L2 · problem 72
gpt-5.5-priority · original · l12_default_gpt
← all variants← original← gpt-5.5-prioritygenerated 2026-05-28 03:33:16 UTC
incorrect 72_ConvTranspose3d_BatchNorm_AvgPool_AvgPool.py
model
gpt-5.5-priority
turns / max
10 / 10
tool calls
10
tokens (total)
0
wall clock
—
truncated
no
backend
cuda fp32
variant
original
runtime
—
ref runtime
12.20 μs
speedup
—
started
2026-05-02T09:00:38Z
correctness issue: Output mismatch
timingkernelbench.timing
kernel runtime
—
ref runtime
12.20 μs
speedup
—
kernel mean / median / std
—
ref mean / median / std
12.20 / — / 0.07 μs
SOL (speed-of-light)nsight_hardware_counters
SOL score (0–1)
0.550
dram util %
7.4%
compute util %
55.0%
bottleneck
compute-bound
dominant pipe
FP32
arithmetic intensity
—
ridge point
20.00
roofline / occupancynsight_hardware_counters
dram bandwidth
248.44 GB/s
dram util %
7.4%
fp32 tflops
—
fp32 util %
—
fp16 tflops
—
occupancy %
184.2%
bottleneck
compute-bound
dominant pipe %
55.0%
arithmetic intensity
—
ridge point
20.00
L1 hit %
—
L2 hit %
369.9%
ld sectors / req
13.22
st sectors / req
—
regs / thread
136
smem / block
8192 B
block size
1024
peak bw
3350.00 GB/s
peak fp32 tflops
67.00
peak fp16 tflops
1979.00
warp stalls (top 5)nsight
long_scoreboard
3415.4%
short_scoreboard
609.8%
mio_throttle
24.5%
reference PyTorch source (the prompt the agent sees)
import torch
import torch.nn as nn

class Model(nn.Module):
    """
    A model that performs a 3D transposed convolution, followed by batch normalization, 
    two average pooling layers.
    """
    def __init__(self, in_channels, out_channels, kernel_size, stride, padding, bias_shape):
        super(Model, self).__init__()
        self.conv_transpose = nn.ConvTranspose3d(in_channels, out_channels, kernel_size, stride=stride, padding=padding)
        self.batch_norm = nn.BatchNorm3d(out_channels)
        self.avg_pool1 = nn.AvgPool3d(kernel_size=2)
        self.avg_pool2 = nn.AvgPool3d(kernel_size=2)

    def forward(self, x):
        x = self.conv_transpose(x)
        x = self.batch_norm(x)
        x = self.avg_pool1(x)
        x = self.avg_pool2(x)
        return x


batch_size = 64
in_channels = 3
out_channels = 16
depth, height, width = 32, 32, 32
kernel_size = 3
stride = 2
padding = 1
bias_shape = (out_channels, 1, 1, 1)

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

def get_init_inputs():
    return [in_channels, out_channels, kernel_size, stride, padding, bias_shape]
turn 09.6s turn · 9.6s 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) · 45.4s turn · 45.4s LLM · 8,019 chars (click to expand)
turn 145.4s turn · 45.4s LLM · 0.0s tools
reasoning
**Writing code for training** I realize I need to write some code, and I'm wondering if I should include a model in the training process. I think it’s crucial to incorporate the necessary libraries. Specifically, I’ll have to include `<torch/extension.h>` and consider CUDA support. It seems straightforward, but I want to make sure everything's set up correctly for the model to function properly. Let’s get this figured out! **Considering model evaluation** I'm thinking through how the eval function interacts with running stats. If training is false, then if ModelNew defaults to training being true, that could create issues. If the harness calls eval on both, that should work, but what if it doesn't? I might need to override the training setting somehow. BatchNorm does compute batch stats, but many benchmark harnesses opt for eval to minimize randomness. The prompt often requests forward computation, which might skip eval call. The correctness tool should help clarify things.
call → compile_kernel
callcompile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n_cpp = r'''\n#include <torch/extension.h>\n\ntorch::Tensor fused_forward_cuda(torch::Tensor x, torch::Tensor w, torch::Tensor conv_b,\n torch::Tensor bn_w, torch::Tensor bn_b,\n torch::Tensor running_mean, torch::Tensor running_var, double eps);\n\ntorch::Tensor fused_forward(torch::Tensor x, torch::Tensor w, torch::Tensor conv_b,\n torch::Tensor bn_w, torch::Tensor bn_b,\n torch::Tensor running_mean, torch::Tensor running_var, double eps) {\n return fused_forward_cuda(x, w, conv_b, bn_w, bn_b, running_mean, running_var, eps);\n}\n'''\n\n_cuda = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n\n__device__ __forceinline__ int k_begin(int t) {\n return (t == 0) ? 1 : 0;\n}\n__device__ __forceinline__ int k_end(int t) {\n return (t == 0) ? 2 : ((t == 1) ? 2 : 0);\n}\n\n__global__ void make_agg_kernel(const float* __restrict__ w, float* __restrict__ agg,\n int CI, int CO) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int total = CO * CI * 27;\n if (idx >= total) return;\n int tmp = idx;\n int tw = tmp % 3; tmp /= 3;\n int th = tmp % 3; tmp /= 3;\n int td = tmp % 3; tmp /= 3;\n int ci = tmp % CI; tmp /= CI;\n int co = tmp;\n float s = 0.0f;\n int db = k_begin(td), de = k_end(td);\n int hb = k_begin(th), he = k_end(th);\n int wb = k_begin(tw), we = k_end(tw);\n // w layout [CI, CO, 3,3,3]\n for (int kd = db; kd <= de; ++kd) {\n for (int kh = hb; kh <= he; ++kh) {\n for (int kw = wb; kw <= we; ++kw) {\n int wi = (((ci * CO + co) * 3 + kd) * 3 + kh) * 3 + kw;\n s += w[wi];\n }\n }\n }\n agg[idx] = s * (1.0f / 64.0f);\n}\n\n__global__ void fused_kernel(const float* __restrict__ x,\n const float* __restrict__ agg,\n const float* __restrict__ conv_b,\n const float* __restrict__ bn_w,\n const float* __restrict__ bn_b,\n const float* __restrict__ mean,\n const float* __restrict__ var,\n float* __restrict__ out,\n int N, int CI, int D, int H, int W, int CO,\n int OD, int OH, int OW, float eps) {\n int64_t idx = (int64_t)blockIdx.x * blockDim.x + threadIdx.x;\n int64_t total = (int64_t)N * CO * OD * OH * OW;\n if (idx >= total) return;\n int ow = idx % OW;\n int64_t t = idx / OW;\n int oh = t % OH; t /= OH;\n int od = t % OD; t /= OD;\n int co = t % CO; t /= CO;\n int n = (int)t;\n\n float acc = conv_b ? conv_b[co] : 0.0f;\n int id0 = od * 2;\n int ih0 = oh * 2;\n int iw0 = ow * 2;\n for (int ci = 0; ci < CI; ++ci) {\n #pragma unroll\n for (int td = 0; td < 3; ++td) {\n int id = id0 + td;\n #pragma unroll\n for (int th = 0; th < 3; ++th) {\n int ih = ih0 + th;\n #pragma unroll\n for (int tw = 0; tw < 3; ++tw) {\n int iw = iw0 + tw;\n float xv = x[((((n * CI + ci) * D + id) * H + ih) * W + iw)];\n float av = agg[(((co * CI + ci) * 3 + td) * 3 + th) * 3 + tw];\n acc = fmaf(xv, av, acc);\n }\n }\n }\n }\n float scale = bn_w[co] * rsqrtf(var[co] + eps);\n float shift = bn_b[co] - mean[co] * scale;\n out[idx] = acc * scale + shift;\n}\n\ntorch::Tensor fused_forward_cuda(torch::Tensor x, torch::Tensor w, torch::Tensor conv_b,\n torch::Tensor bn_w, torch::Tensor bn_b,\n torch::Tensor running_mean, torch::Tensor running_var, double eps) {\n int N = x.size(0), CI = x.size(1), D = x.size(2), H = x.size(3), W = x.size(4);\n int CO = w.size(1);\n // For kernel=3,stride=2,pad=1 then two AvgPool3d(kernel=2,stride=2)\n int convD = (D - 1) * 2 - 2 + 3;\n int convH = (H - 1) * 2 - 2 + 3;\n int convW = (W - 1) * 2 - 2 + 3;\n int OD = ((convD - 2) / 2 + 1 - 2) / 2 + 1;\n int OH = ((convH - 2) / 2 + 1 - 2) / 2 + 1;\n int OW = ((convW - 2) / 2 + 1 - 2) / 2 + 1;\n auto out = torch::empty({N, CO, OD, OH, OW}, x.options());\n auto agg = torch::empty({CO, CI, 3, 3, 3}, x.options());\n int total_agg = CO * CI * 27;\n make_agg_kernel<<<(total_agg + 255)/256, 256>>>(w.data_ptr<float>(), agg.data_ptr<float>(), CI, CO);\n int64_t total = (int64_t)N * CO * OD * OH * OW;\n fused_kernel<<<(total + 255)/256, 256>>>(x.data_ptr<float>(), agg.data_ptr<float>(),\n conv_b.defined() ? conv_b.data_ptr<float>() : nullptr,\n bn_w.data_ptr<float>(), bn_b.data_ptr<float>(), running_mean.data_ptr<float>(), running_var.data_ptr<float>(),\n out.data_ptr<float>(), N, CI, D, H, W, CO, OD, OH, OW, (float)eps);\n return out;\n}\n'''\n\n_ext = load_inline(name='fused_deconv_bn_pool_ext_v1', cpp_sources=_cpp, cuda_sources=_cuda,\n functions=['fused_forward'], verbose=False, extra_cuda_cflags=['-O3', '--use_fast_math'])\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, stride, padding, bias_shape):\n super(ModelNew, self).__init__()\n self.conv_transpose = nn.ConvTranspose3d(in_channels, out_channels, kernel_size, stride=stride, padding=padding)\n self.batch_norm = nn.BatchNorm3d(out_channels)\n self.avg_pool1 = nn.AvgPool3d(kernel_size=2)\n self.avg_pool2 = nn.AvgPool3d(kernel_size=2)\n def forward(self, x):\n return _ext.fused_forward(x, self.conv_transpose.weight, self.conv_transpose.bias,\n self.batch_norm.weight, self.batch_norm.bias,\n self.batch_norm.running_mean, self.batch_norm.running_var,\n float(self.batch_norm.eps))\n" }
result ← compile_kernel (ok)
okcompile_kernel compile_kernel PASSED: kernel compiled without errors.
turn 2 · 1 tool call(s) · 11.3s turn · 11.3s LLM · 7,118 chars (click to expand)
turn 211.3s turn · 11.3s 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\n_cpp = r'''\n#include <torch/extension.h>\n\ntorch::Tensor fused_forward_cuda(torch::Tensor x, torch::Tensor w, torch::Tensor conv_b,\n torch::Tensor bn_w, torch::Tensor bn_b,\n torch::Tensor running_mean, torch::Tensor running_var, double eps);\n\ntorch::Tensor fused_forward(torch::Tensor x, torch::Tensor w, torch::Tensor conv_b,\n torch::Tensor bn_w, torch::Tensor bn_b,\n torch::Tensor running_mean, torch::Tensor running_var, double eps) {\n return fused_forward_cuda(x, w, conv_b, bn_w, bn_b, running_mean, running_var, eps);\n}\n'''\n\n_cuda = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n\n__device__ __forceinline__ int k_begin(int t) {\n return (t == 0) ? 1 : 0;\n}\n__device__ __forceinline__ int k_end(int t) {\n return (t == 0) ? 2 : ((t == 1) ? 2 : 0);\n}\n\n__global__ void make_agg_kernel(const float* __restrict__ w, float* __restrict__ agg,\n int CI, int CO) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int total = CO * CI * 27;\n if (idx >= total) return;\n int tmp = idx;\n int tw = tmp % 3; tmp /= 3;\n int th = tmp % 3; tmp /= 3;\n int td = tmp % 3; tmp /= 3;\n int ci = tmp % CI; tmp /= CI;\n int co = tmp;\n float s = 0.0f;\n int db = k_begin(td), de = k_end(td);\n int hb = k_begin(th), he = k_end(th);\n int wb = k_begin(tw), we = k_end(tw);\n for (int kd = db; kd <= de; ++kd) {\n for (int kh = hb; kh <= he; ++kh) {\n for (int kw = wb; kw <= we; ++kw) {\n int wi = (((ci * CO + co) * 3 + kd) * 3 + kh) * 3 + kw;\n s += w[wi];\n }\n }\n }\n agg[idx] = s * (1.0f / 64.0f);\n}\n\n__global__ void fused_kernel(const float* __restrict__ x,\n const float* __restrict__ agg,\n const float* __restrict__ conv_b,\n const float* __restrict__ bn_w,\n const float* __restrict__ bn_b,\n const float* __restrict__ mean,\n const float* __restrict__ var,\n float* __restrict__ out,\n int N, int CI, int D, int H, int W, int CO,\n int OD, int OH, int OW, float eps) {\n int64_t idx = (int64_t)blockIdx.x * blockDim.x + threadIdx.x;\n int64_t total = (int64_t)N * CO * OD * OH * OW;\n if (idx >= total) return;\n int ow = idx % OW;\n int64_t t = idx / OW;\n int oh = t % OH; t /= OH;\n int od = t % OD; t /= OD;\n int co = t % CO; t /= CO;\n int n = (int)t;\n\n float acc = conv_b ? conv_b[co] : 0.0f;\n int id0 = od * 2;\n int ih0 = oh * 2;\n int iw0 = ow * 2;\n for (int ci = 0; ci < CI; ++ci) {\n #pragma unroll\n for (int td = 0; td < 3; ++td) {\n int id = id0 + td;\n #pragma unroll\n for (int th = 0; th < 3; ++th) {\n int ih = ih0 + th;\n #pragma unroll\n for (int tw = 0; tw < 3; ++tw) {\n int iw = iw0 + tw;\n float xv = x[((((n * CI + ci) * D + id) * H + ih) * W + iw)];\n float av = agg[(((co * CI + ci) * 3 + td) * 3 + th) * 3 + tw];\n acc = fmaf(xv, av, acc);\n }\n }\n }\n }\n float scale = bn_w[co] * rsqrtf(var[co] + eps);\n float shift = bn_b[co] - mean[co] * scale;\n out[idx] = acc * scale + shift;\n}\n\ntorch::Tensor fused_forward_cuda(torch::Tensor x, torch::Tensor w, torch::Tensor conv_b,\n torch::Tensor bn_w, torch::Tensor bn_b,\n torch::Tensor running_mean, torch::Tensor running_var, double eps) {\n int N = x.size(0), CI = x.size(1), D = x.size(2), H = x.size(3), W = x.size(4);\n int CO = w.size(1);\n int convD = (D - 1) * 2 - 2 + 3;\n int convH = (H - 1) * 2 - 2 + 3;\n int convW = (W - 1) * 2 - 2 + 3;\n int OD = ((convD - 2) / 2 + 1 - 2) / 2 + 1;\n int OH = ((convH - 2) / 2 + 1 - 2) / 2 + 1;\n int OW = ((convW - 2) / 2 + 1 - 2) / 2 + 1;\n auto out = torch::empty({N, CO, OD, OH, OW}, x.options());\n auto agg = torch::empty({CO, CI, 3, 3, 3}, x.options());\n int total_agg = CO * CI * 27;\n make_agg_kernel<<<(total_agg + 255)/256, 256>>>(w.data_ptr<float>(), agg.data_ptr<float>(), CI, CO);\n int64_t total = (int64_t)N * CO * OD * OH * OW;\n fused_kernel<<<(total + 255)/256, 256>>>(x.data_ptr<float>(), agg.data_ptr<float>(),\n conv_b.defined() ? conv_b.data_ptr<float>() : nullptr,\n bn_w.data_ptr<float>(), bn_b.data_ptr<float>(), running_mean.data_ptr<float>(), running_var.data_ptr<float>(),\n out.data_ptr<float>(), N, CI, D, H, W, CO, OD, OH, OW, (float)eps);\n return out;\n}\n'''\n\n_ext = load_inline(name='fused_deconv_bn_pool_ext_v1', cpp_sources=_cpp, cuda_sources=_cuda,\n functions=['fused_forward'], verbose=False, extra_cuda_cflags=['-O3', '--use_fast_math'])\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, stride, padding, bias_shape):\n super(ModelNew, self).__init__()\n self.conv_transpose = nn.ConvTranspose3d(in_channels, out_channels, kernel_size, stride=stride, padding=padding)\n self.batch_norm = nn.BatchNorm3d(out_channels)\n self.avg_pool1 = nn.AvgPool3d(kernel_size=2)\n self.avg_pool2 = nn.AvgPool3d(kernel_size=2)\n def forward(self, x):\n return _ext.fused_forward(x, self.conv_transpose.weight, self.conv_transpose.bias,\n self.batch_norm.weight, self.batch_norm.bias,\n self.batch_norm.running_mean, self.batch_norm.running_var,\n float(self.batch_norm.eps))\n" }
result ← run_correctness (fail)
failrun_correctness run_correctness FAILED: (0 / 5) trials did not all match. correctness_issue: Output mismatch max_difference: ['0.492746', '0.453019', '0.455451', '0.491348', '0.451334'] avg_difference: ['0.052123', '0.052214', '0.052214', '0.052186', '0.052301']
turn 3 · 1 tool call(s) · 45.9s turn · 45.9s LLM · 12,629 chars (click to expand)
turn 345.9s turn · 45.9s LLM · 0.0s tools
reasoning
**Evaluating tensor operations** I'm thinking about dynamic tensors and using a partial double type. It seems feasible to create empty tensors with `torch::empty` using 64-bit floats for stats. Even with a large number like 256M for z, the accumulation performance should be alright since I'm not doing too many floating-point operations. I wonder if the numerical matches will hold up, especially since different reduction methods in batch normalization could introduce tiny differences. I need to compile and check everything to ensure it works as expected.
call → compile_kernel
callcompile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n_cpp = r'''\n#include <torch/extension.h>\n\ntorch::Tensor fused_forward_cuda(torch::Tensor x, torch::Tensor w, torch::Tensor conv_b,\n torch::Tensor bn_w, torch::Tensor bn_b,\n torch::Tensor running_mean, torch::Tensor running_var,\n double eps, bool training, double momentum);\n\ntorch::Tensor fused_forward(torch::Tensor x, torch::Tensor w, torch::Tensor conv_b,\n torch::Tensor bn_w, torch::Tensor bn_b,\n torch::Tensor running_mean, torch::Tensor running_var,\n double eps, bool training, double momentum) {\n return fused_forward_cuda(x, w, conv_b, bn_w, bn_b, running_mean, running_var, eps, training, momentum);\n}\n'''\n\n_cuda = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n\n#define BLOCK 256\n\n__device__ __forceinline__ int k_begin(int t) { return (t == 0) ? 1 : 0; }\n__device__ __forceinline__ int k_end(int t) { return (t == 0) ? 2 : ((t == 1) ? 2 : 0); }\n\n__device__ __forceinline__ float deconv_val(const float* __restrict__ x, const float* __restrict__ w,\n const float* __restrict__ b,\n int n, int co, int od, int oh, int ow,\n int N, int CI, int D, int H, int W, int CO) {\n float acc = b ? b[co] : 0.0f;\n for (int ci = 0; ci < CI; ++ci) {\n #pragma unroll\n for (int kd = 0; kd < 3; ++kd) {\n int id2 = od + 1 - kd;\n if ((id2 & 1) != 0) continue;\n int id = id2 >> 1;\n if ((unsigned)id >= (unsigned)D) continue;\n #pragma unroll\n for (int kh = 0; kh < 3; ++kh) {\n int ih2 = oh + 1 - kh;\n if ((ih2 & 1) != 0) continue;\n int ih = ih2 >> 1;\n if ((unsigned)ih >= (unsigned)H) continue;\n #pragma unroll\n for (int kw = 0; kw < 3; ++kw) {\n int iw2 = ow + 1 - kw;\n if ((iw2 & 1) != 0) continue;\n int iw = iw2 >> 1;\n if ((unsigned)iw >= (unsigned)W) continue;\n float xv = x[((((n * CI + ci) * D + id) * H + ih) * W + iw)];\n float wt = w[((((ci * CO + co) * 3 + kd) * 3 + kh) * 3 + kw)];\n acc = fmaf(xv, wt, acc);\n }\n }\n }\n }\n return acc;\n}\n\n__global__ void stats_partial_kernel(const float* __restrict__ x, const float* __restrict__ w,\n const float* __restrict__ b,\n double* __restrict__ psum, double* __restrict__ psq,\n int N, int CI, int D, int H, int W, int CO,\n int CD, int CH, int CW, int NB) {\n int co = blockIdx.y;\n int bid = blockIdx.x;\n int tid = threadIdx.x;\n int64_t M = (int64_t)N * CD * CH * CW;\n double s = 0.0;\n double q = 0.0;\n for (int64_t m = (int64_t)bid * BLOCK + tid; m < M; m += (int64_t)NB * BLOCK) {\n int ow = m % CW;\n int64_t t = m / CW;\n int oh = t % CH; t /= CH;\n int od = t % CD; t /= CD;\n int n = (int)t;\n float v = deconv_val(x, w, b, n, co, od, oh, ow, N, CI, D, H, W, CO);\n s += (double)v;\n q += (double)v * (double)v;\n }\n __shared__ double ss[BLOCK];\n __shared__ double qq[BLOCK];\n ss[tid] = s; qq[tid] = q;\n __syncthreads();\n for (int off = BLOCK/2; off > 0; off >>= 1) {\n if (tid < off) { ss[tid] += ss[tid+off]; qq[tid] += qq[tid+off]; }\n __syncthreads();\n }\n if (tid == 0) { psum[co * NB + bid] = ss[0]; psq[co * NB + bid] = qq[0]; }\n}\n\n__global__ void stats_final_kernel(const double* __restrict__ psum, const double* __restrict__ psq,\n float* __restrict__ mean, float* __restrict__ var,\n float* __restrict__ running_mean, float* __restrict__ running_var,\n int CO, int NB, int64_t M, float momentum) {\n int co = blockIdx.x;\n int tid = threadIdx.x;\n double s = 0.0, q = 0.0;\n for (int i = tid; i < NB; i += BLOCK) { s += psum[co*NB+i]; q += psq[co*NB+i]; }\n __shared__ double ss[BLOCK];\n __shared__ double qq[BLOCK];\n ss[tid]=s; qq[tid]=q; __syncthreads();\n for (int off=BLOCK/2; off>0; off>>=1) { if (tid<off) { ss[tid]+=ss[tid+off]; qq[tid]+=qq[tid+off]; } __syncthreads(); }\n if (tid==0) {\n double mu = ss[0] / (double)M;\n double vv = qq[0] / (double)M - mu * mu;\n if (vv < 0.0) vv = 0.0;\n mean[co] = (float)mu;\n var[co] = (float)vv;\n // Match BatchNorm running-stat update (unbiased variance for running_var)\n running_mean[co] = running_mean[co] * (1.0f - momentum) + (float)mu * momentum;\n double unb = vv * ((double)M / (double)(M - 1));\n running_var[co] = running_var[co] * (1.0f - momentum) + (float)unb * momentum;\n }\n}\n\n__global__ void make_agg_kernel(const float* __restrict__ w, float* __restrict__ agg, int CI, int CO) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int total = CO * CI * 27;\n if (idx >= total) return;\n int tmp = idx;\n int tw = tmp % 3; tmp /= 3;\n int th = tmp % 3; tmp /= 3;\n int td = tmp % 3; tmp /= 3;\n int ci = tmp % CI; tmp /= CI;\n int co = tmp;\n float s = 0.0f;\n for (int kd = k_begin(td); kd <= k_end(td); ++kd)\n for (int kh = k_begin(th); kh <= k_end(th); ++kh)\n for (int kw = k_begin(tw); kw <= k_end(tw); ++kw)\n s += w[((((ci * CO + co) * 3 + kd) * 3 + kh) * 3 + kw)];\n agg[idx] = s * (1.0f / 64.0f);\n}\n\n__global__ void fused_out_kernel(const float* __restrict__ x, const float* __restrict__ agg,\n const float* __restrict__ conv_b, const float* __restrict__ bn_w,\n const float* __restrict__ bn_b, const float* __restrict__ mean,\n const float* __restrict__ var, float* __restrict__ out,\n int N, int CI, int D, int H, int W, int CO,\n int OD, int OH, int OW, float eps) {\n int64_t idx = (int64_t)blockIdx.x * blockDim.x + threadIdx.x;\n int64_t total = (int64_t)N * CO * OD * OH * OW;\n if (idx >= total) return;\n int ow = idx % OW;\n int64_t t = idx / OW;\n int oh = t % OH; t /= OH;\n int od = t % OD; t /= OD;\n int co = t % CO; t /= CO;\n int n = (int)t;\n float acc = conv_b ? conv_b[co] : 0.0f;\n int id0 = od * 2, ih0 = oh * 2, iw0 = ow * 2;\n for (int ci = 0; ci < CI; ++ci) {\n #pragma unroll\n for (int td = 0; td < 3; ++td) {\n int id = id0 + td;\n #pragma unroll\n for (int th = 0; th < 3; ++th) {\n int ih = ih0 + th;\n #pragma unroll\n for (int tw = 0; tw < 3; ++tw) {\n int iw = iw0 + tw;\n float xv = x[((((n * CI + ci) * D + id) * H + ih) * W + iw)];\n float av = agg[(((co * CI + ci) * 3 + td) * 3 + th) * 3 + tw];\n acc = fmaf(xv, av, acc);\n }\n }\n }\n }\n float scale = bn_w[co] * rsqrtf(var[co] + eps);\n float shift = bn_b[co] - mean[co] * scale;\n out[idx] = acc * scale + shift;\n}\n\ntorch::Tensor fused_forward_cuda(torch::Tensor x, torch::Tensor w, torch::Tensor conv_b,\n torch::Tensor bn_w, torch::Tensor bn_b,\n torch::Tensor running_mean, torch::Tensor running_var,\n double eps, bool training, double momentum) {\n int N = x.size(0), CI = x.size(1), D = x.size(2), H = x.size(3), W = x.size(4);\n int CO = w.size(1);\n int CD = (D - 1) * 2 - 2 + 3, CH = (H - 1) * 2 - 2 + 3, CW = (W - 1) * 2 - 2 + 3;\n int OD = ((CD - 2) / 2 + 1 - 2) / 2 + 1;\n int OH = ((CH - 2) / 2 + 1 - 2) / 2 + 1;\n int OW = ((CW - 2) / 2 + 1 - 2) / 2 + 1;\n auto out = torch::empty({N, CO, OD, OH, OW}, x.options());\n auto agg = torch::empty({CO, CI, 3, 3, 3}, x.options());\n make_agg_kernel<<<(CO * CI * 27 + 255)/256, 256>>>(w.data_ptr<float>(), agg.data_ptr<float>(), CI, CO);\n\n torch::Tensor mean_t, var_t;\n if (training) {\n const int NB = 1024;\n auto optsD = x.options().dtype(torch::kFloat64);\n auto psum = torch::empty({CO, NB}, optsD);\n auto psq = torch::empty({CO, NB}, optsD);\n mean_t = torch::empty({CO}, x.options());\n var_t = torch::empty({CO}, x.options());\n dim3 grid(NB, CO);\n stats_partial_kernel<<<grid, BLOCK>>>(x.data_ptr<float>(), w.data_ptr<float>(),\n conv_b.defined() ? conv_b.data_ptr<float>() : nullptr,\n psum.data_ptr<double>(), psq.data_ptr<double>(), N, CI, D, H, W, CO, CD, CH, CW, NB);\n int64_t M = (int64_t)N * CD * CH * CW;\n stats_final_kernel<<<CO, BLOCK>>>(psum.data_ptr<double>(), psq.data_ptr<double>(),\n mean_t.data_ptr<float>(), var_t.data_ptr<float>(), running_mean.data_ptr<float>(), running_var.data_ptr<float>(),\n CO, NB, M, (float)momentum);\n } else {\n mean_t = running_mean;\n var_t = running_var;\n }\n int64_t total = (int64_t)N * CO * OD * OH * OW;\n fused_out_kernel<<<(total + 255)/256, 256>>>(x.data_ptr<float>(), agg.data_ptr<float>(),\n conv_b.defined() ? conv_b.data_ptr<float>() : nullptr,\n bn_w.data_ptr<float>(), bn_b.data_ptr<float>(), mean_t.data_ptr<float>(), var_t.data_ptr<float>(),\n out.data_ptr<float>(), N, CI, D, H, W, CO, OD, OH, OW, (float)eps);\n return out;\n}\n'''\n\n_ext = load_inline(name='fused_deconv_bn_pool_ext_v2', cpp_sources=_cpp, cuda_sources=_cuda,\n functions=['fused_forward'], verbose=False, extra_cuda_cflags=['-O3', '--use_fast_math'])\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, stride, padding, bias_shape):\n super(ModelNew, self).__init__()\n self.conv_transpose = nn.ConvTranspose3d(in_channels, out_channels, kernel_size, stride=stride, padding=padding)\n self.batch_norm = nn.BatchNorm3d(out_channels)\n self.avg_pool1 = nn.AvgPool3d(kernel_size=2)\n self.avg_pool2 = nn.AvgPool3d(kernel_size=2)\n def forward(self, x):\n mom = self.batch_norm.momentum if self.batch_norm.momentum is not None else 0.1\n return _ext.fused_forward(x, self.conv_transpose.weight, self.conv_transpose.bias,\n self.batch_norm.weight, self.batch_norm.bias,\n self.batch_norm.running_mean, self.batch_norm.running_var,\n float(self.batch_norm.eps), bool(self.training), float(mom))\n" }
result ← compile_kernel (ok)
okcompile_kernel compile_kernel PASSED: kernel compiled without errors.
turn 4 · 1 tool call(s) · 19.4s turn · 19.4s LLM · 12,197 chars (click to expand)
turn 419.4s turn · 19.4s 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\n_cpp = r'''\n#include <torch/extension.h>\n\ntorch::Tensor fused_forward_cuda(torch::Tensor x, torch::Tensor w, torch::Tensor conv_b,\n torch::Tensor bn_w, torch::Tensor bn_b,\n torch::Tensor running_mean, torch::Tensor running_var,\n double eps, bool training, double momentum);\n\ntorch::Tensor fused_forward(torch::Tensor x, torch::Tensor w, torch::Tensor conv_b,\n torch::Tensor bn_w, torch::Tensor bn_b,\n torch::Tensor running_mean, torch::Tensor running_var,\n double eps, bool training, double momentum) {\n return fused_forward_cuda(x, w, conv_b, bn_w, bn_b, running_mean, running_var, eps, training, momentum);\n}\n'''\n\n_cuda = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n\n#define BLOCK 256\n\n__device__ __forceinline__ int k_begin(int t) { return (t == 0) ? 1 : 0; }\n__device__ __forceinline__ int k_end(int t) { return (t == 0) ? 2 : ((t == 1) ? 2 : 0); }\n\n__device__ __forceinline__ float deconv_val(const float* __restrict__ x, const float* __restrict__ w,\n const float* __restrict__ b,\n int n, int co, int od, int oh, int ow,\n int N, int CI, int D, int H, int W, int CO) {\n float acc = b ? b[co] : 0.0f;\n for (int ci = 0; ci < CI; ++ci) {\n #pragma unroll\n for (int kd = 0; kd < 3; ++kd) {\n int id2 = od + 1 - kd;\n if ((id2 & 1) != 0) continue;\n int id = id2 >> 1;\n if ((unsigned)id >= (unsigned)D) continue;\n #pragma unroll\n for (int kh = 0; kh < 3; ++kh) {\n int ih2 = oh + 1 - kh;\n if ((ih2 & 1) != 0) continue;\n int ih = ih2 >> 1;\n if ((unsigned)ih >= (unsigned)H) continue;\n #pragma unroll\n for (int kw = 0; kw < 3; ++kw) {\n int iw2 = ow + 1 - kw;\n if ((iw2 & 1) != 0) continue;\n int iw = iw2 >> 1;\n if ((unsigned)iw >= (unsigned)W) continue;\n float xv = x[((((n * CI + ci) * D + id) * H + ih) * W + iw)];\n float wt = w[((((ci * CO + co) * 3 + kd) * 3 + kh) * 3 + kw)];\n acc = fmaf(xv, wt, acc);\n }\n }\n }\n }\n return acc;\n}\n\n__global__ void stats_partial_kernel(const float* __restrict__ x, const float* __restrict__ w,\n const float* __restrict__ b,\n double* __restrict__ psum, double* __restrict__ psq,\n int N, int CI, int D, int H, int W, int CO,\n int CD, int CH, int CW, int NB) {\n int co = blockIdx.y;\n int bid = blockIdx.x;\n int tid = threadIdx.x;\n int64_t M = (int64_t)N * CD * CH * CW;\n double s = 0.0;\n double q = 0.0;\n for (int64_t m = (int64_t)bid * BLOCK + tid; m < M; m += (int64_t)NB * BLOCK) {\n int ow = m % CW;\n int64_t t = m / CW;\n int oh = t % CH; t /= CH;\n int od = t % CD; t /= CD;\n int n = (int)t;\n float v = deconv_val(x, w, b, n, co, od, oh, ow, N, CI, D, H, W, CO);\n s += (double)v;\n q += (double)v * (double)v;\n }\n __shared__ double ss[BLOCK];\n __shared__ double qq[BLOCK];\n ss[tid] = s; qq[tid] = q;\n __syncthreads();\n for (int off = BLOCK/2; off > 0; off >>= 1) {\n if (tid < off) { ss[tid] += ss[tid+off]; qq[tid] += qq[tid+off]; }\n __syncthreads();\n }\n if (tid == 0) { psum[co * NB + bid] = ss[0]; psq[co * NB + bid] = qq[0]; }\n}\n\n__global__ void stats_final_kernel(const double* __restrict__ psum, const double* __restrict__ psq,\n float* __restrict__ mean, float* __restrict__ var,\n float* __restrict__ running_mean, float* __restrict__ running_var,\n int CO, int NB, int64_t M, float momentum) {\n int co = blockIdx.x;\n int tid = threadIdx.x;\n double s = 0.0, q = 0.0;\n for (int i = tid; i < NB; i += BLOCK) { s += psum[co*NB+i]; q += psq[co*NB+i]; }\n __shared__ double ss[BLOCK];\n __shared__ double qq[BLOCK];\n ss[tid]=s; qq[tid]=q; __syncthreads();\n for (int off=BLOCK/2; off>0; off>>=1) { if (tid<off) { ss[tid]+=ss[tid+off]; qq[tid]+=qq[tid+off]; } __syncthreads(); }\n if (tid==0) {\n double mu = ss[0] / (double)M;\n double vv = qq[0] / (double)M - mu * mu;\n if (vv < 0.0) vv = 0.0;\n mean[co] = (float)mu;\n var[co] = (float)vv;\n running_mean[co] = running_mean[co] * (1.0f - momentum) + (float)mu * momentum;\n double unb = vv * ((double)M / (double)(M - 1));\n running_var[co] = running_var[co] * (1.0f - momentum) + (float)unb * momentum;\n }\n}\n\n__global__ void make_agg_kernel(const float* __restrict__ w, float* __restrict__ agg, int CI, int CO) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int total = CO * CI * 27;\n if (idx >= total) return;\n int tmp = idx;\n int tw = tmp % 3; tmp /= 3;\n int th = tmp % 3; tmp /= 3;\n int td = tmp % 3; tmp /= 3;\n int ci = tmp % CI; tmp /= CI;\n int co = tmp;\n float s = 0.0f;\n for (int kd = k_begin(td); kd <= k_end(td); ++kd)\n for (int kh = k_begin(th); kh <= k_end(th); ++kh)\n for (int kw = k_begin(tw); kw <= k_end(tw); ++kw)\n s += w[((((ci * CO + co) * 3 + kd) * 3 + kh) * 3 + kw)];\n agg[idx] = s * (1.0f / 64.0f);\n}\n\n__global__ void fused_out_kernel(const float* __restrict__ x, const float* __restrict__ agg,\n const float* __restrict__ conv_b, const float* __restrict__ bn_w,\n const float* __restrict__ bn_b, const float* __restrict__ mean,\n const float* __restrict__ var, float* __restrict__ out,\n int N, int CI, int D, int H, int W, int CO,\n int OD, int OH, int OW, float eps) {\n int64_t idx = (int64_t)blockIdx.x * blockDim.x + threadIdx.x;\n int64_t total = (int64_t)N * CO * OD * OH * OW;\n if (idx >= total) return;\n int ow = idx % OW;\n int64_t t = idx / OW;\n int oh = t % OH; t /= OH;\n int od = t % OD; t /= OD;\n int co = t % CO; t /= CO;\n int n = (int)t;\n float acc = conv_b ? conv_b[co] : 0.0f;\n int id0 = od * 2, ih0 = oh * 2, iw0 = ow * 2;\n for (int ci = 0; ci < CI; ++ci) {\n #pragma unroll\n for (int td = 0; td < 3; ++td) {\n int id = id0 + td;\n #pragma unroll\n for (int th = 0; th < 3; ++th) {\n int ih = ih0 + th;\n #pragma unroll\n for (int tw = 0; tw < 3; ++tw) {\n int iw = iw0 + tw;\n float xv = x[((((n * CI + ci) * D + id) * H + ih) * W + iw)];\n float av = agg[(((co * CI + ci) * 3 + td) * 3 + th) * 3 + tw];\n acc = fmaf(xv, av, acc);\n }\n }\n }\n }\n float scale = bn_w[co] * rsqrtf(var[co] + eps);\n float shift = bn_b[co] - mean[co] * scale;\n out[idx] = acc * scale + shift;\n}\n\ntorch::Tensor fused_forward_cuda(torch::Tensor x, torch::Tensor w, torch::Tensor conv_b,\n torch::Tensor bn_w, torch::Tensor bn_b,\n torch::Tensor running_mean, torch::Tensor running_var,\n double eps, bool training, double momentum) {\n int N = x.size(0), CI = x.size(1), D = x.size(2), H = x.size(3), W = x.size(4);\n int CO = w.size(1);\n int CD = (D - 1) * 2 - 2 + 3, CH = (H - 1) * 2 - 2 + 3, CW = (W - 1) * 2 - 2 + 3;\n int OD = ((CD - 2) / 2 + 1 - 2) / 2 + 1;\n int OH = ((CH - 2) / 2 + 1 - 2) / 2 + 1;\n int OW = ((CW - 2) / 2 + 1 - 2) / 2 + 1;\n auto out = torch::empty({N, CO, OD, OH, OW}, x.options());\n auto agg = torch::empty({CO, CI, 3, 3, 3}, x.options());\n make_agg_kernel<<<(CO * CI * 27 + 255)/256, 256>>>(w.data_ptr<float>(), agg.data_ptr<float>(), CI, CO);\n\n torch::Tensor mean_t, var_t;\n if (training) {\n const int NB = 1024;\n auto optsD = x.options().dtype(torch::kFloat64);\n auto psum = torch::empty({CO, NB}, optsD);\n auto psq = torch::empty({CO, NB}, optsD);\n mean_t = torch::empty({CO}, x.options());\n var_t = torch::empty({CO}, x.options());\n dim3 grid(NB, CO);\n stats_partial_kernel<<<grid, BLOCK>>>(x.data_ptr<float>(), w.data_ptr<float>(),\n conv_b.defined() ? conv_b.data_ptr<float>() : nullptr,\n psum.data_ptr<double>(), psq.data_ptr<double>(), N, CI, D, H, W, CO, CD, CH, CW, NB);\n int64_t M = (int64_t)N * CD * CH * CW;\n stats_final_kernel<<<CO, BLOCK>>>(psum.data_ptr<double>(), psq.data_ptr<double>(),\n mean_t.data_ptr<float>(), var_t.data_ptr<float>(), running_mean.data_ptr<float>(), running_var.data_ptr<float>(),\n CO, NB, M, (float)momentum);\n } else {\n mean_t = running_mean;\n var_t = running_var;\n }\n int64_t total = (int64_t)N * CO * OD * OH * OW;\n fused_out_kernel<<<(total + 255)/256, 256>>>(x.data_ptr<float>(), agg.data_ptr<float>(),\n conv_b.defined() ? conv_b.data_ptr<float>() : nullptr,\n bn_w.data_ptr<float>(), bn_b.data_ptr<float>(), mean_t.data_ptr<float>(), var_t.data_ptr<float>(),\n out.data_ptr<float>(), N, CI, D, H, W, CO, OD, OH, OW, (float)eps);\n return out;\n}\n'''\n\n_ext = load_inline(name='fused_deconv_bn_pool_ext_v2', cpp_sources=_cpp, cuda_sources=_cuda,\n functions=['fused_forward'], verbose=False, extra_cuda_cflags=['-O3', '--use_fast_math'])\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, stride, padding, bias_shape):\n super(ModelNew, self).__init__()\n self.conv_transpose = nn.ConvTranspose3d(in_channels, out_channels, kernel_size, stride=stride, padding=padding)\n self.batch_norm = nn.BatchNorm3d(out_channels)\n self.avg_pool1 = nn.AvgPool3d(kernel_size=2)\n self.avg_pool2 = nn.AvgPool3d(kernel_size=2)\n def forward(self, x):\n mom = self.batch_norm.momentum if self.batch_norm.momentum is not None else 0.1\n return _ext.fused_forward(x, self.conv_transpose.weight, self.conv_transpose.bias,\n self.batch_norm.weight, self.batch_norm.bias,\n self.batch_norm.running_mean, self.batch_norm.running_var,\n float(self.batch_norm.eps), bool(self.training), float(mom))\n" }
result ← run_correctness (fail)
failrun_correctness run_correctness FAILED: (0 / 5) trials did not all match. correctness_issue: Output mismatch max_difference: ['0.000203', '0.000233', '0.000230', '0.000212', '0.000228'] avg_difference: ['0.000022', '0.000022', '0.000022', '0.000022', '0.000022']
turn 5 · 1 tool call(s) · 20.2s turn · 20.2s LLM · 21,537 chars (click to expand)
turn 520.2s turn · 20.2s LLM · 0.0s tools
reasoning
**Improving numerical output** I need to enhance the final numerical output to align with what PyTorch produces. The difference may stem from average aggregation or using fast math for rsqrt. Removing the fast math option might yield more precision. I also need to compute averages in a way that mimics PyTorch after batch normalization, possibly by adjusting the pooling order. I'll explore whether using sqrtf instead of rsqrt could help reduce differences, as we're seeing small discrepancies in the outputs and stats. **Testing aggregation order** I suspect that the final aggregation order might be contributing to the issue, so I think the first step is to test without using fast math. By removing fast math, I hope to see if that makes a difference in the output. This approach will help clarify whether the aggregation order is indeed the culprit or if there's something else at play. I'll proceed with this test and keep a close eye on the results!
call → compile_kernel
callcompile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n_cpp = r'''\n#include <torch/extension.h>\ntorch::Tensor fused_forward_cuda(torch::Tensor x, torch::Tensor w, torch::Tensor conv_b, torch::Tensor bn_w, torch::Tensor bn_b, torch::Tensor running_mean, torch::Tensor running_var, double eps, bool training, double momentum);\ntorch::Tensor fused_forward(torch::Tensor x, torch::Tensor w, torch::Tensor conv_b, torch::Tensor bn_w, torch::Tensor bn_b, torch::Tensor running_mean, torch::Tensor running_var, double eps, bool training, double momentum) { return fused_forward_cuda(x,w,conv_b,bn_w,bn_b,running_mean,running_var,eps,training,momentum); }\n'''\n_cuda = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n#define BLOCK 256\n__device__ __forceinline__ int k_begin(int t){return (t==0)?1:0;} __device__ __forceinline__ int k_end(int t){return (t==0)?2:((t==1)?2:0);} \n__device__ __forceinline__ float deconv_val(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ b,int n,int co,int od,int oh,int ow,int N,int CI,int D,int H,int W,int CO){float acc=b?b[co]:0.0f; for(int ci=0;ci<CI;++ci){#pragma unroll\nfor(int kd=0;kd<3;++kd){int id2=od+1-kd; if((id2&1)!=0) continue; int id=id2>>1; if((unsigned)id>=(unsigned)D) continue; #pragma unroll\nfor(int kh=0;kh<3;++kh){int ih2=oh+1-kh; if((ih2&1)!=0) continue; int ih=ih2>>1; if((unsigned)ih>=(unsigned)H) continue; #pragma unroll\nfor(int kw=0;kw<3;++kw){int iw2=ow+1-kw; if((iw2&1)!=0) continue; int iw=iw2>>1; if((unsigned)iw>=(unsigned)W) continue; float xv=x[((((n*CI+ci)*D+id)*H+ih)*W+iw)]; float wt=w[((((ci*CO+co)*3+kd)*3+kh)*3+kw)]; acc=fmaf(xv,wt,acc);}}}} return acc;}\n__global__ void stats_partial_kernel(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ b,double* __restrict__ psum,double* __restrict__ psq,int N,int CI,int D,int H,int W,int CO,int CD,int CH,int CW,int NB){int co=blockIdx.y,bid=blockIdx.x,tid=threadIdx.x; int64_t M=(int64_t)N*CD*CH*CW; double s=0.0,q=0.0; for(int64_t m=(int64_t)bid*BLOCK+tid;m<M;m+=(int64_t)NB*BLOCK){int ow=m%CW; int64_t t=m/CW; int oh=t%CH; t/=CH; int od=t%CD; t/=CD; int n=(int)t; float v=deconv_val(x,w,b,n,co,od,oh,ow,N,CI,D,H,W,CO); s+=(double)v; q+=(double)v*(double)v;} __shared__ double ss[BLOCK],qq[BLOCK]; ss[tid]=s; qq[tid]=q; __syncthreads(); for(int off=BLOCK/2;off>0;off>>=1){if(tid<off){ss[tid]+=ss[tid+off]; qq[tid]+=qq[tid+off];} __syncthreads();} if(tid==0){psum[co*NB+bid]=ss[0]; psq[co*NB+bid]=qq[0];}}\n__global__ void stats_final_kernel(const double* __restrict__ psum,const double* __restrict__ psq,float* __restrict__ mean,float* __restrict__ var,float* __restrict__ running_mean,float* __restrict__ running_var,int CO,int NB,int64_t M,float momentum){int co=blockIdx.x,tid=threadIdx.x; double s=0.0,q=0.0; for(int i=tid;i<NB;i+=BLOCK){s+=psum[co*NB+i]; q+=psq[co*NB+i];} __shared__ double ss[BLOCK],qq[BLOCK]; ss[tid]=s; qq[tid]=q; __syncthreads(); for(int off=BLOCK/2;off>0;off>>=1){if(tid<off){ss[tid]+=ss[tid+off]; qq[tid]+=qq[tid+off];} __syncthreads();} if(tid==0){double mu=ss[0]/(double)M; double vv=qq[0]/(double)M-mu*mu; if(vv<0.0) vv=0.0; mean[co]=(float)mu; var[co]=(float)vv; running_mean[co]=running_mean[co]*(1.0f-momentum)+(float)mu*momentum; double unb=vv*((double)M/(double)(M-1)); running_var[co]=running_var[co]*(1.0f-momentum)+(float)unb*momentum;}}\n__global__ void make_agg_kernel(const float* __restrict__ w,float* __restrict__ agg,int CI,int CO){int idx=blockIdx.x*blockDim.x+threadIdx.x,total=CO*CI*27; if(idx>=total)return; int tmp=idx; int tw=tmp%3; tmp/=3; int th=tmp%3; tmp/=3; int td=tmp%3; tmp/=3; int ci=tmp%CI; tmp/=CI; int co=tmp; float s=0.0f; for(int kd=k_begin(td);kd<=k_end(td);++kd) for(int kh=k_begin(th);kh<=k_end(th);++kh) for(int kw=k_begin(tw);kw<=k_end(tw);++kw) s+=w[((((ci*CO+co)*3+kd)*3+kh)*3+kw)]; agg[idx]=s*(1.0f/64.0f);} \n__global__ void fused_out_kernel(const float* __restrict__ x,const float* __restrict__ agg,const float* __restrict__ conv_b,const float* __restrict__ bn_w,const float* __restrict__ bn_b,const float* __restrict__ mean,const float* __restrict__ var,float* __restrict__ out,int N,int CI,int D,int H,int W,int CO,int OD,int OH,int OW,float eps){int64_t idx=(int64_t)blockIdx.x*blockDim.x+threadIdx.x,total=(int64_t)N*CO*OD*OH*OW; if(idx>=total)return; int ow=idx%OW; int64_t t=idx/OW; int oh=t%OH; t/=OH; int od=t%OD; t/=OD; int co=t%CO; t/=CO; int n=(int)t; float acc=conv_b?conv_b[co]:0.0f; int id0=od*2,ih0=oh*2,iw0=ow*2; for(int ci=0;ci<CI;++ci){#pragma unroll\nfor(int td=0;td<3;++td){int id=id0+td; #pragma unroll\nfor(int th=0;th<3;++th){int ih=ih0+th; #pragma unroll\nfor(int tw=0;tw<3;++tw){int iw=iw0+tw; float xv=x[((((n*CI+ci)*D+id)*H+ih)*W+iw)]; float av=agg[(((co*CI+ci)*3+td)*3+th)*3+tw]; acc=fmaf(xv,av,acc);}}}} float inv=1.0f/sqrtf(var[co]+eps); float scale=bn_w[co]*inv; float shift=bn_b[co]-mean[co]*scale; out[idx]=acc*scale+shift;}\ntorch::Tensor fused_forward_cuda(torch::Tensor x,torch::Tensor w,torch::Tensor conv_b,torch::Tensor bn_w,torch::Tensor bn_b,torch::Tensor running_mean,torch::Tensor running_var,double eps,bool training,double momentum){int N=x.size(0),CI=x.size(1),D=x.size(2),H=x.size(3),W=x.size(4),CO=w.size(1); int CD=(D-1)*2-2+3,CH=(H-1)*2-2+3,CW=(W-1)*2-2+3; int OD=((CD-2)/2+1-2)/2+1,OH=((CH-2)/2+1-2)/2+1,OW=((CW-2)/2+1-2)/2+1; auto out=torch::empty({N,CO,OD,OH,OW},x.options()); auto agg=torch::empty({CO,CI,3,3,3},x.options()); make_agg_kernel<<<(CO*CI*27+255)/256,256>>>(w.data_ptr<float>(),agg.data_ptr<float>(),CI,CO); torch::Tensor mean_t,var_t; if(training){const int NB=1024; auto optsD=x.options().dtype(torch::kFloat64); auto psum=torch::empty({CO,NB},optsD), psq=torch::empty({CO,NB},optsD); mean_t=torch::empty({CO},x.options()); var_t=torch::empty({CO},x.options()); stats_partial_kernel<<<dim3(NB,CO),BLOCK>>>(x.data_ptr<float>(),w.data_ptr<float>(),conv_b.defined()?conv_b.data_ptr<float>():nullptr,psum.data_ptr<double>(),psq.data_ptr<double>(),N,CI,D,H,W,CO,CD,CH,CW,NB); int64_t M=(int64_t)N*CD*CH*CW; stats_final_kernel<<<CO,BLOCK>>>(psum.data_ptr<double>(),psq.data_ptr<double>(),mean_t.data_ptr<float>(),var_t.data_ptr<float>(),running_mean.data_ptr<float>(),running_var.data_ptr<float>(),CO,NB,M,(float)momentum);} else {mean_t=running_mean; var_t=running_var;} int64_t total=(int64_t)N*CO*OD*OH*OW; fused_out_kernel<<<(total+255)/256,256>>>(x.data_ptr<float>(),agg.data_ptr<float>(),conv_b.defined()?conv_b.data_ptr<float>():nullptr,bn_w.data_ptr<float>(),bn_b.data_ptr<float>(),mean_t.data_ptr<float>(),var_t.data_ptr<float>(),out.data_ptr<float>(),N,CI,D,H,W,CO,OD,OH,OW,(float)eps); return out;}\n'''\n_ext = load_inline(name='fused_deconv_bn_pool_ext_v3test', cpp_sources=_cpp, cuda_sources=_cuda, functions=['fused_forward'], verbose=False, extra_cuda_cflags=['-O3'])\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, stride, padding, bias_shape):\n super(ModelNew,self).__init__(); self.conv_transpose=nn.ConvTranspose3d(in_channels,out_channels,kernel_size,stride=stride,padding=padding); self.batch_norm=nn.BatchNorm3d(out_channels); self.avg_pool1=nn.AvgPool3d(kernel_size=2); self.avg_pool2=nn.AvgPool3d(kernel_size=2)\n def forward(self,x):\n mom=self.batch_norm.momentum if self.batch_norm.momentum is not None else 0.1\n return _ext.fused_forward(x,self.conv_transpose.weight,self.conv_transpose.bias,self.batch_norm.weight,self.batch_norm.bias,self.batch_norm.running_mean,self.batch_norm.running_var,float(self.batch_norm.eps),bool(self.training),float(mom))\n" }
result ← compile_kernel (fail)
failcompile_kernel compile_kernel FAILED: RuntimeError. RuntimeError: Error building extension 'fused_deconv_bn_pool_ext_v3test': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=fused_deconv_bn_pool_ext_v3test -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/tejas/PopcornBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/tejas/PopcornBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /home/prabhune/.local/share/uv/python/cpython-3.10-linux-x86_64-gnu/include/python3.10 -fPIC -std=c++17 -c /scratch/tejas/PopcornBench/runs/l12_default/original/gpt-5.5-priority/level_2_problem_72_cache/k_47dd7c47eeb8/fused_deconv_bn_pool_ext_v3test/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=fused_deconv_bn_pool_ext_v3test -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/tejas/PopcornBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/tejas/PopcornBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /home/prabhune/.local/share/uv/python/cpython-3.10-linux-x86_64-gnu/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/tejas/PopcornBench/runs/l12_default/original/gpt-5.5-priority/level_2_problem_72_cache/k_47dd7c47eeb8/fused_deconv_bn_pool_ext_v3test/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=fused_deconv_bn_pool_ext_v3test -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/tejas/PopcornBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/tejas/PopcornBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /home/prabhune/.local/share/uv/python/cpython-3.10-linux-x86_64-gnu/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/tejas/PopcornBench/runs/l12_default/original/gpt-5.5-priority/level_2_problem_72_cache/k_47dd7c47eeb8/fused_deconv_bn_pool_ext_v3test/cuda.cu -o cuda.cuda.o nvcc warning : incompatible redefinition for option 'compiler-bindir', the last value of this option was used /scratch/tejas/PopcornBench/runs/l12_default/original/gpt-5.5-priority/level_2_problem_72_cache/k_47dd7c47eeb8/fused_deconv_bn_pool_ext_v3test/cuda.cu(11): error: "#" not expected here float deconv_val(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ b,int n,int co,int od,int oh,int ow,int N,int CI,int D,int H,int W,int CO){float acc=b?b[co]:0.0f; for(int ci=0;ci<CI;++ci){#pragma unroll ^ /scratch/tejas/PopcornBench/runs/l12_default/original/gpt-5.5-priority/level_2_problem_72_cache/k_47dd7c47eeb8/fused_deconv_bn_pool_ext_v3test/cuda.cu(11): error: expected an expression float deconv_val(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ b,int n,int co,int od,int oh,int ow,int N,int CI,int D,int H,int W,int CO){float acc=b?b[co]:0.0f; for(int ci=0;ci<CI;++ci){#pragma unroll ^ /scratch/tejas/PopcornBench/runs/l12_default/original/gpt-5.5-priority/level_2_problem_72_cache/k_47dd7c47eeb8/fused_deconv_bn_pool_ext_v3test/cuda.cu(12): error: "#" not expected here for(int kd=0;kd<3;++kd){int id2=od+1-kd; if((id2&1)!=0) continue; int id=id2>>1; if((unsigned)id>=(unsigned)D) continue; #pragma unroll ^ /scratch/tejas/PopcornBench/runs/l12_default/original/gpt-5.5-priority/level_2_problem_72_cache/k_47dd7c47eeb8/fused_deconv_bn_pool_ext_v3test/cuda.cu(13): error: "#" not expected here for(int kh=0;kh<3;++kh){int ih2=oh+1-kh; if((ih2&1)!=0) continue; int ih=ih2>>1; if((unsigned)ih>=(unsigned)H) continue; #pragma unroll ^ /scratch/tejas/PopcornBench/runs/l12_default/original/gpt-5.5-priority/level_2_problem_72_cache/k_47dd7c47eeb8/fused_deconv_bn_pool_ext_v3test/cuda.cu(14): warning #12-D: parsing restarts here after previous syntax error for(int kw=0;kw<3;++kw){int iw2=ow+1-kw; if((iw2&1)!=0) continue; int iw=iw2>>1; if((unsigned)iw>=(unsigned)W) continue; float xv=x[((((n*CI+ci)*D+id)*H+ih)*W+iw)]; float wt=w[((((ci*CO+co)*3+kd)*3+kh)*3+kw)]; acc=fmaf(xv,wt,acc);}}}} return acc;} ^ Remark: The warnings can be suppressed with "-diag-suppress <warning-number>" /scratch/tejas/PopcornBench/runs/l12_default/original/gpt-5.5-priority/level_2_problem_72_cache/k_47dd7c47eeb8/fused_deconv_bn_pool_ext_v3test/cuda.cu(14): error: expected a ";" for(int kw=0;kw<3;++kw){int iw2=ow+1-kw; if((iw2&1)!=0) continue; int iw=iw2>>1; if((unsigned)iw>=(unsigned)W) continue; float xv=x[((((n*CI+ci)*D+id)*H+ih)*W+iw)]; float wt=w[((((ci*CO+co)*3+kd)*3+kh)*3+kw)]; acc=fmaf(xv,wt,acc);}}}} return acc;} ^ /scratch/tejas/PopcornBench/runs/l12_default/original/gpt-5.5-priority/level_2_problem_72_cache/k_47dd7c47eeb8/fused_deconv_bn_pool_ext_v3test/cuda.cu(18): error: "#" not expected here void fused_out_kernel(const float* __restrict__ x,const float* __restrict__ agg,const float* __restrict__ conv_b,const float* __restrict__ bn_w,const float* __restrict__ bn_b,const float* __restrict__ mean,const float* __restrict__ var,float* __restrict__ out,int N,int CI,int D,int H,int W,int CO,int OD,int OH,int OW,float eps){int64_t idx=(int64_t)blockIdx.x*blockDim.x+threadIdx.x,total=(int64_t)N*CO*OD*OH*OW; if(idx>=total)return; int ow=idx%OW; int64_t t=idx/OW; int oh=t%OH; t/=OH; int od=t%OD; t/=OD; int co=t%CO; t/=CO; int n=(int)t; float acc=conv_b?conv_b[co]:0.0f; int id0=od*2,ih0=oh*2,iw0=ow*2; for(int ci=0;ci<CI;++ci){#pragma unroll ^ /scratch/tejas/PopcornBench/runs/l12_default/original/gpt-5.5-priority/level_2_problem_72_cache/k_47dd7c47eeb8/fused_deconv_bn_pool_ext_v3test/cuda.cu(18): error: expected an expression void fused_out_kernel(const float* __restrict__ x,const float* __restrict__ agg,const float* __restrict__ conv_b,const float* __restrict__ bn_w,const float* __restrict__ bn_b,const float* __restrict__ mean,const float* __restrict__ var,float* __restrict__ out,int N,int CI,int D,int H,int W,int CO,int OD,int OH,int OW,float eps){int64_t idx=(int64_t)blockIdx.x*blockDim.x+threadIdx.x,total=(int64_t)N*CO*OD*OH*OW; if(idx>=total)return; int ow=idx%OW; int64_t t=idx/OW; int oh=t%OH; t/=OH; int od=t%OD; t/=OD; int co=t%CO; t/=CO; int n=(int)t; float acc=conv_b?conv_b[co]:0.0f; int id0=od*2,ih0=oh*2,iw0=ow*2; for(int ci=0;ci<CI;++ci){#pragma unroll ^ /scratch/tejas/PopcornBench/runs/l12_default/original/gpt-5.5-priority/level_2_problem_72_cache/k_47dd7c47eeb8/fused_deconv_bn_pool_ext_v3test/cuda.cu(19): error: "#" not expected here for(int td=0;td<3;++td){int id=id0+td; #pragma unroll ^ /scratch/tejas/PopcornBench/runs/l12_default/original/gpt-5.5-priority/level_2_problem_72_cache/k_47dd7c47eeb8/fused_deconv_bn_pool_ext_v3test/cuda.cu(20): error: "#" not expected here for(int th=0;th<3;++th){int ih=ih0+th; #pragma unroll ^ /scratch/tejas/PopcornBench/runs/l12_default/original/gpt-5.5-priority/level_2_problem_72_cache/k_47dd7c47eeb8/fused_deconv_bn_pool_ext_v3test/cuda.cu(21): warning #12-D: parsing restarts here after previous syntax error for(int tw=0;tw<3;++tw){int iw=iw0+tw; float xv=x[((((n*CI+ci)*D+id)*H+ih)*W+iw)]; float av=agg[(((co*CI+ci)*3+td)*3+th)*3+tw]; acc=fmaf(xv,av,acc);}}}} float inv=1.0f/sqrtf(var[co]+eps); float scale=bn_w[co]*inv; float shift=bn_b[co]-mean[co]*scale; out[idx]=acc*scale+shift;} ^ /scratch/tejas/PopcornBench/runs/l12_default/original/gpt-5.5-priority/level_2_problem_72_cache/k_47dd7c47eeb8/fused_deconv_bn_pool_ext_v3test/cuda.cu(21): error: expected a ";" for(int tw=0;tw<3;++tw){int iw=iw0+tw; float xv=x[((((n*CI+ci)*D+id)*H+ih)*W+iw)]; float av=agg[(((co*CI+ci)*3+td)*3+th)*3+tw]; acc=fmaf(xv,av,acc);}}}} float inv=1.0f/sqrtf(var[co]+eps); float scale=bn_w[co]*inv; float shift=bn_b[co]-mean[co]*scale; out[idx]=acc*scale+shift;} ^ 10 errors detected in the compilation of "/scratch/tejas/PopcornBench/runs/l12_default/original/gpt-5.5-priority/level_2_problem_72_cache/k_47dd7c47eeb8/fused_deconv_bn_pool_ext_v3test/cuda.cu". ninja: build stopped: subcommand failed.
turn 6 · 1 tool call(s) · 29.0s turn · 29.0s LLM · 10,230 chars (click to expand)
turn 629.0s turn · 29.0s LLM · 0.0s tools
reasoning
**Calculating pooling in PyTorch** I'm thinking about how PyTorch handles avgpool1 and avgpool2. It seems that avgpool1 divides the output by 8 after the first pooling, and then avgpool2 divides further. If I compute the total sum divided by 64, that might yield a different result. I realize that avgpool1 outputs the sum of 8 values divided by 8, while pool2 sums those 8 values again and divides by 8.
call → compile_kernel
callcompile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n_cpp = r'''\n#include <torch/extension.h>\ntorch::Tensor fused_forward_cuda(torch::Tensor x, torch::Tensor w, torch::Tensor conv_b,\n torch::Tensor bn_w, torch::Tensor bn_b,\n torch::Tensor running_mean, torch::Tensor running_var,\n double eps, bool training, double momentum);\ntorch::Tensor fused_forward(torch::Tensor x, torch::Tensor w, torch::Tensor conv_b,\n torch::Tensor bn_w, torch::Tensor bn_b,\n torch::Tensor running_mean, torch::Tensor running_var,\n double eps, bool training, double momentum) {\n return fused_forward_cuda(x,w,conv_b,bn_w,bn_b,running_mean,running_var,eps,training,momentum);\n}\n'''\n_cuda = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n#define BLOCK 256\n\n__device__ __forceinline__ float deconv_val(const float* __restrict__ x, const float* __restrict__ w,\n const float* __restrict__ b,\n int n, int co, int od, int oh, int ow,\n int N, int CI, int D, int H, int W, int CO) {\n float acc = b ? b[co] : 0.0f;\n for (int ci=0; ci<CI; ++ci) {\n#pragma unroll\n for (int kd=0; kd<3; ++kd) {\n int id2 = od + 1 - kd;\n if ((id2 & 1) != 0) continue;\n int id = id2 >> 1;\n if ((unsigned)id >= (unsigned)D) continue;\n#pragma unroll\n for (int kh=0; kh<3; ++kh) {\n int ih2 = oh + 1 - kh;\n if ((ih2 & 1) != 0) continue;\n int ih = ih2 >> 1;\n if ((unsigned)ih >= (unsigned)H) continue;\n#pragma unroll\n for (int kw=0; kw<3; ++kw) {\n int iw2 = ow + 1 - kw;\n if ((iw2 & 1) != 0) continue;\n int iw = iw2 >> 1;\n if ((unsigned)iw >= (unsigned)W) continue;\n float xv = x[((((n*CI+ci)*D+id)*H+ih)*W+iw)];\n float wt = w[((((ci*CO+co)*3+kd)*3+kh)*3+kw)];\n acc = fmaf(xv, wt, acc);\n }\n }\n }\n }\n return acc;\n}\n\n__global__ void stats_partial_kernel(const float* __restrict__ x, const float* __restrict__ w,\n const float* __restrict__ b, double* __restrict__ psum, double* __restrict__ psq,\n int N,int CI,int D,int H,int W,int CO,int CD,int CH,int CW,int NB) {\n int co=blockIdx.y, bid=blockIdx.x, tid=threadIdx.x;\n int64_t M=(int64_t)N*CD*CH*CW;\n double s=0.0, q=0.0;\n for (int64_t m=(int64_t)bid*BLOCK+tid; m<M; m+=(int64_t)NB*BLOCK) {\n int ow=m%CW; int64_t t=m/CW; int oh=t%CH; t/=CH; int od=t%CD; t/=CD; int n=(int)t;\n float v=deconv_val(x,w,b,n,co,od,oh,ow,N,CI,D,H,W,CO);\n s += (double)v; q += (double)v*(double)v;\n }\n __shared__ double ss[BLOCK], qq[BLOCK]; ss[tid]=s; qq[tid]=q; __syncthreads();\n for (int off=BLOCK/2; off>0; off>>=1) { if (tid<off) { ss[tid]+=ss[tid+off]; qq[tid]+=qq[tid+off]; } __syncthreads(); }\n if (tid==0) { psum[co*NB+bid]=ss[0]; psq[co*NB+bid]=qq[0]; }\n}\n\n__global__ void stats_final_kernel(const double* __restrict__ psum, const double* __restrict__ psq,\n float* __restrict__ mean, float* __restrict__ var,\n float* __restrict__ running_mean, float* __restrict__ running_var,\n int NB, int64_t M, float momentum) {\n int co=blockIdx.x, tid=threadIdx.x; double s=0.0, q=0.0;\n for (int i=tid; i<NB; i+=BLOCK) { s += psum[co*NB+i]; q += psq[co*NB+i]; }\n __shared__ double ss[BLOCK], qq[BLOCK]; ss[tid]=s; qq[tid]=q; __syncthreads();\n for (int off=BLOCK/2; off>0; off>>=1) { if (tid<off) { ss[tid]+=ss[tid+off]; qq[tid]+=qq[tid+off]; } __syncthreads(); }\n if (tid==0) {\n double mu=ss[0]/(double)M; double vv=qq[0]/(double)M - mu*mu; if (vv<0.0) vv=0.0;\n mean[co]=(float)mu; var[co]=(float)vv;\n running_mean[co] = running_mean[co]*(1.0f-momentum) + (float)mu*momentum;\n double unb = vv*((double)M/(double)(M-1));\n running_var[co] = running_var[co]*(1.0f-momentum) + (float)unb*momentum;\n }\n}\n\n__global__ void exact_out_kernel(const float* __restrict__ x, const float* __restrict__ w,\n const float* __restrict__ conv_b, const float* __restrict__ bn_w,\n const float* __restrict__ bn_b, const float* __restrict__ mean,\n const float* __restrict__ var, float* __restrict__ out,\n int N,int CI,int D,int H,int W,int CO,int OD,int OH,int OW,float eps) {\n int64_t idx=(int64_t)blockIdx.x*blockDim.x+threadIdx.x;\n int64_t total=(int64_t)N*CO*OD*OH*OW; if (idx>=total) return;\n int ow=idx%OW; int64_t t=idx/OW; int oh=t%OH; t/=OH; int od=t%OD; t/=OD; int co=t%CO; t/=CO; int n=(int)t;\n float scale = bn_w[co] / sqrtf(var[co] + eps);\n float shift = bn_b[co] - mean[co]*scale;\n float sum2 = 0.0f;\n#pragma unroll\n for (int pd=0; pd<2; ++pd) {\n#pragma unroll\n for (int ph=0; ph<2; ++ph) {\n#pragma unroll\n for (int pw=0; pw<2; ++pw) {\n float sum1 = 0.0f;\n#pragma unroll\n for (int dd=0; dd<2; ++dd) {\n#pragma unroll\n for (int dh=0; dh<2; ++dh) {\n#pragma unroll\n for (int dw=0; dw<2; ++dw) {\n int zd = od*4 + pd*2 + dd;\n int zh = oh*4 + ph*2 + dh;\n int zw = ow*4 + pw*2 + dw;\n float z = deconv_val(x,w,conv_b,n,co,zd,zh,zw,N,CI,D,H,W,CO);\n sum1 += z * scale + shift;\n }\n }\n }\n sum2 += sum1 * 0.125f;\n }\n }\n }\n out[idx] = sum2 * 0.125f;\n}\n\ntorch::Tensor fused_forward_cuda(torch::Tensor x, torch::Tensor w, torch::Tensor conv_b,\n torch::Tensor bn_w, torch::Tensor bn_b,\n torch::Tensor running_mean, torch::Tensor running_var,\n double eps, bool training, double momentum) {\n int N=x.size(0), CI=x.size(1), D=x.size(2), H=x.size(3), W=x.size(4), CO=w.size(1);\n int CD=(D-1)*2-2+3, CH=(H-1)*2-2+3, CW=(W-1)*2-2+3;\n int OD=((CD-2)/2+1-2)/2+1, OH=((CH-2)/2+1-2)/2+1, OW=((CW-2)/2+1-2)/2+1;\n auto out=torch::empty({N,CO,OD,OH,OW}, x.options());\n torch::Tensor mean_t, var_t;\n if (training) {\n const int NB=1024;\n auto optsD=x.options().dtype(torch::kFloat64);\n auto psum=torch::empty({CO,NB}, optsD); auto psq=torch::empty({CO,NB}, optsD);\n mean_t=torch::empty({CO}, x.options()); var_t=torch::empty({CO}, x.options());\n stats_partial_kernel<<<dim3(NB,CO),BLOCK>>>(x.data_ptr<float>(), w.data_ptr<float>(), conv_b.defined()?conv_b.data_ptr<float>():nullptr,\n psum.data_ptr<double>(), psq.data_ptr<double>(), N,CI,D,H,W,CO,CD,CH,CW,NB);\n int64_t M=(int64_t)N*CD*CH*CW;\n stats_final_kernel<<<CO,BLOCK>>>(psum.data_ptr<double>(), psq.data_ptr<double>(), mean_t.data_ptr<float>(), var_t.data_ptr<float>(),\n running_mean.data_ptr<float>(), running_var.data_ptr<float>(), NB, M, (float)momentum);\n } else { mean_t=running_mean; var_t=running_var; }\n int64_t total=(int64_t)N*CO*OD*OH*OW;\n exact_out_kernel<<<(total+255)/256,256>>>(x.data_ptr<float>(), w.data_ptr<float>(), conv_b.defined()?conv_b.data_ptr<float>():nullptr,\n bn_w.data_ptr<float>(), bn_b.data_ptr<float>(), mean_t.data_ptr<float>(), var_t.data_ptr<float>(), out.data_ptr<float>(),\n N,CI,D,H,W,CO,OD,OH,OW,(float)eps);\n return out;\n}\n'''\n_ext=load_inline(name='fused_deconv_bn_pool_ext_exact1', cpp_sources=_cpp, cuda_sources=_cuda, functions=['fused_forward'], verbose=False, extra_cuda_cflags=['-O3'])\nclass ModelNew(nn.Module):\n def __init__(self,in_channels,out_channels,kernel_size,stride,padding,bias_shape):\n super(ModelNew,self).__init__(); self.conv_transpose=nn.ConvTranspose3d(in_channels,out_channels,kernel_size,stride=stride,padding=padding); self.batch_norm=nn.BatchNorm3d(out_channels); self.avg_pool1=nn.AvgPool3d(kernel_size=2); self.avg_pool2=nn.AvgPool3d(kernel_size=2)\n def forward(self,x):\n mom=self.batch_norm.momentum if self.batch_norm.momentum is not None else 0.1\n return _ext.fused_forward(x,self.conv_transpose.weight,self.conv_transpose.bias,self.batch_norm.weight,self.batch_norm.bias,self.batch_norm.running_mean,self.batch_norm.running_var,float(self.batch_norm.eps),bool(self.training),float(mom))\n" }
result ← compile_kernel (ok)
okcompile_kernel compile_kernel PASSED: kernel compiled without errors.
turn 7 · 1 tool call(s) · 14.1s turn · 14.1s LLM · 10,042 chars (click to expand)
turn 714.1s turn · 14.1s 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\n\n_cpp = r'''\n#include <torch/extension.h>\ntorch::Tensor fused_forward_cuda(torch::Tensor x, torch::Tensor w, torch::Tensor conv_b,\n torch::Tensor bn_w, torch::Tensor bn_b,\n torch::Tensor running_mean, torch::Tensor running_var,\n double eps, bool training, double momentum);\ntorch::Tensor fused_forward(torch::Tensor x, torch::Tensor w, torch::Tensor conv_b,\n torch::Tensor bn_w, torch::Tensor bn_b,\n torch::Tensor running_mean, torch::Tensor running_var,\n double eps, bool training, double momentum) {\n return fused_forward_cuda(x,w,conv_b,bn_w,bn_b,running_mean,running_var,eps,training,momentum);\n}\n'''\n_cuda = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n#define BLOCK 256\n\n__device__ __forceinline__ float deconv_val(const float* __restrict__ x, const float* __restrict__ w,\n const float* __restrict__ b,\n int n, int co, int od, int oh, int ow,\n int N, int CI, int D, int H, int W, int CO) {\n float acc = b ? b[co] : 0.0f;\n for (int ci=0; ci<CI; ++ci) {\n#pragma unroll\n for (int kd=0; kd<3; ++kd) {\n int id2 = od + 1 - kd;\n if ((id2 & 1) != 0) continue;\n int id = id2 >> 1;\n if ((unsigned)id >= (unsigned)D) continue;\n#pragma unroll\n for (int kh=0; kh<3; ++kh) {\n int ih2 = oh + 1 - kh;\n if ((ih2 & 1) != 0) continue;\n int ih = ih2 >> 1;\n if ((unsigned)ih >= (unsigned)H) continue;\n#pragma unroll\n for (int kw=0; kw<3; ++kw) {\n int iw2 = ow + 1 - kw;\n if ((iw2 & 1) != 0) continue;\n int iw = iw2 >> 1;\n if ((unsigned)iw >= (unsigned)W) continue;\n float xv = x[((((n*CI+ci)*D+id)*H+ih)*W+iw)];\n float wt = w[((((ci*CO+co)*3+kd)*3+kh)*3+kw)];\n acc = fmaf(xv, wt, acc);\n }\n }\n }\n }\n return acc;\n}\n\n__global__ void stats_partial_kernel(const float* __restrict__ x, const float* __restrict__ w,\n const float* __restrict__ b, double* __restrict__ psum, double* __restrict__ psq,\n int N,int CI,int D,int H,int W,int CO,int CD,int CH,int CW,int NB) {\n int co=blockIdx.y, bid=blockIdx.x, tid=threadIdx.x;\n int64_t M=(int64_t)N*CD*CH*CW;\n double s=0.0, q=0.0;\n for (int64_t m=(int64_t)bid*BLOCK+tid; m<M; m+=(int64_t)NB*BLOCK) {\n int ow=m%CW; int64_t t=m/CW; int oh=t%CH; t/=CH; int od=t%CD; t/=CD; int n=(int)t;\n float v=deconv_val(x,w,b,n,co,od,oh,ow,N,CI,D,H,W,CO);\n s += (double)v; q += (double)v*(double)v;\n }\n __shared__ double ss[BLOCK], qq[BLOCK]; ss[tid]=s; qq[tid]=q; __syncthreads();\n for (int off=BLOCK/2; off>0; off>>=1) { if (tid<off) { ss[tid]+=ss[tid+off]; qq[tid]+=qq[tid+off]; } __syncthreads(); }\n if (tid==0) { psum[co*NB+bid]=ss[0]; psq[co*NB+bid]=qq[0]; }\n}\n\n__global__ void stats_final_kernel(const double* __restrict__ psum, const double* __restrict__ psq,\n float* __restrict__ mean, float* __restrict__ var,\n float* __restrict__ running_mean, float* __restrict__ running_var,\n int NB, int64_t M, float momentum) {\n int co=blockIdx.x, tid=threadIdx.x; double s=0.0, q=0.0;\n for (int i=tid; i<NB; i+=BLOCK) { s += psum[co*NB+i]; q += psq[co*NB+i]; }\n __shared__ double ss[BLOCK], qq[BLOCK]; ss[tid]=s; qq[tid]=q; __syncthreads();\n for (int off=BLOCK/2; off>0; off>>=1) { if (tid<off) { ss[tid]+=ss[tid+off]; qq[tid]+=qq[tid+off]; } __syncthreads(); }\n if (tid==0) {\n double mu=ss[0]/(double)M; double vv=qq[0]/(double)M - mu*mu; if (vv<0.0) vv=0.0;\n mean[co]=(float)mu; var[co]=(float)vv;\n running_mean[co] = running_mean[co]*(1.0f-momentum) + (float)mu*momentum;\n double unb = vv*((double)M/(double)(M-1));\n running_var[co] = running_var[co]*(1.0f-momentum) + (float)unb*momentum;\n }\n}\n\n__global__ void exact_out_kernel(const float* __restrict__ x, const float* __restrict__ w,\n const float* __restrict__ conv_b, const float* __restrict__ bn_w,\n const float* __restrict__ bn_b, const float* __restrict__ mean,\n const float* __restrict__ var, float* __restrict__ out,\n int N,int CI,int D,int H,int W,int CO,int OD,int OH,int OW,float eps) {\n int64_t idx=(int64_t)blockIdx.x*blockDim.x+threadIdx.x;\n int64_t total=(int64_t)N*CO*OD*OH*OW; if (idx>=total) return;\n int ow=idx%OW; int64_t t=idx/OW; int oh=t%OH; t/=OH; int od=t%OD; t/=OD; int co=t%CO; t/=CO; int n=(int)t;\n float scale = bn_w[co] / sqrtf(var[co] + eps);\n float shift = bn_b[co] - mean[co]*scale;\n float sum2 = 0.0f;\n#pragma unroll\n for (int pd=0; pd<2; ++pd) {\n#pragma unroll\n for (int ph=0; ph<2; ++ph) {\n#pragma unroll\n for (int pw=0; pw<2; ++pw) {\n float sum1 = 0.0f;\n#pragma unroll\n for (int dd=0; dd<2; ++dd) {\n#pragma unroll\n for (int dh=0; dh<2; ++dh) {\n#pragma unroll\n for (int dw=0; dw<2; ++dw) {\n int zd = od*4 + pd*2 + dd;\n int zh = oh*4 + ph*2 + dh;\n int zw = ow*4 + pw*2 + dw;\n float z = deconv_val(x,w,conv_b,n,co,zd,zh,zw,N,CI,D,H,W,CO);\n sum1 += z * scale + shift;\n }\n }\n }\n sum2 += sum1 * 0.125f;\n }\n }\n }\n out[idx] = sum2 * 0.125f;\n}\n\ntorch::Tensor fused_forward_cuda(torch::Tensor x, torch::Tensor w, torch::Tensor conv_b,\n torch::Tensor bn_w, torch::Tensor bn_b,\n torch::Tensor running_mean, torch::Tensor running_var,\n double eps, bool training, double momentum) {\n int N=x.size(0), CI=x.size(1), D=x.size(2), H=x.size(3), W=x.size(4), CO=w.size(1);\n int CD=(D-1)*2-2+3, CH=(H-1)*2-2+3, CW=(W-1)*2-2+3;\n int OD=((CD-2)/2+1-2)/2+1, OH=((CH-2)/2+1-2)/2+1, OW=((CW-2)/2+1-2)/2+1;\n auto out=torch::empty({N,CO,OD,OH,OW}, x.options());\n torch::Tensor mean_t, var_t;\n if (training) {\n const int NB=1024;\n auto optsD=x.options().dtype(torch::kFloat64);\n auto psum=torch::empty({CO,NB}, optsD); auto psq=torch::empty({CO,NB}, optsD);\n mean_t=torch::empty({CO}, x.options()); var_t=torch::empty({CO}, x.options());\n stats_partial_kernel<<<dim3(NB,CO),BLOCK>>>(x.data_ptr<float>(), w.data_ptr<float>(), conv_b.defined()?conv_b.data_ptr<float>():nullptr,\n psum.data_ptr<double>(), psq.data_ptr<double>(), N,CI,D,H,W,CO,CD,CH,CW,NB);\n int64_t M=(int64_t)N*CD*CH*CW;\n stats_final_kernel<<<CO,BLOCK>>>(psum.data_ptr<double>(), psq.data_ptr<double>(), mean_t.data_ptr<float>(), var_t.data_ptr<float>(),\n running_mean.data_ptr<float>(), running_var.data_ptr<float>(), NB, M, (float)momentum);\n } else { mean_t=running_mean; var_t=running_var; }\n int64_t total=(int64_t)N*CO*OD*OH*OW;\n exact_out_kernel<<<(total+255)/256,256>>>(x.data_ptr<float>(), w.data_ptr<float>(), conv_b.defined()?conv_b.data_ptr<float>():nullptr,\n bn_w.data_ptr<float>(), bn_b.data_ptr<float>(), mean_t.data_ptr<float>(), var_t.data_ptr<float>(), out.data_ptr<float>(),\n N,CI,D,H,W,CO,OD,OH,OW,(float)eps);\n return out;\n}\n'''\n_ext=load_inline(name='fused_deconv_bn_pool_ext_exact1', cpp_sources=_cpp, cuda_sources=_cuda, functions=['fused_forward'], verbose=False, extra_cuda_cflags=['-O3'])\nclass ModelNew(nn.Module):\n def __init__(self,in_channels,out_channels,kernel_size,stride,padding,bias_shape):\n super(ModelNew,self).__init__(); self.conv_transpose=nn.ConvTranspose3d(in_channels,out_channels,kernel_size,stride=stride,padding=padding); self.batch_norm=nn.BatchNorm3d(out_channels); self.avg_pool1=nn.AvgPool3d(kernel_size=2); self.avg_pool2=nn.AvgPool3d(kernel_size=2)\n def forward(self,x):\n mom=self.batch_norm.momentum if self.batch_norm.momentum is not None else 0.1\n return _ext.fused_forward(x,self.conv_transpose.weight,self.conv_transpose.bias,self.batch_norm.weight,self.batch_norm.bias,self.batch_norm.running_mean,self.batch_norm.running_var,float(self.batch_norm.eps),bool(self.training),float(mom))\n" }
result ← run_correctness (fail)
failrun_correctness run_correctness FAILED: (0 / 5) trials did not all match. correctness_issue: Output mismatch max_difference: ['0.000203', '0.000234', '0.000230', '0.000212', '0.000227'] avg_difference: ['0.000022', '0.000022', '0.000022', '0.000022', '0.000022']
turn 8 · 1 tool call(s) · 24.7s turn · 24.7s LLM · 9,870 chars (click to expand)
turn 824.7s turn · 24.7s LLM · 0.0s tools
reasoning
**Fixing stat mismatches** I need to address the correctness and check the mismatch of 2e-4 due to BN stats, aiming for within 1e-4. Maybe the training in PyTorch’s BatchNorm is using cuDNN stats, which could cause differences since it may be floating point, not double. The average is at 2e-5 with a max of 2e-4. There’s a possibility my convolution and pooling order is off, resulting in biased variance. Should I consider evaluating the model's states? I should also check if I can use simpler batch norm calls. **Addressing variance mismatch** I'm noticing a mismatch in statistics from variance. I need to ensure that the output from PyTorch's conv_transpose matches exactly. Our deconvolution might use a non-deterministic accumulation order, which could be why the global scaling is small and leading to a 2e-4 output mismatch, which may be acceptable without fast_math. The average mismatch is 2e-5 and might result from precision. I’ll try using float reductions for statistics and implement partial sums to see if that brings us closer to an accurate output. **Considering batch normalization options** I'm thinking about using ATen's batch_norm, but I want to ensure the output is fast. If I use it in a host call with at::native_batch_norm, I realize that performing separate convolutions is not possible. Another option would be to set batch_norm to eval mode in the forward function, but I can't reference the training mode then. So, my next step might be to replace doubles with floats to see if that helps improve performance.
call → compile_kernel
callcompile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n_cpp=r'''\n#include <torch/extension.h>\ntorch::Tensor fused_forward_cuda(torch::Tensor x, torch::Tensor w, torch::Tensor conv_b, torch::Tensor bn_w, torch::Tensor bn_b, torch::Tensor running_mean, torch::Tensor running_var, double eps, bool training, double momentum);\ntorch::Tensor fused_forward(torch::Tensor x, torch::Tensor w, torch::Tensor conv_b, torch::Tensor bn_w, torch::Tensor bn_b, torch::Tensor running_mean, torch::Tensor running_var, double eps, bool training, double momentum){return fused_forward_cuda(x,w,conv_b,bn_w,bn_b,running_mean,running_var,eps,training,momentum);}\n'''\n_cuda=r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n#define BLOCK 256\n__device__ __forceinline__ int k_begin(int t){return (t==0)?1:0;} __device__ __forceinline__ int k_end(int t){return (t==0)?2:((t==1)?2:0);} \n__device__ __forceinline__ float deconv_val(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ b,int n,int co,int od,int oh,int ow,int N,int CI,int D,int H,int W,int CO){float acc=b?b[co]:0.0f; for(int ci=0;ci<CI;++ci){for(int kd=0;kd<3;++kd){int id2=od+1-kd; if(id2&1) continue; int id=id2>>1; if((unsigned)id>=(unsigned)D) continue; for(int kh=0;kh<3;++kh){int ih2=oh+1-kh; if(ih2&1) continue; int ih=ih2>>1; if((unsigned)ih>=(unsigned)H) continue; for(int kw=0;kw<3;++kw){int iw2=ow+1-kw; if(iw2&1) continue; int iw=iw2>>1; if((unsigned)iw>=(unsigned)W) continue; acc=fmaf(x[((((n*CI+ci)*D+id)*H+ih)*W+iw)],w[((((ci*CO+co)*3+kd)*3+kh)*3+kw)],acc);}}}} return acc;}\n__global__ void stats_partial_kernel(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ b,float* __restrict__ psum,float* __restrict__ psq,int N,int CI,int D,int H,int W,int CO,int CD,int CH,int CW,int NB){int co=blockIdx.y,bid=blockIdx.x,tid=threadIdx.x; int64_t M=(int64_t)N*CD*CH*CW; float s=0.0f,q=0.0f; for(int64_t m=(int64_t)bid*BLOCK+tid;m<M;m+=(int64_t)NB*BLOCK){int ow=m%CW; int64_t t=m/CW; int oh=t%CH; t/=CH; int od=t%CD; t/=CD; int n=(int)t; float v=deconv_val(x,w,b,n,co,od,oh,ow,N,CI,D,H,W,CO); s+=v; q+=v*v;} __shared__ float ss[BLOCK],qq[BLOCK]; ss[tid]=s; qq[tid]=q; __syncthreads(); for(int off=BLOCK/2;off>0;off>>=1){if(tid<off){ss[tid]+=ss[tid+off]; qq[tid]+=qq[tid+off];} __syncthreads();} if(tid==0){psum[co*NB+bid]=ss[0]; psq[co*NB+bid]=qq[0];}}\n__global__ void stats_final_kernel(const float* __restrict__ psum,const float* __restrict__ psq,float* __restrict__ mean,float* __restrict__ var,float* __restrict__ running_mean,float* __restrict__ running_var,int NB,int64_t M,float momentum){int co=blockIdx.x,tid=threadIdx.x; float s=0.0f,q=0.0f; for(int i=tid;i<NB;i+=BLOCK){s+=psum[co*NB+i]; q+=psq[co*NB+i];} __shared__ float ss[BLOCK],qq[BLOCK]; ss[tid]=s; qq[tid]=q; __syncthreads(); for(int off=BLOCK/2;off>0;off>>=1){if(tid<off){ss[tid]+=ss[tid+off]; qq[tid]+=qq[tid+off];} __syncthreads();} if(tid==0){float mu=ss[0]/(float)M; float vv=qq[0]/(float)M-mu*mu; if(vv<0) vv=0; mean[co]=mu; var[co]=vv; running_mean[co]=running_mean[co]*(1.0f-momentum)+mu*momentum; running_var[co]=running_var[co]*(1.0f-momentum)+(vv*((float)M/(float)(M-1)))*momentum;}}\n__global__ void make_agg_kernel(const float* __restrict__ w,float* __restrict__ agg,int CI,int CO){int idx=blockIdx.x*blockDim.x+threadIdx.x,total=CO*CI*27; if(idx>=total)return; int tmp=idx; int tw=tmp%3; tmp/=3; int th=tmp%3; tmp/=3; int td=tmp%3; tmp/=3; int ci=tmp%CI; tmp/=CI; int co=tmp; float s=0; for(int kd=k_begin(td);kd<=k_end(td);++kd)for(int kh=k_begin(th);kh<=k_end(th);++kh)for(int kw=k_begin(tw);kw<=k_end(tw);++kw)s+=w[((((ci*CO+co)*3+kd)*3+kh)*3+kw)]; agg[idx]=s*(1.0f/64.0f);} \n__global__ void fused_out_kernel(const float* __restrict__ x,const float* __restrict__ agg,const float* __restrict__ conv_b,const float* __restrict__ bn_w,const float* __restrict__ bn_b,const float* __restrict__ mean,const float* __restrict__ var,float* __restrict__ out,int N,int CI,int D,int H,int W,int CO,int OD,int OH,int OW,float eps){int64_t idx=(int64_t)blockIdx.x*blockDim.x+threadIdx.x,total=(int64_t)N*CO*OD*OH*OW; if(idx>=total)return; int ow=idx%OW; int64_t t=idx/OW; int oh=t%OH; t/=OH; int od=t%OD; t/=OD; int co=t%CO; t/=CO; int n=(int)t; float acc=conv_b?conv_b[co]:0.0f; int id0=od*2,ih0=oh*2,iw0=ow*2; for(int ci=0;ci<CI;++ci)for(int td=0;td<3;++td){int id=id0+td; for(int th=0;th<3;++th){int ih=ih0+th; for(int tw=0;tw<3;++tw){int iw=iw0+tw; acc=fmaf(x[((((n*CI+ci)*D+id)*H+ih)*W+iw)],agg[(((co*CI+ci)*3+td)*3+th)*3+tw],acc);}}} float scale=bn_w[co]/sqrtf(var[co]+eps); out[idx]=acc*scale+(bn_b[co]-mean[co]*scale);}\ntorch::Tensor fused_forward_cuda(torch::Tensor x,torch::Tensor w,torch::Tensor conv_b,torch::Tensor bn_w,torch::Tensor bn_b,torch::Tensor running_mean,torch::Tensor running_var,double eps,bool training,double momentum){int N=x.size(0),CI=x.size(1),D=x.size(2),H=x.size(3),W=x.size(4),CO=w.size(1); int CD=(D-1)*2-2+3,CH=(H-1)*2-2+3,CW=(W-1)*2-2+3; int OD=((CD-2)/2+1-2)/2+1,OH=((CH-2)/2+1-2)/2+1,OW=((CW-2)/2+1-2)/2+1; auto out=torch::empty({N,CO,OD,OH,OW},x.options()); auto agg=torch::empty({CO,CI,3,3,3},x.options()); make_agg_kernel<<<(CO*CI*27+255)/256,256>>>(w.data_ptr<float>(),agg.data_ptr<float>(),CI,CO); torch::Tensor mean_t,var_t; if(training){const int NB=1024; auto psum=torch::empty({CO,NB},x.options()),psq=torch::empty({CO,NB},x.options()); mean_t=torch::empty({CO},x.options()); var_t=torch::empty({CO},x.options()); stats_partial_kernel<<<dim3(NB,CO),BLOCK>>>(x.data_ptr<float>(),w.data_ptr<float>(),conv_b.defined()?conv_b.data_ptr<float>():nullptr,psum.data_ptr<float>(),psq.data_ptr<float>(),N,CI,D,H,W,CO,CD,CH,CW,NB); int64_t M=(int64_t)N*CD*CH*CW; stats_final_kernel<<<CO,BLOCK>>>(psum.data_ptr<float>(),psq.data_ptr<float>(),mean_t.data_ptr<float>(),var_t.data_ptr<float>(),running_mean.data_ptr<float>(),running_var.data_ptr<float>(),NB,M,(float)momentum);}else{mean_t=running_mean;var_t=running_var;} int64_t total=(int64_t)N*CO*OD*OH*OW; fused_out_kernel<<<(total+255)/256,256>>>(x.data_ptr<float>(),agg.data_ptr<float>(),conv_b.defined()?conv_b.data_ptr<float>():nullptr,bn_w.data_ptr<float>(),bn_b.data_ptr<float>(),mean_t.data_ptr<float>(),var_t.data_ptr<float>(),out.data_ptr<float>(),N,CI,D,H,W,CO,OD,OH,OW,(float)eps); return out;}\n'''\n_ext=load_inline(name='fused_deconv_bn_pool_ext_floatstats',cpp_sources=_cpp,cuda_sources=_cuda,functions=['fused_forward'],verbose=False,extra_cuda_cflags=['-O3'])\nclass ModelNew(nn.Module):\n def __init__(self,in_channels,out_channels,kernel_size,stride,padding,bias_shape):\n super().__init__(); self.conv_transpose=nn.ConvTranspose3d(in_channels,out_channels,kernel_size,stride=stride,padding=padding); self.batch_norm=nn.BatchNorm3d(out_channels); self.avg_pool1=nn.AvgPool3d(2); self.avg_pool2=nn.AvgPool3d(2)\n def forward(self,x):\n mom=self.batch_norm.momentum if self.batch_norm.momentum is not None else 0.1\n return _ext.fused_forward(x,self.conv_transpose.weight,self.conv_transpose.bias,self.batch_norm.weight,self.batch_norm.bias,self.batch_norm.running_mean,self.batch_norm.running_var,float(self.batch_norm.eps),bool(self.training),float(mom))\n" }
result ← compile_kernel (ok)
okcompile_kernel compile_kernel PASSED: kernel compiled without errors.
turn 9 · FINAL · 1 tool call(s) · 16.7s turn · 16.7s LLM · 8,537 chars (click to expand)
turn 916.7s turn · 16.7s 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\n_cpp=r'''\n#include <torch/extension.h>\ntorch::Tensor fused_forward_cuda(torch::Tensor x, torch::Tensor w, torch::Tensor conv_b, torch::Tensor bn_w, torch::Tensor bn_b, torch::Tensor running_mean, torch::Tensor running_var, double eps, bool training, double momentum);\ntorch::Tensor fused_forward(torch::Tensor x, torch::Tensor w, torch::Tensor conv_b, torch::Tensor bn_w, torch::Tensor bn_b, torch::Tensor running_mean, torch::Tensor running_var, double eps, bool training, double momentum){return fused_forward_cuda(x,w,conv_b,bn_w,bn_b,running_mean,running_var,eps,training,momentum);}\n'''\n_cuda=r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n#define BLOCK 256\n__device__ __forceinline__ int k_begin(int t){return (t==0)?1:0;} __device__ __forceinline__ int k_end(int t){return (t==0)?2:((t==1)?2:0);} \n__device__ __forceinline__ float deconv_val(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ b,int n,int co,int od,int oh,int ow,int N,int CI,int D,int H,int W,int CO){float acc=b?b[co]:0.0f; for(int ci=0;ci<CI;++ci){for(int kd=0;kd<3;++kd){int id2=od+1-kd; if(id2&1) continue; int id=id2>>1; if((unsigned)id>=(unsigned)D) continue; for(int kh=0;kh<3;++kh){int ih2=oh+1-kh; if(ih2&1) continue; int ih=ih2>>1; if((unsigned)ih>=(unsigned)H) continue; for(int kw=0;kw<3;++kw){int iw2=ow+1-kw; if(iw2&1) continue; int iw=iw2>>1; if((unsigned)iw>=(unsigned)W) continue; acc=fmaf(x[((((n*CI+ci)*D+id)*H+ih)*W+iw)],w[((((ci*CO+co)*3+kd)*3+kh)*3+kw)],acc);}}}} return acc;}\n__global__ void stats_partial_kernel(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ b,float* __restrict__ psum,float* __restrict__ psq,int N,int CI,int D,int H,int W,int CO,int CD,int CH,int CW,int NB){int co=blockIdx.y,bid=blockIdx.x,tid=threadIdx.x; int64_t M=(int64_t)N*CD*CH*CW; float s=0.0f,q=0.0f; for(int64_t m=(int64_t)bid*BLOCK+tid;m<M;m+=(int64_t)NB*BLOCK){int ow=m%CW; int64_t t=m/CW; int oh=t%CH; t/=CH; int od=t%CD; t/=CD; int n=(int)t; float v=deconv_val(x,w,b,n,co,od,oh,ow,N,CI,D,H,W,CO); s+=v; q+=v*v;} __shared__ float ss[BLOCK],qq[BLOCK]; ss[tid]=s; qq[tid]=q; __syncthreads(); for(int off=BLOCK/2;off>0;off>>=1){if(tid<off){ss[tid]+=ss[tid+off]; qq[tid]+=qq[tid+off];} __syncthreads();} if(tid==0){psum[co*NB+bid]=ss[0]; psq[co*NB+bid]=qq[0];}}\n__global__ void stats_final_kernel(const float* __restrict__ psum,const float* __restrict__ psq,float* __restrict__ mean,float* __restrict__ var,float* __restrict__ running_mean,float* __restrict__ running_var,int NB,int64_t M,float momentum){int co=blockIdx.x,tid=threadIdx.x; float s=0.0f,q=0.0f; for(int i=tid;i<NB;i+=BLOCK){s+=psum[co*NB+i]; q+=psq[co*NB+i];} __shared__ float ss[BLOCK],qq[BLOCK]; ss[tid]=s; qq[tid]=q; __syncthreads(); for(int off=BLOCK/2;off>0;off>>=1){if(tid<off){ss[tid]+=ss[tid+off]; qq[tid]+=qq[tid+off];} __syncthreads();} if(tid==0){float mu=ss[0]/(float)M; float vv=qq[0]/(float)M-mu*mu; if(vv<0) vv=0; mean[co]=mu; var[co]=vv; running_mean[co]=running_mean[co]*(1.0f-momentum)+mu*momentum; running_var[co]=running_var[co]*(1.0f-momentum)+(vv*((float)M/(float)(M-1)))*momentum;}}\n__global__ void make_agg_kernel(const float* __restrict__ w,float* __restrict__ agg,int CI,int CO){int idx=blockIdx.x*blockDim.x+threadIdx.x,total=CO*CI*27; if(idx>=total)return; int tmp=idx; int tw=tmp%3; tmp/=3; int th=tmp%3; tmp/=3; int td=tmp%3; tmp/=3; int ci=tmp%CI; tmp/=CI; int co=tmp; float s=0; for(int kd=k_begin(td);kd<=k_end(td);++kd)for(int kh=k_begin(th);kh<=k_end(th);++kh)for(int kw=k_begin(tw);kw<=k_end(tw);++kw)s+=w[((((ci*CO+co)*3+kd)*3+kh)*3+kw)]; agg[idx]=s*(1.0f/64.0f);} \n__global__ void fused_out_kernel(const float* __restrict__ x,const float* __restrict__ agg,const float* __restrict__ conv_b,const float* __restrict__ bn_w,const float* __restrict__ bn_b,const float* __restrict__ mean,const float* __restrict__ var,float* __restrict__ out,int N,int CI,int D,int H,int W,int CO,int OD,int OH,int OW,float eps){int64_t idx=(int64_t)blockIdx.x*blockDim.x+threadIdx.x,total=(int64_t)N*CO*OD*OH*OW; if(idx>=total)return; int ow=idx%OW; int64_t t=idx/OW; int oh=t%OH; t/=OH; int od=t%OD; t/=OD; int co=t%CO; t/=CO; int n=(int)t; float acc=conv_b?conv_b[co]:0.0f; int id0=od*2,ih0=oh*2,iw0=ow*2; for(int ci=0;ci<CI;++ci)for(int td=0;td<3;++td){int id=id0+td; for(int th=0;th<3;++th){int ih=ih0+th; for(int tw=0;tw<3;++tw){int iw=iw0+tw; acc=fmaf(x[((((n*CI+ci)*D+id)*H+ih)*W+iw)],agg[(((co*CI+ci)*3+td)*3+th)*3+tw],acc);}}} float scale=bn_w[co]/sqrtf(var[co]+eps); out[idx]=acc*scale+(bn_b[co]-mean[co]*scale);}\ntorch::Tensor fused_forward_cuda(torch::Tensor x,torch::Tensor w,torch::Tensor conv_b,torch::Tensor bn_w,torch::Tensor bn_b,torch::Tensor running_mean,torch::Tensor running_var,double eps,bool training,double momentum){int N=x.size(0),CI=x.size(1),D=x.size(2),H=x.size(3),W=x.size(4),CO=w.size(1); int CD=(D-1)*2-2+3,CH=(H-1)*2-2+3,CW=(W-1)*2-2+3; int OD=((CD-2)/2+1-2)/2+1,OH=((CH-2)/2+1-2)/2+1,OW=((CW-2)/2+1-2)/2+1; auto out=torch::empty({N,CO,OD,OH,OW},x.options()); auto agg=torch::empty({CO,CI,3,3,3},x.options()); make_agg_kernel<<<(CO*CI*27+255)/256,256>>>(w.data_ptr<float>(),agg.data_ptr<float>(),CI,CO); torch::Tensor mean_t,var_t; if(training){const int NB=1024; auto psum=torch::empty({CO,NB},x.options()),psq=torch::empty({CO,NB},x.options()); mean_t=torch::empty({CO},x.options()); var_t=torch::empty({CO},x.options()); stats_partial_kernel<<<dim3(NB,CO),BLOCK>>>(x.data_ptr<float>(),w.data_ptr<float>(),conv_b.defined()?conv_b.data_ptr<float>():nullptr,psum.data_ptr<float>(),psq.data_ptr<float>(),N,CI,D,H,W,CO,CD,CH,CW,NB); int64_t M=(int64_t)N*CD*CH*CW; stats_final_kernel<<<CO,BLOCK>>>(psum.data_ptr<float>(),psq.data_ptr<float>(),mean_t.data_ptr<float>(),var_t.data_ptr<float>(),running_mean.data_ptr<float>(),running_var.data_ptr<float>(),NB,M,(float)momentum);}else{mean_t=running_mean;var_t=running_var;} int64_t total=(int64_t)N*CO*OD*OH*OW; fused_out_kernel<<<(total+255)/256,256>>>(x.data_ptr<float>(),agg.data_ptr<float>(),conv_b.defined()?conv_b.data_ptr<float>():nullptr,bn_w.data_ptr<float>(),bn_b.data_ptr<float>(),mean_t.data_ptr<float>(),var_t.data_ptr<float>(),out.data_ptr<float>(),N,CI,D,H,W,CO,OD,OH,OW,(float)eps); return out;}\n'''\n_ext=load_inline(name='fused_deconv_bn_pool_ext_floatstats',cpp_sources=_cpp,cuda_sources=_cuda,functions=['fused_forward'],verbose=False,extra_cuda_cflags=['-O3'])\nclass ModelNew(nn.Module):\n def __init__(self,in_channels,out_channels,kernel_size,stride,padding,bias_shape):\n super().__init__(); self.conv_transpose=nn.ConvTranspose3d(in_channels,out_channels,kernel_size,stride=stride,padding=padding); self.batch_norm=nn.BatchNorm3d(out_channels); self.avg_pool1=nn.AvgPool3d(2); self.avg_pool2=nn.AvgPool3d(2)\n def forward(self,x):\n mom=self.batch_norm.momentum if self.batch_norm.momentum is not None else 0.1\n return _ext.fused_forward(x,self.conv_transpose.weight,self.conv_transpose.bias,self.batch_norm.weight,self.batch_norm.bias,self.batch_norm.running_mean,self.batch_norm.running_var,float(self.batch_norm.eps),bool(self.training),float(mom))\n" }
result ← submit_kernel (fail)
failsubmit_kernel submit_kernel FAILED: correctness check did not pass ((0 / 5) trials). correctness_issue: Output mismatch max_difference: ['0.000203', '0.000233', '0.000230', '0.000212', '0.000228'] avg_difference: ['0.000022', '0.000022', '0.000022', '0.000022', '0.000022']
final submitted kernel (30 lines, 7,212 chars) — full source, untruncated
import torch
import torch.nn as nn
from torch.utils.cpp_extension import load_inline
_cpp=r'''
#include <torch/extension.h>
torch::Tensor fused_forward_cuda(torch::Tensor x, torch::Tensor w, torch::Tensor conv_b, torch::Tensor bn_w, torch::Tensor bn_b, torch::Tensor running_mean, torch::Tensor running_var, double eps, bool training, double momentum);
torch::Tensor fused_forward(torch::Tensor x, torch::Tensor w, torch::Tensor conv_b, torch::Tensor bn_w, torch::Tensor bn_b, torch::Tensor running_mean, torch::Tensor running_var, double eps, bool training, double momentum){return fused_forward_cuda(x,w,conv_b,bn_w,bn_b,running_mean,running_var,eps,training,momentum);}
'''
_cuda=r'''
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <math.h>
#define BLOCK 256
__device__ __forceinline__ int k_begin(int t){return (t==0)?1:0;} __device__ __forceinline__ int k_end(int t){return (t==0)?2:((t==1)?2:0);} 
__device__ __forceinline__ float deconv_val(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ b,int n,int co,int od,int oh,int ow,int N,int CI,int D,int H,int W,int CO){float acc=b?b[co]:0.0f; for(int ci=0;ci<CI;++ci){for(int kd=0;kd<3;++kd){int id2=od+1-kd; if(id2&1) continue; int id=id2>>1; if((unsigned)id>=(unsigned)D) continue; for(int kh=0;kh<3;++kh){int ih2=oh+1-kh; if(ih2&1) continue; int ih=ih2>>1; if((unsigned)ih>=(unsigned)H) continue; for(int kw=0;kw<3;++kw){int iw2=ow+1-kw; if(iw2&1) continue; int iw=iw2>>1; if((unsigned)iw>=(unsigned)W) continue; acc=fmaf(x[((((n*CI+ci)*D+id)*H+ih)*W+iw)],w[((((ci*CO+co)*3+kd)*3+kh)*3+kw)],acc);}}}} return acc;}
__global__ void stats_partial_kernel(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ b,float* __restrict__ psum,float* __restrict__ psq,int N,int CI,int D,int H,int W,int CO,int CD,int CH,int CW,int NB){int co=blockIdx.y,bid=blockIdx.x,tid=threadIdx.x; int64_t M=(int64_t)N*CD*CH*CW; float s=0.0f,q=0.0f; for(int64_t m=(int64_t)bid*BLOCK+tid;m<M;m+=(int64_t)NB*BLOCK){int ow=m%CW; int64_t t=m/CW; int oh=t%CH; t/=CH; int od=t%CD; t/=CD; int n=(int)t; float v=deconv_val(x,w,b,n,co,od,oh,ow,N,CI,D,H,W,CO); s+=v; q+=v*v;} __shared__ float ss[BLOCK],qq[BLOCK]; ss[tid]=s; qq[tid]=q; __syncthreads(); for(int off=BLOCK/2;off>0;off>>=1){if(tid<off){ss[tid]+=ss[tid+off]; qq[tid]+=qq[tid+off];} __syncthreads();} if(tid==0){psum[co*NB+bid]=ss[0]; psq[co*NB+bid]=qq[0];}}
__global__ void stats_final_kernel(const float* __restrict__ psum,const float* __restrict__ psq,float* __restrict__ mean,float* __restrict__ var,float* __restrict__ running_mean,float* __restrict__ running_var,int NB,int64_t M,float momentum){int co=blockIdx.x,tid=threadIdx.x; float s=0.0f,q=0.0f; for(int i=tid;i<NB;i+=BLOCK){s+=psum[co*NB+i]; q+=psq[co*NB+i];} __shared__ float ss[BLOCK],qq[BLOCK]; ss[tid]=s; qq[tid]=q; __syncthreads(); for(int off=BLOCK/2;off>0;off>>=1){if(tid<off){ss[tid]+=ss[tid+off]; qq[tid]+=qq[tid+off];} __syncthreads();} if(tid==0){float mu=ss[0]/(float)M; float vv=qq[0]/(float)M-mu*mu; if(vv<0) vv=0; mean[co]=mu; var[co]=vv; running_mean[co]=running_mean[co]*(1.0f-momentum)+mu*momentum; running_var[co]=running_var[co]*(1.0f-momentum)+(vv*((float)M/(float)(M-1)))*momentum;}}
__global__ void make_agg_kernel(const float* __restrict__ w,float* __restrict__ agg,int CI,int CO){int idx=blockIdx.x*blockDim.x+threadIdx.x,total=CO*CI*27; if(idx>=total)return; int tmp=idx; int tw=tmp%3; tmp/=3; int th=tmp%3; tmp/=3; int td=tmp%3; tmp/=3; int ci=tmp%CI; tmp/=CI; int co=tmp; float s=0; for(int kd=k_begin(td);kd<=k_end(td);++kd)for(int kh=k_begin(th);kh<=k_end(th);++kh)for(int kw=k_begin(tw);kw<=k_end(tw);++kw)s+=w[((((ci*CO+co)*3+kd)*3+kh)*3+kw)]; agg[idx]=s*(1.0f/64.0f);} 
__global__ void fused_out_kernel(const float* __restrict__ x,const float* __restrict__ agg,const float* __restrict__ conv_b,const float* __restrict__ bn_w,const float* __restrict__ bn_b,const float* __restrict__ mean,const float* __restrict__ var,float* __restrict__ out,int N,int CI,int D,int H,int W,int CO,int OD,int OH,int OW,float eps){int64_t idx=(int64_t)blockIdx.x*blockDim.x+threadIdx.x,total=(int64_t)N*CO*OD*OH*OW; if(idx>=total)return; int ow=idx%OW; int64_t t=idx/OW; int oh=t%OH; t/=OH; int od=t%OD; t/=OD; int co=t%CO; t/=CO; int n=(int)t; float acc=conv_b?conv_b[co]:0.0f; int id0=od*2,ih0=oh*2,iw0=ow*2; for(int ci=0;ci<CI;++ci)for(int td=0;td<3;++td){int id=id0+td; for(int th=0;th<3;++th){int ih=ih0+th; for(int tw=0;tw<3;++tw){int iw=iw0+tw; acc=fmaf(x[((((n*CI+ci)*D+id)*H+ih)*W+iw)],agg[(((co*CI+ci)*3+td)*3+th)*3+tw],acc);}}} float scale=bn_w[co]/sqrtf(var[co]+eps); out[idx]=acc*scale+(bn_b[co]-mean[co]*scale);}
torch::Tensor fused_forward_cuda(torch::Tensor x,torch::Tensor w,torch::Tensor conv_b,torch::Tensor bn_w,torch::Tensor bn_b,torch::Tensor running_mean,torch::Tensor running_var,double eps,bool training,double momentum){int N=x.size(0),CI=x.size(1),D=x.size(2),H=x.size(3),W=x.size(4),CO=w.size(1); int CD=(D-1)*2-2+3,CH=(H-1)*2-2+3,CW=(W-1)*2-2+3; int OD=((CD-2)/2+1-2)/2+1,OH=((CH-2)/2+1-2)/2+1,OW=((CW-2)/2+1-2)/2+1; auto out=torch::empty({N,CO,OD,OH,OW},x.options()); auto agg=torch::empty({CO,CI,3,3,3},x.options()); make_agg_kernel<<<(CO*CI*27+255)/256,256>>>(w.data_ptr<float>(),agg.data_ptr<float>(),CI,CO); torch::Tensor mean_t,var_t; if(training){const int NB=1024; auto psum=torch::empty({CO,NB},x.options()),psq=torch::empty({CO,NB},x.options()); mean_t=torch::empty({CO},x.options()); var_t=torch::empty({CO},x.options()); stats_partial_kernel<<<dim3(NB,CO),BLOCK>>>(x.data_ptr<float>(),w.data_ptr<float>(),conv_b.defined()?conv_b.data_ptr<float>():nullptr,psum.data_ptr<float>(),psq.data_ptr<float>(),N,CI,D,H,W,CO,CD,CH,CW,NB); int64_t M=(int64_t)N*CD*CH*CW; stats_final_kernel<<<CO,BLOCK>>>(psum.data_ptr<float>(),psq.data_ptr<float>(),mean_t.data_ptr<float>(),var_t.data_ptr<float>(),running_mean.data_ptr<float>(),running_var.data_ptr<float>(),NB,M,(float)momentum);}else{mean_t=running_mean;var_t=running_var;} int64_t total=(int64_t)N*CO*OD*OH*OW; fused_out_kernel<<<(total+255)/256,256>>>(x.data_ptr<float>(),agg.data_ptr<float>(),conv_b.defined()?conv_b.data_ptr<float>():nullptr,bn_w.data_ptr<float>(),bn_b.data_ptr<float>(),mean_t.data_ptr<float>(),var_t.data_ptr<float>(),out.data_ptr<float>(),N,CI,D,H,W,CO,OD,OH,OW,(float)eps); return out;}
'''
_ext=load_inline(name='fused_deconv_bn_pool_ext_floatstats',cpp_sources=_cpp,cuda_sources=_cuda,functions=['fused_forward'],verbose=False,extra_cuda_cflags=['-O3'])
class ModelNew(nn.Module):
 def __init__(self,in_channels,out_channels,kernel_size,stride,padding,bias_shape):
  super().__init__(); self.conv_transpose=nn.ConvTranspose3d(in_channels,out_channels,kernel_size,stride=stride,padding=padding); self.batch_norm=nn.BatchNorm3d(out_channels); self.avg_pool1=nn.AvgPool3d(2); self.avg_pool2=nn.AvgPool3d(2)
 def forward(self,x):
  mom=self.batch_norm.momentum if self.batch_norm.momentum is not None else 0.1
  return _ext.fused_forward(x,self.conv_transpose.weight,self.conv_transpose.bias,self.batch_norm.weight,self.batch_norm.bias,self.batch_norm.running_mean,self.batch_norm.running_var,float(self.batch_norm.eps),bool(self.training),float(mom))