[Submodule] Turning flash-attention integration into 3rd party submod (…

…pytorch#146372)

Summary:
Pull Request resolved: pytorch#146372

Pull Request resolved: pytorch#144120

# Summary

### Sticky points

Cuda-graph rng handling has changed / deviated from original implementation. We will be left with a dangling 'offset' val and confusing naming due to BC

## Dependencies
- Flash PR: Dao-AILab/flash-attention#1419

### Other Points
- The BC linter is complaining about losing generate.py and its functions which is not real BC surface
cc albanD

imported-using-ghimport

Test Plan:
Imported from OSS

Building in dev
`buck build @//mode/dev-nosan -c fbcode.nvcc_arch=h100a  //caffe2:ATen-cu --show-full-output    `

I and Nming the .so I do see that the flash symbols are correctly named:
```
0000000001c3dfb0 t pytorch_flash::run_mha_bwd(pytorch_flash::Flash_bwd_params&, CUstream_st*)::$_0::operator()() const::{lambda()pytorch#1}::operator()() const::{lambda()pytorch#1}::operator()() const::{lambda()pytorch#7}::operator()() const
0000000001c36080 t pytorch_flash::run_mha_fwd(pytorch_flash::Flash_fwd_params&, CUstream_st*, bool)::$_0::operator()() const::{lambda()pytorch#2}::operator()() const::{lambda()pytorch#1}::operator()() const::{lambda()pytorch#6}::operator()() const
0000000001c360e0 t pytorch_flash::run_mha_fwd(pytorch_flash::Flash_fwd_params&, CUstream_st*, bool)::$_0::operator()() const::{lambda()pytorch#2}::operator()() const::{lambda()pytorch#1}::operator()() const::{lambda()pytorch#7}::operator()() const
0000000001c35fc0 t pytorch_flash::run_mha_fwd(pytorch_flash::Flash_fwd_params&, CUstream_st*, bool)::$_0::operator()() const::{lambda()pytorch#1}::operator()() const::{lambda()pytorch#1}::operator()() const::{lambda()pytorch#6}::operator()() const
0000000001c36020 t pytorch_flash::run_mha_fwd(pytorch_flash::Flash_fwd_params&, CUstream_st*, bool)::$_0::operator()() const::{lambda()pytorch#1}::operator()() const::{lambda()pytorch#1}::operator()() const::{lambda()pytorch#7}::operator()() const
```

Reviewed By: vkuzo

Differential Revision: D68502879

Pulled By: drisspg

CMakeLists.txt

@@ -872,11 +872,6 @@ cmake_dependent_option(
   "USE_CUDA OR USE_ROCM;NOT MSVC"
   OFF)
 
-# We are currenlty not using alibi attention for Flash So we disable this
-# feature by default We dont currently document this feature because we don't
-# Suspect users building from source will need this
-add_definitions(-DFLASHATTENTION_DISABLE_ALIBI)
-
 # CAVEAT: Again, Flash Attention2 will error while building for sm52 while Mem
 # Eff Attention won't
 cmake_dependent_option(

aten/src/ATen/CMakeLists.txt

@@ -164,9 +164,12 @@ file(GLOB native_quantized_cudnn_hip_cpp "native/quantized/cudnn/hip/*.cpp")
 file(GLOB native_utils_cpp "native/utils/*.cpp")
 
 # flash_attention sources
-file(GLOB flash_attention_cuda_cu "native/transformers/cuda/flash_attn/*.cu")
-file(GLOB flash_attention_cuda_kernels_cu "native/transformers/cuda/flash_attn/kernels/*.cu")
-file(GLOB flash_attention_cuda_cpp "native/transformers/cuda/flash_attn/*.cpp")
+file(GLOB flash_attention_cuda_kernels_cu ${PROJECT_SOURCE_DIR}/third_party/flash-attention/csrc/flash_attn/src/*.cu)
+# Flash attention C++ sources
+file(GLOB flash_attention_cuda_cpp
+    "${PROJECT_SOURCE_DIR}/third_party/flash-attention/csrc/flash_attn/src/*.cpp"
+    "native/transformers/cuda/flash_attn/flash_api.cpp"
+)
 
 # flash_attention hip sources
 file(GLOB flash_attention_hip_hip "native/transformers/hip/flash_attn/*.hip")

aten/src/ATen/native/native_functions.yaml

@@ -14862,7 +14862,7 @@
     MPS: _scaled_dot_product_attention_math_mps
   tags: nondeterministic_seeded
 
-- func: _scaled_dot_product_flash_attention(Tensor query, Tensor key, Tensor value, float dropout_p=0.0, bool is_causal=False, bool return_debug_mask=False, *, float? scale=None) -> (Tensor output, Tensor logsumexp, Tensor cum_seq_q, Tensor cum_seq_k, SymInt max_q, SymInt max_k, Tensor philox_seed, Tensor philox_offset, Tensor debug_attn_mask)
+- func: _scaled_dot_product_flash_attention(Tensor query, Tensor key, Tensor value, float dropout_p=0.0, bool is_causal=False, bool return_debug_mask=False, *, float? scale=None) -> (Tensor output, Tensor logsumexp, Tensor cum_seq_q, Tensor cum_seq_k, SymInt max_q, SymInt max_k, Tensor rng_state, Tensor unused, Tensor debug_attn_mask)
   dispatch:
     CUDA: _scaled_dot_product_flash_attention_cuda
     NestedTensorCUDA: _scaled_dot_product_flash_attention_nestedtensor_cuda
@@ -14919,13 +14919,13 @@
     CUDA: _scaled_dot_product_cudnn_attention_backward_cuda
   tags: nondeterministic_seeded
 
-- func: _flash_attention_forward(Tensor query, Tensor key, Tensor value, Tensor? cum_seq_q, Tensor? cum_seq_k, SymInt max_q, SymInt max_k, float dropout_p, bool is_causal, bool return_debug_mask, *, float? scale=None, SymInt? window_size_left=None, SymInt? window_size_right=None, Tensor? seqused_k=None, Tensor? alibi_slopes=None) -> (Tensor output, Tensor softmax_logsumexp, Tensor philox_seed, Tensor philox_offset, Tensor debug_attn_mask)
+- func: _flash_attention_forward(Tensor query, Tensor key, Tensor value, Tensor? cum_seq_q, Tensor? cum_seq_k, SymInt max_q, SymInt max_k, float dropout_p, bool is_causal, bool return_debug_mask, *, float? scale=None, SymInt? window_size_left=None, SymInt? window_size_right=None, Tensor? seqused_k=None, Tensor? alibi_slopes=None) -> (Tensor output, Tensor softmax_logsumexp, Tensor rng_state, Tensor unused, Tensor debug_attn_mask)
   variants: function
   dispatch:
     CUDA: _flash_attention_forward
   tags: nondeterministic_seeded
 
-- func: _flash_attention_backward(Tensor grad_out, Tensor query, Tensor key, Tensor value, Tensor out, Tensor logsumexp, Tensor cum_seq_q, Tensor cum_seq_k, SymInt max_q, SymInt max_k, float dropout_p, bool is_causal, Tensor philox_seed, Tensor philox_offset, *, float? scale=None, SymInt? window_size_left=None, SymInt? window_size_right=None) -> (Tensor, Tensor, Tensor)
+- func: _flash_attention_backward(Tensor grad_out, Tensor query, Tensor key, Tensor value, Tensor out, Tensor logsumexp, Tensor cum_seq_q, Tensor cum_seq_k, SymInt max_q, SymInt max_k, float dropout_p, bool is_causal, Tensor rng_state, Tensor unused, *, float? scale=None, SymInt? window_size_left=None, SymInt? window_size_right=None) -> (Tensor, Tensor, Tensor)
   device_check: NoCheck
   variants: function
   dispatch:

aten/src/ATen/native/transformers/cuda/attention.cu

@@ -70,6 +70,9 @@
 #ifdef USE_FLASH_ATTENTION
 // FlashAttention Specific Imports
 #include <ATen/native/transformers/cuda/flash_attn/flash_api.h>
+#if !defined(__HIP_PLATFORM_AMD__)
+#include <namespace_config.h>
+#endif
 #endif
 #ifdef USE_MEM_EFF_ATTENTION
 #ifndef USE_ROCM
@@ -916,6 +919,7 @@ _flash_attention_forward(
   std::optional<Tensor> seqused_k = _seqused_k;
   std::optional<at::Tensor> block_table = std::nullopt;  // we are not using the block table yet
   std::optional<Tensor> alibi_slopes = _alibi_slopes;
+  const float softcap = 0.0;
 
   const int non_null_window_left = window_size_left.has_value() ? window_size_left.value() : -1;
   const int non_null_window_right = window_size_right.has_value() ? window_size_right.value() : -1;
@@ -939,7 +943,7 @@ _flash_attention_forward(
         philox_seed,
         philox_offset,
         debug_attn_mask) =
-        pytorch_flash::mha_varlen_fwd(
+        FLASH_NAMESPACE::mha_varlen_fwd(
             query,
             key,
             value,
@@ -957,6 +961,7 @@ _flash_attention_forward(
             is_causal,
             non_null_window_left,
             non_null_window_right,
+            softcap,
             return_debug_mask,
             std::nullopt /*gen_*/);
   } else {
@@ -969,7 +974,7 @@ _flash_attention_forward(
         philox_seed,
         philox_offset,
         debug_attn_mask) =
-        pytorch_flash::mha_fwd(
+        FLASH_NAMESPACE::mha_fwd(
             query,
             key,
             value,
@@ -980,6 +985,7 @@ _flash_attention_forward(
             is_causal,
             non_null_window_left,
             non_null_window_right,
+            softcap,
             return_debug_mask, /*return_softmax (this is used for testing)*/
             std::nullopt);
   }

aten/src/ATen/native/transformers/cuda/attention_backward.cu

@@ -94,6 +94,7 @@ std::tuple<Tensor, Tensor, Tensor> _flash_attention_backward(
 
   // Currently unused args:
   std::optional<at::Tensor> alibi_slopes{std::nullopt};
+  const float softcap = 0.0;
 
   bool determinisitic{false};
   auto& ctx = at::globalContext();
@@ -111,7 +112,7 @@ std::tuple<Tensor, Tensor, Tensor> _flash_attention_backward(
   // in order to determine whether we are using varlen or dense forward
   if (cumulative_sequence_length_q.defined()) {
     // Varlen forward
-    auto [dQuery, dKey, dValue, dSoftmax] = pytorch_flash::mha_varlen_bwd(
+    auto [dQuery, dKey, dValue, dSoftmax] = FLASH_NAMESPACE::mha_varlen_bwd(
         contiguous_grad_out,
         query,
         key,
@@ -132,13 +133,14 @@ std::tuple<Tensor, Tensor, Tensor> _flash_attention_backward(
         is_causal,
         non_null_window_left,
         non_null_window_right,
+        softcap,
         determinisitic,
         philox_seed,
         philox_offset);
     return std::make_tuple(std::move(dQuery), std::move(dKey), std::move(dValue));
   } else {
     // Dense forward
-    auto [dQuery, dKey, dValue, dSoftmax] = pytorch_flash::mha_bwd(
+    auto [dQuery, dKey, dValue, dSoftmax] = FLASH_NAMESPACE::mha_bwd(
         contiguous_grad_out,
         query,
         key,
@@ -154,6 +156,7 @@ std::tuple<Tensor, Tensor, Tensor> _flash_attention_backward(
         is_causal,
         non_null_window_left,
         non_null_window_right,
+        softcap,
         determinisitic,
         philox_seed,
         philox_offset);