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Commit 61416180 authored by hubertlu-tw's avatar hubertlu-tw
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Hipify self_multihead_attn_bias 

Fix some spacing
parent 8bdbb502
Hide whitespace changes
Inline Side-by-side
Showing with 384 additions and 99 deletions
apex/contrib/csrc/multihead_attn/self_multihead_attn_bias_cpp.cpp
View file @ 61416180
......@@ -3,7 +3,7 @@
namespace multihead_attn {
namespace self_bias {
namespace cublas_gemmex {
namespace rocblas_gemm_ex {
std::vector<torch::Tensor> fwd_cuda(
bool use_time_mask,
......@@ -128,12 +128,12 @@ std::vector<torch::Tensor> bwd(
);
}
} // end namespace cublas_gemmex
} // end namespace rocblas_gemm_ex
} // end namespace self
} // end namespace multihead_attn
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
m.def("forward", &multihead_attn::self_bias::cublas_gemmex::fwd, "Self Multihead Attention with Bias -- Forward.");
m.def("backward", &multihead_attn::self_bias::cublas_gemmex::bwd, "Self Multihead Attention with Bias -- Backward.");
m.def("forward", &multihead_attn::self_bias::rocblas_gemm_ex::fwd, "Self Multihead Attention with Bias -- Forward.");
m.def("backward", &multihead_attn::self_bias::rocblas_gemm_ex::bwd, "Self Multihead Attention with Bias -- Backward.");
}
apex/contrib/csrc/multihead_attn/self_multihead_attn_bias_cuda.cu
View file @ 61416180
......@@ -21,7 +21,7 @@ extern THCState *state;
namespace multihead_attn {
namespace self_bias {
namespace cublas_gemmex {
namespace rocblas_gemmex {
std::vector<torch::Tensor> fwd_cuda(
bool use_time_mask,
......@@ -80,11 +80,12 @@ std::vector<torch::Tensor> fwd_cuda(
char a_layout_t{'t'};
char a_layout_n{'n'};
char b_layout_n{'n'};
THCublasCheck(cublasSetMathMode(handle, CUBLAS_TENSOR_OP_MATH));
// TODO (OK)
// THCublasCheck(cublasSetMathMode(handle, CUBLAS_TENSOR_OP_MATH));
// Input Linear Fwd
input_lin_results.copy_(input_biases);
THCublasCheck(cublasGemmEx(handle,
// TODO (OK)
THCublasCheck(rocblas_gemm_ex(handle,
CUBLAS_OP_T,
CUBLAS_OP_N,
output_lin_dim,
......@@ -92,19 +93,45 @@ std::vector<torch::Tensor> fwd_cuda(
embed_dim,
static_cast<const void*>(&alpha),
static_cast<const void*>(input_weights.data_ptr()),
CUDA_R_16F,
rocblas_datatype_f16_r,
embed_dim,
static_cast<const void*>(inputs.data_ptr()),
CUDA_R_16F,
rocblas_datatype_f16_r,
embed_dim,
static_cast<const void*>(&beta_one),
q_lin_results_ptr,
CUDA_R_16F,
rocblas_datatype_f16_r,
output_lin_dim,
CUDA_R_32F,
CUBLAS_GEMM_DEFAULT_TENSOR_OP));
q_lin_results_ptr, //
rocblas_datatype_f16_r, //
output_lin_dim, //
rocblas_datatype_f32_r,
algo,
solution_index,
flags));
// THCublasCheck(cublasGemmEx(handle,
// CUBLAS_OP_T,
// CUBLAS_OP_N,
// output_lin_dim,
// batches,
// embed_dim,
// static_cast<const void*>(&alpha),
// static_cast<const void*>(input_weights.data_ptr()),
// CUDA_R_16F,
// embed_dim,
// static_cast<const void*>(inputs.data_ptr()),
// CUDA_R_16F,
// embed_dim,
// static_cast<const void*>(&beta_one),
// q_lin_results_ptr,
// CUDA_R_16F,
// output_lin_dim,
// CUDA_R_32F,
// CUBLAS_GEMM_DEFAULT_TENSOR_OP));
// MatMul1 of Dot-Product Attention Plus scaling by 1/Sqrt(head size)
// TODO (OK)
gemm_switch_fp32accum( state,
a_layout_t,
b_layout_n,
......@@ -122,7 +149,28 @@ std::vector<torch::Tensor> fwd_cuda(
static_cast<half*>(softmax_results_ptr),
k_seq_len,
k_seq_len*q_seq_len,
static_cast<half*>(softmax_results_ptr),
k_seq_len,
k_seq_len*q_seq_len,
attn_batches);
// gemm_switch_fp32accum( state,
// a_layout_t,
// b_layout_n,
// k_seq_len,
// q_seq_len,
// head_dim,
// scale,
// static_cast<const half*>(k_lin_results_ptr),
// lead_dim,
// batch_stride,
// static_cast<const half*>(q_lin_results_ptr),
// lead_dim,
// batch_stride,
// beta_zero,
// static_cast<half*>(softmax_results_ptr),
// k_seq_len,
// k_seq_len*q_seq_len,
// attn_batches);
// Padded Softmax
bool softmax_success = false;
if (pad_mask == nullptr) {
......@@ -163,6 +211,7 @@ std::vector<torch::Tensor> fwd_cuda(
}
// Matmul2
// TODO (OK)
gemm_switch_fp32accum( state,
a_layout_n,
b_layout_n,
......@@ -180,12 +229,34 @@ std::vector<torch::Tensor> fwd_cuda(
static_cast<half*>(matmul2_results.data_ptr()),
head_dim*attn_batches,
head_dim,
static_cast<half*>(matmul2_results.data_ptr()),
head_dim*attn_batches,
head_dim,
attn_batches);
// gemm_switch_fp32accum( state,
// a_layout_n,
// b_layout_n,
// head_dim,
// q_seq_len,
// k_seq_len,
// alpha,
// static_cast<const half*>(v_lin_results_ptr),
// lead_dim,
// batch_stride,
// (is_training) ? static_cast<const half*>(dropout_results.data_ptr()) : static_cast<const half*>(softmax_results.data_ptr()) ,
// k_seq_len,
// k_seq_len*q_seq_len,
// beta_zero,
// static_cast<half*>(matmul2_results.data_ptr()),
// head_dim*attn_batches,
// head_dim,
// attn_batches);
outputs.copy_(output_biases);
// Output Linear
THCublasCheck(cublasGemmEx(handle,
// TODO (OK)
THCublasCheck(rocblas_gemm_ex(handle,
CUBLAS_OP_T,
CUBLAS_OP_N,
embed_dim,
......@@ -193,20 +264,44 @@ std::vector<torch::Tensor> fwd_cuda(
embed_dim,
static_cast<const void*>(&alpha),
static_cast<const void*>(output_weights.data_ptr()),
CUDA_R_16F,
rocblas_datatype_f16_r,
embed_dim,
static_cast<const void*>(matmul2_results.data_ptr()),
CUDA_R_16F,
rocblas_datatype_f16_r,
embed_dim,
static_cast<const void*>(&beta_one),
static_cast<void*>(outputs.data_ptr()),
CUDA_R_16F,
rocblas_datatype_f16_r,
embed_dim,
CUDA_R_32F,
//CUBLAS_GEMM_ALGO1_TENSOR_OP));
CUBLAS_GEMM_DEFAULT_TENSOR_OP));
THCublasCheck(cublasSetMathMode(handle, CUBLAS_DEFAULT_MATH));
static_cast<void*>(outputs.data_ptr()),
rocblas_datatype_f16_r,
embed_dim,
rocblas_datatype_f32_r,
algo,
solution_index,
flags));
// THCublasCheck(cublasGemmEx(handle,
// CUBLAS_OP_T,
// CUBLAS_OP_N,
// embed_dim,
// batches,
// embed_dim,
// static_cast<const void*>(&alpha),
// static_cast<const void*>(output_weights.data_ptr()),
// CUDA_R_16F,
// embed_dim,
// static_cast<const void*>(matmul2_results.data_ptr()),
// CUDA_R_16F,
// embed_dim,
// static_cast<const void*>(&beta_one),
// static_cast<void*>(outputs.data_ptr()),
// CUDA_R_16F,
// embed_dim,
// CUDA_R_32F,
// //CUBLAS_GEMM_ALGO1_TENSOR_OP));
// CUBLAS_GEMM_DEFAULT_TENSOR_OP));
// TODO (OK)
// THCublasCheck(cublasSetMathMode(handle, CUBLAS_DEFAULT_MATH));
return {
input_lin_results,
......@@ -274,11 +369,12 @@ std::vector<torch::Tensor> bwd_cuda(
char a_layout_t{'t'};
char b_layout_n{'n'};
char b_layout_t{'t'};
THCublasCheck(cublasSetMathMode(handle, CUBLAS_TENSOR_OP_MATH));
// TODO (OK)
// THCublasCheck(cublasSetMathMode(handle, CUBLAS_TENSOR_OP_MATH));
// Output Linear Dgrad
THCublasCheck(cublasGemmEx(handle,
// TODO (OK)
THCublasCheck(rocblas_gemm_ex(handle,
CUBLAS_OP_N,
CUBLAS_OP_N,
embed_dim,
......@@ -286,19 +382,45 @@ std::vector<torch::Tensor> bwd_cuda(
embed_dim,
static_cast<const void*>(&alpha),
static_cast<const void*>(output_weights.data_ptr()),
CUDA_R_16F,
rocblas_datatype_f16_r,
embed_dim,
static_cast<const void*>(output_grads.data_ptr()),
CUDA_R_16F,
rocblas_datatype_f16_r,
embed_dim,
static_cast<const void*>(&beta),
static_cast<void*>(output_lin_grads.data_ptr()),
CUDA_R_16F,
rocblas_datatype_f16_r,
embed_dim,
static_cast<void*>(output_lin_grads.data_ptr()),
rocblas_datatype_f16_r,
embed_dim,
CUDA_R_32F,
CUBLAS_GEMM_DEFAULT_TENSOR_OP));
rocblas_datatype_f32_r,
algo,
solution_index,
flags));
// THCublasCheck(cublasGemmEx(handle,
// CUBLAS_OP_N,
// CUBLAS_OP_N,
// embed_dim,
// batches,
// embed_dim,
// static_cast<const void*>(&alpha),
// static_cast<const void*>(output_weights.data_ptr()),
// CUDA_R_16F,
// embed_dim,
// static_cast<const void*>(output_grads.data_ptr()),
// CUDA_R_16F,
// embed_dim,
// static_cast<const void*>(&beta),
// static_cast<void*>(output_lin_grads.data_ptr()),
// CUDA_R_16F,
// embed_dim,
// CUDA_R_32F,
// CUBLAS_GEMM_DEFAULT_TENSOR_OP));
// Output Linear Wgrad
THCublasCheck(cublasGemmEx(handle,
// TODO (OK)
THCublasCheck(rocblas_gemm_ex(handle,
CUBLAS_OP_N,
CUBLAS_OP_T,
embed_dim,
......@@ -306,20 +428,45 @@ std::vector<torch::Tensor> bwd_cuda(
batches,
static_cast<const void*>(&alpha),
static_cast<const void*>(matmul2_results.data_ptr()),
CUDA_R_16F,
rocblas_datatype_f16_r,
embed_dim,
static_cast<const void*>(output_grads.data_ptr()),
CUDA_R_16F,
rocblas_datatype_f16_r,
embed_dim,
static_cast<const void*>(&beta),
static_cast<void*>(output_weight_grads.data_ptr()),
CUDA_R_16F,
rocblas_datatype_f16_r,
embed_dim,
static_cast<void*>(output_weight_grads.data_ptr()),
rocblas_datatype_f16_r,
embed_dim,
CUDA_R_32F,
CUBLAS_GEMM_DEFAULT_TENSOR_OP));
rocblas_datatype_f32_r,
algo,
solution_index,
flags));
// THCublasCheck(cublasGemmEx(handle,
// CUBLAS_OP_N,
// CUBLAS_OP_T,
// embed_dim,
// embed_dim,
// batches,
// static_cast<const void*>(&alpha),
// static_cast<const void*>(matmul2_results.data_ptr()),
// CUDA_R_16F,
// embed_dim,
// static_cast<const void*>(output_grads.data_ptr()),
// CUDA_R_16F,
// embed_dim,
// static_cast<const void*>(&beta),
// static_cast<void*>(output_weight_grads.data_ptr()),
// CUDA_R_16F,
// embed_dim,
// CUDA_R_32F,
// CUBLAS_GEMM_DEFAULT_TENSOR_OP));
auto output_bias_grads = output_grads.view({-1, embed_dim}) .sum(0, false);
// MatMul2 Dgrad1
// TODO (OK)
gemm_switch_fp32accum( state,
a_layout_t,
b_layout_n,
......@@ -337,9 +484,31 @@ std::vector<torch::Tensor> bwd_cuda(
static_cast<half*>(matmul2_grads.data_ptr()),
k_seq_len,
k_seq_len*q_seq_len,
static_cast<half*>(matmul2_grads.data_ptr()),
k_seq_len,
k_seq_len*q_seq_len,
attn_batches);
// gemm_switch_fp32accum( state,
// a_layout_t,
// b_layout_n,
// k_seq_len,
// q_seq_len,
// head_dim,
// alpha,
// static_cast<const half*>(v_lin_results_ptr),
// lead_dim,
// batch_stride,
// static_cast<const half*>(output_lin_grads.data_ptr()),
// head_dim*attn_batches,
// head_dim,
// beta,
// static_cast<half*>(matmul2_grads.data_ptr()),
// k_seq_len,
// k_seq_len*q_seq_len,
// attn_batches);
// Matmul2 Dgrad2
// TODO (OK)
gemm_switch_fp32accum( state,
a_layout_n,
b_layout_t,
......@@ -357,7 +526,28 @@ std::vector<torch::Tensor> bwd_cuda(
v_lin_grads_ptr,
lead_dim,
batch_stride,
v_lin_grads_ptr,
lead_dim,
batch_stride,
attn_batches);
// gemm_switch_fp32accum( state,
// a_layout_n,
// b_layout_t,
// head_dim,
// k_seq_len,
// q_seq_len,
// alpha,
// static_cast<const half*>(output_lin_grads.data_ptr()),
// head_dim*attn_batches,
// head_dim,
// static_cast<const half*>(dropout_results.data_ptr()),
// k_seq_len,
// k_seq_len*q_seq_len,
// beta,
// v_lin_grads_ptr,
// lead_dim,
// batch_stride,
// attn_batches);
// Apply Dropout Mask and Scale by Dropout Probability
// Softmax Grad
......@@ -372,6 +562,7 @@ std::vector<torch::Tensor> bwd_cuda(
attn_batches*q_seq_len, stream);
// Matmul1 Dgrad1
// TODO (OK)
gemm_switch_fp32accum( state,
a_layout_n,
b_layout_n,
......@@ -385,13 +576,35 @@ std::vector<torch::Tensor> bwd_cuda(
static_cast<half*>(matmul2_grads.data_ptr()),
k_seq_len,
k_seq_len*q_seq_len,
beta,
beta,
q_lin_grads_ptr,
lead_dim,
batch_stride,
q_lin_grads_ptr,
lead_dim,
batch_stride,
attn_batches);
// gemm_switch_fp32accum( state,
// a_layout_n,
// b_layout_n,
// head_dim,
// q_seq_len,
// k_seq_len,
// scale,
// k_lin_results_ptr,
// lead_dim,
// batch_stride,
// static_cast<half*>(matmul2_grads.data_ptr()),
// k_seq_len,
// k_seq_len*q_seq_len,
// beta,
// q_lin_grads_ptr,
// lead_dim,
// batch_stride,
// attn_batches);
// Matmul1 Dgrad2
// TODO (OK)
gemm_switch_fp32accum( state,
a_layout_n,
b_layout_t,
......@@ -408,10 +621,32 @@ std::vector<torch::Tensor> bwd_cuda(
beta,
k_lin_grads_ptr,
lead_dim,
batch_stride,
k_lin_grads_ptr,
lead_dim,
batch_stride,
attn_batches);
// gemm_switch_fp32accum( state,
// a_layout_n,
// b_layout_t,
// head_dim,
// k_seq_len,
// q_seq_len,
// scale,
// q_lin_results_ptr,
// lead_dim,
// batch_stride,
// static_cast<half*>(matmul2_grads.data_ptr()),
// k_seq_len,
// k_seq_len*q_seq_len,
// beta,
// k_lin_grads_ptr,
// lead_dim,
// batch_stride,
// attn_batches);
// Input Linear Dgrad
THCublasCheck(cublasGemmEx(handle,
// TODO (OK)
THCublasCheck(rocblas_gemm_ex(handle,
CUBLAS_OP_N,
CUBLAS_OP_N,
embed_dim,
......@@ -419,22 +654,47 @@ std::vector<torch::Tensor> bwd_cuda(
output_lin_dim,
static_cast<const void*>(&alpha),
static_cast<const void*>(input_weights.data_ptr()),
CUDA_R_16F,
rocblas_datatype_f16_r,
embed_dim,
static_cast<const void*>(input_lin_output_grads.data_ptr()),
//static_cast<const void*>(q_lin_grads_ptr),
CUDA_R_16F,
static_cast<const void*>(input_lin_output_grads.data_ptr()),
rocblas_datatype_f16_r,
output_lin_dim,
static_cast<const void*>(&beta),
static_cast<void*>(input_grads.data_ptr()),
CUDA_R_16F,
rocblas_datatype_f16_r,
embed_dim,
CUDA_R_32F,
//CUBLAS_GEMM_ALGO10_TENSOR_OP));
CUBLAS_GEMM_DEFAULT_TENSOR_OP));
static_cast<void*>(input_grads.data_ptr()),
rocblas_datatype_f16_r,
embed_dim,
rocblas_datatype_f32_r,
algo,
solution_index,
flags));
// THCublasCheck(cublasGemmEx(handle,
// CUBLAS_OP_N,
// CUBLAS_OP_N,
// embed_dim,
// batches,
// output_lin_dim,
// static_cast<const void*>(&alpha),
// static_cast<const void*>(input_weights.data_ptr()),
// CUDA_R_16F,
// embed_dim,
// static_cast<const void*>(input_lin_output_grads.data_ptr()),
// //static_cast<const void*>(q_lin_grads_ptr),
// CUDA_R_16F,
// output_lin_dim,
// static_cast<const void*>(&beta),
// static_cast<void*>(input_grads.data_ptr()),
// CUDA_R_16F,
// embed_dim,
// CUDA_R_32F,
// //CUBLAS_GEMM_ALGO10_TENSOR_OP));
// CUBLAS_GEMM_DEFAULT_TENSOR_OP));
// Input Linear Wgrad
THCublasCheck(cublasGemmEx(handle,
// TODO (OK)
THCublasCheck(rocblas_gemm_ex(handle,
CUBLAS_OP_N,
CUBLAS_OP_T,
embed_dim,
......@@ -442,20 +702,45 @@ std::vector<torch::Tensor> bwd_cuda(
batches,
static_cast<const void*>(&alpha),
static_cast<const void*>(inputs.data_ptr()),
CUDA_R_16F,
rocblas_datatype_f16_r,
embed_dim,
static_cast<const void*>(q_lin_grads_ptr),
CUDA_R_16F,
rocblas_datatype_f16_r,
output_lin_dim,
static_cast<const void*>(&beta),
static_cast<void*>(input_weight_grads.data_ptr()),
CUDA_R_16F,
rocblas_datatype_f16_r,
embed_dim,
static_cast<void*>(input_weight_grads.data_ptr()),
rocblas_datatype_f16_r,
embed_dim,
CUDA_R_32F,
CUBLAS_GEMM_DEFAULT_TENSOR_OP));
rocblas_datatype_f32_r,
algo,
solution_index,
flags));
// THCublasCheck(cublasGemmEx(handle,
// CUBLAS_OP_N,
// CUBLAS_OP_T,
// embed_dim,
// output_lin_dim,
// batches,
// static_cast<const void*>(&alpha),
// static_cast<const void*>(inputs.data_ptr()),
// CUDA_R_16F,
// embed_dim,
// static_cast<const void*>(q_lin_grads_ptr),
// CUDA_R_16F,
// output_lin_dim,
// static_cast<const void*>(&beta),
// static_cast<void*>(input_weight_grads.data_ptr()),
// CUDA_R_16F,
// embed_dim,
// CUDA_R_32F,
// CUBLAS_GEMM_DEFAULT_TENSOR_OP));
auto input_bias_grads = input_lin_output_grads.view({-1, output_lin_dim}).sum(0, false);
THCublasCheck(cublasSetMathMode(handle, CUBLAS_DEFAULT_MATH));
// TODO (OK)
// THCublasCheck(cublasSetMathMode(handle, CUBLAS_DEFAULT_MATH));
return {
input_grads,
......
apex/contrib/csrc/multihead_attn/self_multihead_attn_norm_add_cuda.cu
View file @ 61416180
......@@ -124,13 +124,13 @@ std::vector<torch::Tensor> fwd_cuda(
q_lin_results_ptr,
c_type,
output_lin_dim,
q_lin_results_ptr,
d_type,
output_lin_dim,
q_lin_results_ptr,
d_type,
output_lin_dim,
compute_type,
algo,
solution_index,
flags));
solution_index,
flags));
// MatMul1 of Dot-Product Attention Plus scaling by 1/Sqrt(head size)
gemm_switch_fp32accum( state,
......@@ -150,9 +150,9 @@ std::vector<torch::Tensor> fwd_cuda(
static_cast<half*>(softmax_results_ptr),
k_seq_len,
k_seq_len*q_seq_len,
static_cast<half*>(softmax_results_ptr),
k_seq_len,
k_seq_len*q_seq_len,
static_cast<half*>(softmax_results_ptr),
k_seq_len,
k_seq_len*q_seq_len,
attn_batches);
// Padded Softmax
......@@ -215,9 +215,9 @@ std::vector<torch::Tensor> fwd_cuda(
static_cast<half*>(matmul2_results.data_ptr()),
head_dim*attn_batches,
head_dim,
static_cast<half*>(matmul2_results.data_ptr()),
head_dim*attn_batches,
head_dim,
static_cast<half*>(matmul2_results.data_ptr()),
head_dim*attn_batches,
head_dim,
attn_batches);
// Output Linear
......@@ -238,13 +238,13 @@ std::vector<torch::Tensor> fwd_cuda(
static_cast<void*>(output_lin_results.data_ptr()),
c_type,
embed_dim,
static_cast<void*>(output_lin_results.data_ptr()),
d_type,
embed_dim,
static_cast<void*>(output_lin_results.data_ptr()),
d_type,
embed_dim,
compute_type,
algo,
solution_index,
flags));
solution_index,
flags));
// End-of-block Dropout-Add
......@@ -372,13 +372,13 @@ std::vector<torch::Tensor> bwd_cuda(
static_cast<void*>(output_lin_grads.data_ptr()),
c_type,
embed_dim,
static_cast<void*>(output_lin_grads.data_ptr()),
d_type,
embed_dim,
static_cast<void*>(output_lin_grads.data_ptr()),
d_type,
embed_dim,
compute_type,
algo,
solution_index,
flags));
solution_index,
flags));
// Output Linear Wgrad
THCublasCheck(rocblas_gemm_ex(handle,
......@@ -398,13 +398,13 @@ std::vector<torch::Tensor> bwd_cuda(
static_cast<void*>(output_weight_grads.data_ptr()),
c_type,
embed_dim,
static_cast<void*>(output_weight_grads.data_ptr()),
d_type,
embed_dim,
static_cast<void*>(output_weight_grads.data_ptr()),
d_type,
embed_dim,
compute_type,
algo,
solution_index,
flags));
solution_index,
flags));
// MatMul2 Dgrad1
gemm_switch_fp32accum( state,
......@@ -424,9 +424,9 @@ std::vector<torch::Tensor> bwd_cuda(
static_cast<half*>(matmul2_grads.data_ptr()),
k_seq_len,
k_seq_len*q_seq_len,
static_cast<half*>(matmul2_grads.data_ptr()),
k_seq_len,
k_seq_len*q_seq_len,
static_cast<half*>(matmul2_grads.data_ptr()),
k_seq_len,
k_seq_len*q_seq_len,
attn_batches);
// Matmul2 Dgrad2
......@@ -447,9 +447,9 @@ std::vector<torch::Tensor> bwd_cuda(
v_lin_grads_ptr,
lead_dim,
batch_stride,
v_lin_grads_ptr,
lead_dim,
batch_stride,
v_lin_grads_ptr,
lead_dim,
batch_stride,
attn_batches);
// Apply Dropout Mask and Scale by Dropout Probability
......@@ -489,9 +489,9 @@ std::vector<torch::Tensor> bwd_cuda(
q_lin_grads_ptr,
lead_dim,
batch_stride,
q_lin_grads_ptr,
lead_dim,
batch_stride,
q_lin_grads_ptr,
lead_dim,
batch_stride,
attn_batches);
// Matmul1 Dgrad2
......@@ -512,7 +512,7 @@ std::vector<torch::Tensor> bwd_cuda(
k_lin_grads_ptr,
lead_dim,
batch_stride,
k_lin_grads_ptr,
k_lin_grads_ptr,
lead_dim,
batch_stride,
attn_batches);
......@@ -536,13 +536,13 @@ std::vector<torch::Tensor> bwd_cuda(
static_cast<void*>(input_lin_grads.data_ptr()),
c_type,
embed_dim,
static_cast<void*>(input_lin_grads.data_ptr()),
d_type,
embed_dim,
static_cast<void*>(input_lin_grads.data_ptr()),
d_type,
embed_dim,
compute_type,
algo,
solution_index,
flags));
solution_index,
flags));
// Input Linear Wgrad
THCublasCheck(rocblas_gemm_ex(handle,
......@@ -563,13 +563,13 @@ std::vector<torch::Tensor> bwd_cuda(
static_cast<void*>(input_weight_grads.data_ptr()),
c_type,
embed_dim,
static_cast<void*>(input_weight_grads.data_ptr()),
d_type,
embed_dim,
static_cast<void*>(input_weight_grads.data_ptr()),
d_type,
embed_dim,
compute_type,
algo,
solution_index,
flags));
solution_index,
flags));
// Fused Layer Norm Bwd with Residual Add
HostLayerNormGradient<half,float>(
......
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