streaming version of app_januspro

demo/app_januspro_stream.py

@@ -0,0 +1,420 @@
+#GW
+import datetime
+print(datetime.datetime.now().strftime("%Y/%m/%d %H:%M:%S"), "Starting DeepSeek-over-Juice multi-modal demo (model=Janus-Pro-1B). Please wait...")
+VERBOSE=False
+if VERBOSE: print("before imports", datetime.datetime.now())
+last_time = datetime.datetime.now()
+#GW
+
+#GW
+import warnings
+warnings.filterwarnings("ignore")
+with warnings.catch_warnings():
+    warnings.filterwarnings("ignore", category=DeprecationWarning)
+    import collections
+from transformers.utils import logging
+logging.set_verbosity_error() 
+import os
+os.environ["TRANSFORMERS_VERBOSITY"] = "error"
+#GW 
+
+import gradio as gr
+import torch
+from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, TextStreamer
+from janus.models import MultiModalityCausalLM, VLChatProcessor
+from janus.utils.io import load_pil_images
+from PIL import Image
+
+import numpy as np
+#GW import os
+import time
+
+#GW
+import warnings
+warnings.filterwarnings("ignore")
+import traceback
+from threading import Thread
+from transformers import (
+    TextIteratorStreamer
+)
+#GW
+
+# import spaces  # Import spaces for ZeroGPU compatibility
+
+#GW
+if VERBOSE: print("after imports", datetime.datetime.now())
+this_time = datetime.datetime.now()
+print(this_time.strftime("%Y/%m/%d %H:%M:%S"), "Done importing Python packages.", "It took %.1f" % (this_time-last_time).total_seconds(),"seconds. Please wait...")
+last_time = this_time
+#GW
+
+# Load model and processor
+#GW model_path = "deepseek-ai/Janus-Pro-7B"
+model_path = "deepseek-ai/Janus-Pro-1B"
+
+config = AutoConfig.from_pretrained(model_path)
+language_config = config.language_config
+language_config._attn_implementation = 'eager'
+
+#GW
+if VERBOSE: print()
+a = datetime.datetime.now()
+if VERBOSE: print("automodel before from_pretrained", a)
+#GW
+
+vl_gpt = AutoModelForCausalLM.from_pretrained(model_path,
+                                             language_config=language_config,
+                                             trust_remote_code=True)
+
+#GW
+b = datetime.datetime.now()
+if VERBOSE: print("automodel after from_pretrained", b, (b-a).total_seconds())
+#GW
+
+if torch.cuda.is_available():
+    #GW
+    c = datetime.datetime.now()
+    if VERBOSE: print("cuda is available", c, (c-b).total_seconds())
+    #GW
+
+    #GW vl_gpt = vl_gpt.to(torch.bfloat16).cuda()
+    vl_gpt = vl_gpt.to(torch.bfloat16)
+
+    d = datetime.datetime.now()
+    if VERBOSE: print("to blfoat16", d, (d-c).total_seconds())
+
+    vl_gpt = vl_gpt.cuda()
+
+    e = datetime.datetime.now()
+    if VERBOSE: print("to cuda", e, (e-d).total_seconds(), (e-a).total_seconds())
+    print(e.strftime("%Y/%m/%d %H:%M:%S"), "Done initializing model.", "It took %.1f" % (e-last_time).total_seconds(),"seconds. Please wait...")
+    last_time = e
+    #GW
+
+else:
+    if VERBOSE: print("cuda is not available", datetime.datetime.now())
+
+    vl_gpt = vl_gpt.to(torch.float16)
+
+    #GW
+    c = datetime.datetime.now()
+    if VERBOSE: print("model to float16", (c-b).total_seconds(), (c-a).total_seconds())
+    #GW
+
+# GW
+if VERBOSE: 
+    print()
+    print("processor before from_pretrained", datetime.datetime.now())
+#GW
+vl_chat_processor = VLChatProcessor.from_pretrained(model_path)
+
+if VERBOSE: print("processor after from_pretrained", datetime.datetime.now())
+
+tokenizer = vl_chat_processor.tokenizer
+cuda_device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+#GW
+if VERBOSE: print("cuda device", cuda_device, datetime.datetime.now())
+global_thread = None
+global_time = None
+#GW
+
+@torch.inference_mode()
+# @spaces.GPU(duration=120) 
+# Multimodal Understanding function
+#GW def multimodal_understanding(image, question, seed, top_p, temperature):
+def multimodal_understanding(image, question, seed, top_p, temperature,streamer, vl_gpt):
+    if VERBOSE: print("start understanding", type(streamer), datetime.datetime.now())
+#GW
+
+    # Clear CUDA cache before generating
+    torch.cuda.empty_cache()
+   
+    if VERBOSE: print("after empty cache", datetime.datetime.now())
+
+    # set seed
+    torch.manual_seed(seed)
+    np.random.seed(seed)
+    torch.cuda.manual_seed(seed)
+    
+    conversation = [
+        {
+            "role": "<|User|>",
+            "content": f"<image_placeholder>\n{question}",
+            "images": [image],
+        },
+        {"role": "<|Assistant|>", "content": ""},
+    ]
+   
+    if VERBOSE: print("before prepare image", datetime.datetime.now())
+    pil_images = [Image.fromarray(image)]
+    prepare_inputs = vl_chat_processor(
+        conversations=conversation, images=pil_images, force_batchify=True
+    ).to(cuda_device, dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float16)
+    if VERBOSE: print("after prepare image", datetime.datetime.now())
+    
+    inputs_embeds = vl_gpt.prepare_inputs_embeds(**prepare_inputs)
+
+#GW
+    if VERBOSE:
+        print("after embeds", datetime.datetime.now())
+        print("inputs=", inputs_embeds, inputs_embeds.shape)
+        print()
+    b = datetime.datetime.now()
+    if VERBOSE: print("before generate", b)
+#GW
+
+    outputs = vl_gpt.language_model.generate(
+        inputs_embeds=inputs_embeds,
+        attention_mask=prepare_inputs.attention_mask,
+        pad_token_id=tokenizer.eos_token_id,
+        bos_token_id=tokenizer.bos_token_id,
+        eos_token_id=tokenizer.eos_token_id,
+        max_new_tokens=512,
+        do_sample=False if temperature == 0 else True,
+        use_cache=True,
+        temperature=temperature,
+        top_p=top_p,
+#GW
+        streamer = streamer
+#GW
+    )
+
+#GW
+    if VERBOSE: print("outputs=", outputs, outputs.shape)
+    a = datetime.datetime.now()
+    if VERBOSE:
+        print("after generate tokens len-", len( tokenizer.decode(outputs[0].cpu().tolist() )) , a, (a-b).total_seconds())
+        print()
+#GW
+    answer = tokenizer.decode(outputs[0].cpu().tolist(), skip_special_tokens=True)
+    if VERBOSE: print("after decode", datetime.datetime.now())
+    return answer
+
+#GW
+def multimodal_understanding_thread(image, question, seed, top_p, temperature):
+    try:
+        global global_time
+        global_time = datetime.datetime.now()
+        print(global_time.strftime("%Y/%m/%d %H:%M:%S"), "Preparing model for decoding the image and prompt.  Please wait...")
+        tok = AutoTokenizer.from_pretrained(model_path)
+        if VERBOSE: print("after tokenzer create", datetime.datetime.now())
+        streamer = TextIteratorStreamer(
+            tokenizer=tok, timeout=60.0, skip_prompt=True, skip_special_tokens=True
+        )
+        if VERBOSE: print("created tokenzier and streamer", type(streamer), datetime.datetime.now())
+        global global_thread
+        if global_thread:
+            if VERBOSE: print("killing current thread")
+            try:
+                global_thread.stop()
+            except:
+                traceback.print_exc()
+
+        # launch new thread
+        if VERBOSE: print("launching decode thread...")
+        global_thread = Thread(target=multimodal_understanding, args=[image, question, seed, top_p, temperature, streamer, vl_gpt])
+        global_thread.start()
+
+        generated_text = ""
+        count = 0
+        last_time = None
+        for i in streamer:
+            generated_text += i
+            if count==0:
+                count += 1
+                last_time = datetime.datetime.now()
+                print(last_time.strftime("%Y/%m/%d %H:%M:%S"),"Model is ready to decode.", "Model prep took %.1f" % (last_time - global_time).total_seconds(),"seconds. Decode commencing...")
+            else:
+                count += 1
+                this_time = datetime.datetime.now()
+                token_rate = count*1.0 / ( (this_time - last_time).total_seconds() )
+                print(this_time.strftime("%Y/%m/%d %H:%M:%S"), "Model is decoding! Current cecode stats: total tokens=", count, "token rate=", "%.1f" % token_rate,"...")
+
+            yield generated_text
+
+    except:
+        traceback.print_exc()
+#GW
+
+def generate(input_ids,
+             width,
+             height,
+             temperature: float = 1,
+             parallel_size: int = 5,
+             cfg_weight: float = 5,
+             image_token_num_per_image: int = 576,
+             patch_size: int = 16):
+    if VERBOSE: print("generate top", datetime.datetime.now())
+    # Clear CUDA cache before generating
+    torch.cuda.empty_cache()
+    if VERBOSE: print("generate after empty cache", datetime.datetime.now())
+    
+    tokens = torch.zeros((parallel_size * 2, len(input_ids)), dtype=torch.int).to(cuda_device)
+    for i in range(parallel_size * 2):
+        tokens[i, :] = input_ids
+        if i % 2 != 0:
+            tokens[i, 1:-1] = vl_chat_processor.pad_id
+    inputs_embeds = vl_gpt.language_model.get_input_embeddings()(tokens)
+    generated_tokens = torch.zeros((parallel_size, image_token_num_per_image), dtype=torch.int).to(cuda_device)
+
+    pkv = None
+    for i in range(image_token_num_per_image):
+        if VERBOSE: print("for loop top", i, datetime.datetime.now())
+        with torch.no_grad():
+            if VERBOSE: print("before model", i, datetime.datetime.now())
+            outputs = vl_gpt.language_model.model(inputs_embeds=inputs_embeds,
+                                                use_cache=True,
+                                                past_key_values=pkv)
+            if VERBOSE: print("after model", i, datetime.datetime.now())
+            pkv = outputs.past_key_values
+            hidden_states = outputs.last_hidden_state
+            logits = vl_gpt.gen_head(hidden_states[:, -1, :])
+            logit_cond = logits[0::2, :]
+            logit_uncond = logits[1::2, :]
+            logits = logit_uncond + cfg_weight * (logit_cond - logit_uncond)
+            probs = torch.softmax(logits / temperature, dim=-1)
+            next_token = torch.multinomial(probs, num_samples=1)
+            generated_tokens[:, i] = next_token.squeeze(dim=-1)
+            next_token = torch.cat([next_token.unsqueeze(dim=1), next_token.unsqueeze(dim=1)], dim=1).view(-1)
+
+            img_embeds = vl_gpt.prepare_gen_img_embeds(next_token)
+            inputs_embeds = img_embeds.unsqueeze(dim=1)
+            if VERBOSE: print("for loop bottom", i, datetime.datetime.now())
+
+    if VERBOSE: print("before decode", datetime.datetime.now())
+    patches = vl_gpt.gen_vision_model.decode_code(generated_tokens.to(dtype=torch.int),
+                                                 shape=[parallel_size, 8, width // patch_size, height // patch_size])
+    if VERBOSE: print("after decode", datetime.datetime.now())
+
+    return generated_tokens.to(dtype=torch.int), patches
+
+def unpack(dec, width, height, parallel_size=5):
+    dec = dec.to(torch.float32).cpu().numpy().transpose(0, 2, 3, 1)
+    dec = np.clip((dec + 1) / 2 * 255, 0, 255)
+
+    visual_img = np.zeros((parallel_size, width, height, 3), dtype=np.uint8)
+    visual_img[:, :, :] = dec
+
+    return visual_img
+
+
+
+@torch.inference_mode()
+# @spaces.GPU(duration=120)  # Specify a duration to avoid timeout
+def generate_image(prompt,
+                   seed=None,
+                   guidance=5,
+                   t2i_temperature=1.0):
+    # Clear CUDA cache and avoid tracking gradients
+    torch.cuda.empty_cache()
+    # Set the seed for reproducible results
+    if seed is not None:
+        torch.manual_seed(seed)
+        torch.cuda.manual_seed(seed)
+        np.random.seed(seed)
+    width = 384
+    height = 384
+    parallel_size = 5
+    
+    with torch.no_grad():
+        messages = [{'role': '<|User|>', 'content': prompt},
+                    {'role': '<|Assistant|>', 'content': ''}]
+        text = vl_chat_processor.apply_sft_template_for_multi_turn_prompts(conversations=messages,
+                                                                   sft_format=vl_chat_processor.sft_format,
+                                                                   system_prompt='')
+        text = text + vl_chat_processor.image_start_tag
+        
+        input_ids = torch.LongTensor(tokenizer.encode(text))
+        output, patches = generate(input_ids,
+                                   width // 16 * 16,
+                                   height // 16 * 16,
+                                   cfg_weight=guidance,
+                                   parallel_size=parallel_size,
+                                   temperature=t2i_temperature)
+        images = unpack(patches,
+                        width // 16 * 16,
+                        height // 16 * 16,
+                        parallel_size=parallel_size)
+
+        return [Image.fromarray(images[i]).resize((768, 768), Image.LANCZOS) for i in range(parallel_size)]
+        
+
+# Gradio interface
+#GW with gr.Blocks() as demo:
+with gr.Blocks(analytics_enabled=False) as demo:
+    gr.Markdown(value="# Multimodal Understanding: DeepSeek JanusPro1B via JUICE")
+    with gr.Row():
+        image_input = gr.Image()
+        with gr.Column():
+            question_input = gr.Textbox(label="Question")
+            und_seed_input = gr.Number(label="Seed", precision=0, value=42)
+            top_p = gr.Slider(minimum=0, maximum=1, value=0.95, step=0.05, label="top_p")
+            temperature = gr.Slider(minimum=0, maximum=1, value=0.1, step=0.05, label="temperature")
+        
+    understanding_button = gr.Button("Chat")
+    understanding_output = gr.Textbox(label="Response")
+
+    examples_inpainting = gr.Examples(
+        label="Multimodal Understanding examples",
+        examples=[
+            [
+                "explain this meme",
+                "images/doge.png",
+            ],
+            [
+                "Convert the formula into latex code.",
+                "images/equation.png",
+            ],
+        ],
+        inputs=[question_input, image_input],
+    )
+    
+        
+    gr.Markdown(value="# Text-to-Image Generation")
+
+    
+    
+    with gr.Row():
+        cfg_weight_input = gr.Slider(minimum=1, maximum=10, value=5, step=0.5, label="CFG Weight")
+        t2i_temperature = gr.Slider(minimum=0, maximum=1, value=1.0, step=0.05, label="temperature")
+
+    prompt_input = gr.Textbox(label="Prompt. (Prompt in more detail can help produce better images!)")
+    seed_input = gr.Number(label="Seed (Optional)", precision=0, value=12345)
+
+    generation_button = gr.Button("Generate Images")
+
+    image_output = gr.Gallery(label="Generated Images", columns=2, rows=2, height=300)
+
+    examples_t2i = gr.Examples(
+        label="Text to image generation examples.",
+        examples=[
+            "Master shifu racoon wearing drip attire as a street gangster.",
+            "The face of a beautiful girl",
+            "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
+            "A glass of red wine on a reflective surface.",
+            "A cute and adorable baby fox with big brown eyes, autumn leaves in the background enchanting,immortal,fluffy, shiny mane,Petals,fairyism,unreal engine 5 and Octane Render,highly detailed, photorealistic, cinematic, natural colors.",
+            "The image features an intricately designed eye set against a circular backdrop adorned with ornate swirl patterns that evoke both realism and surrealism. At the center of attention is a strikingly vivid blue iris surrounded by delicate veins radiating outward from the pupil to create depth and intensity. The eyelashes are long and dark, casting subtle shadows on the skin around them which appears smooth yet slightly textured as if aged or weathered over time.\n\nAbove the eye, there's a stone-like structure resembling part of classical architecture, adding layers of mystery and timeless elegance to the composition. This architectural element contrasts sharply but harmoniously with the organic curves surrounding it. Below the eye lies another decorative motif reminiscent of baroque artistry, further enhancing the overall sense of eternity encapsulated within each meticulously crafted detail. \n\nOverall, the atmosphere exudes a mysterious aura intertwined seamlessly with elements suggesting timelessness, achieved through the juxtaposition of realistic textures and surreal artistic flourishes. Each component\u2014from the intricate designs framing the eye to the ancient-looking stone piece above\u2014contributes uniquely towards creating a visually captivating tableau imbued with enigmatic allure.",
+        ],
+        inputs=prompt_input,
+    )
+    
+    understanding_button.click(
+        multimodal_understanding_thread, #GW multimodal_understanding
+        inputs=[image_input, question_input, und_seed_input, top_p, temperature],
+        outputs=understanding_output
+    )
+    
+    generation_button.click(
+        fn=generate_image,
+        inputs=[prompt_input, seed_input, cfg_weight_input, t2i_temperature],
+        outputs=image_output
+    )
+
+if VERBOSE: print("before demo launch...", datetime.datetime.now())
+#GW 
+# demo.launch(share=True)
+demo.queue()
+demo.launch(server_name="0.0.0.0", server_port=7860, quiet=True)
+#GW 
+# demo.queue(concurrency_count=1, max_size=10).launch(server_name="0.0.0.0", server_port=37906, root_path="/path")