Fix YaRN ramp calculation and add --yarn-orig-ctx

common/common.cpp

@@ -220,6 +220,12 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
                 break;
             }
             params.rope_freq_scale = 1.0f/std::stof(argv[i]);
+        } else if (arg == "--yarn-orig-ctx") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.yarn_orig_ctx = std::stoi(argv[i]);
         } else if (arg == "--yarn-ext-factor") {
             if (++i >= argc) {
                 invalid_param = true;
@@ -737,6 +743,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
     printf("  --rope-scale N        RoPE context scaling factor, expands context by a factor of N\n");
     printf("  --rope-freq-base N    RoPE base frequency, used by NTK-aware scaling (default: loaded from model)\n");
     printf("  --rope-freq-scale N   RoPE frequency scaling factor, expands context by a factor of 1/N\n");
+    printf("  --yarn-orig-ctx N     YaRN: original context size of model (default: 0 = model training context size)\n");
     printf("  --yarn-ext-factor N   YaRN: extrapolation mix factor (default: 1.0, 0.0 = full interpolation)\n");
     printf("  --yarn-attn-factor N  YaRN: scale sqrt(t) or attention magnitude (default: 1.0)\n");
     printf("  --yarn-beta-slow N    YaRN: high correction dim or alpha (default: %.1f)\n", params.yarn_beta_slow);
@@ -861,6 +868,7 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
     cparams.yarn_attn_factor  = params.yarn_attn_factor;
     cparams.yarn_beta_fast    = params.yarn_beta_fast;
     cparams.yarn_beta_slow    = params.yarn_beta_slow;
+    cparams.yarn_orig_ctx     = params.yarn_orig_ctx;
 
     return cparams;
 }

common/common.h

@@ -57,8 +57,9 @@ struct gpt_params {
     float   rope_freq_scale                 = 0.0f; // RoPE frequency scaling factor
     float   yarn_ext_factor                 = NAN;  // YaRN extrapolation mix factor
     float   yarn_attn_factor                = 1.0f; // YaRN magnitude scaling factor
-    float   yarn_beta_fast                  = 32.0f; // YaRN low correction dim
-    float   yarn_beta_slow                  = 1.0f;  // YaRN high correction dim
+    float   yarn_beta_fast                  = 32.0f;// YaRN low correction dim
+    float   yarn_beta_slow                  = 1.0f; // YaRN high correction dim
+    int32_t yarn_orig_ctx                   = 0;    // YaRN original context length
     int8_t  rope_scaling_type               = LLAMA_ROPE_SCALING_UNSPECIFIED;
 
     // // sampling parameters

ggml-cuda.cu

@@ -4406,7 +4406,7 @@ static __global__ void cpy_f32_f16(const char * cx, char * cdst, const int ne,
 }
 
 static __device__ float rope_yarn_ramp(const float low, const float high, const int i0) {
-    const float y = (i0 / 2 - low) / min(0.001f, high - low);
+    const float y = (i0 / 2 - low) / max(0.001f, high - low);
     return 1.0f - min(1.0f, max(0.0f, y));
 }
 
@@ -4426,11 +4426,10 @@ static __device__ void rope_yarn(
     if (ext_factor != 0.0f) {
         float ramp_mix = rope_yarn_ramp(corr_dims.v[0], corr_dims.v[1], i0) * ext_factor;
         theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix;
-    }
 
-    // Get n-d magnitude scaling corrected for interpolation
-    if (freq_scale < 1.0f)
+        // Get n-d magnitude scaling corrected for interpolation
         mscale *= 1.0f + 0.1f * logf(1.0f / freq_scale);
+    }
     *cos_theta = cosf(theta) * mscale;
     *sin_theta = sinf(theta) * mscale;
 }

ggml-metal.metal

@@ -880,7 +880,7 @@ kernel void kernel_alibi_f32(
 }
 
 static float rope_yarn_ramp(const float low, const float high, const int i0) {
-    const float y = (i0 / 2 - low) / min(0.001f, high - low);
+    const float y = (i0 / 2 - low) / max(0.001f, high - low);
     return 1.0f - min(1.0f, max(0.0f, y));
 }
 
@@ -896,11 +896,10 @@ static void rope_yarn(
     if (ext_factor != 0.0f) {
         ramp_mix = rope_yarn_ramp(corr_dims[0], corr_dims[1], i0) * ext_factor;
         theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix;
-    }
 
-    // Get n-d magnitude scaling corrected for interpolation
-    if (freq_scale < 1.0f)
+        // Get n-d magnitude scaling corrected for interpolation
         mscale *= 1.0f + 0.1f * logf(1.0f / freq_scale);
+    }
     *cos_theta = cosf(theta) * mscale;
     *sin_theta = sinf(theta) * mscale;
 }

ggml.c

@@ -13345,7 +13345,7 @@ static void ggml_compute_forward_clamp(
 // ggml_compute_forward_rope
 
 static float rope_yarn_ramp(const float low, const float high, const int i0) {
-    const float y = (i0 / 2 - low) / MIN(0.001f, high - low);
+    const float y = (i0 / 2 - low) / MAX(0.001f, high - low);
     return 1 - MIN(1, MAX(0, y));
 }
 
@@ -13361,11 +13361,10 @@ static void rope_yarn(
     if (ext_factor != 0.0f) {
         float ramp_mix = rope_yarn_ramp(corr_dims[0], corr_dims[1], i0) * ext_factor;
         theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix;
-    }
 
-    // Get n-d magnitude scaling corrected for interpolation
-    if (freq_scale < 1.0f)
+        // Get n-d magnitude scaling corrected for interpolation
         mscale *= 1.0f + 0.1f * logf(1.0f / freq_scale);
+    }
     *cos_theta = cosf(theta) * mscale;
     *sin_theta = sinf(theta) * mscale;
 }

llama.cpp

@@ -1113,6 +1113,7 @@ struct llama_cparams {
     float    rope_freq_base;
     float    rope_freq_scale;
 
+    uint32_t n_yarn_orig_ctx;
     // These hyperparameters are not exposed in GGUF, because all
     // existing YaRN models use the same values for them.
     float yarn_ext_factor;
@@ -3028,7 +3029,7 @@ static struct ggml_cgraph * llm_build_llama(
     const int32_t n_embd      = hparams.n_embd;
     const int32_t n_layer     = hparams.n_layer;
     const int32_t n_ctx       = cparams.n_ctx;
-    const int32_t n_orig_ctx  = hparams.n_yarn_orig_ctx;
+    const int32_t n_orig_ctx  = cparams.n_yarn_orig_ctx;
     const int32_t n_head      = hparams.n_head;
     const int32_t n_head_kv   = hparams.n_head_kv;
     const int32_t n_embd_head = hparams.n_embd_head();
@@ -3430,7 +3431,7 @@ static struct ggml_cgraph * llm_build_baichaun(
     const int32_t n_embd      = hparams.n_embd;
     const int32_t n_layer     = hparams.n_layer;
     const int32_t n_ctx       = cparams.n_ctx;
-    const int32_t n_orig_ctx  = hparams.n_yarn_orig_ctx;
+    const int32_t n_orig_ctx  = cparams.n_yarn_orig_ctx;
     const int32_t n_head      = hparams.n_head;
     const int32_t n_head_kv   = hparams.n_head_kv;
     const int32_t n_embd_head = hparams.n_embd_head();
@@ -4194,7 +4195,7 @@ static struct ggml_cgraph * llm_build_falcon(
     const int32_t n_embd      = hparams.n_embd;
     const int32_t n_layer     = hparams.n_layer;
     const int32_t n_ctx       = cparams.n_ctx;
-    const int32_t n_orig_ctx  = hparams.n_yarn_orig_ctx;
+    const int32_t n_orig_ctx  = cparams.n_yarn_orig_ctx;
     const int32_t n_head      = hparams.n_head;
     const int32_t n_head_kv   = hparams.n_head_kv;
     const int32_t n_embd_head = hparams.n_embd_head();
@@ -4818,7 +4819,7 @@ static struct ggml_cgraph * llm_build_persimmon(
     const int64_t n_embd      = hparams.n_embd;
     const int64_t n_layer     = hparams.n_layer;
     const int64_t n_ctx       = cparams.n_ctx;
-    const int32_t n_orig_ctx  = hparams.n_yarn_orig_ctx;
+    const int32_t n_orig_ctx  = cparams.n_yarn_orig_ctx;
     const int64_t n_head_kv   = hparams.n_head_kv;
     const int64_t n_head      = hparams.n_head;
     const int64_t n_embd_head = hparams.n_embd_head();
@@ -8676,6 +8677,7 @@ struct llama_context * llama_new_context_with_model(
     cparams.mul_mat_q        = params.mul_mat_q;
 
     cparams.n_ctx            = params.n_ctx           == 0    ? hparams.n_ctx_train           : params.n_ctx;
+    cparams.n_yarn_orig_ctx  = params.yarn_orig_ctx   == 0    ? hparams.n_ctx_train           : params.yarn_orig_ctx;
     cparams.rope_freq_base   = params.rope_freq_base  == 0.0f ? hparams.rope_freq_base_train  : params.rope_freq_base;
     cparams.rope_freq_scale  = params.rope_freq_scale == 0.0f ? hparams.rope_freq_scale_train : params.rope_freq_scale;
 

llama.h

@@ -182,12 +182,13 @@ extern "C" {
         int8_t   rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type`
 
         // ref: https://github.com/ggerganov/llama.cpp/pull/2054
-        float rope_freq_base;   // RoPE base frequency, 0 = from model
-        float rope_freq_scale;  // RoPE frequency scaling factor, 0 = from model
-        float yarn_ext_factor;  // YaRN extrapolation mix factor, NaN = from model
-        float yarn_attn_factor; // YaRN magnitude scaling factor
-        float yarn_beta_fast;   // YaRN low correction dim
-        float yarn_beta_slow;   // YaRN high correction dim
+        float    rope_freq_base;   // RoPE base frequency, 0 = from model
+        float    rope_freq_scale;  // RoPE frequency scaling factor, 0 = from model
+        float    yarn_ext_factor;  // YaRN extrapolation mix factor, NaN = from model
+        float    yarn_attn_factor; // YaRN magnitude scaling factor
+        float    yarn_beta_fast;   // YaRN low correction dim
+        float    yarn_beta_slow;   // YaRN high correction dim
+        uint32_t yarn_orig_ctx;    // YaRN original context size
 
         // Keep the booleans together to avoid misalignment during copy-by-value.
         bool mul_mat_q;  // if true, use experimental mul_mat_q kernels