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Commit 0e4243f2 authored by Po-Yen, Chen's avatar Po-Yen, Chen
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Use initializer_list<> to init Tensor<> directly

parent e4e99a49
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with 26 additions and 26 deletions
+26 -26
example/02_gemm_bilinear/gemm_bilinear_xdl_fp16.cpp
View file @ 0e4243f2
......@@ -272,7 +272,7 @@ int main(int argc, char* argv[])
if(do_verification)
{
Tensor<CShuffleDataType> c_m_n(HostTensorDescriptor({M, N}));
Tensor<CShuffleDataType> c_m_n({M, N});
using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
BDataType,
......
example/18_batched_gemm_reduce/batched_gemm_reduce_xdl_fp16.cpp
View file @ 0e4243f2
......@@ -150,13 +150,13 @@ int main(int argc, char* argv[])
Tensor<CDataType> c_g_m_n_host_result(
f_host_tensor_descriptor(BatchCount, M, N, StrideC, CLayout{}));
Tensor<ReduceDataType> d0_g_m_host_result(HostTensorDescriptor({BatchCount, M}));
Tensor<ReduceDataType> d1_g_m_host_result(HostTensorDescriptor({BatchCount, M}));
Tensor<ReduceDataType> d0_g_m_host_result({BatchCount, M});
Tensor<ReduceDataType> d1_g_m_host_result({BatchCount, M});
Tensor<CDataType> c_g_m_n_device_result(
f_host_tensor_descriptor(BatchCount, M, N, StrideC, CLayout{}));
Tensor<ReduceDataType> d0_g_m_device_result(HostTensorDescriptor({BatchCount, M}));
Tensor<ReduceDataType> d1_g_m_device_result(HostTensorDescriptor({BatchCount, M}));
Tensor<ReduceDataType> d0_g_m_device_result({BatchCount, M});
Tensor<ReduceDataType> d1_g_m_device_result({BatchCount, M});
std::cout << "a_g_m_k: " << a_g_m_k.GetDesc() << std::endl;
std::cout << "b_g_k_n: " << b_g_k_n.GetDesc() << std::endl;
......
example/21_gemm_layernorm/gemm_xdl_layernorm_single_kernel_fp16.cpp
View file @ 0e4243f2
......@@ -151,10 +151,10 @@ int main(int argc, char* argv[])
Tensor<CDataType> c_m_n_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
Tensor<CDataType> c_m_n_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
Tensor<AccDataType> acc_m_n_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
Tensor<C0DataType> c0_n_bias(HostTensorDescriptor(std::vector<size_t>({size_t(N)})));
Tensor<C0DataType> c0_n_bias({N});
Tensor<C0DataType> c0_m_n_add(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
Tensor<C0DataType> c0_n_gamma(HostTensorDescriptor(std::vector<size_t>({size_t(N)})));
Tensor<C0DataType> c0_n_beta(HostTensorDescriptor(std::vector<size_t>({size_t(N)})));
Tensor<C0DataType> c0_n_gamma({N});
Tensor<C0DataType> c0_n_beta({N});
std::cout << "a_m_k: " << a_m_k.GetDesc() << std::endl;
std::cout << "b_k_n: " << b_k_n.GetDesc() << std::endl;
......
library/include/ck/library/reference_tensor_operation/cpu/reference_gemm_layernorm.hpp
View file @ 0e4243f2
......@@ -46,8 +46,8 @@ struct ReferenceGemmLayernorm : public device::BaseOperator
size_t M = acc.GetLengths()[0];
size_t N = acc.GetLengths()[1];
Tensor<ComputeDataType> avg_acc_sq(HostTensorDescriptor(std::vector<size_t>({M})));
Tensor<ComputeDataType> avg_acc(HostTensorDescriptor(std::vector<size_t>({M})));
Tensor<ComputeDataType> avg_acc_sq({M});
Tensor<ComputeDataType> avg_acc({M});
Tensor<ComputeDataType> acc_layernorm(acc);
// reduce N dim
......
profiler/include/profile_batched_gemm_reduce_impl.hpp
View file @ 0e4243f2
......@@ -96,13 +96,13 @@ bool profile_batched_gemm_reduce_impl(int do_verification,
Tensor<CDataType> c_g_m_n_host_result(
f_host_tensor_descriptor(BatchCount, M, N, StrideC, CLayout{}));
Tensor<ReduceDataType> d0_g_m_host_result(HostTensorDescriptor({BatchCount, M}));
Tensor<ReduceDataType> d1_g_m_host_result(HostTensorDescriptor({BatchCount, M}));
Tensor<ReduceDataType> d0_g_m_host_result({BatchCount, M});
Tensor<ReduceDataType> d1_g_m_host_result({BatchCount, M});
Tensor<CDataType> c_g_m_n_device_result(
f_host_tensor_descriptor(BatchCount, M, N, StrideC, CLayout{}));
Tensor<ReduceDataType> d0_g_m_device_result(HostTensorDescriptor({BatchCount, M}));
Tensor<ReduceDataType> d1_g_m_device_result(HostTensorDescriptor({BatchCount, M}));
Tensor<ReduceDataType> d0_g_m_device_result({BatchCount, M});
Tensor<ReduceDataType> d1_g_m_device_result({BatchCount, M});
std::cout << "a_g_m_k: " << a_g_m_k.GetDesc() << std::endl;
std::cout << "b_g_k_n: " << b_g_k_n.GetDesc() << std::endl;
......
profiler/include/profile_conv_fwd_bias_relu_add_impl.hpp
View file @ 0e4243f2
......@@ -93,7 +93,7 @@ void profile_conv_fwd_bias_relu_add_impl(int do_verification,
f_host_tensor_descriptor(N, K, Ho, Wo, OutLayout{}));
// bias: assume contiguous 1d vector
Tensor<OutDataType> bias_k(HostTensorDescriptor({K}));
Tensor<OutDataType> bias_k({K});
// residual: assume same layout as output tensor
Tensor<OutDataType> resi_n_k_ho_wo(f_host_tensor_descriptor(N, K, Ho, Wo, OutLayout{}));
......
profiler/include/profile_conv_fwd_bias_relu_impl.hpp
View file @ 0e4243f2
......@@ -93,7 +93,7 @@ void profile_conv_fwd_bias_relu_impl(int do_verification,
f_host_tensor_descriptor(N, K, Ho, Wo, OutLayout{}));
// bias: assume contiguous 1d vector
Tensor<OutDataType> bias_k(HostTensorDescriptor({K}));
Tensor<OutDataType> bias_k({K});
std::cout << "in_n_c_hi_wi: " << in_n_c_hi_wi.GetDesc() << std::endl;
std::cout << "wei_k_c_y_x: " << wei_k_c_y_x.GetDesc() << std::endl;
......
profiler/include/profile_gemm_add_add_fastgelu_impl.hpp
View file @ 0e4243f2
......@@ -123,7 +123,7 @@ bool profile_gemm_add_add_fastgelu_impl(int do_verification,
// run reference
if(do_verification)
{
Tensor<AccDataType> c_m_n(HostTensorDescriptor({M, N}));
Tensor<AccDataType> c_m_n({M, N});
using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
BDataType,
......
profiler/include/profile_gemm_bias_add_reduce_impl.hpp
View file @ 0e4243f2
......@@ -99,12 +99,12 @@ void profile_gemm_bias_add_reduce_impl(int do_verification,
Tensor<CDataType> c_m_n_host_result(f_host_tensor_descriptor2d(M, N, StrideC, CLayout{}));
Tensor<BiasDataType> bias_n(f_host_tensor_descriptor1d(N, 1));
Tensor<D0DataType> d0_m_n(f_host_tensor_descriptor2d(M, N, StrideC, CLayout{}));
Tensor<ReduceDataType> reduce0_m_host_result(HostTensorDescriptor({M}));
Tensor<ReduceDataType> reduce1_m_host_result(HostTensorDescriptor({M}));
Tensor<ReduceDataType> reduce0_m_host_result({M});
Tensor<ReduceDataType> reduce1_m_host_result({M});
Tensor<CDataType> c_m_n_device_result(f_host_tensor_descriptor2d(M, N, StrideC, CLayout{}));
Tensor<ReduceDataType> reduce0_m_device_result(HostTensorDescriptor({M}));
Tensor<ReduceDataType> reduce1_m_device_result(HostTensorDescriptor({M}));
Tensor<ReduceDataType> reduce0_m_device_result({M});
Tensor<ReduceDataType> reduce1_m_device_result({M});
std::cout << "a_m_k: " << a_m_k.GetDesc() << std::endl;
std::cout << "b_k_n: " << b_k_n.GetDesc() << std::endl;
......
profiler/include/profile_gemm_bilinear_impl.hpp
View file @ 0e4243f2
......@@ -118,7 +118,7 @@ bool profile_gemm_bilinear_impl(int do_verification,
// run reference
if(do_verification)
{
Tensor<AccDataType> c_m_n(HostTensorDescriptor({M, N}));
Tensor<AccDataType> c_m_n({M, N});
using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
BDataType,
......
profiler/include/profile_gemm_reduce_impl.hpp
View file @ 0e4243f2
......@@ -92,12 +92,12 @@ bool profile_gemm_reduce_impl(int do_verification,
Tensor<BDataType> b_k_n(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
Tensor<CDataType> c_m_n_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
Tensor<ReduceDataType> reduce0_m_host_result(HostTensorDescriptor({M}));
Tensor<ReduceDataType> reduce1_m_host_result(HostTensorDescriptor({M}));
Tensor<ReduceDataType> reduce0_m_host_result({M});
Tensor<ReduceDataType> reduce1_m_host_result({M});
Tensor<CDataType> c_m_n_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
Tensor<ReduceDataType> reduce0_m_device_result(HostTensorDescriptor({M}));
Tensor<ReduceDataType> reduce1_m_device_result(HostTensorDescriptor({M}));
Tensor<ReduceDataType> reduce0_m_device_result({M});
Tensor<ReduceDataType> reduce1_m_device_result({M});
std::cout << "a_m_k: " << a_m_k.GetDesc() << std::endl;
std::cout << "b_k_n: " << b_k_n.GetDesc() << std::endl;
......
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