Delete cutlass_mla, non-Marlin GPTQ, AWQ AOT kernel, and Dual Chunk Flash Attention (#32114)
Co-authored-by: Brayden Zhong <brayden@radixark.ai>
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co-authored by
Brayden Zhong
parent
7152c14384
commit
c0b790cf7f
@@ -11,13 +11,6 @@ from sglang.test.ci.ci_register import register_cuda_ci
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register_cuda_ci(est_time=9, stage="base-b-kernel-unit", runner_config="1-gpu-large")
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try:
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from sgl_kernel import awq_dequantize as aot_awq_dequantize
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AOT_AVAILABLE = True
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except ImportError:
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AOT_AVAILABLE = False
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def reverse_awq_order(t: torch.Tensor):
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bits = 4
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@@ -117,54 +110,5 @@ def test_awq_dequantize_jit_vs_torch(
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)
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@pytest.mark.parametrize(
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"qweight_row,qweight_col,is_bf16_act",
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list(
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itertools.product(
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[128, 256, 512, 1024, 3584],
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[16, 32, 64, 128, 448],
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[True, False],
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)
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),
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)
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def test_awq_dequantize_jit_vs_aot(
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qweight_row: int, qweight_col: int, is_bf16_act: bool
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):
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if not AOT_AVAILABLE:
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pytest.skip("sgl_kernel AOT not available")
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device = torch.device("cuda")
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qweight = torch.randint(
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0,
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torch.iinfo(torch.int32).max,
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(qweight_row, qweight_col),
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dtype=torch.int32,
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device=device,
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)
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group_size = qweight_row
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scales_row = qweight_row // group_size
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scales_col = qweight_col * 8
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if is_bf16_act:
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scales = torch.rand(scales_row, scales_col, dtype=torch.bfloat16, device=device)
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else:
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scales = torch.rand(scales_row, scales_col, dtype=torch.float16, device=device)
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qzeros = torch.randint(
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0,
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torch.iinfo(torch.int32).max,
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(scales_row, qweight_col),
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dtype=torch.int32,
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device=device,
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)
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# Run both implementations
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aot_out = aot_awq_dequantize(qweight, scales, qzeros)
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jit_out = jit_awq_dequantize(qweight, scales, qzeros)
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# Bitwise equality
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torch.testing.assert_close(jit_out, aot_out, rtol=0, atol=0)
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if __name__ == "__main__":
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sys.exit(pytest.main([__file__, "-v", "-s"]))
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