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llama : replace ggml_diag_mask_inf with ggml_add (custom -inf mask)

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Georgi Gerganov 2023-09-17 19:42:39 +03:00
parent c5df72e848
commit 3b4bab6a38
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GPG Key ID: 449E073F9DC10735
4 changed files with 128 additions and 31 deletions
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44
ggml-metal.m
View File

@ -736,25 +736,59 @@ void ggml_metal_graph_compute(
GGML_ASSERT(ggml_is_contiguous(src0));
GGML_ASSERT(ggml_is_contiguous(src1));
// utilize float4
GGML_ASSERT(ne00 % 4 == 0);
const int64_t nb = ne00/4;
bool bcast_row = false;
if (ggml_nelements(src1) == ne10) {
int64_t nb = ne00;
if (ggml_nelements(src1) == ne10 && ne00 % 4 == 0) {
// src1 is a row
GGML_ASSERT(ne11 == 1);
nb = ne00 / 4;
[encoder setComputePipelineState:ctx->pipeline_add_row];
bcast_row = true;
} else {
[encoder setComputePipelineState:ctx->pipeline_add];
}
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
[encoder setBuffer:id_dst offset:offs_dst atIndex:2];
[encoder setBytes:&nb length:sizeof(nb) atIndex:3];
[encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3];
[encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4];
[encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5];
[encoder setBytes:&ne03 length:sizeof(ne03) atIndex:6];
[encoder setBytes:&nb00 length:sizeof(nb00) atIndex:7];
[encoder setBytes:&nb01 length:sizeof(nb01) atIndex:8];
[encoder setBytes:&nb02 length:sizeof(nb02) atIndex:9];
[encoder setBytes:&nb03 length:sizeof(nb03) atIndex:10];
[encoder setBytes:&ne10 length:sizeof(ne10) atIndex:11];
[encoder setBytes:&ne11 length:sizeof(ne11) atIndex:12];
[encoder setBytes:&ne12 length:sizeof(ne12) atIndex:13];
[encoder setBytes:&ne13 length:sizeof(ne13) atIndex:14];
[encoder setBytes:&nb10 length:sizeof(nb10) atIndex:15];
[encoder setBytes:&nb11 length:sizeof(nb11) atIndex:16];
[encoder setBytes:&nb12 length:sizeof(nb12) atIndex:17];
[encoder setBytes:&nb13 length:sizeof(nb13) atIndex:18];
[encoder setBytes:&ne0 length:sizeof(ne0) atIndex:19];
[encoder setBytes:&ne1 length:sizeof(ne1) atIndex:20];
[encoder setBytes:&ne2 length:sizeof(ne2) atIndex:21];
[encoder setBytes:&ne3 length:sizeof(ne3) atIndex:22];
[encoder setBytes:&nb0 length:sizeof(nb0) atIndex:23];
[encoder setBytes:&nb1 length:sizeof(nb1) atIndex:24];
[encoder setBytes:&nb2 length:sizeof(nb2) atIndex:25];
[encoder setBytes:&nb3 length:sizeof(nb3) atIndex:26];
[encoder setBytes:&nb length:sizeof(nb) atIndex:27];
if (bcast_row) {
const int64_t n = ggml_nelements(dst)/4;
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} else {
const int nth = MIN(1024, ne0);
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
}
} break;
case GGML_OP_MUL:
{

59
ggml-metal.metal
View File

@ -24,12 +24,59 @@ typedef struct {
int8_t qs[QK8_0]; // quants
} block_q8_0;
// general-purpose kernel for addition of two tensors
// pros: works for non-contiguous tensors, supports broadcast across dims 1, 2 and 3
// cons: not very efficient
kernel void kernel_add(
device const float4 * src0,
device const float4 * src1,
device float4 * dst,
uint tpig[[thread_position_in_grid]]) {
dst[tpig] = src0[tpig] + src1[tpig];
device const char * src0,
device const char * src1,
device char * dst,
constant int64_t & ne00,
constant int64_t & ne01,
constant int64_t & ne02,
constant int64_t & ne03,
constant int64_t & nb00,
constant int64_t & nb01,
constant int64_t & nb02,
constant int64_t & nb03,
constant int64_t & ne10,
constant int64_t & ne11,
constant int64_t & ne12,
constant int64_t & ne13,
constant int64_t & nb10,
constant int64_t & nb11,
constant int64_t & nb12,
constant int64_t & nb13,
constant int64_t & ne0,
constant int64_t & ne1,
constant int64_t & ne2,
constant int64_t & ne3,
constant int64_t & nb0,
constant int64_t & nb1,
constant int64_t & nb2,
constant int64_t & nb3,
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
uint3 ntg[[threads_per_threadgroup]]) {
const int64_t i03 = tgpig.z;
const int64_t i02 = tgpig.y;
const int64_t i01 = tgpig.x;
const int64_t i13 = i03 % ne13;
const int64_t i12 = i02 % ne12;
const int64_t i11 = i01 % ne11;
device const char * src0_ptr = src0 + i03*nb03 + i02*nb02 + i01*nb01 + tpitg.x*nb00;
device const char * src1_ptr = src1 + i13*nb13 + i12*nb12 + i11*nb11 + tpitg.x*nb10;
device char * dst_ptr = dst + i03*nb3 + i02*nb2 + i01*nb1 + tpitg.x*nb0;
for (int i0 = tpitg.x; i0 < ne0; i0 += ntg.x) {
((device float *)dst_ptr)[0] = ((device float *)src0_ptr)[0] + ((device float *)src1_ptr)[0];
src0_ptr += ntg.x*nb00;
src1_ptr += ntg.x*nb10;
dst_ptr += ntg.x*nb0;
}
}
// assumption: src1 is a row
@ -38,7 +85,7 @@ kernel void kernel_add_row(
device const float4 * src0,
device const float4 * src1,
device float4 * dst,
constant int64_t & nb,
constant int64_t & nb [[buffer(27)]],
uint tpig[[thread_position_in_grid]]) {
dst[tpig] = src0[tpig] + src1[tpig % nb];
}

2
ggml.c
View File

@ -8797,8 +8797,6 @@ static void ggml_compute_forward_add_f32(
#else
ggml_vec_add_f32(ne00, dst_ptr, src0_ptr, src1_ptr);
#endif
// }
// }
}
} else {
// src1 is not contiguous

50
llama.cpp
View File

@ -2404,6 +2404,7 @@ static struct ggml_cgraph * llm_build_llama(
}
#endif // GGML_USE_CUBLAS
// KQ_scale
struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
ggml_allocr_alloc(lctx.alloc, KQ_scale);
if (!ggml_allocr_is_measure(lctx.alloc)) {
@ -2411,6 +2412,22 @@ static struct ggml_cgraph * llm_build_llama(
}
ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)");
// KQ_mask
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_past + N, N, 1);
ggml_allocr_alloc(lctx.alloc, KQ_mask);
if (!ggml_allocr_is_measure(lctx.alloc)) {
float * data = (float *) KQ_mask->data;
memset(data, 0, ggml_nbytes(KQ_mask));
for (int h = 0; h < 1; ++h) {
for (int j = 0; j < N; ++j) {
for (int i = n_past + j + 1; i < n_past + N; ++i) {
data[h*(n_past + N)*N + j*(n_past + N) + i] = -INFINITY;
}
}
}
}
for (int il = 0; il < n_layer; ++il) {
ggml_format_name(inpL, "layer_inp_%d", il);
@ -2447,11 +2464,11 @@ static struct ggml_cgraph * llm_build_llama(
offload_func_kq(tmpq);
ggml_set_name(tmpq, "tmpq");
struct ggml_tensor * Kcur = ggml_rope_custom_inplace(ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd_head, n_head_kv, N), n_past, n_embd_head, 0, 0, freq_base, freq_scale);
struct ggml_tensor * Kcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd_head, n_head_kv, N), n_past, n_embd_head, 0, 0, freq_base, freq_scale);
offload_func_kq(Kcur);
ggml_set_name(Kcur, "Kcur");
struct ggml_tensor * Qcur = ggml_rope_custom_inplace(ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head, N), n_past, n_embd_head, 0, 0, freq_base, freq_scale);
struct ggml_tensor * Qcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head, N), n_past, n_embd_head, 0, 0, freq_base, freq_scale);
offload_func_kq(Qcur);
ggml_set_name(Qcur, "Qcur");
@ -2502,17 +2519,18 @@ static struct ggml_cgraph * llm_build_llama(
// KQ_scaled = KQ / sqrt(n_embd_head)
// KQ_scaled shape [n_past + N, N, n_head, 1]
struct ggml_tensor * KQ_scaled = ggml_scale_inplace(ctx0, KQ, KQ_scale);
struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, KQ_scale);
offload_func_kq(KQ_scaled);
ggml_set_name(KQ_scaled, "KQ_scaled");
// KQ_masked = mask_past(KQ_scaled)
struct ggml_tensor * KQ_masked = ggml_diag_mask_inf_inplace(ctx0, KQ_scaled, n_past);
struct ggml_tensor * KQ_masked = ggml_add(ctx0, KQ_scaled, KQ_mask);
//struct ggml_tensor * KQ_masked = ggml_diag_mask_inf_inplace(ctx0, KQ_scaled, n_past);
offload_func_kq(KQ_masked);
ggml_set_name(KQ_masked, "KQ_masked");
// KQ = soft_max(KQ_masked)
struct ggml_tensor * KQ_soft_max = ggml_soft_max_inplace(ctx0, KQ_masked);
struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
offload_func_v(KQ_soft_max);
ggml_set_name(KQ_soft_max, "KQ_soft_max");
@ -2783,8 +2801,8 @@ static struct ggml_cgraph * llm_build_baichaun(
struct ggml_tensor * Qcur;
switch (model.type) {
case MODEL_7B:
Kcur = ggml_rope_custom_inplace(ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd_head, n_head_kv, N), n_past, n_embd_head, 0, 0, freq_base, freq_scale);
Qcur = ggml_rope_custom_inplace(ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head, N), n_past, n_embd_head, 0, 0, freq_base, freq_scale);
Kcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd_head, n_head_kv, N), n_past, n_embd_head, 0, 0, freq_base, freq_scale);
Qcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head, N), n_past, n_embd_head, 0, 0, freq_base, freq_scale);
break;
case MODEL_13B:
Kcur = ggml_reshape_3d(ctx0, tmpk, n_embd/n_head, n_head, N);
@ -2847,7 +2865,7 @@ static struct ggml_cgraph * llm_build_baichaun(
// KQ_scaled = KQ / sqrt(n_embd_head)
// KQ_scaled shape [n_past + N, N, n_head, 1]
struct ggml_tensor * KQ_scaled = ggml_scale_inplace(ctx0, KQ, KQ_scale);
struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, KQ_scale);
offload_func_kq(KQ_scaled);
ggml_set_name(KQ_scaled, "KQ_scaled");
@ -2856,7 +2874,7 @@ static struct ggml_cgraph * llm_build_baichaun(
switch (model.type) {
case MODEL_7B:
KQ_masked = ggml_diag_mask_inf_inplace(ctx0, KQ_scaled, n_past);
KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, n_past);
break;
case MODEL_13B:
KQ_scaled_alibi =ggml_alibi(ctx0, KQ_scaled, n_past, n_head, 8);
@ -2867,13 +2885,13 @@ static struct ggml_cgraph * llm_build_baichaun(
GGML_ASSERT(false);
}
// KQ_masked = mask_past(KQ_scaled)
// struct ggml_tensor * KQ_masked = ggml_diag_mask_inf_inplace(ctx0, KQ_scaled, n_past);
// struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, n_past);
// struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled_alibi, n_past);
// offload_func_kq(KQ_masked);
// ggml_set_name(KQ_masked, "KQ_masked");
// KQ = soft_max(KQ_masked)
struct ggml_tensor * KQ_soft_max = ggml_soft_max_inplace(ctx0, KQ_masked);
struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
offload_func_v(KQ_soft_max);
ggml_set_name(KQ_soft_max, "KQ_soft_max");
@ -3179,9 +3197,9 @@ static struct ggml_cgraph * llm_build_falcon(
offload_func_v(tmpv);
// using mode = 2 for neox mode
struct ggml_tensor * Qcur = ggml_rope_custom_inplace(ctx0, tmpq, n_past, n_embd_head, 2, 0, freq_base, freq_scale);
struct ggml_tensor * Qcur = ggml_rope_custom(ctx0, tmpq, n_past, n_embd_head, 2, 0, freq_base, freq_scale);
offload_func_kq(Qcur);
struct ggml_tensor * Kcur = ggml_rope_custom_inplace(ctx0, tmpk, n_past, n_embd_head, 2, 0, freq_base, freq_scale);
struct ggml_tensor * Kcur = ggml_rope_custom(ctx0, tmpk, n_past, n_embd_head, 2, 0, freq_base, freq_scale);
offload_func_kq(Kcur);
{
@ -3220,15 +3238,15 @@ static struct ggml_cgraph * llm_build_falcon(
offload_func_kq(KQ);
ggml_set_name(KQ, "KQ");
struct ggml_tensor * KQ_scaled = ggml_scale_inplace(ctx0, KQ, KQ_scale);
struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, KQ_scale);
offload_func_kq(KQ_scaled);
ggml_set_name(KQ_scaled, "KQ_scaled");
struct ggml_tensor * KQ_masked = ggml_diag_mask_inf_inplace(ctx0, KQ_scaled, n_past);
struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, n_past);
offload_func_kq(KQ_masked);
ggml_set_name(KQ_masked, "KQ_masked");
struct ggml_tensor * KQ_soft_max = ggml_soft_max_inplace(ctx0, KQ_masked);
struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
offload_func_v(KQ_soft_max);
ggml_set_name(KQ_soft_max, "KQ_soft_max");