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Imported from GitHub PR openxla/xla#21960 Point nanobind to the commit fixing python/c++ object concurrent accessing: wjakob/nanobind#867 cc @hawkinsp Copybara import of the project: -- 77e693fb39e0b737016770585c3f8786eb141474 by vfdev-5 <vfdev.5@gmail.com>: Updated nanobind commit Merging this change closes #21960 FUTURE_COPYBARA_INTEGRATE_REVIEW=openxla/xla#21960 from vfdev-5:update-nanobind 77e693fb39e0b737016770585c3f8786eb141474 PiperOrigin-RevId: 720567148
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36 changes: 36 additions & 0 deletions
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third_party/xla/xla/backends/cpu/runtime/convolution_lib.cc
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| /* Copyright 2025 The OpenXLA Authors. | ||
| Licensed under the Apache License, Version 2.0 (the "License"); | ||
| you may not use this file except in compliance with the License. | ||
| You may obtain a copy of the License at | ||
| http://www.apache.org/licenses/LICENSE-2.0 | ||
| Unless required by applicable law or agreed to in writing, software | ||
| distributed under the License is distributed on an "AS IS" BASIS, | ||
| WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| See the License for the specific language governing permissions and | ||
| limitations under the License. | ||
| ==============================================================================*/ | ||
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| #include "xla/backends/cpu/runtime/convolution_lib.h" | ||
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| #include <cstdint> | ||
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| #include "absl/container/inlined_vector.h" | ||
| #include "absl/types/span.h" | ||
| #include "xla/runtime/buffer_use.h" | ||
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| namespace xla::cpu { | ||
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| absl::InlinedVector<BufferUse, 4> ConvolutionBufferUses( | ||
| const ConvolutionSlices& slices) { | ||
| return {BufferUse::Read(slices.input_buffer), | ||
| BufferUse::Read(slices.kernel_buffer), | ||
| BufferUse::Write(slices.output_buffer)}; | ||
| } | ||
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| ConvolutionCanonicalDims::Dims::Dims(absl::Span<const int64_t> dims) | ||
| : rank(dims.size()), x(dims[0]), y(dims[1]), z(rank == 3 ? dims[2] : 0) {} | ||
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| } // namespace xla::cpu |
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third_party/xla/xla/backends/cpu/runtime/convolution_lib.h
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| /* Copyright 2025 The OpenXLA Authors. | ||
| Licensed under the Apache License, Version 2.0 (the "License"); | ||
| you may not use this file except in compliance with the License. | ||
| You may obtain a copy of the License at | ||
| http://www.apache.org/licenses/LICENSE-2.0 | ||
| Unless required by applicable law or agreed to in writing, software | ||
| distributed under the License is distributed on an "AS IS" BASIS, | ||
| WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| See the License for the specific language governing permissions and | ||
| limitations under the License. | ||
| ==============================================================================*/ | ||
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| #ifndef XLA_BACKENDS_CPU_RUNTIME_CONVOLUTION_LIB_H_ | ||
| #define XLA_BACKENDS_CPU_RUNTIME_CONVOLUTION_LIB_H_ | ||
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| #include <cstddef> | ||
| #include <cstdint> | ||
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| #include "absl/container/inlined_vector.h" | ||
| #include "absl/strings/str_format.h" | ||
| #include "absl/types/span.h" | ||
| #include "xla/runtime/buffer_use.h" | ||
| #include "xla/service/buffer_assignment.h" | ||
| #include "xla/shape.h" | ||
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| namespace xla::cpu { | ||
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| // Allocation slices of the convolution operation. | ||
| struct ConvolutionSlices { | ||
| BufferAllocation::Slice input_buffer; | ||
| Shape input_shape; | ||
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| BufferAllocation::Slice kernel_buffer; | ||
| Shape kernel_shape; | ||
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| BufferAllocation::Slice output_buffer; | ||
| Shape output_shape; | ||
| }; | ||
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| // Returns buffer uses of the dot operation. | ||
| absl::InlinedVector<BufferUse, 4> ConvolutionBufferUses( | ||
| const ConvolutionSlices& slices); | ||
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| // Convolution dimensions in canonical form inferred from the operands shapes | ||
| // and convolution parameters. | ||
| struct ConvolutionCanonicalDims { | ||
| // A helper struct to store the x, y and z dimensions of a tensor, introduced | ||
| // for readability. In case of 2D convolution, only the x and y dimensions are | ||
| // used and z is set to 0. | ||
| struct Dims { | ||
| explicit Dims(absl::Span<const int64_t> dims); | ||
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| template <typename Sink> | ||
| friend void AbslStringify(Sink& sink, const Dims& d); | ||
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| int64_t rank; | ||
| int64_t x; | ||
| int64_t y; | ||
| int64_t z; | ||
| }; | ||
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| template <typename Sink> | ||
| friend void AbslStringify(Sink& sink, const ConvolutionCanonicalDims& d); | ||
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| size_t convolution_rank() const { return input_dims.rank; } | ||
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| int64_t input_batch; | ||
| Dims input_dims; | ||
| int64_t input_channels; | ||
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| Dims kernel_dims; | ||
| int64_t kernel_channels; | ||
| int64_t kernel_filters; | ||
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| Dims output_dims; | ||
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| Dims strides; | ||
| Dims padding_before; | ||
| Dims padding_after; | ||
| Dims base_dilation; | ||
| Dims window_dilation; | ||
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| int64_t feature_group_count; | ||
| }; | ||
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| template <typename Sink> | ||
| void AbslStringify(Sink& sink, const ConvolutionCanonicalDims::Dims& d) { | ||
| switch (d.rank) { | ||
| case 2: | ||
| absl::Format(&sink, "[%d,%d]", d.x, d.y); | ||
| break; | ||
| case 3: | ||
| absl::Format(&sink, "[%d,%d,%d]", d.x, d.y, d.z); | ||
| break; | ||
| default: | ||
| absl::Format(&sink, "[invalid rank %d]", d.rank); | ||
| } | ||
| } | ||
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| template <typename Sink> | ||
| void AbslStringify(Sink& sink, const ConvolutionCanonicalDims& d) { | ||
| absl::Format(&sink, | ||
| "convolution_rank=%d input_batch=%d input_dims=%v " | ||
| "input_channels=%d kernel_dims=%v kernel_channels=%d " | ||
| "kernel_filters=%d output_dims=%v strides=%v padding_before=%v " | ||
| "padding_after=%v base_dilation=%v window_dilation=%v " | ||
| "feature_group_count=%d", | ||
| d.convolution_rank(), d.input_batch, d.input_dims, | ||
| d.input_channels, d.kernel_dims, d.kernel_channels, | ||
| d.kernel_filters, d.output_dims, d.strides, d.padding_before, | ||
| d.padding_after, d.base_dilation, d.window_dilation, | ||
| d.feature_group_count); | ||
| } | ||
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| } // namespace xla::cpu | ||
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| #endif // XLA_BACKENDS_CPU_RUNTIME_CONVOLUTION_LIB_H_ |
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