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Add numerical correctness unittest and fix typo of PR[8041] #8044

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Add numerical correctness unittest and fix typo of PR[8041] #8044

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51 changes: 46 additions & 5 deletions python/oneflow/test/modules/test_parital_fc.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,22 +18,63 @@
from oneflow.test_utils.automated_test_util import *
import oneflow as flow
import oneflow.unittest
import numpy as np


@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases")
class TestParitalFC(flow.unittest.TestCase):
def test_parital_fc(test_case):
class TestPartialFC(flow.unittest.TestCase):
def test_partial_fc(test_case):
p = flow.env.all_device_placement("cuda")
w = flow.randn(50000, 128, placement=p, sbp=flow.sbp.broadcast, requires_grad=True)
w = flow.randn(
50000, 128, placement=p, sbp=flow.sbp.broadcast, requires_grad=True
)
label = flow.randint(0, 50000, (512,), placement=p, sbp=flow.sbp.broadcast)
num_sample = 5000
out = flow.distributed_partial_fc_sample(w, label, num_sample)
test_case.assertTrue(out[0].shape == flow.Size([512]))
test_case.assertTrue(out[1].shape == flow.Size([5000]))
test_case.assertTrue(out[2].shape == flow.Size([5000, 128]))

# test gradient function
sample_weight = out[2]
sample_weight.sum().backward()
x = flow.randn(
512, 128, placement=p, sbp=flow.sbp.broadcast, requires_grad=True
)
_, sample_labels, sample_weights = out
flow.matmul(x, sample_weights, transpose_b=True).sum().backward()

# test numerical correctness
sample_mask = flow.BoolTensor(w.shape[0], placement=p, sbp=flow.sbp.broadcast)
sample_mask[:] = False
sample_mask[sample_labels] = True

test_case.assertTrue(
np.allclose(
w.grad[sample_labels].numpy(),
np.repeat(flow.sum(x, dim=0, keepdim=True).numpy(), num_sample, axis=0),
rtol=0.01,
atol=0.01,
)
)
test_case.assertTrue(
np.allclose(
w.grad[~sample_mask].numpy(),
np.zeros((w.shape[0] - num_sample, w.shape[1])),
rtol=0.0001,
atol=0.0001,
)
)
test_case.assertTrue(
np.allclose(
x.grad.numpy(),
np.repeat(
flow.sum(w[sample_labels], dim=0, keepdim=True).numpy(),
x.shape[0],
axis=0,
),
rtol=0.01,
atol=0.01,
)
)


if __name__ == "__main__":
Expand Down