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12_Gemm_Multiply_LeakyReLU • 12_gemm_warp_primitives_base

Level 2 • Task 12
import torch
import torch.nn as nn
import torch.nn.functional as F


def module_fn(
    x: torch.Tensor,
    multiplier: float,
    negative_slope: float,
    weight: torch.Tensor,
    bias: torch.Tensor,
) -> torch.Tensor:
    """
    Applies linear transformation, multiplies by scalar, and applies LeakyReLU.

    Args:
        x (torch.Tensor): Input tensor of shape (batch_size, in_features)
        multiplier (float): Scalar multiplier
        negative_slope (float): Negative slope for LeakyReLU
        weight (torch.Tensor): Weight matrix of shape (out_features, in_features)
        bias (torch.Tensor): Bias vector of shape (out_features)

    Returns:
        torch.Tensor: Output tensor of shape (batch_size, out_features)
    """
    x = F.linear(x, weight, bias)
    x = x * multiplier
    x = F.leaky_relu(x, negative_slope=negative_slope)
    return x


class Model(nn.Module):
    """
    Simple model that performs a Gemm, multiplies the result, and applies LeakyReLU.
    """

    def __init__(self, in_features, out_features, multiplier, negative_slope):
        super(Model, self).__init__()
        gemm = nn.Linear(in_features, out_features)
        self.weight = gemm.weight
        self.bias = gemm.bias

    def forward(self, x, fn=module_fn):
        return fn(x, multiplier, negative_slope, self.weight, self.bias)


batch_size = 128
in_features = 1024
out_features = 512
multiplier = 2.0
negative_slope = 0.1


def get_inputs():
    return [torch.randn(batch_size, in_features)]


def get_init_inputs():
    return [in_features, out_features, multiplier, negative_slope]

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Simple model that performs a Gemm, multiplies the result, and applies LeakyReLU.
    """
    def __init__(self, in_features, out_features, multiplier, negative_slope):
        super(Model, self).__init__()
        self.gemm = nn.Linear(in_features, out_features)
        self.multiplier = multiplier
        self.leaky_relu = nn.LeakyReLU(negative_slope)

    def forward(self, x):
        x = self.gemm(x)
        x = x * self.multiplier
        x = self.leaky_relu(x)
        return x

batch_size = 128
in_features = 1024
out_features = 512
multiplier = 2.0
negative_slope = 0.1

def get_inputs():
    return [torch.randn(batch_size, in_features)]

def get_init_inputs():
    return [in_features, out_features, multiplier, negative_slope]
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Kernel Information

Operation Name 12_Gemm_Multiply_LeakyReLU
Level ID 2
Task ID 12
Kernel Name 12_gemm_warp_primitives_base
CUDA Speedup (Native) 1.296x
CUDA Speedup (Compile) 2.324x
CUDA Runtime 0.032 ms
PyTorch Runtime (Native) 0.041 ms
PyTorch Runtime (Compile) 0.074 ms
Correct True
Max Diff (vs. Reference) 0.000000
Model bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0
Temperature 1.00
View Experiment Progress Details

Related Kernels (Level 2, Task 12 • 12_Gemm_Multiply_LeakyReLU)

Rank Kernel Name Runtime (ms) Speedup Native Speedup Compile
🥇 12_gemm_warp_primitives_base 0.03 1.30 2.32
🥈 12_gemm_ldg_optimization_base 0.04 1.18 2.12
🥈 12_gemm_warp_vec4_edit_1_base_edit_1 0.04 1.18 2.12
🥈 12_gemm_ldg_optimization_edit_1 0.04 1.18 2.12
5 12_gemm_warp_vec4_edit_1_base_base 0.04 1.04 1.86
6 gemm_tiled_grid_edit_1 0.05 0.83 1.49
7 12_gemm_constant_memory_edit_1 0.05 0.80 1.43
8 gemm_unrolled_base 0.05 0.77 1.38
8 gemm_unrolled_edit_1 0.05 0.77 1.38
8 12_gemm_constant_memory_base 0.05 0.77 1.38
11 gemm_tiled_grid_block_32_base 0.07 0.60 1.08
12 gemm_tiled_grid_base 0.07 0.59 1.06
12 gemm_tiled_shared_base 0.07 0.59 1.06
12 gemm_tiled_grid_block_32_edit_1 0.07 0.59 1.06
15 12_gemm_tiled_coalesced_edit_1 0.07 0.58 1.05
16 gemm_tiled_streamed_base 0.07 0.56 1.00
17 optimized_gemm_leakyrelu_base 0.07 0.55 0.99
17 atomic_optimization_gemm_edit_1 0.07 0.55 0.99
17 optimized_gemm_leakyrelu_edit_1 0.07 0.55 0.99
20 optimized_thread_block_indexing_gemm_base 0.08 0.51 0.91 
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>

__device__ __inline__ float warp_reduce_sum(float val) {
    for (int offset = 16; offset > 0; offset /= 2) {
        val += __shfl_down_sync(0xffffffff, val, offset);
    }
    return val;
}

__global__ void module_fn_kernel(
    const float* __restrict__ x,
    const float* __restrict__ weight,
    const float* __restrict__ bias,
    float* __restrict__ output,
    const int batch_size,
    const int in_features,
    const int out_features,
    const float multiplier,
    const float negative_slope
) {
    const int row = blockIdx.x;
    const int col = blockIdx.y * blockDim.y + threadIdx.y;
    const int lane_id = threadIdx.x;
    
    if (row >= batch_size || col >= out_features) return;

    const float* x_row = x + row * in_features;
    const float* weight_col = weight + col * in_features;
    
    float thread_sum = 0.0f;
    for (int k = lane_id; k < in_features; k += 32) {
        thread_sum += x_row[k] * weight_col[k];
    }
    
    float sum = warp_reduce_sum(thread_sum);
    
    if (lane_id == 0) {
        sum += bias[col];
        sum *= multiplier;
        output[row * out_features + col] = sum > 0 ? sum : sum * negative_slope;
    }
}

torch::Tensor module_fn_forward(
    torch::Tensor x,
    float multiplier,
    float negative_slope,
    torch::Tensor weight,
    torch::Tensor bias
) {
    TORCH_CHECK(x.device().is_cuda(), "x must be a CUDA tensor");
    TORCH_CHECK(weight.device().is_cuda(), "weight must be a CUDA tensor");
    TORCH_CHECK(bias.device().is_cuda(), "bias must be a CUDA tensor");

    const int batch_size = x.size(0);
    const int in_features = x.size(1);
    const int out_features = weight.size(0);

    TORCH_CHECK(weight.size(1) == in_features, "Weight in_features must match x in_features");
    TORCH_CHECK(bias.size(0) == out_features, "Bias size must match weight out_features");

    auto output = torch::zeros({batch_size, out_features}, x.options());

    dim3 block(32, 16);
    dim3 grid(
        batch_size,
        (out_features + block.y - 1) / block.y
    );

    module_fn_kernel<<<grid, block>>>(
        x.data_ptr<float>(),
        weight.data_ptr<float>(),
        bias.data_ptr<float>(),
        output.data_ptr<float>(),
        batch_size,
        in_features,
        out_features,
        multiplier,
        negative_slope
    );

    return output;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &module_fn_forward, "Module function forward CUDA with warp primitives");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 2.274 inst/cycle 0.000 5
Executed Ipc Elapsed 2.078 inst/cycle 0.000 5
Issue Slots Busy 56.968 % 0.022 5
Issued Ipc Active 2.278 inst/cycle 0.000 5
SM Busy 56.968 % 0.022 5
Memory Throughput 85160445830.716 byte/second 246532052853514400.000 5
Mem Busy 77.578 % 0.088 5
Max Bandwidth 75.448 % 0.091 5
L1/TEX Hit Rate 53.290 % 0.034 5
L2 Hit Rate 96.776 % 1.255 5
Mem Pipes Busy 75.448 % 0.091 5
Warp Cycles Per Issued Instruction 25.522 cycle 0.019 5
Warp Cycles Per Executed Instruction 25.560 cycle 0.019 5
Avg. Active Threads Per Warp 29.690 0.000 5
Avg. Not Predicated Off Threads Per Warp 28.880 0.000 5
Max Active Clusters 0.000 cluster 0.000 5
Max Cluster Size 8.000 block 0.000 5
Overall GPU Occupancy 0.000 % 0.000 5
Cluster Occupancy 0.000 % 0.000 5
Block Limit SM 32.000 block 0.000 5
Block Limit Registers 4.000 block 0.000 5
Block Limit Shared Mem 16.000 block 0.000 5
Block Limit Warps 4.000 block 0.000 5
Theoretical Active Warps per SM 64.000 warp 0.000 5
Theoretical Occupancy 100.000 % 0.000 5
Achieved Occupancy 90.940 % 0.019 5
Achieved Active Warps Per SM 58.198 warp 0.008 5
Analysis Rules
Rule Description
INF HighPipeUtilization ALU is the highest-utilized pipeline (22.6%) based on active cycles, taking into account the rates of its different instructions. It executes integer and logic operations. It is well-utilized, but should not be a bottleneck.
INF CPIStall Check the Warp Stall Sampling (All Cycles) table for the top stall locations in your source based on sampling data. The Kernel Profiling Guide (https://docs.nvidia.com/nsight-compute/ProfilingGuide/index.html#metrics-reference) provides more details on each stall reason.
INF Occupancy This kernel's theoretical occupancy is not impacted by any block limit.
Operation / Metric Value Unit
aten::to
CPU Time 427551.51 μs
Device Time 201.92 μs
Self CPU Time 64.12 μs
Self Device Time 0.00 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
aten::zeros
CPU Time 5527234.80 μs
Device Time 111875.26 μs
Self CPU Time 128742.20 μs
Self Device Time 0.00 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
aten::zero_
CPU Time 6689145.67 μs
Device Time 6241978.99 μs
Self CPU Time 252220.72 μs
Self Device Time 0.00 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
aten::fill_
CPU Time 6436927.04 μs
Device Time 6241978.99 μs
Self CPU Time 348120.83 μs
Self Device Time 6241978.99 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
cudaLaunchKernel
CPU Time 6378798.01 μs
Device Time 271994.24 μs
Self CPU Time 6378798.01 μs
Self Device Time 271994.24 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
module_fn_kernel(float const*, float const*, float const*, float*, int, int, int, float, float)
CPU Time 0.00 μs
Device Time 2106335.81 μs
Self CPU Time 0.00 μs
Self Device Time 2106335.81 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor<int>, at::detail::Array<char*, 1> >(int, at::native::FillFunctor<int>, at::detail::Array<char*, 1>)
CPU Time 0.00 μs
Device Time 6130103.74 μs
Self CPU Time 0.00 μs
Self Device Time 6130103.74 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
Status: Completed 
45292 warnings generated when compiling for host.
Suppressed 45325 warnings (45278 in non-user code, 47 NOLINT).
Use -header-filter=.* to display errors from all non-system headers. Use -system-headers to display errors from system headers as well.
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_12/b3_s1_12_gemm_warp_primitives/base/base.cu:13:5 bugprone-easily-swappable-parameters
13 | const float* __restrict__ x,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~
14 | const float* __restrict__ weight,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
15 | const float* __restrict__ bias,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_12/b3_s1_12_gemm_warp_primitives/base/base.cu:13:31: note: the first parameter in the range is 'x'
13 | const float* __restrict__ x,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_12/b3_s1_12_gemm_warp_primitives/base/base.cu:15:31: note: the last parameter in the range is 'bias'
15 | const float* __restrict__ bias,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_12/b3_s1_12_gemm_warp_primitives/base/base.cu:17:5: warning: 2 adjacent parameters of 'module_fn_kernel' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
17 | const int batch_size,
| ^~~~~~~~~~~~~~~~~~~~~
18 | const int in_features,
| ~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_12/b3_s1_12_gemm_warp_primitives/base/base.cu:17:15: note: the first parameter in the range is 'batch_size'
17 | const int batch_size,
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_12/b3_s1_12_gemm_warp_primitives/base/base.cu:18:15: note: the last parameter in the range is 'in_features'
18 | const int in_features,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_12/b3_s1_12_gemm_warp_primitives/base/base.cu:19:5: warning: 2 adjacent parameters of 'module_fn_kernel' of convertible types are easily swapped by mistake [bugprone-easily-swappable-parameters]
19 | const int out_features,
| ^~~~~~~~~~~~~~~~~~~~~~~
20 | const float multiplier,
| ~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_12/b3_s1_12_gemm_warp_primitives/base/base.cu:19:15: note: the first parameter in the range is 'out_features'
19 | const int out_features,
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_12/b3_s1_12_gemm_warp_primitives/base/base.cu:20:17: note: the last parameter in the range is 'multiplier'
20 | const float multiplier,
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_12/b3_s1_12_gemm_warp_primitives/base/base.cu:20:5: note: 'const int' and 'const float' may be implicitly converted: 'const int' (as 'int') -> 'const float' (as 'float'), 'const float' (as 'float') -> 'const int' (as 'int')
20 | const float multiplier,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_12/b3_s1_12_gemm_warp_primitives/base/base.cu:23:21: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
23 | const int row = blockIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_12/b3_s1_12_gemm_warp_primitives/base/base.cu:24:21: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
24 | const int col = blockIdx.y * blockDim.y + threadIdx.y;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_12/b3_s1_12_gemm_warp_primitives/base/base.cu:25:25: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
25 | const int lane_id = threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_12/b3_s1_12_gemm_warp_primitives/base/base.cu:29:26: warning: result of multiplication in type 'int' is used as a pointer offset after an implicit widening conversion to type 'ptrdiff_t' [bugprone-implicit-widening-of-multiplication-result]
29 | const float* x_row = x + row * in_features;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_12/b3_s1_12_gemm_warp_primitives/base/base.cu:29:30: note: make conversion explicit to silence this warning
4 | const float* x_row = x + row * in_features;
| ^~~~~~~~~~~~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_12/b3_s1_12_gemm_warp_primitives/base/base.cu:29:30: note: perform multiplication in a wider type
29 | const float* x_row = x + row * in_features;
| ^~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_12/b3_s1_12_gemm_warp_primitives/base/base.cu:30:31: warning: result of multiplication in type 'int' is used as a pointer offset after an implicit widening conversion to type 'ptrdiff_t' [bugprone-implicit-widening-of-multiplication-result]
30 | const float* weight_col = weight + col * in_features;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_12/b3_s1_12_gemm_warp_primitives/base/base.cu:30:40: note: make conversion explicit to silence this warning
30 | const float* weight_col = weight + col * in_features;
| ^~~~~~~~~~~~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_12/b3_s1_12_gemm_warp_primitives/base/base.cu:30:40: note: perform multiplication in a wider type
30 | const float* weight_col = weight + col * in_features;
| ^~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_12/b3_s1_12_gemm_warp_primitives/base/base.cu:47:19: warning: the parameter 'x' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
47 | torch::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_12/b3_s1_12_gemm_warp_primitives/base/base.cu:50:19: warning: the parameter 'weight' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
50 | torch::Tensor weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_12/b3_s1_12_gemm_warp_primitives/base/base.cu:51:19: warning: the parameter 'bias' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
51 | torch::Tensor bias
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_12/b3_s1_12_gemm_warp_primitives/base/base.cu:57:28: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
57 | const int batch_size = x.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_12/b3_s1_12_gemm_warp_primitives/base/base.cu:58:29: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
58 | const int in_features = x.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_12/b3_s1_12_gemm_warp_primitives/base/base.cu:59:30: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
59 | const int out_features = weight.size(0);
| ^