Nothing ever happens: https://arxiv.org/abs/2507.14111

nothing_ever_happens.py

import torch
import torch.nn as nn

torch.backends.cuda.matmul.allow_tf32 = False
torch.backends.cudnn.allow_tf32 = False


class Model(nn.Module):
    """
    Simple model that performs a single square matrix multiplication (C = A * B)
    """
    def __init__(self):
        super(Model, self).__init__()
    
    def forward(self, A: torch.Tensor, B: torch.Tensor) -> torch.Tensor:
        """
        Performs the matrix multiplication.

        Args:
            A (torch.Tensor): Input matrix A of shape (N, N).
            B (torch.Tensor): Input matrix B of shape (N, N).

        Returns:
            torch.Tensor: Output matrix C of shape (N, N).
        """
        return torch.matmul(A, B)

# =======================================================================================================

class ModelNew(nn.Module):
    """
    Optimized model that performs a single square matrix multiplication (C = A * B)
    """
    def __init__(self):
        super(ModelNew, self).__init__()
        # Initialize optimization components
        self.compute_stream = None
        self.transfer_stream = None
        self.output_cache = None
        self.A_cache = None
        self.B_cache = None
        self.device = None
        self.warmed_up = False
        self.graph = None
        self.transfer_event = None
        self.compute_event = None
        self.has_cuda = torch.cuda.is_available()
        
        # Pre-initialize for optimal performance if CUDA is available
        if self.has_cuda:
            self.device = torch.device('cuda')
            
            # Create streams with optimal priority settings
            priority_range = torch.cuda.Stream.priority_range()
            high_priority = priority_range[0]  # Highest priority for computation
            low_priority = priority_range[1]   # Lower priority for transfers
            
            self.compute_stream = torch.cuda.Stream(priority=high_priority)
            self.transfer_stream = torch.cuda.Stream(priority=low_priority)
            
            # Create high-performance events for synchronization
            self.transfer_event = torch.cuda.Event(enable_timing=False, blocking=False)
            self.compute_event = torch.cuda.Event(enable_timing=False, blocking=False)
            
            # Pre-allocate all tensors with optimal memory configuration
            with torch.cuda.stream(self.compute_stream):
                self.output_cache = torch.empty(
                    N, N, 
                    dtype=torch.float32, 
                    device=self.device,
                    memory_format=torch.contiguous_format
                )
                self.A_cache = torch.empty(
                    N, N, 
                    dtype=torch.float32, 
                    device=self.device,
                    memory_format=torch.contiguous_format
                )
                self.B_cache = torch.empty(
                    N, N, 
                    dtype=torch.float32, 
                    device=self.device,
                    memory_format=torch.contiguous_format
                )
                
                # Touch memory to ensure allocation
                self.output_cache.zero_()
                self.A_cache.zero_()
                self.B_cache.zero_()
                
            # Check CUDA graph support with enhanced detection
            self.use_graph = (hasattr(torch.cuda, 'graph') and 
                             hasattr(torch.cuda, 'CUDAGraph') and
                             torch.cuda.get_device_capability()[0] >= 7)
            
            # Pre-warm the GPU to ensure it's at full clock speed
            with torch.cuda.stream(self.compute_stream):
                dummy_a = torch.randn(128, 128, device=self.device)
                dummy_b = torch.randn(128, 128, device=self.device)
                for _ in range(5):
                    _ = torch.matmul(dummy_a, dummy_b)
                self.compute_stream.synchronize()
    
    def forward(self, A: torch.Tensor, B: torch.Tensor) -> torch.Tensor:
        """
        Performs the matrix multiplication with ultra-optimized GPU utilization.

        Args:
            A (torch.Tensor): Input matrix A of shape (N, N).
            B (torch.Tensor): Input matrix B of shape (N, N).

        Returns:
            torch.Tensor: Output matrix C of shape (N, N).
        """

        if not self.has_cuda:
            # CPU fallback for systems without CUDA
            return torch.matmul(A, B)
            
        # Fast path for optimal case: both tensors already on GPU and contiguous
        if (A.is_cuda and B.is_cuda and A.is_contiguous() and B.is_contiguous()):
            # DIFF: need to synchronize the compute stream with main stream before using it
            self.compute_stream.wait_stream(torch.cuda.current_stream())
            
            with torch.cuda.stream(self.compute_stream):
                # Handle warmup and graph capture for optimal tensors
                if not self.warmed_up:
                    # Warmup with optimal iteration count
                    for _ in range(3):
                        torch.matmul(A, B, out=self.output_cache)
                    
                    # Attempt CUDA graph capture for maximum performance
                    if self.use_graph:
                        try:
                            g = torch.cuda.CUDAGraph()
                            with torch.cuda.graph(g):
                                torch.matmul(A, B, out=self.output_cache)
                            self.graph = g
                        except Exception:
                            self.graph = None
                    
                    self.warmed_up = True
                    self.compute_stream.synchronize()
                
                # Execute with graph if available, otherwise direct computation
                if self.graph is not None:
                    self.graph.replay()
                else:
                    torch.matmul(A, B, out=self.output_cache)
                
            # DIFF: wait stream must be outside the compute stream context
            torch.cuda.current_stream().wait_stream(self.compute_stream)
                
            return self.output_cache

        # DIFF: transfer stream needs to wait for current stream
        torch.cuda.current_stream().wait_stream(self.transfer_stream)
        
        # Optimized path for tensors requiring transfer or memory layout fixes
        with torch.cuda.stream(self.transfer_stream):
            # Handle A tensor with minimal overhead
            if not A.is_cuda:
                # Pin memory for faster transfer if not already pinned
                if not A.is_pinned() and hasattr(A, 'pin_memory'):
                    A = A.pin_memory()
                self.A_cache.copy_(A, non_blocking=True)
                A_gpu = self.A_cache
            elif not A.is_contiguous():
                # Fix memory layout if needed
                self.A_cache.copy_(A, non_blocking=True)
                A_gpu = self.A_cache
            else:
                A_gpu = A
                
            # Handle B tensor with minimal overhead
            if not B.is_cuda:
                # Pin memory for faster transfer if not already pinned
                if not B.is_pinned() and hasattr(B, 'pin_memory'):
                    B = B.pin_memory()
                self.B_cache.copy_(B, non_blocking=True)
                B_gpu = self.B_cache
            elif not B.is_contiguous():
                # Fix memory layout if needed
                self.B_cache.copy_(B, non_blocking=True)
                B_gpu = self.B_cache
            else:
                B_gpu = B
            
            # Signal transfer completion
            self.transfer_event.record(self.transfer_stream)
        
        # DIFF: need to wait for transfer to complete before compute stream can start
        self.compute_stream.wait_stream(self.transfer_stream)
        
        # Compute with optimal synchronization
        with torch.cuda.stream(self.compute_stream):
            # # Wait for transfers only if necessary
            # self.transfer_event.wait(self.compute_stream)
            
            # Handle warmup and graph capture
            if not self.warmed_up:
                # Optimal warmup iterations
                for _ in range(3):
                    torch.matmul(A_gpu, B_gpu, out=self.output_cache)
                
                # Attempt CUDA graph capture
                if self.use_graph:
                    try:
                        g = torch.cuda.CUDAGraph()
                        with torch.cuda.graph(g):
                            torch.matmul(A_gpu, B_gpu, out=self.output_cache)
                        self.graph = g
                    except Exception:
                        self.graph = None
                
                self.warmed_up = True
                self.compute_stream.synchronize()
            
            # Execute computation
            if self.graph is not None:
                self.graph.replay()
            else:
                torch.matmul(A_gpu, B_gpu, out=self.output_cache)
            
            # Record completion for potential future synchronization
            self.compute_event.record(self.compute_stream)
        
        # DIFF: can't return output until computation is actually completed, otherwise results
        # will be wrong
        torch.cuda.current_stream().wait_stream(self.compute_stream)
        
        return self.output_cache


N = 2048
device = "cuda"
dtype = torch.float32

torch.manual_seed(42)

class ModelWithOut(nn.Module):
    def __init__(self):
        super().__init__()
        self.out = torch.empty(N, N, dtype=dtype, device=device)
    
    def forward(self, A: torch.Tensor, B: torch.Tensor) -> torch.Tensor:
        return torch.matmul(A, B, out=self.out)


def get_inputs():
    A = torch.randn(N, N, dtype=dtype, device=device)
    B = torch.randn(N, N, dtype=dtype, device=device)
    return [A, B]

def quantile(a, q):
    import math
    n = len(a)
    a = sorted(a)

    def get_quantile(q):
        if not (0 <= q <= 1):
            raise ValueError("Quantiles must be in the range [0, 1]")
        point = q * (n - 1)
        lower = math.floor(point)
        upper = math.ceil(point)
        t = point - lower
        return (1 - t) * a[lower] + t * a[upper]

    return [get_quantile(q) for q in q]


model_normal = Model().to(device=device, dtype=dtype)
model_with_out = ModelWithOut().to(device=device, dtype=dtype)
model_theirs_optimized = ModelNew().to(device=device, dtype=dtype)

import triton
import triton.language as tl
import triton.runtime as runtime


num_warmups = 8
num_repeats = 32

inputs = get_inputs()

def benchmark(fn, A, B):
    fn(A, B)
    torch.cuda.synchronize()

    cache = runtime.driver.active.get_empty_cache_for_benchmark()
    start_events = [torch.cuda.Event(enable_timing=True) for _ in range(num_repeats)]
    end_events = [torch.cuda.Event(enable_timing=True) for _ in range(num_repeats)]
    
    for i in range(num_warmups):
        runtime.driver.active.clear_cache(cache)
        fn(A, B)
    torch.cuda.synchronize()

    for i in range(num_repeats):
        runtime.driver.active.clear_cache(cache)
        start_events[i].record()
        fn(A, B)
        end_events[i].record()
    torch.cuda.synchronize()

    elapsed_times = [start.elapsed_time(end) for start, end in zip(start_events, end_events)]
    mean_time = sum(elapsed_times) / len(elapsed_times)
    quantile_times = quantile(elapsed_times, [0.5, 0.2, 0.8])

    print(f"Benchmarking {fn.__class__.__name__}")
    print(f"Mean time: {mean_time:.3f} ms")
    print(f"Quantiles (0.5, 0.2, 0.8): {quantile_times}")
    print()

    return mean_time


normal_time = benchmark(model_normal, *inputs)
with_out_time = benchmark(model_with_out, *inputs)
theirs_optimized_time = benchmark(model_theirs_optimized, *inputs)

with_out_speedup = normal_time / with_out_time
theirs_speedup = normal_time / theirs_optimized_time
print(f"Speedup with out= {with_out_speedup:.2f}x")
print(f"Speedup with theirs optimized= {theirs_speedup:.2f}x")

Line 157 is incorrect: should be transfer stream waits for default stream