app.py

#!/usr/bin/env python3
import gradio as gr
import torch
import time
import numpy as np
import spaces  # ZeroGPU import

# ASI V2.5 - ZeroGPU H200 Optimized Version
print("🚀 Loading ASI V2.5 for ZeroGPU H200...")

# ZeroGPU specs
ZEROGPU_SPECS = {
    "hardware": "NVIDIA H200",
    "vram": "70GB",
    "device": "cuda",
    "cost": "FREE with Pro",
    "quota": "8x Pro quota"
}

# Vraie implémentation ASI optimisée GPU
class ZeroGPU_ASI_Attention(torch.nn.Module):
    """
    ASI V2.5 optimisé pour ZeroGPU H200
    - Tire parti des 70GB VRAM
    - Optimisé CUDA pour vraies performances
    - Séquences longues 4096+ tokens
    """
    
    def __init__(self, hidden_size=768, num_heads=12, threshold=512, feature_dim=64):
        super().__init__()
        self.hidden_size = hidden_size
        self.num_heads = num_heads
        self.head_dim = hidden_size // num_heads
        self.threshold = threshold
        self.feature_dim = feature_dim
        
        # GPU-optimized projections
        self.qkv_proj = torch.nn.Linear(hidden_size, hidden_size * 3, bias=False)
        self.o_proj = torch.nn.Linear(hidden_size, hidden_size, bias=False)
        
        # ASI feature mapping pour GPU
        self.feature_map = torch.nn.Sequential(
            torch.nn.Linear(self.head_dim, feature_dim, bias=False),
            torch.nn.ReLU(),
            torch.nn.Linear(feature_dim, feature_dim, bias=False)
        )
        
        self.scale = (self.head_dim ** -0.5)
        
    def forward(self, hidden_states, attention_mask=None):
        """GPU-optimized forward pass"""
        batch_size, seq_len, _ = hidden_states.shape
        device = hidden_states.device
        
        # Single QKV projection (GPU efficient)
        qkv = self.qkv_proj(hidden_states)
        q, k, v = qkv.chunk(3, dim=-1)
        
        # Multi-head reshape
        q = q.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
        k = k.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
        v = v.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
        
        # ASI adaptive attention - optimisé pour H200
        if seq_len <= self.threshold:
            # Exact attention pour courtes séquences
            attn_output = self._gpu_exact_attention(q, k, v, attention_mask)
        else:
            # Linear attention pour longues séquences (où ASI excelle!)
            attn_output = self._gpu_linear_attention(q, k, v, attention_mask)
        
        # Reshape et output
        attn_output = attn_output.transpose(1, 2).contiguous().view(
            batch_size, seq_len, self.hidden_size
        )
        attn_output = self.o_proj(attn_output)
        
        return attn_output
    
    def _gpu_exact_attention(self, q, k, v, attention_mask=None):
        """GPU-optimized exact attention"""
        # Use CUDA-optimized scaled_dot_product_attention si disponible
        if hasattr(torch.nn.functional, 'scaled_dot_product_attention'):
            return torch.nn.functional.scaled_dot_product_attention(
                q, k, v, attn_mask=attention_mask, scale=self.scale
            )
        
        # Fallback standard
        scores = torch.matmul(q, k.transpose(-2, -1)) * self.scale
        if attention_mask is not None:
            scores = scores.masked_fill(attention_mask == 0, -1e9)
        
        attn_weights = torch.softmax(scores, dim=-1)
        return torch.matmul(attn_weights, v)
    
    def _gpu_linear_attention(self, q, k, v, attention_mask=None):
        """GPU-optimized linear attention - où ASI V2.5 excelle!"""
        batch_size, num_heads, seq_len, head_dim = q.shape
        
        # Feature mapping sur GPU
        q_flat = q.reshape(-1, head_dim)
        k_flat = k.reshape(-1, head_dim)
        
        # GPU-optimized feature projection
        q_feat = self.feature_map(q_flat).view(batch_size, num_heads, seq_len, self.feature_dim)
        k_feat = self.feature_map(k_flat).view(batch_size, num_heads, seq_len, self.feature_dim)
        
        # Apply mask sur GPU
        if attention_mask is not None:
            mask = attention_mask.unsqueeze(1).unsqueeze(-1).float()
            k_feat = k_feat * mask
        
        # GPU-optimized linear attention computation
        # K^T @ V - utilise les tensor cores H200
        kv = torch.matmul(k_feat.transpose(-2, -1), v)  # [B, H, F, D]
        
        # Q @ (K^T @ V) - calcul linéaire O(L)
        attn_output = torch.matmul(q_feat, kv)  # [B, H, L, D]
        
        # Normalization optimisée GPU
        k_sum = k_feat.sum(dim=-2, keepdim=True)  # [B, H, 1, F]
        q_k_sum = torch.matmul(q_feat, k_sum.transpose(-2, -1))  # [B, H, L, 1]
        
        # Éviter division par zéro
        attn_output = attn_output / (q_k_sum + 1e-8)
        
        return attn_output

@spaces.GPU  # ZeroGPU decorator - CRUCIAL!
def run_zerogpu_asi_benchmark(threshold, feature_dim, num_heads, dim, seq_lengths_text, num_runs):
    """
    ASI V2.5 Benchmark sur ZeroGPU H200 - VRAIES PERFORMANCES!
    """
    try:
        # ZeroGPU = CUDA H200!
        device = "cuda"
        print(f"🚀 Running on ZeroGPU: {ZEROGPU_SPECS['hardware']} with {ZEROGPU_SPECS['vram']} VRAM")
        
        # Parse séquences - longues pour voir les vrais speedups ASI!
        seq_lengths = [int(x.strip()) for x in seq_lengths_text.split(',')]
        seq_lengths = [max(256, min(8192, sl)) for sl in seq_lengths]  # H200 peut gérer de longues séquences!
        
        # Créer l'ASI GPU-optimisé
        asi_attention = ZeroGPU_ASI_Attention(
            hidden_size=dim,
            num_heads=num_heads,
            threshold=threshold,
            feature_dim=feature_dim
        ).to(device)
        
        print(f"✅ ASI V2.5 loaded on {device} - ZeroGPU H200!")
        
        results = {
            "device": device,
            "zerogpu_specs": ZEROGPU_SPECS,
            "config": {
                "threshold": threshold,
                "feature_dim": feature_dim,
                "num_heads": num_heads,
                "dim": dim
            },
            "metrics": []
        }
        
        report = f"""# 🚀 ASI V2.5 Performance Test - ZeroGPU H200

**Device**: {device.upper()} (ZeroGPU {ZEROGPU_SPECS['hardware']})
**VRAM**: {ZEROGPU_SPECS['vram']} 
**ASI Status**: 🚀 REAL GPU ASI V2.5
**Configuration**: threshold={threshold}, feature_dim={feature_dim}, heads={num_heads}, dim={dim}

## Performance Results (REAL GPU!)

| Sequence Length | Standard (ms) | ASI V2.5 (ms) | Speedup | Attention Type |
|----------------|---------------|---------------|---------|----------------|"""

        for seq_len in seq_lengths:
            batch_size = 1
            
            # Test data sur GPU
            hidden_states = torch.randn(batch_size, seq_len, dim, device=device, dtype=torch.float16)
            
            # Synchronisation GPU pour mesures précises
            torch.cuda.synchronize()
            
            # Test attention standard sur GPU
            standard_times = []
            for _ in range(num_runs):
                torch.cuda.synchronize()
                start = time.time()
                
                # Standard O(L²) attention sur GPU
                q = k = v = hidden_states
                scores = torch.matmul(q, k.transpose(-2, -1)) / (dim ** 0.5)
                attn_weights = torch.softmax(scores, dim=-1)
                output = torch.matmul(attn_weights, v)
                
                torch.cuda.synchronize()
                standard_times.append((time.time() - start) * 1000)
            
            # Test ASI V2.5 sur GPU H200
            asi_times = []
            for _ in range(num_runs):
                torch.cuda.synchronize()
                start = time.time()
                
                # VRAI test ASI V2.5 sur H200!
                with torch.cuda.amp.autocast():  # Mixed precision pour H200
                    asi_output = asi_attention(hidden_states)
                
                torch.cuda.synchronize()
                asi_times.append((time.time() - start) * 1000)
            
            std_time = np.mean(standard_times)
            asi_time = np.mean(asi_times)
            speedup = std_time / asi_time
            
            # Déterminer le type d'attention utilisé
            attention_type = "🔧 Linear" if seq_len > threshold else "🎯 Exact"
            
            # Couleur selon performance
            speedup_color = "🟢" if speedup > 1.5 else "🟡" if speedup > 1.0 else "🔴"
            
            report += f"\n| {seq_len:,} | {std_time:.1f} | {asi_time:.1f} | **{speedup:.2f}x** {speedup_color} | {attention_type} |"
            
            results["metrics"].append({
                "seq_len": seq_len,
                "standard_ms": round(std_time, 2),
                "asi_ms": round(asi_time, 2),
                "speedup": round(speedup, 2),
                "attention_type": attention_type
            })
            
            # Clear GPU memory
            del hidden_states, asi_output
            torch.cuda.empty_cache()
        
        avg_speedup = np.mean([m["speedup"] for m in results["metrics"]])
        max_speedup = max([m["speedup"] for m in results["metrics"]])
        
        report += f"""

## Summary
- **Average Speedup**: {avg_speedup:.2f}x
- **Maximum Speedup**: {max_speedup:.2f}x  
- **ZeroGPU Hardware**: {ZEROGPU_SPECS['hardware']} ({ZEROGPU_SPECS['vram']} VRAM)
- **Cost**: {ZEROGPU_SPECS['cost']}

## ZeroGPU Performance Analysis
✅ **REAL GPU ASI V2.5 TEST COMPLETE!**
- Tested on {ZEROGPU_SPECS['hardware']} with {ZEROGPU_SPECS['vram']} VRAM
- Mixed precision FP16 optimization
- CUDA tensor cores utilization
- {"🚀 **EXCELLENT SPEEDUPS!**" if avg_speedup > 1.5 else "🟡 **GOOD PERFORMANCE**" if avg_speedup > 1.0 else "⚠️ **OPTIMIZATION NEEDED**"}

### ZeroGPU Advantages
- ✅ FREE with Pro subscription
- ✅ 70GB VRAM for long sequences
- ✅ NVIDIA H200 latest architecture
- ✅ 8x quota with Pro priority
"""
        
        return report, str(results)
        
    except Exception as e:
        error_msg = f"""# ⚠️ ZeroGPU Test Error

**Error**: {str(e)}

**ZeroGPU Status**: {ZEROGPU_SPECS['hardware']} available
**Expected**: NVIDIA H200 70GB VRAM

## Troubleshooting
- Verify Pro subscription for ZeroGPU access
- Check @spaces.GPU decorator
- Ensure CUDA operations
"""
        return error_msg, f'{{"error": "{str(e)}", "zerogpu_specs": {ZEROGPU_SPECS}}}'

# Interface Gradio avec ZeroGPU
with gr.Blocks(title="ASI V2.5 ZeroGPU", theme=gr.themes.Soft()) as app:
    gr.HTML(f"""
    <div style="text-align: center; margin-bottom: 30px;">
        <h1>🚀 ASI V2.5: ZeroGPU H200 Performance</h1>
        <h2>REAL GPU Testing - NVIDIA H200 70GB VRAM!</h2>
        <p style="color: #666; font-size: 18px;">
            <strong>ZeroGPU • {ZEROGPU_SPECS['hardware']} • {ZEROGPU_SPECS['vram']} VRAM • {ZEROGPU_SPECS['cost']}</strong><br>
            <span style="color: green;">✅ NVIDIA H200</span> | 
            <span style="color: green;">✅ 70GB VRAM</span> | 
            <span style="color: green;">✅ FREE with Pro</span>
        </p>
    </div>
    """)
    
    with gr.Tab("🚀 ZeroGPU H200 Performance"):
        gr.Markdown("### Real ASI V2.5 Performance on NVIDIA H200 - Finally!")
        
        with gr.Row():
            with gr.Column():
                gr.Markdown("#### ASI Configuration")
                threshold = gr.Slider(64, 2048, value=512, step=64, label="🎯 Threshold (tokens)")
                feature_dim = gr.Slider(16, 128, value=64, step=16, label="🔧 Feature Dimension")
                num_heads = gr.Slider(8, 32, value=12, step=4, label="🏗️ Attention Heads")
                dim = gr.Slider(512, 2048, value=768, step=256, label="📐 Model Dimension")
                
            with gr.Column():
                gr.Markdown("#### Test Configuration")
                seq_lengths = gr.Textbox(
                    value="1024, 2048, 4096, 8192",
                    label="📏 Sequence Lengths (H200 can handle long!)",
                    placeholder="1024, 2048, 4096"
                )
                num_runs = gr.Slider(1, 5, value=3, step=1, label="🔄 Number of Runs")
        
        benchmark_btn = gr.Button("🚀 Run ZeroGPU ASI V2.5 Test", variant="primary", size="lg")
        
        with gr.Row():
            benchmark_results = gr.Markdown()
            benchmark_json = gr.Code(label="GPU Results", language="json")
        
        benchmark_btn.click(
            run_zerogpu_asi_benchmark,
            inputs=[threshold, feature_dim, num_heads, dim, seq_lengths, num_runs],
            outputs=[benchmark_results, benchmark_json]
        )
    
    with gr.Tab("🎯 ZeroGPU Specs"):
        gr.Markdown(f"""
        # 🎯 ZeroGPU Specifications
        
        ## Hardware Details
        - **GPU**: {ZEROGPU_SPECS['hardware']}
        - **VRAM**: {ZEROGPU_SPECS['vram']}
        - **Cost**: {ZEROGPU_SPECS['cost']}
        - **Quota**: {ZEROGPU_SPECS['quota']}
        
        ## ASI V2.5 Optimizations
        - ✅ CUDA tensor cores utilization
        - ✅ Mixed precision FP16
        - ✅ 70GB VRAM for long sequences
        - ✅ GPU-optimized linear attention
        
        ## Expected Performance
        - **Short sequences (≤512)**: 1.0-1.5x speedup
        - **Medium sequences (1024-2048)**: 1.5-2.0x speedup  
        - **Long sequences (4096+)**: 2.0-2.5x speedup
        
        ## ZeroGPU vs CPU Spaces
        | Metric | CPU Spaces | ZeroGPU H200 |
        |--------|------------|--------------|
        | **Hardware** | CPU only | NVIDIA H200 |
        | **Memory** | 16GB RAM | 70GB VRAM |
        | **ASI Speedup** | 0.5-0.8x ❌ | 2.0-2.5x ✅ |
        | **Cost** | Free | Free with Pro |
        
        **🚀 ZeroGPU is THE solution for ASI V2.5!**
        """)

if __name__ == "__main__":
    print("🚀 ASI V2.5 ZeroGPU Demo starting...")
    print(f"ZeroGPU Specs: {ZEROGPU_SPECS}")
    app.launch()