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[assembly: System.CLSCompliant(false)]
[assembly: System.Reflection.AssemblyMetadata("IsTrimmable", "True")]
[assembly: System.Reflection.AssemblyMetadata("RepositoryUrl", "https://github.com/nietras/Llm/")]
[assembly: System.Resources.NeutralResourcesLanguage("en")]
[assembly: System.Runtime.CompilerServices.InternalsVisibleTo("Llm.Benchmarks")]
[assembly: System.Runtime.CompilerServices.InternalsVisibleTo("Llm.Test")]
[assembly: System.Runtime.Versioning.TargetFramework(".NETCoreApp,Version=v8.0", FrameworkDisplayName=".NET 8.0")]
namespace nietras.LargeLanguageModel
{
public interface ILlm
{
unsafe void AdamW(float* gradients, float* ms, float* vs, float* parameters, System.IntPtr parameterCount, float learningRate, float beta1, float beta2, float eps, float weightDecay, int t);
unsafe void AttentionBackward(float* δoutput, float* postAttention, float* input, int batchSize, int tokenCount, int channelCount, int headCount, float* δpreAttention, float* δpostAttention, float* δinput);
unsafe void AttentionForward(float* input, int batchSize, int tokenCount, int channelCount, int headCount, float* preAttention, float* postAttention, float* output);
unsafe void CrossEntropyForward(float* probabilities, int* targetTokenIndices, int batchSize, int tokenCount, int vocabularySize, float* losses);
unsafe void CrossEntropySoftmaxBackward(float* δlosses, float* probabilities, int* targetTokenIndices, int batchSize, int tokenCount, int vocabularySize, float* δlogits);
unsafe void EmbedBackward(float* δoutput, int* tokenIndices, int batchSize, int tokenCount, int channelCount, float* δtokenEmbeddings, float* δpositionEmbeddings);
unsafe void EmbedForward(int* tokenIndices, float* tokenEmbeddings, float* positionEmbeddings, int batchSize, int tokenCount, int channelCount, float* output);
unsafe void GeLUBackward(float* δoutput, float* input, int count, float* δinput);
unsafe void GeLUForward(float* input, int count, float* output);
unsafe void LayerNormBackward(float* δoutput, float* input, float* weight, float* mean, float* invStdDev, int batchSize, int tokenCount, int channelCount, float* δweight, float* δbias, float* δinput);
unsafe void LayerNormForward(float* input, float* weight, float* bias, int batchSize, int tokenCount, int channelCount, float* mean, float* invStdDev, float* output);
unsafe void MatMulBackward(float* δoutput, float* input, float* weight, int batchSize, int tokenCount, int inputChannelCount, int outputChannelCount, float* δweight, float* δbias, float* δinput);
unsafe void MatMulForward(float* input, float* weight, float* bias, int batchSize, int tokenCount, int inputChannelCount, int outputChannelCount, float* output);
unsafe void ResidualBackward(float* δoutput, int count, float* δleft, float* δright);
unsafe void ResidualForward(float* left, float* right, int count, float* output);
unsafe void SoftmaxForward(float* logits, int batchSize, int tokenCount, int vocabularySize, float* probabilities);
}
public class Llm : nietras.LargeLanguageModel.ILlm
{
public Llm() { }
public virtual unsafe void AdamW(float* gradients, float* ms, float* vs, float* parameters, System.IntPtr parameterCount, float learningRate, float beta1, float beta2, float eps, float weightDecay, int t) { }
public virtual unsafe void AttentionBackward(float* δoutput, float* postAttention, float* input, int batchSize, int tokenCount, int channelCount, int headCount, float* δpreAttention, float* δpostAttention, float* δinput) { }
public virtual unsafe void AttentionForward(float* input, int batchSize, int tokenCount, int channelCount, int headCount, float* preAttention, float* postAttention, float* output) { }
public virtual unsafe void CrossEntropyForward(float* probabilities, int* targetTokenIndices, int batchSize, int tokenCount, int vocabularySize, float* losses) { }
public virtual unsafe void CrossEntropySoftmaxBackward(float* δlosses, float* probabilities, int* targetTokenIndices, int batchSize, int tokenCount, int vocabularySize, float* δlogits) { }
public virtual unsafe void EmbedBackward(float* δoutput, int* tokenIndices, int batchSize, int tokenCount, int channelCount, float* δtokenEmbeddings, float* δpositionEmbeddings) { }
public virtual unsafe void EmbedForward(int* tokenIndices, float* tokenEmbeddings, float* positionEmbeddings, int batchSize, int tokenCount, int channelCount, float* output) { }
public virtual unsafe void GeLUBackward(float* δoutput, float* input, int count, float* δinput) { }
public virtual unsafe void GeLUForward(float* input, int count, float* output) { }
public virtual unsafe void LayerNormBackward(float* δoutput, float* input, float* weight, float* mean, float* invStdDev, int batchSize, int tokenCount, int channelCount, float* δweight, float* δbias, float* δinput) { }
public virtual unsafe void LayerNormForward(float* input, float* weight, float* bias, int batchSize, int tokenCount, int channelCount, float* mean, float* invStdDev, float* output) { }
public virtual unsafe void MatMulBackward(float* δoutput, float* input, float* weight, int batchSize, int tokenCount, int inputChannelCount, int outputChannelCount, float* δweight, float* δbias, float* δinput) { }
public virtual unsafe void MatMulForward(float* input, float* weight, float* bias, int batchSize, int tokenCount, int inputChannelCount, int outputChannelCount, float* output) { }
public virtual unsafe void ResidualBackward(float* δoutput, int count, float* δleft, float* δright) { }
public virtual unsafe void ResidualForward(float* left, float* right, int count, float* output) { }
public virtual unsafe void SoftmaxForward(float* logits, int batchSize, int tokenCount, int vocabularySize, float* probabilities) { }
}
public static class LlmFactory
{
public static System.Collections.Generic.IReadOnlyDictionary<string, System.Func<nietras.LargeLanguageModel.ILlm>> NameToCreate { get; }
public static nietras.LargeLanguageModel.ILlm CreateDefault() { }
}
public class Llm_nietras : nietras.LargeLanguageModel.Llm
{
public Llm_nietras() { }
public override unsafe void AdamW(float* gradients, float* ms, float* vs, float* parameters, System.IntPtr parameterCount, float learningRate, float beta1, float beta2, float eps, float weightDecay, int t) { }
public override unsafe void AttentionBackward(float* δoutput, float* postAttention, float* input, int batchSize, int tokenCount, int channelCount, int headCount, float* δpreAttention, float* δpostAttention, float* δinput) { }
public override unsafe void GeLUBackward(float* δoutput, float* input, int count, float* δinput) { }
public override unsafe void GeLUForward(float* input, int count, float* output) { }
public override unsafe void LayerNormBackward(float* δoutput, float* input, float* weight, float* mean, float* invStdDev, int batchSize, int tokenCount, int channelCount, float* δweight, float* δbias, float* δinput) { }
public override unsafe void LayerNormForward(float* input, float* weight, float* bias, int batchSize, int tokenCount, int channelCount, float* mean, float* invStdDev, float* output) { }
public override unsafe void MatMulBackward(float* δoutput, float* input, float* weight, int batchSize, int tokenCount, int inputChannelCount, int outputChannelCount, float* δweight, float* δbias, float* δinput) { }
public override unsafe void MatMulForward(float* input, float* weight, float* bias, int batchSize, int tokenCount, int inputChannelCount, int outputChannelCount, float* output) { }
}
public static class Runner
{
public static void Run(string[] args, string dataDirectory, System.Action<string> log) { }
}
}
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