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cuda : non-cont concat support (ggerganov#7610)
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* tests : add non-cont concat tests

* cuda : non-cont concat support

ggml-ci
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@ggerganov
ggerganov authored May 29, 2024
1 parent 210d991 commit cce3dcf
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Showing 2 changed files with 113 additions and 30 deletions.
110 changes: 88 additions & 22 deletions ggml-cuda/concat.cu
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Original file line number Diff line number Diff line change
@@ -1,5 +1,6 @@
#include "concat.cuh"

// contiguous kernels
static __global__ void concat_f32_dim0(const float * x, const float * y, float * dst, const int ne0, const int ne00) {
int nidx = threadIdx.x + blockIdx.x * blockDim.x;
if (nidx >= ne0) {
Expand Down Expand Up @@ -92,39 +93,104 @@ static void concat_f32_cuda(const float * x, const float * y, float * dst, int n
concat_f32_dim2<<<gridDim, CUDA_CONCAT_BLOCK_SIZE, 0, stream>>>(x, y, dst, ne0, ne02);
}

// non-contiguous kernel (slow)
static __global__ void concat_f32_non_cont(
const char * src0,
const char * src1,
char * dst,
int64_t ne00,
int64_t ne01,
int64_t ne02,
int64_t ne03,
uint64_t nb00,
uint64_t nb01,
uint64_t nb02,
uint64_t nb03,
int64_t /*ne10*/,
int64_t /*ne11*/,
int64_t /*ne12*/,
int64_t /*ne13*/,
uint64_t nb10,
uint64_t nb11,
uint64_t nb12,
uint64_t nb13,
int64_t ne0,
int64_t /*ne1*/,
int64_t /*ne2*/,
int64_t /*ne3*/,
uint64_t nb0,
uint64_t nb1,
uint64_t nb2,
uint64_t nb3,
int32_t dim) {
const int64_t i3 = blockIdx.z;
const int64_t i2 = blockIdx.y;
const int64_t i1 = blockIdx.x;

int64_t o[4] = {0, 0, 0, 0};
o[dim] = dim == 0 ? ne00 : (dim == 1 ? ne01 : (dim == 2 ? ne02 : ne03));

const float * x;

for (int i0 = threadIdx.x; i0 < ne0; i0 += blockDim.x) {
if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) {
x = (const float *)(src0 + (i3 )*nb03 + (i2 )*nb02 + (i1 )*nb01 + (i0 )*nb00);
} else {
x = (const float *)(src1 + (i3 - o[3])*nb13 + (i2 - o[2])*nb12 + (i1 - o[1])*nb11 + (i0 - o[0])*nb10);
}

float * y = (float *)(dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);

*y = *x;
}
}


void ggml_cuda_op_concat(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const ggml_tensor * src0 = dst->src[0];
const ggml_tensor * src1 = dst->src[1];

const float * src0_d = (const float *)src0->data;
const float * src1_d = (const float *)src1->data;

float * dst_d = (float *)dst->data;
cudaStream_t stream = ctx.stream();

const int32_t dim = ((int32_t *) dst->op_params)[0];

GGML_ASSERT(ggml_is_contiguous(src0));
GGML_ASSERT(ggml_is_contiguous(src1));

GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT(src1->type == GGML_TYPE_F32);
GGML_ASSERT(dst->type == GGML_TYPE_F32);

if (dim != 3) {
for (int i3 = 0; i3 < dst->ne[3]; i3++) {
concat_f32_cuda(
src0_d + i3 * (src0->nb[3] / 4),
src1_d + i3 * (src1->nb[3] / 4),
dst_d + i3 * ( dst->nb[3] / 4),
src0->ne[0], src0->ne[1], src0->ne[2],
dst->ne[0], dst->ne[1], dst->ne[2], dim, stream);
GGML_ASSERT(dst->type == GGML_TYPE_F32);

if (ggml_is_contiguous(src0) && ggml_is_contiguous(src1)) {
const float * src0_d = (const float *)src0->data;
const float * src1_d = (const float *)src1->data;

float * dst_d = (float *)dst->data;

if (dim != 3) {
for (int i3 = 0; i3 < dst->ne[3]; i3++) {
concat_f32_cuda(
src0_d + i3 * (src0->nb[3] / 4),
src1_d + i3 * (src1->nb[3] / 4),
dst_d + i3 * ( dst->nb[3] / 4),
src0->ne[0], src0->ne[1], src0->ne[2],
dst->ne[0], dst->ne[1], dst->ne[2], dim, stream);
}
} else {
const size_t size0 = ggml_nbytes(src0);
const size_t size1 = ggml_nbytes(src1);

CUDA_CHECK(cudaMemcpyAsync(dst_d, src0_d, size0, cudaMemcpyDeviceToDevice, stream));
CUDA_CHECK(cudaMemcpyAsync(dst_d + size0/4, src1_d, size1, cudaMemcpyDeviceToDevice, stream));
}
} else {
const size_t size0 = ggml_nbytes(src0);
const size_t size1 = ggml_nbytes(src1);

CUDA_CHECK(cudaMemcpyAsync(dst_d, src0_d, size0, cudaMemcpyDeviceToDevice, stream));
CUDA_CHECK(cudaMemcpyAsync(dst_d + size0/4, src1_d, size1, cudaMemcpyDeviceToDevice, stream));
dim3 grid_dim(dst->ne[1], dst->ne[2], dst->ne[3]);
concat_f32_non_cont<<<grid_dim, CUDA_CONCAT_BLOCK_SIZE, 0, stream>>>(
(const char *)src0->data,
(const char *)src1->data,
( char *)dst->data,
src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3],
src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3],
src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3],
src1->nb[0], src1->nb[1], src1->nb[2], src1->nb[3],
dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3],
dst->nb[0], dst->nb[1], dst->nb[2], dst->nb[3], dim);
}
}
33 changes: 25 additions & 8 deletions tests/test-backend-ops.cpp
View file
Original file line number Diff line number Diff line change
Expand Up @@ -1262,22 +1262,37 @@ struct test_concat : public test_case {
const std::array<int64_t, 4> ne_a;
const int64_t ne_b_d;
const int dim;
const int v; // view (1 << 0: non-cont a, 1 << 1: non-cont b)

std::string vars() override {
return VARS_TO_STR4(type, ne_a, ne_b_d, dim);
return VARS_TO_STR5(type, ne_a, ne_b_d, dim, v);
}

test_concat(ggml_type type = GGML_TYPE_F32,
std::array<int64_t, 4> ne_a = {10, 10, 10, 10},
int64_t ne_b_d = 10,
int dim = 2)
: type(type), ne_a(ne_a), ne_b_d(ne_b_d), dim(dim) {}
int dim = 2, int v = 0)
: type(type), ne_a(ne_a), ne_b_d(ne_b_d), dim(dim), v(v) {}

ggml_tensor * build_graph(ggml_context * ctx) override {
auto ne_b = ne_a;
ne_b[dim] = ne_b_d;
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne_a.data());
ggml_tensor * b = ggml_new_tensor(ctx, type, 4, ne_b.data());
ggml_tensor * a;
if (v & 1) {
auto ne = ne_a; ne[0] *= 2; ne[1] *= 4; ne[2] *= 3;
a = ggml_new_tensor(ctx, type, 4, ne.data());
a = ggml_view_4d(ctx, a, ne_a[0], ne_a[1], ne_a[2], ne_a[3], a->nb[1], a->nb[2], a->nb[3], 0);
} else {
a = ggml_new_tensor(ctx, type, 4, ne_a.data());
}
ggml_tensor * b;
if (v & 2) {
auto ne = ne_b; ne[0] *= 3; ne[1] *= 2; ne[2] *= 4;
b = ggml_new_tensor(ctx, type, 4, ne.data());
b = ggml_view_4d(ctx, b, ne_b[0], ne_b[1], ne_b[2], ne_b[3], b->nb[1], b->nb[2], b->nb[3], 0);
} else {
b = ggml_new_tensor(ctx, type, 4, ne_b.data());
}
ggml_tensor * out = ggml_concat(ctx, a, b, dim);
return out;
}
Expand Down Expand Up @@ -2215,9 +2230,11 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
}
}

for (int dim : { 0, 1, 2, 3, }) {
test_cases.emplace_back(new test_concat(GGML_TYPE_F32, {11, 12, 13, 14}, 7, dim));
test_cases.emplace_back(new test_concat(GGML_TYPE_I32, {11, 12, 13, 14}, 7, dim));
for (int v : { 0, 1, 2, 3 }) {
for (int dim : { 0, 1, 2, 3, }) {
test_cases.emplace_back(new test_concat(GGML_TYPE_F32, {11, 12, 13, 14}, 7, dim, v));
test_cases.emplace_back(new test_concat(GGML_TYPE_I32, {11, 12, 13, 14}, 7, dim, v));
}
}

for (ggml_sort_order order : {GGML_SORT_ORDER_ASC, GGML_SORT_ORDER_DESC}) {
Expand Down

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