[TOPI][CUDA] Enable vectorization on fp16 type #4867
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Please request reviews from reviewers |
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Kindly ping. Thanks! |
vinx13
requested changes
Feb 12, 2020
| if not tvm.runtime.enabled(device): | ||
| print("Skip because %s is not enabled" % device) | ||
| return | ||
| with tvm.target.create(device): |
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add a check whether fp16 is supported here and also verify_relu
https://github.com/apache/incubator-tvm/blob/aaf62e47e64d592be770e915a7aa59d41eddb729/topi/tests/python/test_topi_transform.py#L57-L59
Laurawly
reviewed
Feb 12, 2020
topi/tests/python/test_topi_relu.py
Outdated
| @@ -87,12 +87,12 @@ def _prelu_numpy(x, W): | |||
| tvm.testing.assert_allclose(b.asnumpy(), out_np, rtol=1e-5) | |||
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| def test_relu(): | |||
| verify_relu(10, 128) | |||
| verify_relu(128, 128, "float32") | |||
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Can you keep a test case as before where m and n have different values?
| check_device(device) | ||
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| def test_vectorization(): | ||
| verify_vectorization(128, 128, "float16") |
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Thanks all for the suggestions! Tests updated. |
vinx13
reviewed
Feb 13, 2020
topi/tests/python/test_topi_relu.py
Outdated
| from common import get_all_backend | ||
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| def verify_relu(m, n): | ||
| A = tvm.placeholder((m, n), name='A') | ||
| def skipTest(dtype, device): |
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nit: we prefer skip_test style naming
- This allows to better utilize the memory bandwidth - Note that not all cases are vectorized for fp16 datatype. For instance, when the size is not a multiple of 1024, the inner loop may be an expression that cannot be vectorized. In this case, a small inner loop is still benefical for latency hidding. Signed-off-by: Wei Pan <[email protected]>
vinx13
approved these changes
Feb 13, 2020
alexwong
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to alexwong/tvm
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Feb 26, 2020
- This allows to better utilize the memory bandwidth - Note that not all cases are vectorized for fp16 datatype. For instance, when the size is not a multiple of 1024, the inner loop may be an expression that cannot be vectorized. In this case, a small inner loop is still benefical for latency hidding. Signed-off-by: Wei Pan <[email protected]>
alexwong
pushed a commit
to alexwong/tvm
that referenced
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Feb 28, 2020
- This allows to better utilize the memory bandwidth - Note that not all cases are vectorized for fp16 datatype. For instance, when the size is not a multiple of 1024, the inner loop may be an expression that cannot be vectorized. In this case, a small inner loop is still benefical for latency hidding. Signed-off-by: Wei Pan <[email protected]>
zhiics
pushed a commit
to neo-ai/tvm
that referenced
this pull request
Mar 2, 2020
- This allows to better utilize the memory bandwidth - Note that not all cases are vectorized for fp16 datatype. For instance, when the size is not a multiple of 1024, the inner loop may be an expression that cannot be vectorized. In this case, a small inner loop is still benefical for latency hidding. Signed-off-by: Wei Pan <[email protected]>
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This allows to better utilize the memory bandwidth
Note that not all cases are vectorized for fp16 datatype. For
instance, when the size is not a multiple of 1024, the inner loop
may be an expression that cannot be vectorized. In this case, a
small inner loop is still benefical for latency hidding.
Signed-off-by: Wei Pan [email protected]
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