mirror of
https://github.com/ggerganov/llama.cpp.git
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ggml : sync ggml (ggml_alibi)
This commit is contained in:
Georgi Gerganov
parent
5fba3c016b
commit
55390bcaf2
203
ggml.c
203
ggml.c
@ -4034,7 +4034,7 @@ 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 == 38, "GGML_OP_COUNT != 38");
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static_assert(GGML_OP_COUNT == 39, "GGML_OP_COUNT != 39");
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static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
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"none",
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@ -4082,7 +4082,7 @@ 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 == 38, "GGML_OP_COUNT != 38");
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static_assert(GGML_OP_COUNT == 39, "GGML_OP_COUNT != 39");
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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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@ -6080,6 +6080,37 @@ struct ggml_tensor * ggml_rope(
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return result;
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}
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// ggml_alibi
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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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GGML_ASSERT(n_past >= 0);
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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 = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
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struct ggml_tensor * result = ggml_view_tensor(ctx, a);
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struct ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 2);
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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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result->op = GGML_OP_ALIBI;
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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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@ -9300,6 +9331,162 @@ static void ggml_compute_forward_soft_max(
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}
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}
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// ggml_compute_forward_alibi
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static void ggml_compute_forward_alibi_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 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 ne0 = src0->ne[0]; // all_seq_len = n_past + ne1
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const int ne1 = src0->ne[1]; // seq_len_without_past
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//const int ne2 = src0->ne[2]; // n_head -> this is k
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//const int ne3 = src0->ne[3]; // 1 -> bsz
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const int n = ggml_nrows(src0);
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const int ne2_ne3 = n/ne1; // ne2*ne3
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const int nb0 = src0->nb[0];
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const int nb1 = src0->nb[1];
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const int nb2 = src0->nb[2];
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//const int nb3 = src0->nb[3];
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assert(nb0 == sizeof(float));
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assert(ne1+n_past == ne0);
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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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for (int i = 0; i < ne0; i++) {
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for (int j = 0; j < ne1; j++) {
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for (int k = 0; k < ne2_ne3; k++) {
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float * const src = (float *)((char *) src0->data + i*nb0 + j*nb1 + k*nb2);
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float * pdst = (float *)((char *) dst->data + i*nb0 + j*nb1 + k*nb2);
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// TODO: k*nb2 or k*nb3
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float m_k;
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if (k < n_heads_log2_floor) {
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m_k = powf(m0, k + 1);
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} else {
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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] = (j+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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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 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 ne0 = src0->ne[0]; // all_seq_len = n_past + ne1
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const int ne1 = src0->ne[1]; // seq_len_without_past
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//const int ne2 = src0->ne[2]; // n_head -> this is k
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//const int ne3 = src0->ne[3]; // 1 -> bsz
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const int n = ggml_nrows(src0);
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const int ne2_ne3 = n/ne1; // ne2*ne3
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const int nb0 = src0->nb[0];
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const int nb1 = src0->nb[1];
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const int nb2 = src0->nb[2];
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//const int nb3 = src0->nb[3];
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assert(nb0 == sizeof(ggml_fp16_t));
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assert(ne1+n_past == ne0);
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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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for (int i = 0; i < ne0; i++) {
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for (int j = 0; j < ne1; j++) {
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for (int k = 0; k < ne2_ne3; k++) {
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ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i*nb0 + j*nb1 + k*nb2);
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float * pdst = (float *)((char *) dst->data + i*nb0 + j*nb1 + k*nb2);
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// TODO: k*nb2 or k*nb3
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float m_k;
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if (k < n_heads_log2_floor) {
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m_k = powf(m0, k + 1);
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} else {
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m_k = powf(m1, 2 * (k - n_heads_log2_floor) + 1);
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}
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// we return F32
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pdst[0] = (j+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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}
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static void ggml_compute_forward_alibi(
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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_alibi_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_alibi_f32(params, src0, src1, dst);
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} break;
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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_Q4_2:
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case GGML_TYPE_Q4_3:
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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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@ -10938,6 +11125,10 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
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{
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ggml_compute_forward_rope(params, tensor->src0, tensor->src1, tensor);
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} break;
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case GGML_OP_ALIBI:
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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_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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@ -11140,6 +11331,10 @@ 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_ALIBI:
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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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GGML_ASSERT(false); // TODO: not implemented
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@ -11673,6 +11868,10 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph)
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{
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node->n_tasks = n_threads;
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} break;
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case GGML_OP_ALIBI:
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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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9
ggml.h
9
ggml.h
@ -269,6 +269,7 @@ extern "C" {
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GGML_OP_DIAG_MASK_INF,
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GGML_OP_SOFT_MAX,
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GGML_OP_ROPE,
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GGML_OP_ALIBI,
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GGML_OP_CONV_1D_1S,
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GGML_OP_CONV_1D_2S,
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@ -662,6 +663,14 @@ extern "C" {
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int n_dims,
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int mode);
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// alibi position embedding
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// in-place, returns view(a)
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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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// padding = 1
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// TODO: we don't support extra parameters for now
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// that's why we are hard-coding the stride, padding, and dilation
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