MrPiglr
9dddce666d
- Forked from Godot Engine 4.7-dev (MIT License) - Rebranded to AeThex Engine with cyan/purple theme - Added AI-powered development assistant module - Integrated Claude API for code completion & error fixing - Custom hexagon logo and branding - Multi-platform CI/CD (Windows, Linux, macOS) - Built Linux editor binary (151MB) - Complete source code with all customizations Tech Stack: - C++ game engine core - AI Module: Claude 3.5 Sonnet integration - Build: SCons, 14K+ source files - License: MIT (Godot) + Custom (AeThex features) Ready for Windows build via GitHub Actions!
297 lines
9.5 KiB
C++
297 lines
9.5 KiB
C++
//-------------------------------------------------------------------------------------------------------------------------------------------------------------
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//
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// Metal/MTLTensor.hpp
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//
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// Copyright 2020-2025 Apple Inc.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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//
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//-------------------------------------------------------------------------------------------------------------------------------------------------------------
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#pragma once
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#include "../Foundation/Foundation.hpp"
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#include "MTLDefines.hpp"
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#include "MTLHeaderBridge.hpp"
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#include "MTLPrivate.hpp"
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#include "MTLResource.hpp"
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#include "MTLTypes.hpp"
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namespace MTL
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{
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class Buffer;
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class TensorDescriptor;
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class TensorExtents;
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_MTL_CONST(NS::ErrorDomain, TensorDomain);
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_MTL_ENUM(NS::Integer, TensorDataType) {
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TensorDataTypeNone = 0,
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TensorDataTypeFloat32 = 3,
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TensorDataTypeFloat16 = 16,
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TensorDataTypeBFloat16 = 121,
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TensorDataTypeInt8 = 45,
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TensorDataTypeUInt8 = 49,
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TensorDataTypeInt16 = 37,
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TensorDataTypeUInt16 = 41,
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TensorDataTypeInt32 = 29,
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TensorDataTypeUInt32 = 33,
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};
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_MTL_ENUM(NS::Integer, TensorError) {
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TensorErrorNone = 0,
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TensorErrorInternalError = 1,
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TensorErrorInvalidDescriptor = 2,
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};
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_MTL_OPTIONS(NS::UInteger, TensorUsage) {
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TensorUsageCompute = 1,
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TensorUsageRender = 1 << 1,
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TensorUsageMachineLearning = 1 << 2,
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};
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class TensorExtents : public NS::Referencing<TensorExtents>
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{
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public:
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static TensorExtents* alloc();
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NS::Integer extentAtDimensionIndex(NS::UInteger dimensionIndex);
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TensorExtents* init();
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TensorExtents* init(NS::UInteger rank, const NS::Integer* values);
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NS::UInteger rank() const;
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};
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class TensorDescriptor : public NS::Copying<TensorDescriptor>
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{
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public:
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static TensorDescriptor* alloc();
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CPUCacheMode cpuCacheMode() const;
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TensorDataType dataType() const;
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TensorExtents* dimensions() const;
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HazardTrackingMode hazardTrackingMode() const;
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TensorDescriptor* init();
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ResourceOptions resourceOptions() const;
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void setCpuCacheMode(MTL::CPUCacheMode cpuCacheMode);
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void setDataType(MTL::TensorDataType dataType);
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void setDimensions(const MTL::TensorExtents* dimensions);
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void setHazardTrackingMode(MTL::HazardTrackingMode hazardTrackingMode);
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void setResourceOptions(MTL::ResourceOptions resourceOptions);
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void setStorageMode(MTL::StorageMode storageMode);
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void setStrides(const MTL::TensorExtents* strides);
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void setUsage(MTL::TensorUsage usage);
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StorageMode storageMode() const;
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TensorExtents* strides() const;
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TensorUsage usage() const;
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};
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class Tensor : public NS::Referencing<Tensor, Resource>
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{
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public:
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Buffer* buffer() const;
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NS::UInteger bufferOffset() const;
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TensorDataType dataType() const;
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TensorExtents* dimensions() const;
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void getBytes(void* bytes, const MTL::TensorExtents* strides, const MTL::TensorExtents* sliceOrigin, const MTL::TensorExtents* sliceDimensions);
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ResourceID gpuResourceID() const;
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void replaceSliceOrigin(const MTL::TensorExtents* sliceOrigin, const MTL::TensorExtents* sliceDimensions, const void* bytes, const MTL::TensorExtents* strides);
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TensorExtents* strides() const;
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TensorUsage usage() const;
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};
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}
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_MTL_PRIVATE_DEF_CONST(NS::ErrorDomain, TensorDomain);
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_MTL_INLINE MTL::TensorExtents* MTL::TensorExtents::alloc()
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{
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return NS::Object::alloc<MTL::TensorExtents>(_MTL_PRIVATE_CLS(MTLTensorExtents));
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}
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_MTL_INLINE NS::Integer MTL::TensorExtents::extentAtDimensionIndex(NS::UInteger dimensionIndex)
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{
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return Object::sendMessage<NS::Integer>(this, _MTL_PRIVATE_SEL(extentAtDimensionIndex_), dimensionIndex);
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}
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_MTL_INLINE MTL::TensorExtents* MTL::TensorExtents::init()
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{
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return NS::Object::init<MTL::TensorExtents>();
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}
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_MTL_INLINE MTL::TensorExtents* MTL::TensorExtents::init(NS::UInteger rank, const NS::Integer* values)
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{
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return Object::sendMessage<MTL::TensorExtents*>(this, _MTL_PRIVATE_SEL(initWithRank_values_), rank, values);
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}
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_MTL_INLINE NS::UInteger MTL::TensorExtents::rank() const
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{
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return Object::sendMessage<NS::UInteger>(this, _MTL_PRIVATE_SEL(rank));
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}
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_MTL_INLINE MTL::TensorDescriptor* MTL::TensorDescriptor::alloc()
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{
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return NS::Object::alloc<MTL::TensorDescriptor>(_MTL_PRIVATE_CLS(MTLTensorDescriptor));
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}
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_MTL_INLINE MTL::CPUCacheMode MTL::TensorDescriptor::cpuCacheMode() const
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{
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return Object::sendMessage<MTL::CPUCacheMode>(this, _MTL_PRIVATE_SEL(cpuCacheMode));
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}
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_MTL_INLINE MTL::TensorDataType MTL::TensorDescriptor::dataType() const
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{
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return Object::sendMessage<MTL::TensorDataType>(this, _MTL_PRIVATE_SEL(dataType));
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}
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_MTL_INLINE MTL::TensorExtents* MTL::TensorDescriptor::dimensions() const
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{
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return Object::sendMessage<MTL::TensorExtents*>(this, _MTL_PRIVATE_SEL(dimensions));
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}
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_MTL_INLINE MTL::HazardTrackingMode MTL::TensorDescriptor::hazardTrackingMode() const
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{
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return Object::sendMessage<MTL::HazardTrackingMode>(this, _MTL_PRIVATE_SEL(hazardTrackingMode));
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}
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_MTL_INLINE MTL::TensorDescriptor* MTL::TensorDescriptor::init()
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{
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return NS::Object::init<MTL::TensorDescriptor>();
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}
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_MTL_INLINE MTL::ResourceOptions MTL::TensorDescriptor::resourceOptions() const
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{
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return Object::sendMessage<MTL::ResourceOptions>(this, _MTL_PRIVATE_SEL(resourceOptions));
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}
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_MTL_INLINE void MTL::TensorDescriptor::setCpuCacheMode(MTL::CPUCacheMode cpuCacheMode)
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{
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Object::sendMessage<void>(this, _MTL_PRIVATE_SEL(setCpuCacheMode_), cpuCacheMode);
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}
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_MTL_INLINE void MTL::TensorDescriptor::setDataType(MTL::TensorDataType dataType)
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{
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Object::sendMessage<void>(this, _MTL_PRIVATE_SEL(setDataType_), dataType);
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}
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_MTL_INLINE void MTL::TensorDescriptor::setDimensions(const MTL::TensorExtents* dimensions)
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{
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Object::sendMessage<void>(this, _MTL_PRIVATE_SEL(setDimensions_), dimensions);
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}
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_MTL_INLINE void MTL::TensorDescriptor::setHazardTrackingMode(MTL::HazardTrackingMode hazardTrackingMode)
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{
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Object::sendMessage<void>(this, _MTL_PRIVATE_SEL(setHazardTrackingMode_), hazardTrackingMode);
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}
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_MTL_INLINE void MTL::TensorDescriptor::setResourceOptions(MTL::ResourceOptions resourceOptions)
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{
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Object::sendMessage<void>(this, _MTL_PRIVATE_SEL(setResourceOptions_), resourceOptions);
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}
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_MTL_INLINE void MTL::TensorDescriptor::setStorageMode(MTL::StorageMode storageMode)
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{
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Object::sendMessage<void>(this, _MTL_PRIVATE_SEL(setStorageMode_), storageMode);
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}
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_MTL_INLINE void MTL::TensorDescriptor::setStrides(const MTL::TensorExtents* strides)
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{
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Object::sendMessage<void>(this, _MTL_PRIVATE_SEL(setStrides_), strides);
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}
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_MTL_INLINE void MTL::TensorDescriptor::setUsage(MTL::TensorUsage usage)
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{
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Object::sendMessage<void>(this, _MTL_PRIVATE_SEL(setUsage_), usage);
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}
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_MTL_INLINE MTL::StorageMode MTL::TensorDescriptor::storageMode() const
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{
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return Object::sendMessage<MTL::StorageMode>(this, _MTL_PRIVATE_SEL(storageMode));
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}
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_MTL_INLINE MTL::TensorExtents* MTL::TensorDescriptor::strides() const
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{
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return Object::sendMessage<MTL::TensorExtents*>(this, _MTL_PRIVATE_SEL(strides));
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}
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_MTL_INLINE MTL::TensorUsage MTL::TensorDescriptor::usage() const
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{
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return Object::sendMessage<MTL::TensorUsage>(this, _MTL_PRIVATE_SEL(usage));
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}
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_MTL_INLINE MTL::Buffer* MTL::Tensor::buffer() const
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{
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return Object::sendMessage<MTL::Buffer*>(this, _MTL_PRIVATE_SEL(buffer));
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}
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_MTL_INLINE NS::UInteger MTL::Tensor::bufferOffset() const
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{
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return Object::sendMessage<NS::UInteger>(this, _MTL_PRIVATE_SEL(bufferOffset));
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}
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_MTL_INLINE MTL::TensorDataType MTL::Tensor::dataType() const
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{
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return Object::sendMessage<MTL::TensorDataType>(this, _MTL_PRIVATE_SEL(dataType));
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}
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_MTL_INLINE MTL::TensorExtents* MTL::Tensor::dimensions() const
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{
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return Object::sendMessage<MTL::TensorExtents*>(this, _MTL_PRIVATE_SEL(dimensions));
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}
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_MTL_INLINE void MTL::Tensor::getBytes(void* bytes, const MTL::TensorExtents* strides, const MTL::TensorExtents* sliceOrigin, const MTL::TensorExtents* sliceDimensions)
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{
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Object::sendMessage<void>(this, _MTL_PRIVATE_SEL(getBytes_strides_fromSliceOrigin_sliceDimensions_), bytes, strides, sliceOrigin, sliceDimensions);
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}
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_MTL_INLINE MTL::ResourceID MTL::Tensor::gpuResourceID() const
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{
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return Object::sendMessage<MTL::ResourceID>(this, _MTL_PRIVATE_SEL(gpuResourceID));
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}
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_MTL_INLINE void MTL::Tensor::replaceSliceOrigin(const MTL::TensorExtents* sliceOrigin, const MTL::TensorExtents* sliceDimensions, const void* bytes, const MTL::TensorExtents* strides)
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{
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Object::sendMessage<void>(this, _MTL_PRIVATE_SEL(replaceSliceOrigin_sliceDimensions_withBytes_strides_), sliceOrigin, sliceDimensions, bytes, strides);
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}
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_MTL_INLINE MTL::TensorExtents* MTL::Tensor::strides() const
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{
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return Object::sendMessage<MTL::TensorExtents*>(this, _MTL_PRIVATE_SEL(strides));
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}
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_MTL_INLINE MTL::TensorUsage MTL::Tensor::usage() const
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{
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return Object::sendMessage<MTL::TensorUsage>(this, _MTL_PRIVATE_SEL(usage));
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}
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