import torch import torch.nn as nn import torch.nn.functional as F from safetensors import safe_open import json import gradio as gr from PIL import Image import numpy as np from mistral_common.protocol.instruct.messages import UserMessage, TextChunk, ImageChunk from mistral_common.protocol.instruct.request import ChatCompletionRequest from mistral_common.tokens.tokenizers.mistral import MistralTokenizer from huggingface_hub import snapshot_download import Spaces # Download model files model_path = snapshot_download(repo_id="mistral-community/pixtral-12b-240910") with open('PARAMS.json', 'r') as f: params = json.load(f) with open('TEKKEN.json', 'r') as f: tokenizer_config = json.load(f) class GELU(nn.Module): def __init__(self, dim_in, dim_out, approximate='none', bias=True): super().__init__() self.linear = nn.Linear(dim_in, dim_out, bias=bias) self.approximate = approximate def forward(self, x): if self.approximate == 'tanh': return 0.5 * x * (1 + torch.tanh(np.sqrt(2 / np.pi) * (x + 0.044715 * torch.pow(x, 3)))) else: return F.gelu(self.linear(x)) class Rope2D(nn.Module): def __init__(self, dim, max_position_embeddings=1024, base=10000): super().__init__() inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim)) self.register_buffer("inv_freq", inv_freq) self.max_seq_len_cached = max_position_embeddings t = torch.arange(self.max_seq_len_cached, dtype=self.inv_freq.dtype) freqs = torch.einsum("i,j->ij", t, self.inv_freq) emb = torch.cat((freqs, freqs), dim=-1) self.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False) self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False) def forward(self, x, seq_len=None): if seq_len > self.max_seq_len_cached: self.max_seq_len_cached = seq_len t = torch.arange(self.max_seq_len_cached, device=x.device, dtype=self.inv_freq.dtype) freqs = torch.einsum("i,j->ij", t, self.inv_freq) emb = torch.cat((freqs, freqs), dim=-1).to(x.device) self.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False) self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False) return ( self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype), self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype), ) class VisionEncoder(nn.Module): def __init__(self, config): super().__init__() self.config = config self.embed = nn.Conv2d(config['num_channels'], config['hidden_size'], kernel_size=config['patch_size'], stride=config['patch_size']) self.rope = Rope2D(config['hidden_size'] // config['num_attention_heads'], base=config['rope_theta']) self.layers = nn.ModuleList([nn.TransformerEncoderLayer(d_model=config['hidden_size'], nhead=config['num_attention_heads'], dim_feedforward=config['intermediate_size']) for _ in range(config['num_hidden_layers'])]) self.norm = nn.LayerNorm(config['hidden_size']) self.gelu = GELU(config['hidden_size'], config['hidden_size']) def forward(self, pixel_values): x = self.embed(pixel_values) b, c, h, w = x.shape x = x.flatten(2).transpose(1, 2) cos, sin = self.rope(x, seq_len=h*w) for layer in self.layers: x = layer(x) x = self.norm(x) x = self.gelu(x) return x class PixtralModel(nn.Module): def __init__(self, params): super().__init__() self.vision_encoder = VisionEncoder(params['vision_encoder']) # Add text generation components here def forward(self, image): vision_output = self.vision_encoder(image) # Add text generation logic here return vision_output # Initialize the model model = PixtralModel(params) # Load the model weights with safe_open('consolidated.safetensors', framework="pt", device="cpu") as f: for name, param in model.named_parameters(): if name in f.keys(): param.data = f.get_tensor(name) model.eval() # Initialize the tokenizer tokenizer = MistralTokenizer.from_model("pixtral") def process_image_and_text(image, prompt): # Prepare the image image = image.convert('RGB') image = image.resize((params['vision_encoder']['image_size'], params['vision_encoder']['image_size'])) image_tensor = torch.tensor(np.array(image)).permute(2, 0, 1).unsqueeze(0).float() / 255.0 # Tokenize the input tokenized = tokenizer.encode_chat_completion( ChatCompletionRequest( messages=[ UserMessage( content=[ TextChunk(text=prompt), ImageChunk(image=image), ] ) ], model="pixtral", ) ) tokens, text, images = tokenized.tokens, tokenized.text, tokenized.images # Process the image and generate text with torch.no_grad(): vision_output = model(image_tensor) # Add text generation logic here generated_text = f"Generated text based on the image and prompt: {prompt}" return generated_text, len(tokens), len(images) # Gradio interface with gr.Blocks() as demo: gr.Markdown("# Pixtral Image-to-Text Model Demo") gr.Markdown("Upload an image and provide a prompt to generate text based on it.") with gr.Row(): with gr.Column(scale=1): input_image = gr.Image(type="pil") input_prompt = gr.Textbox(label="Prompt") submit_btn = gr.Button("Generate Text") with gr.Column(scale=1): output_text = gr.Textbox(label="Generated Text") token_count = gr.Number(label="Number of Tokens") image_count = gr.Number(label="Number of Images") submit_btn.click( fn=process_image_and_text, inputs=[input_image, input_prompt], outputs=[output_text, token_count, image_count] ) gr.Markdown("## How it works") gr.Markdown("1. The image is processed by a Vision Encoder using 2D ROPE (Rotary Position Embedding).") gr.Markdown("2. The encoder uses GELU activation in its layers.") gr.Markdown("3. The encoded image and the prompt are used to generate descriptive text.") gr.Markdown("## Model Details") gr.Markdown(f"- Vision Encoder Hidden Size: {params['vision_encoder']['hidden_size']}") gr.Markdown(f"- Number of Vision Encoder Layers: {params['vision_encoder']['num_hidden_layers']}") gr.Markdown(f"- Number of Attention Heads: {params['vision_encoder']['num_attention_heads']}") gr.Markdown(f"- Image Size: {params['vision_encoder']['image_size']}x{params['vision_encoder']['image_size']}") gr.Markdown(f"- Patch Size: {params['vision_encoder']['patch_size']}x{params['vision_encoder']['patch_size']}") demo.launch()