Adds small-batch kernels (#1126)

apex/contrib/csrc/fmha/fmha_api.cpp

@@ -30,28 +30,6 @@
 
 #include "fmha.h"
 
-void run_fmha_fp16_128_64_sm80(const Fused_multihead_attention_fprop_params &params,
-                                  bool is_training,
-                                  cudaStream_t stream);
-void run_fmha_fp16_256_64_sm80(const Fused_multihead_attention_fprop_params &params,
-                                  bool is_training,
-                                  cudaStream_t stream);
-void run_fmha_fp16_384_64_sm80(const Fused_multihead_attention_fprop_params &params,
-                                  bool is_training,
-                                  cudaStream_t stream);
-void run_fmha_fp16_512_64_sm80(const Fused_multihead_attention_fprop_params &params,
-                                  bool is_training,
-                                  cudaStream_t stream);
-
-void run_fmha_dgrad_fp16_128_64_sm80(const Fused_multihead_attention_fprop_params &params,
-                                        cudaStream_t stream);
-void run_fmha_dgrad_fp16_256_64_sm80(const Fused_multihead_attention_fprop_params &params,
-                                        cudaStream_t stream);
-void run_fmha_dgrad_fp16_384_64_sm80(const Fused_multihead_attention_fprop_params &params,
-                                        cudaStream_t stream);
-void run_fmha_dgrad_fp16_512_64_sm80(const Fused_multihead_attention_fprop_params &params,
-                                        cudaStream_t stream);
-
 void set_params(Fused_multihead_attention_fprop_params &params,
                 // sizes
                 const size_t b,
@@ -61,7 +39,6 @@ void set_params(Fused_multihead_attention_fprop_params &params,
                 // device pointers
                 void *qkv_packed_d,
                 void *cu_seqlens_d,
-                void *seqlens_d,
                 void *o_packed_d,
                 void *s_d,
                 float p_dropout) {
@@ -79,7 +56,6 @@ void set_params(Fused_multihead_attention_fprop_params &params,
     params.o_stride_in_bytes = get_size_in_bytes(h * d, data_type);
 
     params.cu_seqlens = static_cast<int *>(cu_seqlens_d);
-    params.seqlens = static_cast<int *>(seqlens_d);
 
     // S = softmax(P)
     params.s_ptr = s_d;
@@ -107,13 +83,9 @@ void set_params(Fused_multihead_attention_fprop_params &params,
     set_alpha(params.scale_dropout, params.rp_dropout, data_type);
 }
 
-constexpr uint32_t NUM_HEADS_DIM = 2;
-constexpr uint32_t THREE_DIM = 1;
-
 std::vector<at::Tensor>
 mha_fwd(const at::Tensor &qkv,  // total x num_heads x 3 x head_size, total := \sum_{i=0}^{b} s_i
         const at::Tensor &cu_seqlens,  // b+1
-        const at::Tensor &seqlens,     // b
         const float p_dropout,
         const int max_seq_len,
         const bool is_training,
@@ -149,17 +121,14 @@ mha_fwd(const at::Tensor &qkv,  // total x num_heads x 3 x head_size, total := \
 
     TORCH_CHECK(qkv.dtype() == torch::kFloat16);
     TORCH_CHECK(cu_seqlens.dtype() == torch::kInt32);
-    TORCH_CHECK(seqlens.dtype() == torch::kInt32);
 
     TORCH_CHECK(qkv.is_cuda())
     TORCH_CHECK(cu_seqlens.is_cuda())
 
     TORCH_CHECK(qkv.is_contiguous())
     TORCH_CHECK(cu_seqlens.is_contiguous())
-    TORCH_CHECK(seqlens.is_contiguous())
 
     TORCH_CHECK(cu_seqlens.dim() == 1);
-    TORCH_CHECK(seqlens.dim() == 1);
     TORCH_CHECK(qkv.dim() == 4);
 
     const auto sizes = qkv.sizes();
@@ -167,10 +136,9 @@ mha_fwd(const at::Tensor &qkv,  // total x num_heads x 3 x head_size, total := \
     TORCH_CHECK(sizes[THREE_DIM] == 3);
 
     const int batch_size = cu_seqlens.numel() - 1;
-    TORCH_CHECK(seqlens.numel() == batch_size);
-    const int total = sizes[0];
-    const int num_heads = sizes[NUM_HEADS_DIM];
-    const int head_size = sizes[3];
+    const int total = sizes[TOTAL_DIM];
+    const int num_heads = sizes[H_DIM];
+    const int head_size = sizes[D_DIM];
     TORCH_CHECK(batch_size > 0);
     TORCH_CHECK(head_size == 64);
     auto opts = qkv.options();
@@ -191,7 +159,6 @@ mha_fwd(const at::Tensor &qkv,  // total x num_heads x 3 x head_size, total := \
                head_size,
                qkv.data_ptr(),
                cu_seqlens.data_ptr(),
-               seqlens.data_ptr(),
                ctx.data_ptr(),
                s.data_ptr(),
                p_dropout);
@@ -217,7 +184,6 @@ mha_bwd(const at::Tensor &dout,  // total x num_heads, x head_size
         const at::Tensor &qkv,   // total x num_heads x 3 x head_size, total := \sum_{i=0}^{b} s_i
         at::Tensor &softmax,     // b x h x s x s softmax and dmask - will be overwritten with dP
         const at::Tensor &cu_seqlens,  // b+1
-        const at::Tensor &seqlens,     // b
         const float p_dropout,         // probability to drop
         const int max_seq_len          // max sequence length to choose the kernel
 ) {
@@ -247,17 +213,14 @@ mha_bwd(const at::Tensor &dout,  // total x num_heads, x head_size
     TORCH_CHECK(dout.dtype() == torch::kFloat16);
     TORCH_CHECK(softmax.dtype() == torch::kFloat16);
     TORCH_CHECK(cu_seqlens.dtype() == torch::kInt32);
-    TORCH_CHECK(seqlens.dtype() == torch::kInt32);
 
     TORCH_CHECK(qkv.is_cuda());
     TORCH_CHECK(cu_seqlens.is_cuda());
 
     TORCH_CHECK(qkv.is_contiguous());
     TORCH_CHECK(cu_seqlens.is_contiguous());
-    TORCH_CHECK(seqlens.is_contiguous());
 
     TORCH_CHECK(cu_seqlens.dim() == 1);
-    TORCH_CHECK(seqlens.dim() == 1);
     TORCH_CHECK(qkv.dim() == 4);
 
     const auto sizes = qkv.sizes();
@@ -265,9 +228,8 @@ mha_bwd(const at::Tensor &dout,  // total x num_heads, x head_size
     TORCH_CHECK(sizes[THREE_DIM] == 3);
 
     const int batch_size = cu_seqlens.numel() - 1;
-    TORCH_CHECK(seqlens.numel() == batch_size);
-    const int num_heads = sizes[NUM_HEADS_DIM];
-    const int head_size = sizes[3];
+    const int num_heads = sizes[H_DIM];
+    const int head_size = sizes[D_DIM];
     TORCH_CHECK(batch_size > 0);
     TORCH_CHECK(head_size == 64);
 
@@ -282,12 +244,11 @@ mha_bwd(const at::Tensor &dout,  // total x num_heads, x head_size
                head_size,
                qkv.data_ptr(),
                cu_seqlens.data_ptr(),
-               seqlens.data_ptr(),
                dout.data_ptr(),     // we set o_ptr to dout
                softmax.data_ptr(),  // softmax gets overwritten by dP!
                p_dropout);
 
-    // we're re-using these scales scales
+    // we're re-using these scales
     Data_type acc_type = DATA_TYPE_FP32;
     set_alpha(params.scale_bmm1, 1.f, acc_type);
     set_alpha(params.scale_softmax, 1.f / sqrtf(head_size), acc_type);
@@ -298,8 +259,174 @@ mha_bwd(const at::Tensor &dout,  // total x num_heads, x head_size
     return { dqkv, softmax };
 }
 
+std::vector<at::Tensor> mha_fwd_nl(const at::Tensor &qkv,         // total x num_heads x 3 x head_size, total := \sum_{i=0}^{b} s_i
+                                const at::Tensor &cu_seqlens,  // b+1
+                                const float p_dropout,
+                                const int max_seq_len,
+                                const bool is_training,
+                                c10::optional<at::Generator> gen_) {
+    int seq_len = 512;
+    auto launch = &run_fmha_fp16_512_64_sm80_nl;
+    TORCH_CHECK(max_seq_len == seq_len);
+
+    constexpr int warps_m = 1;
+    constexpr int warps_n = 4;  // this leads to an upper bound
+    const int mmas_m = seq_len / 16 / warps_m;
+    const int mmas_n = seq_len / 16 / warps_n;
+    // static_assert( mmas_m == 32 );
+    // static_assert( mmas_n == 4 );
+    const int elts_per_thread = 8 * mmas_m * mmas_n;
+
+    auto stream = at::cuda::getCurrentCUDAStream().stream();
+
+    TORCH_CHECK(qkv.is_cuda())
+    TORCH_CHECK(cu_seqlens.is_cuda())
+
+    TORCH_CHECK(qkv.is_contiguous())
+    TORCH_CHECK(cu_seqlens.is_contiguous())
+
+    TORCH_CHECK(cu_seqlens.dim() == 1);
+    TORCH_CHECK(qkv.dim() == 4);
+
+    const auto sizes = qkv.sizes();
+
+    TORCH_CHECK(sizes[THREE_DIM] == 3);
+
+    const int batch_size = cu_seqlens.numel() - 1;
+    const int total = sizes[TOTAL_DIM];
+    const int num_heads = sizes[H_DIM];
+    const int head_size = sizes[D_DIM];
+    TORCH_CHECK(batch_size > 0);
+    TORCH_CHECK(head_size == 64);
+    auto opts = qkv.options();
+
+    auto ctx = torch::empty({ total, num_heads, head_size }, opts);
+
+    auto s = torch::empty({ batch_size, num_heads, seq_len, seq_len }, opts);
+
+    auto gen = at::get_generator_or_default<at::CUDAGeneratorImpl>(gen_, at::cuda::detail::getDefaultCUDAGenerator());
+
+    Fused_multihead_attention_fprop_params params;
+
+    set_params(params,
+               batch_size,
+               seq_len,
+               num_heads,
+               head_size,
+               qkv.data_ptr(),
+               cu_seqlens.data_ptr(),
+               ctx.data_ptr(),
+               s.data_ptr(),
+               p_dropout);
+
+    // number of times random will be generated per thread, to offset philox counter in thc random
+    // state
+    int64_t counter_offset = elts_per_thread;
+    at::PhiloxCudaState rng_engine_inputs;
+
+    if( is_training ) {
+        // See Note [Acquire lock when using random generators]
+        std::lock_guard<std::mutex> lock(gen->mutex_);
+        params.philox_args = gen->philox_cuda_state(counter_offset);
+    }
+    int num_chunks = 3;
+    if(batch_size == 3) {
+        num_chunks = 2;
+    }
+
+    launch(params, is_training, num_chunks, stream);
+
+    return { ctx, s };
+}
+
+std::vector<at::Tensor> mha_bwd_nl(const at::Tensor &dout,        // total x num_heads, x head_size
+                                const at::Tensor &qkv,         // total x num_heads x 3 x head_size, total := \sum_{i=0}^{b} s_i
+                                at::Tensor &softmax,           // b x h x s x s softmax and dmask - will be overwritten with dP
+                                const at::Tensor &cu_seqlens,  // b+1
+                                const float p_dropout,         // probability to drop
+                                const int max_seq_len          // max sequence length to choose the kernel
+) {
+
+    auto stream = at::cuda::getCurrentCUDAStream().stream();
+
+    TORCH_CHECK(qkv.is_cuda())
+    TORCH_CHECK(cu_seqlens.is_cuda())
+
+    TORCH_CHECK(qkv.is_contiguous())
+    TORCH_CHECK(cu_seqlens.is_contiguous())
+
+    TORCH_CHECK(cu_seqlens.dim() == 1);
+
+    TORCH_CHECK(qkv.dim() == 4);
+
+    const auto sizes = qkv.sizes();
+
+    TORCH_CHECK(sizes[THREE_DIM] == 3);
+
+    const int batch_size = cu_seqlens.numel() - 1;
+    
+    const int total = sizes[TOTAL_DIM];
+    const int num_heads = sizes[H_DIM];
+    const int head_size = sizes[D_DIM];
+    TORCH_CHECK(batch_size > 0);
+    TORCH_CHECK(head_size == 64);
+
+    int seq_len = 512;
+    auto launch = &run_fmha_dgrad_fp16_512_64_sm80_nl;
+
+    auto opts = qkv.options();
+
+    auto dqkv = torch::empty_like(qkv);
+
+    int num_chunks = 2;
+    if( batch_size == 1 ) {
+        num_chunks = 4;
+    }else if( batch_size == 2 ) {
+        num_chunks = 3;
+    }
+    auto dkv = torch::empty({total, num_chunks, 2, num_heads, head_size}, opts);
+
+    Fused_multihead_attention_fprop_params params;
+
+    set_params(params,
+               batch_size,
+               seq_len,
+               num_heads,
+               head_size,
+               qkv.data_ptr(),
+               cu_seqlens.data_ptr(),
+               dout.data_ptr(),     // o_ptr = dout
+               softmax.data_ptr(),  // softmax gets overwritten by dP!
+               p_dropout);
+
+    params.dkv_ptr = dkv.data_ptr();
+
+    Data_type acc_type = DATA_TYPE_FP32;
+    set_alpha(params.scale_bmm1, 1.f, acc_type);
+    set_alpha(params.scale_softmax, 1.f / sqrtf(head_size), acc_type);
+    set_alpha(params.scale_bmm2, 1.f, DATA_TYPE_FP16);
+    params.dqkv_ptr = dqkv.data_ptr();
+
+    launch(params, num_chunks, stream);
+
+    //SPLIT-K reduction of num_chunks dK, dV parts
+
+    // The equivalent of the following Pytorch code:
+    // using namespace torch::indexing;
+    // at::Tensor view_out = dqkv.index({Slice(), Slice(1, None, None)});
+    // torch::sum_out(view_out, dkv, 1);
+
+    const int hidden_size = num_heads * head_size;
+    fmha_run_noloop_reduce(
+        dqkv.data_ptr(), dkv.data_ptr(), cu_seqlens.data_ptr<int>(), hidden_size, batch_size, total, num_chunks, stream);
+
+    return { dqkv, softmax, dkv };
+}
+
 PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
     m.doc() = "Fused Multi-head Self-attention for BERT";  
     m.def("fwd", &mha_fwd, "Forward pass");
     m.def("bwd", &mha_bwd, "Backward pass");
+    m.def("fwd_nl", &mha_fwd_nl, "Forward pass (small-batch)");
+    m.def("bwd_nl", &mha_bwd_nl, "Backward pass (small-batch)");
 }

apex/contrib/csrc/fmha/src/fmha.h

@@ -35,6 +35,12 @@
 
 #include <fmha_utils.h>
 
+
+constexpr int TOTAL_DIM = 0;
+constexpr int THREE_DIM = 1;
+constexpr int H_DIM = 2;
+constexpr int D_DIM = 3;
+
 ////////////////////////////////////////////////////////////////////////////////////////////////////
 
 struct Qkv_params {
@@ -43,6 +49,9 @@ struct Qkv_params {
 
     // The stride between rows of the Q, K and V matrices.
     size_t qkv_stride_in_bytes;
+
+    // The number of heads.
+    int h;
 };
 
 ////////////////////////////////////////////////////////////////////////////////////////////////////
@@ -52,6 +61,9 @@ struct Fused_multihead_attention_fprop_params : public Qkv_params {
     // The dQKV matrices.
     void *dqkv_ptr;
 
+    // Temporary for dKV.
+    void *dkv_ptr;
+
     // The O matrix (output).
     void *o_ptr;
 
@@ -64,7 +76,7 @@ struct Fused_multihead_attention_fprop_params : public Qkv_params {
     int64_t s_stride_in_bytes;
 
     // The dimensions.
-    int b, h, s, d;
+    int b, s, d;
 
     // The scaling factors for the kernel.
     uint32_t scale_bmm1, scale_softmax, scale_bmm2;
@@ -72,9 +84,6 @@ struct Fused_multihead_attention_fprop_params : public Qkv_params {
     // array of length b+1 holding starting offset of each sequence.
     int *cu_seqlens;
 
-    // array of length b holding the actual sequence lenghts.
-    int *seqlens;
-
     // The dropout probability (probability of keeping an activation).
     float p_dropout;
 
@@ -90,3 +99,27 @@ struct Fused_multihead_attention_fprop_params : public Qkv_params {
 
 ////////////////////////////////////////////////////////////////////////////////////////////////////
 
+void run_fmha_fp16_128_64_sm80(const Fused_multihead_attention_fprop_params &params, bool is_training, cudaStream_t stream);
+void run_fmha_fp16_256_64_sm80(const Fused_multihead_attention_fprop_params &params, bool is_training, cudaStream_t stream);
+void run_fmha_fp16_384_64_sm80(const Fused_multihead_attention_fprop_params &params, bool is_training, cudaStream_t stream);
+void run_fmha_fp16_512_64_sm80(const Fused_multihead_attention_fprop_params &params, bool is_training, cudaStream_t stream);
+
+void run_fmha_dgrad_fp16_128_64_sm80(const Fused_multihead_attention_fprop_params &params, cudaStream_t stream);
+void run_fmha_dgrad_fp16_256_64_sm80(const Fused_multihead_attention_fprop_params &params, cudaStream_t stream);
+void run_fmha_dgrad_fp16_384_64_sm80(const Fused_multihead_attention_fprop_params &params, cudaStream_t stream);
+void run_fmha_dgrad_fp16_512_64_sm80(const Fused_multihead_attention_fprop_params &params, cudaStream_t stream);
+
+void run_fmha_fp16_512_64_sm80_nl(const Fused_multihead_attention_fprop_params &params, const bool is_training, const int num_chunks, cudaStream_t stream); 
+
+void run_fmha_dgrad_fp16_512_64_sm80_nl(const Fused_multihead_attention_fprop_params &params, const int num_chunks, cudaStream_t stream);
+
+void fmha_run_noloop_reduce(void *out,
+                            const void *in,
+                            const int *cu_seqlens,
+                            const int hidden_size,
+                            const int batch_size,
+                            const int total,
+                            const int num_chunks,
+                            cudaStream_t stream);
+
+

apex/contrib/csrc/fmha/src/fmha/gmem_tile.h

@@ -39,7 +39,9 @@ template<
     // The number of rows of Q, K or V loaded by this tile.
     int ROWS,
     // The number of columns.
-    int COLS
+    int COLS,
+    // The number of matrics.
+    int NUM_MATS = 3
 >
 struct Gmem_tile_qkv {
 
@@ -74,7 +76,7 @@ struct Gmem_tile_qkv {
         // The row offset in the batched GEMM. For each seq element, we store QKV in that order.
         int64_t row_offset = (int64_t)row * params.qkv_stride_in_bytes;
         // Add the block index.
-        row_offset += (int64_t)((binfo.sum_s * 3 + qkv_offset) * binfo.h + binfo.bidh) * BYTES_PER_ROW;
+        row_offset += (int64_t)((binfo.sum_s * NUM_MATS + qkv_offset) * binfo.h + binfo.bidh) * BYTES_PER_ROW;
 
         // Assemble the final pointer.
         qkv_ptr_ += row_offset + col * BYTES_PER_LDG;

apex/contrib/csrc/fmha/src/fmha/smem_tile.h

@@ -1217,7 +1217,8 @@ struct Smem_tile_mma_epilogue : public Base {
     enum { WARPS_M = Base::WARPS_M };
     enum { WARPS_N = Base::WARPS_N };
     static_assert((WARPS_M == 4 || WARPS_N == 8) || WARPS_N == 1);
-    using Fragment = typename Base::Fragment;
+    
+    using Acc = fmha::Fragment_accumulator;
 
     inline __device__ Smem_tile_mma_epilogue(char *smem, int tidx) : Base(smem, tidx) {
         const int read_row = tidx / THREADS_PER_ROW;
@@ -1233,6 +1234,40 @@ struct Smem_tile_mma_epilogue : public Base {
         }
     }
 
+    template<int M, int N>
+    inline __device__ void store(const Acc (&acc)[M][N]){
+        #pragma unroll
+        for( int mi = 0; mi < M; mi++ ) {
+            #pragma unroll
+            for( int ni = 0; ni < N; ni++ ) {
+                // 1st row - 4 elements per row.
+                float tmp00 = acc[mi][ni].elt(0);
+                float tmp01 = acc[mi][ni].elt(1);
+                float tmp02 = acc[mi][ni].elt(4);
+                float tmp03 = acc[mi][ni].elt(5);
+                // 2nd row - 4 elements per row.
+                float tmp10 = acc[mi][ni].elt(2);
+                float tmp11 = acc[mi][ni].elt(3);
+                float tmp12 = acc[mi][ni].elt(6);
+                float tmp13 = acc[mi][ni].elt(7);
+
+                uint32_t x = fmha::float2_to_half2(tmp00, tmp01);
+                uint32_t y = fmha::float2_to_half2(tmp02, tmp03);
+                uint32_t z = fmha::float2_to_half2(tmp10, tmp11);
+                uint32_t w = fmha::float2_to_half2(tmp12, tmp13);
+     
+                size_t offset = (this->write_offset_ ^ (ni * 32)) + mi * WARPS_M * 16 * BYTES_PER_ROW;
+                fmha::sts(this->smem_ + offset + 0 * BYTES_PER_ROW, x);
+                fmha::sts(this->smem_ + offset + 8 * BYTES_PER_ROW, z);
+                offset ^= 4 * Base::BYTES_PER_STS;
+                fmha::sts(this->smem_ + offset + 0 * BYTES_PER_ROW, y);
+                fmha::sts(this->smem_ + offset + 8 * BYTES_PER_ROW, w);
+            }
+        }
+    }
+
+
+
     template<int M, int N>
     inline __device__ void store(const uint4 (&regs)[M][N]) {
         for( int mi = 0; mi < M; mi++ ) {

apex/contrib/csrc/fmha/src/fmha_dgrad_fp16_512_64_kernel.sm80.cu

@@ -27,6 +27,7 @@
 
 #include "fmha.h"
 #include "fmha_dgrad_kernel_1xN_reload.h"
+#include "fmha_dgrad_kernel_1xN_reload_nl.h"
 
 using Kernel_traits = FMHA_kernel_traits< 512, 64, 16, 1, 8, 0x08u>;
 
@@ -35,6 +36,13 @@ extern "C" __global__ void fmha_dgrad_fp16_512_64_sm80_kernel(Fused_multihead_at
     fmha::compute_dq_dk_1xN<Kernel_traits>(params);
 }
 
+template<int CHUNKS>
+__global__
+void fmha_dgrad_fp16_512_64_sm80_nl_kernel(Fused_multihead_attention_fprop_params params){
+    fmha::compute_dv_1xN_nl<CHUNKS, Kernel_traits>(params);
+    fmha::compute_dq_dk_1xN_nl<CHUNKS, Kernel_traits>(params);
+}
+
 void run_fmha_dgrad_fp16_512_64_sm80(const Fused_multihead_attention_fprop_params &params, cudaStream_t stream) {
 
     constexpr int smem_size_softmax = Kernel_traits::Cta_tile_p::M * Kernel_traits::Cta_tile_p::WARPS_N * sizeof(float);
@@ -58,3 +66,40 @@ void run_fmha_dgrad_fp16_512_64_sm80(const Fused_multihead_attention_fprop_param
     dim3 grid(params.h, params.b);
     fmha_dgrad_fp16_512_64_sm80_kernel<<<grid, Kernel_traits::THREADS, smem_size, stream>>>(params);
 }
+
+void run_fmha_dgrad_fp16_512_64_sm80_nl(const Fused_multihead_attention_fprop_params &params, const int num_chunks, cudaStream_t stream) {
+
+    constexpr int smem_size_softmax = Kernel_traits::Cta_tile_p::M * Kernel_traits::Cta_tile_p::WARPS_N * sizeof(float);
+    constexpr int smem_size_q = Kernel_traits::Smem_tile_q::BYTES_PER_TILE;
+    constexpr int smem_size_v = Kernel_traits::Smem_tile_v::BYTES_PER_TILE;
+    constexpr int smem_size_o = Kernel_traits::Smem_tile_o::BYTES_PER_TILE;
+
+    using Smem_tile_s = fmha::Smem_tile_mma_transposed<Kernel_traits::Cta_tile_p>;
+    constexpr int smem_size_s = Smem_tile_s::BYTES_PER_TILE;
+    static_assert(smem_size_s == 16 * 512 * 2);
+    static_assert(smem_size_o == 16 * 64 * 4 * Kernel_traits::Cta_tile_p::WARPS_N);
+
+    constexpr int smem_size_dv = smem_size_s + 2 * smem_size_q + smem_size_v + smem_size_softmax;
+    constexpr int smem_size_dq_dk = smem_size_s + smem_size_o + smem_size_q + smem_size_v;
+    constexpr int smem_size = std::max(smem_size_dv, smem_size_dq_dk);
+
+    auto kernel = fmha_dgrad_fp16_512_64_sm80_nl_kernel<2>;
+       
+    if( num_chunks == 2 ) {
+        kernel = fmha_dgrad_fp16_512_64_sm80_nl_kernel<2>;
+    }else if( num_chunks == 3 ) {
+        kernel = fmha_dgrad_fp16_512_64_sm80_nl_kernel<3>;
+    } else {
+        assert(false && "Unsupperted number of chunks");
+    }
+
+    if( smem_size >= 48 * 1024 ) {
+        FMHA_CHECK_CUDA(cudaFuncSetAttribute(kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, smem_size));
+    }
+
+    dim3 grid(params.h, params.b, num_chunks);
+
+    kernel<<<grid, Kernel_traits::THREADS, smem_size, stream>>>(params);
+
+    FMHA_CHECK_CUDA(cudaPeekAtLastError());
+}

apex/contrib/csrc/fmha/src/fmha_dgrad_kernel_1xN_reload.h

@@ -156,8 +156,10 @@ inline __device__ void compute_dv_1xN(const Params &params) {
     fmha::Fragment_accumulator acc_dv[Mma_tile_dv::MMAS_M][Mma_tile_dv::MMAS_N];
     fmha::Clear_accumulator<fmha::Accumulator_type, Cta_tile_dv::WARPS_K>::apply(acc_dv);
 
+    enum { STEPS = Cta_tile_p::N / Cta_tile_p::M };
     // Load over the entire sequence length.
-    for( int loop = 0, outer = 0; loop < Cta_tile_p::N; loop += Cta_tile_p::M, outer++ ) {
+    for( int l = 0; l < STEPS; l++ ) {
+        const int loop = l * Cta_tile_p::M;
         if( loop >= binfo.actual_seqlen )
             break;
 
@@ -185,6 +187,13 @@ inline __device__ void compute_dv_1xN(const Params &params) {
             int ki = Mma_tile_p::MMAS_K;
             fmha::gemm(acc_p, frag_q[(ki - 1) & 1], frag_k[(ki - 1) & 1]);
         }
+        // Trigger the load for the next Q values. We're using double buffering, so reading qt is safe
+        if( l < STEPS - 1) {
+            smem_q.move_to_next_write_buffer();
+            gmem_q.move();
+            gmem_q.load(smem_q);
+        }
+
 
         // Convert from the accumulator type to FP32 for Softmax.
         softmax.unpack(acc_p);
@@ -203,8 +212,6 @@ inline __device__ void compute_dv_1xN(const Params &params) {
             }
         }
 
-        float d_s[2 * M][4 * N];
-
         #pragma unroll
         for( int mi = 0; mi < M; mi++ ) {
             #pragma unroll
@@ -213,10 +220,11 @@ inline __device__ void compute_dv_1xN(const Params &params) {
                 for( int ni = 0; ni < N; ni++ ) {
                     #pragma unroll
                     for( int jj = 0; jj < 4; jj++ ) {
-                        const float s_dmask = s_mat[2 * mi + ii][4 * ni + jj];
+                        float & s_dmask = s_mat[2 * mi + ii][4 * ni + jj];
                         const bool drop = reinterpret_cast<const uint32_t &>(s_dmask) & 0x80000000;
-                        d_s[2 * mi + ii][4 * ni + jj] = drop ? 0.f : softmax.elt_[2 * mi + ii][4 * ni + jj] * params.rp_dropout;
-                        softmax.elt_[2 * mi + ii][4 * ni + jj] = d_s[2 * mi + ii][4 * ni + jj] * fabsf(s_dmask);
+                        const float d_s = drop ? 0.f : softmax.elt_[2 * mi + ii][4 * ni + jj] * params.rp_dropout;
+                        s_dmask = fabsf(s_dmask);
+                        softmax.elt_[2 * mi + ii][4 * ni + jj] = d_s * fabsf(s_dmask);
                     }
                 }
             }
@@ -225,6 +233,7 @@ inline __device__ void compute_dv_1xN(const Params &params) {
         float p_sum[2 * M];
         softmax.template reduce<fmha::Sum_>(p_sum);
 
+        const float scalef = reinterpret_cast<const float &>(params.scale_softmax);
         #pragma unroll
         for( int mi = 0; mi < M; mi++ ) {
             #pragma unroll
@@ -233,20 +242,12 @@ inline __device__ void compute_dv_1xN(const Params &params) {
                 for( int ni = 0; ni < N; ni++ ) {
                     #pragma unroll
                     for( int jj = 0; jj < 4; jj++ ) {
-                        const float scalef = reinterpret_cast<const float &>(params.scale_softmax);
-                        softmax.elt_[2 * mi + ii][4 * ni + jj] = (d_s[2 * mi + ii][4 * ni + jj] - p_sum[2 * mi + ii]) *
-                                                                 fabsf(s_mat[2 * mi + ii][4 * ni + jj]) * scalef;
+                        softmax.elt_[2 * mi + ii][4 * ni + jj] -= p_sum[2 * mi + ii] * (s_mat[2 * mi + ii][4 * ni + jj]) ;
+                        softmax.elt_[2 * mi + ii][4 * ni + jj] *= scalef;
                     }
                 }
             }
         }
-        // Trigger the load for the next Q values. We're using double buffering, so reading qt is safe
-        if( loop + Cta_tile_p::M < Cta_tile_p::N ) {
-            smem_q.move_to_next_write_buffer();
-            gmem_q.move();
-            gmem_q.load(smem_q);
-        }
-
         typename Smem_tile_st::Fragment frag_s[Mma_tile_dv::MMAS_K][Mma_tile_dv::MMAS_M];
         smem_s.load(frag_s);
         for( int ki = 0; ki < Mma_tile_dv::MMAS_K; ki++ ) {
@@ -275,7 +276,7 @@ inline __device__ void compute_dv_1xN(const Params &params) {
             fmha::gemm(acc_dv, frag_s[(ki - 1)], frag_qt[(ki - 1) & 1]);
         }
         // Commit the values for Q into shared memory.
-        if( loop + Cta_tile_p::M < Cta_tile_p::N ) {
+        if(l < STEPS - 1) {
             gmem_q.commit(smem_q);
         }
 
@@ -295,36 +296,15 @@ inline __device__ void compute_dv_1xN(const Params &params) {
 
     // Epilogue swizzle for dV
     Smem_tile_dv smem_dv(&smem_[Kernel_traits::Smem_tile_q::BYTES_PER_TILE], tidx);
-    uint4 dv[Mma_tile_dv::MMAS_M][Mma_tile_dv::MMAS_N];
-    #pragma unroll
-    for( int mi = 0; mi < Mma_tile_dv::MMAS_M; ++mi ) {
-        #pragma unroll
-        for( int ni = 0; ni < Mma_tile_dv::MMAS_N; ++ni ) {
-            // 1st row - 4 elements per row.
-            float tmp00 = acc_dv[mi][ni].elt(0);
-            float tmp01 = acc_dv[mi][ni].elt(1);
-            float tmp02 = acc_dv[mi][ni].elt(4);
-            float tmp03 = acc_dv[mi][ni].elt(5);
-            // 2nd row - 4 elements per row.
-            float tmp10 = acc_dv[mi][ni].elt(2);
-            float tmp11 = acc_dv[mi][ni].elt(3);
-            float tmp12 = acc_dv[mi][ni].elt(6);
-            float tmp13 = acc_dv[mi][ni].elt(7);
-
-            dv[mi][ni].x = fmha::float2_to_half2(tmp00, tmp01);
-            dv[mi][ni].y = fmha::float2_to_half2(tmp02, tmp03);
-            dv[mi][ni].z = fmha::float2_to_half2(tmp10, tmp11);
-            dv[mi][ni].w = fmha::float2_to_half2(tmp12, tmp13);
-        }
-    }
+    smem_dv.store(acc_dv);
 
-    smem_dv.store(dv);
     __syncthreads();
     uint4 dv_out[Smem_tile_dv::NUM_LDS];
     smem_dv.load(dv_out);
     Qkv_params dv_params;
     dv_params.qkv_ptr = params.dqkv_ptr;
     dv_params.qkv_stride_in_bytes = params.qkv_stride_in_bytes;
+    dv_params.h = params.h;
     Gmem_tile_dv gmem_dv(dv_params, 2, binfo, tidx);
     gmem_dv.store(dv_out);
 }
@@ -447,13 +427,15 @@ inline __device__ void compute_dq_dk_1xN(const Params &params) {
     enum { BITS_PER_ELT_S = sizeof(fmha::A_type) * 8 };
 
     enum { THREADS_PER_ROW = 32 };
+    enum { STEPS = Cta_tile_p::N / Cta_tile_p::M };
 
     // Declare the accumulators for the 2nd gemm.
     fmha::Fragment_accumulator acc_dk[Mma_tile_dk::MMAS_M][Mma_tile_dk::MMAS_N];
     fmha::Clear_accumulator<fmha::Accumulator_type, Cta_tile_dk::WARPS_K>::apply(acc_dk);
 
     // Load over the entire sequence length.
-    for( int loop = 0, outer = 0; loop < Cta_tile_p::N; loop += Cta_tile_p::M, outer++ ) {
+    for( int l=0;l<STEPS;l++) {
+        const int loop = l * Cta_tile_p::M;
         if( loop >= binfo.actual_seqlen )
             break;
 
@@ -492,7 +474,7 @@ inline __device__ void compute_dq_dk_1xN(const Params &params) {
 
         // Store dP to smem for transpose
         smem_s.store(s_regs);
-        if( loop + Cta_tile_p::M < Cta_tile_p::N ) {
+        if(l < STEPS - 1) {
             // Load next part of S
             gmem_s.load(s_regs, mask);
             gmem_s.move();
@@ -544,7 +526,7 @@ inline __device__ void compute_dq_dk_1xN(const Params &params) {
         }
 
         // Commit the values for Q into shared memory.
-        if( loop + Cta_tile_p::M < Cta_tile_p::N ) {
+        if( l < STEPS - 1) {
             gmem_q.commit(smem_q);
         }
 
@@ -559,37 +541,14 @@ inline __device__ void compute_dq_dk_1xN(const Params &params) {
 
     // Epilogue swizzle for dK
     Smem_tile_dk smem_dk(&smem_[0], tidx);
-    uint4 dk[Mma_tile_dk::MMAS_M][Mma_tile_dk::MMAS_N];
-
-    #pragma unroll
-    for( int mi = 0; mi < Mma_tile_dk::MMAS_M; ++mi ) {
-        #pragma unroll
-        for( int ni = 0; ni < Mma_tile_dk::MMAS_N; ++ni ) {
-            // 1st row - 4 elements per row.
-            float tmp00 = acc_dk[mi][ni].elt(0);
-            float tmp01 = acc_dk[mi][ni].elt(1);
-            float tmp02 = acc_dk[mi][ni].elt(4);
-            float tmp03 = acc_dk[mi][ni].elt(5);
-            // 2nd row - 4 elements per row.
-            float tmp10 = acc_dk[mi][ni].elt(2);
-            float tmp11 = acc_dk[mi][ni].elt(3);
-            float tmp12 = acc_dk[mi][ni].elt(6);
-            float tmp13 = acc_dk[mi][ni].elt(7);
-
-            dk[mi][ni].x = fmha::float2_to_half2(tmp00, tmp01);
-            dk[mi][ni].y = fmha::float2_to_half2(tmp02, tmp03);
-            dk[mi][ni].z = fmha::float2_to_half2(tmp10, tmp11);
-            dk[mi][ni].w = fmha::float2_to_half2(tmp12, tmp13);
-        }
-    }
-
-    smem_dk.store(dk);
+    smem_dk.store(acc_dk);
     __syncthreads();
     uint4 dk_out[Smem_tile_dk::NUM_LDS];
     smem_dk.load(dk_out);
     Qkv_params dk_params;
     dk_params.qkv_ptr = params.dqkv_ptr;
     dk_params.qkv_stride_in_bytes = params.qkv_stride_in_bytes;
+    dk_params.h = params.h;
     Gmem_tile_dk gmem_dk(dk_params, 1, binfo, tidx);
     gmem_dk.store(dk_out);
 }

apex/contrib/csrc/fmha/src/fmha_fprop_fp16_512_64_kernel.sm80.cu

@@ -27,6 +27,7 @@
 
 #include "fmha.h"
 #include "fmha_fprop_kernel_1xN.h"
+#include "fmha_fprop_kernel_1xN_nl.h"
 
 using Kernel_traits = FMHA_kernel_traits< 512, 64, 16, 1, 8, 0x08u>;
 
@@ -38,6 +39,17 @@ extern "C" __global__ void fmha_fprop_fp16_512_64_sm80_predict_kernel(Fused_mult
     fmha::device_1xN<Kernel_traits, false>(params);
 }
 
+template<int CHUNKS>
+__global__ void fmha_fprop_fp16_512_64_sm80_train_nl_kernel(Fused_multihead_attention_fprop_params params) {
+    fmha::device_1xN_nl<CHUNKS,Kernel_traits, true>(params);
+}
+
+template<int CHUNKS>
+__global__ void fmha_fprop_fp16_512_64_sm80_predict_nl_kernel(Fused_multihead_attention_fprop_params params) {
+    fmha::device_1xN_nl<CHUNKS, Kernel_traits, false>(params);
+}
+
+
 void run_fmha_fp16_512_64_sm80(const Fused_multihead_attention_fprop_params &params, bool is_training, cudaStream_t stream) {
 
     auto kernel = is_training ? &fmha_fprop_fp16_512_64_sm80_train_kernel : &fmha_fprop_fp16_512_64_sm80_predict_kernel;
@@ -54,3 +66,33 @@ void run_fmha_fp16_512_64_sm80(const Fused_multihead_attention_fprop_params &par
     dim3 grid(params.h, params.b);
     kernel<<<grid, Kernel_traits::THREADS, smem_size, stream>>>(params);
 }
+
+void run_fmha_fp16_512_64_sm80_nl(const Fused_multihead_attention_fprop_params &params, const bool is_training, const int num_chunks, cudaStream_t stream) {
+
+    auto kernel = is_training ? &fmha_fprop_fp16_512_64_sm80_train_nl_kernel<2> : &fmha_fprop_fp16_512_64_sm80_predict_nl_kernel<2>;
+    if( num_chunks == 2 ) {
+        kernel = is_training ? &fmha_fprop_fp16_512_64_sm80_train_nl_kernel<2>
+                             : &fmha_fprop_fp16_512_64_sm80_predict_nl_kernel<2>;
+    } else if( num_chunks == 3 ) {
+        kernel = is_training ? &fmha_fprop_fp16_512_64_sm80_train_nl_kernel<3>
+                             : &fmha_fprop_fp16_512_64_sm80_predict_nl_kernel<3>;
+    } else if( num_chunks == 4 ) {
+        kernel = is_training ? &fmha_fprop_fp16_512_64_sm80_train_nl_kernel<4>
+                             : &fmha_fprop_fp16_512_64_sm80_predict_nl_kernel<4>;
+    } else {
+        assert(false && "Unsupported num_chunks");
+    }
+
+    constexpr int smem_size_softmax = Kernel_traits::Cta_tile_p::M * Kernel_traits::Cta_tile_p::WARPS_N * sizeof(float);
+    constexpr int smem_size_q = Kernel_traits::Smem_tile_q::BYTES_PER_TILE;
+    constexpr int smem_size_v = Kernel_traits::Smem_tile_v::BYTES_PER_TILE;
+    constexpr int smem_size_o = Kernel_traits::Smem_tile_o::BYTES_PER_TILE;
+
+    constexpr int smem_size = smem_size_q + std::max(smem_size_v, smem_size_o + smem_size_softmax);
+    if( smem_size >= 48 * 1024 ) {
+        FMHA_CHECK_CUDA(cudaFuncSetAttribute(kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, smem_size));
+    }
+
+    dim3 grid(params.h, params.b, num_chunks);
+    kernel<<<grid, Kernel_traits::THREADS, smem_size, stream>>>(params);
+}

apex/contrib/csrc/fmha/src/fmha_fprop_kernel_1xN.h

@@ -174,9 +174,11 @@ template <typename Kernel_traits, bool Is_training, typename Params> inline __de
     Softmax softmax(params, &smem_[Smem_tile_q::BYTES_PER_TILE + Smem_tile_o::BYTES_PER_TILE], bidb, tidx);
 
     enum { THREADS_PER_ROW = 32 };
+    enum { STEPS = Cta_tile_p::N / Cta_tile_p::M };
 
     // Load over the entire sequence length.
-    for( int loop = 0, outer = 0; loop < Cta_tile_p::N; loop += Cta_tile_p::M, outer++ ) {
+    for( int l = 0; l < STEPS; l++ ) {
+        const int loop = l * Cta_tile_p::M;
         if( loop >= binfo.actual_seqlen )
             break;
 
@@ -200,12 +202,8 @@ template <typename Kernel_traits, bool Is_training, typename Params> inline __de
             fmha::gemm(acc_p, frag_q[(ki - 1) & 1], frag_k[(ki - 1)]);
         }
 
-        // Store the P matrix.
-#if defined(STORE_P)
-        gmem_p.store(acc_p);
-#endif
         // Load the mask for that iteration.
-        mask.load(outer);
+        mask.load(l);
 
         // Convert from the accumulator type to FP32 for Softmax.
         softmax.unpack(acc_p);
@@ -213,7 +211,7 @@ template <typename Kernel_traits, bool Is_training, typename Params> inline __de
         // Apply the mask.
         softmax.apply_mask(mask);
 
-        if( Kernel_traits::SHARE_SMEM_FOR_K_AND_V && loop == 0 ) {
+        if( Kernel_traits::SHARE_SMEM_FOR_K_AND_V && l == 0 ) {
             // if we share K and V, it could be that V was not fully read yet but we write into smem for reduction
             __syncthreads();
         }
@@ -261,7 +259,7 @@ template <typename Kernel_traits, bool Is_training, typename Params> inline __de
         }
 
         // Trigger the load for the next Q values.
-        if( loop + Cta_tile_p::M < Cta_tile_p::N ) {
+        if(l < STEPS - 1) {
             smem_q.move_to_next_write_buffer();
             gmem_q.move();
             gmem_q.load(smem_q);
@@ -320,7 +318,7 @@ template <typename Kernel_traits, bool Is_training, typename Params> inline __de
         gmem_o.move();
 
         // Commit the values for Q into shared memory.
-        if( loop + Cta_tile_p::M < Cta_tile_p::N ) {
+        if(l < STEPS - 1) {
             gmem_q.commit(smem_q);
         }
 

apex/contrib/csrc/fmha/src/fmha_fprop_kernel_1xN_nl.h

+/******************************************************************************
+ * Copyright (c) 2011-2021, NVIDIA CORPORATION.  All rights reserved.
+ * 
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are met:
+ *     * Redistributions of source code must retain the above copyright
+ *       notice, this list of conditions and the following disclaimer.
+ *     * Redistributions in binary form must reproduce the above copyright
+ *       notice, this list of conditions and the following disclaimer in the
+ *       documentation and/or other materials provided with the distribution.
+ *     * Neither the name of the NVIDIA CORPORATION nor the
+ *       names of its contributors may be used to endorse or promote products
+ *       derived from this software without specific prior written permission.
+ * 
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
+ * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+ * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *
+ ******************************************************************************/
+
+#pragma once
+
+#include "fmha.h"
+#include <fmha/kernel_traits.h>
+#include <fmha/gemm.h>
+
+
+namespace fmha {
+
+////////////////////////////////////////////////////////////////////////////////////////////////////
+
+template<int CHUNKS, typename Kernel_traits, bool Is_training, typename Params>
+inline __device__ void device_1xN_nl(const Params &params) {
+
+    // The description of the CTA tile for the 1st batched GEMM.
+    using Cta_tile_p = typename Kernel_traits::Cta_tile_p;
+    // The description of the CTA tile for the 2nd batched GEMM.
+    using Cta_tile_o = typename Kernel_traits::Cta_tile_o;
+
+    // The MMA tile for the 1st GEMM.
+    using Mma_tile_p = fmha::Hmma_tile<Cta_tile_p>;
+    // The MMA tile for the 2nd GEMM.
+    using Mma_tile_o = fmha::Hmma_tile<Cta_tile_o>;
+
+    // The global memory tile to load Q.
+    using Gmem_tile_q = typename Kernel_traits::Gmem_tile_q;
+    // The shared memory tile to swizzle Q.
+    using Smem_tile_q = typename Kernel_traits::Smem_tile_q;
+
+    // The global memory tile to load K.
+    using Gmem_tile_k = typename Kernel_traits::Gmem_tile_k;
+    // The shared memory tile to swizzle K.
+    using Smem_tile_k = typename Kernel_traits::Smem_tile_k;
+
+    // The global memory tile to load V.
+    using Gmem_tile_v = typename Kernel_traits::Gmem_tile_v;
+    // The shared memory tile to swizzle V.
+    using Smem_tile_v = typename Kernel_traits::Smem_tile_v;
+
+    // The global memory tile to store O.
+    using Gmem_tile_o = typename Kernel_traits::Gmem_tile_o;
+    // The shared memory tile to swizzle O.
+    using Smem_tile_o = typename Kernel_traits::Smem_tile_o;
+
+    // The global memory tile to store S/D.
+    using Gmem_tile_s = typename Kernel_traits::Gmem_tile_s;
+
+    using Noloop = Noloop_traits<CHUNKS, Cta_tile_p>;
+
+    // Shared memory.
+    extern __shared__ char smem_[];
+
+    const int bidc = blockIdx.z;
+    // The block index for the batch.
+    const int bidb = blockIdx.y;
+    // The block index for the head.
+    const int bidh = blockIdx.x;
+    // The thread index.
+    const int tidx = threadIdx.x;
+
+    Noloop nl_traits(bidc);
+
+    const BlockInfoPadded<Kernel_traits::THREADS> binfo(params, bidb, bidh, tidx);
+    if( binfo.stop_early() )
+        return;
+
+    auto seeds = at::cuda::philox::unpack(params.philox_args);
+
+    Philox ph(std::get<0>(seeds), binfo.tidx_global, std::get<1>(seeds));
+
+    fmha::Mask<Cta_tile_p> mask(params, binfo, tidx);
+
+    // Allocate the global memory tile loader for Q.
+    Gmem_tile_q gmem_q(params, 0, binfo, tidx);
+    // Allocate the shared memory tile loader for Q.
+    Smem_tile_q smem_q(&smem_[0], tidx);
+
+    // Allocate the global memory tile loader for K.
+    Gmem_tile_k gmem_k(params, 1, binfo, tidx);
+    // Allocate the shared memory tile loader for K.
+    Smem_tile_k smem_k(&smem_[Smem_tile_q::BYTES_PER_TILE], tidx);
+
+    // Allocate the global memory tile loader for V.
+    Gmem_tile_v gmem_v(params, 2, binfo, tidx);
+    // The base pointer of smem_v;
+    char *smem_v_ = nullptr;
+    if( Kernel_traits::SHARE_SMEM_FOR_K_AND_V ) {
+        smem_v_ = &smem_[Smem_tile_q::BYTES_PER_TILE];
+    } else {
+        smem_v_ = &smem_[Smem_tile_q::BYTES_PER_TILE + Smem_tile_k::BYTES_PER_TILE];
+    }
+    // Allocate the shared memory tile loader for V. We use the same as K so be careful!!!
+    Smem_tile_v smem_v(smem_v_, tidx);
+
+    // Allocate the global memory tile loader for O.
+    Gmem_tile_o gmem_o(params, binfo, tidx);
+    // Allocate the shared memory tile loader for O. We use the same as K so be careful!!!
+    Smem_tile_o smem_o(&smem_[Smem_tile_q::BYTES_PER_TILE], tidx);
+
+    Gmem_tile_s gmem_s(params.s_ptr, params, tidx);
+
+    nl_traits.move_all(gmem_q, gmem_o, gmem_s);
+
+
+    // Trigger the loads for Q.
+    gmem_q.load(smem_q);
+    // Trigger the loads for K.
+    gmem_k.load(smem_k);
+    // Trigger the loads for K.
+    gmem_v.load(smem_v);
+
+
+    // Commit the data for Q and K to shared memory.
+    gmem_q.commit(smem_q);
+    gmem_k.commit(smem_k);
+
+    // Commit the data for V to shared memory.
+    if( !Kernel_traits::SHARE_SMEM_FOR_K_AND_V ) {
+        gmem_v.commit(smem_v);
+    }
+
+    // Make sure the data is in shared memory.
+    __syncthreads();
+
+    // Load the fragments for Q.
+    typename Smem_tile_q::Fragment frag_q[2][Mma_tile_p::MMAS_M];
+    smem_q.load(frag_q[0], 0);
+
+    // Load the fragments for K. We keep the data in registers during the entire kernel.
+    typename Smem_tile_k::Fragment frag_k[Mma_tile_p::MMAS_K][Mma_tile_p::MMAS_N];
+    #pragma unroll
+    for( int ki = 0; ki < Mma_tile_p::MMAS_K; ++ki ) {
+        smem_k.load(frag_k[ki], ki);
+    }
+
+    // Commit the data for V to shared memory if it has not been done already.
+    if( Kernel_traits::SHARE_SMEM_FOR_K_AND_V ) {
+        // Make sure we are done loading the fragments for K.
+        __syncthreads();
+
+        // Commit the data to shared memory for V.
+        gmem_v.commit(smem_v);
+
+        // Make sure the data is in shared memory.
+        __syncthreads();
+    }
+
+    // Load the fragments for V. We keep the data in registers during the entire kernel.
+    typename Smem_tile_v::Fragment frag_v[Mma_tile_o::MMAS_K][Mma_tile_o::MMAS_N];
+    #pragma unroll
+    for( int ki = 0; ki < Mma_tile_o::MMAS_K; ++ki ) {
+        smem_v.load(frag_v[ki], ki);
+    }
+
+    enum { BITS_PER_ELT_S = sizeof(fmha::A_type) * 8 };
+
+
+    // Create the object to do the softmax.
+    using Softmax = fmha::Softmax<Cta_tile_p, Kernel_traits>;
+    Softmax softmax(params, &smem_[Smem_tile_q::BYTES_PER_TILE + Smem_tile_o::BYTES_PER_TILE], bidb, tidx);
+
+    // The number of threads per row.
+    enum { THREADS_PER_ROW = 32 };
+
+    // Load over the entire sequence length.
+    for(int l = 0; l < nl_traits.num_steps_;l++) {
+
+        // Declare the accumulators for the 1st gemm.
+        fmha::Fragment_accumulator acc_p[Mma_tile_p::MMAS_M][Mma_tile_p::MMAS_N];
+        fmha::Clear_accumulator<typename fmha::Accumulator_type, Cta_tile_p::WARPS_K>::apply(acc_p);
+
+        // Do this part of P^T = (Q * K^T)^T.
+        #pragma unroll
+        for( int ki = 1; ki < Mma_tile_p::MMAS_K; ++ki ) {
+
+            // Trigger the load from shared memory for the next series of Q values.
+            smem_q.load(frag_q[ki & 1], ki);
+            // Do the math for the values already in registers.
+            fmha::gemm(acc_p, frag_q[(ki - 1) & 1], frag_k[(ki - 1)]);
+        }
+
+        // Do the final stage of math.
+        {
+            int ki = Mma_tile_p::MMAS_K;
+            fmha::gemm(acc_p, frag_q[(ki - 1) & 1], frag_k[(ki - 1)]);
+        }
+
+        // Trigger the load for the next Q values.
+        if( l < nl_traits.num_steps_- 1) {
+            smem_q.move_to_next_write_buffer();
+            gmem_q.move();
+            gmem_q.load(smem_q);
+        }
+
+
+
+        // Load the mask for that iteration.
+        mask.load(nl_traits.loop_offset_ + l);
+
+        // Convert from the accumulator type to FP32 for Softmax.
+        softmax.unpack(acc_p);
+
+        // Apply the mask.
+        softmax.apply_mask(mask);
+
+        if( Kernel_traits::SHARE_SMEM_FOR_K_AND_V && l == 0 ) {
+            // if we share K and V, it could be that V was not fully read yet but we write into smem for reduction
+            __syncthreads();
+        }
+
+        // Compute the max.
+        float p_max[Mma_tile_p::MMAS_M * 2];
+        softmax.template reduce<fmha::Max_>(p_max);
+
+        // Make sure we are done reading shared memory.
+        __syncthreads();
+
+        // Compute the exponential value.
+        softmax.apply_exp(p_max);
+
+        // Compute the sum.
+        float p_sum[Mma_tile_p::MMAS_M * 2];
+        softmax.template reduce<fmha::Sum_>(p_sum);
+
+        // Finalize softmax on the accumulators of P^T.
+        softmax.scale(p_sum);
+        if( Is_training ) {
+            auto encode_dropout = [](bool keep, float val) { return keep ? val : -val; };
+            #pragma unroll
+            for( int mi = 0; mi < Mma_tile_p::MMAS_M; mi++ ) {
+                #pragma unroll
+                for( int ii = 0; ii < 2; ii++ ) {
+                    #pragma unroll
+                    for( int ni = 0; ni < Mma_tile_p::MMAS_N; ni++ ) {
+                        float4 tmp = uniform4(ph());
+                        // We encode the dropout pattern in the sign bit of the non-negative softmax to distinguish from pre-existing zeros
+                        softmax.elt_[2 * mi + ii][4 * ni + 0] =
+                            encode_dropout(tmp.x <= params.p_dropout, softmax.elt_[2 * mi + ii][4 * ni + 0]);
+                        softmax.elt_[2 * mi + ii][4 * ni + 1] =
+                            encode_dropout(tmp.y <= params.p_dropout, softmax.elt_[2 * mi + ii][4 * ni + 1]);
+                        softmax.elt_[2 * mi + ii][4 * ni + 2] =
+                            encode_dropout(tmp.z <= params.p_dropout, softmax.elt_[2 * mi + ii][4 * ni + 2]);
+                        softmax.elt_[2 * mi + ii][4 * ni + 3] =
+                            encode_dropout(tmp.w <= params.p_dropout, softmax.elt_[2 * mi + ii][4 * ni + 3]);
+                    }
+                }
+            }
+            gmem_s.store(softmax.elt_, mask);
+            gmem_s.move();
+        }
+
+        using Frag_p = fmha::Fragment_a<fmha::Row>;
+        Frag_p frag_p[Mma_tile_o::MMAS_K][Mma_tile_o::MMAS_M];
+        softmax.pack(frag_p);
+        #pragma unroll
+        for( int ki = 0; ki < Mma_tile_o::MMAS_K; ki++ ) {
+            #pragma unroll
+            for( int mi = 0; mi < Mma_tile_o::MMAS_M; mi++ ) {
+                #pragma unroll
+                for( int ii = 0; ii < Frag_p::NUM_REGS; ii++ ) {
+                    //"Apply" the dropout.
+                    frag_p[ki][mi].reg(ii) = fmha::hmul2(frag_p[ki][mi].reg(ii), params.scale_dropout);
+                    frag_p[ki][mi].reg(ii) = fmha::hrelu2(frag_p[ki][mi].reg(ii));
+                }
+            }
+        }
+
+        // Declare the accumulators for the 1st gemm.
+        fmha::Fragment_accumulator acc_o[Mma_tile_o::MMAS_M][Mma_tile_o::MMAS_N];
+        fmha::Clear_accumulator<typename fmha::Accumulator_type, Cta_tile_o::WARPS_K>::apply(acc_o);
+
+        // Do this part of O = P^T * V^T.
+        #pragma unroll
+        for( int ki = 0; ki < Mma_tile_o::MMAS_K; ++ki ) {
+            fmha::gemm(acc_o, frag_p[ki], frag_v[ki]);
+        }
+
+        // Loop over MMAS_M.
+        #pragma unroll
+        for( int ii = 0; ii < Gmem_tile_o::LOOPS; ++ii ) {
+
+            // Swizzle the elements and do the final reduction.
+            smem_o.store(acc_o, ii);
+
+            // Make sure the data is in shared memory.
+            __syncthreads();
+
+            // Load from shared memory.
+            uint4 out[Gmem_tile_o::STGS_PER_LOOP];
+            smem_o.load(out);
+
+            // Make sure the data was read from shared memory.
+            if( ii < Gmem_tile_o::LOOPS - 1 ) {
+                __syncthreads();
+            }
+
+            // Output the values.
+            gmem_o.store(out, ii);
+        }
+
+        // Move to the next part of the output.
+        gmem_o.move();
+
+        // Commit the values for Q into shared memory.
+        if( l < nl_traits.num_steps_- 1) {
+            gmem_q.commit(smem_q);
+            __syncthreads();
+            smem_q.load(frag_q[0], 0);
+        }
+
+    }  // Outer loop over the sequence length.
+}
+
+////////////////////////////////////////////////////////////////////////////////////////////////////
+
+} // namespace fmha

apex/contrib/csrc/fmha/src/fmha_kernel.h

@@ -40,94 +40,130 @@ namespace fmha {
 
 ////////////////////////////////////////////////////////////////////////////////////////////////////
 
-template <int FMHA_VERSION> struct BlockInfo {};
-
-template <> struct BlockInfo<1> {
-
-    int actual_seqlen;
-    int bidx;
-    int sum_s;
-    int bidh;
-    int bidb;
+template<int THREADS_PER_CTA>
+struct BlockInfoPadded {
 
     template<typename Params>
-    __device__ BlockInfo( const Params &params,
-                          const int bidb,
-                          const int bidh,
-                          const int tidx )
-        : bidb( bidb ), bidh( bidh ) {
+    __device__ BlockInfoPadded(const Params &params,
+                               const int bidb,
+                               const int bidh,
+                               const int tidx)
+        : bidb(bidb), bidh(bidh), h(params.h) {
 
         // The block index.
-        sum_s = params.b * params.s;
-        actual_seqlen = params.s;
-        bidx = bidb * params.h + bidh;
+        sum_s = params.cu_seqlens[bidb];
+        actual_seqlen = params.cu_seqlens[bidb + 1] - sum_s;
+        bidx = sum_s * params.h + bidh;
+
+        tidx_global = (bidb * params.h + bidh) * THREADS_PER_CTA + tidx;
     }
 
     __device__ bool stop_early() const {
-        return false;
+        return actual_seqlen == 0;
     }
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <> struct BlockInfo<2> {
 
     int actual_seqlen;
     int bidx;
     int sum_s;
     int bidh;
     int bidb;
+    int tidx_global;
+    int h;
+};
 
-    template<typename Params>
-    __device__ BlockInfo( const Params &params,
-                          const int bidb,
-                          const int bidh,
-                          const int tidx )
-        : bidb( bidb ), bidh( bidh ) {
+////////////////////////////////////////////////////////////////////////////////////////////////////
 
-        // The block index.
-        sum_s = params.cu_seqlens[bidb];
-        actual_seqlen = params.cu_seqlens[bidb + 1] - sum_s;
-        bidx = sum_s * params.h + bidh;
+template<int CHUNKS, typename Cta_tile> 
+struct Noloop_traits{
+    // Interpretation of Cta_tile dims, i.e. Cta_tile_p:
+    enum{ STEP = Cta_tile::M };
+    enum{ SEQLEN = Cta_tile::N };
+
+    // The size of the subsequence this CTA is processing
+    enum { SUBSEQ = SEQLEN / CHUNKS };
+    static_assert(SUBSEQ * CHUNKS == SEQLEN);
+
+    // The number of steps to process the subsequence
+    enum { NUM_STEPS = SUBSEQ / STEP };
+    static_assert(NUM_STEPS  * Cta_tile::M == SUBSEQ);
+
+    inline __device__ Noloop_traits(const int bidc) 
+        : loop_offset_(NUM_STEPS * bidc)
+        , bidc_(bidc) {
     }
 
-    __device__ bool stop_early() const {
-        return actual_seqlen == 0;
+    template<typename ... Tiles> 
+    inline __device__ void move_all(Tiles & ... tiles) const {
+        using expand_type = int[];
+        for( int s = 0; s < loop_offset_; s++ ) {
+            expand_type{ (tiles.move(), 0)... };
+        }
+    }
+
+    inline __device__ int get_idx_dk() const {
+        //return bidc_;
+        return bidc_ * 2 + 0;
+    }
+
+    inline __device__ int get_idx_dv() const {
+        //return CHUNKS + bidc_;
+        return bidc_ * 2 + 1;
+    }
+
+    inline __device__ int offset_loop_count(const int l) {
+        // convert loop counter to position in the outer sequence
+        return (loop_offset_ + l) * STEP;
     }
+
+    const int loop_offset_;
+    const uint32_t bidc_;
+    const int num_steps_ = NUM_STEPS;
 };
 
 ////////////////////////////////////////////////////////////////////////////////////////////////////
 
-template<int THREADS_PER_CTA>
-struct BlockInfoPadded {
+template<typename Cta_tile> 
+struct Noloop_traits<3, Cta_tile>{
+    // Interpretation of Cta_tile dims, i.e. Cta_tile_p:
+    enum{ STEP = Cta_tile::M };
+    enum{ SEQLEN = Cta_tile::N };
 
-    template<typename Params>
-    __device__ BlockInfoPadded( const Params &params,
-                          const int bidb,
-                          const int bidh,
-                          const int tidx )
-        : bidb( bidb ), bidh( bidh ), h(params.h) {
+    static_assert(STEP == 16 && SEQLEN == 512);
 
-        // The block index.
-        sum_s = params.cu_seqlens[bidb];
-        actual_seqlen = params.seqlens[bidb];
-        bidx = sum_s * params.h + bidh;
+    inline __device__ Noloop_traits(const int bidc)
+        : bidc_(bidc)
+        , num_steps_(bidc < 2 ? 11 : 10) 
+        , loop_offset_(bidc * 11) {
+    }
 
-        tidx_global = (bidb * params.h + bidh) * THREADS_PER_CTA + tidx;
+    template<typename ... Tiles> 
+    inline __device__ void move_all(Tiles & ... tiles) const {
+        using expand_type = int[];
+        for( int s = 0; s < loop_offset_; s++ ) {
+            expand_type{ (tiles.move(), 0)... };
+        }
     }
 
-    __device__ bool stop_early() const {
-        return actual_seqlen == 0;
+    inline __device__ int get_idx_dk() const {
+        //return bidc_;
+        return bidc_ * 2 + 0;
     }
 
-    int actual_seqlen;
-    int bidx;
-    int sum_s;
-    int bidh;
-    int bidb;
-    int tidx_global;
-    int h;
+    inline __device__ int get_idx_dv() const {
+        //return CHUNKS + bidc_;
+        return bidc_ * 2 + 1;
+    }
+
+    inline __device__ int offset_loop_count(const int l) {
+        // convert loop counter to position in the outer sequence
+        return (loop_offset_ + l) * STEP;
+    }
+
+    const int loop_offset_;
+    const uint32_t bidc_;
+    const int  num_steps_;
 };
 
+////////////////////////////////////////////////////////////////////////////////////////////////////
 
 }  // namespace fmha

apex/contrib/csrc/fmha/src/fmha_noloop_reduce.cu

+/******************************************************************************
+ * Copyright (c) 2011-2021, NVIDIA CORPORATION.  All rights reserved.
+ * 
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are met:
+ *     * Redistributions of source code must retain the above copyright
+ *       notice, this list of conditions and the following disclaimer.
+ *     * Redistributions in binary form must reproduce the above copyright
+ *       notice, this list of conditions and the following disclaimer in the
+ *       documentation and/or other materials provided with the distribution.
+ *     * Neither the name of the NVIDIA CORPORATION nor the
+ *       names of its contributors may be used to endorse or promote products
+ *       derived from this software without specific prior written permission.
+ * 
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
+ * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+ * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *
+ ******************************************************************************/
+
+#include "fmha.h"
+
+inline __device__ float4 ldg128(const void *ptr) {
+    return *static_cast<const float4 *>(ptr);
+}
+
+inline __device__ void stg128(void *ptr, const float4 &data) {
+    *static_cast<float4 *>(ptr) = data;
+}
+
+template<typename T, int THREADS, int HIDDEN_SIZE, int CHUNKS>
+__global__ __launch_bounds__(THREADS) void fmha_noloop_reduce_kernel(void *__restrict__ out,
+                                                                     const void *__restrict__ in,
+                                                                     const int *__restrict__ cu_seqlens,
+                                                                     const int batch_size) {
+
+    enum { BYTES_PER_LDG = 16 };
+    enum { NUM_ELTS = BYTES_PER_LDG / sizeof(T) };
+
+    // One CTA hidden vector for K and V
+    enum { BYTES_PER_ROW = HIDDEN_SIZE * sizeof(T) * 2 };
+    // The stride in bytes in dQKV
+    enum { OUT_STRIDE_BYTES = 3 * HIDDEN_SIZE * sizeof(T) };
+    // The offset in bytes in dQKV to the dKV part for non-interleaved heads
+    enum { OUT_OFFSET_KV_BYTES = HIDDEN_SIZE * sizeof(T) };
+
+    static_assert(BYTES_PER_ROW == HIDDEN_SIZE * 2 * sizeof(T)); 
+
+    // Size in bytes of the input tile
+    enum { BYTES_PER_TILE = CHUNKS * BYTES_PER_ROW };
+
+    enum { BYTES_PER_CTA = THREADS * BYTES_PER_LDG };
+
+    enum { LDGS = BYTES_PER_ROW / BYTES_PER_CTA };
+    static_assert(BYTES_PER_CTA * LDGS == BYTES_PER_ROW);
+
+    union Vec_t {
+        float4 raw;
+        T elt[NUM_ELTS];
+    };
+
+    // ZERO-OUT invalid positions in dQKV
+    const int total = cu_seqlens[batch_size];
+    if(blockIdx.x >= total){
+        enum { BYTES_PER_QKV_ROW = 3 * HIDDEN_SIZE * sizeof(T) };
+        enum { STGS = BYTES_PER_QKV_ROW / BYTES_PER_LDG };
+
+        const float4 zeros = make_float4(0.f, 0.f, 0.f, 0.f);
+
+        char *base_ptr = static_cast<char *>(out) + blockIdx.x * OUT_STRIDE_BYTES;
+
+        for(int tidx = threadIdx.x; tidx < STGS; tidx += THREADS){
+            stg128(base_ptr + tidx * BYTES_PER_LDG, zeros);
+        }
+
+        return;
+    }
+
+    // SETUP
+    const int offset_in = blockIdx.x * BYTES_PER_TILE + threadIdx.x * BYTES_PER_LDG;
+    const char *ptr_in = static_cast<const char *>(in) + offset_in;
+
+    const int offset_out = blockIdx.x * OUT_STRIDE_BYTES + threadIdx.x * BYTES_PER_LDG;
+    char *ptr_out = static_cast<char *>(out) + OUT_OFFSET_KV_BYTES + offset_out;
+
+    // LOAD
+
+    Vec_t local_in[CHUNKS][LDGS];
+
+    #pragma unroll
+    for( int c = 0; c < CHUNKS; c++ ) {
+        #pragma unroll
+        for( int l = 0; l < LDGS; l++ ) {
+            int offset = c * BYTES_PER_ROW + l * BYTES_PER_CTA;
+            local_in[c][l].raw = ldg128(ptr_in + offset);
+        }
+    }
+
+    // UNPACK
+    float acc[LDGS][NUM_ELTS];
+
+    #pragma unroll
+    for( int l = 0; l < LDGS; l++ ) {
+        #pragma unroll
+        for( int e = 0; e < NUM_ELTS; e++ ) {
+            acc[l][e] = float(local_in[0][l].elt[e]);
+        }
+    }
+
+    // COMPUTE
+    #pragma unroll
+    for( int c = 1; c < CHUNKS; c++ ) {
+        #pragma unroll
+        for( int l = 0; l < LDGS; l++ ) {
+            #pragma unroll
+            for( int e = 0; e < NUM_ELTS; e++ ) {
+                acc[l][e] += float(local_in[c][l].elt[e]);
+            }
+        }
+    }
+
+    // PACK
+    Vec_t local_out[LDGS];
+
+    #pragma unroll
+    for( int l = 0; l < LDGS; l++ ) {
+        #pragma unroll
+        for( int e = 0; e < NUM_ELTS; e++ ) {
+            local_out[l].elt[e] = T(acc[l][e]);
+        }
+    }
+
+    // STORE
+    #pragma unroll
+    for( int l = 0; l < LDGS; l++ ) {
+        const int offset = l * BYTES_PER_CTA;
+        stg128(ptr_out + offset, local_out[l].raw);
+    }
+}
+
+void fmha_run_noloop_reduce(void *out,
+                            const void *in,
+                            const int *cu_seqlens,
+                            const int hidden_size,
+                            const int batch_size,
+                            const int total,
+                            const int num_chunks,
+                            cudaStream_t stream) {
+
+    const int blocks = total;
+
+    if(hidden_size == 1024){
+
+        constexpr int HIDDEN_SIZE = 1024;
+        constexpr int THREADS = 256;
+
+        if( num_chunks == 2 ) {
+            fmha_noloop_reduce_kernel<half, THREADS, HIDDEN_SIZE, 2><<<blocks, THREADS, 0, stream>>>(out, in, cu_seqlens, batch_size);
+        } else if( num_chunks == 3 ) {
+            fmha_noloop_reduce_kernel<half, THREADS, HIDDEN_SIZE, 3><<<blocks, THREADS, 0, stream>>>(out, in, cu_seqlens, batch_size);
+        } else {
+            assert(false && "Unsupported num_chunks");
+        }
+
+    }else{
+        assert(false && "Unsupported hidden_size");
+    }
+
+    FMHA_CHECK_CUDA(cudaPeekAtLastError());
+}

apex/contrib/fmha/fmha.py

@@ -32,11 +32,14 @@ import fmhalib as mha
 
 class FMHAFun(torch.autograd.Function):
     @staticmethod
-    def forward(ctx, qkv, cu_seqlens, seqlens, p_dropout, max_s, is_training):
-        context, S_dmask = mha.fwd(qkv, cu_seqlens, seqlens, p_dropout, max_s, is_training, None)
+    def forward(ctx, qkv, cu_seqlens, p_dropout, max_s, is_training):
+        batch_size = cu_seqlens.numel() - 1
+        if batch_size < 4:
+            context, S_dmask = mha.fwd_nl(qkv, cu_seqlens, p_dropout, max_s, is_training, None)
+        else:
+            context, S_dmask = mha.fwd(qkv, cu_seqlens, p_dropout, max_s, is_training, None)
         ctx.save_for_backward(qkv, S_dmask)
         ctx.cu_seqlens = cu_seqlens
-        ctx.seqlens = seqlens
         ctx.p_dropout = p_dropout
         ctx.max_s = max_s
         return context
@@ -44,7 +47,11 @@ class FMHAFun(torch.autograd.Function):
     @staticmethod
     def backward(ctx, dout):
         qkv, S_dmask = ctx.saved_tensors
-        dqkv, dp = mha.bwd(dout, qkv, S_dmask, ctx.cu_seqlens, ctx.seqlens, ctx.p_dropout, ctx.max_s)
+        batch_size = ctx.cu_seqlens.numel() - 1
+        if batch_size < 4:
+            dqkv, dp, _ = mha.bwd_nl(dout, qkv, S_dmask, ctx.cu_seqlens, ctx.p_dropout, ctx.max_s)
+        else:
+            dqkv, dp = mha.bwd(dout, qkv, S_dmask, ctx.cu_seqlens, ctx.p_dropout, ctx.max_s)
 
         return dqkv, None, None, None, None, None, None
 
@@ -60,8 +67,8 @@ class FMHA(torch.nn.Module):
         self.d = self.hidden_size // self.h
         assert self.d * self.h == self.hidden_size, "Invalid hidden size/num_heads"
 
-    def forward(self, qkv, cu_seqlens, seqlens, max_s, is_training=True):
+    def forward(self, qkv, cu_seqlens, max_s, is_training=True):
 
-        ctx = FMHAFun.apply(qkv.view(-1, 3, self.h, self.d), cu_seqlens, seqlens, self.p_dropout, max_s, is_training)
+        ctx = FMHAFun.apply(qkv.view(-1, 3, self.h, self.d), cu_seqlens, self.p_dropout, max_s, is_training)
 
         return ctx.view(-1, self.hidden_size)

apex/contrib/test/fmha/test_fmha.py

@@ -51,7 +51,8 @@ def py_mha(qkv, amask, b, s, h, d):
 
 class TestFMHA(unittest.TestCase):
 
-    def run_test(self, s):
+    def run_test(self, s, b):
+        print(f'Test s={s} b={b}')
 
         torch.manual_seed(1234)
         torch.cuda.manual_seed(1234)
@@ -59,7 +60,6 @@ class TestFMHA(unittest.TestCase):
         dtype = torch.float16
         device = torch.device('cuda')
 
-        b = 32 
         h = 16 
         d = 64
     
@@ -76,7 +76,10 @@ class TestFMHA(unittest.TestCase):
     
         qkv.requires_grad = True
     
-        ctx, S_ = mha.fwd(qkv_vs, cu_seqlens, seqlens, 0.0, s, True, None)
+        if b < 4:
+            ctx, S_ = mha.fwd_nl(qkv_vs, cu_seqlens, 0.0, s, True, None)
+        else:
+            ctx, S_ = mha.fwd(qkv_vs, cu_seqlens, 0.0, s, True, None)
         ctx = ctx.view(b,s,h,d)
     
         ctx_ref = py_mha(qkv, amask, b,s,h,d)
@@ -91,23 +94,28 @@ class TestFMHA(unittest.TestCase):
     
         dw2 = dw.permute(0,2,1,3).clone().detach().contiguous()
     
-        dqkv2, _ = mha.bwd(dw2, qkv_vs, S_, cu_seqlens, seqlens, 0.0, s)
+        if b < 4:
+            dqkv2, _, _ = mha.bwd_nl(dw2, qkv_vs, S_, cu_seqlens, 0.0, s)
+        else:
+            dqkv2, _ = mha.bwd(dw2, qkv_vs, S_, cu_seqlens, 0.0, s)
         
         dqkv2 = dqkv2.permute(0,2,1,3).view(b,s, h,3,d)
     
         self.assertTrue(torch.allclose(qkv.grad.float(), dqkv2.float(), atol=1e-3))
 
     def test_128(self):
-        self.run_test(128)
+        self.run_test(128, 32)
 
     def test_256(self):
-        self.run_test(256)
+        self.run_test(256, 32)
 
     def test_384(self):
-        self.run_test(384)
+        self.run_test(384, 32)
 
     def test_512(self):
-        self.run_test(512)
+        self.run_test(512, 32)
+        self.run_test(512, 2)
+        self.run_test(512, 3)
 
 if __name__ == '__main__':
     unittest.main()

setup.py

@@ -349,6 +349,7 @@ if "--fmha" in sys.argv:
             CUDAExtension(name='fmhalib',
                           sources=[
                                    'apex/contrib/csrc/fmha/fmha_api.cpp',
+                                   'apex/contrib/csrc/fmha/src/fmha_noloop_reduce.cu',
                                    'apex/contrib/csrc/fmha/src/fmha_fprop_fp16_128_64_kernel.sm80.cu',
                                    'apex/contrib/csrc/fmha/src/fmha_fprop_fp16_256_64_kernel.sm80.cu',
                                    'apex/contrib/csrc/fmha/src/fmha_fprop_fp16_384_64_kernel.sm80.cu',