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#include "set-rows.cuh"
#include "cpy-utils.cuh"
typedef void (*set_rows_kernel_t)(const char * src, char * dst);
// Generic quantized set_rows kernel template
template <typename idx_t, typename block_type, int qk, void (*quantize_func)(const float *, block_type *)>
static __global__ void k_set_rows_quant(const float * __restrict__ src0,
const idx_t * __restrict__ src1,
block_type * __restrict__ dst,
const int64_t ne_total,
const int64_t ne10,
const int64_t ne11,
const int64_t ne12,
const int64_t ne13,
const int64_t s01,
const int64_t s02,
const int64_t s03,
const int64_t s10,
const int64_t s11,
const int64_t s12,
const int64_t s1,
const int64_t s2,
const int64_t s3,
const uint3 ne00,
const uint3 ne01,
const uint3 ne02,
const uint3 ne11_fd,
const uint3 ne12_fd) {
const int64_t i = int64_t(blockDim.x) * blockIdx.x + threadIdx.x;
if (i >= ne_total) {
return;
}
const int64_t i_base = i * qk;
uint32_t tmp = (uint32_t) i_base;
uint2 div_mod;
div_mod = fast_div_modulo(tmp, ne00);
const int64_t i00 = div_mod.y;
tmp = div_mod.x;
div_mod = fast_div_modulo(tmp, ne01);
const int64_t i01 = div_mod.y;
tmp = div_mod.x;
div_mod = fast_div_modulo(tmp, ne02);
const int64_t i02 = div_mod.y;
const int64_t i03 = div_mod.x;
const int64_t i12 = fastmodulo((uint32_t) i03, ne12_fd);
const int64_t i11 = fastmodulo((uint32_t) i02, ne11_fd);
const int64_t i10 = i01;
const int64_t dst_row = *(src1 + i10*s10 + i11*s11 + i12*s12);
const float * src0_row = src0 + i01*s01 + i02*s02 + i03*s03;
block_type * dst_row_ptr = dst + (dst_row*s1 + i02*s2 + i03*s3) / sizeof(block_type);
const float * src_block = src0_row + i00;
block_type * dst_block = dst_row_ptr + i00 / qk;
quantize_func(src_block, dst_block);
GGML_UNUSED(ne10);
GGML_UNUSED(ne11);
GGML_UNUSED(ne12);
GGML_UNUSED(ne13);
}
// Template dispatch function for quantized set_rows
template<typename idx_t, typename block_type, int qk, void (*quantize_func)(const float*, block_type*)>
static void set_rows_cuda_quant(
const float * src0_d, const idx_t * src1_d, block_type * dst_d,
const int64_t ne00, const int64_t ne01, const int64_t ne02, const int64_t ne03,
const int64_t ne10, const int64_t ne11, const int64_t ne12, const int64_t ne13,
const size_t nb01, const size_t nb02, const size_t nb03,
const size_t nb10, const size_t nb11, const size_t nb12,
const size_t nb1, const size_t nb2, const size_t nb3,
cudaStream_t stream) {
GGML_ASSERT(ne00 % qk == 0);
const int64_t ne_total = (ne00 * ne01 * ne02 * ne03) / qk;
const int num_blocks = (ne_total + CUDA_SET_ROWS_BLOCK_SIZE - 1) / CUDA_SET_ROWS_BLOCK_SIZE;
const dim3 block_size(CUDA_SET_ROWS_BLOCK_SIZE);
const dim3 grid_size(num_blocks);
const int64_t s01 = nb01/sizeof(float);
const int64_t s02 = nb02/sizeof(float);
const int64_t s03 = nb03/sizeof(float);
const int64_t s10 = nb10/sizeof(idx_t);
const int64_t s11 = nb11/sizeof(idx_t);
const int64_t s12 = nb12/sizeof(idx_t);
const int64_t s1 = nb1;
const int64_t s2 = nb2;
const int64_t s3 = nb3;
if (ne_total > 0 && ne00 > 0 && ne01 > 0 && ne02 > 0 && ne11 > 0 && ne12 > 0) {
const uint3 ne00_fd = init_fastdiv_values((uint32_t) ne00);
const uint3 ne01_fd = init_fastdiv_values((uint32_t) ne01);
const uint3 ne02_fd = init_fastdiv_values((uint32_t) ne02);
const uint3 ne11_fd = init_fastdiv_values((uint32_t) ne11);
const uint3 ne12_fd = init_fastdiv_values((uint32_t) ne12);
k_set_rows_quant<idx_t, block_type, qk, quantize_func><<<grid_size, block_size, 0, stream>>>(
src0_d, src1_d, dst_d, ne_total, ne10, ne11, ne12, ne13, s01, s02, s03, s10, s11, s12, s1, s2, s3, ne00_fd,
ne01_fd, ne02_fd, ne11_fd, ne12_fd);
}
}
template <typename src_t, typename idx_t, typename dst_t>
static __global__ void k_set_rows(const src_t * __restrict__ src0,
const idx_t * __restrict__ src1,
dst_t * __restrict__ dst,
const int64_t ne_total,
const int64_t ne10,
const int64_t ne11,
const int64_t ne12,
const int64_t ne13,
const int64_t s01,
const int64_t s02,
const int64_t s03,
const int64_t s10,
const int64_t s11,
const int64_t s12,
const int64_t s1,
const int64_t s2,
const int64_t s3,
const uint3 ne00,
const uint3 ne01,
const uint3 ne02,
const uint3 ne11_fd,
const uint3 ne12_fd) {
const int64_t i = int64_t(blockDim.x) * blockIdx.x + threadIdx.x;
if (i >= ne_total) {
return;
}
uint32_t tmp = (uint32_t) i;
uint2 div_mod;
div_mod = fast_div_modulo(tmp, ne00);
const int64_t i00 = div_mod.y;
tmp = div_mod.x;
div_mod = fast_div_modulo(tmp, ne01);
const int64_t i01 = div_mod.y;
tmp = div_mod.x;
div_mod = fast_div_modulo(tmp, ne02);
const int64_t i02 = div_mod.y;
const int64_t i03 = div_mod.x;
const int64_t i12 = fastmodulo((uint32_t) i03, ne12_fd);
const int64_t i11 = fastmodulo((uint32_t) i02, ne11_fd);
const int64_t i10 = i01;
const int64_t dst_row = *(src1 + i10*s10 + i11*s11 + i12*s12);
const src_t * src0_row = src0 + i01*s01 + i02*s02 + i03*s03;
dst_t * dst_row_ptr = dst + dst_row*s1 + i02*s2 + i03*s3;
dst_row_ptr[i00] = ggml_cuda_cast<dst_t>(src0_row[i00]);
GGML_UNUSED(ne10);
GGML_UNUSED(ne11);
GGML_UNUSED(ne12);
GGML_UNUSED(ne13);
}
template<typename src_t, typename idx_t, typename dst_t>
static void set_rows_cuda(
const src_t * src0_d, const idx_t * src1_d, dst_t * dst_d,
const int64_t ne00, const int64_t ne01, const int64_t ne02, const int64_t ne03,
const int64_t ne10, const int64_t ne11, const int64_t ne12, const int64_t ne13,
const size_t nb01, const size_t nb02, const size_t nb03,
const size_t nb10, const size_t nb11, const size_t nb12,
const size_t nb1, const size_t nb2, const size_t nb3,
cudaStream_t stream) {
const int64_t ne_total = ne00 * ne01 * ne02 * ne03;
const int num_blocks = (ne_total + CUDA_SET_ROWS_BLOCK_SIZE - 1) / CUDA_SET_ROWS_BLOCK_SIZE;
const dim3 block_size(CUDA_SET_ROWS_BLOCK_SIZE);
const dim3 grid_size(num_blocks);
const int64_t s01 = nb01/sizeof(src_t);
const int64_t s02 = nb02/sizeof(src_t);
const int64_t s03 = nb03/sizeof(src_t);
const int64_t s10 = nb10/sizeof(idx_t);
const int64_t s11 = nb11/sizeof(idx_t);
const int64_t s12 = nb12/sizeof(idx_t);
const int64_t s1 = nb1/sizeof(dst_t);
const int64_t s2 = nb2/sizeof(dst_t);
const int64_t s3 = nb3/sizeof(dst_t);
if (ne_total > 0 && ne00 > 0 && ne01 > 0 && ne02 > 0 && ne11 > 0 && ne12 > 0) {
const uint3 ne00_fd = init_fastdiv_values((uint32_t) ne00);
const uint3 ne01_fd = init_fastdiv_values((uint32_t) ne01);
const uint3 ne02_fd = init_fastdiv_values((uint32_t) ne02);
const uint3 ne11_fd = init_fastdiv_values((uint32_t) ne11);
const uint3 ne12_fd = init_fastdiv_values((uint32_t) ne12);
k_set_rows<<<grid_size, block_size, 0, stream>>>(src0_d, src1_d, dst_d, ne_total, ne10, ne11, ne12, ne13, s01,
s02, s03, s10, s11, s12, s1, s2, s3, ne00_fd, ne01_fd, ne02_fd,
ne11_fd, ne12_fd);
}
}
template<typename src_t, typename idx_t>
static void set_rows_cuda(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
const src_t * src0_d = (const src_t *)src0->data;
const idx_t * src1_d = (const idx_t *)src1->data;
GGML_TENSOR_BINARY_OP_LOCALS
cudaStream_t stream = ctx.stream();
if (dst->type == GGML_TYPE_F32) {
set_rows_cuda(
src0_d, src1_d, (float*)dst->data,
ne00, ne01, ne02, ne03,
ne10, ne11, ne12, ne13,
nb01, nb02, nb03,
nb10, nb11, nb12,
nb1, nb2, nb3,
stream
);
} else if (dst->type == GGML_TYPE_F16) {
set_rows_cuda(
src0_d, src1_d, (half*)dst->data,
ne00, ne01, ne02, ne03,
ne10, ne11, ne12, ne13,
nb01, nb02, nb03,
nb10, nb11, nb12,
nb1, nb2, nb3,
stream
);
} else if (dst->type == GGML_TYPE_BF16) {
set_rows_cuda(
src0_d, src1_d, (nv_bfloat16*)dst->data,
ne00, ne01, ne02, ne03,
ne10, ne11, ne12, ne13,
nb01, nb02, nb03,
nb10, nb11, nb12,
nb1, nb2, nb3,
stream
);
} else if (dst->type == GGML_TYPE_Q4_0) {
set_rows_cuda_quant<idx_t, block_q4_0, QK4_0, quantize_f32_q4_0_block>(
src0_d, src1_d, (block_q4_0*)dst->data,
ne00, ne01, ne02, ne03,
ne10, ne11, ne12, ne13,
nb01, nb02, nb03,
nb10, nb11, nb12,
nb1, nb2, nb3,
stream
);
} else if (dst->type == GGML_TYPE_Q4_1) {
set_rows_cuda_quant<idx_t, block_q4_1, QK4_1, quantize_f32_q4_1_block>(
src0_d, src1_d, (block_q4_1*)dst->data,
ne00, ne01, ne02, ne03,
ne10, ne11, ne12, ne13,
nb01, nb02, nb03,
nb10, nb11, nb12,
nb1, nb2, nb3,
stream
);
} else if (dst->type == GGML_TYPE_Q5_0) {
set_rows_cuda_quant<idx_t, block_q5_0, QK5_0, quantize_f32_q5_0_block>(
src0_d, src1_d, (block_q5_0*)dst->data,
ne00, ne01, ne02, ne03,
ne10, ne11, ne12, ne13,
nb01, nb02, nb03,
nb10, nb11, nb12,
nb1, nb2, nb3,
stream
);
} else if (dst->type == GGML_TYPE_Q5_1) {
set_rows_cuda_quant<idx_t, block_q5_1, QK5_1, quantize_f32_q5_1_block>(
src0_d, src1_d, (block_q5_1*)dst->data,
ne00, ne01, ne02, ne03,
ne10, ne11, ne12, ne13,
nb01, nb02, nb03,
nb10, nb11, nb12,
nb1, nb2, nb3,
stream
);
} else if (dst->type == GGML_TYPE_Q8_0) {
set_rows_cuda_quant<idx_t, block_q8_0, QK8_0, quantize_f32_q8_0_block>(
src0_d, src1_d, (block_q8_0*)dst->data,
ne00, ne01, ne02, ne03,
ne10, ne11, ne12, ne13,
nb01, nb02, nb03,
nb10, nb11, nb12,
nb1, nb2, nb3,
stream
);
} else if (dst->type == GGML_TYPE_IQ4_NL) {
set_rows_cuda_quant<idx_t, block_iq4_nl, QK4_NL, quantize_f32_iq4_nl_block>(
src0_d, src1_d, (block_iq4_nl*)dst->data,
ne00, ne01, ne02, ne03,
ne10, ne11, ne12, ne13,
nb01, nb02, nb03,
nb10, nb11, nb12,
nb1, nb2, nb3,
stream
);
} else {
GGML_ABORT("unsupported type %s", ggml_type_name(dst->type));
}
}
void ggml_cuda_op_set_rows(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const ggml_tensor * src0 = dst->src[0];
const ggml_tensor * src1 = dst->src[1];
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT(src1->type == GGML_TYPE_I64 || src1->type == GGML_TYPE_I32);
if (src1->type == GGML_TYPE_I64) {
set_rows_cuda<float, int64_t>(ctx, src0, src1, dst);
} else {
set_rows_cuda<float, int32_t>(ctx, src0, src1, dst);
}
}
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