[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/tests/test_gptq_marlin_repack.py

@@ -0,0 +1,101 @@
+import pytest
+import torch
+from sgl_kernel import gptq_marlin_repack as aot_gptq_marlin_repack
+from sgl_kernel.scalar_type import scalar_types
+
+from sglang.jit_kernel.gptq_marlin_repack import gptq_marlin_repack
+from sglang.srt.layers.quantization.utils import (
+    gptq_quantize_weights,
+    pack_rows,
+    sort_weights,
+)
+from sglang.test.test_marlin_utils import get_weight_perm, marlin_weights
+
+MARLIN_K_CHUNKS = [128]
+MARLIN_N_CHUNKS = [64, 256]
+
+MNK_FACTORS = [
+    (1, 1, 1),
+    (1, 4, 8),
+    (1, 7, 5),
+    (13, 17, 67),
+    (26, 37, 13),
+    (67, 13, 11),
+    (257, 13, 11),
+    (658, 13, 11),
+]
+
+
+@pytest.mark.parametrize("k_chunk", MARLIN_K_CHUNKS)
+@pytest.mark.parametrize("n_chunk", MARLIN_N_CHUNKS)
+@pytest.mark.parametrize("quant_type", [scalar_types.uint4b8])
+@pytest.mark.parametrize("group_size", [-1, 32, 64, 128])
+@pytest.mark.parametrize("act_order", [False, True])
+@pytest.mark.parametrize("mnk_factors", MNK_FACTORS)
+def test_gptq_marlin_repack(
+    k_chunk, n_chunk, quant_type, group_size, act_order, mnk_factors
+):
+    m_factor, n_factor, k_factor = mnk_factors
+
+    size_k = k_chunk * k_factor
+    size_n = n_chunk * n_factor
+
+    # Filter act_order
+    if act_order:
+        if group_size == -1:
+            return
+        if group_size == size_k:
+            return
+
+    # Normalize group_size
+    if group_size == -1:
+        group_size = size_k
+    assert group_size <= size_k
+
+    if size_k % group_size != 0:
+        pytest.skip("size_k must be divisible by group_size")
+
+    # Create input
+    b_weight = torch.randn((size_k, size_n), dtype=torch.float16, device="cuda")
+
+    # Quantize (and apply act_order if provided)
+    w_ref, q_w, s, g_idx, rand_perm = gptq_quantize_weights(
+        b_weight, quant_type, group_size, act_order
+    )
+
+    q_w_gptq = pack_rows(q_w, quant_type.size_bits, size_k, size_n)
+
+    # For act_order, sort the "weights" and "g_idx" so that group ids are
+    # increasing
+    sort_indices = torch.empty(0, dtype=torch.int, device=b_weight.device)
+    if act_order:
+        q_w, g_idx, sort_indices = sort_weights(q_w, g_idx)
+
+    marlin_layout_perm = get_weight_perm(quant_type.size_bits)
+    q_w_marlin_ref = marlin_weights(
+        q_w, size_k, size_n, quant_type.size_bits, marlin_layout_perm
+    )
+
+    # Run JIT repack kernel
+    jit_output = gptq_marlin_repack(
+        q_w_gptq, sort_indices, size_k, size_n, quant_type.size_bits
+    )
+
+    # Run AOT repack kernel
+    aot_output = aot_gptq_marlin_repack(
+        q_w_gptq, sort_indices, size_k, size_n, quant_type.size_bits
+    )
+
+    torch.cuda.synchronize()
+
+    # JIT should match the reference (computed from CPU marlin_weights)
+    torch.testing.assert_close(jit_output, q_w_marlin_ref)
+
+    # JIT should produce bitwise identical results to AOT
+    torch.testing.assert_close(jit_output, aot_output, rtol=0, atol=0)
+
+
+if __name__ == "__main__":
+    import subprocess
+
+    subprocess.call(["pytest", "--tb=short", str(__file__)])