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Gemm_c_shuffle (4 layouts) X (fp32 bf16 int8) (#131)

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* [What] Separate fixpoint gemm from gemm example
[Why] let example of gemm_int8 be pure gemm.
[What]
1. Add gemm_requant_relu_requant,
2. Let CDataType be int32 in pure gemm, because no one use int8 CDataType. It is also part of gemm_requant_relu_requant

* Fix path

* Revise cmakelist due to merge develop

* Add gemm fp16 test

* Extract PrepareGemmTensor

* Extract TestGemm

* Add test for different layout

* Add 4 layouts of shuffle version of fp32

* Add 4 layouts of shuffle version of int8

* Add 4 layouts of shuffle version of bf16

* replace all DeviceGemmPtr_ with DeviceGemmNoOpPtr to fit naming convension

* Add test for non-shuffle verstion of gemm

* Fix typo

* Print kernel information

* Add rest of the fp32 kernel to the test

* 1. Add rest of the fp16 device iop.
2. Mark the invalid device operation

Co-authored-by: rocking <chunylai@amd.com>
This commit is contained in:
rocking5566
2022-03-22 04:59:51 +08:00
committed by GitHub
parent b51808d7a5
commit 485ea46a40
24 changed files with 1497 additions and 322 deletions
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10
example/02_gemm_alpha_beta/gemm_xdl_alpha_beta.cpp
View File

@@ -157,9 +157,9 @@ int main(int argc, char* argv[])
Tensor<ADataType> a_m_k(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
Tensor<BDataType> b_k_n(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
Tensor<BDataType> c0_m_n(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
Tensor<BDataType> c_m_n_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
Tensor<BDataType> c_m_n_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
Tensor<CDataType> c0_m_n(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
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{}));
std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
@@ -172,12 +172,12 @@ int main(int argc, char* argv[])
case 1:
a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
c0_m_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
c0_m_n.GenerateTensorValue(GeneratorTensor_2<CDataType>{-5, 5});
break;
default:
a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
c0_m_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
c0_m_n.GenerateTensorValue(GeneratorTensor_3<CDataType>{-0.5, 0.5});
}
DeviceMem a_m_k_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpace());

4
example/03_gemm_bias_relu/gemm_xdl_bias_relu.cpp
View File

@@ -139,8 +139,8 @@ int main(int argc, char* argv[])
Tensor<ADataType> a_m_k(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
Tensor<BDataType> b_k_n(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
Tensor<BDataType> c_m_n_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
Tensor<BDataType> c_m_n_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
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{}));
// c0_n[n]
Tensor<CDataType> c0_n(HostTensorDescriptor(

6
example/04_gemm_bias_relu_add/gemm_xdl_bias_relu_add.cpp
View File

@@ -141,15 +141,15 @@ int main(int argc, char* argv[])
Tensor<ADataType> a_m_k(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
Tensor<BDataType> b_k_n(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
Tensor<BDataType> c_m_n_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
Tensor<BDataType> c_m_n_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
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{}));
// c0_n[n]
Tensor<CDataType> c0_n(HostTensorDescriptor(
std::vector<std::size_t>({static_cast<std::size_t>(N)}), std::vector<std::size_t>({1})));
// c1_m_n[m ,n]
Tensor<BDataType> c1_m_n(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
Tensor<CDataType> c1_m_n(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;