628 lines
28 KiB
Python
628 lines
28 KiB
Python
"""GPU benchmark module — nvbandwidth + PyTorch compute throughput."""
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import json
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import os
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import shutil
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import subprocess
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import time
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from datetime import datetime
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from typing import Optional, List
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from rich.console import Console
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from rich.table import Table
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from rich.progress import Progress, SpinnerColumn, BarColumn, TextColumn, TimeElapsedColumn
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from modules.gpu_specs import detect_gpu_type, get_gpu_specs, get_gpu_label, resolve_tools_dir
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TORCH_AVAILABLE = False
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try:
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import torch
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if torch.cuda.is_available():
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TORCH_AVAILABLE = True
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except ImportError:
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pass
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class Benchmark:
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def __init__(self, config: dict):
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self.config = config
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self.console = Console()
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self.bench_cfg = config.get("benchmark", {})
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self.tools_dir = resolve_tools_dir(config)
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cfg_gpu_type = config.get("gpu_type", "auto")
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self.gpu_type = cfg_gpu_type if cfg_gpu_type != "auto" else detect_gpu_type()
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self.specs = get_gpu_specs(self.gpu_type)
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self.gpu_label = get_gpu_label(self.gpu_type)
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def run(self) -> dict:
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results = {}
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results.update(self.run_memory_benchmark())
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results.update(self.run_compute_benchmark())
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return results
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def _find_nvbandwidth(self) -> Optional[str]:
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# 1. System PATH
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p = shutil.which("nvbandwidth")
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if p:
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return p
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# 2. tools_dir
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local = os.path.join(self.tools_dir, "nvbandwidth", "nvbandwidth")
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if os.path.isfile(local) and os.access(local, os.X_OK):
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return local
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# 3. Common DCGM / system locations
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extra_paths = [
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"/usr/libexec/datacenter-gpu-manager-4/plugins/cuda12/nvbandwidth",
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"/usr/libexec/datacenter-gpu-manager/plugins/cuda12/nvbandwidth",
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"/usr/local/bin/nvbandwidth",
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"/opt/nvidia/nvbandwidth/nvbandwidth",
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]
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for ep in extra_paths:
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if os.path.isfile(ep) and os.access(ep, os.X_OK):
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return ep
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return None
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def run_memory_benchmark(self) -> dict:
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nvbw = self._find_nvbandwidth()
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if nvbw:
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return self._run_nvbandwidth(nvbw)
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self.console.print("[yellow]nvbandwidth not found, falling back to PyTorch[/yellow]")
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return self._run_memory_pytorch()
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def _run_nvbandwidth(self, nvbw_path: str) -> dict:
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mem_cfg = self.bench_cfg.get("memory", {})
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buffer_mb = mem_cfg.get("nvbandwidth_buffer_mb", 512)
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samples = mem_cfg.get("nvbandwidth_samples", 3)
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self.console.print(f"[cyan]Memory Benchmark via nvbandwidth ({nvbw_path})[/cyan]")
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results_by_test = {}
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# Testcases to run — keys used internally, try both old and new names
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testcases = [
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("h2d", ["host_to_device_memcpy_ce", "host_to_device_memcpy_read_ce"]),
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("d2h", ["device_to_host_memcpy_ce", "device_to_host_memcpy_write_ce"]),
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("d2d_write", ["device_to_device_memcpy_write_ce"]),
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("d2d_read", ["device_to_device_memcpy_read_ce"]),
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("d2d_bidir", ["device_to_device_bidirectional_memcpy_write_sm",
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"device_to_device_bidirectional_sm"]),
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]
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# Discover available testcase names
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available_names: list[str] = []
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try:
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list_r = subprocess.run(
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[nvbw_path, "-l"], capture_output=True, text=True, timeout=15,
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)
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if list_r.returncode == 0:
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for line in list_r.stdout.splitlines():
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line = line.strip()
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if line and ", " in line and line[0].isdigit():
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parts = line.split(", ", 1)
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name = parts[1].rstrip(":").strip()
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if name:
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available_names.append(name)
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except (subprocess.TimeoutExpired, FileNotFoundError):
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pass
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with Progress(
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SpinnerColumn(), TextColumn("[progress.description]{task.description}"),
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BarColumn(), TextColumn("{task.completed}/{task.total}"),
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TimeElapsedColumn(), console=self.console,
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) as progress:
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task = progress.add_task("nvbandwidth tests...", total=len(testcases))
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for key, name_candidates in testcases:
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# Pick the first available test name
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tc = None
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for candidate in name_candidates:
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if not available_names or candidate in available_names:
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tc = candidate
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break
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if tc is None:
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progress.advance(task)
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continue
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try:
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# --disableAffinity skips nvbandwidth's CPU affinity setup, which
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# calls nvmlDeviceGetHandleByUUID() — that lookup fails on hosts
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# whose fabricmanager build doesn't expose the UUID format nvml
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# expects (seen on H20-3e with custom 570.172.08-1 fabricmanager).
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cmd = [nvbw_path, "--disableAffinity", "-t", tc,
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"-b", str(buffer_mb), "-i", str(samples), "-j"]
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r = subprocess.run(cmd, capture_output=True, text=True, timeout=120)
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if r.returncode == 0 and r.stdout.strip():
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avg_bw = self._parse_nvbandwidth_json(r.stdout)
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results_by_test[key] = round(avg_bw, 1)
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else:
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results_by_test[key] = 0
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except (subprocess.TimeoutExpired, FileNotFoundError):
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results_by_test[key] = 0
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progress.advance(task)
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d2d_bw = max(
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results_by_test.get("d2d_write", 0),
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results_by_test.get("d2d_read", 0),
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results_by_test.get("d2d_bidir", 0),
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)
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h2d_bw = results_by_test.get("h2d", 0)
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d2h_bw = results_by_test.get("d2h", 0)
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# If every subtest returned 0 the nvbandwidth binary is broken on this host
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# (e.g. CUDA_ERROR_INVALID_CONTEXT, NVML mismatch). Fall back to PyTorch.
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if all(v == 0 for v in results_by_test.values()):
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self.console.print(
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"[yellow]nvbandwidth returned no usable data — "
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"falling back to PyTorch memory benchmark[/yellow]"
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)
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return self._run_memory_pytorch()
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# D2D goes through NVLink — compare to NVLink per-direction bandwidth
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# (nvlink_bandwidth_gbps is bidirectional, so per-direction = /2)
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nvlink_bw = self.specs.get("nvlink_bandwidth_gbps", 0)
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d2d_peak = nvlink_bw / 2 if nvlink_bw else 0
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d2d_efficiency = round((d2d_bw / d2d_peak) * 100, 1) if (d2d_bw and d2d_peak) else None
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# H2D/D2H goes through PCIe — estimate peak from PCIe gen
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pcie_gen = self.specs.get("pcie_gen", 0)
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pcie_peak = {3: 16, 4: 32, 5: 64, 6: 128}.get(pcie_gen, 32) if pcie_gen > 0 else 0 # GB/s x16
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h2d_efficiency = round((h2d_bw / pcie_peak) * 100, 1) if (h2d_bw and pcie_peak) else None
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d2h_efficiency = round((d2h_bw / pcie_peak) * 100, 1) if (d2h_bw and pcie_peak) else None
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return {
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"memory": {
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"source": "nvbandwidth",
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"h2d_bandwidth_gbps": round(h2d_bw, 1),
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"d2h_bandwidth_gbps": round(d2h_bw, 1),
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"d2d_bandwidth_gbps": round(d2d_bw, 1),
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"h2d_peak_gbps": pcie_peak if pcie_peak else None,
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"d2h_peak_gbps": pcie_peak if pcie_peak else None,
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"d2d_peak_gbps": round(d2d_peak, 1) if d2d_peak else None,
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"h2d_efficiency_pct": h2d_efficiency,
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"d2h_efficiency_pct": d2h_efficiency,
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"d2d_efficiency_pct": d2d_efficiency,
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"peak_bandwidth_gbps": self.specs["memory_bandwidth_gbps"],
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"efficiency_pct": d2d_efficiency,
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"results_by_test": results_by_test,
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"per_gpu": [],
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}
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}
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@staticmethod
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def _parse_nvbandwidth_json(raw: str) -> float:
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"""Parse nvbandwidth JSON output (supports v0.5+ and v0.8+ formats)."""
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try:
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data = json.loads(raw)
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except json.JSONDecodeError:
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return 0.0
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# v0.8+ format: {"nvbandwidth": {"testcases": [{"bandwidth_matrix": [...], "sum": N}]}}
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if isinstance(data, dict) and "nvbandwidth" in data:
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testcases = data["nvbandwidth"].get("testcases", [])
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for tc in testcases:
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matrix = tc.get("bandwidth_matrix", [])
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values = []
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for row in matrix:
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for cell in row:
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try:
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v = float(cell)
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except (ValueError, TypeError):
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continue
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# Exclude diagonal entries (intra-device, reported as 0 or
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# N/A) so they don't drag the off-diagonal average down.
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if v > 0:
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values.append(v)
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if values:
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return sum(values) / len(values)
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return 0.0
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# v0.5 format: list of dicts with "results" array
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entries = data if isinstance(data, list) else [data]
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bw_values = []
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for entry in entries:
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if isinstance(entry, dict):
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for row in entry.get("results", []):
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val = row.get("value", 0)
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if isinstance(val, (int, float)):
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bw_values.append(val)
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return sum(bw_values) / len(bw_values) if bw_values else 0.0
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def _run_memory_pytorch(self) -> dict:
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mem_cfg = self.bench_cfg.get("memory", {})
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test_sizes_mb = [1, 4, 16, 64, 256, 1024, 4096]
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iterations = mem_cfg.get("iterations", 10)
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if not TORCH_AVAILABLE:
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self.console.print("[yellow]PyTorch not available - skipping memory benchmark[/yellow]")
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return {"memory": {"error": "pytorch_not_available"}}
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gpu_count = torch.cuda.device_count()
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self.console.print(f"[cyan]Memory Benchmark (PyTorch fallback) - {gpu_count} GPU(s)[/cyan]")
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bandwidth_by_size = {}
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with Progress(
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SpinnerColumn(), TextColumn("[progress.description]{task.description}"),
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BarColumn(), TextColumn("{task.completed}/{task.total}"),
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TimeElapsedColumn(), console=self.console,
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) as progress:
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task = progress.add_task("Testing sizes...", total=len(test_sizes_mb))
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for size_mb in test_sizes_mb:
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size_bytes = size_mb * 1024 * 1024
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h2d_times, d2h_times, d2d_times = [], [], []
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x_cpu = torch.randn(size_bytes // 4, dtype=torch.float32)
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for _ in range(iterations):
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t0 = time.perf_counter()
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x_gpu = x_cpu.cuda()
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torch.cuda.synchronize()
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h2d_times.append(time.perf_counter() - t0)
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t0 = time.perf_counter()
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x_gpu.cpu()
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torch.cuda.synchronize()
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d2h_times.append(time.perf_counter() - t0)
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x_gpu2 = torch.randn_like(x_gpu)
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t0 = time.perf_counter()
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x_gpu2.copy_(x_gpu)
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torch.cuda.synchronize()
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d2d_times.append(time.perf_counter() - t0)
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del x_gpu, x_gpu2
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torch.cuda.empty_cache()
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def median(lst):
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s = sorted(lst)
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return s[len(s) // 2]
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def bw_gb(t, sz):
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return (sz / t) / 1e9
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bandwidth_by_size[str(size_mb)] = {
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"h2d_gbps": round(bw_gb(median(h2d_times), size_bytes), 1),
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"d2h_gbps": round(bw_gb(median(d2h_times), size_bytes), 1),
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"d2d_gbps": round(bw_gb(median(d2d_times), size_bytes), 1),
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}
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progress.advance(task)
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best_d2d = max(v["d2d_gbps"] for v in bandwidth_by_size.values())
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peak_bw = self.specs["memory_bandwidth_gbps"]
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efficiency = round((best_d2d / peak_bw) * 100, 1) if peak_bw else None
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return {
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"memory": {
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"source": "pytorch",
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"h2d_bandwidth_gbps": round(max(v["h2d_gbps"] for v in bandwidth_by_size.values()), 1),
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"d2h_bandwidth_gbps": round(max(v["d2h_gbps"] for v in bandwidth_by_size.values()), 1),
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"d2d_bandwidth_gbps": round(best_d2d, 1),
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"peak_bandwidth_gbps": self.specs["memory_bandwidth_gbps"],
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"efficiency_pct": efficiency,
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"test_sizes_mb": test_sizes_mb,
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"bandwidth_by_size": bandwidth_by_size,
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"per_gpu": [],
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}
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}
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def run_compute_benchmark(self, dtypes: Optional[List[str]] = None) -> dict:
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comp_cfg = self.bench_cfg.get("compute", {})
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configured_dtypes = dtypes or comp_cfg.get("dtypes", ["fp32", "tf32", "fp16", "bf16", "fp8"])
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matrix_size = comp_cfg.get("matrix_size", 4096)
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warmup = comp_cfg.get("warmup", 10)
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iterations = comp_cfg.get("iterations", 100)
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use_compile = comp_cfg.get("use_compile", False)
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if not TORCH_AVAILABLE:
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self.console.print("[yellow]PyTorch not available - skipping compute benchmark[/yellow]")
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return {"compute": {"error": "pytorch_not_available"}}
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gpu_count = torch.cuda.device_count()
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self.console.print(f"[cyan]Compute Benchmark - {gpu_count} GPU(s)[/cyan]")
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# torch.compile(max-autotune) benchmarks cuBLAS vs Triton kernels and picks
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# the fastest for this GPU/shape, typically improving efficiency by 8-15%.
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# compile_warmup must be larger than warmup to absorb JIT + autotuning time.
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mm_fn = torch.matmul
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compile_warmup = warmup
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if use_compile:
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try:
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_compiled = torch.compile(torch.matmul, mode="max-autotune")
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# Trial call to trigger JIT and verify compilation succeeds before the dtype loop.
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_t = torch.randn(64, 64, device="cuda", dtype=torch.float32)
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_compiled(_t, _t)
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torch.cuda.synchronize()
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del _t
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mm_fn = _compiled
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compile_warmup = max(warmup, 50)
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self.console.print("[cyan] torch.compile(max-autotune) enabled[/cyan]")
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except Exception as e:
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self.console.print(f"[yellow] torch.compile unavailable ({type(e).__name__}), using eager[/yellow]")
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dtype_map = {
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"fp32": (torch.float32, self.specs.get("fp32_tflops", 0)),
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"tf32": ("tf32", self.specs.get("tf32_tflops", 0)),
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"fp16": (torch.float16, self.specs.get("fp16_tflops", 0)),
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"bf16": (torch.bfloat16, self.specs.get("bf16_tflops", 0)),
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"fp8": (getattr(torch, "float8_e4m3fn", None), self.specs.get("fp8_tflops", 0)),
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"fp64": (torch.float64, self.specs.get("fp64_tflops", 0)),
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"int8": (torch.int8, self.specs.get("int8_tflops", 0)),
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}
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results_by_dtype = {}
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per_gpu_results = [{"index": i} for i in range(gpu_count)]
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with Progress(
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SpinnerColumn(), TextColumn("[progress.description]{task.description}"),
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BarColumn(), TextColumn("{task.completed}/{task.total}"),
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TimeElapsedColumn(), console=self.console,
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) as progress:
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task = progress.add_task("Testing dtypes...", total=len(configured_dtypes))
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for dtype_name in configured_dtypes:
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if dtype_name not in dtype_map:
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progress.advance(task)
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continue
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# Skip FP8 if GPU architecture doesn't support it
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if dtype_name == "fp8" and self.specs.get("fp8_tflops", 0) == 0:
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arch = self.specs.get("architecture", "unknown")
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results_by_dtype["fp8"] = f"skipped ({arch} does not support FP8)"
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self.console.print(f"[dim] fp8: skipped - {arch} architecture has no FP8 support[/dim]")
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progress.advance(task)
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continue
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gpu_values = []
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errors = []
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for gpu_idx in range(gpu_count):
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try:
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val = self._benchmark_dtype_on_gpu(
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dtype_name, dtype_map[dtype_name][0], matrix_size,
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warmup, compile_warmup, iterations, mm_fn, gpu_idx,
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)
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gpu_values.append(val)
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per_gpu_results[gpu_idx][dtype_name] = round(val, 1)
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except Exception as e:
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errors.append(f"gpu{gpu_idx}: {e}")
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per_gpu_results[gpu_idx][dtype_name] = f"error: {e}"
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finally:
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torch.cuda.empty_cache()
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if gpu_values:
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results_by_dtype[dtype_name] = round(sum(gpu_values) / len(gpu_values), 1)
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else:
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results_by_dtype[dtype_name] = "error: " + "; ".join(errors[:3])
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self.console.print(f"[yellow] {dtype_name}: {results_by_dtype[dtype_name]}[/yellow]")
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progress.advance(task)
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efficiency = {}
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for dt, achieved in results_by_dtype.items():
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if isinstance(achieved, (int, float)) and dt in dtype_map:
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peak_tp = dtype_map[dt][1]
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if peak_tp:
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efficiency[dt] = round((achieved / peak_tp) * 100, 1)
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consistency = {}
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for dt in results_by_dtype:
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vals = [pg.get(dt) for pg in per_gpu_results]
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nums = [v for v in vals if isinstance(v, (int, float))]
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if len(nums) >= 2:
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mean = sum(nums) / len(nums)
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spread_pct = ((max(nums) - min(nums)) / mean * 100) if mean else 0
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consistency[dt] = {
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"mean_tflops": round(mean, 1),
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"min_tflops": round(min(nums), 1),
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"max_tflops": round(max(nums), 1),
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"spread_pct": round(spread_pct, 2),
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"max_allowed_pct": 3,
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"passed": spread_pct <= 3,
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}
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pass_thresholds = dict(self.specs.get("compute_pass_thresholds_tflops") or {})
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threshold_passed = True
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for dt, threshold in pass_thresholds.items():
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|
val = results_by_dtype.get(dt)
|
|
if not isinstance(val, (int, float)) or val < threshold:
|
|
threshold_passed = False
|
|
break
|
|
consistency_passed = all(row.get("passed", False) for row in consistency.values()) if consistency else True
|
|
|
|
return {
|
|
"compute": {
|
|
"passed": threshold_passed and consistency_passed,
|
|
"per_dtype_tflops": results_by_dtype,
|
|
"peak_tflops": {dt: dtype_map[dt][1] for dt in dtype_map},
|
|
"efficiency_pct": efficiency,
|
|
# Absolute TFLOPS PASS thresholds (decoupled from peak). When present,
|
|
# report.py judges PASS/WARN/FAIL against these directly instead of
|
|
# using % of peak. Empty dict => fall back to legacy 80% rule.
|
|
"pass_thresholds_tflops": pass_thresholds,
|
|
"per_gpu": per_gpu_results,
|
|
"consistency": consistency,
|
|
"matrix_size": matrix_size,
|
|
"warmup": warmup,
|
|
"iterations": iterations,
|
|
}
|
|
}
|
|
|
|
def _benchmark_dtype_on_gpu(self, dtype_name: str, dtype_val, matrix_size: int,
|
|
warmup: int, compile_warmup: int, iterations: int,
|
|
mm_fn, gpu_idx: int) -> float:
|
|
if dtype_name == "fp8" and dtype_val is None:
|
|
raise RuntimeError("torch.float8_e4m3fn unavailable")
|
|
device = f"cuda:{gpu_idx}"
|
|
old_tf32 = torch.backends.cuda.matmul.allow_tf32
|
|
try:
|
|
with torch.cuda.device(gpu_idx):
|
|
if dtype_name == "tf32":
|
|
torch.backends.cuda.matmul.allow_tf32 = True
|
|
dtype_val = torch.float32
|
|
|
|
M = N = K = matrix_size
|
|
if dtype_name == "int8" and M > 4096:
|
|
# torch._int_mm on 8192 can be extremely memory hungry because the
|
|
# output is int32. Keep it production-visible, but bounded.
|
|
M = N = K = 4096
|
|
|
|
elem_bytes = 1 if dtype_name in ("fp8", "int8") else torch.tensor([], dtype=dtype_val).element_size()
|
|
pair_bytes = 2 * M * K * elem_bytes
|
|
num_pools = max(4, -(-256 * 1024 * 1024 // pair_bytes))
|
|
|
|
if dtype_name == "fp8":
|
|
if not hasattr(torch, "_scaled_mm"):
|
|
raise RuntimeError("torch._scaled_mm unavailable")
|
|
pools_a = [torch.randn(M, K, device=device, dtype=torch.float32).to(torch.float8_e4m3fn) for _ in range(num_pools)]
|
|
pools_b = [torch.randn(N, K, device=device, dtype=torch.float32).to(torch.float8_e4m3fn) for _ in range(num_pools)]
|
|
scale_a = torch.tensor(1.0, device=device)
|
|
scale_b = torch.tensor(1.0, device=device)
|
|
|
|
def run(i):
|
|
return torch._scaled_mm(pools_a[i], pools_b[i].T, scale_a=scale_a, scale_b=scale_b, out_dtype=torch.bfloat16)
|
|
|
|
effective_warmup = warmup
|
|
elif dtype_name == "int8":
|
|
if not hasattr(torch, "_int_mm"):
|
|
raise RuntimeError("torch._int_mm unavailable")
|
|
pools_a = [torch.randint(-128, 127, (M, K), device=device, dtype=torch.int8) for _ in range(num_pools)]
|
|
pools_b = [torch.randint(-128, 127, (K, N), device=device, dtype=torch.int8) for _ in range(num_pools)]
|
|
|
|
def run(i):
|
|
return torch._int_mm(pools_a[i], pools_b[i])
|
|
|
|
effective_warmup = warmup
|
|
else:
|
|
pools_a = [torch.randn(M, K, device=device, dtype=dtype_val) for _ in range(num_pools)]
|
|
pools_b = [torch.randn(K, N, device=device, dtype=dtype_val) for _ in range(num_pools)]
|
|
|
|
def run(i):
|
|
return mm_fn(pools_a[i], pools_b[i])
|
|
|
|
effective_warmup = compile_warmup
|
|
|
|
for i in range(effective_warmup):
|
|
run(i % num_pools)
|
|
torch.cuda.synchronize()
|
|
start_event = torch.cuda.Event(enable_timing=True)
|
|
end_event = torch.cuda.Event(enable_timing=True)
|
|
start_event.record()
|
|
for i in range(iterations):
|
|
c = run(i % num_pools)
|
|
end_event.record()
|
|
torch.cuda.synchronize()
|
|
elapsed_ms = start_event.elapsed_time(end_event)
|
|
del pools_a, pools_b, c
|
|
flops = 2 * M * N * K * iterations
|
|
return flops / (elapsed_ms / 1000) / 1e12
|
|
finally:
|
|
torch.backends.cuda.matmul.allow_tf32 = old_tf32
|
|
|
|
@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_key, eff_key in [
|
|
("H2D (PCIe)", mem["h2d_bandwidth_gbps"], "h2d_peak_gbps", "h2d_efficiency_pct"),
|
|
("D2H (PCIe)", mem["d2h_bandwidth_gbps"], "d2h_peak_gbps", "d2h_efficiency_pct"),
|
|
("D2D (NVLink)", mem["d2d_bandwidth_gbps"], "d2d_peak_gbps", "d2d_efficiency_pct"),
|
|
]:
|
|
val_str = f"{achieved:.1f} GB/s" if isinstance(achieved, (int, float)) else "N/A"
|
|
peak = mem.get(peak_key, 0)
|
|
peak_str = f"{peak:.0f} GB/s" if peak else "N/A"
|
|
eff = mem.get(eff_key, 0)
|
|
if eff:
|
|
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)
|
|
|
|
consistency = comp.get("consistency", {})
|
|
if consistency:
|
|
t_cons = Table(title="Per-GPU Consistency", box=None, padding=(0, 1))
|
|
t_cons.add_column("DType", style="bold")
|
|
t_cons.add_column("Min", justify="right")
|
|
t_cons.add_column("Mean", justify="right")
|
|
t_cons.add_column("Max", justify="right")
|
|
t_cons.add_column("Spread", justify="right")
|
|
t_cons.add_column("Status", justify="right")
|
|
for dt, row in consistency.items():
|
|
status = "PASS" if row.get("passed") else "FAIL"
|
|
color = "green" if row.get("passed") else "red"
|
|
t_cons.add_row(
|
|
dt.upper(),
|
|
f"{row.get('min_tflops', 0):.1f}",
|
|
f"{row.get('mean_tflops', 0):.1f}",
|
|
f"{row.get('max_tflops', 0):.1f}",
|
|
f"{row.get('spread_pct', 0):.2f}%",
|
|
f"[{color}]{status}[/{color}]",
|
|
)
|
|
c.print(t_cons)
|