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author | Mohammed Suhail Munshi <MohammedSuhail.Munshi@arm.com> | 2024-04-29 22:53:58 +0100 |
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committer | Suhail M <MohammedSuhail.Munshi@arm.com> | 2024-05-08 12:07:31 +0000 |
commit | 2fea13593a4753316ae488edf489cb4b00150153 (patch) | |
tree | 423e6369a74c44b505dd8fd4d62bde0946ec2e32 | |
parent | c22e1263ba3a6945ceb1fdccb33eac512fd156fb (diff) | |
download | ComputeLibrary-2fea13593a4753316ae488edf489cb4b00150153.tar.gz |
Add batched indices support to Scatter GPU Implementation
Resolves: [COMPMID-6897]
Signed-off-by: Mohammed Suhail Munshi <MohammedSuhail.Munshi@arm.com>
Change-Id: I70b1c3c5f0de8484fcb6c3b0cc0d0d8c059b0f58
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/11525
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Gunes Bayir <gunes.bayir@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Benchmark: Arm Jenkins <bsgcomp@arm.com>
-rw-r--r-- | src/gpu/cl/kernels/ClScatterKernel.cpp | 44 | ||||
-rw-r--r-- | tests/datasets/ScatterDataset.h | 19 | ||||
-rw-r--r-- | tests/validation/CL/ScatterLayer.cpp | 4 | ||||
-rw-r--r-- | tests/validation/fixtures/ScatterLayerFixture.h | 15 | ||||
-rw-r--r-- | tests/validation/reference/ScatterLayer.cpp | 1 |
5 files changed, 60 insertions, 23 deletions
diff --git a/src/gpu/cl/kernels/ClScatterKernel.cpp b/src/gpu/cl/kernels/ClScatterKernel.cpp index 21c0253f91..f76a674b27 100644 --- a/src/gpu/cl/kernels/ClScatterKernel.cpp +++ b/src/gpu/cl/kernels/ClScatterKernel.cpp @@ -66,6 +66,7 @@ Status ClScatterKernel::validate(const ITensorInfo *updates, const int32_t upt_dims = upt_shape.num_dimensions(); const int32_t dst_dims = dst_shape.num_dimensions(); const int32_t ind_dims = ind_shape.num_dimensions(); + const int32_t data_dim = upt_dims - (ind_dims - 1); // Number of batch dims is the number of indices dims - 1 const int32_t index_len = ind_shape[0]; @@ -73,14 +74,34 @@ Status ClScatterKernel::validate(const ITensorInfo *updates, ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_NOT_IN(indices, DataType::S32); ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_NOT_IN(dst, DataType::F32, DataType::F16, DataType::S32, DataType::S16, DataType::S8, DataType::U32, DataType::U16, DataType::U8); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(ind_dims > 2, "Only 2D indices tensors are currently supported."); + + // Check data dims in update tensor and output tensor are equal + for (int32_t i = 0; i < data_dim; i++) + { + ARM_COMPUTE_RETURN_ERROR_ON_MSG(upt_shape[i] != dst_shape[i], + "Data dims should be same size in both updates and ouput tensor."); + } + + // Check if batch dims in indices and updates tensor are equal. + for (int32_t i = 0; i < ind_dims - 1; i++) + { + ARM_COMPUTE_RETURN_ERROR_ON_MSG(upt_shape[data_dim + i] != ind_shape[i + 1], + "Batch dimensions should be the same in updates and indices tensor."); + } + + ARM_COMPUTE_RETURN_ERROR_ON_MSG(ind_shape[1] != upt_shape[data_dim], + "Height of indices tensor should match size of highest dimension in updates tensor " + "(Excluding batch dimension)"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG( - ind_shape[1] != upt_shape[upt_dims - 1], - "Height of indices tensor should match size of highest dimension in updates tensor."); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(upt_dims > dst_dims, "Update tensor cannot have more dims than output tensor."); + data_dim >= dst_dims, "Update tensor cannot have more dims than output tensor. (Excluding batch dimensions)"); + ARM_COMPUTE_RETURN_ERROR_ON(index_len != dst_dims - data_dim); + ARM_COMPUTE_RETURN_ERROR_ON_MSG((ind_dims < 2), "Shape of Indices tensor must be at least 2D"); ARM_COMPUTE_RETURN_ERROR_ON_MSG(index_len > max_index_length, "Maximum supported index length is 5!"); - ARM_COMPUTE_RETURN_ERROR_ON(index_len != dst_dims - upt_dims + 1); + ARM_COMPUTE_RETURN_ERROR_ON_MSG( + index_len >= dst_dims && dst_dims != 1, + "Index length should be smaller than number of output dims (or equal to with 1D output)"); return Status{}; } @@ -96,7 +117,7 @@ void ClScatterKernel::configure(const ClCompileContext &compile_context, const TensorShape &dst_shape = dst->tensor_shape(); - const bool is_scalar_block = updates->num_dimensions() == 1; + const bool is_scalar_block = updates->num_dimensions() == 1; // Checks for replacing only a single element. const int n0 = adjust_vec_size(16 / updates->element_size(), is_scalar_block ? 1 : updates->dimension(0)); const int partial_n0 = updates->dimension(0) % n0; @@ -120,9 +141,9 @@ void ClScatterKernel::configure(const ClCompileContext &compile_context, build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(dst->data_type())); build_opts.add_option_if(is_data_type_float(dst->data_type()), "-DIS_FLOAT"); - const int num_dims = dst->num_dimensions(); - - build_opts.add_option("-DNUM_INDICES=" + support::cpp11::to_string(indices->dimension(1))); + const int num_dims = dst->num_dimensions(); + TensorShape ind_collapsed = indices->tensor_shape().collapsed_from(1); + build_opts.add_option("-DNUM_INDICES=" + support::cpp11::to_string(ind_collapsed[1])); build_opts.add_option("-DINDEX_LENGTH=" + support::cpp11::to_string(index_len)); // We provide 5 variables to use in a constant array @@ -187,11 +208,14 @@ void ClScatterKernel::run_op(ITensorPack &tensors, const Window &window, cl::Com const ITensorInfo *dst_info = dst->info(); const int num_dims = dst_info->num_dimensions(); + const int ind_dims = indices->info()->num_dimensions(); const int index_len = indices->info()->dimension(0); // calculate m-dimensional data block strides in updates and destination tensors - const int upt_block_stride = updates->info()->strides_in_bytes()[updates->info()->num_dimensions() - 1]; + const int upt_block_stride = + updates->info()->strides_in_bytes()[updates->info()->num_dimensions() - (ind_dims - 1)]; + const int out_block_stride = dst_info->strides_in_bytes()[num_dims - index_len]; unsigned int idx = 0; diff --git a/tests/datasets/ScatterDataset.h b/tests/datasets/ScatterDataset.h index 9dcf859a8f..4ad269ec85 100644 --- a/tests/datasets/ScatterDataset.h +++ b/tests/datasets/ScatterDataset.h @@ -179,10 +179,21 @@ public: // NOTE: Config is src, updates, indices, output. // NOTE: Updates/Indices tensors are now batched. // NOTE: indices.shape.x = (updates_batched) ? (src.num_dimensions - updates.num_dimensions) + 2 : (src.num_dimensions - updates.num_dimensions) + 1 + // k is the number of batch dimensions + + // k = 2 add_config(TensorShape(6U, 5U), TensorShape(6U, 2U, 2U), TensorShape(1U, 2U, 2U), TensorShape(6U, 5U)); - add_config(TensorShape(6U, 5U, 2U), TensorShape(6U, 2U, 2U), TensorShape(2U, 2U, 2U), TensorShape(6U, 5U, 2U)); - add_config(TensorShape(6U, 5U, 2U, 2U), TensorShape(3U, 2U), TensorShape(4U, 3U, 2U), TensorShape(6U, 5U, 2U, 2U)); - add_config(TensorShape(5U, 5U, 4U, 2U, 2U), TensorShape(6U, 2U), TensorShape(5U, 6U, 2U), TensorShape(5U, 5U, 4U, 2U, 2U)); + add_config(TensorShape(5U, 5U, 4U, 2U, 2U), TensorShape(5U, 5U, 6U, 2U), TensorShape(3U, 6U, 2U), TensorShape(5U, 5U, 4U, 2U, 2U)); + + // k = 3 + add_config(TensorShape(6U, 5U), TensorShape(6U, 2U, 2U, 2U), TensorShape(1U, 2U, 2U, 2U), TensorShape(6U, 5U)); + add_config(TensorShape(5U, 5U, 4U, 2U, 2U), TensorShape(5U, 5U, 3U, 6U, 2U), TensorShape(3U, 3U, 6U, 2U), TensorShape(5U, 5U, 4U, 2U, 2U)); + + // k = 4 + add_config(TensorShape(5U, 5U, 4U, 2U, 2U), TensorShape(5U, 6U, 2U, 3U, 2U), TensorShape(4U, 6U, 2U, 3U, 2U), TensorShape(5U, 5U, 4U, 2U, 2U)); + + // k = 5 + add_config(TensorShape(5U, 5U, 4U, 2U, 2U), TensorShape(5U, 3U, 4U, 3U, 2U, 2U), TensorShape(4U, 3U, 4U, 3U, 2U, 2U), TensorShape(5U, 5U, 4U, 2U, 2U)); } }; @@ -196,7 +207,7 @@ public: add_config(TensorShape(9U, 3U, 4U), TensorShape(9U, 3U, 2U), TensorShape(1U, 2U), TensorShape(9U, 3U, 4U)); add_config(TensorShape(35U, 4U, 3U, 2U, 2U), TensorShape(35U, 4U), TensorShape(4U, 4U), TensorShape(35U, 4U, 3U, 2U, 2U)); add_config(TensorShape(11U, 3U, 3U, 2U, 4U), TensorShape(11U, 3U, 3U, 4U), TensorShape(2U, 4U), TensorShape(11U, 3U, 3U, 2U, 4U)); - // TODO: add_config(TensorShape(6U, 5U, 2U), TensorShape(6U, 2U, 2U), TensorShape(2U, 2U, 2U), TensorShape(6U, 5U, 2U)); + add_config(TensorShape(6U, 5U, 2U), TensorShape(6U, 2U, 2U), TensorShape(2U, 2U, 2U), TensorShape(6U, 5U, 2U)); } }; } // namespace datasets diff --git a/tests/validation/CL/ScatterLayer.cpp b/tests/validation/CL/ScatterLayer.cpp index 2970d82572..e327ff9522 100644 --- a/tests/validation/CL/ScatterLayer.cpp +++ b/tests/validation/CL/ScatterLayer.cpp @@ -164,10 +164,10 @@ FIXTURE_DATA_TEST_CASE(RunSmallMultiIndices, CLScatterLayerFixture<float>, frame } // m+k, k-1-D m+n-D case -FIXTURE_DATA_TEST_CASE(RunSmallBatchedMultiIndices, CLScatterLayerFixture<float>, framework::DatasetMode::DISABLED, +FIXTURE_DATA_TEST_CASE(RunSmallBatchedMultiIndices, CLScatterLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallScatterBatchedDataset(), make("DataType", {DataType::F32}), - make("ScatterFunction", {ScatterFunction::Update, ScatterFunction::Add }), + make("ScatterFunction", {ScatterFunction::Update, ScatterFunction::Add}), make("ZeroInit", {false}), make("Inplace", {false}))) { diff --git a/tests/validation/fixtures/ScatterLayerFixture.h b/tests/validation/fixtures/ScatterLayerFixture.h index 35e6b647f3..5cd9b8115c 100644 --- a/tests/validation/fixtures/ScatterLayerFixture.h +++ b/tests/validation/fixtures/ScatterLayerFixture.h @@ -103,7 +103,7 @@ protected: void fill_indices(U &&tensor, int i, const TensorShape &shape) { // Calculate max indices the shape should contain. Add an arbitrary value to allow testing for some out of bounds values (In this case min dimension) - const int32_t max = std::max({shape[0] , shape[1], shape[2]}); + const int32_t max = std::min({shape[0] , shape[1], shape[2]}) + 1; library->fill_tensor_uniform(tensor, i, static_cast<int32_t>(-2), static_cast<int32_t>(max)); } @@ -197,12 +197,13 @@ protected: TensorShape src_shape = a_shape; TensorShape updates_shape = b_shape; TensorShape indices_shape = c_shape; + const int num_ind_dims = c_shape.num_dimensions(); // 1. Collapse batch index into a single dim if necessary for update tensor and indices tensor. - if(c_shape.num_dimensions() >= 3) + if(num_ind_dims >= 3) { indices_shape = indices_shape.collapsed_from(1); - updates_shape = updates_shape.collapsed_from(updates_shape.num_dimensions() - 2); // Collapses from last 2 dims + updates_shape = updates_shape.collapsed_from(updates_shape.num_dimensions() - (num_ind_dims -1)); // Collapses batch dims } // 2. Collapse data dims into a single dim. @@ -212,16 +213,16 @@ protected: updates_shape.collapse(updates_shape.num_dimensions() - 1); // Collapse data dims (all except last dim which is batch dim) // Create reference tensors - SimpleTensor<T> src{ a_shape, data_type, 1, a_qinfo }; - SimpleTensor<T> updates{b_shape, data_type, 1, QuantizationInfo() }; - SimpleTensor<int32_t> indices{ c_shape, DataType::S32, 1, QuantizationInfo() }; + SimpleTensor<T> src{ src_shape, data_type, 1, a_qinfo }; + SimpleTensor<T> updates{updates_shape, data_type, 1, QuantizationInfo() }; + SimpleTensor<int32_t> indices{ indices_shape, DataType::S32, 1, QuantizationInfo() }; // Fill reference fill(src, 0 + _hash); fill(updates, 1 + _hash); fill_indices(indices, 2 + _hash, out_shape); - // Calculate individual reference. + // Calculate individual reference using collapsed shapes return reference::scatter_layer<T>(src, updates, indices, out_shape, info); } diff --git a/tests/validation/reference/ScatterLayer.cpp b/tests/validation/reference/ScatterLayer.cpp index c9e6035e14..55c48a9002 100644 --- a/tests/validation/reference/ScatterLayer.cpp +++ b/tests/validation/reference/ScatterLayer.cpp @@ -63,6 +63,7 @@ T reduce_op(const T ¤t,const T &update,const ScatterFunction func) } template float reduce_op(const float ¤t,const float &update,const ScatterFunction func); +template half reduce_op(const half ¤t,const half &update,const ScatterFunction func); } // NOTE: This function expects collapsed tensors as input. |