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vulkan: Add VK_NV_cooperative_matrix2 support for mul_mat and flash attention (#10206)

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This commit is contained in:
Jeff Bolz
2024-12-05 13:15:05 -06:00
committed by GitHub
parent 6fe6247831
commit c9c6e01dae
6 changed files with 1665 additions and 97 deletions
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741
ggml/src/ggml-vulkan/ggml-vulkan.cpp
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@@ -167,6 +167,7 @@ struct vk_device_struct {
uint32_t subgroup_size;
uint32_t shader_core_count;
bool uma;
bool coopmat2;
size_t idx;
@@ -176,6 +177,7 @@ struct vk_device_struct {
vk_matmul_pipeline2 pipeline_matmul_f16_f32;
vk_pipeline pipeline_matmul_split_k_reduce;
vk_matmul_pipeline2 pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_COUNT];
vk_matmul_pipeline2 pipeline_dequant_mul_mat_mat[GGML_TYPE_COUNT];
vk_matmul_pipeline pipeline_matmul_id_f32;
@@ -229,6 +231,14 @@ struct vk_device_struct {
vk_pipeline pipeline_timestep_embedding_f32;
vk_pipeline pipeline_pool2d_f32;
// [2][2][2] is for {f16acc,f32acc}x{large,small_rows}x{unaligned, aligned}
vk_pipeline pipeline_flash_attn_f32_f16_D64[GGML_TYPE_COUNT][2][2][2];
vk_pipeline pipeline_flash_attn_f32_f16_D80[GGML_TYPE_COUNT][2][2][2];
vk_pipeline pipeline_flash_attn_f32_f16_D96[GGML_TYPE_COUNT][2][2][2];
vk_pipeline pipeline_flash_attn_f32_f16_D112[GGML_TYPE_COUNT][2][2][2];
vk_pipeline pipeline_flash_attn_f32_f16_D128[GGML_TYPE_COUNT][2][2][2];
vk_pipeline pipeline_flash_attn_f32_f16_D256[GGML_TYPE_COUNT][2][2][2];
std::unordered_map<std::string, vk_pipeline_ref> pipelines;
std::unordered_map<std::string, uint64_t> pipeline_descriptor_set_requirements;
@@ -340,6 +350,40 @@ struct vk_mat_vec_id_push_constants {
uint32_t nei0; uint32_t ne11;
};
struct vk_flash_attn_push_constants {
uint32_t N;
uint32_t KV;
uint32_t ne1;
uint32_t ne2;
uint32_t ne3;
uint32_t neq2;
uint32_t neq3;
uint32_t nek2;
uint32_t nek3;
uint32_t nev2;
uint32_t nev3;
uint32_t nem1;
uint32_t nb02;
uint32_t nb03;
uint32_t nb12;
uint32_t nb13;
uint32_t nb22;
uint32_t nb23;
uint32_t nb31;
float scale;
float max_bias;
float logit_softcap;
uint32_t mask;
uint32_t n_head_log2;
float m0;
float m1;
};
struct vk_op_push_constants {
uint32_t KX;
uint32_t KY;
@@ -1265,6 +1309,23 @@ static void ggml_vk_wait_events(vk_context& ctx, std::vector<vk::Event>&& events
);
}
// number of rows/cols for flash attention shader
static constexpr uint32_t flash_attention_num_small_rows = 32;
static std::array<uint32_t, 2> fa_rows_cols(uint32_t D, uint32_t clamp, ggml_type type, bool small_rows) {
GGML_UNUSED(clamp);
// small rows, large cols
if (small_rows) {
return {flash_attention_num_small_rows, 128};
}
// small cols to reduce register count
if (ggml_is_quantized(type) || D == 256) {
return {64, 32};
}
return {64, 64};
};
static void ggml_vk_load_shaders(vk_device& device) {
VK_LOG_DEBUG("ggml_vk_load_shaders(" << device->name << ")");
@@ -1275,59 +1336,98 @@ static void ggml_vk_load_shaders(vk_device& device) {
// mulmat
std::vector<uint32_t> l_warptile, m_warptile, s_warptile,
l_warptile_mmq, m_warptile_mmq, s_warptile_mmq;
l_warptile_mmq, m_warptile_mmq, s_warptile_mmq,
l_warptile_mmq_k, m_warptile_mmq_k, s_warptile_mmq_k,
l_warptile_mmqid, m_warptile_mmqid, s_warptile_mmqid;
std::array<uint32_t, 3> l_wg_denoms, m_wg_denoms, s_wg_denoms,
l_mmq_wg_denoms, m_mmq_wg_denoms, s_mmq_wg_denoms;
l_mmq_wg_denoms, m_mmq_wg_denoms, s_mmq_wg_denoms,
l_mmq_wg_denoms_k, m_mmq_wg_denoms_k, s_mmq_wg_denoms_k,
l_mmqid_wg_denoms, m_mmqid_wg_denoms, s_mmqid_wg_denoms;
uint32_t l_align, m_align, s_align;
if (device->coopmat2) {
// spec constants and tile sizes for non-quant matmul/matmul_id
l_warptile = { 256, 128, 256, 64 };
m_warptile = { 256, 128, 128, 64 };
s_warptile = { 128, 32, 16, 64 };
l_wg_denoms = {128, 256, 1 };
m_wg_denoms = {128, 128, 1 };
s_wg_denoms = { 32, 16, 1 };
l_warptile = { 128, 128, 128, 16, device->subgroup_size * 2, 64, 2, 4, 4, device->subgroup_size };
m_warptile = { 128, 64, 64, 16, device->subgroup_size, 32, 2, 4, 2, device->subgroup_size };
s_warptile = { subgroup_size_16, 32, 32, 16, 32, 32, 2, 2, 2, device->subgroup_size };
// spec constants and tile sizes for quant matmul (non-Qi_K)
l_warptile_mmq = { 256, 128, 256, 64 };
m_warptile_mmq = { 256, 128, 128, 64 };
s_warptile_mmq = { 256, 128, 128, 64 };
l_mmq_wg_denoms = { 128, 256, 1 };
m_mmq_wg_denoms = { 128, 128, 1 };
s_mmq_wg_denoms = { 128, 128, 1 };
l_warptile_mmq = { 128, 128, 128, 32, device->subgroup_size * 2, 64, 2, 4, 4, device->subgroup_size };
m_warptile_mmq = { 128, 64, 64, 32, device->subgroup_size, 32, 2, 4, 2, device->subgroup_size };
s_warptile_mmq = { subgroup_size_16, 32, 32, 32, 32, 32, 2, 2, 2, device->subgroup_size };
// spec constants and tile sizes for quant matmul (Qi_K)
l_warptile_mmq_k = { 256, 128, 512, 16 };
m_warptile_mmq_k = { 256, 128, 256, 16 };
s_warptile_mmq_k = { 256, 32, 128, 64 };
l_mmq_wg_denoms_k = { 128, 512, 1 };
m_mmq_wg_denoms_k = { 128, 256, 1 };
s_mmq_wg_denoms_k = { 32, 128, 1 };
l_mmq_wg_denoms = l_wg_denoms = {128, 128, 1 };
m_mmq_wg_denoms = m_wg_denoms = { 64, 64, 1 };
s_mmq_wg_denoms = s_wg_denoms = { 32, 32, 1 };
// spec constants and tile sizes for quant matmul_id
l_warptile_mmqid = { 256, 128, 128, 16 };
m_warptile_mmqid = { 256, 128, 64, 16 };
s_warptile_mmqid = { 256, 64, 64, 16 };
l_mmqid_wg_denoms = { 128, 128, 1 };
m_mmqid_wg_denoms = { 128, 64, 1 };
s_mmqid_wg_denoms = { 64, 64, 1 };
l_align = 128;
m_align = 64;
s_align = 32;
l_align = 128;
m_align = 64;
s_align = 32;
} else {
l_warptile = { 128, 128, 128, 16, device->subgroup_size * 2, 64, 2, 4, 4, device->subgroup_size };
m_warptile = { 128, 64, 64, 16, device->subgroup_size, 32, 2, 4, 2, device->subgroup_size };
s_warptile = { subgroup_size_16, 32, 32, 16, 32, 32, 2, 2, 2, device->subgroup_size };
l_warptile_mmq = { 128, 128, 128, 32, device->subgroup_size * 2, 64, 2, 4, 4, device->subgroup_size };
m_warptile_mmq = { 128, 64, 64, 32, device->subgroup_size, 32, 2, 4, 2, device->subgroup_size };
s_warptile_mmq = { subgroup_size_16, 32, 32, 32, 32, 32, 2, 2, 2, device->subgroup_size };
l_mmq_wg_denoms = l_wg_denoms = {128, 128, 1 };
m_mmq_wg_denoms = m_wg_denoms = { 64, 64, 1 };
s_mmq_wg_denoms = s_wg_denoms = { 32, 32, 1 };
l_align = 128;
m_align = 64;
s_align = 32;
// Fallback to smaller sizes if there's not enough shared memory. Given the current shaders
// and tile sizes, this should handle 16KB, 32KB, and 48KB+.
// This logic doesn't explicitly account for the 12KB row_ids in the mul_mat_mat_id shaders.
// But the numbers happen to work out for 32KB shared memory size that when using the medium
// size there's enough room for everything, and we assert for this.
uint32_t shmem_needed = (l_warptile[1] + l_warptile[2]) * (l_warptile[3] + 1) * sizeof(float);
if (shmem_needed > device->properties.limits.maxComputeSharedMemorySize) {
l_warptile = m_warptile;
l_wg_denoms = m_wg_denoms;
shmem_needed = (l_warptile[1] + l_warptile[2]) * (l_warptile[3] + 1) * sizeof(float);
GGML_ASSERT(shmem_needed <= device->properties.limits.maxComputeSharedMemorySize);
}
if (device->properties.limits.maxComputeSharedMemorySize >= 32768) {
// assert mul_mat_mat_id shaders will fit.
GGML_ASSERT(shmem_needed + 3072*4 <= device->properties.limits.maxComputeSharedMemorySize);
}
shmem_needed = (l_warptile_mmq[1] + l_warptile_mmq[2]) * (l_warptile_mmq[3] + 1) * sizeof(float);
if (shmem_needed > device->properties.limits.maxComputeSharedMemorySize) {
if (device->properties.limits.maxComputeSharedMemorySize == 32768) {
l_warptile_mmq = m_warptile_mmq;
l_mmq_wg_denoms = m_mmq_wg_denoms;
} else {
l_warptile_mmq = s_warptile_mmq;
l_mmq_wg_denoms = s_mmq_wg_denoms;
// Fallback to smaller sizes if there's not enough shared memory. Given the current shaders
// and tile sizes, this should handle 16KB, 32KB, and 48KB+.
// This logic doesn't explicitly account for the 12KB row_ids in the mul_mat_mat_id shaders.
// But the numbers happen to work out for 32KB shared memory size that when using the medium
// size there's enough room for everything, and we assert for this.
uint32_t shmem_needed = (l_warptile[1] + l_warptile[2]) * (l_warptile[3] + 1) * sizeof(float);
if (shmem_needed > device->properties.limits.maxComputeSharedMemorySize) {
l_warptile = m_warptile;
l_wg_denoms = m_wg_denoms;
shmem_needed = (l_warptile[1] + l_warptile[2]) * (l_warptile[3] + 1) * sizeof(float);
GGML_ASSERT(shmem_needed <= device->properties.limits.maxComputeSharedMemorySize);
}
if (device->properties.limits.maxComputeSharedMemorySize >= 32768) {
// assert mul_mat_mat_id shaders will fit.
GGML_ASSERT(shmem_needed + 3072*4 <= device->properties.limits.maxComputeSharedMemorySize);
}
shmem_needed = (l_warptile_mmq[1] + l_warptile_mmq[2]) * (l_warptile_mmq[3] + 1) * sizeof(float);
GGML_ASSERT(shmem_needed <= device->properties.limits.maxComputeSharedMemorySize);
}
if (device->properties.limits.maxComputeSharedMemorySize >= 32768) {
// assert mul_mat_mat_id shaders will fit.
GGML_ASSERT(shmem_needed + 3072*4 <= device->properties.limits.maxComputeSharedMemorySize);
if (shmem_needed > device->properties.limits.maxComputeSharedMemorySize) {
if (device->properties.limits.maxComputeSharedMemorySize == 32768) {
l_warptile_mmq = m_warptile_mmq;
l_mmq_wg_denoms = m_mmq_wg_denoms;
} else {
l_warptile_mmq = s_warptile_mmq;
l_mmq_wg_denoms = s_mmq_wg_denoms;
}
shmem_needed = (l_warptile_mmq[1] + l_warptile_mmq[2]) * (l_warptile_mmq[3] + 1) * sizeof(float);
GGML_ASSERT(shmem_needed <= device->properties.limits.maxComputeSharedMemorySize);
}
if (device->properties.limits.maxComputeSharedMemorySize >= 32768) {
// assert mul_mat_mat_id shaders will fit.
GGML_ASSERT(shmem_needed + 3072*4 <= device->properties.limits.maxComputeSharedMemorySize);
}
}
device->pipeline_matmul_f32 = std::make_shared<vk_matmul_pipeline_struct>();
@@ -1362,6 +1462,105 @@ static void ggml_vk_load_shaders(vk_device& device) {
compiles.push_back(std::async(ggml_vk_create_pipeline_func, std::ref(device), std::ref(pipeline), name, spv_size, spv_data, entrypoint, parameter_count, push_constant_size, wg_denoms, specialization_constants, align, disable_robustness));
};
#if defined(VK_NV_cooperative_matrix2)
if (device->coopmat2) {
auto const &fa_wg_denoms = [&](uint32_t D, uint32_t clamp, ggml_type type, bool small_rows) -> std::array<uint32_t, 3> {
return {fa_rows_cols(D, clamp, type, small_rows)[0], 1, 1};
};
auto const &fa_spec_constants = [&](uint32_t D, uint32_t clamp, ggml_type type, bool small_rows) -> std::vector<uint32_t> {
// For large number of rows, 128 invocations seems to work best.
// For small number of rows (e.g. N==1), 256 works better. But matrix granularity for 256 is 32, so we
// can't use 256 for D==80.
uint32_t wg_size = (small_rows && (D % 32) == 0) ? 256 : 128;
auto rows_cols = fa_rows_cols(D, clamp, type, small_rows);
return {wg_size, rows_cols[0], rows_cols[1], (D), clamp};
};
#define CREATE_FA2(TYPE, NAMELC, D) \
ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16_D ## D[TYPE][0][0][0], "flash_attn_f32_f16_D" #D "_f16acc" #NAMELC, flash_attn_f32_f16_ ## NAMELC ## _f16acc_cm2_len, flash_attn_f32_f16_ ## NAMELC ## _f16acc_cm2_data, "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(D,1,TYPE,false), fa_spec_constants(D,1,TYPE,false), 1); \
ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16_D ## D[TYPE][0][0][1], "flash_attn_f32_f16_D" #D "_aligned_f16acc" #NAMELC, flash_attn_f32_f16_ ## NAMELC ## _f16acc_cm2_len, flash_attn_f32_f16_ ## NAMELC ## _f16acc_cm2_data, "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(D,0,TYPE,false), fa_spec_constants(D,0,TYPE,false), fa_rows_cols(D,0,TYPE,false)[1]); \
ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16_D ## D[TYPE][1][0][0], "flash_attn_f32_f16_D" #D "_f32acc" #NAMELC, flash_attn_f32_f16_ ## NAMELC ## _cm2_len, flash_attn_f32_f16_ ## NAMELC ## _cm2_data, "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(D,1,TYPE,false), fa_spec_constants(D,1,TYPE,false), 1); \
ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16_D ## D[TYPE][1][0][1], "flash_attn_f32_f16_D" #D "_aligned_f32acc" #NAMELC, flash_attn_f32_f16_ ## NAMELC ## _cm2_len, flash_attn_f32_f16_ ## NAMELC ## _cm2_data, "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(D,0,TYPE,false), fa_spec_constants(D,0,TYPE,false), fa_rows_cols(D,0,TYPE,false)[1]); \
ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16_D ## D[TYPE][0][1][0], "flash_attn_f32_f16_D" #D "_f16acc_smallrows" #NAMELC, flash_attn_f32_f16_ ## NAMELC ## _f16acc_cm2_len, flash_attn_f32_f16_ ## NAMELC ## _f16acc_cm2_data, "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(D,1,TYPE,true), fa_spec_constants(D,1,TYPE,true), 1); \
ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16_D ## D[TYPE][0][1][1], "flash_attn_f32_f16_D" #D "_aligned_f16acc_smallrows" #NAMELC, flash_attn_f32_f16_ ## NAMELC ## _f16acc_cm2_len, flash_attn_f32_f16_ ## NAMELC ## _f16acc_cm2_data, "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(D,0,TYPE,true), fa_spec_constants(D,0,TYPE,true), fa_rows_cols(D,0,TYPE,true)[1]); \
ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16_D ## D[TYPE][1][1][0], "flash_attn_f32_f16_D" #D "_f32acc_smallrows" #NAMELC, flash_attn_f32_f16_ ## NAMELC ## _cm2_len, flash_attn_f32_f16_ ## NAMELC ## _cm2_data, "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(D,1,TYPE,true), fa_spec_constants(D,1,TYPE,true), 1); \
ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16_D ## D[TYPE][1][1][1], "flash_attn_f32_f16_D" #D "_aligned_f32acc_smallrows" #NAMELC, flash_attn_f32_f16_ ## NAMELC ## _cm2_len, flash_attn_f32_f16_ ## NAMELC ## _cm2_data, "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(D,0,TYPE,true), fa_spec_constants(D,0,TYPE,true), fa_rows_cols(D,0,TYPE,true)[1]); \
#define CREATE_FA(TYPE, NAMELC) \
CREATE_FA2(TYPE, NAMELC, 64) \
CREATE_FA2(TYPE, NAMELC, 80) \
CREATE_FA2(TYPE, NAMELC, 96) \
CREATE_FA2(TYPE, NAMELC, 112) \
CREATE_FA2(TYPE, NAMELC, 128) \
CREATE_FA2(TYPE, NAMELC, 256)
CREATE_FA(GGML_TYPE_F16, f16)
CREATE_FA(GGML_TYPE_Q4_0, q4_0)
CREATE_FA(GGML_TYPE_Q4_1, q4_1)
CREATE_FA(GGML_TYPE_Q5_0, q5_0)
CREATE_FA(GGML_TYPE_Q5_1, q5_1)
CREATE_FA(GGML_TYPE_Q8_0, q8_0)
// K dequants currently disabled because D dimension is rounded up to 256 and runs inefficiently
//CREATE_FA(GGML_TYPE_Q2_K, q2_k)
//CREATE_FA(GGML_TYPE_Q3_K, q3_k)
//CREATE_FA(GGML_TYPE_Q4_K, q4_k)
//CREATE_FA(GGML_TYPE_Q5_K, q5_k)
//CREATE_FA(GGML_TYPE_Q6_K, q6_k)
CREATE_FA(GGML_TYPE_IQ4_NL, iq4_nl)
#undef CREATE_FA
// Create 6 variants, {s,m,l}x{unaligned,aligned}
#define CREATE_MM(PIPELINE_NAME, NAMELC, F16ACC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT) \
ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->l, #NAMELC #F16ACC "_l", NAMELC ## F16ACC ## _cm2_len, NAMELC ## F16ACC ## _cm2_data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, 1); \
ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->m, #NAMELC #F16ACC "_m", NAMELC ## F16ACC ## _cm2_len, NAMELC ## F16ACC ## _cm2_data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, 1); \
ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->s, #NAMELC #F16ACC "_s", NAMELC ## F16ACC ## _cm2_len, NAMELC ## F16ACC ## _cm2_data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, 1); \
ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_l, #NAMELC #F16ACC "_aligned_l", NAMELC ## _aligned ## F16ACC ## _cm2_len, NAMELC ## _aligned ## F16ACC ## _cm2_data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, l_align); \
ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_m, #NAMELC #F16ACC "_aligned_m", NAMELC ## _aligned ## F16ACC ## _cm2_len, NAMELC ## _aligned ## F16ACC ## _cm2_data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, m_align); \
ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_s, #NAMELC #F16ACC "_aligned_s", NAMELC ## _aligned ## F16ACC ## _cm2_len, NAMELC ## _aligned ## F16ACC ## _cm2_data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, s_align); \
// Create 2 variants, {f16,f32} accumulator
#define CREATE_MM2(PIPELINE_NAME, NAMELC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT) \
CREATE_MM(PIPELINE_NAME . f16acc, NAMELC, _f16acc, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT) \
CREATE_MM(PIPELINE_NAME . f32acc, NAMELC, , WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT) \
CREATE_MM(pipeline_matmul_f32, matmul_f32_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 3)
CREATE_MM(pipeline_matmul_f32_f16, matmul_f32_f16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3)
CREATE_MM2(pipeline_matmul_f16, matmul_f16, wg_denoms, warptile, vk_mat_mat_push_constants, 3)
CREATE_MM2(pipeline_matmul_f16_f32, matmul_f16_f32, wg_denoms, warptile, vk_mat_mat_push_constants, 3)
CREATE_MM(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q4_0].f16acc, matmul_q4_0_f16, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3)
CREATE_MM(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q4_1].f16acc, matmul_q4_1_f16, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3)
CREATE_MM(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q5_0].f16acc, matmul_q5_0_f16, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3)
CREATE_MM(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q5_1].f16acc, matmul_q5_1_f16, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3)
CREATE_MM(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q8_0].f16acc, matmul_q8_0_f16, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3)
CREATE_MM(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q2_K].f16acc, matmul_q2_k_f16, _f16acc, mmq_wg_denoms_k, warptile_mmq_k, vk_mat_mat_push_constants, 3)
CREATE_MM(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q3_K].f16acc, matmul_q3_k_f16, _f16acc, mmq_wg_denoms_k, warptile_mmq_k, vk_mat_mat_push_constants, 3)
CREATE_MM(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q4_K].f16acc, matmul_q4_k_f16, _f16acc, mmq_wg_denoms_k, warptile_mmq_k, vk_mat_mat_push_constants, 3)
CREATE_MM(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q5_K].f16acc, matmul_q5_k_f16, _f16acc, mmq_wg_denoms_k, warptile_mmq_k, vk_mat_mat_push_constants, 3)
CREATE_MM(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q6_K].f16acc, matmul_q6_k_f16, _f16acc, mmq_wg_denoms_k, warptile_mmq_k, vk_mat_mat_push_constants, 3)
CREATE_MM(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_IQ4_NL].f16acc, matmul_iq4_nl_f16, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3)
CREATE_MM(pipeline_matmul_id_f32, matmul_id_f32_f32, , wg_denoms, warptile, vk_mat_mat_id_push_constants, 4)
CREATE_MM(pipeline_matmul_id_f16, matmul_id_f16, , wg_denoms, warptile, vk_mat_mat_id_push_constants, 4)
CREATE_MM(pipeline_matmul_id_f16_f32, matmul_id_f16_f32, , wg_denoms, warptile, vk_mat_mat_id_push_constants, 4)
CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0], matmul_id_q4_0_f32, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1], matmul_id_q4_1_f32, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0], matmul_id_q5_0_f32, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1], matmul_id_q5_1_f32, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0], matmul_id_q8_0_f32, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K], matmul_id_q2_k_f32, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K], matmul_id_q3_k_f32, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K], matmul_id_q4_k_f32, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K], matmul_id_q5_k_f32, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K], matmul_id_q6_k_f32, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL], matmul_id_iq4_nl_f32, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
#undef CREATE_MM
#undef CREATE_MM2
} else
#endif
if (device->fp16) {
// Create 6 variants, {s,m,l}x{unaligned,aligned}
#define CREATE_MM(PIPELINE_NAME, NAMELC, F16ACC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT) \
@@ -1648,15 +1847,28 @@ static vk_device ggml_vk_get_device(size_t idx) {
device->physical_device = physical_devices[dev_num];
const std::vector<vk::ExtensionProperties> ext_props = device->physical_device.enumerateDeviceExtensionProperties();
bool fp16_storage = false;
bool fp16_compute = false;
bool maintenance4_support = false;
bool sm_builtins = false;
bool pipeline_robustness = false;
bool coopmat2_support = false;
// Check if maintenance4 is supported
for (const auto& properties : ext_props) {
if (strcmp("VK_KHR_maintenance4", properties.extensionName) == 0) {
maintenance4_support = true;
} else if (strcmp("VK_KHR_16bit_storage", properties.extensionName) == 0) {
fp16_storage = true;
} else if (strcmp("VK_KHR_shader_float16_int8", properties.extensionName) == 0) {
fp16_compute = true;
} else if (strcmp("VK_NV_shader_sm_builtins", properties.extensionName) == 0) {
sm_builtins = true;
} else if (strcmp("VK_EXT_pipeline_robustness", properties.extensionName) == 0) {
pipeline_robustness = true;
} else if (strcmp("VK_NV_cooperative_matrix2", properties.extensionName) == 0 &&
!getenv("GGML_VULKAN_DISABLE_COOPMAT2")) {
coopmat2_support = true;
}
}
@@ -1679,6 +1891,14 @@ static vk_device ggml_vk_get_device(size_t idx) {
last_struct = (VkBaseOutStructure *)&sm_props;
}
#if defined(VK_NV_cooperative_matrix2)
vk::PhysicalDeviceCooperativeMatrix2PropertiesNV coopmat2_props;
if (coopmat2_support) {
last_struct->pNext = (VkBaseOutStructure *)&coopmat2_props;
last_struct = (VkBaseOutStructure *)&coopmat2_props;
}
#endif
device->physical_device.getProperties2(&props2);
device->properties = props2.properties;
@@ -1701,20 +1921,6 @@ static vk_device ggml_vk_get_device(size_t idx) {
device->shader_core_count = 0;
}
bool fp16_storage = false;
bool fp16_compute = false;
bool pipeline_robustness = false;
for (const auto& properties : ext_props) {
if (strcmp("VK_KHR_16bit_storage", properties.extensionName) == 0) {
fp16_storage = true;
} else if (strcmp("VK_KHR_shader_float16_int8", properties.extensionName) == 0) {
fp16_compute = true;
} else if (strcmp("VK_EXT_pipeline_robustness", properties.extensionName) == 0) {
pipeline_robustness = true;
}
}
const char* GGML_VK_DISABLE_F16 = getenv("GGML_VK_DISABLE_F16");
const bool force_disable_f16 = GGML_VK_DISABLE_F16 != nullptr;
@@ -1757,22 +1963,112 @@ static vk_device ggml_vk_get_device(size_t idx) {
vk12_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_2_FEATURES;
vk11_features.pNext = &vk12_features;
last_struct = (VkBaseOutStructure *)&vk12_features;
VkPhysicalDevicePipelineRobustnessFeaturesEXT pl_robustness_features;
pl_robustness_features.pNext = nullptr;
pl_robustness_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_PIPELINE_ROBUSTNESS_FEATURES_EXT;
pl_robustness_features.pipelineRobustness = VK_FALSE;
if (pipeline_robustness) {
vk12_features.pNext = &pl_robustness_features;
last_struct->pNext = (VkBaseOutStructure *)&pl_robustness_features;
last_struct = (VkBaseOutStructure *)&pl_robustness_features;
device_extensions.push_back("VK_EXT_pipeline_robustness");
}
#if defined(VK_NV_cooperative_matrix2)
VkPhysicalDeviceCooperativeMatrix2FeaturesNV coopmat2_features {};
coopmat2_features.pNext = nullptr;
coopmat2_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_COOPERATIVE_MATRIX_2_FEATURES_NV;
if (coopmat2_support) {
last_struct->pNext = (VkBaseOutStructure *)&coopmat2_features;
last_struct = (VkBaseOutStructure *)&coopmat2_features;
device_extensions.push_back("VK_NV_cooperative_matrix2");
}
#endif
vkGetPhysicalDeviceFeatures2(device->physical_device, &device_features2);
device->fp16 = device->fp16 && vk12_features.shaderFloat16;
device->pipeline_robustness = pl_robustness_features.pipelineRobustness;
if (coopmat2_support) {
#if defined(VK_NV_cooperative_matrix2)
if (coopmat2_features.cooperativeMatrixWorkgroupScope &&
coopmat2_features.cooperativeMatrixFlexibleDimensions &&
coopmat2_features.cooperativeMatrixReductions &&
coopmat2_features.cooperativeMatrixConversions &&
coopmat2_features.cooperativeMatrixPerElementOperations &&
coopmat2_features.cooperativeMatrixTensorAddressing &&
coopmat2_features.cooperativeMatrixBlockLoads &&
vk12_features.bufferDeviceAddress) {
std::vector<VkCooperativeMatrixFlexibleDimensionsPropertiesNV> flexible_dimensions;
uint32_t count = 0;
PFN_vkGetPhysicalDeviceCooperativeMatrixFlexibleDimensionsPropertiesNV
_vkGetPhysicalDeviceCooperativeMatrixFlexibleDimensionsPropertiesNV =
(PFN_vkGetPhysicalDeviceCooperativeMatrixFlexibleDimensionsPropertiesNV)
vk_instance.instance.getProcAddr("vkGetPhysicalDeviceCooperativeMatrixFlexibleDimensionsPropertiesNV");
_vkGetPhysicalDeviceCooperativeMatrixFlexibleDimensionsPropertiesNV(device->physical_device, &count, nullptr);
VkCooperativeMatrixFlexibleDimensionsPropertiesNV empty_prop {};
empty_prop.sType = VK_STRUCTURE_TYPE_COOPERATIVE_MATRIX_FLEXIBLE_DIMENSIONS_PROPERTIES_NV;
flexible_dimensions.resize(count, empty_prop);
_vkGetPhysicalDeviceCooperativeMatrixFlexibleDimensionsPropertiesNV(device->physical_device, &count, flexible_dimensions.data());
bool found_fp16_128 = false,
found_fp16_256 = false,
found_fp32_128 = false,
found_fp32_256 = false;
// need to support fp16*fp16 with fp16/fp32 accumulator, for workgroupsize 128
// with 32x16x16 and 256 with 32x32x16.
for (auto &prop : flexible_dimensions) {
if (prop.saturatingAccumulation == VK_FALSE &&
prop.scope == VK_SCOPE_WORKGROUP_KHR &&
prop.AType == VK_COMPONENT_TYPE_FLOAT16_KHR &&
prop.BType == VK_COMPONENT_TYPE_FLOAT16_KHR) {
if (prop.workgroupInvocations == 128 &&
prop.MGranularity <= 32 &&
prop.NGranularity <= 16 &&
prop.KGranularity <= 16) {
if (prop.CType == VK_COMPONENT_TYPE_FLOAT16_KHR &&
prop.ResultType == VK_COMPONENT_TYPE_FLOAT16_KHR) {
found_fp16_128 = true;
}
if (prop.CType == VK_COMPONENT_TYPE_FLOAT32_KHR &&
prop.ResultType == VK_COMPONENT_TYPE_FLOAT32_KHR) {
found_fp32_128 = true;
}
}
if (prop.workgroupInvocations == 256 &&
prop.MGranularity <= 32 &&
prop.NGranularity <= 32 &&
prop.KGranularity <= 16) {
if (prop.CType == VK_COMPONENT_TYPE_FLOAT16_KHR &&
prop.ResultType == VK_COMPONENT_TYPE_FLOAT16_KHR) {
found_fp16_256 = true;
}
if (prop.CType == VK_COMPONENT_TYPE_FLOAT32_KHR &&
prop.ResultType == VK_COMPONENT_TYPE_FLOAT32_KHR) {
found_fp32_256 = true;
}
}
}
}
if (found_fp16_128 && found_fp16_256 &&
found_fp32_128 && found_fp32_256 &&
coopmat2_props.cooperativeMatrixFlexibleDimensionsMaxDimension >= 512) {
device->coopmat2 = true;
}
}
#endif
}
if (!vk11_features.storageBuffer16BitAccess) {
std::cerr << "ggml_vulkan: device " << GGML_VK_NAME << idx << " does not support 16-bit storage." << std::endl;
throw std::runtime_error("Unsupported device");
@@ -2124,7 +2420,7 @@ static vk_pipeline ggml_vk_get_to_fp16(ggml_backend_vk_context * ctx, ggml_type
return ctx->device->pipeline_dequant[type];
}
static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_pipeline(ggml_backend_vk_context * ctx, ggml_type src0_type, ggml_type src1_type) {
static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_pipeline(ggml_backend_vk_context * ctx, ggml_type src0_type, ggml_type src1_type, ggml_prec prec) {
VK_LOG_DEBUG("ggml_vk_get_mul_mat_mat_pipeline(" << ggml_type_name(src0_type) << ", " << ggml_type_name(src1_type) << ")");
if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F32) {
return ctx->device->pipeline_matmul_f32;
@@ -2132,14 +2428,23 @@ static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_pipeline(ggml_backend_vk_conte
if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F16) {
return ctx->device->pipeline_matmul_f32_f16;
}
if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F32) {
return ctx->device->pipeline_matmul_f16_f32.f32acc;
}
if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F16) {
return ctx->device->pipeline_matmul_f16.f32acc;
if (prec == GGML_PREC_DEFAULT && ctx->device->coopmat2) {
if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F32) {
return ctx->device->pipeline_matmul_f16_f32.f16acc;
}
if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F16) {
return ctx->device->pipeline_matmul_f16.f16acc;
}
} else {
if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F32) {
return ctx->device->pipeline_matmul_f16_f32.f32acc;
}
if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F16) {
return ctx->device->pipeline_matmul_f16.f32acc;
}
}
if (src1_type != GGML_TYPE_F32) {
if (src1_type != GGML_TYPE_F32 && !ctx->device->coopmat2) {
return nullptr;
}
@@ -2160,6 +2465,10 @@ static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_pipeline(ggml_backend_vk_conte
return nullptr;
}
if (ctx->device->coopmat2) {
assert(src1_type == GGML_TYPE_F16);
return ctx->device->pipeline_dequant_mul_mat_mat_f16[src0_type].f16acc;
}
return ctx->device->pipeline_dequant_mul_mat_mat[src0_type].f32acc;
}
@@ -2844,6 +3153,16 @@ static vk_pipeline ggml_vk_guess_matmul_pipeline(ggml_backend_vk_context * ctx,
break;
}
if (ctx->device->coopmat2) {
if ((m % mmp->l->wg_denoms[0]) == 0 && (n % mmp->l->wg_denoms[1]) == 0) {
return aligned ? mmp->a_l : mmp->l;
}
if ((m % mmp->m->wg_denoms[0]) == 0 && (n % mmp->m->wg_denoms[1]) == 0) {
return aligned ? mmp->a_m : mmp->m;
}
return aligned ? mmp->a_s : mmp->s;
}
if (m <= 32 || n <= 32) {
return aligned ? mmp->a_s : mmp->s;
}
@@ -3008,18 +3327,20 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context& sub
}
const bool x_non_contig = !ggml_vk_dim01_contiguous(src0);
const bool y_non_contig = !ggml_vk_dim01_contiguous(src1);
// Reformat and convert to fp16 if src1 is non-contiguous, or for coopmat2 for better perf
const bool y_non_contig = (ctx->device->coopmat2 && src1->type == GGML_TYPE_F32) ||
!ggml_vk_dim01_contiguous(src1);
const bool y_f32_kernel = src1->type == GGML_TYPE_F32 && !y_non_contig;
vk_matmul_pipeline mmp = ggml_vk_get_mul_mat_mat_pipeline(ctx, src0->type, y_non_contig ? GGML_TYPE_F16 : src1->type);
vk_matmul_pipeline mmp = ggml_vk_get_mul_mat_mat_pipeline(ctx, src0->type, y_non_contig ? GGML_TYPE_F16 : src1->type, (ggml_prec)dst->op_params[0]);
const bool qx_needs_dequant = mmp == nullptr || x_non_contig;
const bool qy_needs_dequant = (src1->type != GGML_TYPE_F16 && !y_f32_kernel) || y_non_contig;
if (qx_needs_dequant) {
// Fall back to dequant + f16 mulmat
mmp = ggml_vk_get_mul_mat_mat_pipeline(ctx, GGML_TYPE_F16, y_f32_kernel ? GGML_TYPE_F32 : GGML_TYPE_F16);
mmp = ggml_vk_get_mul_mat_mat_pipeline(ctx, GGML_TYPE_F16, y_f32_kernel ? GGML_TYPE_F32 : GGML_TYPE_F16, (ggml_prec)dst->op_params[0]);
}
// Not implemented
@@ -3930,6 +4251,167 @@ static void ggml_vk_mul_mat_id(ggml_backend_vk_context * ctx, vk_context& subctx
}
}
static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * q, const ggml_tensor * k, const ggml_tensor * v, const ggml_tensor * mask, ggml_tensor * dst, bool dryrun = false) {
VK_LOG_DEBUG("ggml_vk_flash_attn((" << q << ", name=" << q->name << ", type=" << q->type << ", ne0=" << q->ne[0] << ", ne1=" << q->ne[1] << ", ne2=" << q->ne[2] << ", ne3=" << q->ne[3] << ", nb0=" << q->nb[0] << ", nb1=" << q->nb[1] << ", nb2=" << q->nb[2] << ", nb3=" << q->nb[3];
std::cerr << "), (" << k << ", name=" << k->name << ", type=" << k->type << ", ne0=" << k->ne[0] << ", ne1=" << k->ne[1] << ", ne2=" << k->ne[2] << ", ne3=" << k->ne[3] << ", nb0=" << k->nb[0] << ", nb1=" << k->nb[1] << ", nb2=" << k->nb[2] << ", nb3=" << k->nb[3];
std::cerr << "), (" << v << ", name=" << v->name << ", type=" << v->type << ", ne0=" << v->ne[0] << ", ne1=" << v->ne[1] << ", ne2=" << v->ne[2] << ", ne3=" << v->ne[3] << ", nb0=" << v->nb[0] << ", nb1=" << v->nb[1] << ", nb2=" << v->nb[2] << ", nb3=" << v->nb[3];
std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3];
std::cerr << "), " << (dryrun ? "dryrun" : "") << ")");
GGML_TENSOR_LOCALS(int64_t, neq, q, ne)
GGML_TENSOR_LOCALS(size_t, nbq, q, nb)
GGML_TENSOR_LOCALS(int64_t, nek, k, ne)
GGML_TENSOR_LOCALS(size_t, nbk, k, nb)
GGML_TENSOR_LOCALS(int64_t, nev, v, ne)
GGML_TENSOR_LOCALS(size_t, nbv, v, nb)
GGML_TENSOR_LOCALS(int64_t, ne, dst, ne)
GGML_TENSOR_LOCALS(size_t, nb, dst, nb)
const uint32_t nem1 = mask ? mask->ne[1] : 0;
const uint32_t nbm1 = mask ? mask->nb[1] : 0;
const uint32_t D = neq0;
const uint32_t N = neq1;
const uint32_t KV = nek1;
GGML_ASSERT(ne0 == D);
GGML_ASSERT(ne2 == N);
// input tensor rows must be contiguous
GGML_ASSERT(nbq0 == ggml_type_size(q->type));
GGML_ASSERT(nbk0 == ggml_type_size(k->type));
GGML_ASSERT(nbv0 == ggml_type_size(v->type));
GGML_ASSERT(neq0 == D);
GGML_ASSERT(nek0 == D);
GGML_ASSERT(nev0 == D);
GGML_ASSERT(neq1 == N);
GGML_ASSERT(nev0 == D);
GGML_ASSERT(nev1 == nek1);
// dst cannot be transposed or permuted
GGML_ASSERT(nb0 == sizeof(float));
GGML_ASSERT(nb0 <= nb1);
GGML_ASSERT(nb1 <= nb2);
GGML_ASSERT(nb2 <= nb3);
assert(dst->type == GGML_TYPE_F32);
assert(q->type == GGML_TYPE_F32);
assert(k->type == v->type);
vk_pipeline *pipelines;
// XXX TODO other backends may be changing accumulator precision to default to f32 soon
bool f32acc = dst->op_params[3] == GGML_PREC_F32;
bool small_rows = N <= flash_attention_num_small_rows;
switch (D) {
case 64: pipelines = &ctx->device->pipeline_flash_attn_f32_f16_D64[k->type][f32acc][small_rows][0]; break;
case 80: pipelines = &ctx->device->pipeline_flash_attn_f32_f16_D80[k->type][f32acc][small_rows][0]; break;
case 96: pipelines = &ctx->device->pipeline_flash_attn_f32_f16_D96[k->type][f32acc][small_rows][0]; break;
case 112: pipelines = &ctx->device->pipeline_flash_attn_f32_f16_D112[k->type][f32acc][small_rows][0]; break;
case 128: pipelines = &ctx->device->pipeline_flash_attn_f32_f16_D128[k->type][f32acc][small_rows][0]; break;
case 256: pipelines = &ctx->device->pipeline_flash_attn_f32_f16_D256[k->type][f32acc][small_rows][0]; break;
default:
assert(!"unsupported D value");
return;
}
assert(pipelines);
bool aligned = (KV % pipelines[1]->align) == 0;
vk_pipeline pipeline = pipelines[aligned];
assert(pipeline);
if (dryrun) {
// Request descriptor sets
ggml_pipeline_request_descriptor_sets(ctx->device, pipeline, 1);
return;
}
float scale = 1.0f;
float max_bias = 0.0f;
float logit_softcap = 0.0f;
memcpy(&scale, (const float *) dst->op_params + 0, sizeof(float));
memcpy(&max_bias, (const float *) dst->op_params + 1, sizeof(float));
memcpy(&logit_softcap, (const float *) dst->op_params + 2, sizeof(float));
if (logit_softcap != 0) {
scale /= logit_softcap;
}
const uint32_t n_head_kv = neq2;
const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head_kv));
const float m0 = powf(2.0f, -(max_bias ) / n_head_log2);
const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
ggml_vk_sync_buffers(subctx);
vk_buffer d_Q, d_K, d_V, d_D, d_M;
uint64_t q_buf_offset, k_buf_offset, v_buf_offset, d_buf_offset, m_buf_offset;
bool Q_uma = false, K_uma = false, V_uma = false, D_uma = false, M_uma = false;
if (ctx->device->uma) {
ggml_vk_host_get(ctx->device, q->data, d_Q, q_buf_offset);
ggml_vk_host_get(ctx->device, k->data, d_K, q_buf_offset);
ggml_vk_host_get(ctx->device, v->data, d_V, q_buf_offset);
ggml_vk_host_get(ctx->device, dst->data, d_D, q_buf_offset);
Q_uma = d_Q != nullptr;
K_uma = d_K != nullptr;
V_uma = d_V != nullptr;
D_uma = d_D != nullptr;
if (mask) {
ggml_vk_host_get(ctx->device, mask->data, d_M, q_buf_offset);
M_uma = d_M != nullptr;
}
}
ggml_backend_vk_buffer_context * d_buf_ctx = (ggml_backend_vk_buffer_context *)dst->buffer->context;
ggml_backend_vk_buffer_context * q_buf_ctx = (ggml_backend_vk_buffer_context *)q->buffer->context;
ggml_backend_vk_buffer_context * k_buf_ctx = (ggml_backend_vk_buffer_context *)k->buffer->context;
ggml_backend_vk_buffer_context * v_buf_ctx = (ggml_backend_vk_buffer_context *)v->buffer->context;
if (!Q_uma) {
d_Q = q_buf_ctx->dev_buffer;
q_buf_offset = vk_tensor_offset(q) + q->view_offs;
}
if (!K_uma) {
d_K = k_buf_ctx->dev_buffer;
k_buf_offset = vk_tensor_offset(k) + k->view_offs;
}
if (!V_uma) {
d_V = v_buf_ctx->dev_buffer;
v_buf_offset = vk_tensor_offset(v) + v->view_offs;
}
if (!D_uma) {
d_D = d_buf_ctx->dev_buffer;
d_buf_offset = vk_tensor_offset(dst) + dst->view_offs;
}
if (!M_uma) {
d_M = d_Q;
m_buf_offset = q_buf_offset;
if (mask) {
ggml_backend_vk_buffer_context * m_buf_ctx = (ggml_backend_vk_buffer_context*)mask->buffer->context;
d_M = m_buf_ctx->dev_buffer;
m_buf_offset = vk_tensor_offset(mask) + mask->view_offs;
}
}
const vk_flash_attn_push_constants pc = { N, KV, (uint32_t)ne1, (uint32_t)ne2, (uint32_t)ne3, (uint32_t)neq2, (uint32_t)neq3, (uint32_t)nek2, (uint32_t)nek3, (uint32_t)nev2, (uint32_t)nev3, nem1, (uint32_t)nbq2, (uint32_t)nbq3, (uint32_t)nbk2, (uint32_t)nbk3, (uint32_t)nbv2, (uint32_t)nbv3, nbm1, scale, max_bias, logit_softcap, mask != nullptr, n_head_log2, m0, m1 };
ggml_vk_dispatch_pipeline(ctx, subctx, pipeline,
{
vk_subbuffer{d_Q, q_buf_offset, VK_WHOLE_SIZE},
vk_subbuffer{d_K, k_buf_offset, VK_WHOLE_SIZE},
vk_subbuffer{d_V, v_buf_offset, VK_WHOLE_SIZE},
vk_subbuffer{d_M, m_buf_offset, VK_WHOLE_SIZE},
vk_subbuffer{d_D, d_buf_offset, VK_WHOLE_SIZE},
},
sizeof(vk_flash_attn_push_constants), &pc, { (uint32_t)neq1, (uint32_t)neq2, (uint32_t)neq3 });
}
static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst, ggml_op op) {
switch (op) {
case GGML_OP_GET_ROWS:
@@ -5044,16 +5526,16 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t
ggml_vk_buffer_write(d_Y, 0, y, sizeof(Y_TYPE) * k * n * batch);
vk_context subctx = ggml_vk_create_context(ctx, ctx->device->compute_queue);
ggml_vk_ctx_begin(ctx->device, subctx);
for (size_t i = 0; i < num_it; i++) {
ggml_vk_ctx_begin(ctx->device, subctx);
ggml_vk_matmul(
ctx, subctx, p, ggml_vk_subbuffer(d_X), ggml_vk_subbuffer(d_Y), ggml_vk_subbuffer(d_D), ggml_vk_subbuffer(ctx->prealloc_split_k),
m, n, k,
k, k, m, k*m, k*n, m*n,
split_k, batch, batch, batch, 1, 1
);
ggml_vk_ctx_end(subctx);
}
ggml_vk_ctx_end(subctx);
auto begin = std::chrono::high_resolution_clock::now();
ggml_vk_submit(subctx, ctx->fence);
@@ -5391,16 +5873,16 @@ static void ggml_vk_test_dequant_matmul(ggml_backend_vk_context * ctx, size_t m,
ggml_vk_buffer_write(y_buf, 0, y, y_sz);
vk_context subctx = ggml_vk_create_context(ctx, ctx->device->compute_queue);
ggml_vk_ctx_begin(ctx->device, subctx);
for (size_t i = 0; i < num_it; i++) {
ggml_vk_ctx_begin(ctx->device, subctx);
ggml_vk_matmul(
ctx, subctx, p, ggml_vk_subbuffer(qx_buf), ggml_vk_subbuffer(y_buf), ggml_vk_subbuffer(d_buf), ggml_vk_subbuffer(ctx->prealloc_split_k),
m, n, k,
k, k, m, k*m, k*n, m*n,
split_k, batch, batch, batch, 1, 1
);
ggml_vk_ctx_end(subctx);
}
ggml_vk_ctx_end(subctx);
auto begin = std::chrono::high_resolution_clock::now();
@@ -5621,7 +6103,8 @@ static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx) {
4096, 512, 11008,
32000, 512, 4096,
};
const size_t num_it = 1;
const size_t num_it = 100;
for (size_t i = 0; i < vals.size(); i += 3) {
ggml_vk_test_matmul<ggml_fp16_t, float>(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 0);
ggml_vk_test_matmul<ggml_fp16_t, float>(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 1);
@@ -5676,6 +6159,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod
const ggml_tensor * src0 = node->src[0];
const ggml_tensor * src1 = node->src[1];
const ggml_tensor * src2 = node->src[2];
const ggml_tensor * src3 = node->src[3];
switch (node->op) {
// Return on empty ops to avoid generating a compute_ctx and setting exit_tensor
@@ -5728,6 +6212,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod
case GGML_OP_TIMESTEP_EMBEDDING:
case GGML_OP_POOL_2D:
case GGML_OP_LEAKY_RELU:
case GGML_OP_FLASH_ATTN_EXT:
break;
default:
std::cerr << "ggml_vulkan: Error: Missing op: " << ggml_op_name(node->op) << std::endl;
@@ -5920,6 +6405,11 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod
case GGML_OP_MUL_MAT_ID:
ggml_vk_mul_mat_id(ctx, compute_ctx, src0, src1, src2, node, dryrun);
break;
case GGML_OP_FLASH_ATTN_EXT:
ggml_vk_flash_attn(ctx, compute_ctx, src0, src1, src2, src3, node, dryrun);
break;
default:
return false;
@@ -6020,6 +6510,7 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_tensor *
break;
case GGML_OP_MUL_MAT:
case GGML_OP_MUL_MAT_ID:
case GGML_OP_FLASH_ATTN_EXT:
buf = tensor->buffer;
break;
@@ -6751,6 +7242,57 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
return true;
} break;
case GGML_OP_FLASH_ATTN_EXT:
{
ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context;
if (!ggml_vk_get_device(ctx->device)->coopmat2) {
return false;
}
switch (op->src[0]->ne[0]) {
case 64:
case 80:
case 96:
case 112:
case 128:
case 256:
break;
default:
return false;
}
if (op->src[0]->type != GGML_TYPE_F32) {
return false;
}
if (op->type != GGML_TYPE_F32) {
return false;
}
if (op->src[3] && op->src[3]->type != GGML_TYPE_F16) {
return false;
}
// It's straightforward to support different K/V dequant, but would
// significantly increase the number of pipelines
if (op->src[1]->type != op->src[2]->type) {
return false;
}
switch (op->src[1]->type) {
case GGML_TYPE_F16:
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:
// K dequants currently disabled because D dimension is rounded up to 256 and runs inefficiently
//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_IQ4_NL:
break;
default:
return false;
}
return true;
}
case GGML_OP_GET_ROWS:
{
switch (op->src[0]->type) {
@@ -7065,6 +7607,7 @@ static void ggml_vk_check_results_0(ggml_tensor * tensor) {
ggml_tensor * src0 = tensor->src[0];
ggml_tensor * src1 = tensor->src[1];
ggml_tensor * src2 = tensor->src[2];
ggml_tensor * src3 = tensor->src[3];
struct ggml_init_params iparams = {
/*.mem_size =*/ 2ul*1024ul*1024ul*1024ul,
@@ -7077,15 +7620,18 @@ static void ggml_vk_check_results_0(ggml_tensor * tensor) {
struct ggml_tensor * src0_clone = nullptr;
struct ggml_tensor * src1_clone = nullptr;
struct ggml_tensor * src2_clone = nullptr;
struct ggml_tensor * src3_clone = nullptr;
struct ggml_tensor * tensor_clone = nullptr;
size_t src0_size;
size_t src1_size;
size_t src2_size;
size_t src3_size;
void * src0_buffer = nullptr;
void * src1_buffer = nullptr;
void * src2_buffer = nullptr;
void * src3_buffer = nullptr;
if (src0 != nullptr) {
src0_clone = ggml_dup_tensor(ggml_ctx, src0);
@@ -7213,8 +7759,53 @@ static void ggml_vk_check_results_0(ggml_tensor * tensor) {
ggml_vk_print_tensor(src2, "src2");
}
}
if (src3 != nullptr) {
src3_clone = ggml_dup_tensor(ggml_ctx, src3);
if (tensor->op == GGML_OP_MUL_MAT) {
src3_size = ggml_nbytes(src3);
src3_buffer = malloc(src3_size);
src3_clone->data = src3_buffer;
if (ggml_backend_buffer_is_host(src3->buffer)) {
memcpy(src3_clone->data, src3->data, src3_size);
memcpy(src3_clone->nb, src3->nb, sizeof(size_t) * GGML_MAX_DIMS);
} else if (ggml_backend_buffer_is_vk(src3->buffer)) {
ggml_backend_vk_buffer_context * buf_ctx = (ggml_backend_vk_buffer_context *)src3->buffer->context;
vk_buffer& buffer_gpu = buf_ctx->dev_buffer;
uint64_t offset = vk_tensor_offset(src3) + src3->view_offs;
if (!ggml_is_contiguous(src3) && ggml_vk_dim01_contiguous(src3)) {
for (int i3 = 0; i3 < src3->ne[3]; i3++) {
for (int i2 = 0; i2 < src3->ne[2]; i2++) {
const int idx = i3*src3->ne[2] + i2;
ggml_vk_buffer_read(buffer_gpu, offset + idx * src3->nb[2], ((char *)src3_clone->data + idx * src3_clone->nb[2]), src3->ne[1] * src3->nb[1]);
}
}
src3_clone->nb[0] = src3->nb[0];
src3_clone->nb[1] = src3->nb[1];
for (int i = 2; i < GGML_MAX_DIMS; i++) {
src3_clone->nb[i] = src3_clone->nb[i - 1]*src3_clone->ne[i - 1];
}
} else {
if (offset + src3_size >= buffer_gpu->size) {
src3_size = buffer_gpu->size - offset;
}
ggml_vk_buffer_read(buffer_gpu, offset, src3_clone->data, src3_size);
memcpy(src3_clone->nb, src3->nb, sizeof(size_t) * GGML_MAX_DIMS);
}
} else {
GGML_ABORT("fatal error");
}
if (vk_output_tensor > 0 && vk_output_tensor == check_counter) {
ggml_vk_print_tensor(src3, "src3");
}
}
if (tensor->op == GGML_OP_FLASH_ATTN_EXT) {
const float *params = (const float *)tensor->op_params;
tensor_clone = ggml_flash_attn_ext(ggml_ctx, src0_clone, src1_clone, src2_clone, src3_clone, params[0], params[1], params[2]);
} else if (tensor->op == GGML_OP_MUL_MAT) {
tensor_clone = ggml_mul_mat(ggml_ctx, src0_clone, src1_clone);
} else if (tensor->op == GGML_OP_MUL_MAT_ID) {
tensor_clone = ggml_mul_mat_id(ggml_ctx, src0_clone, src1_clone, src2_clone);

2
ggml/src/ggml-vulkan/vulkan-shaders/CMakeLists.txt
View File

@@ -1,7 +1,9 @@
find_package (Threads REQUIRED)
find_package(Vulkan COMPONENTS glslc REQUIRED)
set(TARGET vulkan-shaders-gen)
add_executable(${TARGET} vulkan-shaders-gen.cpp)
install(TARGETS ${TARGET} RUNTIME)
target_compile_features(${TARGET} PRIVATE cxx_std_17)
target_link_libraries(vulkan-shaders-gen PUBLIC Threads::Threads)
target_link_libraries(vulkan-shaders-gen PRIVATE Vulkan::Vulkan)

305
ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs_cm2.comp Normal file
View File

@@ -0,0 +1,305 @@
#include "types.comp"
layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufQ4_0 {
block_q4_0_packed16 block;
};
float16_t dequantFuncQ4_0(const in decodeBufQ4_0 bl, const in uint blockCoords[2], const in uint coordInBlock[2])
{
const float16_t d = bl.block.d;
const uint idx = coordInBlock[1];
const uint shift = (idx & 0x10) >> 2;
uint32_t qs = unpack8(uint32_t(bl.block.qs[(idx & 0xE) >> 1]))[idx & 1];
qs >>= shift;
qs &= 0xF;
float16_t ret = (float16_t(qs) - float16_t(8)) * d;
return ret;
}
layout(buffer_reference, std430, buffer_reference_align = 4) buffer decodeBufQ4_1 {
block_q4_1 block;
};
float16_t dequantFuncQ4_1(const in decodeBufQ4_1 bl, const in uint blockCoords[2], const in uint coordInBlock[2])
{
const float16_t d = bl.block.d;
const float16_t m = bl.block.m;
const uint idx = coordInBlock[1];
const uint iqs = idx & 0xF;
const uint shift = (idx & 0x10) >> 2;
uint32_t qs = bl.block.qs[iqs];
qs >>= shift;
qs &= 0xF;
float16_t ret = float16_t(qs) * d + m;
return ret;
}
layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufQ5_0 {
block_q5_0 block;
};
float16_t dequantFuncQ5_0(const in decodeBufQ5_0 bl, const in uint blockCoords[2], const in uint coordInBlock[2])
{
const float16_t d = bl.block.d;
const uint idx = coordInBlock[1];
const uint iqs = idx & 0xF;
const uint uint_qh = uint(bl.block.qh[1]) << 16 | bl.block.qh[0];
const uint qh = ((uint_qh >> idx) << 4) & 0x10;
const uint shift = (idx & 0x10) >> 2;
uint32_t qs = bl.block.qs[iqs];
qs >>= shift;
qs &= 0xF;
float16_t ret = (float16_t(qs | qh) - float16_t(16)) * d;
return ret;
}
layout(buffer_reference, std430, buffer_reference_align = 8) buffer decodeBufQ5_1 {
block_q5_1 block;
};
float16_t dequantFuncQ5_1(const in decodeBufQ5_1 bl, const in uint blockCoords[2], const in uint coordInBlock[2])
{
const float16_t d = bl.block.d;
const float16_t m = bl.block.m;
const uint idx = coordInBlock[1];
const uint iqs = idx & 0xF;
const uint uint_qh = bl.block.qh;
const uint qh = ((uint_qh >> idx) << 4) & 0x10;
const uint shift = (idx & 0x10) >> 2;
uint32_t qs = bl.block.qs[iqs];
qs >>= shift;
qs &= 0xF;
float16_t ret = float16_t(qs | qh) * d + m;
return ret;
}
layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufQ8_0 {
block_q8_0_packed16 block;
};
float16_t dequantFuncQ8_0(const in decodeBufQ8_0 bl, const in uint blockCoords[2], const in uint coordInBlock[2])
{
const float16_t d = bl.block.d;
const uint idx = coordInBlock[1];
const uint iqs = idx;
// Load 16b and select the byte for this element
int32_t qs = unpack8(int32_t(bl.block.qs[(iqs & 0x1E) >> 1]))[iqs & 1];
float16_t ret = float16_t(qs) * d;
return ret;
}
layout(buffer_reference, std430, buffer_reference_align = 4) buffer decodeBufQ2_K {
block_q2_K block;
};
float16_t dequantFuncQ2_K(const in decodeBufQ2_K bl, const in uint blockCoords[2], const in uint coordInBlock[2])
{
const f16vec2 d = bl.block.d;
const uint idx = coordInBlock[1];
const uint iqs = idx;
const uint qsi = (iqs / 128) * 32 + (iqs % 32); // 0..31
const uint scalesi = iqs / 16; // 0..15
const uint qsshift = ((iqs % 128) / 32) * 2; // 0,2,4,6
uint32_t qs = bl.block.qs[qsi];
const uint scales = bl.block.scales[scalesi];
float16_t ret = d.x * float16_t(scales & 0xF) * float16_t((qs >> qsshift) & 3) - d.y * float16_t(scales >> 4);
return ret;
}
layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufQ3_K {
block_q3_K block;
};
float16_t dequantFuncQ3_K(const in decodeBufQ3_K bl, const in uint blockCoords[2], const in uint coordInBlock[2])
{
const uint idx = coordInBlock[1];
const uint iqs = idx;
const uint n = iqs / 128; // 0,1
const uint qsi = n * 32 + (iqs % 32); // 0..63
const uint hmi = (iqs % 32); // 0..31
const uint j = (iqs % 128) / 8; // 0..15
const uint is = iqs / 16; // 0..15
const uint halfsplit = ((iqs % 128) / 32); // 0,1,2,3
const uint qsshift = halfsplit * 2; // 0,2,4,6
const uint m = 1 << (4 * n + halfsplit); // 1,2,4,8,16,32,64,128
uint32_t scaleidx0 = (is < 8) ? is : (is-8);
uint32_t scaleidx0shift = (is < 8) ? 0 : 4;
uint32_t scaleidx1 = is + 8 - (is/4)*4;
uint32_t scaleidx1shift = (is/4)*2;
const int8_t us = int8_t(((bl.block.scales[scaleidx0] >> scaleidx0shift) & 0xF) | (((bl.block.scales[scaleidx1] >> scaleidx1shift) & 3) << 4));
const float16_t dl = bl.block.d * float16_t(us - 32);
float16_t ret = dl * float16_t(int8_t((bl.block.qs[qsi ] >> qsshift) & 3) - (((bl.block.hmask[hmi ] & m) != 0) ? 0 : 4));
return ret;
}
layout(buffer_reference, std430, buffer_reference_align = 16) buffer decodeBufQ4_K {
block_q4_K block;
};
float16_t dequantFuncQ4_K(const in decodeBufQ4_K bl, const in uint blockCoords[2], const in uint coordInBlock[2])
{
const uint idx = coordInBlock[1];
const uint iqs = idx;
const uint n = iqs / 64; // 0,1,2,3
const uint b = (iqs % 64) / 32; // 0,1
const uint is = (idx & 0xE0) >> 5; // 0..7
const uint qsi = n * 32 + (iqs % 32); // 0..127
const f16vec2 loadd = bl.block.d;
uint32_t sc;
uint32_t mbyte;
uint32_t scidx0 = (is < 4) ? is : (is + 4);
uint32_t scidx1 = (is < 4) ? is : (is - 4);
uint32_t scidxmask1 = (is < 4) ? 0x30 : 0xC0;
uint32_t scidxshift1 = (is < 4) ? 0 : 2;
uint32_t mbidx0 = is + 4;
uint32_t mbidx1 = (is < 4) ? is + 4 : is;
uint32_t mbidxmask0 = (is < 4) ? 0xF : 0xF0;
uint32_t mbidxshift0 = (is < 4) ? 0 : 4;
uint32_t mbidxmask1 = (is < 4) ? 0x30 : 0xC0;
uint32_t mbidxshift1 = (is < 4) ? 0 : 2;
sc = uint8_t((bl.block.scales[scidx0] & 0xF) | ((bl.block.scales[scidx1] & scidxmask1) >> scidxshift1));
mbyte = uint8_t(((bl.block.scales[mbidx0] & mbidxmask0) >> mbidxshift0) | ((bl.block.scales[mbidx1] & mbidxmask1) >> mbidxshift1));
const float16_t d = loadd.x * float16_t(sc);
const float16_t m = loadd.y * float16_t(mbyte);
uint32_t dmask = 0xF << (b * 4);
float16_t ret = d * float16_t((bl.block.qs[qsi ] & dmask) >> (b * 4)) - m;
return ret;
}
layout(buffer_reference, std430, buffer_reference_align = 16) buffer decodeBufQ5_K {
block_q5_K block;
};
float16_t dequantFuncQ5_K(const in decodeBufQ5_K bl, const in uint blockCoords[2], const in uint coordInBlock[2])
{
const uint idx = coordInBlock[1];
const uint iqs = idx;
const uint n = iqs / 64; // 0,1,2,3
const uint b = (iqs % 64) / 32; // 0,1
const uint is = (idx & 0xE0) >> 5; // 0..7
const uint qsi = n * 32 + (iqs % 32); // 0..127
const uint qhi = (iqs % 32); // 0..31
const uint8_t hm = uint8_t(1 << (iqs / 32));
const f16vec2 loadd = bl.block.d;
uint32_t sc;
uint32_t mbyte;
uint32_t scidx0 = (is < 4) ? is : (is + 4);
uint32_t scidx1 = (is < 4) ? is : (is - 4);
uint32_t scidxmask1 = (is < 4) ? 0x30 : 0xC0;
uint32_t scidxshift1 = (is < 4) ? 0 : 2;
uint32_t mbidx0 = is + 4;
uint32_t mbidx1 = (is < 4) ? is + 4 : is;
uint32_t mbidxmask0 = (is < 4) ? 0xF : 0xF0;
uint32_t mbidxshift0 = (is < 4) ? 0 : 4;
uint32_t mbidxmask1 = (is < 4) ? 0x30 : 0xC0;
uint32_t mbidxshift1 = (is < 4) ? 0 : 2;
sc = uint8_t((bl.block.scales[scidx0] & 0xF) | ((bl.block.scales[scidx1] & scidxmask1) >> scidxshift1));
mbyte = uint8_t(((bl.block.scales[mbidx0] & mbidxmask0) >> mbidxshift0) | ((bl.block.scales[mbidx1] & mbidxmask1) >> mbidxshift1));
const float16_t d = loadd.x * float16_t(sc);
const float16_t m = loadd.y * float16_t(mbyte);
uint32_t dmask = 0xF << (b * 4);
float16_t ret = d * (float16_t((bl.block.qs[qsi ] & dmask) >> (b * 4)) + float16_t((bl.block.qh[qhi ] & hm) != 0 ? 16 : 0)) - m;
return ret;
}
layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufQ6_K {
block_q6_K block;
};
float16_t dequantFuncQ6_K(const in decodeBufQ6_K bl, const in uint blockCoords[2], const in uint coordInBlock[2])
{
const uint idx = coordInBlock[1];
const uint iqs = idx;
const uint n = iqs / 128; // 0,1
const uint b = (iqs % 128) / 64; // 0,1
const uint is_b = (iqs % 32) / 16; // 0,1
const uint qhshift = ((iqs % 128) / 32) * 2;// 0,2,4,6
const uint is = 8 * n + qhshift + is_b; // 0..15
const uint qsi = n * 64 + (iqs % 64); // 0..127
const uint qhi = n * 32 + (iqs % 32); // 0..63
const float16_t dscale = bl.block.d * float16_t(bl.block.scales[is]);
float16_t ret = dscale * float16_t(int8_t(((bl.block.ql[qsi ] >> (b * 4)) & 0xF) | (((bl.block.qh[qhi ] >> qhshift) & 3) << 4)) - 32);
return ret;
}
#if defined(DATA_A_IQ4_NL)
layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufIQ4_NL {
block_iq4_nl block;
};
float16_t dequantFuncIQ4_NL(const in decodeBufIQ4_NL bl, const in uint blockCoords[2], const in uint coordInBlock[2])
{
const float16_t d = bl.block.d;
const uint idx = coordInBlock[1];
const uint iqs = idx & 0xF;
const uint shift = (idx & 0x10) >> 2;
uint32_t qs = bl.block.qs[iqs];
qs >>= shift;
qs &= 0xF;
float16_t ret = float16_t(kvalues_iq4nl[qs]) * d;
return ret;
}
#endif
#if defined(DATA_A_Q4_0)
#define dequantFuncA dequantFuncQ4_0
#elif defined(DATA_A_Q4_1)
#define dequantFuncA dequantFuncQ4_1
#elif defined(DATA_A_Q5_0)
#define dequantFuncA dequantFuncQ5_0
#elif defined(DATA_A_Q5_1)
#define dequantFuncA dequantFuncQ5_1
#elif defined(DATA_A_Q8_0)
#define dequantFuncA dequantFuncQ8_0
#elif defined(DATA_A_Q2_K)
#define dequantFuncA dequantFuncQ2_K
#elif defined(DATA_A_Q3_K)
#define dequantFuncA dequantFuncQ3_K
#elif defined(DATA_A_Q4_K)
#define dequantFuncA dequantFuncQ4_K
#elif defined(DATA_A_Q5_K)
#define dequantFuncA dequantFuncQ5_K
#elif defined(DATA_A_Q6_K)
#define dequantFuncA dequantFuncQ6_K
#elif defined(DATA_A_IQ4_NL)
#define dequantFuncA dequantFuncIQ4_NL
#endif

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#version 450
#extension GL_EXT_control_flow_attributes : enable
#extension GL_EXT_shader_16bit_storage : require
#extension GL_EXT_shader_explicit_arithmetic_types_float16 : require
#extension GL_EXT_shader_explicit_arithmetic_types_int8 : require
#extension GL_EXT_shader_explicit_arithmetic_types_int32 : require
#extension GL_EXT_shader_explicit_arithmetic_types_int16 : require
#extension GL_KHR_memory_scope_semantics : enable
#extension GL_KHR_cooperative_matrix : enable
#extension GL_NV_cooperative_matrix2 : enable
#extension GL_EXT_buffer_reference : enable
#extension GL_KHR_shader_subgroup_ballot : enable
#extension GL_KHR_shader_subgroup_vote : enable
#extension GL_EXT_null_initializer : enable
#include "types.comp"
#include "dequant_funcs_cm2.comp"
layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
layout (constant_id = 1) const uint32_t Br = 32;
layout (constant_id = 2) const uint32_t Bc = 32;
layout (constant_id = 3) const uint32_t D = 32;
layout (constant_id = 4) const uint32_t Clamp = gl_CooperativeMatrixClampModeConstantNV;
layout (push_constant) uniform parameter {
uint32_t N;
uint32_t KV;
uint32_t ne1;
uint32_t ne2;
uint32_t ne3;
uint32_t neq2;
uint32_t neq3;
uint32_t nek2;
uint32_t nek3;
uint32_t nev2;
uint32_t nev3;
uint32_t nem1;
uint32_t nb02;
uint32_t nb03;
uint32_t nb12;
uint32_t nb13;
uint32_t nb22;
uint32_t nb23;
uint32_t nb31;
float scale;
float max_bias;
float logit_softcap;
uint32_t mask;
uint32_t n_head_log2;
float m0;
float m1;
} p;
layout (binding = 0) readonly buffer Q {uint8_t data_q[];};
layout (binding = 1) readonly buffer K {uint8_t data_k[];};
layout (binding = 2) readonly buffer V {uint8_t data_v[];};
layout (binding = 3) readonly buffer M {uint8_t data_m[];};
layout (binding = 4) writeonly buffer O {D_TYPE data_o[];};
#define CEIL_DIV(a, b) (((a) + (b) - 1) / (b))
ACC_TYPE maxReduce(const in ACC_TYPE x, const in ACC_TYPE y) {
return max(x, y);
}
ACC_TYPE smearReduce(const in ACC_TYPE x, const in ACC_TYPE y) {
return x;
}
// Replace matrix elements >= numRows or numCols with 'replace'
ACC_TYPE replacePadding(const in uint32_t row, const in uint32_t col, const in ACC_TYPE elem, const in ACC_TYPE replace, const in uint32_t numRows, const in uint32_t numCols) {
if (row >= numRows || col >= numCols) {
return replace;
}
return elem;
}
ACC_TYPE Exp(const in uint32_t row, const in uint32_t col, const in ACC_TYPE elem)
{
return exp(elem);
}
ACC_TYPE Max(const in uint32_t row, const in uint32_t col, const in ACC_TYPE elem0, const in ACC_TYPE elem1)
{
return max(elem0, elem1);
}
#if defined(BLOCK_SIZE)
#define DECODEFUNC , DEQUANTFUNC
#else
#define DECODEFUNC
#endif
void main() {
#if defined(DATA_A_IQ4_NL)
init_iq4nl_shmem();
#endif
const uint32_t N = p.N;
const uint32_t KV = p.KV;
const uint32_t Tr = CEIL_DIV(N, Br);
const uint32_t Tc = CEIL_DIV(KV, Bc);
const uint32_t i = gl_WorkGroupID.x;
const uint32_t iq2 = gl_WorkGroupID.y;
const uint32_t iq3 = gl_WorkGroupID.z;
// broadcast factors
const uint32_t rk2 = p.neq2/p.nek2;
const uint32_t rk3 = p.neq3/p.nek3;
const uint32_t rv2 = p.neq2/p.nev2;
const uint32_t rv3 = p.neq3/p.nev3;
// k indices
const uint32_t ik3 = iq3 / rk3;
const uint32_t ik2 = iq2 / rk2;
// v indices
const uint32_t iv3 = iq3 / rv3;
const uint32_t iv2 = iq2 / rv2;
tensorLayoutNV<2, gl_CooperativeMatrixClampModeConstantNV> tensorLayoutQ = createTensorLayoutNV(2, gl_CooperativeMatrixClampModeConstantNV);
tensorLayoutNV<2, Clamp> tensorLayoutK = createTensorLayoutNV(2, Clamp);
tensorLayoutNV<2, Clamp> tensorLayoutV = createTensorLayoutNV(2, Clamp);
tensorViewNV<2, false, 1, 0> tensorViewTranspose = createTensorViewNV(2, false, 1, 0);
#if defined(BLOCK_SIZE)
tensorLayoutK = setTensorLayoutBlockSizeNV(tensorLayoutK, 1, BLOCK_SIZE);
tensorLayoutV = setTensorLayoutBlockSizeNV(tensorLayoutV, 1, BLOCK_SIZE);
#endif
tensorLayoutQ = setTensorLayoutDimensionNV(tensorLayoutQ, N, D);
tensorLayoutK = setTensorLayoutDimensionNV(tensorLayoutK, KV, D);
tensorLayoutV = setTensorLayoutDimensionNV(tensorLayoutV, KV, D);
coopmat<Q_TYPE, gl_ScopeWorkgroup, Br, D, gl_MatrixUseA> Q;
coopmat<float16_t, gl_ScopeWorkgroup, Br, D, gl_MatrixUseA> Qf16;
uint32_t q_offset = iq2*p.nb02+iq3*p.nb03;
coopMatLoadTensorNV(Q, data_q, q_offset, sliceTensorLayoutNV(tensorLayoutQ, i * Br, Br, 0, D));
Qf16 = coopmat<float16_t, gl_ScopeWorkgroup, Br, D, gl_MatrixUseA>(Q);
Qf16 *= float16_t(p.scale);
coopmat<ACC_TYPE, gl_ScopeWorkgroup, Br, D, gl_MatrixUseAccumulator> O = coopmat<ACC_TYPE, gl_ScopeWorkgroup, Br, D, gl_MatrixUseAccumulator>(0);
coopmat<ACC_TYPE, gl_ScopeWorkgroup, Br, Bc, gl_MatrixUseAccumulator> L, M;
L = coopmat<ACC_TYPE, gl_ScopeWorkgroup, Br, Bc, gl_MatrixUseAccumulator>(0);
M = coopmat<ACC_TYPE, gl_ScopeWorkgroup, Br, Bc, gl_MatrixUseAccumulator>(-1.0/0.0);
ACC_TYPE slope = ACC_TYPE(1.0);
// ALiBi
if (p.max_bias > 0.0f) {
const uint32_t h = iq2;
const ACC_TYPE base = ACC_TYPE(h < p.n_head_log2 ? p.m0 : p.m1);
const int exph = int(h < p.n_head_log2 ? h + 1 : 2*(h - p.n_head_log2) + 1);
slope = pow(base, ACC_TYPE(exph));
}
[[dont_unroll]]
for (uint32_t j = 0; j < Tc; ++j) {
coopmat<ACC_TYPE, gl_ScopeWorkgroup, Br, Bc, gl_MatrixUseAccumulator> S = coopmat<ACC_TYPE, gl_ScopeWorkgroup, Br, Bc, gl_MatrixUseAccumulator>(0);
coopmat<float16_t, gl_ScopeWorkgroup, D, Bc, gl_MatrixUseB> K_T;
uint32_t k_offset = ik2*p.nb12 + ik3*p.nb13;
coopMatLoadTensorNV(K_T, data_k, k_offset, sliceTensorLayoutNV(tensorLayoutK, j * Bc, Bc, 0, D), tensorViewTranspose DECODEFUNC);
S = coopMatMulAdd(Qf16, K_T, S);
if (p.logit_softcap != 0.0f) {
[[unroll]]
for (int k = 0; k < S.length(); ++k) {
S[k] = ACC_TYPE(p.logit_softcap)*tanh(S[k]);
}
}
if (p.mask != 0) {
tensorLayoutNV<2, gl_CooperativeMatrixClampModeConstantNV> tensorLayoutM = createTensorLayoutNV(2, gl_CooperativeMatrixClampModeConstantNV);
tensorLayoutM = setTensorLayoutDimensionNV(tensorLayoutM, p.nem1, KV);
coopmat<float16_t, gl_ScopeWorkgroup, Br, Bc, gl_MatrixUseAccumulator> mv;
coopMatLoadTensorNV(mv, data_m, 0, sliceTensorLayoutNV(tensorLayoutM, i * Br, Br, j * Bc, Bc));
S += slope*coopmat<ACC_TYPE, gl_ScopeWorkgroup, Br, Bc, gl_MatrixUseAccumulator>(mv);
}
// Clear padding elements to -inf, so they don't contribute to rowmax
if (Clamp != 0 &&
((j + 1) * Bc > KV ||
(i + 1) * Br > N)) {
uint R = ((i + 1) * Br > N) ? (N % Br) : Br;
uint C = ((j + 1) * Bc > KV) ? (KV % Bc) : Bc;
coopMatPerElementNV(S, S, replacePadding, ACC_TYPE(-1.0/0.0), R, C);
}
coopmat<ACC_TYPE, gl_ScopeWorkgroup, Br, Bc, gl_MatrixUseAccumulator> rowmax, P, rowsum, eM;
coopMatReduceNV(rowmax, S, gl_CooperativeMatrixReduceRowNV, maxReduce);
coopmat<ACC_TYPE, gl_ScopeWorkgroup, Br, Bc, gl_MatrixUseAccumulator> Mold = M;
// M = max(rowmax, Mold)
// P = e^(S - M)
// eM = e^(Mold - M)
coopMatPerElementNV(M, rowmax, Max, Mold);
coopMatPerElementNV(P, S - M, Exp);
coopMatPerElementNV(eM, Mold - M, Exp);
// Clear padding elements to 0, so they don't contribute to rowsum
if (Clamp != 0 &&
((j + 1) * Bc > KV ||
(i + 1) * Br > N)) {
uint R = ((i + 1) * Br > N) ? (N % Br) : Br;
uint C = ((j + 1) * Bc > KV) ? (KV % Bc) : Bc;
coopMatPerElementNV(P, P, replacePadding, ACC_TYPE(0.0), R, C);
}
coopmat<float16_t, gl_ScopeWorkgroup, Br, Bc, gl_MatrixUseA> P_A = coopmat<float16_t, gl_ScopeWorkgroup, Br, Bc, gl_MatrixUseA>(P);
// compute rowsum by multiplying by matrix of all ones.
coopmat<float16_t, gl_ScopeWorkgroup, Bc, Bc, gl_MatrixUseB> One = coopmat<float16_t, gl_ScopeWorkgroup, Bc, Bc, gl_MatrixUseB>(1.0);
rowsum = coopmat<ACC_TYPE, gl_ScopeWorkgroup, Br, Bc, gl_MatrixUseAccumulator>(0.0);
rowsum = coopMatMulAdd(P_A, One, rowsum);
coopmat<float16_t, gl_ScopeWorkgroup, Bc, D, gl_MatrixUseB> V;
uint32_t v_offset = iv2*p.nb22 + iv3*p.nb23;
coopMatLoadTensorNV(V, data_v, v_offset, sliceTensorLayoutNV(tensorLayoutV, j * Bc, Bc, 0, D) DECODEFUNC);
L = eM*L + rowsum;
// This is the "diagonal" matrix in the paper, but since we do componentwise
// multiply rather than matrix multiply it has the diagonal element smeared
// across the row
coopmat<ACC_TYPE, gl_ScopeWorkgroup, Br, D, gl_MatrixUseAccumulator> eMdiag;
// resize eM by using smear/reduce
coopMatReduceNV(eMdiag, eM, gl_CooperativeMatrixReduceRowNV, smearReduce);
O = eMdiag * O;
O = coopMatMulAdd(P_A, V, O);
}
coopmat<ACC_TYPE, gl_ScopeWorkgroup, Br, D, gl_MatrixUseAccumulator> Ldiag;
// resize L by using smear/reduce
coopMatReduceNV(Ldiag, L, gl_CooperativeMatrixReduceRowNV, smearReduce);
[[unroll]]
for (int k = 0; k < Ldiag.length(); ++k) {
Ldiag[k] = ACC_TYPE(1.0) / Ldiag[k];
}
O = Ldiag*O;
tensorLayoutNV<3, gl_CooperativeMatrixClampModeConstantNV> tensorLayoutD = createTensorLayoutNV(3, gl_CooperativeMatrixClampModeConstantNV);
tensorLayoutD = setTensorLayoutDimensionNV(tensorLayoutD, p.ne2, p.ne1, D);
// permute dimensions
tensorViewNV<3, false, 1, 0, 2> tensorViewPermute = createTensorViewNV(3, false, 1, 0, 2);
uint32_t o_offset = iq3*p.ne2*p.ne1;
coopmat<D_TYPE, gl_ScopeWorkgroup, Br, D, gl_MatrixUseAccumulator> O_D = coopmat<D_TYPE, gl_ScopeWorkgroup, Br, D, gl_MatrixUseAccumulator>(O);
coopMatStoreTensorNV(O_D, data_o, o_offset, sliceTensorLayoutNV(tensorLayoutD, i * Br, Br, iq2, 1, 0, D), tensorViewPermute);
}

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#version 450
#extension GL_EXT_control_flow_attributes : enable
#extension GL_EXT_shader_16bit_storage : require
#extension GL_EXT_shader_explicit_arithmetic_types_float16 : require
#extension GL_EXT_shader_explicit_arithmetic_types_int8 : require
#extension GL_EXT_shader_explicit_arithmetic_types_int32 : require
#extension GL_EXT_shader_explicit_arithmetic_types_int16 : require
#extension GL_KHR_memory_scope_semantics : enable
#extension GL_KHR_cooperative_matrix : enable
#extension GL_NV_cooperative_matrix2 : enable
#extension GL_EXT_buffer_reference : enable
#extension GL_KHR_shader_subgroup_ballot : enable
#extension GL_KHR_shader_subgroup_vote : enable
#include "types.comp"
layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
layout (constant_id = 1) const uint BM = 64;
layout (constant_id = 2) const uint BN = 64;
layout (constant_id = 3) const uint BK = 16; // Assumed to be 32 if working with a quant
layout (push_constant) uniform parameter
{
uint M;
uint N;
uint K;
uint stride_a;
uint stride_b;
uint stride_d;
uint batch_stride_a;
uint batch_stride_b;
uint batch_stride_d;
#ifdef MUL_MAT_ID
uint nei0;
uint nei1;
uint nbi1;
uint ne11;
#else
uint k_split;
uint ne02;
uint ne12;
uint broadcast2;
uint broadcast3;
#endif
} p;
layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
layout (binding = 1) readonly buffer B {B_TYPE data_b[];};
layout (binding = 2) writeonly buffer D {D_TYPE data_d[];};
#if QUANT_K > 1
#define DECODEFUNCA , dequantFuncA
#define MAT_A_TYPE float16_t
#include "dequant_funcs_cm2.comp"
#else
#define DECODEFUNCA
#define MAT_A_TYPE A_TYPE
#endif
#define MAT_B_TYPE B_TYPE
#ifdef MUL_MAT_ID
layout (binding = 3) readonly buffer IDS {int data_ids[];};
shared u16vec4 row_ids[3072];
layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufB {
B_TYPE b[];
};
uint _ne1;
shared uint _ne1_sh;
B_TYPE decodeFuncB(const in decodeBufB bl, const in uint blockCoords[2], const in uint coordInBlock[2])
{
const uint row_i = blockCoords[0];
if (row_i >= _ne1) {
return B_TYPE(0.0);
}
const u16vec4 row_idx = row_ids[row_i];
B_TYPE ret = data_b[row_idx.y * p.batch_stride_b + row_idx.x * p.stride_b + blockCoords[1]];
return ret;
}
D_TYPE perElemOpD(const in uint32_t r, const in uint32_t c, const in D_TYPE elem, const in uint32_t ir, const in uint32_t ic)
{
uint dr = ir * BM + r;
uint dc = ic * BN + c;
if (dr < p.M && dc < _ne1) {
uint row_i = dc;
const u16vec4 row_idx = row_ids[row_i];
data_d[row_idx.y * p.batch_stride_d + row_idx.z * p.stride_d + dr] = elem;
}
return elem;
}
#endif
void main() {
#if defined(DATA_A_IQ4_NL)
init_iq4nl_shmem();
#endif
#ifdef MUL_MAT_ID
const uint expert_idx = gl_GlobalInvocationID.z;
#else
const uint batch_idx = gl_GlobalInvocationID.z;
const uint i13 = batch_idx / p.ne12;
const uint i12 = batch_idx % p.ne12;
const uint i03 = i13 / p.broadcast3;
const uint i02 = i12 / p.broadcast2;
const uint batch_idx_a = i03 * p.ne02 + i02;
#endif
const uint blocks_m = (p.M + BM - 1) / BM;
const uint ir = gl_WorkGroupID.x % blocks_m;
const uint ik = gl_WorkGroupID.x / blocks_m;
const uint ic = gl_WorkGroupID.y;
#ifdef MUL_MAT_ID
// Spread the search across all elements in the first subgroup
if (gl_SubgroupID == 0) {
_ne1 = 0;
uint num_elements = p.nei1 * p.nei0;
for (uint i = gl_SubgroupInvocationID; subgroupAny(i < num_elements); i += gl_SubgroupSize) {
bool in_range = i < num_elements;
uint ii0 = i % p.nei0;
uint ii1 = i / p.nei0;
uint id = in_range ? data_ids[ii1*p.nbi1 + ii0] : 0;
uvec4 ballot = subgroupBallot(in_range && id == expert_idx);
uint idx = subgroupBallotExclusiveBitCount(ballot);
if (in_range && id == expert_idx) {
row_ids[_ne1 + idx] = u16vec4(ii0 % p.ne11, ii1, ii0, 0);
}
_ne1 += subgroupBallotBitCount(ballot);
}
_ne1_sh = _ne1;
}
barrier();
_ne1 = _ne1_sh;
// Workgroup has no work
if (ic * BN >= _ne1) return;
#endif
#ifdef MUL_MAT_ID
uint start_k = 0;
const uint end_k = p.K;
#else
uint start_k = ik * p.k_split;
const uint end_k = min(p.K, (ik + 1) * p.k_split);
#endif
coopmat<ACC_TYPE, gl_ScopeWorkgroup, BM, BN, gl_MatrixUseAccumulator> sum;
sum = coopmat<ACC_TYPE, gl_ScopeWorkgroup, BM, BN, gl_MatrixUseAccumulator>(0.0);
#ifdef MUL_MAT_ID
uint pos_a = (expert_idx * p.batch_stride_a) / QUANT_K;
uint pos_b = 0;
#else
uint pos_a = (batch_idx_a * p.batch_stride_a) / QUANT_K;
uint pos_b = batch_idx * p.batch_stride_b;
#endif
uint stride_a = p.stride_a / QUANT_K;
uint stride_b = p.stride_b;
// Hint to the compiler that values are aligned (want 16B alignment).
// Quants are always block-aligned, no alignment needed.
#if ALIGNED
#if QUANT_K == 1
stride_a &= ~7;
#endif
stride_b &= ~7;
#endif
// Create layouts for both clamped and unclamped accesses
tensorLayoutNV<2> tensorLayoutA = createTensorLayoutNV(2);
tensorLayoutNV<2, gl_CooperativeMatrixClampModeConstantNV> tensorLayoutAClamp = createTensorLayoutNV(2, gl_CooperativeMatrixClampModeConstantNV);
tensorLayoutNV<2> tensorLayoutB = createTensorLayoutNV(2);
tensorLayoutNV<2, gl_CooperativeMatrixClampModeConstantNV> tensorLayoutBClamp = createTensorLayoutNV(2, gl_CooperativeMatrixClampModeConstantNV);
tensorLayoutNV<2, gl_CooperativeMatrixClampModeConstantNV> tensorLayoutD = createTensorLayoutNV(2, gl_CooperativeMatrixClampModeConstantNV);
#if QUANT_K > 1
tensorLayoutA = setTensorLayoutBlockSizeNV(tensorLayoutA, 1, QUANT_K);
tensorLayoutAClamp = setTensorLayoutBlockSizeNV(tensorLayoutAClamp, 1, QUANT_K);
#endif
// Use end_k rather than p.K as the dimension because that's what
// we need to bound check against when using split_k
tensorLayoutA = setTensorLayoutDimensionNV(tensorLayoutA, p.M, end_k);
tensorLayoutB = setTensorLayoutDimensionNV(tensorLayoutB, p.N, end_k);
tensorLayoutD = setTensorLayoutDimensionNV(tensorLayoutD, p.N, p.M);
tensorLayoutAClamp = setTensorLayoutDimensionNV(tensorLayoutAClamp, p.M, end_k);
tensorLayoutBClamp = setTensorLayoutDimensionNV(tensorLayoutBClamp, p.N, end_k);
tensorViewNV<2, false, 1, 0> tensorViewTranspose = createTensorViewNV(2, false, 1, 0);
#if !defined(MUL_MAT_ID)
// Detect a fast path where all loads are entirely in bounds and no clamping is required
if ((ir + 1) * BM <= p.M && (ic + 1) * BN <= p.N && (start_k % BK) == 0 && (end_k % BK) == 0 &&
#if QUANT_K == 1
(stride_a % 8) == 0 &&
#endif
(stride_b % 8) == 0 && (start_k % 8) == 0) {
// Hint to the compiler that values are aligned (want 16B alignment)
start_k &= ~7;
stride_b &= ~7;
#if QUANT_K == 1
stride_a &= ~7;
#endif
tensorLayoutA = setTensorLayoutStrideNV(tensorLayoutA, stride_a, 1);
tensorLayoutB = setTensorLayoutStrideNV(tensorLayoutB, stride_b, 1);
uint k_iters = (end_k - start_k + BK - 1) / BK;
for (uint block_k = start_k, i = 0; i < k_iters; block_k += BK, ++i) {
coopmat<MAT_A_TYPE, gl_ScopeWorkgroup, BM, BK, gl_MatrixUseA> mat_a;
coopmat<MAT_B_TYPE, gl_ScopeWorkgroup, BK, BN, gl_MatrixUseB> mat_b;
coopMatLoadTensorNV(mat_a, data_a, pos_a, sliceTensorLayoutNV(tensorLayoutA, ir * BM, BM, block_k, BK) DECODEFUNCA);
coopmat<FLOAT_TYPE, gl_ScopeWorkgroup, BM, BK, gl_MatrixUseA> mat_a_ft = coopmat<FLOAT_TYPE, gl_ScopeWorkgroup, BM, BK, gl_MatrixUseA>(mat_a);
coopMatLoadTensorNV(mat_b, data_b, pos_b, sliceTensorLayoutNV(tensorLayoutB, ic * BN, BN, block_k, BK), tensorViewTranspose);
coopmat<FLOAT_TYPE, gl_ScopeWorkgroup, BK, BN, gl_MatrixUseB> mat_b_ft = coopmat<FLOAT_TYPE, gl_ScopeWorkgroup, BK, BN, gl_MatrixUseB>(mat_b);
sum = coopMatMulAdd(mat_a_ft, mat_b_ft, sum);
}
} else
#endif // !defined(MUL_MAT_ID)
{
tensorLayoutA = setTensorLayoutStrideNV(tensorLayoutA, stride_a, 1);
tensorLayoutAClamp = setTensorLayoutStrideNV(tensorLayoutAClamp, stride_a, 1);
tensorLayoutB = setTensorLayoutStrideNV(tensorLayoutB, stride_b, 1);
tensorLayoutBClamp = setTensorLayoutStrideNV(tensorLayoutBClamp, stride_b, 1);
[[dont_unroll]]
for (uint block_k = start_k; block_k < end_k; block_k += BK) {
coopmat<MAT_A_TYPE, gl_ScopeWorkgroup, BM, BK, gl_MatrixUseA> mat_a;
coopmat<MAT_B_TYPE, gl_ScopeWorkgroup, BK, BN, gl_MatrixUseB> mat_b;
coopmat<FLOAT_TYPE, gl_ScopeWorkgroup, BM, BK, gl_MatrixUseA> mat_a_ft;
coopmat<FLOAT_TYPE, gl_ScopeWorkgroup, BK, BN, gl_MatrixUseB> mat_b_ft;
// Clamping is expensive, so detect different code paths for each combination
// of A and B needing clamping.
bool unclampedA = (ir + 1) * BM <= p.M && block_k + BK <= end_k && (block_k % 8) == 0;
#ifdef MUL_MAT_ID
bool unclampedB = true;
#else
bool unclampedB = (ic + 1) * BN <= p.N && block_k + BK <= end_k && (block_k % 8) == 0;
#endif
if (unclampedA && unclampedB) {
coopMatLoadTensorNV(mat_a, data_a, pos_a, sliceTensorLayoutNV(tensorLayoutA, ir * BM, BM, (block_k & ~7), BK) DECODEFUNCA);
#ifdef MUL_MAT_ID
coopMatLoadTensorNV(mat_b, data_b, pos_b, sliceTensorLayoutNV(tensorLayoutB, ic * BN, BN, block_k, BK), tensorViewTranspose, decodeFuncB);
#else
coopMatLoadTensorNV(mat_b, data_b, pos_b, sliceTensorLayoutNV(tensorLayoutB, ic * BN, BN, (block_k & ~7), BK), tensorViewTranspose);
#endif
mat_a_ft = coopmat<FLOAT_TYPE, gl_ScopeWorkgroup, BM, BK, gl_MatrixUseA>(mat_a);
mat_b_ft = coopmat<FLOAT_TYPE, gl_ScopeWorkgroup, BK, BN, gl_MatrixUseB>(mat_b);
sum = coopMatMulAdd(mat_a_ft, mat_b_ft, sum);
} else if (unclampedA && !unclampedB) {
coopMatLoadTensorNV(mat_a, data_a, pos_a, sliceTensorLayoutNV(tensorLayoutA, ir * BM, BM, (block_k & ~7), BK) DECODEFUNCA);
coopMatLoadTensorNV(mat_b, data_b, pos_b, sliceTensorLayoutNV(tensorLayoutBClamp, ic * BN, BN, block_k, BK), tensorViewTranspose);
mat_a_ft = coopmat<FLOAT_TYPE, gl_ScopeWorkgroup, BM, BK, gl_MatrixUseA>(mat_a);
mat_b_ft = coopmat<FLOAT_TYPE, gl_ScopeWorkgroup, BK, BN, gl_MatrixUseB>(mat_b);
sum = coopMatMulAdd(mat_a_ft, mat_b_ft, sum);
} else if (!unclampedA && unclampedB) {
coopMatLoadTensorNV(mat_a, data_a, pos_a, sliceTensorLayoutNV(tensorLayoutAClamp, ir * BM, BM, block_k, BK) DECODEFUNCA);
#ifdef MUL_MAT_ID
coopMatLoadTensorNV(mat_b, data_b, pos_b, sliceTensorLayoutNV(tensorLayoutB, ic * BN, BN, block_k, BK), tensorViewTranspose, decodeFuncB);
#else
coopMatLoadTensorNV(mat_b, data_b, pos_b, sliceTensorLayoutNV(tensorLayoutB, ic * BN, BN, (block_k & ~7), BK), tensorViewTranspose);
#endif
mat_a_ft = coopmat<FLOAT_TYPE, gl_ScopeWorkgroup, BM, BK, gl_MatrixUseA>(mat_a);
mat_b_ft = coopmat<FLOAT_TYPE, gl_ScopeWorkgroup, BK, BN, gl_MatrixUseB>(mat_b);
sum = coopMatMulAdd(mat_a_ft, mat_b_ft, sum);
} else if (!unclampedA && !unclampedB) {
coopMatLoadTensorNV(mat_a, data_a, pos_a, sliceTensorLayoutNV(tensorLayoutAClamp, ir * BM, BM, block_k, BK) DECODEFUNCA);
coopMatLoadTensorNV(mat_b, data_b, pos_b, sliceTensorLayoutNV(tensorLayoutBClamp, ic * BN, BN, block_k, BK), tensorViewTranspose);
mat_a_ft = coopmat<FLOAT_TYPE, gl_ScopeWorkgroup, BM, BK, gl_MatrixUseA>(mat_a);
mat_b_ft = coopmat<FLOAT_TYPE, gl_ScopeWorkgroup, BK, BN, gl_MatrixUseB>(mat_b);
sum = coopMatMulAdd(mat_a_ft, mat_b_ft, sum);
}
}
}
// Convert from ACC_TYPE to D_TYPE
coopmat<D_TYPE, gl_ScopeWorkgroup, BM, BN, gl_MatrixUseAccumulator> mat_d;
mat_d = coopmat<D_TYPE, gl_ScopeWorkgroup, BM, BN, gl_MatrixUseAccumulator>(sum);
#ifdef MUL_MAT_ID
// Call callback to store each element, remapping row through shared memory
coopMatPerElementNV(mat_d, mat_d, perElemOpD, ir, ic);
#else
tensorLayoutD = setTensorLayoutStrideNV(tensorLayoutD, p.stride_d, 1);
uint pos_d = batch_idx * p.batch_stride_d + ik * p.batch_stride_d * gl_NumWorkGroups.z;
coopMatStoreTensorNV(mat_d, data_d, pos_d, sliceTensorLayoutNV(tensorLayoutD, ic * BN, BN, ir * BM, BM), tensorViewTranspose);
#endif
}

97
ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
View File

@@ -30,6 +30,8 @@
#include <fcntl.h>
#endif
#include <vulkan/vulkan_core.h>
#define ASYNCIO_CONCURRENCY 64
std::mutex lock;
@@ -196,15 +198,17 @@ static uint32_t compile_count = 0;
static std::mutex compile_count_mutex;
static std::condition_variable compile_count_cond;
void string_to_spv_func(const std::string& _name, const std::string& in_fname, const std::map<std::string, std::string>& defines, bool fp16 = true) {
std::string name = _name + (fp16 ? "" : "_fp32");
void string_to_spv_func(const std::string& _name, const std::string& in_fname, const std::map<std::string, std::string>& defines, bool fp16 = true, bool coopmat2 = false, bool f16acc = false) {
std::string name = _name + (f16acc ? "_f16acc" : "") + (coopmat2 ? "_cm2" : (fp16 ? "" : "_fp32"));
std::string out_fname = join_paths(output_dir, name + ".spv");
std::string in_path = join_paths(input_dir, in_fname);
std::string target_env = (name.find("_cm2") != std::string::npos) ? "--target-env=vulkan1.3" : "--target-env=vulkan1.2";
#ifdef _WIN32
std::vector<std::string> cmd = {GLSLC, "-fshader-stage=compute", "--target-env=vulkan1.2", "-O", "\"" + in_path + "\"", "-o", "\"" + out_fname + "\""};
std::vector<std::string> cmd = {GLSLC, "-fshader-stage=compute", target_env, "-O", "\"" + in_path + "\"", "-o", "\"" + out_fname + "\""};
#else
std::vector<std::string> cmd = {GLSLC, "-fshader-stage=compute", "--target-env=vulkan1.2", "-O", in_path, "-o", out_fname};
std::vector<std::string> cmd = {GLSLC, "-fshader-stage=compute", target_env, "-O", in_path, "-o", out_fname};
#endif
#ifdef GGML_VULKAN_SHADER_DEBUG_INFO
@@ -254,7 +258,7 @@ std::map<std::string, std::string> merge_maps(const std::map<std::string, std::s
}
static std::vector<std::future<void>> compiles;
void string_to_spv(const std::string& _name, const std::string& in_fname, const std::map<std::string, std::string>& defines, bool fp16 = true) {
void string_to_spv(const std::string& _name, const std::string& in_fname, const std::map<std::string, std::string>& defines, bool fp16 = true, bool coopmat2 = false, bool f16acc = false) {
{
// wait until fewer than N compiles are in progress.
// 16 is an arbitrary limit, the goal is to avoid "failed to create pipe" errors.
@@ -265,15 +269,15 @@ void string_to_spv(const std::string& _name, const std::string& in_fname, const
}
compile_count++;
}
compiles.push_back(std::async(string_to_spv_func, _name, in_fname, defines, fp16));
compiles.push_back(std::async(string_to_spv_func, _name, in_fname, defines, fp16, coopmat2, f16acc));
}
void matmul_shaders(bool fp16, bool matmul_id) {
std::string load_vec = fp16 ? "8" : "4";
std::string aligned_b_type_f32 = fp16 ? "mat2x4" : "vec4";
std::string aligned_b_type_f16 = fp16 ? "f16mat2x4" : "f16vec4";
void matmul_shaders(bool fp16, bool matmul_id, bool coopmat2, bool f16acc) {
std::string load_vec = coopmat2 ? "1" : fp16 ? "8" : "4";
std::string aligned_b_type_f32 = coopmat2 ? "float" : fp16 ? "mat2x4" : "vec4";
std::string aligned_b_type_f16 = coopmat2 ? "float16_t" : fp16 ? "f16mat2x4" : "f16vec4";
std::map<std::string, std::string> base_dict = {{"FLOAT_TYPE", fp16 ? "float16_t" : "float"}};
std::map<std::string, std::string> base_dict = {{"FLOAT_TYPE", (coopmat2 || fp16) ? "float16_t" : "float"}};
std::string shader_name = "matmul";
if (matmul_id) {
@@ -285,21 +289,31 @@ void matmul_shaders(bool fp16, bool matmul_id) {
base_dict["FLOAT16"] = "1";
}
// Shaders with f16 B_TYPE
string_to_spv(shader_name + "_f32_f16", "mul_mm.comp", merge_maps(base_dict, {{"DATA_A_F32", "1"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}}), fp16);
string_to_spv(shader_name + "_f32_f16_aligned", "mul_mm.comp", merge_maps(base_dict, {{"DATA_A_F32", "1"}, {"LOAD_VEC_A", load_vec}, {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f16}, {"D_TYPE", "float"}}), fp16);
base_dict["ACC_TYPE"] = f16acc ? "float16_t" : "float";
string_to_spv(shader_name + "_f16", "mul_mm.comp", merge_maps(base_dict, {{"DATA_A_F16", "1"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}}), fp16);
string_to_spv(shader_name + "_f16_aligned", "mul_mm.comp", merge_maps(base_dict, {{"DATA_A_F16", "1"}, {"LOAD_VEC_A", load_vec}, {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f16}, {"D_TYPE", "float"}}), fp16);
std::string source_name = coopmat2 ? "mul_mm_cm2.comp" : "mul_mm.comp";
// Shaders with f16 B_TYPE
string_to_spv(shader_name + "_f32_f16", source_name, merge_maps(base_dict, {{"DATA_A_F32", "1"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}, }), fp16, coopmat2, f16acc);
string_to_spv(shader_name + "_f32_f16_aligned", source_name, merge_maps(base_dict, {{"DATA_A_F32", "1"}, {"LOAD_VEC_A", load_vec}, {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f16}, {"D_TYPE", "float"}, {"ALIGNED", "1"}}), fp16, coopmat2, f16acc);
string_to_spv(shader_name + "_f16_aligned", source_name, merge_maps(base_dict, {{"DATA_A_F16", "1"}, {"LOAD_VEC_A", load_vec}, {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f16}, {"D_TYPE", "float"}, {"ALIGNED", "1"}}), fp16, coopmat2, f16acc);
string_to_spv(shader_name + "_f16", source_name, merge_maps(base_dict, {{"DATA_A_F16", "1"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}}), fp16, coopmat2, f16acc);
for (const auto& tname : type_names) {
std::string data_a_key = "DATA_A_" + to_uppercase(tname);
// For unaligned, load one at a time for f32/f16, or two at a time for quants
std::string load_vec_a_unaligned = (tname == "f32" || tname == "f16") ? "1" : "2";
std::string load_vec_a_unaligned = (coopmat2 || tname == "f32" || tname == "f16") ? "1" : "2";
// For aligned matmul loads
std::string load_vec_a = (tname == "f32" || tname == "f16") ? load_vec : "2";
string_to_spv(shader_name + "_" + tname + "_f32", "mul_mm.comp", merge_maps(base_dict, {{data_a_key, "1"}, {"LOAD_VEC_A", load_vec_a_unaligned}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}), fp16);
string_to_spv(shader_name + "_" + tname + "_f32_aligned", "mul_mm.comp", merge_maps(base_dict, {{data_a_key, "1"}, {"LOAD_VEC_A", load_vec_a}, {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f32}, {"D_TYPE", "float"}}), fp16);
std::string load_vec_a = (coopmat2 || tname == "f32" || tname == "f16") ? load_vec : "2";
string_to_spv(shader_name + "_" + tname + "_f32", source_name, merge_maps(base_dict, {{data_a_key, "1"}, {"LOAD_VEC_A", load_vec_a_unaligned}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"B_IS_FLOAT", "1"}}), fp16, coopmat2, f16acc);
string_to_spv(shader_name + "_" + tname + "_f32_aligned", source_name, merge_maps(base_dict, {{data_a_key, "1"}, {"LOAD_VEC_A", load_vec_a}, {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f32}, {"D_TYPE", "float"}, {"B_IS_FLOAT", "1"}, {"ALIGNED", "1"}}), fp16, coopmat2, f16acc);
if (tname != "f16" && tname != "f32") {
string_to_spv(shader_name + "_" + tname + "_f16", source_name, merge_maps(base_dict, {{data_a_key, "1"}, {"LOAD_VEC_A", load_vec_a_unaligned}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}, {"B_IS_FLOAT", "1"}}), fp16, coopmat2, f16acc);
string_to_spv(shader_name + "_" + tname + "_f16_aligned", source_name, merge_maps(base_dict, {{data_a_key, "1"}, {"LOAD_VEC_A", load_vec_a}, {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f16}, {"D_TYPE", "float"}, {"B_IS_FLOAT", "1"}, {"ALIGNED", "1"}}), fp16, coopmat2, f16acc);
}
}
}
@@ -307,11 +321,50 @@ void process_shaders() {
std::cout << "ggml_vulkan: Generating and compiling shaders to SPIR-V" << std::endl;
std::map<std::string, std::string> base_dict = {{"FLOAT_TYPE", "float"}};
// matmul
for (const auto& fp16 : {false, true}) {
matmul_shaders(fp16, false);
matmul_shaders(fp16, true);
for (const auto& matmul_id : {false, true}) {
for (const auto& coopmat2 : {false, true}) {
for (const auto& f16acc : {false, true}) {
#if !defined(VK_NV_cooperative_matrix2)
if (coopmat2) {
continue;
}
#endif
if (coopmat2 && !fp16) {
continue;
}
if (!coopmat2 && f16acc) {
continue;
}
matmul_shaders(fp16, matmul_id, coopmat2, f16acc);
}
}
}
}
#if defined(VK_NV_cooperative_matrix2)
// flash attention
for (const auto& f16acc : {false, true}) {
std::string acctype = f16acc ? "float16_t" : "float";
for (const auto& tname : type_names) {
if (tname == "f32") {
continue;
}
if (tname == "f16") {
string_to_spv("flash_attn_f32_f16_" + tname, "flash_attn_cm2.comp",
merge_maps(base_dict, {{"Q_TYPE", "float"}, {"D_TYPE", "float"}, {"ACC_TYPE", acctype}}), true, true, f16acc);
} else {
std::string data_a_key = "DATA_A_" + to_uppercase(tname);
string_to_spv("flash_attn_f32_f16_" + tname, "flash_attn_cm2.comp",
merge_maps(base_dict, {{data_a_key, "1"}, {"Q_TYPE", "float"}, {"D_TYPE", "float"}, {"ACC_TYPE", acctype}, {"DEQUANTFUNC", "dequantFunc"+to_uppercase(tname) }, {"BLOCK_SIZE", "QUANT_K_"+to_uppercase(tname) }}), true, true, f16acc);
}
}
}
#endif
for (const auto& tname : type_names) {
// mul mat vec
std::string data_a_key = "DATA_A_" + to_uppercase(tname);