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metal : optimize FA kernels (ggerganov#10171)
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* ggml : add ggml_flash_attn_ext_get_prec

* metal : use F16 precision in FA kernels

ggml-ci

* metal : minor clean-up

* metal : compile-guard bf16 FA kernels

ggml-ci

* build : remove obsolete compile flag [no ci]

* metal : prevent int overflows [no ci]

* cuda : disable BF16 FA

ggml-ci

* metal : fix BF16 requirement for FA kernels

ggml-ci

* make : clean-up [no ci]
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@ggerganov
ggerganov authored Nov 8, 2024
1 parent d05b312 commit 841f27a
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Showing 8 changed files with 504 additions and 345 deletions.
3 changes: 3 additions & 0 deletions examples/llama-bench/llama-bench.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -256,6 +256,9 @@ static ggml_type ggml_type_from_name(const std::string & s) {
if (s == "f16") {
return GGML_TYPE_F16;
}
if (s == "bf16") {
return GGML_TYPE_BF16;
}
if (s == "q8_0") {
return GGML_TYPE_Q8_0;
}
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3 changes: 3 additions & 0 deletions ggml/include/ggml.h
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Original file line number Diff line number Diff line change
Expand Up @@ -1746,6 +1746,9 @@ extern "C" {
struct ggml_tensor * a,
enum ggml_prec prec);

GGML_API enum ggml_prec ggml_flash_attn_ext_get_prec(
const struct ggml_tensor * a);

// TODO: needs to be adapted to ggml_flash_attn_ext
GGML_API struct ggml_tensor * ggml_flash_attn_back(
struct ggml_context * ctx,
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3 changes: 3 additions & 0 deletions ggml/src/ggml-cuda.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -3159,6 +3159,9 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
#ifndef FLASH_ATTN_AVAILABLE
return false;
#endif
if (op->src[1]->type == GGML_TYPE_BF16 || op->src[2]->type == GGML_TYPE_BF16) {
return false;
}
if (op->src[0]->ne[0] == 64 && op->src[1]->type == GGML_TYPE_F16) {
return true;
}
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10 changes: 5 additions & 5 deletions ggml/src/ggml-cuda/fattn.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -13,9 +13,9 @@ static void ggml_cuda_flash_attn_ext_wmma_f16(ggml_backend_cuda_context & ctx, g
const ggml_tensor * KQV = dst;
const ggml_tensor * Q = dst->src[0];

const int32_t precision = KQV->op_params[3];
const enum ggml_prec prec = ggml_flash_attn_ext_get_prec(KQV);

if (precision != GGML_PREC_DEFAULT) {
if (prec != GGML_PREC_DEFAULT) {
if (Q->ne[1] <= 32 || Q->ne[0] > 128) {
constexpr int cols_per_block = 16;
switch (Q->ne[0]) {
Expand Down Expand Up @@ -301,11 +301,11 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst

ggml_cuda_set_device(ctx.device);
const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
const int32_t precision = KQV->op_params[3];
const enum ggml_prec prec = ggml_flash_attn_ext_get_prec(KQV);

// On AMD the tile kernels perform poorly, use the vec kernel instead:
if (cc >= CC_OFFSET_AMD) {
if (precision == GGML_PREC_DEFAULT && fast_fp16_available(cc)) {
if (prec == GGML_PREC_DEFAULT && fast_fp16_available(cc)) {
ggml_cuda_flash_attn_ext_vec_f16(ctx, dst);
} else {
ggml_cuda_flash_attn_ext_vec_f32(ctx, dst);
Expand All @@ -332,7 +332,7 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst
}

if (Q->ne[1] == 1 && Q->ne[0] % (2*WARP_SIZE) == 0) {
if (precision == GGML_PREC_DEFAULT) {
if (prec == GGML_PREC_DEFAULT) {
ggml_cuda_flash_attn_ext_vec_f16(ctx, dst);
return;
} else if(Q->ne[0] <= 128) {
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74 changes: 56 additions & 18 deletions ggml/src/ggml-metal.m
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Original file line number Diff line number Diff line change
Expand Up @@ -269,6 +269,12 @@ static void ggml_backend_metal_device_rel(struct ggml_backend_metal_device_conte
GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112,
GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128,
GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256,
GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H64,
GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H80,
GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H96,
GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H112,
GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H128,
GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H256,
GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H64,
GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H80,
GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H96,
Expand Down Expand Up @@ -300,12 +306,14 @@ static void ggml_backend_metal_device_rel(struct ggml_backend_metal_device_conte
GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H128,
GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H256,
GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128,
GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_H128,
GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_H128,
GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_1_H128,
GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_H128,
GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_1_H128,
GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q8_0_H128,
GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256,
GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_H256,
GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_H256,
GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_1_H256,
GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_H256,
Expand Down Expand Up @@ -585,6 +593,9 @@ @implementation GGMLMetalClass
struct ggml_metal_kernel * kernel = &ctx->kernels[e]; \
id<MTLFunction> metal_function = [metal_library newFunctionWithName:@"kernel_"#name]; \
kernel->pipeline = [device newComputePipelineStateWithFunction:metal_function error:&error]; \
GGML_LOG_INFO("%s: loaded %-40s %16p | th_max = %4d | th_width = %4d\n", __func__, "kernel_"#name, (void *) kernel->pipeline, \
(int) kernel->pipeline.maxTotalThreadsPerThreadgroup, \
(int) kernel->pipeline.threadExecutionWidth); \
[metal_function release]; \
if (error) { \
GGML_LOG_ERROR("%s: error: load pipeline error: %s\n", __func__, [[error description] UTF8String]); \
Expand Down Expand Up @@ -777,6 +788,12 @@ @implementation GGMLMetalClass
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112, flash_attn_ext_f16_h112, has_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128, flash_attn_ext_f16_h128, has_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256, flash_attn_ext_f16_h256, has_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H64, flash_attn_ext_bf16_h64, has_simdgroup_mm && has_bfloat);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H80, flash_attn_ext_bf16_h80, has_simdgroup_mm && has_bfloat);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H96, flash_attn_ext_bf16_h96, has_simdgroup_mm && has_bfloat);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H112, flash_attn_ext_bf16_h112, has_simdgroup_mm && has_bfloat);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H128, flash_attn_ext_bf16_h128, has_simdgroup_mm && has_bfloat);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H256, flash_attn_ext_bf16_h256, has_simdgroup_mm && has_bfloat);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H64, flash_attn_ext_q4_0_h64, has_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H80, flash_attn_ext_q4_0_h80, has_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H96, flash_attn_ext_q4_0_h96, has_simdgroup_mm);
Expand Down Expand Up @@ -808,12 +825,14 @@ @implementation GGMLMetalClass
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H128, flash_attn_ext_q8_0_h128, has_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H256, flash_attn_ext_q8_0_h256, has_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128, flash_attn_ext_vec_f16_h128, has_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_H128, flash_attn_ext_vec_bf16_h128, has_simdgroup_reduction && has_bfloat);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_H128, flash_attn_ext_vec_q4_0_h128, has_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_1_H128, flash_attn_ext_vec_q4_1_h128, has_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_H128, flash_attn_ext_vec_q5_0_h128, has_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_1_H128, flash_attn_ext_vec_q5_1_h128, has_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q8_0_H128, flash_attn_ext_vec_q8_0_h128, has_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256, flash_attn_ext_vec_f16_h256, has_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_H256, flash_attn_ext_vec_bf16_h256, has_simdgroup_reduction && has_bfloat);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_H256, flash_attn_ext_vec_q4_0_h256, has_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_1_H256, flash_attn_ext_vec_q4_1_h256, has_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_H256, flash_attn_ext_vec_q5_0_h256, has_simdgroup_reduction);
Expand Down Expand Up @@ -1111,7 +1130,7 @@ static void ggml_metal_encode_node(
const uint64_t nb20 = src2 ? src2->nb[0] : 0; GGML_UNUSED(nb20);
const uint64_t nb21 = src2 ? src2->nb[1] : 0;
const uint64_t nb22 = src2 ? src2->nb[2] : 0;
const uint64_t nb23 = src2 ? src2->nb[3] : 0;
const uint64_t nb23 = src2 ? src2->nb[3] : 0; GGML_UNUSED(nb23);

const int64_t ne0 = dst ? dst->ne[0] : 0;
const int64_t ne1 = dst ? dst->ne[1] : 0;
Expand Down Expand Up @@ -3033,6 +3052,23 @@ static void ggml_metal_encode_node(
}
}
} break;
case GGML_TYPE_BF16:
{
switch (ne00) {
case 64: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H64 ].pipeline; break;
case 80: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H80 ].pipeline; break;
case 96: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H96 ].pipeline; break;
case 112: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H112].pipeline; break;
case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H128].pipeline; break;
case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H256].pipeline; break;
default:
{
GGML_LOG_ERROR("unsupported size: %lld\n", ne00);
GGML_LOG_ERROR("add template specialization for this size\n");
GGML_ABORT("add template specialization for this size");
}
}
} break;
case GGML_TYPE_Q4_0:
{
switch (ne00) {
Expand Down Expand Up @@ -3133,6 +3169,7 @@ static void ggml_metal_encode_node(
{
switch (src1->type) {
case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128].pipeline; break;
case GGML_TYPE_BF16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_H128].pipeline; break;
case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_H128].pipeline; break;
case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_1_H128].pipeline; break;
case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_H128].pipeline; break;
Expand All @@ -3150,6 +3187,7 @@ static void ggml_metal_encode_node(
{
switch (src1->type) {
case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256].pipeline; break;
case GGML_TYPE_BF16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_H256].pipeline; break;
case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_H256].pipeline; break;
case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_1_H256].pipeline; break;
case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_H256].pipeline; break;
Expand Down Expand Up @@ -3194,18 +3232,15 @@ static void ggml_metal_encode_node(
[encoder setBytes:&nb11 length:sizeof(uint64_t) atIndex:14];
[encoder setBytes:&nb12 length:sizeof(uint64_t) atIndex:15];
[encoder setBytes:&nb13 length:sizeof(uint64_t) atIndex:16];
[encoder setBytes:&nb21 length:sizeof(uint64_t) atIndex:17];
[encoder setBytes:&nb22 length:sizeof(uint64_t) atIndex:18];
[encoder setBytes:&nb23 length:sizeof(uint64_t) atIndex:19];
[encoder setBytes:&nb31 length:sizeof(uint64_t) atIndex:20];
[encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:21];
[encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:22];
[encoder setBytes:&scale length:sizeof( float) atIndex:23];
[encoder setBytes:&max_bias length:sizeof( float) atIndex:24];
[encoder setBytes:&m0 length:sizeof(m0) atIndex:25];
[encoder setBytes:&m1 length:sizeof(m1) atIndex:26];
[encoder setBytes:&n_head_log2 length:sizeof(n_head_log2) atIndex:27];
[encoder setBytes:&logit_softcap length:sizeof(logit_softcap) atIndex:28];
[encoder setBytes:&nb31 length:sizeof(uint64_t) atIndex:17];
[encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:18];
[encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:19];
[encoder setBytes:&scale length:sizeof( float) atIndex:20];
[encoder setBytes:&max_bias length:sizeof( float) atIndex:21];
[encoder setBytes:&m0 length:sizeof(m0) atIndex:22];
[encoder setBytes:&m1 length:sizeof(m1) atIndex:23];
[encoder setBytes:&n_head_log2 length:sizeof(n_head_log2) atIndex:24];
[encoder setBytes:&logit_softcap length:sizeof(logit_softcap) atIndex:25];

if (!use_vec_kernel) {
// half8x8 kernel
Expand All @@ -3216,11 +3251,14 @@ static void ggml_metal_encode_node(
GGML_ASSERT(nqptg % 8 == 0);
GGML_ASSERT(ncpsg % 32 == 0);

// 2*(2*ncpsg + nqptg)*(nsg)
// ncpsg soft_max values + ncpsg mask values + a diagonal scaling matrix (in float)
//
// 16*32*(nsg)
// the shared memory needed for the simdgroups to load the KV cache
// each thread loads (dequantizes) 16 head elements, there are 32 threads in th SG
//
#define FATTN_SMEM(nsg) (GGML_PAD((nqptg*(ne00 + 2*(ncpsg + nqptg)*(nsg)) + 16*32*(nsg))*(sizeof(float)/2), 16))
#define FATTN_SMEM(nsg) (GGML_PAD((nqptg*(ne00 + 2*(2*ncpsg + nqptg)*(nsg)) + 16*32*(nsg))*(sizeof(float)/2), 16))

int64_t nsgmax = 2;

Expand Down Expand Up @@ -3254,12 +3292,12 @@ static void ggml_metal_encode_node(

// ne00 + 2*ncpsg*(nsg)
// for each query, we load it as f16 in shared memory (ne00)
// and store the attention scores (nqptg x ncpsg) as f32
// and store the soft_max values and the mask
//
// 2*ne00*(nsg)
// each simdgroup has a full f32 head vector in shared mem to accumulate results
// ne00*(nsg)
// each simdgroup has a full f16 head vector in shared mem to accumulate results
//
#define FATTN_SMEM(nsg) (GGML_PAD((nqptg*(ne00 + 2*ncpsg*(nsg)) + 2*ne00*(nsg))*(sizeof(float)/2), 16))
#define FATTN_SMEM(nsg) (GGML_PAD((nqptg*(ne00 + 2*ncpsg*(nsg)) + ne00*(nsg))*(sizeof(float)/2), 16))

int64_t nsgmax = 2;

Expand Down
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