states = generate_output.hidden_states
+        last_hidden_states = [item[-1][0] for item in hidden_states]
+        last_hidden_states = torch.cat(last_hidden_states, dim=0)
+
+        # get cls tokens
+        bg_cls_token_id = torch.as_tensor([self.bg_cls_token_idx,], dtype=ids.dtype, device=ids.device)
+        bg_cls_embedding = self.language_model.get_input_embeddings()(bg_cls_token_id).clone()
+        output_ids = generate_output.sequences[0][:-1]
+        cls_token_mask = ids == self.cls_token_idx
+
+        # get seg tokens
+        seg_token_mask = (output_ids >= self.the_first_seg_token_idx) & (output_ids <= self.the_last_seg_token_idx)
+
+        do_pano_seg = torch.any(cls_token_mask) & torch.any(seg_token_mask)
+
+        reason_cls_token_mask = output_ids == self.cls_token_idx
+
+        do_reason_seg = torch.any(reason_cls_token_mask) & torch.any(seg_token_mask)
+
+        if not do_pano_seg and not do_reason_seg:
+            return {'prediction': predict, 'prediction_masks': ret_masks, 'm2f_outputs': None}
+        
+        # get seg tokens
+        seg_hidden_states = last_hidden_states[-len(seg_token_mask):][seg_token_mask].unsqueeze(0)
+        seg_hidden_states = self.llm_to_m2f(seg_hidden_states)
+        
+        if do_pano_seg:
+            cls_hidden_states = last_hidden_states[:len(cls_token_mask)][cls_token_mask]
+            text_classifier = self.llm_to_cls(torch.cat([cls_hidden_states, bg_cls_embedding], dim=0))
+            seg_hidden_states = seg_hidden_states.transpose(0, 1)
+
+            # proposals go through mask2former decoder layers
+            m2f_outputs = self.mask2former.forward_second_part(
+                query_features=seg_hidden_states[:, :, :self.mask2former.config.hidden_dim], # q, b, c
+                query_embeddings=seg_hidden_states[:, :, self.mask2former.config.hidden_dim:], # q, b, c
+                pixel_level_module_output=pixel_level_module_output,
+                text_classifier=[text_classifier, ],
+                mask_labels=None,
+                class_labels=None,
+                **m2f_inputs
+            )
+
+            tags = re.findall(r'<p>(.*?)</p>', predict)
+            label_id_to_text = {id: tag for id, tag in enumerate(tags)}
+            
+            class_queries_logits = m2f_outputs.class_queries_logits
+            masks_queries_logits = m2f_outputs.masks_queries_logits
+
+            m2f_masks = {'label_id_to_text': label_id_to_text,
+                        'class_queries_logits': class_queries_logits,
+                        'masks_queries_logits': masks_queries_logits}
+
+            return {'prediction': predict, 'prediction_masks': ret_masks, 'm2f_outputs': m2f_masks}
+        elif do_reason_seg:
+            raise NotImplementedError
+        else:
+            raise NotImplementedError
+    
+    def post_process_panoptic_segmentation(
+        self,
+        class_queries_logits,
+        masks_queries_logits,
+        threshold: float = 0.5,
+        mask_threshold: float = 0.5,
+        overlap_mask_area_threshold: float = 0.8,
+        label_ids_to_fuse: Optional[Set[int]] = None,
+        target_sizes: Optional[List[Tuple[int, int]]] = None,
+    ) -> List[Dict]:
+        
+        if label_ids_to_fuse is None:
+            logger.warning("`label_ids_to_fuse` unset. No instance will be fused.")
+            label_ids_to_fuse = set()
+
+        batch_size = len(class_queries_logits)
+
+        # Loop over items in batch size
+        results: List[Dict[str, TensorType]] = []
+
+        for i in range(batch_size):
+            height, width = target_sizes[i]
+            long_edge = height if height > width else width
+            masks_queries_logits_i = torch.nn.functional.interpolate(
+                masks_queries_logits[i:i+1], size=(long_edge, long_edge), mode="bilinear", align_corners=False
+            )
+            
+            mask_probs = masks_queries_logits_i[0].sigmoid()
+
+            num_labels = class_queries_logits[i].shape[-1] - 1
+
+            pred_scores, pred_labels = nn.functional.softmax(class_queries_logits[i], dim=-1).max(-1)
+
+            mask_probs_item, pred_scores_item, pred_labels_item = remove_low_and_no_objects(
+                mask_probs, pred_scores, pred_labels, threshold, num_labels
+            )
+            
+            # No mask found
+            if mask_probs_item.shape[0] <= 0:
+                segmentation = torch.zeros((height, width)) - 1
+                results.append({"segmentation": segmentation, "segments_info": []})
+                continue
+
+            # Get segmentation map and segment information of batch item
+            target_size = target_sizes[i] if target_sizes is not None else None
+            segmentation, segments = compute_segments(
+                mask_probs=mask_probs_item,
+                pred_scores=pred_scores_item,
+                pred_labels=pred_labels_item,
+                mask_threshold=mask_threshold,
+                overlap_mask_area_threshold=overlap_mask_area_threshold,
+                label_ids_to_fuse=label_ids_to_fuse,
+                target_size=target_size,
+            )
+            
+            results.append({"segmentation": segmentation, "segments_info": segments})
+
+        return results
+
+def get_seg_hidden_states(hidden_states, output_ids, seg_id):
+    seg_mask = output_ids == seg_id
+    n_out = len(seg_mask)
+    if n_out == 0:
+        return hidden_states[0:0]
+    return hidden_states[-n_out:][seg_mask]
+
+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
+    return best_ratio
+
+def dynamic_preprocess(image,
+                       min_num=1,
+                       max_num=6,
+                       image_size=448,
+                       use_thumbnail=False):
+    orig_width, orig_height = image.size
+    aspect_ratio = orig_width / orig_height
+
+    # calculate the existing image aspect ratio
+    target_ratios = {(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}
+    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)
+
+    # 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
+
+
+from transformers.cache_utils import Cache, DynamicCache
+
+def prepare_inputs_for_generation_phi3(
+        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
+):
+    if past_key_values is not None:
+        if isinstance(past_key_values, Cache):
+            cache_length = past_key_values.get_seq_length()
+            past_length = past_key_values.seen_tokens
+            max_cache_length = past_key_values.get_max_length()
+        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 `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 or len(past_key_values)==0):
+        model_inputs = {'inputs_embeds': inputs_embeds}
+    else:
+        model_inputs = {'input_ids': input_ids}
+
+    model_inputs.update(
+        {
+            'position_ids': position_ids,
+            'past_key_values': past_key_values,
+            'use_cache': kwargs.get('use_cache'),
+            'attention_mask': attention_mask,
+        }
+    )
+    return model_inputs
+
+
+# Copied from transformers.models.detr.image_processing_detr.compute_segments
+def compute_segments(
+    mask_probs,
+    pred_scores,
+    pred_labels,
+    mask_threshold: float = 0.5,
+    overlap_mask_area_threshold: float = 0.8,
+    label_ids_to_fuse: Optional[Set[int]] = None,
+    target_size: Tuple[int, int] = None,
+):
+    height = mask_probs.shape[1] if target_size is None else target_size[0]
+    width = mask_probs.shape[2] if target_size is None else target_size[1]
+
+    segmentation = torch.zeros((height, width), dtype=torch.int32, device=mask_probs.device)
+    segments: List[Dict] = []
+
+    if target_size is not None:
+        mask_probs = mask_probs[..., :height, :width]
+
+    current_segment_id = 0
+
+    # Weigh each mask by its prediction score
+    mask_probs *= pred_scores.view(-1, 1, 1)
+    mask_labels = mask_probs.argmax(0)  # [height, width]
+
+    # Keep track of instances of each class
+    stuff_memory_list: Dict[str, int] = {}
+    for k in range(pred_labels.shape[0]):
+        pred_class = pred_labels[k].item()
+        should_fuse = pred_class in label_ids_to_fuse
+
+        # Check if mask exists and large enough to be a segment
+        mask_exists, mask_k = check_segment_validity(
+            mask_labels, mask_probs, k, mask_threshold, overlap_mask_area_threshold
+        )
+
+        if mask_exists:
+            if pred_class in stuff_memory_list:
+                current_segment_id = stuff_memory_list[pred_class]
+            else:
+                current_segment_id += 1
+
+            # Add current object segment to final segmentation map
+            segmentation[mask_k] = current_segment_id
+            segment_score = round(pred_scores[k].item(), 6)
+            segments.append(
+                {
+                    "id": current_segment_id,
+                    "label_id": pred_class,
+                    "was_fused": should_fuse,
+                    "score": segment_score,
+                }
+            )
+            if should_fuse:
+                stuff_memory_list[pred_class] = current_segment_id
+
+    return segmentation, segments

special_tokens_map.json

@@ -0,0 +1,40 @@
+{
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>",
+    "<img>",
+    "</img>",
+    "<IMG_CONTEXT>",
+    "<quad>",
+    "</quad>",
+    "<ref>",
+    "</ref>",
+    "<box>",
+    "</box>"
+  ],
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

templates.py

@@ -0,0 +1,170 @@
+
+PROMPT_TEMPLATE = dict(
+    default=dict(
+        SYSTEM='<|System|>:{system}\n',
+        INSTRUCTION='<|User|>:{input}\n<|Bot|>:',
+        SEP='\n'),
+    zephyr=dict(
+        SYSTEM='<|system|>\n{system}\n',
+        INSTRUCTION='<|user|>\n{input}\n<|assistant|>\n',
+        SEP='\n'),
+    internlm_chat=dict(
+        SYSTEM='<|System|>:{system}\n',
+        INSTRUCTION='<|User|>:{input}<eoh>\n<|Bot|>:',
+        SUFFIX='<eoa>',
+        SUFFIX_AS_EOS=True,
+        SEP='\n',
+        STOP_WORDS=['<eoa>']),
+    internlm2_chat=dict(
+        SYSTEM='<|im_start|>system\n{system}<|im_end|>\n',
+        INSTRUCTION=('<|im_start|>user\n{input}<|im_end|>\n'
+                     '<|im_start|>assistant\n'),
+        SUFFIX='<|im_end|>',
+        SUFFIX_AS_EOS=True,
+        SEP='\n',
+        STOP_WORDS=['<|im_end|>']),
+    moss_sft=dict(
+        SYSTEM='{system}\n',
+        INSTRUCTION='<|Human|>: {input}<eoh>\n',
+        SEP='\n',
+        STOP_WORDS=['<eoc>', '<eom>']),
+    llama2_chat=dict(
+        SYSTEM=(
+            '[INST] <<SYS>>\n You are a helpful, respectful and honest '
+            'assistant. Always answer as helpfully as possible, while being '
+            'safe. Your answers should not include any harmful, unethical, '
+            'racist, sexist, toxic, dangerous, or illegal content. Please '
+            'ensure that your responses are socially unbiased and positive in '
+            'nature.\n{system}\n<</SYS>>\n [/INST] '),
+        INSTRUCTION='[INST] {input} [/INST]',
+        SEP='\n'),
+    code_llama_chat=dict(
+        SYSTEM='{system}\n', INSTRUCTION='[INST] {input} [/INST]'),
+    chatglm2=dict(
+        SYSTEM='{system}\n',
+        INSTRUCTION='[Round {round}]\n\n问：{input}\n\n答：',
+        SEP='\n\n'),
+    chatglm3=dict(
+        SYSTEM='<|system|>\n{system}',
+        INSTRUCTION='<|user|>\n{input}<|assistant|>\n',
+        SEP='\n'),
+    qwen_chat=dict(
+        SYSTEM=('<|im_start|>system\n{system}<|im_end|>\n'),
+        INSTRUCTION=('<|im_start|>user\n{input}<|im_end|>\n'
+                     '<|im_start|>assistant\n'),
+        SUFFIX='<|im_end|>',
+        SUFFIX_AS_EOS=True,
+        SEP='\n',
+        STOP_WORDS=['<|im_end|>', '<|endoftext|>']),
+    baichuan_chat=dict(
+        SYSTEM='{system}\n',
+        INSTRUCTION='<reserved_102>{input}<reserved_103>',
+        SEP='\n'),
+    baichuan2_chat=dict(
+        SYSTEM='{system}\n',
+        INSTRUCTION='<reserved_106>{input}<reserved_107>',
+        SEP='\n'),
+    wizardlm=dict(
+        SYSTEM=('A chat between a curious user and an artificial '
+                'intelligence assistant. The assistant gives '
+                'helpful, detailed, and polite answers to the '
+                'user\'s questions. {system}\n '),
+        INSTRUCTION=('USER: {input} ASSISTANT:'),
+        SEP='\n'),
+    wizardcoder=dict(
+        SYSTEM=(
+            'Below is an instruction that describes a task. '
+            'Write a response that appropriately completes the request.\n\n'
+            '{system}\n '),
+        INSTRUCTION=('### Instruction:\n{input}\n\n### Response:'),
+        SEP='\n\n'),
+    vicuna=dict(
+        SYSTEM=('A chat between a curious user and an artificial '
+                'intelligence assistant. The assistant gives '
+                'helpful, detailed, and polite answers to the '
+                'user\'s questions. {system}\n '),
+        INSTRUCTION=('USER: {input} ASSISTANT:'),
+        SEP='\n'),
+    deepseek_coder=dict(
+        SYSTEM=('You are an AI programming assistant, utilizing '
+                'the DeepSeek Coder model, developed by DeepSeek'
+                'Company, and you only answer questions related '
+                'to computer science. For politically sensitive '
+                'questions, security and privacy issues, and '
+                'other non-computer science questions, you will '
+                'refuse to answer. {system}\n'),
+        INSTRUCTION=('### Instruction:\n{input}\n### Response:\n'),
+        SEP='\n'),
+    # TODO: deprecation, v0.2.0
+    deepseekcoder=dict(
+        SYSTEM=('You are an AI programming assistant, utilizing '
+                'the DeepSeek Coder model, developed by DeepSeek'
+                'Company, and you only answer questions related '
+                'to computer science. For politically sensitive '
+                'questions, security and privacy issues, and '
+                'other non-computer science questions, you will '
+                'refuse to answer. {system}\n'),
+        INSTRUCTION=('### Instruction:\n{input}\n### Response:\n'),
+        SEP='\n'),
+    deepseek_moe=dict(
+        SYSTEM=('[INST] {system} [/INST]\n'),
+        INSTRUCTION=('[INST] {input} [/INST]'),
+        SEP='\n'),
+    deepseek_v2=dict(
+        SYSTEM='{system}\n\n',
+        INSTRUCTION='User: {input}\n\nAssistant: ',
+        SUFFIX='<｜end▁of▁sentence｜>',
+        SUFFIX_AS_EOS=True,
+        STOP_WORDS=['<｜end▁of▁sentence｜>']),
+    mistral=dict(
+        SYSTEM=('[INST] {system} [/INST]\n'),
+        INSTRUCTION=('[INST] {input} [/INST]'),
+        SEP='\n'),
+    mixtral=dict(
+        SYSTEM=('[INST] {system} [/INST]\n'),
+        INSTRUCTION=('[INST] {input} [/INST]'),
+        SEP='\n'),
+    minicpm=dict(INSTRUCTION=('<用户> {input} <AI>'), SEP='\n'),
+    minicpm3=dict(
+        SYSTEM=('<|im_start|>system\n{system}<|im_end|>\n'),
+        INSTRUCTION=('<|im_start|>user\n{input}<|im_end|>\n'
+                     '<|im_start|>assistant\n'),
+        SUFFIX='<|im_end|>',
+        SUFFIX_AS_EOS=True,
+        SEP='\n',
+        STOP_WORDS=['<|im_end|>', '<|endoftext|>']),
+    gemma=dict(
+        # `system` field is extended by xtuner
+        SYSTEM=('<start_of_turn>system\n{system}<end_of_turn>\n'),
+        INSTRUCTION=('<start_of_turn>user\n{input}<end_of_turn>\n'
+                     '<start_of_turn>model\n'),
+        SUFFIX='<end_of_turn>',
+        SUFFIX_AS_EOS=False,
+        SEP='\n',
+        STOP_WORDS=['<end_of_turn>']),
+    cohere_chat=dict(
+        SYSTEM=('<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>{system}'
+                '<|END_OF_TURN_TOKEN|>'),
+        INSTRUCTION=(
+            '<|START_OF_TURN_TOKEN|><|USER_TOKEN|>{input}<|END_OF_TURN_TOKEN|>'
+            '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>'),
+        SUFFIX='<|END_OF_TURN_TOKEN|>',
+        SUFFIX_AS_EOS=True,
+        STOP_WORDS=['<|END_OF_TURN_TOKEN|>']),
+    llama3_chat=dict(
+        SYSTEM=('<|start_header_id|>system<|end_header_id|>\n\n'
+                '{system}<|eot_id|>'),
+        INSTRUCTION=(
+            '<|start_header_id|>user<|end_header_id|>\n\n{input}<|eot_id|>'
+            '<|start_header_id|>assistant<|end_header_id|>\n\n'),
+        SUFFIX='<|eot_id|>',
+        SUFFIX_AS_EOS=True,
+        STOP_WORDS=['<|eot_id|>']),
+    phi3_chat=dict(
+        SYSTEM='<|system|>\n{system}<|end|>\n',
+        INSTRUCTION='<|user|>\n{input}<|end|>\n<|assistant|>\n',
+        SUFFIX='<|end|>',
+        SUFFIX_AS_EOS=True,
+        SEP='\n',
+        STOP_WORDS=['<|end|>']),
+)

tokenization_internlm2.py

@@ -0,0 +1,235 @@
+# Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on transformers/src/transformers/models/llama/tokenization_llama.py
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Tokenization classes for InternLM."""
+import os
+from shutil import copyfile
+from typing import Any, Dict, List, Optional, Tuple
+
+import sentencepiece as spm
+from transformers.tokenization_utils import PreTrainedTokenizer
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {'vocab_file': './tokenizer.model'}
+
+PRETRAINED_VOCAB_FILES_MAP = {}
+
+
+# Modified from transformers.model.llama.tokenization_llama.LlamaTokenizer
+class InternLM2Tokenizer(PreTrainedTokenizer):
+    """
+    Construct a InternLM2 tokenizer. Based on byte-level Byte-Pair-Encoding.
+
+    Args:
+        vocab_file (`str`):
+            Path to the vocabulary file.
+    """
+
+    vocab_files_names = VOCAB_FILES_NAMES
+    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
+    model_input_names = ['input_ids', 'attention_mask']
+    _auto_class = 'AutoTokenizer'
+
+    def __init__(
+        self,
+        vocab_file,
+        unk_token='<unk>',
+        bos_token='<s>',
+        eos_token='</s>',
+        pad_token='</s>',
+        sp_model_kwargs: Optional[Dict[str, Any]] = None,
+        add_bos_token=True,
+        add_eos_token=False,
+        decode_with_prefix_space=False,
+        clean_up_tokenization_spaces=False,
+        **kwargs,
+    ):
+        self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
+        self.vocab_file = vocab_file
+        self.add_bos_token = add_bos_token
+        self.add_eos_token = add_eos_token
+        self.decode_with_prefix_space = decode_with_prefix_space
+        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
+        self.sp_model.Load(vocab_file)
+        self._no_prefix_space_tokens = None
+        super().__init__(
+            bos_token=bos_token,
+            eos_token=eos_token,
+            unk_token=unk_token,
+            pad_token=pad_token,
+            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+            **kwargs,
+        )
+
+    @property
+    def no_prefix_space_tokens(self):
+        if self._no_prefix_space_tokens is None:
+            vocab = self.convert_ids_to_tokens(list(range(self.vocab_size)))
+            self._no_prefix_space_tokens = {i for i, tok in enumerate(vocab) if not tok.startswith('▁')}
+        return self._no_prefix_space_tokens
+
+    @property
+    def vocab_size(self):
+        """Returns vocab size"""
+        return self.sp_model.get_piece_size()
+
+    @property
+    def bos_token_id(self) -> Optional[int]:
+        return self.sp_model.bos_id()
+
+    @property
+    def eos_token_id(self) -> Optional[int]:
+        return self.sp_model.eos_id()
+
+    def get_vocab(self):
+        """Returns vocab as a dict"""
+        vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
+        vocab.update(self.added_tokens_encoder)
+        return vocab
+
+    def _tokenize(self, text):
+        """Returns a tokenized string."""
+        return self.sp_model.encode(text, out_type=str)
+
+    def _convert_token_to_id(self, token):
+        """Converts a token (str) in an id using the vocab."""
+        return self.sp_model.piece_to_id(token)
+
+    def _convert_id_to_token(self, index):
+        """Converts an index (integer) in a token (str) using the vocab."""
+        token = self.sp_model.IdToPiece(index)
+        return token
+
+    def _maybe_add_prefix_space(self, tokens, decoded):
+        if tokens and tokens[0] not in self.no_prefix_space_tokens:
+            return ' ' + decoded
+        else:
+            return decoded
+
+    def convert_tokens_to_string(self, tokens):
+        """Converts a sequence of tokens (string) in a single string."""
+        current_sub_tokens = []
+        out_string = ''
+        prev_is_special = False
+        for token in tokens:
+            # make sure that special tokens are not decoded using sentencepiece model
+            if token in self.all_special_tokens:
+                if not prev_is_special:
+                    out_string += ' '
+                out_string += self.sp_model.decode(current_sub_tokens) + token
+                prev_is_special = True
+                current_sub_tokens = []
+            else:
+                current_sub_tokens.append(token)
+                prev_is_special = False
+        out_string += self.sp_model.decode(current_sub_tokens)
+        out_string = self.clean_up_tokenization(out_string)
+        out_string = self._maybe_add_prefix_space(tokens=tokens, decoded=out_string)
+        return out_string[1:]
+
+    def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:
+        """
+        Save the vocabulary and special tokens file to a directory.
+
+        Args:
+            save_directory (`str`):
+                The directory in which to save the vocabulary.
+
+        Returns:
+            `Tuple(str)`: Paths to the files saved.
+        """
+        if not os.path.isdir(save_directory):
+            logger.error(f'Vocabulary path ({save_directory}) should be a directory')
+            return
+        out_vocab_file = os.path.join(
+            save_directory, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']
+        )
+
+        if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
+            copyfile(self.vocab_file, out_vocab_file)
+        elif not os.path.isfile(self.vocab_file):
+            with open(out_vocab_file, 'wb') as fi:
+                content_spiece_model = self.sp_model.serialized_model_proto()
+                fi.write(content_spiece_model)
+
+        return (out_vocab_file,)
+
+    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
+        if self.add_bos_token:
+            bos_token_ids = [self.bos_token_id]
+        else:
+            bos_token_ids = []
+
+        output = bos_token_ids + token_ids_0
+
+        if token_ids_1 is not None:
+            output = output + token_ids_1
+
+        if self.add_eos_token:
+            output = output + [self.eos_token_id]
+
+        return output
+
+    def get_special_tokens_mask(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
+    ) -> List[int]:
+        """
+        Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
+        special tokens using the tokenizer `prepare_for_model` method.
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
+                Whether or not the token list is already formatted with special tokens for the model.
+
+        Returns:
+            `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+        """
+        if already_has_special_tokens:
+            return super().get_special_tokens_mask(
+                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
+            )
+
+        if token_ids_1 is None:
+            return [1] + ([0] * len(token_ids_0)) + [1]
+        return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
+
+    def create_token_type_ids_from_sequences(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        """
+        Create a mask from the two sequences passed to be used in a sequence-pair classification task. T5 does not make
+        use of token type ids, therefore a list of zeros is returned.
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+
+        Returns:
+            `List[int]`: List of zeros.
+        """
+        eos = [self.eos_token_id]
+
+        if token_ids_1 is None:
+            return len(token_ids_0 + eos) * [0]
+        return len(token_ids_0 + eos + token_ids_1 + eos) * [0]

tokenization_internlm2_fast.py

@@ -0,0 +1,211 @@
+# Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on transformers/src/transformers/models/llama/tokenization_llama_fast.py
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Tokenization Fast class for InternLM."""
+import os
+from shutil import copyfile
+from typing import Any, Dict, Optional, Tuple
+
+from tokenizers import Tokenizer, decoders, normalizers, processors
+from tokenizers.models import BPE
+from transformers.convert_slow_tokenizer import (SLOW_TO_FAST_CONVERTERS,
+                                                 SentencePieceExtractor,
+                                                 SpmConverter)
+from transformers.tokenization_utils_fast import PreTrainedTokenizerFast
+from transformers.utils import logging
+
+from .tokenization_internlm2 import InternLM2Tokenizer
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {'vocab_file': './tokenizer.model'}
+
+
+# Modified from transformers.convert_slow_tokenizer.LlamaConverter
+class InternLM2Converter(SpmConverter):
+    handle_byte_fallback = True
+
+    def vocab(self, proto):
+        vocab = [
+            ('<unk>', 0.0),
+            ('<s>', 0.0),
+            ('</s>', 0.0),
+        ]
+        vocab += [(piece.piece, piece.score) for piece in proto.pieces[3:]]
+        return vocab
+
+    def unk_id(self, proto):
+        unk_id = 0
+        return unk_id
+
+    def decoder(self, replacement, add_prefix_space):
+        return decoders.Sequence(
+            [
+                decoders.Replace('▁', ' '),
+                decoders.ByteFallback(),
+                decoders.Fuse(),
+                decoders.Strip(content=' ', left=1),
+            ]
+        )
+
+    def tokenizer(self, proto):
+        model_type = proto.trainer_spec.model_type
+        vocab_scores = self.vocab(proto)
+        # special tokens
+        added_tokens = self.original_tokenizer.added_tokens_decoder
+        for i in range(len(vocab_scores)):
+            piece, score = vocab_scores[i]
+            if i in added_tokens:
+                vocab_scores[i] = (added_tokens[i].content, score)
+        if model_type == 1:
+            raise RuntimeError('InternLM2 is supposed to be a BPE model!')
+
+        elif model_type == 2:
+            _, merges = SentencePieceExtractor(self.original_tokenizer.vocab_file).extract(vocab_scores)
+            bpe_vocab = {word: i for i, (word, _score) in enumerate(vocab_scores)}
+            tokenizer = Tokenizer(
+                BPE(bpe_vocab, merges, unk_token=proto.trainer_spec.unk_piece, fuse_unk=True, byte_fallback=True)
+            )
+            tokenizer.add_special_tokens(
+                [ added_token for index, added_token in added_tokens.items()]
+            )
+        else:
+            raise Exception(
+                "You're trying to run a `Unigram` model but you're file was trained with a different algorithm"
+            )
+
+        return tokenizer
+
+    def normalizer(self, proto):
+        normalizers_list = []
+        if proto.normalizer_spec.add_dummy_prefix:
+            normalizers_list.append(normalizers.Prepend(prepend='▁'))
+        normalizers_list.append(normalizers.Replace(pattern=' ', content='▁'))
+        return normalizers.Sequence(normalizers_list)
+
+    def pre_tokenizer(self, replacement, add_prefix_space):
+        return None
+
+
+SLOW_TO_FAST_CONVERTERS['InternLM2Tokenizer'] = InternLM2Converter
+
+
+# Modified from transformers.model.llama.tokenization_llama_fast.LlamaTokenizerFast -> InternLM2TokenizerFast
+class InternLM2TokenizerFast(PreTrainedTokenizerFast):
+    vocab_files_names = VOCAB_FILES_NAMES
+    slow_tokenizer_class = InternLM2Tokenizer
+    padding_side = 'left'
+    model_input_names = ['input_ids', 'attention_mask']
+    _auto_class = 'AutoTokenizer'
+
+    def __init__(
+        self,
+        vocab_file,
+        unk_token='<unk>',
+        bos_token='<s>',
+        eos_token='</s>',
+        pad_token='</s>',
+        sp_model_kwargs: Optional[Dict[str, Any]] = None,
+        add_bos_token=True,
+        add_eos_token=False,
+        decode_with_prefix_space=False,
+        clean_up_tokenization_spaces=False,
+        **kwargs,
+    ):
+        super().__init__(
+            vocab_file=vocab_file,
+            unk_token=unk_token,
+            bos_token=bos_token,
+            eos_token=eos_token,
+            pad_token=pad_token,
+            sp_model_kwargs=sp_model_kwargs,
+            add_bos_token=add_bos_token,
+            add_eos_token=add_eos_token,
+            decode_with_prefix_space=decode_with_prefix_space,
+            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+            **kwargs,
+        )
+        self._add_bos_token = add_bos_token
+        self._add_eos_token = add_eos_token
+        self.update_post_processor()
+        self.vocab_file = vocab_file
+
+    @property
+    def can_save_slow_tokenizer(self) -> bool:
+        return os.path.isfile(self.vocab_file) if self.vocab_file else False
+
+    def update_post_processor(self):
+        """
+        Updates the underlying post processor with the current `bos_token` and `eos_token`.
+        """
+        bos = self.bos_token
+        bos_token_id = self.bos_token_id
+        if bos is None and self.add_bos_token:
+            raise ValueError('add_bos_token = True but bos_token = None')
+
+        eos = self.eos_token
+        eos_token_id = self.eos_token_id
+        if eos is None and self.add_eos_token:
+            raise ValueError('add_eos_token = True but eos_token = None')
+
+        single = f"{(bos+':0 ') if self.add_bos_token else ''}$A:0{(' '+eos+':0') if self.add_eos_token else ''}"
+        pair = f"{single}{(' '+bos+':1') if self.add_bos_token else ''} $B:1{(' '+eos+':1') if self.add_eos_token else ''}"
+
+        special_tokens = []
+        if self.add_bos_token:
+            special_tokens.append((bos, bos_token_id))
+        if self.add_eos_token:
+            special_tokens.append((eos, eos_token_id))
+        self._tokenizer.post_processor = processors.TemplateProcessing(
+            single=single, pair=pair, special_tokens=special_tokens
+        )
+
+    @property
+    def add_eos_token(self):
+        return self._add_eos_token
+
+    @property
+    def add_bos_token(self):
+        return self._add_bos_token
+
+    @add_eos_token.setter
+    def add_eos_token(self, value):
+        self._add_eos_token = value
+        self.update_post_processor()
+
+    @add_bos_token.setter
+    def add_bos_token(self, value):
+        self._add_bos_token = value
+        self.update_post_processor()
+
+    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
+        if not self.can_save_slow_tokenizer:
+            raise ValueError(
+                'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
+                'tokenizer.'
+            )
+
+        if not os.path.isdir(save_directory):
+            logger.error(f'Vocabulary path ({save_directory}) should be a directory')
+            return
+        out_vocab_file = os.path.join(
+            save_directory, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']
+        )
+
+        if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file):
+            copyfile(self.vocab_file, out_vocab_file)
+
+        return (out_vocab_file,)

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d257d75be50ec94137a76982b1ba699695a69d25a660733e8d0e2073bf50328b
+size 11443325

tokenizer_config.json

@@ -0,0 +1,1147 @@
+{
+  "add_bos_token": false,
+  "add_eos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "151643": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151644": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151645": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151646": {
+      "content": "<|object_ref_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151647": {
+      "content": "<|object_ref_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151648": {
+      "content": "<|box_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151649": {
+      "content": "<|box_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151650": {
+      "content": "<|quad_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151651": {
+      "content": "<|quad_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151652": {
+      "content": "<|vision_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151653": {
+      "content": "<|vision_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151654": {
+      "content": "<|vision_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151655": {
+      "content": "<|image_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151656": {
+      "content": "<|video_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151657": {
+      "content": "<tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151658": {
+      "content": "</tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151659": {
+      "content": "<|fim_prefix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151660": {
+      "content": "<|fim_middle|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151661": {
+      "content": "<|fim_suffix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151662": {
+      "content": "<|fim_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151663": {
+      "content": "<|repo_name|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151664": {
+      "content": "<|file_sep|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151665": {
+      "content": "<img>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151666": {
+      "content": "</img>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151667": {
+      "content": "<IMG_CONTEXT>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151668": {
+      "content": "<quad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151669": {
+      "content": "</quad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151670": {
+      "content": "<ref>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151671": {
+      "content": "</ref>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151672": {
+      "content": "<box>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151673": {
+      "content": "</box>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151674": {
+      "content": "<p>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151675": {
+      "content": "</p>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151676": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151677": {
+      "content": "[BG_CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151678": {
+      "content": "<obj>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151679": {
+      "content": "</obj>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151680": {
+      "content": "<OBJ_CONTEXT>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151681": {
+      "content": "[SEG000]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151682": {
+      "content": "[SEG001]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151683": {
+      "content": "[SEG002]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151684": {
+      "content": "[SEG003]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151685": {
+      "content": "[SEG004]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151686": {
+      "content": "[SEG005]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151687": {
+      "content": "[SEG006]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151688": {
+      "content": "[SEG007]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151689": {
+      "content": "[SEG008]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151690": {
+      "content": "[SEG009]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151691": {
+      "content": "[SEG010]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151692": {
+      "content": "[SEG011]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151693": {
+      "content": "[SEG012]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151694": {
+      "content": "[SEG013]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151695": {
+      "content": "[SEG014]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151696": {
+      "content": "[SEG015]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151697": {
+      "content": "[SEG016]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151698": {
+      "content": "[SEG017]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151699": {
+      "content": "[SEG018]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151700": {
+      "content": "[SEG019]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151701": {
+      "content": "[SEG020]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151702": {
+      "content": "[SEG021]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151703": {
+      "content": "[SEG022]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151704": {
+      "content": "[SEG023]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151705": {
+      "content": "[SEG024]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151706": {
+      "content": "[SEG025]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151707": {
+      "content": "[SEG026]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151708": {
+      "content": "[SEG027]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151709": {
+      "content": "[SEG028]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151710": {
+      "content": "[SEG029]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151711": {
+      "content": "[SEG030]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151712": {
+      "content": "[SEG031]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151713": {
+      "content": "[SEG032]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151714": {
+      "content": "[SEG033]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151715": {
+      "content": "[SEG034]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151716": {
+      "content": "[SEG035]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151717": {
+      "content": "[SEG036]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151718": {
+      "content": "[SEG037]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151719": {
+      "content": "[SEG038]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151720": {
+      "content": "[SEG039]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151721": {
+      "content": "[SEG040]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151722": {
+      "content": "[SEG041]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151723": {
+      "content": "[SEG042]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151724": {
+      "content": "[SEG043]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151725": {
+      "content": "[SEG044]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151726": {
+      "content": "[SEG045]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151727": {
+      "content": "[SEG046]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151728": {
+      "content": "[SEG047]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151729": {
+      "content": "[SEG048]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151730": {
+      "content": "[SEG049]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151731": {
+      "content": "[SEG050]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151732": {
+      "content": "[SEG051]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151733": {
+      "content": "[SEG052]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151734": {
+      "content": "[SEG053]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151735": {
+      "content": "[SEG054]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151736": {
+      "content": "[SEG055]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151737": {
+      "content": "[SEG056]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151738": {
+      "content": "[SEG057]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151739": {
+      "content": "[SEG058]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151740": {
+      "content": "[SEG059]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151741": {
+      "content": "[SEG060]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151742": {
+      "content": "[SEG061]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151743": {
+      "content": "[SEG062]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151744": {
+      "content": "[SEG063]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151745": {
+      "content": "[SEG064]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151746": {
+      "content": "[SEG065]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151747": {
+      "content": "[SEG066]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151748": {
+      "content": "[SEG067]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151749": {
+      "content": "[SEG068]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151750": {
+      "content": "[SEG069]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151751": {
+      "content": "[SEG070]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151752": {
+      "content": "[SEG071]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151753": {
+      "content": "[SEG072]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151754": {
+      "content": "[SEG073]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151755": {
+      "content": "[SEG074]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151756": {
+      "content": "[SEG075]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151757": {
+      "content": "[SEG076]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151758": {
+      "content": "[SEG077]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151759": {
+      "content": "[SEG078]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151760": {
+      "content": "[SEG079]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151761": {
+      "content": "[SEG080]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151762": {
+      "content": "[SEG081]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151763": {
+      "content": "[SEG082]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151764": {
+      "content": "[SEG083]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151765": {
+      "content": "[SEG084]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151766": {
+      "content": "[SEG085]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151767": {
+      "content": "[SEG086]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151768": {
+      "content": "[SEG087]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151769": {
+      "content": "[SEG088]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151770": {
+      "content": "[SEG089]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151771": {
+      "content": "[SEG090]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151772": {
+      "content": "[SEG091]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151773": {
+      "content": "[SEG092]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151774": {
+      "content": "[SEG093]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151775": {
+      "content": "[SEG094]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151776": {
+      "content": "[SEG095]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151777": {
+      "content": "[SEG096]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151778": {
+      "content": "[SEG097]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151779": {
+      "content": "[SEG098]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151780": {
+      "content": "[SEG099]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>",
+    "<img>",
+    "</img>",
+    "<IMG_CONTEXT>",
+    "<quad>",
+    "</quad>",
+    "<ref>",
+    "</ref>",
+    "<box>",
+    "</box>"
+  ],
+  "bos_token": null,
+  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- messages[0]['content'] }}\n    {%- else %}\n        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n    {%- endif %}\n    {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n    {%- else %}\n        {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {{- '<|im_start|>' + message.role }}\n        {%- if message.content %}\n            {{- '\\n' + message.content }}\n        {%- endif %}\n        {%- for tool_call in message.tool_calls %}\n            {%- if tool_call.function is defined %}\n                {%- set tool_call = tool_call.function %}\n            {%- endif %}\n            {{- '\\n<tool_call>\\n{\"name\": \"' }}\n            {{- tool_call.name }}\n            {{- '\", \"arguments\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- '}\\n</tool_call>' }}\n        {%- endfor %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- message.content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "model_max_length": 16384,
+  "pad_token": "<|endoftext|>",
+  "padding_side": "right",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}