[common] Remove kvpacked and qkvpacked attention functions for every kernel type. (#2287)

* code drop
Signed-off-by: Pawel Gadzinski <pgadzinski@nvidia.com>

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* fix
Signed-off-by: Pawel Gadzinski <pgadzinski@nvidia.com>

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* fix
Signed-off-by: Pawel Gadzinski <pgadzinski@nvidia.com>

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* fix
Signed-off-by: Pawel Gadzinski <pgadzinski@nvidia.com>

* depracted compile time warning + \warning -> \deprecated
Signed-off-by: Pawel Gadzinski <pgadzinski@nvidia.com>

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* [pre-commit.ci] auto fixes from pre-commit.com hooks

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---------
Signed-off-by: Pawel Gadzinski <pgadzinski@nvidia.com>
Signed-off-by: Charlene Yang <8636796+cyanguwa@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Charlene Yang <8636796+cyanguwa@users.noreply.github.com>

transformer_engine/common/fused_attn/fused_attn_f16_arbitrary_seqlen.h

@@ -18,53 +18,6 @@
 
 namespace transformer_engine {
 #if (CUDNN_VERSION >= 8900)
-void fused_attn_arbitrary_seqlen_fwd_qkvpacked(
-    size_t batch, size_t num_attn_heads, size_t max_seqlen, size_t head_dim, size_t num_tokens,
-    bool is_training, bool return_max_logit, float attn_scale, float p_dropout,
-    NVTE_QKV_Layout qkv_layout, NVTE_Bias_Type bias_type, NVTE_Mask_Type mask_type,
-    NVTE_Softmax_Type softmax_type, int64_t window_size_left, int64_t window_size_right,
-    const Tensor *input_QKV, const Tensor *input_Bias, const Tensor *input_SoftmaxOffset,
-    Tensor *output_O, NVTETensorPack *Aux_CTX_Tensors, const Tensor *cu_seqlens,
-    const Tensor *cu_seqlens_padded, const Tensor *rng_state, Tensor *workspace,
-    cudaStream_t stream, cudnnHandle_t handle);
-
-void fused_attn_arbitrary_seqlen_bwd_qkvpacked(
-    size_t batch, size_t num_attn_heads, size_t max_seqlen, size_t head_dim, size_t num_tokens,
-    float attn_scale, float p_dropout, NVTE_QKV_Layout qkv_layout, NVTE_Bias_Type bias_type,
-    NVTE_Mask_Type mask_type, NVTE_Softmax_Type softmax_type, int64_t window_size_left,
-    int64_t window_size_right, bool deterministic, const Tensor *input_QKV, const Tensor *input_O,
-    const Tensor *input_dO, const Tensor *input_Bias, const Tensor *input_SoftmaxOffset,
-    Tensor *output_S, Tensor *output_dQKV, Tensor *output_dBias, Tensor *output_dSoftmaxOffset,
-    const Tensor *cu_seqlens, const Tensor *cu_seqlens_padded, const Tensor *rng_state,
-    Tensor *workspace, cudaStream_t stream, cudnnHandle_t handle);
-
-void fused_attn_arbitrary_seqlen_fwd_kvpacked(
-    size_t batch, size_t num_attn_heads, size_t num_gqa_groups, size_t max_seqlen_q,
-    size_t max_seqlen_kv, size_t head_dim, size_t num_tokens_q, size_t num_tokens_kv,
-    size_t num_pages_k, size_t num_pages_v, size_t page_size_k, size_t page_size_v,
-    size_t max_pages_per_seq_k, size_t max_pages_per_seq_v, bool is_training, bool return_max_logit,
-    float attn_scale, float p_dropout, NVTE_QKV_Layout qkv_layout, NVTE_Bias_Type bias_type,
-    NVTE_Mask_Type mask_type, NVTE_Softmax_Type softmax_type, int64_t window_size_left,
-    int64_t window_size_right, const Tensor *input_Q, const Tensor *input_KV,
-    const Tensor *input_Bias, const Tensor *input_SoftmaxOffset, Tensor *output_O,
-    NVTETensorPack *Aux_CTX_Tensors, const Tensor *cu_seqlens_q, const Tensor *cu_seqlens_kv,
-    const Tensor *cu_seqlens_q_padded, const Tensor *cu_seqlens_kv_padded,
-    const Tensor *page_table_k, const Tensor *page_table_v, const Tensor *rng_state,
-    Tensor *workspace, cudaStream_t stream, cudnnHandle_t handle);
-
-void fused_attn_arbitrary_seqlen_bwd_kvpacked(
-    size_t batch, size_t num_attn_heads, size_t num_gqa_groups, size_t max_seqlen_q,
-    size_t max_seqlen_kv, size_t head_dim, size_t num_tokens_q, size_t num_tokens_kv,
-    float attn_scale, float p_dropout, NVTE_QKV_Layout qkv_layout, NVTE_Bias_Type bias_type,
-    NVTE_Mask_Type mask_type, NVTE_Softmax_Type softmax_type, int64_t window_size_left,
-    int64_t window_size_right, bool deterministic, const Tensor *input_Q, const Tensor *input_KV,
-    const Tensor *input_O, const Tensor *input_dO, const Tensor *input_Bias,
-    const Tensor *input_SoftmaxOffset, Tensor *output_S, Tensor *output_dQ, Tensor *output_dKV,
-    Tensor *output_dBias, Tensor *output_dSoftmaxOffset, const Tensor *cu_seqlens_q,
-    const Tensor *cu_seqlens_kv, const Tensor *cu_seqlens_q_padded,
-    const Tensor *cu_seqlens_kv_padded, const Tensor *rng_state, Tensor *workspace,
-    cudaStream_t stream, cudnnHandle_t handle);
-
 void fused_attn_arbitrary_seqlen_fwd(
     size_t batch, size_t num_attn_heads, size_t num_gqa_groups, size_t max_seqlen_q,
     size_t max_seqlen_kv, size_t head_dim_qk, size_t head_dim_v, size_t num_tokens_q,

transformer_engine/common/fused_attn/fused_attn_f16_max512_seqlen.cu

@@ -1215,150 +1215,6 @@ void fused_attn_max_512_bwd_impl(int64_t b, int64_t h, int64_t s_q, int64_t s_kv
 }  // namespace fused_attn
 
 using namespace transformer_engine::fused_attn;
-void fused_attn_max_512_fwd_qkvpacked(
-    size_t batch, size_t num_head, size_t max_seqlen, size_t head_dim, bool is_training,
-    float attn_scale, float p_dropout, NVTE_QKV_Layout qkv_layout, NVTE_Bias_Type bias_type,
-    NVTE_Mask_Type mask_type, const Tensor *input_QKV, const Tensor *input_Bias, Tensor *output_O,
-    NVTETensorPack *Aux_CTX_Tensors, const Tensor *cu_seqlens, const Tensor *rng_state,
-    Tensor *workspace, cudaStream_t stream, cudnnHandle_t handle) {
-  using namespace transformer_engine;
-
-  // QKV shape is [b, s, 3, h, d]
-  void *devPtrQKV = input_QKV->data.dptr;
-  const auto stride = 2 * num_head * head_dim;
-
-  void *devPtrQ = static_cast<void *>(devPtrQKV);
-  void *devPtrK = static_cast<void *>(static_cast<int8_t *>(devPtrQKV) + stride);
-  void *devPtrV = static_cast<void *>(static_cast<int8_t *>(devPtrQKV) + 2 * stride);
-
-  void *devPtrBias = static_cast<void *>(input_Bias->data.dptr);
-
-  void *devPtrO = output_O->data.dptr;
-
-  void *devPtrS = nullptr;
-
-  if (Aux_CTX_Tensors->size == 0) {
-    Aux_CTX_Tensors->size = 1;
-    Tensor *output_S = convertNVTETensorCheck(Aux_CTX_Tensors->tensors[0]);
-    output_S->data.dptr = nullptr;
-    output_S->data.shape = {batch, num_head, max_seqlen, max_seqlen};
-    output_S->data.dtype = input_QKV->data.dtype;
-  } else if (Aux_CTX_Tensors->size == 1) {
-    Tensor *output_S = convertNVTETensorCheck(Aux_CTX_Tensors->tensors[0]);
-    devPtrS = output_S->data.dptr;
-  } else {
-    NVTE_ERROR("Unexpected Aux_CTX_Tensors->size.");
-  }
-
-  void *devPtrCuSeqlen = cu_seqlens->data.dptr;
-
-  const DType rng_state_type = rng_state->data.dtype;
-  NVTE_CHECK(rng_state_type == DType::kInt64);
-  void *devPtrDropoutSeed = rng_state->data.dptr;
-  void *devPtrDropoutOffset =
-      static_cast<void *>(static_cast<uint64_t *>(rng_state->data.dptr) + 1);
-
-  const DType QKV_type = input_QKV->data.dtype;
-  size_t workspace_size = 0;
-
-  fused_attn_max_512_fwd_impl(
-      batch, num_head, max_seqlen, max_seqlen, head_dim, is_training, attn_scale, p_dropout,
-      qkv_layout, bias_type, mask_type, devPtrQ, devPtrK, devPtrV, devPtrS, devPtrO, devPtrBias,
-      devPtrCuSeqlen, devPtrCuSeqlen, devPtrDropoutSeed, devPtrDropoutOffset, workspace->data.dptr,
-      &workspace_size, get_cudnn_dtype(QKV_type), stream, handle);
-
-  if (workspace_size > 0) {
-    if (workspace->data.dptr == nullptr) {
-      workspace->data.shape = {workspace_size};
-      workspace->data.dtype = DType::kByte;
-      return;
-    }
-  } else if (workspace_size == 0) {
-    workspace->data.shape = {1};
-    workspace->data.dtype = DType::kByte;
-    return;
-  } else {
-    NVTE_ERROR("Unexpected workspace_size.");
-  }
-}
-
-void fused_attn_max_512_fwd_kvpacked(size_t batch, size_t num_head, size_t q_max_seqlen,
-                                     size_t kv_max_seqlen, size_t head_dim, bool is_training,
-                                     float attn_scale, float p_dropout, NVTE_QKV_Layout qkv_layout,
-                                     NVTE_Bias_Type bias_type, NVTE_Mask_Type mask_type,
-                                     const Tensor *input_Q, const Tensor *input_KV,
-                                     const Tensor *input_Bias, Tensor *output_O,
-                                     NVTETensorPack *Aux_CTX_Tensors, const Tensor *q_cu_seqlens,
-                                     const Tensor *kv_cu_seqlens, const Tensor *rng_state,
-                                     Tensor *workspace, cudaStream_t stream, cudnnHandle_t handle) {
-  using namespace transformer_engine;
-
-  NVTE_CHECK(bias_type == NVTE_Bias_Type::NVTE_NO_BIAS ||
-                 bias_type == NVTE_Bias_Type::NVTE_POST_SCALE_BIAS,
-             "NVTE_PRE_SCALE_BIAS is not implemented in fused_attn_max_512.");
-
-  // Q shape is [b, s, h, d]
-  void *devPtrQ = input_Q->data.dptr;
-
-  // KV shape is [b, s, 2, h, d]
-  const auto stride = 2 * num_head * head_dim;
-  void *devPtrK = input_KV->data.dptr;
-  void *devPtrV = static_cast<void *>(static_cast<int8_t *>(devPtrK) + stride);
-
-  void *devPtrBias = input_Bias->data.dptr;
-
-  void *devPtrO = output_O->data.dptr;
-
-  void *devPtrS = nullptr;
-
-  const DType q_type = input_Q->data.dtype;
-  const DType kv_type = input_KV->data.dtype;
-  NVTE_CHECK(q_type == kv_type, "data type of Q must be equal to data type of KV.");
-
-  if (Aux_CTX_Tensors->size == 0) {
-    Aux_CTX_Tensors->size = 1;
-    Tensor *output_S = convertNVTETensorCheck(Aux_CTX_Tensors->tensors[0]);
-    output_S->data.dptr = nullptr;
-    output_S->data.shape = {batch, num_head, q_max_seqlen, kv_max_seqlen};
-    output_S->data.dtype = q_type;
-  } else if (Aux_CTX_Tensors->size == 1) {
-    Tensor *output_S = convertNVTETensorCheck(Aux_CTX_Tensors->tensors[0]);
-    devPtrS = output_S->data.dptr;
-  } else {
-    NVTE_ERROR("Unexpected Aux_CTX_Tensors->size.");
-  }
-
-  void *devQCuSeqlen = q_cu_seqlens->data.dptr;
-  void *devKVCuSeqlen = kv_cu_seqlens->data.dptr;
-
-  const DType rng_state_type = rng_state->data.dtype;
-  NVTE_CHECK(rng_state_type == DType::kInt64);
-  void *devPtrDropoutSeed = rng_state->data.dptr;
-  void *devPtrDropoutOffset =
-      static_cast<void *>(static_cast<uint64_t *>(rng_state->data.dptr) + 1);
-
-  size_t workspace_size = 0;
-
-  fused_attn_max_512_fwd_impl(
-      batch, num_head, q_max_seqlen, kv_max_seqlen, head_dim, is_training, attn_scale, p_dropout,
-      qkv_layout, bias_type, mask_type, devPtrQ, devPtrK, devPtrV, devPtrS, devPtrO, devPtrBias,
-      devQCuSeqlen, devKVCuSeqlen, devPtrDropoutSeed, devPtrDropoutOffset, workspace->data.dptr,
-      &workspace_size, get_cudnn_dtype(q_type), stream, handle);
-
-  if (workspace_size > 0) {
-    if (workspace->data.dptr == nullptr) {
-      workspace->data.shape = {workspace_size};
-      workspace->data.dtype = DType::kByte;
-      return;
-    }
-  } else if (workspace_size == 0) {
-    workspace->data.shape = {1};
-    workspace->data.dtype = DType::kByte;
-    return;
-  } else {
-    NVTE_ERROR("Unexpected workspace_size.");
-  }
-}
 void fused_attn_max_512_fwd(size_t batch, size_t num_head, size_t q_max_seqlen,
                             size_t kv_max_seqlen, size_t head_dim, bool is_training,
                             float attn_scale, float p_dropout, NVTE_QKV_Layout qkv_layout,
@@ -1429,126 +1285,6 @@ void fused_attn_max_512_fwd(size_t batch, size_t num_head, size_t q_max_seqlen,
   }
 }
 
-void fused_attn_max_512_bwd_qkvpacked(size_t batch, size_t num_head, size_t max_seqlen,
-                                      size_t head_dim, float attn_scale, float p_dropout,
-                                      NVTE_QKV_Layout qkv_layout, NVTE_Bias_Type bias_type,
-                                      NVTE_Mask_Type mask_type, const Tensor *input_QKV,
-                                      const Tensor *input_dO, Tensor *output_S, Tensor *output_dQKV,
-                                      Tensor *output_dBias, const Tensor *cu_seqlens,
-                                      Tensor *workspace, cudaStream_t stream,
-                                      cudnnHandle_t handle) {
-  using namespace transformer_engine;
-
-  // QKV shape is [b, s, 3, h, d]
-  void *devPtrQKV = input_QKV->data.dptr;
-
-  auto stride = 2 * num_head * head_dim;
-  void *devPtrQ = devPtrQKV;
-  void *devPtrK = static_cast<void *>(static_cast<int8_t *>(devPtrQKV) + stride);
-  void *devPtrV = static_cast<void *>(static_cast<int8_t *>(devPtrQKV) + 2 * stride);
-
-  void *devPtrdO = input_dO->data.dptr;
-
-  // dQKV shape is [b, s, 3, h, d]
-  void *devPtrdQKV = output_dQKV->data.dptr;
-  void *devPtrdQ = devPtrdQKV;
-  void *devPtrdK = static_cast<void *>(static_cast<int8_t *>(devPtrdQKV) + stride);
-  void *devPtrdV = static_cast<void *>(static_cast<int8_t *>(devPtrdQKV) + 2 * stride);
-
-  void *devPtrdBias = output_dBias->data.dptr;
-
-  void *devPtrS = output_S->data.dptr;
-
-  // devPtrdS reuses the memory of devPtrS
-  void *devPtrdS = devPtrS;
-
-  void *devPtrCuSeqlens = cu_seqlens->data.dptr;
-
-  const auto qkv_type = input_QKV->data.dtype;
-  size_t workspace_size = 0;
-
-  fused_attn_max_512_bwd_impl(batch, num_head, max_seqlen, max_seqlen, head_dim, attn_scale,
-                              p_dropout, qkv_layout, mask_type, bias_type, devPtrQ, devPtrK,
-                              devPtrV, devPtrS, devPtrdQ, devPtrdK, devPtrdV, devPtrdO, devPtrdS,
-                              devPtrdBias, devPtrCuSeqlens, devPtrCuSeqlens, workspace->data.dptr,
-                              &workspace_size, get_cudnn_dtype(qkv_type), stream, handle);
-
-  if (workspace_size > 0) {
-    if (workspace->data.dptr == nullptr) {
-      workspace->data.shape = {workspace_size};
-      workspace->data.dtype = DType::kByte;
-      return;
-    }
-  } else if (workspace_size == 0) {
-    workspace->data.shape = {1};
-    workspace->data.dtype = DType::kByte;
-    return;
-  } else {
-    NVTE_ERROR("Unexpected workspace_size.");
-  }
-}
-
-void fused_attn_max_512_bwd_kvpacked(size_t batch, size_t num_head, size_t q_max_seqlen,
-                                     size_t kv_max_seqlen, size_t head_dim, float attn_scale,
-                                     float p_dropout, NVTE_QKV_Layout qkv_layout,
-                                     NVTE_Bias_Type bias_type, NVTE_Mask_Type mask_type,
-                                     const Tensor *input_Q, const Tensor *input_KV,
-                                     const Tensor *input_dO, Tensor *output_S, Tensor *output_dQ,
-                                     Tensor *output_dKV, Tensor *output_dBias,
-                                     const Tensor *q_cu_seqlens, const Tensor *kv_cu_seqlens,
-                                     Tensor *workspace, cudaStream_t stream, cudnnHandle_t handle) {
-  using namespace transformer_engine;
-
-  // Q shape is [b, s, h, d]
-  // KV shape is [b, s, 2, h, d]
-  auto stride = 2 * num_head * head_dim;
-  void *devPtrQ = input_Q->data.dptr;
-  void *devPtrK = input_KV->data.dptr;
-  void *devPtrV = static_cast<void *>(static_cast<int8_t *>(devPtrK) + stride);
-
-  void *devPtrdO = input_dO->data.dptr;
-
-  // dQ shape is [b, s, h, d]
-  // dKV shape is [b, s, 2, h, d]
-  void *devPtrdQ = output_dQ->data.dptr;
-  void *devPtrdK = output_dKV->data.dptr;
-  void *devPtrdV = static_cast<void *>(static_cast<int8_t *>(devPtrdK) + stride);
-
-  void *devPtrdBias = output_dBias->data.dptr;
-
-  void *devPtrS = output_S->data.dptr;
-
-  // devPtrdS reuses the memory of devPtrS
-  void *devPtrdS = devPtrS;
-
-  void *devPtrQCuSeqlens = q_cu_seqlens->data.dptr;
-  void *devPtrKVCuSeqlens = kv_cu_seqlens->data.dptr;
-
-  const auto q_type = input_Q->data.dtype;
-  const auto kv_type = input_KV->data.dtype;
-  NVTE_CHECK(q_type == kv_type, "data type of Q must be equal to data type of KV.");
-  size_t workspace_size = 0;
-
-  fused_attn_max_512_bwd_impl(
-      batch, num_head, q_max_seqlen, kv_max_seqlen, head_dim, attn_scale, p_dropout, qkv_layout,
-      mask_type, bias_type, devPtrQ, devPtrK, devPtrV, devPtrS, devPtrdQ, devPtrdK, devPtrdV,
-      devPtrdO, devPtrdS, devPtrdBias, devPtrQCuSeqlens, devPtrKVCuSeqlens, workspace->data.dptr,
-      &workspace_size, get_cudnn_dtype(q_type), stream, handle);
-
-  if (workspace_size > 0) {
-    if (workspace->data.dptr == nullptr) {
-      workspace->data.shape = {workspace_size};
-      workspace->data.dtype = DType::kByte;
-      return;
-    }
-  } else if (workspace_size == 0) {
-    workspace->data.shape = {1};
-    workspace->data.dtype = DType::kByte;
-    return;
-  } else {
-    NVTE_ERROR("Unexpected workspace_size.");
-  }
-}
 void fused_attn_max_512_bwd(size_t batch, size_t num_head, size_t q_max_seqlen,
                             size_t kv_max_seqlen, size_t head_dim, float attn_scale,
                             float p_dropout, NVTE_QKV_Layout qkv_layout, NVTE_Bias_Type bias_type,

transformer_engine/common/fused_attn/fused_attn_f16_max512_seqlen.h

@@ -18,25 +18,6 @@
 
 namespace transformer_engine {
 #if (CUDNN_VERSION >= 8901)
-void fused_attn_max_512_fwd_qkvpacked(size_t batch, size_t num_head, size_t max_seqlen,
-                                      size_t head_size, bool is_training, float attn_scale,
-                                      float p_dropout, NVTE_QKV_Layout qkv_layout,
-                                      NVTE_Bias_Type bias_type, NVTE_Mask_Type mask_type,
-                                      const Tensor *input_QKV, const Tensor *input_Bias,
-                                      Tensor *output_O, NVTETensorPack *Aux_CTX_Tensors,
-                                      const Tensor *cu_seqlens, const Tensor *rng_state,
-                                      Tensor *workspace, cudaStream_t stream, cudnnHandle_t handle);
-
-void fused_attn_max_512_fwd_kvpacked(size_t batch, size_t num_head, size_t q_max_seqlen,
-                                     size_t kv_max_seqlen, size_t head_dim, bool is_training,
-                                     float attn_scale, float p_dropout, NVTE_QKV_Layout qkv_layout,
-                                     NVTE_Bias_Type bias_type, NVTE_Mask_Type mask_type,
-                                     const Tensor *input_Q, const Tensor *input_KV,
-                                     const Tensor *input_Bias, Tensor *output_O,
-                                     NVTETensorPack *Aux_CTX_Tensors, const Tensor *q_cu_seqlens,
-                                     const Tensor *kv_cu_seqlens, const Tensor *rng_state,
-                                     Tensor *workspace, cudaStream_t stream, cudnnHandle_t handle);
-
 void fused_attn_max_512_fwd(size_t batch, size_t num_head, size_t q_max_seqlen,
                             size_t kv_max_seqlen, size_t head_dim, bool is_training,
                             float attn_scale, float p_dropout, NVTE_QKV_Layout qkv_layout,
@@ -47,24 +28,6 @@ void fused_attn_max_512_fwd(size_t batch, size_t num_head, size_t q_max_seqlen,
                             const Tensor *kv_cu_seqlens, const Tensor *rng_state, Tensor *workspace,
                             cudaStream_t stream, cudnnHandle_t handle);
 
-void fused_attn_max_512_bwd_qkvpacked(size_t batch, size_t num_head, size_t max_seqlen,
-                                      size_t head_dim, float attn_scale, float p_dropout,
-                                      NVTE_QKV_Layout qkv_layout, NVTE_Bias_Type bias_type,
-                                      NVTE_Mask_Type mask_type, const Tensor *input_QKV,
-                                      const Tensor *input_dO, Tensor *output_S, Tensor *output_dQKV,
-                                      Tensor *output_dBias, const Tensor *cu_seqlens,
-                                      Tensor *workspace, cudaStream_t stream, cudnnHandle_t handle);
-
-void fused_attn_max_512_bwd_kvpacked(size_t batch, size_t num_head, size_t q_max_seqlen,
-                                     size_t kv_max_seqlen, size_t head_dim, float attn_scale,
-                                     float p_dropout, NVTE_QKV_Layout qkv_layout,
-                                     NVTE_Bias_Type bias_type, NVTE_Mask_Type mask_type,
-                                     const Tensor *input_Q, const Tensor *input_KV,
-                                     const Tensor *input_dO, Tensor *output_S, Tensor *output_dQ,
-                                     Tensor *output_dKV, Tensor *output_dBias,
-                                     const Tensor *q_cu_seqlens, const Tensor *kv_cu_seqlens,
-                                     Tensor *workspace, cudaStream_t stream, cudnnHandle_t handle);
-
 void fused_attn_max_512_bwd(size_t batch, size_t num_head, size_t q_max_seqlen,
                             size_t kv_max_seqlen, size_t head_dim, float attn_scale,
                             float p_dropout, NVTE_QKV_Layout qkv_layout, NVTE_Bias_Type bias_type,

transformer_engine/common/fused_attn/fused_attn_fp8.h

@@ -13,47 +13,6 @@
 
 namespace transformer_engine {
 #if (CUDNN_VERSION >= 8900)
-// fused attention FWD FP8 with packed QKV
-void fused_attn_fp8_fwd_qkvpacked(size_t batch, size_t num_attn_heads, size_t max_seqlen,
-                                  size_t head_dim, bool is_training, float attn_scale,
-                                  float p_dropout, NVTE_QKV_Layout qkv_layout,
-                                  NVTE_Bias_Type bias_type, NVTE_Mask_Type mask_type,
-                                  const Tensor *input_QKV, Tensor *input_output_S, Tensor *output_O,
-                                  NVTETensorPack *Aux_CTX_Tensors, const Tensor *cu_seqlens,
-                                  const Tensor *rng_state, Tensor *workspace, cudaStream_t stream,
-                                  cudnnHandle_t handle);
-
-// fused attention BWD FP8 with packed QKV
-void fused_attn_fp8_bwd_qkvpacked(
-    size_t batch, size_t num_attn_heads, size_t max_seqlen, size_t head_dim, float attn_scale,
-    float p_dropout, NVTE_QKV_Layout qkv_layout, NVTE_Bias_Type bias_type, NVTE_Mask_Type mask_type,
-    const Tensor *input_QKV, const Tensor *input_O, const Tensor *input_dO, const Tensor *input_M,
-    const Tensor *input_ZInv, const Tensor *input_S, Tensor *input_output_dP,
-    const Tensor *output_dQKV, const Tensor *cu_seqlens, const Tensor *rng_state, Tensor *workspace,
-    cudaStream_t stream, cudnnHandle_t handle);
-
-// fused attention FWD FP8 with packed KV
-void fused_attn_fp8_fwd_kvpacked(size_t batch, size_t num_attn_heads, size_t num_gqa_groups,
-                                 size_t max_seqlen_q, size_t max_seqlen_kv, size_t head_dim,
-                                 bool is_training, float attn_scale, float p_dropout,
-                                 NVTE_QKV_Layout qkv_layout, NVTE_Bias_Type bias_type,
-                                 NVTE_Mask_Type mask_type, const Tensor *input_Q,
-                                 const Tensor *input_KV, Tensor *input_output_S, Tensor *output_O,
-                                 NVTETensorPack *Aux_CTX_Tensors, const Tensor *cu_seqlens_q,
-                                 const Tensor *cu_seqlens_kv, const Tensor *rng_state,
-                                 Tensor *workspace, cudaStream_t stream, cudnnHandle_t handle);
-
-// fused attention BWD FP8 with packed KV
-void fused_attn_fp8_bwd_kvpacked(
-    size_t batch, size_t num_attn_heads, size_t num_gqa_groups, size_t max_seqlen_q,
-    size_t max_seqlen_kv, size_t head_dim, float attn_scale, float p_dropout,
-    NVTE_QKV_Layout qkv_layout, NVTE_Bias_Type bias_type, NVTE_Mask_Type mask_type,
-    const Tensor *input_Q, const Tensor *input_KV, const Tensor *input_O, const Tensor *input_dO,
-    const Tensor *input_M, const Tensor *input_ZInv, const Tensor *input_S, Tensor *input_output_dP,
-    const Tensor *output_dQ, const Tensor *output_dKV, const Tensor *cu_seqlens_q,
-    const Tensor *cu_seqlens_kv, const Tensor *rng_state, Tensor *workspace, cudaStream_t stream,
-    cudnnHandle_t handle);
-
 // fused attention FWD FP8 with separate Q, K, V
 void fused_attn_fp8_fwd(size_t batch, size_t num_attn_heads, size_t num_gqa_groups,
                         size_t max_seqlen_q, size_t max_seqlen_kv, size_t head_dim,

transformer_engine/common/include/transformer_engine/fused_attn.h

@@ -217,6 +217,8 @@ NVTE_Fused_Attn_Backend nvte_get_fused_attn_backend(
     int64_t window_size_right, bool return_max_logit, bool cuda_graph);
 
 /*! \brief Compute dot product attention with packed QKV input.
+ *
+ * \deprecated Please use `nvte_fused_attn_fwd` with separate Q, K, V tensors instead.
  *
  * Computes:
  *  - P = Q * Transpose(K) + Bias
@@ -270,6 +272,9 @@ NVTE_Fused_Attn_Backend nvte_get_fused_attn_backend(
  *  \param[in]     workspace                Workspace tensor.
  *  \param[in]     stream                   CUDA stream used for this operation.
  */
+[[deprecated(
+    "nvte_fused_attn_fwd_qkvpacked() is deprecated. Please use nvte_fused_attn_fwd() with separate "
+    "Q, K, V tensors instead.")]]
 void nvte_fused_attn_fwd_qkvpacked(const NVTETensor QKV, const NVTETensor Bias,
                                    const NVTETensor SoftmaxOffset, NVTETensor S, NVTETensor O,
                                    NVTETensorPack *Aux_CTX_Tensors, const NVTETensor cu_seqlens,
@@ -282,6 +287,8 @@ void nvte_fused_attn_fwd_qkvpacked(const NVTETensor QKV, const NVTETensor Bias,
                                    NVTETensor workspace, cudaStream_t stream);
 
 /*! \brief Compute the backward of the dot product attention with packed QKV input.
+ *
+ * \deprecated Please use `nvte_fused_attn_bwd` with separate Q, K, V tensors instead.
  *
  * Support Matrix:
    \verbatim
@@ -330,6 +337,9 @@ void nvte_fused_attn_fwd_qkvpacked(const NVTETensor QKV, const NVTETensor Bias,
  *  \param[in]     workspace                Workspace tensor.
  *  \param[in]     stream                   CUDA stream used for this operation.
  */
+[[deprecated(
+    "nvte_fused_attn_bwd_qkvpacked() is deprecated. Please use nvte_fused_attn_bwd() with separate "
+    "Q, K, V tensors instead.")]]
 void nvte_fused_attn_bwd_qkvpacked(
     const NVTETensor QKV, const NVTETensor O, const NVTETensor dO, const NVTETensor S,
     NVTETensor dP, const NVTETensorPack *Aux_CTX_Tensors, NVTETensor dQKV, NVTETensor dBias,
@@ -340,6 +350,8 @@ void nvte_fused_attn_bwd_qkvpacked(
     NVTETensor workspace, cudaStream_t stream);
 
 /*! \brief Compute dot product attention with packed KV input.
+ *
+ * \deprecated Please use `nvte_fused_attn_fwd` with separate Q, K, V tensors instead.
  *
  * Computes:
  *  - P = Q * Transpose(K) + Bias
@@ -401,6 +413,9 @@ void nvte_fused_attn_bwd_qkvpacked(
  *  \param[in]     workspace                 Workspace tensor.
  *  \param[in]     stream                    CUDA stream used for this operation.
  */
+[[deprecated(
+    "nvte_fused_attn_fwd_kvpacked() is deprecated. Please use nvte_fused_attn_fwd() with separate "
+    "Q, K, V tensors instead.")]]
 void nvte_fused_attn_fwd_kvpacked(
     const NVTETensor Q, const NVTETensor KV, const NVTETensor Bias, const NVTETensor SoftmaxOffset,
     NVTETensor S, NVTETensor O, NVTETensorPack *Aux_CTX_Tensors, const NVTETensor cu_seqlens_q,
@@ -413,6 +428,8 @@ void nvte_fused_attn_fwd_kvpacked(
     int64_t window_size_right, NVTETensor workspace, cudaStream_t stream);
 
 /*! \brief Compute the backward of the dot product attention with packed KV input.
+ *
+ * \deprecated Please use `nvte_fused_attn_bwd` with separate Q, K, V tensors instead.
  *
  * Support Matrix:
    \verbatim
@@ -467,6 +484,9 @@ void nvte_fused_attn_fwd_kvpacked(
  *  \param[in]     workspace                 Workspace tensor.
  *  \param[in]     stream                    CUDA stream used for this operation.
  */
+[[deprecated(
+    "nvte_fused_attn_bwd_kvpacked() is deprecated. Please use nvte_fused_attn_bwd() with separate "
+    "Q, K, V tensors instead.")]]
 void nvte_fused_attn_bwd_kvpacked(
     const NVTETensor Q, const NVTETensor KV, const NVTETensor O, const NVTETensor dO,
     const NVTETensor S, NVTETensor dP, const NVTETensorPack *Aux_CTX_Tensors, NVTETensor dQ,