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82 lines
2.5 KiB
Python
82 lines
2.5 KiB
Python
import sys
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import pytest
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import torch
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from sglang.test.ci.ci_register import register_amd_ci, register_cuda_ci
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register_cuda_ci(est_time=4, stage="base-b-kernel-unit", runner_config="1-gpu-large")
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register_amd_ci(est_time=4, stage="jit-kernel-unit", runner_config="amd")
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DEVICE = "cuda"
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def reference_sigmoid_gate_mul(x, gate):
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return x * torch.sigmoid(gate)
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@pytest.mark.parametrize("dtype", [torch.bfloat16, torch.float16, torch.float32])
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@pytest.mark.parametrize(
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"shape",
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[
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(1, 4096),
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(4, 4096),
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(8, 4096),
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(32, 8192),
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(1, 128),
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(16, 16384),
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],
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)
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def test_sigmoid_gate_mul_correctness(shape, dtype):
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from sglang.jit_kernel.triton.sigmoid_gate_mul import sigmoid_gate_mul
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torch.manual_seed(42)
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x = torch.randn(shape, dtype=dtype, device=DEVICE)
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gate = torch.randn(shape, dtype=dtype, device=DEVICE)
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ref = reference_sigmoid_gate_mul(x, gate)
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out = sigmoid_gate_mul(x, gate)
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rtol = 1e-2 if dtype == torch.bfloat16 else 1e-3
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atol = 2e-2 if dtype == torch.bfloat16 else 1e-3
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torch.testing.assert_close(out, ref, rtol=rtol, atol=atol)
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@pytest.mark.parametrize("shape", [(4, 4096), (1, 128)])
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def test_sigmoid_gate_mul_does_not_modify_inputs(shape):
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from sglang.jit_kernel.triton.sigmoid_gate_mul import sigmoid_gate_mul
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torch.manual_seed(42)
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x = torch.randn(shape, dtype=torch.bfloat16, device=DEVICE)
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gate = torch.randn(shape, dtype=torch.bfloat16, device=DEVICE)
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x_orig = x.clone()
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gate_orig = gate.clone()
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sigmoid_gate_mul(x, gate)
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torch.testing.assert_close(x, x_orig, rtol=0, atol=0)
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torch.testing.assert_close(gate, gate_orig, rtol=0, atol=0)
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def test_sigmoid_gate_mul_output_dtype():
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from sglang.jit_kernel.triton.sigmoid_gate_mul import sigmoid_gate_mul
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for dtype in [torch.bfloat16, torch.float16, torch.float32]:
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x = torch.randn(4, 4096, dtype=dtype, device=DEVICE)
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gate = torch.randn(4, 4096, dtype=dtype, device=DEVICE)
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out = sigmoid_gate_mul(x, gate)
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assert out.dtype == dtype, f"Expected {dtype}, got {out.dtype}"
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def test_sigmoid_gate_mul_contiguous_output():
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from sglang.jit_kernel.triton.sigmoid_gate_mul import sigmoid_gate_mul
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x = torch.randn(4, 4096, dtype=torch.bfloat16, device=DEVICE)
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gate = torch.randn(4, 4096, dtype=torch.bfloat16, device=DEVICE)
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out = sigmoid_gate_mul(x, gate)
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assert out.is_contiguous()
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if __name__ == "__main__":
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sys.exit(pytest.main([__file__, "-v"]))
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