Erase GPU operator #1971
Erase GPU operator #1971
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Signed-off-by: Rafal <Banas.Rafal97@gmail.com>
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| private: | ||
| OpSpec spec_; |
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That smells of bad design - base Operator class has this field - maybe we should just rework OpImplBase into something that doesn't require this kind of ugly tricks.
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changed to const OpSpec&
Signed-off-by: Rafal <Banas.Rafal97@gmail.com>
Signed-off-by: Rafal <Banas.Rafal97@gmail.com>
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!build |
dali/kernels/erase/erase_gpu.h
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| @@ -309,7 +310,7 @@ struct do_copy_or_erase { | |||
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| template <int channel_dim = -1, typename T, int ndim = 2> | |||
| __global__ void erase_gpu_impl(erase_sample_desc<T, ndim> *samples, ivec<ndim> region_shape, | |||
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| __global__ void erase_gpu_impl(erase_sample_desc<T, ndim> *samples, ivec<ndim> region_shape, | |
| __global__ void erase_gpu_impl(const erase_sample_desc<T, ndim> *samples, ivec<ndim> region_shape, |
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CI MESSAGE: [1337975]: BUILD STARTED |
dali/kernels/erase/erase_gpu.h
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| auto *sample_desc_gpu = ctx.scratchpad->ToGPU(stream, make_span(sample_desc_cpu, num_samples)); | ||
| auto* fill_values_gpu = | ||
| ctx.scratchpad->ToGPU(stream, make_span(fill_values_cpu, num_fill_values)); |
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Better option would be to use ToContiguousGPU - it will issue just one cudaMemcpy.
It's not in the scope of this task, I guess, however, if we keep some sane limit on number of channels, then the fill value could be copied to a __constant__ - it should improve the performance, since the fill_value won't have to be read from global memory and will not compete for cache with input.
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Used ToContiguousGPU
dali/kernels/erase/erase_gpu.h
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| const T *in = nullptr; | ||
| T* out = nullptr; |
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| const T *in = nullptr; | |
| T* out = nullptr; | |
| const T *__restrict__ in = nullptr; | |
| T *__restrict__ out = nullptr; |
Signed-off-by: Rafal <Banas.Rafal97@gmail.com>
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| return {reinterpret_cast<ptrs_t>(tlv.data.data()), new_shape}; | ||
| } | ||
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| template <int ndim, typename Storage> |
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I don't think this overload is necessary - a non-const TensorListView should convert implicitly to const one.
| @@ -126,8 +126,8 @@ std::tuple<std::remove_cv_t<element_t<Collections>>*...> | |||
| ToContiguousGPUMem(Scratchpad &scratchpad, cudaStream_t stream, const Collections &... c) { | |||
| const size_t N = sizeof...(Collections); | |||
| static_assert( | |||
| all_of<std::is_pod<std::remove_cv_t<element_t<Collections>>>::value...>::value, | |||
| "ToContiguousGPUMem must be used with collections of POD types"); | |||
| all_of<std::is_trivially_copyable<std::remove_cv_t<element_t<Collections>>>::value...>::value, | |||
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There's one more is_pod in this file - please change it, too.
| regions_shape.set_tensor_shape(i, {n_regions, 2, Dims}); | ||
| } | ||
| TensorList<CPUBackend> regions_cpu; | ||
| regions_cpu.set_type(TypeTable::GetTypeInfo(TypeTable::GetTypeID<int32_t>())); |
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Why not:
| regions_cpu.set_type(TypeTable::GetTypeInfo(TypeTable::GetTypeID<int32_t>())); | |
| regions_cpu.set_type(TypeInfo::Create<ibox<Dims>()); |
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| TensorList<CPUBackend> regions_cpu; | ||
| regions_cpu.set_type(TypeTable::GetTypeInfo(TypeTable::GetTypeID<int32_t>())); | ||
| regions_cpu.Resize(regions_shape); | ||
| auto regions_tlv = detail::as_boxes<Dims>(view<int32_t, 3>(regions_cpu)); |
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And you don't need this as_boxes thing at all, you can take a view<ibox<Dims>>(regions_cpu) and place the data in it directly. AFAIK it should work.
Signed-off-by: Rafal <Banas.Rafal97@gmail.com>
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!build |
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CI MESSAGE: [1340155]: BUILD STARTED |
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CI MESSAGE: [1340155]: BUILD PASSED |
Why we need this PR?
Pick one, remove the rest
What happened in this PR?
Fill relevant points, put NA otherwise. Replace anything inside []
The gpu erase kernel was modified to support different number of erase regions for each sample. The operator implementation is mostly just instantiating the kernel.
GPU erase kernel and a new file with GPU operator.
Instantiating the kernel.
Existing python test was extended to GPU. Kernel test was extended to cover different number of erase regions per sample.
N/A
JIRA TASK: [DALI-1245]