gpu-optimizer: Скилл для оптимизации GPU производительности

--- name: gpu-optimizer description: Expert GPU optimization for modern consumer GPUs (8-24GB VRAM). Use this skill when you need to optimize GPU training, speed up CUDA code, reduce OOM errors, tune XGBoost for GPU, migrate NumPy to CuPy, make a model faster, manage GPU memory, optimize VRAM usage, or benchmark PyTorch. Covers mixed precision, gradient checkpointing, XGBoost GPU acceleration, CuPy/cuDF migration, vectorization, torch.compile, and diagnostics. NVIDIA GPUs only. PyTorch, XGBoost, and RAPIDS frameworks. metadata: version: 1.1.0 category: data tags: [gpu, cuda, vram, pytorch] difficulty: advanced --- # GPU Optimizer Expert GPU optimization for consumer GPUs with 8–24GB VRAM. Evidence-based patterns only. ## Hardware Profile Fill in your hardware before applying optimizations: | Property | Your Value | | ----------------- | ------------------------------------------------ | | GPU model | (e.g., RTX 4080 Mobile, RTX 3090, RTX 4090) | | VRAM | (e.g., 12GB, 16GB, 24GB) | | CUDA version | (`nvidia-smi` → top-right) | | TDP / power limit | (laptop vs desktop affects sustained throughput) | | Driver version | (`nvidia-smi` → top-left) | Key constraint: VRAM capacity determines which strategies apply. Patterns below are annotated with minimum VRAM requirements where relevant. ## Optimization Categories ### 1. XGBoost GPU Acceleration **DMatrix vs QuantileDMatrix:** ```python # GPU-optimized: QuantileDMatrix is 1.8x faster dtrain = xgb.QuantileDMatrix(X_train.astype(np.float32)) dval = xgb.QuantileDMatrix(X_val.astype(np.float32)) # Standard: DMatrix (use for inference only) dtest = xgb.DMatrix(X_test.astype(np.float32)) ``` **Critical Parameters:** ```python params = { 'tree_method': 'hist', # GPU-accelerated histogram 'device': 'cuda:0', # Explicit GPU device 'max_bin': 256, # Higher bins = better splits (VRAM permitting) 'grow_policy': 'depthwise', # vs 'lossguide' for imbalanced data 'predictor': 'gpu_predictor', # GPU inference } # Training with explicit device model = xgb.train(params, dtrain, num_boost_round=100) ``` **GPU Verification (fail-fast):** ```python def verify_gpu(): """Verify XGBoost GPU availability. Raises if unavailable.""" import subprocess try: result = subprocess.run(["nvidia-smi"], capture_output=True, text=True) if result.returncode != 0: raise RuntimeError("nvidia-smi failed - no GPU available") except FileNotFoundError: raise RuntimeError("nvidia-smi not found - no GPU available") build_info = xgb.build_info() if not build_info.get("USE_CUDA"): raise RuntimeError("XGBoost not compiled with CUDA support") ``` **Memory Management:** ```python # Single-pass training (reuse QuantileDMatrix across slots) dtrain = xgb.QuantileDMatrix(X_train.astype(np.float32)) for slot_idx in range(num_slots): dtrain.set_label(y_train[:, slot_idx]) # Reuse matrix model = xgb.train(params, dtrain, num_boost_round=100) ``` ### 2. PyTorch Mixed Precision **BF16 (preferred) vs FP16:** ```python from torch.amp import autocast, GradScaler # Auto-detect best precision if torch.cuda.is_bf16_supported(): amp_dtype = torch.bfloat16 # Ampere+ GPUs support BF16 else: amp_dtype = torch.float16 # Training step scaler = GradScaler('cuda') if amp_dtype == torch.float16 else None with autocast('cuda', dtype=amp_dtype): output = model(input_ids, attention_mask) loss = criterion(output, targets) # Backward with scaling (FP16 only) if scaler: scaler.scale(loss).backward() scaler.step(optimizer) scaler.update() else: loss.backward() optimizer.step() ``` **Why BF16 > FP16:** - Same exponent range as FP32 (no overflow/underflow) - No GradScaler needed (simpler code) - Ampere and later GPUs have native BF16 Tensor cores ### 3. VRAM Management **Gradient Checkpointing:** ```python # Saves ~40% VRAM, adds ~20% compute time model.gradient_checkpointing_enable() # For transformers: model.base_model.model.gradient_checkpointing_enable() ``` **VRAM Monitoring:** ```python import torch torch.cuda.reset_peak_memory_stats() # ... training ... peak_vram_gb = torch.cuda.max_memory_allocated() / 1024**3 print(f"Peak VRAM: {peak_vram_gb:.2f} GB") # Clear cache between experiments torch.cuda.empty_cache() ``` **Gradient Accumulation:** ```python # Simulate larger batch size without OOM grad_accum_steps = max(1, target_batch_size // actual_batch_size) for i, batch in enumerate(dataloader): loss = model(batch) / grad_accum_steps loss.backward() if (i + 1) % grad_accum_steps == 0: optimizer.step() optimizer.zero_grad() ``` **DoE for VRAM Optimization:** ```python EXPERIMENTS = [ {"batch_size": 2, "seq_len": 128, "grad_ckpt": True, "amp": "bf16"}, {"batch_size": 4, "seq_len": 256, "grad_ckpt": True, "amp": "bf16"}, {"batch_size": 8, "seq_len": 512, "grad_ckpt": False, "amp": "bf16"}, {"batch_size": 16, "seq_len": 256, "grad_ckpt": False, "amp": "bf16"}, ] ``` ### 4. Aggressive Vectorization **Tensor Lookups (not Python loops):** ```python # Slow: Python loop for i, token_id in enumerate(input_ids): type_id = token_to_type[token_id] embeddings[i] = type_embeddings[type_id] # Fast: Vectorized type_ids = token_to_type[input_ids] # Broadcast lookup embeddings = type_embeddings[type_ids] # Single GPU kernel ``` **Registered Buffers (persistent GPU data):** ```python class Model(nn.Module): def __init__(self): super().__init__() # Build lookup tensors once type_ids = torch.zeros(vocab_size, dtype=torch.long) self.register_buffer('_type_ids', type_ids) # Stays on GPU def forward(self, input_ids): return self._type_ids[input_ids] # Vectorized lookup ``` **Batch Operations:** ```python # Slow: Per-sample processing outputs = [model(x.unsqueeze(0)) for x in batch] # Fast: Batched outputs = model(batch) # Single forward pass ``` ### 5. CuPy Migration (NumPy → GPU) **When to Use CuPy:** - Large array operations (>1M elements) - Repeated NumPy calls in tight loops - Preprocessing pipelines before PyTorch/XGBoost **Migration Pattern:** ```python import cupy as cp import numpy as np # NumPy (CPU) x = np.random.randn(10000, 1000) y = np.dot(x, x.T) # CuPy (GPU) - SAME API x_gpu = cp.random.randn(10000, 1000) y_gpu = cp.dot(x_gpu, x_gpu.T) # Transfer back if needed y_cpu = cp.asnumpy(y_gpu) ``` **Interop with PyTorch:** ```python # CuPy → PyTorch (zero-copy) x_cupy = cp.random.randn(1000, 1000) x_torch = torch.as_tensor(x_cupy, device='cuda') # PyTorch → CuPy (zero-copy) x_torch = torch.randn(1000, 1000, device='cuda') x_cupy = cp.asarray(x_torch) ``` **Install:** ```bash uv pip install cupy-cuda12x # For CUDA 12.x ``` ### 6. cuDF Migration (Pandas → GPU) **When to Use cuDF:** - DataFrames >1GB - Groupby/aggregation on large data - ETL pipelines before model training **Migration Pattern:** ```python import cudf import pandas as pd # Pandas (CPU) df = pd.read_csv('large.csv') grouped = df.groupby('category')['value'].mean() # cuDF (GPU) - SAME API df_gpu = cudf.read_csv('large.csv') grouped_gpu = df_gpu.groupby('category')['value'].mean() # Transfer back grouped_cpu = grouped_gpu.to_pandas() ``` **XGBoost Integration:** ```python import cudf import xgboost as xgb # Load data on GPU df = cudf.read_csv('train.csv') X = df[feature_cols] y = df['target'] # Create DMatrix directly from cuDF (no CPU copy) dtrain = xgb.DMatrix(X, label=y) ``` **Install:** ```bash # RAPIDS (includes cuDF, cuML, cuGraph) uv pip install cudf-cu12 --extra-index-url=https://pypi.nvidia.com ``` ### 7. PyTorch Compilation & Optimization **Fused Optimizer:** ```python # Check availability use_fused = ( torch.cuda.is_available() and "fused" in torch.optim.AdamW.__init__.__code__.co_varnames ) optimizer = torch.optim.AdamW( model.parameters(), lr=1e-3, fused=use_fused, # Single GPU kernel (2-3x faster) ) ``` **Torch Compile:** ```python # PyTorch 2.0+ compile if hasattr(torch, "compile"): model = torch.compile(model, mode="reduce-overhead") ``` **cuDNN Benchmarking:** ```python # Auto-tune kernels (slower startup, faster training) torch.backends.cudnn.benchmark = True # Disable for determinism torch.backends.cudnn.deterministic = True ``` ### 8. Advanced Loss Functions **Weighted Slot Loss:** ```python class WeightedSlotLoss(nn.Module): def __init__(self, slot_weights): super().__init__() self.slot_weights = torch.tensor(slot_weights) def forward(self, logits_list, targets): weighted_losses = [] for i, logits in enumerate(logits_list): loss = F.cross_entropy(logits, targets[:, i]) weighted_losses.append(loss * self.slot_weights[i]) return torch.stack(weighted_losses).sum() / self.slot_weights.sum() ``` **Focal Loss (hard example mining):** ```python class FocalLoss(nn.Module): def __init__(self, gamma=2.0): super().__init__() self.gamma = gamma def forward(self, logits, targets): ce_loss = F.cross_entropy(logits, targets, reduction='none') pt = torch.exp(-ce_loss) focal_loss = ((1 - pt) ** self.gamma) * ce_loss return focal_loss.mean() ``` ### 9. Caching & Precomputation **Position Embedding Cache:** ```python class Model(nn.Module): def __init__(self): super().__init__() self._pos_cache = {} # {seq_len: positions} def forward(self, x): T = x.size(1) if T not in self._pos_cache: self._pos_cache[T] = torch.arange(T, device=x.device) # Limit cache size if len(self._pos_cache) > 10: self._pos_cache.pop(next(iter(self._pos_cache))) return self.pos_embed(self._pos_cache[T]) ``` **Attention Mask Cache:** ```python def _create_causal_mask(self, T, device): if T not in self._mask_cache: mask = torch.triu(torch.ones(T, T), diagonal=1).bool() self._mask_cache[T] = mask.to(device) return self._mask_cache[T] ``` ## Quick Diagnostics **Check GPU Utilization:** ```bash watch -n 1 nvidia-smi # Monitor in real-time ``` **Profile PyTorch:** ```python with torch.profiler.profile( activities=[torch.profiler.ProfilerActivity.GPU], with_stack=True, ) as prof: model(batch) print(prof.key_averages().table(sort_by="cuda_time_total")) ``` **Bottleneck Detection:** ```python import torch.utils.bottleneck as bottleneck bottleneck.main(['script.py']) ``` ## Migration Checklist - [ ] **XGBoost**: Use `QuantileDMatrix`, set `device='cuda:0'` - [ ] **PyTorch**: Enable BF16/FP16, fused optimizer, torch.compile - [ ] **VRAM**: Gradient checkpointing if approaching VRAM limit - [ ] **NumPy→CuPy**: For preprocessing >1M elements - [ ] **Pandas→cuDF**: For DataFrames >1GB - [ ] **Vectorization**: Replace Python loops with tensor ops - [ ] **Caching**: Precompute positions, masks, embeddings - [ ] **Monitor**: Track VRAM usage, profile GPU kernels ## Anti-Patterns **Avoid:** - Using `.cpu()` in training loop (kills GPU pipeline) - Creating tensors on CPU then moving to GPU (create on GPU directly) - Using Python loops over tensors (vectorize) - Ignoring VRAM monitoring (leads to OOM crashes) - Using FP32 when BF16/FP16 works (wastes bandwidth) - Calling `torch.cuda.synchronize()` unnecessarily (breaks async) ## References **Documentation:** - XGBoost GPU: https://xgboost.readthedocs.io/en/stable/gpu/ - PyTorch AMP: https://pytorch.org/docs/stable/amp.html - CuPy: https://docs.cupy.dev/en/stable/ - cuDF: https://docs.rapids.ai/api/cudf/stable/ ## Error Handling - CUDA not available at runtime: run `nvidia-smi` first to confirm the GPU is visible; if the command fails, verify driver installation with `sudo nvidia-smi` or reinstall drivers before proceeding. - XGBoost raises `RuntimeError: XGBoost not compiled with CUDA support`: install the CUDA build via `uv pip install xgboost` from a CUDA-enabled environment, or build from source with `-DUSE_CUDA=ON`. - OOM during training: reduce batch size first (halve it), then enable gradient checkpointing; if OOM persists after both, enable gradient accumulation to simulate the original batch size. - CuPy import failure (`ImportError` or version mismatch): verify CUDA toolkit version with `nvcc --version` and install the matching CuPy wheel (e.g., `cupy-cuda12x` for CUDA 12.x). - cuDF install fails or produces CUDA version errors: use the NVIDIA PyPI index (`--extra-index-url=https://pypi.nvidia.com`) and match the `cudf-cu12` suffix to your CUDA major version. - `torch.compile` produces incorrect results or crashes: disable with `model = model` (no compile) to isolate; known to fail on some custom ops — fall back to eager mode for those layers. ## Limitations - NVIDIA GPUs only — AMD (ROCm) and Intel Arc GPUs are not covered by these patterns. - Assumes a single-GPU setup; multi-GPU (DDP, FSDP) requires additional configuration not covered here. - Patterns are calibrated for consumer GPUs (8–24GB VRAM); datacenter GPUs (A100, H100) have different memory hierarchies and may benefit from different strategies. - Framework coverage: PyTorch, XGBoost, and RAPIDS (CuPy/cuDF) only — JAX, TensorFlow, and MXNet are out of scope. - Laptop GPU TDP limits sustained throughput; power-throttled performance can differ significantly from desktop benchmarks even at the same VRAM capacity. ## Output Format Each optimization recommendation includes a before/after code pair showing the original pattern and the GPU-optimized equivalent. Performance gain estimates are provided as ranges (e.g., "1.8x faster", "~40% VRAM reduction") based on typical consumer GPU benchmarks — actual gains depend on workload and hardware. Where a change introduces a trade-off (e.g., gradient checkpointing adds compute time), the trade-off is stated explicitly inline.