ROCm/composable_kernel
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fix nswizzle = true

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This commit is contained in:
coderfeli
2025-02-19 02:28:25 +00:00
parent 77e2385f0e
commit 24d8024f0e
2 changed files with 31 additions and 18 deletions
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18
example/65_gemm_multiply_multiply/moe_gemm1.cpp
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@@ -139,7 +139,7 @@ static constexpr ck::index_t BLOCKSIZE = 256;
static constexpr ck::index_t NPerBlock = 128;
static constexpr ck::index_t MNPerXDL = 32;
static constexpr ck::index_t KPerBlock = 128 / sizeof(A0DataType);
static constexpr ck::index_t Nswizzle = false;
static constexpr ck::index_t Nswizzle = true;
static constexpr ck::index_t AK1 = 16 / sizeof(A0DataType);
static constexpr ck::index_t BK1 = 16 / sizeof(B0DataType);
static constexpr ck::index_t EVec = 16 / sizeof(EDataType);
@@ -199,7 +199,7 @@ int main(int argc, char* argv[])
ck::index_t valid_tile_num = 13;
ck::index_t sorted_size = sorted_tile_num * MPerBlock;
ck::index_t valid_size = valid_tile_num * MPerBlock;
ck::index_t tokens = 64;
ck::index_t tokens = 544;
ck::index_t topk = 2;
// ck::index_t tokens = batch * topk;
@@ -245,17 +245,18 @@ int main(int argc, char* argv[])
Tensor<ck::index_t> expert_ids(HostTensorDescriptor({sorted_tile_num}, {1}));
Tensor<ck::index_t> sorted_token_ids(HostTensorDescriptor({sorted_size}, {1}));
Tensor<ck::index_t> max_token_id(HostTensorDescriptor({1 + sorted_tile_num}));
max_token_id.mData = {valid_size, 2, 2, 1, 1, 2, 2, 2,2, 2, 2, 1, 2,2,0,0,0};
int eids[] = {0, 0,1, 2,3, 3, 4,4, 5, 5, 6, 7, 7, 3, 3, 3}; // {2, 1, 1, 2, 2, 2, 1, 2}
// max_token_id.mData = {valid_size, 2, 2, 1, 1, 2, 2, 2,2, 2, 2, 2, 2,1,0,0,0};
max_token_id.mData = {valid_size, 0, 2, 3, 4, 6, 8, 10, 12, 13};
int eids[] = {0, 0,1, 2,3, 3, 4,4, 5, 5, 6, 6, 7, 3, 3, 3}; // {2, 1, 1, 2, 2, 2, 1, 2}
for (int i = 0; i < sorted_tile_num; i++) {
expert_ids.mData[i] = eids[i];
}
int token_per_tile = tokens * topk / valid_tile_num;
int token_per_tile = (tokens * topk + valid_tile_num - 1) / valid_tile_num;
int tokenid = 0;
// sorted_token_ids.mData[0] = 0;
for (int i = 0; i < sorted_size; i++) {
int tile_off = i % MPerBlock;
if(tile_off < token_per_tile)
if(tile_off < token_per_tile && tokenid < tokens * topk)
{
sorted_token_ids.mData[i] = (tokenid % tokens) | ((tokenid / tokens) << 24);
tokenid++;
@@ -274,7 +275,6 @@ int main(int argc, char* argv[])
Tensor<D1DataType> d1_e_n(HostTensorDescriptor({experts, N}, {1, StrideDs[1]}));
Tensor<EDataType> e_t_n_host_result(HostTensorDescriptor({tokens, topk, N}, {topk * N, N, 1}));
Tensor<EDataType> e_t_n_device_result(HostTensorDescriptor({tokens, topk, N}, {topk * N, N, 1}));
std::cout << "a0_t_k: " << a0_t_k.mDesc << std::endl;
std::cout << "b0_e_n_k: " << b0_e_n_k.mDesc << std::endl;
std::cout << "d1_e_n: " << d1_e_n.mDesc << std::endl;
@@ -287,8 +287,8 @@ int main(int argc, char* argv[])
case 1:
a0_t_k.GenerateTensorValue(GeneratorTensor_2<A0DataType>{-2, 2});
b0_e_n_k.GenerateTensorValue(GeneratorTensor_2<B0DataType>{-2, 2});
d0_t_n.GenerateTensorValue(GeneratorTensor_2<D0DataType>{-2, 2});
d1_e_n.GenerateTensorValue(GeneratorTensor_2<D1DataType>{-2, 2});
d0_t_n.GenerateTensorValue(GeneratorTensor_2<D0DataType>{1, 2});
d1_e_n.GenerateTensorValue(GeneratorTensor_2<D1DataType>{1, 2});
break;
case 2:
a0_t_k.GenerateTensorValue(GeneratorTensor_1<A0DataType>{});