[WIP] 2 pass sdpav

mlx/backend/cuda/primitives.cpp

@@ -4,6 +4,8 @@
 #include "mlx/fast_primitives.h"
 #include "mlx/primitives.h"
 
+namespace mlx::core {
+
 #define NO_GPU_MULTI(func)                                             \
   void func::eval_gpu(                                                 \
       const std::vector<array>& inputs, std::vector<array>& outputs) { \

mlx/backend/cuda/scaled_dot_product_attention.cu

@@ -15,14 +15,632 @@
 #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 {} // namespace cu
+namespace cu {
+
+namespace cg = cooperative_groups;
+
+#define PRAGMA_LOOP_UNROLL #pragma unroll
+
+struct AttnParams {
+  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];
+};
+
+template <typename T, bool do_causal, int D>
+__global__ void kernel_sdpav_1pass(
+    const T* Q,
+    const T* K,
+    const T* V,
+    T* O,
+    __grid_constant__ const AttnParams params) {
+  constexpr int BN = 32;
+  constexpr int BD = 32;
+
+  constexpr int v_per_thread = D / BD;
+
+  const int inner_k_stride = BN * int(params.K_strides[2]);
+  const int inner_v_stride = BN * int(params.V_strides[2]);
+
+  typedef float U;
+
+  U q[v_per_thread];
+  U k[v_per_thread];
+  U o[v_per_thread];
+
+  __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;
+
+  auto block = cg::this_thread_block();
+  auto warp = cg::tiled_partition<32>(block);
+
+  const int lane_idx = warp.thread_rank();
+  const int warp_idx = warp.meta_group_rank();
+
+  // Adjust to thread block and thread
+  const int batch_idx = blockIdx.z;
+  const int head_idx = blockIdx.x;
+  const int kv_head_idx = head_idx / params.gqa_factor;
+
+  const int q_seq_idx = blockIdx.y;
+  const int kv_seq_idx = warp_idx;
+
+  Q += batch_idx * params.Q_strides[0] + // Batch
+      head_idx * params.Q_strides[1] + // Head
+      q_seq_idx * params.Q_strides[2]; // Sequence
+
+  K += batch_idx * params.K_strides[0] + // Batch
+      kv_head_idx * params.K_strides[1] + // Head
+      kv_seq_idx * params.K_strides[2]; // Sequence
+
+  V += batch_idx * params.V_strides[0] + // Batch
+      kv_head_idx * params.V_strides[1] + // Head
+      kv_seq_idx * params.V_strides[2]; // Sequence
+
+  O += batch_idx * params.O_strides[0] + // Batch
+      head_idx * params.O_strides[1] + // Head
+      q_seq_idx * params.O_strides[2]; // Sequence
+
+  // Read the query and 0 the output accumulator
+  PRAGMA_LOOP_UNROLL
+  for (int i = 0; i < v_per_thread; i++) {
+    q[i] = scale_log2 * static_cast<U>(Q[v_per_thread * lane_idx + i]);
+  }
+
+  PRAGMA_LOOP_UNROLL
+  for (int i = 0; i < v_per_thread; i++) {
+    o[i] = 0.f;
+  }
+
+  U max_score = -INFINITY;
+  U sum_exp_score = 0.f;
+
+  // For each key
+  for (int i = kv_seq_idx; i < params.kL; i += BN) {
+    bool use_key = true;
+    if constexpr (do_causal) {
+      use_key = i <= (params.kL - params.qL + q_seq_idx);
+    }
+
+    if (use_key) {
+      // Read the key
+      PRAGMA_LOOP_UNROLL
+      for (int j = 0; j < v_per_thread; j++) {
+        k[j] = K[v_per_thread * lane_idx + j];
+      }
+
+      // Compute the i-th score
+      U score = 0.f;
+      PRAGMA_LOOP_UNROLL
+      for (int j = 0; j < v_per_thread; j++) {
+        score += q[j] * k[j];
+      }
+
+      // Warp sum
+      score = cg::reduce(warp, score, cg::plus<U>());
+
+      // Update the accumulators
+      U new_max = max(max_score, score);
+      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;
+
+      // Update the output accumulator
+      PRAGMA_LOOP_UNROLL
+      for (int j = 0; j < v_per_thread; j++) {
+        o[j] = o[j] * factor +
+            exp_score * static_cast<U>(V[v_per_thread * lane_idx + j]);
+      }
+    }
+
+    // Move the pointers to the next kv
+    K += inner_k_stride;
+    V += inner_v_stride;
+  }
+
+  if (lane_idx == 0) {
+    max_scores[warp_idx] = max_score;
+    sum_exp_scores[warp_idx] = sum_exp_score;
+  }
+  block.sync();
+
+  max_score = max_scores[lane_idx];
+  U new_max = cg::reduce(warp, max_score, cg::greater<U>());
+  U factor = exp2f(max_score - new_max);
+  sum_exp_score =
+      cg::reduce(warp, sum_exp_scores[lane_idx] * factor, cg::plus<U>());
+  sum_exp_score = __frcp_rn(sum_exp_score);
+
+  // Now we need to aggregate all the outputs
+  PRAGMA_LOOP_UNROLL
+  for (int i = 0; i < v_per_thread; i++) {
+    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;
+    block.sync();
+  }
+
+  // And write the output
+  if (lane_idx == 0) {
+    PRAGMA_LOOP_UNROLL
+    for (int i = 0; i < v_per_thread; i++) {
+      O[v_per_thread * warp_idx + i] = static_cast<T>(o[i]);
+    }
+  }
+}
+
+template <typename T, bool do_causal, int D>
+__global__ void kernel_sdpav_2pass_1(
+    const T* Q,
+    const T* K,
+    const T* V,
+    float* partials,
+    float* sums,
+    float* maxs,
+    __grid_constant__ const AttnParams params) {
+  constexpr int BN = 8;
+  constexpr int BD = 32;
+  constexpr int blocks = 32;
+
+  constexpr int v_per_thread = D / BD;
+
+  const int inner_k_stride = blocks * BN * int(params.K_strides[2]);
+  const int inner_v_stride = blocks * BN * int(params.V_strides[2]);
+
+  typedef float U;
+
+  U q[v_per_thread];
+  U k[v_per_thread];
+  U o[v_per_thread];
+
+  __shared__ U outputs[BD][BN + 1];
+  __shared__ U max_scores[BN];
+  __shared__ U sum_exp_scores[BN];
+
+  const U scale_log2 = params.scale; // * 1.44269504089f;
+
+  auto block = cg::this_thread_block();
+  auto warp = cg::tiled_partition<32>(block);
+
+  const int lane_idx = warp.thread_rank();
+  const int warp_idx = warp.meta_group_rank();
+
+  // Adjust to thread block and thread
+  const int batch_idx = 0; // 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;
+
+  const int q_seq_idx = blockIdx.y;
+  const int kv_seq_idx = block_idx * BN + warp_idx;
+
+  Q += batch_idx * params.Q_strides[0] + // Batch
+      head_idx * params.Q_strides[1] + // Head
+      q_seq_idx * params.Q_strides[2]; // Sequence
+
+  K += batch_idx * params.K_strides[0] + // Batch
+      kv_head_idx * params.K_strides[1] + // Head
+      kv_seq_idx * params.K_strides[2]; // Sequence
+
+  V += batch_idx * params.V_strides[0] + // Batch
+      kv_head_idx * params.V_strides[1] + // Head
+      kv_seq_idx * params.V_strides[2]; // Sequence
+
+  const int p_stride_s = blocks;
+  const int p_stride_h = params.qL * p_stride_s;
+  const int p_stride_b = params.H * p_stride_h;
+  const int p_offset = batch_idx * p_stride_b + // Batch
+      head_idx * p_stride_h + // Head
+      q_seq_idx * p_stride_s + // Sequence
+      block_idx; // Block
+
+  partials += p_offset * D;
+  sums += p_offset;
+  maxs += p_offset;
+
+  // Read the query and 0 the output accumulator
+  PRAGMA_LOOP_UNROLL
+  for (int i = 0; i < v_per_thread; i++) {
+    q[i] = scale_log2 * static_cast<U>(Q[v_per_thread * lane_idx + i]);
+  }
+
+  PRAGMA_LOOP_UNROLL
+  for (int i = 0; i < v_per_thread; i++) {
+    o[i] = 0.f;
+  }
+
+  U max_score = -1e9;
+  U sum_exp_score = 0.f;
+
+  // For each key
+  for (int i = kv_seq_idx; i < params.kL; i += blocks * BN) {
+    bool use_key = true;
+    if constexpr (do_causal) {
+      use_key = i <= (params.kL - params.qL + q_seq_idx);
+    }
+
+    if (use_key) {
+      // Read the key
+      PRAGMA_LOOP_UNROLL
+      for (int j = 0; j < v_per_thread; j++) {
+        k[j] = K[v_per_thread * lane_idx + j];
+      }
+
+      // Compute the i-th score
+      U score = 0.f;
+      PRAGMA_LOOP_UNROLL
+      for (int j = 0; j < v_per_thread; j++) {
+        score += q[j] * k[j];
+      }
+
+      // Warp sum
+      score = cg::reduce(warp, score, cg::plus<U>());
+
+      // Update the accumulators
+      U new_max = max(max_score, score);
+      U factor = expf(max_score - new_max);
+      U exp_score = expf(score - new_max);
+
+      max_score = new_max;
+      sum_exp_score = sum_exp_score * factor + exp_score;
+
+      // Update the output accumulator
+      PRAGMA_LOOP_UNROLL
+      for (int j = 0; j < v_per_thread; j++) {
+        o[j] = o[j] * factor +
+            exp_score * static_cast<U>(V[v_per_thread * lane_idx + j]);
+      }
+    }
+
+    // Move the pointers to the next kv
+    K += inner_k_stride;
+    V += inner_v_stride;
+  }
+
+  if (lane_idx == 0) {
+    max_scores[warp_idx] = max_score;
+    sum_exp_scores[warp_idx] = sum_exp_score;
+  }
+
+  block.sync();
+
+  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);
+  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>());
+
+  // Write the sum and new max
+  if (warp_idx == 0) {
+    sums[0] = sum_exp_score;
+    maxs[0] = new_max;
+  }
+
+  // Now we need to aggregate all the outputs
+  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);
+    block.sync();
+
+    if (warp_idx == 0) {
+      U ot = outputs[lane_idx][0];
+
+      PRAGMA_LOOP_UNROLL
+      for (int j = 1; j < BN; j++) {
+        ot += outputs[lane_idx][0];
+      }
+
+      // o[i] = ot;
+      partials[v_per_thread * lane_idx + 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];
+  //   }
+  // }
+}
+
+template <typename T, bool do_causal, int D>
+__global__ void kernel_sdpav_2pass_2(
+    const float* partials,
+    const float* sums,
+    const float* maxs,
+    T* O,
+    __grid_constant__ const AttnParams params) {
+  constexpr int BN = 32;
+  constexpr int BD = 32;
+  constexpr int blocks = 32;
+
+  constexpr int v_per_thread = D / BD;
+
+  typedef float U;
+
+  U o[v_per_thread];
+  __shared__ U outputs[BN][BD + 1];
+
+  auto block = cg::this_thread_block();
+  auto warp = cg::tiled_partition<32>(block);
+
+  const int lane_idx = warp.thread_rank();
+  const int warp_idx = warp.meta_group_rank();
+
+  // Adjust to thread block and thread
+  const int batch_idx = blockIdx.z;
+  const int head_idx = blockIdx.x;
+  const int q_seq_idx = blockIdx.y;
+
+  const int p_stride_s = blocks;
+  const int p_stride_h = params.qL * p_stride_s;
+  const int p_stride_b = params.H * p_stride_h;
+  const int p_offset = batch_idx * p_stride_b + // Batch
+      head_idx * p_stride_h + // Head
+      q_seq_idx * p_stride_s; // Sequence
+
+  partials += p_offset * D + warp_idx * D;
+  sums += p_offset;
+  maxs += p_offset;
+
+  O += batch_idx * params.O_strides[0] + // Batch
+      head_idx * params.O_strides[1] + // Head
+      q_seq_idx * params.O_strides[2]; // Sequence
+
+  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 sum_exp_score = cg::reduce(warp, sums[lane_idx] * factor, cg::plus<U>());
+  // sum_exp_score = __frcp_rn(sum_exp_score);
+
+  PRAGMA_LOOP_UNROLL
+  for (int i = 0; i < v_per_thread; i++) {
+    o[i] = partials[v_per_thread * lane_idx + i];
+  }
+
+  // Now we need to aggregate all the outputs
+  PRAGMA_LOOP_UNROLL
+  for (int i = 0; i < v_per_thread; i++) {
+    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;
+    block.sync();
+  }
+
+  // And write the output
+  if (lane_idx == 0) {
+    PRAGMA_LOOP_UNROLL
+    for (int i = 0; i < v_per_thread; i++) {
+      O[v_per_thread * warp_idx + i] = static_cast<T>(o[i]);
+    }
+  }
+}
+
+} // namespace cu
 
 namespace {
 
+template <typename F>
+void dispatch_headdim(int n, F&& f) {
+  switch (n) {
+    case 64:
+      f(std::integral_constant<int, 64>{});
+      break;
+    case 96:
+      f(std::integral_constant<int, 96>{});
+      break;
+    case 128:
+      f(std::integral_constant<int, 128>{});
+      break;
+  }
+}
+
+void sdpa_vector_1pass_fallback(
+    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);
+
+  cu::AttnParams params{
+      /* int B = */ q.shape(0),
+      /* int H = */ q.shape(1),
+      /* int D = */ q.shape(3),
+
+      /* int qL = */ q.shape(2),
+      /* int kL = */ k.shape(2),
+
+      /* int gqa_factor = */ q.shape(1) / k.shape(1),
+      /* 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)}};
+
+  dim3 grid_dim(params.H, params.qL, params.B);
+  dim3 block_dim(1024, 1, 1);
+
+  dispatch_float_types(o.dtype(), "kernel_sdpav_1pass", [&](auto type_tag) {
+    dispatch_bool(do_causal_, [&](auto do_causal) {
+      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()>;
+        encoder.add_kernel_node(
+            kernel,
+            grid_dim,
+            block_dim,
+            q.data<DataType>(),
+            k.data<DataType>(),
+            v.data<DataType>(),
+            o.data<DataType>(),
+            params);
+      });
+    });
+  });
+}
+
+void sdpa_vector_2pass_fallback(
+    const Stream& s,
+    cu::CommandEncoder& encoder,
+    const array& q,
+    const array& k,
+    const array& v,
+    const float scale,
+    array& o,
+    bool do_causal_ = false) {
+  cu::AttnParams params{
+      /* int B = */ q.shape(0),
+      /* int H = */ q.shape(1),
+      /* int D = */ q.shape(3),
+
+      /* int qL = */ q.shape(2),
+      /* int kL = */ k.shape(2),
+
+      /* int gqa_factor = */ q.shape(1) / k.shape(1),
+      /* 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)}};
+
+  // Allocate the intermediates
+  int blocks = 32;
+
+  Shape intermediate_shape;
+  intermediate_shape.reserve(o.ndim() + 1);
+  intermediate_shape.insert(
+      intermediate_shape.end(), o.shape().begin(), o.shape().end() - 1);
+  intermediate_shape.push_back(blocks);
+  intermediate_shape.push_back(o.shape().back());
+
+  array intermediate(intermediate_shape, float32, nullptr, {});
+  intermediate_shape.pop_back();
+  array sums(intermediate_shape, float32, nullptr, {});
+  array maxs(std::move(intermediate_shape), float32, nullptr, {});
+
+  intermediate.set_data(allocator::malloc(intermediate.nbytes()));
+  sums.set_data(allocator::malloc(sums.nbytes()));
+  maxs.set_data(allocator::malloc(maxs.nbytes()));
+
+  encoder.add_temporary(intermediate);
+  encoder.add_temporary(sums);
+  encoder.add_temporary(maxs);
+
+  dispatch_float_types(o.dtype(), "kernel_sdpav_2pass", [&](auto type_tag) {
+    dispatch_bool(do_causal_, [&](auto do_causal) {
+      dispatch_headdim(params.D, [&](auto headdim) {
+        using DataType = cuda_type_t<MLX_GET_TYPE(type_tag)>;
+
+        {
+          auto kernel =
+              cu::kernel_sdpav_2pass_1<DataType, do_causal(), headdim()>;
+
+          encoder.set_input_array(q);
+          encoder.set_input_array(k);
+          encoder.set_input_array(v);
+          encoder.set_output_array(intermediate);
+          encoder.set_output_array(sums);
+          encoder.set_output_array(maxs);
+
+          dim3 grid_dim(params.H, params.qL, params.B * 32);
+          dim3 block_dim(8 * 32, 1, 1);
+
+          encoder.add_kernel_node(
+              kernel,
+              grid_dim,
+              block_dim,
+              q.data<DataType>(),
+              k.data<DataType>(),
+              v.data<DataType>(),
+              intermediate.data<float>(),
+              sums.data<float>(),
+              maxs.data<float>(),
+              params);
+        }
+
+        {
+          auto kernel =
+              cu::kernel_sdpav_2pass_2<DataType, do_causal(), headdim()>;
+
+          encoder.set_input_array(intermediate);
+          encoder.set_input_array(sums);
+          encoder.set_input_array(maxs);
+          encoder.set_output_array(o);
+
+          dim3 grid_dim(params.H, params.qL, params.B);
+          dim3 block_dim(1024, 1, 1);
+
+          encoder.add_kernel_node(
+              kernel,
+              grid_dim,
+              block_dim,
+              intermediate.data<float>(),
+              sums.data<float>(),
+              maxs.data<float>(),
+              o.data<DataType>(),
+              params);
+        }
+      });
+    });
+  });
+}
+
+void sdpa_vector_fallback(
+    const Stream& s,
+    cu::CommandEncoder& encoder,
+    const array& q,
+    const array& k,
+    const array& v,
+    const float scale,
+    array& o,
+    bool do_causal_ = false) {
+  int kL = k.shape(2);
+
+  if (false && kL > 1024) {
+    return sdpa_vector_2pass_fallback(
+        s, encoder, q, k, v, scale, o, do_causal_);
+  } else {
+    return sdpa_vector_1pass_fallback(
+        s, encoder, q, k, v, scale, o, do_causal_);
+  }
+}
+
 struct SDPACacheKey {
   int device_id;
   fe::DataType_t cudnn_type;
@@ -67,8 +685,6 @@ std::shared_ptr<fe::graph::Graph> get_sdpa_forward_graph(
     return it->second;
   }
 
-  nvtx3::scoped_range r("get_sdpa_forward_graph");
-
   // Set up new graph
   auto graph = std::make_shared<fe::graph::Graph>();
 
@@ -143,8 +759,6 @@ std::shared_ptr<fe::graph::Graph> get_sdpa_forward_graph(
 
   // cuDNN only supports native CUDA graphs for sdpa in 9.6 or above.
   if (cudnnGetVersion() < 90600) {
-    nvtx3::scoped_range r("get_sdpa_forward_graph::graph_building");
-
     auto build_status = graph->build(handle, {fe::HeurMode_t::A});
     if (!build_status.is_good()) {
       throw std::runtime_error(
@@ -331,11 +945,6 @@ bool ScaledDotProductAttention::use_fallback(
     return true;
   }
 
-  auto& cu_device = cu::device(s.device);
-  if (cu_device.compute_capability_major() < 8) {
-    return true;
-  }
-
   const int value_head_dim = v.shape(-1);
   const int query_head_dim = q.shape(-1);
   const int query_sequence_length = q.shape(2);
@@ -344,11 +953,7 @@ 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);
 
-  const bool supported_dtype = q.dtype() == float16 || q.dtype() == bfloat16;
+  return has_arr_mask || !sdpa_supported_head_dim;
-
-  const bool supported_config = supported_dtype && sdpa_supported_head_dim;
-
-  return has_arr_mask || !supported_config;
 }
 
 void ScaledDotProductAttention::eval_gpu(
@@ -432,7 +1037,8 @@ void ScaledDotProductAttention::eval_gpu(
       o.set_data(allocator::malloc(o.nbytes()));
     }
 
-    return sdpa_cudnn(s, encoder, q, k, v, scale_, o, do_causal_);
+    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