[Kernel Slimming] Migrate GPTQ-Marlin repack kernel to JIT (#18543)

Co-authored-by: Xiaoyu Zhang <35585791+BBuf@users.noreply.github.com>

python/sglang/jit_kernel/benchmark/bench_gptq_marlin_repack.py

@@ -0,0 +1,104 @@
+import os
+
+import torch
+import triton
+import triton.testing
+from sgl_kernel.scalar_type import scalar_types
+
+from sglang.jit_kernel.gptq_marlin_repack import gptq_marlin_repack as jit_fn
+from sglang.srt.layers.quantization.utils import gptq_quantize_weights, pack_rows
+
+try:
+    from sgl_kernel import gptq_marlin_repack as aot_fn
+
+    AOT_AVAILABLE = True
+except ImportError:
+    AOT_AVAILABLE = False
+
+IS_CI = (
+    os.getenv("CI", "false").lower() == "true"
+    or os.getenv("GITHUB_ACTIONS", "false").lower() == "true"
+)
+
+# Fixed problem dimensions
+SIZE_N = 4096
+NUM_BITS = 4
+QUANT_TYPE = scalar_types.uint4b8
+GROUP_SIZE = 128
+
+# Pre-compute quantized weight for each size_k in the sweep
+_cache = {}
+
+
+def _get_inputs(size_k):
+    if size_k not in _cache:
+        size_n = SIZE_N
+        b_weight = torch.randn((size_k, size_n), dtype=torch.float16, device="cuda")
+        _, q_w, _, _, _ = gptq_quantize_weights(
+            b_weight, QUANT_TYPE, GROUP_SIZE, act_order=False
+        )
+        q_w_gptq = pack_rows(q_w, NUM_BITS, size_k, size_n)
+        sort_indices = torch.empty(0, dtype=torch.int, device="cuda")
+        _cache[size_k] = (q_w_gptq, sort_indices)
+    return _cache[size_k]
+
+
+def check_correctness():
+    if not AOT_AVAILABLE:
+        print("sgl_kernel AOT not available, skipping correctness check")
+        return
+    size_k = 4096
+    q_w_gptq, sort_indices = _get_inputs(size_k)
+    out_jit = jit_fn(q_w_gptq, sort_indices, size_k, SIZE_N, NUM_BITS)
+    out_aot = aot_fn(q_w_gptq, sort_indices, size_k, SIZE_N, NUM_BITS)
+    torch.testing.assert_close(out_jit, out_aot, rtol=0, atol=0)
+    print("Correctness check passed (JIT vs AOT)")
+
+
+if IS_CI:
+    k_range = [128, 1024, 4096]
+else:
+    k_range = [128, 256, 512, 1024, 2048, 4096, 8192]
+
+if AOT_AVAILABLE:
+    line_vals = ["jit", "aot"]
+    line_names = ["JIT Kernel", "AOT Kernel"]
+    styles = [("blue", "-"), ("green", "-")]
+else:
+    line_vals = ["jit"]
+    line_names = ["JIT Kernel"]
+    styles = [("blue", "-")]
+
+
+@triton.testing.perf_report(
+    triton.testing.Benchmark(
+        x_names=["size_k"],
+        x_vals=k_range,
+        line_arg="provider",
+        line_vals=line_vals,
+        line_names=line_names,
+        styles=styles,
+        ylabel="us",
+        plot_name="gptq-marlin-repack-performance",
+        args={},
+    )
+)
+def benchmark(size_k, provider):
+    q_w_gptq, sort_indices = _get_inputs(size_k)
+
+    quantiles = [0.5, 0.2, 0.8]
+
+    if provider == "jit":
+        fn = lambda: jit_fn(q_w_gptq, sort_indices, size_k, SIZE_N, NUM_BITS)
+    elif provider == "aot":
+        fn = lambda: aot_fn(q_w_gptq, sort_indices, size_k, SIZE_N, NUM_BITS)
+    else:
+        raise ValueError(f"Unknown provider: {provider}")
+
+    ms, min_ms, max_ms = triton.testing.do_bench_cudagraph(fn, quantiles=quantiles)
+    return 1000 * ms, 1000 * max_ms, 1000 * min_ms
+
+
+if __name__ == "__main__":
+    check_correctness()
+    benchmark.run(print_data=True)