L1 · problem 78
gpt-5.5-priority · original · l12_all_gpt
← all variants← original← gpt-5.5-prioritygenerated 2026-05-28 03:33:15 UTC
correct 78_conv_transposed_2D_asymmetric_input_asymmetric_kernel___padded__.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
24.70 μs
ref runtime
2.41 μs
speedup
0.10x
started
2026-05-04T03:13:03Z
timingkernelbench.timing
kernel runtime
24.70 μs
ref runtime
2.41 μs
speedup
0.10x
kernel mean / median / std
24.70 / — / 0.18 μs
ref mean / median / std
2.41 / — / 0.14 μs
numerical precisioneval_kernel_against_ref
max abs error
6.69e-05
mean abs error
1.19e-06
max rel error
856.51
mean rel error
3.65e-05
gpu memoryextended_metrics.measure_memory
kernel peak
1057.16 MB
ref peak
2081.25 MB
kernel / ref
0.51x
kernel launches / fusiontorch.profiler
kernel launches
2
ref launches
4
fusion ratio (ref / kernel)
2.00x
total cuda time
31 μs
energyNVML (pynvml)
kernel mJ / run
8384.98
ref mJ / run
1092.42
ratio (ref / kernel)
0.13x
avg power
338.40 W
ref avg power
457.13 W
SOL (speed-of-light)nsight_hardware_counters
SOL score (0–1)
0.194
dram util %
6.0%
compute util %
19.4%
bottleneck
compute-bound
dominant pipe
FP32
arithmetic intensity
—
ridge point
20.00
roofline / occupancynsight_hardware_counters
dram bandwidth
200.85 GB/s
dram util %
6.0%
fp32 tflops
—
fp32 util %
—
fp16 tflops
—
occupancy %
34.2%
bottleneck
compute-bound
dominant pipe %
19.4%
arithmetic intensity
—
ridge point
20.00
L1 hit %
—
L2 hit %
68.8%
ld sectors / req
4.07
st sectors / req
—
regs / thread
64
smem / block
1024 B
block size
128
peak bw
3350.00 GB/s
peak fp32 tflops
67.00
peak fp16 tflops
1979.00
warp stalls (top 5)nsight
long_scoreboard
150.2%
short_scoreboard
6.3%
mio_throttle
0.1%
reference PyTorch source (the prompt the agent sees)
import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Performs a 2D transposed convolution operation with asymmetric input and kernel, with optional padding.

    Args:
        in_channels (int): Number of channels in the input tensor.
        out_channels (int): Number of channels produced by the convolution.
        kernel_size (tuple): Size of the convolution kernel (height, width).
        stride (tuple, optional): Stride of the convolution (height, width). Defaults to (1, 1).
        padding (tuple, optional): Padding applied to the input (height, width). Defaults to (0, 0).
        bias (bool, optional): If `True`, adds a learnable bias to the output. Defaults to `False`.
    """
    def __init__(self, in_channels: int, out_channels: int, kernel_size: tuple, stride: tuple = (1, 1), padding: tuple = (0, 0), bias: bool = False):
        super(Model, self).__init__()
        self.conv_transpose2d = nn.ConvTranspose2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, bias=bias)
        
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Performs the 2D transposed convolution.

        Args:
            x (torch.Tensor): Input tensor of shape (batch_size, in_channels, height, width).

        Returns:
            torch.Tensor: Output tensor of shape (batch_size, out_channels, height_out, width_out).
        """
        return self.conv_transpose2d(x)

# Test code
batch_size = 8
in_channels = 32
out_channels = 32
kernel_size = (3, 7)
height = 512
width = 1024
stride = (1, 1)
padding = (1, 3)

def get_inputs():
    x = torch.rand(batch_size, in_channels, height, width)
    return [x]

def get_init_inputs():
    return [in_channels, out_channels, kernel_size, stride, padding]
turn 08.4s turn · 8.4s LLM · 0.0s tools
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) · 78.5s turn · 78.5s LLM · 10,057 chars (click to expand)
turn 178.5s turn · 78.5s 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>\n\ntorch::Tensor conv_transpose2d_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, int64_t sH, int64_t sW, int64_t pH, int64_t pW, bool has_bias);\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\n__global__ void deconv2d_oc4w4_kernel(const float* __restrict__ x,\n const float* __restrict__ w,\n const float* __restrict__ bias,\n float* __restrict__ y,\n int N, int H, int W, int has_bias) {\n // fixed Cin=Cout=32, kH=3,kW=7,pad=(1,3), stride=1, Hout=H,Wout=W\n int tid = blockIdx.x * blockDim.x + threadIdx.x;\n int W4 = (W + 3) >> 2;\n int total = N * 8 * H * W4; // 8 oc groups of 4\n if (tid >= total) return;\n int t = tid;\n int ow4 = t % W4; t /= W4;\n int oh = t % H; t /= H;\n int ocg = t & 7; t >>= 3;\n int n = t;\n int ow = ow4 << 2;\n int oc0 = ocg << 2;\n\n float a00=0.f,a01=0.f,a02=0.f,a03=0.f;\n float a10=0.f,a11=0.f,a12=0.f,a13=0.f;\n float a20=0.f,a21=0.f,a22=0.f,a23=0.f;\n float a30=0.f,a31=0.f,a32=0.f,a33=0.f;\n\n bool interior = (oh > 0 && oh < H-1 && ow >= 3 && ow + 6 < W); // all 4 lanes valid for all kw\n if (interior) {\n #pragma unroll\n for (int ic=0; ic<32; ++ic) {\n #pragma unroll\n for (int kh=0; kh<3; ++kh) {\n int ih = oh + 1 - kh;\n const float* xp = x + ((n*32 + ic)*H + ih)*W + (ow - 3);\n const float* wp = w + ((ic*32 + oc0)*3 + kh)*7;\n #pragma unroll\n for (int kw=0; kw<7; ++kw) {\n float xv0 = __ldg(xp + kw + 0);\n float xv1 = __ldg(xp + kw + 1);\n float xv2 = __ldg(xp + kw + 2);\n float xv3 = __ldg(xp + kw + 3);\n float w0 = __ldg(wp + 0*3*7 + kw);\n float w1 = __ldg(wp + 1*3*7 + kw);\n float w2 = __ldg(wp + 2*3*7 + kw);\n float w3 = __ldg(wp + 3*3*7 + kw);\n a00 = fmaf(xv0,w0,a00); a01 = fmaf(xv1,w0,a01); a02 = fmaf(xv2,w0,a02); a03 = fmaf(xv3,w0,a03);\n a10 = fmaf(xv0,w1,a10); a11 = fmaf(xv1,w1,a11); a12 = fmaf(xv2,w1,a12); a13 = fmaf(xv3,w1,a13);\n a20 = fmaf(xv0,w2,a20); a21 = fmaf(xv1,w2,a21); a22 = fmaf(xv2,w2,a22); a23 = fmaf(xv3,w2,a23);\n a30 = fmaf(xv0,w3,a30); a31 = fmaf(xv1,w3,a31); a32 = fmaf(xv2,w3,a32); a33 = fmaf(xv3,w3,a33);\n }\n }\n }\n } else {\n #pragma unroll\n for (int ic=0; ic<32; ++ic) {\n #pragma unroll\n for (int kh=0; kh<3; ++kh) {\n int ih = oh + 1 - kh;\n if ((unsigned)ih >= (unsigned)H) continue;\n #pragma unroll\n for (int kw=0; kw<7; ++kw) {\n int iw0 = ow + 3 - kw;\n float w0 = __ldg(w + ((ic*32 + oc0+0)*3 + kh)*7 + kw);\n float w1 = __ldg(w + ((ic*32 + oc0+1)*3 + kh)*7 + kw);\n float w2 = __ldg(w + ((ic*32 + oc0+2)*3 + kh)*7 + kw);\n float w3 = __ldg(w + ((ic*32 + oc0+3)*3 + kh)*7 + kw);\n if ((unsigned)(iw0+0) < (unsigned)W) { float xv=__ldg(x + ((n*32+ic)*H+ih)*W + iw0+0); a00=fmaf(xv,w0,a00); a10=fmaf(xv,w1,a10); a20=fmaf(xv,w2,a20); a30=fmaf(xv,w3,a30); }\n if ((unsigned)(iw0+1) < (unsigned)W) { float xv=__ldg(x + ((n*32+ic)*H+ih)*W + iw0+1); a01=fmaf(xv,w0,a01); a11=fmaf(xv,w1,a11); a21=fmaf(xv,w2,a21); a31=fmaf(xv,w3,a31); }\n if ((unsigned)(iw0+2) < (unsigned)W) { float xv=__ldg(x + ((n*32+ic)*H+ih)*W + iw0+2); a02=fmaf(xv,w0,a02); a12=fmaf(xv,w1,a12); a22=fmaf(xv,w2,a22); a32=fmaf(xv,w3,a32); }\n if ((unsigned)(iw0+3) < (unsigned)W) { float xv=__ldg(x + ((n*32+ic)*H+ih)*W + iw0+3); a03=fmaf(xv,w0,a03); a13=fmaf(xv,w1,a13); a23=fmaf(xv,w2,a23); a33=fmaf(xv,w3,a33); }\n }\n }\n }\n }\n if (has_bias) {\n float b0=__ldg(bias+oc0), b1=__ldg(bias+oc0+1), b2=__ldg(bias+oc0+2), b3=__ldg(bias+oc0+3);\n a00+=b0; a01+=b0; a02+=b0; a03+=b0;\n a10+=b1; a11+=b1; a12+=b1; a13+=b1;\n a20+=b2; a21+=b2; a22+=b2; a23+=b2;\n a30+=b3; a31+=b3; a32+=b3; a33+=b3;\n }\n float* y0 = y + ((n*32 + oc0+0)*H + oh)*W + ow;\n float* y1 = y + ((n*32 + oc0+1)*H + oh)*W + ow;\n float* y2 = y + ((n*32 + oc0+2)*H + oh)*W + ow;\n float* y3 = y + ((n*32 + oc0+3)*H + oh)*W + ow;\n if (ow + 3 < W) {\n y0[0]=a00; y0[1]=a01; y0[2]=a02; y0[3]=a03;\n y1[0]=a10; y1[1]=a11; y1[2]=a12; y1[3]=a13;\n y2[0]=a20; y2[1]=a21; y2[2]=a22; y2[3]=a23;\n y3[0]=a30; y3[1]=a31; y3[2]=a32; y3[3]=a33;\n } else {\n if (ow+0 < W) { y0[0]=a00; y1[0]=a10; y2[0]=a20; y3[0]=a30; }\n if (ow+1 < W) { y0[1]=a01; y1[1]=a11; y2[1]=a21; y3[1]=a31; }\n if (ow+2 < W) { y0[2]=a02; y1[2]=a12; y2[2]=a22; y3[2]=a32; }\n }\n}\n\n__global__ void deconv2d_generic_kernel(const float* __restrict__ x, const float* __restrict__ w,\n const float* __restrict__ bias, float* __restrict__ y,\n int N, int Cin, int H, int W, int Cout, int kH, int kW, int outH, int outW,\n int sH, int sW, int pH, int pW, int has_bias) {\n long long idx = (long long)blockIdx.x * blockDim.x + threadIdx.x;\n long long total = (long long)N*Cout*outH*outW;\n if (idx >= total) return;\n int ow = idx % outW; idx /= outW;\n int oh = idx % outH; idx /= outH;\n int oc = idx % Cout; int n = idx / Cout;\n float sum = has_bias ? bias[oc] : 0.f;\n for (int ic=0; ic<Cin; ++ic) {\n for (int kh=0; kh<kH; ++kh) {\n int th = oh + pH - kh;\n if (th % sH) continue;\n int ih = th / sH;\n if ((unsigned)ih >= (unsigned)H) continue;\n for (int kw=0; kw<kW; ++kw) {\n int tw = ow + pW - kw;\n if (tw % sW) continue;\n int iw = tw / sW;\n if ((unsigned)iw < (unsigned)W) {\n sum = fmaf(x[((n*Cin+ic)*H+ih)*W+iw], w[((ic*Cout+oc)*kH+kh)*kW+kw], sum);\n }\n }\n }\n }\n y[((n*Cout+oc)*outH+oh)*outW+ow] = sum;\n}\n\ntorch::Tensor conv_transpose2d_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, int64_t sH, int64_t sW, int64_t pH, int64_t pW, bool has_bias) {\n TORCH_CHECK(x.is_cuda() && weight.is_cuda(), \"CUDA tensors expected\");\n TORCH_CHECK(x.scalar_type() == torch::kFloat32, \"float32 only\");\n x = x.contiguous(); weight = weight.contiguous();\n int N=x.size(0), Cin=x.size(1), H=x.size(2), W=x.size(3);\n int kH=weight.size(2), kW=weight.size(3), Cout=weight.size(1);\n int outH = (H-1)*(int)sH - 2*(int)pH + kH;\n int outW = (W-1)*(int)sW - 2*(int)pW + kW;\n auto y = torch::empty({N, Cout, outH, outW}, x.options());\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n if (Cin==32 && Cout==32 && kH==3 && kW==7 && sH==1 && sW==1 && pH==1 && pW==3 && outH==H && outW==W) {\n int W4=(W+3)/4; int total=N*8*H*W4;\n deconv2d_oc4w4_kernel<<<(total+127)/128,128,0,stream>>>(x.data_ptr<float>(), weight.data_ptr<float>(), has_bias?bias.data_ptr<float>():nullptr, y.data_ptr<float>(), N,H,W,has_bias?1:0);\n } else {\n long long total=(long long)N*Cout*outH*outW;\n deconv2d_generic_kernel<<<(total+255)/256,256,0,stream>>>(x.data_ptr<float>(), weight.data_ptr<float>(), has_bias?bias.data_ptr<float>():nullptr, y.data_ptr<float>(), N,Cin,H,W,Cout,kH,kW,outH,outW,(int)sH,(int)sW,(int)pH,(int)pW,has_bias?1:0);\n }\n return y;\n}\n'''\n\n_ext = load_inline(name='deconv2d_ext_v1', cpp_sources=cpp_src, cuda_sources=cuda_src, functions=['conv_transpose2d_forward'], extra_cuda_cflags=['-O3', '--use_fast_math'], extra_cflags=['-O3'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels: int, out_channels: int, kernel_size: tuple, stride: tuple = (1, 1), padding: tuple = (0, 0), bias: bool = False):\n super().__init__()\n self.conv_transpose2d = nn.ConvTranspose2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, bias=bias)\n self.stride = stride\n self.padding = padding\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n b = self.conv_transpose2d.bias\n if b is None:\n b = torch.empty(0, device=x.device, dtype=x.dtype)\n has_bias = False\n else:\n has_bias = True\n return _ext.conv_transpose2d_forward(x, self.conv_transpose2d.weight, b, int(self.stride[0]), int(self.stride[1]), int(self.padding[0]), int(self.padding[1]), has_bias)\n" }
result ← compile_kernel (ok)
okcompile_kernel compile_kernel PASSED: kernel compiled without errors.
turn 2 · 1 tool call(s) · 19.2s turn · 19.2s LLM · 10,367 chars (click to expand)
turn 219.2s turn · 19.2s 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>\n\ntorch::Tensor conv_transpose2d_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, int64_t sH, int64_t sW, int64_t pH, int64_t pW, bool has_bias);\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\n__global__ void deconv2d_oc4w4_kernel(const float* __restrict__ x,\n const float* __restrict__ w,\n const float* __restrict__ bias,\n float* __restrict__ y,\n int N, int H, int W, int has_bias) {\n // fixed Cin=Cout=32, kH=3,kW=7,pad=(1,3), stride=1, Hout=H,Wout=W\n int tid = blockIdx.x * blockDim.x + threadIdx.x;\n int W4 = (W + 3) >> 2;\n int total = N * 8 * H * W4; // 8 oc groups of 4\n if (tid >= total) return;\n int t = tid;\n int ow4 = t % W4; t /= W4;\n int oh = t % H; t /= H;\n int ocg = t & 7; t >>= 3;\n int n = t;\n int ow = ow4 << 2;\n int oc0 = ocg << 2;\n\n float a00=0.f,a01=0.f,a02=0.f,a03=0.f;\n float a10=0.f,a11=0.f,a12=0.f,a13=0.f;\n float a20=0.f,a21=0.f,a22=0.f,a23=0.f;\n float a30=0.f,a31=0.f,a32=0.f,a33=0.f;\n\n bool interior = (oh > 0 && oh < H-1 && ow >= 3 && ow + 6 < W); // all 4 lanes valid for all kw\n if (interior) {\n #pragma unroll\n for (int ic=0; ic<32; ++ic) {\n #pragma unroll\n for (int kh=0; kh<3; ++kh) {\n int ih = oh + 1 - kh;\n const float* xp = x + ((n*32 + ic)*H + ih)*W + (ow - 3);\n const float* wp = w + ((ic*32 + oc0)*3 + kh)*7;\n #pragma unroll\n for (int kw=0; kw<7; ++kw) {\n float xv0 = __ldg(xp + kw + 0);\n float xv1 = __ldg(xp + kw + 1);\n float xv2 = __ldg(xp + kw + 2);\n float xv3 = __ldg(xp + kw + 3);\n float w0 = __ldg(wp + 0*3*7 + kw);\n float w1 = __ldg(wp + 1*3*7 + kw);\n float w2 = __ldg(wp + 2*3*7 + kw);\n float w3 = __ldg(wp + 3*3*7 + kw);\n a00 = fmaf(xv0,w0,a00); a01 = fmaf(xv1,w0,a01); a02 = fmaf(xv2,w0,a02); a03 = fmaf(xv3,w0,a03);\n a10 = fmaf(xv0,w1,a10); a11 = fmaf(xv1,w1,a11); a12 = fmaf(xv2,w1,a12); a13 = fmaf(xv3,w1,a13);\n a20 = fmaf(xv0,w2,a20); a21 = fmaf(xv1,w2,a21); a22 = fmaf(xv2,w2,a22); a23 = fmaf(xv3,w2,a23);\n a30 = fmaf(xv0,w3,a30); a31 = fmaf(xv1,w3,a31); a32 = fmaf(xv2,w3,a32); a33 = fmaf(xv3,w3,a33);\n }\n }\n }\n } else {\n #pragma unroll\n for (int ic=0; ic<32; ++ic) {\n #pragma unroll\n for (int kh=0; kh<3; ++kh) {\n int ih = oh + 1 - kh;\n if ((unsigned)ih >= (unsigned)H) continue;\n #pragma unroll\n for (int kw=0; kw<7; ++kw) {\n int iw0 = ow + 3 - kw;\n float w0 = __ldg(w + ((ic*32 + oc0+0)*3 + kh)*7 + kw);\n float w1 = __ldg(w + ((ic*32 + oc0+1)*3 + kh)*7 + kw);\n float w2 = __ldg(w + ((ic*32 + oc0+2)*3 + kh)*7 + kw);\n float w3 = __ldg(w + ((ic*32 + oc0+3)*3 + kh)*7 + kw);\n if ((unsigned)(iw0+0) < (unsigned)W) { float xv=__ldg(x + ((n*32+ic)*H+ih)*W + iw0+0); a00=fmaf(xv,w0,a00); a10=fmaf(xv,w1,a10); a20=fmaf(xv,w2,a20); a30=fmaf(xv,w3,a30); }\n if ((unsigned)(iw0+1) < (unsigned)W) { float xv=__ldg(x + ((n*32+ic)*H+ih)*W + iw0+1); a01=fmaf(xv,w0,a01); a11=fmaf(xv,w1,a11); a21=fmaf(xv,w2,a21); a31=fmaf(xv,w3,a31); }\n if ((unsigned)(iw0+2) < (unsigned)W) { float xv=__ldg(x + ((n*32+ic)*H+ih)*W + iw0+2); a02=fmaf(xv,w0,a02); a12=fmaf(xv,w1,a12); a22=fmaf(xv,w2,a22); a32=fmaf(xv,w3,a32); }\n if ((unsigned)(iw0+3) < (unsigned)W) { float xv=__ldg(x + ((n*32+ic)*H+ih)*W + iw0+3); a03=fmaf(xv,w0,a03); a13=fmaf(xv,w1,a13); a23=fmaf(xv,w2,a23); a33=fmaf(xv,w3,a33); }\n }\n }\n }\n }\n if (has_bias) {\n float b0=__ldg(bias+oc0), b1=__ldg(bias+oc0+1), b2=__ldg(bias+oc0+2), b3=__ldg(bias+oc0+3);\n a00+=b0; a01+=b0; a02+=b0; a03+=b0;\n a10+=b1; a11+=b1; a12+=b1; a13+=b1;\n a20+=b2; a21+=b2; a22+=b2; a23+=b2;\n a30+=b3; a31+=b3; a32+=b3; a33+=b3;\n }\n float* y0 = y + ((n*32 + oc0+0)*H + oh)*W + ow;\n float* y1 = y + ((n*32 + oc0+1)*H + oh)*W + ow;\n float* y2 = y + ((n*32 + oc0+2)*H + oh)*W + ow;\n float* y3 = y + ((n*32 + oc0+3)*H + oh)*W + ow;\n if (ow + 3 < W) {\n y0[0]=a00; y0[1]=a01; y0[2]=a02; y0[3]=a03;\n y1[0]=a10; y1[1]=a11; y1[2]=a12; y1[3]=a13;\n y2[0]=a20; y2[1]=a21; y2[2]=a22; y2[3]=a23;\n y3[0]=a30; y3[1]=a31; y3[2]=a32; y3[3]=a33;\n } else {\n if (ow+0 < W) { y0[0]=a00; y1[0]=a10; y2[0]=a20; y3[0]=a30; }\n if (ow+1 < W) { y0[1]=a01; y1[1]=a11; y2[1]=a21; y3[1]=a31; }\n if (ow+2 < W) { y0[2]=a02; y1[2]=a12; y2[2]=a22; y3[2]=a32; }\n }\n}\n\n__global__ void deconv2d_generic_kernel(const float* __restrict__ x, const float* __restrict__ w,\n const float* __restrict__ bias, float* __restrict__ y,\n int N, int Cin, int H, int W, int Cout, int kH, int kW, int outH, int outW,\n int sH, int sW, int pH, int pW, int has_bias) {\n long long idx = (long long)blockIdx.x * blockDim.x + threadIdx.x;\n long long total = (long long)N*Cout*outH*outW;\n if (idx >= total) return;\n int ow = idx % outW; idx /= outW;\n int oh = idx % outH; idx /= outH;\n int oc = idx % Cout; int n = idx / Cout;\n float sum = has_bias ? bias[oc] : 0.f;\n for (int ic=0; ic<Cin; ++ic) {\n for (int kh=0; kh<kH; ++kh) {\n int th = oh + pH - kh;\n if (th % sH) continue;\n int ih = th / sH;\n if ((unsigned)ih >= (unsigned)H) continue;\n for (int kw=0; kw<kW; ++kw) {\n int tw = ow + pW - kw;\n if (tw % sW) continue;\n int iw = tw / sW;\n if ((unsigned)iw < (unsigned)W) {\n sum = fmaf(x[((n*Cin+ic)*H+ih)*W+iw], w[((ic*Cout+oc)*kH+kh)*kW+kw], sum);\n }\n }\n }\n }\n y[((n*Cout+oc)*outH+oh)*outW+ow] = sum;\n}\n\ntorch::Tensor conv_transpose2d_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, int64_t sH, int64_t sW, int64_t pH, int64_t pW, bool has_bias) {\n TORCH_CHECK(x.is_cuda() && weight.is_cuda(), \"CUDA tensors expected\");\n TORCH_CHECK(x.scalar_type() == torch::kFloat32, \"float32 only\");\n x = x.contiguous(); weight = weight.contiguous();\n int N=x.size(0), Cin=x.size(1), H=x.size(2), W=x.size(3);\n int kH=weight.size(2), kW=weight.size(3), Cout=weight.size(1);\n int outH = (H-1)*(int)sH - 2*(int)pH + kH;\n int outW = (W-1)*(int)sW - 2*(int)pW + kW;\n auto y = torch::empty({N, Cout, outH, outW}, x.options());\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n if (Cin==32 && Cout==32 && kH==3 && kW==7 && sH==1 && sW==1 && pH==1 && pW==3 && outH==H && outW==W) {\n int W4=(W+3)/4; int total=N*8*H*W4;\n deconv2d_oc4w4_kernel<<<(total+127)/128,128,0,stream>>>(x.data_ptr<float>(), weight.data_ptr<float>(), has_bias?bias.data_ptr<float>():nullptr, y.data_ptr<float>(), N,H,W,has_bias?1:0);\n } else {\n long long total=(long long)N*Cout*outH*outW;\n deconv2d_generic_kernel<<<(total+255)/256,256,0,stream>>>(x.data_ptr<float>(), weight.data_ptr<float>(), has_bias?bias.data_ptr<float>():nullptr, y.data_ptr<float>(), N,Cin,H,W,Cout,kH,kW,outH,outW,(int)sH,(int)sW,(int)pH,(int)pW,has_bias?1:0);\n }\n return y;\n}\n'''\n\n_ext = load_inline(name='deconv2d_ext_v1', cpp_sources=cpp_src, cuda_sources=cuda_src, functions=['conv_transpose2d_forward'], extra_cuda_cflags=['-O3', '--use_fast_math'], extra_cflags=['-O3'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels: int, out_channels: int, kernel_size: tuple, stride: tuple = (1, 1), padding: tuple = (0, 0), bias: bool = False):\n super().__init__()\n self.conv_transpose2d = nn.ConvTranspose2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, bias=bias)\n self.stride = stride\n self.padding = padding\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n b = self.conv_transpose2d.bias\n if b is None:\n b = torch.empty(0, device=x.device, dtype=x.dtype)\n has_bias = False\n else:\n has_bias = True\n return _ext.conv_transpose2d_forward(x, self.conv_transpose2d.weight, b, int(self.stride[0]), int(self.stride[1]), int(self.padding[0]), int(self.padding[1]), has_bias)\n" }
result ← run_correctness (fail)
failrun_correctness run_correctness FAILED: (0 / 5) trials did not all match. correctness_issue: Output mismatch max_difference: ['1.335410', '1.304272', '1.304715', '1.328136', '1.312199'] avg_difference: ['0.172806', '0.172790', '0.172800', '0.172799', '0.172825']
turn 3 · 1 tool call(s) · 25.1s turn · 25.1s LLM · 9,206 chars (click to expand)
turn 325.1s turn · 25.1s LLM · 0.0s tools
reasoning
**Considering code efficiency** I need to create concise code that I can compile and run later. I might want to include flags like --use_fast_math and fmaf, as they should pass. There's something about the order being different from PyTorch, maybe with a limit around 1e-6? That seems good for accuracy. I’ll also need to speed up profiling, so I should use the updated version. Let’s make sure everything is set for optimal performance!
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 conv_transpose2d_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, int64_t sH, int64_t sW, int64_t pH, int64_t pW, bool has_bias);\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\n__global__ void deconv2d_oc4w4_kernel(const float* __restrict__ x, const float* __restrict__ w, const float* __restrict__ bias, float* __restrict__ y, int N, int H, int W, int has_bias) {\n int tid = blockIdx.x * blockDim.x + threadIdx.x;\n int W4 = (W + 3) >> 2;\n int total = N * 8 * H * W4;\n if (tid >= total) return;\n int t = tid;\n int ow4 = t % W4; t /= W4;\n int oh = t % H; t /= H;\n int ocg = t & 7; t >>= 3;\n int n = t;\n int ow = ow4 << 2;\n int oc0 = ocg << 2;\n float a00=0.f,a01=0.f,a02=0.f,a03=0.f,a10=0.f,a11=0.f,a12=0.f,a13=0.f,a20=0.f,a21=0.f,a22=0.f,a23=0.f,a30=0.f,a31=0.f,a32=0.f,a33=0.f;\n bool interior = (oh > 0 && oh < H-1 && ow >= 3 && ow + 6 < W);\n if (interior) {\n #pragma unroll\n for (int ic=0; ic<32; ++ic) {\n #pragma unroll\n for (int kh=0; kh<3; ++kh) {\n int ih = oh + 1 - kh;\n const float* xp = x + ((n*32 + ic)*H + ih)*W + (ow - 3);\n const float* wp = w + ((ic*32 + oc0)*3 + kh)*7;\n #pragma unroll\n for (int q=0; q<7; ++q) {\n float xv0 = __ldg(xp + q + 0);\n float xv1 = __ldg(xp + q + 1);\n float xv2 = __ldg(xp + q + 2);\n float xv3 = __ldg(xp + q + 3);\n int kw = 6 - q;\n float w0 = __ldg(wp + 0*3*7 + kw);\n float w1 = __ldg(wp + 1*3*7 + kw);\n float w2 = __ldg(wp + 2*3*7 + kw);\n float w3 = __ldg(wp + 3*3*7 + kw);\n a00=fmaf(xv0,w0,a00); a01=fmaf(xv1,w0,a01); a02=fmaf(xv2,w0,a02); a03=fmaf(xv3,w0,a03);\n a10=fmaf(xv0,w1,a10); a11=fmaf(xv1,w1,a11); a12=fmaf(xv2,w1,a12); a13=fmaf(xv3,w1,a13);\n a20=fmaf(xv0,w2,a20); a21=fmaf(xv1,w2,a21); a22=fmaf(xv2,w2,a22); a23=fmaf(xv3,w2,a23);\n a30=fmaf(xv0,w3,a30); a31=fmaf(xv1,w3,a31); a32=fmaf(xv2,w3,a32); a33=fmaf(xv3,w3,a33);\n }\n }\n }\n } else {\n #pragma unroll\n for (int ic=0; ic<32; ++ic) {\n #pragma unroll\n for (int kh=0; kh<3; ++kh) {\n int ih = oh + 1 - kh;\n if ((unsigned)ih >= (unsigned)H) continue;\n #pragma unroll\n for (int kw=0; kw<7; ++kw) {\n int iw0 = ow + 3 - kw;\n float w0=__ldg(w+((ic*32+oc0+0)*3+kh)*7+kw), w1=__ldg(w+((ic*32+oc0+1)*3+kh)*7+kw), w2=__ldg(w+((ic*32+oc0+2)*3+kh)*7+kw), w3=__ldg(w+((ic*32+oc0+3)*3+kh)*7+kw);\n if ((unsigned)(iw0+0)<(unsigned)W) { float xv=__ldg(x+((n*32+ic)*H+ih)*W+iw0+0); a00=fmaf(xv,w0,a00); a10=fmaf(xv,w1,a10); a20=fmaf(xv,w2,a20); a30=fmaf(xv,w3,a30); }\n if ((unsigned)(iw0+1)<(unsigned)W) { float xv=__ldg(x+((n*32+ic)*H+ih)*W+iw0+1); a01=fmaf(xv,w0,a01); a11=fmaf(xv,w1,a11); a21=fmaf(xv,w2,a21); a31=fmaf(xv,w3,a31); }\n if ((unsigned)(iw0+2)<(unsigned)W) { float xv=__ldg(x+((n*32+ic)*H+ih)*W+iw0+2); a02=fmaf(xv,w0,a02); a12=fmaf(xv,w1,a12); a22=fmaf(xv,w2,a22); a32=fmaf(xv,w3,a32); }\n if ((unsigned)(iw0+3)<(unsigned)W) { float xv=__ldg(x+((n*32+ic)*H+ih)*W+iw0+3); a03=fmaf(xv,w0,a03); a13=fmaf(xv,w1,a13); a23=fmaf(xv,w2,a23); a33=fmaf(xv,w3,a33); }\n }\n }\n }\n }\n if (has_bias) { float b0=__ldg(bias+oc0),b1=__ldg(bias+oc0+1),b2=__ldg(bias+oc0+2),b3=__ldg(bias+oc0+3); a00+=b0;a01+=b0;a02+=b0;a03+=b0; a10+=b1;a11+=b1;a12+=b1;a13+=b1; a20+=b2;a21+=b2;a22+=b2;a23+=b2; a30+=b3;a31+=b3;a32+=b3;a33+=b3; }\n float* y0=y+((n*32+oc0+0)*H+oh)*W+ow; float* y1=y+((n*32+oc0+1)*H+oh)*W+ow; float* y2=y+((n*32+oc0+2)*H+oh)*W+ow; float* y3=y+((n*32+oc0+3)*H+oh)*W+ow;\n y0[0]=a00; y0[1]=a01; y0[2]=a02; y0[3]=a03; y1[0]=a10; y1[1]=a11; y1[2]=a12; y1[3]=a13; y2[0]=a20; y2[1]=a21; y2[2]=a22; y2[3]=a23; y3[0]=a30; y3[1]=a31; y3[2]=a32; y3[3]=a33;\n}\n\n__global__ void deconv2d_generic_kernel(const float* __restrict__ x, const float* __restrict__ w, const float* __restrict__ bias, float* __restrict__ y, int N,int Cin,int H,int W,int Cout,int kH,int kW,int outH,int outW,int sH,int sW,int pH,int pW,int has_bias) {\n long long idx=(long long)blockIdx.x*blockDim.x+threadIdx.x, total=(long long)N*Cout*outH*outW; if(idx>=total) return; int ow=idx%outW; idx/=outW; int oh=idx%outH; idx/=outH; int oc=idx%Cout; int n=idx/Cout; float sum=has_bias?bias[oc]:0.f;\n for(int ic=0;ic<Cin;++ic) for(int kh=0;kh<kH;++kh){ int th=oh+pH-kh; if(th%sH) continue; int ih=th/sH; if((unsigned)ih>=(unsigned)H) continue; for(int kw=0;kw<kW;++kw){ int tw=ow+pW-kw; if(tw%sW) continue; int iw=tw/sW; if((unsigned)iw<(unsigned)W) sum=fmaf(x[((n*Cin+ic)*H+ih)*W+iw], w[((ic*Cout+oc)*kH+kh)*kW+kw], sum); }}\n y[((n*Cout+oc)*outH+oh)*outW+ow]=sum;\n}\n\ntorch::Tensor conv_transpose2d_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, int64_t sH, int64_t sW, int64_t pH, int64_t pW, bool has_bias) {\n x=x.contiguous(); weight=weight.contiguous(); int N=x.size(0),Cin=x.size(1),H=x.size(2),W=x.size(3),kH=weight.size(2),kW=weight.size(3),Cout=weight.size(1); int outH=(H-1)*(int)sH-2*(int)pH+kH, outW=(W-1)*(int)sW-2*(int)pW+kW; auto y=torch::empty({N,Cout,outH,outW}, x.options()); cudaStream_t stream=at::cuda::getCurrentCUDAStream();\n if(Cin==32&&Cout==32&&kH==3&&kW==7&&sH==1&&sW==1&&pH==1&&pW==3&&outH==H&&outW==W){ int total=N*8*H*((W+3)/4); deconv2d_oc4w4_kernel<<<(total+127)/128,128,0,stream>>>(x.data_ptr<float>(),weight.data_ptr<float>(),has_bias?bias.data_ptr<float>():nullptr,y.data_ptr<float>(),N,H,W,has_bias?1:0); }\n else { long long total=(long long)N*Cout*outH*outW; deconv2d_generic_kernel<<<(total+255)/256,256,0,stream>>>(x.data_ptr<float>(),weight.data_ptr<float>(),has_bias?bias.data_ptr<float>():nullptr,y.data_ptr<float>(),N,Cin,H,W,Cout,kH,kW,outH,outW,(int)sH,(int)sW,(int)pH,(int)pW,has_bias?1:0); }\n return y;\n}\n'''\n\n_ext = load_inline(name='deconv2d_ext_v2', cpp_sources=cpp_src, cuda_sources=cuda_src, functions=['conv_transpose2d_forward'], extra_cuda_cflags=['-O3','--use_fast_math'], extra_cflags=['-O3'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels: int, out_channels: int, kernel_size: tuple, stride: tuple = (1, 1), padding: tuple = (0, 0), bias: bool = False):\n super().__init__(); self.conv_transpose2d=nn.ConvTranspose2d(in_channels,out_channels,kernel_size,stride=stride,padding=padding,bias=bias); self.stride=stride; self.padding=padding\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n b=self.conv_transpose2d.bias; has_bias=b is not None\n if b is None: b=torch.empty(0,device=x.device,dtype=x.dtype)\n return _ext.conv_transpose2d_forward(x,self.conv_transpose2d.weight,b,int(self.stride[0]),int(self.stride[1]),int(self.padding[0]),int(self.padding[1]),has_bias)\n" }
result ← run_correctness (fail)
failrun_correctness run_correctness FAILED: (0 / 5) trials did not all match. correctness_issue: Output mismatch max_difference: ['0.000477', '0.000520', '0.000484', '0.000525', '0.000496'] avg_difference: ['0.000071', '0.000071', '0.000071', '0.000071', '0.000071']
turn 4 · 1 tool call(s) · 21.1s turn · 21.1s LLM · 7,441 chars (click to expand)
turn 421.1s turn · 21.1s 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\ncpp_src=r'''\n#include <torch/extension.h>\ntorch::Tensor conv_transpose2d_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, int64_t sH, int64_t sW, int64_t pH, int64_t pW, bool has_bias);\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__global__ void deconv2d_oc4w4_kernel(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ bias,float* __restrict__ y,int N,int H,int W,int has_bias){\n int tid=blockIdx.x*blockDim.x+threadIdx.x; int W4=(W+3)>>2; int total=N*8*H*W4; if(tid>=total)return; int t=tid; int ow4=t%W4; t/=W4; int oh=t%H; t/=H; int ocg=t&7; t>>=3; int n=t; int ow=ow4<<2; int oc0=ocg<<2;\n float a00=0.f,a01=0.f,a02=0.f,a03=0.f,a10=0.f,a11=0.f,a12=0.f,a13=0.f,a20=0.f,a21=0.f,a22=0.f,a23=0.f,a30=0.f,a31=0.f,a32=0.f,a33=0.f;\n bool interior=(oh>0&&oh<H-1&&ow>=3&&ow+6<W);\n if(interior){\n #pragma unroll\n for(int ic=0;ic<32;++ic){\n #pragma unroll\n for(int kh=0;kh<3;++kh){ int ih=oh+1-kh; const float* xp=x+((n*32+ic)*H+ih)*W+(ow+3); const float* wp=w+((ic*32+oc0)*3+kh)*7;\n #pragma unroll\n for(int kw=0;kw<7;++kw){ float xv0=__ldg(xp-kw+0),xv1=__ldg(xp-kw+1),xv2=__ldg(xp-kw+2),xv3=__ldg(xp-kw+3); float w0=__ldg(wp+0*21+kw),w1=__ldg(wp+1*21+kw),w2=__ldg(wp+2*21+kw),w3=__ldg(wp+3*21+kw);\n a00=fmaf(xv0,w0,a00);a01=fmaf(xv1,w0,a01);a02=fmaf(xv2,w0,a02);a03=fmaf(xv3,w0,a03); a10=fmaf(xv0,w1,a10);a11=fmaf(xv1,w1,a11);a12=fmaf(xv2,w1,a12);a13=fmaf(xv3,w1,a13); a20=fmaf(xv0,w2,a20);a21=fmaf(xv1,w2,a21);a22=fmaf(xv2,w2,a22);a23=fmaf(xv3,w2,a23); a30=fmaf(xv0,w3,a30);a31=fmaf(xv1,w3,a31);a32=fmaf(xv2,w3,a32);a33=fmaf(xv3,w3,a33); }\n }\n }\n }else{\n #pragma unroll\n for(int ic=0;ic<32;++ic){\n #pragma unroll\n for(int kh=0;kh<3;++kh){ int ih=oh+1-kh; if((unsigned)ih>=(unsigned)H)continue;\n #pragma unroll\n for(int kw=0;kw<7;++kw){ int iw0=ow+3-kw; float w0=__ldg(w+((ic*32+oc0+0)*3+kh)*7+kw),w1=__ldg(w+((ic*32+oc0+1)*3+kh)*7+kw),w2=__ldg(w+((ic*32+oc0+2)*3+kh)*7+kw),w3=__ldg(w+((ic*32+oc0+3)*3+kh)*7+kw);\n if((unsigned)(iw0+0)<(unsigned)W){float xv=__ldg(x+((n*32+ic)*H+ih)*W+iw0+0);a00=fmaf(xv,w0,a00);a10=fmaf(xv,w1,a10);a20=fmaf(xv,w2,a20);a30=fmaf(xv,w3,a30);} if((unsigned)(iw0+1)<(unsigned)W){float xv=__ldg(x+((n*32+ic)*H+ih)*W+iw0+1);a01=fmaf(xv,w0,a01);a11=fmaf(xv,w1,a11);a21=fmaf(xv,w2,a21);a31=fmaf(xv,w3,a31);} if((unsigned)(iw0+2)<(unsigned)W){float xv=__ldg(x+((n*32+ic)*H+ih)*W+iw0+2);a02=fmaf(xv,w0,a02);a12=fmaf(xv,w1,a12);a22=fmaf(xv,w2,a22);a32=fmaf(xv,w3,a32);} if((unsigned)(iw0+3)<(unsigned)W){float xv=__ldg(x+((n*32+ic)*H+ih)*W+iw0+3);a03=fmaf(xv,w0,a03);a13=fmaf(xv,w1,a13);a23=fmaf(xv,w2,a23);a33=fmaf(xv,w3,a33);} }\n }\n }\n }\n if(has_bias){float b0=__ldg(bias+oc0),b1=__ldg(bias+oc0+1),b2=__ldg(bias+oc0+2),b3=__ldg(bias+oc0+3);a00+=b0;a01+=b0;a02+=b0;a03+=b0;a10+=b1;a11+=b1;a12+=b1;a13+=b1;a20+=b2;a21+=b2;a22+=b2;a23+=b2;a30+=b3;a31+=b3;a32+=b3;a33+=b3;}\n float* y0=y+((n*32+oc0+0)*H+oh)*W+ow;float* y1=y+((n*32+oc0+1)*H+oh)*W+ow;float* y2=y+((n*32+oc0+2)*H+oh)*W+ow;float* y3=y+((n*32+oc0+3)*H+oh)*W+ow; y0[0]=a00;y0[1]=a01;y0[2]=a02;y0[3]=a03;y1[0]=a10;y1[1]=a11;y1[2]=a12;y1[3]=a13;y2[0]=a20;y2[1]=a21;y2[2]=a22;y2[3]=a23;y3[0]=a30;y3[1]=a31;y3[2]=a32;y3[3]=a33;\n}\n__global__ void deconv2d_generic_kernel(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ bias,float* __restrict__ y,int N,int Cin,int H,int W,int Cout,int kH,int kW,int outH,int outW,int sH,int sW,int pH,int pW,int has_bias){long long idx=(long long)blockIdx.x*blockDim.x+threadIdx.x,total=(long long)N*Cout*outH*outW;if(idx>=total)return;int ow=idx%outW;idx/=outW;int oh=idx%outH;idx/=outH;int oc=idx%Cout;int n=idx/Cout;float sum=has_bias?bias[oc]:0.f;for(int ic=0;ic<Cin;++ic)for(int kh=0;kh<kH;++kh){int th=oh+pH-kh;if(th%sH)continue;int ih=th/sH;if((unsigned)ih>=(unsigned)H)continue;for(int kw=0;kw<kW;++kw){int tw=ow+pW-kw;if(tw%sW)continue;int iw=tw/sW;if((unsigned)iw<(unsigned)W)sum=fmaf(x[((n*Cin+ic)*H+ih)*W+iw],w[((ic*Cout+oc)*kH+kh)*kW+kw],sum);}}y[((n*Cout+oc)*outH+oh)*outW+ow]=sum;}\ntorch::Tensor conv_transpose2d_forward(torch::Tensor x,torch::Tensor weight,torch::Tensor bias,int64_t sH,int64_t sW,int64_t pH,int64_t pW,bool has_bias){x=x.contiguous();weight=weight.contiguous();int N=x.size(0),Cin=x.size(1),H=x.size(2),W=x.size(3),kH=weight.size(2),kW=weight.size(3),Cout=weight.size(1);int outH=(H-1)*(int)sH-2*(int)pH+kH,outW=(W-1)*(int)sW-2*(int)pW+kW;auto y=torch::empty({N,Cout,outH,outW},x.options());cudaStream_t stream=at::cuda::getCurrentCUDAStream();if(Cin==32&&Cout==32&&kH==3&&kW==7&&sH==1&&sW==1&&pH==1&&pW==3&&outH==H&&outW==W){int total=N*8*H*((W+3)/4);deconv2d_oc4w4_kernel<<<(total+127)/128,128,0,stream>>>(x.data_ptr<float>(),weight.data_ptr<float>(),has_bias?bias.data_ptr<float>():nullptr,y.data_ptr<float>(),N,H,W,has_bias?1:0);}else{long long total=(long long)N*Cout*outH*outW;deconv2d_generic_kernel<<<(total+255)/256,256,0,stream>>>(x.data_ptr<float>(),weight.data_ptr<float>(),has_bias?bias.data_ptr<float>():nullptr,y.data_ptr<float>(),N,Cin,H,W,Cout,kH,kW,outH,outW,(int)sH,(int)sW,(int)pH,(int)pW,has_bias?1:0);}return y;}\n'''\n_ext=load_inline(name='deconv2d_ext_v3',cpp_sources=cpp_src,cuda_sources=cuda_src,functions=['conv_transpose2d_forward'],extra_cuda_cflags=['-O3','--use_fast_math'],extra_cflags=['-O3'],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels:int,out_channels:int,kernel_size:tuple,stride:tuple=(1,1),padding:tuple=(0,0),bias:bool=False):\n super().__init__();self.conv_transpose2d=nn.ConvTranspose2d(in_channels,out_channels,kernel_size,stride=stride,padding=padding,bias=bias);self.stride=stride;self.padding=padding\n def forward(self,x:torch.Tensor)->torch.Tensor:\n b=self.conv_transpose2d.bias;has_bias=b is not None\n if b is None:b=torch.empty(0,device=x.device,dtype=x.dtype)\n return _ext.conv_transpose2d_forward(x,self.conv_transpose2d.weight,b,int(self.stride[0]),int(self.stride[1]),int(self.padding[0]),int(self.padding[1]),has_bias)\n" }
result ← run_correctness (fail)
failrun_correctness run_correctness FAILED: (0 / 5) trials did not all match. correctness_issue: Output mismatch max_difference: ['0.000477', '0.000520', '0.000484', '0.000525', '0.000496'] avg_difference: ['0.000071', '0.000071', '0.000071', '0.000071', '0.000071']
turn 512.9s turn · 12.9s LLM · 0.0s tools
reasoning
**Analyzing formula discrepancies** I’m wondering if my formula has a small bug, particularly in the interior. The edges are small, with a maximum around 5e-4. I might be comparing it to ConvTranspose, which does cross-correlation instead of convolution. My formula’s orientation could be off. I did get close, but rounding might be affecting accuracy. The average difference of 7e-5 seems acceptable in some cases, but I need to reduce the difference. Maybe I should consider double accumulation for comparison.
call → run_correctness
callrun_correctness { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\ncpp_src=r'''\n#include <torch/extension.h>\ntorch::Tensor conv_transpose2d_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, int64_t sH, int64_t sW, int64_t pH, int64_t pW, bool has_bias);\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__global__ void deconv2d_scalar_double(const float* x,const float* w,const float* bias,float* y,int N,int H,int W,int has_bias){long long idx=(long long)blockIdx.x*blockDim.x+threadIdx.x,total=(long long)N*32*H*W;if(idx>=total)return;int ow=idx%W;idx/=W;int oh=idx%H;idx/=H;int oc=idx%32;int n=idx/32; double s=0.0; for(int ic=0;ic<32;++ic)for(int kh=0;kh<3;++kh){int ih=oh+1-kh;if((unsigned)ih>=(unsigned)H)continue;for(int kw=0;kw<7;++kw){int iw=ow+3-kw;if((unsigned)iw<(unsigned)W)s+=(double)x[((n*32+ic)*H+ih)*W+iw]*(double)w[((ic*32+oc)*3+kh)*7+kw];}} float sf=(float)s; if(has_bias)sf+=bias[oc]; y[((n*32+oc)*H+oh)*W+ow]=sf;}\ntorch::Tensor conv_transpose2d_forward(torch::Tensor x,torch::Tensor weight,torch::Tensor bias,int64_t sH,int64_t sW,int64_t pH,int64_t pW,bool has_bias){int N=x.size(0),H=x.size(2),W=x.size(3);auto y=torch::empty({N,32,H,W},x.options());long long total=(long long)N*32*H*W;deconv2d_scalar_double<<<(total+255)/256,256,0,at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(),weight.data_ptr<float>(),has_bias?bias.data_ptr<float>():nullptr,y.data_ptr<float>(),N,H,W,has_bias?1:0);return y;}\n'''\n_ext=load_inline(name='deconv2d_ext_dbltest',cpp_sources=cpp_src,cuda_sources=cuda_src,functions=['conv_transpose2d_forward'],extra_cuda_cflags=['-O3'],extra_cflags=['-O3'],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels:int,out_channels:int,kernel_size:tuple,stride:tuple=(1,1),padding:tuple=(0,0),bias:bool=False):\n super().__init__();self.conv_transpose2d=nn.ConvTranspose2d(in_channels,out_channels,kernel_size,stride=stride,padding=padding,bias=bias);self.stride=stride;self.padding=padding\n def forward(self,x):\n b=self.conv_transpose2d.bias;has=b is not None\n if b is None:b=torch.empty(0,device=x.device,dtype=x.dtype)\n return _ext.conv_transpose2d_forward(x,self.conv_transpose2d.weight,b,1,1,1,3,has)\n" }
result ← run_correctness (fail)
failrun_correctness run_correctness FAILED: (0 / 5) trials did not all match. correctness_issue: Output mismatch max_difference: ['0.000477', '0.000520', '0.000484', '0.000525', '0.000496'] avg_difference: ['0.000071', '0.000071', '0.000071', '0.000071', '0.000071']
turn 69.2s turn · 9.2s 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\ncpp_src=r'''\n#include <torch/extension.h>\ntorch::Tensor conv_transpose2d_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, int64_t sH, int64_t sW, int64_t pH, int64_t pW, bool has_bias);\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__global__ void k(const float* x,const float* w,const float* bias,float* y,int N,int H,int W,int has){long long idx=(long long)blockIdx.x*blockDim.x+threadIdx.x,total=(long long)N*32*H*W;if(idx>=total)return;int ow=idx%W;idx/=W;int oh=idx%H;idx/=H;int oc=idx%32;int n=idx/32;float s=0.f;for(int ic=0;ic<32;++ic)for(int kh=0;kh<3;++kh){int ih=oh+1-kh;if((unsigned)ih>=(unsigned)H)continue;for(int kw=0;kw<7;++kw){int iw=ow+3-kw;if((unsigned)iw<(unsigned)W){float a=__float_to_tf32(x[((n*32+ic)*H+ih)*W+iw]);float b=__float_to_tf32(w[((ic*32+oc)*3+kh)*7+kw]);s=fmaf(a,b,s);}}}if(has)s+=bias[oc];y[((n*32+oc)*H+oh)*W+ow]=s;}\ntorch::Tensor conv_transpose2d_forward(torch::Tensor x,torch::Tensor weight,torch::Tensor bias,int64_t sH,int64_t sW,int64_t pH,int64_t pW,bool has){int N=x.size(0),H=x.size(2),W=x.size(3);auto y=torch::empty({N,32,H,W},x.options());long long total=(long long)N*32*H*W;k<<<(total+255)/256,256,0,at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(),weight.data_ptr<float>(),has?bias.data_ptr<float>():nullptr,y.data_ptr<float>(),N,H,W,has?1:0);return y;}\n'''\n_ext=load_inline(name='deconv2d_ext_tf32test',cpp_sources=cpp_src,cuda_sources=cuda_src,functions=['conv_transpose2d_forward'],extra_cuda_cflags=['-O3'],extra_cflags=['-O3'],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels:int,out_channels:int,kernel_size:tuple,stride:tuple=(1,1),padding:tuple=(0,0),bias:bool=False):\n super().__init__();self.conv_transpose2d=nn.ConvTranspose2d(in_channels,out_channels,kernel_size,stride=stride,padding=padding,bias=bias);self.stride=stride;self.padding=padding\n def forward(self,x):\n b=self.conv_transpose2d.bias;has=b is not None\n if b is None:b=torch.empty(0,device=x.device,dtype=x.dtype)\n return _ext.conv_transpose2d_forward(x,self.conv_transpose2d.weight,b,1,1,1,3,has)\n" }
result ← run_correctness (fail)
failrun_correctness run_correctness FAILED: persistent build/lock contention after retries. Please try a different kernel.
turn 7 · 1 tool call(s) · 6.6s turn · 6.6s LLM · 7,648 chars (click to expand)
turn 76.6s turn · 6.6s LLM · 0.0s tools
reasoning
**Considering naming issues** I'm pondering whether the build lock contention is due to the same issue with naming. It feels like I should try a different name to avoid conflicts. However, if it's truly a different compilation, maybe the previous one is still compiling and causing the hold-up. I wonder if I should just go ahead and use "compile" as a potential solution. That seems like a logical step to troubleshoot this!
call → compile_kernel
callcompile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\ncpp_src=r'''\n#include <torch/extension.h>\ntorch::Tensor conv_transpose2d_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, int64_t sH, int64_t sW, int64_t pH, int64_t pW, bool has_bias);\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__global__ void k(const float* x,const float* w,const float* bias,float* y,int N,int H,int W,int has){long long idx=(long long)blockIdx.x*blockDim.x+threadIdx.x,total=(long long)N*32*H*W;if(idx>=total)return;int ow=idx%W;idx/=W;int oh=idx%H;idx/=H;int oc=idx%32;int n=idx/32;float s=0.f;for(int ic=0;ic<32;++ic)for(int kh=0;kh<3;++kh){int ih=oh+1-kh;if((unsigned)ih>=(unsigned)H)continue;for(int kw=0;kw<7;++kw){int iw=ow+3-kw;if((unsigned)iw<(unsigned)W){float a=__float_to_tf32(x[((n*32+ic)*H+ih)*W+iw]);float b=__float_to_tf32(w[((ic*32+oc)*3+kh)*7+kw]);s=fmaf(a,b,s);}}}if(has)s+=bias[oc];y[((n*32+oc)*H+oh)*W+ow]=s;}\ntorch::Tensor conv_transpose2d_forward(torch::Tensor x,torch::Tensor weight,torch::Tensor bias,int64_t sH,int64_t sW,int64_t pH,int64_t pW,bool has){int N=x.size(0),H=x.size(2),W=x.size(3);auto y=torch::empty({N,32,H,W},x.options());long long total=(long long)N*32*H*W;k<<<(total+255)/256,256,0,at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(),weight.data_ptr<float>(),has?bias.data_ptr<float>():nullptr,y.data_ptr<float>(),N,H,W,has?1:0);return y;}\n'''\n_ext=load_inline(name='deconv2d_ext_tf32test2',cpp_sources=cpp_src,cuda_sources=cuda_src,functions=['conv_transpose2d_forward'],extra_cuda_cflags=['-O3'],extra_cflags=['-O3'],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels:int,out_channels:int,kernel_size:tuple,stride:tuple=(1,1),padding:tuple=(0,0),bias:bool=False):\n super().__init__();self.conv_transpose2d=nn.ConvTranspose2d(in_channels,out_channels,kernel_size,stride=stride,padding=padding,bias=bias);self.stride=stride;self.padding=padding\n def forward(self,x):\n b=self.conv_transpose2d.bias;has=b is not None\n if b is None:b=torch.empty(0,device=x.device,dtype=x.dtype)\n return _ext.conv_transpose2d_forward(x,self.conv_transpose2d.weight,b,1,1,1,3,has)\n" }
result ← compile_kernel (fail)
failcompile_kernel compile_kernel FAILED: RuntimeError. RuntimeError: Error building extension 'deconv2d_ext_tf32test2': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=deconv2d_ext_tf32test2 -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 -O3 -c /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_78_cache/k_13399e3587aa/deconv2d_ext_tf32test2/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=deconv2d_ext_tf32test2 -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_78_cache/k_13399e3587aa/deconv2d_ext_tf32test2/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=deconv2d_ext_tf32test2 -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_78_cache/k_13399e3587aa/deconv2d_ext_tf32test2/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_78_cache/k_13399e3587aa/deconv2d_ext_tf32test2/cuda.cu(9): error: identifier "__float_to_tf32" is undefined void k(const float* x,const float* w,const float* bias,float* y,int N,int H,int W,int has){long long idx=(long long)blockIdx.x*blockDim.x+threadIdx.x,total=(long long)N*32*H*W;if(idx>=total)return;int ow=idx%W;idx/=W;int oh=idx%H;idx/=H;int oc=idx%32;int n=idx/32;float s=0.f;for(int ic=0;ic<32;++ic)for(int kh=0;kh<3;++kh){int ih=oh+1-kh;if((unsigned)ih>=(unsigned)H)continue;for(int kw=0;kw<7;++kw){int iw=ow+3-kw;if((unsigned)iw<(unsigned)W){float a=__float_to_tf32(x[((n*32+ic)*H+ih)*W+iw]);float b=__float_to_tf32(w[((ic*32+oc)*3+kh)*7+kw]);s=fmaf(a,b,s);}}}if(has)s+=bias[oc];y[((n*32+oc)*H+oh)*W+ow]=s;} ^ 1 error detected in the compilation of "/scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_78_cache/k_13399e3587aa/deconv2d_ext_tf32test2/cuda.cu". ninja: build stopped: subcommand failed.
turn 8 · 1 tool call(s) · 8.7s turn · 8.7s LLM · 10,963 chars (click to expand)
turn 88.7s turn · 8.7s 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\ncpp_src=r'''\n#include <torch/extension.h>\ntorch::Tensor conv_transpose2d_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, int64_t sH, int64_t sW, int64_t pH, int64_t pW, bool has_bias);\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__device__ __forceinline__ float tf32c(float x){float y; asm(\"cvt.rna.tf32.f32 %0, %1;\" : \"=f\"(y) : \"f\"(x)); return y;}\n__global__ void k(const float* x,const float* w,const float* bias,float* y,int N,int H,int W,int has){long long idx=(long long)blockIdx.x*blockDim.x+threadIdx.x,total=(long long)N*32*H*W;if(idx>=total)return;int ow=idx%W;idx/=W;int oh=idx%H;idx/=H;int oc=idx%32;int n=idx/32;float s=0.f;for(int ic=0;ic<32;++ic)for(int kh=0;kh<3;++kh){int ih=oh+1-kh;if((unsigned)ih>=(unsigned)H)continue;for(int kw=0;kw<7;++kw){int iw=ow+3-kw;if((unsigned)iw<(unsigned)W){float a=tf32c(x[((n*32+ic)*H+ih)*W+iw]);float b=tf32c(w[((ic*32+oc)*3+kh)*7+kw]);s=fmaf(a,b,s);}}}if(has)s+=bias[oc];y[((n*32+oc)*H+oh)*W+ow]=s;}\ntorch::Tensor conv_transpose2d_forward(torch::Tensor x,torch::Tensor weight,torch::Tensor bias,int64_t sH,int64_t sW,int64_t pH,int64_t pW,bool has){int N=x.size(0),H=x.size(2),W=x.size(3);auto y=torch::empty({N,32,H,W},x.options());long long total=(long long)N*32*H*W;k<<<(total+255)/256,256,0,at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(),weight.data_ptr<float>(),has?bias.data_ptr<float>():nullptr,y.data_ptr<float>(),N,H,W,has?1:0);return y;}\n'''\n_ext=load_inline(name='deconv2d_ext_tf32test3',cpp_sources=cpp_src,cuda_sources=cuda_src,functions=['conv_transpose2d_forward'],extra_cuda_cflags=['-O3'],extra_cflags=['-O3'],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels:int,out_channels:int,kernel_size:tuple,stride:tuple=(1,1),padding:tuple=(0,0),bias:bool=False):\n super().__init__();self.conv_transpose2d=nn.ConvTranspose2d(in_channels,out_channels,kernel_size,stride=stride,padding=padding,bias=bias);self.stride=stride;self.padding=padding\n def forward(self,x):\n b=self.conv_transpose2d.bias;has=b is not None\n if b is None:b=torch.empty(0,device=x.device,dtype=x.dtype)\n return _ext.conv_transpose2d_forward(x,self.conv_transpose2d.weight,b,1,1,1,3,has)\n" }
result ← run_correctness (fail)
failrun_correctness run_correctness FAILED: kernel did not compile. Error building extension 'deconv2d_ext_tf32test3': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=deconv2d_ext_tf32test3 -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 -O3 -c /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_78_cache/k_e2df1d2b9f6b/deconv2d_ext_tf32test3/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=deconv2d_ext_tf32test3 -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_78_cache/k_e2df1d2b9f6b/deconv2d_ext_tf32test3/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=deconv2d_ext_tf32test3 -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_78_cache/k_e2df1d2b9f6b/deconv2d_ext_tf32test3/cuda.cu -o cuda.cuda.o nvcc warning : incompatible redefinition for option 'compiler-bindir', the last value of this option was used ptxas /var/tmp/tmpxft_0020996f_00000000-6_cuda.ptx, line 175; error : Arguments mismatch for instruction 'cvt' ptxas /var/tmp/tmpxft_0020996f_00000000-6_cuda.ptx, line 179; error : Arguments mismatch for instruction 'cvt' ptxas /var/tmp/tmpxft_0020996f_00000000-6_cuda.ptx, line 189; error : Arguments mismatch for instruction 'cvt' ptxas /var/tmp/tmpxft_0020996f_00000000-6_cuda.ptx, line 193; error : Arguments mismatch for instruction 'cvt' ptxas /var/tmp/tmpxft_0020996f_00000000-6_cuda.ptx, line 203; error : Arguments mismatch for instruction 'cvt' ptxas /var/tmp/tmpxft_0020996f_00000000-6_cuda.ptx, line 207; error : Arguments mismatch for instruction 'cvt' ptxas /var/tmp/tmpxft_0020996f_00000000-6_cuda.ptx, line 217; error : Arguments mismatch for instruction 'cvt' ptxas /var/tmp/tmpxft_0020996f_00000000-6_cuda.ptx, line 221; error : Arguments mismatch for instruction 'cvt' ptxas /var/tmp/tmpxft_0020996f_00000000-6_cuda.ptx, line 231; error : Arguments mismatch for instruction 'cvt' ptxas /var/tmp/tmpxft_0020996f_00000000-6_cuda.ptx, line 235; error : Arguments mismatch for instruction 'cvt' ptxas /var/tmp/tmpxft_0020996f_00000000-6_cuda.ptx, line 245; error : Arguments mismatch for instruction 'cvt' ptxas /var/tmp/tmpxft_0020996f_00000000-6_cuda.ptx, line 249; error : Arguments mismatch for instruction 'cvt' ptxas /var/tmp/tmpxft_0020996f_00000000-6_cuda.ptx, line 259; error : Arguments mismatch for instruction 'cvt' ptxas /var/tmp/tmpxft_0020996f_00000000-6_cuda.ptx, line 263; error : Arguments mismatch for instruction 'cvt' ptxas /var/tmp/tmpxft_0020996f_00000000-6_cuda.ptx, line 279; error : Arguments mismatch for instruction 'cvt' ptxas /var/tmp/tmpxft_0020996f_00000000-6_cuda.ptx, line 283; error : Arguments mismatch for instruction 'cvt' ptxas /var/tmp/tmpxft_0020996f_00000000-6_cuda.ptx, line 293; error : Arguments mismatch for instruction 'cvt' ptxas /var/tmp/tmpxft_0020996f_00000000-6_cuda.ptx, line 297; error : Arguments mismatch for instruction 'cvt' ptxas /var/tmp/tmpxft_0020996f_00000000-6_cuda.ptx, line 307; error : Arguments mismatch for instruction 'cvt' ptxas /var/tmp/tmpxft_0020996f_00000000-6_cuda.ptx, line 311; error : Arguments mismatch for instruction 'cvt' ptxas /var/tmp/tmpxft_0020996f_00000000-6_cuda.ptx, line 321; error : Arguments mismatch for instruction 'cvt' ptxas /var/tmp/tmpxft_0020996f_00000000-6_cuda.ptx, line 325; error : Arguments mismatch for instruction 'cvt' ptxas /var/tmp/tmpxft_0020996f_00000000-6_cuda.ptx, line 335; error : Arguments mismatch for instruction 'cvt' ptxas /var/tmp/tmpxft_0020996f_00000000-6_cuda.ptx, line 339; error : Arguments mismatch for instruction 'cvt' ptxas /var/tmp/tmpxft_0020996f_00000000-6_cuda.ptx, line 349; error : Arguments mismatch for instruction 'cvt' ptxas /var/tmp/tmpxft_0020996f_00000000-6_cuda.ptx, line 353; error : Arguments mismatch for instruction 'cvt' ptxas /var/tmp/tmpxft_0020996f_00000000-6_cuda.ptx, line 363; error : Arguments mismatch for instruction 'cvt' ptxas /var/tmp/tmpxft_0020996f_00000000-6_cuda.ptx, line 367; error : Arguments mismatch for instruction 'cvt' ptxas /var/tmp/tmpxft_0020996f_00000000-6_cuda.ptx, line 383; error : Arguments mismatch for instruction 'cvt' ptxas /var/tmp/tmpxft_0020996f_00000000-6_cuda.ptx, line 387; error : Arguments mismatch for instruction 'cvt' ptxas /var/tmp/tmpxft_0020996f_00000000-6_cuda.ptx, line 397; error : Arguments mismatch for instruction 'cvt' ptxas /var/tmp/tmpxft_0020996f_00000000-6_cuda.ptx, line 401; error : Arguments mismatch for instruction 'cvt' ptxas /var/tmp/tmpxft_0020996f_00000000-6_cuda.ptx, line 411; error : Arguments mismatch for instruction 'cvt' ptxas /var/tmp/tmpxft_0020996f_00000000-6_cuda.ptx, line 415; error : Arguments mismatch for instruction 'cvt' ptxas /var/tmp/tmpxft_0020996f_00000000-6_cuda.ptx, line 425; error : Arguments mismatch for instruction 'cvt' ptxas /var/tmp/tmpxft_0020996f_00000000-6_cuda.ptx, line 429; error : Arguments mismatch for instruction 'cvt' ptxas /var/tmp/tmpxft_0020996f_00000000-6_cuda.ptx, line 439; error : Arguments mismatch for instruction 'cvt' ptxas /var/tmp/tmpxft_0020996f_00000000-6_cuda.ptx, line 443; error : Arguments mismatch for instruction 'cvt' ptxas /var/tmp/tmpxft_0020996f_00000000-6_cuda.ptx, line 453; error : Arguments mismatch for instruction 'cvt' ptxas /var/tmp/tmpxft_0020996f_00000000-6_cuda.ptx, line 457; error : Arguments mismatch for instruction 'cvt' ptxas /var/tmp/tmpxft_0020996f_00000000-6_cuda.ptx, line 467; error : Arguments mismatch for instruction 'cvt' ptxas /var/tmp/tmpxft_0020996f_00000000-6_cuda.ptx, line 471; error : Arguments mismatch for instruction 'cvt' ptxas fatal : Ptx assembly aborted due to errors ninja: build stopped: subcommand failed.
turn 911.1s turn · 11.1s 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\ncpp_src=r'''\n#include <torch/extension.h>\ntorch::Tensor conv_transpose2d_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, int64_t sH, int64_t sW, int64_t pH, int64_t pW, bool has_bias);\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__device__ __forceinline__ float tf32c(float x){unsigned y; asm(\"cvt.rna.tf32.f32 %0, %1;\" : \"=r\"(y) : \"f\"(x)); return __uint_as_float(y);}\n__global__ void k(const float* x,const float* w,const float* bias,float* y,int N,int H,int W,int has){long long idx=(long long)blockIdx.x*blockDim.x+threadIdx.x,total=(long long)N*32*H*W;if(idx>=total)return;int ow=idx%W;idx/=W;int oh=idx%H;idx/=H;int oc=idx%32;int n=idx/32;float s=0.f;for(int ic=0;ic<32;++ic)for(int kh=0;kh<3;++kh){int ih=oh+1-kh;if((unsigned)ih>=(unsigned)H)continue;for(int kw=0;kw<7;++kw){int iw=ow+3-kw;if((unsigned)iw<(unsigned)W){float a=tf32c(x[((n*32+ic)*H+ih)*W+iw]);float b=tf32c(w[((ic*32+oc)*3+kh)*7+kw]);s=fmaf(a,b,s);}}}if(has)s+=bias[oc];y[((n*32+oc)*H+oh)*W+ow]=s;}\ntorch::Tensor conv_transpose2d_forward(torch::Tensor x,torch::Tensor weight,torch::Tensor bias,int64_t sH,int64_t sW,int64_t pH,int64_t pW,bool has){int N=x.size(0),H=x.size(2),W=x.size(3);auto y=torch::empty({N,32,H,W},x.options());long long total=(long long)N*32*H*W;k<<<(total+255)/256,256,0,at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(),weight.data_ptr<float>(),has?bias.data_ptr<float>():nullptr,y.data_ptr<float>(),N,H,W,has?1:0);return y;}\n'''\n_ext=load_inline(name='deconv2d_ext_tf32test4',cpp_sources=cpp_src,cuda_sources=cuda_src,functions=['conv_transpose2d_forward'],extra_cuda_cflags=['-O3'],extra_cflags=['-O3'],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels:int,out_channels:int,kernel_size:tuple,stride:tuple=(1,1),padding:tuple=(0,0),bias:bool=False):\n super().__init__();self.conv_transpose2d=nn.ConvTranspose2d(in_channels,out_channels,kernel_size,stride=stride,padding=padding,bias=bias);self.stride=stride;self.padding=padding\n def forward(self,x):\n b=self.conv_transpose2d.bias;has=b is not None\n if b is None:b=torch.empty(0,device=x.device,dtype=x.dtype)\n return _ext.conv_transpose2d_forward(x,self.conv_transpose2d.weight,b,1,1,1,3,has)\n" }
result ← run_correctness (ok)
okrun_correctness run_correctness PASSED: (5 / 5) trials all matched the reference. Numerical precision: max_abs_err=6.69e-05 mean_abs_err=1.19e-06 max_rel_err=8.57e+02
turn 10 · 1 tool call(s) · 20.3s turn · 20.3s LLM · 8,065 chars (click to expand)
turn 1020.3s turn · 20.3s LLM · 0.0s tools
reasoning
**Considering tensor optimizations** Alright, I need to incorporate tf32c into optimized oc4, using tf32c for x and weight. This will increase the conversion instructions, but tensor cores could speed things up. However, I wonder if our scalar tf32 might end up being slower. I could also think about using mma tensor cores, but there might be overhead with ConvTranspose. I really should profile the speed first before diving into the correctness of oc4 tf32 in the run.
call → run_correctness
callrun_correctness { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\ncpp_src=r'''\n#include <torch/extension.h>\ntorch::Tensor conv_transpose2d_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, int64_t sH, int64_t sW, int64_t pH, int64_t pW, bool has_bias);\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__device__ __forceinline__ float tf32c(float x){unsigned y; asm(\"cvt.rna.tf32.f32 %0, %1;\" : \"=r\"(y) : \"f\"(x)); return __uint_as_float(y);} \n__global__ void deconv2d_oc4w4_kernel(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ bias,float* __restrict__ y,int N,int H,int W,int has_bias){\n int tid=blockIdx.x*blockDim.x+threadIdx.x; int W4=(W+3)>>2; int total=N*8*H*W4; if(tid>=total)return; int t=tid; int ow4=t%W4; t/=W4; int oh=t%H; t/=H; int ocg=t&7; t>>=3; int n=t; int ow=ow4<<2; int oc0=ocg<<2;\n float a00=0.f,a01=0.f,a02=0.f,a03=0.f,a10=0.f,a11=0.f,a12=0.f,a13=0.f,a20=0.f,a21=0.f,a22=0.f,a23=0.f,a30=0.f,a31=0.f,a32=0.f,a33=0.f;\n bool interior=(oh>0&&oh<H-1&&ow>=3&&ow+6<W);\n if(interior){\n #pragma unroll\n for(int ic=0;ic<32;++ic){\n #pragma unroll\n for(int kh=0;kh<3;++kh){ int ih=oh+1-kh; const float* xp=x+((n*32+ic)*H+ih)*W+(ow+3); const float* wp=w+((ic*32+oc0)*3+kh)*7;\n #pragma unroll\n for(int kw=0;kw<7;++kw){ float xv0=tf32c(__ldg(xp-kw+0)),xv1=tf32c(__ldg(xp-kw+1)),xv2=tf32c(__ldg(xp-kw+2)),xv3=tf32c(__ldg(xp-kw+3)); float w0=tf32c(__ldg(wp+0*21+kw)),w1=tf32c(__ldg(wp+1*21+kw)),w2=tf32c(__ldg(wp+2*21+kw)),w3=tf32c(__ldg(wp+3*21+kw));\n a00=fmaf(xv0,w0,a00);a01=fmaf(xv1,w0,a01);a02=fmaf(xv2,w0,a02);a03=fmaf(xv3,w0,a03); a10=fmaf(xv0,w1,a10);a11=fmaf(xv1,w1,a11);a12=fmaf(xv2,w1,a12);a13=fmaf(xv3,w1,a13); a20=fmaf(xv0,w2,a20);a21=fmaf(xv1,w2,a21);a22=fmaf(xv2,w2,a22);a23=fmaf(xv3,w2,a23); a30=fmaf(xv0,w3,a30);a31=fmaf(xv1,w3,a31);a32=fmaf(xv2,w3,a32);a33=fmaf(xv3,w3,a33); }\n }\n }\n }else{\n #pragma unroll\n for(int ic=0;ic<32;++ic){\n #pragma unroll\n for(int kh=0;kh<3;++kh){ int ih=oh+1-kh; if((unsigned)ih>=(unsigned)H)continue;\n #pragma unroll\n for(int kw=0;kw<7;++kw){ int iw0=ow+3-kw; float w0=tf32c(__ldg(w+((ic*32+oc0+0)*3+kh)*7+kw)),w1=tf32c(__ldg(w+((ic*32+oc0+1)*3+kh)*7+kw)),w2=tf32c(__ldg(w+((ic*32+oc0+2)*3+kh)*7+kw)),w3=tf32c(__ldg(w+((ic*32+oc0+3)*3+kh)*7+kw));\n if((unsigned)(iw0+0)<(unsigned)W){float xv=tf32c(__ldg(x+((n*32+ic)*H+ih)*W+iw0+0));a00=fmaf(xv,w0,a00);a10=fmaf(xv,w1,a10);a20=fmaf(xv,w2,a20);a30=fmaf(xv,w3,a30);} if((unsigned)(iw0+1)<(unsigned)W){float xv=tf32c(__ldg(x+((n*32+ic)*H+ih)*W+iw0+1));a01=fmaf(xv,w0,a01);a11=fmaf(xv,w1,a11);a21=fmaf(xv,w2,a21);a31=fmaf(xv,w3,a31);} if((unsigned)(iw0+2)<(unsigned)W){float xv=tf32c(__ldg(x+((n*32+ic)*H+ih)*W+iw0+2));a02=fmaf(xv,w0,a02);a12=fmaf(xv,w1,a12);a22=fmaf(xv,w2,a22);a32=fmaf(xv,w3,a32);} if((unsigned)(iw0+3)<(unsigned)W){float xv=tf32c(__ldg(x+((n*32+ic)*H+ih)*W+iw0+3));a03=fmaf(xv,w0,a03);a13=fmaf(xv,w1,a13);a23=fmaf(xv,w2,a23);a33=fmaf(xv,w3,a33);} }\n }\n }\n }\n if(has_bias){float b0=__ldg(bias+oc0),b1=__ldg(bias+oc0+1),b2=__ldg(bias+oc0+2),b3=__ldg(bias+oc0+3);a00+=b0;a01+=b0;a02+=b0;a03+=b0;a10+=b1;a11+=b1;a12+=b1;a13+=b1;a20+=b2;a21+=b2;a22+=b2;a23+=b2;a30+=b3;a31+=b3;a32+=b3;a33+=b3;}\n float* y0=y+((n*32+oc0+0)*H+oh)*W+ow;float* y1=y+((n*32+oc0+1)*H+oh)*W+ow;float* y2=y+((n*32+oc0+2)*H+oh)*W+ow;float* y3=y+((n*32+oc0+3)*H+oh)*W+ow; y0[0]=a00;y0[1]=a01;y0[2]=a02;y0[3]=a03;y1[0]=a10;y1[1]=a11;y1[2]=a12;y1[3]=a13;y2[0]=a20;y2[1]=a21;y2[2]=a22;y2[3]=a23;y3[0]=a30;y3[1]=a31;y3[2]=a32;y3[3]=a33;\n}\n__global__ void deconv2d_generic_kernel(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ bias,float* __restrict__ y,int N,int Cin,int H,int W,int Cout,int kH,int kW,int outH,int outW,int sH,int sW,int pH,int pW,int has_bias){long long idx=(long long)blockIdx.x*blockDim.x+threadIdx.x,total=(long long)N*Cout*outH*outW;if(idx>=total)return;int ow=idx%outW;idx/=outW;int oh=idx%outH;idx/=outH;int oc=idx%Cout;int n=idx/Cout;float sum=has_bias?bias[oc]:0.f;for(int ic=0;ic<Cin;++ic)for(int kh=0;kh<kH;++kh){int th=oh+pH-kh;if(th%sH)continue;int ih=th/sH;if((unsigned)ih>=(unsigned)H)continue;for(int kw=0;kw<kW;++kw){int tw=ow+pW-kw;if(tw%sW)continue;int iw=tw/sW;if((unsigned)iw<(unsigned)W)sum=fmaf(tf32c(x[((n*Cin+ic)*H+ih)*W+iw]),tf32c(w[((ic*Cout+oc)*kH+kh)*kW+kw]),sum);}}y[((n*Cout+oc)*outH+oh)*outW+ow]=sum;}\ntorch::Tensor conv_transpose2d_forward(torch::Tensor x,torch::Tensor weight,torch::Tensor bias,int64_t sH,int64_t sW,int64_t pH,int64_t pW,bool has_bias){x=x.contiguous();weight=weight.contiguous();int N=x.size(0),Cin=x.size(1),H=x.size(2),W=x.size(3),kH=weight.size(2),kW=weight.size(3),Cout=weight.size(1);int outH=(H-1)*(int)sH-2*(int)pH+kH,outW=(W-1)*(int)sW-2*(int)pW+kW;auto y=torch::empty({N,Cout,outH,outW},x.options());cudaStream_t stream=at::cuda::getCurrentCUDAStream();if(Cin==32&&Cout==32&&kH==3&&kW==7&&sH==1&&sW==1&&pH==1&&pW==3&&outH==H&&outW==W){int total=N*8*H*((W+3)/4);deconv2d_oc4w4_kernel<<<(total+127)/128,128,0,stream>>>(x.data_ptr<float>(),weight.data_ptr<float>(),has_bias?bias.data_ptr<float>():nullptr,y.data_ptr<float>(),N,H,W,has_bias?1:0);}else{long long total=(long long)N*Cout*outH*outW;deconv2d_generic_kernel<<<(total+255)/256,256,0,stream>>>(x.data_ptr<float>(),weight.data_ptr<float>(),has_bias?bias.data_ptr<float>():nullptr,y.data_ptr<float>(),N,Cin,H,W,Cout,kH,kW,outH,outW,(int)sH,(int)sW,(int)pH,(int)pW,has_bias?1:0);}return y;}\n'''\n_ext=load_inline(name='deconv2d_ext_v4_tf32',cpp_sources=cpp_src,cuda_sources=cuda_src,functions=['conv_transpose2d_forward'],extra_cuda_cflags=['-O3'],extra_cflags=['-O3'],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels:int,out_channels:int,kernel_size:tuple,stride:tuple=(1,1),padding:tuple=(0,0),bias:bool=False):\n super().__init__();self.conv_transpose2d=nn.ConvTranspose2d(in_channels,out_channels,kernel_size,stride=stride,padding=padding,bias=bias);self.stride=stride;self.padding=padding\n def forward(self,x:torch.Tensor)->torch.Tensor:\n b=self.conv_transpose2d.bias;has=b is not None\n if b is None:b=torch.empty(0,device=x.device,dtype=x.dtype)\n return _ext.conv_transpose2d_forward(x,self.conv_transpose2d.weight,b,int(self.stride[0]),int(self.stride[1]),int(self.padding[0]),int(self.padding[1]),has)\n" }
result ← run_correctness (ok)
okrun_correctness run_correctness PASSED: (5 / 5) trials all matched the reference. Numerical precision: max_abs_err=6.69e-05 mean_abs_err=1.19e-06 max_rel_err=8.57e+02
turn 11 · 1 tool call(s) · 17.0s turn · 17.0s LLM · 9,446 chars (click to expand)
turn 1117.0s turn · 17.0s LLM · 0.0s tools
reasoning
**Determining timing options** I’m thinking about timing. Should I use a profile or submit? It seems like using a profile might provide the runtime information I need. I wonder how quickly I could get the results that way compared to submitting directly. It’s important to weigh these options to ensure I’m making the best decision for efficiency. Let’s see which route offers the most useful insights!
call → profile_kernel
callprofile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\ncpp_src=r'''\n#include <torch/extension.h>\ntorch::Tensor conv_transpose2d_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, int64_t sH, int64_t sW, int64_t pH, int64_t pW, bool has_bias);\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__device__ __forceinline__ float tf32c(float x){unsigned y; asm(\"cvt.rna.tf32.f32 %0, %1;\" : \"=r\"(y) : \"f\"(x)); return __uint_as_float(y);} \n__global__ void deconv2d_oc4w4_kernel(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ bias,float* __restrict__ y,int N,int H,int W,int has_bias){\n int tid=blockIdx.x*blockDim.x+threadIdx.x; int W4=(W+3)>>2; int total=N*8*H*W4; if(tid>=total)return; int t=tid; int ow4=t%W4; t/=W4; int oh=t%H; t/=H; int ocg=t&7; t>>=3; int n=t; int ow=ow4<<2; int oc0=ocg<<2;\n float a00=0.f,a01=0.f,a02=0.f,a03=0.f,a10=0.f,a11=0.f,a12=0.f,a13=0.f,a20=0.f,a21=0.f,a22=0.f,a23=0.f,a30=0.f,a31=0.f,a32=0.f,a33=0.f;\n bool interior=(oh>0&&oh<H-1&&ow>=3&&ow+6<W);\n if(interior){\n #pragma unroll\n for(int ic=0;ic<32;++ic){\n #pragma unroll\n for(int kh=0;kh<3;++kh){ int ih=oh+1-kh; const float* xp=x+((n*32+ic)*H+ih)*W+(ow+3); const float* wp=w+((ic*32+oc0)*3+kh)*7;\n #pragma unroll\n for(int kw=0;kw<7;++kw){ float xv0=tf32c(__ldg(xp-kw+0)),xv1=tf32c(__ldg(xp-kw+1)),xv2=tf32c(__ldg(xp-kw+2)),xv3=tf32c(__ldg(xp-kw+3)); float w0=tf32c(__ldg(wp+0*21+kw)),w1=tf32c(__ldg(wp+1*21+kw)),w2=tf32c(__ldg(wp+2*21+kw)),w3=tf32c(__ldg(wp+3*21+kw));\n a00=fmaf(xv0,w0,a00);a01=fmaf(xv1,w0,a01);a02=fmaf(xv2,w0,a02);a03=fmaf(xv3,w0,a03); a10=fmaf(xv0,w1,a10);a11=fmaf(xv1,w1,a11);a12=fmaf(xv2,w1,a12);a13=fmaf(xv3,w1,a13); a20=fmaf(xv0,w2,a20);a21=fmaf(xv1,w2,a21);a22=fmaf(xv2,w2,a22);a23=fmaf(xv3,w2,a23); a30=fmaf(xv0,w3,a30);a31=fmaf(xv1,w3,a31);a32=fmaf(xv2,w3,a32);a33=fmaf(xv3,w3,a33); }\n }\n }\n }else{\n #pragma unroll\n for(int ic=0;ic<32;++ic){\n #pragma unroll\n for(int kh=0;kh<3;++kh){ int ih=oh+1-kh; if((unsigned)ih>=(unsigned)H)continue;\n #pragma unroll\n for(int kw=0;kw<7;++kw){ int iw0=ow+3-kw; float w0=tf32c(__ldg(w+((ic*32+oc0+0)*3+kh)*7+kw)),w1=tf32c(__ldg(w+((ic*32+oc0+1)*3+kh)*7+kw)),w2=tf32c(__ldg(w+((ic*32+oc0+2)*3+kh)*7+kw)),w3=tf32c(__ldg(w+((ic*32+oc0+3)*3+kh)*7+kw));\n if((unsigned)(iw0+0)<(unsigned)W){float xv=tf32c(__ldg(x+((n*32+ic)*H+ih)*W+iw0+0));a00=fmaf(xv,w0,a00);a10=fmaf(xv,w1,a10);a20=fmaf(xv,w2,a20);a30=fmaf(xv,w3,a30);} if((unsigned)(iw0+1)<(unsigned)W){float xv=tf32c(__ldg(x+((n*32+ic)*H+ih)*W+iw0+1));a01=fmaf(xv,w0,a01);a11=fmaf(xv,w1,a11);a21=fmaf(xv,w2,a21);a31=fmaf(xv,w3,a31);} if((unsigned)(iw0+2)<(unsigned)W){float xv=tf32c(__ldg(x+((n*32+ic)*H+ih)*W+iw0+2));a02=fmaf(xv,w0,a02);a12=fmaf(xv,w1,a12);a22=fmaf(xv,w2,a22);a32=fmaf(xv,w3,a32);} if((unsigned)(iw0+3)<(unsigned)W){float xv=tf32c(__ldg(x+((n*32+ic)*H+ih)*W+iw0+3));a03=fmaf(xv,w0,a03);a13=fmaf(xv,w1,a13);a23=fmaf(xv,w2,a23);a33=fmaf(xv,w3,a33);} }\n }\n }\n }\n if(has_bias){float b0=__ldg(bias+oc0),b1=__ldg(bias+oc0+1),b2=__ldg(bias+oc0+2),b3=__ldg(bias+oc0+3);a00+=b0;a01+=b0;a02+=b0;a03+=b0;a10+=b1;a11+=b1;a12+=b1;a13+=b1;a20+=b2;a21+=b2;a22+=b2;a23+=b2;a30+=b3;a31+=b3;a32+=b3;a33+=b3;}\n float* y0=y+((n*32+oc0+0)*H+oh)*W+ow;float* y1=y+((n*32+oc0+1)*H+oh)*W+ow;float* y2=y+((n*32+oc0+2)*H+oh)*W+ow;float* y3=y+((n*32+oc0+3)*H+oh)*W+ow; y0[0]=a00;y0[1]=a01;y0[2]=a02;y0[3]=a03;y1[0]=a10;y1[1]=a11;y1[2]=a12;y1[3]=a13;y2[0]=a20;y2[1]=a21;y2[2]=a22;y2[3]=a23;y3[0]=a30;y3[1]=a31;y3[2]=a32;y3[3]=a33;\n}\n__global__ void deconv2d_generic_kernel(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ bias,float* __restrict__ y,int N,int Cin,int H,int W,int Cout,int kH,int kW,int outH,int outW,int sH,int sW,int pH,int pW,int has_bias){long long idx=(long long)blockIdx.x*blockDim.x+threadIdx.x,total=(long long)N*Cout*outH*outW;if(idx>=total)return;int ow=idx%outW;idx/=outW;int oh=idx%outH;idx/=outH;int oc=idx%Cout;int n=idx/Cout;float sum=has_bias?bias[oc]:0.f;for(int ic=0;ic<Cin;++ic)for(int kh=0;kh<kH;++kh){int th=oh+pH-kh;if(th%sH)continue;int ih=th/sH;if((unsigned)ih>=(unsigned)H)continue;for(int kw=0;kw<kW;++kw){int tw=ow+pW-kw;if(tw%sW)continue;int iw=tw/sW;if((unsigned)iw<(unsigned)W)sum=fmaf(tf32c(x[((n*Cin+ic)*H+ih)*W+iw]),tf32c(w[((ic*Cout+oc)*kH+kh)*kW+kw]),sum);}}y[((n*Cout+oc)*outH+oh)*outW+ow]=sum;}\ntorch::Tensor conv_transpose2d_forward(torch::Tensor x,torch::Tensor weight,torch::Tensor bias,int64_t sH,int64_t sW,int64_t pH,int64_t pW,bool has_bias){x=x.contiguous();weight=weight.contiguous();int N=x.size(0),Cin=x.size(1),H=x.size(2),W=x.size(3),kH=weight.size(2),kW=weight.size(3),Cout=weight.size(1);int outH=(H-1)*(int)sH-2*(int)pH+kH,outW=(W-1)*(int)sW-2*(int)pW+kW;auto y=torch::empty({N,Cout,outH,outW},x.options());cudaStream_t stream=at::cuda::getCurrentCUDAStream();if(Cin==32&&Cout==32&&kH==3&&kW==7&&sH==1&&sW==1&&pH==1&&pW==3&&outH==H&&outW==W){int total=N*8*H*((W+3)/4);deconv2d_oc4w4_kernel<<<(total+127)/128,128,0,stream>>>(x.data_ptr<float>(),weight.data_ptr<float>(),has_bias?bias.data_ptr<float>():nullptr,y.data_ptr<float>(),N,H,W,has_bias?1:0);}else{long long total=(long long)N*Cout*outH*outW;deconv2d_generic_kernel<<<(total+255)/256,256,0,stream>>>(x.data_ptr<float>(),weight.data_ptr<float>(),has_bias?bias.data_ptr<float>():nullptr,y.data_ptr<float>(),N,Cin,H,W,Cout,kH,kW,outH,outW,(int)sH,(int)sW,(int)pH,(int)pW,has_bias?1:0);}return y;}\n'''\n_ext=load_inline(name='deconv2d_ext_v4_tf32_prof',cpp_sources=cpp_src,cuda_sources=cuda_src,functions=['conv_transpose2d_forward'],extra_cuda_cflags=['-O3'],extra_cflags=['-O3'],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels:int,out_channels:int,kernel_size:tuple,stride:tuple=(1,1),padding:tuple=(0,0),bias:bool=False):\n super().__init__();self.conv_transpose2d=nn.ConvTranspose2d(in_channels,out_channels,kernel_size,stride=stride,padding=padding,bias=bias);self.stride=stride;self.padding=padding\n def forward(self,x:torch.Tensor)->torch.Tensor:\n b=self.conv_transpose2d.bias;has=b is not None\n if b is None:b=torch.empty(0,device=x.device,dtype=x.dtype)\n return _ext.conv_transpose2d_forward(x,self.conv_transpose2d.weight,b,int(self.stride[0]),int(self.stride[1]),int(self.padding[0]),int(self.padding[1]),has)\n" }
result ← profile_kernel (ok)
okprofile_kernel profile_kernel PASSED: profiling complete. === Kernel Profile Summary === --- Kernel Breakdown (by GPU time) --- #1: aten::empty 2578.0μs (99%) calls=1 #2: aten::empty 35.0μs (1%) calls=1 --- Timing --- GPU time: 24685.44 μs --- Memory Bandwidth --- DRAM BW: 201.1/3350 GB/s (6.0%) read=179.4 write=21.7 GB/s Hit rates: L2=69.7% --- Compute Throughput --- Dominant pipe: FP32 util=19.3% Pipe util: FMA=19.3%, Tensor=0.3% --- Roofline Analysis --- Ridge point (FP32): 20.00 FLOPs/byte Classification: COMPUTE-BOUND --- Occupancy --- Achieved: 34.2% Eligible warps/cycle: 0.87 WARNING: <1 eligible warp/cycle = latency-bound, needs more parallelism Launch: regs/thread=64, smem/blk=1024B, block=128, grid=65536 --- Memory Access Quality --- Global ld coalescing: 4.07 sectors/req (4.1x wasted BW) --- Warp Stall Reasons (% of issue cycles) --- long_scoreboard 151.5% #################### short_scoreboard 6.4% # --- Optimization Hints --- -> 152% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=4.1 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set. -> Global load coalescing=4.1x (ideal=1.0) -> 4x wasted DRAM BW. Make per-warp accesses contiguous. -> Low eligible warps + low BW% + low compute% = LATENCY-BOUND. Need more parallelism (larger grid, more ILP, prefetching). -> Compute-bound but NOT using tensor cores. If applicable, use FP16/BF16 with torch.matmul or WMMA intrinsics.
turn 12 · 1 tool call(s) · 30.3s turn · 30.3s LLM · 31,474 chars (click to expand)
turn 1230.3s turn · 30.3s LLM · 0.0s tools
reasoning
**Testing boundary efficiency** I'm thinking about how boundaries relate to both first and last groups of rows, along with how they store loops. It seems like these might compile to arrays that create local memory. I'm considering using `#pragma unroll` with constant indices. It sounds like performing tests for correctness and profiling might be a good approach to see if everything is working efficiently. Let's quickly check these assumptions!
call → run_correctness
callrun_correctness { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\ncpp_src=r'''\n#include <torch/extension.h>\ntorch::Tensor conv_transpose2d_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, int64_t sH, int64_t sW, int64_t pH, int64_t pW, bool has_bias);\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__device__ __forceinline__ float tf32c(float x){unsigned y; asm(\"cvt.rna.tf32.f32 %0, %1;\" : \"=r\"(y) : \"f\"(x)); return __uint_as_float(y);} \n__global__ void k48(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ bias,float* __restrict__ y,int N,int H,int W,int has){\n int tid=blockIdx.x*blockDim.x+threadIdx.x; int W8=(W+7)>>3; int total=N*8*H*W8; if(tid>=total)return; int t=tid; int ow8=t%W8; t/=W8; int oh=t%H; t/=H; int ocg=t&7; t>>=3; int n=t; int ow=ow8<<3; int oc0=ocg<<2;\n float a00=0,a01=0,a02=0,a03=0,a04=0,a05=0,a06=0,a07=0,a10=0,a11=0,a12=0,a13=0,a14=0,a15=0,a16=0,a17=0,a20=0,a21=0,a22=0,a23=0,a24=0,a25=0,a26=0,a27=0,a30=0,a31=0,a32=0,a33=0,a34=0,a35=0,a36=0,a37=0;\n bool interior=(oh>0&&oh<H-1&&ow>=3&&ow+10<W);\n if(interior){#pragma unroll\n for(int ic=0;ic<32;++ic){#pragma unroll\n for(int kh=0;kh<3;++kh){int ih=oh+1-kh; const float* xp=x+((n*32+ic)*H+ih)*W+(ow+3); const float* wp=w+((ic*32+oc0)*3+kh)*7;#pragma unroll\n for(int kw=0;kw<7;++kw){float x0=tf32c(__ldg(xp-kw+0)),x1=tf32c(__ldg(xp-kw+1)),x2=tf32c(__ldg(xp-kw+2)),x3=tf32c(__ldg(xp-kw+3)),x4=tf32c(__ldg(xp-kw+4)),x5=tf32c(__ldg(xp-kw+5)),x6=tf32c(__ldg(xp-kw+6)),x7=tf32c(__ldg(xp-kw+7));float w0=tf32c(__ldg(wp+0*21+kw)),w1=tf32c(__ldg(wp+1*21+kw)),w2=tf32c(__ldg(wp+2*21+kw)),w3=tf32c(__ldg(wp+3*21+kw));\n a00=fmaf(x0,w0,a00);a01=fmaf(x1,w0,a01);a02=fmaf(x2,w0,a02);a03=fmaf(x3,w0,a03);a04=fmaf(x4,w0,a04);a05=fmaf(x5,w0,a05);a06=fmaf(x6,w0,a06);a07=fmaf(x7,w0,a07);a10=fmaf(x0,w1,a10);a11=fmaf(x1,w1,a11);a12=fmaf(x2,w1,a12);a13=fmaf(x3,w1,a13);a14=fmaf(x4,w1,a14);a15=fmaf(x5,w1,a15);a16=fmaf(x6,w1,a16);a17=fmaf(x7,w1,a17);a20=fmaf(x0,w2,a20);a21=fmaf(x1,w2,a21);a22=fmaf(x2,w2,a22);a23=fmaf(x3,w2,a23);a24=fmaf(x4,w2,a24);a25=fmaf(x5,w2,a25);a26=fmaf(x6,w2,a26);a27=fmaf(x7,w2,a27);a30=fmaf(x0,w3,a30);a31=fmaf(x1,w3,a31);a32=fmaf(x2,w3,a32);a33=fmaf(x3,w3,a33);a34=fmaf(x4,w3,a34);a35=fmaf(x5,w3,a35);a36=fmaf(x6,w3,a36);a37=fmaf(x7,w3,a37);}}}\n }else{#pragma unroll\n for(int ic=0;ic<32;++ic){#pragma unroll\n for(int kh=0;kh<3;++kh){int ih=oh+1-kh;if((unsigned)ih>=(unsigned)H)continue;#pragma unroll\n for(int kw=0;kw<7;++kw){int iw=ow+3-kw;float w0=tf32c(__ldg(w+((ic*32+oc0+0)*3+kh)*7+kw)),w1=tf32c(__ldg(w+((ic*32+oc0+1)*3+kh)*7+kw)),w2=tf32c(__ldg(w+((ic*32+oc0+2)*3+kh)*7+kw)),w3=tf32c(__ldg(w+((ic*32+oc0+3)*3+kh)*7+kw));#define DO(L) if((unsigned)(iw+L)<(unsigned)W){float xv=tf32c(__ldg(x+((n*32+ic)*H+ih)*W+iw+L)); a0##L=fmaf(xv,w0,a0##L); a1##L=fmaf(xv,w1,a1##L); a2##L=fmaf(xv,w2,a2##L); a3##L=fmaf(xv,w3,a3##L);} DO(0) DO(1) DO(2) DO(3) DO(4) DO(5) DO(6) DO(7) #undef DO }} }\n }\n if(has){float b0=__ldg(bias+oc0),b1=__ldg(bias+oc0+1),b2=__ldg(bias+oc0+2),b3=__ldg(bias+oc0+3);a00+=b0;a01+=b0;a02+=b0;a03+=b0;a04+=b0;a05+=b0;a06+=b0;a07+=b0;a10+=b1;a11+=b1;a12+=b1;a13+=b1;a14+=b1;a15+=b1;a16+=b1;a17+=b1;a20+=b2;a21+=b2;a22+=b2;a23+=b2;a24+=b2;a25+=b2;a26+=b2;a27+=b2;a30+=b3;a31+=b3;a32+=b3;a33+=b3;a34+=b3;a35+=b3;a36+=b3;a37+=b3;}\n float* y0=y+((n*32+oc0+0)*H+oh)*W+ow;float* y1=y+((n*32+oc0+1)*H+oh)*W+ow;float* y2=y+((n*32+oc0+2)*H+oh)*W+ow;float* y3=y+((n*32+oc0+3)*H+oh)*W+ow; y0[0]=a00;y0[1]=a01;y0[2]=a02;y0[3]=a03;y0[4]=a04;y0[5]=a05;y0[6]=a06;y0[7]=a07;y1[0]=a10;y1[1]=a11;y1[2]=a12;y1[3]=a13;y1[4]=a14;y1[5]=a15;y1[6]=a16;y1[7]=a17;y2[0]=a20;y2[1]=a21;y2[2]=a22;y2[3]=a23;y2[4]=a24;y2[5]=a25;y2[6]=a26;y2[7]=a27;y3[0]=a30;y3[1]=a31;y3[2]=a32;y3[3]=a33;y3[4]=a34;y3[5]=a35;y3[6]=a36;y3[7]=a37;\n}\ntorch::Tensor conv_transpose2d_forward(torch::Tensor x,torch::Tensor weight,torch::Tensor bias,int64_t sH,int64_t sW,int64_t pH,int64_t pW,bool has){int N=x.size(0),H=x.size(2),W=x.size(3);auto y=torch::empty({N,32,H,W},x.options());int total=N*8*H*((W+7)/8);k48<<<(total+127)/128,128,0,at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(),weight.data_ptr<float>(),has?bias.data_ptr<float>():nullptr,y.data_ptr<float>(),N,H,W,has?1:0);return y;}\n'''\n_ext=load_inline(name='deconv2d_ext_w8try',cpp_sources=cpp_src,cuda_sources=cuda_src,functions=['conv_transpose2d_forward'],extra_cuda_cflags=['-O3'],extra_cflags=['-O3'],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels:int,out_channels:int,kernel_size:tuple,stride:tuple=(1,1),padding:tuple=(0,0),bias:bool=False):\n super().__init__();self.conv_transpose2d=nn.ConvTranspose2d(in_channels,out_channels,kernel_size,stride=stride,padding=padding,bias=bias);self.stride=stride;self.padding=padding\n def forward(self,x):\n b=self.conv_transpose2d.bias;has=b is not None\n if b is None:b=torch.empty(0,device=x.device,dtype=x.dtype)\n return _ext.conv_transpose2d_forward(x,self.conv_transpose2d.weight,b,1,1,1,3,has)\n" }
result ← run_correctness (fail)
failrun_correctness run_correctness FAILED: kernel did not compile. Error building extension 'deconv2d_ext_w8try': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=deconv2d_ext_w8try -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 -O3 -c /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_78_cache/k_974af43d29dc/deconv2d_ext_w8try/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=deconv2d_ext_w8try -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_78_cache/k_974af43d29dc/deconv2d_ext_w8try/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=deconv2d_ext_w8try -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_78_cache/k_974af43d29dc/deconv2d_ext_w8try/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_78_cache/k_974af43d29dc/deconv2d_ext_w8try/cuda.cu(14): error: "#" not expected here if(interior){#pragma unroll ^ /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_78_cache/k_974af43d29dc/deconv2d_ext_w8try/cuda.cu(14): error: expected an expression if(interior){#pragma unroll ^ /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_78_cache/k_974af43d29dc/deconv2d_ext_w8try/cuda.cu(15): error: "#" not expected here for(int ic=0;ic<32;++ic){#pragma unroll ^ /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_78_cache/k_974af43d29dc/deconv2d_ext_w8try/cuda.cu(16): error: "#" not expected here for(int kh=0;kh<3;++kh){int ih=oh+1-kh; const float* xp=x+((n*32+ic)*H+ih)*W+(ow+3); const float* wp=w+((ic*32+oc0)*3+kh)*7;#pragma unroll ^ /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_78_cache/k_974af43d29dc/deconv2d_ext_w8try/cuda.cu(19): warning #12-D: parsing restarts here after previous syntax error }else{#pragma unroll ^ Remark: The warnings can be suppressed with "-diag-suppress <warning-number>" /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_78_cache/k_974af43d29dc/deconv2d_ext_w8try/cuda.cu(19): error: expected a ";" }else{#pragma unroll ^ /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_78_cache/k_974af43d29dc/deconv2d_ext_w8try/cuda.cu(19): error: "#" not expected here }else{#pragma unroll ^ /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_78_cache/k_974af43d29dc/deconv2d_ext_w8try/cuda.cu(19): error: expected an expression }else{#pragma unroll ^ /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_78_cache/k_974af43d29dc/deconv2d_ext_w8try/cuda.cu(20): error: "#" not expected here for(int ic=0;ic<32;++ic){#pragma unroll ^ /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_78_cache/k_974af43d29dc/deconv2d_ext_w8try/cuda.cu(21): error: "#" not expected here for(int kh=0;kh<3;++kh){int ih=oh+1-kh;if((unsigned)ih>=(unsigned)H)continue;#pragma unroll ^ /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_78_cache/k_974af43d29dc/deconv2d_ext_w8try/cuda.cu(22): error: "#" not expected here for(int kw=0;kw<7;++kw){int iw=ow+3-kw;float w0=tf32c(__ldg(w+((ic*32+oc0+0)*3+kh)*7+kw)),w1=tf32c(__ldg(w+((ic*32+oc0+1)*3+kh)*7+kw)),w2=tf32c(__ldg(w+((ic*32+oc0+2)*3+kh)*7+kw)),w3=tf32c(__ldg(w+((ic*32+oc0+3)*3+kh)*7+kw));#define DO(L) if((unsigned)(iw+L)<(unsigned)W){float xv=tf32c(__ldg(x+((n*32+ic)*H+ih)*W+iw+L)); a0##L=fmaf(xv,w0,a0##L); a1##L=fmaf(xv,w1,a1##L); a2##L=fmaf(xv,w2,a2##L); a3##L=fmaf(xv,w3,a3##L);} DO(0) DO(1) DO(2) DO(3) DO(4) DO(5) DO(6) DO(7) #undef DO }} } ^ /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_78_cache/k_974af43d29dc/deconv2d_ext_w8try/cuda.cu(22): error: "#" not expected here for(int kw=0;kw<7;++kw){int iw=ow+3-kw;float w0=tf32c(__ldg(w+((ic*32+oc0+0)*3+kh)*7+kw)),w1=tf32c(__ldg(w+((ic*32+oc0+1)*3+kh)*7+kw)),w2=tf32c(__ldg(w+((ic*32+oc0+2)*3+kh)*7+kw)),w3=tf32c(__ldg(w+((ic*32+oc0+3)*3+kh)*7+kw));#define DO(L) if((unsigned)(iw+L)<(unsigned)W){float xv=tf32c(__ldg(x+((n*32+ic)*H+ih)*W+iw+L)); a0##L=fmaf(xv,w0,a0##L); a1##L=fmaf(xv,w1,a1##L); a2##L=fmaf(xv,w2,a2##L); a3##L=fmaf(xv,w3,a3##L);} DO(0) DO(1) DO(2) DO(3) DO(4) DO(5) DO(6) DO(7) #undef DO }} } ^ /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_78_cache/k_974af43d29dc/deconv2d_ext_w8try/cuda.cu(22): error: "#" not expected here for(int kw=0;kw<7;++kw){int iw=ow+3-kw;float w0=tf32c(__ldg(w+((ic*32+oc0+0)*3+kh)*7+kw)),w1=tf32c(__ldg(w+((ic*32+oc0+1)*3+kh)*7+kw)),w2=tf32c(__ldg(w+((ic*32+oc0+2)*3+kh)*7+kw)),w3=tf32c(__ldg(w+((ic*32+oc0+3)*3+kh)*7+kw));#define DO(L) if((unsigned)(iw+L)<(unsigned)W){float xv=tf32c(__ldg(x+((n*32+ic)*H+ih)*W+iw+L)); a0##L=fmaf(xv,w0,a0##L); a1##L=fmaf(xv,w1,a1##L); a2##L=fmaf(xv,w2,a2##L); a3##L=fmaf(xv,w3,a3##L);} DO(0) DO(1) DO(2) DO(3) DO(4) DO(5) DO(6) DO(7) #undef DO }} } ^ /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_78_cache/k_974af43d29dc/deconv2d_ext_w8try/cuda.cu(22): error: "#" not expected here for(int kw=0;kw<7;++kw){int iw=ow+3-kw;float w0=tf32c(__ldg(w+((ic*32+oc0+0)*3+kh)*7+kw)),w1=tf32c(__ldg(w+((ic*32+oc0+1)*3+kh)*7+kw)),w2=tf32c(__ldg(w+((ic*32+oc0+2)*3+kh)*7+kw)),w3=tf32c(__ldg(w+((ic*32+oc0+3)*3+kh)*7+kw));#define DO(L) if((unsigned)(iw+L)<(unsigned)W){float xv=tf32c(__ldg(x+((n*32+ic)*H+ih)*W+iw+L)); a0##L=fmaf(xv,w0,a0##L); a1##L=fmaf(xv,w1,a1##L); a2##L=fmaf(xv,w2,a2##L); a3##L=fmaf(xv,w3,a3##L);} DO(0) DO(1) DO(2) DO(3) DO(4) DO(5) DO(6) DO(7) #undef DO }} } ^ /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_78_cache/k_974af43d29dc/deconv2d_ext_w8try/cuda.cu(22): error: "#" not expected here for(int kw=0;kw<7;++kw){int iw=ow+3-kw;float w0=tf32c(__ldg(w+((ic*32+oc0+0)*3+kh)*7+kw)),w1=tf32c(__ldg(w+((ic*32+oc0+1)*3+kh)*7+kw)),w2=tf32c(__ldg(w+((ic*32+oc0+2)*3+kh)*7+kw)),w3=tf32c(__ldg(w+((ic*32+oc0+3)*3+kh)*7+kw));#define DO(L) if((unsigned)(iw+L)<(unsigned)W){float xv=tf32c(__ldg(x+((n*32+ic)*H+ih)*W+iw+L)); a0##L=fmaf(xv,w0,a0##L); a1##L=fmaf(xv,w1,a1##L); a2##L=fmaf(xv,w2,a2##L); a3##L=fmaf(xv,w3,a3##L);} DO(0) DO(1) DO(2) DO(3) DO(4) DO(5) DO(6) DO(7) #undef DO }} } ^ /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_78_cache/k_974af43d29dc/deconv2d_ext_w8try/cuda.cu(22): error: "#" not expected here for(int kw=0;kw<7;++kw){int iw=ow+3-kw;float w0=tf32c(__ldg(w+((ic*32+oc0+0)*3+kh)*7+kw)),w1=tf32c(__ldg(w+((ic*32+oc0+1)*3+kh)*7+kw)),w2=tf32c(__ldg(w+((ic*32+oc0+2)*3+kh)*7+kw)),w3=tf32c(__ldg(w+((ic*32+oc0+3)*3+kh)*7+kw));#define DO(L) if((unsigned)(iw+L)<(unsigned)W){float xv=tf32c(__ldg(x+((n*32+ic)*H+ih)*W+iw+L)); a0##L=fmaf(xv,w0,a0##L); a1##L=fmaf(xv,w1,a1##L); a2##L=fmaf(xv,w2,a2##L); a3##L=fmaf(xv,w3,a3##L);} DO(0) DO(1) DO(2) DO(3) DO(4) DO(5) DO(6) DO(7) #undef DO }} } ^ /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_78_cache/k_974af43d29dc/deconv2d_ext_w8try/cuda.cu(22): error: "#" not expected here for(int kw=0;kw<7;++kw){int iw=ow+3-kw;float w0=tf32c(__ldg(w+((ic*32+oc0+0)*3+kh)*7+kw)),w1=tf32c(__ldg(w+((ic*32+oc0+1)*3+kh)*7+kw)),w2=tf32c(__ldg(w+((ic*32+oc0+2)*3+kh)*7+kw)),w3=tf32c(__ldg(w+((ic*32+oc0+3)*3+kh)*7+kw));#define DO(L) if((unsigned)(iw+L)<(unsigned)W){float xv=tf32c(__ldg(x+((n*32+ic)*H+ih)*W+iw+L)); a0##L=fmaf(xv,w0,a0##L); a1##L=fmaf(xv,w1,a1##L); a2##L=fmaf(xv,w2,a2##L); a3##L=fmaf(xv,w3,a3##L);} DO(0) DO(1) DO(2) DO(3) DO(4) DO(5) DO(6) DO(7) #undef DO }} } ^ /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_78_cache/k_974af43d29dc/deconv2d_ext_w8try/cuda.cu(22): error: "#" not expected here for(int kw=0;kw<7;++kw){int iw=ow+3-kw;float w0=tf32c(__ldg(w+((ic*32+oc0+0)*3+kh)*7+kw)),w1=tf32c(__ldg(w+((ic*32+oc0+1)*3+kh)*7+kw)),w2=tf32c(__ldg(w+((ic*32+oc0+2)*3+kh)*7+kw)),w3=tf32c(__ldg(w+((ic*32+oc0+3)*3+kh)*7+kw));#define DO(L) if((unsigned)(iw+L)<(unsigned)W){float xv=tf32c(__ldg(x+((n*32+ic)*H+ih)*W+iw+L)); a0##L=fmaf(xv,w0,a0##L); a1##L=fmaf(xv,w1,a1##L); a2##L=fmaf(xv,w2,a2##L); a3##L=fmaf(xv,w3,a3##L);} DO(0) DO(1) DO(2) DO(3) DO(4) DO(5) DO(6) DO(7) #undef DO }} } ^ /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_78_cache/k_974af43d29dc/deconv2d_ext_w8try/cuda.cu(22): error: "#" not expected here for(int kw=0;kw<7;++kw){int iw=ow+3-kw;float w0=tf32c(__ldg(w+((ic*32+oc0+0)*3+kh)*7+kw)),w1=tf32c(__ldg(w+((ic*32+oc0+1)*3+kh)*7+kw)),w2=tf32c(__ldg(w+((ic*32+oc0+2)*3+kh)*7+kw)),w3=tf32c(__ldg(w+((ic*32+oc0+3)*3+kh)*7+kw));#define DO(L) if((unsigned)(iw+L)<(unsigned)W){float xv=tf32c(__ldg(x+((n*32+ic)*H+ih)*W+iw+L)); a0##L=fmaf(xv,w0,a0##L); a1##L=fmaf(xv,w1,a1##L); a2##L=fmaf(xv,w2,a2##L); a3##L=fmaf(xv,w3,a3##L);} DO(0) DO(1) DO(2) DO(3) DO(4) DO(5) DO(6) DO(7) #undef DO }} } ^ /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_78_cache/k_974af43d29dc/deconv2d_ext_w8try/cuda.cu(22): error: "#" not expected here for(int kw=0;kw<7;++kw){int iw=ow+3-kw;float w0=tf32c(__ldg(w+((ic*32+oc0+0)*3+kh)*7+kw)),w1=tf32c(__ldg(w+((ic*32+oc0+1)*3+kh)*7+kw)),w2=tf32c(__ldg(w+((ic*32+oc0+2)*3+kh)*7+kw)),w3=tf32c(__ldg(w+((ic*32+oc0+3)*3+kh)*7+kw));#define DO(L) if((unsigned)(iw+L)<(unsigned)W){float xv=tf32c(__ldg(x+((n*32+ic)*H+ih)*W+iw+L)); a0##L=fmaf(xv,w0,a0##L); a1##L=fmaf(xv,w1,a1##L); a2##L=fmaf(xv,w2,a2##L); a3##L=fmaf(xv,w3,a3##L);} DO(0) DO(1) DO(2) DO(3) DO(4) DO(5) DO(6) DO(7) #undef DO }} } ^ /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_78_cache/k_974af43d29dc/deconv2d_ext_w8try/cuda.cu(22): error: "#" not expected here for(int kw=0;kw<7;++kw){int iw=ow+3-kw;float w0=tf32c(__ldg(w+((ic*32+oc0+0)*3+kh)*7+kw)),w1=tf32c(__ldg(w+((ic*32+oc0+1)*3+kh)*7+kw)),w2=tf32c(__ldg(w+((ic*32+oc0+2)*3+kh)*7+kw)),w3=tf32c(__ldg(w+((ic*32+oc0+3)*3+kh)*7+kw));#define DO(L) if((unsigned)(iw+L)<(unsigned)W){float xv=tf32c(__ldg(x+((n*32+ic)*H+ih)*W+iw+L)); a0##L=fmaf(xv,w0,a0##L); a1##L=fmaf(xv,w1,a1##L); a2##L=fmaf(xv,w2,a2##L); a3##L=fmaf(xv,w3,a3##L);} DO(0) DO(1) DO(2) DO(3) DO(4) DO(5) DO(6) DO(7) #undef DO }} } ^ /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_78_cache/k_974af43d29dc/deconv2d_ext_w8try/cuda.cu(22): error: "#" not expected here for(int kw=0;kw<7;++kw){int iw=ow+3-kw;float w0=tf32c(__ldg(w+((ic*32+oc0+0)*3+kh)*7+kw)),w1=tf32c(__ldg(w+((ic*32+oc0+1)*3+kh)*7+kw)),w2=tf32c(__ldg(w+((ic*32+oc0+2)*3+kh)*7+kw)),w3=tf32c(__ldg(w+((ic*32+oc0+3)*3+kh)*7+kw));#define DO(L) if((unsigned)(iw+L)<(unsigned)W){float xv=tf32c(__ldg(x+((n*32+ic)*H+ih)*W+iw+L)); a0##L=fmaf(xv,w0,a0##L); a1##L=fmaf(xv,w1,a1##L); a2##L=fmaf(xv,w2,a2##L); a3##L=fmaf(xv,w3,a3##L);} DO(0) DO(1) DO(2) DO(3) DO(4) DO(5) DO(6) DO(7) #undef DO }} } ^ /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_78_cache/k_974af43d29dc/deconv2d_ext_w8try/cuda.cu(22): error: "#" not expected here for(int kw=0;kw<7;++kw){int iw=ow+3-kw;float w0=tf32c(__ldg(w+((ic*32+oc0+0)*3+kh)*7+kw)),w1=tf32c(__ldg(w+((ic*32+oc0+1)*3+kh)*7+kw)),w2=tf32c(__ldg(w+((ic*32+oc0+2)*3+kh)*7+kw)),w3=tf32c(__ldg(w+((ic*32+oc0+3)*3+kh)*7+kw));#define DO(L) if((unsigned)(iw+L)<(unsigned)W){float xv=tf32c(__ldg(x+((n*32+ic)*H+ih)*W+iw+L)); a0##L=fmaf(xv,w0,a0##L); a1##L=fmaf(xv,w1,a1##L); a2##L=fmaf(xv,w2,a2##L); a3##L=fmaf(xv,w3,a3##L);} DO(0) DO(1) DO(2) DO(3) DO(4) DO(5) DO(6) DO(7) #undef DO }} } ^ /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_78_cache/k_974af43d29dc/deconv2d_ext_w8try/cuda.cu(22): error: "#" not expected here for(int kw=0;kw<7;++kw){int iw=ow+3-kw;float w0=tf32c(__ldg(w+((ic*32+oc0+0)*3+kh)*7+kw)),w1=tf32c(__ldg(w+((ic*32+oc0+1)*3+kh)*7+kw)),w2=tf32c(__ldg(w+((ic*32+oc0+2)*3+kh)*7+kw)),w3=tf32c(__ldg(w+((ic*32+oc0+3)*3+kh)*7+kw));#define DO(L) if((unsigned)(iw+L)<(unsigned)W){float xv=tf32c(__ldg(x+((n*32+ic)*H+ih)*W+iw+L)); a0##L=fmaf(xv,w0,a0##L); a1##L=fmaf(xv,w1,a1##L); a2##L=fmaf(xv,w2,a2##L); a3##L=fmaf(xv,w3,a3##L);} DO(0) DO(1) DO(2) DO(3) DO(4) DO(5) DO(6) DO(7) #undef DO }} } ^ /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_78_cache/k_974af43d29dc/deconv2d_ext_w8try/cuda.cu(22): error: "#" not expected here for(int kw=0;kw<7;++kw){int iw=ow+3-kw;float w0=tf32c(__ldg(w+((ic*32+oc0+0)*3+kh)*7+kw)),w1=tf32c(__ldg(w+((ic*32+oc0+1)*3+kh)*7+kw)),w2=tf32c(__ldg(w+((ic*32+oc0+2)*3+kh)*7+kw)),w3=tf32c(__ldg(w+((ic*32+oc0+3)*3+kh)*7+kw));#define DO(L) if((unsigned)(iw+L)<(unsigned)W){float xv=tf32c(__ldg(x+((n*32+ic)*H+ih)*W+iw+L)); a0##L=fmaf(xv,w0,a0##L); a1##L=fmaf(xv,w1,a1##L); a2##L=fmaf(xv,w2,a2##L); a3##L=fmaf(xv,w3,a3##L);} DO(0) DO(1) DO(2) DO(3) DO(4) DO(5) DO(6) DO(7) #undef DO }} } ^ /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_78_cache/k_974af43d29dc/deconv2d_ext_w8try/cuda.cu(22): error: "#" not expected here for(int kw=0;kw<7;++kw){int iw=ow+3-kw;float w0=tf32c(__ldg(w+((ic*32+oc0+0)*3+kh)*7+kw)),w1=tf32c(__ldg(w+((ic*32+oc0+1)*3+kh)*7+kw)),w2=tf32c(__ldg(w+((ic*32+oc0+2)*3+kh)*7+kw)),w3=tf32c(__ldg(w+((ic*32+oc0+3)*3+kh)*7+kw));#define DO(L) if((unsigned)(iw+L)<(unsigned)W){float xv=tf32c(__ldg(x+((n*32+ic)*H+ih)*W+iw+L)); a0##L=fmaf(xv,w0,a0##L); a1##L=fmaf(xv,w1,a1##L); a2##L=fmaf(xv,w2,a2##L); a3##L=fmaf(xv,w3,a3##L);} DO(0) DO(1) DO(2) DO(3) DO(4) DO(5) DO(6) DO(7) #undef DO }} } ^ /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_78_cache/k_974af43d29dc/deconv2d_ext_w8try/cuda.cu(22): error: "#" not expected here for(int kw=0;kw<7;++kw){int iw=ow+3-kw;float w0=tf32c(__ldg(w+((ic*32+oc0+0)*3+kh)*7+kw)),w1=tf32c(__ldg(w+((ic*32+oc0+1)*3+kh)*7+kw)),w2=tf32c(__ldg(w+((ic*32+oc0+2)*3+kh)*7+kw)),w3=tf32c(__ldg(w+((ic*32+oc0+3)*3+kh)*7+kw));#define DO(L) if((unsigned)(iw+L)<(unsigned)W){float xv=tf32c(__ldg(x+((n*32+ic)*H+ih)*W+iw+L)); a0##L=fmaf(xv,w0,a0##L); a1##L=fmaf(xv,w1,a1##L); a2##L=fmaf(xv,w2,a2##L); a3##L=fmaf(xv,w3,a3##L);} DO(0) DO(1) DO(2) DO(3) DO(4) DO(5) DO(6) DO(7) #undef DO }} } ^ /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_78_cache/k_974af43d29dc/deconv2d_ext_w8try/cuda.cu(22): error: "#" not expected here for(int kw=0;kw<7;++kw){int iw=ow+3-kw;float w0=tf32c(__ldg(w+((ic*32+oc0+0)*3+kh)*7+kw)),w1=tf32c(__ldg(w+((ic*32+oc0+1)*3+kh)*7+kw)),w2=tf32c(__ldg(w+((ic*32+oc0+2)*3+kh)*7+kw)),w3=tf32c(__ldg(w+((ic*32+oc0+3)*3+kh)*7+kw));#define DO(L) if((unsigned)(iw+L)<(unsigned)W){float xv=tf32c(__ldg(x+((n*32+ic)*H+ih)*W+iw+L)); a0##L=fmaf(xv,w0,a0##L); a1##L=fmaf(xv,w1,a1##L); a2##L=fmaf(xv,w2,a2##L); a3##L=fmaf(xv,w3,a3##L);} DO(0) DO(1) DO(2) DO(3) DO(4) DO(5) DO(6) DO(7) #undef DO }} } ^ /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_78_cache/k_974af43d29dc/deconv2d_ext_w8try/cuda.cu(23): warning #12-D: parsing restarts here after previous syntax error } ^ /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_78_cache/k_974af43d29dc/deconv2d_ext_w8try/cuda.cu(23): error: expected a ";" } ^ 28 errors detected in the compilation of "/scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_78_cache/k_974af43d29dc/deconv2d_ext_w8try/cuda.cu". ninja: build stopped: subcommand failed.
turn 13 · 1 tool call(s) · 29.4s turn · 29.4s LLM · 7,403 chars (click to expand)
turn 1329.4s turn · 29.4s LLM · 0.0s tools
call → static_check
callstatic_check { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\ncpp_src=r'''\n#include <torch/extension.h>\ntorch::Tensor conv_transpose2d_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, int64_t sH, int64_t sW, int64_t pH, int64_t pW, bool has_bias);\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__device__ __forceinline__ float tf32c(float x){unsigned y; asm(\"cvt.rna.tf32.f32 %0, %1;\" : \"=r\"(y) : \"f\"(x)); return __uint_as_float(y);} \n__global__ void deconv2d_oc4w4_kernel(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ bias,float* __restrict__ y,int N,int H,int W,int has_bias){\n int tid=blockIdx.x*blockDim.x+threadIdx.x; int W4=(W+3)>>2; int total=N*8*H*W4; if(tid>=total)return; int t=tid; int ow4=t%W4; t/=W4; int oh=t%H; t/=H; int ocg=t&7; t>>=3; int n=t; int ow=ow4<<2; int oc0=ocg<<2;\n float a00=0.f,a01=0.f,a02=0.f,a03=0.f,a10=0.f,a11=0.f,a12=0.f,a13=0.f,a20=0.f,a21=0.f,a22=0.f,a23=0.f,a30=0.f,a31=0.f,a32=0.f,a33=0.f;\n bool interior=(oh>0&&oh<H-1&&ow>=3&&ow+6<W);\n if(interior){\n #pragma unroll\n for(int ic=0;ic<32;++ic){\n #pragma unroll\n for(int kh=0;kh<3;++kh){ int ih=oh+1-kh; const float* xp=x+((n*32+ic)*H+ih)*W+(ow+3); const float* wp=w+((ic*32+oc0)*3+kh)*7;\n #pragma unroll\n for(int kw=0;kw<7;++kw){ float xv0=tf32c(__ldg(xp-kw+0)),xv1=tf32c(__ldg(xp-kw+1)),xv2=tf32c(__ldg(xp-kw+2)),xv3=tf32c(__ldg(xp-kw+3)); float w0=tf32c(__ldg(wp+0*21+kw)),w1=tf32c(__ldg(wp+1*21+kw)),w2=tf32c(__ldg(wp+2*21+kw)),w3=tf32c(__ldg(wp+3*21+kw));\n a00=fmaf(xv0,w0,a00);a01=fmaf(xv1,w0,a01);a02=fmaf(xv2,w0,a02);a03=fmaf(xv3,w0,a03); a10=fmaf(xv0,w1,a10);a11=fmaf(xv1,w1,a11);a12=fmaf(xv2,w1,a12);a13=fmaf(xv3,w1,a13); a20=fmaf(xv0,w2,a20);a21=fmaf(xv1,w2,a21);a22=fmaf(xv2,w2,a22);a23=fmaf(xv3,w2,a23); a30=fmaf(xv0,w3,a30);a31=fmaf(xv1,w3,a31);a32=fmaf(xv2,w3,a32);a33=fmaf(xv3,w3,a33); }\n }\n }\n }else{\n #pragma unroll\n for(int ic=0;ic<32;++ic){\n #pragma unroll\n for(int kh=0;kh<3;++kh){ int ih=oh+1-kh; if((unsigned)ih>=(unsigned)H)continue;\n #pragma unroll\n for(int kw=0;kw<7;++kw){ int iw0=ow+3-kw; float w0=tf32c(__ldg(w+((ic*32+oc0+0)*3+kh)*7+kw)),w1=tf32c(__ldg(w+((ic*32+oc0+1)*3+kh)*7+kw)),w2=tf32c(__ldg(w+((ic*32+oc0+2)*3+kh)*7+kw)),w3=tf32c(__ldg(w+((ic*32+oc0+3)*3+kh)*7+kw));\n if((unsigned)(iw0+0)<(unsigned)W){float xv=tf32c(__ldg(x+((n*32+ic)*H+ih)*W+iw0+0));a00=fmaf(xv,w0,a00);a10=fmaf(xv,w1,a10);a20=fmaf(xv,w2,a20);a30=fmaf(xv,w3,a30);} if((unsigned)(iw0+1)<(unsigned)W){float xv=tf32c(__ldg(x+((n*32+ic)*H+ih)*W+iw0+1));a01=fmaf(xv,w0,a01);a11=fmaf(xv,w1,a11);a21=fmaf(xv,w2,a21);a31=fmaf(xv,w3,a31);} if((unsigned)(iw0+2)<(unsigned)W){float xv=tf32c(__ldg(x+((n*32+ic)*H+ih)*W+iw0+2));a02=fmaf(xv,w0,a02);a12=fmaf(xv,w1,a12);a22=fmaf(xv,w2,a22);a32=fmaf(xv,w3,a32);} if((unsigned)(iw0+3)<(unsigned)W){float xv=tf32c(__ldg(x+((n*32+ic)*H+ih)*W+iw0+3));a03=fmaf(xv,w0,a03);a13=fmaf(xv,w1,a13);a23=fmaf(xv,w2,a23);a33=fmaf(xv,w3,a33);} }\n }\n }\n }\n if(has_bias){float b0=__ldg(bias+oc0),b1=__ldg(bias+oc0+1),b2=__ldg(bias+oc0+2),b3=__ldg(bias+oc0+3);a00+=b0;a01+=b0;a02+=b0;a03+=b0;a10+=b1;a11+=b1;a12+=b1;a13+=b1;a20+=b2;a21+=b2;a22+=b2;a23+=b2;a30+=b3;a31+=b3;a32+=b3;a33+=b3;}\n float* y0=y+((n*32+oc0+0)*H+oh)*W+ow;float* y1=y+((n*32+oc0+1)*H+oh)*W+ow;float* y2=y+((n*32+oc0+2)*H+oh)*W+ow;float* y3=y+((n*32+oc0+3)*H+oh)*W+ow; y0[0]=a00;y0[1]=a01;y0[2]=a02;y0[3]=a03;y1[0]=a10;y1[1]=a11;y1[2]=a12;y1[3]=a13;y2[0]=a20;y2[1]=a21;y2[2]=a22;y2[3]=a23;y3[0]=a30;y3[1]=a31;y3[2]=a32;y3[3]=a33;\n}\n__global__ void deconv2d_generic_kernel(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ bias,float* __restrict__ y,int N,int Cin,int H,int W,int Cout,int kH,int kW,int outH,int outW,int sH,int sW,int pH,int pW,int has_bias){long long idx=(long long)blockIdx.x*blockDim.x+threadIdx.x,total=(long long)N*Cout*outH*outW;if(idx>=total)return;int ow=idx%outW;idx/=outW;int oh=idx%outH;idx/=outH;int oc=idx%Cout;int n=idx/Cout;float sum=has_bias?bias[oc]:0.f;for(int ic=0;ic<Cin;++ic)for(int kh=0;kh<kH;++kh){int th=oh+pH-kh;if(th%sH)continue;int ih=th/sH;if((unsigned)ih>=(unsigned)H)continue;for(int kw=0;kw<kW;++kw){int tw=ow+pW-kw;if(tw%sW)continue;int iw=tw/sW;if((unsigned)iw<(unsigned)W)sum=fmaf(tf32c(x[((n*Cin+ic)*H+ih)*W+iw]),tf32c(w[((ic*Cout+oc)*kH+kh)*kW+kw]),sum);}}y[((n*Cout+oc)*outH+oh)*outW+ow]=sum;}\ntorch::Tensor conv_transpose2d_forward(torch::Tensor x,torch::Tensor weight,torch::Tensor bias,int64_t sH,int64_t sW,int64_t pH,int64_t pW,bool has_bias){x=x.contiguous();weight=weight.contiguous();int N=x.size(0),Cin=x.size(1),H=x.size(2),W=x.size(3),kH=weight.size(2),kW=weight.size(3),Cout=weight.size(1);int outH=(H-1)*(int)sH-2*(int)pH+kH,outW=(W-1)*(int)sW-2*(int)pW+kW;auto y=torch::empty({N,Cout,outH,outW},x.options());cudaStream_t stream=at::cuda::getCurrentCUDAStream();if(Cin==32&&Cout==32&&kH==3&&kW==7&&sH==1&&sW==1&&pH==1&&pW==3&&outH==H&&outW==W){int total=N*8*H*((W+3)/4);deconv2d_oc4w4_kernel<<<(total+127)/128,128,0,stream>>>(x.data_ptr<float>(),weight.data_ptr<float>(),has_bias?bias.data_ptr<float>():nullptr,y.data_ptr<float>(),N,H,W,has_bias?1:0);}else{long long total=(long long)N*Cout*outH*outW;deconv2d_generic_kernel<<<(total+255)/256,256,0,stream>>>(x.data_ptr<float>(),weight.data_ptr<float>(),has_bias?bias.data_ptr<float>():nullptr,y.data_ptr<float>(),N,Cin,H,W,Cout,kH,kW,outH,outW,(int)sH,(int)sW,(int)pH,(int)pW,has_bias?1:0);}return y;}\n'''\n_ext=load_inline(name='deconv2d_ext_v4_tf32_final',cpp_sources=cpp_src,cuda_sources=cuda_src,functions=['conv_transpose2d_forward'],extra_cuda_cflags=['-O3'],extra_cflags=['-O3'],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels:int,out_channels:int,kernel_size:tuple,stride:tuple=(1,1),padding:tuple=(0,0),bias:bool=False):\n super().__init__();self.conv_transpose2d=nn.ConvTranspose2d(in_channels,out_channels,kernel_size,stride=stride,padding=padding,bias=bias);self.stride=stride;self.padding=padding\n def forward(self,x:torch.Tensor)->torch.Tensor:\n b=self.conv_transpose2d.bias;has=b is not None\n if b is None:b=torch.empty(0,device=x.device,dtype=x.dtype)\n return _ext.conv_transpose2d_forward(x,self.conv_transpose2d.weight,b,int(self.stride[0]),int(self.stride[1]),int(self.padding[0]),int(self.padding[1]),has)\n" }
result ← static_check (ok)
okstatic_check static_check PASSED: no violations or warnings detected.
turn 14 · FINAL · 1 tool call(s) · 18.8s turn · 18.8s LLM · 7,770 chars (click to expand)
turn 1418.8s turn · 18.8s LLM · 0.0s tools · FINAL
call → submit_kernel
callsubmit_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\ncpp_src=r'''\n#include <torch/extension.h>\ntorch::Tensor conv_transpose2d_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, int64_t sH, int64_t sW, int64_t pH, int64_t pW, bool has_bias);\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__device__ __forceinline__ float tf32c(float x){unsigned y; asm(\"cvt.rna.tf32.f32 %0, %1;\" : \"=r\"(y) : \"f\"(x)); return __uint_as_float(y);} \n__global__ void deconv2d_oc4w4_kernel(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ bias,float* __restrict__ y,int N,int H,int W,int has_bias){\n int tid=blockIdx.x*blockDim.x+threadIdx.x; int W4=(W+3)>>2; int total=N*8*H*W4; if(tid>=total)return; int t=tid; int ow4=t%W4; t/=W4; int oh=t%H; t/=H; int ocg=t&7; t>>=3; int n=t; int ow=ow4<<2; int oc0=ocg<<2;\n float a00=0.f,a01=0.f,a02=0.f,a03=0.f,a10=0.f,a11=0.f,a12=0.f,a13=0.f,a20=0.f,a21=0.f,a22=0.f,a23=0.f,a30=0.f,a31=0.f,a32=0.f,a33=0.f;\n bool interior=(oh>0&&oh<H-1&&ow>=3&&ow+6<W);\n if(interior){\n #pragma unroll\n for(int ic=0;ic<32;++ic){\n #pragma unroll\n for(int kh=0;kh<3;++kh){ int ih=oh+1-kh; const float* xp=x+((n*32+ic)*H+ih)*W+(ow+3); const float* wp=w+((ic*32+oc0)*3+kh)*7;\n #pragma unroll\n for(int kw=0;kw<7;++kw){ float xv0=tf32c(__ldg(xp-kw+0)),xv1=tf32c(__ldg(xp-kw+1)),xv2=tf32c(__ldg(xp-kw+2)),xv3=tf32c(__ldg(xp-kw+3)); float w0=tf32c(__ldg(wp+0*21+kw)),w1=tf32c(__ldg(wp+1*21+kw)),w2=tf32c(__ldg(wp+2*21+kw)),w3=tf32c(__ldg(wp+3*21+kw));\n a00=fmaf(xv0,w0,a00);a01=fmaf(xv1,w0,a01);a02=fmaf(xv2,w0,a02);a03=fmaf(xv3,w0,a03); a10=fmaf(xv0,w1,a10);a11=fmaf(xv1,w1,a11);a12=fmaf(xv2,w1,a12);a13=fmaf(xv3,w1,a13); a20=fmaf(xv0,w2,a20);a21=fmaf(xv1,w2,a21);a22=fmaf(xv2,w2,a22);a23=fmaf(xv3,w2,a23); a30=fmaf(xv0,w3,a30);a31=fmaf(xv1,w3,a31);a32=fmaf(xv2,w3,a32);a33=fmaf(xv3,w3,a33); }\n }\n }\n }else{\n #pragma unroll\n for(int ic=0;ic<32;++ic){\n #pragma unroll\n for(int kh=0;kh<3;++kh){ int ih=oh+1-kh; if((unsigned)ih>=(unsigned)H)continue;\n #pragma unroll\n for(int kw=0;kw<7;++kw){ int iw0=ow+3-kw; float w0=tf32c(__ldg(w+((ic*32+oc0+0)*3+kh)*7+kw)),w1=tf32c(__ldg(w+((ic*32+oc0+1)*3+kh)*7+kw)),w2=tf32c(__ldg(w+((ic*32+oc0+2)*3+kh)*7+kw)),w3=tf32c(__ldg(w+((ic*32+oc0+3)*3+kh)*7+kw));\n if((unsigned)(iw0+0)<(unsigned)W){float xv=tf32c(__ldg(x+((n*32+ic)*H+ih)*W+iw0+0));a00=fmaf(xv,w0,a00);a10=fmaf(xv,w1,a10);a20=fmaf(xv,w2,a20);a30=fmaf(xv,w3,a30);} if((unsigned)(iw0+1)<(unsigned)W){float xv=tf32c(__ldg(x+((n*32+ic)*H+ih)*W+iw0+1));a01=fmaf(xv,w0,a01);a11=fmaf(xv,w1,a11);a21=fmaf(xv,w2,a21);a31=fmaf(xv,w3,a31);} if((unsigned)(iw0+2)<(unsigned)W){float xv=tf32c(__ldg(x+((n*32+ic)*H+ih)*W+iw0+2));a02=fmaf(xv,w0,a02);a12=fmaf(xv,w1,a12);a22=fmaf(xv,w2,a22);a32=fmaf(xv,w3,a32);} if((unsigned)(iw0+3)<(unsigned)W){float xv=tf32c(__ldg(x+((n*32+ic)*H+ih)*W+iw0+3));a03=fmaf(xv,w0,a03);a13=fmaf(xv,w1,a13);a23=fmaf(xv,w2,a23);a33=fmaf(xv,w3,a33);} }\n }\n }\n }\n if(has_bias){float b0=__ldg(bias+oc0),b1=__ldg(bias+oc0+1),b2=__ldg(bias+oc0+2),b3=__ldg(bias+oc0+3);a00+=b0;a01+=b0;a02+=b0;a03+=b0;a10+=b1;a11+=b1;a12+=b1;a13+=b1;a20+=b2;a21+=b2;a22+=b2;a23+=b2;a30+=b3;a31+=b3;a32+=b3;a33+=b3;}\n float* y0=y+((n*32+oc0+0)*H+oh)*W+ow;float* y1=y+((n*32+oc0+1)*H+oh)*W+ow;float* y2=y+((n*32+oc0+2)*H+oh)*W+ow;float* y3=y+((n*32+oc0+3)*H+oh)*W+ow; y0[0]=a00;y0[1]=a01;y0[2]=a02;y0[3]=a03;y1[0]=a10;y1[1]=a11;y1[2]=a12;y1[3]=a13;y2[0]=a20;y2[1]=a21;y2[2]=a22;y2[3]=a23;y3[0]=a30;y3[1]=a31;y3[2]=a32;y3[3]=a33;\n}\n__global__ void deconv2d_generic_kernel(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ bias,float* __restrict__ y,int N,int Cin,int H,int W,int Cout,int kH,int kW,int outH,int outW,int sH,int sW,int pH,int pW,int has_bias){long long idx=(long long)blockIdx.x*blockDim.x+threadIdx.x,total=(long long)N*Cout*outH*outW;if(idx>=total)return;int ow=idx%outW;idx/=outW;int oh=idx%outH;idx/=outH;int oc=idx%Cout;int n=idx/Cout;float sum=has_bias?bias[oc]:0.f;for(int ic=0;ic<Cin;++ic)for(int kh=0;kh<kH;++kh){int th=oh+pH-kh;if(th%sH)continue;int ih=th/sH;if((unsigned)ih>=(unsigned)H)continue;for(int kw=0;kw<kW;++kw){int tw=ow+pW-kw;if(tw%sW)continue;int iw=tw/sW;if((unsigned)iw<(unsigned)W)sum=fmaf(tf32c(x[((n*Cin+ic)*H+ih)*W+iw]),tf32c(w[((ic*Cout+oc)*kH+kh)*kW+kw]),sum);}}y[((n*Cout+oc)*outH+oh)*outW+ow]=sum;}\ntorch::Tensor conv_transpose2d_forward(torch::Tensor x,torch::Tensor weight,torch::Tensor bias,int64_t sH,int64_t sW,int64_t pH,int64_t pW,bool has_bias){x=x.contiguous();weight=weight.contiguous();int N=x.size(0),Cin=x.size(1),H=x.size(2),W=x.size(3),kH=weight.size(2),kW=weight.size(3),Cout=weight.size(1);int outH=(H-1)*(int)sH-2*(int)pH+kH,outW=(W-1)*(int)sW-2*(int)pW+kW;auto y=torch::empty({N,Cout,outH,outW},x.options());cudaStream_t stream=at::cuda::getCurrentCUDAStream();if(Cin==32&&Cout==32&&kH==3&&kW==7&&sH==1&&sW==1&&pH==1&&pW==3&&outH==H&&outW==W){int total=N*8*H*((W+3)/4);deconv2d_oc4w4_kernel<<<(total+127)/128,128,0,stream>>>(x.data_ptr<float>(),weight.data_ptr<float>(),has_bias?bias.data_ptr<float>():nullptr,y.data_ptr<float>(),N,H,W,has_bias?1:0);}else{long long total=(long long)N*Cout*outH*outW;deconv2d_generic_kernel<<<(total+255)/256,256,0,stream>>>(x.data_ptr<float>(),weight.data_ptr<float>(),has_bias?bias.data_ptr<float>():nullptr,y.data_ptr<float>(),N,Cin,H,W,Cout,kH,kW,outH,outW,(int)sH,(int)sW,(int)pH,(int)pW,has_bias?1:0);}return y;}\n'''\n_ext=load_inline(name='deconv2d_ext_v4_tf32_final',cpp_sources=cpp_src,cuda_sources=cuda_src,functions=['conv_transpose2d_forward'],extra_cuda_cflags=['-O3'],extra_cflags=['-O3'],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels:int,out_channels:int,kernel_size:tuple,stride:tuple=(1,1),padding:tuple=(0,0),bias:bool=False):\n super().__init__();self.conv_transpose2d=nn.ConvTranspose2d(in_channels,out_channels,kernel_size,stride=stride,padding=padding,bias=bias);self.stride=stride;self.padding=padding\n def forward(self,x:torch.Tensor)->torch.Tensor:\n b=self.conv_transpose2d.bias;has=b is not None\n if b is None:b=torch.empty(0,device=x.device,dtype=x.dtype)\n return _ext.conv_transpose2d_forward(x,self.conv_transpose2d.weight,b,int(self.stride[0]),int(self.stride[1]),int(self.padding[0]),int(self.padding[1]),has)\n" }
result ← submit_kernel (ok)
oksubmit_kernel submit_kernel PASSED: (5 / 5) correctness trials all passed. Kernel runtime: 24.70 μs Runtime stats: mean=24.70μs median=?μs std=0.18μs Numerical precision: max_abs_err=6.69e-05 mean_abs_err=1.19e-06 max_rel_err=8.57e+02 Memory: 1057.16 MB peak (ref: 2081.25 MB, ratio: 0.5079x) Kernel launches: 2 (ref: 4, fusion ratio: 2.0) Energy: 8384.98 mJ/run (ref: 1092.42 mJ, ratio: 0.1303x) SOL score: 0.1943
final submitted kernel (53 lines, 6,376 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 conv_transpose2d_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, int64_t sH, int64_t sW, int64_t pH, int64_t pW, bool has_bias);
'''
cuda_src=r'''
#include <torch/extension.h>
#include <ATen/cuda/CUDAContext.h>
#include <cuda.h>
#include <cuda_runtime.h>
__device__ __forceinline__ float tf32c(float x){unsigned y; asm("cvt.rna.tf32.f32 %0, %1;" : "=r"(y) : "f"(x)); return __uint_as_float(y);} 
__global__ void deconv2d_oc4w4_kernel(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ bias,float* __restrict__ y,int N,int H,int W,int has_bias){
 int tid=blockIdx.x*blockDim.x+threadIdx.x; int W4=(W+3)>>2; int total=N*8*H*W4; if(tid>=total)return; int t=tid; int ow4=t%W4; t/=W4; int oh=t%H; t/=H; int ocg=t&7; t>>=3; int n=t; int ow=ow4<<2; int oc0=ocg<<2;
 float a00=0.f,a01=0.f,a02=0.f,a03=0.f,a10=0.f,a11=0.f,a12=0.f,a13=0.f,a20=0.f,a21=0.f,a22=0.f,a23=0.f,a30=0.f,a31=0.f,a32=0.f,a33=0.f;
 bool interior=(oh>0&&oh<H-1&&ow>=3&&ow+6<W);
 if(interior){
  #pragma unroll
  for(int ic=0;ic<32;++ic){
   #pragma unroll
   for(int kh=0;kh<3;++kh){ int ih=oh+1-kh; const float* xp=x+((n*32+ic)*H+ih)*W+(ow+3); const float* wp=w+((ic*32+oc0)*3+kh)*7;
    #pragma unroll
    for(int kw=0;kw<7;++kw){ float xv0=tf32c(__ldg(xp-kw+0)),xv1=tf32c(__ldg(xp-kw+1)),xv2=tf32c(__ldg(xp-kw+2)),xv3=tf32c(__ldg(xp-kw+3)); float w0=tf32c(__ldg(wp+0*21+kw)),w1=tf32c(__ldg(wp+1*21+kw)),w2=tf32c(__ldg(wp+2*21+kw)),w3=tf32c(__ldg(wp+3*21+kw));
     a00=fmaf(xv0,w0,a00);a01=fmaf(xv1,w0,a01);a02=fmaf(xv2,w0,a02);a03=fmaf(xv3,w0,a03); a10=fmaf(xv0,w1,a10);a11=fmaf(xv1,w1,a11);a12=fmaf(xv2,w1,a12);a13=fmaf(xv3,w1,a13); a20=fmaf(xv0,w2,a20);a21=fmaf(xv1,w2,a21);a22=fmaf(xv2,w2,a22);a23=fmaf(xv3,w2,a23); a30=fmaf(xv0,w3,a30);a31=fmaf(xv1,w3,a31);a32=fmaf(xv2,w3,a32);a33=fmaf(xv3,w3,a33); }
   }
  }
 }else{
  #pragma unroll
  for(int ic=0;ic<32;++ic){
   #pragma unroll
   for(int kh=0;kh<3;++kh){ int ih=oh+1-kh; if((unsigned)ih>=(unsigned)H)continue;
    #pragma unroll
    for(int kw=0;kw<7;++kw){ int iw0=ow+3-kw; float w0=tf32c(__ldg(w+((ic*32+oc0+0)*3+kh)*7+kw)),w1=tf32c(__ldg(w+((ic*32+oc0+1)*3+kh)*7+kw)),w2=tf32c(__ldg(w+((ic*32+oc0+2)*3+kh)*7+kw)),w3=tf32c(__ldg(w+((ic*32+oc0+3)*3+kh)*7+kw));
     if((unsigned)(iw0+0)<(unsigned)W){float xv=tf32c(__ldg(x+((n*32+ic)*H+ih)*W+iw0+0));a00=fmaf(xv,w0,a00);a10=fmaf(xv,w1,a10);a20=fmaf(xv,w2,a20);a30=fmaf(xv,w3,a30);} if((unsigned)(iw0+1)<(unsigned)W){float xv=tf32c(__ldg(x+((n*32+ic)*H+ih)*W+iw0+1));a01=fmaf(xv,w0,a01);a11=fmaf(xv,w1,a11);a21=fmaf(xv,w2,a21);a31=fmaf(xv,w3,a31);} if((unsigned)(iw0+2)<(unsigned)W){float xv=tf32c(__ldg(x+((n*32+ic)*H+ih)*W+iw0+2));a02=fmaf(xv,w0,a02);a12=fmaf(xv,w1,a12);a22=fmaf(xv,w2,a22);a32=fmaf(xv,w3,a32);} if((unsigned)(iw0+3)<(unsigned)W){float xv=tf32c(__ldg(x+((n*32+ic)*H+ih)*W+iw0+3));a03=fmaf(xv,w0,a03);a13=fmaf(xv,w1,a13);a23=fmaf(xv,w2,a23);a33=fmaf(xv,w3,a33);} }
   }
  }
 }
 if(has_bias){float b0=__ldg(bias+oc0),b1=__ldg(bias+oc0+1),b2=__ldg(bias+oc0+2),b3=__ldg(bias+oc0+3);a00+=b0;a01+=b0;a02+=b0;a03+=b0;a10+=b1;a11+=b1;a12+=b1;a13+=b1;a20+=b2;a21+=b2;a22+=b2;a23+=b2;a30+=b3;a31+=b3;a32+=b3;a33+=b3;}
 float* y0=y+((n*32+oc0+0)*H+oh)*W+ow;float* y1=y+((n*32+oc0+1)*H+oh)*W+ow;float* y2=y+((n*32+oc0+2)*H+oh)*W+ow;float* y3=y+((n*32+oc0+3)*H+oh)*W+ow; y0[0]=a00;y0[1]=a01;y0[2]=a02;y0[3]=a03;y1[0]=a10;y1[1]=a11;y1[2]=a12;y1[3]=a13;y2[0]=a20;y2[1]=a21;y2[2]=a22;y2[3]=a23;y3[0]=a30;y3[1]=a31;y3[2]=a32;y3[3]=a33;
}
__global__ void deconv2d_generic_kernel(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ bias,float* __restrict__ y,int N,int Cin,int H,int W,int Cout,int kH,int kW,int outH,int outW,int sH,int sW,int pH,int pW,int has_bias){long long idx=(long long)blockIdx.x*blockDim.x+threadIdx.x,total=(long long)N*Cout*outH*outW;if(idx>=total)return;int ow=idx%outW;idx/=outW;int oh=idx%outH;idx/=outH;int oc=idx%Cout;int n=idx/Cout;float sum=has_bias?bias[oc]:0.f;for(int ic=0;ic<Cin;++ic)for(int kh=0;kh<kH;++kh){int th=oh+pH-kh;if(th%sH)continue;int ih=th/sH;if((unsigned)ih>=(unsigned)H)continue;for(int kw=0;kw<kW;++kw){int tw=ow+pW-kw;if(tw%sW)continue;int iw=tw/sW;if((unsigned)iw<(unsigned)W)sum=fmaf(tf32c(x[((n*Cin+ic)*H+ih)*W+iw]),tf32c(w[((ic*Cout+oc)*kH+kh)*kW+kw]),sum);}}y[((n*Cout+oc)*outH+oh)*outW+ow]=sum;}
torch::Tensor conv_transpose2d_forward(torch::Tensor x,torch::Tensor weight,torch::Tensor bias,int64_t sH,int64_t sW,int64_t pH,int64_t pW,bool has_bias){x=x.contiguous();weight=weight.contiguous();int N=x.size(0),Cin=x.size(1),H=x.size(2),W=x.size(3),kH=weight.size(2),kW=weight.size(3),Cout=weight.size(1);int outH=(H-1)*(int)sH-2*(int)pH+kH,outW=(W-1)*(int)sW-2*(int)pW+kW;auto y=torch::empty({N,Cout,outH,outW},x.options());cudaStream_t stream=at::cuda::getCurrentCUDAStream();if(Cin==32&&Cout==32&&kH==3&&kW==7&&sH==1&&sW==1&&pH==1&&pW==3&&outH==H&&outW==W){int total=N*8*H*((W+3)/4);deconv2d_oc4w4_kernel<<<(total+127)/128,128,0,stream>>>(x.data_ptr<float>(),weight.data_ptr<float>(),has_bias?bias.data_ptr<float>():nullptr,y.data_ptr<float>(),N,H,W,has_bias?1:0);}else{long long total=(long long)N*Cout*outH*outW;deconv2d_generic_kernel<<<(total+255)/256,256,0,stream>>>(x.data_ptr<float>(),weight.data_ptr<float>(),has_bias?bias.data_ptr<float>():nullptr,y.data_ptr<float>(),N,Cin,H,W,Cout,kH,kW,outH,outW,(int)sH,(int)sW,(int)pH,(int)pW,has_bias?1:0);}return y;}
'''
_ext=load_inline(name='deconv2d_ext_v4_tf32_final',cpp_sources=cpp_src,cuda_sources=cuda_src,functions=['conv_transpose2d_forward'],extra_cuda_cflags=['-O3'],extra_cflags=['-O3'],verbose=False)
class ModelNew(nn.Module):
 def __init__(self,in_channels:int,out_channels:int,kernel_size:tuple,stride:tuple=(1,1),padding:tuple=(0,0),bias:bool=False):
  super().__init__();self.conv_transpose2d=nn.ConvTranspose2d(in_channels,out_channels,kernel_size,stride=stride,padding=padding,bias=bias);self.stride=stride;self.padding=padding
 def forward(self,x:torch.Tensor)->torch.Tensor:
  b=self.conv_transpose2d.bias;has=b is not None
  if b is None:b=torch.empty(0,device=x.device,dtype=x.dtype)
  return _ext.conv_transpose2d_forward(x,self.conv_transpose2d.weight,b,int(self.stride[0]),int(self.stride[1]),int(self.padding[0]),int(self.padding[1]),has)