gemini / gemini-3.5-flash — KernelBench-Hard

harness

gemini

model

gemini-3.5-flash

turns

tools called

events

input toks

412,996

output toks

9,919

cache hit

243,721

solution.py

benchmark.log

check.log

result.json

view raw solution.py

import torch
import torch.nn as nn
import triton
import triton.language as tl

OP_TYPE = "gemm"
SUPPORTED_PRECISIONS = ["fp8_e4m3"]
HARDWARE_REQUIRED = ["RTX_PRO_6000", "H100", "B200"]
E4M3_MAX = 448.0

@triton.jit
def matmul_kernel(
    a_ptr, b_ptr, c_ptr, scale_ptr,
    M, N, K,
    stride_am, stride_ak,
    stride_bn, stride_bk,
    stride_cm, stride_cn,
    stride_scale,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
):
    pid = tl.program_id(axis=0)
    num_pid_m = tl.cdiv(M, BLOCK_M)
    num_pid_n = tl.cdiv(N, BLOCK_N)
    pid_m = pid // num_pid_n
    pid_n = pid % num_pid_n

    offs_am = (pid_m * BLOCK_M + tl.arange(0, BLOCK_M)) % M
    offs_bn = (pid_n * BLOCK_N + tl.arange(0, BLOCK_N)) % N
    offs_k = tl.arange(0, BLOCK_K)

    a_ptrs = a_ptr + offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak
    b_ptrs = b_ptr + offs_bn[None, :] * stride_bn + offs_k[:, None] * stride_bk

    accumulator = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, tl.cdiv(K, BLOCK_K)):
        a = tl.load(a_ptrs, mask=offs_k[None, :] < K - k * BLOCK_K, other=0.0)
        b = tl.load(b_ptrs, mask=offs_k[:, None] < K - k * BLOCK_K, other=0.0)
        
        accumulator = tl.dot(a, b, accumulator)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk

    # Load scales
    scale_ptrs = scale_ptr + offs_bn * stride_scale
    scale = tl.load(scale_ptrs, mask=offs_bn < N, other=1.0)
    accumulator = accumulator * scale[None, :]

    offs_cm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_cn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    c_ptrs = c_ptr + offs_cm[:, None] * stride_cm + offs_cn[None, :] * stride_cn
    c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N)
    tl.store(c_ptrs, accumulator.to(tl.bfloat16), mask=c_mask)

class Model(nn.Module):
    def __init__(self, M: int, N: int, K: int):
        super().__init__()
        self.M, self.N, self.K = M, N, K
        # Setup temporary buffers matching reference
        w = torch.empty(N, K, dtype=torch.bfloat16)
        nn.init.normal_(w, std=0.02)
        s = (w.float().abs().amax(dim=1, keepdim=True) / E4M3_MAX).clamp(min=1e-12)
        w_fp8 = (w.float() / s).to(torch.float8_e4m3fn)
        self.register_buffer("weight", w_fp8)
        self.register_buffer("weight_scale", s.squeeze(1).to(torch.float32))

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        # Run our Triton GEMM
        M, K = x.shape
        N = self.weight.shape[0]
        c = torch.empty((M, N), device=x.device, dtype=torch.bfloat16)
        
        BLOCK_M = 64
        BLOCK_N = 64
        BLOCK_K = 64
        
        grid = lambda META: (triton.cdiv(M, META['BLOCK_M']) * triton.cdiv(N, META['BLOCK_N']),)
        
        matmul_kernel[grid](
            x, self.weight, c, self.weight_scale,
            M, N, K,
            x.stride(0), x.stride(1),
            self.weight.stride(0), self.weight.stride(1),
            c.stride(0), c.stride(1),
            self.weight_scale.stride(0),
            BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N, BLOCK_K=BLOCK_K,
        )
        return c

M = 4096
N = 4096
K = 4096

def get_inputs():
    x = (torch.rand(M, K) * 8 - 4).to(torch.float8_e4m3fn)
    return [x]

def get_init_inputs():
    return [M, N, K]

warning: `VIRTUAL_ENV=/home/infatoshi/kernelbench.com/benchmarks/hard/.venv` does not match the project environment path `/home/infatoshi/kernelbench.com/benchmarks/hard/outputs/runs/20260614_170713_gemini_gemini-3.5-flash_01_fp8_gemm/repo/.venv` and will be ignored; use `--active` to target the active environment instead
benchmark_event event=variant_start shape=0 variant=solution ts=2026-06-14T23:18:03.212258+00:00
benchmark_event event=variant_end shape=0 variant=solution ts=2026-06-14T23:18:03.432224+00:00 elapsed_s=0.220 ms=0.329888
shape=0 variant=solution tflops=416.623 gbps=203.429 ms=0.330
shape=0 solution_peak_fraction=0.4166
benchmark_event event=variant_start shape=1 variant=solution ts=2026-06-14T23:18:03.967603+00:00
benchmark_event event=variant_end shape=1 variant=solution ts=2026-06-14T23:18:04.001937+00:00 elapsed_s=0.034 ms=0.780672
shape=1 variant=solution tflops=177.385 gbps=86.288 ms=0.781
shape=1 solution_peak_fraction=0.1774
benchmark_event event=variant_start shape=2 variant=solution ts=2026-06-14T23:18:05.908728+00:00
benchmark_event event=variant_end shape=2 variant=solution ts=2026-06-14T23:18:05.914285+00:00 elapsed_s=0.006 ms=0.095840
shape=2 variant=solution tflops=44.814 gbps=708.423 ms=0.096
shape=2 solution_peak_fraction=0.0448
benchmark_event event=variant_start shape=3 variant=solution ts=2026-06-14T23:18:07.636303+00:00
benchmark_event event=variant_end shape=3 variant=solution ts=2026-06-14T23:18:07.681664+00:00 elapsed_s=0.045 ms=1.074864
shape=3 variant=solution tflops=447.532 gbps=179.500 ms=1.075
shape=3 solution_peak_fraction=0.4475
peak_fraction: 0.1962
RESULT: OK

warning: `VIRTUAL_ENV=/home/infatoshi/kernelbench.com/benchmarks/hard/.venv` does not match the project environment path `/home/infatoshi/kernelbench.com/benchmarks/hard/outputs/runs/20260614_170713_gemini_gemini-3.5-flash_01_fp8_gemm/repo/.venv` and will be ignored; use `--active` to target the active environment instead
warning: Ignoring existing virtual environment linked to non-existent Python interpreter: /home/infatoshi/kernelbench.com/benchmarks/hard/outputs/runs/20260614_170713_gemini_gemini-3.5-flash_01_fp8_gemm/repo/.venv/bin/python3 -> python
Using CPython 3.11.15
Removed virtual environment at: /home/infatoshi/kernelbench.com/benchmarks/hard/outputs/runs/20260614_170713_gemini_gemini-3.5-flash_01_fp8_gemm/repo/.venv
Creating virtual environment at: /home/infatoshi/kernelbench.com/benchmarks/hard/outputs/runs/20260614_170713_gemini_gemini-3.5-flash_01_fp8_gemm/repo/.venv
   Building kernelbench-hard @ file:///home/infatoshi/kernelbench.com/benchmarks/hard/outputs/runs/20260614_170713_gemini_gemini-3.5-flash_01_fp8_gemm/repo
      Built kernelbench-hard @ file:///home/infatoshi/kernelbench.com/benchmarks/hard/outputs/runs/20260614_170713_gemini_gemini-3.5-flash_01_fp8_gemm/repo
Installed 56 packages in 67ms
PASS

{
    "run_id": "20260614_170713_gemini_gemini-3.5-flash_01_fp8_gemm",
    "run_group": "",
    "problem": "01_fp8_gemm",
    "harness": "gemini",
    "model": "gemini-3.5-flash",
    "reasoning_effort": "",
    "started_at": "2026-06-14T17:07:14-06:00",
    "harness_finished_at": "2026-06-14T17:17:53-06:00",
    "finished_at": "2026-06-14T17:18:07-06:00",
    "start_epoch": 1781478434,
    "harness_end_epoch": 1781479073,
    "end_epoch": 1781479087,
    "has_solution": true,
    "correct": true,
    "failure_reason": "pass",
    "retryable_infra_failure": false,
    "minimum_useful_output_tokens": 5000,
    "peak_fraction": 0.1962,
    "template_mutated": false,
    "elapsed_seconds": 639,
    "total_elapsed_seconds": 653,
    "check_elapsed_seconds": 9,
    "benchmark_elapsed_seconds": 5,
    "check_timeout_seconds": 180,
    "benchmark_timeout_seconds": 1800,
    "check_exit_code": 0,
    "benchmark_exit_code": 0,
    "harness_exit_code": 0,
    "session_complete": true,
    "agent_cuda_disabled": false,
    "agent_container": true,
    "agent_container_image": "nvcr.io/nvidia/tensorrt-llm/release:latest",
    "agent_container_network": "bridge",
    "gpu_queue_mode": "agent_container_native_profiling_path_wrapper_gpu_lock",
    "output_tokens_per_second": 15.522691705790297,
    "usage": {"input_tokens": 412996, "output_tokens": 9919, "cache_read_tokens": 243721, "cache_creation_tokens": null, "reasoning_tokens": null, "total_cost_usd": null}
}

timeline (72 events)

system — init

session start model=gemini-3.5-flash

user

I need you to optimize an FP8 GEMM for the RTX PRO 6000 (SM120 Blackwell, GDDR7, 1.8 TB/s). The reference implementation is in reference.py and your kernel goes in solution.py. You can make whatever mess you want in this directory — scratch files, intermediate kernels, build artifacts, profiling traces — but the final answer has to be in solution.py with the same Model, get_inputs, and get_init_inputs interface as reference.py. This is a genuine fp8 x fp8 GEMM. Both operands are fp8_e4m3. The activation x is fp8_e4m3 (M, K). The weight is given to you ALREADY quantized: the Model holds `weight` as fp8_e4m3 (N, K) normalized into the e4m3 range, plus `weight_scale` (N,), a per-output-channel dequant scale. You return y = (x @ weight.T) * weight_scale as bf16 (M, N). Read reference.py — it defines this exactly; your Model must register the same `weight` and `weight_scale` buffers so the graded state_dict loads. To actually score well you must run a real fp8 x fp8 tensor-core MMA (fp8 inputs, fp32 accumulate) and apply the per-channel scale. Upcasting both operands to bf16 and doing a bf16 GEMM is correct but it caps at roughly HALF the roofline — the roofline here is the fp8 peak (~1000 TFLOPS) and bf16 tensor cores run at ~half that. So a bf16 fallback passes correctness but leaves ~2x on the table; the fp8 path is the point. Correctness tolerance on the bf16 output is ~0.2 abs/rel (fp8 has real accumulation noise, so the bar is loose), but it has to match the reference within that across every shape and the numeric-stress cases. The shapes you must handle are M=4096 N=4096 K=4096 (square aligned, the easy case), M=4096 N=4096 K=4127 (K not a multiple of 128, which forces predicated tails on tile-quantized kernels), M=32 N=8192 K=8192 (skinny M, decode-style, memory-bound), and M=4096 N=14336 K=4096 (Llama-3 up-proj). This needs to be a real custom kernel. Don't reach for torch._scaled_mm or torch.ops.aten._scaled_mm — they're off-limits and using them fails correctness. Triton tl.dot on fp8, CUDA C++ via torch.utils.cpp_extension.load_inline, inline PTX (mma.sync ... e4m3), or CUTLASS all work — whatever fits. Anything you're uncertain about, look up PTX docs, clone CUTLASS or other reference repos, read library headers, and investigate. Your flywheel is implement, profile (ncu, nsys, torch.profiler — whatever's useful) and time it with benchmark.py, verify correctness by running `python check.py` and reading the output, then iterate. Don't substitute your own one-off correctness snippets for check.py — it iterates over every shape, your spot-check almost certainly won't. If `python check.py` hasn't printed PASS, you're not done. Take as long as you need to actually push the number up. Workspace isolation note: you are already running inside the archive-local problem workspace, /workspace/problems/01_fp8_gemm. Write the final answer to solution.py in the current directory only. The source repository's problems/ tree is not mounted. Container note: inside this run, the visible workspace path is /workspace/problems/01_fp8_gemm. The source repository, old runs, leaderboards, and host harness memory are not mounted. Container network mode is bridge. Run all Python through `uv run ...` so you use the workspace uv environment; it is built from the same uv.lock as the official scoring environment. The container image's system python has a different torch build and is NOT the scoring environment.

assistant

update_topic

args

{
  "strategic_intent": "Initiating optimization of FP8 GEMM on RTX PRO 6000 by studying the files in the workspace.",
  "summary": "Starting the optimization of the FP8 GEMM. We will first read reference.py, check.py, and other files in the workspace to understand the constraints, input formats, model interface, and testing framework. then we will build our strategy around Triton or inline PTX / CUTLASS.",
  "title": "Researching Workspace and Reference Implementation"
}

assistant

read_file

args

{
  "file_path": "reference.py",
  "start_line": 1,
  "end_line": 100
}

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read_file

args

{
  "file_path": "check.py",
  "start_line": 1,
  "end_line": 100
}

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read_file

args

{
  "start_line": 1,
  "end_line": 100,
  "file_path": "benchmark.py"
}

tool

stdout (477 chars)

## 📂 Topic: **Researching Workspace and Reference Implementation**

**Summary:**
Starting the optimization of the FP8 GEMM. We will first read reference.py, check.py, and other files in the workspace to understand the constraints, input formats, model interface, and testing framework. then we will build our strategy around Triton or inline PTX / CUTLASS.

> [!STRATEGY]
> **Intent:** Initiating optimization of FP8 GEMM on RTX PRO 6000 by studying the files in the workspace.

tool

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tool

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Read lines 1-100 of 120 from check.py

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Read lines 1-100 of 148 from benchmark.py

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read_file

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{
  "file_path": "problem.yaml",
  "start_line": 1,
  "end_line": 100
}

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read_file

args

{
  "file_path": "shapes.py",
  "end_line": 100,
  "start_line": 1
}

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glob

args

{
  "pattern": "*.py"
}

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stdout (24 chars)

Found 5 matching file(s)

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read_file

args

{
  "end_line": 100,
  "file_path": "sota.py",
  "start_line": 1
}

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assistant

I will run a python one-liner in the workspace uv environment to check Py

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Torch version, Triton availability, and details about the GPU.

assistant

run_shell_command

args

{
  "description": "Check PyTorch, Triton, and CUDA device information.",
  "command": "uv run python -c \"import torch; import triton; print('PyTorch:', torch.__version__); print('Triton:', triton.__version__); print('Device:', torch.cuda.get_device_name(0))\""
}

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I will write a temporary test script `test_triton.py

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` to experiment with a basic Triton FP8 GEMM kernel and see if it compiles and produces the correct output.

assistant

write_file

args

{
  "content": "<2600 chars \u2014 see below>",
  "file_path": "test_triton.py"
}

content (2600 chars)

import torch
import triton
import triton.language as tl

@triton.jit
def matmul_kernel(
    a_ptr, b_ptr, c_ptr,
    M, N, K,
    stride_am, stride_ak,
    stride_bn, stride_bk,
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
):
    pid = tl.program_id(axis=0)
    num_pid_m = tl.cdiv(M, BLOCK_M)
    num_pid_n = tl.cdiv(N, BLOCK_N)
    pid_m = pid // num_pid_n
    pid_n = pid % num_pid_n

    offs_am = (pid_m * BLOCK_M + tl.arange(0, BLOCK_M)) % M
    offs_bn = (pid_n * BLOCK_N + tl.arange(0, BLOCK_N)) % N
    offs_k = tl.arange(0, BLOCK_K)

    a_ptrs = a_ptr + offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak
    b_ptrs = b_ptr + offs_bn[None, :] * stride_bn + offs_k[:, None] * stride_bk

    accumulator = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, tl.cdiv(K, BLOCK_K)):
        # Load blocks (with masking if needed, but let's do a simple mask)
        a = tl.load(a_ptrs, mask=offs_k[None, :] < K - k * BLOCK_K, other=0.0)
        # B is shape (K, N) or stored as (N, K) and we load from B.
        # Here we assume b_ptr is weight of shape (N, K), so strides are stride_bn, stride_bk.
        # So b_ptrs offset is offs_bn[None, :] * stride_bn + (k * BLOCK_K + offs_k[:, None]) * stride_bk
        b = tl.load(b_ptrs, mask=offs_k[:, None] < K - k * BLOCK_K, other=0.0)
        
        accumulator = tl.dot(a, b, accumulator)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk

    offs_cm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_cn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    c_ptrs = c_ptr + offs_cm[:, None] * stride_cm + offs_cn[None, :] * stride_cn
    c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N)
    tl.store(c_ptrs, accumulator, mask=c_mask)

def triton_gemm(a, b, M, N, K):
    # a: (M, K)
    # b: (N, K)
    # c: (M, N)
    c = torch.empty((M, N), device=a.device, dtype=torch.float32)
    grid = lambda META: (triton.cdiv(M, META['BLOCK_M']) * triton.cdiv(N, META['BLOCK_N']),)
    matmul_kernel[grid](
        a, b, c,
        M, N, K,
        a.stride(0), a.stride(1),
        b.stride(0), b.stride(1),
        c.stride(0), c.stride(1),
        BLOCK_M=64, BLOCK_N=64, BLOCK_K=64,
    )
    return c

# Quick test
device = torch.device("cuda:0")
M, N, K = 128, 128, 128
x = torch.randn(M, K, device=device).to(torch.float8_e4m3fn)
w = torch.randn(N, K, device=device).to(torch.float8_e4m3fn)
y = triton_gemm(x, w, M, N, K)
ref_y = (x.to(torch.float32) @ w.to(torch.float32).T)
diff = (y - ref_y).abs().max()
print("Diff:", diff.item())

tool

stdout (0 chars)

assistant

I will run the `test_triton.py` script to see if a simple Triton FP

assistant

8 GEMM kernel compiles and produces the correct output.

assistant

run_shell_command

args

{
  "command": "uv run python test_triton.py",
  "description": "Run test_triton.py to verify FP8 GEMM with Triton."
}

tool

stdout (0 chars)

assistant

I will modify `test_triton.py` to

assistant

test non-power-of-two values for `K` (such as `K = 130

assistant

` with `BLOCK_K = 64`) to confirm that our masking scheme works correctly.

assistant

write_file

args

{
  "file_path": "test_triton.py",
  "content": "<2665 chars \u2014 see below>"
}

content (2665 chars)

import torch
import triton
import triton.language as tl

@triton.jit
def matmul_kernel(
    a_ptr, b_ptr, c_ptr, scale_ptr,
    M, N, K,
    stride_am, stride_ak,
    stride_bn, stride_bk,
    stride_cm, stride_cn,
    stride_scale,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
):
    pid = tl.program_id(axis=0)
    num_pid_m = tl.cdiv(M, BLOCK_M)
    num_pid_n = tl.cdiv(N, BLOCK_N)
    pid_m = pid // num_pid_n
    pid_n = pid % num_pid_n

    offs_am = (pid_m * BLOCK_M + tl.arange(0, BLOCK_M)) % M
    offs_bn = (pid_n * BLOCK_N + tl.arange(0, BLOCK_N)) % N
    offs_k = tl.arange(0, BLOCK_K)

    a_ptrs = a_ptr + offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak
    b_ptrs = b_ptr + offs_bn[None, :] * stride_bn + offs_k[:, None] * stride_bk

    accumulator = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, tl.cdiv(K, BLOCK_K)):
        # Correctly mask along the K dimension
        a = tl.load(a_ptrs, mask=offs_k[None, :] < K - k * BLOCK_K, other=0.0)
        b = tl.load(b_ptrs, mask=offs_k[:, None] < K - k * BLOCK_K, other=0.0)
        
        accumulator = tl.dot(a, b, accumulator)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk

    # Load scales
    scale_ptrs = scale_ptr + offs_bn * stride_scale
    scale = tl.load(scale_ptrs, mask=offs_bn < N, other=1.0)
    accumulator = accumulator * scale[None, :]

    offs_cm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_cn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    c_ptrs = c_ptr + offs_cm[:, None] * stride_cm + offs_cn[None, :] * stride_cn
    c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N)
    tl.store(c_ptrs, accumulator.to(tl.bfloat16), mask=c_mask)

def triton_gemm(a, b, scale, M, N, K):
    c = torch.empty((M, N), device=a.device, dtype=torch.bfloat16)
    grid = lambda META: (triton.cdiv(M, META['BLOCK_M']) * triton.cdiv(N, META['BLOCK_N']),)
    matmul_kernel[grid](
        a, b, scale,
        M, N, K,
        a.stride(0), a.stride(1),
        b.stride(0), b.stride(1),
        c.stride(0), c.stride(1),
        scale.stride(0),
        BLOCK_M=64, BLOCK_N=64, BLOCK_K=64,
    )
    return c

# Quick test with unaligned K and weight scale
device = torch.device("cuda:0")
M, N, K = 128, 128, 130
x = torch.randn(M, K, device=device).to(torch.float8_e4m3fn)
w = torch.randn(N, K, device=device).to(torch.float8_e4m3fn)
scale = torch.randn(N, 1, device=device) # (N, 1) to match (N,)
y = triton_gemm(x, w, scale, M, N, K)
ref_y = (x.to(torch.float32) @ w.to(torch.float32).T) * scale.T
ref_y = ref_y.to(torch.bfloat16)
diff = (y - ref_y).abs().max()
print("Diff:", diff.item())

tool

stdout (0 chars)

assistant

I will

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run the modified `test_triton.py` script.

assistant

run_shell_command

args

{
  "description": "Verify unaligned K and scale correctness in Triton.",
  "command": "uv run python test_triton.py"
}

tool

stdout (0 chars)

assistant

I will correct the arguments in the `test_triton.py` script and run it again

assistant

write_file

args

{
  "file_path": "test_triton.py",
  "content": "<2679 chars \u2014 see below>"
}

content (2679 chars)

import torch
import triton
import triton.language as tl

@triton.jit
def matmul_kernel(
    a_ptr, b_ptr, c_ptr, scale_ptr,
    M, N, K,
    stride_am, stride_ak,
    stride_bn, stride_bk,
    stride_cm, stride_cn,
    stride_scale,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
):
    pid = tl.program_id(axis=0)
    num_pid_m = tl.cdiv(M, BLOCK_M)
    num_pid_n = tl.cdiv(N, BLOCK_N)
    pid_m = pid // num_pid_n
    pid_n = pid % num_pid_n

    offs_am = (pid_m * BLOCK_M + tl.arange(0, BLOCK_M)) % M
    offs_bn = (pid_n * BLOCK_N + tl.arange(0, BLOCK_N)) % N
    offs_k = tl.arange(0, BLOCK_K)

    a_ptrs = a_ptr + offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak
    b_ptrs = b_ptr + offs_bn[None, :] * stride_bn + offs_k[:, None] * stride_bk

    accumulator = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, tl.cdiv(K, BLOCK_K)):
        # Correctly mask along the K dimension
        a = tl.load(a_ptrs, mask=offs_k[None, :] < K - k * BLOCK_K, other=0.0)
        b = tl.load(b_ptrs, mask=offs_k[:, None] < K - k * BLOCK_K, other=0.0)
        
        accumulator = tl.dot(a, b, accumulator)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk

    # Load scales
    scale_ptrs = scale_ptr + offs_bn * stride_scale
    scale = tl.load(scale_ptrs, mask=offs_bn < N, other=1.0)
    accumulator = accumulator * scale[None, :]

    offs_cm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_cn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    c_ptrs = c_ptr + offs_cm[:, None] * stride_cm + offs_cn[None, :] * stride_cn
    c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N)
    tl.store(c_ptrs, accumulator.to(tl.bfloat16), mask=c_mask)

def triton_gemm(a, b, scale, M, N, K):
    c = torch.empty((M, N), device=a.device, dtype=torch.bfloat16)
    grid = lambda META: (triton.cdiv(M, META['BLOCK_M']) * triton.cdiv(N, META['BLOCK_N']),)
    matmul_kernel[grid](
        a, b, c, scale,
        M, N, K,
        a.stride(0), a.stride(1),
        b.stride(0), b.stride(1),
        c.stride(0), c.stride(1),
        scale.stride(0),
        BLOCK_M=64, BLOCK_N=64, BLOCK_K=64,
    )
    return c

# Quick test with unaligned K and weight scale
device = torch.device("cuda:0")
M, N, K = 128, 128, 130
x = torch.randn(M, K, device=device).to(torch.float8_e4m3fn)
w = torch.randn(N, K, device=device).to(torch.float8_e4m3fn)
scale = torch.randn(N, 1, device=device) # (N, 1) to match (N,)
y = triton_gemm(x, w, scale.squeeze(1), M, N, K)
ref_y = (x.to(torch.float32) @ w.to(torch.float32).T) * scale.T
ref_y = ref_y.to(torch.bfloat16)
diff = (y - ref_y).abs().max()
print("Diff:", diff.item())

tool

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assistant

I will run the corrected

assistant

`test_triton.py` script.

assistant

run_shell_command

args

{
  "description": "Verify the corrected Triton FP8 GEMM test.",
  "command": "uv run python test_triton.py"
}

tool

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assistant

I will write the initial draft of `solution.py

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` incorporating the Triton kernel and the required `Model`, `get_inputs`, and `get_init_inputs` interfaces.

assistant

write_file

args

{
  "content": "<3305 chars \u2014 see below>",
  "file_path": "solution.py"
}

content (3305 chars)

import torch
import torch.nn as nn
import triton
import triton.language as tl

OP_TYPE = "gemm"
SUPPORTED_PRECISIONS = ["fp8_e4m3"]
HARDWARE_REQUIRED = ["RTX_PRO_6000", "H100", "B200"]
E4M3_MAX = 448.0

@triton.jit
def matmul_kernel(
    a_ptr, b_ptr, c_ptr, scale_ptr,
    M, N, K,
    stride_am, stride_ak,
    stride_bn, stride_bk,
    stride_cm, stride_cn,
    stride_scale,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
):
    pid = tl.program_id(axis=0)
    num_pid_m = tl.cdiv(M, BLOCK_M)
    num_pid_n = tl.cdiv(N, BLOCK_N)
    pid_m = pid // num_pid_n
    pid_n = pid % num_pid_n

    offs_am = (pid_m * BLOCK_M + tl.arange(0, BLOCK_M)) % M
    offs_bn = (pid_n * BLOCK_N + tl.arange(0, BLOCK_N)) % N
    offs_k = tl.arange(0, BLOCK_K)

    a_ptrs = a_ptr + offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak
    b_ptrs = b_ptr + offs_bn[None, :] * stride_bn + offs_k[:, None] * stride_bk

    accumulator = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, tl.cdiv(K, BLOCK_K)):
        a = tl.load(a_ptrs, mask=offs_k[None, :] < K - k * BLOCK_K, other=0.0)
        b = tl.load(b_ptrs, mask=offs_k[:, None] < K - k * BLOCK_K, other=0.0)
        
        accumulator = tl.dot(a, b, accumulator)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk

    # Load scales
    scale_ptrs = scale_ptr + offs_bn * stride_scale
    scale = tl.load(scale_ptrs, mask=offs_bn < N, other=1.0)
    accumulator = accumulator * scale[None, :]

    offs_cm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_cn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    c_ptrs = c_ptr + offs_cm[:, None] * stride_cm + offs_cn[None, :] * stride_cn
    c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N)
    tl.store(c_ptrs, accumulator.to(tl.bfloat16), mask=c_mask)

class Model(nn.Module):
    def __init__(self, M: int, N: int, K: int):
        super().__init__()
        self.M, self.N, self.K = M, N, K
        # Setup temporary buffers matching reference
        w = torch.empty(N, K, dtype=torch.bfloat16)
        nn.init.normal_(w, std=0.02)
        s = (w.float().abs().amax(dim=1, keepdim=True) / E4M3_MAX).clamp(min=1e-12)
        w_fp8 = (w.float() / s).to(torch.float8_e4m3fn)
        self.register_buffer("weight", w_fp8)
        self.register_buffer("weight_scale", s.squeeze(1).to(torch.float32))

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        # Run our Triton GEMM
        M, K = x.shape
        N = self.weight.shape[0]
        c = torch.empty((M, N), device=x.device, dtype=torch.bfloat16)
        
        BLOCK_M = 64
        BLOCK_N = 64
        BLOCK_K = 64
        
        grid = lambda META: (triton.cdiv(M, META['BLOCK_M']) * triton.cdiv(N, META['BLOCK_N']),)
        
        matmul_kernel[grid](
            x, self.weight, c, self.weight_scale,
            M, N, K,
            x.stride(0), x.stride(1),
            self.weight.stride(0), self.weight.stride(1),
            c.stride(0), c.stride(1),
            self.weight_scale.stride(0),
            BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N, BLOCK_K=BLOCK_K,
        )
        return c

M = 4096
N = 4096
K = 4096

def get_inputs():
    x = (torch.rand(M, K) * 8 - 4).to(torch.float8_e4m3fn)
    return [x]

def get_init_inputs():
    return [M, N, K]

tool

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assistant

I will execute

assistant

`check.py` to verify our draft solution's correctness.

assistant

run_shell_command

args

{
  "description": "Run correctness checks.",
  "command": "uv run python check.py"
}

tool

stdout (0 chars)

assistant

I will run `benchmark.py` to measure our baseline performance across the canonical shapes.

assistant

run_shell_command

args

{
  "command": "uv run python benchmark.py",
  "description": "Run performance benchmarks on the baseline."
}

tool

stdout (0 chars)

assistant

I will write a sweeping script `test_

assistant

tuning.py` to find the optimal Triton configurations (tile sizes, warps, stages) for Shape 0 (

assistant

4096x4096x4096).

assistant

write_file

args

{
  "file_path": "test_tuning.py",
  "content": "<4052 chars \u2014 see below>"
}

content (4052 chars — TRUNCATED)

import torch
import triton
import triton.language as tl
import time

@triton.jit
def matmul_kernel(
    a_ptr, b_ptr, c_ptr, scale_ptr,
    M, N, K,
    stride_am, stride_ak,
    stride_bn, stride_bk,
    stride_cm, stride_cn,
    stride_scale,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
):
    pid = tl.program_id(axis=0)
    num_pid_m = tl.cdiv(M, BLOCK_M)
    num_pid_n = tl.cdiv(N, BLOCK_N)
    pid_m = pid // num_pid_n
    pid_n = pid % num_pid_n

    offs_am = (pid_m * BLOCK_M + tl.arange(0, BLOCK_M)) % M
    offs_bn = (pid_n * BLOCK_N + tl.arange(0, BLOCK_N)) % N
    offs_k = tl.arange(0, BLOCK_K)

    a_ptrs = a_ptr + offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak
    b_ptrs = b_ptr + offs_bn[None, :] * stride_bn + offs_k[:, None] * stride_bk

    accumulator = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, tl.cdiv(K, BLOCK_K)):
        a = tl.load(a_ptrs, mask=offs_k[None, :] < K - k * BLOCK_K, other=0.0)
        b = tl.load(b_ptrs, mask=offs_k[:, None] < K - k * BLOCK_K, other=0.0)
        
        accumulator = tl.dot(a, b, accumulator)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk

    # Load scales
    scale_ptrs = scale_ptr + offs_bn * stride_scale
    scale = tl.load(scale_ptrs, mask=offs_bn < N, other=1.0)
    accumulator = accumulator * scale[None, :]

    offs_cm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_cn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    c_ptrs = c_ptr + offs_cm[:, None] * stride_cm + offs_cn[None, :] * stride_cn
    c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N)
    tl.store(c_ptrs, accumulator.to(tl.bfloat16), mask=c_mask)

def benchmark_config(M, N, K, BLOCK_M, BLOCK_N, BLOCK_K, num_warps, num_stages):
    device = torch.device("cuda:0")
    a = torch.randn(M, K, device=device).to(torch.float8_e4m3fn)
    b = torch.randn(N, K, device=device).to(torch.float8_e4m3fn)
    scale = torch.randn(N, device=device)
    c = torch.empty((M, N), device=device, dtype=torch.bfloat16)

    grid = lambda META: (triton.cdiv(M, META['BLOCK_M']) * triton.cdiv(N, META['BLOCK_N']),)

    # Warmup
    for _ in range(5):
        matmul_kernel[grid](
            a, b, c, scale,
            M, N, K,
            a.stride(0), a.stride(1),
            b.stride(0), b.stride(1),
            c.stride(0), c.stride(1),
            scale.stride(0),
            BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N, BLOCK_K=BLOCK_K,
            num_warps=num_warps, num_stages=num_stages,
        )
    
    torch.cuda.synchronize()
    start = time.perf_counter()
    iters = 50
    for _ in range(iters):
        matmul_kernel[grid](
            a, b, c, scale,
            M, N, K,
            a.stride(0), a.stride(1),
            b.stride(0), b.stride(1),
            c.stride(0), c.stride(1),
            scale.stride(0),
            BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N, BLOCK_K=BLOCK_K,
            num_warps=num_warps, num_stages=num_stages,
        )
    torch.cuda.synchronize()
    end = time.perf_counter()
    ms = (end - start) * 1000 / iters
    flops = 2 * M * N * K
    tflops = (flops * 1e-12) / (ms * 1e-3)
    return ms, tflops

M, N, K = 4096, 4096, 4096
configs = [
    # (BLOCK_M, BLOCK_N, BLOCK_K, num_warps, num_stages)
    (128, 128, 64, 4, 3),
    (128, 128, 64, 8, 4),
    (128, 128, 64, 8, 5),
    (128, 256, 64, 8, 4),
    (256, 128, 64, 8, 4),
    (128, 128, 128, 8, 4),
    (128, 128, 128, 8, 5),
    (128, 128, 128, 4, 3),
    (128, 128, 128, 8, 3),
    (256, 256, 64, 8, 4),
    (256, 256, 128, 8, 4),
    (64, 128, 64, 4, 4),
    (128, 64, 64, 4, 4),
    (128, 128, 32, 4, 4),
]

best_tflops = 0
best_config = None

for cfg in configs:
    try:
        ms, tflops = benchmark_config(M, N, K, *cfg)
        print(f"Config {cfg}: {ms:.3f} ms, {tflops:.3f} TFLOPS")
        if tflops > best_tflops:
            best_tflops = tflops
            best_config = cfg
    except Exception as e:
        print(f"Config {cfg} failed: {e}")

print(f"

... (52 more chars)

tool

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assistant

I will execute the tuning sweep script `test_tuning.py` to evaluate various kernel configurations for square inputs.

assistant

run_shell_command

args

{
  "command": "uv run python test_tuning.py",
  "description": "Run the tuning sweep on Shape 0."
}

tool

stdout (0 chars)

assistant

I will write `test

assistant

_tuning_all.py` to sweep and find the best Triton configurations (block dimensions, warps, stages) specifically

assistant

for each of the four target shapes.

assistant

write_file

args

{
  "content": "<5169 chars \u2014 see below>",
  "file_path": "test_tuning_all.py"
}

content (5169 chars — TRUNCATED)

import torch
import triton
import triton.language as tl
import time

@triton.jit
def matmul_kernel(
    a_ptr, b_ptr, c_ptr, scale_ptr,
    M, N, K,
    stride_am, stride_ak,
    stride_bn, stride_bk,
    stride_cm, stride_cn,
    stride_scale,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
):
    pid = tl.program_id(axis=0)
    num_pid_m = tl.cdiv(M, BLOCK_M)
    num_pid_n = tl.cdiv(N, BLOCK_N)
    pid_m = pid // num_pid_n
    pid_n = pid % num_pid_n

    offs_am = (pid_m * BLOCK_M + tl.arange(0, BLOCK_M)) % M
    offs_bn = (pid_n * BLOCK_N + tl.arange(0, BLOCK_N)) % N
    offs_k = tl.arange(0, BLOCK_K)

    a_ptrs = a_ptr + offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak
    b_ptrs = b_ptr + offs_bn[None, :] * stride_bn + offs_k[:, None] * stride_bk

    accumulator = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, tl.cdiv(K, BLOCK_K)):
        a = tl.load(a_ptrs, mask=offs_k[None, :] < K - k * BLOCK_K, other=0.0)
        b = tl.load(b_ptrs, mask=offs_k[:, None] < K - k * BLOCK_K, other=0.0)
        
        accumulator = tl.dot(a, b, accumulator)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk

    # Load scales
    scale_ptrs = scale_ptr + offs_bn * stride_scale
    scale = tl.load(scale_ptrs, mask=offs_bn < N, other=1.0)
    accumulator = accumulator * scale[None, :]

    offs_cm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_cn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    c_ptrs = c_ptr + offs_cm[:, None] * stride_cm + offs_cn[None, :] * stride_cn
    c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N)
    tl.store(c_ptrs, accumulator.to(tl.bfloat16), mask=c_mask)

def benchmark_config(M, N, K, BLOCK_M, BLOCK_N, BLOCK_K, num_warps, num_stages):
    device = torch.device("cuda:0")
    a = torch.randn(M, K, device=device).to(torch.float8_e4m3fn)
    b = torch.randn(N, K, device=device).to(torch.float8_e4m3fn)
    scale = torch.randn(N, device=device)
    c = torch.empty((M, N), device=device, dtype=torch.bfloat16)

    grid = lambda META: (triton.cdiv(M, META['BLOCK_M']) * triton.cdiv(N, META['BLOCK_N']),)

    # Warmup
    for _ in range(5):
        matmul_kernel[grid](
            a, b, c, scale,
            M, N, K,
            a.stride(0), a.stride(1),
            b.stride(0), b.stride(1),
            c.stride(0), c.stride(1),
            scale.stride(0),
            BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N, BLOCK_K=BLOCK_K,
            num_warps=num_warps, num_stages=num_stages,
        )
    
    torch.cuda.synchronize()
    start = time.perf_counter()
    iters = 30
    for _ in range(iters):
        matmul_kernel[grid](
            a, b, c, scale,
            M, N, K,
            a.stride(0), a.stride(1),
            b.stride(0), b.stride(1),
            c.stride(0), c.stride(1),
            scale.stride(0),
            BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N, BLOCK_K=BLOCK_K,
            num_warps=num_warps, num_stages=num_stages,
        )
    torch.cuda.synchronize()
    end = time.perf_counter()
    ms = (end - start) * 1000 / iters
    flops = 2 * M * N * K
    tflops = (flops * 1e-12) / (ms * 1e-3)
    bytes_moved = M*K + K*N + M*N*2
    gbps = (bytes_moved * 1e-9) / (ms * 1e-3)
    return ms, tflops, gbps

SHAPES = [
    {"M": 4096, "N": 4096, "K": 4096},        # square aligned
    {"M": 4096, "N": 4096, "K": 4127},        # K not multiple of 128 -> predicated tails
    {"M": 32,   "N": 8192, "K": 8192},        # skinny M (decode)
    {"M": 4096, "N": 14336, "K": 4096},       # Llama3 up-proj shape
]

# Standard configs for M large
configs_large = [
    (128, 256, 64, 8, 4),
    (256, 128, 64, 8, 4),
    (128, 128, 64, 4, 3),
    (128, 128, 64, 8, 4),
    (128, 128, 128, 8, 3),
    (128, 128, 128, 8, 4),
    (64, 128, 64, 4, 4),
    (128, 64, 64, 4, 4),
    (256, 128, 128, 8, 3),
    (128, 256, 128, 8, 3),
]

# Configs for M small (skinny)
configs_skinny = [
    (16, 128, 64, 4, 3),
    (16, 256, 64, 4, 3),
    (32, 128, 64, 4,

... (1169 more chars)

tool

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system — result

result: error