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6c9004a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 | #!/usr/bin/env python3
import torch
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from datetime import datetime
import subprocess
import time
import psutil
import re
from collections import deque
import threading
import signal
import sys
class FP16BenchmarkFixed:
def __init__(self):
self.max_temp = 85
self.temperatures = deque(maxlen=200)
self.tflops_history = deque(maxlen=200)
self.load_level = deque(maxlen=200)
self.power_watts = deque(maxlen=200)
self.voltage_volts = deque(maxlen=200)
self.current_amps = deque(maxlen=200)
self.peak_tflops = 0
self.peak_power = 0
self.running = True
self.current_load = 1
self.matrix_size = 10240
self.num_operations = 1
self.num_streams = 1
plt.ion()
self.fig, ((self.ax1, self.ax2), (self.ax3, self.ax4)) = plt.subplots(2, 2, figsize=(16, 10))
self.fig.suptitle('BENCHMARK FP16 COMPLETO - Radeon Pro VII', fontsize=16, weight='bold')
self.last_temp_check = time.time()
self.temp_rising_fast = False
signal.signal(signal.SIGINT, self.signal_handler)
def signal_handler(self, sig, frame):
print("\n\n🛑 Interrompido pelo usuário")
self.running = False
sys.exit(0)
def get_gpu_metrics(self):
"""Obtém temperatura, potência via sensors e rocm-smi"""
temp = 0
power = 0
# Temperatura via sensors
try:
result = subprocess.run(['sensors'], capture_output=True, text=True, timeout=0.5)
for line in result.stdout.split('\n'):
if 'edge:' in line.lower():
match = re.search(r'([+-]?\d+\.?\d*)\s*°C', line)
if match:
temp = float(match.group(1))
except:
pass
# Potência via rocm-smi
try:
result = subprocess.run(['rocm-smi', '--showpower'],
capture_output=True, text=True, timeout=0.5)
for line in result.stdout.split('\n'):
# Captura: "Current Socket Graphics Package Power (W): 19.0"
if 'Power (W)' in line or 'Power: ' in line:
match = re.search(r':\s*(\d+\.?\d*)', line)
if match:
power = float(match.group(1))
except:
pass
# Tensão estimada baseada na potência (V = P/I, estimando ~200A max)
# Radeon VII tipicamente opera em ~1.0-1.2V
voltage = 1.05 # Valor típico
return temp, power, voltage
def check_system_health(self):
try:
start = time.time()
cpu = psutil.cpu_percent(interval=0.05)
response = time.time() - start
if response > 0.4 or cpu > 95:
return False
return True
except:
return False
def calculate_tflops(self, matrix_size, elapsed_time, num_ops, num_streams):
operations = 2 * (matrix_size ** 3) * num_ops * num_streams
return (operations / elapsed_time) / 1e12
def increase_load(self):
if self.current_load < 10:
self.current_load += 1
if self.current_load >= 2 and self.num_streams < 4:
self.num_streams += 1
if self.current_load >= 4 and self.num_operations < 30:
self.num_operations += 5
if self.current_load >= 6 and self.matrix_size < 16384:
self.matrix_size = min(self.matrix_size + 1024, 16384)
def decrease_load(self):
if self.current_load > 1:
self.current_load -= 1
if self.matrix_size > 8192:
self.matrix_size = max(self.matrix_size - 512, 8192)
if self.num_operations > 5:
self.num_operations = max(self.num_operations - 5, 1)
def stress_gpu(self):
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
if device.type == 'cpu':
print("❌ ERRO: GPU não detectada!")
self.running = False
return
props = torch.cuda.get_device_properties(0)
print(f"🎯 GPU: {torch.cuda.get_device_name(0)}")
print(f"💾 VRAM: {props.total_memory / 1e9:.1f} GB")
print(f"🔥 Modo: FP16 (Half Precision)")
print(f"📊 TFLOPS Teórico FP16: ~26.88")
print(f"⚡ TDP: 300W")
print(f"⚠️ Limite: {self.max_temp}°C\n")
streams = [torch.cuda.Stream() for _ in range(4)]
last_temp = 0
stable_cycles = 0
while self.running:
current_time = time.time()
if current_time - self.last_temp_check > 0.1:
temp, power, voltage = self.get_gpu_metrics()
self.last_temp_check = current_time
current = power / voltage if voltage > 0 and power > 0 else 0
if len(self.temperatures) > 0:
temp_delta = temp - last_temp
if temp_delta > 2:
self.temp_rising_fast = True
else:
self.temp_rising_fast = False
if temp >= self.max_temp:
print(f"\n🚨 TEMPERATURA: {temp}°C - ABORTANDO!")
self.running = False
break
if temp >= self.max_temp - 3:
self.decrease_load()
self.decrease_load()
if self.temp_rising_fast and temp > 75:
self.decrease_load()
last_temp = temp
else:
temp, power, voltage = last_temp, self.power_watts[-1] if self.power_watts else 0, 1.05
current = power / voltage if voltage > 0 and power > 0 else 0
if not self.check_system_health():
print(f"\n🚨 SISTEMA INSTÁVEL!")
self.running = False
break
try:
torch.cuda.synchronize()
start = time.time()
for i in range(self.num_streams):
with torch.cuda.stream(streams[i]):
a = torch.randn(self.matrix_size, self.matrix_size, device=device, dtype=torch.float16)
b = torch.randn(self.matrix_size, self.matrix_size, device=device, dtype=torch.float16)
for _ in range(self.num_operations):
c = torch.mm(a, b)
a = b
b = c
torch.cuda.synchronize()
elapsed = time.time() - start
tflops = self.calculate_tflops(self.matrix_size, elapsed,
self.num_operations, self.num_streams)
self.temperatures.append(temp)
self.tflops_history.append(tflops)
self.load_level.append(self.current_load)
self.power_watts.append(power)
self.voltage_volts.append(voltage)
self.current_amps.append(current)
if tflops > self.peak_tflops:
self.peak_tflops = tflops
if power > self.peak_power:
self.peak_power = power
print(f"TFLOPS: {tflops:6.2f} | Temp: {temp:5.1f}°C | {power:6.1f}W | {voltage:5.3f}V | {current:6.1f}A | Peak: {self.peak_tflops:.2f}", end='\r')
if temp < 75 and stable_cycles > 10:
self.increase_load()
stable_cycles = 0
elif temp < 80:
stable_cycles += 1
else:
stable_cycles = 0
time.sleep(0.02)
except RuntimeError as e:
if "out of memory" in str(e):
self.decrease_load()
torch.cuda.empty_cache()
else:
print(f"\n🚨 ERRO: {e}")
self.running = False
break
except Exception as e:
print(f"\n🚨 ERRO: {e}")
self.running = False
break
def update_plot(self, frame):
if len(self.tflops_history) == 0:
return
for ax in [self.ax1, self.ax2, self.ax3, self.ax4]:
ax.clear()
if len(self.tflops_history) > 0:
self.ax1.plot(list(self.tflops_history), 'b-', linewidth=2.5)
self.ax1.axhline(y=self.peak_tflops, color='g', linestyle='--', linewidth=2,
label=f'Peak: {self.peak_tflops:.2f}')
self.ax1.axhline(y=26.88, color='orange', linestyle=':', linewidth=2,
label='Teórico: 26.88')
self.ax1.set_ylabel('TFLOPS', fontsize=11, weight='bold')
self.ax1.set_title('Performance FP16', fontsize=11, weight='bold')
self.ax1.legend(loc='upper left', fontsize=9)
self.ax1.grid(True, alpha=0.3)
self.ax1.set_ylim(0, 30)
if len(self.temperatures) > 0:
temps = list(self.temperatures)
self.ax2.plot(temps, 'r-', linewidth=2.5)
self.ax2.axhline(y=self.max_temp, color='red', linestyle='--', linewidth=2)
self.ax2.fill_between(range(len(temps)), temps, self.max_temp,
where=[t >= self.max_temp - 5 for t in temps],
alpha=0.3, color='orange')
self.ax2.set_ylabel('Temperatura (°C)', fontsize=11, weight='bold')
self.ax2.set_title('Temperatura', fontsize=11, weight='bold')
self.ax2.grid(True, alpha=0.3)
self.ax2.set_ylim(30, 95)
if len(self.power_watts) > 0:
powers = list(self.power_watts)
self.ax3.plot(powers, 'green', linewidth=2.5)
self.ax3.axhline(y=300, color='red', linestyle='--', linewidth=2,
label='TDP: 300W')
self.ax3.axhline(y=self.peak_power, color='orange', linestyle=':', linewidth=2,
label=f'Peak: {self.peak_power:.1f}W')
self.ax3.fill_between(range(len(powers)), powers, alpha=0.3, color='green')
self.ax3.set_ylabel('Potência (W)', fontsize=11, weight='bold')
self.ax3.set_xlabel('Amostras', fontsize=11, weight='bold')
self.ax3.set_title('Consumo', fontsize=11, weight='bold')
self.ax3.legend(loc='upper left', fontsize=9)
self.ax3.grid(True, alpha=0.3)
self.ax3.set_ylim(0, 350)
if len(self.current_amps) > 0:
amps = list(self.current_amps)
self.ax4.plot(amps, 'purple', linewidth=2.5)
self.ax4.fill_between(range(len(amps)), amps, alpha=0.3, color='purple')
self.ax4.set_ylabel('Corrente (A)', fontsize=11, weight='bold')
self.ax4.set_xlabel('Amostras', fontsize=11, weight='bold')
self.ax4.set_title('Corrente Estimada', fontsize=11, weight='bold')
self.ax4.grid(True, alpha=0.3)
def run(self):
stress_thread = threading.Thread(target=self.stress_gpu)
stress_thread.daemon = True
stress_thread.start()
while self.running and stress_thread.is_alive():
self.update_plot(None)
plt.pause(0.3)
avg_power = sum(self.power_watts) / len(self.power_watts) if self.power_watts else 0
avg_current = sum(self.current_amps) / len(self.current_amps) if self.current_amps else 0
print(f"\n\n{'='*70}")
print(f"{'RESULTADO FINAL':^70}")
print(f"{'='*70}")
print(f"🏆 PEAK TFLOPS (FP16): {self.peak_tflops:.2f}")
print(f"📊 Teórico: 26.88 TFLOPS")
print(f"📈 Eficiência: {(self.peak_tflops / 26.88) * 100:.1f}%")
print(f"🌡️ Temp Máx: {max(self.temperatures) if self.temperatures else 0:.1f}°C")
print(f"⚡ Potência Peak: {self.peak_power:.1f}W")
print(f"⚡ Potência Média: {avg_power:.1f}W")
print(f"🔌 Corrente Média: {avg_current:.1f}A")
print(f"🔥 Carga Máx: {max(self.load_level) if self.load_level else 0}/10")
print(f"{'='*70}\n")
plt.ioff()
plt.show()
if __name__ == "__main__":
bench = FP16BenchmarkFixed()
bench.run()
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