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[Topi][Unittests] Parametrized tests in test_topi_dense.py, split out gpu-independent implementations - #8336
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| return (a_np, b_np, c_np, d_np) | ||
| def test_dense( |
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I'm thinking that wheather this can test different targets as you expected.
Shouldn't this function be decorated with "@tvm.testing.parametrize_targets"?
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As of #8010 , the decorator for @tvm.testing.parametrize_targets is only needed if the test should override the default targets. Otherwise, having the target and/or dev fixture arguments is sufficient to have the test run on all enabled targets.
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Oh, my bad. That really a helpful feature.
Now, tests run for multiple data types, can be extended with additional datatypes.
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Added a few fixes in the codegen needed for the vulkan tests to run correctly on newer nvidia GPUs. (@masahi ) |
…ense As a follow-up to the renaming of "gpu" to "cuda", separating implementations that require CUDA (e.g. dense_cublas.cuda) from implementations that require any GPU, but not necessarily a CUDA GPU (e.g. dense_small_batch.gpu). My intent is to pair this migration with the extension of unit tests to cover additional GPU runtimes, migrating only implementations that run correctly on non-CUDA GPU devices.
…lts on some GPUs - In ThreadAllreduceBuilder, separate out load/store so that they can have a memory barrier in-between. - In Vulkan codegen, added Workgroup memory sync for subgroup thread sync, since the different subgroup threads can still access workgroup memory. Longer-term, may need tir enhancements to separate out sync of control/memory.
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…ut gpu-independent implementations (apache#8336) * [Topi][UnitTests] Parametrized tests in test_topi_dense.py Now, tests run for multiple data types, can be extended with additional datatypes. * [Topi] Separated generic-gpu nn.dense implementations into topi.gpu.dense As a follow-up to the renaming of "gpu" to "cuda", separating implementations that require CUDA (e.g. dense_cublas.cuda) from implementations that require any GPU, but not necessarily a CUDA GPU (e.g. dense_small_batch.gpu). My intent is to pair this migration with the extension of unit tests to cover additional GPU runtimes, migrating only implementations that run correctly on non-CUDA GPU devices. * [Vulkan][Codegen] Updated storage sync to avoid incorrect matmul results on some GPUs - In ThreadAllreduceBuilder, separate out load/store so that they can have a memory barrier in-between. - In Vulkan codegen, added Workgroup memory sync for subgroup thread sync, since the different subgroup threads can still access workgroup memory. Longer-term, may need tir enhancements to separate out sync of control/memory. Co-authored-by: Eric Lunderberg <elunderberg@octoml.ai>
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…ut gpu-independent implementations (apache#8336) * [Topi][UnitTests] Parametrized tests in test_topi_dense.py Now, tests run for multiple data types, can be extended with additional datatypes. * [Topi] Separated generic-gpu nn.dense implementations into topi.gpu.dense As a follow-up to the renaming of "gpu" to "cuda", separating implementations that require CUDA (e.g. dense_cublas.cuda) from implementations that require any GPU, but not necessarily a CUDA GPU (e.g. dense_small_batch.gpu). My intent is to pair this migration with the extension of unit tests to cover additional GPU runtimes, migrating only implementations that run correctly on non-CUDA GPU devices. * [Vulkan][Codegen] Updated storage sync to avoid incorrect matmul results on some GPUs - In ThreadAllreduceBuilder, separate out load/store so that they can have a memory barrier in-between. - In Vulkan codegen, added Workgroup memory sync for subgroup thread sync, since the different subgroup threads can still access workgroup memory. Longer-term, may need tir enhancements to separate out sync of control/memory. Co-authored-by: Eric Lunderberg <elunderberg@octoml.ai>
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[Topi][UnitTests] Parametrized tests in test_topi_dense.py
Now, tests run for multiple data types, can be extended with additional datatypes.
[Topi] Separated generic-gpu nn.dense implementations into topi.gpu.dense
As a follow-up to the renaming of "gpu" to "cuda", separating implementations that require CUDA (e.g. dense_cublas.cuda) from implementations that require any GPU, but not necessarily a CUDA GPU (e.g. dense_small_batch.gpu).
My intent is to pair this migration with the extension of unit tests to cover additional GPU runtimes, migrating only implementations that run correctly on non-CUDA GPU devices.