cuda : non-cont concat support (llama/7610)

* tests : add non-cont concat tests

* cuda : non-cont concat support

ggml-ci

src/ggml-cuda/concat.cu

@@ -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) {
@@ -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);
  }
 }

tests/test-backend-ops.cpp

@@ -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;
  }
@@ -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}) {