ggml : add ggml_clamp() (#1539)
* ggml : add ggml_clamp() * ggml : indentation
This commit is contained in:
Georgi Gerganov
GitHub
parent
affc76edfd
commit
3de84b2606
158
ggml.c
158
ggml.c
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@ -3472,6 +3472,7 @@ static const char * GGML_OP_LABEL[GGML_OP_COUNT] = {
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"ROPE",
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"ROPE_BACK",
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"ALIBI",
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"CLAMP",
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"CONV_1D_1S",
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"CONV_1D_2S",
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@ -3482,7 +3483,8 @@ static const char * GGML_OP_LABEL[GGML_OP_COUNT] = {
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"MAP_BINARY",
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};
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static_assert(GGML_OP_COUNT == 50, "GGML_OP_COUNT != 50");
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static_assert(GGML_OP_COUNT == 51, "GGML_OP_COUNT != 51");
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static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
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"none",
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@ -3532,6 +3534,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
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"rope(x)",
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"rope_back(x)",
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"alibi(x)",
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"clamp(x)",
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"conv_1d_1s(x)",
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"conv_1d_2s(x)",
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@ -3542,7 +3545,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
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"f(x,y)",
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};
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static_assert(GGML_OP_COUNT == 50, "GGML_OP_COUNT != 50");
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static_assert(GGML_OP_COUNT == 51, "GGML_OP_COUNT != 51");
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static_assert(sizeof(struct ggml_object)%GGML_MEM_ALIGN == 0, "ggml_object size must be a multiple of GGML_MEM_ALIGN");
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static_assert(sizeof(struct ggml_tensor)%GGML_MEM_ALIGN == 0, "ggml_tensor size must be a multiple of GGML_MEM_ALIGN");
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@ -6214,7 +6217,8 @@ struct ggml_tensor * ggml_alibi(
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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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int n_head) {
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int n_head,
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float bias_max) {
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GGML_ASSERT(n_past >= 0);
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bool is_node = false;
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@ -6233,6 +6237,8 @@ struct ggml_tensor * ggml_alibi(
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((int32_t *) b->data)[0] = n_past;
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((int32_t *) b->data)[1] = n_head;
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GGML_ASSERT(sizeof(float) == sizeof(int32_t));
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(((float *) b->data)[2]) = bias_max;
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ggml_scratch_load(ctx);
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@ -6244,6 +6250,40 @@ struct ggml_tensor * ggml_alibi(
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return result;
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}
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// ggml_clamp
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struct ggml_tensor * ggml_clamp(
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struct ggml_context * ctx,
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struct ggml_tensor * a,
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float min,
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float max) {
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bool is_node = false;
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if (a->grad) {
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GGML_ASSERT(false); // TODO: implement backward
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is_node = true;
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}
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// TODO: when implement backward, fix this:
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struct ggml_tensor * result = ggml_view_tensor(ctx, a);
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ggml_scratch_save(ctx);
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struct ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 3);
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((float *) b->data)[0] = min;
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((float *) b->data)[1] = max;
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ggml_scratch_load(ctx);
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result->op = GGML_OP_CLAMP;
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result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
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result->src0 = a;
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result->src1 = b;
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return result;
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}
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// ggml_conv_1d_1s
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struct ggml_tensor * ggml_conv_1d_1s(
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@ -10553,6 +10593,7 @@ static void ggml_compute_forward_diag_mask_f32(
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const int n_past = ((int32_t *) src1->data)[0];
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const bool inplace = (bool)((int32_t *) src1->data)[1];
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assert(n_past >= 0);
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if (!inplace && (params->type == GGML_TASK_INIT)) {
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@ -10723,14 +10764,15 @@ static void ggml_compute_forward_alibi_f32(
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struct ggml_tensor * dst) {
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assert(params->ith == 0);
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assert(src1->type == GGML_TYPE_I32);
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assert(ggml_nelements(src1) == 2);
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assert(ggml_nelements(src1) == 3);
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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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const int n_past = ((int32_t *) src1->data)[0];
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const int n_head = ((int32_t *) src1->data)[1];
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const int n_past = ((int32_t *) src1->data)[0];
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const int n_head = ((int32_t *) src1->data)[1];
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const float max_bias = ((float *) src1->data)[2];
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assert(n_past >= 0);
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@ -10753,8 +10795,8 @@ static void ggml_compute_forward_alibi_f32(
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// add alibi to src0 (KQ_scaled)
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const int n_heads_log2_floor = 1 << (int) floor(log2(n_head));
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const float m0 = powf(2.0f, -8.0f / n_heads_log2_floor);
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const float m1 = powf(2.0f, -4.0f / n_heads_log2_floor);
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const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor);
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const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor);
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for (int i = 0; i < ne0; i++) {
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for (int j = 0; j < ne1; j++) {
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@ -10772,13 +10814,13 @@ static void ggml_compute_forward_alibi_f32(
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m_k = powf(m1, 2 * (k - n_heads_log2_floor) + 1);
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}
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pdst[0] = i * m_k + src[0];
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pdst[0] = (i-ne0+1) * m_k + src[0];
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}
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}
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}
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}
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static void ggml_compute_forward_alibi_f16(
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const struct ggml_compute_params * params,
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const struct ggml_tensor * src0,
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@ -10786,14 +10828,15 @@ static void ggml_compute_forward_alibi_f16(
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struct ggml_tensor * dst) {
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assert(params->ith == 0);
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assert(src1->type == GGML_TYPE_I32);
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assert(ggml_nelements(src1) == 2);
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assert(ggml_nelements(src1) == 3);
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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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const int n_past = ((int32_t *) src1->data)[0];
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const int n_head = ((int32_t *) src1->data)[1];
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const int n_past = ((int32_t *) src1->data)[0];
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const int n_head = ((int32_t *) src1->data)[1];
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const float max_bias = ((float *) src1->data)[2];
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assert(n_past >= 0);
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@ -10816,8 +10859,8 @@ static void ggml_compute_forward_alibi_f16(
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// add alibi to src0 (KQ_scaled)
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const int n_heads_log2_floor = 1 << (int) floor(log2(n_head));
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const float m0 = powf(2.0f, -8.0f / n_heads_log2_floor);
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const float m1 = powf(2.0f, -4.0f / n_heads_log2_floor);
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const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor);
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const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor);
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for (int i = 0; i < ne0; i++) {
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for (int j = 0; j < ne1; j++) {
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@ -10836,7 +10879,7 @@ static void ggml_compute_forward_alibi_f16(
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}
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// we return F32
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pdst[0] = i * m_k + GGML_FP16_TO_FP32(src[0]);
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pdst[0] = (i-ne0+1) * m_k + GGML_FP16_TO_FP32(src[0]);
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}
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}
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}
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@ -10872,6 +10915,77 @@ static void ggml_compute_forward_alibi(
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}
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}
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// ggml_compute_forward_clamp
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static void ggml_compute_forward_clamp_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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assert(params->ith == 0);
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assert(src1->type == GGML_TYPE_I32);
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assert(ggml_nelements(src1) == 2);
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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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const int min = ((float *) src1->data)[0];
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const int max = ((float *) src1->data)[1];
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const int ith = params->ith;
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const int nth = params->nth;
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const int n = ggml_nrows(src0);
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const int nc = src0->ne[0];
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const size_t nb00 = src0->nb[0];
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const size_t nb01 = src0->nb[1];
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const size_t nb0 = dst->nb[0];
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const size_t nb1 = dst->nb[1];
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GGML_ASSERT( nb0 == sizeof(float));
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GGML_ASSERT(nb00 == sizeof(float));
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for (int j = ith; j < n; j += nth) {
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float * dst_ptr = (float *) ((char *) dst->data + j*nb1);
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float * src0_ptr = (float *) ((char *) src0->data + j*nb01);
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for (int i = 0; i < nc; i++) {
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dst_ptr[i] = MAX(MIN(src0_ptr[i], max), min);
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}
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}
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}
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static void ggml_compute_forward_clamp(
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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_F32:
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{
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ggml_compute_forward_clamp_f32(params, src0, src1, dst);
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} break;
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case GGML_TYPE_F16:
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case GGML_TYPE_Q4_0:
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case GGML_TYPE_Q4_1:
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case GGML_TYPE_Q5_0:
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case GGML_TYPE_Q5_1:
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case GGML_TYPE_Q8_0:
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case GGML_TYPE_Q8_1:
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case GGML_TYPE_I8:
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case GGML_TYPE_I16:
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case GGML_TYPE_I32:
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case GGML_TYPE_COUNT:
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{
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GGML_ASSERT(false);
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} break;
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}
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}
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// ggml_compute_forward_rope
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static void ggml_compute_forward_rope_f32(
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@ -12853,6 +12967,10 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
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{
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ggml_compute_forward_alibi(params, tensor->src0, tensor->src1, tensor);
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} break;
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case GGML_OP_CLAMP:
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{
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ggml_compute_forward_clamp(params, tensor->src0, tensor->src1, tensor);
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} break;
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case GGML_OP_CONV_1D_1S:
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{
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ggml_compute_forward_conv_1d_1s(params, tensor->src0, tensor->src1, tensor);
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@ -13160,6 +13278,10 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
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{
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GGML_ASSERT(false); // TODO: not implemented
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} break;
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case GGML_OP_CLAMP:
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{
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GGML_ASSERT(false); // TODO: not implemented
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} break;
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case GGML_OP_SILU:
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{
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// necessary for llama
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@ -14039,6 +14161,10 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph)
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{
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node->n_tasks = 1; //TODO
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} break;
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case GGML_OP_CLAMP:
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{
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node->n_tasks = 1; //TODO
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} break;
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case GGML_OP_CONV_1D_1S:
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case GGML_OP_CONV_1D_2S:
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{
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14
ggml.h
14
ggml.h
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@ -313,6 +313,7 @@ extern "C" {
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GGML_OP_ROPE,
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GGML_OP_ROPE_BACK,
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GGML_OP_ALIBI,
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GGML_OP_CLAMP,
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GGML_OP_CONV_1D_1S,
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GGML_OP_CONV_1D_2S,
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@ -849,7 +850,7 @@ extern "C" {
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int n_past);
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// in-place, returns view(a)
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GGML_API struct ggml_tensor * gml_diag_mask_zero_inplace(
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GGML_API struct ggml_tensor * ggml_diag_mask_zero_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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@ -897,7 +898,16 @@ extern "C" {
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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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int n_head);
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int n_head,
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float bias_max);
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// clamp
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// in-place, returns view(a)
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struct ggml_tensor * ggml_clamp(
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struct ggml_context * ctx,
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struct ggml_tensor * a,
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float min,
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float max);
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// padding = 1
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// TODO: we don't support extra parameters for now
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