Llm.cs - C# port of @karpathy llm.c
⭐ Please star this project if you like it. ⭐
- Install .NET SDK matching version in global.json
- Restore and build the project (working directory where
Llm.sln):dotnet restore dotnet build -c Release - Run (currently defaults to running train "test"/verification):
NOTE: First time this is run it will download binary files e.g. weights and input tokens from nietras/llm.bin on Hugging Face. This means there is no need to run any Python here to get data or similar. Clone and run ✅
dotnet run -c Release --project .\src\Llm\Llm.csproj
- Output should then be something like:
ProcessorCount: 32 [GPT-2] MaxTokenCount: 1024 VocabularySize: 50257 LayerCount: 12 HeadCount: 12 ChannelCount: 768 ParameterCount: 124439808 [State] BatchSize: 4 TokenCount: 64 OutputCount: 73323776 Logits TENSOR OK MaxAbsDiff 0.000534 δTokenEmbeddings TENSOR OK MaxAbsDiff 0.001185 δPositionEmbeddings TENSOR OK MaxAbsDiff 0.000037 δLayerNorm1Weights TENSOR OK MaxAbsDiff 0.003039 δLayerNorm1Bias TENSOR OK MaxAbsDiff 0.001283 δQKVWeights TENSOR OK MaxAbsDiff 0.000474 δQKVBias TENSOR OK MaxAbsDiff 0.000257 δAttentionProjectionWeights TENSOR OK MaxAbsDiff 0.000200 δAttentionProjectionBias TENSOR OK MaxAbsDiff 0.000179 δLayerNorm2Weights TENSOR OK MaxAbsDiff 0.009708 δLayerNorm2Bias TENSOR OK MaxAbsDiff 0.000819 δFullConnectWeights TENSOR OK MaxAbsDiff 0.000794 δFullConnectBias TENSOR OK MaxAbsDiff 0.000193 δFullConnectProjectionWeights TENSOR OK MaxAbsDiff 0.000385 δFullConnectProjectionBias TENSOR OK MaxAbsDiff 0.000118 δLayerNormFinalWeights TENSOR OK MaxAbsDiff 0.000362 δLayerNormFinalBias TENSOR OK MaxAbsDiff 0.000066 0: loss 5.269892 exp. 5.270007 OK ( 1386 ms = Forward 490 ms ZeroGrad 90 ms Backward 632 ms Update 174 ms) JIT/WARMUP 1: loss 4.059388 exp. 4.059707 OK ( 875 ms = Forward 279 ms ZeroGrad 28 ms Backward 463 ms Update 106 ms) JIT/WARMUP 2: loss 3.374209 exp. 3.375123 OK ( 1005 ms = Forward 407 ms ZeroGrad 28 ms Backward 459 ms Update 110 ms) JIT/WARMUP 3: loss 2.800130 exp. 2.800783 OK ( 867 ms = Forward 266 ms ZeroGrad 28 ms Backward 474 ms Update 99 ms) 4: loss 2.315308 exp. 2.315382 OK ( 847 ms = Forward 238 ms ZeroGrad 28 ms Backward 477 ms Update 103 ms) 5: loss 1.849346 exp. 1.849029 OK ( 884 ms = Forward 234 ms ZeroGrad 28 ms Backward 516 ms Update 106 ms) 6: loss 1.395217 exp. 1.394656 OK ( 884 ms = Forward 282 ms ZeroGrad 28 ms Backward 468 ms Update 106 ms) 7: loss 0.998617 exp. 0.999147 OK ( 839 ms = Forward 231 ms ZeroGrad 28 ms Backward 474 ms Update 106 ms) 8: loss 0.625541 exp. 0.624080 OK ( 887 ms = Forward 309 ms ZeroGrad 28 ms Backward 449 ms Update 102 ms) 9: loss 0.378010 exp. 0.376511 OK ( 915 ms = Forward 311 ms ZeroGrad 28 ms Backward 485 ms Update 91 ms) All okay: True 0.Forward 00 EmbedForward 0% count: 7 sum: 1.2 min: 0.2 mean: 0.2 max: 0.2 [ms] 0.Forward 01 LayerNormForward 0% count: 84 sum: 7.2 min: 0.1 mean: 0.1 max: 0.1 [ms] 0.Forward 02 MatMulForward 3% count: 84 sum: 197.5 min: 1.9 mean: 2.4 max: 4.4 [ms] 0.Forward 03 AttentionForward 1% count: 84 sum: 67.0 min: 0.7 mean: 0.8 max: 1.6 [ms] 0.Forward 04 MatMulForward 1% count: 84 sum: 69.5 min: 0.7 mean: 0.8 max: 1.4 [ms] 0.Forward 05 ResidualForward 0% count: 84 sum: 7.5 min: 0.1 mean: 0.1 max: 0.3 [ms] 0.Forward 06 LayerNormForward 0% count: 84 sum: 6.8 min: 0.1 mean: 0.1 max: 0.1 [ms] 0.Forward 07 MatMulForward 4% count: 84 sum: 252.7 min: 2.3 mean: 3.0 max: 5.2 [ms] 0.Forward 08 GeLUForward 1% count: 84 sum: 74.9 min: 0.7 mean: 0.9 max: 1.7 [ms] 0.Forward 09 MatMulForward 4% count: 84 sum: 250.0 min: 2.4 mean: 3.0 max: 5.9 [ms] 0.Forward 10 ResidualForward 0% count: 84 sum: 7.2 min: 0.1 mean: 0.1 max: 0.1 [ms] 0.Forward 11 LayerNormForward 0% count: 7 sum: 0.5 min: 0.1 mean: 0.1 max: 0.1 [ms] 0.Forward 12 MatMulForward 15% count: 7 sum: 887.3 min: 93.7 mean: 126.8 max: 175.8 [ms] 0.Forward 13 SoftmaxForward 1% count: 7 sum: 38.6 min: 3.8 mean: 5.5 max: 9.4 [ms] 0.Forward 14 CrossEntropyForward 0% count: 7 sum: 0.2 min: 0.0 mean: 0.0 max: 0.0 [ms] 1.ZeroGrad 00 Zero 2% count: 7 sum: 123.0 min: 17.5 mean: 17.6 max: 17.6 [ms] 1.ZeroGrad 01 Zero 1% count: 7 sum: 72.2 min: 10.3 mean: 10.3 max: 10.4 [ms] 2.Backward 00 CrossEntropySoftmaxBackward 1% count: 7 sum: 45.4 min: 6.4 mean: 6.5 max: 6.6 [ms] 2.Backward 01 MatMulBackward 18% count: 7 sum: 1116.7 min: 139.0 mean: 159.5 max: 173.6 [ms] 2.Backward 02 LayerNormBackward 0% count: 7 sum: 4.1 min: 0.5 mean: 0.6 max: 0.6 [ms] 2.Backward 03 ResidualBackward 0% count: 84 sum: 14.1 min: 0.1 mean: 0.2 max: 0.3 [ms] 2.Backward 04 MatMulBackward 11% count: 84 sum: 679.4 min: 6.8 mean: 8.1 max: 14.3 [ms] 2.Backward 05 GeLUBackward 2% count: 84 sum: 124.7 min: 1.3 mean: 1.5 max: 2.0 [ms] 2.Backward 06 MatMulBackward 8% count: 84 sum: 519.8 min: 5.7 mean: 6.2 max: 10.2 [ms] 2.Backward 07 LayerNormBackward 1% count: 84 sum: 39.3 min: 0.4 mean: 0.5 max: 1.1 [ms] 2.Backward 08 ResidualBackward 0% count: 84 sum: 12.2 min: 0.1 mean: 0.1 max: 0.3 [ms] 2.Backward 09 MatMulBackward 3% count: 84 sum: 158.9 min: 1.6 mean: 1.9 max: 7.1 [ms] 2.Backward 10 AttentionBackward 3% count: 84 sum: 159.1 min: 1.4 mean: 1.9 max: 4.6 [ms] 2.Backward 11 MatMulBackward 7% count: 84 sum: 423.6 min: 4.3 mean: 5.0 max: 8.1 [ms] 2.Backward 12 LayerNormBackward 1% count: 84 sum: 42.5 min: 0.4 mean: 0.5 max: 0.8 [ms] 2.Backward 13 EmbedBackward 0% count: 7 sum: 1.3 min: 0.2 mean: 0.2 max: 0.2 [ms] 3.Update 00 AdamW 12% count: 7 sum: 713.1 min: 91.1 mean: 101.9 max: 106.3 [ms] MatMulBackward 47% sum: 2898 [ms] per step: 414 [ms] MatMulForward 27% sum: 1657 [ms] per step: 237 [ms] AdamW 12% sum: 713 [ms] per step: 102 [ms] Zero 3% sum: 195 [ms] per step: 28 [ms] AttentionBackward 3% sum: 159 [ms] per step: 23 [ms] GeLUBackward 2% sum: 125 [ms] per step: 18 [ms] LayerNormBackward 1% sum: 86 [ms] per step: 12 [ms] GeLUForward 1% sum: 75 [ms] per step: 11 [ms] AttentionForward 1% sum: 67 [ms] per step: 10 [ms] CrossEntropySoftmaxBackward 1% sum: 45 [ms] per step: 6 [ms] SoftmaxForward 1% sum: 39 [ms] per step: 6 [ms] ResidualBackward 0% sum: 26 [ms] per step: 4 [ms] ResidualForward 0% sum: 15 [ms] per step: 2 [ms] LayerNormForward 0% sum: 14 [ms] per step: 2 [ms] EmbedBackward 0% sum: 1 [ms] per step: 0 [ms] EmbedForward 0% sum: 1 [ms] per step: 0 [ms] CrossEntropyForward 0% sum: 0 [ms] per step: 0 [ms] Total 100% sum: 6117 [ms] per step: 874 [ms]
[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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