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[API Proposal]: Tensor Operations Per Dimension #113068

Description

@michaelgsharp

Background and motivation

We have exposed lots of functions we can use with Tensor<T>. These currently all operate on the entire backing memory of the Tensor. The problem with this is that many times you want to do the operation on whole dimensions, rather than the whole data, and we have no easy way of providing this currently. For a very simple example say your Tensor<T> is 2d, think of it like an Excel table. In Excel we can sum the whole table, but we can also sum each row, for example. This is the functionality we need to add to Tensor<T>.

The approach we have taken means we don't have to add overloads to every operation that could do this type of behavior. Currently we are planning on these methods needing this behavior.

CosineSimilarity
StdDev
Average
Max
MaxMagnitude
MaxMagnitudeNumber
MaxNumber
Min
MinMagnitude
MinMagnitudeNumber
MinNumber
Norm
Product
SoftMax
Sum
SumOfSquares
IndexOfMax
IndexOfMaxMagnitude
IndexOfMin
IndexOfMinMagnitude

API Proposal

namespaceSystem.Numerics.Tensors;publicinterfaceIReadOnlyTensor<TSelf,T>:IEnumerable<T>whereTSelf:IReadOnlyTensor<TSelf,T>{//used to easily get a sub dimension of a tensor. Like all the Rows of a tensor for example.TSelfSliceAlongDimension(intdimension,nintindex);}publicstaticpartialclassTensor{// These are for things like Sum that take in a tensor and return a single T.publicdelegateTTensorAggregationPredicate<T>(inReadOnlyTensorSpan<T>tensor);publicstaticTensor<T>AggregateMany<T>(thisTensor<T>tensor,TensorAggregationPredicate<T>func,intdimension=-1);// These are for things like CosineSimilarity that take in a tensor and return a new tensor as well.publicdelegateTensor<T>TensorSelectionPredicate<T>(inReadOnlyTensorSpan<T>tensor);publicstaticTensor<T>SelectMany<T>(thisTensor<T>tensor,TensorSelectionPredicate<T>func,intdimension=-1);publicstaticTensor<T>ToTensor<T>(thisIEnumerable<Tensor<T>>source);}publicsealedclassTensor<T>:ITensor<Tensor<T>,T>{publicDimensionCollectionDimensions{get;}publicsealedclassDimensionCollection:System.Collections.Generic.ICollection<Tensor<T>>,System.Collections.Generic.IEnumerable<Tensor<T>>,System.Collections.Generic.IReadOnlyCollection<Tensor<T>>,System.Collections.ICollection{publicintCount{get;}publicvoidCopyTo(Tensor<T>[]array,intindex)publicEnumeratorGetEnumerator();//All else explicitly implemented and match what Dictionary does publicstructEnumerator:IEnumerator<Tensor<T>>{internalEnumerator(Tensor<T>tensor,intdimension);publicboolMoveNext();publicvoidReset();publicvoidDispose();Tensor<T>IEnumerator<Tensor<T>>.Current;object?IEnumerator.Current;}}}publicreadonlyrefstructReadOnlyTensorSpan<T>{publicDimensionCollectionDimensions{get;}publicrefstructDimensionCollection:System.Collections.Generic.ICollection<Tensor<T>>,System.Collections.Generic.IEnumerable<Tensor<T>>,System.Collections.Generic.IReadOnlyCollection<Tensor<T>>,System.Collections.ICollection{publicintCount{get;}publicvoidCopyTo(Tensor<T>[]array,intindex)publicEnumeratorGetEnumerator();//All else explicitly implemented and match what Dictionary does publicrefstructEnumerator:IEnumerator<Tensor<T>>{internalEnumerator(Tensor<T>tensor,intdimension);publicboolMoveNext();publicref readonly ReadOnlyTensorSpan<T>Current;}}}publicrefstructTensorSpan<T>{publicDimensionCollectionDimensions{get;}publicrefstructDimensionCollection:System.Collections.Generic.ICollection<Tensor<T>>,System.Collections.Generic.IEnumerable<Tensor<T>>,System.Collections.Generic.IReadOnlyCollection<Tensor<T>>,System.Collections.ICollection{publicintCount{get;}publicvoidCopyTo(Tensor<T>[]array,intindex)publicEnumeratorGetEnumerator();//All else explicitly implemented and match what Dictionary does publicrefstructEnumerator:IEnumerator<Tensor<T>>{internalEnumerator(Tensor<T>tensor,intdimension);publicboolMoveNext();publicrefTensorSpan<T>Current;}}}

API Usage

Tensor<int>t1=Tensor.Create([1,2,3,4,5,6,7,8,9],[3,3]);// Will be [1,2,3]Tensor<int>slice=t1.SliceAlongDimension(0,0);// Will be [7, 8, 9]slice=t1.SliceAlongDimension(0,2);for(inti=0;i<3;i++){// Get each slicet1.SliceAlongDimension(0,i);// Can now do something which each sub slice.}Tensor<int>tensor=Tensor.Create([1,2,3,4],[2,2]);// Sum tensor will be [3, 7]Tensor<int>sum=t.AggregateMany(t =>Sum(t),0);// CosineTensor would be [1,1,1]Tensor<float>tensor1=Create<float>([1,2,3,4,5,6,7,8,9],[3,3],[],false);Tensor<float>cosineTensor=tensor1.SelectMany(t =>CosineSimilarity<T>(t,t),0);

Alternative Designs

We could explicitly overload every method that has this behavior. That would mean an additional 20 overloads just for now. As time goes on this number would grow. If a user wanted to do this with a method we hadn't overloaded yet, they would either have to do it themselves (which we have had happen recently), or they would have to wait. Our current approach allows us to avoid both of these problems.

Risks

Low risk because it's a preview type and its all new features.

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api-approvedAPI was approved in API review, it can be implementedarea-System.Numerics.Tensorsin-prThere is an active PR which will close this issue when it is merged

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    [API Proposal]: Tensor Operations Per Dimension · Issue #113068 · dotnet/runtime · GitHub
    Skip to content

    [API Proposal]: Tensor Operations Per Dimension #113068

    Description

    @michaelgsharp

    Background and motivation

    We have exposed lots of functions we can use with Tensor<T>. These currently all operate on the entire backing memory of the Tensor. The problem with this is that many times you want to do the operation on whole dimensions, rather than the whole data, and we have no easy way of providing this currently. For a very simple example say your Tensor<T> is 2d, think of it like an Excel table. In Excel we can sum the whole table, but we can also sum each row, for example. This is the functionality we need to add to Tensor<T>.

    The approach we have taken means we don't have to add overloads to every operation that could do this type of behavior. Currently we are planning on these methods needing this behavior.

    CosineSimilarity
    StdDev
    Average
    Max
    MaxMagnitude
    MaxMagnitudeNumber
    MaxNumber
    Min
    MinMagnitude
    MinMagnitudeNumber
    MinNumber
    Norm
    Product
    SoftMax
    Sum
    SumOfSquares
    IndexOfMax
    IndexOfMaxMagnitude
    IndexOfMin
    IndexOfMinMagnitude

    API Proposal

    namespaceSystem.Numerics.Tensors;publicinterfaceIReadOnlyTensor<TSelf,T>:IEnumerable<T>whereTSelf:IReadOnlyTensor<TSelf,T>{//used to easily get a sub dimension of a tensor. Like all the Rows of a tensor for example.TSelfSliceAlongDimension(intdimension,nintindex);}publicstaticpartialclassTensor{// These are for things like Sum that take in a tensor and return a single T.publicdelegateTTensorAggregationPredicate<T>(inReadOnlyTensorSpan<T>tensor);publicstaticTensor<T>AggregateMany<T>(thisTensor<T>tensor,TensorAggregationPredicate<T>func,intdimension=-1);// These are for things like CosineSimilarity that take in a tensor and return a new tensor as well.publicdelegateTensor<T>TensorSelectionPredicate<T>(inReadOnlyTensorSpan<T>tensor);publicstaticTensor<T>SelectMany<T>(thisTensor<T>tensor,TensorSelectionPredicate<T>func,intdimension=-1);publicstaticTensor<T>ToTensor<T>(thisIEnumerable<Tensor<T>>source);}publicsealedclassTensor<T>:ITensor<Tensor<T>,T>{publicDimensionCollectionDimensions{get;}publicsealedclassDimensionCollection:System.Collections.Generic.ICollection<Tensor<T>>,System.Collections.Generic.IEnumerable<Tensor<T>>,System.Collections.Generic.IReadOnlyCollection<Tensor<T>>,System.Collections.ICollection{publicintCount{get;}publicvoidCopyTo(Tensor<T>[]array,intindex)publicEnumeratorGetEnumerator();//All else explicitly implemented and match what Dictionary does publicstructEnumerator:IEnumerator<Tensor<T>>{internalEnumerator(Tensor<T>tensor,intdimension);publicboolMoveNext();publicvoidReset();publicvoidDispose();Tensor<T>IEnumerator<Tensor<T>>.Current;object?IEnumerator.Current;}}}publicreadonlyrefstructReadOnlyTensorSpan<T>{publicDimensionCollectionDimensions{get;}publicrefstructDimensionCollection:System.Collections.Generic.ICollection<Tensor<T>>,System.Collections.Generic.IEnumerable<Tensor<T>>,System.Collections.Generic.IReadOnlyCollection<Tensor<T>>,System.Collections.ICollection{publicintCount{get;}publicvoidCopyTo(Tensor<T>[]array,intindex)publicEnumeratorGetEnumerator();//All else explicitly implemented and match what Dictionary does publicrefstructEnumerator:IEnumerator<Tensor<T>>{internalEnumerator(Tensor<T>tensor,intdimension);publicboolMoveNext();publicref readonly ReadOnlyTensorSpan<T>Current;}}}publicrefstructTensorSpan<T>{publicDimensionCollectionDimensions{get;}publicrefstructDimensionCollection:System.Collections.Generic.ICollection<Tensor<T>>,System.Collections.Generic.IEnumerable<Tensor<T>>,System.Collections.Generic.IReadOnlyCollection<Tensor<T>>,System.Collections.ICollection{publicintCount{get;}publicvoidCopyTo(Tensor<T>[]array,intindex)publicEnumeratorGetEnumerator();//All else explicitly implemented and match what Dictionary does publicrefstructEnumerator:IEnumerator<Tensor<T>>{internalEnumerator(Tensor<T>tensor,intdimension);publicboolMoveNext();publicrefTensorSpan<T>Current;}}}

    API Usage

    Tensor<int>t1=Tensor.Create([1,2,3,4,5,6,7,8,9],[3,3]);// Will be [1,2,3]Tensor<int>slice=t1.SliceAlongDimension(0,0);// Will be [7, 8, 9]slice=t1.SliceAlongDimension(0,2);for(inti=0;i<3;i++){// Get each slicet1.SliceAlongDimension(0,i);// Can now do something which each sub slice.}Tensor<int>tensor=Tensor.Create([1,2,3,4],[2,2]);// Sum tensor will be [3, 7]Tensor<int>sum=t.AggregateMany(t =>Sum(t),0);// CosineTensor would be [1,1,1]Tensor<float>tensor1=Create<float>([1,2,3,4,5,6,7,8,9],[3,3],[],false);Tensor<float>cosineTensor=tensor1.SelectMany(t =>CosineSimilarity<T>(t,t),0);

    Alternative Designs

    We could explicitly overload every method that has this behavior. That would mean an additional 20 overloads just for now. As time goes on this number would grow. If a user wanted to do this with a method we hadn't overloaded yet, they would either have to do it themselves (which we have had happen recently), or they would have to wait. Our current approach allows us to avoid both of these problems.

    Risks

    Low risk because it's a preview type and its all new features.

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    Metadata

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    Labels

    api-approvedAPI was approved in API review, it can be implementedarea-System.Numerics.Tensorsin-prThere is an active PR which will close this issue when it is merged

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      , 'i'); if (__m === '*' || __re.test(location.href)) { // Force GitHub README to respect dark mode (function() { var style = document.createElement('style'); style.textContent = ' .markdown-body { color-scheme: dark light; } .markdown-body pre { background: #161b22 !important; } .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; } .markdown-body table th, .markdown-body table td { border-color: #30363d !important; } .markdown-body img { background: #0d1117; } .markdown-body blockquote { border-left-color: #8b949e; } .markdown-body hr { border-color: #30363d; } '; document.head.appendChild(style); })(); } } catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + ' [API Proposal]: Tensor Operations Per Dimension · Issue #113068 · dotnet/runtime · GitHub
      Skip to content

      [API Proposal]: Tensor Operations Per Dimension #113068

      Description

      @michaelgsharp

      Background and motivation

      We have exposed lots of functions we can use with Tensor<T>. These currently all operate on the entire backing memory of the Tensor. The problem with this is that many times you want to do the operation on whole dimensions, rather than the whole data, and we have no easy way of providing this currently. For a very simple example say your Tensor<T> is 2d, think of it like an Excel table. In Excel we can sum the whole table, but we can also sum each row, for example. This is the functionality we need to add to Tensor<T>.

      The approach we have taken means we don't have to add overloads to every operation that could do this type of behavior. Currently we are planning on these methods needing this behavior.

      CosineSimilarity
      StdDev
      Average
      Max
      MaxMagnitude
      MaxMagnitudeNumber
      MaxNumber
      Min
      MinMagnitude
      MinMagnitudeNumber
      MinNumber
      Norm
      Product
      SoftMax
      Sum
      SumOfSquares
      IndexOfMax
      IndexOfMaxMagnitude
      IndexOfMin
      IndexOfMinMagnitude

      API Proposal

      namespaceSystem.Numerics.Tensors;publicinterfaceIReadOnlyTensor<TSelf,T>:IEnumerable<T>whereTSelf:IReadOnlyTensor<TSelf,T>{//used to easily get a sub dimension of a tensor. Like all the Rows of a tensor for example.TSelfSliceAlongDimension(intdimension,nintindex);}publicstaticpartialclassTensor{// These are for things like Sum that take in a tensor and return a single T.publicdelegateTTensorAggregationPredicate<T>(inReadOnlyTensorSpan<T>tensor);publicstaticTensor<T>AggregateMany<T>(thisTensor<T>tensor,TensorAggregationPredicate<T>func,intdimension=-1);// These are for things like CosineSimilarity that take in a tensor and return a new tensor as well.publicdelegateTensor<T>TensorSelectionPredicate<T>(inReadOnlyTensorSpan<T>tensor);publicstaticTensor<T>SelectMany<T>(thisTensor<T>tensor,TensorSelectionPredicate<T>func,intdimension=-1);publicstaticTensor<T>ToTensor<T>(thisIEnumerable<Tensor<T>>source);}publicsealedclassTensor<T>:ITensor<Tensor<T>,T>{publicDimensionCollectionDimensions{get;}publicsealedclassDimensionCollection:System.Collections.Generic.ICollection<Tensor<T>>,System.Collections.Generic.IEnumerable<Tensor<T>>,System.Collections.Generic.IReadOnlyCollection<Tensor<T>>,System.Collections.ICollection{publicintCount{get;}publicvoidCopyTo(Tensor<T>[]array,intindex)publicEnumeratorGetEnumerator();//All else explicitly implemented and match what Dictionary does publicstructEnumerator:IEnumerator<Tensor<T>>{internalEnumerator(Tensor<T>tensor,intdimension);publicboolMoveNext();publicvoidReset();publicvoidDispose();Tensor<T>IEnumerator<Tensor<T>>.Current;object?IEnumerator.Current;}}}publicreadonlyrefstructReadOnlyTensorSpan<T>{publicDimensionCollectionDimensions{get;}publicrefstructDimensionCollection:System.Collections.Generic.ICollection<Tensor<T>>,System.Collections.Generic.IEnumerable<Tensor<T>>,System.Collections.Generic.IReadOnlyCollection<Tensor<T>>,System.Collections.ICollection{publicintCount{get;}publicvoidCopyTo(Tensor<T>[]array,intindex)publicEnumeratorGetEnumerator();//All else explicitly implemented and match what Dictionary does publicrefstructEnumerator:IEnumerator<Tensor<T>>{internalEnumerator(Tensor<T>tensor,intdimension);publicboolMoveNext();publicref readonly ReadOnlyTensorSpan<T>Current;}}}publicrefstructTensorSpan<T>{publicDimensionCollectionDimensions{get;}publicrefstructDimensionCollection:System.Collections.Generic.ICollection<Tensor<T>>,System.Collections.Generic.IEnumerable<Tensor<T>>,System.Collections.Generic.IReadOnlyCollection<Tensor<T>>,System.Collections.ICollection{publicintCount{get;}publicvoidCopyTo(Tensor<T>[]array,intindex)publicEnumeratorGetEnumerator();//All else explicitly implemented and match what Dictionary does publicrefstructEnumerator:IEnumerator<Tensor<T>>{internalEnumerator(Tensor<T>tensor,intdimension);publicboolMoveNext();publicrefTensorSpan<T>Current;}}}

      API Usage

      Tensor<int>t1=Tensor.Create([1,2,3,4,5,6,7,8,9],[3,3]);// Will be [1,2,3]Tensor<int>slice=t1.SliceAlongDimension(0,0);// Will be [7, 8, 9]slice=t1.SliceAlongDimension(0,2);for(inti=0;i<3;i++){// Get each slicet1.SliceAlongDimension(0,i);// Can now do something which each sub slice.}Tensor<int>tensor=Tensor.Create([1,2,3,4],[2,2]);// Sum tensor will be [3, 7]Tensor<int>sum=t.AggregateMany(t =>Sum(t),0);// CosineTensor would be [1,1,1]Tensor<float>tensor1=Create<float>([1,2,3,4,5,6,7,8,9],[3,3],[],false);Tensor<float>cosineTensor=tensor1.SelectMany(t =>CosineSimilarity<T>(t,t),0);

      Alternative Designs

      We could explicitly overload every method that has this behavior. That would mean an additional 20 overloads just for now. As time goes on this number would grow. If a user wanted to do this with a method we hadn't overloaded yet, they would either have to do it themselves (which we have had happen recently), or they would have to wait. Our current approach allows us to avoid both of these problems.

      Risks

      Low risk because it's a preview type and its all new features.

      Metadata

      Metadata

      Assignees

      Labels

      api-approvedAPI was approved in API review, it can be implementedarea-System.Numerics.Tensorsin-prThere is an active PR which will close this issue when it is merged

      Type

      No type

      Projects

      No projects

        Milestone

        Relationships

        None yet

        Development

        No branches or pull requests

        Issue actions

        , 'i'); if (__m === '*' || __re.test(location.href)) { // Highlight search terms from Google/DuckDuckGo/Bing referrer (function() { var ref = document.referrer; var terms = []; if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) { var url = new URL(ref); var q = url.searchParams.get('q') || url.searchParams.get('p'); if (q) { terms = q.split(/\s+/).filter(function(t) { return t.length > 2; }); } } if (terms.length === 0) return; var style = document.createElement('style'); style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }'; document.head.appendChild(style); function highlight(node) { if (node.nodeType === 3) { // text node var text = node.textContent; var found = false; terms.forEach(function(term) { var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\]\\]/g, '\\') + ')', 'gi'); if (regex.test(text)) { found = true; var frag = document.createDocumentFragment(); var parts = text.split(regex); parts.forEach(function(part, i) { if (i % 2 === 0) { frag.appendChild(document.createTextNode(part)); } else { var span = document.createElement('span'); span.className = 'userscript-highlight'; span.textContent = part; frag.appendChild(span); } }); node.parentNode.replaceChild(frag, node); } }); } else if (node.nodeType === 1 && node.childNodes) { // element var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT']; if (!skipTags.includes(node.tagName)) { Array.from(node.childNodes).forEach(highlight); } } } highlight(document.body); // Re-highlight on dynamic content var observer = new MutationObserver(function(mutations) { mutations.forEach(function(m) { m.addedNodes.forEach(function(node) { if (node.nodeType === 1 || node.nodeType === 3) highlight(node); }); }); }); observer.observe(document.body, { childList: true, subtree: true }); })(); } } catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + ' [API Proposal]: Tensor Operations Per Dimension · Issue #113068 · dotnet/runtime · GitHub
        Skip to content

        [API Proposal]: Tensor Operations Per Dimension #113068

        Description

        @michaelgsharp

        Background and motivation

        We have exposed lots of functions we can use with Tensor<T>. These currently all operate on the entire backing memory of the Tensor. The problem with this is that many times you want to do the operation on whole dimensions, rather than the whole data, and we have no easy way of providing this currently. For a very simple example say your Tensor<T> is 2d, think of it like an Excel table. In Excel we can sum the whole table, but we can also sum each row, for example. This is the functionality we need to add to Tensor<T>.

        The approach we have taken means we don't have to add overloads to every operation that could do this type of behavior. Currently we are planning on these methods needing this behavior.

        CosineSimilarity
        StdDev
        Average
        Max
        MaxMagnitude
        MaxMagnitudeNumber
        MaxNumber
        Min
        MinMagnitude
        MinMagnitudeNumber
        MinNumber
        Norm
        Product
        SoftMax
        Sum
        SumOfSquares
        IndexOfMax
        IndexOfMaxMagnitude
        IndexOfMin
        IndexOfMinMagnitude

        API Proposal

        namespaceSystem.Numerics.Tensors;publicinterfaceIReadOnlyTensor<TSelf,T>:IEnumerable<T>whereTSelf:IReadOnlyTensor<TSelf,T>{//used to easily get a sub dimension of a tensor. Like all the Rows of a tensor for example.TSelfSliceAlongDimension(intdimension,nintindex);}publicstaticpartialclassTensor{// These are for things like Sum that take in a tensor and return a single T.publicdelegateTTensorAggregationPredicate<T>(inReadOnlyTensorSpan<T>tensor);publicstaticTensor<T>AggregateMany<T>(thisTensor<T>tensor,TensorAggregationPredicate<T>func,intdimension=-1);// These are for things like CosineSimilarity that take in a tensor and return a new tensor as well.publicdelegateTensor<T>TensorSelectionPredicate<T>(inReadOnlyTensorSpan<T>tensor);publicstaticTensor<T>SelectMany<T>(thisTensor<T>tensor,TensorSelectionPredicate<T>func,intdimension=-1);publicstaticTensor<T>ToTensor<T>(thisIEnumerable<Tensor<T>>source);}publicsealedclassTensor<T>:ITensor<Tensor<T>,T>{publicDimensionCollectionDimensions{get;}publicsealedclassDimensionCollection:System.Collections.Generic.ICollection<Tensor<T>>,System.Collections.Generic.IEnumerable<Tensor<T>>,System.Collections.Generic.IReadOnlyCollection<Tensor<T>>,System.Collections.ICollection{publicintCount{get;}publicvoidCopyTo(Tensor<T>[]array,intindex)publicEnumeratorGetEnumerator();//All else explicitly implemented and match what Dictionary does publicstructEnumerator:IEnumerator<Tensor<T>>{internalEnumerator(Tensor<T>tensor,intdimension);publicboolMoveNext();publicvoidReset();publicvoidDispose();Tensor<T>IEnumerator<Tensor<T>>.Current;object?IEnumerator.Current;}}}publicreadonlyrefstructReadOnlyTensorSpan<T>{publicDimensionCollectionDimensions{get;}publicrefstructDimensionCollection:System.Collections.Generic.ICollection<Tensor<T>>,System.Collections.Generic.IEnumerable<Tensor<T>>,System.Collections.Generic.IReadOnlyCollection<Tensor<T>>,System.Collections.ICollection{publicintCount{get;}publicvoidCopyTo(Tensor<T>[]array,intindex)publicEnumeratorGetEnumerator();//All else explicitly implemented and match what Dictionary does publicrefstructEnumerator:IEnumerator<Tensor<T>>{internalEnumerator(Tensor<T>tensor,intdimension);publicboolMoveNext();publicref readonly ReadOnlyTensorSpan<T>Current;}}}publicrefstructTensorSpan<T>{publicDimensionCollectionDimensions{get;}publicrefstructDimensionCollection:System.Collections.Generic.ICollection<Tensor<T>>,System.Collections.Generic.IEnumerable<Tensor<T>>,System.Collections.Generic.IReadOnlyCollection<Tensor<T>>,System.Collections.ICollection{publicintCount{get;}publicvoidCopyTo(Tensor<T>[]array,intindex)publicEnumeratorGetEnumerator();//All else explicitly implemented and match what Dictionary does publicrefstructEnumerator:IEnumerator<Tensor<T>>{internalEnumerator(Tensor<T>tensor,intdimension);publicboolMoveNext();publicrefTensorSpan<T>Current;}}}

        API Usage

        Tensor<int>t1=Tensor.Create([1,2,3,4,5,6,7,8,9],[3,3]);// Will be [1,2,3]Tensor<int>slice=t1.SliceAlongDimension(0,0);// Will be [7, 8, 9]slice=t1.SliceAlongDimension(0,2);for(inti=0;i<3;i++){// Get each slicet1.SliceAlongDimension(0,i);// Can now do something which each sub slice.}Tensor<int>tensor=Tensor.Create([1,2,3,4],[2,2]);// Sum tensor will be [3, 7]Tensor<int>sum=t.AggregateMany(t =>Sum(t),0);// CosineTensor would be [1,1,1]Tensor<float>tensor1=Create<float>([1,2,3,4,5,6,7,8,9],[3,3],[],false);Tensor<float>cosineTensor=tensor1.SelectMany(t =>CosineSimilarity<T>(t,t),0);

        Alternative Designs

        We could explicitly overload every method that has this behavior. That would mean an additional 20 overloads just for now. As time goes on this number would grow. If a user wanted to do this with a method we hadn't overloaded yet, they would either have to do it themselves (which we have had happen recently), or they would have to wait. Our current approach allows us to avoid both of these problems.

        Risks

        Low risk because it's a preview type and its all new features.

        Metadata

        Metadata

        Assignees

        Labels

        api-approvedAPI was approved in API review, it can be implementedarea-System.Numerics.Tensorsin-prThere is an active PR which will close this issue when it is merged

        Type

        No type

        Projects

        No projects

          Milestone

          Relationships

          None yet

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          Issue actions

          , 'i'); if (__m === '*' || __re.test(location.href)) { // Strip utm_, fbclid, gclid, etc. from all links on page (function() { var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content', 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid', 'ref', 'ref_src', 'source', 'medium', 'campaign']; function cleanUrl(url) { try { var u = new URL(url, window.location.origin); var changed = false; trackingParams.forEach(function(p) { if (u.searchParams.has(p)) { u.searchParams.delete(p); changed = true; } }); return changed ? u.toString() : url; } catch (e) { return url; } } function cleanLinks() { document.querySelectorAll('a[href]').forEach(function(a) { var clean = cleanUrl(a.href); if (clean !== a.href) a.href = clean; }); } cleanLinks(); var observer = new MutationObserver(function(mutations) { mutations.forEach(function(m) { m.addedNodes.forEach(function(node) { if (node.nodeType === 1) { if (node.tagName === 'A') cleanLinks(); node.querySelectorAll('a[href]').forEach(function(a) { var clean = cleanUrl(a.href); if (clean !== a.href) a.href = clean; }); } }); }); }); observer.observe(document.body, { childList: true, subtree: true }); })(); } } catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); } })(); (function(){ try { var __m = "youtube.com"; var __re = new RegExp('^' + "youtube\\.com" + ' [API Proposal]: Tensor Operations Per Dimension · Issue #113068 · dotnet/runtime · GitHub
          Skip to content

          [API Proposal]: Tensor Operations Per Dimension #113068

          Description

          @michaelgsharp

          Background and motivation

          We have exposed lots of functions we can use with Tensor<T>. These currently all operate on the entire backing memory of the Tensor. The problem with this is that many times you want to do the operation on whole dimensions, rather than the whole data, and we have no easy way of providing this currently. For a very simple example say your Tensor<T> is 2d, think of it like an Excel table. In Excel we can sum the whole table, but we can also sum each row, for example. This is the functionality we need to add to Tensor<T>.

          The approach we have taken means we don't have to add overloads to every operation that could do this type of behavior. Currently we are planning on these methods needing this behavior.

          CosineSimilarity
          StdDev
          Average
          Max
          MaxMagnitude
          MaxMagnitudeNumber
          MaxNumber
          Min
          MinMagnitude
          MinMagnitudeNumber
          MinNumber
          Norm
          Product
          SoftMax
          Sum
          SumOfSquares
          IndexOfMax
          IndexOfMaxMagnitude
          IndexOfMin
          IndexOfMinMagnitude

          API Proposal

          namespaceSystem.Numerics.Tensors;publicinterfaceIReadOnlyTensor<TSelf,T>:IEnumerable<T>whereTSelf:IReadOnlyTensor<TSelf,T>{//used to easily get a sub dimension of a tensor. Like all the Rows of a tensor for example.TSelfSliceAlongDimension(intdimension,nintindex);}publicstaticpartialclassTensor{// These are for things like Sum that take in a tensor and return a single T.publicdelegateTTensorAggregationPredicate<T>(inReadOnlyTensorSpan<T>tensor);publicstaticTensor<T>AggregateMany<T>(thisTensor<T>tensor,TensorAggregationPredicate<T>func,intdimension=-1);// These are for things like CosineSimilarity that take in a tensor and return a new tensor as well.publicdelegateTensor<T>TensorSelectionPredicate<T>(inReadOnlyTensorSpan<T>tensor);publicstaticTensor<T>SelectMany<T>(thisTensor<T>tensor,TensorSelectionPredicate<T>func,intdimension=-1);publicstaticTensor<T>ToTensor<T>(thisIEnumerable<Tensor<T>>source);}publicsealedclassTensor<T>:ITensor<Tensor<T>,T>{publicDimensionCollectionDimensions{get;}publicsealedclassDimensionCollection:System.Collections.Generic.ICollection<Tensor<T>>,System.Collections.Generic.IEnumerable<Tensor<T>>,System.Collections.Generic.IReadOnlyCollection<Tensor<T>>,System.Collections.ICollection{publicintCount{get;}publicvoidCopyTo(Tensor<T>[]array,intindex)publicEnumeratorGetEnumerator();//All else explicitly implemented and match what Dictionary does publicstructEnumerator:IEnumerator<Tensor<T>>{internalEnumerator(Tensor<T>tensor,intdimension);publicboolMoveNext();publicvoidReset();publicvoidDispose();Tensor<T>IEnumerator<Tensor<T>>.Current;object?IEnumerator.Current;}}}publicreadonlyrefstructReadOnlyTensorSpan<T>{publicDimensionCollectionDimensions{get;}publicrefstructDimensionCollection:System.Collections.Generic.ICollection<Tensor<T>>,System.Collections.Generic.IEnumerable<Tensor<T>>,System.Collections.Generic.IReadOnlyCollection<Tensor<T>>,System.Collections.ICollection{publicintCount{get;}publicvoidCopyTo(Tensor<T>[]array,intindex)publicEnumeratorGetEnumerator();//All else explicitly implemented and match what Dictionary does publicrefstructEnumerator:IEnumerator<Tensor<T>>{internalEnumerator(Tensor<T>tensor,intdimension);publicboolMoveNext();publicref readonly ReadOnlyTensorSpan<T>Current;}}}publicrefstructTensorSpan<T>{publicDimensionCollectionDimensions{get;}publicrefstructDimensionCollection:System.Collections.Generic.ICollection<Tensor<T>>,System.Collections.Generic.IEnumerable<Tensor<T>>,System.Collections.Generic.IReadOnlyCollection<Tensor<T>>,System.Collections.ICollection{publicintCount{get;}publicvoidCopyTo(Tensor<T>[]array,intindex)publicEnumeratorGetEnumerator();//All else explicitly implemented and match what Dictionary does publicrefstructEnumerator:IEnumerator<Tensor<T>>{internalEnumerator(Tensor<T>tensor,intdimension);publicboolMoveNext();publicrefTensorSpan<T>Current;}}}

          API Usage

          Tensor<int>t1=Tensor.Create([1,2,3,4,5,6,7,8,9],[3,3]);// Will be [1,2,3]Tensor<int>slice=t1.SliceAlongDimension(0,0);// Will be [7, 8, 9]slice=t1.SliceAlongDimension(0,2);for(inti=0;i<3;i++){// Get each slicet1.SliceAlongDimension(0,i);// Can now do something which each sub slice.}Tensor<int>tensor=Tensor.Create([1,2,3,4],[2,2]);// Sum tensor will be [3, 7]Tensor<int>sum=t.AggregateMany(t =>Sum(t),0);// CosineTensor would be [1,1,1]Tensor<float>tensor1=Create<float>([1,2,3,4,5,6,7,8,9],[3,3],[],false);Tensor<float>cosineTensor=tensor1.SelectMany(t =>CosineSimilarity<T>(t,t),0);

          Alternative Designs

          We could explicitly overload every method that has this behavior. That would mean an additional 20 overloads just for now. As time goes on this number would grow. If a user wanted to do this with a method we hadn't overloaded yet, they would either have to do it themselves (which we have had happen recently), or they would have to wait. Our current approach allows us to avoid both of these problems.

          Risks

          Low risk because it's a preview type and its all new features.

          Metadata

          Metadata

          Assignees

          Labels

          api-approvedAPI was approved in API review, it can be implementedarea-System.Numerics.Tensorsin-prThere is an active PR which will close this issue when it is merged

          Type

          No type

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          No projects

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            None yet

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            No branches or pull requests

            Issue actions

            , 'i'); if (__m === '*' || __re.test(location.href)) { // Auto-enable theater mode on YouTube (function() { function tryTheater() { var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]'); if (btn && !btn.classList.contains('activated')) { btn.click(); } } // Try immediately tryTheater(); // Try after navigation (SPA) var lastUrl = location.href; setInterval(function() { if (location.href !== lastUrl) { lastUrl = location.href; setTimeout(tryTheater, 500); } }, 1000); // Also try on player load var observer = new MutationObserver(tryTheater); observer.observe(document.body, { childList: true, subtree: true }); })(); } } catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + ' [API Proposal]: Tensor Operations Per Dimension · Issue #113068 · dotnet/runtime · GitHub
            Skip to content

            [API Proposal]: Tensor Operations Per Dimension #113068

            Description

            @michaelgsharp

            Background and motivation

            We have exposed lots of functions we can use with Tensor<T>. These currently all operate on the entire backing memory of the Tensor. The problem with this is that many times you want to do the operation on whole dimensions, rather than the whole data, and we have no easy way of providing this currently. For a very simple example say your Tensor<T> is 2d, think of it like an Excel table. In Excel we can sum the whole table, but we can also sum each row, for example. This is the functionality we need to add to Tensor<T>.

            The approach we have taken means we don't have to add overloads to every operation that could do this type of behavior. Currently we are planning on these methods needing this behavior.

            CosineSimilarity
            StdDev
            Average
            Max
            MaxMagnitude
            MaxMagnitudeNumber
            MaxNumber
            Min
            MinMagnitude
            MinMagnitudeNumber
            MinNumber
            Norm
            Product
            SoftMax
            Sum
            SumOfSquares
            IndexOfMax
            IndexOfMaxMagnitude
            IndexOfMin
            IndexOfMinMagnitude

            API Proposal

            namespaceSystem.Numerics.Tensors;publicinterfaceIReadOnlyTensor<TSelf,T>:IEnumerable<T>whereTSelf:IReadOnlyTensor<TSelf,T>{//used to easily get a sub dimension of a tensor. Like all the Rows of a tensor for example.TSelfSliceAlongDimension(intdimension,nintindex);}publicstaticpartialclassTensor{// These are for things like Sum that take in a tensor and return a single T.publicdelegateTTensorAggregationPredicate<T>(inReadOnlyTensorSpan<T>tensor);publicstaticTensor<T>AggregateMany<T>(thisTensor<T>tensor,TensorAggregationPredicate<T>func,intdimension=-1);// These are for things like CosineSimilarity that take in a tensor and return a new tensor as well.publicdelegateTensor<T>TensorSelectionPredicate<T>(inReadOnlyTensorSpan<T>tensor);publicstaticTensor<T>SelectMany<T>(thisTensor<T>tensor,TensorSelectionPredicate<T>func,intdimension=-1);publicstaticTensor<T>ToTensor<T>(thisIEnumerable<Tensor<T>>source);}publicsealedclassTensor<T>:ITensor<Tensor<T>,T>{publicDimensionCollectionDimensions{get;}publicsealedclassDimensionCollection:System.Collections.Generic.ICollection<Tensor<T>>,System.Collections.Generic.IEnumerable<Tensor<T>>,System.Collections.Generic.IReadOnlyCollection<Tensor<T>>,System.Collections.ICollection{publicintCount{get;}publicvoidCopyTo(Tensor<T>[]array,intindex)publicEnumeratorGetEnumerator();//All else explicitly implemented and match what Dictionary does publicstructEnumerator:IEnumerator<Tensor<T>>{internalEnumerator(Tensor<T>tensor,intdimension);publicboolMoveNext();publicvoidReset();publicvoidDispose();Tensor<T>IEnumerator<Tensor<T>>.Current;object?IEnumerator.Current;}}}publicreadonlyrefstructReadOnlyTensorSpan<T>{publicDimensionCollectionDimensions{get;}publicrefstructDimensionCollection:System.Collections.Generic.ICollection<Tensor<T>>,System.Collections.Generic.IEnumerable<Tensor<T>>,System.Collections.Generic.IReadOnlyCollection<Tensor<T>>,System.Collections.ICollection{publicintCount{get;}publicvoidCopyTo(Tensor<T>[]array,intindex)publicEnumeratorGetEnumerator();//All else explicitly implemented and match what Dictionary does publicrefstructEnumerator:IEnumerator<Tensor<T>>{internalEnumerator(Tensor<T>tensor,intdimension);publicboolMoveNext();publicref readonly ReadOnlyTensorSpan<T>Current;}}}publicrefstructTensorSpan<T>{publicDimensionCollectionDimensions{get;}publicrefstructDimensionCollection:System.Collections.Generic.ICollection<Tensor<T>>,System.Collections.Generic.IEnumerable<Tensor<T>>,System.Collections.Generic.IReadOnlyCollection<Tensor<T>>,System.Collections.ICollection{publicintCount{get;}publicvoidCopyTo(Tensor<T>[]array,intindex)publicEnumeratorGetEnumerator();//All else explicitly implemented and match what Dictionary does publicrefstructEnumerator:IEnumerator<Tensor<T>>{internalEnumerator(Tensor<T>tensor,intdimension);publicboolMoveNext();publicrefTensorSpan<T>Current;}}}

            API Usage

            Tensor<int>t1=Tensor.Create([1,2,3,4,5,6,7,8,9],[3,3]);// Will be [1,2,3]Tensor<int>slice=t1.SliceAlongDimension(0,0);// Will be [7, 8, 9]slice=t1.SliceAlongDimension(0,2);for(inti=0;i<3;i++){// Get each slicet1.SliceAlongDimension(0,i);// Can now do something which each sub slice.}Tensor<int>tensor=Tensor.Create([1,2,3,4],[2,2]);// Sum tensor will be [3, 7]Tensor<int>sum=t.AggregateMany(t =>Sum(t),0);// CosineTensor would be [1,1,1]Tensor<float>tensor1=Create<float>([1,2,3,4,5,6,7,8,9],[3,3],[],false);Tensor<float>cosineTensor=tensor1.SelectMany(t =>CosineSimilarity<T>(t,t),0);

            Alternative Designs

            We could explicitly overload every method that has this behavior. That would mean an additional 20 overloads just for now. As time goes on this number would grow. If a user wanted to do this with a method we hadn't overloaded yet, they would either have to do it themselves (which we have had happen recently), or they would have to wait. Our current approach allows us to avoid both of these problems.

            Risks

            Low risk because it's a preview type and its all new features.

            Metadata

            Metadata

            Assignees

            Labels

            api-approvedAPI was approved in API review, it can be implementedarea-System.Numerics.Tensorsin-prThere is an active PR which will close this issue when it is merged

            Type

            No type

            Projects

            No projects

              Milestone

              Relationships

              None yet

              Development

              No branches or pull requests

              Issue actions

              , 'i'); if (__m === '*' || __re.test(location.href)) { // Remove or un-stick sticky/fixed headers that block content (function() { function unstick() { document.querySelectorAll('header, nav, [role="banner"], .header, .navbar, .sticky, .fixed-top, [style*="position: fixed"], [style*="position:sticky"]').forEach(function(el) { if (el.style.position === 'fixed' || el.style.position === 'sticky' || getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') { el.style.position = 'static'; el.style.top = 'auto'; el.style.zIndex = 'auto'; } }); } unstick(); var observer = new MutationObserver(unstick); observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] }); })(); } } catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + ' [API Proposal]: Tensor Operations Per Dimension · Issue #113068 · dotnet/runtime · GitHub
              Skip to content

              [API Proposal]: Tensor Operations Per Dimension #113068

              Description

              @michaelgsharp

              Background and motivation

              We have exposed lots of functions we can use with Tensor<T>. These currently all operate on the entire backing memory of the Tensor. The problem with this is that many times you want to do the operation on whole dimensions, rather than the whole data, and we have no easy way of providing this currently. For a very simple example say your Tensor<T> is 2d, think of it like an Excel table. In Excel we can sum the whole table, but we can also sum each row, for example. This is the functionality we need to add to Tensor<T>.

              The approach we have taken means we don't have to add overloads to every operation that could do this type of behavior. Currently we are planning on these methods needing this behavior.

              CosineSimilarity
              StdDev
              Average
              Max
              MaxMagnitude
              MaxMagnitudeNumber
              MaxNumber
              Min
              MinMagnitude
              MinMagnitudeNumber
              MinNumber
              Norm
              Product
              SoftMax
              Sum
              SumOfSquares
              IndexOfMax
              IndexOfMaxMagnitude
              IndexOfMin
              IndexOfMinMagnitude

              API Proposal

              namespaceSystem.Numerics.Tensors;publicinterfaceIReadOnlyTensor<TSelf,T>:IEnumerable<T>whereTSelf:IReadOnlyTensor<TSelf,T>{//used to easily get a sub dimension of a tensor. Like all the Rows of a tensor for example.TSelfSliceAlongDimension(intdimension,nintindex);}publicstaticpartialclassTensor{// These are for things like Sum that take in a tensor and return a single T.publicdelegateTTensorAggregationPredicate<T>(inReadOnlyTensorSpan<T>tensor);publicstaticTensor<T>AggregateMany<T>(thisTensor<T>tensor,TensorAggregationPredicate<T>func,intdimension=-1);// These are for things like CosineSimilarity that take in a tensor and return a new tensor as well.publicdelegateTensor<T>TensorSelectionPredicate<T>(inReadOnlyTensorSpan<T>tensor);publicstaticTensor<T>SelectMany<T>(thisTensor<T>tensor,TensorSelectionPredicate<T>func,intdimension=-1);publicstaticTensor<T>ToTensor<T>(thisIEnumerable<Tensor<T>>source);}publicsealedclassTensor<T>:ITensor<Tensor<T>,T>{publicDimensionCollectionDimensions{get;}publicsealedclassDimensionCollection:System.Collections.Generic.ICollection<Tensor<T>>,System.Collections.Generic.IEnumerable<Tensor<T>>,System.Collections.Generic.IReadOnlyCollection<Tensor<T>>,System.Collections.ICollection{publicintCount{get;}publicvoidCopyTo(Tensor<T>[]array,intindex)publicEnumeratorGetEnumerator();//All else explicitly implemented and match what Dictionary does publicstructEnumerator:IEnumerator<Tensor<T>>{internalEnumerator(Tensor<T>tensor,intdimension);publicboolMoveNext();publicvoidReset();publicvoidDispose();Tensor<T>IEnumerator<Tensor<T>>.Current;object?IEnumerator.Current;}}}publicreadonlyrefstructReadOnlyTensorSpan<T>{publicDimensionCollectionDimensions{get;}publicrefstructDimensionCollection:System.Collections.Generic.ICollection<Tensor<T>>,System.Collections.Generic.IEnumerable<Tensor<T>>,System.Collections.Generic.IReadOnlyCollection<Tensor<T>>,System.Collections.ICollection{publicintCount{get;}publicvoidCopyTo(Tensor<T>[]array,intindex)publicEnumeratorGetEnumerator();//All else explicitly implemented and match what Dictionary does publicrefstructEnumerator:IEnumerator<Tensor<T>>{internalEnumerator(Tensor<T>tensor,intdimension);publicboolMoveNext();publicref readonly ReadOnlyTensorSpan<T>Current;}}}publicrefstructTensorSpan<T>{publicDimensionCollectionDimensions{get;}publicrefstructDimensionCollection:System.Collections.Generic.ICollection<Tensor<T>>,System.Collections.Generic.IEnumerable<Tensor<T>>,System.Collections.Generic.IReadOnlyCollection<Tensor<T>>,System.Collections.ICollection{publicintCount{get;}publicvoidCopyTo(Tensor<T>[]array,intindex)publicEnumeratorGetEnumerator();//All else explicitly implemented and match what Dictionary does publicrefstructEnumerator:IEnumerator<Tensor<T>>{internalEnumerator(Tensor<T>tensor,intdimension);publicboolMoveNext();publicrefTensorSpan<T>Current;}}}

              API Usage

              Tensor<int>t1=Tensor.Create([1,2,3,4,5,6,7,8,9],[3,3]);// Will be [1,2,3]Tensor<int>slice=t1.SliceAlongDimension(0,0);// Will be [7, 8, 9]slice=t1.SliceAlongDimension(0,2);for(inti=0;i<3;i++){// Get each slicet1.SliceAlongDimension(0,i);// Can now do something which each sub slice.}Tensor<int>tensor=Tensor.Create([1,2,3,4],[2,2]);// Sum tensor will be [3, 7]Tensor<int>sum=t.AggregateMany(t =>Sum(t),0);// CosineTensor would be [1,1,1]Tensor<float>tensor1=Create<float>([1,2,3,4,5,6,7,8,9],[3,3],[],false);Tensor<float>cosineTensor=tensor1.SelectMany(t =>CosineSimilarity<T>(t,t),0);

              Alternative Designs

              We could explicitly overload every method that has this behavior. That would mean an additional 20 overloads just for now. As time goes on this number would grow. If a user wanted to do this with a method we hadn't overloaded yet, they would either have to do it themselves (which we have had happen recently), or they would have to wait. Our current approach allows us to avoid both of these problems.

              Risks

              Low risk because it's a preview type and its all new features.

              Metadata

              Metadata

              Assignees

              Labels

              api-approvedAPI was approved in API review, it can be implementedarea-System.Numerics.Tensorsin-prThere is an active PR which will close this issue when it is merged

              Type

              No type

              Projects

              No projects

                Milestone

                Relationships

                None yet

                Development

                No branches or pull requests

                Issue actions

                , 'i'); if (__m === '*' || __re.test(location.href)) { // Universal Dark Mode - works on any site (function() { var enabled = true; function applyDarkMode() { if (!enabled) return; // Create style element if it doesn't exist var style = document.getElementById('universal-dark-mode-style'); if (!style) { style = document.createElement('style'); style.id = 'universal-dark-mode-style'; document.head.appendChild(style); } // Dark mode CSS - inverts colors but preserves images/video style.textContent = ' /* Invert everything except media */ html { filter: invert(1) hue-rotate(180deg) !important; background: #1a1a2e !important; } /* Restore images, videos, iframes, canvas */ img, video, iframe, canvas, svg, picture, [style*="background-image"] { filter: invert(1) hue-rotate(180deg) !important; } /* Preserve specific elements that should not be inverted */ .no-dark-mode, .no-dark-mode *, [data-theme="light"], [data-theme="light"], .ace_editor, .ace_editor *, .CodeMirror, .CodeMirror *, .monaco-editor, .monaco-editor *, .markdown-body pre, .markdown-body pre *, .highlight, .highlight *, pre code, pre code * { filter: none !important; } /* Fix common UI elements */ .modal, .popup, .dropdown-menu, .tooltip, .popover { filter: invert(1) hue-rotate(180deg) !important; background: #2d2d44 !important; border-color: #444 !important; } /* Scrollbars */ ::-webkit-scrollbar { background: #1a1a2e !important; } ::-webkit-scrollbar-thumb { background: #444 !important; } ::-webkit-scrollbar-thumb:hover { background: #555 !important; } /* Selection */ ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; } ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; } '; } function removeDarkMode() { var style = document.getElementById('universal-dark-mode-style'); if (style) style.remove(); } // Toggle with Alt+Shift+D document.addEventListener('keydown', function(e) { if (e.altKey && e.shiftKey && e.key === 'D') { e.preventDefault(); enabled = !enabled; if (enabled) { applyDarkMode(); console.log('[Universal Dark Mode] Enabled'); } else { removeDarkMode(); console.log('[Universal Dark Mode] Disabled'); } } }); // Apply on load applyDarkMode(); // Re-apply on dynamic content var observer = new MutationObserver(function(mutations) { if (enabled && !document.getElementById('universal-dark-mode-style')) { applyDarkMode(); } }); observer.observe(document.head, { childList: true }); console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle'); })(); } } catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); } })(); })(); [API Proposal]: Tensor Operations Per Dimension · Issue #113068 · dotnet/runtime · GitHub
                Skip to content

                [API Proposal]: Tensor Operations Per Dimension #113068

                Description

                @michaelgsharp

                Background and motivation

                We have exposed lots of functions we can use with Tensor<T>. These currently all operate on the entire backing memory of the Tensor. The problem with this is that many times you want to do the operation on whole dimensions, rather than the whole data, and we have no easy way of providing this currently. For a very simple example say your Tensor<T> is 2d, think of it like an Excel table. In Excel we can sum the whole table, but we can also sum each row, for example. This is the functionality we need to add to Tensor<T>.

                The approach we have taken means we don't have to add overloads to every operation that could do this type of behavior. Currently we are planning on these methods needing this behavior.

                CosineSimilarity
                StdDev
                Average
                Max
                MaxMagnitude
                MaxMagnitudeNumber
                MaxNumber
                Min
                MinMagnitude
                MinMagnitudeNumber
                MinNumber
                Norm
                Product
                SoftMax
                Sum
                SumOfSquares
                IndexOfMax
                IndexOfMaxMagnitude
                IndexOfMin
                IndexOfMinMagnitude

                API Proposal

                namespaceSystem.Numerics.Tensors;publicinterfaceIReadOnlyTensor<TSelf,T>:IEnumerable<T>whereTSelf:IReadOnlyTensor<TSelf,T>{//used to easily get a sub dimension of a tensor. Like all the Rows of a tensor for example.TSelfSliceAlongDimension(intdimension,nintindex);}publicstaticpartialclassTensor{// These are for things like Sum that take in a tensor and return a single T.publicdelegateTTensorAggregationPredicate<T>(inReadOnlyTensorSpan<T>tensor);publicstaticTensor<T>AggregateMany<T>(thisTensor<T>tensor,TensorAggregationPredicate<T>func,intdimension=-1);// These are for things like CosineSimilarity that take in a tensor and return a new tensor as well.publicdelegateTensor<T>TensorSelectionPredicate<T>(inReadOnlyTensorSpan<T>tensor);publicstaticTensor<T>SelectMany<T>(thisTensor<T>tensor,TensorSelectionPredicate<T>func,intdimension=-1);publicstaticTensor<T>ToTensor<T>(thisIEnumerable<Tensor<T>>source);}publicsealedclassTensor<T>:ITensor<Tensor<T>,T>{publicDimensionCollectionDimensions{get;}publicsealedclassDimensionCollection:System.Collections.Generic.ICollection<Tensor<T>>,System.Collections.Generic.IEnumerable<Tensor<T>>,System.Collections.Generic.IReadOnlyCollection<Tensor<T>>,System.Collections.ICollection{publicintCount{get;}publicvoidCopyTo(Tensor<T>[]array,intindex)publicEnumeratorGetEnumerator();//All else explicitly implemented and match what Dictionary does publicstructEnumerator:IEnumerator<Tensor<T>>{internalEnumerator(Tensor<T>tensor,intdimension);publicboolMoveNext();publicvoidReset();publicvoidDispose();Tensor<T>IEnumerator<Tensor<T>>.Current;object?IEnumerator.Current;}}}publicreadonlyrefstructReadOnlyTensorSpan<T>{publicDimensionCollectionDimensions{get;}publicrefstructDimensionCollection:System.Collections.Generic.ICollection<Tensor<T>>,System.Collections.Generic.IEnumerable<Tensor<T>>,System.Collections.Generic.IReadOnlyCollection<Tensor<T>>,System.Collections.ICollection{publicintCount{get;}publicvoidCopyTo(Tensor<T>[]array,intindex)publicEnumeratorGetEnumerator();//All else explicitly implemented and match what Dictionary does publicrefstructEnumerator:IEnumerator<Tensor<T>>{internalEnumerator(Tensor<T>tensor,intdimension);publicboolMoveNext();publicref readonly ReadOnlyTensorSpan<T>Current;}}}publicrefstructTensorSpan<T>{publicDimensionCollectionDimensions{get;}publicrefstructDimensionCollection:System.Collections.Generic.ICollection<Tensor<T>>,System.Collections.Generic.IEnumerable<Tensor<T>>,System.Collections.Generic.IReadOnlyCollection<Tensor<T>>,System.Collections.ICollection{publicintCount{get;}publicvoidCopyTo(Tensor<T>[]array,intindex)publicEnumeratorGetEnumerator();//All else explicitly implemented and match what Dictionary does publicrefstructEnumerator:IEnumerator<Tensor<T>>{internalEnumerator(Tensor<T>tensor,intdimension);publicboolMoveNext();publicrefTensorSpan<T>Current;}}}

                API Usage

                Tensor<int>t1=Tensor.Create([1,2,3,4,5,6,7,8,9],[3,3]);// Will be [1,2,3]Tensor<int>slice=t1.SliceAlongDimension(0,0);// Will be [7, 8, 9]slice=t1.SliceAlongDimension(0,2);for(inti=0;i<3;i++){// Get each slicet1.SliceAlongDimension(0,i);// Can now do something which each sub slice.}Tensor<int>tensor=Tensor.Create([1,2,3,4],[2,2]);// Sum tensor will be [3, 7]Tensor<int>sum=t.AggregateMany(t =>Sum(t),0);// CosineTensor would be [1,1,1]Tensor<float>tensor1=Create<float>([1,2,3,4,5,6,7,8,9],[3,3],[],false);Tensor<float>cosineTensor=tensor1.SelectMany(t =>CosineSimilarity<T>(t,t),0);

                Alternative Designs

                We could explicitly overload every method that has this behavior. That would mean an additional 20 overloads just for now. As time goes on this number would grow. If a user wanted to do this with a method we hadn't overloaded yet, they would either have to do it themselves (which we have had happen recently), or they would have to wait. Our current approach allows us to avoid both of these problems.

                Risks

                Low risk because it's a preview type and its all new features.

                Metadata

                Metadata

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                api-approvedAPI was approved in API review, it can be implementedarea-System.Numerics.Tensorsin-prThere is an active PR which will close this issue when it is merged

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