L1 · problem 93
gpt-5.5-priority · original · l12_all_gpt
← all variants← original← gpt-5.5-prioritygenerated 2026-05-28 03:33:15 UTC
error 93_masked_cumsum.py
model
gpt-5.5-priority
turns / max
15 / 15
tool calls
15
tokens (total)
0
wall clock
—
truncated
no
backend
cuda fp32
variant
original
runtime
—
ref runtime
0.00 μs
speedup
—
started
2026-05-04T04:09:37Z
reference PyTorch source (the prompt the agent sees)
import torch
import torch.nn as nn

class Model(nn.Module):
    """
    A model that performs a masked cumulative sum, only summing elements that satisfy a condition.

    Parameters:
        dim (int): The dimension along which to perform the masked cumulative sum.
    """

    def __init__(self, dim):
        super(Model, self).__init__()
        self.dim = dim

    def forward(self, x, mask):
        """
        Args:
            x (torch.Tensor): Input tensor of shape (batch_size, *input_shape).
            mask (torch.Tensor): Boolean mask of the same shape as x.

        Returns:
            torch.Tensor: Cumulative sum of elements where mask is True.
        """
        return torch.cumsum(x * mask, dim=self.dim)

batch_size = 32768
input_shape = (32768,)
dim = 1

def get_inputs():
    x = torch.rand(batch_size, *input_shape)
    mask = torch.randint(0, 2, x.shape).bool()  # Random boolean mask
    return [x, mask]

def get_init_inputs():
    return [dim]
turn 019.5s turn · 19.5s LLM · 0.0s tools
reasoning
**Optimizing masked cumsum** I'm considering how to craft an optimized masked cumulative sum over dimensions, managing a huge dataset with around 1 billion elements. The challenge is to create a method that's faster than the existing PyTorch implementation that uses a multiplication followed by a cumulative sum. I need to ensure that the numerical equivalence adheres to a tolerance of 1e-4, while maintaining correctness. Additionally, the method must be efficient as I anticipate significant memory bandwidth usage. Implementing an efficient per-row solution for this size is crucial. **Exploring block scan options** I'm figuring out the best way to implement a block scan across 32,768 elements using 1,024 threads, where each thread handles 32 elements. I need to consider using CUB's BlockScan functionality, which should help with managing row elements. The goal is to ensure that loading and accessing data is efficient, possibly through coalesced memory access. However, if I use striped loading, I could run into non-coalesced issues. I’ll need to consider how to handle this for optimal performance. **Examining CUB BlockScan capabilities** I'm looking into whether CUB BlockScan supports an arrangement based on ITEMS_PER_THREAD in a blocked order. It seems like it scans items in thread order, so for example, it would process items in this sequence: [thread0 item0 to item31, then thread1, and so on] if I set up the arrays this way. I want to ensure that I'm utilizing this functionality correctly for optimal performance in my implementation. **Analyzing thread performance** I'm considering the performance impact of a setup with 1024 threads, where the block scan cost is quite high. Each streaming multiprocessor (SM) can handle a maximum of one block, which consists of 32 warps. Given that there are 32 items, the total number of elements is 1 billion. I'm trying to weigh whether the current configuration is sufficient for the operations needed. It's a bit complex, so I need to think through the efficiency here.
call → get_gpu_specs
callget_gpu_specs {}
result ← get_gpu_specs (ok)
okget_gpu_specs GPU specs for NVIDIA H100 80GB HBM3: total memory (runtime): 79.1 GB spec entry: H100 GPU Architecture: Hopper GPU Memory: 80GB Memory Bandwidth: 3.35 TB/s FP64 TFLOPS: 34 FP64 Tensor Core TFLOPS: 67 FP32 TFLOPS: 67 TF32 Tensor Core TFLOPS: 989 with sparsity BFLOAT16 Tensore Core TFLOPS: 1979 with sparsity FP16 Tensor Core TFLOPS: 1979 with sparsity FP8 Tensor Core TFLOPS: 3958 with sparsity INT8 Tensor Core TOPS: 3958 with sparsity Register File Size: 64K 32-bit registers per SM Maximum number of registers per thread: 255 Maximum number of thread blocks per SM: 32 Shared memory capacity per SM: 228 KB Maximum shared memory per thread block: 227 KB
turn 1 · 1 tool call(s) · 8.2s turn · 8.2s LLM · 6,881 chars (click to expand)
turn 18.2s turn · 8.2s LLM · 0.0s tools
reasoning
**Implementing CUB compiler** I’m thinking about implementing CUB with inclusion, and I might need to include `load_inline`. It seems I need to add some extra flags like `-O3` for optimization. My idea is that the class constructor will store dimensions and the forward method will call the extension function, possibly checking dimensions. I wonder if I should handle cases where the dimension isn’t 1, but maybe I could just fallback to that. I should compile soon!
call → compile_kernel
callcompile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncpp_src = r'''\n#include <torch/extension.h>\n\ntorch::Tensor masked_cumsum_forward(torch::Tensor x, torch::Tensor mask);\n'''\n\ncuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <cub/cub.cuh>\n\nconstexpr int BLOCK_THREADS = 1024;\nconstexpr int ITEMS_PER_THREAD = 32;\nconstexpr int TILE = BLOCK_THREADS * ITEMS_PER_THREAD;\n\n__global__ __launch_bounds__(BLOCK_THREADS, 1)\nvoid masked_cumsum_kernel(const float* __restrict__ x,\n const unsigned char* __restrict__ mask,\n float* __restrict__ out,\n int64_t rows) {\n int row = blockIdx.x;\n if (row >= rows) return;\n int64_t base = (int64_t)row * TILE;\n\n float vals[ITEMS_PER_THREAD];\n unsigned char mvals[ITEMS_PER_THREAD];\n\n using LoadFloat = cub::BlockLoad<float, BLOCK_THREADS, ITEMS_PER_THREAD, cub::BLOCK_LOAD_WARP_TRANSPOSE>;\n using LoadByte = cub::BlockLoad<unsigned char, BLOCK_THREADS, ITEMS_PER_THREAD, cub::BLOCK_LOAD_WARP_TRANSPOSE>;\n using Scan = cub::BlockScan<float, BLOCK_THREADS>;\n using StoreFloat = cub::BlockStore<float, BLOCK_THREADS, ITEMS_PER_THREAD, cub::BLOCK_STORE_WARP_TRANSPOSE>;\n\n __shared__ typename LoadFloat::TempStorage load_x_storage;\n __shared__ typename LoadByte::TempStorage load_m_storage;\n __shared__ typename Scan::TempStorage scan_storage;\n __shared__ typename StoreFloat::TempStorage store_storage;\n\n LoadFloat(load_x_storage).Load(x + base, vals);\n __syncthreads();\n LoadByte(load_m_storage).Load(mask + base, mvals);\n __syncthreads();\n\n #pragma unroll\n for (int i = 0; i < ITEMS_PER_THREAD; ++i) {\n vals[i] = mvals[i] ? vals[i] : 0.0f;\n }\n __syncthreads();\n Scan(scan_storage).InclusiveSum(vals, vals);\n __syncthreads();\n StoreFloat(store_storage).Store(out + base, vals);\n}\n\ntorch::Tensor masked_cumsum_forward(torch::Tensor x, torch::Tensor mask) {\n auto out = torch::empty_like(x);\n int64_t rows = x.numel() / TILE;\n dim3 grid(rows);\n dim3 block(BLOCK_THREADS);\n masked_cumsum_kernel<<<grid, block, 0, at::cuda::getCurrentCUDAStream()>>>(\n x.data_ptr<float>(), reinterpret_cast<const unsigned char*>(mask.data_ptr<bool>()), out.data_ptr<float>(), rows);\n return out;\n}\n'''\n\n_ext = load_inline(name='masked_cumsum_ext_v1', cpp_sources=cpp_src, cuda_sources=cuda_src, functions=['masked_cumsum_forward'], extra_cuda_cflags=['-O3'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, dim):\n super(ModelNew, self).__init__()\n self.dim = dim\n def forward(self, x, mask):\n return _ext.masked_cumsum_forward(x, mask)\n" }
result ← compile_kernel (fail)
failcompile_kernel compile_kernel FAILED: RuntimeError. RuntimeError: Error building extension 'masked_cumsum_ext_v1': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=masked_cumsum_ext_v1 -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -fPIC -std=c++17 -c /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_93_cache/k_672ece4513a7/masked_cumsum_ext_v1/main.cpp -o main.o [2/3] /usr/local/cuda/bin/nvcc -MD -MF cuda.cuda.o.d -ccbin /opt/conda/bin/x86_64-conda-linux-gnu-cc -DTORCH_EXTENSION_NAME=masked_cumsum_ext_v1 -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr -gencode=arch=compute_90,code=compute_90 -gencode=arch=compute_90,code=sm_90 --compiler-options '-fPIC' -O3 -std=c++17 -c /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_93_cache/k_672ece4513a7/masked_cumsum_ext_v1/cuda.cu -o cuda.cuda.o FAILED: [code=255] cuda.cuda.o /usr/local/cuda/bin/nvcc -MD -MF cuda.cuda.o.d -ccbin /opt/conda/bin/x86_64-conda-linux-gnu-cc -DTORCH_EXTENSION_NAME=masked_cumsum_ext_v1 -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr -gencode=arch=compute_90,code=compute_90 -gencode=arch=compute_90,code=sm_90 --compiler-options '-fPIC' -O3 -std=c++17 -c /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_93_cache/k_672ece4513a7/masked_cumsum_ext_v1/cuda.cu -o cuda.cuda.o nvcc warning : incompatible redefinition for option 'compiler-bindir', the last value of this option was used ptxas error : Entry function '_Z20masked_cumsum_kernelPKfPKhPfl' uses too much shared data (0x4b4b0 bytes, 0xc000 max) ninja: build stopped: subcommand failed.
turn 221.0s turn · 21.0s LLM · 0.0s tools
call → compile_kernel
callcompile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor masked_cumsum_forward(torch::Tensor x, torch::Tensor mask);\n'''\n\ncuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\nconstexpr int BLOCK_THREADS = 1024;\nconstexpr int COLS = 32768;\nconstexpr int TILES = COLS / BLOCK_THREADS;\n\n__device__ __forceinline__ float warp_inclusive_scan(float v) {\n unsigned mask = 0xffffffffu;\n #pragma unroll\n for (int offset = 1; offset < 32; offset <<= 1) {\n float n = __shfl_up_sync(mask, v, offset);\n if ((threadIdx.x & 31) >= offset) v += n;\n }\n return v;\n}\n\n__global__ __launch_bounds__(BLOCK_THREADS, 1)\nvoid masked_cumsum_kernel(const float* __restrict__ x,\n const unsigned char* __restrict__ mask,\n float* __restrict__ out,\n int rows) {\n int row = blockIdx.x;\n if (row >= rows) return;\n int tid = threadIdx.x;\n int lane = tid & 31;\n int warp = tid >> 5;\n __shared__ float warp_sums[32];\n int64_t base = (int64_t)row * COLS;\n float carry = 0.0f;\n\n #pragma unroll\n for (int t = 0; t < TILES; ++t) {\n int64_t idx = base + t * BLOCK_THREADS + tid;\n float v = mask[idx] ? x[idx] : 0.0f;\n float s = warp_inclusive_scan(v);\n if (lane == 31) warp_sums[warp] = s;\n __syncthreads();\n if (warp == 0) {\n float w = warp_sums[lane];\n float ws = warp_inclusive_scan(w);\n warp_sums[lane] = ws;\n }\n __syncthreads();\n if (warp != 0) s += warp_sums[warp - 1];\n out[idx] = s + carry;\n float total = warp_sums[31];\n carry += total;\n __syncthreads();\n }\n}\n\ntorch::Tensor masked_cumsum_forward(torch::Tensor x, torch::Tensor mask) {\n auto out = torch::empty_like(x);\n int rows = (int)(x.numel() / COLS);\n masked_cumsum_kernel<<<rows, BLOCK_THREADS, 0, at::cuda::getCurrentCUDAStream()>>>(\n x.data_ptr<float>(), reinterpret_cast<const unsigned char*>(mask.data_ptr<bool>()), out.data_ptr<float>(), rows);\n return out;\n}\n'''\n\n_ext = load_inline(name='masked_cumsum_ext_v2', cpp_sources=cpp_src, cuda_sources=cuda_src, functions=['masked_cumsum_forward'], extra_cuda_cflags=['-O3'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, dim):\n super(ModelNew, self).__init__()\n self.dim = dim\n def forward(self, x, mask):\n return _ext.masked_cumsum_forward(x, mask)\n" }
result ← compile_kernel (ok)
okcompile_kernel compile_kernel PASSED: kernel compiled without errors.
turn 35.5s turn · 5.5s LLM · 0.0s tools
call → run_correctness
callrun_correctness { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor masked_cumsum_forward(torch::Tensor x, torch::Tensor mask);\n'''\n\ncuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\nconstexpr int BLOCK_THREADS = 1024;\nconstexpr int COLS = 32768;\nconstexpr int TILES = COLS / BLOCK_THREADS;\n\n__device__ __forceinline__ float warp_inclusive_scan(float v) {\n unsigned mask = 0xffffffffu;\n #pragma unroll\n for (int offset = 1; offset < 32; offset <<= 1) {\n float n = __shfl_up_sync(mask, v, offset);\n if ((threadIdx.x & 31) >= offset) v += n;\n }\n return v;\n}\n\n__global__ __launch_bounds__(BLOCK_THREADS, 1)\nvoid masked_cumsum_kernel(const float* __restrict__ x,\n const unsigned char* __restrict__ mask,\n float* __restrict__ out,\n int rows) {\n int row = blockIdx.x;\n if (row >= rows) return;\n int tid = threadIdx.x;\n int lane = tid & 31;\n int warp = tid >> 5;\n __shared__ float warp_sums[32];\n int64_t base = (int64_t)row * COLS;\n float carry = 0.0f;\n\n #pragma unroll\n for (int t = 0; t < TILES; ++t) {\n int64_t idx = base + t * BLOCK_THREADS + tid;\n float v = mask[idx] ? x[idx] : 0.0f;\n float s = warp_inclusive_scan(v);\n if (lane == 31) warp_sums[warp] = s;\n __syncthreads();\n if (warp == 0) {\n float w = warp_sums[lane];\n float ws = warp_inclusive_scan(w);\n warp_sums[lane] = ws;\n }\n __syncthreads();\n if (warp != 0) s += warp_sums[warp - 1];\n out[idx] = s + carry;\n float total = warp_sums[31];\n carry += total;\n __syncthreads();\n }\n}\n\ntorch::Tensor masked_cumsum_forward(torch::Tensor x, torch::Tensor mask) {\n auto out = torch::empty_like(x);\n int rows = (int)(x.numel() / COLS);\n masked_cumsum_kernel<<<rows, BLOCK_THREADS, 0, at::cuda::getCurrentCUDAStream()>>>(\n x.data_ptr<float>(), reinterpret_cast<const unsigned char*>(mask.data_ptr<bool>()), out.data_ptr<float>(), rows);\n return out;\n}\n'''\n\n_ext = load_inline(name='masked_cumsum_ext_v2c', cpp_sources=cpp_src, cuda_sources=cuda_src, functions=['masked_cumsum_forward'], extra_cuda_cflags=['-O3'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, dim):\n super(ModelNew, self).__init__()\n self.dim = dim\n def forward(self, x, mask):\n return _ext.masked_cumsum_forward(x, mask)\n" }
result ← run_correctness (fail)
failrun_correctness run_correctness FAILED: ? trials did not all match. runtime_error: expected scalar type Bool but found Float
turn 413.2s turn · 13.2s LLM · 0.0s tools
call → run_correctness
callrun_correctness { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor masked_cumsum_forward(torch::Tensor x, torch::Tensor mask);\n'''\n\ncuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\nconstexpr int BLOCK_THREADS = 1024;\nconstexpr int COLS = 32768;\nconstexpr int TILES = COLS / BLOCK_THREADS;\n\n__device__ __forceinline__ float warp_inclusive_scan(float v) {\n unsigned mask = 0xffffffffu;\n #pragma unroll\n for (int offset = 1; offset < 32; offset <<= 1) {\n float n = __shfl_up_sync(mask, v, offset);\n if ((threadIdx.x & 31) >= offset) v += n;\n }\n return v;\n}\n\n__global__ __launch_bounds__(BLOCK_THREADS, 1)\nvoid masked_cumsum_kernel(const float* __restrict__ x,\n const unsigned char* __restrict__ mask,\n float* __restrict__ out,\n int rows) {\n int row = blockIdx.x;\n int tid = threadIdx.x;\n int lane = tid & 31;\n int warp = tid >> 5;\n __shared__ float warp_sums[32];\n int64_t base = (int64_t)row * COLS;\n float carry = 0.0f;\n #pragma unroll\n for (int t = 0; t < TILES; ++t) {\n int64_t idx = base + t * BLOCK_THREADS + tid;\n float v = mask[idx] ? x[idx] : 0.0f;\n float s = warp_inclusive_scan(v);\n if (lane == 31) warp_sums[warp] = s;\n __syncthreads();\n if (warp == 0) {\n float w = warp_sums[lane];\n float ws = warp_inclusive_scan(w);\n warp_sums[lane] = ws;\n }\n __syncthreads();\n if (warp != 0) s += warp_sums[warp - 1];\n out[idx] = s + carry;\n carry += warp_sums[31];\n __syncthreads();\n }\n}\n\ntorch::Tensor masked_cumsum_forward(torch::Tensor x, torch::Tensor mask) {\n auto out = torch::empty_like(x);\n int rows = (int)(x.numel() / COLS);\n const unsigned char* mp = static_cast<const unsigned char*>(mask.data_ptr());\n masked_cumsum_kernel<<<rows, BLOCK_THREADS, 0, at::cuda::getCurrentCUDAStream()>>>(\n x.data_ptr<float>(), mp, out.data_ptr<float>(), rows);\n return out;\n}\n'''\n\n_ext = load_inline(name='masked_cumsum_ext_v3', cpp_sources=cpp_src, cuda_sources=cuda_src, functions=['masked_cumsum_forward'], extra_cuda_cflags=['-O3'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, dim):\n super(ModelNew, self).__init__()\n self.dim = dim\n def forward(self, x, mask):\n return _ext.masked_cumsum_forward(x, mask)\n" }
result ← run_correctness (fail)
failrun_correctness run_correctness FAILED: (0 / 5) trials did not all match. correctness_issue: Output mismatch max_difference: ['4390.419922', '4415.218750', '4454.874512', '4430.512695', '4424.504395'] avg_difference: ['2048.065430', '2048.068848', '2048.586670', '2048.410156', '2048.246826']
turn 536.1s turn · 36.1s LLM · 0.0s tools
reasoning
**Verifying PyTorch C++ Behavior** I’m trying to verify whether bool maps to c10::ScalarType::Bool in PyTorch C++. It seems that data_ptr<bool>() should expect Bool, but I wonder if it’s actually mapping correctly. I might be facing issues with the parameter order or possible name collisions from previous compilations. There’s also a chance of mismatches between mask tensor types—if it’s float when it should be bool, that could lead to problems. Let’s explore these areas further! **Handling Float Masks in Computation** I’m considering how to handle the case when the mask is a float, even though the task specifies it should be a bool. It seems like the tool might be treating the mask as x, leading to a possible correctness issue. If I'm multiplying x by a random float mask, the results could yield all true values with a significant average. The cumulative sum of this product looks to have an average discrepancy. I really need to clarify this behavior! **Considering function parsing** I’m contemplating whether it might parse functions in the list. But instead of relying on that, I think it’s better to include a wrapper in the cpp_sources instead of just in the CUDA files. Generally, I define the CUDA launch function within cpp_sources, which then calls the specific CUDA function in the CUDA files. This way, it feels more structured and reduces potential dependencies. It’s all about making it clearer! **Determining tile logic** I'm considering the logic for handling each tile in a loop: if the index is less than the row end, I should load it; otherwise, store it as 0 if it's valid. I think carrying continues for any column length, which seems good. I'm also hardcoding a limit of 1024 for most cases and 32768 for very large ones. I wonder about needing to implement this in a way that maintains runtime without unrolling unless it hits max.
call → run_correctness
callrun_correctness { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor masked_cumsum_forward(torch::Tensor x, torch::Tensor mask);\n'''\n\ncuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\nconstexpr int BLOCK_THREADS = 1024;\n\n__device__ __forceinline__ float warp_inclusive_scan(float v) {\n unsigned m = 0xffffffffu;\n #pragma unroll\n for (int offset = 1; offset < 32; offset <<= 1) {\n float n = __shfl_up_sync(m, v, offset);\n if ((threadIdx.x & 31) >= offset) v += n;\n }\n return v;\n}\n\n__global__ __launch_bounds__(BLOCK_THREADS, 1)\nvoid masked_cumsum_kernel(const float* __restrict__ x,\n const unsigned char* __restrict__ mask,\n float* __restrict__ out,\n int rows, int cols) {\n int row = blockIdx.x;\n int tid = threadIdx.x;\n int lane = tid & 31;\n int warp = tid >> 5;\n __shared__ float warp_sums[32];\n int64_t base = (int64_t)row * cols;\n float carry = 0.0f;\n int tiles = (cols + BLOCK_THREADS - 1) / BLOCK_THREADS;\n for (int t = 0; t < tiles; ++t) {\n int col = t * BLOCK_THREADS + tid;\n int64_t idx = base + col;\n float v = 0.0f;\n if (col < cols) v = mask[idx] ? x[idx] : 0.0f;\n float s = warp_inclusive_scan(v);\n if (lane == 31) warp_sums[warp] = s;\n __syncthreads();\n if (warp == 0) {\n float w = warp_sums[lane];\n float ws = warp_inclusive_scan(w);\n warp_sums[lane] = ws;\n }\n __syncthreads();\n if (warp != 0) s += warp_sums[warp - 1];\n if (col < cols) out[idx] = s + carry;\n carry += warp_sums[31];\n __syncthreads();\n }\n}\n\ntorch::Tensor masked_cumsum_forward(torch::Tensor x, torch::Tensor mask) {\n auto out = torch::empty_like(x);\n int rows = (int)x.size(0);\n int cols = (int)x.size(1);\n const unsigned char* mp = static_cast<const unsigned char*>(mask.data_ptr());\n masked_cumsum_kernel<<<rows, BLOCK_THREADS, 0, at::cuda::getCurrentCUDAStream()>>>(\n x.data_ptr<float>(), mp, out.data_ptr<float>(), rows, cols);\n return out;\n}\n'''\n\n_ext = load_inline(name='masked_cumsum_ext_v4', cpp_sources=cpp_src, cuda_sources=cuda_src, functions=['masked_cumsum_forward'], extra_cuda_cflags=['-O3'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, dim):\n super(ModelNew, self).__init__()\n self.dim = dim\n def forward(self, x, mask):\n return _ext.masked_cumsum_forward(x, mask)\n" }
result ← run_correctness (fail)
failrun_correctness run_correctness FAILED: (0 / 5) trials did not all match. correctness_issue: Output mismatch max_difference: ['4390.419922', '4415.218750', '4454.874512', '4430.512695', '4424.504395'] avg_difference: ['2048.065430', '2048.068848', '2048.586670', '2048.410156', '2048.246826']
turn 617.5s turn · 17.5s LLM · 0.0s tools
reasoning
**Evaluating mask types** I’m considering that I might need to handle both float and boolean masks. The runtime error I'm getting suggests that I need to ensure the input types are correctly managed. The user's input indicates a boolean expectation, but it seems like the tool is handling a float mask instead. I should explore how to support both types through different kernels based on the mask's data type. It appears there’s a potential optimization by checking the mask type while ensuring accuracy in calculations. **Implementing type checks** I’m focusing on how to implement a boolean branch in my logic. I’ll check if the mask's scalar type is boolean using `if (mask.scalar_type() == at::kBool)`; otherwise, I'll assume it's a float. This static check seems like a good approach for ensuring correctness throughout the operation. It feels straightforward, and I want to make sure I’m handling the types appropriately to avoid any issues down the line. Logic like this is essential for maintaining accuracy.
call → run_correctness
callrun_correctness { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor masked_cumsum_forward(torch::Tensor x, torch::Tensor mask);\n'''\n\ncuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\nconstexpr int BLOCK_THREADS = 1024;\n\n__device__ __forceinline__ float warp_inclusive_scan(float v) {\n unsigned m = 0xffffffffu;\n #pragma unroll\n for (int offset = 1; offset < 32; offset <<= 1) {\n float n = __shfl_up_sync(m, v, offset);\n if ((threadIdx.x & 31) >= offset) v += n;\n }\n return v;\n}\n\ntemplate <typename MaskT, bool MASK_IS_FLOAT>\n__global__ __launch_bounds__(BLOCK_THREADS, 1)\nvoid masked_cumsum_kernel(const float* __restrict__ x,\n const MaskT* __restrict__ mask,\n float* __restrict__ out,\n int rows, int cols) {\n int row = blockIdx.x;\n int tid = threadIdx.x;\n int lane = tid & 31;\n int warp = tid >> 5;\n __shared__ float warp_sums[32];\n int64_t base = (int64_t)row * cols;\n float carry = 0.0f;\n int tiles = (cols + BLOCK_THREADS - 1) / BLOCK_THREADS;\n for (int t = 0; t < tiles; ++t) {\n int col = t * BLOCK_THREADS + tid;\n int64_t idx = base + col;\n float v = 0.0f;\n if (col < cols) {\n if constexpr (MASK_IS_FLOAT) {\n v = x[idx] * mask[idx];\n } else {\n v = mask[idx] ? x[idx] : 0.0f;\n }\n }\n float s = warp_inclusive_scan(v);\n if (lane == 31) warp_sums[warp] = s;\n __syncthreads();\n if (warp == 0) {\n float w = warp_sums[lane];\n float ws = warp_inclusive_scan(w);\n warp_sums[lane] = ws;\n }\n __syncthreads();\n if (warp != 0) s += warp_sums[warp - 1];\n if (col < cols) out[idx] = s + carry;\n carry += warp_sums[31];\n __syncthreads();\n }\n}\n\ntorch::Tensor masked_cumsum_forward(torch::Tensor x, torch::Tensor mask) {\n auto out = torch::empty_like(x);\n int rows = (int)x.size(0);\n int cols = (int)x.size(1);\n dim3 grid(rows);\n dim3 block(BLOCK_THREADS);\n if (mask.scalar_type() == at::kBool) {\n const unsigned char* mp = static_cast<const unsigned char*>(mask.data_ptr());\n masked_cumsum_kernel<unsigned char, false><<<grid, block, 0, at::cuda::getCurrentCUDAStream()>>>(\n x.data_ptr<float>(), mp, out.data_ptr<float>(), rows, cols);\n } else {\n masked_cumsum_kernel<float, true><<<grid, block, 0, at::cuda::getCurrentCUDAStream()>>>(\n x.data_ptr<float>(), mask.data_ptr<float>(), out.data_ptr<float>(), rows, cols);\n }\n return out;\n}\n'''\n\n_ext = load_inline(name='masked_cumsum_ext_v5', cpp_sources=cpp_src, cuda_sources=cuda_src, functions=['masked_cumsum_forward'], extra_cuda_cflags=['-O3'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, dim):\n super(ModelNew, self).__init__()\n self.dim = dim\n def forward(self, x, mask):\n return _ext.masked_cumsum_forward(x, mask)\n" }
result ← run_correctness (ok)
okrun_correctness run_correctness PASSED: (5 / 5) trials all matched the reference. Numerical precision: max_abs_err=3.91e-02 mean_abs_err=2.61e-03 max_rel_err=4.81e-06
turn 76.9s turn · 6.9s LLM · 0.0s tools
call → profile_kernel
callprofile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor masked_cumsum_forward(torch::Tensor x, torch::Tensor mask);\n'''\n\ncuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\nconstexpr int BLOCK_THREADS = 1024;\n__device__ __forceinline__ float warp_inclusive_scan(float v) { unsigned m=0xffffffffu; #pragma unroll for(int offset=1; offset<32; offset<<=1){ float n=__shfl_up_sync(m,v,offset); if((threadIdx.x&31)>=offset) v+=n;} return v; }\ntemplate <typename MaskT, bool MASK_IS_FLOAT>\n__global__ __launch_bounds__(BLOCK_THREADS,1) void masked_cumsum_kernel(const float* __restrict__ x,const MaskT* __restrict__ mask,float* __restrict__ out,int rows,int cols){int row=blockIdx.x; int tid=threadIdx.x; int lane=tid&31; int warp=tid>>5; __shared__ float warp_sums[32]; int64_t base=(int64_t)row*cols; float carry=0.0f; int tiles=(cols+BLOCK_THREADS-1)/BLOCK_THREADS; for(int t=0;t<tiles;++t){int col=t*BLOCK_THREADS+tid; int64_t idx=base+col; float v=0.0f; if(col<cols){ if constexpr (MASK_IS_FLOAT) v=x[idx]*mask[idx]; else v=mask[idx]?x[idx]:0.0f;} float s=warp_inclusive_scan(v); if(lane==31) warp_sums[warp]=s; __syncthreads(); if(warp==0){float w=warp_sums[lane]; float ws=warp_inclusive_scan(w); warp_sums[lane]=ws;} __syncthreads(); if(warp!=0) s+=warp_sums[warp-1]; if(col<cols) out[idx]=s+carry; carry+=warp_sums[31]; __syncthreads();}}\ntorch::Tensor masked_cumsum_forward(torch::Tensor x, torch::Tensor mask){auto out=torch::empty_like(x); int rows=(int)x.size(0); int cols=(int)x.size(1); dim3 grid(rows), block(BLOCK_THREADS); if(mask.scalar_type()==at::kBool){const unsigned char* mp=static_cast<const unsigned char*>(mask.data_ptr()); masked_cumsum_kernel<unsigned char,false><<<grid,block,0,at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(),mp,out.data_ptr<float>(),rows,cols);} else {masked_cumsum_kernel<float,true><<<grid,block,0,at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(),mask.data_ptr<float>(),out.data_ptr<float>(),rows,cols);} return out;}\n'''\n_ext = load_inline(name='masked_cumsum_ext_v5p', cpp_sources=cpp_src, cuda_sources=cuda_src, functions=['masked_cumsum_forward'], extra_cuda_cflags=['-O3'], verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self, dim): super(ModelNew,self).__init__(); self.dim=dim\n def forward(self,x,mask): return _ext.masked_cumsum_forward(x,mask)\n" }
result ← profile_kernel (fail)
failprofile_kernel profile_kernel FAILED: worker exited with code 1. ve_scan(float v) { unsigned m=0xffffffffu; #pragma unroll for(int offset=1; offset<32; offset<<=1){ float n=__shfl_up_sync(m,v,offset); if((threadIdx.x&31)>=offset) v+=n;} return v; } ^ 2 errors detected in the compilation of "/scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_93_cache/k_e0b5e4ac454e/masked_cumsum_ext_v5p/cuda.cu". ninja: build stopped: subcommand failed.
turn 86.5s turn · 6.5s LLM · 0.0s tools
call → profile_kernel
callprofile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor masked_cumsum_forward(torch::Tensor x, torch::Tensor mask);\n'''\n\ncuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\nconstexpr int BLOCK_THREADS = 1024;\n\n__device__ __forceinline__ float warp_inclusive_scan(float v) {\n unsigned m = 0xffffffffu;\n #pragma unroll\n for (int offset = 1; offset < 32; offset <<= 1) {\n float n = __shfl_up_sync(m, v, offset);\n if ((threadIdx.x & 31) >= offset) v += n;\n }\n return v;\n}\n\ntemplate <typename MaskT, bool MASK_IS_FLOAT>\n__global__ __launch_bounds__(BLOCK_THREADS, 1)\nvoid masked_cumsum_kernel(const float* __restrict__ x,\n const MaskT* __restrict__ mask,\n float* __restrict__ out,\n int rows, int cols) {\n int row = blockIdx.x;\n int tid = threadIdx.x;\n int lane = tid & 31;\n int warp = tid >> 5;\n __shared__ float warp_sums[32];\n int64_t base = (int64_t)row * cols;\n float carry = 0.0f;\n int tiles = (cols + BLOCK_THREADS - 1) / BLOCK_THREADS;\n for (int t = 0; t < tiles; ++t) {\n int col = t * BLOCK_THREADS + tid;\n int64_t idx = base + col;\n float v = 0.0f;\n if (col < cols) {\n if constexpr (MASK_IS_FLOAT) v = x[idx] * mask[idx];\n else v = mask[idx] ? x[idx] : 0.0f;\n }\n float s = warp_inclusive_scan(v);\n if (lane == 31) warp_sums[warp] = s;\n __syncthreads();\n if (warp == 0) {\n float w = warp_sums[lane];\n float ws = warp_inclusive_scan(w);\n warp_sums[lane] = ws;\n }\n __syncthreads();\n if (warp != 0) s += warp_sums[warp - 1];\n if (col < cols) out[idx] = s + carry;\n carry += warp_sums[31];\n __syncthreads();\n }\n}\n\ntorch::Tensor masked_cumsum_forward(torch::Tensor x, torch::Tensor mask) {\n auto out = torch::empty_like(x);\n int rows = (int)x.size(0);\n int cols = (int)x.size(1);\n dim3 grid(rows), block(BLOCK_THREADS);\n if (mask.scalar_type() == at::kBool) {\n const unsigned char* mp = static_cast<const unsigned char*>(mask.data_ptr());\n masked_cumsum_kernel<unsigned char, false><<<grid, block, 0, at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(), mp, out.data_ptr<float>(), rows, cols);\n } else {\n masked_cumsum_kernel<float, true><<<grid, block, 0, at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(), mask.data_ptr<float>(), out.data_ptr<float>(), rows, cols);\n }\n return out;\n}\n'''\n\n_ext = load_inline(name='masked_cumsum_ext_v5prof', cpp_sources=cpp_src, cuda_sources=cuda_src, functions=['masked_cumsum_forward'], extra_cuda_cflags=['-O3'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, dim):\n super(ModelNew, self).__init__()\n self.dim = dim\n def forward(self, x, mask):\n return _ext.masked_cumsum_forward(x, mask)\n" }
result ← profile_kernel (ok)
okprofile_kernel profile_kernel PASSED: profiling complete. === Kernel Profile Summary === --- Kernel Breakdown (by GPU time) --- #1: aten::empty_like 574340.0μs (50%) calls=1 #2: aten::empty_strided 574300.0μs (50%) calls=1 --- Timing --- GPU time: 5610.24 μs --- Memory Bandwidth --- DRAM BW: 2295.0/3350 GB/s (68.5%) read=1531.1 write=763.9 GB/s Hit rates: L2=33.3% --- Compute Throughput --- Dominant pipe: FP32 util=8.9% Pipe util: FMA=8.9%, Tensor=1.1% --- Roofline Analysis --- Ridge point (FP32): 20.00 FLOPs/byte Classification: MEMORY-BOUND --- Occupancy --- Achieved: 99.5% Eligible warps/cycle: 1.19 Launch: regs/thread=24, smem/blk=1152B, block=1024, grid=32768 --- Memory Access Quality --- Global ld coalescing: 4.00 sectors/req (4.0x wasted BW) --- Warp Stall Reasons (% of issue cycles) --- long_scoreboard 2065.1% #################### short_scoreboard 386.1% #################### mio_throttle 20.1% #### --- Optimization Hints --- -> 2065% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=4.0 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set. -> Global load coalescing=4.0x (ideal=1.0) -> 4x wasted DRAM BW. Make per-warp accesses contiguous.
turn 9 · 1 tool call(s) · 22.6s turn · 22.6s LLM · 6,384 chars (click to expand)
turn 922.6s turn · 22.6s LLM · 0.0s tools
reasoning
**Considering implementation complexities** I'm thinking about how to support arbitrary columns, specifically using the condition if idx + 3 is less than row_end. Since the shape is fixed, I can use a fast path vector. However, for dynamic correctness, I'm wondering if I should still consider multiple columns. It seems likely that's necessary. I should also include a generic scalar tail. Alright, let's proceed with implementing version 6 while ensuring correctness.
call → run_correctness
callrun_correctness { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor masked_cumsum_forward(torch::Tensor x, torch::Tensor mask);\n'''\n\ncuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\nconstexpr int BLOCK_THREADS = 256;\nconstexpr int ITEMS = 4;\nconstexpr int TILE = BLOCK_THREADS * ITEMS;\n\n__device__ __forceinline__ float warp_inclusive_scan(float v) {\n unsigned m = 0xffffffffu;\n #pragma unroll\n for (int offset = 1; offset < 32; offset <<= 1) {\n float n = __shfl_up_sync(m, v, offset);\n if ((threadIdx.x & 31) >= offset) v += n;\n }\n return v;\n}\n\ntemplate <typename MaskT, bool MASK_IS_FLOAT>\n__global__ __launch_bounds__(BLOCK_THREADS, 2)\nvoid masked_cumsum_vec4_kernel(const float* __restrict__ x,\n const MaskT* __restrict__ mask,\n float* __restrict__ out,\n int rows, int cols) {\n int row = blockIdx.x;\n int tid = threadIdx.x;\n int lane = tid & 31;\n int warp = tid >> 5;\n __shared__ float warp_sums[8];\n int64_t base = (int64_t)row * cols;\n int tiles = (cols + TILE - 1) / TILE;\n float carry = 0.0f;\n\n for (int t = 0; t < tiles; ++t) {\n int col0 = t * TILE + tid * ITEMS;\n int64_t idx = base + col0;\n float a0=0.f, a1=0.f, a2=0.f, a3=0.f;\n if (col0 + 3 < cols) {\n float4 xv = reinterpret_cast<const float4*>(x + idx)[0];\n if constexpr (MASK_IS_FLOAT) {\n float4 mv = reinterpret_cast<const float4*>(mask + idx)[0];\n a0 = xv.x * mv.x; a1 = xv.y * mv.y; a2 = xv.z * mv.z; a3 = xv.w * mv.w;\n } else {\n uchar4 mv = reinterpret_cast<const uchar4*>(mask + idx)[0];\n a0 = mv.x ? xv.x : 0.f; a1 = mv.y ? xv.y : 0.f; a2 = mv.z ? xv.z : 0.f; a3 = mv.w ? xv.w : 0.f;\n }\n } else {\n if (col0 + 0 < cols) { if constexpr (MASK_IS_FLOAT) a0 = x[idx+0] * mask[idx+0]; else a0 = mask[idx+0] ? x[idx+0] : 0.f; }\n if (col0 + 1 < cols) { if constexpr (MASK_IS_FLOAT) a1 = x[idx+1] * mask[idx+1]; else a1 = mask[idx+1] ? x[idx+1] : 0.f; }\n if (col0 + 2 < cols) { if constexpr (MASK_IS_FLOAT) a2 = x[idx+2] * mask[idx+2]; else a2 = mask[idx+2] ? x[idx+2] : 0.f; }\n if (col0 + 3 < cols) { if constexpr (MASK_IS_FLOAT) a3 = x[idx+3] * mask[idx+3]; else a3 = mask[idx+3] ? x[idx+3] : 0.f; }\n }\n float p0 = a0;\n float p1 = p0 + a1;\n float p2 = p1 + a2;\n float p3 = p2 + a3;\n float thread_sum = p3;\n float s = warp_inclusive_scan(thread_sum);\n if (lane == 31) warp_sums[warp] = s;\n __syncthreads();\n if (warp == 0) {\n float w = (lane < 8) ? warp_sums[lane] : 0.0f;\n float ws = warp_inclusive_scan(w);\n if (lane < 8) warp_sums[lane] = ws;\n }\n __syncthreads();\n float offset = carry;\n if (warp != 0) offset += warp_sums[warp - 1];\n offset += s - thread_sum;\n float4 ov; ov.x = offset + p0; ov.y = offset + p1; ov.z = offset + p2; ov.w = offset + p3;\n if (col0 + 3 < cols) {\n reinterpret_cast<float4*>(out + idx)[0] = ov;\n } else {\n if (col0 + 0 < cols) out[idx+0] = ov.x;\n if (col0 + 1 < cols) out[idx+1] = ov.y;\n if (col0 + 2 < cols) out[idx+2] = ov.z;\n if (col0 + 3 < cols) out[idx+3] = ov.w;\n }\n carry += warp_sums[7];\n __syncthreads();\n }\n}\n\ntorch::Tensor masked_cumsum_forward(torch::Tensor x, torch::Tensor mask) {\n auto out = torch::empty_like(x);\n int rows = (int)x.size(0);\n int cols = (int)x.size(1);\n dim3 grid(rows), block(BLOCK_THREADS);\n if (mask.scalar_type() == at::kBool) {\n const unsigned char* mp = static_cast<const unsigned char*>(mask.data_ptr());\n masked_cumsum_vec4_kernel<unsigned char, false><<<grid, block, 0, at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(), mp, out.data_ptr<float>(), rows, cols);\n } else {\n masked_cumsum_vec4_kernel<float, true><<<grid, block, 0, at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(), mask.data_ptr<float>(), out.data_ptr<float>(), rows, cols);\n }\n return out;\n}\n'''\n\n_ext = load_inline(name='masked_cumsum_ext_v6', cpp_sources=cpp_src, cuda_sources=cuda_src, functions=['masked_cumsum_forward'], extra_cuda_cflags=['-O3'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, dim):\n super(ModelNew, self).__init__()\n self.dim = dim\n def forward(self, x, mask):\n return _ext.masked_cumsum_forward(x, mask)\n" }
result ← run_correctness (ok)
okrun_correctness run_correctness PASSED: (5 / 5) trials all matched the reference. Numerical precision: max_abs_err=3.91e-02 mean_abs_err=2.62e-03 max_rel_err=1.17e-04
turn 10 · 1 tool call(s) · 9.9s turn · 9.9s LLM · 7,285 chars (click to expand)
turn 109.9s turn · 9.9s LLM · 0.0s tools
call → profile_kernel
callprofile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor masked_cumsum_forward(torch::Tensor x, torch::Tensor mask);\n'''\n\ncuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\nconstexpr int BLOCK_THREADS = 256;\nconstexpr int ITEMS = 4;\nconstexpr int TILE = BLOCK_THREADS * ITEMS;\n\n__device__ __forceinline__ float warp_inclusive_scan(float v) {\n unsigned m = 0xffffffffu;\n #pragma unroll\n for (int offset = 1; offset < 32; offset <<= 1) {\n float n = __shfl_up_sync(m, v, offset);\n if ((threadIdx.x & 31) >= offset) v += n;\n }\n return v;\n}\n\ntemplate <typename MaskT, bool MASK_IS_FLOAT>\n__global__ __launch_bounds__(BLOCK_THREADS, 2)\nvoid masked_cumsum_vec4_kernel(const float* __restrict__ x,\n const MaskT* __restrict__ mask,\n float* __restrict__ out,\n int rows, int cols) {\n int row = blockIdx.x;\n int tid = threadIdx.x;\n int lane = tid & 31;\n int warp = tid >> 5;\n __shared__ float warp_sums[8];\n int64_t base = (int64_t)row * cols;\n int tiles = (cols + TILE - 1) / TILE;\n float carry = 0.0f;\n for (int t = 0; t < tiles; ++t) {\n int col0 = t * TILE + tid * ITEMS;\n int64_t idx = base + col0;\n float a0=0.f, a1=0.f, a2=0.f, a3=0.f;\n if (col0 + 3 < cols) {\n float4 xv = reinterpret_cast<const float4*>(x + idx)[0];\n if constexpr (MASK_IS_FLOAT) {\n float4 mv = reinterpret_cast<const float4*>(mask + idx)[0];\n a0 = xv.x * mv.x; a1 = xv.y * mv.y; a2 = xv.z * mv.z; a3 = xv.w * mv.w;\n } else {\n uchar4 mv = reinterpret_cast<const uchar4*>(mask + idx)[0];\n a0 = mv.x ? xv.x : 0.f; a1 = mv.y ? xv.y : 0.f; a2 = mv.z ? xv.z : 0.f; a3 = mv.w ? xv.w : 0.f;\n }\n } else {\n if (col0 + 0 < cols) { if constexpr (MASK_IS_FLOAT) a0 = x[idx+0] * mask[idx+0]; else a0 = mask[idx+0] ? x[idx+0] : 0.f; }\n if (col0 + 1 < cols) { if constexpr (MASK_IS_FLOAT) a1 = x[idx+1] * mask[idx+1]; else a1 = mask[idx+1] ? x[idx+1] : 0.f; }\n if (col0 + 2 < cols) { if constexpr (MASK_IS_FLOAT) a2 = x[idx+2] * mask[idx+2]; else a2 = mask[idx+2] ? x[idx+2] : 0.f; }\n if (col0 + 3 < cols) { if constexpr (MASK_IS_FLOAT) a3 = x[idx+3] * mask[idx+3]; else a3 = mask[idx+3] ? x[idx+3] : 0.f; }\n }\n float p0 = a0;\n float p1 = p0 + a1;\n float p2 = p1 + a2;\n float p3 = p2 + a3;\n float thread_sum = p3;\n float s = warp_inclusive_scan(thread_sum);\n if (lane == 31) warp_sums[warp] = s;\n __syncthreads();\n if (warp == 0) {\n float w = (lane < 8) ? warp_sums[lane] : 0.0f;\n float ws = warp_inclusive_scan(w);\n if (lane < 8) warp_sums[lane] = ws;\n }\n __syncthreads();\n float offset = carry;\n if (warp != 0) offset += warp_sums[warp - 1];\n offset += s - thread_sum;\n float4 ov; ov.x = offset + p0; ov.y = offset + p1; ov.z = offset + p2; ov.w = offset + p3;\n if (col0 + 3 < cols) reinterpret_cast<float4*>(out + idx)[0] = ov;\n else { if (col0+0<cols) out[idx+0]=ov.x; if (col0+1<cols) out[idx+1]=ov.y; if (col0+2<cols) out[idx+2]=ov.z; if (col0+3<cols) out[idx+3]=ov.w; }\n carry += warp_sums[7];\n __syncthreads();\n }\n}\n\ntorch::Tensor masked_cumsum_forward(torch::Tensor x, torch::Tensor mask) {\n auto out = torch::empty_like(x);\n int rows = (int)x.size(0);\n int cols = (int)x.size(1);\n dim3 grid(rows), block(BLOCK_THREADS);\n if (mask.scalar_type() == at::kBool) {\n const unsigned char* mp = static_cast<const unsigned char*>(mask.data_ptr());\n masked_cumsum_vec4_kernel<unsigned char, false><<<grid, block, 0, at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(), mp, out.data_ptr<float>(), rows, cols);\n } else {\n masked_cumsum_vec4_kernel<float, true><<<grid, block, 0, at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(), mask.data_ptr<float>(), out.data_ptr<float>(), rows, cols);\n }\n return out;\n}\n'''\n\n_ext = load_inline(name='masked_cumsum_ext_v6prof', cpp_sources=cpp_src, cuda_sources=cuda_src, functions=['masked_cumsum_forward'], extra_cuda_cflags=['-O3'], verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self, dim):\n super(ModelNew, self).__init__(); self.dim = dim\n def forward(self, x, mask):\n return _ext.masked_cumsum_forward(x, mask)\n" }
result ← profile_kernel (ok)
okprofile_kernel profile_kernel PASSED: profiling complete. === Kernel Profile Summary === --- Kernel Breakdown (by GPU time) --- #1: aten::empty_like 25785.0μs (50%) calls=1 #2: aten::empty_strided 25748.0μs (50%) calls=1 --- Timing --- GPU time: 4348.22 μs [↓1262.0μs ok] --- Memory Bandwidth --- DRAM BW: 2960.7/3350 GB/s (88.4%) [↑19.9% ok] read=1975.5 write=985.2 GB/s Hit rates: L2=34.2% --- Compute Throughput --- Pipe util: FMA=4.7%, Tensor=0.0% --- Roofline Analysis --- Ridge point (FP32): 20.00 FLOPs/byte Classification: UNKNOWN --- Occupancy --- Achieved: 99.3% [↓0.2% REGRESSED] Eligible warps/cycle: 0.19 WARNING: <1 eligible warp/cycle = latency-bound, needs more parallelism Launch: regs/thread=29, smem/blk=1152B, block=256, grid=32768 --- Memory Access Quality --- Global ld coalescing: 16.00 sectors/req (16.0x wasted BW) --- Warp Stall Reasons (% of issue cycles) --- long_scoreboard 6587.1% #################### short_scoreboard 323.1% #################### mio_throttle 0.7% --- Optimization Hints --- -> 6587% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=16.0 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set. -> Global load coalescing=16.0x (ideal=1.0) -> 16x wasted DRAM BW. Make per-warp accesses contiguous. --- Delta vs Previous Iteration --- GPU time: 5610.24→4348.22μs (↓1262.02) ok DRAM BW%: 68.51→88.38% (↑19.87) ok Occupancy: 99.54→99.32% (↓0.21) REGRESSED Eligible warps/cyc: 1.19→0.19 (↓1.00) REGRESSED Ld sectors/req: 4.00→16.00 (↑12.00) REGRESSED L2 hit%: 33.33→34.17% (↑0.84) ok
turn 11 · 1 tool call(s) · 11.3s turn · 11.3s LLM · 41,165 chars (click to expand)
turn 1111.3s turn · 11.3s LLM · 0.0s tools
reasoning
**Exploring performance improvements** I'm analyzing performance metrics and consider running v6 faster at 4.35ms, close to peak performance. I think I could improve by using ITEMS=8 with 256 threads and a tile size of 2048 across 16 iterations. Each thread could load 8 contiguous items. I wonder if reducing barriers and adjusting memory could help, particularly with register usage. I'm curious about the impact of coalescing requests on bandwidth, and I need to consider potential error tolerance with more sequential groups.
call → run_correctness
callrun_correctness { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor masked_cumsum_forward(torch::Tensor x, torch::Tensor mask);\n'''\n\ncuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\nconstexpr int BLOCK_THREADS=256; constexpr int ITEMS=8; constexpr int TILE=BLOCK_THREADS*ITEMS;\n__device__ __forceinline__ float warp_inclusive_scan(float v){unsigned m=0xffffffffu; #pragma unroll for(int offset=1; offset<32; offset<<=1){float n=__shfl_up_sync(m,v,offset); if((threadIdx.x&31)>=offset) v+=n;} return v;}\ntemplate<typename MaskT,bool MF>\n__global__ __launch_bounds__(BLOCK_THREADS,2) void kern(const float* __restrict__ x,const MaskT* __restrict__ mask,float* __restrict__ out,int rows,int cols){int row=blockIdx.x,tid=threadIdx.x,lane=tid&31,warp=tid>>5; __shared__ float warp_sums[8]; int64_t base=(int64_t)row*cols; int tiles=(cols+TILE-1)/TILE; float carry=0.f; for(int t=0;t<tiles;++t){int col0=t*TILE+tid*ITEMS; int64_t idx=base+col0; float a[ITEMS]; #pragma unroll for(int i=0;i<ITEMS;++i) a[i]=0.f; if(col0+ITEMS-1<cols){ float4 x0=reinterpret_cast<const float4*>(x+idx)[0]; float4 x1=reinterpret_cast<const float4*>(x+idx+4)[0]; if constexpr(MF){float4 m0=reinterpret_cast<const float4*>(mask+idx)[0]; float4 m1=reinterpret_cast<const float4*>(mask+idx+4)[0]; a[0]=x0.x*m0.x; a[1]=x0.y*m0.y; a[2]=x0.z*m0.z; a[3]=x0.w*m0.w; a[4]=x1.x*m1.x; a[5]=x1.y*m1.y; a[6]=x1.z*m1.z; a[7]=x1.w*m1.w;} else {uchar4 m0=reinterpret_cast<const uchar4*>(mask+idx)[0]; uchar4 m1=reinterpret_cast<const uchar4*>(mask+idx+4)[0]; a[0]=m0.x?x0.x:0.f; a[1]=m0.y?x0.y:0.f; a[2]=m0.z?x0.z:0.f; a[3]=m0.w?x0.w:0.f; a[4]=m1.x?x1.x:0.f; a[5]=m1.y?x1.y:0.f; a[6]=m1.z?x1.z:0.f; a[7]=m1.w?x1.w:0.f;}} else {#pragma unroll for(int i=0;i<ITEMS;++i){if(col0+i<cols){if constexpr(MF) a[i]=x[idx+i]*mask[idx+i]; else a[i]=mask[idx+i]?x[idx+i]:0.f;}}} float p[ITEMS]; p[0]=a[0]; #pragma unroll for(int i=1;i<ITEMS;++i) p[i]=p[i-1]+a[i]; float thread_sum=p[ITEMS-1]; float s=warp_inclusive_scan(thread_sum); if(lane==31) warp_sums[warp]=s; __syncthreads(); if(warp==0){float w=(lane<8)?warp_sums[lane]:0.f; float ws=warp_inclusive_scan(w); if(lane<8) warp_sums[lane]=ws;} __syncthreads(); float offset=carry + (warp?warp_sums[warp-1]:0.f) + s-thread_sum; if(col0+ITEMS-1<cols){float4 o0,o1; o0.x=offset+p[0]; o0.y=offset+p[1]; o0.z=offset+p[2]; o0.w=offset+p[3]; o1.x=offset+p[4]; o1.y=offset+p[5]; o1.z=offset+p[6]; o1.w=offset+p[7]; reinterpret_cast<float4*>(out+idx)[0]=o0; reinterpret_cast<float4*>(out+idx+4)[0]=o1;} else {#pragma unroll for(int i=0;i<ITEMS;++i) if(col0+i<cols) out[idx+i]=offset+p[i];} carry += warp_sums[7]; __syncthreads();}}\ntorch::Tensor masked_cumsum_forward(torch::Tensor x, torch::Tensor mask){auto out=torch::empty_like(x); int rows=(int)x.size(0), cols=(int)x.size(1); dim3 grid(rows),block(BLOCK_THREADS); if(mask.scalar_type()==at::kBool){const unsigned char* mp=static_cast<const unsigned char*>(mask.data_ptr()); kern<unsigned char,false><<<grid,block,0,at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(),mp,out.data_ptr<float>(),rows,cols);} else {kern<float,true><<<grid,block,0,at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(),mask.data_ptr<float>(),out.data_ptr<float>(),rows,cols);} return out;}\n'''\n_ext=load_inline(name='masked_cumsum_ext_v7', cpp_sources=cpp_src, cuda_sources=cuda_src, functions=['masked_cumsum_forward'], extra_cuda_cflags=['-O3'], verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self, dim): super(ModelNew,self).__init__(); self.dim=dim\n def forward(self,x,mask): return _ext.masked_cumsum_forward(x,mask)\n" }
result ← run_correctness (fail)
failrun_correctness run_correctness FAILED: kernel did not compile. Error building extension 'masked_cumsum_ext_v7': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=masked_cumsum_ext_v7 -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -fPIC -std=c++17 -c /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_93_cache/k_d9fdaf292eb8/masked_cumsum_ext_v7/main.cpp -o main.o [2/3] /usr/local/cuda/bin/nvcc -MD -MF cuda.cuda.o.d -ccbin /opt/conda/bin/x86_64-conda-linux-gnu-cc -DTORCH_EXTENSION_NAME=masked_cumsum_ext_v7 -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr -gencode=arch=compute_90,code=compute_90 -gencode=arch=compute_90,code=sm_90 --compiler-options '-fPIC' -O3 -std=c++17 -c /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_93_cache/k_d9fdaf292eb8/masked_cumsum_ext_v7/cuda.cu -o cuda.cuda.o FAILED: [code=2] cuda.cuda.o /usr/local/cuda/bin/nvcc -MD -MF cuda.cuda.o.d -ccbin /opt/conda/bin/x86_64-conda-linux-gnu-cc -DTORCH_EXTENSION_NAME=masked_cumsum_ext_v7 -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr -gencode=arch=compute_90,code=compute_90 -gencode=arch=compute_90,code=sm_90 --compiler-options '-fPIC' -O3 -std=c++17 -c /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_93_cache/k_d9fdaf292eb8/masked_cumsum_ext_v7/cuda.cu -o cuda.cuda.o nvcc warning : incompatible redefinition for option 'compiler-bindir', the last value of this option was used /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_93_cache/k_d9fdaf292eb8/masked_cumsum_ext_v7/cuda.cu(10): error: "#" not expected here float warp_inclusive_scan(float v){unsigned m=0xffffffffu; #pragma unroll for(int offset=1; offset<32; offset<<=1){float n=__shfl_up_sync(m,v,offset); if((threadIdx.x&31)>=offset) v+=n;} return v;} ^ /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_93_cache/k_d9fdaf292eb8/masked_cumsum_ext_v7/cuda.cu(10): error: expected an expression float warp_inclusive_scan(float v){unsigned m=0xffffffffu; #pragma unroll for(int offset=1; offset<32; offset<<=1){float n=__shfl_up_sync(m,v,offset); if((threadIdx.x&31)>=offset) v+=n;} return v;} ^ /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_93_cache/k_d9fdaf292eb8/masked_cumsum_ext_v7/cuda.cu(12): error: "#" not expected here float warp_sums[8]; int64_t base=(int64_t)row*cols; int tiles=(cols+TILE-1)/TILE; float carry=0.f; for(int t=0;t<tiles;++t){int col0=t*TILE+tid*ITEMS; int64_t idx=base+col0; float a[ITEMS]; #pragma unroll for(int i=0;i<ITEMS;++i) a[i]=0.f; if(col0+ITEMS-1<cols){ float4 x0=reinterpret_cast<const float4*>(x+idx)[0]; float4 x1=reinterpret_cast<const float4*>(x+idx+4)[0]; if constexpr(MF){float4 m0=reinterpret_cast<const float4*>(mask+idx)[0]; float4 m1=reinterpret_cast<const float4*>(mask+idx+4)[0]; a[0]=x0.x*m0.x; a[1]=x0.y*m0.y; a[2]=x0.z*m0.z; a[3]=x0.w*m0.w; a[4]=x1.x*m1.x; a[5]=x1.y*m1.y; a[6]=x1.z*m1.z; a[7]=x1.w*m1.w;} else {uchar4 m0=reinterpret_cast<const uchar4*>(mask+idx)[0]; uchar4 m1=reinterpret_cast<const uchar4*>(mask+idx+4)[0]; a[0]=m0.x?x0.x:0.f; a[1]=m0.y?x0.y:0.f; a[2]=m0.z?x0.z:0.f; a[3]=m0.w?x0.w:0.f; a[4]=m1.x?x1.x:0.f; a[5]=m1.y?x1.y:0.f; a[6]=m1.z?x1.z:0.f; a[7]=m1.w?x1.w:0.f;}} else {#pragma unroll for(int i=0;i<ITEMS;++i){if(col0+i<cols){if constexpr(MF) a[i]=x[idx+i]*mask[idx+i]; else a[i]=mask[idx+i]?x[idx+i]:0.f;}}} float p[ITEMS]; p[0]=a[0]; #pragma unroll for(int i=1;i<ITEMS;++i) p[i]=p[i-1]+a[i]; float thread_sum=p[ITEMS-1]; float s=warp_inclusive_scan(thread_sum); if(lane==31) warp_sums[warp]=s; __syncthreads(); if(warp==0){float w=(lane<8)?warp_sums[lane]:0.f; float ws=warp_inclusive_scan(w); if(lane<8) warp_sums[lane]=ws;} __syncthreads(); float offset=carry + (warp?warp_sums[warp-1]:0.f) + s-thread_sum; if(col0+ITEMS-1<cols){float4 o0,o1; o0.x=offset+p[0]; o0.y=offset+p[1]; o0.z=offset+p[2]; o0.w=offset+p[3]; o1.x=offset+p[4]; o1.y=offset+p[5]; o1.z=offset+p[6]; o1.w=offset+p[7]; reinterpret_cast<float4*>(out+idx)[0]=o0; reinterpret_cast<float4*>(out+idx+4)[0]=o1;} else {#pragma unroll for(int i=0;i<ITEMS;++i) if(col0+i<cols) out[idx+i]=offset+p[i];} carry += warp_sums[7]; __syncthreads();}} ^ /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_93_cache/k_d9fdaf292eb8/masked_cumsum_ext_v7/cuda.cu(12): error: "#" not expected here float warp_sums[8]; int64_t base=(int64_t)row*cols; int tiles=(cols+TILE-1)/TILE; float carry=0.f; for(int t=0;t<tiles;++t){int col0=t*TILE+tid*ITEMS; int64_t idx=base+col0; float a[ITEMS]; #pragma unroll for(int i=0;i<ITEMS;++i) a[i]=0.f; if(col0+ITEMS-1<cols){ float4 x0=reinterpret_cast<const float4*>(x+idx)[0]; float4 x1=reinterpret_cast<const float4*>(x+idx+4)[0]; if constexpr(MF){float4 m0=reinterpret_cast<const float4*>(mask+idx)[0]; float4 m1=reinterpret_cast<const float4*>(mask+idx+4)[0]; a[0]=x0.x*m0.x; a[1]=x0.y*m0.y; a[2]=x0.z*m0.z; a[3]=x0.w*m0.w; a[4]=x1.x*m1.x; a[5]=x1.y*m1.y; a[6]=x1.z*m1.z; a[7]=x1.w*m1.w;} else {uchar4 m0=reinterpret_cast<const uchar4*>(mask+idx)[0]; uchar4 m1=reinterpret_cast<const uchar4*>(mask+idx+4)[0]; a[0]=m0.x?x0.x:0.f; a[1]=m0.y?x0.y:0.f; a[2]=m0.z?x0.z:0.f; a[3]=m0.w?x0.w:0.f; a[4]=m1.x?x1.x:0.f; a[5]=m1.y?x1.y:0.f; a[6]=m1.z?x1.z:0.f; a[7]=m1.w?x1.w:0.f;}} else {#pragma unroll for(int i=0;i<ITEMS;++i){if(col0+i<cols){if constexpr(MF) a[i]=x[idx+i]*mask[idx+i]; else a[i]=mask[idx+i]?x[idx+i]:0.f;}}} float p[ITEMS]; p[0]=a[0]; #pragma unroll for(int i=1;i<ITEMS;++i) p[i]=p[i-1]+a[i]; float thread_sum=p[ITEMS-1]; float s=warp_inclusive_scan(thread_sum); if(lane==31) warp_sums[warp]=s; __syncthreads(); if(warp==0){float w=(lane<8)?warp_sums[lane]:0.f; float ws=warp_inclusive_scan(w); if(lane<8) warp_sums[lane]=ws;} __syncthreads(); float offset=carry + (warp?warp_sums[warp-1]:0.f) + s-thread_sum; if(col0+ITEMS-1<cols){float4 o0,o1; o0.x=offset+p[0]; o0.y=offset+p[1]; o0.z=offset+p[2]; o0.w=offset+p[3]; o1.x=offset+p[4]; o1.y=offset+p[5]; o1.z=offset+p[6]; o1.w=offset+p[7]; reinterpret_cast<float4*>(out+idx)[0]=o0; reinterpret_cast<float4*>(out+idx+4)[0]=o1;} else {#pragma unroll for(int i=0;i<ITEMS;++i) if(col0+i<cols) out[idx+i]=offset+p[i];} carry += warp_sums[7]; __syncthreads();}} ^ /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_93_cache/k_d9fdaf292eb8/masked_cumsum_ext_v7/cuda.cu(12): error: "#" not expected here float warp_sums[8]; int64_t base=(int64_t)row*cols; int tiles=(cols+TILE-1)/TILE; float carry=0.f; for(int t=0;t<tiles;++t){int col0=t*TILE+tid*ITEMS; int64_t idx=base+col0; float a[ITEMS]; #pragma unroll for(int i=0;i<ITEMS;++i) a[i]=0.f; if(col0+ITEMS-1<cols){ float4 x0=reinterpret_cast<const float4*>(x+idx)[0]; float4 x1=reinterpret_cast<const float4*>(x+idx+4)[0]; if constexpr(MF){float4 m0=reinterpret_cast<const float4*>(mask+idx)[0]; float4 m1=reinterpret_cast<const float4*>(mask+idx+4)[0]; a[0]=x0.x*m0.x; a[1]=x0.y*m0.y; a[2]=x0.z*m0.z; a[3]=x0.w*m0.w; a[4]=x1.x*m1.x; a[5]=x1.y*m1.y; a[6]=x1.z*m1.z; a[7]=x1.w*m1.w;} else {uchar4 m0=reinterpret_cast<const uchar4*>(mask+idx)[0]; uchar4 m1=reinterpret_cast<const uchar4*>(mask+idx+4)[0]; a[0]=m0.x?x0.x:0.f; a[1]=m0.y?x0.y:0.f; a[2]=m0.z?x0.z:0.f; a[3]=m0.w?x0.w:0.f; a[4]=m1.x?x1.x:0.f; a[5]=m1.y?x1.y:0.f; a[6]=m1.z?x1.z:0.f; a[7]=m1.w?x1.w:0.f;}} else {#pragma unroll for(int i=0;i<ITEMS;++i){if(col0+i<cols){if constexpr(MF) a[i]=x[idx+i]*mask[idx+i]; else a[i]=mask[idx+i]?x[idx+i]:0.f;}}} float p[ITEMS]; p[0]=a[0]; #pragma unroll for(int i=1;i<ITEMS;++i) p[i]=p[i-1]+a[i]; float thread_sum=p[ITEMS-1]; float s=warp_inclusive_scan(thread_sum); if(lane==31) warp_sums[warp]=s; __syncthreads(); if(warp==0){float w=(lane<8)?warp_sums[lane]:0.f; float ws=warp_inclusive_scan(w); if(lane<8) warp_sums[lane]=ws;} __syncthreads(); float offset=carry + (warp?warp_sums[warp-1]:0.f) + s-thread_sum; if(col0+ITEMS-1<cols){float4 o0,o1; o0.x=offset+p[0]; o0.y=offset+p[1]; o0.z=offset+p[2]; o0.w=offset+p[3]; o1.x=offset+p[4]; o1.y=offset+p[5]; o1.z=offset+p[6]; o1.w=offset+p[7]; reinterpret_cast<float4*>(out+idx)[0]=o0; reinterpret_cast<float4*>(out+idx+4)[0]=o1;} else {#pragma unroll for(int i=0;i<ITEMS;++i) if(col0+i<cols) out[idx+i]=offset+p[i];} carry += warp_sums[7]; __syncthreads();}} ^ /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_93_cache/k_d9fdaf292eb8/masked_cumsum_ext_v7/cuda.cu(12): error: "#" not expected here float warp_sums[8]; int64_t base=(int64_t)row*cols; int tiles=(cols+TILE-1)/TILE; float carry=0.f; for(int t=0;t<tiles;++t){int col0=t*TILE+tid*ITEMS; int64_t idx=base+col0; float a[ITEMS]; #pragma unroll for(int i=0;i<ITEMS;++i) a[i]=0.f; if(col0+ITEMS-1<cols){ float4 x0=reinterpret_cast<const float4*>(x+idx)[0]; float4 x1=reinterpret_cast<const float4*>(x+idx+4)[0]; if constexpr(MF){float4 m0=reinterpret_cast<const float4*>(mask+idx)[0]; float4 m1=reinterpret_cast<const float4*>(mask+idx+4)[0]; a[0]=x0.x*m0.x; a[1]=x0.y*m0.y; a[2]=x0.z*m0.z; a[3]=x0.w*m0.w; a[4]=x1.x*m1.x; a[5]=x1.y*m1.y; a[6]=x1.z*m1.z; a[7]=x1.w*m1.w;} else {uchar4 m0=reinterpret_cast<const uchar4*>(mask+idx)[0]; uchar4 m1=reinterpret_cast<const uchar4*>(mask+idx+4)[0]; a[0]=m0.x?x0.x:0.f; a[1]=m0.y?x0.y:0.f; a[2]=m0.z?x0.z:0.f; a[3]=m0.w?x0.w:0.f; a[4]=m1.x?x1.x:0.f; a[5]=m1.y?x1.y:0.f; a[6]=m1.z?x1.z:0.f; a[7]=m1.w?x1.w:0.f;}} else {#pragma unroll for(int i=0;i<ITEMS;++i){if(col0+i<cols){if constexpr(MF) a[i]=x[idx+i]*mask[idx+i]; else a[i]=mask[idx+i]?x[idx+i]:0.f;}}} float p[ITEMS]; p[0]=a[0]; #pragma unroll for(int i=1;i<ITEMS;++i) p[i]=p[i-1]+a[i]; float thread_sum=p[ITEMS-1]; float s=warp_inclusive_scan(thread_sum); if(lane==31) warp_sums[warp]=s; __syncthreads(); if(warp==0){float w=(lane<8)?warp_sums[lane]:0.f; float ws=warp_inclusive_scan(w); if(lane<8) warp_sums[lane]=ws;} __syncthreads(); float offset=carry + (warp?warp_sums[warp-1]:0.f) + s-thread_sum; if(col0+ITEMS-1<cols){float4 o0,o1; o0.x=offset+p[0]; o0.y=offset+p[1]; o0.z=offset+p[2]; o0.w=offset+p[3]; o1.x=offset+p[4]; o1.y=offset+p[5]; o1.z=offset+p[6]; o1.w=offset+p[7]; reinterpret_cast<float4*>(out+idx)[0]=o0; reinterpret_cast<float4*>(out+idx+4)[0]=o1;} else {#pragma unroll for(int i=0;i<ITEMS;++i) if(col0+i<cols) out[idx+i]=offset+p[i];} carry += warp_sums[7]; __syncthreads();}} ^ /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_93_cache/k_d9fdaf292eb8/masked_cumsum_ext_v7/cuda.cu(12): error: expected an expression float warp_sums[8]; int64_t base=(int64_t)row*cols; int tiles=(cols+TILE-1)/TILE; float carry=0.f; for(int t=0;t<tiles;++t){int col0=t*TILE+tid*ITEMS; int64_t idx=base+col0; float a[ITEMS]; #pragma unroll for(int i=0;i<ITEMS;++i) a[i]=0.f; if(col0+ITEMS-1<cols){ float4 x0=reinterpret_cast<const float4*>(x+idx)[0]; float4 x1=reinterpret_cast<const float4*>(x+idx+4)[0]; if constexpr(MF){float4 m0=reinterpret_cast<const float4*>(mask+idx)[0]; float4 m1=reinterpret_cast<const float4*>(mask+idx+4)[0]; a[0]=x0.x*m0.x; a[1]=x0.y*m0.y; a[2]=x0.z*m0.z; a[3]=x0.w*m0.w; a[4]=x1.x*m1.x; a[5]=x1.y*m1.y; a[6]=x1.z*m1.z; a[7]=x1.w*m1.w;} else {uchar4 m0=reinterpret_cast<const uchar4*>(mask+idx)[0]; uchar4 m1=reinterpret_cast<const uchar4*>(mask+idx+4)[0]; a[0]=m0.x?x0.x:0.f; a[1]=m0.y?x0.y:0.f; a[2]=m0.z?x0.z:0.f; a[3]=m0.w?x0.w:0.f; a[4]=m1.x?x1.x:0.f; a[5]=m1.y?x1.y:0.f; a[6]=m1.z?x1.z:0.f; a[7]=m1.w?x1.w:0.f;}} else {#pragma unroll for(int i=0;i<ITEMS;++i){if(col0+i<cols){if constexpr(MF) a[i]=x[idx+i]*mask[idx+i]; else a[i]=mask[idx+i]?x[idx+i]:0.f;}}} float p[ITEMS]; p[0]=a[0]; #pragma unroll for(int i=1;i<ITEMS;++i) p[i]=p[i-1]+a[i]; float thread_sum=p[ITEMS-1]; float s=warp_inclusive_scan(thread_sum); if(lane==31) warp_sums[warp]=s; __syncthreads(); if(warp==0){float w=(lane<8)?warp_sums[lane]:0.f; float ws=warp_inclusive_scan(w); if(lane<8) warp_sums[lane]=ws;} __syncthreads(); float offset=carry + (warp?warp_sums[warp-1]:0.f) + s-thread_sum; if(col0+ITEMS-1<cols){float4 o0,o1; o0.x=offset+p[0]; o0.y=offset+p[1]; o0.z=offset+p[2]; o0.w=offset+p[3]; o1.x=offset+p[4]; o1.y=offset+p[5]; o1.z=offset+p[6]; o1.w=offset+p[7]; reinterpret_cast<float4*>(out+idx)[0]=o0; reinterpret_cast<float4*>(out+idx+4)[0]=o1;} else {#pragma unroll for(int i=0;i<ITEMS;++i) if(col0+i<cols) out[idx+i]=offset+p[i];} carry += warp_sums[7]; __syncthreads();}} ^ /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_93_cache/k_d9fdaf292eb8/masked_cumsum_ext_v7/cuda.cu(12): error: expected an expression float warp_sums[8]; int64_t base=(int64_t)row*cols; int tiles=(cols+TILE-1)/TILE; float carry=0.f; for(int t=0;t<tiles;++t){int col0=t*TILE+tid*ITEMS; int64_t idx=base+col0; float a[ITEMS]; #pragma unroll for(int i=0;i<ITEMS;++i) a[i]=0.f; if(col0+ITEMS-1<cols){ float4 x0=reinterpret_cast<const float4*>(x+idx)[0]; float4 x1=reinterpret_cast<const float4*>(x+idx+4)[0]; if constexpr(MF){float4 m0=reinterpret_cast<const float4*>(mask+idx)[0]; float4 m1=reinterpret_cast<const float4*>(mask+idx+4)[0]; a[0]=x0.x*m0.x; a[1]=x0.y*m0.y; a[2]=x0.z*m0.z; a[3]=x0.w*m0.w; a[4]=x1.x*m1.x; a[5]=x1.y*m1.y; a[6]=x1.z*m1.z; a[7]=x1.w*m1.w;} else {uchar4 m0=reinterpret_cast<const uchar4*>(mask+idx)[0]; uchar4 m1=reinterpret_cast<const uchar4*>(mask+idx+4)[0]; a[0]=m0.x?x0.x:0.f; a[1]=m0.y?x0.y:0.f; a[2]=m0.z?x0.z:0.f; a[3]=m0.w?x0.w:0.f; a[4]=m1.x?x1.x:0.f; a[5]=m1.y?x1.y:0.f; a[6]=m1.z?x1.z:0.f; a[7]=m1.w?x1.w:0.f;}} else {#pragma unroll for(int i=0;i<ITEMS;++i){if(col0+i<cols){if constexpr(MF) a[i]=x[idx+i]*mask[idx+i]; else a[i]=mask[idx+i]?x[idx+i]:0.f;}}} float p[ITEMS]; p[0]=a[0]; #pragma unroll for(int i=1;i<ITEMS;++i) p[i]=p[i-1]+a[i]; float thread_sum=p[ITEMS-1]; float s=warp_inclusive_scan(thread_sum); if(lane==31) warp_sums[warp]=s; __syncthreads(); if(warp==0){float w=(lane<8)?warp_sums[lane]:0.f; float ws=warp_inclusive_scan(w); if(lane<8) warp_sums[lane]=ws;} __syncthreads(); float offset=carry + (warp?warp_sums[warp-1]:0.f) + s-thread_sum; if(col0+ITEMS-1<cols){float4 o0,o1; o0.x=offset+p[0]; o0.y=offset+p[1]; o0.z=offset+p[2]; o0.w=offset+p[3]; o1.x=offset+p[4]; o1.y=offset+p[5]; o1.z=offset+p[6]; o1.w=offset+p[7]; reinterpret_cast<float4*>(out+idx)[0]=o0; reinterpret_cast<float4*>(out+idx+4)[0]=o1;} else {#pragma unroll for(int i=0;i<ITEMS;++i) if(col0+i<cols) out[idx+i]=offset+p[i];} carry += warp_sums[7]; __syncthreads();}} ^ /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_93_cache/k_d9fdaf292eb8/masked_cumsum_ext_v7/cuda.cu(12): error: expected a ";" float warp_sums[8]; int64_t base=(int64_t)row*cols; int tiles=(cols+TILE-1)/TILE; float carry=0.f; for(int t=0;t<tiles;++t){int col0=t*TILE+tid*ITEMS; int64_t idx=base+col0; float a[ITEMS]; #pragma unroll for(int i=0;i<ITEMS;++i) a[i]=0.f; if(col0+ITEMS-1<cols){ float4 x0=reinterpret_cast<const float4*>(x+idx)[0]; float4 x1=reinterpret_cast<const float4*>(x+idx+4)[0]; if constexpr(MF){float4 m0=reinterpret_cast<const float4*>(mask+idx)[0]; float4 m1=reinterpret_cast<const float4*>(mask+idx+4)[0]; a[0]=x0.x*m0.x; a[1]=x0.y*m0.y; a[2]=x0.z*m0.z; a[3]=x0.w*m0.w; a[4]=x1.x*m1.x; a[5]=x1.y*m1.y; a[6]=x1.z*m1.z; a[7]=x1.w*m1.w;} else {uchar4 m0=reinterpret_cast<const uchar4*>(mask+idx)[0]; uchar4 m1=reinterpret_cast<const uchar4*>(mask+idx+4)[0]; a[0]=m0.x?x0.x:0.f; a[1]=m0.y?x0.y:0.f; a[2]=m0.z?x0.z:0.f; a[3]=m0.w?x0.w:0.f; a[4]=m1.x?x1.x:0.f; a[5]=m1.y?x1.y:0.f; a[6]=m1.z?x1.z:0.f; a[7]=m1.w?x1.w:0.f;}} else {#pragma unroll for(int i=0;i<ITEMS;++i){if(col0+i<cols){if constexpr(MF) a[i]=x[idx+i]*mask[idx+i]; else a[i]=mask[idx+i]?x[idx+i]:0.f;}}} float p[ITEMS]; p[0]=a[0]; #pragma unroll for(int i=1;i<ITEMS;++i) p[i]=p[i-1]+a[i]; float thread_sum=p[ITEMS-1]; float s=warp_inclusive_scan(thread_sum); if(lane==31) warp_sums[warp]=s; __syncthreads(); if(warp==0){float w=(lane<8)?warp_sums[lane]:0.f; float ws=warp_inclusive_scan(w); if(lane<8) warp_sums[lane]=ws;} __syncthreads(); float offset=carry + (warp?warp_sums[warp-1]:0.f) + s-thread_sum; if(col0+ITEMS-1<cols){float4 o0,o1; o0.x=offset+p[0]; o0.y=offset+p[1]; o0.z=offset+p[2]; o0.w=offset+p[3]; o1.x=offset+p[4]; o1.y=offset+p[5]; o1.z=offset+p[6]; o1.w=offset+p[7]; reinterpret_cast<float4*>(out+idx)[0]=o0; reinterpret_cast<float4*>(out+idx+4)[0]=o1;} else {#pragma unroll for(int i=0;i<ITEMS;++i) if(col0+i<cols) out[idx+i]=offset+p[i];} carry += warp_sums[7]; __syncthreads();}} ^ /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_93_cache/k_d9fdaf292eb8/masked_cumsum_ext_v7/cuda.cu(12): error: expected an expression float warp_sums[8]; int64_t base=(int64_t)row*cols; int tiles=(cols+TILE-1)/TILE; float carry=0.f; for(int t=0;t<tiles;++t){int col0=t*TILE+tid*ITEMS; int64_t idx=base+col0; float a[ITEMS]; #pragma unroll for(int i=0;i<ITEMS;++i) a[i]=0.f; if(col0+ITEMS-1<cols){ float4 x0=reinterpret_cast<const float4*>(x+idx)[0]; float4 x1=reinterpret_cast<const float4*>(x+idx+4)[0]; if constexpr(MF){float4 m0=reinterpret_cast<const float4*>(mask+idx)[0]; float4 m1=reinterpret_cast<const float4*>(mask+idx+4)[0]; a[0]=x0.x*m0.x; a[1]=x0.y*m0.y; a[2]=x0.z*m0.z; a[3]=x0.w*m0.w; a[4]=x1.x*m1.x; a[5]=x1.y*m1.y; a[6]=x1.z*m1.z; a[7]=x1.w*m1.w;} else {uchar4 m0=reinterpret_cast<const uchar4*>(mask+idx)[0]; uchar4 m1=reinterpret_cast<const uchar4*>(mask+idx+4)[0]; a[0]=m0.x?x0.x:0.f; a[1]=m0.y?x0.y:0.f; a[2]=m0.z?x0.z:0.f; a[3]=m0.w?x0.w:0.f; a[4]=m1.x?x1.x:0.f; a[5]=m1.y?x1.y:0.f; a[6]=m1.z?x1.z:0.f; a[7]=m1.w?x1.w:0.f;}} else {#pragma unroll for(int i=0;i<ITEMS;++i){if(col0+i<cols){if constexpr(MF) a[i]=x[idx+i]*mask[idx+i]; else a[i]=mask[idx+i]?x[idx+i]:0.f;}}} float p[ITEMS]; p[0]=a[0]; #pragma unroll for(int i=1;i<ITEMS;++i) p[i]=p[i-1]+a[i]; float thread_sum=p[ITEMS-1]; float s=warp_inclusive_scan(thread_sum); if(lane==31) warp_sums[warp]=s; __syncthreads(); if(warp==0){float w=(lane<8)?warp_sums[lane]:0.f; float ws=warp_inclusive_scan(w); if(lane<8) warp_sums[lane]=ws;} __syncthreads(); float offset=carry + (warp?warp_sums[warp-1]:0.f) + s-thread_sum; if(col0+ITEMS-1<cols){float4 o0,o1; o0.x=offset+p[0]; o0.y=offset+p[1]; o0.z=offset+p[2]; o0.w=offset+p[3]; o1.x=offset+p[4]; o1.y=offset+p[5]; o1.z=offset+p[6]; o1.w=offset+p[7]; reinterpret_cast<float4*>(out+idx)[0]=o0; reinterpret_cast<float4*>(out+idx+4)[0]=o1;} else {#pragma unroll for(int i=0;i<ITEMS;++i) if(col0+i<cols) out[idx+i]=offset+p[i];} carry += warp_sums[7]; __syncthreads();}} ^ /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_93_cache/k_d9fdaf292eb8/masked_cumsum_ext_v7/cuda.cu(12): error: expected an expression float warp_sums[8]; int64_t base=(int64_t)row*cols; int tiles=(cols+TILE-1)/TILE; float carry=0.f; for(int t=0;t<tiles;++t){int col0=t*TILE+tid*ITEMS; int64_t idx=base+col0; float a[ITEMS]; #pragma unroll for(int i=0;i<ITEMS;++i) a[i]=0.f; if(col0+ITEMS-1<cols){ float4 x0=reinterpret_cast<const float4*>(x+idx)[0]; float4 x1=reinterpret_cast<const float4*>(x+idx+4)[0]; if constexpr(MF){float4 m0=reinterpret_cast<const float4*>(mask+idx)[0]; float4 m1=reinterpret_cast<const float4*>(mask+idx+4)[0]; a[0]=x0.x*m0.x; a[1]=x0.y*m0.y; a[2]=x0.z*m0.z; a[3]=x0.w*m0.w; a[4]=x1.x*m1.x; a[5]=x1.y*m1.y; a[6]=x1.z*m1.z; a[7]=x1.w*m1.w;} else {uchar4 m0=reinterpret_cast<const uchar4*>(mask+idx)[0]; uchar4 m1=reinterpret_cast<const uchar4*>(mask+idx+4)[0]; a[0]=m0.x?x0.x:0.f; a[1]=m0.y?x0.y:0.f; a[2]=m0.z?x0.z:0.f; a[3]=m0.w?x0.w:0.f; a[4]=m1.x?x1.x:0.f; a[5]=m1.y?x1.y:0.f; a[6]=m1.z?x1.z:0.f; a[7]=m1.w?x1.w:0.f;}} else {#pragma unroll for(int i=0;i<ITEMS;++i){if(col0+i<cols){if constexpr(MF) a[i]=x[idx+i]*mask[idx+i]; else a[i]=mask[idx+i]?x[idx+i]:0.f;}}} float p[ITEMS]; p[0]=a[0]; #pragma unroll for(int i=1;i<ITEMS;++i) p[i]=p[i-1]+a[i]; float thread_sum=p[ITEMS-1]; float s=warp_inclusive_scan(thread_sum); if(lane==31) warp_sums[warp]=s; __syncthreads(); if(warp==0){float w=(lane<8)?warp_sums[lane]:0.f; float ws=warp_inclusive_scan(w); if(lane<8) warp_sums[lane]=ws;} __syncthreads(); float offset=carry + (warp?warp_sums[warp-1]:0.f) + s-thread_sum; if(col0+ITEMS-1<cols){float4 o0,o1; o0.x=offset+p[0]; o0.y=offset+p[1]; o0.z=offset+p[2]; o0.w=offset+p[3]; o1.x=offset+p[4]; o1.y=offset+p[5]; o1.z=offset+p[6]; o1.w=offset+p[7]; reinterpret_cast<float4*>(out+idx)[0]=o0; reinterpret_cast<float4*>(out+idx+4)[0]=o1;} else {#pragma unroll for(int i=0;i<ITEMS;++i) if(col0+i<cols) out[idx+i]=offset+p[i];} carry += warp_sums[7]; __syncthreads();}} ^ 11 errors detected in the compilation of "/scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_93_cache/k_d9fdaf292eb8/masked_cumsum_ext_v7/cuda.cu". ninja: build stopped: subcommand failed.
turn 12 · 1 tool call(s) · 17.2s turn · 17.2s LLM · 7,532 chars (click to expand)
turn 1217.2s turn · 17.2s LLM · 0.0s tools
reasoning
**Optimizing thread synchronization** I’m thinking about reducing syncthreads by comparing different versions: v6 using 32 iterations versus v8 with 16 iterations. That seems promising! Each block is 256 rows, leading to 32,768 blocks, which provides enough occupancy for my needs. I wonder if formatting it as v8 will yield better efficiency. Let’s give this a try and see what the results look like! It feels like I might be on the right track here.
call → run_correctness
callrun_correctness { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor masked_cumsum_forward(torch::Tensor x, torch::Tensor mask);\n'''\n\ncuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\nconstexpr int BLOCK_THREADS = 256;\nconstexpr int ITEMS = 8;\nconstexpr int TILE = BLOCK_THREADS * ITEMS;\n\n__device__ __forceinline__ float warp_inclusive_scan(float v) {\n unsigned m = 0xffffffffu;\n #pragma unroll\n for (int offset = 1; offset < 32; offset <<= 1) {\n float n = __shfl_up_sync(m, v, offset);\n if ((threadIdx.x & 31) >= offset) v += n;\n }\n return v;\n}\n\ntemplate <typename MaskT, bool MASK_IS_FLOAT>\n__global__ __launch_bounds__(BLOCK_THREADS, 2)\nvoid masked_cumsum_vec8_kernel(const float* __restrict__ x,\n const MaskT* __restrict__ mask,\n float* __restrict__ out,\n int rows, int cols) {\n int row = blockIdx.x;\n int tid = threadIdx.x;\n int lane = tid & 31;\n int warp = tid >> 5;\n __shared__ float warp_sums[8];\n int64_t base = (int64_t)row * cols;\n int tiles = (cols + TILE - 1) / TILE;\n float carry = 0.0f;\n for (int t = 0; t < tiles; ++t) {\n int col0 = t * TILE + tid * ITEMS;\n int64_t idx = base + col0;\n float a0=0.f,a1=0.f,a2=0.f,a3=0.f,a4=0.f,a5=0.f,a6=0.f,a7=0.f;\n if (col0 + 7 < cols) {\n float4 x0 = reinterpret_cast<const float4*>(x + idx)[0];\n float4 x1 = reinterpret_cast<const float4*>(x + idx + 4)[0];\n if constexpr (MASK_IS_FLOAT) {\n float4 m0 = reinterpret_cast<const float4*>(mask + idx)[0];\n float4 m1 = reinterpret_cast<const float4*>(mask + idx + 4)[0];\n a0=x0.x*m0.x; a1=x0.y*m0.y; a2=x0.z*m0.z; a3=x0.w*m0.w;\n a4=x1.x*m1.x; a5=x1.y*m1.y; a6=x1.z*m1.z; a7=x1.w*m1.w;\n } else {\n uchar4 m0 = reinterpret_cast<const uchar4*>(mask + idx)[0];\n uchar4 m1 = reinterpret_cast<const uchar4*>(mask + idx + 4)[0];\n a0=m0.x?x0.x:0.f; a1=m0.y?x0.y:0.f; a2=m0.z?x0.z:0.f; a3=m0.w?x0.w:0.f;\n a4=m1.x?x1.x:0.f; a5=m1.y?x1.y:0.f; a6=m1.z?x1.z:0.f; a7=m1.w?x1.w:0.f;\n }\n } else {\n if (col0+0 < cols) { if constexpr (MASK_IS_FLOAT) a0=x[idx+0]*mask[idx+0]; else a0=mask[idx+0]?x[idx+0]:0.f; }\n if (col0+1 < cols) { if constexpr (MASK_IS_FLOAT) a1=x[idx+1]*mask[idx+1]; else a1=mask[idx+1]?x[idx+1]:0.f; }\n if (col0+2 < cols) { if constexpr (MASK_IS_FLOAT) a2=x[idx+2]*mask[idx+2]; else a2=mask[idx+2]?x[idx+2]:0.f; }\n if (col0+3 < cols) { if constexpr (MASK_IS_FLOAT) a3=x[idx+3]*mask[idx+3]; else a3=mask[idx+3]?x[idx+3]:0.f; }\n if (col0+4 < cols) { if constexpr (MASK_IS_FLOAT) a4=x[idx+4]*mask[idx+4]; else a4=mask[idx+4]?x[idx+4]:0.f; }\n if (col0+5 < cols) { if constexpr (MASK_IS_FLOAT) a5=x[idx+5]*mask[idx+5]; else a5=mask[idx+5]?x[idx+5]:0.f; }\n if (col0+6 < cols) { if constexpr (MASK_IS_FLOAT) a6=x[idx+6]*mask[idx+6]; else a6=mask[idx+6]?x[idx+6]:0.f; }\n if (col0+7 < cols) { if constexpr (MASK_IS_FLOAT) a7=x[idx+7]*mask[idx+7]; else a7=mask[idx+7]?x[idx+7]:0.f; }\n }\n float p0=a0; float p1=p0+a1; float p2=p1+a2; float p3=p2+a3;\n float p4=p3+a4; float p5=p4+a5; float p6=p5+a6; float p7=p6+a7;\n float thread_sum = p7;\n float s = warp_inclusive_scan(thread_sum);\n if (lane == 31) warp_sums[warp] = s;\n __syncthreads();\n if (warp == 0) {\n float w = (lane < 8) ? warp_sums[lane] : 0.0f;\n float ws = warp_inclusive_scan(w);\n if (lane < 8) warp_sums[lane] = ws;\n }\n __syncthreads();\n float offset = carry + (warp ? warp_sums[warp-1] : 0.0f) + s - thread_sum;\n float4 o0, o1;\n o0.x=offset+p0; o0.y=offset+p1; o0.z=offset+p2; o0.w=offset+p3;\n o1.x=offset+p4; o1.y=offset+p5; o1.z=offset+p6; o1.w=offset+p7;\n if (col0 + 7 < cols) {\n reinterpret_cast<float4*>(out + idx)[0] = o0;\n reinterpret_cast<float4*>(out + idx + 4)[0] = o1;\n } else {\n if (col0+0 < cols) out[idx+0]=o0.x;\n if (col0+1 < cols) out[idx+1]=o0.y;\n if (col0+2 < cols) out[idx+2]=o0.z;\n if (col0+3 < cols) out[idx+3]=o0.w;\n if (col0+4 < cols) out[idx+4]=o1.x;\n if (col0+5 < cols) out[idx+5]=o1.y;\n if (col0+6 < cols) out[idx+6]=o1.z;\n if (col0+7 < cols) out[idx+7]=o1.w;\n }\n carry += warp_sums[7];\n __syncthreads();\n }\n}\n\ntorch::Tensor masked_cumsum_forward(torch::Tensor x, torch::Tensor mask) {\n auto out = torch::empty_like(x);\n int rows = (int)x.size(0);\n int cols = (int)x.size(1);\n dim3 grid(rows), block(BLOCK_THREADS);\n if (mask.scalar_type() == at::kBool) {\n const unsigned char* mp = static_cast<const unsigned char*>(mask.data_ptr());\n masked_cumsum_vec8_kernel<unsigned char, false><<<grid, block, 0, at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(), mp, out.data_ptr<float>(), rows, cols);\n } else {\n masked_cumsum_vec8_kernel<float, true><<<grid, block, 0, at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(), mask.data_ptr<float>(), out.data_ptr<float>(), rows, cols);\n }\n return out;\n}\n'''\n\n_ext = load_inline(name='masked_cumsum_ext_v8', cpp_sources=cpp_src, cuda_sources=cuda_src, functions=['masked_cumsum_forward'], extra_cuda_cflags=['-O3'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, dim):\n super(ModelNew, self).__init__()\n self.dim = dim\n def forward(self, x, mask):\n return _ext.masked_cumsum_forward(x, mask)\n" }
result ← run_correctness (ok)
okrun_correctness run_correctness PASSED: (5 / 5) trials all matched the reference. Numerical precision: max_abs_err=3.96e-02 mean_abs_err=2.61e-03 max_rel_err=8.72e-04
turn 13 · 1 tool call(s) · 12.3s turn · 12.3s LLM · 8,594 chars (click to expand)
turn 1312.3s turn · 12.3s LLM · 0.0s tools
call → profile_kernel
callprofile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor masked_cumsum_forward(torch::Tensor x, torch::Tensor mask);\n'''\n\ncuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\nconstexpr int BLOCK_THREADS = 256;\nconstexpr int ITEMS = 8;\nconstexpr int TILE = BLOCK_THREADS * ITEMS;\n\n__device__ __forceinline__ float warp_inclusive_scan(float v) {\n unsigned m = 0xffffffffu;\n #pragma unroll\n for (int offset = 1; offset < 32; offset <<= 1) {\n float n = __shfl_up_sync(m, v, offset);\n if ((threadIdx.x & 31) >= offset) v += n;\n }\n return v;\n}\n\ntemplate <typename MaskT, bool MASK_IS_FLOAT>\n__global__ __launch_bounds__(BLOCK_THREADS, 2)\nvoid masked_cumsum_vec8_kernel(const float* __restrict__ x,\n const MaskT* __restrict__ mask,\n float* __restrict__ out,\n int rows, int cols) {\n int row = blockIdx.x;\n int tid = threadIdx.x;\n int lane = tid & 31;\n int warp = tid >> 5;\n __shared__ float warp_sums[8];\n int64_t base = (int64_t)row * cols;\n int tiles = (cols + TILE - 1) / TILE;\n float carry = 0.0f;\n for (int t = 0; t < tiles; ++t) {\n int col0 = t * TILE + tid * ITEMS;\n int64_t idx = base + col0;\n float a0=0.f,a1=0.f,a2=0.f,a3=0.f,a4=0.f,a5=0.f,a6=0.f,a7=0.f;\n if (col0 + 7 < cols) {\n float4 x0 = reinterpret_cast<const float4*>(x + idx)[0];\n float4 x1 = reinterpret_cast<const float4*>(x + idx + 4)[0];\n if constexpr (MASK_IS_FLOAT) {\n float4 m0 = reinterpret_cast<const float4*>(mask + idx)[0];\n float4 m1 = reinterpret_cast<const float4*>(mask + idx + 4)[0];\n a0=x0.x*m0.x; a1=x0.y*m0.y; a2=x0.z*m0.z; a3=x0.w*m0.w;\n a4=x1.x*m1.x; a5=x1.y*m1.y; a6=x1.z*m1.z; a7=x1.w*m1.w;\n } else {\n uchar4 m0 = reinterpret_cast<const uchar4*>(mask + idx)[0];\n uchar4 m1 = reinterpret_cast<const uchar4*>(mask + idx + 4)[0];\n a0=m0.x?x0.x:0.f; a1=m0.y?x0.y:0.f; a2=m0.z?x0.z:0.f; a3=m0.w?x0.w:0.f;\n a4=m1.x?x1.x:0.f; a5=m1.y?x1.y:0.f; a6=m1.z?x1.z:0.f; a7=m1.w?x1.w:0.f;\n }\n } else {\n if (col0+0 < cols) { if constexpr (MASK_IS_FLOAT) a0=x[idx+0]*mask[idx+0]; else a0=mask[idx+0]?x[idx+0]:0.f; }\n if (col0+1 < cols) { if constexpr (MASK_IS_FLOAT) a1=x[idx+1]*mask[idx+1]; else a1=mask[idx+1]?x[idx+1]:0.f; }\n if (col0+2 < cols) { if constexpr (MASK_IS_FLOAT) a2=x[idx+2]*mask[idx+2]; else a2=mask[idx+2]?x[idx+2]:0.f; }\n if (col0+3 < cols) { if constexpr (MASK_IS_FLOAT) a3=x[idx+3]*mask[idx+3]; else a3=mask[idx+3]?x[idx+3]:0.f; }\n if (col0+4 < cols) { if constexpr (MASK_IS_FLOAT) a4=x[idx+4]*mask[idx+4]; else a4=mask[idx+4]?x[idx+4]:0.f; }\n if (col0+5 < cols) { if constexpr (MASK_IS_FLOAT) a5=x[idx+5]*mask[idx+5]; else a5=mask[idx+5]?x[idx+5]:0.f; }\n if (col0+6 < cols) { if constexpr (MASK_IS_FLOAT) a6=x[idx+6]*mask[idx+6]; else a6=mask[idx+6]?x[idx+6]:0.f; }\n if (col0+7 < cols) { if constexpr (MASK_IS_FLOAT) a7=x[idx+7]*mask[idx+7]; else a7=mask[idx+7]?x[idx+7]:0.f; }\n }\n float p0=a0; float p1=p0+a1; float p2=p1+a2; float p3=p2+a3;\n float p4=p3+a4; float p5=p4+a5; float p6=p5+a6; float p7=p6+a7;\n float thread_sum = p7;\n float s = warp_inclusive_scan(thread_sum);\n if (lane == 31) warp_sums[warp] = s;\n __syncthreads();\n if (warp == 0) {\n float w = (lane < 8) ? warp_sums[lane] : 0.0f;\n float ws = warp_inclusive_scan(w);\n if (lane < 8) warp_sums[lane] = ws;\n }\n __syncthreads();\n float offset = carry + (warp ? warp_sums[warp-1] : 0.0f) + s - thread_sum;\n float4 o0, o1;\n o0.x=offset+p0; o0.y=offset+p1; o0.z=offset+p2; o0.w=offset+p3;\n o1.x=offset+p4; o1.y=offset+p5; o1.z=offset+p6; o1.w=offset+p7;\n if (col0 + 7 < cols) {\n reinterpret_cast<float4*>(out + idx)[0] = o0;\n reinterpret_cast<float4*>(out + idx + 4)[0] = o1;\n } else {\n if (col0+0 < cols) out[idx+0]=o0.x;\n if (col0+1 < cols) out[idx+1]=o0.y;\n if (col0+2 < cols) out[idx+2]=o0.z;\n if (col0+3 < cols) out[idx+3]=o0.w;\n if (col0+4 < cols) out[idx+4]=o1.x;\n if (col0+5 < cols) out[idx+5]=o1.y;\n if (col0+6 < cols) out[idx+6]=o1.z;\n if (col0+7 < cols) out[idx+7]=o1.w;\n }\n carry += warp_sums[7];\n __syncthreads();\n }\n}\n\ntorch::Tensor masked_cumsum_forward(torch::Tensor x, torch::Tensor mask) {\n auto out = torch::empty_like(x);\n int rows = (int)x.size(0);\n int cols = (int)x.size(1);\n dim3 grid(rows), block(BLOCK_THREADS);\n if (mask.scalar_type() == at::kBool) {\n const unsigned char* mp = static_cast<const unsigned char*>(mask.data_ptr());\n masked_cumsum_vec8_kernel<unsigned char, false><<<grid, block, 0, at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(), mp, out.data_ptr<float>(), rows, cols);\n } else {\n masked_cumsum_vec8_kernel<float, true><<<grid, block, 0, at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(), mask.data_ptr<float>(), out.data_ptr<float>(), rows, cols);\n }\n return out;\n}\n'''\n_ext = load_inline(name='masked_cumsum_ext_v8p', cpp_sources=cpp_src, cuda_sources=cuda_src, functions=['masked_cumsum_forward'], extra_cuda_cflags=['-O3'], verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self, dim): super(ModelNew,self).__init__(); self.dim=dim\n def forward(self,x,mask): return _ext.masked_cumsum_forward(x,mask)\n" }
result ← profile_kernel (ok)
okprofile_kernel profile_kernel PASSED: profiling complete. === Kernel Profile Summary === --- Kernel Breakdown (by GPU time) --- #1: aten::empty_like 24436.0μs (50%) calls=1 #2: aten::empty_strided 24405.0μs (50%) calls=1 --- Timing --- GPU time: 4374.27 μs [↑26.0μs REGRESSED] --- Memory Bandwidth --- DRAM BW: 2946.9/3350 GB/s (88.0%) [↓0.4% REGRESSED] read=1967.5 write=979.4 GB/s Hit rates: L2=47.7% --- Compute Throughput --- Pipe util: FMA=3.7%, Tensor=0.2% --- Roofline Analysis --- Ridge point (FP32): 20.00 FLOPs/byte Classification: UNKNOWN --- Occupancy --- Achieved: 74.4% [↓24.9% REGRESSED] Eligible warps/cycle: 0.13 WARNING: <1 eligible warp/cycle = latency-bound, needs more parallelism Launch: regs/thread=36, smem/blk=1152B, block=256, grid=32768 --- Memory Access Quality --- Global ld coalescing: 32.00 sectors/req (32.0x wasted BW) --- Warp Stall Reasons (% of issue cycles) --- long_scoreboard 6719.7% #################### short_scoreboard 2779.5% #################### mio_throttle 602.1% #################### --- Optimization Hints --- -> 6720% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=32.0 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set. -> Global load coalescing=32.0x (ideal=1.0) -> 32x wasted DRAM BW. Make per-warp accesses contiguous. --- Delta vs Previous Iteration --- GPU time: 4348.22→4374.27μs (↑26.05) REGRESSED DRAM BW%: 88.38→87.97% (↓0.41) REGRESSED Occupancy: 99.32→74.38% (↓24.95) REGRESSED Eligible warps/cyc: 0.19→0.13 (↓0.06) REGRESSED Ld sectors/req: 16.00→32.00 (↑16.00) REGRESSED L2 hit%: 34.17→47.68% (↑13.51) ok
turn 14 · 1 tool call(s) · 10.3s turn · 10.3s LLM · 7,229 chars (click to expand)
turn 1410.3s turn · 10.3s LLM · 0.0s tools
call → submit_kernel
callsubmit_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor masked_cumsum_forward(torch::Tensor x, torch::Tensor mask);\n'''\n\ncuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\nconstexpr int BLOCK_THREADS = 256;\nconstexpr int ITEMS = 4;\nconstexpr int TILE = BLOCK_THREADS * ITEMS;\n\n__device__ __forceinline__ float warp_inclusive_scan(float v) {\n unsigned m = 0xffffffffu;\n #pragma unroll\n for (int offset = 1; offset < 32; offset <<= 1) {\n float n = __shfl_up_sync(m, v, offset);\n if ((threadIdx.x & 31) >= offset) v += n;\n }\n return v;\n}\n\ntemplate <typename MaskT, bool MASK_IS_FLOAT>\n__global__ __launch_bounds__(BLOCK_THREADS, 2)\nvoid masked_cumsum_vec4_kernel(const float* __restrict__ x,\n const MaskT* __restrict__ mask,\n float* __restrict__ out,\n int rows, int cols) {\n int row = blockIdx.x;\n int tid = threadIdx.x;\n int lane = tid & 31;\n int warp = tid >> 5;\n __shared__ float warp_sums[8];\n int64_t base = (int64_t)row * cols;\n int tiles = (cols + TILE - 1) / TILE;\n float carry = 0.0f;\n\n for (int t = 0; t < tiles; ++t) {\n int col0 = t * TILE + tid * ITEMS;\n int64_t idx = base + col0;\n float a0=0.f, a1=0.f, a2=0.f, a3=0.f;\n if (col0 + 3 < cols) {\n float4 xv = reinterpret_cast<const float4*>(x + idx)[0];\n if constexpr (MASK_IS_FLOAT) {\n float4 mv = reinterpret_cast<const float4*>(mask + idx)[0];\n a0 = xv.x * mv.x; a1 = xv.y * mv.y; a2 = xv.z * mv.z; a3 = xv.w * mv.w;\n } else {\n uchar4 mv = reinterpret_cast<const uchar4*>(mask + idx)[0];\n a0 = mv.x ? xv.x : 0.f; a1 = mv.y ? xv.y : 0.f; a2 = mv.z ? xv.z : 0.f; a3 = mv.w ? xv.w : 0.f;\n }\n } else {\n if (col0 + 0 < cols) { if constexpr (MASK_IS_FLOAT) a0 = x[idx+0] * mask[idx+0]; else a0 = mask[idx+0] ? x[idx+0] : 0.f; }\n if (col0 + 1 < cols) { if constexpr (MASK_IS_FLOAT) a1 = x[idx+1] * mask[idx+1]; else a1 = mask[idx+1] ? x[idx+1] : 0.f; }\n if (col0 + 2 < cols) { if constexpr (MASK_IS_FLOAT) a2 = x[idx+2] * mask[idx+2]; else a2 = mask[idx+2] ? x[idx+2] : 0.f; }\n if (col0 + 3 < cols) { if constexpr (MASK_IS_FLOAT) a3 = x[idx+3] * mask[idx+3]; else a3 = mask[idx+3] ? x[idx+3] : 0.f; }\n }\n float p0 = a0;\n float p1 = p0 + a1;\n float p2 = p1 + a2;\n float p3 = p2 + a3;\n float thread_sum = p3;\n float s = warp_inclusive_scan(thread_sum);\n if (lane == 31) warp_sums[warp] = s;\n __syncthreads();\n if (warp == 0) {\n float w = (lane < 8) ? warp_sums[lane] : 0.0f;\n float ws = warp_inclusive_scan(w);\n if (lane < 8) warp_sums[lane] = ws;\n }\n __syncthreads();\n float offset = carry;\n if (warp != 0) offset += warp_sums[warp - 1];\n offset += s - thread_sum;\n float4 ov; ov.x = offset + p0; ov.y = offset + p1; ov.z = offset + p2; ov.w = offset + p3;\n if (col0 + 3 < cols) reinterpret_cast<float4*>(out + idx)[0] = ov;\n else {\n if (col0 + 0 < cols) out[idx+0] = ov.x;\n if (col0 + 1 < cols) out[idx+1] = ov.y;\n if (col0 + 2 < cols) out[idx+2] = ov.z;\n if (col0 + 3 < cols) out[idx+3] = ov.w;\n }\n carry += warp_sums[7];\n __syncthreads();\n }\n}\n\ntorch::Tensor masked_cumsum_forward(torch::Tensor x, torch::Tensor mask) {\n auto out = torch::empty_like(x);\n int rows = (int)x.size(0);\n int cols = (int)x.size(1);\n dim3 grid(rows), block(BLOCK_THREADS);\n if (mask.scalar_type() == at::kBool) {\n const unsigned char* mp = static_cast<const unsigned char*>(mask.data_ptr());\n masked_cumsum_vec4_kernel<unsigned char, false><<<grid, block, 0, at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(), mp, out.data_ptr<float>(), rows, cols);\n } else {\n masked_cumsum_vec4_kernel<float, true><<<grid, block, 0, at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(), mask.data_ptr<float>(), out.data_ptr<float>(), rows, cols);\n }\n return out;\n}\n'''\n\n_ext = load_inline(name='masked_cumsum_ext_final', cpp_sources=cpp_src, cuda_sources=cuda_src, functions=['masked_cumsum_forward'], extra_cuda_cflags=['-O3'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, dim):\n super(ModelNew, self).__init__()\n self.dim = dim\n def forward(self, x, mask):\n return _ext.masked_cumsum_forward(x, mask)\n" }
result ← submit_kernel (fail)
failsubmit_kernel submit_kernel FAILED: unexpected error during tool execution. _WorkItemTimeout: work item exceeded soft timeout budget Traceback (most recent call last): File "/scratch/abaner/KernelBench/src/kernelbench/agent/agent.py", line 627, in _execute_tool return tool.execute(self.ctx, **args) File "/scratch/abaner/KernelBench/src/kernelbench/agent/tools.py", line 770, in execute return ctx.eval_client.submit_kernel(ctx, kernel_code) File "/scratch/abaner/KernelBench/src/kernelbench/agent/eval_client.py", line 50, in submit_kernel return self._call("submit", args) File "/scratch/abaner/KernelBench/src/kernelbench/agent/eval_client.py", line 117, in _call payload = self._response_q.get(timeout=remaining) File "<string>", line 2, in get File "/opt/conda/lib/python3.10/multiprocessing/managers.py", line 818, in _callmethod kind, result = conn.recv() File "/opt/conda/lib/python3.10/multiprocessing/connection.py", line 250, in recv buf = self._recv_bytes() File "/opt/conda/lib/python3.10/multiprocessing/connection.py", line 414, in _recv_bytes buf = self._recv(4) File "/opt/conda/lib/python3.10/multiprocessing/connection.py", line 379, in _recv chunk = read(handle, remaining) File "/scratch/abaner/KernelBench/scripts/run_sweep.py", line 161, in _alarm_handler raise _WorkItemTimeout("work item exceeded soft timeout budget") __mp_main__._WorkItemTimeout: work item exceeded soft timeout budget
final submitted kernel (117 lines, 4,738 chars) — full source, untruncated
import torch
import torch.nn as nn
from torch.utils.cpp_extension import load_inline

cpp_src = r'''
#include <torch/extension.h>
torch::Tensor masked_cumsum_forward(torch::Tensor x, torch::Tensor mask);
'''

cuda_src = r'''
#include <torch/extension.h>
#include <ATen/cuda/CUDAContext.h>
#include <cuda.h>
#include <cuda_runtime.h>

constexpr int BLOCK_THREADS = 256;
constexpr int ITEMS = 4;
constexpr int TILE = BLOCK_THREADS * ITEMS;

__device__ __forceinline__ float warp_inclusive_scan(float v) {
    unsigned m = 0xffffffffu;
    #pragma unroll
    for (int offset = 1; offset < 32; offset <<= 1) {
        float n = __shfl_up_sync(m, v, offset);
        if ((threadIdx.x & 31) >= offset) v += n;
    }
    return v;
}

template <typename MaskT, bool MASK_IS_FLOAT>
__global__ __launch_bounds__(BLOCK_THREADS, 2)
void masked_cumsum_vec4_kernel(const float* __restrict__ x,
                               const MaskT* __restrict__ mask,
                               float* __restrict__ out,
                               int rows, int cols) {
    int row = blockIdx.x;
    int tid = threadIdx.x;
    int lane = tid & 31;
    int warp = tid >> 5;
    __shared__ float warp_sums[8];
    int64_t base = (int64_t)row * cols;
    int tiles = (cols + TILE - 1) / TILE;
    float carry = 0.0f;

    for (int t = 0; t < tiles; ++t) {
        int col0 = t * TILE + tid * ITEMS;
        int64_t idx = base + col0;
        float a0=0.f, a1=0.f, a2=0.f, a3=0.f;
        if (col0 + 3 < cols) {
            float4 xv = reinterpret_cast<const float4*>(x + idx)[0];
            if constexpr (MASK_IS_FLOAT) {
                float4 mv = reinterpret_cast<const float4*>(mask + idx)[0];
                a0 = xv.x * mv.x; a1 = xv.y * mv.y; a2 = xv.z * mv.z; a3 = xv.w * mv.w;
            } else {
                uchar4 mv = reinterpret_cast<const uchar4*>(mask + idx)[0];
                a0 = mv.x ? xv.x : 0.f; a1 = mv.y ? xv.y : 0.f; a2 = mv.z ? xv.z : 0.f; a3 = mv.w ? xv.w : 0.f;
            }
        } else {
            if (col0 + 0 < cols) { if constexpr (MASK_IS_FLOAT) a0 = x[idx+0] * mask[idx+0]; else a0 = mask[idx+0] ? x[idx+0] : 0.f; }
            if (col0 + 1 < cols) { if constexpr (MASK_IS_FLOAT) a1 = x[idx+1] * mask[idx+1]; else a1 = mask[idx+1] ? x[idx+1] : 0.f; }
            if (col0 + 2 < cols) { if constexpr (MASK_IS_FLOAT) a2 = x[idx+2] * mask[idx+2]; else a2 = mask[idx+2] ? x[idx+2] : 0.f; }
            if (col0 + 3 < cols) { if constexpr (MASK_IS_FLOAT) a3 = x[idx+3] * mask[idx+3]; else a3 = mask[idx+3] ? x[idx+3] : 0.f; }
        }
        float p0 = a0;
        float p1 = p0 + a1;
        float p2 = p1 + a2;
        float p3 = p2 + a3;
        float thread_sum = p3;
        float s = warp_inclusive_scan(thread_sum);
        if (lane == 31) warp_sums[warp] = s;
        __syncthreads();
        if (warp == 0) {
            float w = (lane < 8) ? warp_sums[lane] : 0.0f;
            float ws = warp_inclusive_scan(w);
            if (lane < 8) warp_sums[lane] = ws;
        }
        __syncthreads();
        float offset = carry;
        if (warp != 0) offset += warp_sums[warp - 1];
        offset += s - thread_sum;
        float4 ov; ov.x = offset + p0; ov.y = offset + p1; ov.z = offset + p2; ov.w = offset + p3;
        if (col0 + 3 < cols) reinterpret_cast<float4*>(out + idx)[0] = ov;
        else {
            if (col0 + 0 < cols) out[idx+0] = ov.x;
            if (col0 + 1 < cols) out[idx+1] = ov.y;
            if (col0 + 2 < cols) out[idx+2] = ov.z;
            if (col0 + 3 < cols) out[idx+3] = ov.w;
        }
        carry += warp_sums[7];
        __syncthreads();
    }
}

torch::Tensor masked_cumsum_forward(torch::Tensor x, torch::Tensor mask) {
    auto out = torch::empty_like(x);
    int rows = (int)x.size(0);
    int cols = (int)x.size(1);
    dim3 grid(rows), block(BLOCK_THREADS);
    if (mask.scalar_type() == at::kBool) {
        const unsigned char* mp = static_cast<const unsigned char*>(mask.data_ptr());
        masked_cumsum_vec4_kernel<unsigned char, false><<<grid, block, 0, at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(), mp, out.data_ptr<float>(), rows, cols);
    } else {
        masked_cumsum_vec4_kernel<float, true><<<grid, block, 0, at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(), mask.data_ptr<float>(), out.data_ptr<float>(), rows, cols);
    }
    return out;
}
'''

_ext = load_inline(name='masked_cumsum_ext_final', cpp_sources=cpp_src, cuda_sources=cuda_src, functions=['masked_cumsum_forward'], extra_cuda_cflags=['-O3'], verbose=False)

class ModelNew(nn.Module):
    def __init__(self, dim):
        super(ModelNew, self).__init__()
        self.dim = dim
    def forward(self, x, mask):
        return _ext.masked_cumsum_forward(x, mask)