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tengqmtengqm
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Split accuracy test for general reduction ops (#2613)
1 parent 93e4d30 commit a8a4452

11 files changed

Lines changed: 765 additions & 623 deletions

‎tests/test_all.py‎

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import pytest
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import torch
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import flag_gems
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from . import accuracy_utils as utils
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from . import conftest as cfg
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if cfg.QUICK_MODE:
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FLOAT_DTYPES = [torch.float32]
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DIMS_LIST = [1]
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KIND_KEEPDIM_DIMS_SHAPE = [("normal", True, 1, utils.REDUCTION_SHAPES[0])]
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else:
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FLOAT_DTYPES = utils.FLOAT_DTYPES
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DIMS_LIST = [0, 1, [0, 1], [1, 0]]
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KIND_KEEPDIM_DIMS_SHAPE = list(
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zip(
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["normal", "allTrue"] * 2,
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[True, False] * 2,
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DIMS_LIST,
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utils.REDUCTION_SHAPES + [(7, 4, 11, 1)],
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)
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)
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@pytest.mark.all
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@pytest.mark.parametrize("shape", utils.REDUCTION_SHAPES)
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@pytest.mark.parametrize("dtype", FLOAT_DTYPES + [torch.bool])
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@pytest.mark.parametrize("kind", ["normal", "allTrue"])
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def test_all(shape, dtype, kind):
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if kind == "allTrue":
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inp = torch.ones(shape, dtype=dtype, device=flag_gems.device)
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else:
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inp = torch.randint(0, 2, shape, dtype=dtype, device="cpu").to(flag_gems.device)
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ref_inp = utils.to_reference(inp)
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ref_out = torch.all(ref_inp)
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with flag_gems.use_gems():
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res_out = torch.all(inp)
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utils.gems_assert_equal(res_out, ref_out)
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@pytest.mark.all_dims
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@pytest.mark.skipif(
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utils.SkipVersion("torch", "<2.2"), reason="Skipping Pytorch version."
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)
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@pytest.mark.parametrize("kind, keepdim, dim, shape", KIND_KEEPDIM_DIMS_SHAPE)
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@pytest.mark.parametrize("dtype", FLOAT_DTYPES + [torch.bool])
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def test_all_dims(shape, dim, keepdim, dtype, kind):
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if kind == "allTrue":
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inp = torch.ones(shape, dtype=dtype, device=flag_gems.device)
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else:
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inp = torch.randint(0, 2, shape, dtype=dtype, device="cpu").to(flag_gems.device)
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ref_inp = utils.to_reference(inp)
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ref_out = torch.all(ref_inp, dim=dim, keepdim=keepdim)
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with flag_gems.use_gems():
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res_out = torch.all(inp, dim=dim, keepdim=keepdim)
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utils.gems_assert_equal(res_out, ref_out)

‎tests/test_allclose.py‎

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import pytest
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import torch
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import flag_gems
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from . import accuracy_utils as utils
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from . import conftest as cfg
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if cfg.QUICK_MODE:
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FLOAT_DTYPES = [torch.float32]
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else:
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FLOAT_DTYPES = utils.FLOAT_DTYPES
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@pytest.mark.allclose
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@pytest.mark.parametrize("shape", utils.POINTWISE_SHAPES)
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@pytest.mark.parametrize("dtype", utils.ALL_FLOAT_DTYPES + utils.ALL_INT_DTYPES)
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@pytest.mark.parametrize("equal_nan", [False, True])
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@pytest.mark.parametrize("gen_nan", [0, 1, 2, 3, 4])
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def test_allclose(shape, dtype, equal_nan, gen_nan):
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# [gen_nan] 1: nan, 2: inf, 3: -inf, 4: inf vs -inf
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rtol = torch.rand(1, dtype=torch.float32, device=flag_gems.device).item() * (
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0.0001 if dtype in [torch.bfloat16, torch.float16] else 0.01
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)
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if dtype in utils.ALL_FLOAT_DTYPES:
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atol = (
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torch.finfo(dtype).tiny
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* torch.randint(0, 4, (1,), device=flag_gems.device).item()
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)
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inp1 = torch.full(shape, 1.234, dtype=dtype, device=flag_gems.device)
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inp2 = torch.full(shape, 1.234, dtype=dtype, device=flag_gems.device)
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if gen_nan:
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nan_num = torch.full(
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(1,),
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float("nan" if gen_nan == 1 else "inf"),
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dtype=dtype,
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device=flag_gems.device,
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)
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# FIXME: Neg doesn't support double on torch_musa, so workaround temporarily.
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inp1.view(-1)[0] = (
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(-nan_num.cpu()).to(flag_gems.device) if gen_nan == 3 else nan_num
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)
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inp2.view(-1)[0] = (
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(-nan_num.cpu()).to(flag_gems.device) if gen_nan >= 3 else nan_num
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)
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else:
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atol = (
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torch.finfo(torch.float16).eps
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* torch.randint(0, 10, (1,), device=flag_gems.device).item()
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)
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inp1 = torch.randint(-1000, 1000, shape, device=flag_gems.device).to(dtype)
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inp2 = torch.randint(-1000, 1000, shape, device=flag_gems.device).to(dtype)
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ref_inp1 = utils.to_reference(inp1, False)
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ref_inp2 = utils.to_reference(inp2, False)
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with flag_gems.use_gems():
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res_out = torch.allclose(inp1, inp2, rtol, atol, equal_nan=equal_nan)
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ref_out = torch.allclose(ref_inp1, ref_inp2, rtol, atol, equal_nan=equal_nan)
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assert res_out == ref_out

‎tests/test_any.py‎

Lines changed: 61 additions & 0 deletions
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import pytest
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import torch
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import flag_gems
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from . import accuracy_utils as utils
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from . import conftest as cfg
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if cfg.QUICK_MODE:
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FLOAT_DTYPES = [torch.float32]
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DIMS_LIST = [1]
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KIND_KEEPDIM_DIMS_SHAPE = [("normal", True, 1, utils.REDUCTION_SHAPES[0])]
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else:
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FLOAT_DTYPES = utils.FLOAT_DTYPES
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DIMS_LIST = [0, 1, [0, 1], [1, 0]]
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KIND_KEEPDIM_DIMS_SHAPE = list(
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zip(
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["normal", "allTrue"] * 2,
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[True, False] * 2,
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DIMS_LIST,
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utils.REDUCTION_SHAPES + [(7, 4, 11, 1)],
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)
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)
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@pytest.mark.any
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@pytest.mark.parametrize("shape", utils.REDUCTION_SHAPES)
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@pytest.mark.parametrize("dtype", FLOAT_DTYPES + [torch.bool])
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@pytest.mark.parametrize("kind", ["normal", "allFalse"])
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def test_any(shape, dtype, kind):
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if kind == "allFalse":
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inp = torch.zeros(shape, dtype=dtype, device=flag_gems.device)
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else:
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inp = torch.randint(0, 2, shape, dtype=dtype, device="cpu").to(flag_gems.device)
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ref_inp = utils.to_reference(inp)
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ref_out = torch.any(ref_inp)
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with flag_gems.use_gems():
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res_out = torch.any(inp)
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utils.gems_assert_equal(res_out, ref_out)
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@pytest.mark.any_dims
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@pytest.mark.skipif(
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utils.SkipVersion("torch", "<2.2"), reason="Skipping Pytorch version."
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)
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@pytest.mark.parametrize("kind, keepdim, dim, shape", KIND_KEEPDIM_DIMS_SHAPE)
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@pytest.mark.parametrize("dtype", FLOAT_DTYPES + [torch.bool])
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def test_any_dims(shape, dim, keepdim, dtype, kind):
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if kind == "allFalse":
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inp = torch.zeros(shape, dtype=dtype, device=flag_gems.device)
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else:
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inp = torch.randint(0, 2, shape, dtype=dtype, device="cpu").to(flag_gems.device)
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ref_inp = utils.to_reference(inp)
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ref_out = torch.any(ref_inp, dim=dim, keepdim=keepdim)
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with flag_gems.use_gems():
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res_out = torch.any(inp, dim=dim, keepdim=keepdim)
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utils.gems_assert_equal(res_out, ref_out)

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