ggml : full ALiBi support (#7192)
* ggml : full ALiBi support * ggml : update ggml_soft_max_ext() CUDA, SYCL * ggml : ggml_flash_attn_ext() support ALiBi (CPU) * ggml : ggml_flash_attn_ext() support ALiBi (Metal) * ggml : fix warning * ggml : ggml_flash_attn_ext() support ALiBi (CUDA) ggml-ci * ggml : fix assert message * vulkan : add dev notes * ggml : require mask when using ALiBi ggml-ci * convert : fix convert for refact models
This commit is contained in:
Georgi Gerganov
GitHub
parent
e849648888
commit
9cb317f77e
16 changed files with 350 additions and 825 deletions
309
ggml.c
309
ggml.c
|
|
@ -2185,7 +2185,6 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
|
|||
"SOFT_MAX_BACK",
|
||||
"ROPE",
|
||||
"ROPE_BACK",
|
||||
"ALIBI",
|
||||
"CLAMP",
|
||||
"CONV_TRANSPOSE_1D",
|
||||
"IM2COL",
|
||||
|
|
@ -2227,7 +2226,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
|
|||
"CROSS_ENTROPY_LOSS_BACK",
|
||||
};
|
||||
|
||||
static_assert(GGML_OP_COUNT == 77, "GGML_OP_COUNT != 77");
|
||||
static_assert(GGML_OP_COUNT == 76, "GGML_OP_COUNT != 76");
|
||||
|
||||
static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
|
||||
"none",
|
||||
|
|
@ -2276,7 +2275,6 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
|
|||
"soft_max_back(x)",
|
||||
"rope(x)",
|
||||
"rope_back(x)",
|
||||
"alibi(x)",
|
||||
"clamp(x)",
|
||||
"conv_transpose_1d(x)",
|
||||
"im2col(x)",
|
||||
|
|
@ -2318,7 +2316,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
|
|||
"cross_entropy_loss_back(x,y)",
|
||||
};
|
||||
|
||||
static_assert(GGML_OP_COUNT == 77, "GGML_OP_COUNT != 77");
|
||||
static_assert(GGML_OP_COUNT == 76, "GGML_OP_COUNT != 76");
|
||||
|
||||
static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2");
|
||||
|
||||
|
|
@ -5646,7 +5644,6 @@ static struct ggml_tensor * ggml_soft_max_impl(
|
|||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * mask,
|
||||
struct ggml_tensor * pos,
|
||||
float scale,
|
||||
float max_bias,
|
||||
bool inplace) {
|
||||
|
|
@ -5660,18 +5657,8 @@ static struct ggml_tensor * ggml_soft_max_impl(
|
|||
GGML_ASSERT(mask->ne[1] >= a->ne[1]);
|
||||
}
|
||||
|
||||
if (pos) {
|
||||
GGML_ASSERT(ggml_is_vector(pos));
|
||||
GGML_ASSERT(pos->type == GGML_TYPE_F16 || pos->type == GGML_TYPE_F32);
|
||||
GGML_ASSERT(pos->ne[0] == a->ne[0]);
|
||||
}
|
||||
|
||||
if (pos && mask) {
|
||||
GGML_ASSERT(pos->type == mask->type);
|
||||
}
|
||||
|
||||
if (max_bias > 0.0f) {
|
||||
GGML_ASSERT(pos);
|
||||
GGML_ASSERT(mask);
|
||||
}
|
||||
|
||||
bool is_node = false;
|
||||
|
|
@ -5689,7 +5676,6 @@ static struct ggml_tensor * ggml_soft_max_impl(
|
|||
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
||||
result->src[0] = a;
|
||||
result->src[1] = mask;
|
||||
result->src[2] = pos;
|
||||
|
||||
return result;
|
||||
}
|
||||
|
|
@ -5697,23 +5683,22 @@ static struct ggml_tensor * ggml_soft_max_impl(
|
|||
struct ggml_tensor * ggml_soft_max(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a) {
|
||||
return ggml_soft_max_impl(ctx, a, NULL, NULL, 1.0f, 0.0f, false);
|
||||
return ggml_soft_max_impl(ctx, a, NULL, 1.0f, 0.0f, false);
|
||||
}
|
||||
|
||||
struct ggml_tensor * ggml_soft_max_inplace(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a) {
|
||||
return ggml_soft_max_impl(ctx, a, NULL, NULL, 1.0f, 0.0f, true);
|
||||
return ggml_soft_max_impl(ctx, a, NULL, 1.0f, 0.0f, true);
|
||||
}
|
||||
|
||||
struct ggml_tensor * ggml_soft_max_ext(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * mask,
|
||||
struct ggml_tensor * pos,
|
||||
float scale,
|
||||
float max_bias) {
|
||||
return ggml_soft_max_impl(ctx, a, mask, pos, scale, max_bias, false);
|
||||
return ggml_soft_max_impl(ctx, a, mask, scale, max_bias, false);
|
||||
}
|
||||
|
||||
// ggml_soft_max_back
|
||||
|
|
@ -5928,37 +5913,6 @@ struct ggml_tensor * ggml_rope_back(
|
|||
return result;
|
||||
}
|
||||
|
||||
// ggml_alibi
|
||||
|
||||
struct ggml_tensor * ggml_alibi(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
int n_past,
|
||||
int n_head,
|
||||
float bias_max) {
|
||||
GGML_ASSERT(n_past >= 0);
|
||||
bool is_node = false;
|
||||
|
||||
if (a->grad) {
|
||||
GGML_ASSERT(false); // TODO: implement backward
|
||||
is_node = true;
|
||||
}
|
||||
|
||||
// TODO: when implement backward, fix this:
|
||||
//struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
|
||||
struct ggml_tensor * result = ggml_view_tensor(ctx, a);
|
||||
|
||||
int32_t op_params[3] = { n_past, n_head };
|
||||
memcpy(op_params + 2, &bias_max, sizeof(float));
|
||||
ggml_set_op_params(result, op_params, sizeof(op_params));
|
||||
|
||||
result->op = GGML_OP_ALIBI;
|
||||
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
||||
result->src[0] = a;
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
// ggml_clamp
|
||||
|
||||
struct ggml_tensor * ggml_clamp(
|
||||
|
|
@ -6486,9 +6440,11 @@ struct ggml_tensor * ggml_flash_attn_ext(
|
|||
struct ggml_tensor * k,
|
||||
struct ggml_tensor * v,
|
||||
struct ggml_tensor * mask,
|
||||
float scale) {
|
||||
float scale,
|
||||
float max_bias) {
|
||||
GGML_ASSERT(ggml_can_mul_mat(k, q));
|
||||
// TODO: check if vT can be multiplied by (k*qT)
|
||||
|
||||
if (mask) {
|
||||
GGML_ASSERT(ggml_is_contiguous(mask));
|
||||
GGML_ASSERT(mask->ne[2] == 1);
|
||||
|
|
@ -6498,6 +6454,10 @@ struct ggml_tensor * ggml_flash_attn_ext(
|
|||
//GGML_ASSERT(ggml_can_repeat_rows(mask, qk));
|
||||
}
|
||||
|
||||
if (max_bias > 0.0f) {
|
||||
GGML_ASSERT(mask);
|
||||
}
|
||||
|
||||
bool is_node = false;
|
||||
|
||||
if (q->grad || k->grad || v->grad) {
|
||||
|
|
@ -6508,7 +6468,7 @@ struct ggml_tensor * ggml_flash_attn_ext(
|
|||
int64_t ne[4] = { q->ne[0], q->ne[2], q->ne[1], q->ne[3] };
|
||||
struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
|
||||
|
||||
float params[] = { scale };
|
||||
float params[] = { scale, max_bias };
|
||||
ggml_set_op_params(result, params, sizeof(params));
|
||||
|
||||
result->op = GGML_OP_FLASH_ATTN_EXT;
|
||||
|
|
@ -6528,7 +6488,7 @@ void ggml_flash_attn_ext_set_prec(
|
|||
|
||||
const int32_t prec_i32 = (int32_t) prec;
|
||||
|
||||
ggml_set_op_params_i32(a, 1, prec_i32); // scale is on first pos
|
||||
ggml_set_op_params_i32(a, 2, prec_i32); // scale is on first pos, max_bias on second
|
||||
}
|
||||
|
||||
// ggml_flash_ff
|
||||
|
|
@ -13333,7 +13293,6 @@ static void ggml_compute_forward_soft_max_f32(
|
|||
|
||||
const struct ggml_tensor * src0 = dst->src[0];
|
||||
const struct ggml_tensor * src1 = dst->src[1];
|
||||
const struct ggml_tensor * src2 = dst->src[2];
|
||||
|
||||
assert(ggml_is_contiguous(dst));
|
||||
assert(ggml_are_same_shape(src0, dst));
|
||||
|
|
@ -13359,8 +13318,8 @@ static void ggml_compute_forward_soft_max_f32(
|
|||
|
||||
// TODO: is this supposed to be ceil instead of floor?
|
||||
// https://huggingface.co/mosaicml/mpt-7b/blob/main/attention.py#L370
|
||||
const uint32_t n_head_kv = ne02;
|
||||
const uint32_t n_head_log2 = 1u << (uint32_t) floor(log2(n_head_kv));
|
||||
const uint32_t n_head = ne02;
|
||||
const uint32_t n_head_log2 = 1u << (uint32_t) floor(log2(n_head));
|
||||
|
||||
const float m0 = powf(2.0f, -(max_bias ) / n_head_log2);
|
||||
const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
|
||||
|
|
@ -13377,13 +13336,13 @@ static void ggml_compute_forward_soft_max_f32(
|
|||
|
||||
float * wp = (float *) params->wdata + (nc + CACHE_LINE_SIZE_F32) * ith;
|
||||
|
||||
// when max_bias <= 0.0f, src2 is not used and we default it to src0 to avoid branching
|
||||
ggml_fp16_t * pos_f16 = src2 ? (ggml_fp16_t *) src2->data : src0->data;
|
||||
float * pos_f32 = src2 ? (float *) src2->data : src0->data;
|
||||
|
||||
const bool use_f16 = (src1 && src1->type == GGML_TYPE_F16) || (src2 && src2->type == GGML_TYPE_F16);
|
||||
const bool use_f16 = (src1 && src1->type == GGML_TYPE_F16);
|
||||
|
||||
for (int i1 = ir0; i1 < ir1; i1++) {
|
||||
// ALiBi
|
||||
const uint32_t h = (i1/ne01)%ne02; // head
|
||||
const float slope = (max_bias > 0.0f) ? h < n_head_log2 ? powf(m0, h + 1) : powf(m1, 2*(h - n_head_log2) + 1) : 1.0f;
|
||||
|
||||
float * sp = (float *)((char *) src0->data + i1*src0->nb[1]);
|
||||
float * dp = (float *)((char *) dst->data + i1*dst->nb[1]);
|
||||
|
||||
|
|
@ -13396,27 +13355,11 @@ static void ggml_compute_forward_soft_max_f32(
|
|||
if (mp_f32) {
|
||||
if (use_f16) {
|
||||
for (int i = 0; i < nc; ++i) {
|
||||
wp[i] += GGML_FP16_TO_FP32(mp_f16[i]);
|
||||
wp[i] += slope*GGML_FP16_TO_FP32(mp_f16[i]);
|
||||
}
|
||||
} else {
|
||||
for (int i = 0; i < nc; ++i) {
|
||||
wp[i] += mp_f32[i];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// ALiBi bias
|
||||
if (max_bias > 0.0f) {
|
||||
const uint32_t h = (i1/ne01)%ne02; // head
|
||||
const float slope = h < n_head_log2 ? powf(m0, h + 1) : powf(m1, 2*(h - n_head_log2) + 1);
|
||||
|
||||
if (use_f16) {
|
||||
for (int i = 0; i < nc; ++i) {
|
||||
wp[i] += slope*GGML_FP16_TO_FP32(pos_f16[i]);
|
||||
}
|
||||
} else {
|
||||
for (int i = 0; i < nc; ++i) {
|
||||
wp[i] += slope*pos_f32[i];
|
||||
wp[i] += slope*mp_f32[i];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -13578,178 +13521,6 @@ static void ggml_compute_forward_soft_max_back(
|
|||
}
|
||||
}
|
||||
|
||||
// ggml_compute_forward_alibi
|
||||
|
||||
static void ggml_compute_forward_alibi_f32(
|
||||
const struct ggml_compute_params * params,
|
||||
struct ggml_tensor * dst) {
|
||||
|
||||
const struct ggml_tensor * src0 = dst->src[0];
|
||||
|
||||
assert(params->ith == 0);
|
||||
|
||||
if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
|
||||
return;
|
||||
}
|
||||
|
||||
//const int n_past = ((int32_t *) dst->op_params)[0];
|
||||
const int n_head = ((int32_t *) dst->op_params)[1];
|
||||
float max_bias;
|
||||
memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float));
|
||||
|
||||
const int64_t ne0 = src0->ne[0]; // all_seq_len = n_past + ne1
|
||||
const int64_t ne1 = src0->ne[1]; // seq_len_without_past
|
||||
const int64_t ne2 = src0->ne[2]; // n_head -> this is k
|
||||
//const int64_t ne3 = src0->ne[3]; // 1 -> bsz
|
||||
|
||||
const int64_t n = ggml_nrows(src0);
|
||||
const int64_t ne2_ne3 = n/ne1; // ne2*ne3
|
||||
|
||||
const size_t nb0 = src0->nb[0];
|
||||
const size_t nb1 = src0->nb[1];
|
||||
const size_t nb2 = src0->nb[2];
|
||||
//const int nb3 = src0->nb[3];
|
||||
|
||||
GGML_ASSERT(nb0 == sizeof(float));
|
||||
GGML_ASSERT(n_head == ne2);
|
||||
|
||||
// add alibi to src0 (KQ_scaled)
|
||||
const int n_heads_log2_floor = 1 << (int) floor(log2(n_head));
|
||||
|
||||
const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor);
|
||||
const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor);
|
||||
|
||||
for (int64_t k = 0; k < ne2_ne3; k++) {
|
||||
// TODO: k*nb2 or k*nb3
|
||||
float m_k;
|
||||
|
||||
if (k < n_heads_log2_floor) {
|
||||
m_k = powf(m0, k + 1);
|
||||
} else {
|
||||
m_k = powf(m1, 2 * (k - n_heads_log2_floor) + 1);
|
||||
}
|
||||
|
||||
for (int64_t i = 0; i < ne0; i++) {
|
||||
for (int64_t j = 0; j < ne1; j++) {
|
||||
float * const src = (float *)((char *) src0->data + i*nb0 + j*nb1 + k*nb2);
|
||||
float * pdst = (float *)((char *) dst->data + i*nb0 + j*nb1 + k*nb2);
|
||||
pdst[0] = i * m_k + src[0];
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
static void ggml_compute_forward_alibi_f16(
|
||||
const struct ggml_compute_params * params,
|
||||
struct ggml_tensor * dst) {
|
||||
|
||||
const struct ggml_tensor * src0 = dst->src[0];
|
||||
|
||||
assert(params->ith == 0);
|
||||
|
||||
if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
|
||||
return;
|
||||
}
|
||||
|
||||
//const int n_past = ((int32_t *) dst->op_params)[0];
|
||||
const int n_head = ((int32_t *) dst->op_params)[1];
|
||||
float max_bias;
|
||||
memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float));
|
||||
|
||||
const int ne0 = src0->ne[0]; // all_seq_len = n_past + ne1
|
||||
const int ne1 = src0->ne[1]; // seq_len_without_past
|
||||
const int ne2 = src0->ne[2]; // n_head -> this is k
|
||||
//const int ne3 = src0->ne[3]; // 1 -> bsz
|
||||
|
||||
const int n = ggml_nrows(src0);
|
||||
const int ne2_ne3 = n/ne1; // ne2*ne3
|
||||
|
||||
const int nb0 = src0->nb[0];
|
||||
const int nb1 = src0->nb[1];
|
||||
const int nb2 = src0->nb[2];
|
||||
//const int nb3 = src0->nb[3];
|
||||
|
||||
GGML_ASSERT(nb0 == sizeof(ggml_fp16_t));
|
||||
//GGML_ASSERT(ne1 + n_past == ne0); (void) n_past;
|
||||
GGML_ASSERT(n_head == ne2);
|
||||
|
||||
// add alibi to src0 (KQ_scaled)
|
||||
const int n_heads_log2_floor = 1 << (int) floor(log2(n_head));
|
||||
|
||||
const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor);
|
||||
const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor);
|
||||
|
||||
for (int k = 0; k < ne2_ne3; k++) {
|
||||
// TODO: k*nb2 or k*nb3
|
||||
float m_k;
|
||||
|
||||
if (k < n_heads_log2_floor) {
|
||||
m_k = powf(m0, k + 1);
|
||||
} else {
|
||||
m_k = powf(m1, 2 * (k - n_heads_log2_floor) + 1);
|
||||
}
|
||||
|
||||
for (int i = 0; i < ne0; i++) {
|
||||
for (int j = 0; j < ne1; j++) {
|
||||
ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i*nb0 + j*nb1 + k*nb2);
|
||||
float * pdst = (float *)((char *) dst->data + i*nb0 + j*nb1 + k*nb2);
|
||||
|
||||
// we return F32
|
||||
pdst[0] = i * m_k + GGML_FP16_TO_FP32(src[0]);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
static void ggml_compute_forward_alibi(
|
||||
const struct ggml_compute_params * params,
|
||||
struct ggml_tensor * dst) {
|
||||
|
||||
const struct ggml_tensor * src0 = dst->src[0];
|
||||
|
||||
switch (src0->type) {
|
||||
case GGML_TYPE_F16:
|
||||
{
|
||||
ggml_compute_forward_alibi_f16(params, dst);
|
||||
} break;
|
||||
case GGML_TYPE_F32:
|
||||
{
|
||||
ggml_compute_forward_alibi_f32(params, dst);
|
||||
} break;
|
||||
case GGML_TYPE_BF16:
|
||||
case GGML_TYPE_Q4_0:
|
||||
case GGML_TYPE_Q4_1:
|
||||
case GGML_TYPE_Q5_0:
|
||||
case GGML_TYPE_Q5_1:
|
||||
case GGML_TYPE_Q8_0:
|
||||
case GGML_TYPE_Q8_1:
|
||||
case GGML_TYPE_Q2_K:
|
||||
case GGML_TYPE_Q3_K:
|
||||
case GGML_TYPE_Q4_K:
|
||||
case GGML_TYPE_Q5_K:
|
||||
case GGML_TYPE_Q6_K:
|
||||
case GGML_TYPE_IQ2_XXS:
|
||||
case GGML_TYPE_IQ2_XS:
|
||||
case GGML_TYPE_IQ3_XXS:
|
||||
case GGML_TYPE_IQ1_S:
|
||||
case GGML_TYPE_IQ1_M:
|
||||
case GGML_TYPE_IQ4_NL:
|
||||
case GGML_TYPE_IQ4_XS:
|
||||
case GGML_TYPE_IQ3_S:
|
||||
case GGML_TYPE_IQ2_S:
|
||||
case GGML_TYPE_Q8_K:
|
||||
case GGML_TYPE_I8:
|
||||
case GGML_TYPE_I16:
|
||||
case GGML_TYPE_I32:
|
||||
case GGML_TYPE_I64:
|
||||
case GGML_TYPE_F64:
|
||||
case GGML_TYPE_COUNT:
|
||||
{
|
||||
GGML_ASSERT(false);
|
||||
} break;
|
||||
}
|
||||
}
|
||||
|
||||
// ggml_compute_forward_clamp
|
||||
|
||||
static void ggml_compute_forward_clamp_f32(
|
||||
|
|
@ -15763,8 +15534,17 @@ static void ggml_compute_forward_flash_attn_ext_f16(
|
|||
const int ir0 = dr*ith;
|
||||
const int ir1 = MIN(ir0 + dr, nr);
|
||||
|
||||
float scale = 1.0f;
|
||||
memcpy(&scale, (float *) dst->op_params + 0, sizeof(float));
|
||||
float scale = 1.0f;
|
||||
float max_bias = 0.0f;
|
||||
|
||||
memcpy(&scale, (float *) dst->op_params + 0, sizeof(float));
|
||||
memcpy(&max_bias, (float *) dst->op_params + 1, sizeof(float));
|
||||
|
||||
const uint32_t n_head = neq2;
|
||||
const uint32_t n_head_log2 = 1u << (uint32_t) floor(log2(n_head));
|
||||
|
||||
const float m0 = powf(2.0f, -(max_bias ) / n_head_log2);
|
||||
const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
|
||||
|
||||
// loop over n_batch and n_head
|
||||
for (int ir = ir0; ir < ir1; ++ir) {
|
||||
|
|
@ -15773,6 +15553,9 @@ static void ggml_compute_forward_flash_attn_ext_f16(
|
|||
const int iq2 = (ir - iq3*neq2*neq1)/neq1;
|
||||
const int iq1 = (ir - iq3*neq2*neq1 - iq2*neq1);
|
||||
|
||||
const uint32_t h = iq2; // head
|
||||
const float slope = (max_bias > 0.0f) ? h < n_head_log2 ? powf(m0, h + 1) : powf(m1, 2*(h - n_head_log2) + 1) : 1.0f;
|
||||
|
||||
float S = 0.0f;
|
||||
float M = -INFINITY;
|
||||
|
||||
|
|
@ -15796,7 +15579,7 @@ static void ggml_compute_forward_flash_attn_ext_f16(
|
|||
// loop over n_kv and n_head_kv
|
||||
// ref: https://arxiv.org/pdf/2112.05682.pdf
|
||||
for (int64_t ic = 0; ic < nek1; ++ic) {
|
||||
const float mv = mp ? GGML_FP16_TO_FP32(mp[ic]) : 0.0f;
|
||||
const float mv = mp ? slope*GGML_FP16_TO_FP32(mp[ic]) : 0.0f;
|
||||
if (mv == -INFINITY) {
|
||||
continue;
|
||||
}
|
||||
|
|
@ -15867,7 +15650,7 @@ static void ggml_compute_forward_flash_attn_ext(
|
|||
const struct ggml_tensor * v,
|
||||
const struct ggml_tensor * mask,
|
||||
struct ggml_tensor * dst) {
|
||||
switch (dst->op_params[1]) {
|
||||
switch (dst->op_params[2]) {
|
||||
case GGML_PREC_DEFAULT:
|
||||
case GGML_PREC_F32:
|
||||
{
|
||||
|
|
@ -17630,10 +17413,6 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
|
|||
{
|
||||
ggml_compute_forward_rope_back(params, tensor);
|
||||
} break;
|
||||
case GGML_OP_ALIBI:
|
||||
{
|
||||
ggml_compute_forward_alibi(params, tensor);
|
||||
} break;
|
||||
case GGML_OP_CLAMP:
|
||||
{
|
||||
ggml_compute_forward_clamp(params, tensor);
|
||||
|
|
@ -18652,10 +18431,6 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
|
|||
zero_table);
|
||||
}
|
||||
} break;
|
||||
case GGML_OP_ALIBI:
|
||||
{
|
||||
GGML_ASSERT(false); // TODO: not implemented
|
||||
} break;
|
||||
case GGML_OP_CLAMP:
|
||||
{
|
||||
GGML_ASSERT(false); // TODO: not implemented
|
||||
|
|
@ -19428,10 +19203,6 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads, int n_cur_
|
|||
{
|
||||
n_tasks = n_threads;
|
||||
} break;
|
||||
case GGML_OP_ALIBI:
|
||||
{
|
||||
n_tasks = 1; //TODO
|
||||
} break;
|
||||
case GGML_OP_CLAMP:
|
||||
{
|
||||
n_tasks = 1; //TODO
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue