ggml : sync ggml (add GPT-NeoX RoPE implementation)

ggml.c

@@ -8653,9 +8653,11 @@ static void ggml_compute_forward_rope_f32(
 
     const float theta_scale = powf(10000.0, -2.0f/n_dims);
 
+    const bool is_neox = mode & 2;
+
     for (int64_t i3 = 0; i3 < ne3; i3++) {
-        for (int64_t i2 = (mode == 0 ? 0 : n_past); i2 < ne2; i2++) {
-            const int p = (mode == 0 ? n_past + i2 : i2);
+        for (int64_t i2 = ((mode & 1) == 0 ? 0 : n_past); i2 < ne2; i2++) {
+            const int p = ((mode & 1) == 0 ? n_past + i2 : i2);
             for (int64_t i1 = 0; i1 < ne1; i1++) {
                 if (ir++ < ir0) continue;
                 if (ir   > ir1) break;
@@ -8668,14 +8670,25 @@ static void ggml_compute_forward_rope_f32(
 
                     theta *= theta_scale;
 
-                    const float * const src = (float *)((char *) src0->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
-                          float * dst_data  = (float *)((char *)  dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
+                    if (!is_neox) {
+                        const float * const src = (float *)((char *) src0->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
+                              float * dst_data  = (float *)((char *)  dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
+
+                        const float x0 = src[0];
+                        const float x1 = src[1];
 
-                    const float x0 = src[0];
-                    const float x1 = src[1];
+                        dst_data[0] = x0*cos_theta - x1*sin_theta;
+                        dst_data[1] = x0*sin_theta + x1*cos_theta;
+                    } else {
+                        const float * const src = (float *)((char *) src0->data + i3*nb3 + i2*nb2 + i1*nb1 + (i0/2)*nb0);
+                              float * dst_data  = (float *)((char *)  dst->data + i3*nb3 + i2*nb2 + i1*nb1 + (i0/2)*nb0);
 
-                    dst_data[0] = x0*cos_theta - x1*sin_theta;
-                    dst_data[1] = x0*sin_theta + x1*cos_theta;
+                        const float x0 = src[0];
+                        const float x1 = src[n_dims/2];
+
+                        dst_data[0]        = x0*cos_theta - x1*sin_theta;
+                        dst_data[n_dims/2] = x0*sin_theta + x1*cos_theta;
+                    }
                 }
             }
         }
@@ -8730,9 +8743,11 @@ static void ggml_compute_forward_rope_f16(
 
     const float theta_scale = powf(10000.0, -2.0f/n_dims);
 
+    const bool is_neox = mode & 2;
+
     for (int64_t i3 = 0; i3 < ne3; i3++) {
-        for (int64_t i2 = (mode == 0 ? 0 : n_past); i2 < ne2; i2++) {
-            const int p = (mode == 0 ? n_past + i2 : i2);
+        for (int64_t i2 = ((mode & 1) == 0 ? 0 : n_past); i2 < ne2; i2++) {
+            const int p = ((mode & 1) == 0 ? n_past + i2 : i2);
             for (int64_t i1 = 0; i1 < ne1; i1++) {
                 if (ir++ < ir0) continue;
                 if (ir   > ir1) break;
@@ -8745,14 +8760,25 @@ static void ggml_compute_forward_rope_f16(
 
                     theta *= theta_scale;
 
-                    const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
-                          ggml_fp16_t * dst_data  = (ggml_fp16_t *)((char *)  dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
+                    if (!is_neox) {
+                        const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
+                              ggml_fp16_t * dst_data  = (ggml_fp16_t *)((char *)  dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
+
+                        const float x0 = GGML_FP16_TO_FP32(src[0]);
+                        const float x1 = GGML_FP16_TO_FP32(src[1]);
 
-                    const float x0 = GGML_FP16_TO_FP32(src[0]);
-                    const float x1 = GGML_FP16_TO_FP32(src[1]);
+                        dst_data[0] = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta);
+                        dst_data[1] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta);
+                    } else {
+                        const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb3 + i2*nb2 + i1*nb1 + (i0/2)*nb0);
+                              ggml_fp16_t * dst_data  = (ggml_fp16_t *)((char *)  dst->data + i3*nb3 + i2*nb2 + i1*nb1 + (i0/2)*nb0);
 
-                    dst_data[0] = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta);
-                    dst_data[1] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta);
+                        const float x0 = GGML_FP16_TO_FP32(src[0]);
+                        const float x1 = GGML_FP16_TO_FP32(src[n_dims/2]);
+
+                        dst_data[0]        = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta);
+                        dst_data[n_dims/2] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta);
+                    }
                 }
             }
         }

ggml.h

@@ -630,7 +630,8 @@ struct ggml_tensor * ggml_soft_max(
 
 // rotary position embedding
 // in-place, returns view(a)
-// if mode == 1, skip n_past elements
+// if mode & 1 == 1, skip n_past elements
+// if mode & 2 == 1, GPT-NeoX style
 // TODO: avoid creating a new tensor every time
 struct ggml_tensor * ggml_rope(
         struct ggml_context * ctx,

llama.cpp

@@ -1618,6 +1618,11 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
         // quantize only 2D tensors
         quantize &= (tensor.ne.size() == 2);
 
+        // GG: uncomment this to keep the output layer in FP16
+        //if (tensor.name.rfind("output")) {
+        //    quantize = false;
+        //}
+
         enum ggml_type new_type;
         void * new_data;
         size_t new_size;