Create main.py

main.py

@@ -0,0 +1,383 @@
+import torch
+from transformers import (
+    AutoTokenizer,
+    AutoModelForCausalLM,
+    AutoModelForSeq2SeqLM,
+    AutoProcessor,
+    AutoModelForSpeechSeq2Seq,
+    AutoModelForTextToWaveform
+)
+from diffusers import DiffusionPipeline
+import time
+import os
+from dotenv import load_dotenv
+from huggingface_hub import HfApi, HfFolder, Repository
+
+load_dotenv()
+
+def prune_model(model, amount=0.5):
+    from torch.nn.utils import prune
+    for name, module in model.named_modules():
+        if isinstance(module, (torch.nn.Linear, torch.nn.Conv2d)):
+            prune.l1_unstructured(module, name='weight', amount=amount)
+            prune.remove(module, 'weight')
+    return model
+
+def quantize_to_q1_with_min(tensor, min_value=-1):
+    tensor = torch.sign(tensor)
+    tensor[tensor < min_value] = min_value
+    return tensor
+
+def quantize_model_to_q1_with_min(model, min_value=-1):
+    for name, param in model.named_parameters():
+        if param.dtype in [torch.float32, torch.float16]:
+            with torch.no_grad():
+                param.copy_(quantize_to_q1_with_min(param.data, min_value))
+
+def disable_unnecessary_components(model):
+    for name, module in model.named_modules():
+        if isinstance(module, torch.nn.Dropout):
+            module.p = 0.0
+        elif isinstance(module, torch.nn.BatchNorm1d):
+            module.eval()
+
+def ultra_max_compress(model):
+    model = prune_model(model, amount=0.8)
+    quantize_model_to_q1_with_min(model, min_value=-0.05)
+    disable_unnecessary_components(model)
+    with torch.no_grad():
+        for name, param in model.named_parameters():
+            if param.requires_grad:
+                param.requires_grad = False
+                param.data = torch.nn.functional.hardtanh(param.data, min_val=-1.0, max_val=1.0)
+                param.data = param.data.half()
+    try:
+        model = torch.jit.script(model)
+    except Exception:
+        pass
+    prune_model(model, amount=0.9)
+    model.eval()
+    for buffer_name, buffer in model.named_buffers():
+        if buffer.numel() == 0:
+            model._buffers.pop(buffer_name)
+    return model
+
+def optimize_model_resources(model):
+    torch.set_grad_enabled(False)
+    model.eval()
+    for name, param in model.named_parameters():
+        param.requires_grad = False
+        if param.dtype == torch.float32:
+            param.data = param.data.half()
+    if hasattr(model, 'config'):
+        if hasattr(model.config, 'max_position_embeddings'):
+            model.config.max_position_embeddings = min(model.config.max_position_embeddings, 512)
+        if hasattr(model.config, 'hidden_size'):
+            model.config.hidden_size = min(model.config.hidden_size, 768)
+    model = torch.jit.optimize_for_inference(model)
+    return model
+
+def generate_random_responses(model, tokenizer, prompt, num_responses=5, max_length=50):
+    responses = []
+    for _ in range(num_responses):
+        input_ids = tokenizer.encode(prompt, return_tensors="pt")
+        output = model.generate(input_ids, max_length=max_length, do_sample=True, top_k=50)
+        response = tokenizer.decode(output[0], skip_special_tokens=True)
+        responses.append(response)
+    return responses
+
+def patched_distilbert_forward(self, input_ids=None, attention_mask=None, head_mask=None, inputs_embeds=None, output_attentions=None, output_hidden_states=None, return_dict=None):
+    return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+    outputs = DistilBertModel.forward(self, input_ids, attention_mask=attention_mask, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
+    if not return_dict:
+        output_tuple = []
+        for v in [outputs.last_hidden_state, outputs.hidden_states, outputs.attentions]:
+            if v is not None:
+                output_tuple.append(v)
+        return tuple(output_tuple)
+    return outputs
+
+def patched_forward(self, input_ids=None, attention_mask=None, head_mask=None, inputs_embeds=None, labels=None, output_attentions=None, output_hidden_states=None, return_dict=None):
+    return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+    outputs = self.distilbert(input_ids, attention_mask=attention_mask, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
+    hidden_state = outputs[0]
+    pooled_output = self.pre_classifier(hidden_state[:, 0])
+    pooled_output = self.dropout(pooled_output)
+    logits = self.classifier(pooled_output)
+    if not return_dict:
+        output = (logits,) + outputs[1:]
+        return output
+    return logits
+
+def patched_roberta_forward(self, input_ids=None, attention_mask=None, head_mask=None, inputs_embeds=None, labels=None, output_attentions=None, output_hidden_states=None, return_dict=None):
+    return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+    outputs = self.roberta(input_ids, attention_mask=attention_mask, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
+    hidden_state = outputs[0]
+    pooled_output = hidden_state[:, 0]
+    pooled_output = self.dropout(pooled_output)
+    logits = self.classifier(pooled_output)
+    if not return_dict:
+        output = (logits,) + outputs[1:]
+        return output
+    return logits
+
+def optimize_for_low_resources(model):
+    model = ultra_max_compress(model)
+    model = optimize_model_resources(model)
+    model.config.max_position_embeddings = 256
+    model.config.hidden_size = 384
+    return model
+
+def optimize_for_very_low_resources(model):
+    model = ultra_max_compress(model)
+    model = optimize_model_resources(model)
+    model.config.max_position_embeddings = 128
+    model.config.hidden_size = 256
+    return model
+
+def remove_unused_model_components(model):
+    for name, param in model.named_parameters():
+        if param.numel() == 0:
+            model._parameters.pop(name)
+    return model
+
+def auto_train_model(model, train_data, epochs=3):
+    optimizer = torch.optim.Adam(model.parameters(), lr=1e-5)
+    model.train()
+    for epoch in range(epochs):
+        for batch in train_data:
+            inputs, labels = batch
+            optimizer.zero_grad()
+            outputs = model(**inputs, labels=labels)
+            loss = outputs.loss
+            loss.backward()
+            optimizer.step()
+    return model
+
+def apply_extreme_filters(model):
+    model = ultra_max_compress(model)
+    model = optimize_model_resources(model)
+    model.config.max_position_embeddings = 128
+    model.config.hidden_size = 256
+    model = torch.jit.optimize_for_inference(model)
+    model = prune_model(model, amount=0.95)
+    quantize_model_to_q1_with_min(model, min_value=-0.1)
+    return model
+
+def reduce_latency(model, tokenizer, prompt, num_responses=5, max_length=50):
+    responses = []
+    start_time = time.time()
+    for _ in range(num_responses):
+        input_ids = tokenizer.encode(prompt, return_tensors="pt")
+        output = model.generate(input_ids, max_length=max_length, do_sample=True, top_k=50)
+        response = tokenizer.decode(output[0], skip_special_tokens=True)
+        responses.append(response)
+    end_time = time.time()
+    latency = (end_time - start_time) / num_responses * 1000
+    return responses, latency
+
+def create_gpt_distill_model():
+    gpt_model = GPT2LMHeadModel.from_pretrained("gpt2")
+    gpt_tokenizer = AutoTokenizer.from_pretrained("gpt2")
+    return gpt_model, gpt_tokenizer
+
+def create_gemma_distill_model():
+    gemma_model = AutoModelForCausalLM.from_pretrained("google/gemma-2-9b")
+    gemma_tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-9b")
+    return gemma_model, gemma_tokenizer
+
+def measure_performance(model, tokenizer, sequence_length=20, num_tokens=100):
+    inputs = tokenizer("A" * sequence_length, return_tensors="pt")
+    start_time = time.time()
+    for _ in range(num_tokens):
+        model.generate(**inputs)
+    end_time = time.time()
+    latency = (end_time - start_time) / num_tokens * 1000
+    tokens_per_second = num_tokens / (end_time - start_time)
+    return latency, tokens_per_second
+
+def apply_diffusion_pipeline(prompt):
+    diffusion_pipeline = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell")
+    images = diffusion_pipeline(prompt).images
+    return images
+
+def generate_responses_with_diffusion(prompt, use_diffusion):
+    if "imagina" in prompt.lower() or "imagine" in prompt.lower():
+        images = apply_diffusion_pipeline(prompt)
+        return images
+    return None
+
+def generate_summary_with_bart(prompt):
+    tokenizer = AutoTokenizer.from_pretrained("facebook/bart-large-cnn")
+    model = AutoModelForSeq2SeqLM.from_pretrained("facebook/bart-large-cnn")
+    inputs = tokenizer.encode(prompt, return_tensors="pt")
+    summary_ids = model.generate(inputs, max_length=130, min_length=30, length_penalty=2.0, num_beams=4, early_stopping=True)
+    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+    return summary
+
+def generate_responses_with_bart(prompt):
+    if "resumir" in prompt.lower() or "resumime" in prompt.lower():
+        summary = generate_summary_with_bart(prompt)
+        return summary
+    return None
+
+def apply_whisper_pipeline(prompt):
+    processor = AutoProcessor.from_pretrained("openai/whisper-small")
+    model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-small")
+    inputs = processor(prompt, return_tensors="pt")
+    outputs = model.generate(**inputs)
+    transcription = processor.batch_decode(outputs, skip_special_tokens=True)
+    return transcription
+
+def generate_transcription_with_whisper(prompt):
+    if "transcribe" in prompt.lower() or "transcribime" in prompt.lower():
+        transcription = apply_whisper_pipeline(prompt)
+        return transcription
+    return None
+
+def apply_translation_pipeline(prompt):
+    tokenizer = AutoTokenizer.from_pretrained("google-t5/t5-base")
+    model = AutoModelForSeq2SeqLM.from_pretrained("google-t5/t5-base")
+    inputs = tokenizer.encode(prompt, return_tensors="pt")
+    translated_ids = model.generate(inputs, max_length=50)
+    translated_text = tokenizer.decode(translated_ids[0], skip_special_tokens=True)
+    return translated_text
+
+def generate_translation_with_t5(prompt):
+    if "traducir" in prompt.lower() or "traducime" in prompt.lower():
+        translation = apply_translation_pipeline(prompt)
+        return translation
+    return None
+
+def apply_musicgen_pipeline(prompt):
+    tokenizer = AutoTokenizer.from_pretrained("facebook/musicgen-small")
+    model = AutoModelForTextToWaveform.from_pretrained("facebook/musicgen-small")
+    inputs = tokenizer(prompt, return_tensors="pt")
+    audio = model.generate(inputs)
+    return audio
+
+def generate_music_with_musicgen(prompt):
+    if "música" in prompt.lower() or "canción" in prompt.lower():
+        music = apply_musicgen_pipeline(prompt)
+        return music
+    return None
+
+def apply_musicgen_melody_pipeline(prompt):
+    tokenizer = AutoTokenizer.from_pretrained("facebook/musicgen-melody")
+    model = AutoModelForTextToWaveform.from_pretrained("facebook/musicgen-melody")
+    inputs = tokenizer(prompt, return_tensors="pt")
+    audio = model.generate(inputs)
+    return audio
+
+def generate_music_with_musicgen_melody(prompt):
+    if "melodía" in prompt.lower() or "melodia" in prompt.lower():
+        music = apply_musicgen_melody_pipeline(prompt)
+        return music
+    return None
+
+def apply_stable_diffusion_pipeline(prompt):
+    pipeline = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1")
+    images = pipeline(prompt).images
+    return images
+
+def generate_responses_with_stable_diffusion(prompt):
+    if "imagen" in prompt.lower() or "image" in prompt.lower():
+        images = apply_stable_diffusion_pipeline(prompt)
+        return images
+    return None
+
+def unify_models(*models):
+    combined_model = torch.nn.ModuleList(models)
+    return combined_model
+
+def combined_filter(model):
+    model = ultra_max_compress(model)
+    model = optimize_model_resources(model)
+    model.config.max_position_embeddings = 128
+    model.config.hidden_size = 256
+    model = torch.jit.optimize_for_inference(model)
+    model = prune_model(model, amount=0.95)
+    quantize_model_to_q1_with_min(model, min_value=-0.1)
+    return model
+
+def apply_filters_and_unify(model):
+    model = combined_filter(model)
+    model = remove_unused_model_components(model)
+    return model
+
+def upload_to_huggingface(model, repo_name):
+    api = HfApi()
+    try:
+        api.create_repo(repo_id=repo_name, repo_type="model")
+    except Exception:
+        pass
+    model.save_pretrained(repo_name)
+    tokenizer.save_pretrained(repo_name)
+    repo = Repository(repo_name)
+    repo.push_to_hub()
+
+def apply_extreme_filters_and_upload(model, repo_name):
+    model = apply_extreme_filters(model)
+    upload_to_huggingface(model, repo_name)
+
+model_infos = [
+    {"model_name": "gpt2", "class": GPT2LMHeadModel},
+    {"model_name": "google/gemma-2-9b", "class": AutoModelForCausalLM}
+]
+
+for model_info in model_infos:
+    model = model_info["class"].from_pretrained(model_info["model_name"])
+    tokenizer = AutoTokenizer.from_pretrained(model_info["model_name"])
+    optimized_model, responses, latency = optimize_model_with_all_optimizations(model, tokenizer, "Sample prompt for optimization.")
+    print(f"Model: {model_info['model_name']}")
+    print(f"Latency: {latency:.2f} ms")
+    print(f"Sample Responses: {responses}")
+
+gpt_model, gpt_tokenizer = create_gpt_distill_model()
+gemma_model, gemma_tokenizer = create_gemma_distill_model()
+
+optimized_gpt_model, gpt_responses, gpt_latency = optimize_model_with_all_optimizations(gpt_model, gpt_tokenizer, "Sample prompt for GPT optimization.")
+optimized_gemma_model, gemma_responses, gemma_latency = optimize_model_with_all_optimizations(gemma_model, gemma_tokenizer, "Sample prompt for Gemma optimization.")
+
+combined_model = unify_models(optimized_gpt_model, optimized_gemma_model)
+
+optimized_gpt_model_1gb = optimize_for_1gb_ram(optimized_gpt_model)
+optimized_gemma_model_1gb = optimize_for_1gb_ram(optimized_gemma_model)
+optimized_gpt_model_low = optimize_for_very_low_resources(optimized_gpt_model)
+optimized_gemma_model_low = optimize_for_very_low_resources(optimized_gemma_model)
+optimized_gpt_model_cpu = optimize_for_old_cpu(optimized_gpt_model)
+optimized_gemma_model_cpu = optimize_for_old_cpu(optimized_gemma_model)
+optimized_gpt_model_gpu = optimize_for_old_gpu(optimized_gpt_model)
+optimized_gemma_model_gpu = optimize_for_old_gpu(optimized_gemma_model)
+
+print("Models optimized for various resource constraints.")
+
+diffusion_response = generate_responses_with_diffusion("Imagine a serene landscape", True)
+if diffusion_response:
+    print("Diffusion response generated.")
+
+summary_response = generate_responses_with_bart("Resumir este texto para obtener un resumen efectivo.", True)
+if summary_response:
+    print("Summary response generated.")
+
+transcription_response = generate_transcription_with_whisper("Transcribe this audio file.", True)
+if transcription_response:
+    print("Transcription response generated.")
+
+translation_response = generate_translation_with_t5("Traducir este texto al inglés.", True)
+if translation_response:
+    print("Translation response generated.")
+
+music_response = generate_music_with_musicgen("Música para una tarde tranquila.", True)
+if music_response:
+    print("Music response generated.")
+
+melody_music_response = generate_music_with_musicgen_melody("Melodía para relajación.", True)
+if melody_music_response:
+    print("Melody music response generated.")
+
+image_response = generate_responses_with_stable_diffusion("Imagen de un paisaje sereno.", True)
+if image_response:
+    print("Image response generated.")
+
+upload_to_huggingface(combined_model, "xilixmeaty40/my_model")