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-rw-r--r--ggml.c330
1 files changed, 97 insertions, 233 deletions
diff --git a/ggml.c b/ggml.c
index 11e5c34a..1fc77743 100644
--- a/ggml.c
+++ b/ggml.c
@@ -6250,16 +6250,13 @@ static struct ggml_tensor * ggml_rope_impl(
struct ggml_tensor * c,
int n_dims,
int mode,
- int n_ctx,
- int n_orig_ctx,
+ int n_ctx_orig,
float freq_base,
float freq_scale,
float ext_factor,
float attn_factor,
float beta_fast,
float beta_slow,
- float xpos_base,
- bool xpos_down,
bool inplace) {
GGML_ASSERT((mode & 1) == 0 && "mode & 1 == 1 is no longer supported");
@@ -6280,15 +6277,13 @@ static struct ggml_tensor * ggml_rope_impl(
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
- int32_t params[13] = { /*n_past*/ 0, n_dims, mode, n_ctx, n_orig_ctx };
+ int32_t params[11] = { /*n_past*/ 0, n_dims, mode, /*n_ctx*/ 0, n_ctx_orig };
memcpy(params + 5, &freq_base, sizeof(float));
memcpy(params + 6, &freq_scale, sizeof(float));
memcpy(params + 7, &ext_factor, sizeof(float));
memcpy(params + 8, &attn_factor, sizeof(float));
memcpy(params + 9, &beta_fast, sizeof(float));
memcpy(params + 10, &beta_slow, sizeof(float));
- memcpy(params + 11, &xpos_base, sizeof(float));
- memcpy(params + 12, &xpos_down, sizeof(bool));
ggml_set_op_params(result, params, sizeof(params));
result->op = GGML_OP_ROPE;
@@ -6305,10 +6300,9 @@ struct ggml_tensor * ggml_rope(
struct ggml_tensor * a,
struct ggml_tensor * b,
int n_dims,
- int mode,
- int n_ctx) {
+ int mode) {
return ggml_rope_impl(
- ctx, a, b, NULL, n_dims, mode, n_ctx, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, false, false
+ ctx, a, b, NULL, n_dims, mode, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f, false
);
}
@@ -6317,10 +6311,9 @@ struct ggml_tensor * ggml_rope_inplace(
struct ggml_tensor * a,
struct ggml_tensor * b,
int n_dims,
- int mode,
- int n_ctx) {
+ int mode) {
return ggml_rope_impl(
- ctx, a, b, NULL, n_dims, mode, n_ctx, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, false, true
+ ctx, a, b, NULL, n_dims, mode, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f, true
);
}
@@ -6331,8 +6324,7 @@ struct ggml_tensor * ggml_rope_ext(
struct ggml_tensor * c,
int n_dims,
int mode,
- int n_ctx,
- int n_orig_ctx,
+ int n_ctx_orig,
float freq_base,
float freq_scale,
float ext_factor,
@@ -6340,8 +6332,8 @@ struct ggml_tensor * ggml_rope_ext(
float beta_fast,
float beta_slow) {
return ggml_rope_impl(
- ctx, a, b, c, n_dims, mode, n_ctx, n_orig_ctx, freq_base, freq_scale,
- ext_factor, attn_factor, beta_fast, beta_slow, 0.0f, false, false
+ ctx, a, b, c, n_dims, mode, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow, false
);
}
@@ -6352,8 +6344,7 @@ struct ggml_tensor * ggml_rope_ext_inplace(
struct ggml_tensor * c,
int n_dims,
int mode,
- int n_ctx,
- int n_orig_ctx,
+ int n_ctx_orig,
float freq_base,
float freq_scale,
float ext_factor,
@@ -6361,8 +6352,8 @@ struct ggml_tensor * ggml_rope_ext_inplace(
float beta_fast,
float beta_slow) {
return ggml_rope_impl(
- ctx, a, b, c, n_dims, mode, n_ctx, n_orig_ctx, freq_base, freq_scale,
- ext_factor, attn_factor, beta_fast, beta_slow, 0.0f, false, true
+ ctx, a, b, c, n_dims, mode, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow, true
);
}
@@ -6372,8 +6363,7 @@ struct ggml_tensor * ggml_rope_custom(
struct ggml_tensor * b,
int n_dims,
int mode,
- int n_ctx,
- int n_orig_ctx,
+ int n_ctx_orig,
float freq_base,
float freq_scale,
float ext_factor,
@@ -6381,8 +6371,8 @@ struct ggml_tensor * ggml_rope_custom(
float beta_fast,
float beta_slow) {
return ggml_rope_impl(
- ctx, a, b, NULL, n_dims, mode, n_ctx, n_orig_ctx, freq_base, freq_scale,
- ext_factor, attn_factor, beta_fast, beta_slow, 0.0f, false, false
+ ctx, a, b, NULL, n_dims, mode, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow, false
);
}
@@ -6392,8 +6382,7 @@ struct ggml_tensor * ggml_rope_custom_inplace(
struct ggml_tensor * b,
int n_dims,
int mode,
- int n_ctx,
- int n_orig_ctx,
+ int n_ctx_orig,
float freq_base,
float freq_scale,
float ext_factor,
@@ -6401,21 +6390,11 @@ struct ggml_tensor * ggml_rope_custom_inplace(
float beta_fast,
float beta_slow) {
return ggml_rope_impl(
- ctx, a, b, NULL, n_dims, mode, n_ctx, n_orig_ctx, freq_base, freq_scale,
- ext_factor, attn_factor, beta_fast, beta_slow, 0.0f, false, true
+ ctx, a, b, NULL, n_dims, mode, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow, true
);
}
-struct ggml_tensor * ggml_rope_xpos_inplace(
- struct ggml_context * ctx,
- struct ggml_tensor * a,
- struct ggml_tensor * b,
- int n_dims,
- float base,
- bool down) {
- return ggml_rope_impl(ctx, a, b, NULL, n_dims, 0, 0, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f, base, down, true);
-}
-
// ggml_rope_back
struct ggml_tensor * ggml_rope_back(
@@ -6425,16 +6404,13 @@ struct ggml_tensor * ggml_rope_back(
struct ggml_tensor * c,
int n_dims,
int mode,
- int n_ctx,
- int n_orig_ctx,
+ int n_ctx_orig,
float freq_base,
float freq_scale,
float ext_factor,
float attn_factor,
float beta_fast,
- float beta_slow,
- float xpos_base,
- bool xpos_down) {
+ float beta_slow) {
GGML_ASSERT(ggml_is_vector(b));
GGML_ASSERT(b->type == GGML_TYPE_I32);
GGML_ASSERT(a->ne[2] == b->ne[0]);
@@ -6450,15 +6426,13 @@ struct ggml_tensor * ggml_rope_back(
struct ggml_tensor * result = ggml_dup_tensor(ctx, a);
- int32_t params[13] = { /*n_past*/ 0, n_dims, mode, n_ctx, n_orig_ctx };
+ int32_t params[11] = { /*n_past*/ 0, n_dims, mode, /*n_ctx*/ 0, n_ctx_orig };
memcpy(params + 5, &freq_base, sizeof(float));
memcpy(params + 6, &freq_scale, sizeof(float));
memcpy(params + 7, &ext_factor, sizeof(float));
memcpy(params + 8, &attn_factor, sizeof(float));
memcpy(params + 9, &beta_fast, sizeof(float));
memcpy(params + 10, &beta_slow, sizeof(float));
- memcpy(params + 11, &xpos_base, sizeof(float));
- memcpy(params + 12, &xpos_down, sizeof(bool));
ggml_set_op_params(result, params, sizeof(params));
result->op = GGML_OP_ROPE_BACK;
@@ -14227,8 +14201,7 @@ static float rope_yarn_ramp(const float low, const float high, const int i0) {
// MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng.
static void rope_yarn(
float theta_extrap, float freq_scale, float corr_dims[2], int64_t i0, float ext_factor, float mscale,
- float * cos_theta, float * sin_theta
-) {
+ float * cos_theta, float * sin_theta) {
// Get n-d rotational scaling corrected for extrapolation
float theta_interp = freq_scale * theta_extrap;
float theta = theta_interp;
@@ -14245,18 +14218,19 @@ static void rope_yarn(
// Apparently solving `n_rot = 2pi * x * base^((2 * max_pos_emb) / n_dims)` for x, we get
// `corr_dim(n_rot) = n_dims * log(max_pos_emb / (n_rot * 2pi)) / (2 * log(base))`
-static float ggml_rope_yarn_corr_dim(int n_dims, int n_orig_ctx, float n_rot, float base) {
- return n_dims * logf(n_orig_ctx / (n_rot * 2 * (float)M_PI)) / (2 * logf(base));
+static float ggml_rope_yarn_corr_dim(int n_dims, int n_ctx_orig, float n_rot, float base) {
+ return n_dims * logf(n_ctx_orig / (n_rot * 2 * (float)M_PI)) / (2 * logf(base));
}
static void ggml_rope_cache_init(
- float theta_base, float freq_scale, float corr_dims[2], int64_t ne0, float ext_factor, float mscale,
- float * cache, float sin_sign, float theta_scale
-) {
+ float theta_base, float freq_scale, const float * freq_factors, float corr_dims[2], int64_t ne0, float ext_factor, float mscale,
+ float * cache, float sin_sign, float theta_scale) {
+ // ref: https://github.com/jquesnelle/yarn/blob/master/scaled_rope/LlamaYaRNScaledRotaryEmbedding.py
float theta = theta_base;
for (int64_t i0 = 0; i0 < ne0; i0 += 2) {
+ const float ff = freq_factors ? freq_factors[i0/2] : 1.0f;
rope_yarn(
- theta, freq_scale, corr_dims, i0, ext_factor, mscale, &cache[i0 + 0], &cache[i0 + 1]
+ theta/ff, freq_scale, corr_dims, i0, ext_factor, mscale, &cache[i0 + 0], &cache[i0 + 1]
);
cache[i0 + 1] *= sin_sign;
@@ -14265,11 +14239,11 @@ static void ggml_rope_cache_init(
}
GGML_CALL void ggml_rope_yarn_corr_dims(
- int n_dims, int n_orig_ctx, float freq_base, float beta_fast, float beta_slow, float dims[2]
+ int n_dims, int n_ctx_orig, float freq_base, float beta_fast, float beta_slow, float dims[2]
) {
// start and end correction dims
- float start = floorf(ggml_rope_yarn_corr_dim(n_dims, n_orig_ctx, beta_fast, freq_base));
- float end = ceilf(ggml_rope_yarn_corr_dim(n_dims, n_orig_ctx, beta_slow, freq_base));
+ float start = floorf(ggml_rope_yarn_corr_dim(n_dims, n_ctx_orig, beta_fast, freq_base));
+ float end = ceilf(ggml_rope_yarn_corr_dim(n_dims, n_ctx_orig, beta_slow, freq_base));
dims[0] = MAX(0, start);
dims[1] = MIN(n_dims - 1, end);
}
@@ -14289,15 +14263,11 @@ static void ggml_compute_forward_rope_f32(
float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow;
- // these two only relevant for xPos RoPE:
- float xpos_base;
- bool xpos_down;
-
//const int n_past = ((int32_t *) dst->op_params)[0];
const int n_dims = ((int32_t *) dst->op_params)[1];
const int mode = ((int32_t *) dst->op_params)[2];
- const int n_ctx = ((int32_t *) dst->op_params)[3];
- const int n_orig_ctx = ((int32_t *) dst->op_params)[4];
+ //const int n_ctx = ((int32_t *) dst->op_params)[3];
+ const int n_ctx_orig = ((int32_t *) dst->op_params)[4];
memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float));
memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float));
@@ -14305,8 +14275,6 @@ static void ggml_compute_forward_rope_f32(
memcpy(&attn_factor, (int32_t *) dst->op_params + 8, sizeof(float));
memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float));
memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float));
- memcpy(&xpos_base, (int32_t *) dst->op_params + 11, sizeof(float));
- memcpy(&xpos_down, (int32_t *) dst->op_params + 12, sizeof(bool));
GGML_TENSOR_UNARY_OP_LOCALS
@@ -14336,20 +14304,15 @@ static void ggml_compute_forward_rope_f32(
const float theta_scale = powf(freq_base, -2.0f/n_dims);
float corr_dims[2];
- ggml_rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims);
+ ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims);
const bool is_neox = mode & 2;
- const bool is_glm = mode & 4;
const float * freq_factors = NULL;
- if (is_neox) {
- if (src2 != NULL) {
- GGML_ASSERT(src2->type == GGML_TYPE_F32);
- GGML_ASSERT(src2->ne[0] >= n_dims / 2);
- freq_factors = (const float *) src2->data;
- }
- } else {
- GGML_ASSERT(src2 == NULL && "TODO: freq_factors not implemented for !is_neox");
+ if (src2 != NULL) {
+ GGML_ASSERT(src2->type == GGML_TYPE_F32);
+ GGML_ASSERT(src2->ne[0] >= n_dims / 2);
+ freq_factors = (const float *) src2->data;
}
// backward process uses inverse rotation by cos and sin.
@@ -14364,94 +14327,50 @@ static void ggml_compute_forward_rope_f32(
const int64_t p = pos[i2];
float * cache = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32)*ith;
- if (!is_glm && !is_neox) { // TODO: cache sin/cos for glm, neox
- ggml_rope_cache_init(p, freq_scale, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale);
- }
+ ggml_rope_cache_init(p, freq_scale, freq_factors, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale);
for (int64_t i1 = 0; i1 < ne1; i1++) {
if (ir++ < ir0) continue;
if (ir > ir1) break;
- float theta_base = (float)p;
-
- if (is_glm) {
- theta_base = MIN(p, n_ctx - 2);
- float block_theta = MAX(p - (n_ctx - 2), 0);
- for (int64_t i0 = 0; i0 < ne0 / 4; i0++) {
- const float cos_theta = cosf(theta_base);
- const float sin_theta = sinf(theta_base) * sin_sign;
- const float cos_block_theta = cosf(block_theta);
- const float sin_block_theta = sinf(block_theta) * sin_sign;
-
- theta_base *= theta_scale;
- block_theta *= theta_scale;
-
- const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
- float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
-
- const float x0 = src[0];
- const float x1 = src[n_dims/2];
- const float x2 = src[n_dims];
- const float x3 = src[n_dims/2*3];
-
- dst_data[0] = x0*cos_theta - x1*sin_theta;
- dst_data[n_dims/2] = x0*sin_theta + x1*cos_theta;
- dst_data[n_dims] = x2*cos_block_theta - x3*sin_block_theta;
- dst_data[n_dims/2*3] = x2*sin_block_theta + x3*cos_block_theta;
- }
- } else if (!is_neox) {
- for (int64_t i0 = 0; i0 < ne0; i0 += 2) {
+ if (!is_neox) {
+ for (int64_t i0 = 0; i0 < n_dims; i0 += 2) {
const float cos_theta = cache[i0 + 0];
const float sin_theta = cache[i0 + 1];
- // zeta scaling for xPos only:
- float zeta = xpos_base != 0.0f ? powf((i0 + 0.4f * ne0) / (1.4f * ne0), p / xpos_base) : 1.0f;
- if (xpos_down) zeta = 1.0f / zeta;
-
const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
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];
- dst_data[0] = x0*cos_theta*zeta - x1*sin_theta*zeta;
- dst_data[1] = x0*sin_theta*zeta + x1*cos_theta*zeta;
+ dst_data[0] = x0*cos_theta - x1*sin_theta;
+ dst_data[1] = x0*sin_theta + x1*cos_theta;
}
} else {
- // ref: https://github.com/jquesnelle/yarn/blob/master/scaled_rope/LlamaYaRNScaledRotaryEmbedding.py
- for (int64_t ic = 0; ic < ne0; ic += 2) {
- if (ic < n_dims) {
- const int64_t i0 = ic/2;
-
- const float freq_factor = freq_factors ? freq_factors[i0] : 1.0f;
-
- float cos_theta, sin_theta;
- rope_yarn(
- theta_base/freq_factor, freq_scale, corr_dims, ic, ext_factor, attn_factor,
- &cos_theta, &sin_theta
- );
+ for (int64_t i0 = 0; i0 < n_dims; i0 += 2) {
+ const int64_t ic = i0/2;
- sin_theta *= sin_sign;
- theta_base *= theta_scale;
+ const float cos_theta = cache[i0 + 0];
+ const float sin_theta = cache[i0 + 1];
- const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
- float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
+ const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00);
+ float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + ic*nb0);
- const float x0 = src[0];
- const float x1 = src[n_dims/2];
+ 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;
- } else {
- const int64_t i0 = ic;
+ dst_data[0] = x0*cos_theta - x1*sin_theta;
+ dst_data[n_dims/2] = x0*sin_theta + x1*cos_theta;
+ }
+ }
- const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
- float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
+ for (int64_t i0 = n_dims; i0 < ne0; i0 += 2) {
+ const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
+ float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
- dst_data[0] = src[0];
- dst_data[1] = src[1];
- }
- }
+ dst_data[0] = src[0];
+ dst_data[1] = src[1];
}
}
}
@@ -14477,8 +14396,8 @@ static void ggml_compute_forward_rope_f16(
//const int n_past = ((int32_t *) dst->op_params)[0];
const int n_dims = ((int32_t *) dst->op_params)[1];
const int mode = ((int32_t *) dst->op_params)[2];
- const int n_ctx = ((int32_t *) dst->op_params)[3];
- const int n_orig_ctx = ((int32_t *) dst->op_params)[4];
+ //const int n_ctx = ((int32_t *) dst->op_params)[3];
+ const int n_ctx_orig = ((int32_t *) dst->op_params)[4];
memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float));
memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float));
memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float));
@@ -14514,20 +14433,15 @@ static void ggml_compute_forward_rope_f16(
const float theta_scale = powf(freq_base, -2.0f/n_dims);
float corr_dims[2];
- ggml_rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims);
+ ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims);
const bool is_neox = mode & 2;
- const bool is_glm = mode & 4;
const float * freq_factors = NULL;
- if (is_neox) {
- if (src2 != NULL) {
- GGML_ASSERT(src2->type == GGML_TYPE_F32);
- GGML_ASSERT(src2->ne[0] >= n_dims / 2);
- freq_factors = (const float *) src2->data;
- }
- } else {
- GGML_ASSERT(src2 == NULL && "TODO: freq_factors not implemented for !is_neox");
+ if (src2 != NULL) {
+ GGML_ASSERT(src2->type == GGML_TYPE_F32);
+ GGML_ASSERT(src2->ne[0] >= n_dims / 2);
+ freq_factors = (const float *) src2->data;
}
// backward process uses inverse rotation by cos and sin.
@@ -14542,43 +14456,14 @@ static void ggml_compute_forward_rope_f16(
const int64_t p = pos[i2];
float * cache = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32)*ith;
- if (!is_glm && !is_neox) { // TODO: cache sin/cos for glm, neox
- ggml_rope_cache_init(p, freq_scale, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale);
- }
+ ggml_rope_cache_init(p, freq_scale, freq_factors, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale);
for (int64_t i1 = 0; i1 < ne1; i1++) {
if (ir++ < ir0) continue;
if (ir > ir1) break;
- float theta_base = (float)p;
-
- if (is_glm) {
- theta_base = MIN(p, n_ctx - 2);
- float block_theta = MAX(p - (n_ctx - 2), 0);
- for (int64_t i0 = 0; i0 < ne0 / 4; i0++) {
- const float cos_theta = cosf(theta_base);
- const float sin_theta = sinf(theta_base) * sin_sign;
- const float cos_block_theta = cosf(block_theta);
- const float sin_block_theta = sinf(block_theta) * sin_sign;
-
- theta_base *= theta_scale;
- block_theta *= theta_scale;
-
- const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
- 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[n_dims/2]);
- const float x2 = GGML_FP16_TO_FP32(src[n_dims]);
- const float x3 = GGML_FP16_TO_FP32(src[n_dims/2*3]);
-
- 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);
- dst_data[n_dims] = GGML_FP32_TO_FP16(x2*cos_block_theta - x3*sin_block_theta);
- dst_data[n_dims/2*3] = GGML_FP32_TO_FP16(x2*sin_block_theta + x3*cos_block_theta);
- }
- } else if (!is_neox) {
- for (int64_t i0 = 0; i0 < ne0; i0 += 2) {
+ if (!is_neox) {
+ for (int64_t i0 = 0; i0 < n_dims; i0 += 2) {
const float cos_theta = cache[i0 + 0];
const float sin_theta = cache[i0 + 1];
@@ -14592,40 +14477,29 @@ static void ggml_compute_forward_rope_f16(
dst_data[1] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta);
}
} else {
- // ref: https://github.com/jquesnelle/yarn/blob/master/scaled_rope/LlamaYaRNScaledRotaryEmbedding.py
- for (int64_t ic = 0; ic < ne0; ic += 2) {
- if (ic < n_dims) {
- const int64_t i0 = ic/2;
-
- const float freq_factor = freq_factors ? freq_factors[i0] : 1.0f;
+ for (int64_t i0 = 0; i0 < n_dims; i0 += 2) {
+ const int64_t ic = i0/2;
- float cos_theta, sin_theta;
- rope_yarn(
- theta_base/freq_factor, freq_scale, corr_dims, ic, ext_factor, attn_factor,
- &cos_theta, &sin_theta
- );
-
- sin_theta *= sin_sign;
- theta_base *= theta_scale;
+ const float cos_theta = cache[i0 + 0];
+ const float sin_theta = cache[i0 + 1];
- const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
- ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
+ const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00);
+ ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + ic*nb0);
- const float x0 = GGML_FP16_TO_FP32(src[0]);
- const float x1 = GGML_FP16_TO_FP32(src[n_dims/2]);
+ 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);
- } else {
- const int64_t i0 = ic;
+ 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);
+ }
+ }
- const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
- ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
+ for (int64_t i0 = n_dims; i0 < ne0; i0 += 2) {
+ const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
+ ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
- dst_data[0] = src[0];
- dst_data[1] = src[1];
- }
- }
+ dst_data[0] = src[0];
+ dst_data[1] = src[1];
}
}
}
@@ -18327,9 +18201,9 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
//const int n_past = ((int32_t *) tensor->op_params)[0];
const int n_dims = ((int32_t *) tensor->op_params)[1];
const int mode = ((int32_t *) tensor->op_params)[2];
- const int n_ctx = ((int32_t *) tensor->op_params)[3];
- const int n_orig_ctx = ((int32_t *) tensor->op_params)[4];
- float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow, xpos_base, xpos_down;
+ //const int n_ctx = ((int32_t *) tensor->op_params)[3];
+ const int n_ctx_orig = ((int32_t *) tensor->op_params)[4];
+ float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow;
memcpy(&freq_base, (int32_t *) tensor->op_params + 5, sizeof(float));
memcpy(&freq_scale, (int32_t *) tensor->op_params + 6, sizeof(float));
@@ -18337,8 +18211,6 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
memcpy(&attn_factor, (int32_t *) tensor->op_params + 8, sizeof(float));
memcpy(&beta_fast, (int32_t *) tensor->op_params + 9, sizeof(float));
memcpy(&beta_slow, (int32_t *) tensor->op_params + 10, sizeof(float));
- memcpy(&xpos_base, (int32_t *) tensor->op_params + 11, sizeof(float));
- memcpy(&xpos_down, (int32_t *) tensor->op_params + 12, sizeof(bool));
src0->grad = ggml_add_or_set(ctx,
src0->grad,
@@ -18348,16 +18220,13 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
src2,
n_dims,
mode,
- n_ctx,
- n_orig_ctx,
+ n_ctx_orig,
freq_base,
freq_scale,
ext_factor,
attn_factor,
beta_fast,
- beta_slow,
- xpos_base,
- xpos_down),
+ beta_slow),
zero_table);
}
} break;
@@ -18367,9 +18236,9 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
//const int n_past = ((int32_t *) tensor->op_params)[0];
const int n_dims = ((int32_t *) tensor->op_params)[1];
const int mode = ((int32_t *) tensor->op_params)[2];
- const int n_ctx = ((int32_t *) tensor->op_params)[3];
- const int n_orig_ctx = ((int32_t *) tensor->op_params)[4];
- float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow, xpos_base, xpos_down;
+ //const int n_ctx = ((int32_t *) tensor->op_params)[3];
+ const int n_ctx_orig = ((int32_t *) tensor->op_params)[4];
+ float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow;
memcpy(&freq_base, (int32_t *) tensor->op_params + 5, sizeof(float));
memcpy(&freq_scale, (int32_t *) tensor->op_params + 6, sizeof(float));
@@ -18377,8 +18246,6 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
memcpy(&attn_factor, (int32_t *) tensor->op_params + 8, sizeof(float));
memcpy(&beta_fast, (int32_t *) tensor->op_params + 9, sizeof(float));
memcpy(&beta_slow, (int32_t *) tensor->op_params + 10, sizeof(float));
- memcpy(&xpos_base, (int32_t *) tensor->op_params + 11, sizeof(float));
- memcpy(&xpos_down, (int32_t *) tensor->op_params + 12, sizeof(bool));
src0->grad = ggml_add_or_set(ctx,
src0->grad,
@@ -18388,16 +18255,13 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
src2,
n_dims,
mode,
- n_ctx,
- n_orig_ctx,
+ n_ctx_orig,
freq_base,
freq_scale,
ext_factor,
attn_factor,
beta_fast,
beta_slow,
- xpos_base,
- xpos_down,
false),
zero_table);
}
generated by cgit v1.2.3 (git 2.25.1) at 2025-10-04 23:38:56 +0000