3e967dd34a
- Expand GPU specs database to include A100/A800 with Ampere architecture parameters - Rename h200_tester.py to gpu_tester.py for architecture-neutral branding - Add driver/CUDA compatibility validation per GPU generation - Enhance report module with HTML and Markdown output formats - Improve nvbandwidth binary discovery (system paths, DCGM locations) - Add pyproject.toml with uv for dependency management - Update install_deps.sh, configs, and README for multi-architecture support 🤖 Generated with [Qoder][https://qoder.com]
446 lines
18 KiB
Python
446 lines
18 KiB
Python
"""GPU benchmark module — nvbandwidth + PyTorch compute throughput."""
|
|
|
|
import json
|
|
import os
|
|
import shutil
|
|
import subprocess
|
|
import time
|
|
from datetime import datetime
|
|
from typing import Optional, List
|
|
|
|
from rich.console import Console
|
|
from rich.table import Table
|
|
from rich.progress import Progress, SpinnerColumn, BarColumn, TextColumn, TimeElapsedColumn
|
|
|
|
from modules.gpu_specs import detect_gpu_type, get_gpu_specs, get_gpu_label, resolve_tools_dir
|
|
|
|
TORCH_AVAILABLE = False
|
|
try:
|
|
import torch
|
|
if torch.cuda.is_available():
|
|
TORCH_AVAILABLE = True
|
|
except ImportError:
|
|
pass
|
|
|
|
|
|
class Benchmark:
|
|
|
|
def __init__(self, config: dict):
|
|
self.config = config
|
|
self.console = Console()
|
|
self.bench_cfg = config.get("benchmark", {})
|
|
self.tools_dir = resolve_tools_dir(config)
|
|
self.gpu_type = detect_gpu_type()
|
|
self.specs = get_gpu_specs(self.gpu_type)
|
|
self.gpu_label = get_gpu_label(self.gpu_type)
|
|
|
|
def run(self) -> dict:
|
|
results = {}
|
|
results.update(self.run_memory_benchmark())
|
|
results.update(self.run_compute_benchmark())
|
|
return results
|
|
|
|
def _find_nvbandwidth(self) -> Optional[str]:
|
|
# 1. System PATH
|
|
p = shutil.which("nvbandwidth")
|
|
if p:
|
|
return p
|
|
# 2. tools_dir
|
|
local = os.path.join(self.tools_dir, "nvbandwidth", "nvbandwidth")
|
|
if os.path.isfile(local) and os.access(local, os.X_OK):
|
|
return local
|
|
# 3. Common DCGM / system locations
|
|
extra_paths = [
|
|
"/usr/libexec/datacenter-gpu-manager-4/plugins/cuda12/nvbandwidth",
|
|
"/usr/libexec/datacenter-gpu-manager/plugins/cuda12/nvbandwidth",
|
|
"/usr/local/bin/nvbandwidth",
|
|
"/opt/nvidia/nvbandwidth/nvbandwidth",
|
|
]
|
|
for ep in extra_paths:
|
|
if os.path.isfile(ep) and os.access(ep, os.X_OK):
|
|
return ep
|
|
return None
|
|
|
|
def run_memory_benchmark(self) -> dict:
|
|
nvbw = self._find_nvbandwidth()
|
|
|
|
if nvbw:
|
|
return self._run_nvbandwidth(nvbw)
|
|
|
|
self.console.print("[yellow]nvbandwidth not found, falling back to PyTorch[/yellow]")
|
|
return self._run_memory_pytorch()
|
|
|
|
def _run_nvbandwidth(self, nvbw_path: str) -> dict:
|
|
mem_cfg = self.bench_cfg.get("memory", {})
|
|
buffer_mb = mem_cfg.get("nvbandwidth_buffer_mb", 512)
|
|
samples = mem_cfg.get("nvbandwidth_samples", 3)
|
|
|
|
self.console.print(f"[cyan]Memory Benchmark via nvbandwidth ({nvbw_path})[/cyan]")
|
|
|
|
results_by_test = {}
|
|
per_gpu_d2d = []
|
|
|
|
testcases = [
|
|
"host_to_device_memcpy_read_ce",
|
|
"device_to_host_memcpy_write_ce",
|
|
"device_to_device_memcpy_write_ce",
|
|
"device_to_device_memcpy_read_ce",
|
|
"device_to_device_bidirectional_sm",
|
|
]
|
|
|
|
try:
|
|
list_r = subprocess.run(
|
|
[nvbw_path, "-l", "-j"],
|
|
capture_output=True, text=True, timeout=15,
|
|
)
|
|
available = []
|
|
if list_r.returncode == 0:
|
|
try:
|
|
avail_list = json.loads(list_r.stdout)
|
|
available = [t.get("name", "") for t in avail_list if isinstance(t, dict)]
|
|
except json.JSONDecodeError:
|
|
pass
|
|
except (subprocess.TimeoutExpired, FileNotFoundError):
|
|
available = []
|
|
|
|
with Progress(
|
|
SpinnerColumn(), TextColumn("[progress.description]{task.description}"),
|
|
BarColumn(), TextColumn("{task.completed}/{task.total}"),
|
|
TimeElapsedColumn(), console=self.console,
|
|
) as progress:
|
|
task = progress.add_task("nvbandwidth tests...", total=len(testcases))
|
|
|
|
for tc in testcases:
|
|
if available and tc not in available:
|
|
progress.advance(task)
|
|
continue
|
|
|
|
try:
|
|
cmd = [
|
|
nvbw_path,
|
|
f"-b{buffer_mb}",
|
|
f"-i{samples}",
|
|
"-j",
|
|
f"-t{tc}",
|
|
]
|
|
r = subprocess.run(cmd, capture_output=True, text=True, timeout=120)
|
|
|
|
if r.returncode == 0 and r.stdout.strip():
|
|
try:
|
|
data = json.loads(r.stdout)
|
|
bw_values = []
|
|
for entry in data if isinstance(data, list) else [data]:
|
|
if isinstance(entry, dict):
|
|
for row in entry.get("results", []):
|
|
val = row.get("value", 0)
|
|
if isinstance(val, (int, float)):
|
|
bw_values.append(val)
|
|
avg_bw = sum(bw_values) / len(bw_values) if bw_values else 0
|
|
results_by_test[tc] = round(avg_bw, 1)
|
|
except json.JSONDecodeError:
|
|
results_by_test[tc] = 0
|
|
else:
|
|
results_by_test[tc] = 0
|
|
except (subprocess.TimeoutExpired, FileNotFoundError):
|
|
results_by_test[tc] = 0
|
|
|
|
progress.advance(task)
|
|
|
|
d2d_bw = max(
|
|
results_by_test.get("device_to_device_memcpy_write_ce", 0),
|
|
results_by_test.get("device_to_device_memcpy_read_ce", 0),
|
|
results_by_test.get("device_to_device_bidirectional_sm", 0),
|
|
)
|
|
h2d_bw = results_by_test.get("host_to_device_memcpy_read_ce", 0)
|
|
d2h_bw = results_by_test.get("device_to_host_memcpy_write_ce", 0)
|
|
peak_bw = self.specs["memory_bandwidth_gbps"]
|
|
efficiency = (
|
|
(d2d_bw / peak_bw) * 100 if (d2d_bw and peak_bw) else 0
|
|
)
|
|
|
|
return {
|
|
"memory": {
|
|
"source": "nvbandwidth",
|
|
"h2d_bandwidth_gbps": round(h2d_bw, 1),
|
|
"d2h_bandwidth_gbps": round(d2h_bw, 1),
|
|
"d2d_bandwidth_gbps": round(d2d_bw, 1),
|
|
"peak_bandwidth_gbps": self.specs["memory_bandwidth_gbps"],
|
|
"efficiency_pct": round(efficiency, 1),
|
|
"results_by_test": results_by_test,
|
|
"per_gpu": per_gpu_d2d,
|
|
}
|
|
}
|
|
|
|
def _run_memory_pytorch(self) -> dict:
|
|
mem_cfg = self.bench_cfg.get("memory", {})
|
|
test_sizes_mb = [1, 4, 16, 64, 256, 1024, 4096]
|
|
iterations = mem_cfg.get("iterations", 10)
|
|
|
|
if not TORCH_AVAILABLE:
|
|
self.console.print("[yellow]PyTorch not available - skipping memory benchmark[/yellow]")
|
|
return {"memory": {"error": "pytorch_not_available"}}
|
|
|
|
gpu_count = torch.cuda.device_count()
|
|
self.console.print(f"[cyan]Memory Benchmark (PyTorch fallback) - {gpu_count} GPU(s)[/cyan]")
|
|
|
|
bandwidth_by_size = {}
|
|
|
|
with Progress(
|
|
SpinnerColumn(), TextColumn("[progress.description]{task.description}"),
|
|
BarColumn(), TextColumn("{task.completed}/{task.total}"),
|
|
TimeElapsedColumn(), console=self.console,
|
|
) as progress:
|
|
task = progress.add_task("Testing sizes...", total=len(test_sizes_mb))
|
|
|
|
for size_mb in test_sizes_mb:
|
|
size_bytes = size_mb * 1024 * 1024
|
|
h2d_times, d2h_times, d2d_times = [], [], []
|
|
x_cpu = torch.randn(size_bytes // 4, dtype=torch.float32)
|
|
|
|
for _ in range(iterations):
|
|
t0 = time.perf_counter()
|
|
x_gpu = x_cpu.cuda()
|
|
torch.cuda.synchronize()
|
|
h2d_times.append(time.perf_counter() - t0)
|
|
|
|
t0 = time.perf_counter()
|
|
x_gpu.cpu()
|
|
torch.cuda.synchronize()
|
|
d2h_times.append(time.perf_counter() - t0)
|
|
|
|
x_gpu2 = torch.randn_like(x_gpu)
|
|
t0 = time.perf_counter()
|
|
x_gpu2.copy_(x_gpu)
|
|
torch.cuda.synchronize()
|
|
d2d_times.append(time.perf_counter() - t0)
|
|
|
|
del x_gpu, x_gpu2
|
|
torch.cuda.empty_cache()
|
|
|
|
def median(lst):
|
|
s = sorted(lst)
|
|
return s[len(s) // 2]
|
|
|
|
def bw_gb(t, sz):
|
|
return (sz / t) / 1e9
|
|
|
|
bandwidth_by_size[str(size_mb)] = {
|
|
"h2d_gbps": round(bw_gb(median(h2d_times), size_bytes), 1),
|
|
"d2h_gbps": round(bw_gb(median(d2h_times), size_bytes), 1),
|
|
"d2d_gbps": round(bw_gb(median(d2d_times), size_bytes), 1),
|
|
}
|
|
progress.advance(task)
|
|
|
|
best_d2d = max(v["d2d_gbps"] for v in bandwidth_by_size.values())
|
|
peak_bw = self.specs["memory_bandwidth_gbps"]
|
|
efficiency = (best_d2d / peak_bw) * 100 if peak_bw else 0.0
|
|
|
|
return {
|
|
"memory": {
|
|
"source": "pytorch",
|
|
"h2d_bandwidth_gbps": round(max(v["h2d_gbps"] for v in bandwidth_by_size.values()), 1),
|
|
"d2h_bandwidth_gbps": round(max(v["d2h_gbps"] for v in bandwidth_by_size.values()), 1),
|
|
"d2d_bandwidth_gbps": round(best_d2d, 1),
|
|
"peak_bandwidth_gbps": self.specs["memory_bandwidth_gbps"],
|
|
"efficiency_pct": round(efficiency, 1),
|
|
"test_sizes_mb": test_sizes_mb,
|
|
"bandwidth_by_size": bandwidth_by_size,
|
|
"per_gpu": [],
|
|
}
|
|
}
|
|
|
|
def run_compute_benchmark(self, dtypes: Optional[List[str]] = None) -> dict:
|
|
comp_cfg = self.bench_cfg.get("compute", {})
|
|
configured_dtypes = dtypes or comp_cfg.get("dtypes", ["fp32", "tf32", "fp16", "bf16", "fp8"])
|
|
matrix_size = comp_cfg.get("matrix_size", 4096)
|
|
warmup = comp_cfg.get("warmup", 10)
|
|
iterations = comp_cfg.get("iterations", 100)
|
|
|
|
if not TORCH_AVAILABLE:
|
|
self.console.print("[yellow]PyTorch not available - skipping compute benchmark[/yellow]")
|
|
return {"compute": {"error": "pytorch_not_available"}}
|
|
|
|
gpu_count = torch.cuda.device_count()
|
|
self.console.print(f"[cyan]Compute Benchmark - {gpu_count} GPU(s)[/cyan]")
|
|
|
|
dtype_map = {
|
|
"fp32": (torch.float32, self.specs["fp32_tflops"]),
|
|
"tf32": ("tf32", self.specs["tf32_tflops"]),
|
|
"fp16": (torch.float16, self.specs["fp16_tflops"]),
|
|
"bf16": (torch.bfloat16, self.specs["bf16_tflops"]),
|
|
"fp8": (torch.float8_e4m3fn, self.specs["fp8_tflops"]),
|
|
}
|
|
|
|
results_by_dtype = {}
|
|
per_gpu_results = [{"index": i} for i in range(gpu_count)]
|
|
|
|
with Progress(
|
|
SpinnerColumn(), TextColumn("[progress.description]{task.description}"),
|
|
BarColumn(), TextColumn("{task.completed}/{task.total}"),
|
|
TimeElapsedColumn(), console=self.console,
|
|
) as progress:
|
|
task = progress.add_task("Testing dtypes...", total=len(configured_dtypes))
|
|
|
|
for dtype_name in configured_dtypes:
|
|
if dtype_name not in dtype_map:
|
|
progress.advance(task)
|
|
continue
|
|
|
|
# Skip FP8 if GPU architecture doesn't support it
|
|
if dtype_name == "fp8" and self.specs.get("fp8_tflops", 0) == 0:
|
|
arch = self.specs.get("architecture", "unknown")
|
|
results_by_dtype["fp8"] = f"skipped ({arch} does not support FP8)"
|
|
self.console.print(f"[dim] fp8: skipped - {arch} architecture has no FP8 support[/dim]")
|
|
progress.advance(task)
|
|
continue
|
|
|
|
dtype_val, peak_tflops = dtype_map[dtype_name]
|
|
|
|
try:
|
|
if dtype_name == "tf32":
|
|
old_tf32 = torch.backends.cuda.matmul.allow_tf32
|
|
torch.backends.cuda.matmul.allow_tf32 = True
|
|
dtype_val = torch.float32
|
|
|
|
M = N = K = matrix_size
|
|
|
|
if dtype_name == "fp8":
|
|
a = torch.randn(M, K, device="cuda", dtype=torch.float32).to(torch.float8_e4m3fn)
|
|
b = torch.randn(K, N, device="cuda", dtype=torch.float32).to(torch.float8_e4m3fn)
|
|
else:
|
|
a = torch.randn(M, K, device="cuda", dtype=dtype_val)
|
|
b = torch.randn(K, N, device="cuda", dtype=dtype_val)
|
|
|
|
for _ in range(warmup):
|
|
torch.matmul(a, b)
|
|
torch.cuda.synchronize()
|
|
|
|
start_event = torch.cuda.Event(enable_timing=True)
|
|
end_event = torch.cuda.Event(enable_timing=True)
|
|
start_event.record()
|
|
for _ in range(iterations):
|
|
c = torch.matmul(a, b)
|
|
end_event.record()
|
|
torch.cuda.synchronize()
|
|
|
|
elapsed_ms = start_event.elapsed_time(end_event)
|
|
flops = 2 * M * N * K * iterations
|
|
tflops = flops / (elapsed_ms / 1000) / 1e12
|
|
results_by_dtype[dtype_name] = round(tflops, 1)
|
|
|
|
for pg in per_gpu_results:
|
|
pg[dtype_name] = round(tflops, 1)
|
|
|
|
if dtype_name == "tf32":
|
|
torch.backends.cuda.matmul.allow_tf32 = old_tf32
|
|
|
|
del a, b, c
|
|
torch.cuda.empty_cache()
|
|
|
|
except Exception as e:
|
|
results_by_dtype[dtype_name] = f"error: {e}"
|
|
self.console.print(f"[yellow] {dtype_name}: {e}[/yellow]")
|
|
|
|
progress.advance(task)
|
|
|
|
efficiency = {}
|
|
for dt, achieved in results_by_dtype.items():
|
|
if isinstance(achieved, (int, float)) and dt in dtype_map:
|
|
peak_tp = dtype_map[dt][1]
|
|
if peak_tp:
|
|
efficiency[dt] = round((achieved / peak_tp) * 100, 1)
|
|
|
|
return {
|
|
"compute": {
|
|
"per_dtype_tflops": results_by_dtype,
|
|
"peak_tflops": {dt: dtype_map[dt][1] for dt in dtype_map},
|
|
"efficiency_pct": efficiency,
|
|
"per_gpu": per_gpu_results,
|
|
"matrix_size": matrix_size,
|
|
"warmup": warmup,
|
|
"iterations": iterations,
|
|
}
|
|
}
|
|
|
|
@staticmethod
|
|
def print_results(results: dict, console: Console = None):
|
|
c = console or Console()
|
|
|
|
if "memory" in results and "error" not in results["memory"]:
|
|
mem = results["memory"]
|
|
source = mem.get("source", "unknown")
|
|
c.print(f"\n[bold cyan]Memory Bandwidth Results (via {source})[/bold cyan]")
|
|
|
|
table = Table(box=None, padding=(0, 1))
|
|
table.add_column("Metric", style="bold")
|
|
table.add_column("Value", justify="right")
|
|
table.add_column("Peak", justify="right")
|
|
table.add_column("Efficiency", justify="right")
|
|
|
|
for label, achieved, peak in [
|
|
("H2D (PCIe)", mem["h2d_bandwidth_gbps"], None),
|
|
("D2H (PCIe)", mem["d2h_bandwidth_gbps"], None),
|
|
("D2D (HBM3e)", mem["d2d_bandwidth_gbps"], mem["peak_bandwidth_gbps"]),
|
|
]:
|
|
val_str = f"{achieved:.1f} GB/s" if isinstance(achieved, (int, float)) else "N/A"
|
|
peak_str = f"{peak:.0f} GB/s" if peak else "N/A"
|
|
if peak and isinstance(achieved, (int, float)) and achieved > 0:
|
|
eff = (achieved / peak) * 100
|
|
ec = "green" if eff >= 80 else ("yellow" if eff >= 50 else "red")
|
|
eff_str = f"[{ec}]{eff:.1f}%[/{ec}]"
|
|
else:
|
|
eff_str = "N/A"
|
|
table.add_row(label, val_str, peak_str, eff_str)
|
|
|
|
c.print(table)
|
|
|
|
by_test = mem.get("results_by_test", {})
|
|
if by_test:
|
|
c.print("\n [dim]nvbandwidth breakdown:[/dim]")
|
|
for tc, bw in sorted(by_test.items()):
|
|
c.print(f" {tc}: {bw} GB/s")
|
|
|
|
by_size = mem.get("bandwidth_by_size", {})
|
|
if by_size:
|
|
t2 = Table(title="Bandwidth by Transfer Size", box=None, padding=(0, 1))
|
|
t2.add_column("Size (MB)", style="bold", justify="right")
|
|
t2.add_column("H2D (GB/s)", justify="right")
|
|
t2.add_column("D2H (GB/s)", justify="right")
|
|
t2.add_column("D2D (GB/s)", justify="right")
|
|
for sz, vals in sorted(by_size.items(), key=lambda x: int(x[0])):
|
|
peak = mem["peak_bandwidth_gbps"]
|
|
if peak:
|
|
d2d_eff = (vals["d2d_gbps"] / peak) * 100
|
|
ec = "green" if d2d_eff >= 80 else ("yellow" if d2d_eff >= 50 else "red")
|
|
d2d_cell = f"[{ec}]{vals['d2d_gbps']:.1f}[/{ec}]"
|
|
else:
|
|
d2d_cell = f"{vals['d2d_gbps']:.1f}"
|
|
t2.add_row(sz, f"{vals['h2d_gbps']:.1f}", f"{vals['d2h_gbps']:.1f}", d2d_cell)
|
|
c.print(t2)
|
|
|
|
if "compute" in results and "error" not in results["compute"]:
|
|
comp = results["compute"]
|
|
c.print(f"\n[bold cyan]Compute Throughput Results[/bold cyan]")
|
|
|
|
table = Table(box=None, padding=(0, 1))
|
|
table.add_column("DType", style="bold")
|
|
table.add_column("Achieved (TFLOPS)", justify="right")
|
|
table.add_column("Peak", justify="right")
|
|
table.add_column("Efficiency", justify="right")
|
|
|
|
peak = comp.get("peak_tflops", {})
|
|
per_dtype = comp.get("per_dtype_tflops", {})
|
|
eff = comp.get("efficiency_pct", {})
|
|
|
|
for dt in per_dtype:
|
|
achieved = per_dtype[dt]
|
|
if isinstance(achieved, str):
|
|
table.add_row(dt, f"[red]{achieved}[/red]", str(peak.get(dt, "N/A")), "N/A")
|
|
continue
|
|
pk = peak.get(dt, 0)
|
|
ef = eff.get(dt, 0)
|
|
ec = "green" if ef >= 80 else ("yellow" if ef >= 50 else "red")
|
|
table.add_row(dt.upper(), f"{achieved:.1f}", f"{pk:.0f}",
|
|
f"[{ec}]{ef:.1f}%[/{ec}]")
|
|
c.print(table)
|