mirror of
https://github.com/ggerganov/whisper.cpp.git
synced 2025-06-16 22:08:07 +00:00
feat: add new sin and cos operators (ggml/919)
* ggml : add sin/cos operators * ggml-cuda : add sin/cos operators * ggml : add corresponding tests for sin/cos * ggml : add backward computation for sin/cos operators * ggml-vulkan : add sin/cos operators * ggml-vulkan : add sin/cos shader source * metal : add sin, cos --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit is contained in:
Ronsor
Georgi Gerganov
202
ggml/src/ggml.c
202
ggml/src/ggml.c
@ -2310,7 +2310,9 @@ inline static void ggml_vec_scale_f16(const int n, ggml_fp16_t * y, const float
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inline static void ggml_vec_norm_f32 (const int n, float * s, const float * x) { ggml_vec_dot_f32(n, s, 0, x, 0, x, 0, 1); *s = sqrtf(*s); }
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inline static void ggml_vec_sqr_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = x[i]*x[i]; }
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inline static void ggml_vec_sqrt_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = sqrtf(x[i]); }
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inline static void ggml_vec_log_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = logf(x[i]); }
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inline static void ggml_vec_log_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = logf(x[i]); }
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inline static void ggml_vec_sin_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = sinf(x[i]); }
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inline static void ggml_vec_cos_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = cosf(x[i]); }
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inline static void ggml_vec_abs_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = fabsf(x[i]); }
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inline static void ggml_vec_sgn_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? 1.f : ((x[i] < 0.f) ? -1.f : 0.f); }
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inline static void ggml_vec_step_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? 1.f : 0.f; }
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@ -2760,6 +2762,8 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
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"SQR",
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"SQRT",
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"LOG",
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"SIN",
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"COS",
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"SUM",
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"SUM_ROWS",
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"MEAN",
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@ -2833,7 +2837,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
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"CROSS_ENTROPY_LOSS_BACK",
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};
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static_assert(GGML_OP_COUNT == 74, "GGML_OP_COUNT != 74");
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static_assert(GGML_OP_COUNT == 76, "GGML_OP_COUNT != 76");
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static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
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"none",
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@ -2848,6 +2852,8 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
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"x^2",
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"√x",
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"log(x)",
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"sin(x)",
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"cos(x)",
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"Σx",
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"Σx_k",
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"Σx/n",
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@ -2921,7 +2927,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
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"cross_entropy_loss_back(x,y)",
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};
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static_assert(GGML_OP_COUNT == 74, "GGML_OP_COUNT != 74");
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static_assert(GGML_OP_COUNT == 76, "GGML_OP_COUNT != 76");
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static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2");
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@ -4882,6 +4888,72 @@ struct ggml_tensor * ggml_log_inplace(
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return ggml_log_impl(ctx, a, true);
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}
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// ggml_sin
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static struct ggml_tensor * ggml_sin_impl(
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struct ggml_context * ctx,
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struct ggml_tensor * a,
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bool inplace) {
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bool is_node = false;
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if (!inplace && (a->grad)) {
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is_node = true;
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}
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struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
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result->op = GGML_OP_SIN;
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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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return result;
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}
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struct ggml_tensor * ggml_sin(
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struct ggml_context * ctx,
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struct ggml_tensor * a) {
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return ggml_sin_impl(ctx, a, false);
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}
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struct ggml_tensor * ggml_sin_inplace(
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struct ggml_context * ctx,
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struct ggml_tensor * a) {
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return ggml_sin_impl(ctx, a, true);
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}
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// ggml_cos
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static struct ggml_tensor * ggml_cos_impl(
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struct ggml_context * ctx,
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struct ggml_tensor * a,
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bool inplace) {
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bool is_node = false;
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if (!inplace && (a->grad)) {
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is_node = true;
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}
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struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
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result->op = GGML_OP_COS;
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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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return result;
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}
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struct ggml_tensor * ggml_cos(
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struct ggml_context * ctx,
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struct ggml_tensor * a) {
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return ggml_cos_impl(ctx, a, false);
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}
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struct ggml_tensor * ggml_cos_inplace(
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struct ggml_context * ctx,
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struct ggml_tensor * a) {
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return ggml_cos_impl(ctx, a, true);
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}
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// ggml_sum
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struct ggml_tensor * ggml_sum(
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@ -10512,6 +10584,96 @@ static void ggml_compute_forward_log(
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}
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}
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// ggml_compute_forward_sin
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static void ggml_compute_forward_sin_f32(
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const struct ggml_compute_params * params,
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struct ggml_tensor * dst) {
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const struct ggml_tensor * src0 = dst->src[0];
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if (params->ith != 0) {
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return;
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}
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GGML_ASSERT(ggml_are_same_shape(src0, dst));
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const int n = ggml_nrows(src0);
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const int nc = src0->ne[0];
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GGML_ASSERT( dst->nb[0] == sizeof(float));
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GGML_ASSERT(src0->nb[0] == sizeof(float));
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for (int i = 0; i < n; i++) {
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ggml_vec_sin_f32(nc,
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(float *) ((char *) dst->data + i*( dst->nb[1])),
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(float *) ((char *) src0->data + i*(src0->nb[1])));
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}
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}
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static void ggml_compute_forward_sin(
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const struct ggml_compute_params * params,
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struct ggml_tensor * dst) {
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const struct ggml_tensor * src0 = dst->src[0];
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switch (src0->type) {
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case GGML_TYPE_F32:
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{
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ggml_compute_forward_sin_f32(params, dst);
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} break;
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default:
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{
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GGML_ABORT("fatal error");
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}
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}
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}
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// ggml_compute_forward_cos
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static void ggml_compute_forward_cos_f32(
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const struct ggml_compute_params * params,
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struct ggml_tensor * dst) {
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const struct ggml_tensor * src0 = dst->src[0];
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if (params->ith != 0) {
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return;
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}
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GGML_ASSERT(ggml_are_same_shape(src0, dst));
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const int n = ggml_nrows(src0);
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const int nc = src0->ne[0];
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GGML_ASSERT( dst->nb[0] == sizeof(float));
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GGML_ASSERT(src0->nb[0] == sizeof(float));
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for (int i = 0; i < n; i++) {
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ggml_vec_cos_f32(nc,
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(float *) ((char *) dst->data + i*( dst->nb[1])),
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(float *) ((char *) src0->data + i*(src0->nb[1])));
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}
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}
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static void ggml_compute_forward_cos(
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const struct ggml_compute_params * params,
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struct ggml_tensor * dst) {
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const struct ggml_tensor * src0 = dst->src[0];
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switch (src0->type) {
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case GGML_TYPE_F32:
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{
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ggml_compute_forward_cos_f32(params, dst);
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} break;
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default:
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{
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GGML_ABORT("fatal error");
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}
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}
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}
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// ggml_compute_forward_sum
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static void ggml_compute_forward_sum_f32(
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@ -16787,6 +16949,14 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
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{
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ggml_compute_forward_log(params, tensor);
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} break;
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case GGML_OP_SIN:
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{
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ggml_compute_forward_sin(params, tensor);
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} break;
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case GGML_OP_COS:
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{
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ggml_compute_forward_cos(params, tensor);
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} break;
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case GGML_OP_SUM:
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{
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ggml_compute_forward_sum(params, tensor);
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@ -17433,6 +17603,30 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
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zero_table);
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}
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} break;
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case GGML_OP_SIN:
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{
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if (src0->grad) {
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src0->grad =
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ggml_add_or_set(ctx,
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src0->grad,
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ggml_mul(ctx,
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tensor->grad,
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ggml_cos(ctx, src0)),
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zero_table);
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}
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} break;
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case GGML_OP_COS:
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{
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if (src0->grad) {
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src0->grad =
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ggml_sub_or_set(ctx,
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src0->grad,
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ggml_mul(ctx,
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tensor->grad,
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ggml_sin(ctx, src0)),
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zero_table);
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}
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} break;
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case GGML_OP_SUM:
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{
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if (src0->grad) {
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@ -18520,6 +18714,8 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
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case GGML_OP_SQR:
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case GGML_OP_SQRT:
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case GGML_OP_LOG:
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case GGML_OP_SIN:
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case GGML_OP_COS:
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case GGML_OP_SUM:
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case GGML_OP_SUM_ROWS:
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case GGML_OP_MEAN:
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