Upload rth_tcn_ops.cpp with huggingface_hub

rth_tcn_ops.cpp

@@ -0,0 +1,67 @@
+/**
+ * @file rth_tcn_ops.cpp
+ * @brief Implementation of custom TCN operators for llama.cpp.
+ */
+
+#include "ggml.h"
+#include <cmath>
+#include <algorithm>
+
+// --- CAUSAL CONV1D (CPU REFERENCE) ---
+// This is the reference implementation for the causal convolution.
+// In production, this would be optimized with AVX/NEON or CUDA.
+
+void ggml_compute_forward_causal_conv1d(
+    const struct ggml_compute_params * params,
+    const struct ggml_tensor * src0, // Input: [N, C, T]
+    const struct ggml_tensor * src1, // Kernel: [C, 1, K]
+    struct ggml_tensor * dst) {
+    
+    const int nc = src0->ne[1]; // Channels
+    const int nt = src0->ne[0]; // Time steps
+    const int nk = src1->ne[0]; // Kernel size
+    const int dilation = params->ith; // We use params->ith to pass dilation for simplicity in prototype
+
+    // Causal Convolution Logic:
+    // Output[c, t] = sum_{k=0}^{nk-1} Input[c, t - k * dilation] * Kernel[c, k]
+    // If t - k * dilation < 0, Input is 0 (causal padding).
+
+    for (int c = 0; c < nc; c++) {
+        for (int t = 0; t < nt; t++) {
+            float sum = 0.0f;
+            for (int k = 0; k < nk; k++) {
+                int src_t = t - k * dilation;
+                if (src_t >= 0) {
+                    float val = ((float*)src0->data)[c * nt + src_t];
+                    float weight = ((float*)src1->data)[c * nk + k];
+                    sum += val * weight;
+                }
+            }
+            ((float*)dst->data)[c * nt + t] = sum;
+        }
+    }
+}
+
+// --- FRACTAL GATE (CPU REFERENCE) ---
+// Y = SiLU(path_a) * Sigmoid(path_b)
+// Note: In ZetaGrid, gating is applied after convolution.
+
+void ggml_compute_forward_fractal_gate(
+    const struct ggml_compute_params * params,
+    const struct ggml_tensor * src0, // Path A (Mixed)
+    const struct ggml_tensor * src1, // Path B (Gate)
+    struct ggml_tensor * dst) {
+    
+    const int ne = ggml_nelements(src0);
+    const float * a = (const float *)src0->data;
+    const float * b = (const float *)src1->data;
+    float * out = (float *)dst->data;
+
+    for (int i = 0; i < ne; i++) {
+        // SiLU(x) = x * sigmoid(x)
+        float silu_a = a[i] * (1.0f / (1.0f + std::exp(-a[i])));
+        // Sigmoid(g)
+        float sig_b = 1.0f / (1.0f + std::exp(-b[i]));
+        out[i] = silu_a * sig_b;
+    }
+}