correct
12_DeepSeekMoEDispatchPermute.py
started
2026-05-11T19:03:55Z
timingkernelbench.timing
kernel mean / median / std
0.10 / — / 0.06 μs
ref mean / median / std
82.30 / — / 0.66 μs
numerical precisioneval_kernel_against_ref
gpu memoryextended_metrics.measure_memory
kernel launches / fusiontorch.profiler
fusion ratio (ref / kernel)
1741.20x
SOL (speed-of-light)nsight_hardware_counters
roofline / occupancynsight_hardware_counters
dram bandwidth
101.82 GB/s
warp stalls (top 5)nsight
reference PyTorch source (the prompt the agent sees)
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Pack tokens into expert-major order for MoE execution.
Each token is assigned to one expert and one slot within that expert's
buffer. The kernel writes token activations into the packed dispatch layout.
"""
def __init__(self):
super().__init__()
def forward(
self,
token_hidden: torch.Tensor,
expert_idx: torch.Tensor,
slot_idx: torch.Tensor,
expert_offsets: torch.Tensor,
) -> torch.Tensor:
num_rows = int(expert_offsets[-1].item())
out = torch.zeros(num_rows, token_hidden.shape[1], dtype=token_hidden.dtype, device=token_hidden.device)
for token in range(token_hidden.shape[0]):
expert = int(expert_idx[token].item())
row = int(expert_offsets[expert].item() + slot_idx[token].item())
out[row] = token_hidden[token]
return out
num_tokens = 2048
hidden_dim = 128
num_experts = 16
def get_inputs():
return popcorn_pri.moe_dispatch_inputs(num_tokens, hidden_dim, num_experts)
def get_init_inputs():
return []
turn 0 · 1 tool call(s) · 233.2s turn · 16.0s LLM · tokens in=2,225 · out=1,437 · total=3,662 · 7,798 chars (click to expand)
turn 0233.2s turn · 16.0s LLM · 217.2s tools · tokens in=2,225 · out=1,437 · total=3,662
reasoning
Let me analyze this task. The reference implementation is packing tokens into expert-major order for Mixture of Experts (MoE) execution.
Looking at the reference:
1. `token_hidden` has shape `(num_tokens, hidden_dim)` - the input tokens
2. `expert_idx` has shape `(num_tokens,)` - which expert each token goes to
3. `slot_idx` has shape `(num_tokens,)` - the slot within each expert's buffer
4. `expert_offsets` has shape `(num_experts + 1,)` - cumulative offsets for each expert
The output is `num_rows x hidden_dim` where `num_rows = expert_offsets[-1]`.
For each token:
- Get its expert index
- Compute row = expert_offsets[expert] + slot_idx[token]
- Copy token_hidden[token] to out[row]
This is essentially a scatter operation where we're scattering tokens to specific output rows based on expert assignment and slot.
Let me design a CUDA kernel for this:
1. Each thread can handle one token (or one element of the hidden dimension)
2. We need to handle the case where multiple tokens might go to different rows
A simple approach:
- Launch one thread per token
- Each thread copies the entire hidden dimension for that token
Better approach for coalesced memory access:
- Launch one thread per element (token, hidden_dim_index)
- This gives better memory coalescing
Let me write the kernel:
```cpp
__global__ void pack_tokens_kernel(
const float* __restrict__ token_hidden,
const int* __restrict__ expert_idx,
const int* __restrict__ slot_idx,
const int* __restrict__ expert_offsets,
float* __restrict__ out,
int num_tokens,
int hidden_dim
) {
int token = blockIdx.x * blockDim.x + threadIdx.x;
int dim = blockIdx.y * blockDim.y + threadIdx.y;
if (token < num_tokens && dim < hidden_dim) {
int expert = expert_idx[token];
int row = expert_offsets[expert] + slot_idx[token];
out[row * hidden_dim + dim] = token_hidden[token * hidden_dim + dim];
}
}
```
Wait, but this has uncoalesced writes since `row` can be arbitrary. Let me think about this more carefully.
Actually, the writes are scattered by nature of the problem. The reads from `token_hidden` could be coalesced if threads in a warp access consecutive tokens and the same dimension, but the writes will be scattered.
One optimization could be to use shared memory to buffer writes for each expert, but that gets complicated.
Let me start with a simple kernel and see how it performs.
Actually, let me think about a different approach. If we process one token per thread (instead of one element per thread), we get:
- Coalesced reads from token_hidden (if threads access consecutive tokens)
- Scattered writes to out (but this is inherent to the problem)
For hidden_dim = 128, having each thread handle one element might give better parallelism. Let me try that approach.
Actually, let me look at the dimensions:
- num_tokens = 2048
- hidden_dim = 128
With one thread per element, we'd have 2048 * 128 = 262,144 threads, which is good for GPU.
Let me write a 2D kernel where:
- x dimension: token index
- y dimension: hidden dimension index
This way, threads with consecutive x values and same y value read consecutive elements from token_hidden (coalesced reads). The writes will be scattered, but that's unavoidable.
Let me write the kernel and Python wrapper:
```python
import torch
import torch.nn as nn
from torch.utils.cpp_extension import load_inline
# Define CUDA kernel
cuda_source = """
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>
__global__ void pack_tokens_kernel(
const float* __restrict__ token_hidden,
const int* __restrict__ expert_idx,
const int* __restrict__ slot_idx,
const int* __restrict__ expert_offsets,
float* __restrict__ out,
int num_tokens,
int hidden_dim
) {
int token = blockIdx.x * blockDim.x + threadIdx.x;
int dim = blockIdx.y * blockDim.y + threadIdx.y;
if (token < num_tokens && dim < hidden_dim) {
int expert = expert_idx[token];
int row = expert_offsets[expert] + slot_idx[token];
out[row * hidden_dim + dim] = token_hidden[token * hidden_dim + dim];
}
}
torch::Tensor pack_tokens_cuda(
torch::Tensor token_hidden,
torch::Tensor expert_idx,
torch::Tensor slot_idx,
torch::Tensor expert_offsets
) {
int num_tokens = token_hidden.size(0);
int hidden_dim = token_hidden.size(1);
int num_rows = expert_offsets[-1].item<int>();
auto out = torch::zeros({num_rows, hidden_dim}, token_hidden.options());
dim3 block(32, 4);
dim3 grid((num_tokens + block.x - 1) / block.x, (hidden_dim + block.y - 1) / block.y);
pack_tokens_kernel<<<grid, block>>>(
token_hidden.data_ptr<float>(),
expert_idx.data_ptr<int>(),
slot_idx.data_ptr<int>(),
expert_offsets.data_ptr<int>(),
out.data_ptr<float>(),
num_tokens,
hidden_dim
);
return out;
}
"""
cpp_source = """
torch::Tensor pack_tokens_cuda(
torch::Tensor token_hidden,
torch::Tensor expert_idx,
torch::Tensor slot_idx,
torch::Tensor expert_offsets
);
"""
# Load the extension
pack_module = load_inline(
name='pack_tokens',
cpp_sources=cpp_source,
cuda_sources=cuda_source,
functions=['pack_tokens_cuda'],
verbose=True
)
class ModelNew(nn.Module):
def __init__(self):
super().__init__()
def forward(
self,
token_hidden: torch.Tensor,
expert_idx: torch.Tensor,
slot_idx: torch.Tensor,
expert_offsets: torch.Tensor,
) -> torch.Tensor:
return pack_module.pack_tokens_cuda(token_hidden, expert_idx, slot_idx, expert_offsets)
```
Wait, I need to be careful about the tensor types. The expert_idx is int32, and I should ensure the CUDA kernel handles that properly.
Also, I should handle edge cases and maybe add bounds checking.
Let me refine this and compile it to check for errors.
assistantLooking at this task, I need to implement a token packing kernel for MoE (Mixture of Experts) that scatters tokens to specific output rows based on expert assignments.
Let me start by getting GPU specs and understanding the problem better.
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 1268.0s turn · 6.6s LLM · 261.4s tools · tokens in=2,521 · out=688 · total=3,209
reasoning
Good. Now let me write the initial kernel. The operation is:
- For each token (row), compute the destination row: expert_offsets[expert_idx[token]] + slot_idx[token]
- Copy token_hidden[token] to out[row]
This is essentially a scatter operation. Let me write a straightforward kernel first.
assistantNow let me write the initial CUDA kernel implementation:
call → compile_kernel
callcompile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n# Define the CUDA kernel\ncuda_source = \"\"\"\n__global__ void pack_tokens_kernel(\n const float* __restrict__ token_hidden,\n const int* __restrict__ expert_idx,\n const int* __restrict__ slot_idx,\n const int* __restrict__ expert_offsets,\n float* __restrict__ out,\n int num_tokens,\n int hidden_dim) {\n \n int token = blockIdx.x * blockDim.x + threadIdx.x;\n if (token >= num_tokens) return;\n \n int expert = expert_idx[token];\n int row = expert_offsets[expert] + slot_idx[token];\n \n // Copy the hidden vector\n for (int i = 0; i < hidden_dim; i++) {\n out[row * hidden_dim + i] = token_hidden[token * hidden_dim + i];\n }\n}\n\nvoid pack_tokens_launcher(\n const float* token_hidden,\n const int* expert_idx,\n const int* slot_idx,\n const int* expert_offsets,\n float* out,\n int num_tokens,\n int hidden_dim) {\n \n int threads = 256;\n int blocks = (num_tokens + threads - 1) / threads;\n \n pack_tokens_kernel<<<blocks, threads>>>(\n token_hidden, expert_idx, slot_idx, expert_offsets, out, num_tokens, hidden_dim);\n}\n\"\"\"\n\n# Load the CUDA extension\npack_extension = load_inline(\n name='pack_tokens',\n cpp_sources=\"\"\"\n void pack_tokens_launcher(\n const float* token_hidden,\n const int* expert_idx,\n const int* slot_idx,\n const int* expert_offsets,\n float* out,\n int num_tokens,\n int hidden_dim);\n \"\"\",\n cuda_sources=cuda_source,\n functions=['pack_tokens_launcher'],\n extra_cuda_cflags=['-O3'],\n verbose=False\n)\n\n\nclass ModelNew(nn.Module):\n \"\"\"\n Pack tokens into expert-major order for MoE execution.\n\n Each token is assigned to one expert and one slot within that expert's\n buffer. The kernel writes token activations into the packed dispatch layout.\n \"\"\"\n\n def __init__(self):\n super().__init__()\n\n def forward(\n self,\n token_hidden: torch.Tensor,\n expert_idx: torch.Tensor,\n slot_idx: torch.Tensor,\n expert_offsets: torch.Tensor,\n ) -> torch.Tensor:\n num_rows = int(expert_offsets[-1].item())\n out = torch.zeros(num_rows, token_hidden.shape[1], dtype=token_hidden.dtype, device=token_hidden.device)\n \n pack_extension.pack_tokens_launcher(\n token_hidden.data_ptr(),\n expert_idx.data_ptr(),\n slot_idx.data_ptr(),\n expert_offsets.data_ptr(),\n out.data_ptr(),\n token_hidden.shape[0],\n token_hidden.shape[1]\n )\n \n return out"
}
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 2247.5s turn · 6.3s LLM · 241.2s tools · tokens in=3,157 · out=626 · total=3,783
reasoning
The kernel compiled. Now let me run the correctness check.
call → run_correctness
callrun_correctness
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n# Define the CUDA kernel\ncuda_source = \"\"\"\n__global__ void pack_tokens_kernel(\n const float* __restrict__ token_hidden,\n const int* __restrict__ expert_idx,\n const int* __restrict__ slot_idx,\n const int* __restrict__ expert_offsets,\n float* __restrict__ out,\n int num_tokens,\n int hidden_dim) {\n \n int token = blockIdx.x * blockDim.x + threadIdx.x;\n if (token >= num_tokens) return;\n \n int expert = expert_idx[token];\n int row = expert_offsets[expert] + slot_idx[token];\n \n // Copy the hidden vector\n for (int i = 0; i < hidden_dim; i++) {\n out[row * hidden_dim + i] = token_hidden[token * hidden_dim + i];\n }\n}\n\nvoid pack_tokens_launcher(\n const float* token_hidden,\n const int* expert_idx,\n const int* slot_idx,\n const int* expert_offsets,\n float* out,\n int num_tokens,\n int hidden_dim) {\n \n int threads = 256;\n int blocks = (num_tokens + threads - 1) / threads;\n \n pack_tokens_kernel<<<blocks, threads>>>(\n token_hidden, expert_idx, slot_idx, expert_offsets, out, num_tokens, hidden_dim);\n}\n\"\"\"\n\n# Load the CUDA extension\npack_extension = load_inline(\n name='pack_tokens',\n cpp_sources=\"\"\"\n void pack_tokens_launcher(\n const float* token_hidden,\n const int* expert_idx,\n const int* slot_idx,\n const int* expert_offsets,\n float* out,\n int num_tokens,\n int hidden_dim);\n \"\"\",\n cuda_sources=cuda_source,\n functions=['pack_tokens_launcher'],\n extra_cuda_cflags=['-O3'],\n verbose=False\n)\n\n\nclass ModelNew(nn.Module):\n \"\"\"\n Pack tokens into expert-major order for MoE execution.\n\n Each token is assigned to one expert and one slot within that expert's\n buffer. The kernel writes token activations into the packed dispatch layout.\n \"\"\"\n\n def __init__(self):\n super().__init__()\n\n def forward(\n self,\n token_hidden: torch.Tensor,\n expert_idx: torch.Tensor,\n slot_idx: torch.Tensor,\n expert_offsets: torch.Tensor,\n ) -> torch.Tensor:\n num_rows = int(expert_offsets[-1].item())\n out = torch.zeros(num_rows, token_hidden.shape[1], dtype=token_hidden.dtype, device=token_hidden.device)\n \n pack_extension.pack_tokens_launcher(\n token_hidden.data_ptr(),\n expert_idx.data_ptr(),\n slot_idx.data_ptr(),\n expert_offsets.data_ptr(),\n out.data_ptr(),\n token_hidden.shape[0],\n token_hidden.shape[1]\n )\n \n return out"
}
result ← run_correctness (fail)
failrun_correctness
run_correctness FAILED: ? trials did not all match.
runtime_error: pack_tokens_launcher(): incompatible function arguments. The following argument types are supported:
1. (arg0: typing.SupportsFloat, arg1: typing.SupportsInt, arg2: typing.SupportsInt, arg3: ty...
turn 3 · 1 tool call(s) · 154.8s turn · 6.0s LLM · tokens in=3,838 · out=625 · total=4,463 · 6,663 chars (click to expand)
turn 3154.8s turn · 6.0s LLM · 148.8s tools · tokens in=3,838 · out=625 · total=4,463
reasoning
There's an issue with the function signature. The CUDA function expects specific types. I need to convert the arguments properly. Let me fix the Python bindings by ensuring correct types are passed.
assistantI need to fix the argument types for the CUDA function. Let me correct this:
call → compile_kernel
callcompile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n# Define the CUDA kernel\ncuda_source = \"\"\"\n__global__ void pack_tokens_kernel(\n const float* __restrict__ token_hidden,\n const int* __restrict__ expert_idx,\n const int* __restrict__ slot_idx,\n const int* __restrict__ expert_offsets,\n float* __restrict__ out,\n int num_tokens,\n int hidden_dim) {\n \n int token = blockIdx.x * blockDim.x + threadIdx.x;\n if (token >= num_tokens) return;\n \n int expert = expert_idx[token];\n int row = expert_offsets[expert] + slot_idx[token];\n \n // Copy the hidden vector\n for (int i = 0; i < hidden_dim; i++) {\n out[row * hidden_dim + i] = token_hidden[token * hidden_dim + i];\n }\n}\n\nvoid pack_tokens_launcher(\n at::Tensor token_hidden,\n at::Tensor expert_idx,\n at::Tensor slot_idx,\n at::Tensor expert_offsets,\n at::Tensor out) {\n \n int num_tokens = token_hidden.size(0);\n int hidden_dim = token_hidden.size(1);\n \n int threads = 256;\n int blocks = (num_tokens + threads - 1) / threads;\n \n pack_tokens_kernel<<<blocks, threads>>>(\n token_hidden.data_ptr<float>(),\n expert_idx.data_ptr<int>(),\n slot_idx.data_ptr<int>(),\n expert_offsets.data_ptr<int>(),\n out.data_ptr<float>(),\n num_tokens, hidden_dim);\n}\n\"\"\"\n\n# Load the CUDA extension\npack_extension = load_inline(\n name='pack_tokens',\n cpp_sources=\"\",\n cuda_sources=cuda_source,\n functions=['pack_tokens_launcher'],\n extra_cuda_cflags=['-O3'],\n verbose=False\n)\n\n\nclass ModelNew(nn.Module):\n \"\"\"\n Pack tokens into expert-major order for MoE execution.\n\n Each token is assigned to one expert and one slot within that expert's\n buffer. The kernel writes token activations into the packed dispatch layout.\n \"\"\"\n\n def __init__(self):\n super().__init__()\n\n def forward(\n self,\n token_hidden: torch.Tensor,\n expert_idx: torch.Tensor,\n slot_idx: torch.Tensor,\n expert_offsets: torch.Tensor,\n ) -> torch.Tensor:\n num_rows = int(expert_offsets[-1].item())\n out = torch.zeros(num_rows, token_hidden.shape[1], dtype=token_hidden.dtype, device=token_hidden.device)\n \n pack_extension.pack_tokens_launcher(\n token_hidden,\n expert_idx,\n slot_idx,\n expert_offsets,\n out\n )\n \n return out"
}
result ← compile_kernel (fail)
failcompile_kernel
compile_kernel FAILED: RuntimeError.
RuntimeError: Error building extension 'pack_tokens_v1': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=pack_tokens_v1 -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -fPIC -std=c++17 -c /scratch/abaner/KernelBench/runs/pop_l123_all_gpt/popcorn/FW-GLM-5-1/level_1_problem_12_cache/k_3a90ff5ec99f/pack_tokens/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=pack_tokens_v1 -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -fPIC -std=c++17 -c /scratch/abaner/KernelBench/runs/pop_l123_all_gpt/popcorn/FW-GLM-5-1/level_1_problem_12_cache/k_3a90ff5ec99f/pack_tokens/main.cpp -o main.o
/scratch/abaner/KernelBench/runs/pop_l123_all_gpt/popcorn/FW-GLM-5-1/level_1_problem_12_cache/k_3a90ff5ec99f/pack_tokens/main.cpp: In function 'void pybind11_init_pack_tokens_v1(pybind11::module_&)':
/scratch/abaner/KernelBench/runs/pop_l123_all_gpt/popcorn/FW-GLM-5-1/level_1_problem_12_cache/k_3a90ff5ec99f/pack_tokens/main.cpp:4:59: error: 'pack_tokens_launcher' was not declared in this scope
4 | m.def("pack_tokens_launcher", torch::wrap_pybind_function(pack_tokens_launcher), "pack_tokens_launcher");
| ^~~~~~~~~~~~~~~~~~~~
[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=pack_tokens_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/pop_l123_all_gpt/popcorn/FW-GLM-5-1/level_1_problem_12_cache/k_3a90ff5ec99f/pack_tokens/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 4 · 1 tool call(s) · 252.2s turn · 5.4s LLM · tokens in=5,329 · out=599 · total=5,928 · 6,423 chars (click to expand)
turn 4252.2s turn · 5.4s LLM · 246.8s tools · tokens in=5,329 · out=599 · total=5,928
reasoning
I need to include the ATen headers in the CUDA source. Let me fix the compilation.
call → compile_kernel
result ← compile_kernel (fail)
failcompile_kernel
compile_kernel FAILED: RuntimeError.
RuntimeError: Error building extension 'pack_tokens_v2': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=pack_tokens_v2 -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -fPIC -std=c++17 -c /scratch/abaner/KernelBench/runs/pop_l123_all_gpt/popcorn/FW-GLM-5-1/level_1_problem_12_cache/k_20d5975f09b0/pack_tokens/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=pack_tokens_v2 -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -fPIC -std=c++17 -c /scratch/abaner/KernelBench/runs/pop_l123_all_gpt/popcorn/FW-GLM-5-1/level_1_problem_12_cache/k_20d5975f09b0/pack_tokens/main.cpp -o main.o
/scratch/abaner/KernelBench/runs/pop_l123_all_gpt/popcorn/FW-GLM-5-1/level_1_problem_12_cache/k_20d5975f09b0/pack_tokens/main.cpp: In function 'void pybind11_init_pack_tokens_v2(pybind11::module_&)':
/scratch/abaner/KernelBench/runs/pop_l123_all_gpt/popcorn/FW-GLM-5-1/level_1_problem_12_cache/k_20d5975f09b0/pack_tokens/main.cpp:4:59: error: 'pack_tokens_launcher' was not declared in this scope
4 | m.def("pack_tokens_launcher", torch::wrap_pybind_function(pack_tokens_launcher), "pack_tokens_launcher");
| ^~~~~~~~~~~~~~~~~~~~
[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=pack_tokens_v2 -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/pop_l123_all_gpt/popcorn/FW-GLM-5-1/level_1_problem_12_cache/k_20d5975f09b0/pack_tokens/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 5185.5s turn · 6.8s LLM · 178.7s tools · tokens in=6,817 · out=666 · total=7,483
reasoning
The error shows that `pack_tokens_launcher` is not being found during C++ compilation. The function needs to be declared in the cpp_sources section as well. Let me include it in both places.
call → compile_kernel
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 6163.3s turn · 5.6s LLM · 157.7s tools · tokens in=7,456 · out=636 · total=8,092
reasoning
Great, now let me test for correctness.
call → run_correctness
result ← run_correctness (fail)
failrun_correctness
run_correctness FAILED: ? trials did not all match.
runtime_error: expected scalar type Int but found Float
turn 7175.2s turn · 7.4s LLM · 167.7s tools · tokens in=8,108 · out=800 · total=8,908
reasoning
There's a type mismatch. The expert_idx and slot_idx tensors are int32, but the offsets might be the wrong type. Let me look at the input generation more carefully.
Looking at the input generation:
```python
expert_idx = torch.arange(num_tokens, dtype=torch.int32) % num_experts
...
expert_offsets = torch.zeros(num_experts + 1, dtype=torch.int32)
slot_idx = torch.empty(num_tokens, dtype=torch.int32)
```
So all are int32. The issue is the PyTorch tensor might need to be contiguous, or there's something else going on. Let me try to ensure contiguous tensors and make sure the types are correct.
call → compile_kernel
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 8248.6s turn · 6.4s LLM · 242.2s tools · tokens in=8,782 · out=669 · total=9,451
reasoning
Now let's run correctness again.
call → run_correctness
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (3 / 3) trials all matched the reference.
Numerical precision: max_abs_err=0.00e+00 mean_abs_err=0.00e+00 max_rel_err=0.00e+00
turn 9191.9s turn · 6.7s LLM · 185.2s tools · tokens in=9,501 · out=695 · total=10,196
reasoning
The kernel is correct. Now let me profile it to understand performance and optimize it.
assistantThe kernel is correct. Now let me profile it to see how we can optimize:
call → profile_kernel
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::to 10815.0μs (18%) calls=1
#2: aten::_to_copy 10806.0μs (18%) calls=1
#3: aten::copy_ 10770.0μs (18%) calls=1
#4: aten::zeros 7341.0μs (12%) calls=1
#5: aten::zero_ 6653.0μs (11%) calls=1
... +15 more kernels
--- Timing ---
GPU time: 64.35 μs
--- Memory Bandwidth ---
DRAM BW: 50.0/3350 GB/s (1.5%)
read=50.0 write=0.0 GB/s
Hit rates: L2=479.6%
--- Compute Throughput ---
Pipe util: FMA=3.1%, Tensor=0.0%
--- Roofline Analysis ---
Ridge point (FP32): 20.00 FLOPs/byte
Classification: UNKNOWN
--- Occupancy ---
Achieved: 40.8%
Eligible warps/cycle: 0.15
WARNING: <1 eligible warp/cycle = latency-bound, needs more parallelism
Launch: regs/thread=144, smem/blk=5120B, block=768, grid=273
--- Memory Access Quality ---
Global ld coalescing: 42.34 sectors/req (42.3x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 13078.2% ####################
short_scoreboard 679.9% ####################
--- Optimization Hints ---
-> 13078% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=42.3 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=42.3x (ideal=1.0) -> 42x wasted DRAM BW. Make per-warp accesses contiguous.
-> Low eligible warps + low BW% + low compute% = LATENCY-BOUND. Need more parallelism (larger grid, more ILP, prefetching).
turn 10343.2s turn · 8.7s LLM · 334.5s tools · tokens in=10,721 · out=979 · total=11,700
reasoning
The profiling reveals several issues:
1. The kernel has very poor memory access coalescing - 42.3x wasted bandwidth
2. The hidden_dim loop is slow - each thread sequentially reads 128 elements
Let me optimize this with:
1. Vectorized loads using float4 (4 floats at a time)
2. Coalesced memory access
Since hidden_dim = 128, I can use float4 (16 bytes = 4 floats) to read 32 elements at a time, which will greatly improve coalescing.
Actually, the issue is that each thread is reading a row from `token_hidden` and writing to a (potentially different) row in `out`. The writes might not be coalesced because they're scattered based on expert assignment.
Let me use vectorized loads/stores to at least improve the throughput:
assistantI see the issues - the loop over hidden_dim is very slow and causes poor coalescing. Let me optimize using vectorized loads with float4:
call → compile_kernel
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 1130.6s turn · 6.9s LLM · 23.7s tools · tokens in=11,540 · out=783 · total=12,323
reasoning
Good, let me test correctness.
call → run_correctness
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (3 / 3) trials all matched the reference.
Numerical precision: max_abs_err=0.00e+00 mean_abs_err=0.00e+00 max_rel_err=0.00e+00
turn 12 · 1 tool call(s) · 42.5s turn · 7.7s LLM · tokens in=12,405 · out=820 · total=13,225 · 6,468 chars (click to expand)
turn 1242.5s turn · 7.7s LLM · 34.7s tools · tokens in=12,405 · out=820 · total=13,225
reasoning
The kernel is correct and uses vectorized loads. Let me profile it to see if there are any performance issues, and then I can decide whether to submit or optimize further.
assistantLet me profile the optimized kernel to check performance:
call → profile_kernel
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::to 12248.0μs (20%) calls=1
#2: aten::_to_copy 12240.0μs (20%) calls=1
#3: aten::copy_ 12196.0μs (20%) calls=1
#4: aten::zeros 7304.0μs (12%) calls=1
#5: aten::zero_ 7088.0μs (12%) calls=1
... +15 more kernels
--- Timing ---
GPU time: 30.98 μs [↓33.4μs ok]
--- Memory Bandwidth ---
DRAM BW: 100.0/3350 GB/s (3.0%) [↑1.5% ok]
read=100.0 write=0.0 GB/s
Hit rates: L2=469.5%
--- Compute Throughput ---
Pipe util: FMA=3.3%, Tensor=0.0%
--- Roofline Analysis ---
Ridge point (FP32): 20.00 FLOPs/byte
Classification: UNKNOWN
--- Occupancy ---
Achieved: 40.8% [↓0.0% REGRESSED]
Eligible warps/cycle: 0.16
WARNING: <1 eligible warp/cycle = latency-bound, needs more parallelism
Launch: regs/thread=152, smem/blk=5120B, block=768, grid=273
--- Memory Access Quality ---
Global ld coalescing: 40.54 sectors/req (40.5x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 17321.5% ####################
short_scoreboard 734.0% ####################
--- Optimization Hints ---
-> 17322% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=40.5 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=40.5x (ideal=1.0) -> 41x wasted DRAM BW. Make per-warp accesses contiguous.
-> Low eligible warps + low BW% + low compute% = LATENCY-BOUND. Need more parallelism (larger grid, more ILP, prefetching).
--- Delta vs Previous Iteration ---
GPU time: 64.35→30.98μs (↓33.38) ok
DRAM BW%: 1.49→2.98% (↑1.49) ok
Occupancy: 40.80→40.75% (↓0.05) REGRESSED
Eligible warps/cyc: 0.15→0.16 (↑0.01) ok
Ld sectors/req: 42.34→40.54 (↓1.80) ok
L2 hit%: 479.56→469.49% (↓10.07) REGRESSED
turn 13208.9s turn · 7.9s LLM · 201.0s tools · tokens in=13,937 · out=845 · total=14,782 · FINAL
reasoning
The kernel is now correct and shows improvement (GPU time reduced from 64.35μs to 30.98μs). The profiling shows there's still room for optimization (coalescing issues, latency-bound), but the kernel is functional and faster than the initial version.
Let me submit this kernel now since it's correct and optimized.
call → submit_kernel
result ← submit_kernel (ok)
oksubmit_kernel
submit_kernel PASSED: (3 / 3) correctness trials all passed.
Kernel runtime: 0.10 μs
Runtime stats: mean=0.10μs median=?μs std=0.06μs
Numerical precision: max_abs_err=0.00e+00 mean_abs_err=0.00e+00 max_rel_err=0.00e+00
Memory: 35.11 MB peak (ref: 35.09 MB, ratio: 1.0005x)
Kernel launches: 20 (ref: 34824, fusion ratio: 1741.2)
SOL score: 0.0304
Flagged for excessive speedup — this submission may have been rejected by automated review.
final submitted kernel (112 lines, 3,232 chars) — full source, untruncated
import torch
import torch.nn as nn
from torch.utils.cpp_extension import load_inline
# Define the CUDA kernel
cuda_source = """
#include <ATen/ATen.h>
__global__ void pack_tokens_kernel(
const float* __restrict__ token_hidden,
const int32_t* __restrict__ expert_idx,
const int32_t* __restrict__ slot_idx,
const int32_t* __restrict__ expert_offsets,
float* __restrict__ out,
int num_tokens,
int hidden_dim) {
int token = blockIdx.x * blockDim.x + threadIdx.x;
if (token >= num_tokens) return;
int expert = expert_idx[token];
int row = expert_offsets[expert] + slot_idx[token];
// Use vectorized loads/stores for better bandwidth
const float4* src_vec = reinterpret_cast<const float4*>(token_hidden + token * hidden_dim);
float4* dst_vec = reinterpret_cast<float4*>(out + row * hidden_dim);
int vec_len = hidden_dim / 4; // Number of float4 elements
for (int i = 0; i < vec_len; i++) {
dst_vec[i] = src_vec[i];
}
// Handle remaining elements if hidden_dim is not divisible by 4
for (int i = vec_len * 4; i < hidden_dim; i++) {
out[row * hidden_dim + i] = token_hidden[token * hidden_dim + i];
}
}
void pack_tokens_launcher(
at::Tensor token_hidden,
at::Tensor expert_idx,
at::Tensor slot_idx,
at::Tensor expert_offsets,
at::Tensor out) {
int num_tokens = token_hidden.size(0);
int hidden_dim = token_hidden.size(1);
int threads = 256;
int blocks = (num_tokens + threads - 1) / threads;
pack_tokens_kernel<<<blocks, threads>>>(
token_hidden.data_ptr<float>(),
expert_idx.data_ptr<int32_t>(),
slot_idx.data_ptr<int32_t>(),
expert_offsets.data_ptr<int32_t>(),
out.data_ptr<float>(),
num_tokens, hidden_dim);
}
"""
cpp_source = """
void pack_tokens_launcher(
at::Tensor token_hidden,
at::Tensor expert_idx,
at::Tensor slot_idx,
at::Tensor expert_offsets,
at::Tensor out);
"""
# Load the CUDA extension
pack_extension = load_inline(
name='pack_tokens',
cpp_sources=cpp_source,
cuda_sources=cuda_source,
functions=['pack_tokens_launcher'],
extra_cuda_cflags=['-O3'],
verbose=False
)
class ModelNew(nn.Module):
"""
Pack tokens into expert-major order for MoE execution.
Each token is assigned to one expert and one slot within that expert's
buffer. The kernel writes token activations into the packed dispatch layout.
"""
def __init__(self):
super().__init__()
def forward(
self,
token_hidden: torch.Tensor,
expert_idx: torch.Tensor,
slot_idx: torch.Tensor,
expert_offsets: torch.Tensor,
) -> torch.Tensor:
num_rows = int(expert_offsets[-1].item())
out = torch.zeros(num_rows, token_hidden.shape[1], dtype=token_hidden.dtype, device=token_hidden.device)
pack_extension.pack_tokens_launcher(
token_hidden.contiguous(),
expert_idx.contiguous().to(torch.int32),
slot_idx.contiguous().to(torch.int32),
expert_offsets.contiguous().to(torch.int32),
out
)
return out