error
15_GQAKVHeadExpansionAttention.py
started
2026-05-02T22:06:21Z
reference PyTorch source (the prompt the agent sees)
import math
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Causal grouped-query attention with KV-head expansion.
Query heads outnumber KV heads. Keys and values are expanded by repeating
each KV head across its assigned query-head group before causal attention.
"""
def __init__(self):
super().__init__()
def forward(
self,
q: torch.Tensor,
k: torch.Tensor,
v: torch.Tensor,
) -> torch.Tensor:
batch_size, seq_len, num_q_heads, head_dim = q.shape
num_kv_heads = k.shape[2]
group_size = num_q_heads // num_kv_heads
expanded_k = k.repeat_interleave(group_size, dim=2)
expanded_v = v.repeat_interleave(group_size, dim=2)
out = torch.zeros_like(q)
scale = 1.0 / math.sqrt(head_dim)
for b in range(batch_size):
for h in range(num_q_heads):
for t in range(seq_len):
q_t = q[b, t, h]
k_hist = expanded_k[b, : t + 1, h]
v_hist = expanded_v[b, : t + 1, h]
scores = (k_hist * q_t.unsqueeze(0)).sum(dim=-1) * scale
attn = torch.softmax(scores, dim=0)
out[b, t, h] = (attn.unsqueeze(-1) * v_hist).sum(dim=0)
return out
batch_size = 2
seq_len = 80
num_q_heads = 8
num_kv_heads = 2
head_dim = 32
def get_inputs():
p = popcorn_pri
q = torch.randn(p.jitter_int(batch_size), p.jitter_int(seq_len), p.jitter_int(num_q_heads), p.jitter_int(head_dim, align=8), dtype=torch.float32)
k = torch.randn(p.jitter_int(batch_size), p.jitter_int(seq_len), p.jitter_int(num_kv_heads), p.jitter_int(head_dim, align=8), dtype=torch.float32)
v = torch.randn(p.jitter_int(batch_size), p.jitter_int(seq_len), p.jitter_int(num_kv_heads), p.jitter_int(head_dim, align=8), dtype=torch.float32)
return [q, k, v]
def get_init_inputs():
return []
turn 014.3s turn · 14.3s LLM · 0.0s tools
reasoning
**Crafting CUDA attention code**
I need to create a CUDA implementation for attention with small, fixed sizes. To do that, I'll need to produce complete Python code. It sounds like I should utilize certain tools to help with this process. First, I’ll gather specifications relevant to the task, ensuring I fully understand the requirements. Once I have everything I need, I can start crafting the code!
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) · 73.0s turn · 73.0s LLM · 8,215 chars (click to expand)
turn 173.0s turn · 73.0s LLM · 0.0s tools
reasoning
**Evaluating code structure**
I think static might be okay, but I should consider a fallback option just in case. If the inputs are non-contiguous, then the extension is probably wrong. I need to evaluate that. Wait, I’ll check if the constructor arguments are correct too. Alright, let’s get started on writing the first piece of code. It feels like I might need to compile it to see how it works together.
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_src = r'''\n#include <torch/extension.h>\ntorch::Tensor causal_gqa_forward(torch::Tensor q, torch::Tensor k, torch::Tensor v);\n'''\n\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math_constants.h>\n\n__global__ void causal_gqa_kernel(const float* __restrict__ q,\n const float* __restrict__ k,\n const float* __restrict__ v,\n float* __restrict__ out,\n int B, int T, int Hq, int Hkv, int D) {\n int row = blockIdx.x;\n int t = row % T;\n int tmp = row / T;\n int h = tmp % Hq;\n int b = tmp / Hq;\n int group = Hq / Hkv;\n int hk = h / group;\n int tid = threadIdx.x;\n\n extern __shared__ float smem[];\n float* scores = smem; // blockDim.x\n float* red = smem + blockDim.x;\n\n float sc = -CUDART_INF_F;\n if (tid <= t) {\n const float* qptr = q + (((b * T + t) * Hq + h) * D);\n const float* kptr = k + (((b * T + tid) * Hkv + hk) * D);\n float dot = 0.0f;\n #pragma unroll\n for (int d = 0; d < 32; ++d) {\n dot = fmaf(qptr[d], kptr[d], dot);\n }\n sc = dot * rsqrtf((float)D);\n }\n scores[tid] = sc;\n red[tid] = sc;\n __syncthreads();\n\n // reduce max over 128 threads\n for (int stride = blockDim.x >> 1; stride > 0; stride >>= 1) {\n if (tid < stride) {\n float other = red[tid + stride];\n red[tid] = fmaxf(red[tid], other);\n }\n __syncthreads();\n }\n float m = red[0];\n\n float e = (tid <= t) ? __expf(sc - m) : 0.0f;\n scores[tid] = e;\n red[tid] = e;\n __syncthreads();\n\n for (int stride = blockDim.x >> 1; stride > 0; stride >>= 1) {\n if (tid < stride) {\n red[tid] += red[tid + stride];\n }\n __syncthreads();\n }\n float inv_sum = 1.0f / red[0];\n __syncthreads();\n\n for (int d = tid; d < D; d += blockDim.x) {\n float acc = 0.0f;\n #pragma unroll\n for (int s = 0; s < 80; ++s) {\n if (s <= t) {\n float w = scores[s] * inv_sum;\n const float* vptr = v + (((b * T + s) * Hkv + hk) * D);\n acc = fmaf(w, vptr[d], acc);\n }\n }\n out[(((b * T + t) * Hq + h) * D) + d] = acc;\n }\n}\n\ntorch::Tensor causal_gqa_forward(torch::Tensor q, torch::Tensor k, torch::Tensor v) {\n int B = (int)q.size(0);\n int T = (int)q.size(1);\n int Hq = (int)q.size(2);\n int D = (int)q.size(3);\n int Hkv = (int)k.size(2);\n auto out = torch::empty_like(q);\n int blocks = B * Hq * T;\n const int threads = 128;\n size_t shmem = 2 * threads * sizeof(float);\n causal_gqa_kernel<<<blocks, threads, shmem, at::cuda::getCurrentCUDAStream()>>>(\n q.data_ptr<float>(), k.data_ptr<float>(), v.data_ptr<float>(), out.data_ptr<float>(), B, T, Hq, Hkv, D);\n return out;\n}\n'''\n\n_ext = load_inline(name='causal_gqa_ext_v1', cpp_sources=[_cpp_src], cuda_sources=[_cuda_src], functions=['causal_gqa_forward'], extra_cuda_cflags=['-O3', '--use_fast_math'], extra_cflags=['-O3'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n def forward(self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor) -> torch.Tensor:\n return _ext.causal_gqa_forward(q, k, v)\n"
}
result ← compile_kernel (fail)
failcompile_kernel
compile_kernel FAILED: RuntimeError.
RuntimeError: Error building extension 'causal_gqa_ext_v1': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=causal_gqa_ext_v1 -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 -O3 -c /scratch/tejas/PopcornBench/runs/l12_default/popcorn/gpt-5.5-priority/level_2_problem_15_cache/k_245bf80c576a/causal_gqa_ext_v1/main.cpp -o main.o
[2/3] /usr/local/cuda/bin/nvcc -MD -MF cuda.cuda.o.d -ccbin /opt/conda/bin/x86_64-conda-linux-gnu-cc -DTORCH_EXTENSION_NAME=causal_gqa_ext_v1 -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 --use_fast_math -std=c++17 -c /scratch/tejas/PopcornBench/runs/l12_default/popcorn/gpt-5.5-priority/level_2_problem_15_cache/k_245bf80c576a/causal_gqa_ext_v1/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=causal_gqa_ext_v1 -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 --use_fast_math -std=c++17 -c /scratch/tejas/PopcornBench/runs/l12_default/popcorn/gpt-5.5-priority/level_2_problem_15_cache/k_245bf80c576a/causal_gqa_ext_v1/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/popcorn/gpt-5.5-priority/level_2_problem_15_cache/k_245bf80c576a/causal_gqa_ext_v1/cuda.cu(91): error: namespace "at::cuda" has no member "getCurrentCUDAStream"
causal_gqa_kernel<<<blocks, threads, shmem, at::cuda::getCurrentCUDAStream()>>>(
^
1 error detected in the compilation of "/scratch/tejas/PopcornBench/runs/l12_default/popcorn/gpt-5.5-priority/level_2_problem_15_cache/k_245bf80c576a/causal_gqa_ext_v1/cuda.cu".
ninja: build stopped: subcommand failed.
turn 217.9s turn · 17.9s LLM · 0.0s tools
reasoning
**Resolving compilation issues**
I'm thinking about the need to include the CUDAContext.h file from the ATen/cuda directory. There's also an issue with the D loop unrolling, which assumes D equals 32. That might be okay, but what if D could be different? I should address that. It seems I need to compile everything after making these adjustments to ensure it runs smoothly. Let's work through this step by step.
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_src = r'''\n#include <torch/extension.h>\ntorch::Tensor causal_gqa_forward(torch::Tensor q, torch::Tensor k, torch::Tensor v);\n'''\n\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math_constants.h>\n\n__global__ void causal_gqa_kernel(const float* __restrict__ q,\n const float* __restrict__ k,\n const float* __restrict__ v,\n float* __restrict__ out,\n int B, int T, int Hq, int Hkv, int D) {\n int row = blockIdx.x;\n int t = row % T;\n int tmp = row / T;\n int h = tmp % Hq;\n int b = tmp / Hq;\n int group = Hq / Hkv;\n int hk = h / group;\n int tid = threadIdx.x;\n\n extern __shared__ float smem[];\n float* scores = smem;\n float* red = smem + blockDim.x;\n\n float sc = -CUDART_INF_F;\n if (tid <= t) {\n const float* qptr = q + (((b * T + t) * Hq + h) * D);\n const float* kptr = k + (((b * T + tid) * Hkv + hk) * D);\n float dot = 0.0f;\n #pragma unroll\n for (int d = 0; d < 32; ++d) dot = fmaf(qptr[d], kptr[d], dot);\n sc = dot * rsqrtf((float)D);\n }\n scores[tid] = sc;\n red[tid] = sc;\n __syncthreads();\n for (int stride = blockDim.x >> 1; stride > 0; stride >>= 1) {\n if (tid < stride) red[tid] = fmaxf(red[tid], red[tid + stride]);\n __syncthreads();\n }\n float m = red[0];\n float e = (tid <= t) ? __expf(sc - m) : 0.0f;\n scores[tid] = e;\n red[tid] = e;\n __syncthreads();\n for (int stride = blockDim.x >> 1; stride > 0; stride >>= 1) {\n if (tid < stride) red[tid] += red[tid + stride];\n __syncthreads();\n }\n float inv_sum = 1.0f / red[0];\n __syncthreads();\n for (int d = tid; d < D; d += blockDim.x) {\n float acc = 0.0f;\n #pragma unroll\n for (int s = 0; s < 80; ++s) {\n if (s <= t) {\n const float* vptr = v + (((b * T + s) * Hkv + hk) * D);\n acc = fmaf(scores[s] * inv_sum, vptr[d], acc);\n }\n }\n out[(((b * T + t) * Hq + h) * D) + d] = acc;\n }\n}\n\ntorch::Tensor causal_gqa_forward(torch::Tensor q, torch::Tensor k, torch::Tensor v) {\n int B = (int)q.size(0), T = (int)q.size(1), Hq = (int)q.size(2), D = (int)q.size(3), Hkv = (int)k.size(2);\n auto out = torch::empty_like(q);\n const int threads = 128;\n causal_gqa_kernel<<<B * Hq * T, threads, 2 * threads * sizeof(float), at::cuda::getCurrentCUDAStream()>>>(\n q.data_ptr<float>(), k.data_ptr<float>(), v.data_ptr<float>(), out.data_ptr<float>(), B, T, Hq, Hkv, D);\n return out;\n}\n'''\n\n_ext = load_inline(name='causal_gqa_ext_v2', cpp_sources=[_cpp_src], cuda_sources=[_cuda_src], functions=['causal_gqa_forward'], extra_cuda_cflags=['-O3', '--use_fast_math'], extra_cflags=['-O3'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n def forward(self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor) -> torch.Tensor:\n return _ext.causal_gqa_forward(q, k, v)\n"
}
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 320.7s turn · 20.7s LLM · 0.0s tools
call → run_correctness
callrun_correctness
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n_cpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor causal_gqa_forward(torch::Tensor q, torch::Tensor k, torch::Tensor v);\n'''\n\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math_constants.h>\n\n__global__ void causal_gqa_kernel(const float* __restrict__ q,\n const float* __restrict__ k,\n const float* __restrict__ v,\n float* __restrict__ out,\n int B, int T, int Hq, int Hkv, int D) {\n int row = blockIdx.x;\n int t = row % T;\n int tmp = row / T;\n int h = tmp % Hq;\n int b = tmp / Hq;\n int group = Hq / Hkv;\n int hk = h / group;\n int tid = threadIdx.x;\n\n extern __shared__ float smem[];\n float* scores = smem;\n float* red = smem + blockDim.x;\n\n float sc = -CUDART_INF_F;\n if (tid <= t) {\n const float* qptr = q + (((b * T + t) * Hq + h) * D);\n const float* kptr = k + (((b * T + tid) * Hkv + hk) * D);\n float dot = 0.0f;\n #pragma unroll\n for (int d = 0; d < 32; ++d) dot = fmaf(qptr[d], kptr[d], dot);\n sc = dot * rsqrtf((float)D);\n }\n scores[tid] = sc;\n red[tid] = sc;\n __syncthreads();\n for (int stride = blockDim.x >> 1; stride > 0; stride >>= 1) {\n if (tid < stride) red[tid] = fmaxf(red[tid], red[tid + stride]);\n __syncthreads();\n }\n float m = red[0];\n float e = (tid <= t) ? __expf(sc - m) : 0.0f;\n scores[tid] = e;\n red[tid] = e;\n __syncthreads();\n for (int stride = blockDim.x >> 1; stride > 0; stride >>= 1) {\n if (tid < stride) red[tid] += red[tid + stride];\n __syncthreads();\n }\n float inv_sum = 1.0f / red[0];\n __syncthreads();\n for (int d = tid; d < D; d += blockDim.x) {\n float acc = 0.0f;\n #pragma unroll\n for (int s = 0; s < 80; ++s) {\n if (s <= t) {\n const float* vptr = v + (((b * T + s) * Hkv + hk) * D);\n acc = fmaf(scores[s] * inv_sum, vptr[d], acc);\n }\n }\n out[(((b * T + t) * Hq + h) * D) + d] = acc;\n }\n}\n\ntorch::Tensor causal_gqa_forward(torch::Tensor q, torch::Tensor k, torch::Tensor v) {\n int B = (int)q.size(0), T = (int)q.size(1), Hq = (int)q.size(2), D = (int)q.size(3), Hkv = (int)k.size(2);\n auto out = torch::empty_like(q);\n const int threads = 128;\n causal_gqa_kernel<<<B * Hq * T, threads, 2 * threads * sizeof(float), at::cuda::getCurrentCUDAStream()>>>(\n q.data_ptr<float>(), k.data_ptr<float>(), v.data_ptr<float>(), out.data_ptr<float>(), B, T, Hq, Hkv, D);\n return out;\n}\n'''\n\n_ext = load_inline(name='causal_gqa_ext_v2c', cpp_sources=[_cpp_src], cuda_sources=[_cuda_src], functions=['causal_gqa_forward'], extra_cuda_cflags=['-O3', '--use_fast_math'], extra_cflags=['-O3'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n def forward(self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor) -> torch.Tensor:\n return _ext.causal_gqa_forward(q, k, v)\n"
}
result ← run_correctness (fail)
failrun_correctness
run_correctness FAILED: (4 / 5) trials did not all match.
correctness_issue: Output mismatch
max_difference: ['1.493197']
avg_difference: ['0.000301']
turn 411.6s turn · 11.6s LLM · 0.0s tools
reasoning
**Considering accuracy in math functions**
I find that I need to use `expf` instead of `__expf` and avoid the `fast_math` option for more accuracy. It's important to maintain precision in calculations, especially when dealing with large values. So, I'll focus on using a general approach with `expf` without the fast math optimizations to ensure I get reliable results. It seems like a sensible plan for maintaining accuracy.
call → run_correctness
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (5 / 5) trials all matched the reference.
Numerical precision: max_abs_err=8.94e-07 mean_abs_err=4.47e-08 max_rel_err=3.10e-02
turn 5 · 1 tool call(s) · 26.6s turn · 26.6s LLM · 6,400 chars (click to expand)
turn 526.6s turn · 26.6s LLM · 0.0s tools
reasoning
**Optimizing runtime approach**
I’m looking to optimize timing, and I think there might be room for improvement. I need to determine the runtime since the submission might only reveal certain aspects. I can probably tailor optimizations for fixed D=32 and T=80 without fast math, focusing on correctness. The previous mismatches might stem from varying D/T, but if I specialize for D=32 and T=80, I could potentially use unroll techniques. Additionally, I’m considering warp reductions to reduce sync threads; current settings use about 14 sync threads.
**Optimizing softmax computation**
I need to compute softmax for each t and output the results. With only 16 blocks, each block's work involves around 80*80*32 dot products, which feels moderate. While I have 1280 blocks, maybe one block per row works. The kernel launch overhead is about 5 microseconds, while computation is roughly 20 microseconds.
The reference in PyTorch uses Python loops, and I need to finalize my code, looking for specialized outputs. Also, I think using fewer threads could help, so perhaps 64 threads, depending on the scenario.
**Improving softmax efficiency**
I'm considering performing softmax reductions within a warp, but I might need to store up to 80 weights, which could be challenging. The current setup is okay, but what if I combined 4 rows per block with 4 warps? That might reduce block overhead and increase occupancy. Each warp could compute one row. For each row, 32 lanes would compute scores for s, s+32, s+64, and up to 80. I'll definitely need to calculate the dot product for each s serially for D.
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_src = r'''\n#include <torch/extension.h>\ntorch::Tensor causal_gqa_forward(torch::Tensor q, torch::Tensor k, torch::Tensor v);\n'''\n\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math_constants.h>\n\n__inline__ __device__ float warp_reduce_sum(float val) {\n unsigned mask = 0xffffffffu;\n val += __shfl_down_sync(mask, val, 16);\n val += __shfl_down_sync(mask, val, 8);\n val += __shfl_down_sync(mask, val, 4);\n val += __shfl_down_sync(mask, val, 2);\n val += __shfl_down_sync(mask, val, 1);\n return val;\n}\n__inline__ __device__ float warp_reduce_max(float val) {\n unsigned mask = 0xffffffffu;\n val = fmaxf(val, __shfl_down_sync(mask, val, 16));\n val = fmaxf(val, __shfl_down_sync(mask, val, 8));\n val = fmaxf(val, __shfl_down_sync(mask, val, 4));\n val = fmaxf(val, __shfl_down_sync(mask, val, 2));\n val = fmaxf(val, __shfl_down_sync(mask, val, 1));\n return val;\n}\n\n__global__ void causal_gqa_warp_kernel(const float* __restrict__ q,\n const float* __restrict__ k,\n const float* __restrict__ v,\n float* __restrict__ out,\n int B, int T, int Hq, int Hkv, int D) {\n int lane = threadIdx.x & 31;\n int warp = threadIdx.x >> 5;\n int row = blockIdx.x * 4 + warp;\n int total = B * Hq * T;\n if (row >= total) return;\n int t = row % T;\n int tmp = row / T;\n int h = tmp % Hq;\n int b = tmp / Hq;\n int hk = h / (Hq / Hkv);\n extern __shared__ float smem[];\n float* scores = smem + warp * 128;\n const float* qptr = q + (((b * T + t) * Hq + h) * D);\n float lmax = -CUDART_INF_F;\n for (int s = lane; s <= t; s += 32) {\n const float* kptr = k + (((b * T + s) * Hkv + hk) * D);\n float dot = 0.0f;\n for (int d = 0; d < D; ++d) dot += qptr[d] * kptr[d];\n float sc = dot * rsqrtf((float)D);\n scores[s] = sc;\n lmax = fmaxf(lmax, sc);\n }\n float m = warp_reduce_max(lmax);\n m = __shfl_sync(0xffffffffu, m, 0);\n __syncwarp();\n float lsum = 0.0f;\n for (int s = lane; s <= t; s += 32) {\n float e = expf(scores[s] - m);\n scores[s] = e;\n lsum += e;\n }\n float sum = warp_reduce_sum(lsum);\n sum = __shfl_sync(0xffffffffu, sum, 0);\n float inv = 1.0f / sum;\n __syncwarp();\n for (int d = lane; d < D; d += 32) {\n float acc = 0.0f;\n for (int s = 0; s <= t; ++s) {\n const float* vptr = v + (((b * T + s) * Hkv + hk) * D);\n acc += (scores[s] * inv) * vptr[d];\n }\n out[(((b * T + t) * Hq + h) * D) + d] = acc;\n }\n}\n\ntorch::Tensor causal_gqa_forward(torch::Tensor q, torch::Tensor k, torch::Tensor v) {\n int B = (int)q.size(0), T = (int)q.size(1), Hq = (int)q.size(2), D = (int)q.size(3), Hkv = (int)k.size(2);\n auto out = torch::empty_like(q);\n int total = B * Hq * T;\n causal_gqa_warp_kernel<<<(total + 3) / 4, 128, 4 * 128 * sizeof(float), at::cuda::getCurrentCUDAStream()>>>(\n q.data_ptr<float>(), k.data_ptr<float>(), v.data_ptr<float>(), out.data_ptr<float>(), B, T, Hq, Hkv, D);\n return out;\n}\n'''\n\n_ext = load_inline(name='causal_gqa_ext_warp1', cpp_sources=[_cpp_src], cuda_sources=[_cuda_src], functions=['causal_gqa_forward'], extra_cuda_cflags=['-O3'], extra_cflags=['-O3'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n def forward(self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor) -> torch.Tensor:\n return _ext.causal_gqa_forward(q, k, v)\n"
}
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (5 / 5) trials all matched the reference.
Numerical precision: max_abs_err=8.94e-07 mean_abs_err=4.47e-08 max_rel_err=3.10e-02
turn 66593.4s turn · 6593.4s LLM · 0.0s tools
feedbackLLM call failed: _WorkItemTimeout: work item exceeded soft timeout budget