diff options
Diffstat (limited to 'ggml.c')
| -rw-r--r-- | ggml.c | 50 |
1 files changed, 38 insertions, 12 deletions
@@ -6956,6 +6956,8 @@ struct ggml_tensor * ggml_rope_impl( int n_past, int n_dims, int mode, + float freq_base, + float freq_scale, int n_ctx, bool inplace) { GGML_ASSERT(n_past >= 0); @@ -6969,12 +6971,14 @@ struct ggml_tensor * ggml_rope_impl( ggml_scratch_save(ctx); - struct ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 4); + struct ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 6); ((int32_t *) b->data)[0] = n_past; ((int32_t *) b->data)[1] = n_dims; ((int32_t *) b->data)[2] = mode; ((int32_t *) b->data)[3] = n_ctx; + memcpy((int32_t *) b->data + 4, &freq_base, sizeof(float)); + memcpy((int32_t *) b->data + 5, &freq_scale, sizeof(float)); ggml_scratch_load(ctx); @@ -6993,7 +6997,7 @@ struct ggml_tensor * ggml_rope( int n_dims, int mode, int n_ctx) { - return ggml_rope_impl(ctx, a, n_past, n_dims, mode, n_ctx, false); + return ggml_rope_impl(ctx, a, n_past, n_dims, mode, 10000.0f, 1.0f, n_ctx, false); } struct ggml_tensor * ggml_rope_inplace( @@ -7003,7 +7007,19 @@ struct ggml_tensor * ggml_rope_inplace( int n_dims, int mode, int n_ctx) { - return ggml_rope_impl(ctx, a, n_past, n_dims, mode, n_ctx, true); + return ggml_rope_impl(ctx, a, n_past, n_dims, mode, 10000.0f, 1.0f, n_ctx, true); +} + +struct ggml_tensor * ggml_rope_custom_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a, + int n_past, + int n_dims, + int mode, + float freq_base, + float freq_scale, + int n_ctx) { + return ggml_rope_impl(ctx, a, n_past, n_dims, mode, freq_base, freq_scale, n_ctx, true); } // ggml_rope_back @@ -12074,16 +12090,21 @@ static void ggml_compute_forward_rope_f32( const struct ggml_tensor * src1, struct ggml_tensor * dst) { GGML_ASSERT(src1->type == GGML_TYPE_I32); - GGML_ASSERT(ggml_nelements(src1) == 4); + GGML_ASSERT(ggml_nelements(src1) == 6); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } + float freq_base; + float freq_scale; + const int n_past = ((int32_t *) src1->data)[0]; const int n_dims = ((int32_t *) src1->data)[1]; const int mode = ((int32_t *) src1->data)[2]; const int n_ctx = ((int32_t *) src1->data)[3]; + memcpy(&freq_base, (int32_t *) src1->data + 4, sizeof(float)); + memcpy(&freq_scale, (int32_t *) src1->data + 5, sizeof(float)); assert(n_past >= 0); @@ -12112,7 +12133,7 @@ static void ggml_compute_forward_rope_f32( // row index used to determine which thread to use int ir = 0; - const float theta_scale = powf(10000.0, -2.0f/n_dims); + const float theta_scale = powf(freq_base, -2.0f/n_dims); const bool is_neox = mode & 2; const bool is_glm = mode & 4; @@ -12124,7 +12145,7 @@ static void ggml_compute_forward_rope_f32( if (ir++ < ir0) continue; if (ir > ir1) break; - float theta = (float)p; + float theta = freq_scale * (float)p; if (is_glm) { theta = MIN(p, n_ctx - 2); @@ -12201,16 +12222,21 @@ static void ggml_compute_forward_rope_f16( const struct ggml_tensor * src1, struct ggml_tensor * dst) { GGML_ASSERT(src1->type == GGML_TYPE_I32); - GGML_ASSERT(ggml_nelements(src1) == 4); + GGML_ASSERT(ggml_nelements(src1) == 6); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } + float freq_base; + float freq_scale; + const int n_past = ((int32_t *) src1->data)[0]; const int n_dims = ((int32_t *) src1->data)[1]; const int mode = ((int32_t *) src1->data)[2]; const int n_ctx = ((int32_t *) src1->data)[3]; + memcpy(&freq_base, (int32_t *) src1->data + 4, sizeof(float)); + memcpy(&freq_scale, (int32_t *) src1->data + 5, sizeof(float)); assert(n_past >= 0); @@ -12239,7 +12265,7 @@ static void ggml_compute_forward_rope_f16( // row index used to determine which thread to use int ir = 0; - const float theta_scale = powf(10000.0, -2.0f/n_dims); + const float theta_scale = powf(freq_base, -2.0f/n_dims); const bool is_neox = mode & 2; const bool is_glm = mode & 4; @@ -12251,7 +12277,7 @@ static void ggml_compute_forward_rope_f16( if (ir++ < ir0) continue; if (ir > ir1) break; - float theta = (float)p; + float theta = freq_scale * (float)p; if (is_glm) { theta = MIN(p, n_ctx - 2); @@ -12312,7 +12338,7 @@ static void ggml_compute_forward_rope_f16( 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[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); } } @@ -15710,7 +15736,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor // necessary for llama if (src0->grad) { assert(src1->type == GGML_TYPE_I32); - assert(ggml_nelements(src1) == 4); + assert(ggml_nelements(src1) == 6); const int n_past = ((int32_t *) src1->data)[0]; const int n_dims = ((int32_t *) src1->data)[1]; const int mode = ((int32_t *) src1->data)[2]; @@ -15731,7 +15757,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor { if (src0->grad) { assert(src1->type == GGML_TYPE_I32); - assert(ggml_nelements(src1) == 4); + assert(ggml_nelements(src1) == 3); const int n_past = ((int32_t *) src1->data)[0]; const int n_dims = ((int32_t *) src1->data)[1]; const int mode = ((int32_t *) src1->data)[2]; |