[Kernel Slimming] Remove sgl-kernel AOT marlin kernels (#19241)
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@@ -1,6 +1,5 @@
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import pytest
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import torch
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from sgl_kernel import gptq_marlin_gemm as aot_gptq_marlin_gemm
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from sgl_kernel.scalar_type import scalar_types
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from sglang.jit_kernel.gptq_marlin import gptq_marlin_gemm
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@@ -86,26 +85,6 @@ def test_gptq_marlin_gemm(
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is_zp_float=False,
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)
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aot_output = aot_gptq_marlin_gemm(
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a_input,
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None,
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marlin_q_w,
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marlin_s,
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marlin_s2,
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marlin_zp,
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g_idx,
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sort_indices,
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workspace,
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quant_type,
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a_input.shape[0],
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b_weight.shape[1],
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a_input.shape[1],
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is_k_full=True,
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use_atomic_add=False,
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use_fp32_reduce=False,
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is_zp_float=False,
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)
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output_ref = torch.matmul(a_input, w_ref)
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torch.cuda.synchronize()
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@@ -115,9 +94,6 @@ def test_gptq_marlin_gemm(
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)
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assert max_diff < 0.04
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# JIT kernel should produce bitwise identical results to AOT kernel
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torch.testing.assert_close(output, aot_output, rtol=0, atol=0)
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if __name__ == "__main__":
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import subprocess
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@@ -1,6 +1,5 @@
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import pytest
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import torch
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from sgl_kernel import gptq_marlin_repack as aot_gptq_marlin_repack
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from sgl_kernel.scalar_type import scalar_types
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from sglang.jit_kernel.gptq_marlin_repack import gptq_marlin_repack
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@@ -81,19 +80,11 @@ def test_gptq_marlin_repack(
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q_w_gptq, sort_indices, size_k, size_n, quant_type.size_bits
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)
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# Run AOT repack kernel
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aot_output = aot_gptq_marlin_repack(
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q_w_gptq, sort_indices, size_k, size_n, quant_type.size_bits
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)
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torch.cuda.synchronize()
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# JIT should match the reference (computed from CPU marlin_weights)
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torch.testing.assert_close(jit_output, q_w_marlin_ref)
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# JIT should produce bitwise identical results to AOT
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torch.testing.assert_close(jit_output, aot_output, rtol=0, atol=0)
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if __name__ == "__main__":
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import subprocess
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