L3 P35 · original

error 35_LSTM.py

model

gpt-5.5-priority

turns / max

5 / 15

tool calls

tokens (total)

wall clock

—

truncated

backend

cuda fp32

variant

original

runtime

—

ref runtime

0.00 μs

speedup

—

started

2026-05-04T20:53:32Z

reference PyTorch source (the prompt the agent sees)

import torch
import torch.nn as nn

class Model(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, output_size, dropout=0.0):
        """
        Initialize the LSTM model.

        :param input_size: The number of expected features in the input `x`
        :param hidden_size: The number of features in the hidden state `h`
        :param num_layers: Number of recurrent layers
        :param output_size: The number of output features
        :param dropout: If non-zero, introduces a Dropout layer on the outputs of each LSTM layer except the last layer
        """
        super(Model, self).__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.lstm = nn.LSTM(input_size, hidden_size, num_layers,
                            batch_first=True, dropout=dropout, bidirectional=False)
        self.fc = nn.Linear(hidden_size, output_size)

    def forward(self, x, h0=None, c0=None):
        """
        Forward pass through the LSTM model.

        :param x: The input tensor, shape (batch_size, sequence_length, input_size)
        :param h0: Optional initial hidden state (num_layers, batch_size, hidden_size)
        :param c0: Optional initial cell state (num_layers, batch_size, hidden_size)
        :return: The output tensor, shape (batch_size, output_size)
        """
        batch_size = x.size(0)

        if h0 is None:
            h0 = torch.randn(self.num_layers, batch_size, self.hidden_size, device=x.device)
        if c0 is None:
            c0 = torch.randn(self.num_layers, batch_size, self.hidden_size, device=x.device)

        out, _ = self.lstm(x, (h0, c0))  # out: (batch_size, seq_length, hidden_size)
        out = self.fc(out[:, -1, :])     # out: (batch_size, output_size)

        return out

# === Test configuration ===
batch_size = 10
sequence_length = 512
input_size = 128
hidden_size = 256
num_layers = 6
output_size = 10
dropout = 0.0

def get_inputs():
    return [torch.rand(batch_size, sequence_length, input_size)]

def get_init_inputs():
    return [input_size, hidden_size, num_layers, output_size, dropout]

turn 033.6s turn · 33.6s LLM · 0.0s tools

reasoning

**Developing LSTM Implementation** I need to create a complete Python file to implement the LSTM forward pass. I'm wondering if the weights need to be copied and whether the constructor for ModelNew should have the same parameters as Model. Typically, the harness copies the state_dict, so do I need to define modules equivalently with random initialization? It could be tricky if I have to ensure randomness matches reference outputs, especially when considering the RNG state. I’ll also implement an LSTM with specified layers and parameters. **Optimizing LSTM Performance** I’m trying to figure out if there's a way to achieve faster performance than what's provided by cuDNN with a linear operation. The PyTorch reference likely relies on cuDNN, making it tricky for small batch sizes. The LSTM equations are quite specific, involving matrix-vector multiplication for a batch of 10. I'm considering whether I can create a custom CUDA kernel or use cuBLAS, but it's not clear if that's allowed. I might explore writing naive kernels, though I need to ensure they're efficient enough. So many options to evaluate! **Calculating Performance Goals** I need to achieve around 15 GFLOP in performance, aiming for a target execution time of approximately 100 microseconds. This seems like a challenging balance, so I want to ensure I'm optimizing calculations efficiently. I should probably break down my operations to see how I can reach the desired floating-point operation performance within that time frame. It's all about finding the right balance and efficiency here!

call → get_gpu_specs

callget_gpu_specs {}

result ← get_gpu_specs (ok)

okget_gpu_specs GPU specs for NVIDIA H100 80GB HBM3: total memory (runtime): 79.1 GB spec entry: H100 GPU Architecture: Hopper GPU Memory: 80GB Memory Bandwidth: 3.35 TB/s FP64 TFLOPS: 34 FP64 Tensor Core TFLOPS: 67 FP32 TFLOPS: 67 TF32 Tensor Core TFLOPS: 989 with sparsity BFLOAT16 Tensore Core TFLOPS: 1979 with sparsity FP16 Tensor Core TFLOPS: 1979 with sparsity FP8 Tensor Core TFLOPS: 3958 with sparsity INT8 Tensor Core TOPS: 3958 with sparsity Register File Size: 64K 32-bit registers per SM Maximum number of registers per thread: 255 Maximum number of thread blocks per SM: 32 Shared memory capacity per SM: 228 KB Maximum shared memory per thread block: 227 KB

turn 1 · 1 tool call(s) · 192.5s turn · 192.5s LLM · 17,516 chars (click to expand)

turn 1192.5s turn · 192.5s LLM · 0.0s tools

reasoning

**Considering performance optimization** I'm thinking about using cuBLAS with SGEMM for tensor cores; it could enhance speed. For custom recurrent processing per timestep, I wonder if using B*4H * H GEMM could work, but I think B should be 10 to keep it small. Grouping across layers could be challenging. I might consider leveraging PyTorch ATen operations in the forward pass and referencing cuDNN for speed but would require a custom CUDA kernel. Static methods might not be the answer. **Evaluating performance metrics** I'm looking at a calculation involving 2560 FMA per cycle at 1.8 GHz, leading to about 4.6 TFLOP. This indicates that the minimum processing time is around 6 ms, which feels too slow. In contrast, cuDNN typically achieves around 1 ms, so I need to focus on increasing parallelism and utilizing tensor cores effectively. Maybe I should consider using one warp per hidden unit dot product to maximize the parallelization over K and improve performance. **Evaluating recurrent computations** I'm computing gates for all B and H using h_prev at each timestep. I wonder if there needs to be a barrier before activation. If the same kernel computes both hidden states and writes new h and c, a barrier after writing is necessary to prevent reading issues in the next timestep. For layer outputs, inputs are either x or the previous layer's outputs. I’m thinking about the number of kernel launches and how to parallelize effectively, considering blocks and threads for each computation cell.

call → compile_kernel

callcompile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\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\n__inline__ __device__ float warp_reduce_sum(float v) {\n for (int offset = 16; offset > 0; offset >>= 1) {\n v += __shfl_down_sync(0xffffffff, v, offset);\n }\n return v;\n}\n\n__device__ __forceinline__ float sigmoidf_stable(float x) {\n return 1.0f / (1.0f + expf(-x));\n}\n\n__device__ void grid_barrier(unsigned int* bar, int nblocks) {\n __syncthreads();\n __threadfence();\n if (threadIdx.x == 0) {\n unsigned int my_sense = bar[1];\n unsigned int ticket = atomicAdd(&bar[0], 1u);\n if (ticket == (unsigned int)(nblocks - 1)) {\n bar[0] = 0u;\n __threadfence();\n atomicAdd(&bar[1], 1u);\n } else {\n while (bar[1] == my_sense) { }\n }\n }\n __syncthreads();\n}\n\n__global__ void lstm_layer_kernel(\n const float* __restrict__ inp,\n const float* __restrict__ h0,\n const float* __restrict__ c0,\n const float* __restrict__ w_ih,\n const float* __restrict__ w_hh,\n const float* __restrict__ b_ih,\n const float* __restrict__ b_hh,\n float* __restrict__ out,\n float* __restrict__ hA,\n float* __restrict__ hB,\n float* __restrict__ cA,\n float* __restrict__ cB,\n unsigned int* __restrict__ bar,\n int B, int T, int K, int H, int layer_idx, int nblocks)\n{\n int tid = threadIdx.x;\n int lane = tid & 31;\n int warp_in_block = tid >> 5;\n int warps_per_block = blockDim.x >> 5;\n int warp_global = blockIdx.x * warps_per_block + warp_in_block;\n int total_warps = gridDim.x * warps_per_block;\n int BH = B * H;\n\n // initialize state buffers from h0/c0 for this layer\n for (int idx = blockIdx.x * blockDim.x + tid; idx < BH; idx += gridDim.x * blockDim.x) {\n int b = idx / H;\n int h = idx - b * H;\n int off = (layer_idx * B + b) * H + h;\n hA[idx] = h0[off];\n cA[idx] = c0[off];\n }\n grid_barrier(bar, nblocks);\n\n const float* hprev = hA;\n float* hnew = hB;\n const float* cprev = cA;\n float* cnew = cB;\n\n for (int t = 0; t < T; ++t) {\n for (int cell = warp_global; cell < BH; cell += total_warps) {\n int b = cell / H;\n int h = cell - b * H;\n float s0 = b_ih[h] + b_hh[h];\n float s1 = b_ih[H + h] + b_hh[H + h];\n float s2 = b_ih[2*H + h] + b_hh[2*H + h];\n float s3 = b_ih[3*H + h] + b_hh[3*H + h];\n\n const float* xptr = inp + ((b * T + t) * K);\n const float* wi0 = w_ih + h * K;\n const float* wi1 = w_ih + (H + h) * K;\n const float* wi2 = w_ih + (2*H + h) * K;\n const float* wi3 = w_ih + (3*H + h) * K;\n for (int k = lane; k < K; k += 32) {\n float xv = xptr[k];\n s0 = fmaf(xv, wi0[k], s0);\n s1 = fmaf(xv, wi1[k], s1);\n s2 = fmaf(xv, wi2[k], s2);\n s3 = fmaf(xv, wi3[k], s3);\n }\n const float* hhptr = hprev + b * H;\n const float* wh0 = w_hh + h * H;\n const float* wh1 = w_hh + (H + h) * H;\n const float* wh2 = w_hh + (2*H + h) * H;\n const float* wh3 = w_hh + (3*H + h) * H;\n for (int k = lane; k < H; k += 32) {\n float hv = hhptr[k];\n s0 = fmaf(hv, wh0[k], s0);\n s1 = fmaf(hv, wh1[k], s1);\n s2 = fmaf(hv, wh2[k], s2);\n s3 = fmaf(hv, wh3[k], s3);\n }\n s0 = warp_reduce_sum(s0);\n s1 = warp_reduce_sum(s1);\n s2 = warp_reduce_sum(s2);\n s3 = warp_reduce_sum(s3);\n if (lane == 0) {\n float ig = sigmoidf_stable(s0);\n float fg = sigmoidf_stable(s1);\n float gg = tanhf(s2);\n float og = sigmoidf_stable(s3);\n float cv = fg * cprev[cell] + ig * gg;\n float hv = og * tanhf(cv);\n cnew[cell] = cv;\n hnew[cell] = hv;\n out[(b * T + t) * H + h] = hv;\n }\n }\n grid_barrier(bar, nblocks);\n const float* tmp_h_prev = hprev; hprev = hnew; hnew = (float*)tmp_h_prev;\n const float* tmp_c_prev = cprev; cprev = cnew; cnew = (float*)tmp_c_prev;\n }\n}\n\n__global__ void fc_kernel(const float* __restrict__ seq,\n const float* __restrict__ w,\n const float* __restrict__ bias,\n float* __restrict__ y,\n int B, int T, int H, int O) {\n int tid = threadIdx.x;\n int bo = blockIdx.x;\n int b = bo / O;\n int o = bo - b * O;\n float sum = 0.0f;\n const float* x = seq + ((b * T + (T - 1)) * H);\n const float* ww = w + o * H;\n for (int k = tid; k < H; k += blockDim.x) {\n sum = fmaf(x[k], ww[k], sum);\n }\n // block reduce 128 threads using shared memory\n __shared__ float smem[128];\n smem[tid] = sum;\n __syncthreads();\n for (int s = blockDim.x/2; s > 32; s >>= 1) {\n if (tid < s) smem[tid] += smem[tid+s];\n __syncthreads();\n }\n float v = smem[tid];\n if (tid < 32) {\n v = warp_reduce_sum(v);\n if (tid == 0) y[b * O + o] = v + bias[o];\n }\n}\n\ntorch::Tensor lstm6_forward(torch::Tensor x, torch::Tensor h0, torch::Tensor c0,\n torch::Tensor w_ih0, torch::Tensor w_hh0, torch::Tensor b_ih0, torch::Tensor b_hh0,\n torch::Tensor w_ih1, torch::Tensor w_hh1, torch::Tensor b_ih1, torch::Tensor b_hh1,\n torch::Tensor w_ih2, torch::Tensor w_hh2, torch::Tensor b_ih2, torch::Tensor b_hh2,\n torch::Tensor w_ih3, torch::Tensor w_hh3, torch::Tensor b_ih3, torch::Tensor b_hh3,\n torch::Tensor w_ih4, torch::Tensor w_hh4, torch::Tensor b_ih4, torch::Tensor b_hh4,\n torch::Tensor w_ih5, torch::Tensor w_hh5, torch::Tensor b_ih5, torch::Tensor b_hh5,\n torch::Tensor fc_w, torch::Tensor fc_b) {\n\n int B = x.size(0);\n int T = x.size(1);\n int K0 = x.size(2);\n int H = w_hh0.size(1);\n int O = fc_w.size(0);\n auto opts = x.options();\n auto tmp1 = torch::empty({B, T, H}, opts);\n auto tmp2 = torch::empty({B, T, H}, opts);\n auto hA = torch::empty({B, H}, opts);\n auto hB = torch::empty({B, H}, opts);\n auto cA = torch::empty({B, H}, opts);\n auto cB = torch::empty({B, H}, opts);\n auto bar = torch::zeros({2}, torch::TensorOptions().dtype(torch::kUInt32).device(x.device()));\n auto y = torch::empty({B, O}, opts);\n\n int threads = 128;\n int blocks = 80;\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n\n lstm_layer_kernel<<<blocks, threads, 0, stream>>>(x.data_ptr<float>(), h0.data_ptr<float>(), c0.data_ptr<float>(),\n w_ih0.data_ptr<float>(), w_hh0.data_ptr<float>(), b_ih0.data_ptr<float>(), b_hh0.data_ptr<float>(),\n tmp1.data_ptr<float>(), hA.data_ptr<float>(), hB.data_ptr<float>(), cA.data_ptr<float>(), cB.data_ptr<float>(),\n (unsigned int*)bar.data_ptr<unsigned int>(), B, T, K0, H, 0, blocks);\n lstm_layer_kernel<<<blocks, threads, 0, stream>>>(tmp1.data_ptr<float>(), h0.data_ptr<float>(), c0.data_ptr<float>(),\n w_ih1.data_ptr<float>(), w_hh1.data_ptr<float>(), b_ih1.data_ptr<float>(), b_hh1.data_ptr<float>(),\n tmp2.data_ptr<float>(), hA.data_ptr<float>(), hB.data_ptr<float>(), cA.data_ptr<float>(), cB.data_ptr<float>(),\n (unsigned int*)bar.data_ptr<unsigned int>(), B, T, H, H, 1, blocks);\n lstm_layer_kernel<<<blocks, threads, 0, stream>>>(tmp2.data_ptr<float>(), h0.data_ptr<float>(), c0.data_ptr<float>(),\n w_ih2.data_ptr<float>(), w_hh2.data_ptr<float>(), b_ih2.data_ptr<float>(), b_hh2.data_ptr<float>(),\n tmp1.data_ptr<float>(), hA.data_ptr<float>(), hB.data_ptr<float>(), cA.data_ptr<float>(), cB.data_ptr<float>(),\n (unsigned int*)bar.data_ptr<unsigned int>(), B, T, H, H, 2, blocks);\n lstm_layer_kernel<<<blocks, threads, 0, stream>>>(tmp1.data_ptr<float>(), h0.data_ptr<float>(), c0.data_ptr<float>(),\n w_ih3.data_ptr<float>(), w_hh3.data_ptr<float>(), b_ih3.data_ptr<float>(), b_hh3.data_ptr<float>(),\n tmp2.data_ptr<float>(), hA.data_ptr<float>(), hB.data_ptr<float>(), cA.data_ptr<float>(), cB.data_ptr<float>(),\n (unsigned int*)bar.data_ptr<unsigned int>(), B, T, H, H, 3, blocks);\n lstm_layer_kernel<<<blocks, threads, 0, stream>>>(tmp2.data_ptr<float>(), h0.data_ptr<float>(), c0.data_ptr<float>(),\n w_ih4.data_ptr<float>(), w_hh4.data_ptr<float>(), b_ih4.data_ptr<float>(), b_hh4.data_ptr<float>(),\n tmp1.data_ptr<float>(), hA.data_ptr<float>(), hB.data_ptr<float>(), cA.data_ptr<float>(), cB.data_ptr<float>(),\n (unsigned int*)bar.data_ptr<unsigned int>(), B, T, H, H, 4, blocks);\n lstm_layer_kernel<<<blocks, threads, 0, stream>>>(tmp1.data_ptr<float>(), h0.data_ptr<float>(), c0.data_ptr<float>(),\n w_ih5.data_ptr<float>(), w_hh5.data_ptr<float>(), b_ih5.data_ptr<float>(), b_hh5.data_ptr<float>(),\n tmp2.data_ptr<float>(), hA.data_ptr<float>(), hB.data_ptr<float>(), cA.data_ptr<float>(), cB.data_ptr<float>(),\n (unsigned int*)bar.data_ptr<unsigned int>(), B, T, H, H, 5, blocks);\n\n fc_kernel<<<B * O, 128, 0, stream>>>(tmp2.data_ptr<float>(), fc_w.data_ptr<float>(), fc_b.data_ptr<float>(), y.data_ptr<float>(), B, T, H, O);\n return y;\n}\n'''\n\n_ext = load_inline(\n name='lstm6_custom_ext_v1',\n cpp_sources='',\n cuda_sources=_cuda_src,\n functions=['lstm6_forward'],\n extra_cuda_cflags=['-O3'],\n extra_cflags=['-O3'],\n with_cuda=True,\n verbose=False,\n)\n\nclass ModelNew(nn.Module):\n def __init__(self, input_size, hidden_size, num_layers, output_size, dropout=0.0):\n super(ModelNew, self).__init__()\n self.hidden_size = hidden_size\n self.num_layers = num_layers\n self.lstm = nn.LSTM(input_size, hidden_size, num_layers,\n batch_first=True, dropout=dropout, bidirectional=False)\n self.fc = nn.Linear(hidden_size, output_size)\n def forward(self, x, h0=None, c0=None):\n batch_size = x.size(0)\n if h0 is None:\n h0 = torch.randn(self.num_layers, batch_size, self.hidden_size, device=x.device)\n if c0 is None:\n c0 = torch.randn(self.num_layers, batch_size, self.hidden_size, device=x.device)\n if self.num_layers == 6 and self.hidden_size == 256 and x.is_cuda and x.dtype == torch.float32:\n return _ext.lstm6_forward(x.contiguous(), h0.contiguous(), c0.contiguous(),\n self.lstm.weight_ih_l0, self.lstm.weight_hh_l0, self.lstm.bias_ih_l0, self.lstm.bias_hh_l0,\n self.lstm.weight_ih_l1, self.lstm.weight_hh_l1, self.lstm.bias_ih_l1, self.lstm.bias_hh_l1,\n self.lstm.weight_ih_l2, self.lstm.weight_hh_l2, self.lstm.bias_ih_l2, self.lstm.bias_hh_l2,\n self.lstm.weight_ih_l3, self.lstm.weight_hh_l3, self.lstm.bias_ih_l3, self.lstm.bias_hh_l3,\n self.lstm.weight_ih_l4, self.lstm.weight_hh_l4, self.lstm.bias_ih_l4, self.lstm.bias_hh_l4,\n self.lstm.weight_ih_l5, self.lstm.weight_hh_l5, self.lstm.bias_ih_l5, self.lstm.bias_hh_l5,\n self.fc.weight, self.fc.bias)\n out, _ = self.lstm(x, (h0, c0))\n return self.fc(out[:, -1, :])\n" }

result ← compile_kernel (fail)

failcompile_kernel compile_kernel FAILED: RuntimeError. RuntimeError: Error building extension 'lstm6_custom_ext_v1': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=lstm6_custom_ext_v1 -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -fPIC -std=c++17 -O3 -c /scratch/abaner/KernelBench/runs/l3_default_gpt/original/gpt-5.5-priority/level_3_problem_35_cache/k_8aec123b5d60/lstm6_custom_ext_v1/main.cpp -o main.o FAILED: [code=1] main.o /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=lstm6_custom_ext_v1 -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -fPIC -std=c++17 -O3 -c /scratch/abaner/KernelBench/runs/l3_default_gpt/original/gpt-5.5-priority/level_3_problem_35_cache/k_8aec123b5d60/lstm6_custom_ext_v1/main.cpp -o main.o /scratch/abaner/KernelBench/runs/l3_default_gpt/original/gpt-5.5-priority/level_3_problem_35_cache/k_8aec123b5d60/lstm6_custom_ext_v1/main.cpp: In function 'void pybind11_init_lstm6_custom_ext_v1(pybind11::module_&)': /scratch/abaner/KernelBench/runs/l3_default_gpt/original/gpt-5.5-priority/level_3_problem_35_cache/k_8aec123b5d60/lstm6_custom_ext_v1/main.cpp:4:52: error: 'lstm6_forward' was not declared in this scope 4 | m.def("lstm6_forward", torch::wrap_pybind_function(lstm6_forward), "lstm6_forward"); | ^~~~~~~~~~~~~ [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=lstm6_custom_ext_v1 -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr -gencode=arch=compute_90,code=compute_90 -gencode=arch=compute_90,code=sm_90 --compiler-options '-fPIC' -O3 -std=c++17 -c /scratch/abaner/KernelBench/runs/l3_default_gpt/original/gpt-5.5-priority/level_3_problem_35_cache/k_8aec123b5d60/lstm6_custom_ext_v1/cuda.cu -o cuda.cuda.o nvcc warning : incompatible redefinition for option 'compiler-bindir', the last value of this option was used ninja: build stopped: subcommand failed.

turn 2 · 1 tool call(s) · 24.8s turn · 24.8s LLM · 10,664 chars (click to expand)

turn 224.8s turn · 24.8s LLM · 0.0s tools

reasoning

**Updating C++ declarations** I need to make a declaration in the cpp_sources for a function since the autogenerated main.cpp doesn't recognize the CUDA symbol. Typically, I'd place C++ declarations in cpp_sources and add the necessary prototype. There’s also an extension function defined in cuda.cu with torch::Tensor; should I use extern for that? I think I’ll just prototype it in cpp_sources using includes and the correct signature, but I can't have a pybind module. Time to update!

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 lstm6_forward(torch::Tensor x, torch::Tensor h0, torch::Tensor c0,\n torch::Tensor w_ih0, torch::Tensor w_hh0, torch::Tensor b_ih0, torch::Tensor b_hh0,\n torch::Tensor w_ih1, torch::Tensor w_hh1, torch::Tensor b_ih1, torch::Tensor b_hh1,\n torch::Tensor w_ih2, torch::Tensor w_hh2, torch::Tensor b_ih2, torch::Tensor b_hh2,\n torch::Tensor w_ih3, torch::Tensor w_hh3, torch::Tensor b_ih3, torch::Tensor b_hh3,\n torch::Tensor w_ih4, torch::Tensor w_hh4, torch::Tensor b_ih4, torch::Tensor b_hh4,\n torch::Tensor w_ih5, torch::Tensor w_hh5, torch::Tensor b_ih5, torch::Tensor b_hh5,\n torch::Tensor fc_w, torch::Tensor fc_b);\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\n__inline__ __device__ float warp_reduce_sum(float v) {\n for (int offset = 16; offset > 0; offset >>= 1) v += __shfl_down_sync(0xffffffff, v, offset);\n return v;\n}\n__device__ __forceinline__ float sigmoidf_stable(float x) { return 1.0f / (1.0f + expf(-x)); }\n__device__ void grid_barrier(unsigned int* bar, int nblocks) {\n __syncthreads();\n __threadfence();\n if (threadIdx.x == 0) {\n unsigned int my_sense = bar[1];\n unsigned int ticket = atomicAdd(&bar[0], 1u);\n if (ticket == (unsigned int)(nblocks - 1)) {\n bar[0] = 0u;\n __threadfence();\n atomicAdd(&bar[1], 1u);\n } else {\n while (bar[1] == my_sense) { }\n }\n }\n __syncthreads();\n}\n\n__global__ void lstm_layer_kernel(const float* __restrict__ inp, const float* __restrict__ h0, const float* __restrict__ c0,\n const float* __restrict__ w_ih, const float* __restrict__ w_hh, const float* __restrict__ b_ih, const float* __restrict__ b_hh,\n float* __restrict__ out, float* __restrict__ hA, float* __restrict__ hB, float* __restrict__ cA, float* __restrict__ cB,\n unsigned int* __restrict__ bar, int B, int T, int K, int H, int layer_idx, int nblocks) {\n int tid = threadIdx.x, lane = tid & 31, warp_in_block = tid >> 5;\n int total_warps = gridDim.x * (blockDim.x >> 5);\n int warp_global = blockIdx.x * (blockDim.x >> 5) + warp_in_block;\n int BH = B * H;\n for (int idx = blockIdx.x * blockDim.x + tid; idx < BH; idx += gridDim.x * blockDim.x) {\n int b = idx / H, h = idx - b * H;\n int off = (layer_idx * B + b) * H + h;\n hA[idx] = h0[off]; cA[idx] = c0[off];\n }\n grid_barrier(bar, nblocks);\n const float* hprev = hA; float* hnew = hB; const float* cprev = cA; float* cnew = cB;\n for (int t = 0; t < T; ++t) {\n for (int cell = warp_global; cell < BH; cell += total_warps) {\n int b = cell / H, h = cell - b * H;\n float s0 = b_ih[h] + b_hh[h];\n float s1 = b_ih[H+h] + b_hh[H+h];\n float s2 = b_ih[2*H+h] + b_hh[2*H+h];\n float s3 = b_ih[3*H+h] + b_hh[3*H+h];\n const float* xptr = inp + ((b*T + t) * K);\n const float* wi0 = w_ih + h*K; const float* wi1 = w_ih + (H+h)*K; const float* wi2 = w_ih + (2*H+h)*K; const float* wi3 = w_ih + (3*H+h)*K;\n for (int k = lane; k < K; k += 32) { float xv = xptr[k]; s0 = fmaf(xv, wi0[k], s0); s1 = fmaf(xv, wi1[k], s1); s2 = fmaf(xv, wi2[k], s2); s3 = fmaf(xv, wi3[k], s3); }\n const float* hhptr = hprev + b*H;\n const float* wh0 = w_hh + h*H; const float* wh1 = w_hh + (H+h)*H; const float* wh2 = w_hh + (2*H+h)*H; const float* wh3 = w_hh + (3*H+h)*H;\n for (int k = lane; k < H; k += 32) { float hv = hhptr[k]; s0 = fmaf(hv, wh0[k], s0); s1 = fmaf(hv, wh1[k], s1); s2 = fmaf(hv, wh2[k], s2); s3 = fmaf(hv, wh3[k], s3); }\n s0 = warp_reduce_sum(s0); s1 = warp_reduce_sum(s1); s2 = warp_reduce_sum(s2); s3 = warp_reduce_sum(s3);\n if (lane == 0) {\n float ig = sigmoidf_stable(s0), fg = sigmoidf_stable(s1), gg = tanhf(s2), og = sigmoidf_stable(s3);\n float cv = fg * cprev[cell] + ig * gg; float hv = og * tanhf(cv);\n cnew[cell] = cv; hnew[cell] = hv; out[(b*T + t)*H + h] = hv;\n }\n }\n grid_barrier(bar, nblocks);\n const float* th = hprev; hprev = hnew; hnew = (float*)th;\n const float* tc = cprev; cprev = cnew; cnew = (float*)tc;\n }\n}\n\n__global__ void fc_kernel(const float* __restrict__ seq, const float* __restrict__ w, const float* __restrict__ bias,\n float* __restrict__ y, int B, int T, int H, int O) {\n int tid = threadIdx.x, bo = blockIdx.x, b = bo / O, o = bo - b * O;\n float sum = 0.0f; const float* x = seq + ((b*T + (T-1))*H); const float* ww = w + o*H;\n for (int k = tid; k < H; k += blockDim.x) sum = fmaf(x[k], ww[k], sum);\n __shared__ float smem[128]; smem[tid] = sum; __syncthreads();\n for (int s = 64; s > 32; s >>= 1) { if (tid < s) smem[tid] += smem[tid+s]; __syncthreads(); }\n float v = smem[tid];\n if (tid < 32) { v = warp_reduce_sum(v); if (tid == 0) y[b*O + o] = v + bias[o]; }\n}\n\ntorch::Tensor lstm6_forward(torch::Tensor x, torch::Tensor h0, torch::Tensor c0,\n torch::Tensor w_ih0, torch::Tensor w_hh0, torch::Tensor b_ih0, torch::Tensor b_hh0,\n torch::Tensor w_ih1, torch::Tensor w_hh1, torch::Tensor b_ih1, torch::Tensor b_hh1,\n torch::Tensor w_ih2, torch::Tensor w_hh2, torch::Tensor b_ih2, torch::Tensor b_hh2,\n torch::Tensor w_ih3, torch::Tensor w_hh3, torch::Tensor b_ih3, torch::Tensor b_hh3,\n torch::Tensor w_ih4, torch::Tensor w_hh4, torch::Tensor b_ih4, torch::Tensor b_hh4,\n torch::Tensor w_ih5, torch::Tensor w_hh5, torch::Tensor b_ih5, torch::Tensor b_hh5,\n torch::Tensor fc_w, torch::Tensor fc_b) {\n int B = x.size(0), T = x.size(1), K0 = x.size(2), H = w_hh0.size(1), O = fc_w.size(0);\n auto opts = x.options();\n auto tmp1 = torch::empty({B,T,H}, opts), tmp2 = torch::empty({B,T,H}, opts);\n auto hA = torch::empty({B,H}, opts), hB = torch::empty({B,H}, opts), cA = torch::empty({B,H}, opts), cB = torch::empty({B,H}, opts);\n auto bar = torch::zeros({2}, torch::TensorOptions().dtype(torch::kUInt32).device(x.device()));\n auto y = torch::empty({B,O}, opts);\n int threads = 128, blocks = 80; cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n#define RUN_LAYER(INP, OUT, LI, K, WIH, WHH, BIH, BHH) \\\n lstm_layer_kernel<<<blocks, threads, 0, stream>>>(INP.data_ptr<float>(), h0.data_ptr<float>(), c0.data_ptr<float>(), \\\n WIH.data_ptr<float>(), WHH.data_ptr<float>(), BIH.data_ptr<float>(), BHH.data_ptr<float>(), OUT.data_ptr<float>(), \\\n hA.data_ptr<float>(), hB.data_ptr<float>(), cA.data_ptr<float>(), cB.data_ptr<float>(), (unsigned int*)bar.data_ptr<unsigned int>(), B, T, K, H, LI, blocks)\n RUN_LAYER(x, tmp1, 0, K0, w_ih0, w_hh0, b_ih0, b_hh0);\n RUN_LAYER(tmp1, tmp2, 1, H, w_ih1, w_hh1, b_ih1, b_hh1);\n RUN_LAYER(tmp2, tmp1, 2, H, w_ih2, w_hh2, b_ih2, b_hh2);\n RUN_LAYER(tmp1, tmp2, 3, H, w_ih3, w_hh3, b_ih3, b_hh3);\n RUN_LAYER(tmp2, tmp1, 4, H, w_ih4, w_hh4, b_ih4, b_hh4);\n RUN_LAYER(tmp1, tmp2, 5, H, w_ih5, w_hh5, b_ih5, b_hh5);\n#undef RUN_LAYER\n fc_kernel<<<B*O, 128, 0, stream>>>(tmp2.data_ptr<float>(), fc_w.data_ptr<float>(), fc_b.data_ptr<float>(), y.data_ptr<float>(), B, T, H, O);\n return y;\n}\n'''\n\n_ext = load_inline(name='lstm6_custom_ext_v2', cpp_sources=_cpp_src, cuda_sources=_cuda_src, functions=['lstm6_forward'], extra_cuda_cflags=['-O3'], extra_cflags=['-O3'], with_cuda=True, verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, input_size, hidden_size, num_layers, output_size, dropout=0.0):\n super(ModelNew, self).__init__()\n self.hidden_size = hidden_size\n self.num_layers = num_layers\n self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True, dropout=dropout, bidirectional=False)\n self.fc = nn.Linear(hidden_size, output_size)\n def forward(self, x, h0=None, c0=None):\n batch_size = x.size(0)\n if h0 is None:\n h0 = torch.randn(self.num_layers, batch_size, self.hidden_size, device=x.device)\n if c0 is None:\n c0 = torch.randn(self.num_layers, batch_size, self.hidden_size, device=x.device)\n return _ext.lstm6_forward(x.contiguous(), h0.contiguous(), c0.contiguous(),\n self.lstm.weight_ih_l0, self.lstm.weight_hh_l0, self.lstm.bias_ih_l0, self.lstm.bias_hh_l0,\n self.lstm.weight_ih_l1, self.lstm.weight_hh_l1, self.lstm.bias_ih_l1, self.lstm.bias_hh_l1,\n self.lstm.weight_ih_l2, self.lstm.weight_hh_l2, self.lstm.bias_ih_l2, self.lstm.bias_hh_l2,\n self.lstm.weight_ih_l3, self.lstm.weight_hh_l3, self.lstm.bias_ih_l3, self.lstm.bias_hh_l3,\n self.lstm.weight_ih_l4, self.lstm.weight_hh_l4, self.lstm.bias_ih_l4, self.lstm.bias_hh_l4,\n self.lstm.weight_ih_l5, self.lstm.weight_hh_l5, self.lstm.bias_ih_l5, self.lstm.bias_hh_l5,\n self.fc.weight, self.fc.bias)\n" }

result ← compile_kernel (ok)

okcompile_kernel compile_kernel PASSED: kernel compiled without errors.

turn 3 · 1 tool call(s) · 21.1s turn · 21.1s LLM · 10,760 chars (click to expand)

turn 321.1s turn · 21.1s LLM · 0.0s tools

call → run_correctness

callrun_correctness { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n_cpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor lstm6_forward(torch::Tensor x, torch::Tensor h0, torch::Tensor c0,\n torch::Tensor w_ih0, torch::Tensor w_hh0, torch::Tensor b_ih0, torch::Tensor b_hh0,\n torch::Tensor w_ih1, torch::Tensor w_hh1, torch::Tensor b_ih1, torch::Tensor b_hh1,\n torch::Tensor w_ih2, torch::Tensor w_hh2, torch::Tensor b_ih2, torch::Tensor b_hh2,\n torch::Tensor w_ih3, torch::Tensor w_hh3, torch::Tensor b_ih3, torch::Tensor b_hh3,\n torch::Tensor w_ih4, torch::Tensor w_hh4, torch::Tensor b_ih4, torch::Tensor b_hh4,\n torch::Tensor w_ih5, torch::Tensor w_hh5, torch::Tensor b_ih5, torch::Tensor b_hh5,\n torch::Tensor fc_w, torch::Tensor fc_b);\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__inline__ __device__ float warp_reduce_sum(float v) { for (int offset = 16; offset > 0; offset >>= 1) v += __shfl_down_sync(0xffffffff, v, offset); return v; }\n__device__ __forceinline__ float sigmoidf_stable(float x) { return 1.0f / (1.0f + expf(-x)); }\n__device__ void grid_barrier(unsigned int* bar, int nblocks) { __syncthreads(); __threadfence(); if (threadIdx.x == 0) { unsigned int my_sense = bar[1]; unsigned int ticket = atomicAdd(&bar[0], 1u); if (ticket == (unsigned int)(nblocks - 1)) { bar[0] = 0u; __threadfence(); atomicAdd(&bar[1], 1u); } else { while (bar[1] == my_sense) { } } } __syncthreads(); }\n__global__ void lstm_layer_kernel(const float* __restrict__ inp, const float* __restrict__ h0, const float* __restrict__ c0, const float* __restrict__ w_ih, const float* __restrict__ w_hh, const float* __restrict__ b_ih, const float* __restrict__ b_hh, float* __restrict__ out, float* __restrict__ hA, float* __restrict__ hB, float* __restrict__ cA, float* __restrict__ cB, unsigned int* __restrict__ bar, int B, int T, int K, int H, int layer_idx, int nblocks) { int tid = threadIdx.x, lane = tid & 31, warp_in_block = tid >> 5; int total_warps = gridDim.x * (blockDim.x >> 5); int warp_global = blockIdx.x * (blockDim.x >> 5) + warp_in_block; int BH = B * H; for (int idx = blockIdx.x * blockDim.x + tid; idx < BH; idx += gridDim.x * blockDim.x) { int b = idx / H, h = idx - b * H; int off = (layer_idx * B + b) * H + h; hA[idx] = h0[off]; cA[idx] = c0[off]; } grid_barrier(bar, nblocks); const float* hprev = hA; float* hnew = hB; const float* cprev = cA; float* cnew = cB; for (int t = 0; t < T; ++t) { for (int cell = warp_global; cell < BH; cell += total_warps) { int b = cell / H, h = cell - b * H; float s0 = b_ih[h] + b_hh[h]; float s1 = b_ih[H+h] + b_hh[H+h]; float s2 = b_ih[2*H+h] + b_hh[2*H+h]; float s3 = b_ih[3*H+h] + b_hh[3*H+h]; const float* xptr = inp + ((b*T + t) * K); const float* wi0 = w_ih + h*K; const float* wi1 = w_ih + (H+h)*K; const float* wi2 = w_ih + (2*H+h)*K; const float* wi3 = w_ih + (3*H+h)*K; for (int k = lane; k < K; k += 32) { float xv = xptr[k]; s0 = fmaf(xv, wi0[k], s0); s1 = fmaf(xv, wi1[k], s1); s2 = fmaf(xv, wi2[k], s2); s3 = fmaf(xv, wi3[k], s3); } const float* hhptr = hprev + b*H; const float* wh0 = w_hh + h*H; const float* wh1 = w_hh + (H+h)*H; const float* wh2 = w_hh + (2*H+h)*H; const float* wh3 = w_hh + (3*H+h)*H; for (int k = lane; k < H; k += 32) { float hv = hhptr[k]; s0 = fmaf(hv, wh0[k], s0); s1 = fmaf(hv, wh1[k], s1); s2 = fmaf(hv, wh2[k], s2); s3 = fmaf(hv, wh3[k], s3); } s0 = warp_reduce_sum(s0); s1 = warp_reduce_sum(s1); s2 = warp_reduce_sum(s2); s3 = warp_reduce_sum(s3); if (lane == 0) { float ig = sigmoidf_stable(s0), fg = sigmoidf_stable(s1), gg = tanhf(s2), og = sigmoidf_stable(s3); float cv = fg * cprev[cell] + ig * gg; float hv = og * tanhf(cv); cnew[cell] = cv; hnew[cell] = hv; out[(b*T + t)*H + h] = hv; } } grid_barrier(bar, nblocks); const float* th = hprev; hprev = hnew; hnew = (float*)th; const float* tc = cprev; cprev = cnew; cnew = (float*)tc; } }\n__global__ void fc_kernel(const float* __restrict__ seq, const float* __restrict__ w, const float* __restrict__ bias, float* __restrict__ y, int B, int T, int H, int O) { int tid = threadIdx.x, bo = blockIdx.x, b = bo / O, o = bo - b * O; float sum = 0.0f; const float* x = seq + ((b*T + (T-1))*H); const float* ww = w + o*H; for (int k = tid; k < H; k += blockDim.x) sum = fmaf(x[k], ww[k], sum); __shared__ float smem[128]; smem[tid] = sum; __syncthreads(); for (int s = 64; s > 32; s >>= 1) { if (tid < s) smem[tid] += smem[tid+s]; __syncthreads(); } float v = smem[tid]; if (tid < 32) { v = warp_reduce_sum(v); if (tid == 0) y[b*O + o] = v + bias[o]; } }\ntorch::Tensor lstm6_forward(torch::Tensor x, torch::Tensor h0, torch::Tensor c0, torch::Tensor w_ih0, torch::Tensor w_hh0, torch::Tensor b_ih0, torch::Tensor b_hh0, torch::Tensor w_ih1, torch::Tensor w_hh1, torch::Tensor b_ih1, torch::Tensor b_hh1, torch::Tensor w_ih2, torch::Tensor w_hh2, torch::Tensor b_ih2, torch::Tensor b_hh2, torch::Tensor w_ih3, torch::Tensor w_hh3, torch::Tensor b_ih3, torch::Tensor b_hh3, torch::Tensor w_ih4, torch::Tensor w_hh4, torch::Tensor b_ih4, torch::Tensor b_hh4, torch::Tensor w_ih5, torch::Tensor w_hh5, torch::Tensor b_ih5, torch::Tensor b_hh5, torch::Tensor fc_w, torch::Tensor fc_b) { int B = x.size(0), T = x.size(1), K0 = x.size(2), H = w_hh0.size(1), O = fc_w.size(0); auto opts = x.options(); auto tmp1 = torch::empty({B,T,H}, opts), tmp2 = torch::empty({B,T,H}, opts); auto hA = torch::empty({B,H}, opts), hB = torch::empty({B,H}, opts), cA = torch::empty({B,H}, opts), cB = torch::empty({B,H}, opts); auto bar = torch::zeros({2}, torch::TensorOptions().dtype(torch::kUInt32).device(x.device())); auto y = torch::empty({B,O}, opts); int threads = 128, blocks = 80; cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n#define RUN_LAYER(INP, OUT, LI, K, WIH, WHH, BIH, BHH) lstm_layer_kernel<<<blocks, threads, 0, stream>>>(INP.data_ptr<float>(), h0.data_ptr<float>(), c0.data_ptr<float>(), WIH.data_ptr<float>(), WHH.data_ptr<float>(), BIH.data_ptr<float>(), BHH.data_ptr<float>(), OUT.data_ptr<float>(), hA.data_ptr<float>(), hB.data_ptr<float>(), cA.data_ptr<float>(), cB.data_ptr<float>(), (unsigned int*)bar.data_ptr<unsigned int>(), B, T, K, H, LI, blocks)\nRUN_LAYER(x, tmp1, 0, K0, w_ih0, w_hh0, b_ih0, b_hh0); RUN_LAYER(tmp1, tmp2, 1, H, w_ih1, w_hh1, b_ih1, b_hh1); RUN_LAYER(tmp2, tmp1, 2, H, w_ih2, w_hh2, b_ih2, b_hh2); RUN_LAYER(tmp1, tmp2, 3, H, w_ih3, w_hh3, b_ih3, b_hh3); RUN_LAYER(tmp2, tmp1, 4, H, w_ih4, w_hh4, b_ih4, b_hh4); RUN_LAYER(tmp1, tmp2, 5, H, w_ih5, w_hh5, b_ih5, b_hh5);\n#undef RUN_LAYER\nfc_kernel<<<B*O, 128, 0, stream>>>(tmp2.data_ptr<float>(), fc_w.data_ptr<float>(), fc_b.data_ptr<float>(), y.data_ptr<float>(), B, T, H, O); return y; }\n'''\n_ext = load_inline(name='lstm6_custom_ext_v2c', cpp_sources=_cpp_src, cuda_sources=_cuda_src, functions=['lstm6_forward'], extra_cuda_cflags=['-O3'], extra_cflags=['-O3'], with_cuda=True, verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self, input_size, hidden_size, num_layers, output_size, dropout=0.0):\n super(ModelNew, self).__init__(); self.hidden_size = hidden_size; self.num_layers = num_layers; self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True, dropout=dropout, bidirectional=False); self.fc = nn.Linear(hidden_size, output_size)\n def forward(self, x, h0=None, c0=None):\n batch_size = x.size(0)\n if h0 is None: h0 = torch.randn(self.num_layers, batch_size, self.hidden_size, device=x.device)\n if c0 is None: c0 = torch.randn(self.num_layers, batch_size, self.hidden_size, device=x.device)\n return _ext.lstm6_forward(x.contiguous(), h0.contiguous(), c0.contiguous(), self.lstm.weight_ih_l0, self.lstm.weight_hh_l0, self.lstm.bias_ih_l0, self.lstm.bias_hh_l0, self.lstm.weight_ih_l1, self.lstm.weight_hh_l1, self.lstm.bias_ih_l1, self.lstm.bias_hh_l1, self.lstm.weight_ih_l2, self.lstm.weight_hh_l2, self.lstm.bias_ih_l2, self.lstm.bias_hh_l2, self.lstm.weight_ih_l3, self.lstm.weight_hh_l3, self.lstm.bias_ih_l3, self.lstm.bias_hh_l3, self.lstm.weight_ih_l4, self.lstm.weight_hh_l4, self.lstm.bias_ih_l4, self.lstm.bias_hh_l4, self.lstm.weight_ih_l5, self.lstm.weight_hh_l5, self.lstm.bias_ih_l5, self.lstm.bias_hh_l5, self.fc.weight, self.fc.bias)\n" }

result ← run_correctness (fail)

failrun_correctness run_correctness FAILED: unexpected error during tool execution. _WorkItemTimeout: work item exceeded soft timeout budget Traceback (most recent call last): File "/scratch/abaner/KernelBench/src/kernelbench/agent/agent.py", line 627, in _execute_tool return tool.execute(self.ctx, **args) File "/scratch/abaner/KernelBench/src/kernelbench/agent/tools.py", line 365, in execute return ctx.eval_client.run_correctness(ctx, kernel_code) File "/scratch/abaner/KernelBench/src/kernelbench/agent/eval_client.py", line 58, in run_correctness return self._call("correctness", args) File "/scratch/abaner/KernelBench/src/kernelbench/agent/eval_client.py", line 117, in _call payload = self._response_q.get(timeout=remaining) File "<string>", line 2, in get File "/opt/conda/lib/python3.10/multiprocessing/managers.py", line 818, in _callmethod kind, result = conn.recv() File "/opt/conda/lib/python3.10/multiprocessing/connection.py", line 250, in recv buf = self._recv_bytes() File "/opt/conda/lib/python3.10/multiprocessing/connection.py", line 414, in _recv_bytes buf = self._recv(4) File "/opt/conda/lib/python3.10/multiprocessing/connection.py", line 379, in _recv chunk = read(handle, remaining) File "/scratch/abaner/KernelBench/scripts/run_sweep.py", line 161, in _alarm_handler raise _WorkItemTimeout("work item exceeded soft timeout budget") __mp_main__._WorkItemTimeout: work item exceeded soft timeout budget

turn 4575.6s turn · 575.6s LLM · 0.0s tools

feedbackLLM call failed: Empty: