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model : Apertus model implementation (#15852)
* First attempt * No permute during convert (fixes qk tensors), proper norm application. * RoPE = NeoX * Coherence! * Migrate xielu params from tensors to hyperparameters * Simple CUDA kernel * Revert stupid LLM refactorings * Chat template support * configchecker / flake8 errors * Reorder unary.cu * I do conclude that LLMs are, in fact, stupid. * Fix after merge * Final newline * Make xIELU an UNARY_OP * Final newline * Correctly account for parameter shift * Argh. * Update ggml/src/ggml-cpu/unary-ops.cpp Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Refactor: remove unused methods, inline and factorize softplus, add const modifiers * Revert CUDA changes, implement xIELU as a separate OP * Pesky newline * Add float2half / half2float for F16 inputs/outputs * CUDA variants, attempt 2 * Actually, attempt 3 * Update ggml/src/ggml-cuda/unary.cu Co-authored-by: Johannes Gäßler <johannesg@5d6.de> * Missing convert header * Proper formula and reference for xIELU in the comments. * Modify unary-ops.cpp to add the functor-based logic besides the template system to retain optimizations * Apply suggestions from code review Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * Add tensor mappings for Apertus to global list instead * Fix lazy on scalars * Update ggml/src/ggml-cuda/unary.cu Co-authored-by: Johannes Gäßler <johannesg@5d6.de> * Add comment about the constraints on positive/negative alpha * Change `softplus` to `ggml_softplus` --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> Co-authored-by: Johannes Gäßler <johannesg@5d6.de> Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
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@@ -576,6 +576,7 @@ extern "C" {
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GGML_UNARY_OP_HARDSIGMOID,
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GGML_UNARY_OP_EXP,
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GGML_UNARY_OP_GELU_ERF,
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GGML_UNARY_OP_XIELU,
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GGML_UNARY_OP_COUNT,
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};
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@@ -1150,6 +1151,18 @@ extern "C" {
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struct ggml_context * ctx,
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struct ggml_tensor * a);
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// xIELU activation function
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// x = x * (c_a(alpha_n) + c_b(alpha_p, beta) * sigmoid(beta * x)) + eps * (x > 0)
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// where c_a = softplus and c_b(a, b) = softplus(a) + b are constraining functions
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// that constrain the positive and negative source alpha values respectively
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GGML_API struct ggml_tensor * ggml_xielu(
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struct ggml_context * ctx,
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struct ggml_tensor * a,
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float alpha_n,
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float alpha_p,
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float beta,
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float eps);
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// gated linear unit ops
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// A: n columns, r rows,
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// result is n / 2 columns, r rows,
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