ggml : ggml_rope now takes a vector with positions instead of n_past
This commit is contained in:
Georgi Gerganov
parent
3b4bab6a38
commit
1fb033fd85
9 changed files with 270 additions and 131 deletions
113
ggml.c
113
ggml.c
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@ -6968,7 +6968,7 @@ struct ggml_tensor * ggml_soft_max_back_inplace(
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static struct ggml_tensor * ggml_rope_impl(
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struct ggml_context * ctx,
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struct ggml_tensor * a,
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int n_past,
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struct ggml_tensor * b,
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int n_dims,
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int mode,
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int n_ctx,
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@ -6977,6 +6977,10 @@ static struct ggml_tensor * ggml_rope_impl(
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float xpos_base,
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bool xpos_down,
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bool inplace) {
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GGML_ASSERT(ggml_is_vector(b));
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GGML_ASSERT(b->type == GGML_TYPE_I32);
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GGML_ASSERT(a->ne[2] == b->ne[0]);
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bool is_node = false;
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if (a->grad) {
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@ -6985,7 +6989,7 @@ static struct ggml_tensor * ggml_rope_impl(
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struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
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int32_t params[8] = { n_past, n_dims, mode, n_ctx };
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int32_t params[8] = { /*n_past*/ 0, n_dims, mode, n_ctx };
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memcpy(params + 4, &freq_base, sizeof(float));
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memcpy(params + 5, &freq_scale, sizeof(float));
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memcpy(params + 6, &xpos_base, sizeof(float));
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@ -6995,6 +6999,7 @@ static struct ggml_tensor * ggml_rope_impl(
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result->op = GGML_OP_ROPE;
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result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
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result->src[0] = a;
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result->src[1] = b;
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return result;
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}
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@ -7002,55 +7007,55 @@ static struct ggml_tensor * ggml_rope_impl(
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struct ggml_tensor * ggml_rope(
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struct ggml_context * ctx,
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struct ggml_tensor * a,
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int n_past,
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struct ggml_tensor * b,
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int n_dims,
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int mode,
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int n_ctx) {
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return ggml_rope_impl(ctx, a, n_past, n_dims, mode, n_ctx, 10000.0f, 1.0f, 0.0f, false, false);
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return ggml_rope_impl(ctx, a, b, n_dims, mode, n_ctx, 10000.0f, 1.0f, 0.0f, false, false);
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}
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struct ggml_tensor * ggml_rope_inplace(
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struct ggml_context * ctx,
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struct ggml_tensor * a,
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int n_past,
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struct ggml_tensor * b,
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int n_dims,
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int mode,
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int n_ctx) {
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return ggml_rope_impl(ctx, a, n_past, n_dims, mode, n_ctx, 10000.0f, 1.0f, 0.0f, false, true);
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return ggml_rope_impl(ctx, a, b, n_dims, mode, n_ctx, 10000.0f, 1.0f, 0.0f, false, true);
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}
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struct ggml_tensor * ggml_rope_custom(
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struct ggml_context * ctx,
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struct ggml_tensor * a,
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int n_past,
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struct ggml_tensor * b,
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int n_dims,
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int mode,
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int n_ctx,
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float freq_base,
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float freq_scale) {
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return ggml_rope_impl(ctx, a, n_past, n_dims, mode, n_ctx, freq_base, freq_scale, 0.0f, false, false);
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return ggml_rope_impl(ctx, a, b, n_dims, mode, n_ctx, freq_base, freq_scale, 0.0f, false, false);
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}
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struct ggml_tensor * ggml_rope_custom_inplace(
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struct ggml_context * ctx,
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struct ggml_tensor * a,
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int n_past,
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struct ggml_tensor * b,
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int n_dims,
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int mode,
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int n_ctx,
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float freq_base,
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float freq_scale) {
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return ggml_rope_impl(ctx, a, n_past, n_dims, mode, n_ctx, freq_base, freq_scale, 0.0f, false, true);
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return ggml_rope_impl(ctx, a, b, n_dims, mode, n_ctx, freq_base, freq_scale, 0.0f, false, true);
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}
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struct ggml_tensor * ggml_rope_xpos_inplace(
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struct ggml_context * ctx,
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struct ggml_tensor * a,
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int n_past,
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struct ggml_tensor * b,
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int n_dims,
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float base,
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bool down) {
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return ggml_rope_impl(ctx, a, n_past, n_dims, 0, 0, 10000.0f, 1.0f, base, down, true);
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return ggml_rope_impl(ctx, a, b, n_dims, 0, 0, 10000.0f, 1.0f, base, down, true);
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}
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// ggml_rope_back
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@ -7058,7 +7063,7 @@ struct ggml_tensor * ggml_rope_xpos_inplace(
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struct ggml_tensor * ggml_rope_back(
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struct ggml_context * ctx,
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struct ggml_tensor * a,
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int n_past,
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struct ggml_tensor * b,
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int n_dims,
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int mode,
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int n_ctx,
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@ -7066,7 +7071,10 @@ struct ggml_tensor * ggml_rope_back(
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float freq_scale,
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float xpos_base,
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bool xpos_down) {
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GGML_ASSERT(n_past >= 0);
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GGML_ASSERT(ggml_is_vector(b));
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GGML_ASSERT(b->type == GGML_TYPE_I32);
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GGML_ASSERT(a->ne[2] == b->ne[0]);
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GGML_ASSERT((mode & 4) == 0 && "ggml_rope_back() for ChatGLM not implemented yet");
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bool is_node = false;
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@ -7077,7 +7085,7 @@ struct ggml_tensor * ggml_rope_back(
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struct ggml_tensor * result = ggml_dup_tensor(ctx, a);
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int32_t params[8] = { n_past, n_dims, mode, n_ctx };
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int32_t params[8] = { /*n_past*/ 0, n_dims, mode, n_ctx };
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memcpy(params + 4, &freq_base, sizeof(float));
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memcpy(params + 5, &freq_scale, sizeof(float));
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memcpy(params + 6, &xpos_base, sizeof(float));
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@ -7087,6 +7095,7 @@ struct ggml_tensor * ggml_rope_back(
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result->op = GGML_OP_ROPE_BACK;
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result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
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result->src[0] = a;
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result->src[1] = b;
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return result;
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}
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@ -12620,8 +12629,8 @@ static void ggml_compute_forward_clamp(
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static void ggml_compute_forward_rope_f32(
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const struct ggml_compute_params * params,
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const struct ggml_tensor * src0,
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const struct ggml_tensor * src1,
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struct ggml_tensor * dst) {
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if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
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return;
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}
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@ -12631,9 +12640,9 @@ static void ggml_compute_forward_rope_f32(
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// these two only relevant for xPos RoPE:
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float xpos_base;
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bool xpos_down;
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bool xpos_down;
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const int n_past = ((int32_t *) dst->op_params)[0];
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//const int n_past = ((int32_t *) dst->op_params)[0];
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const int n_dims = ((int32_t *) dst->op_params)[1];
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const int mode = ((int32_t *) dst->op_params)[2];
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const int n_ctx = ((int32_t *) dst->op_params)[3];
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@ -12669,14 +12678,14 @@ static void ggml_compute_forward_rope_f32(
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const float theta_scale = powf(freq_base, -2.0f/n_dims);
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const bool is_skip = mode & 1;
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const bool is_neox = mode & 2;
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const bool is_glm = mode & 4;
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const bool is_diff = mode & 8; // TODO: temporary
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const int32_t * pos = (const int32_t *) src1->data;
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for (int64_t i3 = 0; i3 < ne3; i3++) {
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for (int64_t i2 = (is_skip ? n_past : 0); i2 < ne2; i2++) {
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const int64_t p = is_diff ? n_past : is_skip ? i2 : n_past + i2;
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for (int64_t i2 = 0; i2 < ne2; i2++) {
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const int64_t p = pos[i2];
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for (int64_t i1 = 0; i1 < ne1; i1++) {
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if (ir++ < ir0) continue;
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if (ir > ir1) break;
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@ -12713,7 +12722,7 @@ static void ggml_compute_forward_rope_f32(
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const float cos_theta = cosf(theta);
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const float sin_theta = sinf(theta);
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// zeta scaling for xPos only:
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float zeta = xpos_base != 0.0f ? powf((i0 + 0.4f * ne0) / (1.4f * ne0), (n_past + i2) / xpos_base) : 1.0f;
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float zeta = xpos_base != 0.0f ? powf((i0 + 0.4f * ne0) / (1.4f * ne0), p / xpos_base) : 1.0f;
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if (xpos_down) zeta = 1.0f / zeta;
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theta *= theta_scale;
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@ -12758,8 +12767,8 @@ static void ggml_compute_forward_rope_f32(
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static void ggml_compute_forward_rope_f16(
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const struct ggml_compute_params * params,
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const struct ggml_tensor * src0,
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const struct ggml_tensor * src1,
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struct ggml_tensor * dst) {
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if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
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return;
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}
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@ -12767,15 +12776,13 @@ static void ggml_compute_forward_rope_f16(
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float freq_base;
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float freq_scale;
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const int n_past = ((int32_t *) dst->op_params)[0];
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//const int n_past = ((int32_t *) dst->op_params)[0];
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const int n_dims = ((int32_t *) dst->op_params)[1];
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const int mode = ((int32_t *) dst->op_params)[2];
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const int n_ctx = ((int32_t *) dst->op_params)[3];
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memcpy(&freq_base, (int32_t *) dst->op_params + 4, sizeof(float));
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memcpy(&freq_scale, (int32_t *) dst->op_params + 5, sizeof(float));
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assert(n_past >= 0);
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GGML_TENSOR_UNARY_OP_LOCALS;
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//printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3);
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@ -12806,9 +12813,11 @@ static void ggml_compute_forward_rope_f16(
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const bool is_neox = mode & 2;
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const bool is_glm = mode & 4;
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const int32_t * pos = (const int32_t *) src1->data;
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for (int64_t i3 = 0; i3 < ne3; i3++) {
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for (int64_t i2 = ((mode & 1) == 0 ? 0 : n_past); i2 < ne2; i2++) {
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const int64_t p = ((mode & 1) == 0 ? n_past + i2 : i2);
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for (int64_t i2 = 0; i2 < ne2; i2++) {
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const int64_t p = pos[i2];
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for (int64_t i1 = 0; i1 < ne1; i1++) {
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if (ir++ < ir0) continue;
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if (ir > ir1) break;
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@ -12887,15 +12896,16 @@ static void ggml_compute_forward_rope_f16(
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static void ggml_compute_forward_rope(
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const struct ggml_compute_params * params,
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const struct ggml_tensor * src0,
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const struct ggml_tensor * src1,
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struct ggml_tensor * dst) {
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switch (src0->type) {
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case GGML_TYPE_F16:
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{
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ggml_compute_forward_rope_f16(params, src0, dst);
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ggml_compute_forward_rope_f16(params, src0, src1, dst);
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} break;
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case GGML_TYPE_F32:
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{
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ggml_compute_forward_rope_f32(params, src0, dst);
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ggml_compute_forward_rope_f32(params, src0, src1, dst);
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} break;
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default:
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{
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@ -12909,6 +12919,7 @@ static void ggml_compute_forward_rope(
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static void ggml_compute_forward_rope_back_f32(
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const struct ggml_compute_params * params,
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const struct ggml_tensor * src0,
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const struct ggml_tensor * src1,
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struct ggml_tensor * dst) {
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if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
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@ -12926,7 +12937,7 @@ static void ggml_compute_forward_rope_back_f32(
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float xpos_base;
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bool xpos_down;
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const int n_past = ((int32_t *) dst->op_params)[0];
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//const int n_past = ((int32_t *) dst->op_params)[0];
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const int n_dims = ((int32_t *) dst->op_params)[1];
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const int mode = ((int32_t *) dst->op_params)[2];
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const int n_ctx = ((int32_t *) dst->op_params)[3]; UNUSED(n_ctx);
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@ -12935,8 +12946,6 @@ static void ggml_compute_forward_rope_back_f32(
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memcpy(&xpos_base, (int32_t *) dst->op_params + 6, sizeof(float));
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memcpy(&xpos_down, (int32_t *) dst->op_params + 7, sizeof(bool));
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assert(n_past >= 0);
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GGML_TENSOR_UNARY_OP_LOCALS;
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//printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3);
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@ -12963,9 +12972,11 @@ static void ggml_compute_forward_rope_back_f32(
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const bool is_neox = mode & 2;
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const int32_t * pos = (const int32_t *) src1->data;
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for (int64_t i3 = 0; i3 < ne3; i3++) {
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for (int64_t i2 = ((mode & 1) == 0 ? 0 : n_past); i2 < ne2; i2++) {
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const int64_t p = ((mode & 1) == 0 ? n_past + i2 : i2);
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for (int64_t i2 = 0; i2 < ne2; i2++) {
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const int64_t p = pos[i2];
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for (int64_t i1 = 0; i1 < ne1; i1++) {
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if (ir++ < ir0) continue;
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if (ir > ir1) break;
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@ -12977,7 +12988,7 @@ static void ggml_compute_forward_rope_back_f32(
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const float cos_theta = cosf(theta);
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const float sin_theta = sinf(theta);
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// zeta scaling for xPos only:
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float zeta = xpos_base != 0.0f ? powf((i0 + 0.4f * ne0) / (1.4f * ne0), (n_past + i2) / xpos_base) : 1.0f;
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float zeta = xpos_base != 0.0f ? powf((i0 + 0.4f * ne0) / (1.4f * ne0), p / xpos_base) : 1.0f;
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if (xpos_down) zeta = 1.0f / zeta;
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theta *= theta_scale;
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@ -13020,6 +13031,7 @@ static void ggml_compute_forward_rope_back_f32(
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static void ggml_compute_forward_rope_back_f16(
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const struct ggml_compute_params * params,
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const struct ggml_tensor * src0,
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const struct ggml_tensor * src1,
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struct ggml_tensor * dst) {
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if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
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@ -13030,12 +13042,10 @@ static void ggml_compute_forward_rope_back_f16(
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// dx = rope_back(dy, src1)
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// src0 is dy, src1 contains options
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const int n_past = ((int32_t *) dst->op_params)[0];
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//const int n_past = ((int32_t *) dst->op_params)[0];
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const int n_dims = ((int32_t *) dst->op_params)[1];
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const int mode = ((int32_t *) dst->op_params)[2];
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assert(n_past >= 0);
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GGML_TENSOR_UNARY_OP_LOCALS;
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//printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3);
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@ -13062,9 +13072,11 @@ static void ggml_compute_forward_rope_back_f16(
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const bool is_neox = mode & 2;
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const int32_t * pos = (const int32_t *) src1->data;
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for (int64_t i3 = 0; i3 < ne3; i3++) {
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for (int64_t i2 = ((mode & 1) == 0 ? 0 : n_past); i2 < ne2; i2++) {
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const int64_t p = ((mode & 1) == 0 ? n_past + i2 : i2);
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for (int64_t i2 = 0; i2 < ne2; i2++) {
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const int64_t p = pos[i2];
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for (int64_t i1 = 0; i1 < ne1; i1++) {
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if (ir++ < ir0) continue;
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if (ir > ir1) break;
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@ -13116,15 +13128,16 @@ static void ggml_compute_forward_rope_back_f16(
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static void ggml_compute_forward_rope_back(
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const struct ggml_compute_params * params,
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const struct ggml_tensor * src0,
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const struct ggml_tensor * src1,
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struct ggml_tensor * dst) {
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switch (src0->type) {
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case GGML_TYPE_F16:
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{
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ggml_compute_forward_rope_back_f16(params, src0, dst);
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ggml_compute_forward_rope_back_f16(params, src0, src1, dst);
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} break;
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case GGML_TYPE_F32:
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{
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ggml_compute_forward_rope_back_f32(params, src0, dst);
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ggml_compute_forward_rope_back_f32(params, src0, src1, dst);
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} break;
|
||||
default:
|
||||
{
|
||||
|
|
@ -15861,11 +15874,11 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
|
|||
} break;
|
||||
case GGML_OP_ROPE:
|
||||
{
|
||||
ggml_compute_forward_rope(params, tensor->src[0], tensor);
|
||||
ggml_compute_forward_rope(params, tensor->src[0], tensor->src[1], tensor);
|
||||
} break;
|
||||
case GGML_OP_ROPE_BACK:
|
||||
{
|
||||
ggml_compute_forward_rope_back(params, tensor->src[0], tensor);
|
||||
ggml_compute_forward_rope_back(params, tensor->src[0], tensor->src[1], tensor);
|
||||
} break;
|
||||
case GGML_OP_ALIBI:
|
||||
{
|
||||
|
|
@ -16503,7 +16516,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
|
|||
{
|
||||
// necessary for llama
|
||||
if (src0->grad) {
|
||||
const int n_past = ((int32_t *) tensor->op_params)[0];
|
||||
//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];
|
||||
|
|
@ -16520,7 +16533,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
|
|||
src0->grad,
|
||||
ggml_rope_back(ctx,
|
||||
tensor->grad,
|
||||
n_past,
|
||||
src1,
|
||||
n_dims,
|
||||
mode,
|
||||
n_ctx,
|
||||
|
|
@ -16534,7 +16547,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
|
|||
case GGML_OP_ROPE_BACK:
|
||||
{
|
||||
if (src0->grad) {
|
||||
const int n_past = ((int32_t *) tensor->op_params)[0];
|
||||
//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];
|
||||
|
|
@ -16551,7 +16564,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
|
|||
src0->grad,
|
||||
ggml_rope_impl(ctx,
|
||||
tensor->grad,
|
||||
n_past,
|
||||
src1,
|
||||
n_dims,
|
||||
mode,
|
||||
n_ctx,
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue