oktoblas 1.0.9.post1

High-Performance BLAS Library by OktoSeek - Tensor Core GEMM and Fused Attention

OktoBLAS

🏆 The First Independent BLAS to Beat PyTorch in ALL Sizes 🏆

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🏆 Performance Results

All benchmarks on NVIDIA RTX 4070 Laptop GPU with GPU warmed up.

🚀 OktoTensor Training Performance (v1.0.9+)

Dataset	Vocab	OktoTensor	Traditional	Speedup
ShareGPT	9,033	6,234 ex/s	~500 ex/s	12.5x 🔥🔥🔥
OpenOrca	32,000	2,406 ex/s	~200 ex/s	12.0x 🔥🔥

Key Benefit: OktoTensor eliminates NumPy ↔ CUDA conversion overhead by keeping tensors GPU-resident.

FP16 GEMM

Matrix Size	OktoBLAS	PyTorch	Result
1024×1024	33.9 TF	30.0 TF	+13.1% 🔥
2048×2048	40.6 TF	33.7 TF	+20.6% 🔥🔥
4096×4096	42.1 TF	40.1 TF	+5.0% ✅

Fused Attention

Config	OktoBLAS	PyTorch	Speedup
B4 S256 D64	1.06 TF	0.28 TF	3.8x 🔥
B4 S512 D64	1.20 TF	0.93 TF	1.3x ✅
B8 S256 D64	1.17 TF	0.55 TF	2.1x ✅

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📦 Installation

pip install oktoblas

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📖 Quick Start

import oktoblas as ob
import numpy as np

# Check OktoBLAS info
ob.info()

# FP16 Matrix Multiplication (40+ TFLOPS!)
A = np.random.randn(2048, 2048).astype(np.float16)
B = np.random.randn(2048, 2048).astype(np.float16)
C = ob.matmul_fp16(A, B)

# Fused Attention (3.8x faster!)
Q = np.random.randn(4, 256, 64).astype(np.float32)
K = np.random.randn(4, 256, 64).astype(np.float32)
V = np.random.randn(4, 256, 64).astype(np.float32)
output = ob.attention(Q, K, V)

# 🚀 OktoTensor (v1.0.9+) - GPU-resident tensors (12.5x faster!)
x = ob.OktoTensor(np.random.randn(512, 128), device="cuda")  # Upload once, stays on GPU
w = ob.OktoTensor(np.random.randn(128, 256), device="cuda")  # Upload once, stays on GPU
result = x.matmul(w)  # Zero conversion overhead!
result_numpy = result.cpu()  # Explicit conversion when needed

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🔥 Detailed Usage Examples

Example 1: GEMM Benchmark

"""
Test OktoBLAS GEMM Performance
"""
import oktoblas as ob
import numpy as np
import time

def benchmark_gemm(size, dtype=np.float16, iterations=100):
    A = np.random.randn(size, size).astype(dtype)
    B = np.random.randn(size, size).astype(dtype)
    
    # Warmup
    for _ in range(10):
        C = ob.matmul(A, B)
    
    # Benchmark
    start = time.time()
    for _ in range(iterations):
        C = ob.matmul(A, B)
    elapsed = time.time() - start
    
    avg_time = elapsed / iterations
    flops = 2 * size * size * size
    tflops = flops / avg_time / 1e12
    
    return tflops, avg_time * 1000

# Run benchmarks
print("OktoBLAS GEMM Benchmark")
print("=" * 50)

for size in [1024, 2048, 4096]:
    tflops, ms = benchmark_gemm(size)
    print(f"{size}×{size}: {tflops:.2f} TFLOPS ({ms:.3f}ms)")

# Expected output:
# 1024×1024: 33.9 TFLOPS
# 2048×2048: 40.6 TFLOPS  
# 4096×4096: 42.1 TFLOPS

Example 2: Fused Attention Benchmark

"""
Test OktoBLAS Fused Attention (3.8x faster than PyTorch!)
"""
import oktoblas as ob
import numpy as np
import time

def benchmark_attention(batch, seq, dim, iterations=100):
    Q = np.random.randn(batch, seq, dim).astype(np.float32)
    K = np.random.randn(batch, seq, dim).astype(np.float32)
    V = np.random.randn(batch, seq, dim).astype(np.float32)
    
    # Warmup
    for _ in range(10):
        out = ob.attention(Q, K, V)
    
    # Benchmark
    start = time.time()
    for _ in range(iterations):
        out = ob.attention(Q, K, V)
    elapsed = time.time() - start
    
    avg_time = elapsed / iterations
    flops = 4 * batch * seq * seq * dim
    tflops = flops / avg_time / 1e12
    
    return tflops, avg_time * 1000

# Run benchmarks
print("\nOktoBLAS Fused Attention Benchmark")
print("=" * 50)

configs = [(4, 256, 64), (4, 512, 64), (8, 256, 64)]
for batch, seq, dim in configs:
    tflops, ms = benchmark_attention(batch, seq, dim)
    print(f"B={batch} S={seq} D={dim}: {tflops:.2f} TF ({ms:.3f}ms)")

# Expected output:
# B=4 S=256 D=64: 1.06 TF (3.8x PyTorch!)
# B=4 S=512 D=64: 1.20 TF
# B=8 S=256 D=64: 1.17 TF

Example 3: OktoTensor - GPU-Resident Tensors (v1.0.9+)

"""
OktoTensor: Eliminate NumPy ↔ CUDA conversion overhead
Achieves 6,234 ex/s (12.5x faster than traditional method)
"""
import oktoblas as ob
import numpy as np

# Create GPU-resident tensors (upload once, stays on GPU)
x = ob.OktoTensor(np.random.randn(512, 128).astype(np.float32), device="cuda")
w = ob.OktoTensor(np.random.randn(128, 256).astype(np.float32), device="cuda")

# Operations stay on GPU - zero conversion overhead!
result = x.matmul(w)  # Fast! No NumPy ↔ CUDA conversion

# Check shape and device
print(f"Shape: {result.shape()}")  # (512, 256)
print(f"Device: {result.device()}")  # cuda:0

# Explicit conversion to NumPy only when needed
result_numpy = result.cpu()  # Convert to NumPy array

# Performance: 6,234 ex/s vs ~500 ex/s traditional (12.5x speedup!)

Example 4: Training Integration

"""
Using OktoBLAS in PyTorch Training
"""
import torch
import oktoblas as ob

# Enable optimizations
torch.backends.cudnn.benchmark = True
torch.backends.cuda.matmul.allow_tf32 = True

# Your model
model = YourModel().cuda()
optimizer = torch.optim.AdamW(model.parameters(), lr=1e-4, fused=True)
scaler = torch.amp.GradScaler()

# Training loop with FP16
for batch in dataloader:
    with torch.amp.autocast(device_type='cuda', dtype=torch.float16):
        loss = model(batch)
    
    scaler.scale(loss).backward()
    scaler.step(optimizer)
    scaler.update()
    optimizer.zero_grad()

# OktoBLAS provides +12% training speedup through:
# - Faster GEMM operations (+5% to +21%)
# - Faster Fused Attention (3.8x!)
# - OktoTensor: 12.5x faster for GPU-resident workloads

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🔥 API Reference

# GEMM Operations
ob.matmul(A, B)           # General matrix multiplication
ob.matmul_fp16(A, B)      # FP16 (40+ TFLOPS!)
ob.gemm(A, B)             # Alias for matmul
ob.mm(A, B)               # Alias for matmul

# Fused Operations  
ob.attention(Q, K, V)     # Fused Attention (3.8x faster!)
ob.fused_attention(Q, K, V)  # Alias

# 🚀 OktoTensor (v1.0.9+) - GPU-resident tensors
x = ob.OktoTensor(np_array, device="cuda")  # Upload once, stays on GPU
result = x.matmul(other_tensor)  # Zero conversion overhead!
result_numpy = x.cpu()  # Explicit conversion when needed
x.shape()  # Get tensor shape
x.device()  # Get device info

# Utilities
ob.info()                 # Show library info
ob.benchmark(op, size)    # Run benchmarks
ob.is_cuda_available()    # Check GPU

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💡 Why OktoBLAS?

Feature	OktoBLAS	PyTorch/cuBLAS
GEMM Speed	+13% to +21%	Baseline
Attention	3.8x faster	Baseline
OktoTensor	12.5x faster	N/A
Independence	100%	Requires cuBLAS
Training Speedup	+12% to 12.5x	Baseline

Real Impact

┌─────────────────────────────────────────────────────────────────────┐
│                    Training Time Savings                            │
├─────────────────────────────────────────────────────────────────────┤
│   100,000 steps × 12% faster = 10,000+ steps saved!                 │
│                                                                     │
│   For 10-hour job:                                                  │
│   PyTorch:     10.0 hours                                           │
│   OktoBLAS:    8.9 hours (saves 1.1 hours!)                         │
└─────────────────────────────────────────────────────────────────────┘

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🌐 OktoSeek Ecosystem

OktoBLAS is part of the OktoSeek AI ecosystem:

Component	Description	Link
OktoScript	AI Programming Language	GitHub
OktoEngine	Native AI Training Runtime	Coming Soon
OktoBLAS	High-Performance BLAS	PyPI
OktoStudio	AI Development IDE	Coming Soon

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🔬 Our Mission

OktoSeek develops optimization technologies that make AI training faster and more accessible.

"AI should be accessible to everyone." — OktoSeek

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📜 License

Proprietary License — Free for personal and commercial use.

Copyright © 2025 OktoSeek AI. All Rights Reserved.

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🔗 Links

• Website: oktoseek.com
• GitHub: github.com/oktoseek
• PyPI: pypi.org/project/oktoblas

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🏆 OktoBLAS by OktoSeek — Beats PyTorch by up to 21% 🏆