Mirrors/llama.cpp
1
0
Fork 0
mirror of https://github.com/ggerganov/llama.cpp.git synced 2024-12-25 13:58:46 +01:00
Code Issues Packages Projects Releases Wiki Activity

vulkan: Optimize soft_max (#10301)

Browse Source 
* vulkan: Optimize soft_max

Large soft_max could already saturate memory, but small/medium sizes were
pretty slow. The bulk of the gains for them comes from using a smaller
workgroup size, and making the workgroup size match the subgroup size also
makes the barriers much cheaper.

Cache some values in locals to avoid refetching/recomputing. And stamp
out a few "template instantiations" so smaller cases will fully unroll.

Add a missing early return for OOB rows. This happens when there are more
than 512 rows and the dispatch is 512 x H.

* vulkan: Further soft_max optimizations

Restore the workgroup size of 512 case, use it for >1024.

Use unrollable loops for more iteration counts.
This commit is contained in:
Jeff Bolz 2024-11-19 01:25:17 -06:00 committed by GitHub
parent 557924f222
commit b3e585988f
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
3 changed files with 107 additions and 28 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

13
ggml/src/ggml-vulkan/ggml-vulkan.cpp
View File

@ -218,6 +218,7 @@ struct vk_device_struct {
vk_pipeline pipeline_tanh_f32;
vk_pipeline pipeline_diag_mask_inf_f32;
vk_pipeline pipeline_soft_max_f32, pipeline_soft_max_f32_f16;
vk_pipeline pipeline_soft_max_f32_wg512, pipeline_soft_max_f32_f16_wg512;
vk_pipeline pipeline_rope_norm_f32, pipeline_rope_norm_f16;
vk_pipeline pipeline_rope_neox_f32, pipeline_rope_neox_f16;
vk_pipeline pipeline_argsort_f32;
@ -388,6 +389,7 @@ struct vk_op_soft_max_push_constants {
float m0;
float m1;
uint32_t n_head_log2;
uint32_t nrows_x;
};
struct vk_op_argsort_push_constants {
@ -1497,8 +1499,10 @@ static void ggml_vk_load_shaders(vk_device& device) {
ggml_vk_create_pipeline(device, device->pipeline_diag_mask_inf_f32, "diag_mask_inf_f32", diag_mask_inf_f32_len, diag_mask_inf_f32_data, "main", 2, sizeof(vk_op_diag_mask_push_constants), {512, 1, 1}, {}, 1);
ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32, "soft_max_f32", soft_max_f32_len, soft_max_f32_data, "main", 3, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, {}, 1);
ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32_f16, "soft_max_f32_f16", soft_max_f32_f16_len, soft_max_f32_f16_data, "main", 3, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, {}, 1);
ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32, "soft_max_f32", soft_max_f32_len, soft_max_f32_data, "main", 3, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32_wg512, "soft_max_f32_wg512", soft_max_f32_len, soft_max_f32_data, "main", 3, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 512 }, 1);
ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32_f16, "soft_max_f32_f16", soft_max_f32_f16_len, soft_max_f32_f16_data, "main", 3, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32_f16_wg512, "soft_max_f32_f16_wg512", soft_max_f32_f16_len, soft_max_f32_f16_data, "main", 3, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 512 }, 1);
ggml_vk_create_pipeline(device, device->pipeline_rope_norm_f32, "rope_norm_f32", rope_norm_f32_len, rope_norm_f32_data, "main", 4, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1);
ggml_vk_create_pipeline(device, device->pipeline_rope_norm_f16, "rope_norm_f16", rope_norm_f16_len, rope_norm_f16_data, "main", 4, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1);
@ -3932,10 +3936,10 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16);
if (src0->type == GGML_TYPE_F32 && (src1 == nullptr || src1->type == GGML_TYPE_F32) && dst->type == GGML_TYPE_F32) {
return ctx->device->pipeline_soft_max_f32;
return src0->ne[0] > 1024 ? ctx->device->pipeline_soft_max_f32_wg512 : ctx->device->pipeline_soft_max_f32;
}
if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F32) {
return ctx->device->pipeline_soft_max_f32_f16;
return src0->ne[0] > 1024 ? ctx->device->pipeline_soft_max_f32_f16_wg512 : ctx->device->pipeline_soft_max_f32_f16;
}
return nullptr;
case GGML_OP_ROPE:
@ -4581,6 +4585,7 @@ static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context& subctx,
scale, max_bias,
m0, m1,
n_head_log2,
nrows_x,
}, dryrun);
}

114
ggml/src/ggml-vulkan/vulkan-shaders/soft_max.comp
View File

@ -1,6 +1,7 @@
#version 450
#extension GL_EXT_shader_16bit_storage : require
#extension GL_EXT_shader_explicit_arithmetic_types_float16 : require
#extension GL_EXT_control_flow_attributes : enable
layout (push_constant) uniform parameter
{
@ -11,14 +12,13 @@ layout (push_constant) uniform parameter
float m0;
float m1;
uint n_head_log2;
uint nrows_x;
} p;
#include "types.comp"
#extension GL_EXT_control_flow_attributes : enable
#define BLOCK_SIZE 512
layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in;
layout(constant_id = 0) const uint BLOCK_SIZE = 32;
layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
layout (binding = 1) readonly buffer Y {B_TYPE data_b[];};
@ -26,11 +26,18 @@ layout (binding = 2) buffer D {D_TYPE data_d[];};
shared FLOAT_TYPE vals[BLOCK_SIZE];
void main() {
// num_iters is the number of BLOCK_SIZE loop iterations we need to iterate
// over all the columns. The main function tries to pass a constant here,
// as if it were a template function, to allow unrolling.
void soft_max(uint num_iters) {
const uint tid = gl_LocalInvocationID.x;
const uint rowx = gl_WorkGroupID.z * 262144 + gl_WorkGroupID.y * 512 + gl_WorkGroupID.x;
const uint rowy = rowx % p.KY;
if (rowx >= p.nrows_x) {
return;
}
float slope = 1.0f;
// ALiBi
@ -46,19 +53,37 @@ void main() {
// Find max
FLOAT_TYPE max_val = uintBitsToFloat(0xFF800000);
[[unroll]] for (uint col0 = 0; col0 < p.KX; col0 += BLOCK_SIZE) {
// Cache values while we compute the max, so we don't need to read them
// again when we're ready to compute exp(x-max).
const uint DATA_CACHE_SIZE = 16;
FLOAT_TYPE data_cache[DATA_CACHE_SIZE];
[[unroll]] for (uint col0 = 0, idx = 0; idx < num_iters; col0 += BLOCK_SIZE, ++idx) {
const uint col = col0 + tid;
if (col >= p.KX) {
break;
FLOAT_TYPE a = FLOAT_TYPE(0);
if (col < p.KX) {
a = data_a[rowx * p.KX + col];
}
max_val = max(max_val, FLOAT_TYPE(data_a[rowx * p.KX + col]) * p.scale + (p.KY > 0 ? slope * FLOAT_TYPE(data_b[rowy * p.KX + col]) : FLOAT_TYPE(0.0f)));
}
vals[tid] = max_val;
FLOAT_TYPE b = FLOAT_TYPE(0);
if (p.KY > 0 && col < p.KX) {
b = data_b[rowy * p.KX + col];
}
FLOAT_TYPE v = a * p.scale + slope * b;
max_val = max(max_val, v);
if (idx < DATA_CACHE_SIZE) {
data_cache[idx] = v;
}
}
// reduce across the workgroup
vals[tid] = max_val;
barrier();
[[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) {
[[unroll]] for (uint s = BLOCK_SIZE / 2; s > 0; s >>= 1) {
if (tid < s) {
vals[tid] = max(vals[tid], vals[tid + s]);
}
@ -68,39 +93,80 @@ void main() {
max_val = vals[0];
barrier();
// Sum up values
vals[tid] = FLOAT_TYPE(0.0f);
FLOAT_TYPE sum = FLOAT_TYPE(0.0f);
[[unroll]] for (uint col0 = 0; col0 < p.KX; col0 += BLOCK_SIZE) {
// Compute sum{exp(x - max)}
[[unroll]] for (uint col0 = 0, idx = 0; idx < num_iters; col0 += BLOCK_SIZE, ++idx) {
const uint col = col0 + tid;
if (col >= p.KX) {
break;
}
// compute exp(a*scale+b*slope), add it to sum, and cache the new value
// in data_cache if possible.
const uint i = rowx * p.KX + col;
const FLOAT_TYPE val = exp(FLOAT_TYPE(data_a[i]) * p.scale + (p.KY > 0 ? slope * FLOAT_TYPE(data_b[rowy * p.KX + col]) : FLOAT_TYPE(0.0f)) - max_val);
vals[tid] += val;
data_d[i] = D_TYPE(val);
FLOAT_TYPE val;
if (idx < DATA_CACHE_SIZE) {
val = exp(data_cache[idx] - max_val);
} else {
val = exp(FLOAT_TYPE(data_a[i]) * p.scale + (p.KY > 0 ? slope * FLOAT_TYPE(data_b[rowy * p.KX + col]) : FLOAT_TYPE(0.0f)) - max_val);
}
sum += val;
if (idx < DATA_CACHE_SIZE) {
data_cache[idx] = val;
} else {
data_d[i] = D_TYPE(val);
}
}
// reduce across the workgroup
vals[tid] = sum;
barrier();
[[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) {
[[unroll]] for (uint s = BLOCK_SIZE / 2; s > 0; s >>= 1) {
if (tid < s) {
vals[tid] += vals[tid + s];
}
barrier();
}
sum = vals[0];
const D_TYPE divisor = D_TYPE(vals[0]);
FLOAT_TYPE rcpdivisor = 1.0/sum;
[[unroll]] for (uint col0 = 0; col0 < p.KX; col0 += BLOCK_SIZE) {
[[unroll]] for (uint col0 = 0, idx = 0; idx < num_iters; col0 += BLOCK_SIZE, ++idx) {
const uint col = col0 + tid;
if (col >= p.KX) {
break;
continue;
}
data_d[rowx*p.KX + col] /= divisor;
if (idx < DATA_CACHE_SIZE) {
data_d[rowx*p.KX + col] = D_TYPE(data_cache[idx] * rcpdivisor);
} else {
data_d[rowx*p.KX + col] *= D_TYPE(rcpdivisor);
}
}
}
void main() {
// instantiate the soft_max function for several different
// dimensions, to allow loop unrolling
uint num_blocks = (p.KX + BLOCK_SIZE - 1) / BLOCK_SIZE;
if (num_blocks > 32) {
soft_max(num_blocks);
} else if (num_blocks > 16) {
soft_max(32);
} else if (num_blocks > 8) {
soft_max(16);
} else if (num_blocks > 4) {
soft_max(8);
} else if (num_blocks == 4) {
soft_max(4);
} else if (num_blocks == 3) {
soft_max(3);
} else if (num_blocks == 2) {
soft_max(2);
} else if (num_blocks == 1) {
soft_max(1);
}
}

8
tests/test-backend-ops.cpp
View File

@ -3823,6 +3823,14 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_perf() {
test_cases.emplace_back(new test_cpy(GGML_TYPE_F32, GGML_TYPE_F16, {512, 3072, 1, 1}));
test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {4096, 4096, 5, 1}, false, 1.0f, 0.0f));
test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {77, 4096, 5, 1}, false, 1.0f, 0.0f));
test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {1024, 1024, 10, 1}, false, 1.0f, 0.0f));
test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {77, 1024, 10, 1}, false, 1.0f, 0.0f));
test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {256, 256, 20, 1}, false, 1.0f, 0.0f));
test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {64, 64, 20, 1}, false, 1.0f, 0.0f));
test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {77, 64, 20, 1}, false, 1.0f, 0.0f));
for (int bs : {1, 512}) {
for (ggml_type type_a : all_types) {
for (ggml_type type_b : {GGML_TYPE_F32}) {