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Unverified Commit 8fc9b21f authored by Pruthvi Madugundu's avatar Pruthvi Madugundu Committed by GitHub
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Changes to support hipblas migration (#113)

parent 10c74820
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Showing with 296 additions and 274 deletions
apex/contrib/csrc/multihead_attn/encdec_multihead_attn_cuda.cu
View file @ 8fc9b21f
......@@ -90,9 +90,9 @@ std::vector<torch::Tensor> fwd_cuda(bool use_time_mask, bool is_training,
//TORCH_CUDABLAS_CHECK(cublasSetMathMode(handle, CUBLAS_TENSOR_OP_MATH));
// Input Linear Q Fwd
TORCH_CUDABLAS_CHECK(rocblas_gemm_ex(handle,
CUBLAS_OP_T,
CUBLAS_OP_N,
TORCH_CUDABLAS_CHECK(rocBLASStatusToHIPStatus(rocblas_gemm_ex((rocblas_handle) handle,
hipOperationToRocOperation(CUBLAS_OP_T),
hipOperationToRocOperation(CUBLAS_OP_N),
output_lin_q_dim,
batches_q,
embed_dim,
......@@ -113,12 +113,12 @@ std::vector<torch::Tensor> fwd_cuda(bool use_time_mask, bool is_training,
rocblas_datatype_f32_r,
rocblas_gemm_algo_standard /*algo*/,
0 /*solution_index*/,
flags));
flags)));
// Input Linear KV Fwd
TORCH_CUDABLAS_CHECK(rocblas_gemm_ex(handle,
CUBLAS_OP_T,
CUBLAS_OP_N,
TORCH_CUDABLAS_CHECK(rocBLASStatusToHIPStatus(rocblas_gemm_ex((rocblas_handle) handle,
hipOperationToRocOperation(CUBLAS_OP_T),
hipOperationToRocOperation(CUBLAS_OP_N),
output_lin_kv_dim,
batches_kv,
embed_dim,
......@@ -139,7 +139,7 @@ std::vector<torch::Tensor> fwd_cuda(bool use_time_mask, bool is_training,
rocblas_datatype_f32_r,
rocblas_gemm_algo_standard /*algo*/,
0 /*solution_index*/,
flags));
flags)));
// MatMul1 of Dot-Product Attention Plus scaling by 1/Sqrt(head size)
gemm_switch_fp32accum( a_layout_t,
......@@ -219,9 +219,9 @@ std::vector<torch::Tensor> fwd_cuda(bool use_time_mask, bool is_training,
flags);
// Output Linear
TORCH_CUDABLAS_CHECK(rocblas_gemm_ex(handle,
CUBLAS_OP_T,
CUBLAS_OP_N,
TORCH_CUDABLAS_CHECK(rocBLASStatusToHIPStatus(rocblas_gemm_ex((rocblas_handle) handle,
hipOperationToRocOperation(CUBLAS_OP_T),
hipOperationToRocOperation(CUBLAS_OP_N),
embed_dim,
batches_q,
embed_dim,
......@@ -242,7 +242,7 @@ std::vector<torch::Tensor> fwd_cuda(bool use_time_mask, bool is_training,
rocblas_datatype_f32_r,
rocblas_gemm_algo_standard /*algo*/,
0 /*solution_index*/,
flags));
flags)));
//TORCH_CUDABLAS_CHECK(cublasSetMathMode(handle, CUBLAS_DEFAULT_MATH));
return {input_lin_q_results,
......@@ -332,9 +332,9 @@ std::vector<torch::Tensor> bwd_cuda(
#endif
// Output Linear Dgrad
TORCH_CUDABLAS_CHECK(rocblas_gemm_ex(handle,
CUBLAS_OP_N,
CUBLAS_OP_N,
TORCH_CUDABLAS_CHECK(rocBLASStatusToHIPStatus(rocblas_gemm_ex((rocblas_handle) handle,
hipOperationToRocOperation(CUBLAS_OP_N),
hipOperationToRocOperation(CUBLAS_OP_N),
embed_dim,
batches_q,
embed_dim,
......@@ -355,12 +355,12 @@ std::vector<torch::Tensor> bwd_cuda(
rocblas_datatype_f32_r,
rocblas_gemm_algo_standard /*algo*/,
0 /*solution_index*/,
flags));
flags)));
// Output Linear Wgrad
TORCH_CUDABLAS_CHECK(rocblas_gemm_ex(handle,
CUBLAS_OP_N,
CUBLAS_OP_T,
TORCH_CUDABLAS_CHECK(rocBLASStatusToHIPStatus(rocblas_gemm_ex((rocblas_handle) handle,
hipOperationToRocOperation(CUBLAS_OP_N),
hipOperationToRocOperation(CUBLAS_OP_T),
embed_dim,
embed_dim,
batches_q,
......@@ -381,7 +381,7 @@ std::vector<torch::Tensor> bwd_cuda(
rocblas_datatype_f32_r,
rocblas_gemm_algo_standard /*algo*/,
0 /*solution_index*/,
flags));
flags)));
// MatMul2 Dgrad1
gemm_switch_fp32accum( a_layout_t,
......@@ -493,9 +493,9 @@ std::vector<torch::Tensor> bwd_cuda(
flags);
// Input Linear Q Dgrad
TORCH_CUDABLAS_CHECK(rocblas_gemm_ex(handle,
CUBLAS_OP_N,
CUBLAS_OP_N,
TORCH_CUDABLAS_CHECK(rocBLASStatusToHIPStatus(rocblas_gemm_ex((rocblas_handle) handle,
hipOperationToRocOperation(CUBLAS_OP_N),
hipOperationToRocOperation(CUBLAS_OP_N),
embed_dim,
batches_q,
output_lin_q_dim,
......@@ -516,12 +516,12 @@ std::vector<torch::Tensor> bwd_cuda(
rocblas_datatype_f32_r,
rocblas_gemm_algo_standard /*algo*/,
0 /*solution_index*/,
flags));
flags)));
// Input Linear Q Wgrad
TORCH_CUDABLAS_CHECK(rocblas_gemm_ex(handle,
CUBLAS_OP_N,
CUBLAS_OP_T,
TORCH_CUDABLAS_CHECK(rocBLASStatusToHIPStatus(rocblas_gemm_ex((rocblas_handle) handle,
hipOperationToRocOperation(CUBLAS_OP_N),
hipOperationToRocOperation(CUBLAS_OP_T),
embed_dim,
output_lin_q_dim,
batches_q,
......@@ -542,12 +542,12 @@ std::vector<torch::Tensor> bwd_cuda(
rocblas_datatype_f32_r,
rocblas_gemm_algo_standard /*algo*/,
0 /*solution_index*/,
flags));
flags)));
// Input Linear KV Dgrad
TORCH_CUDABLAS_CHECK(rocblas_gemm_ex(handle,
CUBLAS_OP_N,
CUBLAS_OP_N,
TORCH_CUDABLAS_CHECK(rocBLASStatusToHIPStatus(rocblas_gemm_ex((rocblas_handle) handle,
hipOperationToRocOperation(CUBLAS_OP_N),
hipOperationToRocOperation(CUBLAS_OP_N),
embed_dim,
batches_kv,
output_lin_kv_dim,
......@@ -568,12 +568,12 @@ std::vector<torch::Tensor> bwd_cuda(
rocblas_datatype_f32_r,
rocblas_gemm_algo_standard /*algo*/,
0 /*solution_index*/,
flags));
flags)));
// Input Linear KV Wgrad
TORCH_CUDABLAS_CHECK(rocblas_gemm_ex(handle,
CUBLAS_OP_N,
CUBLAS_OP_T,
TORCH_CUDABLAS_CHECK(rocBLASStatusToHIPStatus(rocblas_gemm_ex((rocblas_handle) handle,
hipOperationToRocOperation(CUBLAS_OP_N),
hipOperationToRocOperation(CUBLAS_OP_T),
embed_dim,
output_lin_kv_dim,
batches_kv,
......@@ -594,7 +594,7 @@ std::vector<torch::Tensor> bwd_cuda(
rocblas_datatype_f32_r,
rocblas_gemm_algo_standard /*algo*/,
0 /*solution_index*/,
flags));
flags)));
// TORCH_CUDABLAS_CHECK(cublasSetMathMode(handle, CUBLAS_DEFAULT_MATH));
return {
input_q_grads,
......
apex/contrib/csrc/multihead_attn/encdec_multihead_attn_norm_add_cuda.cu
View file @ 8fc9b21f
......@@ -116,9 +116,9 @@ std::vector<torch::Tensor> fwd_cuda(
static_cast<const at::Half *>(lyr_nrm_beta_weights.data_ptr()));
// Input Linear Q Fwd
TORCH_CUDABLAS_CHECK(rocblas_gemm_ex(handle,
CUBLAS_OP_T,
CUBLAS_OP_N,
TORCH_CUDABLAS_CHECK(rocBLASStatusToHIPStatus(rocblas_gemm_ex((rocblas_handle) handle,
hipOperationToRocOperation(CUBLAS_OP_T),
hipOperationToRocOperation(CUBLAS_OP_N),
output_lin_q_dim,
batches_q,
embed_dim,
......@@ -140,12 +140,12 @@ std::vector<torch::Tensor> fwd_cuda(
rocblas_datatype_f32_r /*compute_type*/,
rocblas_gemm_algo_standard /*algo*/,
0 /*solution_index*/,
flags));
flags)));
// Input Linear KV Fwd
TORCH_CUDABLAS_CHECK(rocblas_gemm_ex(handle,
CUBLAS_OP_T,
CUBLAS_OP_N,
TORCH_CUDABLAS_CHECK(rocBLASStatusToHIPStatus(rocblas_gemm_ex((rocblas_handle) handle,
hipOperationToRocOperation(CUBLAS_OP_T),
hipOperationToRocOperation(CUBLAS_OP_N),
output_lin_kv_dim,
batches_kv,
embed_dim,
......@@ -166,7 +166,7 @@ std::vector<torch::Tensor> fwd_cuda(
rocblas_datatype_f32_r /*compute_type*/,
rocblas_gemm_algo_standard /*algo*/,
0 /*solution_index*/,
flags));
flags)));
// MatMul1 of Dot-Product Attention Plus scaling by 1/Sqrt(head size)
gemm_switch_fp32accum( a_layout_t,
b_layout_n,
......@@ -246,9 +246,9 @@ std::vector<torch::Tensor> fwd_cuda(
flags);
// Output Linear
TORCH_CUDABLAS_CHECK(rocblas_gemm_ex(handle,
CUBLAS_OP_T,
CUBLAS_OP_N,
TORCH_CUDABLAS_CHECK(rocBLASStatusToHIPStatus(rocblas_gemm_ex((rocblas_handle) handle,
hipOperationToRocOperation(CUBLAS_OP_T),
hipOperationToRocOperation(CUBLAS_OP_N),
embed_dim,
batches_q,
embed_dim,
......@@ -269,7 +269,7 @@ std::vector<torch::Tensor> fwd_cuda(
rocblas_datatype_f32_r /*compute_type*/,
rocblas_gemm_algo_standard /*algo*/,
0 /*solution_index*/,
flags));
flags)));
// End-of-block Dropout-Add
if (is_training) {
......@@ -396,9 +396,9 @@ std::vector<torch::Tensor> bwd_cuda(
(1.0 / (1.0 - dropout_prob)));
// Output Linear Dgrad
TORCH_CUDABLAS_CHECK(rocblas_gemm_ex(handle,
CUBLAS_OP_N,
CUBLAS_OP_N,
TORCH_CUDABLAS_CHECK(rocBLASStatusToHIPStatus(rocblas_gemm_ex((rocblas_handle) handle,
hipOperationToRocOperation(CUBLAS_OP_N),
hipOperationToRocOperation(CUBLAS_OP_N),
embed_dim,
batches_q,
embed_dim,
......@@ -419,12 +419,12 @@ std::vector<torch::Tensor> bwd_cuda(
rocblas_datatype_f32_r /*compute_type*/,
rocblas_gemm_algo_standard /*algo*/,
0 /*solution_index*/,
flags));
flags)));
// Output Linear Wgrad
TORCH_CUDABLAS_CHECK(rocblas_gemm_ex(handle,
CUBLAS_OP_N,
CUBLAS_OP_T,
TORCH_CUDABLAS_CHECK(rocBLASStatusToHIPStatus(rocblas_gemm_ex((rocblas_handle) handle,
hipOperationToRocOperation(CUBLAS_OP_N),
hipOperationToRocOperation(CUBLAS_OP_T),
embed_dim,
embed_dim,
batches_q,
......@@ -445,7 +445,7 @@ std::vector<torch::Tensor> bwd_cuda(
rocblas_datatype_f32_r /*compute_type*/,
rocblas_gemm_algo_standard /*algo*/,
0 /*solution_index*/,
flags));
flags)));
// MatMul2 Dgrad1
gemm_switch_fp32accum( a_layout_t,
......@@ -557,9 +557,9 @@ std::vector<torch::Tensor> bwd_cuda(
flags);
// Input Linear Q Dgrad
TORCH_CUDABLAS_CHECK(rocblas_gemm_ex(handle,
CUBLAS_OP_N,
CUBLAS_OP_N,
TORCH_CUDABLAS_CHECK(rocBLASStatusToHIPStatus(rocblas_gemm_ex((rocblas_handle) handle,
hipOperationToRocOperation(CUBLAS_OP_N),
hipOperationToRocOperation(CUBLAS_OP_N),
embed_dim,
batches_q,
output_lin_q_dim,
......@@ -581,12 +581,12 @@ std::vector<torch::Tensor> bwd_cuda(
rocblas_datatype_f32_r /*compute_type*/,
rocblas_gemm_algo_standard /*algo*/,
0 /*solution_index*/,
flags));
flags)));
// Input Linear Q Wgrad
TORCH_CUDABLAS_CHECK(rocblas_gemm_ex(handle,
CUBLAS_OP_N,
CUBLAS_OP_T,
TORCH_CUDABLAS_CHECK(rocBLASStatusToHIPStatus(rocblas_gemm_ex((rocblas_handle) handle,
hipOperationToRocOperation(CUBLAS_OP_N),
hipOperationToRocOperation(CUBLAS_OP_T),
embed_dim,
output_lin_q_dim,
batches_q,
......@@ -607,12 +607,12 @@ std::vector<torch::Tensor> bwd_cuda(
rocblas_datatype_f32_r /*compute_type*/,
rocblas_gemm_algo_standard /*algo*/,
0 /*solution_index*/,
flags));
flags)));
// Input Linear KV Dgrad
TORCH_CUDABLAS_CHECK(rocblas_gemm_ex(handle,
CUBLAS_OP_N,
CUBLAS_OP_N,
TORCH_CUDABLAS_CHECK(rocBLASStatusToHIPStatus(rocblas_gemm_ex((rocblas_handle) handle,
hipOperationToRocOperation(CUBLAS_OP_N),
hipOperationToRocOperation(CUBLAS_OP_N),
embed_dim,
batches_kv,
output_lin_kv_dim,
......@@ -633,12 +633,12 @@ std::vector<torch::Tensor> bwd_cuda(
rocblas_datatype_f32_r /*compute_type*/,
rocblas_gemm_algo_standard /*algo*/,
0 /*solution_index*/,
flags));
flags)));
// Input Linear KV Wgrad
TORCH_CUDABLAS_CHECK(rocblas_gemm_ex(handle,
CUBLAS_OP_N,
CUBLAS_OP_T,
TORCH_CUDABLAS_CHECK(rocBLASStatusToHIPStatus(rocblas_gemm_ex((rocblas_handle) handle,
hipOperationToRocOperation(CUBLAS_OP_N),
hipOperationToRocOperation(CUBLAS_OP_T),
embed_dim,
output_lin_kv_dim,
batches_kv,
......@@ -659,7 +659,7 @@ std::vector<torch::Tensor> bwd_cuda(
rocblas_datatype_f32_r /*compute_type*/,
rocblas_gemm_algo_standard /*algo*/,
0 /*solution_index*/,
flags));
flags)));
// Fused Layer Norm Bwd with Residual Add
HostLayerNormGradient<half,float>(
......@@ -687,4 +687,4 @@ std::vector<torch::Tensor> bwd_cuda(
} // end namespace rocblas_gemmex
} // end namespace encdec_norm_add
} // end namespace multihead_attn
\ No newline at end of file
} // end namespace multihead_attn
apex/contrib/csrc/multihead_attn/self_multihead_attn_bias_additive_mask_cuda.cu
View file @ 8fc9b21f
......@@ -86,9 +86,9 @@ std::vector<torch::Tensor> fwd_cuda(bool use_time_mask, bool is_training,
// Input Linear Fwd
input_lin_results.copy_(input_biases);
TORCH_CUDABLAS_CHECK(rocblas_gemm_ex(handle,
CUBLAS_OP_T,
CUBLAS_OP_N,
TORCH_CUDABLAS_CHECK(rocBLASStatusToHIPStatus(rocblas_gemm_ex((rocblas_handle) handle,
hipOperationToRocOperation(CUBLAS_OP_T),
hipOperationToRocOperation(CUBLAS_OP_N),
output_lin_dim,
batches,
embed_dim,
......@@ -109,7 +109,7 @@ std::vector<torch::Tensor> fwd_cuda(bool use_time_mask, bool is_training,
rocblas_datatype_f32_r,
rocblas_gemm_algo_standard /*algo*/,
0 /*solution_index*/,
flags));
flags)));
// MatMul1 of Dot-Product Attention Plus scaling by 1/Sqrt(head size)
gemm_switch_fp32accum( a_layout_t,
......@@ -183,9 +183,9 @@ std::vector<torch::Tensor> fwd_cuda(bool use_time_mask, bool is_training,
outputs.copy_(output_biases);
// Output Linear
TORCH_CUDABLAS_CHECK(rocblas_gemm_ex(handle,
CUBLAS_OP_T,
CUBLAS_OP_N,
TORCH_CUDABLAS_CHECK(rocBLASStatusToHIPStatus(rocblas_gemm_ex((rocblas_handle) handle,
hipOperationToRocOperation(CUBLAS_OP_T),
hipOperationToRocOperation(CUBLAS_OP_N),
embed_dim,
batches,
embed_dim,
......@@ -206,7 +206,7 @@ std::vector<torch::Tensor> fwd_cuda(bool use_time_mask, bool is_training,
rocblas_datatype_f32_r,
rocblas_gemm_algo_standard /*algo*/,
0 /*solution_index*/,
flags));
flags)));
//TORCH_CUDABLAS_CHECK(cublasSetMathMode(handle, CUBLAS_DEFAULT_MATH));
return {input_lin_results, bmm1_results, dropout_results,
......@@ -281,9 +281,9 @@ std::vector<torch::Tensor> bwd_cuda(
#endif
// Output Linear Dgrad
TORCH_CUDABLAS_CHECK(rocblas_gemm_ex(handle,
CUBLAS_OP_N,
CUBLAS_OP_N,
TORCH_CUDABLAS_CHECK(rocBLASStatusToHIPStatus(rocblas_gemm_ex((rocblas_handle) handle,
hipOperationToRocOperation(CUBLAS_OP_N),
hipOperationToRocOperation(CUBLAS_OP_N),
embed_dim,
batches,
embed_dim,
......@@ -304,12 +304,12 @@ std::vector<torch::Tensor> bwd_cuda(
rocblas_datatype_f32_r,
rocblas_gemm_algo_standard /*algo*/,
0 /*solution_index*/,
flags));
flags)));
// Output Linear Wgrad
TORCH_CUDABLAS_CHECK(rocblas_gemm_ex(handle,
CUBLAS_OP_N,
CUBLAS_OP_T,
TORCH_CUDABLAS_CHECK(rocBLASStatusToHIPStatus(rocblas_gemm_ex((rocblas_handle) handle,
hipOperationToRocOperation(CUBLAS_OP_N),
hipOperationToRocOperation(CUBLAS_OP_T),
embed_dim,
embed_dim,
batches,
......@@ -330,7 +330,7 @@ std::vector<torch::Tensor> bwd_cuda(
rocblas_datatype_f32_r,
rocblas_gemm_algo_standard /*algo*/,
0 /*solution_index*/,
flags));
flags)));
auto output_bias_grads = output_grads.view({-1, embed_dim}) .sum(0, false);
// MatMul2 Dgrad1
......@@ -441,9 +441,9 @@ std::vector<torch::Tensor> bwd_cuda(
flags);
// Input Linear Dgrad
TORCH_CUDABLAS_CHECK(rocblas_gemm_ex(handle,
CUBLAS_OP_N,
CUBLAS_OP_N,
TORCH_CUDABLAS_CHECK(rocBLASStatusToHIPStatus(rocblas_gemm_ex((rocblas_handle) handle,
hipOperationToRocOperation(CUBLAS_OP_N),
hipOperationToRocOperation(CUBLAS_OP_N),
embed_dim,
batches,
output_lin_dim,
......@@ -464,12 +464,12 @@ std::vector<torch::Tensor> bwd_cuda(
rocblas_datatype_f32_r,
rocblas_gemm_algo_standard /*algo*/,
0 /*solution_index*/,
flags));
flags)));
// Input Linear Wgrad
TORCH_CUDABLAS_CHECK(rocblas_gemm_ex(handle,
CUBLAS_OP_N,
CUBLAS_OP_T,
TORCH_CUDABLAS_CHECK(rocBLASStatusToHIPStatus(rocblas_gemm_ex((rocblas_handle) handle,
hipOperationToRocOperation(CUBLAS_OP_N),
hipOperationToRocOperation(CUBLAS_OP_T),
embed_dim,
output_lin_dim,
batches,
......@@ -490,7 +490,7 @@ std::vector<torch::Tensor> bwd_cuda(
rocblas_datatype_f32_r,
rocblas_gemm_algo_standard /*algo*/,
0 /*solution_index*/,
flags));
flags)));
auto input_bias_grads = input_lin_output_grads.view({-1, output_lin_dim}).sum(0, false);
//TORCH_CUDABLAS_CHECK(cublasSetMathMode(handle, CUBLAS_DEFAULT_MATH));
......
apex/contrib/csrc/multihead_attn/self_multihead_attn_bias_cuda.cu
View file @ 8fc9b21f
......@@ -84,9 +84,9 @@ fwd_cuda(bool use_time_mask, bool is_training, int heads,
// Input Linear Fwd
input_lin_results.copy_(input_biases);
TORCH_CUDABLAS_CHECK(rocblas_gemm_ex(handle,
CUBLAS_OP_T,
CUBLAS_OP_N,
TORCH_CUDABLAS_CHECK(rocBLASStatusToHIPStatus(rocblas_gemm_ex((rocblas_handle) handle,
hipOperationToRocOperation(CUBLAS_OP_T),
hipOperationToRocOperation(CUBLAS_OP_N),
output_lin_dim,
batches,
embed_dim,
......@@ -107,7 +107,7 @@ fwd_cuda(bool use_time_mask, bool is_training, int heads,
rocblas_datatype_f32_r,
rocblas_gemm_algo_standard /*algo*/,
0 /*solution_index*/,
flags));
flags)));
// MatMul1 of Dot-Product Attention Plus scaling by 1/Sqrt(head size)
gemm_switch_fp32accum( a_layout_t,
......@@ -189,9 +189,9 @@ fwd_cuda(bool use_time_mask, bool is_training, int heads,
outputs.copy_(output_biases);
// Output Linear
TORCH_CUDABLAS_CHECK(rocblas_gemm_ex(handle,
CUBLAS_OP_T,
CUBLAS_OP_N,
TORCH_CUDABLAS_CHECK(rocBLASStatusToHIPStatus(rocblas_gemm_ex((rocblas_handle) handle,
hipOperationToRocOperation(CUBLAS_OP_T),
hipOperationToRocOperation(CUBLAS_OP_N),
embed_dim,
batches,
embed_dim,
......@@ -212,7 +212,7 @@ fwd_cuda(bool use_time_mask, bool is_training, int heads,
rocblas_datatype_f32_r,
rocblas_gemm_algo_standard /*algo*/,
0 /*solution_index*/,
flags));
flags)));
//TORCH_CUDABLAS_CHECK(cublasSetMathMode(handle, CUBLAS_DEFAULT_MATH));
return {input_lin_results, softmax_results, dropout_results,
......@@ -287,9 +287,9 @@ std::vector<torch::Tensor> bwd_cuda(
#endif
// Output Linear Dgrad
TORCH_CUDABLAS_CHECK(rocblas_gemm_ex(handle,
CUBLAS_OP_N,
CUBLAS_OP_N,
TORCH_CUDABLAS_CHECK(rocBLASStatusToHIPStatus(rocblas_gemm_ex((rocblas_handle) handle,
hipOperationToRocOperation(CUBLAS_OP_N),
hipOperationToRocOperation(CUBLAS_OP_N),
embed_dim,
batches,
embed_dim,
......@@ -310,12 +310,12 @@ std::vector<torch::Tensor> bwd_cuda(
rocblas_datatype_f32_r,
rocblas_gemm_algo_standard /*algo*/,
0 /*solution_index*/,
flags));
flags)));
// Output Linear Wgrad
TORCH_CUDABLAS_CHECK(rocblas_gemm_ex(handle,
CUBLAS_OP_N,
CUBLAS_OP_T,
TORCH_CUDABLAS_CHECK(rocBLASStatusToHIPStatus(rocblas_gemm_ex((rocblas_handle) handle,
hipOperationToRocOperation(CUBLAS_OP_N),
hipOperationToRocOperation(CUBLAS_OP_T),
embed_dim,
embed_dim,
batches,
......@@ -336,7 +336,7 @@ std::vector<torch::Tensor> bwd_cuda(
rocblas_datatype_f32_r,
rocblas_gemm_algo_standard /*algo*/,
0 /*solution_index*/,
flags));
flags)));
auto output_bias_grads = output_grads.view({-1, embed_dim}) .sum(0, false);
// MatMul2 Dgrad1
......@@ -441,9 +441,9 @@ std::vector<torch::Tensor> bwd_cuda(
attn_batches,
flags);
// Input Linear Dgrad
TORCH_CUDABLAS_CHECK(rocblas_gemm_ex(handle,
CUBLAS_OP_N,
CUBLAS_OP_N,
TORCH_CUDABLAS_CHECK(rocBLASStatusToHIPStatus(rocblas_gemm_ex((rocblas_handle) handle,
hipOperationToRocOperation(CUBLAS_OP_N),
hipOperationToRocOperation(CUBLAS_OP_N),
embed_dim,
batches,
output_lin_dim,
......@@ -464,12 +464,12 @@ std::vector<torch::Tensor> bwd_cuda(
rocblas_datatype_f32_r,
rocblas_gemm_algo_standard /*algo*/,
0 /*solution_index*/,
flags));
flags)));
// Input Linear Wgrad
TORCH_CUDABLAS_CHECK(rocblas_gemm_ex(handle,
CUBLAS_OP_N,
CUBLAS_OP_T,
TORCH_CUDABLAS_CHECK(rocBLASStatusToHIPStatus(rocblas_gemm_ex((rocblas_handle) handle,
hipOperationToRocOperation(CUBLAS_OP_N),
hipOperationToRocOperation(CUBLAS_OP_T),
embed_dim,
output_lin_dim,
batches,
......@@ -490,7 +490,7 @@ std::vector<torch::Tensor> bwd_cuda(
rocblas_datatype_f32_r,
rocblas_gemm_algo_standard /*algo*/,
0 /*solution_index*/,
flags));
flags)));
auto input_bias_grads = input_lin_output_grads.view({-1, output_lin_dim}).sum(0, false);
//TORCH_CUDABLAS_CHECK(cublasSetMathMode(handle, CUBLAS_DEFAULT_MATH));
......@@ -501,4 +501,4 @@ std::vector<torch::Tensor> bwd_cuda(
} // end namespace rocblas_gemmex
} // end namespace self
} // end namespace multihead_attn
\ No newline at end of file
} // end namespace multihead_attn
apex/contrib/csrc/multihead_attn/self_multihead_attn_cuda.cu
View file @ 8fc9b21f
......@@ -82,9 +82,9 @@ std::vector<torch::Tensor> fwd_cuda(bool use_time_mask, bool is_training,
//TORCH_CUDABLAS_CHECK(cublasSetMathMode(handle, CUBLAS_TENSOR_OP_MATH));
// Input Linear Fwd
TORCH_CUDABLAS_CHECK(rocblas_gemm_ex(handle,
CUBLAS_OP_T,
CUBLAS_OP_N,
TORCH_CUDABLAS_CHECK(rocBLASStatusToHIPStatus(rocblas_gemm_ex((rocblas_handle) handle,
hipOperationToRocOperation(CUBLAS_OP_T),
hipOperationToRocOperation(CUBLAS_OP_N),
output_lin_dim,
batches,
embed_dim,
......@@ -105,7 +105,7 @@ std::vector<torch::Tensor> fwd_cuda(bool use_time_mask, bool is_training,
rocblas_datatype_f32_r,
rocblas_gemm_algo_standard /*algo*/,
0 /*solution_index*/,
flags));
flags)));
// MatMul1 of Dot-Product Attention Plus scaling by 1/Sqrt(head size)
gemm_switch_fp32accum( a_layout_t,
......@@ -185,9 +185,9 @@ std::vector<torch::Tensor> fwd_cuda(bool use_time_mask, bool is_training,
flags);
// Output Linear
TORCH_CUDABLAS_CHECK(rocblas_gemm_ex(handle,
CUBLAS_OP_T,
CUBLAS_OP_N,
TORCH_CUDABLAS_CHECK(rocBLASStatusToHIPStatus(rocblas_gemm_ex((rocblas_handle) handle,
hipOperationToRocOperation(CUBLAS_OP_T),
hipOperationToRocOperation(CUBLAS_OP_N),
embed_dim,
batches,
embed_dim,
......@@ -208,7 +208,7 @@ std::vector<torch::Tensor> fwd_cuda(bool use_time_mask, bool is_training,
rocblas_datatype_f32_r,
rocblas_gemm_algo_standard /*algo*/,
0 /*solution_index*/,
flags));
flags)));
//TORCH_CUDABLAS_CHECK(cublasSetMathMode(handle, CUBLAS_DEFAULT_MATH));
return {input_lin_results, softmax_results, dropout_results,
......@@ -283,9 +283,9 @@ std::vector<torch::Tensor> bwd_cuda(
#endif
// Output Linear Dgrad
TORCH_CUDABLAS_CHECK(rocblas_gemm_ex(handle,
CUBLAS_OP_N,
CUBLAS_OP_N,
TORCH_CUDABLAS_CHECK(rocBLASStatusToHIPStatus(rocblas_gemm_ex((rocblas_handle) handle,
hipOperationToRocOperation(CUBLAS_OP_N),
hipOperationToRocOperation(CUBLAS_OP_N),
embed_dim,
batches,
embed_dim,
......@@ -306,12 +306,12 @@ std::vector<torch::Tensor> bwd_cuda(
rocblas_datatype_f32_r,
rocblas_gemm_algo_standard /*algo*/,
0 /*solution_index*/,
flags));
flags)));
// Output Linear Wgrad
TORCH_CUDABLAS_CHECK(rocblas_gemm_ex(handle,
CUBLAS_OP_N,
CUBLAS_OP_T,
TORCH_CUDABLAS_CHECK(rocBLASStatusToHIPStatus(rocblas_gemm_ex((rocblas_handle) handle,
hipOperationToRocOperation(CUBLAS_OP_N),
hipOperationToRocOperation(CUBLAS_OP_T),
embed_dim,
embed_dim,
batches,
......@@ -332,7 +332,7 @@ std::vector<torch::Tensor> bwd_cuda(
rocblas_datatype_f32_r,
rocblas_gemm_algo_standard /*algo*/,
0 /*solution_index*/,
flags));
flags)));
// MatMul2 Dgrad1
gemm_switch_fp32accum( a_layout_t,
......@@ -444,9 +444,9 @@ std::vector<torch::Tensor> bwd_cuda(
flags);
// Input Linear Dgrad
TORCH_CUDABLAS_CHECK(rocblas_gemm_ex(handle,
CUBLAS_OP_N,
CUBLAS_OP_N,
TORCH_CUDABLAS_CHECK(rocBLASStatusToHIPStatus(rocblas_gemm_ex((rocblas_handle) handle,
hipOperationToRocOperation(CUBLAS_OP_N),
hipOperationToRocOperation(CUBLAS_OP_N),
embed_dim,
batches,
output_lin_dim,
......@@ -467,12 +467,12 @@ std::vector<torch::Tensor> bwd_cuda(
rocblas_datatype_f32_r,
rocblas_gemm_algo_standard /*algo*/,
0 /*solution_index*/,
flags));
flags)));
// Input Linear Wgrad
TORCH_CUDABLAS_CHECK(rocblas_gemm_ex(handle,
CUBLAS_OP_N,
CUBLAS_OP_T,
TORCH_CUDABLAS_CHECK(rocBLASStatusToHIPStatus(rocblas_gemm_ex((rocblas_handle) handle,
hipOperationToRocOperation(CUBLAS_OP_N),
hipOperationToRocOperation(CUBLAS_OP_T),
embed_dim,
output_lin_dim,
batches,
......@@ -493,7 +493,7 @@ std::vector<torch::Tensor> bwd_cuda(
rocblas_datatype_f32_r,
rocblas_gemm_algo_standard /*algo*/,
0 /*solution_index*/,
flags));
flags)));
//TORCH_CUDABLAS_CHECK(cublasSetMathMode(handle, CUBLAS_DEFAULT_MATH));
return {
......
apex/contrib/csrc/multihead_attn/self_multihead_attn_norm_add_cuda.cu
View file @ 8fc9b21f
......@@ -103,9 +103,9 @@ std::vector<torch::Tensor> fwd_cuda(bool use_time_mask, bool is_training,
static_cast<const at::Half *>(lyr_nrm_beta_weights.data_ptr()));
// Input Linear Fwd
TORCH_CUDABLAS_CHECK(rocblas_gemm_ex(handle,
CUBLAS_OP_T,
CUBLAS_OP_N,
TORCH_CUDABLAS_CHECK(rocBLASStatusToHIPStatus(rocblas_gemm_ex((rocblas_handle) handle,
hipOperationToRocOperation(CUBLAS_OP_T),
hipOperationToRocOperation(CUBLAS_OP_N),
output_lin_dim,
batches,
embed_dim,
......@@ -127,7 +127,7 @@ std::vector<torch::Tensor> fwd_cuda(bool use_time_mask, bool is_training,
rocblas_datatype_f32_r /*compute_type*/,
rocblas_gemm_algo_standard /*algo*/,
0 /*solution_index*/,
flags));
flags)));
// MatMul1 of Dot-Product Attention Plus scaling by 1/Sqrt(head size)
gemm_switch_fp32accum( a_layout_t,
......@@ -208,9 +208,9 @@ std::vector<torch::Tensor> fwd_cuda(bool use_time_mask, bool is_training,
flags);
// Output Linear
TORCH_CUDABLAS_CHECK(rocblas_gemm_ex(handle,
CUBLAS_OP_T,
CUBLAS_OP_N,
TORCH_CUDABLAS_CHECK(rocBLASStatusToHIPStatus(rocblas_gemm_ex((rocblas_handle) handle,
hipOperationToRocOperation(CUBLAS_OP_T),
hipOperationToRocOperation(CUBLAS_OP_N),
embed_dim,
batches,
embed_dim,
......@@ -231,7 +231,7 @@ std::vector<torch::Tensor> fwd_cuda(bool use_time_mask, bool is_training,
rocblas_datatype_f32_r /*compute_type*/,
rocblas_gemm_algo_standard /*algo*/,
0 /*solution_index*/,
flags));
flags)));
// End-of-block Dropout-Add
......@@ -341,9 +341,9 @@ std::vector<torch::Tensor> bwd_cuda(
(1.0 / (1.0 - dropout_prob)));
// Output Linear Dgrad
TORCH_CUDABLAS_CHECK(rocblas_gemm_ex(handle,
CUBLAS_OP_N,
CUBLAS_OP_N,
TORCH_CUDABLAS_CHECK(rocBLASStatusToHIPStatus(rocblas_gemm_ex((rocblas_handle) handle,
hipOperationToRocOperation(CUBLAS_OP_N),
hipOperationToRocOperation(CUBLAS_OP_N),
embed_dim,
batches,
embed_dim,
......@@ -364,12 +364,12 @@ std::vector<torch::Tensor> bwd_cuda(
rocblas_datatype_f32_r /*compute_type*/,
rocblas_gemm_algo_standard /*algo*/,
0 /*solution_index*/,
flags));
flags)));
// Output Linear Wgrad
TORCH_CUDABLAS_CHECK(rocblas_gemm_ex(handle,
CUBLAS_OP_N,
CUBLAS_OP_T,
TORCH_CUDABLAS_CHECK(rocBLASStatusToHIPStatus(rocblas_gemm_ex((rocblas_handle) handle,
hipOperationToRocOperation(CUBLAS_OP_N),
hipOperationToRocOperation(CUBLAS_OP_T),
embed_dim,
embed_dim,
batches,
......@@ -390,7 +390,7 @@ std::vector<torch::Tensor> bwd_cuda(
rocblas_datatype_f32_r /*compute_type*/,
rocblas_gemm_algo_standard /*algo*/,
0 /*solution_index*/,
flags));
flags)));
// MatMul2 Dgrad1
gemm_switch_fp32accum( a_layout_t,
......@@ -502,9 +502,9 @@ std::vector<torch::Tensor> bwd_cuda(
flags);
// Input Linear Dgrad
TORCH_CUDABLAS_CHECK(rocblas_gemm_ex(handle,
CUBLAS_OP_N,
CUBLAS_OP_N,
TORCH_CUDABLAS_CHECK(rocBLASStatusToHIPStatus(rocblas_gemm_ex((rocblas_handle) handle,
hipOperationToRocOperation(CUBLAS_OP_N),
hipOperationToRocOperation(CUBLAS_OP_N),
embed_dim,
batches,
output_lin_dim,
......@@ -526,12 +526,12 @@ std::vector<torch::Tensor> bwd_cuda(
rocblas_datatype_f32_r /*compute_type*/,
rocblas_gemm_algo_standard /*algo*/,
0 /*solution_index*/,
flags));
flags)));
// Input Linear Wgrad
TORCH_CUDABLAS_CHECK(rocblas_gemm_ex(handle,
CUBLAS_OP_N,
CUBLAS_OP_T,
TORCH_CUDABLAS_CHECK(rocBLASStatusToHIPStatus(rocblas_gemm_ex((rocblas_handle) handle,
hipOperationToRocOperation(CUBLAS_OP_N),
hipOperationToRocOperation(CUBLAS_OP_T),
embed_dim,
output_lin_dim,
batches,
......@@ -553,7 +553,7 @@ std::vector<torch::Tensor> bwd_cuda(
rocblas_datatype_f32_r /*compute_type*/,
rocblas_gemm_algo_standard /*algo*/,
0 /*solution_index*/,
flags));
flags)));
// Fused Layer Norm Bwd with Residual Add
HostLayerNormGradient<half, float>(
......@@ -577,4 +577,4 @@ std::vector<torch::Tensor> bwd_cuda(
} // end namespace rocblas_gemmex
} // end namespace self_norm_add
} // end namespace multihead_attn
\ No newline at end of file
} // end namespace multihead_attn
apex/contrib/csrc/multihead_attn/strided_batched_gemm.cuh
View file @ 8fc9b21f
......@@ -7,6 +7,8 @@
//#include <cuda_profiler_api.h>
#include <cuda_runtime.h>
#include <rocblas/rocblas.h>
//#include <ATen/ATen.h>
#include <ATen/cuda/CUDAContext.h>
#include <ATen/cuda/Exceptions.h>
......@@ -42,6 +44,52 @@ cublasOperation_t convertTransToCublasOperation(char trans) {
}
}
// needed to work around calling rocblas API instead of hipblas API
static rocblas_operation hipOperationToRocOperation(hipblasOperation_t op)
{
switch(op)
{
case HIPBLAS_OP_N:
return rocblas_operation_none;
case HIPBLAS_OP_T:
return rocblas_operation_transpose;
case HIPBLAS_OP_C:
return rocblas_operation_conjugate_transpose;
}
AT_ERROR("HIPBLAS_STATUS_INVALID_ENUM");
}
static hipblasStatus_t rocBLASStatusToHIPStatus(rocblas_status error)
{
switch(error)
{
case rocblas_status_size_unchanged:
case rocblas_status_size_increased:
case rocblas_status_success:
case rocblas_status_continue:
return HIPBLAS_STATUS_SUCCESS;
case rocblas_status_invalid_handle:
return HIPBLAS_STATUS_NOT_INITIALIZED;
case rocblas_status_not_implemented:
case rocblas_status_excluded_from_build:
return HIPBLAS_STATUS_NOT_SUPPORTED;
case rocblas_status_invalid_pointer:
case rocblas_status_invalid_size:
case rocblas_status_invalid_value:
case rocblas_status_size_query_mismatch:
return HIPBLAS_STATUS_INVALID_VALUE;
case rocblas_status_memory_error:
return HIPBLAS_STATUS_ALLOC_FAILED;
case rocblas_status_internal_error:
case rocblas_status_perf_degraded:
case rocblas_status_check_numerics_fail:
return HIPBLAS_STATUS_INTERNAL_ERROR;
case rocblas_status_arch_mismatch:
return HIPBLAS_STATUS_ARCH_MISMATCH;
}
AT_ERROR("HIPBLAS_STATUS_INVALID_ENUM");
}
void RocblasStridedBatchedGemm(char transa, char transb, long m, long n, long k,
float alpha, const half *a, long lda, long strideA, const half *b, long ldb, long strideB,
float beta, half *c, long ldc, long strideC, half *d, long ldd, long strideD, long batchCount, rocblas_gemm_algo algo, rocblas_int flags) {
......@@ -54,13 +102,13 @@ void RocblasStridedBatchedGemm(char transa, char transb, long m, long n, long k,
float fAlpha = alpha;
float fBeta = beta;
//THCublasCheck(cublasSetMathMode(handle, CUBLAS_TENSOR_OP_MATH));
TORCH_CUDABLAS_CHECK(rocblas_gemm_strided_batched_ex(handle,
opa, opb, (int)m, (int)n, (int)k,
TORCH_CUDABLAS_CHECK(rocBLASStatusToHIPStatus(rocblas_gemm_strided_batched_ex((rocblas_handle)handle,
hipOperationToRocOperation(opa), hipOperationToRocOperation(opb), (int)m, (int)n, (int)k,
(void*)&fAlpha, a, rocblas_datatype_f16_r /*a_type*/, (int)lda, strideA,
b, rocblas_datatype_f16_r /*b_type*/, (int)ldb, strideB,
(void*)&fBeta, c, rocblas_datatype_f16_r /*c_type*/, (int)ldc, strideC,
d, rocblas_datatype_f16_r /*d_type*/, int(ldd), strideD,
(int)batchCount, rocblas_datatype_f32_r /*compute_type*/, algo, 0 /*solution_index*/, flags));
(int)batchCount, rocblas_datatype_f32_r /*compute_type*/, algo, 0 /*solution_index*/, flags)));
}
void gemm_switch_fp32accum(char transa, char transb, long m, long n, long k,
......
csrc/fused_dense_cuda.cu
View file @ 8fc9b21f
......@@ -10,10 +10,21 @@
#include <cublas_v2.h>
#include <cuda_runtime.h>
#include <rocblas/rocblas.h>
#if defined(CUBLAS_VERSION) && CUBLAS_VERSION >= 11000
// includes cublaslt
#include <cublasLt.h>
#endif
// until we use hipblas v2
// hipify correctly maps things like CUDA_R_16F to HIP_R_16F,
// however hipblas v1 is still using its custom type
#define HIP_R_64F HIPBLAS_R_64F
#define HIP_R_32F HIPBLAS_R_32F
#define HIP_R_16F HIPBLAS_R_16F
// FP64 Wrapper around cublas GEMMEx
cublasStatus_t gemm_bias(
cublasHandle_t handle,
......@@ -30,33 +41,6 @@ cublasStatus_t gemm_bias(
const float* beta,
double* C,
int ldc) {
#ifdef __HIP_PLATFORM_HCC__
return rocblas_gemm_ex(
handle,
transa,
transb,
m,
n,
k,
alpha,
A,
rocblas_datatype_f64_r,
lda,
B,
rocblas_datatype_f64_r,
ldb,
beta,
C,
rocblas_datatype_f64_r,
ldc,
C,
rocblas_datatype_f64_r,
ldc,
rocblas_datatype_f64_r,
rocblas_gemm_algo_standard,
0,
0);
#else
return cublasGemmEx(
handle,
transa,
......@@ -77,7 +61,6 @@ cublasStatus_t gemm_bias(
ldc,
CUDA_R_64F,
CUBLAS_GEMM_DEFAULT);
#endif
}
// FP32 Wrapper around cublas GEMMEx
......@@ -96,34 +79,6 @@ cublasStatus_t gemm_bias(
const float* beta,
float* C,
int ldc) {
#ifdef __HIP_PLATFORM_HCC__
return rocblas_gemm_ex(
handle,
transa,
transb,
m,
n,
k,
alpha,
A,
rocblas_datatype_f32_r,
lda,
B,
rocblas_datatype_f32_r,
ldb,
beta,
C,
rocblas_datatype_f32_r,
ldc,
C,
rocblas_datatype_f32_r,
ldc,
rocblas_datatype_f32_r,
rocblas_gemm_algo_standard,
0,
0);
#else
return cublasGemmEx(
handle,
transa,
......@@ -144,7 +99,6 @@ cublasStatus_t gemm_bias(
ldc,
CUDA_R_32F,
CUBLAS_GEMM_DEFAULT);
#endif
}
// FP16 Tensor core wrapper around cublas GEMMEx
......@@ -163,33 +117,6 @@ cublasStatus_t gemm_bias(
const float* beta,
at::Half* C,
int ldc) {
#ifdef __HIP_PLATFORM_HCC__
return rocblas_gemm_ex(
handle,
transa,
transb,
m,
n,
k,
alpha,
A,
rocblas_datatype_f16_r,
lda,
B,
rocblas_datatype_f16_r,
ldb,
beta,
C,
rocblas_datatype_f16_r,
ldc,
C,
rocblas_datatype_f16_r,
ldc,
rocblas_datatype_f32_r,
rocblas_gemm_algo_standard,
0,
0);
#else
return cublasGemmEx(
handle,
transa,
......@@ -210,7 +137,6 @@ cublasStatus_t gemm_bias(
ldc,
CUDA_R_32F,
CUBLAS_GEMM_DEFAULT_TENSOR_OP);
#endif
}
......
csrc/mlp_cuda.cu
View file @ 8fc9b21f
......@@ -12,6 +12,8 @@
#include <cublas_v2.h>
#include <cuda_runtime.h>
#include <rocblas/rocblas.h>
#if defined(CUBLAS_VERSION) && CUBLAS_VERSION >= 11000
// includes cublaslt
#include <cublasLt.h>
......@@ -58,6 +60,52 @@ __device__ __inline__ float sigmoid(float a) {
return (retf);
}
// needed to work around calling rocblas API instead of hipblas API
static rocblas_operation hipOperationToRocOperation(hipblasOperation_t op)
{
switch(op)
{
case HIPBLAS_OP_N:
return rocblas_operation_none;
case HIPBLAS_OP_T:
return rocblas_operation_transpose;
case HIPBLAS_OP_C:
return rocblas_operation_conjugate_transpose;
}
AT_ERROR("HIPBLAS_STATUS_INVALID_ENUM");
}
static hipblasStatus_t rocBLASStatusToHIPStatus(rocblas_status error)
{
switch(error)
{
case rocblas_status_size_unchanged:
case rocblas_status_size_increased:
case rocblas_status_success:
case rocblas_status_continue:
return HIPBLAS_STATUS_SUCCESS;
case rocblas_status_invalid_handle:
return HIPBLAS_STATUS_NOT_INITIALIZED;
case rocblas_status_not_implemented:
case rocblas_status_excluded_from_build:
return HIPBLAS_STATUS_NOT_SUPPORTED;
case rocblas_status_invalid_pointer:
case rocblas_status_invalid_size:
case rocblas_status_invalid_value:
case rocblas_status_size_query_mismatch:
return HIPBLAS_STATUS_INVALID_VALUE;
case rocblas_status_memory_error:
return HIPBLAS_STATUS_ALLOC_FAILED;
case rocblas_status_internal_error:
case rocblas_status_perf_degraded:
case rocblas_status_check_numerics_fail:
return HIPBLAS_STATUS_INTERNAL_ERROR;
case rocblas_status_arch_mismatch:
return HIPBLAS_STATUS_ARCH_MISMATCH;
}
AT_ERROR("HIPBLAS_STATUS_INVALID_ENUM");
}
// FP64 Wrapper around cublas GEMMEx
cublasStatus_t mlp_gemm(
cublasHandle_t handle,
......@@ -76,10 +124,10 @@ cublasStatus_t mlp_gemm(
int ldc,
int flag) {
#ifdef __HIP_PLATFORM_HCC__
return rocblas_gemm_ex(
handle,
transa,
transb,
return rocBLASStatusToHIPStatus(rocblas_gemm_ex(
(rocblas_handle) handle,
hipOperationToRocOperation(transa),
hipOperationToRocOperation(transb),
m,
n,
k,
......@@ -100,7 +148,7 @@ cublasStatus_t mlp_gemm(
rocblas_datatype_f64_r,
rocblas_gemm_algo_standard,
0,
flag);
flag));
#else
return cublasGemmEx(
handle,
......@@ -143,10 +191,10 @@ cublasStatus_t mlp_gemm(
int ldc,
int flag) {
#ifdef __HIP_PLATFORM_HCC__
return rocblas_gemm_ex(
handle,
transa,
transb,
return rocBLASStatusToHIPStatus(rocblas_gemm_ex(
(rocblas_handle) handle,
hipOperationToRocOperation(transa),
hipOperationToRocOperation(transb),
m,
n,
k,
......@@ -167,7 +215,7 @@ cublasStatus_t mlp_gemm(
rocblas_datatype_f32_r,
rocblas_gemm_algo_standard,
0,
flag);
flag));
#else
return cublasGemmEx(
......@@ -211,10 +259,10 @@ cublasStatus_t mlp_gemm(
int ldc,
int flag) {
#ifdef __HIP_PLATFORM_HCC__
return rocblas_gemm_ex(
handle,
transa,
transb,
return rocBLASStatusToHIPStatus(rocblas_gemm_ex(
(rocblas_handle) handle,
hipOperationToRocOperation(transa),
hipOperationToRocOperation(transb),
m,
n,
k,
......@@ -235,7 +283,7 @@ cublasStatus_t mlp_gemm(
rocblas_datatype_f32_r,
rocblas_gemm_algo_standard,
0,
flag);
flag));
#else
return cublasGemmEx(
handle,
......
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