Uploading checkpoints, tokenizers, and license

Dockerfile

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+FROM nvcr.io/nvidia/pytorch:23.09-py3
+
+RUN pip install transformers==4.39.3
+
+RUN pip install accelerate==0.34.2
+
+RUN pip install datasets==2.18.0
+
+RUN pip install timm==1.0.9
+
+RUN pip install anls==0.0.2
+
+RUN pip install pycocoevalcap==1.2
+

README.md

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+---
+license: cc-by-nc-4.0
+language:
+- en
+pipeline_tag: text-generation
+tags:
+- nvidia
+- NVLM
+- pytorch
+- multimodal
+- conversational
+---
+
+
+## Model Details
+
+Today (September 17th, 2024), we introduce [NVLM 1.0](https://arxiv.org/abs/2409.11402), a family of frontier-class multimodal large language models (LLMs) that achieve state-of-the-art results on vision-language tasks, rivaling the leading proprietary models (e.g., GPT-4o) and open-access models (e.g., Llama 3-V 405B and InternVL 2). Remarkably, NVLM 1.0 shows improved text-only performance over its LLM backbone after multimodal training. We are open-sourcing the model weights and code for the community.
+
+## Other Resources
+[Inference Code (HF)](https://huggingface.co/nvidia/NVLM-1.0-D-72B/tree/main)   [Training Code (Coming soon)]()   [Website](https://nvlm-project.github.io/)   [Paper](https://arxiv.org/abs/2409.11402)
+
+## Benchmark Results
+We train our model with legacy [Megatron-LM](https://github.com/NVIDIA/Megatron-LM/tree/main/megatron/legacy) and adapt the codebase to Huggingface for model hosting, reproducibility, and inference.
+We observe numerical differences between the Megatron and Huggingface codebases, which are within the expected range of variation. 
+We provide the results from both the Huggingface codebase and the Megatron codebase for reproducibility and comparison with other models.
+
+Results (as of September 17th, 2024) in the multimodal benchmarks are as follows:
+
+| Benchmark                    | MMMU (val / test) | MathVista | OCRBench | AI2D | ChartQA | DocVQA | TextVQA | RealWorldQA | VQAv2 |
+|------------------------------|-------------------|-----------|----------|------|---------|--------|---------|-------------|-------|
+| NVLM-D 1.0 72B (Huggingface) | 58.7 / 54.9       | 65.2      | 852      | 94.2 | 86.0    | 92.6   | 82.6    | 69.5        | 85.4  |
+| NVLM-D 1.0 72B (Megatron)    | 59.7 / 54.6       | 65.2      | 853      | 94.2 | 86.0    | 92.6   | 82.1    | 69.7        | 85.4  |
+| Llama 3.2 90B                | 60.3 / -          | 57.3      | -        | 92.3 | 85.5    | 90.1   | -       | -           | 78.1  |
+| Llama 3-V 70B                | 60.6 / -          | -         | -        | 93.0 | 83.2    | 92.2   | 83.4    | -           | 79.1  |
+| Llama 3-V 405B               | 64.5 / -          | -         | -        | 94.1 | 85.8    | 92.6   | 84.8    | -           | 80.2  |
+| InternVL2-Llama3-76B         | 55.2 / -          | 65.5      | 839      | 94.8 | 88.4    | 94.1   | 84.4    | 72.2        | -     |
+| GPT-4V                       | 56.8 / 55.7       | 49.9      | 645      | 78.2 | 78.5    | 88.4   | 78.0    | 61.4        | 77.2  |
+| GPT-4o                       | 69.1 / -          | 63.8      | 736      | 94.2 | 85.7    | 92.8   | -       | -           | -     |
+| Claude 3.5 Sonnet            | 68.3 / -          | 67.7      | 788      | 94.7 | 90.8    | 95.2   | -       | -           | -     |
+| Gemini 1.5 Pro (Aug 2024)    | 62.2 / -          | 63.9      | 754      | 94.4 | 87.2    | 93.1   | 78.7    | 70.4        | 80.2  |
+
+
+
+## How to use
+
+When converting Megatron checkpoint to Huggingface, we adapt [InternVL codebase](https://huggingface.co/OpenGVLab/InternVL2-Llama3-76B) to support model loading and multi-GPU inference in HF. For training, please refer to [Megatron-LM (Coming soon)]().
+
+### Prepare the environment
+
+We provide a docker build file in the [Dockerfile](Dockerfile) for reproduction. 
+
+The docker image is based on `nvcr.io/nvidia/pytorch:23.09-py3`. 
+
+*Note: We observe that different transformer versions / CUDA versions / docker versions can lead to slight benchmark number differences. We recommend using the Dockerfile above for precise reproduction.*
+
+### Model loading
+
+```python
+import torch
+from transformers import AutoModel
+
+path = "nvidia/NVLM-D-72B"
+model = AutoModel.from_pretrained(
+    path,
+    torch_dtype=torch.bfloat16,
+    low_cpu_mem_usage=True,
+    use_flash_attn=False,
+    trust_remote_code=True).eval()
+```
+
+### Multiple GPUs
+
+The model can be loaded on multiple GPUs as follows:
+
+```python
+import torch
+import math
+from transformers import AutoModel
+
+def split_model():
+    device_map = {}
+    world_size = torch.cuda.device_count()
+    num_layers = 80
+    # Since the first GPU will be used for ViT, treat it as half a GPU.
+    num_layers_per_gpu = math.ceil(num_layers / (world_size - 0.5))
+    num_layers_per_gpu = [num_layers_per_gpu] * world_size
+    num_layers_per_gpu[0] = math.ceil(num_layers_per_gpu[0] * 0.5)
+    layer_cnt = 0
+    for i, num_layer in enumerate(num_layers_per_gpu):
+        for j in range(num_layer):
+            device_map[f'language_model.model.layers.{layer_cnt}'] = i
+            layer_cnt += 1
+    device_map['vision_model'] = 0
+    device_map['mlp1'] = 0
+    device_map['language_model.model.tok_embeddings'] = 0
+    device_map['language_model.model.embed_tokens'] = 0
+    device_map['language_model.output'] = 0
+    device_map['language_model.model.norm'] = 0
+    device_map['language_model.lm_head'] = 0
+    device_map[f'language_model.model.layers.{num_layers - 1}'] = 0
+
+    return device_map
+
+path = "nvidia/NVLM-D-72B"
+device_map = split_model()
+model = AutoModel.from_pretrained(
+    path,
+    torch_dtype=torch.bfloat16,
+    low_cpu_mem_usage=True,
+    use_flash_attn=False,
+    trust_remote_code=True,
+    device_map=device_map).eval()
+```
+
+
+### Inference
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModel
+import math
+from PIL import Image
+import torchvision.transforms as T
+from torchvision.transforms.functional import InterpolationMode
+
+
+def split_model():
+    device_map = {}
+    world_size = torch.cuda.device_count()
+    num_layers = 80
+    # Since the first GPU will be used for ViT, treat it as half a GPU.
+    num_layers_per_gpu = math.ceil(num_layers / (world_size - 0.5))
+    num_layers_per_gpu = [num_layers_per_gpu] * world_size
+    num_layers_per_gpu[0] = math.ceil(num_layers_per_gpu[0] * 0.5)
+    layer_cnt = 0
+    for i, num_layer in enumerate(num_layers_per_gpu):
+        for j in range(num_layer):
+            device_map[f'language_model.model.layers.{layer_cnt}'] = i
+            layer_cnt += 1
+    device_map['vision_model'] = 0
+    device_map['mlp1'] = 0
+    device_map['language_model.model.tok_embeddings'] = 0
+    device_map['language_model.model.embed_tokens'] = 0
+    device_map['language_model.output'] = 0
+    device_map['language_model.model.norm'] = 0
+    device_map['language_model.lm_head'] = 0
+    device_map[f'language_model.model.layers.{num_layers - 1}'] = 0
+
+    return device_map
+
+
+IMAGENET_MEAN = (0.485, 0.456, 0.406)
+IMAGENET_STD = (0.229, 0.224, 0.225)
+
+
+def build_transform(input_size):
+    MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
+    transform = T.Compose([
+        T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
+        T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
+        T.ToTensor(),
+        T.Normalize(mean=MEAN, std=STD)
+    ])
+    return transform
+
+
+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=12, 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 = set(
+        (i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
+        i * j <= max_num and i * j >= min_num)
+    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
+
+
+def load_image(image_file, input_size=448, max_num=12):
+    image = Image.open(image_file).convert('RGB')
+    transform = build_transform(input_size=input_size)
+    images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num)
+    pixel_values = [transform(image) for image in images]
+    pixel_values = torch.stack(pixel_values)
+    return pixel_values
+
+path = "nvidia/NVLM-D-72B"
+device_map = split_model()
+model = AutoModel.from_pretrained(
+    path,
+    torch_dtype=torch.bfloat16,
+    low_cpu_mem_usage=True,
+    use_flash_attn=False,
+    trust_remote_code=True,
+    device_map=device_map).eval()
+
+print(model)
+
+tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True, use_fast=False)
+generation_config = dict(max_new_tokens=1024, do_sample=False)
+
+# pure-text conversation
+question = 'Hello, who are you?'
+response, history = model.chat(tokenizer, None, question, generation_config, history=None, return_history=True)
+print(f'User: {question}\nAssistant: {response}')
+
+# single-image single-round conversation
+pixel_values = load_image('path/to/your/example/image.jpg', max_num=6).to(
+    torch.bfloat16)
+question = '<image>\nPlease describe the image shortly.'
+response = model.chat(tokenizer, pixel_values, question, generation_config)
+print(f'User: {question}\nAssistant: {response}')
+```
+
+
+## Correspondence to
+Wenliang Dai* (wdai@nvidia.com), Nayeon Lee* (nayeonl@nvidia.com), Boxin Wang* (boxinw@nvidia.com), Zhuolin Yang* (zhuoliny@nvidia.com), Wei Ping* (wping@nvidia.com)
+
+*Equal contribution
+
+## Citation
+<pre>
+@article{nvlm2024,
+  title={NVLM: Open Frontier-Class Multimodal LLMs},
+  author={Dai, Wenliang and Lee, Nayeon and Wang, Boxin and Yang, Zhuolin and Liu, Zihan and Barker, Jon and Rintamaki, Tuomas and Shoeybi, Mohammad and Catanzaro, Bryan and Ping, Wei},
+  journal={arXiv preprint},
+  year={2024}}
+</pre>
+
+
+## License
+The use of this model is governed by the [cc-by-nc-4.0](https://spdx.org/licenses/CC-BY-NC-4.0)

config.json

@@ -0,0 +1,141 @@
+{
+  "_commit_hash": null,
+  "architectures": [
+    "NVLM_D"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_nvlm_d.NVLM_D_Config",
+    "AutoModel": "modeling_nvlm_d.NVLM_D_Model",
+    "AutoModelForCausalLM": "modeling_nvlm_d.NVLM_D_Model"
+  },
+  "downsample_ratio": 0.5,
+  "dynamic_image_size": true,
+  "force_image_size": 448,
+  "llm_config": {
+    "_name_or_path": "Qwen/Qwen2-72B-Instruct",
+    "add_cross_attention": false,
+    "architectures": [
+      "Qwen2ForCausalLM"
+    ],
+    "attention_bias": true,
+    "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": 8192,
+    "id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1"
+    },
+    "initializer_range": 0.02,
+    "intermediate_size": 29568,
+    "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,
+    "min_length": 0,
+    "mlp_bias": false,
+    "model_type": "llama",
+    "no_repeat_ngram_size": 0,
+    "num_attention_heads": 64,
+    "num_beam_groups": 1,
+    "num_beams": 1,
+    "num_hidden_layers": 80,
+    "num_key_value_heads": 8,
+    "num_return_sequences": 1,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "output_scores": false,
+    "pad_token_id": null,
+    "prefix": null,
+    "pretraining_tp": 1,
+    "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": {
+      "factor": 3.0,
+      "type": "dynamic"
+    },
+    "rope_theta": 1000000.0,
+    "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": false,
+    "tokenizer_class": null,
+    "top_k": 1,
+    "top_p": 0,
+    "torch_dtype": "bfloat16",
+    "torchscript": false,
+    "transformers_version": "4.39.3",
+    "typical_p": 1.0,
+    "use_bfloat16": true,
+    "use_cache": true,
+    "vocab_size": 152064
+  },
+  "max_dynamic_patch": 6,
+  "min_dynamic_patch": 1,
+  "model_type": "NVLM_D",
+  "ps_version": "v2",
+  "select_layer": -1,
+  "template": "chatml",
+  "torch_dtype": "bfloat16",
+  "transformers_version": null,
+  "use_backbone_lora": 0,
+  "use_llm_lora": 0,
+  "use_thumbnail": true,
+  "vision_config": {
+    "architectures": [
+      "InternVisionModel"
+    ],
+    "attention_dropout": 0.0,
+    "drop_path_rate": 0.0,
+    "dropout": 0.0,
+    "hidden_act": "gelu",
+    "hidden_size": 3200,
+    "image_size": 448,
+    "initializer_factor": 0.1,
+    "initializer_range": 1e-10,
+    "intermediate_size": 12800,
+    "layer_norm_eps": 1e-06,
+    "model_type": "intern_vit_6b",
+    "norm_type": "rms_norm",
+    "num_attention_heads": 25,
+    "num_channels": 3,
+    "num_hidden_layers": 45,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "patch_size": 14,
+    "qk_normalization": true,
+    "qkv_bias": false,
+    "return_dict": true,
+    "torch_dtype": "bfloat16",
+    "transformers_version": "4.39.3",
+    "use_bfloat16": true,
+    "use_flash_attn": true
+  }
+}

incl_licenses/LICENSE

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+MIT License
+
+Copyright (c) 2023 OpenGVLab
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

incl_licenses/LICENSE_2

@@ -0,0 +1,201 @@
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