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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "cdad6b21-030a-40d3-9b31-a229e5b6196d",
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, AutoTokenizer, AutoConfig, CLIPImageProcessor"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "1f832710-0e8c-42ec-b581-1b15fd2a6acc",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[2024-01-25 14:31:58,511] [INFO] [real_accelerator.py:110:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n"
]
}
],
"source": [
"from model import LlavaPhiForCausalLM"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "9e68f1d4-1ae3-4d45-b818-4600218d2215",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e5e13e666e3a43d4ad26cc70904abee8",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Loading checkpoint shards: 0%| | 0/3 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"model_name = \"RaviNaik/Llava-Phi2\"\n",
"model = LlavaPhiForCausalLM.from_pretrained(model_name)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "49edfa0d-e08a-4d3c-a1d6-34068b122419",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
]
}
],
"source": [
"tokenizer = AutoTokenizer.from_pretrained(model_name)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "dcec20cd-d946-42d7-8e10-c198cd49b486",
"metadata": {},
"outputs": [],
"source": [
"image_processor = CLIPImageProcessor.from_pretrained(model_name)\n",
"mm_use_im_start_end = getattr(model.config, \"mm_use_im_start_end\", False)\n",
"mm_use_im_patch_token = getattr(model.config, \"mm_use_im_patch_token\", True)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "443c13c4-b7e6-4bc5-b6c7-c577bd4708f6",
"metadata": {},
"outputs": [],
"source": [
"if mm_use_im_patch_token:\n",
" tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True)\n",
"if mm_use_im_start_end:\n",
" tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True)\n",
" \n",
"if hasattr(model.config, \"max_sequence_length\"):\n",
" context_len = model.config.max_sequence_length\n",
"else:\n",
" context_len = 2048"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "d8caee43-0d2a-46d4-bdbc-2cfc7dec9e52",
"metadata": {},
"outputs": [],
"source": [
"from transformers import WhisperProcessor, WhisperForConditionalGeneration"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "3acea526-d8ae-4eb6-8dfc-4ea72651b547",
"metadata": {},
"outputs": [],
"source": [
"class AudioLanguageConnector:\n",
" def __init__(self, projection_dim):\n",
" model_name = \"microsoft/phi-2\"\n",
" self.phi2_tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
" self.phi2_tokenizer.pad_token = self.phi2_tokenizer.eos_token\n",
" self.phi2_tokenizer.max_length = projection_dim\n",
"\n",
" def __call__(self, text):\n",
" text = f\"<audio_start> {text} <audio_end>\"\n",
" tokens = self.phi2_tokenizer(text, return_tensors=\"pt\", return_attention_mask=False)\n",
" return tokens\n",
" \n",
"\n",
"class WhisperWithProjection:\n",
" def __init__(self, projection_dim, device):\n",
" self.device = device\n",
" self.processor = WhisperProcessor.from_pretrained(\"openai/whisper-tiny\", device_map=device)\n",
" self.model = WhisperForConditionalGeneration.from_pretrained(\"openai/whisper-tiny\", device_map=device)\n",
" self.model.config.forced_decoder_ids = None\n",
" # self.audio_language_connector = AudioLanguageConnector(projection_dim)\n",
" \n",
" def __call__(self, audio):\n",
" input_features = self.processor(audio[\"array\"],\n",
" sampling_rate=audio[\"sampling_rate\"],\n",
" return_tensors=\"pt\").input_features\n",
" # generate token ids\n",
" predicted_ids = self.model.generate(input_features.to(self.device))\n",
" # decode token ids to text \n",
" transcription = self.processor.batch_decode(predicted_ids, skip_special_tokens=True)\n",
"\n",
" # audio_embeddings = self.audio_language_connector(transcription)\n",
" return transcription"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "a2757c91-2ec1-4fe7-9216-03740bf80061",
"metadata": {},
"outputs": [],
"source": [
"IGNORE_INDEX = -100\n",
"IMAGE_TOKEN_INDEX = -200\n",
"DEFAULT_IMAGE_TOKEN = \"<image>\"\n",
"DEFAULT_IMAGE_PATCH_TOKEN = \"<im_patch>\"\n",
"DEFAULT_IM_START_TOKEN = \"<im_start>\"\n",
"DEFAULT_IM_END_TOKEN = \"<im_end>\"\n",
"\n",
"from conversation import conv_templates, SeparatorStyle\n",
"\n",
"class MultiModalPhi2:\n",
" def __init__(self, modelname_or_path=\"RaviNaik/Llava-Phi2\",\n",
" temperature=0.2,\n",
" max_new_tokens=1024,\n",
" device=\"cuda:0\"):\n",
" self.model_name = modelname_or_path\n",
" self.temperature = temperature\n",
" self.max_new_tokens = max_new_tokens\n",
" self.device = device\n",
" self.disable_torch_init()\n",
" self.whisper_w_proj = WhisperWithProjection(projection_dim=512, device=device)\n",
" self.load_pretrained_model()\n",
" \n",
" def disable_torch_init(self):\n",
" \"\"\"\n",
" Disable the redundant torch default initialization to accelerate model creation.\n",
" \"\"\"\n",
" setattr(torch.nn.Linear, \"reset_parameters\", lambda self: None)\n",
" setattr(torch.nn.LayerNorm, \"reset_parameters\", lambda self: None)\n",
" \n",
" def load_pretrained_model(self):\n",
" self.model = LlavaPhiForCausalLM.from_pretrained(self.model_name, device_map=self.device)\n",
" self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)\n",
" self.image_processor = CLIPImageProcessor.from_pretrained(self.model_name)\n",
" mm_use_im_start_end = getattr(self.model.config, \"mm_use_im_start_end\", False)\n",
" mm_use_im_patch_token = getattr(self.model.config, \"mm_use_im_patch_token\", True)\n",
" if mm_use_im_patch_token:\n",
" self.tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True)\n",
" if mm_use_im_start_end:\n",
" self.tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True)\n",
" \n",
" def tokenizer_image_token(self, prompt, tokenizer, image_token_index=IMAGE_TOKEN_INDEX, return_tensors=None):\n",
" prompt_chunks = [tokenizer(chunk).input_ids for chunk in prompt.split('<image>')]\n",
" \n",
" def insert_separator(X, sep):\n",
" return [ele for sublist in zip(X, [sep]*len(X)) for ele in sublist][:-1]\n",
" \n",
" input_ids = []\n",
" offset = 0\n",
" if len(prompt_chunks) > 0 and len(prompt_chunks[0]) > 0 and prompt_chunks[0][0] == tokenizer.bos_token_id:\n",
" offset = 1\n",
" input_ids.append(prompt_chunks[0][0])\n",
" for x in insert_separator(prompt_chunks, [image_token_index] * (offset + 1)):\n",
" input_ids.extend(x[offset:])\n",
" \n",
" if return_tensors is not None:\n",
" if return_tensors == 'pt':\n",
" return torch.tensor(input_ids, dtype=torch.long)\n",
" raise ValueError(f'Unsupported tensor type: {return_tensors}')\n",
" return input_ids\n",
" \n",
" def __call__(self, text, audio, image):\n",
" qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\\n' + text\n",
" conv = conv_templates[\"phi-2_v0\"].copy()\n",
" conv.append_message(conv.roles[0], qs)\n",
" conv.append_message(conv.roles[1], None)\n",
" prompt = conv.get_prompt()\n",
"\n",
" image_tensor = self.image_processor.preprocess(image, return_tensors='pt')['pixel_values'].to(self.device)\n",
" \n",
" input_ids = self.tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0)\n",
" if audio is not None:\n",
" audio_transcript = self.whisper_w_proj(audio)\n",
" audio_embed = self.tokenizer(audio_transcript, return_tensors='pt')[\"input_ids\"]\n",
" input_ids = torch.concat([input_ids, audio_embed], dim=1)\n",
" input_ids = input_ids.to(self.device)\n",
" \n",
" stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2\n",
"\n",
" with torch.inference_mode():\n",
" output_ids = self.model.generate(\n",
" input_ids,\n",
" images=image_tensor,\n",
" do_sample=True,\n",
" temperature=self.temperature,\n",
" max_new_tokens=self.max_new_tokens,\n",
" eos_token_id=self.tokenizer.eos_token_id, # End of sequence token\n",
" pad_token_id=self.tokenizer.eos_token_id, # Pad token\n",
" use_cache=True,\n",
" )\n",
"\n",
" input_token_len = input_ids.shape[1]\n",
" n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item()\n",
" if n_diff_input_output > 0:\n",
" print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids')\n",
" outputs = self.tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0]\n",
" outputs = outputs.strip()\n",
" if outputs.endswith(stop_str):\n",
" outputs = outputs[:-len(stop_str)]\n",
" outputs = outputs.strip()\n",
" return outputs"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "cc47e6a0-3544-4a60-930f-ccae87ef945a",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9ef56077307d4cef907e25b092061611",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Loading checkpoint shards: 0%| | 0/3 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
]
}
],
"source": [
"multimodal_phi2 = MultiModalPhi2()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "cb8aca1b-7d75-45e7-b5a4-71d151f792e1",
"metadata": {},
"outputs": [],
"source": [
"from PIL import Image\n",
"import requests\n",
"\n",
"url = \"https://www.ilankelman.org/stopsigns/australia.jpg\"\n",
"image = Image.open(requests.get(url, stream=True).raw)\n",
"\n",
"from datasets import load_dataset\n",
"audio_ds = load_dataset(\"hf-internal-testing/librispeech_asr_dummy\", \"clean\", split=\"validation\")\n",
"audio = audio_ds[0][\"audio\"]\n",
"\n",
"text = \"tell me about the image\""
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "6767efc6-be4f-44d3-84ff-34db57d9f940",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'In the image, there is a Chinese writing on a pole in a foreign language. This suggests that the image was taken in a foreign country, possibly in a foreign country. The sign is in a foreign language, which might be in a foreign language. The sign is written in Japanese, which is a common language in Japan. The sign is also written in two different languages, which suggests that it is written in a language that is not in the native language.'"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"multimodal_phi2(text, None, image)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0bdd0b8a-709b-4c82-ac1d-dc746d3a0748",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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