Remove batch sdpa

mlx/backend/cuda/scaled_dot_product_attention.cu

@@ -2,24 +2,19 @@
 
 #include "mlx/backend/cuda/device.h"
 #include "mlx/backend/cuda/device/config.h"
+#include "mlx/backend/cuda/device/utils.cuh"
 #include "mlx/backend/cuda/kernel_utils.cuh"
 #include "mlx/backend/cuda/lru_cache.h"
 #include "mlx/backend/gpu/copy.h"
 #include "mlx/dtype_utils.h"
 #include "mlx/fast_primitives.h"
+#include "mlx/transforms_impl.h"
 
-// cudnn_frontend.h redefines this macro.
-#undef CHECK_CUDA_ERROR
-
-#include <cudnn_frontend.h>
-#include <fmt/format.h>
 #include <nvtx3/nvtx3.hpp>
 
 #include <cooperative_groups.h>
 #include <cooperative_groups/reduce.h>
 
-namespace fe = cudnn_frontend;
-
 namespace mlx::core {
 
 namespace cu {
@@ -217,11 +212,11 @@ __global__ void kernel_sdpav_2pass_1(
   U k[v_per_thread];
   U o[v_per_thread];
 
-  __shared__ U outputs[BD][BN + 1];
+  __shared__ U outputs[BN][BD + 1];
   __shared__ U max_scores[BN];
   __shared__ U sum_exp_scores[BN];
 
-  const U scale_log2 = params.scale; // * 1.44269504089f;
+  const U scale_log2 = params.scale * 1.44269504089f;
 
   auto block = cg::this_thread_block();
   auto warp = cg::tiled_partition<32>(block);
@@ -230,7 +225,7 @@ __global__ void kernel_sdpav_2pass_1(
   const int warp_idx = warp.meta_group_rank();
 
   // Adjust to thread block and thread
-  const int batch_idx = 0; // blockIdx.z / blocks;
+  const int batch_idx = blockIdx.z / blocks;
   const int block_idx = blockIdx.z % blocks;
   const int head_idx = blockIdx.x;
   const int kv_head_idx = head_idx / params.gqa_factor;
@@ -302,8 +297,8 @@ __global__ void kernel_sdpav_2pass_1(
 
       // Update the accumulators
       U new_max = max(max_score, score);
-      U factor = expf(max_score - new_max);
-      U exp_score = expf(score - new_max);
+      U factor = exp2f(max_score - new_max);
+      U exp_score = exp2f(score - new_max);
 
       max_score = new_max;
       sum_exp_score = sum_exp_score * factor + exp_score;
@@ -330,7 +325,7 @@ __global__ void kernel_sdpav_2pass_1(
 
   max_score = (lane_idx < BN) ? max_scores[lane_idx] : -1e9;
   U new_max = cg::reduce(warp, max_score, cg::greater<U>());
-  U factor = expf(max_score - new_max);
+  U factor = exp2f(max_score - new_max);
   sum_exp_score = (lane_idx < BN) ? sum_exp_scores[lane_idx] : 0.f;
   sum_exp_score = cg::reduce(warp, sum_exp_score * factor, cg::plus<U>());
 
@@ -341,31 +336,30 @@ __global__ void kernel_sdpav_2pass_1(
   }
 
   // Now we need to aggregate all the outputs
+  auto ff = exp2f(max_scores[warp_idx] - new_max);
   PRAGMA_LOOP_UNROLL
   for (int i = 0; i < v_per_thread; i++) {
-    outputs[lane_idx][warp_idx] = o[i] * expf(max_scores[warp_idx] - new_max);
+    outputs[warp_idx][lane_idx] = o[i] * ff;
     block.sync();
 
     if (warp_idx == 0) {
-      U ot = outputs[lane_idx][0];
-
+      U ot = outputs[0][lane_idx];
       PRAGMA_LOOP_UNROLL
       for (int j = 1; j < BN; j++) {
-        ot += outputs[lane_idx][0];
+        ot += outputs[j][lane_idx];
+        warp.sync();
       }
-
-      // o[i] = ot;
-      partials[v_per_thread * lane_idx + i] = ot;
+      o[i] = ot;
     }
     block.sync();
   }
 
-  // if(warp_idx == 0) {
-  //   PRAGMA_LOOP_UNROLL
-  //   for (int i = 0; i < v_per_thread; i++) {
-  //     partials[v_per_thread * lane_idx + i] = o[i];
-  //   }
-  // }
+  if (warp_idx == 0) {
+    PRAGMA_LOOP_UNROLL
+    for (int i = 0; i < v_per_thread; i++) {
+      partials[v_per_thread * lane_idx + i] = o[i];
+    }
+  }
 }
 
 template <typename T, bool do_causal, int D>
@@ -414,9 +408,9 @@ __global__ void kernel_sdpav_2pass_2(
 
   U max_score = maxs[lane_idx];
   U new_max = cg::reduce(warp, max_score, cg::greater<U>());
-  U factor = expf(max_score - new_max);
+  U factor = exp2f(max_score - new_max);
   U sum_exp_score = cg::reduce(warp, sums[lane_idx] * factor, cg::plus<U>());
-  // sum_exp_score = __frcp_rn(sum_exp_score);
+  sum_exp_score = __frcp_rn(sum_exp_score);
 
   PRAGMA_LOOP_UNROLL
   for (int i = 0; i < v_per_thread; i++) {
@@ -429,7 +423,7 @@ __global__ void kernel_sdpav_2pass_2(
     outputs[lane_idx][warp_idx] = o[i];
     block.sync();
     U ot = outputs[warp_idx][lane_idx] * factor;
-    o[i] = cg::reduce(warp, ot, cg::plus<U>()) / sum_exp_score;
+    o[i] = cg::reduce(warp, ot, cg::plus<U>()) * sum_exp_score;
     block.sync();
   }
 
@@ -499,11 +493,13 @@ void sdpa_vector_1pass_fallback(
       dispatch_headdim(params.D, [&](auto headdim) {
         using DataType = cuda_type_t<MLX_GET_TYPE(type_tag)>;
 
-        auto kernel = cu::kernel_sdpav_1pass<DataType, do_causal(), headdim()>;
+        auto kernel =
+            cu::kernel_sdpav_1pass<DataType, do_causal.value, headdim.value>;
         encoder.add_kernel_node(
             kernel,
             grid_dim,
             block_dim,
+            0,
             q.data<DataType>(),
             k.data<DataType>(),
             v.data<DataType>(),
@@ -568,8 +564,8 @@ void sdpa_vector_2pass_fallback(
         using DataType = cuda_type_t<MLX_GET_TYPE(type_tag)>;
 
         {
-          auto kernel =
-              cu::kernel_sdpav_2pass_1<DataType, do_causal(), headdim()>;
+          auto kernel = cu::
+              kernel_sdpav_2pass_1<DataType, do_causal.value, headdim.value>;
 
           encoder.set_input_array(q);
           encoder.set_input_array(k);
@@ -585,6 +581,7 @@ void sdpa_vector_2pass_fallback(
               kernel,
               grid_dim,
               block_dim,
+              0,
               q.data<DataType>(),
               k.data<DataType>(),
               v.data<DataType>(),
@@ -595,8 +592,8 @@ void sdpa_vector_2pass_fallback(
         }
 
         {
-          auto kernel =
-              cu::kernel_sdpav_2pass_2<DataType, do_causal(), headdim()>;
+          auto kernel = cu::
+              kernel_sdpav_2pass_2<DataType, do_causal.value, headdim.value>;
 
           encoder.set_input_array(intermediate);
           encoder.set_input_array(sums);
@@ -610,6 +607,7 @@ void sdpa_vector_2pass_fallback(
               kernel,
               grid_dim,
               block_dim,
+              0,
               intermediate.data<float>(),
               sums.data<float>(),
               maxs.data<float>(),
@@ -632,7 +630,7 @@ void sdpa_vector_fallback(
     bool do_causal_ = false) {
   int kL = k.shape(2);
 
-  if (false && kL > 1024) {
+  if (kL > 1024) {
     return sdpa_vector_2pass_fallback(
         s, encoder, q, k, v, scale, o, do_causal_);
   } else {
@@ -641,294 +639,6 @@ void sdpa_vector_fallback(
   }
 }
 
-struct SDPACacheKey {
-  int device_id;
-  fe::DataType_t cudnn_type;
-
-  int B;
-  int H;
-  int D;
-
-  int qL;
-  int kL;
-
-  int gqa_factor;
-  float scale;
-
-  int64_t Q_strides[3];
-  int64_t K_strides[3];
-  int64_t V_strides[3];
-  int64_t O_strides[3];
-
-  bool generate_stats;
-  bool causal_mask;
-};
-
-auto& sdpa_cache() {
-  static LRUBytesKeyCache<SDPACacheKey, std::shared_ptr<fe::graph::Graph>>
-      cache(
-          /* capacity */ 128);
-  return cache;
-}
-
-#define Q_UID 1
-#define K_UID 2
-#define V_UID 3
-#define O_UID 4
-#define STATS_UID 5
-
-std::shared_ptr<fe::graph::Graph> get_sdpa_forward_graph(
-    cu::CommandEncoder& encoder,
-    const SDPACacheKey& cache_key) {
-  // Check if graph has already been fully built
-  if (auto it = sdpa_cache().find(cache_key); it != sdpa_cache().end()) {
-    return it->second;
-  }
-
-  // Set up new graph
-  auto graph = std::make_shared<fe::graph::Graph>();
-
-  graph->set_io_data_type(cache_key.cudnn_type)
-      .set_intermediate_data_type(fe::DataType_t::FLOAT)
-      .set_compute_data_type(fe::DataType_t::FLOAT);
-
-  auto Q = graph->tensor(
-      fe::graph::Tensor_attributes()
-          .set_name("Q")
-          .set_uid(Q_UID)
-          .set_dim({cache_key.B, cache_key.H, cache_key.qL, cache_key.D})
-          .set_stride(
-              {cache_key.Q_strides[0],
-               cache_key.Q_strides[1],
-               cache_key.Q_strides[2],
-               1}));
-
-  int h_kv = cache_key.H / cache_key.gqa_factor;
-  auto K =
-      graph->tensor(fe::graph::Tensor_attributes()
-                        .set_name("K")
-                        .set_uid(K_UID)
-                        .set_dim({cache_key.B, h_kv, cache_key.kL, cache_key.D})
-                        .set_stride(
-                            {cache_key.K_strides[0],
-                             cache_key.K_strides[1],
-                             cache_key.V_strides[2],
-                             1}));
-
-  auto V =
-      graph->tensor(fe::graph::Tensor_attributes()
-                        .set_name("V")
-                        .set_uid(V_UID)
-                        .set_dim({cache_key.B, h_kv, cache_key.kL, cache_key.D})
-                        .set_stride(
-                            {cache_key.V_strides[0],
-                             cache_key.V_strides[1],
-                             cache_key.V_strides[2],
-                             1}));
-
-  auto sdpa_options = fe::graph::SDPA_attributes()
-                          .set_name("flash_attention")
-                          .set_is_inference(!cache_key.generate_stats)
-                          .set_attn_scale(cache_key.scale);
-
-  if (cache_key.causal_mask && cache_key.qL > 1) {
-    sdpa_options.set_diagonal_alignment(fe::DiagonalAlignment_t::TOP_LEFT)
-        .set_diagonal_band_right_bound(0);
-  }
-
-  auto [O, Stats] = graph->sdpa(Q, K, V, sdpa_options);
-
-  O->set_output(true)
-      .set_uid(O_UID)
-      .set_dim({cache_key.B, cache_key.H, cache_key.qL, cache_key.D})
-      .set_stride(
-          {cache_key.O_strides[0],
-           cache_key.O_strides[1],
-           cache_key.O_strides[2],
-           1});
-
-  if (cache_key.generate_stats) {
-    Stats->set_output(true)
-        .set_data_type(fe::DataType_t::FLOAT)
-        .set_uid(STATS_UID);
-  }
-
-  // Build and Validate cudnn graph
-
-  auto handle = encoder.device().cudnn_handle();
-
-  // cuDNN only supports native CUDA graphs for sdpa in 9.6 or above.
-  if (cudnnGetVersion() < 90600) {
-    auto build_status = graph->build(handle, {fe::HeurMode_t::A});
-    if (!build_status.is_good()) {
-      throw std::runtime_error(
-          "Unable to build cudnn graph for attention."
-          " Failed with message: " +
-          build_status.get_message());
-    }
-
-  } else {
-    auto val_status = graph->validate();
-    auto op_status = graph->build_operation_graph(handle);
-
-    auto plan_stauts =
-        graph->create_execution_plans({cudnn_frontend::HeurMode_t::A});
-    if (!plan_stauts.is_good()) {
-      throw std::runtime_error(
-          "Unable to create exec plan for cudnn attention."
-          " Failed with message: " +
-          plan_stauts.get_message());
-    }
-
-    graph->select_behavior_notes(
-        {cudnn_frontend::BehaviorNote_t::SUPPORTS_CUDA_GRAPH_NATIVE_API});
-
-    auto support_status = graph->check_support(handle);
-    if (!support_status.is_good()) {
-      throw std::runtime_error(
-          "No cuda graph support for cudnn attention."
-          " Failed with message: " +
-          support_status.get_message());
-    }
-
-    auto build_status = graph->build_plans(handle);
-    if (!build_status.is_good()) {
-      throw std::runtime_error(
-          "Unable to build cudnn graph for attention."
-          " Failed with message: " +
-          build_status.get_message());
-    }
-  }
-
-  auto [it, _] = sdpa_cache().emplace(cache_key, graph);
-
-  return it->second;
-}
-
-inline fe::DataType_t dtype_to_cudnn_type(Dtype dtype) {
-  switch (dtype) {
-    case int8:
-      return fe::DataType_t::INT8;
-    case int32:
-      return fe::DataType_t::INT32;
-    case uint8:
-      return fe::DataType_t::UINT8;
-    case float16:
-      return fe::DataType_t::HALF;
-    case bfloat16:
-      return fe::DataType_t::BFLOAT16;
-    case float32:
-      return fe::DataType_t::FLOAT;
-    case float64:
-      return fe::DataType_t::DOUBLE;
-    default:
-      throw std::runtime_error(fmt::format(
-          "Unsupported dtype in SDPA: {}.", dtype_to_string(dtype)));
-  }
-}
-
-void sdpa_cudnn(
-    const Stream& s,
-    cu::CommandEncoder& encoder,
-    const array& q,
-    const array& k,
-    const array& v,
-    const float scale,
-    array& o,
-    bool do_causal_ = false) {
-  encoder.set_input_array(q);
-  encoder.set_input_array(k);
-  encoder.set_input_array(v);
-  encoder.set_output_array(o);
-
-  auto cudnn_type = dtype_to_cudnn_type(q.dtype());
-
-  int B = q.shape(0);
-  int H = q.shape(1);
-  int D = q.shape(3);
-  int gqa_factor = q.shape(1) / k.shape(1);
-
-  int qL = q.shape(2);
-  int kL = k.shape(2);
-
-  SDPACacheKey cache_key{
-      /* int device_id = */ encoder.device().cuda_device(),
-      /* fe::DataType_t cudnn_type = */ cudnn_type,
-
-      /* int B = */ B,
-      /* int H = */ H,
-      /* int D = */ D,
-
-      /* int qL = */ qL,
-      /* int kL = */ kL,
-
-      /* int gqa_factor = */ gqa_factor,
-      /* float scale = */ scale,
-
-      /* int64_t Q_strides[3] = */ {q.strides(0), q.strides(1), q.strides(2)},
-      /* int64_t K_strides[3] = */ {k.strides(0), k.strides(1), k.strides(2)},
-      /* int64_t V_strides[3] = */ {v.strides(0), v.strides(1), v.strides(2)},
-      /* int64_t O_strides[3] = */ {o.strides(0), o.strides(1), o.strides(2)},
-
-      /* bool generate_stats = */ false,
-      /* bool causal_mask = */ do_causal_};
-
-  auto graph = get_sdpa_forward_graph(encoder, cache_key);
-
-  int64_t workspace_size = 0;
-  auto workspace_status = graph->get_workspace_size(workspace_size);
-  if (!workspace_status.is_good()) {
-    throw std::runtime_error("Unable to get workspace for cudnn attention.");
-  }
-
-  array workspace(
-      allocator::malloc(workspace_size), {int(workspace_size)}, uint8);
-  auto workspace_ptr = workspace.data<void>();
-
-  std::unordered_map<int64_t, void*> variant_pack = {
-      {Q_UID, const_cast<void*>(q.data<void>())},
-      {K_UID, const_cast<void*>(k.data<void>())},
-      {V_UID, const_cast<void*>(v.data<void>())},
-      {O_UID, o.data<void>()}};
-
-  auto handle = encoder.device().cudnn_handle();
-  cudnnSetStream(handle, encoder.stream());
-
-  // cuDNN only supports native CUDA graphs for sdpa in 9.6 or above.
-  if (cudnnGetVersion() < 90600) {
-    auto capture = encoder.capture_context();
-    auto exec_status = graph->execute(handle, variant_pack, workspace_ptr);
-
-    if (!exec_status.is_good()) {
-      capture.discard = true;
-      throw std::runtime_error(
-          "Unable to execute cudnn attention."
-          " Failed with message: " +
-          exec_status.get_message());
-    }
-  } else {
-    cudaGraph_t cu_graph;
-    cudaGraphCreate(&cu_graph, 0);
-
-    std::unique_ptr<cudaGraph_t, void (*)(cudaGraph_t*)> graph_freer(
-        &cu_graph, [](cudaGraph_t* p) { cudaGraphDestroy(*p); });
-
-    auto cu_graph_status = graph->populate_cuda_graph(
-        handle, variant_pack, workspace_ptr, cu_graph);
-
-    if (!cu_graph_status.is_good()) {
-      throw std::runtime_error(
-          "Unable to add cuda graph for cudnn attention."
-          " Failed with message: " +
-          cu_graph_status.get_message());
-    }
-
-    encoder.add_graph_node(cu_graph);
-  }
-
-  encoder.add_temporary(workspace);
-}
-
 } // namespace
 
 namespace fast {
@@ -941,6 +651,9 @@ bool ScaledDotProductAttention::use_fallback(
     bool has_arr_mask,
     bool do_causal,
     Stream s) {
+  if (detail::in_grad_tracing()) {
+    return true;
+  }
   if (s.device == Device::cpu) {
     return true;
   }
@@ -953,7 +666,12 @@ bool ScaledDotProductAttention::use_fallback(
   const bool sdpa_supported_head_dim = query_head_dim == value_head_dim &&
       (query_head_dim == 64 || query_head_dim == 96 || query_head_dim == 128);
 
-  return has_arr_mask || !sdpa_supported_head_dim;
+  const bool supported_vector_config =
+      sdpa_supported_head_dim && query_sequence_length < 4;
+
+  const bool supported_config = supported_vector_config;
+
+  return has_arr_mask || !supported_config;
 }
 
 void ScaledDotProductAttention::eval_gpu(
@@ -985,12 +703,8 @@ void ScaledDotProductAttention::eval_gpu(
     }
   };
 
-  auto is_matrix_contiguous = [](const array& arr) {
-    return arr.strides(-1) == 1;
-  };
-
   // We are in vector mode ie single query
-  if (q_pre.shape(2) <= 1) {
+  if (q_pre.shape(2) < 4) {
     auto q_copy_unless = [](const array& arr) {
       if (arr.flags().row_contiguous) {
         return true;
@@ -1034,45 +748,34 @@ void ScaledDotProductAttention::eval_gpu(
     if (q.is_donatable() && q.flags().row_contiguous && q.size() == o.size()) {
       o.copy_shared_buffer(q);
     } else {
-      o.set_data(allocator::malloc(o.nbytes()));
+      int64_t str_oD = 1;
+      int64_t str_oH = o.shape(3);
+      int64_t str_oL = o.shape(1) * str_oH;
+      int64_t str_oB = o.shape(2) * str_oL;
+      size_t data_size = o.shape(0) * str_oB;
+
+      array::Flags flags{
+          /* bool contiguous = */ 1,
+          /* bool row_contiguous = */ o.shape(2) == 1,
+          /* bool col_contiguous = */ 0,
+      };
+
+      o.set_data(
+          allocator::malloc(o.nbytes()),
+          data_size,
+          {str_oB, str_oH, str_oL, str_oD},
+          flags);
     }
 
     return sdpa_vector_fallback(s, encoder, q, k, v, scale_, o, do_causal_);
-    // return sdpa_cudnn(s, encoder, q, k, v, scale_, o, do_causal_);
   }
 
-  // Full attention mode
+  // Full attention mode should never reach here
   else {
-    const auto& q = copy_unless(is_matrix_contiguous, q_pre);
-    const auto& k = copy_unless(is_matrix_contiguous, k_pre);
-    const auto& v = copy_unless(is_matrix_contiguous, v_pre);
-
-    for (const auto& cp : copies) {
-      encoder.add_temporary(cp);
-    }
-
-    int64_t str_oD = 1;
-    int64_t str_oH = o.shape(3);
-    int64_t str_oL = o.shape(1) * str_oH;
-    int64_t str_oB = o.shape(2) * str_oL;
-    size_t data_size = o.shape(0) * str_oB;
-
-    array::Flags flags{
-        /* bool contiguous = */ 1,
-        /* bool row_contiguous = */ 0,
-        /* bool col_contiguous = */ 0,
-    };
-
-    o.set_data(
-        allocator::malloc(o.nbytes()),
-        data_size,
-        {str_oB, str_oH, str_oL, str_oD},
-        flags);
-
-    return sdpa_cudnn(s, encoder, q, k, v, scale_, o, do_causal_);
+    throw std::runtime_error("Doesn't support matrix yet.");
   }
 }
 
 } // namespace fast
 
-} // namespace mlx::core
+} // namespace mlx::core