L2 P13 · popcorn

correct 13_QuantVLA_SelectiveLayoutFusion.py

model

FW-GLM-5-1

turns / max

15 / 15

tool calls

tokens (total)

216665

wall clock

4867.5s

truncated

backend

cuda fp32

variant

popcorn

runtime

0.04 μs

ref runtime

0.09 μs

speedup

2.26x

started

2026-05-11T19:31:12Z

timingkernelbench.timing

kernel runtime

0.04 μs

ref runtime

0.09 μs

speedup

2.26x

kernel mean / median / std

0.04 / — / 0.00 μs

ref mean / median / std

0.09 / — / 0.03 μs

numerical precisioneval_kernel_against_ref

max abs error

1.19e-06

mean abs error

7.03e-08

max rel error

0.06

mean rel error

1.43e-06

gpu memoryextended_metrics.measure_memory

kernel peak

33.69 MB

ref peak

34.44 MB

kernel / ref

0.98x

kernel launches / fusiontorch.profiler

kernel launches

ref launches

fusion ratio (ref / kernel)

1.68x

total cuda time

2476 μs

SOL (speed-of-light)nsight_hardware_counters

SOL score (0–1)

0.167

dram util %

4.2%

compute util %

16.7%

bottleneck

compute-bound

dominant pipe

FP32

arithmetic intensity

—

ridge point

20.00

roofline / occupancynsight_hardware_counters

dram bandwidth

141.77 GB/s

dram util %

4.2%

fp32 tflops

—

fp32 util %

—

fp16 tflops

—

occupancy %

57.0%

bottleneck

compute-bound

dominant pipe %

16.7%

arithmetic intensity

—

ridge point

20.00

L1 hit %

—

L2 hit %

360.0%

ld sectors / req

39.98

st sectors / req

—

regs / thread

smem / block

5248 B

block size

640

peak bw

3350.00 GB/s

peak fp32 tflops

67.00

peak fp16 tflops

1979.00

warp stalls (top 5)nsight

long_scoreboard

3578.9%

short_scoreboard

835.5%

mio_throttle

6.0%

reference PyTorch source (the prompt the agent sees)

import torch
import torch.nn as nn

# Default ModelNew is PyTorch-only. run_and_check: use check_kernel=False with backend=cuda
# until the candidate file contains a real CUDA kernel (see kernel_static_checker).


def _fake_quant_symmetric_int8(x: torch.Tensor, scale: torch.Tensor) -> torch.Tensor:
    """Symmetric int8 fake-quant: round(x/scale) clamped to [-127, 127], times scale."""
    q = torch.clamp(torch.round(x / scale), -127.0, 127.0)
    return q * scale


class Model(nn.Module):
    """
    **QuantVLA selective layout fusion** (reference target for a fused CUDA/Triton kernel).

    Vision–language–action stacks often use **token-wise mixed precision**: some
    timesteps or modalities tolerate aggressive quantization while others stay in
    full precision. This module fuses in **one forward**:

      1. **Layout routing** — ``sigmoid(layout_logits)`` yields a per-token mix
         coefficient in ``(0, 1)`` (quant vs FP residual).
      2. **Quantitative path** — global symmetric **int8-style fake quantization**
         (scale from the tensor max, ``/ 127``).
      3. **Selective blend** — ``α · x_quant + (1 - α) · x``.
      4. **Head** — linear projection on the mixed activations.

    This matches a “quantitative selective layout” block: the layout logits (from a
    router, modality tags, or calibration) choose how strongly each position uses the
    quantized layout vs the identity layout.
    """

    def __init__(self, dim: int):
        super().__init__()
        self.dim = dim
        self.out_proj = nn.Linear(dim, dim, bias=True)

    def forward(self, x: torch.Tensor, layout_logits: torch.Tensor) -> torch.Tensor:
        """
        Args:
            x: (B, T, D) VLA token activations (fused modalities).
            layout_logits: (B, T) unbounded logits; larger → more mass on quantized path.
        Returns:
            (B, T, D) updated activations.
        """
        B, T, D = x.shape
        assert D == self.dim
        assert layout_logits.shape == (B, T)

        alpha = torch.sigmoid(layout_logits).unsqueeze(-1)
        with torch.no_grad():
            amax = x.detach().abs().max().clamp(min=1e-8)
        scale = amax / 127.0
        x_q = _fake_quant_symmetric_int8(x, scale)
        mixed = alpha * x_q + (1.0 - alpha) * x
        return self.out_proj(mixed)


class ModelNew(nn.Module):
    """KernelBench candidate entry point; default delegates to ``Model``."""

    def __init__(self, dim: int):
        super().__init__()
        self._impl = Model(dim)

    def forward(self, x: torch.Tensor, layout_logits: torch.Tensor) -> torch.Tensor:
        return self._impl(x, layout_logits)


batch_size = 8
seq_len = 64
dim = 128


def get_inputs():
    p = popcorn_pri
    x = torch.randn(p.jitter_int(batch_size), p.jitter_int(seq_len), dim)
    layout_logits = torch.randn(p.jitter_int(batch_size), p.jitter_int(seq_len))
    return [x, layout_logits]


def get_init_inputs():
    return [dim]

turn 0384.6s turn · 3.7s LLM · 380.8s tools · tokens in=2,541 · out=231 · total=2,772

reasoning

Let me understand the task: 1. I need to create a custom CUDA kernel that replaces the PyTorch reference implementation 2. The model does: - Compute sigmoid of layout_logits to get alpha (B, T) -> (B, T, 1) - Compute the absolute max of x (detached) clamped to min 1e-8 - Compute scale = amax / 127.0 - Fake quantize: round(x/scale) clamped to [-127, 127], then multiply by scale - Mix: alpha * x_quant + (1-alpha) * x - Linear projection on the result 3. The input shapes are: - x: (B, T, D) = (8, 64, 128) - layout_logits: (B, T) = (8, 64) - Output: (B, T, D) = (8, 64, 128) Let me start by getting the GPU specs and understanding the hardware.

call → get_gpu_specs

callget_gpu_specs {}

call → ert_roofline

callert_roofline {}

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

result ← ert_roofline (ok)

okert_roofline ert_roofline PASSED: empirical roofline benchmarks complete. === Empirical Roofline Model === Device: NVIDIA H100 80GB HBM3 --- Measured Bandwidth --- L1 13.6 GB/s (working set: 32 KB) L2 927.6 GB/s (working set: 2048 KB) DRAM 2577.3 GB/s (working set: 262144 KB) --- Measured Compute Throughput --- FP32 51.85 TFLOPS FP16 (tensor core) 758.77 TFLOPS Ridge point (FP32): 20.12 FLOPs/byte Kernels with arithmetic intensity below the ridge point are memory-bound; above it they are compute-bound.

turn 1 · 1 tool call(s) · 331.4s turn · 24.7s LLM · tokens in=2,975 · out=2,535 · total=5,510 · 10,730 chars (click to expand)

turn 1331.4s turn · 24.7s LLM · 306.7s tools · tokens in=2,975 · out=2,535 · total=5,510

reasoning

Good, I have the GPU specs. The H100 has very high memory bandwidth (3.35 TB/s theoretical, 2577 GB/s measured) and high FP32 compute (67 TFLOPS theoretical, 51.85 TFLOPS measured). Now let me analyze the problem: - Input: x (B, T, D) and layout_logits (B, T) - Operations: 1. sigmoid(layout_logits) -> alpha (B, T, 1) 2. amax = max(abs(x)) across all elements 3. scale = amax / 127.0 4. x_q = clamp(round(x / scale), -127, 127) * scale 5. mixed = alpha * x_q + (1 - alpha) * x 6. out_proj(mixed) -> output (B, T, D) The out_proj is a linear layer with bias. I need to handle all these operations in CUDA. Let me write the CUDA kernel. The key operations are: 1. Sigmoid computation 2. Max reduction (global) 3. Fake quantization 4. Blend 5. Linear projection I'll write a fused kernel that does most of these operations, but the linear projection might need to be separate or I can try to fuse everything. Let me start with a basic implementation:

assistantLet me analyze this problem. I need to fuse: 1. Sigmoid on layout_logits → alpha 2. Global max reduction for amax 3. Fake int8 quantization 4. Blend: alpha * x_q + (1-alpha) * x 5. Linear projection Let me write a fused CUDA kernel:

call → compile_kernel

callcompile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\nimport math\n\n# Define the CUDA kernel source\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <cmath>\n#include <limits>\n\n// Sigmoid function\n__device__ __forceinline__ float sigmoid(float x) {\n return 1.0f / (1.0f + expf(-x));\n}\n\n// Warp reduction for max\n__device__ float warp_reduce_max(float val) {\n for (int mask = 16; mask > 0; mask >>= 1) {\n val = fmaxf(val, __shfl_xor_sync(0xffffffff, val, mask));\n }\n return val;\n}\n\n// Block reduction for max\n__device__ float block_reduce_max(float val, float* shared) {\n int lane = threadIdx.x & 31;\n int wid = threadIdx.x >> 5;\n \n val = warp_reduce_max(val);\n \n __syncthreads();\n if (lane == 0) {\n shared[wid] = val;\n }\n __syncthreads();\n \n val = (threadIdx.x < (blockDim.x >> 5)) ? shared[threadIdx.x] : -INFINITY;\n if (wid == 0) {\n val = warp_reduce_max(val);\n }\n return val;\n}\n\n// Kernel to compute amax and blend in one pass (after we know scale)\n__global__ void quant_blend_kernel(\n const float* __restrict__ x,\n const float* __restrict__ layout_logits,\n float* __restrict__ out,\n const float* __restrict__ weight,\n const float* __restrict__ bias,\n float scale,\n int B, int T, int D) {\n \n // Each thread handles one element of the blend output\n int total_elements = B * T * D;\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n \n if (idx < total_elements) {\n int d = idx % D;\n int t = (idx / D) % T;\n int b = idx / (D * T);\n \n float x_val = x[idx];\n \n // Compute alpha for this (b, t)\n int logit_idx = b * T + t;\n float alpha = sigmoid(layout_logits[logit_idx]);\n \n // Fake quantize\n float q = roundf(x_val / scale);\n q = fmaxf(-127.0f, fminf(127.0f, q));\n float x_q = q * scale;\n \n // Blend\n float mixed = alpha * x_q + (1.0f - alpha) * x_val;\n \n // Store blended result (we'll do projection separately)\n out[idx] = mixed;\n }\n}\n\n// Kernel for max reduction (compute amax)\n__global__ void amax_reduce_kernel(\n const float* __restrict__ x,\n float* __restrict__ partial_max,\n int total_elements,\n int elements_per_block) {\n \n extern __shared__ float shared_max[];\n \n int tid = threadIdx.x;\n int start_idx = blockIdx.x * elements_per_block;\n int end_idx = min(start_idx + elements_per_block, total_elements);\n \n float local_max = 0.0f;\n \n // Each thread processes multiple elements\n for (int i = start_idx + tid; i < end_idx; i += blockDim.x) {\n float val = fabsf(x[i]);\n local_max = fmaxf(local_max, val);\n }\n \n // Block reduction\n local_max = block_reduce_max(local_max, shared_max);\n \n if (threadIdx.x == 0) {\n partial_max[blockIdx.x] = local_max;\n }\n}\n\n// Final reduction kernel\n__global__ void final_max_kernel(\n float* __restrict__ partial_max,\n float* __restrict__ global_max,\n int num_blocks) {\n \n extern __shared__ float shared_max[];\n \n float local_max = 0.0f;\n \n for (int i = threadIdx.x; i < num_blocks; i += blockDim.x) {\n local_max = fmaxf(local_max, partial_max[i]);\n }\n \n local_max = block_reduce_max(local_max, shared_max);\n \n if (threadIdx.x == 0) {\n *global_max = local_max;\n }\n}\n\n// Fused linear projection kernel\n__global__ void linear_kernel(\n const float* __restrict__ input,\n const float* __restrict__ weight,\n const float* __restrict__ bias,\n float* __restrict__ output,\n int B, int T, int D) {\n \n int bt = blockIdx.x; // One block per (b, t) pair\n int b = bt / T;\n int t = bt % T;\n int d = threadIdx.x; // One thread per output dimension\n \n if (d < D) {\n float sum = bias[d];\n for (int k = 0; k < D; k++) {\n sum += input[b * T * D + t * D + k] * weight[d * D + k];\n }\n output[b * T * D + t * D + d] = sum;\n }\n}\n\n// Optimized fused kernel - compute amax, blend, and project all in one\n__global__ void fused_quant_blend_project_kernel(\n const float* __restrict__ x,\n const float* __restrict__ layout_logits,\n const float* __restrict__ weight,\n const float* __restrict__ bias,\n float* __restrict__ output,\n float scale,\n int B, int T, int D) {\n \n // Each block handles one (b, t) pair, computing all D outputs\n int bt = blockIdx.x;\n int b = bt / T;\n int t = bt % T;\n int d = threadIdx.x;\n \n float alpha = sigmoid(layout_logits[b * T + t]);\n \n // Compute blend for all D elements in this (b, t)\n // Thread d handles dimension d\n extern __shared__ float shared_blend[];\n \n if (d < D) {\n float x_val = x[b * T * D + t * D + d];\n float q = roundf(x_val / scale);\n q = fmaxf(-127.0f, fminf(127.0f, q));\n float x_q = q * scale;\n float mixed = alpha * x_q + (1.0f - alpha) * x_val;\n \n shared_blend[d] = mixed;\n }\n __syncthreads();\n \n // Now compute linear projection for dimension d\n if (d < D) {\n float sum = bias[d];\n for (int k = 0; k < D; k++) {\n sum += shared_blend[k] * weight[d * D + k];\n }\n output[b * T * D + t * D + d] = sum;\n }\n}\n\n// Host function to run the full pipeline\ntorch::Tensor forward_cuda(\n torch::Tensor x,\n torch::Tensor layout_logits,\n torch::Tensor weight,\n torch::Tensor bias) {\n \n int B = x.size(0);\n int T = x.size(1);\n int D = x.size(2);\n int total_elements = B * T * D;\n \n // Step 1: Compute amax using reduction\n int threads = 256;\n int elements_per_block = 4096;\n int num_blocks = (total_elements + elements_per_block - 1) / elements_per_block;\n \n auto partial_max = torch::empty({num_blocks}, x.options());\n \n amax_reduce_kernel<<<num_blocks, threads, 32 * sizeof(float)>>>(\n x.data_ptr<float>(),\n partial_max.data_ptr<float>(),\n total_elements,\n elements_per_block);\n \n auto global_max = torch::empty({1}, x.options());\n \n final_max_kernel<<<1, threads, 32 * sizeof(float)>>>(\n partial_max.data_ptr<float>(),\n global_max.data_ptr<float>(),\n num_blocks);\n \n float amax = global_max.item<float>();\n amax = fmaxf(amax, 1e-8f);\n float scale = amax / 127.0f;\n \n // Step 2: Fused blend + projection\n auto output = torch::empty({B, T, D}, x.options());\n \n int bt_blocks = B * T;\n int blend_threads = D;\n size_t shared_mem = D * sizeof(float);\n \n fused_quant_blend_project_kernel<<<bt_blocks, blend_threads, shared_mem>>>(\n x.data_ptr<float>(),\n layout_logits.data_ptr<float>(),\n weight.data_ptr<float>(),\n bias.data_ptr<float>(),\n output.data_ptr<float>(),\n scale,\n B, T, D);\n \n return output;\n}\n\"\"\"\n\n# Load the CUDA extension\nmodule = load_inline(\n name=\"quant_vla_cuda\",\n cpp_sources=[],\n cuda_sources=[cuda_source],\n functions=[\"forward_cuda\"],\n verbose=True\n)\n\nclass ModelNew(nn.Module):\n def __init__(self, dim: int):\n super().__init__()\n self.dim = dim\n # Create the linear layer with proper initialization\n self.weight = nn.Parameter(torch.empty(dim, dim))\n self.bias = nn.Parameter(torch.empty(dim))\n # Initialize same as nn.Linear\n nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))\n fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight)\n bound = 1 / math.sqrt(fan_in) if fan_in > 0 else 0\n nn.init.uniform_(self.bias, -bound, bound)\n\n def forward(self, x: torch.Tensor, layout_logits: torch.Tensor) -> torch.Tensor:\n # Ensure contiguous and correct dtype\n x = x.contiguous().float()\n layout_logits = layout_logits.contiguous().float()\n weight = self.weight.contiguous().float()\n bias = self.bias.contiguous().float()\n \n return module.forward_cuda(x, layout_logits, weight, bias)" }

result ← compile_kernel (fail)

failcompile_kernel compile_kernel FAILED: RuntimeError. RuntimeError: Error building extension 'quant_vla_cuda'

turn 2 · 1 tool call(s) · 308.9s turn · 11.6s LLM · tokens in=5,245 · out=1,338 · total=6,583 · 6,013 chars (click to expand)

turn 2308.9s turn · 11.6s LLM · 297.4s tools · tokens in=5,245 · out=1,338 · total=6,583

reasoning

Let me try to get more details about the error. The compilation failed but I don't see the actual error message. Let me simplify the kernel and try again with more verbose output.

call → compile_kernel

result ← compile_kernel (fail)

failcompile_kernel compile_kernel FAILED: RuntimeError. RuntimeError: Error building extension 'quant_vla_cuda_v1'

turn 3 · 1 tool call(s) · 116.5s turn · 11.7s LLM · tokens in=6,569 · out=1,156 · total=7,725 · 7,683 chars (click to expand)

turn 3116.5s turn · 11.7s LLM · 104.8s tools · tokens in=6,569 · out=1,156 · total=7,725

reasoning

Still failing. The error message is not very descriptive. Let me try a simpler approach and also check if there's an issue with my kernel code. Let me try to debug by removing verbose=True and adding more specific error handling, or try a simpler kernel first.

call → compile_kernel

result ← compile_kernel (fail)

failcompile_kernel compile_kernel FAILED: RuntimeError. RuntimeError: Error building extension 'qvla': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=qvla -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_2_problem_13_cache/k_08eed61739f2/qvla/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=qvla -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_2_problem_13_cache/k_08eed61739f2/qvla/main.cpp -o main.o /scratch/abaner/KernelBench/runs/pop_l123_all_gpt/popcorn/FW-GLM-5-1/level_2_problem_13_cache/k_08eed61739f2/qvla/main.cpp: In function 'void pybind11_init_qvla(pybind11::module_&)': /scratch/abaner/KernelBench/runs/pop_l123_all_gpt/popcorn/FW-GLM-5-1/level_2_problem_13_cache/k_08eed61739f2/qvla/main.cpp:3:51: error: 'forward_cuda' was not declared in this scope 3 | m.def("forward_cuda", torch::wrap_pybind_function(forward_cuda), "forward_cuda"); | ^~~~~~~~~~~~ [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=qvla -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_2_problem_13_cache/k_08eed61739f2/qvla/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 4237.5s turn · 10.1s LLM · 227.4s tools · tokens in=8,548 · out=1,159 · total=9,707

reasoning

I need to add a C++ wrapper. The function needs to be declared in the C++ part as well. Let me add a cpp_sources section.

call → compile_kernel

result ← compile_kernel (ok)

okcompile_kernel compile_kernel PASSED: kernel compiled without errors.

turn 5254.8s turn · 10.2s LLM · 244.6s tools · tokens in=9,689 · out=1,140 · total=10,829

reasoning

Great, it compiled! Now let me test correctness.

call → run_correctness

callrun_correctness { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\nimport math\n\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__device__ float sigmoid_d(float x) {\n return 1.0f / (1.0f + expf(-x));\n}\n\n__global__ void amax_kernel(const float* x, float* out, int n) {\n __shared__ float s[32];\n float m = 0.0f;\n for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < n; i += blockDim.x * gridDim.x) {\n m = fmaxf(m, fabsf(x[i]));\n }\n // warp reduce\n for (int mask = 16; mask > 0; mask >>= 1) {\n m = fmaxf(m, __shfl_xor_sync(0xffffffff, m, mask));\n }\n int lane = threadIdx.x & 31;\n int wid = threadIdx.x >> 5;\n if (lane == 0) s[wid] = m;\n __syncthreads();\n if (threadIdx.x < (blockDim.x >> 5)) m = s[threadIdx.x];\n else m = 0.0f;\n if (wid == 0) {\n for (int mask = 16; mask > 0; mask >>= 1) {\n m = fmaxf(m, __shfl_xor_sync(0xffffffff, m, mask));\n }\n }\n if (threadIdx.x == 0) {\n atomicMax(reinterpret_cast<int*>(out), __float_as_int(m));\n }\n}\n\n__global__ void blend_proj_kernel(\n const float* __restrict__ x,\n const float* __restrict__ layout_logits,\n const float* __restrict__ weight,\n const float* __restrict__ bias,\n float* __restrict__ out,\n float scale, int B, int T, int D) {\n \n extern __shared__ float sb[];\n int bt = blockIdx.x;\n int b = bt / T, t = bt % T, d = threadIdx.x;\n float alpha = sigmoid_d(layout_logits[b * T + t]);\n if (d < D) {\n float xv = x[b * T * D + t * D + d];\n float q = roundf(xv / scale);\n q = fmaxf(-127.0f, fminf(127.0f, q));\n sb[d] = alpha * q * scale + (1.0f - alpha) * xv;\n }\n __syncthreads();\n if (d < D) {\n float sum = bias[d];\n for (int k = 0; k < D; k++) sum += sb[k] * weight[d * D + k];\n out[b * T * D + t * D + d] = sum;\n }\n}\n\ntorch::Tensor forward_cuda(torch::Tensor x, torch::Tensor layout_logits, torch::Tensor weight, torch::Tensor bias) {\n int B = x.size(0), T = x.size(1), D = x.size(2), N = B * T * D;\n auto amax_t = torch::zeros({1}, x.options());\n int th = 256, bl = min(1024, (N + th - 1) / th);\n amax_kernel<<<bl, th>>>(x.data_ptr<float>(), amax_t.data_ptr<float>(), N);\n float amax = amax_t.item<float>();\n amax = fmaxf(amax, 1e-8f);\n float scale = amax / 127.0f;\n \n auto out = torch::empty({B, T, D}, x.options());\n blend_proj_kernel<<>>(\n x.data_ptr<float>(), layout_logits.data_ptr<float>(),\n weight.data_ptr<float>(), bias.data_ptr<float>(),\n out.data_ptr<float>(), scale, B, T, D);\n return out;\n}\n\"\"\"\n\nmodule = load_inline(\n name=\"qvla\",\n cpp_sources=[\"torch::Tensor forward_cuda(torch::Tensor x, torch::Tensor layout_logits, torch::Tensor weight, torch::Tensor bias);\"],\n cuda_sources=[cuda_source],\n functions=[\"forward_cuda\"],\n extra_cuda_cflags=[\"-O3\"]\n)\n\nclass ModelNew(nn.Module):\n def __init__(self, dim: int):\n super().__init__()\n self.dim = dim\n self.weight = nn.Parameter(torch.empty(dim, dim))\n self.bias = nn.Parameter(torch.empty(dim))\n nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))\n fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight)\n bound = 1 / math.sqrt(fan_in) if fan_in > 0 else 0\n nn.init.uniform_(self.bias, -bound, bound)\n\n def forward(self, x: torch.Tensor, layout_logits: torch.Tensor) -> torch.Tensor:\n return module.forward_cuda(\n x.contiguous().float(),\n layout_logits.contiguous().float(),\n self.weight.contiguous().float(),\n self.bias.contiguous().float()\n )" }

result ← run_correctness (ok)

okrun_correctness run_correctness PASSED: (3 / 3) trials all matched the reference. Numerical precision: max_abs_err=1.19e-06 mean_abs_err=6.97e-08 max_rel_err=6.38e-02

turn 6 · 1 tool call(s) · 248.4s turn · 9.4s LLM · tokens in=10,875 · out=1,146 · total=12,021 · 6,544 chars (click to expand)

turn 6248.4s turn · 9.4s LLM · 239.0s tools · tokens in=10,875 · out=1,146 · total=12,021

reasoning

Correctness passed. Now let me profile the kernel to see performance and identify optimization opportunities.

call → profile_kernel

callprofile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\nimport math\n\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__device__ float sigmoid_d(float x) {\n return 1.0f / (1.0f + expf(-x));\n}\n\n__global__ void amax_kernel(const float* x, float* out, int n) {\n __shared__ float s[32];\n float m = 0.0f;\n for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < n; i += blockDim.x * gridDim.x) {\n m = fmaxf(m, fabsf(x[i]));\n }\n // warp reduce\n for (int mask = 16; mask > 0; mask >>= 1) {\n m = fmaxf(m, __shfl_xor_sync(0xffffffff, m, mask));\n }\n int lane = threadIdx.x & 31;\n int wid = threadIdx.x >> 5;\n if (lane == 0) s[wid] = m;\n __syncthreads();\n if (threadIdx.x < (blockDim.x >> 5)) m = s[threadIdx.x];\n else m = 0.0f;\n if (wid == 0) {\n for (int mask = 16; mask > 0; mask >>= 1) {\n m = fmaxf(m, __shfl_xor_sync(0xffffffff, m, mask));\n }\n }\n if (threadIdx.x == 0) {\n atomicMax(reinterpret_cast<int*>(out), __float_as_int(m));\n }\n}\n\n__global__ void blend_proj_kernel(\n const float* __restrict__ x,\n const float* __restrict__ layout_logits,\n const float* __restrict__ weight,\n const float* __restrict__ bias,\n float* __restrict__ out,\n float scale, int B, int T, int D) {\n \n extern __shared__ float sb[];\n int bt = blockIdx.x;\n int b = bt / T, t = bt % T, d = threadIdx.x;\n float alpha = sigmoid_d(layout_logits[b * T + t]);\n if (d < D) {\n float xv = x[b * T * D + t * D + d];\n float q = roundf(xv / scale);\n q = fmaxf(-127.0f, fminf(127.0f, q));\n sb[d] = alpha * q * scale + (1.0f - alpha) * xv;\n }\n __syncthreads();\n if (d < D) {\n float sum = bias[d];\n for (int k = 0; k < D; k++) sum += sb[k] * weight[d * D + k];\n out[b * T * D + t * D + d] = sum;\n }\n}\n\ntorch::Tensor forward_cuda(torch::Tensor x, torch::Tensor layout_logits, torch::Tensor weight, torch::Tensor bias) {\n int B = x.size(0), T = x.size(1), D = x.size(2), N = B * T * D;\n auto amax_t = torch::zeros({1}, x.options());\n int th = 256, bl = min(1024, (N + th - 1) / th);\n amax_kernel<<<bl, th>>>(x.data_ptr<float>(), amax_t.data_ptr<float>(), N);\n float amax = amax_t.item<float>();\n amax = fmaxf(amax, 1e-8f);\n float scale = amax / 127.0f;\n \n auto out = torch::empty({B, T, D}, x.options());\n blend_proj_kernel<<>>(\n x.data_ptr<float>(), layout_logits.data_ptr<float>(),\n weight.data_ptr<float>(), bias.data_ptr<float>(),\n out.data_ptr<float>(), scale, B, T, D);\n return out;\n}\n\"\"\"\n\nmodule = load_inline(\n name=\"qvla\",\n cpp_sources=[\"torch::Tensor forward_cuda(torch::Tensor x, torch::Tensor layout_logits, torch::Tensor weight, torch::Tensor bias);\"],\n cuda_sources=[cuda_source],\n functions=[\"forward_cuda\"],\n extra_cuda_cflags=[\"-O3\"]\n)\n\nclass ModelNew(nn.Module):\n def __init__(self, dim: int):\n super().__init__()\n self.dim = dim\n self.weight = nn.Parameter(torch.empty(dim, dim))\n self.bias = nn.Parameter(torch.empty(dim))\n nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))\n fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight)\n bound = 1 / math.sqrt(fan_in) if fan_in > 0 else 0\n nn.init.uniform_(self.bias, -bound, bound)\n\n def forward(self, x: torch.Tensor, layout_logits: torch.Tensor) -> torch.Tensor:\n return module.forward_cuda(\n x.contiguous().float(),\n layout_logits.contiguous().float(),\n self.weight.contiguous().float(),\n self.bias.contiguous().float()\n )" }

result ← profile_kernel (ok)

okprofile_kernel profile_kernel PASSED: profiling complete. === Kernel Profile Summary === --- Kernel Breakdown (by GPU time) --- #1: aten::zeros 6013.0μs (30%) calls=1 #2: aten::zero_ 5964.0μs (30%) calls=1 #3: aten::fill_ 5943.0μs (30%) calls=1 #4: aten::item 892.0μs (5%) calls=1 #5: aten::_local_scalar_dense 870.0μs (4%) calls=1 ... +6 more kernels --- Timing --- GPU time: 44.74 μs --- Memory Bandwidth --- DRAM BW: 85.4/3350 GB/s (2.5%) read=85.4 write=0.0 GB/s Hit rates: L2=272.9% --- Compute Throughput --- Pipe util: FMA=2.7%, Tensor=0.0% --- Roofline Analysis --- Ridge point (FP32): 20.00 FLOPs/byte Classification: UNKNOWN --- Occupancy --- Achieved: 51.6% Eligible warps/cycle: 0.18 WARNING: <1 eligible warp/cycle = latency-bound, needs more parallelism Launch: regs/thread=64, smem/blk=3712B, block=512, grid=769 --- Memory Access Quality --- Global ld coalescing: 35.34 sectors/req (35.3x wasted BW) --- Warp Stall Reasons (% of issue cycles) --- long_scoreboard 8487.9% #################### short_scoreboard 3666.9% #################### mio_throttle 2270.6% #################### --- Optimization Hints --- -> 8488% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=35.3 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set. -> Global load coalescing=35.3x (ideal=1.0) -> 35x 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 7 · 1 tool call(s) · 248.4s turn · 19.9s LLM · tokens in=12,565 · out=1,946 · total=14,511 · 8,069 chars (click to expand)

turn 7248.4s turn · 19.9s LLM · 228.5s tools · tokens in=12,565 · out=1,946 · total=14,511

reasoning

The profiling shows issues: 1. Very high percentage of stalls (long_scoreboard, short_scoreboard, mio_throttle) 2. Poor global load coalescing (35.3x wasted BW) 3. Memory latency bound The kernel needs optimization: 1. Better memory access patterns - the blend_proj_kernel has uncoalesced reads for weight matrix 2. Need to tile the computation better Let me look at the weight access pattern. Currently I'm reading `weight[d * D + k]` which means each thread is reading D elements that are stride-D apart. This is terrible for coalescing. Better approach: 1. Transpose the weight matrix to make accesses coalesced 2. Or use shared memory to load weight tiles Let me try a different approach - load the weight matrix into shared memory in a coalesced way and then use it.

assistantThe profiling shows poor memory coalescing (35x wasted bandwidth) and the kernel is latency-bound. Let me optimize the memory access patterns:

call → compile_kernel

callcompile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\nimport math\n\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__device__ __forceinline__ float sigmoid_f(float x) {\n return 1.0f / (1.0f + expf(-x));\n}\n\n__global__ void amax_kernel(const float* __restrict__ x, float* __restrict__ out, int n) {\n float m = 0.0f;\n for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < n; i += blockDim.x * gridDim.x) {\n m = fmaxf(m, fabsf(x[i]));\n }\n // Warp reduce\n for (int mask = 16; mask > 0; mask >>= 1) {\n m = fmaxf(m, __shfl_xor_sync(0xffffffff, m, mask));\n }\n __shared__ float smem[32];\n int lane = threadIdx.x & 31;\n int wid = threadIdx.x >> 5;\n if (lane == 0) smem[wid] = m;\n __syncthreads();\n if (wid == 0) {\n m = (lane < (blockDim.x >> 5)) ? smem[lane] : 0.0f;\n for (int mask = 16; mask > 0; mask >>= 1) {\n m = fmaxf(m, __shfl_xor_sync(0xffffffff, m, mask));\n }\n if (lane == 0) atomicMax(reinterpret_cast<int*>(out), __float_as_int(m));\n }\n}\n\n__global__ void fused_kernel(\n const float* __restrict__ x,\n const float* __restrict__ layout_logits,\n const float* __restrict__ weight_t, // transposed weight (D x D)\n const float* __restrict__ bias,\n float* __restrict__ out,\n float scale, int B, int T, int D) {\n \n // One block per (b, t) pair, threads = D\n int bt = blockIdx.x;\n int b = bt / T, t = bt % T, d = threadIdx.x;\n \n __shared__ float sb_blend[128]; // blended values\n __shared__ float sb_wcol[128]; // weight column (for output d)\n \n float alpha = sigmoid_f(layout_logits[b * T + t]);\n \n // Step 1: Compute blend values (coalesced read of x, store to shared)\n if (d < D) {\n float xv = x[b * T * D + t * D + d];\n float q = roundf(xv / scale);\n q = fmaxf(-127.0f, fminf(127.0f, q));\n sb_blend[d] = alpha * q * scale + (1.0f - alpha) * xv;\n }\n __syncthreads();\n \n // Step 2: Compute output for this thread's d\n // weight_t is D x D stored row-major, so weight_t[d * D + k] is the weight for output d from input k\n // Thread d reads row d of weight_t (coalesced when consecutive threads read consecutive k)\n float sum = bias[d];\n for (int k = 0; k < D; k++) {\n // Thread d needs weight_t[d * D + k] - but this is NOT coalesced across threads\n // Thread 0 reads w[0*D+0], thread 1 reads w[1*D+0] = stride D\n // Instead load weight column into shared memory cooperatively\n }\n \n // Better: load weight column into shared memory\n // Each iteration, all threads load one column element each\n // Thread d loads weight_t[d * D + k] -> this is stride D, still bad\n \n // Actually let's re-think: We need weight_t[d][k] for each d to multiply with blend[k]\n // Thread d needs D weight values, all threads need different weight values\n \n // Let's use shared memory tiling\n // Load blend values (already in shared)\n // Load weight tile into shared memory\n \n // Actually, simpler: use the original weight layout but load in coalesced way\n // If we store weight as (D, D) row-major, then weight[k, d] is at index k*D + d\n // Thread d reads weight[k*D + d] for all k - this IS coalesced! Same k, consecutive d\n \n // But the original weight is (out_dim, in_dim) = (D, D), so weight[d, k] for output d from input k\n // Thread d needs weight[d, k] = weight[d*D + k] - stride 1 for k, but different d have stride D\n \n // Let's use weight transposed: weight_t[k, d] = weight[d, k]\n // Then thread d reads weight_t[k*D + d] for k=0..D-1\n // For a fixed k, thread d reads weight_t[k*D + d] - consecutive d, consecutive memory -> coalesced!\n \n // So we need to pass transposed weight\n for (int k = 0; k < D; k++) {\n sum += sb_blend[k] * weight_t[k * D + d]; // Coalesced read!\n }\n \n if (d < D) {\n out[b * T * D + t * D + d] = sum;\n }\n}\n\ntorch::Tensor forward_cuda(torch::Tensor x, torch::Tensor layout_logits, torch::Tensor weight, torch::Tensor bias) {\n int B = x.size(0), T = x.size(1), D = x.size(2), N = B * T * D;\n \n // Compute amax\n auto amax_t = torch::zeros({1}, x.options());\n int th = 256, bl = min(1024, (N + th - 1) / th);\n amax_kernel<<<bl, th>>>(x.data_ptr<float>(), amax_t.data_ptr<float>(), N);\n float amax = amax_t.item<float>();\n amax = fmaxf(amax, 1e-8f);\n float scale = amax / 127.0f;\n \n // Transpose weight for coalesced access\n auto weight_t = weight.t().contiguous();\n \n auto out = torch::empty({B, T, D}, x.options());\n fused_kernel<<>>(\n x.data_ptr<float>(), layout_logits.data_ptr<float>(),\n weight_t.data_ptr<float>(), bias.data_ptr<float>(),\n out.data_ptr<float>(), scale, B, T, D);\n return out;\n}\n\"\"\"\n\nmodule = load_inline(\n name=\"qvla\",\n cpp_sources=[\"torch::Tensor forward_cuda(torch::Tensor x, torch::Tensor layout_logits, torch::Tensor weight, torch::Tensor bias);\"],\n cuda_sources=[cuda_source],\n functions=[\"forward_cuda\"],\n extra_cuda_cflags=[\"-O3\"]\n)\n\nclass ModelNew(nn.Module):\n def __init__(self, dim: int):\n super().__init__()\n self.dim = dim\n self.weight = nn.Parameter(torch.empty(dim, dim))\n self.bias = nn.Parameter(torch.empty(dim))\n nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))\n fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight)\n bound = 1 / math.sqrt(fan_in) if fan_in > 0 else 0\n nn.init.uniform_(self.bias, -bound, bound)\n\n def forward(self, x: torch.Tensor, layout_logits: torch.Tensor) -> torch.Tensor:\n return module.forward_cuda(\n x.contiguous().float(),\n layout_logits.contiguous().float(),\n self.weight.contiguous().float(),\n self.bias.contiguous().float()\n )" }

result ← compile_kernel (ok)

okcompile_kernel compile_kernel PASSED: kernel compiled without errors.

turn 8307.6s turn · 11.5s LLM · 296.1s tools · tokens in=14,345 · out=1,301 · total=15,646

reasoning

Let me check correctness first.

call → run_correctness

callrun_correctness { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\nimport math\n\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__device__ __forceinline__ float sigmoid_f(float x) {\n return 1.0f / (1.0f + expf(-x));\n}\n\n__global__ void amax_kernel(const float* __restrict__ x, float* __restrict__ out, int n) {\n float m = 0.0f;\n for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < n; i += blockDim.x * gridDim.x) {\n m = fmaxf(m, fabsf(x[i]));\n }\n // Warp reduce\n for (int mask = 16; mask > 0; mask >>= 1) {\n m = fmaxf(m, __shfl_xor_sync(0xffffffff, m, mask));\n }\n __shared__ float smem[32];\n int lane = threadIdx.x & 31;\n int wid = threadIdx.x >> 5;\n if (lane == 0) smem[wid] = m;\n __syncthreads();\n if (wid == 0) {\n m = (lane < (blockDim.x >> 5)) ? smem[lane] : 0.0f;\n for (int mask = 16; mask > 0; mask >>= 1) {\n m = fmaxf(m, __shfl_xor_sync(0xffffffff, m, mask));\n }\n if (lane == 0) atomicMax(reinterpret_cast<int*>(out), __float_as_int(m));\n }\n}\n\n__global__ void fused_kernel(\n const float* __restrict__ x,\n const float* __restrict__ layout_logits,\n const float* __restrict__ weight_t, // transposed weight (D x D)\n const float* __restrict__ bias,\n float* __restrict__ out,\n float scale, int B, int T, int D) {\n \n // One block per (b, t) pair, threads = D\n int bt = blockIdx.x;\n int b = bt / T, t = bt % T, d = threadIdx.x;\n \n __shared__ float sb_blend[128]; // blended values\n \n float alpha = sigmoid_f(layout_logits[b * T + t]);\n \n // Step 1: Compute blend values (coalesced read of x, store to shared)\n if (d < D) {\n float xv = x[b * T * D + t * D + d];\n float q = roundf(xv / scale);\n q = fmaxf(-127.0f, fminf(127.0f, q));\n sb_blend[d] = alpha * q * scale + (1.0f - alpha) * xv;\n }\n __syncthreads();\n \n // Step 2: Compute output for this thread's d\n // weight_t is D x D stored row-major, so weight_t[k * D + d] is the weight for output d from input k\n // Thread d reads weight_t[k * D + d] for k=0..D-1 - coalesced access!\n float sum = bias[d];\n for (int k = 0; k < D; k++) {\n sum += sb_blend[k] * weight_t[k * D + d];\n }\n \n if (d < D) {\n out[b * T * D + t * D + d] = sum;\n }\n}\n\ntorch::Tensor forward_cuda(torch::Tensor x, torch::Tensor layout_logits, torch::Tensor weight, torch::Tensor bias) {\n int B = x.size(0), T = x.size(1), D = x.size(2), N = B * T * D;\n \n // Compute amax\n auto amax_t = torch::zeros({1}, x.options());\n int th = 256, bl = min(1024, (N + th - 1) / th);\n amax_kernel<<<bl, th>>>(x.data_ptr<float>(), amax_t.data_ptr<float>(), N);\n float amax = amax_t.item<float>();\n amax = fmaxf(amax, 1e-8f);\n float scale = amax / 127.0f;\n \n // Transpose weight for coalesced access\n auto weight_t = weight.t().contiguous();\n \n auto out = torch::empty({B, T, D}, x.options());\n fused_kernel<<>>(\n x.data_ptr<float>(), layout_logits.data_ptr<float>(),\n weight_t.data_ptr<float>(), bias.data_ptr<float>(),\n out.data_ptr<float>(), scale, B, T, D);\n return out;\n}\n\"\"\"\n\nmodule = load_inline(\n name=\"qvla\",\n cpp_sources=[\"torch::Tensor forward_cuda(torch::Tensor x, torch::Tensor layout_logits, torch::Tensor weight, torch::Tensor bias);\"],\n cuda_sources=[cuda_source],\n functions=[\"forward_cuda\"],\n extra_cuda_cflags=[\"-O3\"]\n)\n\nclass ModelNew(nn.Module):\n def __init__(self, dim: int):\n super().__init__()\n self.dim = dim\n self.weight = nn.Parameter(torch.empty(dim, dim))\n self.bias = nn.Parameter(torch.empty(dim))\n nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))\n fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight)\n bound = 1 / math.sqrt(fan_in) if fan_in > 0 else 0\n nn.init.uniform_(self.bias, -bound, bound)\n\n def forward(self, x: torch.Tensor, layout_logits: torch.Tensor) -> torch.Tensor:\n return module.forward_cuda(\n x.contiguous().float(),\n layout_logits.contiguous().float(),\n self.weight.contiguous().float(),\n self.bias.contiguous().float()\n )" }

result ← run_correctness (ok)

okrun_correctness run_correctness PASSED: (3 / 3) trials all matched the reference. Numerical precision: max_abs_err=1.19e-06 mean_abs_err=6.97e-08 max_rel_err=6.38e-02

turn 9 · 1 tool call(s) · 407.0s turn · 10.9s LLM · tokens in=15,697 · out=1,303 · total=17,000 · 7,505 chars (click to expand)

turn 9407.0s turn · 10.9s LLM · 396.0s tools · tokens in=15,697 · out=1,303 · total=17,000

reasoning

Let me profile again to see the improvement.

call → profile_kernel

callprofile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\nimport math\n\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__device__ __forceinline__ float sigmoid_f(float x) {\n return 1.0f / (1.0f + expf(-x));\n}\n\n__global__ void amax_kernel(const float* __restrict__ x, float* __restrict__ out, int n) {\n float m = 0.0f;\n for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < n; i += blockDim.x * gridDim.x) {\n m = fmaxf(m, fabsf(x[i]));\n }\n // Warp reduce\n for (int mask = 16; mask > 0; mask >>= 1) {\n m = fmaxf(m, __shfl_xor_sync(0xffffffff, m, mask));\n }\n __shared__ float smem[32];\n int lane = threadIdx.x & 31;\n int wid = threadIdx.x >> 5;\n if (lane == 0) smem[wid] = m;\n __syncthreads();\n if (wid == 0) {\n m = (lane < (blockDim.x >> 5)) ? smem[lane] : 0.0f;\n for (int mask = 16; mask > 0; mask >>= 1) {\n m = fmaxf(m, __shfl_xor_sync(0xffffffff, m, mask));\n }\n if (lane == 0) atomicMax(reinterpret_cast<int*>(out), __float_as_int(m));\n }\n}\n\n__global__ void fused_kernel(\n const float* __restrict__ x,\n const float* __restrict__ layout_logits,\n const float* __restrict__ weight_t, // transposed weight (D x D)\n const float* __restrict__ bias,\n float* __restrict__ out,\n float scale, int B, int T, int D) {\n \n // One block per (b, t) pair, threads = D\n int bt = blockIdx.x;\n int b = bt / T, t = bt % T, d = threadIdx.x;\n \n __shared__ float sb_blend[128]; // blended values\n \n float alpha = sigmoid_f(layout_logits[b * T + t]);\n \n // Step 1: Compute blend values (coalesced read of x, store to shared)\n if (d < D) {\n float xv = x[b * T * D + t * D + d];\n float q = roundf(xv / scale);\n q = fmaxf(-127.0f, fminf(127.0f, q));\n sb_blend[d] = alpha * q * scale + (1.0f - alpha) * xv;\n }\n __syncthreads();\n \n // Step 2: Compute output for this thread's d\n // weight_t is D x D stored row-major, so weight_t[k * D + d] is the weight for output d from input k\n // Thread d reads weight_t[k * D + d] for k=0..D-1 - coalesced access!\n float sum = bias[d];\n for (int k = 0; k < D; k++) {\n sum += sb_blend[k] * weight_t[k * D + d];\n }\n \n if (d < D) {\n out[b * T * D + t * D + d] = sum;\n }\n}\n\ntorch::Tensor forward_cuda(torch::Tensor x, torch::Tensor layout_logits, torch::Tensor weight, torch::Tensor bias) {\n int B = x.size(0), T = x.size(1), D = x.size(2), N = B * T * D;\n \n // Compute amax\n auto amax_t = torch::zeros({1}, x.options());\n int th = 256, bl = min(1024, (N + th - 1) / th);\n amax_kernel<<<bl, th>>>(x.data_ptr<float>(), amax_t.data_ptr<float>(), N);\n float amax = amax_t.item<float>();\n amax = fmaxf(amax, 1e-8f);\n float scale = amax / 127.0f;\n \n // Transpose weight for coalesced access\n auto weight_t = weight.t().contiguous();\n \n auto out = torch::empty({B, T, D}, x.options());\n fused_kernel<<>>(\n x.data_ptr<float>(), layout_logits.data_ptr<float>(),\n weight_t.data_ptr<float>(), bias.data_ptr<float>(),\n out.data_ptr<float>(), scale, B, T, D);\n return out;\n}\n\"\"\"\n\nmodule = load_inline(\n name=\"qvla\",\n cpp_sources=[\"torch::Tensor forward_cuda(torch::Tensor x, torch::Tensor layout_logits, torch::Tensor weight, torch::Tensor bias);\"],\n cuda_sources=[cuda_source],\n functions=[\"forward_cuda\"],\n extra_cuda_cflags=[\"-O3\"]\n)\n\nclass ModelNew(nn.Module):\n def __init__(self, dim: int):\n super().__init__()\n self.dim = dim\n self.weight = nn.Parameter(torch.empty(dim, dim))\n self.bias = nn.Parameter(torch.empty(dim))\n nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))\n fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight)\n bound = 1 / math.sqrt(fan_in) if fan_in > 0 else 0\n nn.init.uniform_(self.bias, -bound, bound)\n\n def forward(self, x: torch.Tensor, layout_logits: torch.Tensor) -> torch.Tensor:\n return module.forward_cuda(\n x.contiguous().float(),\n layout_logits.contiguous().float(),\n self.weight.contiguous().float(),\n self.bias.contiguous().float()\n )" }

result ← profile_kernel (ok)

okprofile_kernel profile_kernel PASSED: profiling complete. === Kernel Profile Summary === --- Kernel Breakdown (by GPU time) --- #1: aten::contiguous 7355.0μs (17%) calls=1 #2: aten::clone 7345.0μs (17%) calls=1 #3: aten::copy_ 7285.0μs (17%) calls=1 #4: aten::zeros 6605.0μs (15%) calls=1 #5: aten::zero_ 6554.0μs (15%) calls=1 ... +14 more kernels --- Timing --- GPU time: 17.95 μs [↓26.8μs ok] --- Memory Bandwidth --- DRAM BW: 138.6/3350 GB/s (4.1%) [↑1.6% ok] read=138.6 write=0.0 GB/s Hit rates: L2=360.2% --- Compute Throughput --- Dominant pipe: FP32 util=16.9% Pipe util: FMA=16.9%, Tensor=0.3% --- Roofline Analysis --- Ridge point (FP32): 20.00 FLOPs/byte Classification: COMPUTE-BOUND --- Occupancy --- Achieved: 57.1% [↑5.5% ok] Eligible warps/cycle: 0.40 WARNING: <1 eligible warp/cycle = latency-bound, needs more parallelism Launch: regs/thread=88, smem/blk=4736B, block=640, grid=833 --- Memory Access Quality --- Global ld coalescing: 39.98 sectors/req (40.0x wasted BW) --- Warp Stall Reasons (% of issue cycles) --- long_scoreboard 3700.8% #################### short_scoreboard 789.2% #################### mio_throttle 6.9% # --- Optimization Hints --- -> 3701% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=40.0 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set. -> Global load coalescing=40.0x (ideal=1.0) -> 40x wasted DRAM BW. Make per-warp accesses contiguous. -> Low eligible warps + low BW% + low compute% = LATENCY-BOUND. Need more parallelism (larger grid, more ILP, prefetching). -> Compute-bound but NOT using tensor cores. If applicable, use FP16/BF16 with torch.matmul or WMMA intrinsics. --- Delta vs Previous Iteration --- GPU time: 44.74→17.95μs (↓26.78) ok DRAM BW%: 2.55→4.14% (↑1.59) ok Occupancy: 51.59→57.07% (↑5.48) ok Eligible warps/cyc: 0.18→0.40 (↑0.22) ok Ld sectors/req: 35.34→39.98 (↑4.64) REGRESSED L2 hit%: 272.90→360.19% (↑87.29) ok

turn 10 · 1 tool call(s) · 326.8s turn · 25.3s LLM · tokens in=17,762 · out=2,580 · total=20,342 · 10,572 chars (click to expand)

turn 10326.8s turn · 25.3s LLM · 301.5s tools · tokens in=17,762 · out=2,580 · total=20,342

reasoning

The kernel time improved significantly (44.74μs → 17.95μs), but the profiling still shows uncoalesced memory access (40x wasted bandwidth). The issue is with the weight matrix access pattern. The problem is: - In the loop `sum += sb_blend[k] * weight_t[k * D + d]`, thread d reads weight_t[k*D + d] - For consecutive threads (d=0,1,2...), they access addresses weight_t[k*D], weight_t[k*D+1], weight_t[k*D+2] - these ARE consecutive! - But the weight is D x D = 128 x 128, so weight_t[k*D + d] should be coalesced... Wait, the issue is actually in reading the weight matrix across iterations. Let me check: - For k=0: threads 0..D-1 read addresses 0..D-1 (stride 1, coalesced) ✓ - For k=1: threads 0..D-1 read addresses D..2D-1 (stride 1, coalesced) ✓ Hmm, this should be coalesced... unless there's an issue with the read-only cache or the memory transaction size. Actually, looking at the profiler output, the GPU time is now 17.95μs which is compute-bound. The uncoalescing warning might be due to how the profiler counts things, or it could be from the amax kernel. Let me try to use a more optimized approach with proper tiling to reduce register pressure and improve memory access. Also, I should consider: 1. Using __ldg for read-only data (weight matrix) 2. Pre-loading weight matrix in shared memory tiles 3. Using warp-level matrix operations if applicable But first, let me check if the current version is already faster than PyTorch. Let me also look at eliminating the overhead of the weight transpose by storing the weight transposed in the model itself. Actually, I notice the `item()` call and `zeros()` operations are taking significant time. Let me optimize those: 1. Instead of `zeros` for amax output, use a single value initialized to 0 2. Reduce overhead of .item() call Let me also try a simpler approach: do the reduction on device and launch the fused kernel directly, avoiding the CPU synchronization.

assistantGood progress! The kernel time improved from 44.74μs to 17.95μs. But there's still uncoalesced memory access. Let me optimize further by using shared memory tiling for the weight matrix and avoiding the CPU synchronization:

call → compile_kernel

callcompile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\nimport math\n\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__device__ __forceinline__ float sigmoid_f(float x) {\n return 1.0f / (1.0f + expf(-x));\n}\n\n__global__ void amax_kernel(const float* __restrict__ x, float* __restrict__ out, int n) {\n float m = 0.0f;\n for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < n; i += blockDim.x * gridDim.x) {\n m = fmaxf(m, fabsf(x[i]));\n }\n for (int mask = 16; mask > 0; mask >>= 1) {\n m = fmaxf(m, __shfl_xor_sync(0xffffffff, m, mask));\n }\n __shared__ float smem[32];\n int lane = threadIdx.x & 31;\n int wid = threadIdx.x >> 5;\n if (lane == 0) smem[wid] = m;\n __syncthreads();\n if (wid == 0) {\n m = (lane < (blockDim.x >> 5)) ? smem[lane] : 0.0f;\n for (int mask = 16; mask > 0; mask >>= 1) {\n m = fmaxf(m, __shfl_xor_sync(0xffffffff, m, mask));\n }\n if (lane == 0) atomicMax(reinterpret_cast<int*>(out), __float_as_int(m));\n }\n}\n\n// Optimized kernel: use shared memory tiles for weights\n__global__ void fused_kernel_opt(\n const float* __restrict__ x,\n const float* __restrict__ layout_logits,\n const float* __restrict__ weight_t, // transposed weight (D x D)\n const float* __restrict__ bias,\n float* __restrict__ out,\n float scale, int B, int T, int D) {\n \n int bt = blockIdx.x;\n int b = bt / T, t = bt % T;\n int d = threadIdx.x;\n int lane = threadIdx.x & 31;\n int warp_id = threadIdx.x >> 5;\n int num_warps = blockDim.x >> 5;\n \n __shared__ float sb_blend[128];\n __shared__ float sb_weight[128]; // tile of weight matrix\n \n float alpha = sigmoid_f(layout_logits[b * T + t]);\n \n // Step 1: Compute blend values\n if (d < D) {\n float xv = x[b * T * D + t * D + d];\n float q = roundf(xv / scale);\n q = fmaxf(-127.0f, fminf(127.0f, q));\n sb_blend[d] = alpha * q * scale + (1.0f - alpha) * xv;\n }\n __syncthreads();\n \n // Step 2: Compute output using tiled weight access\n // Each thread computes one output element\n float sum = bias[d];\n \n // Process weight matrix in tiles\n const int TILE_SIZE = 32; // Match warp size\n for (int tile_start = 0; tile_start < D; tile_start += TILE_SIZE) {\n // Cooperatively load a tile of weights into shared memory\n // Each warp loads one row of the tile\n // warp_id loads weight_t[(tile_start + lane) * D + ?]\n // We need weight_t[k * D + d] for all k in [tile_start, tile_start+TILE_SIZE)\n \n // Actually, let's load the tile column by column\n // For each column (output dimension), we need D weights\n // This is complex, let me simplify\n \n // Simpler approach: each thread loads its own weight values from global memory\n // but do it with explicit __ldg for read-only cache\n }\n \n // Even simpler: just use __ldg and let the cache work\n for (int k = 0; k < D; k++) {\n sum += sb_blend[k] * __ldg(&weight_t[k * D + d]);\n }\n \n if (d < D) {\n out[b * T * D + t * D + d] = sum;\n }\n}\n\n// Fully fused kernel that computes amax internally\n__global__ void fully_fused_kernel(\n const float* __restrict__ x,\n const float* __restrict__ layout_logits,\n const float* __restrict__ weight_t,\n const float* __restrict__ bias,\n float* __restrict__ out,\n float* __restrict__ global_amax,\n int B, int T, int D) {\n \n extern __shared__ float smem[];\n float* sb_blend = smem;\n float* sb_amax = smem + D;\n \n int bt = blockIdx.x;\n int b = bt / T, t = bt % T;\n int d = threadIdx.x;\n \n // Step 1: Each thread finds local max\n int total = B * T * D;\n float local_max = 0.0f;\n for (int i = threadIdx.x; i < total; i += blockDim.x) {\n local_max = fmaxf(local_max, fabsf(x[i]));\n }\n \n // Warp reduce max\n for (int mask = 16; mask > 0; mask >>= 1) {\n local_max = fmaxf(local_max, __shfl_xor_sync(0xffffffff, local_max, mask));\n }\n \n // Store to shared memory\n int lane = threadIdx.x & 31;\n int wid = threadIdx.x >> 5;\n int num_warps = blockDim.x >> 5;\n \n if (lane == 0) sb_amax[wid] = local_max;\n __syncthreads();\n \n // First warp reduces\n if (wid == 0) {\n local_max = (lane < num_warps) ? sb_amax[lane] : 0.0f;\n for (int mask = 16; mask > 0; mask >>= 1) {\n local_max = fmaxf(local_max, __shfl_xor_sync(0xffffffff, local_max, mask));\n }\n if (lane == 0) {\n atomicMax(reinterpret_cast<int*>(global_amax), __float_as_int(local_max));\n }\n }\n \n // Need to wait for all blocks to contribute to amax\n __threadfence();\n \n // This is problematic - we can't easily sync across blocks\n // Let's use a different approach\n}\n\ntorch::Tensor forward_cuda(torch::Tensor x, torch::Tensor layout_logits, torch::Tensor weight, torch::Tensor bias) {\n int B = x.size(0), T = x.size(1), D = x.size(2), N = B * T * D;\n \n // Compute amax\n auto amax_t = torch::zeros({1}, x.options());\n int th = 256, bl = min(1024, (N + th - 1) / th);\n amax_kernel<<<bl, th>>>(x.data_ptr<float>(), amax_t.data_ptr<float>(), N);\n float amax = amax_t.item<float>();\n amax = fmaxf(amax, 1e-8f);\n float scale = amax / 127.0f;\n \n // Transpose weight for coalesced access (but do it once during init, not every forward)\n auto weight_t = weight.t().contiguous();\n \n auto out = torch::empty({B, T, D}, x.options());\n fused_kernel_opt<<>>(\n x.data_ptr<float>(), layout_logits.data_ptr<float>(),\n weight_t.data_ptr<float>(), bias.data_ptr<float>(),\n out.data_ptr<float>(), scale, B, T, D);\n return out;\n}\n\"\"\"\n\nmodule = load_inline(\n name=\"qvla\",\n cpp_sources=[\"torch::Tensor forward_cuda(torch::Tensor x, torch::Tensor layout_logits, torch::Tensor weight, torch::Tensor bias);\"],\n cuda_sources=[cuda_source],\n functions=[\"forward_cuda\"],\n extra_cuda_cflags=[\"-O3\", \"-use_fast_math\"]\n)\n\nclass ModelNew(nn.Module):\n def __init__(self, dim: int):\n super().__init__()\n self.dim = dim\n # Store weight transposed for coalesced access\n self.weight = nn.Parameter(torch.empty(dim, dim))\n self.bias = nn.Parameter(torch.empty(dim))\n nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))\n fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight)\n bound = 1 / math.sqrt(fan_in) if fan_in > 0 else 0\n nn.init.uniform_(self.bias, -bound, bound)\n # Store transposed weight\n self.weight_t = nn.Parameter(self.weight.data.t().clone())\n\n def forward(self, x: torch.Tensor, layout_logits: torch.Tensor) -> torch.Tensor:\n return module.forward_cuda(\n x.contiguous().float(),\n layout_logits.contiguous().float(),\n self.weight.contiguous().float(),\n self.bias.contiguous().float()\n )" }

result ← compile_kernel (ok)

okcompile_kernel compile_kernel PASSED: kernel compiled without errors.

turn 11244.9s turn · 10.9s LLM · 234.0s tools · tokens in=19,863 · out=1,265 · total=21,128

reasoning

Let me fix the code - I'm using `__ldg` which is a read-only cache directive. But I should also check correctness. The issue is that I'm creating both `weight` and `weight_t` as parameters which is redundant. Let me clean this up and also use the transpose properly.

call → run_correctness

callrun_correctness { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\nimport math\n\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__device__ __forceinline__ float sigmoid_f(float x) {\n return 1.0f / (1.0f + expf(-x));\n}\n\n__global__ void amax_kernel(const float* __restrict__ x, float* __restrict__ out, int n) {\n float m = 0.0f;\n for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < n; i += blockDim.x * gridDim.x) {\n m = fmaxf(m, fabsf(x[i]));\n }\n for (int mask = 16; mask > 0; mask >>= 1) {\n m = fmaxf(m, __shfl_xor_sync(0xffffffff, m, mask));\n }\n __shared__ float smem[32];\n int lane = threadIdx.x & 31;\n int wid = threadIdx.x >> 5;\n if (lane == 0) smem[wid] = m;\n __syncthreads();\n if (wid == 0) {\n m = (lane < (blockDim.x >> 5)) ? smem[lane] : 0.0f;\n for (int mask = 16; mask > 0; mask >>= 1) {\n m = fmaxf(m, __shfl_xor_sync(0xffffffff, m, mask));\n }\n if (lane == 0) atomicMax(reinterpret_cast<int*>(out), __float_as_int(m));\n }\n}\n\n__global__ void fused_kernel_opt(\n const float* __restrict__ x,\n const float* __restrict__ layout_logits,\n const float* __restrict__ weight_t,\n const float* __restrict__ bias,\n float* __restrict__ out,\n float scale, int B, int T, int D) {\n \n int bt = blockIdx.x;\n int b = bt / T, t = bt % T;\n int d = threadIdx.x;\n \n __shared__ float sb_blend[128];\n \n float alpha = sigmoid_f(layout_logits[b * T + t]);\n \n // Compute blend values\n if (d < D) {\n float xv = x[b * T * D + t * D + d];\n float q = roundf(xv / scale);\n q = fmaxf(-127.0f, fminf(127.0f, q));\n sb_blend[d] = alpha * q * scale + (1.0f - alpha) * xv;\n }\n __syncthreads();\n \n // Compute output using __ldg for read-only cache\n float sum = bias[d];\n for (int k = 0; k < D; k++) {\n sum += sb_blend[k] * __ldg(&weight_t[k * D + d]);\n }\n \n if (d < D) {\n out[b * T * D + t * D + d] = sum;\n }\n}\n\ntorch::Tensor forward_cuda(torch::Tensor x, torch::Tensor layout_logits, torch::Tensor weight, torch::Tensor bias) {\n int B = x.size(0), T = x.size(1), D = x.size(2), N = B * T * D;\n \n // Compute amax\n auto amax_t = torch::zeros({1}, x.options());\n int th = 256, bl = min(1024, (N + th - 1) / th);\n amax_kernel<<<bl, th>>>(x.data_ptr<float>(), amax_t.data_ptr<float>(), N);\n float amax = amax_t.item<float>();\n amax = fmaxf(amax, 1e-8f);\n float scale = amax / 127.0f;\n \n // Transpose weight for coalesced access\n auto weight_t = weight.t().contiguous();\n \n auto out = torch::empty({B, T, D}, x.options());\n fused_kernel_opt<<>>(\n x.data_ptr<float>(), layout_logits.data_ptr<float>(),\n weight_t.data_ptr<float>(), bias.data_ptr<float>(),\n out.data_ptr<float>(), scale, B, T, D);\n return out;\n}\n\"\"\"\n\nmodule = load_inline(\n name=\"qvla\",\n cpp_sources=[\"torch::Tensor forward_cuda(torch::Tensor x, torch::Tensor layout_logits, torch::Tensor weight, torch::Tensor bias);\"],\n cuda_sources=[cuda_source],\n functions=[\"forward_cuda\"],\n extra_cuda_cflags=[\"-O3\", \"-use_fast_math\"]\n)\n\nclass ModelNew(nn.Module):\n def __init__(self, dim: int):\n super().__init__()\n self.dim = dim\n self.weight = nn.Parameter(torch.empty(dim, dim))\n self.bias = nn.Parameter(torch.empty(dim))\n nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))\n fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight)\n bound = 1 / math.sqrt(fan_in) if fan_in > 0 else 0\n nn.init.uniform_(self.bias, -bound, bound)\n\n def forward(self, x: torch.Tensor, layout_logits: torch.Tensor) -> torch.Tensor:\n return module.forward_cuda(\n x.contiguous().float(),\n layout_logits.contiguous().float(),\n self.weight.contiguous().float(),\n self.bias.contiguous().float()\n )" }

result ← run_correctness (ok)

okrun_correctness run_correctness PASSED: (3 / 3) trials all matched the reference. Numerical precision: max_abs_err=1.19e-06 mean_abs_err=7.03e-08 max_rel_err=6.38e-02

turn 12 · 1 tool call(s) · 334.7s turn · 11.8s LLM · tokens in=21,154 · out=1,364 · total=22,518 · 7,979 chars (click to expand)

turn 12334.7s turn · 11.8s LLM · 322.9s tools · tokens in=21,154 · out=1,364 · total=22,518

reasoning

The kernel is correct and working. Let me profile it to see if there are any more optimizations to make, or if I should submit now. Actually, looking at the previous profile, the kernel is showing: - GPU time: 17.95 μs - Compute-bound classification - Still has 40x wasted bandwidth on global loads The main issue is that the weight access pattern isn't optimal. But the kernel is already correct and faster. Let me check if the current implementation is good enough to submit. Given the constraints, I should probably submit now since: 1. Kernel is correct 2. It's already showing improvement (from 44.74μs to 17.95μs GPU time) 3. Further optimizations would require more complex restructuring Let me submit the kernel.

call → profile_kernel

callprofile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\nimport math\n\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__device__ __forceinline__ float sigmoid_f(float x) {\n return 1.0f / (1.0f + expf(-x));\n}\n\n__global__ void amax_kernel(const float* __restrict__ x, float* __restrict__ out, int n) {\n float m = 0.0f;\n for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < n; i += blockDim.x * gridDim.x) {\n m = fmaxf(m, fabsf(x[i]));\n }\n for (int mask = 16; mask > 0; mask >>= 1) {\n m = fmaxf(m, __shfl_xor_sync(0xffffffff, m, mask));\n }\n __shared__ float smem[32];\n int lane = threadIdx.x & 31;\n int wid = threadIdx.x >> 5;\n if (lane == 0) smem[wid] = m;\n __syncthreads();\n if (wid == 0) {\n m = (lane < (blockDim.x >> 5)) ? smem[lane] : 0.0f;\n for (int mask = 16; mask > 0; mask >>= 1) {\n m = fmaxf(m, __shfl_xor_sync(0xffffffff, m, mask));\n }\n if (lane == 0) atomicMax(reinterpret_cast<int*>(out), __float_as_int(m));\n }\n}\n\n__global__ void fused_kernel_opt(\n const float* __restrict__ x,\n const float* __restrict__ layout_logits,\n const float* __restrict__ weight_t,\n const float* __restrict__ bias,\n float* __restrict__ out,\n float scale, int B, int T, int D) {\n \n int bt = blockIdx.x;\n int b = bt / T, t = bt % T;\n int d = threadIdx.x;\n \n __shared__ float sb_blend[128];\n \n float alpha = sigmoid_f(layout_logits[b * T + t]);\n \n // Compute blend values\n if (d < D) {\n float xv = x[b * T * D + t * D + d];\n float q = roundf(xv / scale);\n q = fmaxf(-127.0f, fminf(127.0f, q));\n sb_blend[d] = alpha * q * scale + (1.0f - alpha) * xv;\n }\n __syncthreads();\n \n // Compute output using __ldg for read-only cache\n float sum = bias[d];\n for (int k = 0; k < D; k++) {\n sum += sb_blend[k] * __ldg(&weight_t[k * D + d]);\n }\n \n if (d < D) {\n out[b * T * D + t * D + d] = sum;\n }\n}\n\ntorch::Tensor forward_cuda(torch::Tensor x, torch::Tensor layout_logits, torch::Tensor weight, torch::Tensor bias) {\n int B = x.size(0), T = x.size(1), D = x.size(2), N = B * T * D;\n \n // Compute amax\n auto amax_t = torch::zeros({1}, x.options());\n int th = 256, bl = min(1024, (N + th - 1) / th);\n amax_kernel<<<bl, th>>>(x.data_ptr<float>(), amax_t.data_ptr<float>(), N);\n float amax = amax_t.item<float>();\n amax = fmaxf(amax, 1e-8f);\n float scale = amax / 127.0f;\n \n // Transpose weight for coalesced access\n auto weight_t = weight.t().contiguous();\n \n auto out = torch::empty({B, T, D}, x.options());\n fused_kernel_opt<<>>(\n x.data_ptr<float>(), layout_logits.data_ptr<float>(),\n weight_t.data_ptr<float>(), bias.data_ptr<float>(),\n out.data_ptr<float>(), scale, B, T, D);\n return out;\n}\n\"\"\"\n\nmodule = load_inline(\n name=\"qvla\",\n cpp_sources=[\"torch::Tensor forward_cuda(torch::Tensor x, torch::Tensor layout_logits, torch::Tensor weight, torch::Tensor bias);\"],\n cuda_sources=[cuda_source],\n functions=[\"forward_cuda\"],\n extra_cuda_cflags=[\"-O3\", \"-use_fast_math\"]\n)\n\nclass ModelNew(nn.Module):\n def __init__(self, dim: int):\n super().__init__()\n self.dim = dim\n self.weight = nn.Parameter(torch.empty(dim, dim))\n self.bias = nn.Parameter(torch.empty(dim))\n nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))\n fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight)\n bound = 1 / math.sqrt(fan_in) if fan_in > 0 else 0\n nn.init.uniform_(self.bias, -bound, bound)\n\n def forward(self, x: torch.Tensor, layout_logits: torch.Tensor) -> torch.Tensor:\n return module.forward_cuda(\n x.contiguous().float(),\n layout_logits.contiguous().float(),\n self.weight.contiguous().float(),\n self.bias.contiguous().float()\n )" }

result ← profile_kernel (ok)

okprofile_kernel profile_kernel PASSED: profiling complete. === Kernel Profile Summary === --- Kernel Breakdown (by GPU time) --- #1: aten::contiguous 10267.0μs (17%) calls=1 #2: aten::clone 10243.0μs (17%) calls=1 #3: aten::copy_ 10147.0μs (17%) calls=1 #4: aten::zeros 8460.0μs (14%) calls=1 #5: aten::zero_ 8388.0μs (14%) calls=1 ... +14 more kernels --- Timing --- GPU time: 25.22 μs [↑7.3μs REGRESSED] --- Memory Bandwidth --- DRAM BW: 120.7/3350 GB/s (3.6%) [↓0.5% REGRESSED] read=120.7 write=0.0 GB/s Hit rates: L2=358.8% --- Compute Throughput --- Dominant pipe: FP32 util=8.1% [↓8.7% REGRESSED] Pipe util: FMA=8.1%, Tensor=0.1% --- Roofline Analysis --- Ridge point (FP32): 20.00 FLOPs/byte Classification: COMPUTE-BOUND --- Occupancy --- Achieved: 57.2% [↑0.1% ok] Eligible warps/cycle: 0.27 WARNING: <1 eligible warp/cycle = latency-bound, needs more parallelism Launch: regs/thread=75, smem/blk=5248B, block=640, grid=833 --- Memory Access Quality --- Global ld coalescing: 39.98 sectors/req (40.0x wasted BW) --- Warp Stall Reasons (% of issue cycles) --- long_scoreboard 5574.2% #################### short_scoreboard 747.9% #################### mio_throttle 5.6% # --- Optimization Hints --- -> 5574% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=40.0 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set. -> Global load coalescing=40.0x (ideal=1.0) -> 40x wasted DRAM BW. Make per-warp accesses contiguous. -> Low eligible warps + low BW% + low compute% = LATENCY-BOUND. Need more parallelism (larger grid, more ILP, prefetching). -> Compute-bound but NOT using tensor cores. If applicable, use FP16/BF16 with torch.matmul or WMMA intrinsics. --- Delta vs Previous Iteration --- GPU time: 17.95→25.22μs (↑7.26) REGRESSED DRAM BW%: 4.14→3.60% (↓0.53) REGRESSED Compute%: 16.87→8.13% (↓8.74) REGRESSED Occupancy: 57.07→57.20% (↑0.13) ok Eligible warps/cyc: 0.40→0.27 (↓0.13) REGRESSED L2 hit%: 360.19→358.76% (↓1.43) REGRESSED

turn 13 · 1 tool call(s) · 276.0s turn · 13.3s LLM · tokens in=23,172 · out=1,432 · total=24,604 · 6,256 chars (click to expand)

turn 13276.0s turn · 13.3s LLM · 262.7s tools · tokens in=23,172 · out=1,432 · total=24,604

reasoning

The kernel performance regressed with the __ldg addition. Let me go back to the previous version without __ldg which had better performance (17.95μs GPU time). Let me also try to cache the weight transpose to avoid doing it every forward pass. Actually, looking at the profiling results, a lot of the time is spent on PyTorch overhead operations like `contiguous`, `clone`, `copy_`, `zeros`, `zero_`, and `item`. The actual kernel time is relatively small. Let me try a few optimizations: 1. Avoid the weight transpose on every forward pass by caching it 2. Reduce PyTorch overhead by using a pre-allocated buffer for amax Let me try caching the transposed weight in the module.

assistantLet me try caching the transposed weight and reducing PyTorch overhead:

call → compile_kernel

result ← compile_kernel (ok)

okcompile_kernel compile_kernel PASSED: kernel compiled without errors.

turn 14840.1s turn · 11.0s LLM · 829.1s tools · tokens in=24,549 · out=1,220 · total=25,769 · FINAL

reasoning

I need to submit my best kernel. Let me first check correctness, then submit.

call → run_correctness

callrun_correctness { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\nimport math\n\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__device__ __forceinline__ float sigmoid_f(float x) {\n return 1.0f / (1.0f + expf(-x));\n}\n\n__global__ void amax_kernel(const float* __restrict__ x, float* __restrict__ out, int n) {\n float m = 0.0f;\n for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < n; i += blockDim.x * gridDim.x) {\n m = fmaxf(m, fabsf(x[i]));\n }\n for (int mask = 16; mask > 0; mask >>= 1) {\n m = fmaxf(m, __shfl_xor_sync(0xffffffff, m, mask));\n }\n __shared__ float smem[32];\n int lane = threadIdx.x & 31;\n int wid = threadIdx.x >> 5;\n if (lane == 0) smem[wid] = m;\n __syncthreads();\n if (wid == 0) {\n m = (lane < (blockDim.x >> 5)) ? smem[lane] : 0.0f;\n for (int mask = 16; mask > 0; mask >>= 1) {\n m = fmaxf(m, __shfl_xor_sync(0xffffffff, m, mask));\n }\n if (lane == 0) atomicMax(reinterpret_cast<int*>(out), __float_as_int(m));\n }\n}\n\n__global__ void fused_kernel(\n const float* __restrict__ x,\n const float* __restrict__ layout_logits,\n const float* __restrict__ weight_t,\n const float* __restrict__ bias,\n float* __restrict__ out,\n float scale, int B, int T, int D) {\n \n int bt = blockIdx.x;\n int b = bt / T, t = bt % T;\n int d = threadIdx.x;\n \n __shared__ float sb_blend[128];\n \n float alpha = sigmoid_f(layout_logits[b * T + t]);\n \n if (d < D) {\n float xv = x[b * T * D + t * D + d];\n float q = roundf(xv / scale);\n q = fmaxf(-127.0f, fminf(127.0f, q));\n sb_blend[d] = alpha * q * scale + (1.0f - alpha) * xv;\n }\n __syncthreads();\n \n float sum = bias[d];\n for (int k = 0; k < D; k++) {\n sum += sb_blend[k] * weight_t[k * D + d];\n }\n \n if (d < D) {\n out[b * T * D + t * D + d] = sum;\n }\n}\n\ntorch::Tensor forward_cuda(torch::Tensor x, torch::Tensor layout_logits, torch::Tensor weight_t, torch::Tensor bias) {\n int B = x.size(0), T = x.size(1), D = x.size(2), N = B * T * D;\n \n auto amax_buffer = torch::zeros({1}, x.options());\n \n int th = 256, bl = min(1024, (N + th - 1) / th);\n amax_kernel<<<bl, th>>>(x.data_ptr<float>(), amax_buffer.data_ptr<float>(), N);\n \n float amax = amax_buffer.item<float>();\n amax = fmaxf(amax, 1e-8f);\n float scale = amax / 127.0f;\n \n auto out = torch::empty({B, T, D}, x.options());\n fused_kernel<<>>(\n x.data_ptr<float>(), layout_logits.data_ptr<float>(),\n weight_t.data_ptr<float>(), bias.data_ptr<float>(),\n out.data_ptr<float>(), scale, B, T, D);\n return out;\n}\n\"\"\"\n\nmodule = load_inline(\n name=\"qvla_final\",\n cpp_sources=[\"torch::Tensor forward_cuda(torch::Tensor x, torch::Tensor layout_logits, torch::Tensor weight_t, torch::Tensor bias);\"],\n cuda_sources=[cuda_source],\n functions=[\"forward_cuda\"],\n extra_cuda_cflags=[\"-O3\", \"-use_fast_math\"]\n)\n\nclass ModelNew(nn.Module):\n def __init__(self, dim: int):\n super().__init__()\n self.dim = dim\n self.weight = nn.Parameter(torch.empty(dim, dim))\n self.bias = nn.Parameter(torch.empty(dim))\n nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))\n fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight)\n bound = 1 / math.sqrt(fan_in) if fan_in > 0 else 0\n nn.init.uniform_(self.bias, -bound, bound)\n # Cache transposed weight for coalesced memory access\n self.register_buffer('weight_t', self.weight.data.t().contiguous())\n\n def forward(self, x: torch.Tensor, layout_logits: torch.Tensor) -> torch.Tensor:\n # Update cached weight_t if weight changed\n self.weight_t = self.weight.data.t().contiguous()\n return module.forward_cuda(\n x.contiguous().float(),\n layout_logits.contiguous().float(),\n self.weight_t.float(),\n self.bias.contiguous().float()\n )" }

result ← run_correctness (ok)

okrun_correctness run_correctness PASSED: (3 / 3) trials all matched the reference. Numerical precision: max_abs_err=1.19e-06 mean_abs_err=7.03e-08 max_rel_err=6.38e-02

result ← submit_kernel (ok)

oksubmit_kernel submit_kernel PASSED: (3 / 3) correctness trials all passed. Kernel runtime: 0.04 μs Runtime stats: mean=0.04μs median=?μs std=0.00μs Numerical precision: max_abs_err=1.19e-06 mean_abs_err=7.03e-08 max_rel_err=6.38e-02 Memory: 33.69 MB peak (ref: 34.44 MB, ratio: 0.9782x) Kernel launches: 19 (ref: 32, fusion ratio: 1.6842) SOL score: 0.1668

final submitted kernel (121 lines, 4,075 chars) — full source, untruncated

import torch
import torch.nn as nn
from torch.utils.cpp_extension import load_inline
import math

cuda_source = """
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>

__device__ __forceinline__ float sigmoid_f(float x) {
    return 1.0f / (1.0f + expf(-x));
}

__global__ void amax_kernel(const float* __restrict__ x, float* __restrict__ out, int n) {
    float m = 0.0f;
    for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < n; i += blockDim.x * gridDim.x) {
        m = fmaxf(m, fabsf(x[i]));
    }
    for (int mask = 16; mask > 0; mask >>= 1) {
        m = fmaxf(m, __shfl_xor_sync(0xffffffff, m, mask));
    }
    __shared__ float smem[32];
    int lane = threadIdx.x & 31;
    int wid = threadIdx.x >> 5;
    if (lane == 0) smem[wid] = m;
    __syncthreads();
    if (wid == 0) {
        m = (lane < (blockDim.x >> 5)) ? smem[lane] : 0.0f;
        for (int mask = 16; mask > 0; mask >>= 1) {
            m = fmaxf(m, __shfl_xor_sync(0xffffffff, m, mask));
        }
        if (lane == 0) atomicMax(reinterpret_cast<int*>(out), __float_as_int(m));
    }
}

__global__ void fused_kernel(
    const float* __restrict__ x,
    const float* __restrict__ layout_logits,
    const float* __restrict__ weight_t,
    const float* __restrict__ bias,
    float* __restrict__ out,
    float scale, int B, int T, int D) {
    
    int bt = blockIdx.x;
    int b = bt / T, t = bt % T;
    int d = threadIdx.x;
    
    __shared__ float sb_blend[128];
    
    float alpha = sigmoid_f(layout_logits[b * T + t]);
    
    if (d < D) {
        float xv = x[b * T * D + t * D + d];
        float q = roundf(xv / scale);
        q = fmaxf(-127.0f, fminf(127.0f, q));
        sb_blend[d] = alpha * q * scale + (1.0f - alpha) * xv;
    }
    __syncthreads();
    
    float sum = bias[d];
    for (int k = 0; k < D; k++) {
        sum += sb_blend[k] * weight_t[k * D + d];
    }
    
    if (d < D) {
        out[b * T * D + t * D + d] = sum;
    }
}

torch::Tensor forward_cuda(torch::Tensor x, torch::Tensor layout_logits, torch::Tensor weight_t, torch::Tensor bias) {
    int B = x.size(0), T = x.size(1), D = x.size(2), N = B * T * D;
    
    auto amax_buffer = torch::zeros({1}, x.options());
    
    int th = 256, bl = min(1024, (N + th - 1) / th);
    amax_kernel<<<bl, th>>>(x.data_ptr<float>(), amax_buffer.data_ptr<float>(), N);
    
    float amax = amax_buffer.item<float>();
    amax = fmaxf(amax, 1e-8f);
    float scale = amax / 127.0f;
    
    auto out = torch::empty({B, T, D}, x.options());
    fused_kernel<<<B * T, D, 128 * sizeof(float)>>>(
        x.data_ptr<float>(), layout_logits.data_ptr<float>(),
        weight_t.data_ptr<float>(), bias.data_ptr<float>(),
        out.data_ptr<float>(), scale, B, T, D);
    return out;
}
"""

module = load_inline(
    name="qvla_final",
    cpp_sources=["torch::Tensor forward_cuda(torch::Tensor x, torch::Tensor layout_logits, torch::Tensor weight_t, torch::Tensor bias);"],
    cuda_sources=[cuda_source],
    functions=["forward_cuda"],
    extra_cuda_cflags=["-O3", "-use_fast_math"]
)

class ModelNew(nn.Module):
    def __init__(self, dim: int):
        super().__init__()
        self.dim = dim
        self.weight = nn.Parameter(torch.empty(dim, dim))
        self.bias = nn.Parameter(torch.empty(dim))
        nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))
        fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight)
        bound = 1 / math.sqrt(fan_in) if fan_in > 0 else 0
        nn.init.uniform_(self.bias, -bound, bound)
        # Cache transposed weight for coalesced memory access
        self.register_buffer('weight_t', self.weight.data.t().contiguous())

    def forward(self, x: torch.Tensor, layout_logits: torch.Tensor) -> torch.Tensor:
        # Update cached weight_t if weight changed
        self.weight_t = self.weight.data.t().contiguous()
        return module.forward_cuda(
            x.contiguous().float(),
            layout_logits.contiguous().float(),
            self.weight_t.float(),
            self.bias.contiguous().float()
        )