Add a 2-pass col reduce for CUDA (#2863)

mlx/backend/cuda/reduce/col_reduce.cu

@@ -89,9 +89,13 @@ template <
     int NDIM,
     int BM,
     int BN,
-    int N_READS = 4>
-__global__ void
-col_reduce_looped(T* in, U* out, const __grid_constant__ ColReduceArgs args) {
+    int N_READS = 4,
+    int BLOCKS = 1>
+__global__ void col_reduce_looped(
+    T* in,
+    U* out,
+    const __grid_constant__ ColReduceArgs args,
+    int64_t out_size) {
   auto grid = cg::this_grid();
   auto block = cg::this_thread_block();
   auto warp = cg::tiled_partition<WARP_SIZE>(block);
@@ -102,6 +106,8 @@ col_reduce_looped(T* in, U* out, const __grid_constant__ ColReduceArgs args) {
   size_t tile_idx = grid.block_rank();
   size_t tile_x = tile_idx % ((args.reduction_stride + BN - 1) / BN);
   size_t tile_y = tile_idx / ((args.reduction_stride + BN - 1) / BN);
+  size_t tile_out = tile_y / out_size;
+  tile_y = tile_y % out_size;
 
   // Compute the indices for the thread within the tile
   short thread_x = block.thread_rank() % threads_per_row;
@@ -118,12 +124,23 @@ col_reduce_looped(T* in, U* out, const __grid_constant__ ColReduceArgs args) {
     totals[i] = ReduceInit<Op, T>::value();
   }
 
-  LoopedElemToLoc<NDIM, (NDIM > 2)> loop(args.reduce_ndim);
-  loop.next(thread_y, args.reduce_shape.data(), args.reduce_strides.data());
   size_t total = args.non_col_reductions * args.reduction_size;
+  size_t per_block, start, end;
+  if constexpr (BLOCKS > 1) {
+    per_block = (total + BLOCKS - 1) / BLOCKS;
+    start = tile_out * per_block + thread_y;
+    end = min((tile_out + 1) * per_block, total);
+  } else {
+    per_block = total;
+    start = thread_y;
+    end = total;
+  }
+
+  LoopedElemToLoc<NDIM, (NDIM > 2)> loop(args.reduce_ndim);
+  loop.next(start, args.reduce_shape.data(), args.reduce_strides.data());
   if (tile_x * BN + BN <= args.reduction_stride) {
     if (args.reduction_stride % N_READS == 0) {
-      for (size_t r = thread_y; r < total; r += BM) {
+      for (size_t r = start; r < end; r += BM) {
         T vals[N_READS];
         cub::LoadDirectBlockedVectorized(thread_x, in + loop.location(), vals);
         for (int i = 0; i < N_READS; i++) {
@@ -132,7 +149,7 @@ col_reduce_looped(T* in, U* out, const __grid_constant__ ColReduceArgs args) {
         loop.next(BM, args.reduce_shape.data(), args.reduce_strides.data());
       }
     } else {
-      for (size_t r = thread_y; r < total; r += BM) {
+      for (size_t r = start; r < end; r += BM) {
         T vals[N_READS];
         cub::LoadDirectBlocked(thread_x, in + loop.location(), vals);
         for (int i = 0; i < N_READS; i++) {
@@ -142,7 +159,7 @@ col_reduce_looped(T* in, U* out, const __grid_constant__ ColReduceArgs args) {
       }
     }
   } else {
-    for (size_t r = thread_y; r < total; r += BM) {
+    for (size_t r = start; r < end; r += BM) {
       T vals[N_READS];
       cub::LoadDirectBlocked(
           thread_x,
@@ -173,6 +190,9 @@ col_reduce_looped(T* in, U* out, const __grid_constant__ ColReduceArgs args) {
 
   // Write result.
   if (warp.thread_rank() == 0) {
+    if (BLOCKS > 1) {
+      out += tile_out * out_size * args.reduction_stride;
+    }
     cub::StoreDirectBlocked(
         warp.meta_group_rank(),
         out + tile_y * args.reduction_stride + tile_x * BN,
@@ -227,11 +247,12 @@ __global__ void col_reduce_small(
 inline auto output_grid_for_col_reduce(
     const array& out,
     const cu::ColReduceArgs& args,
-    int bn) {
+    int bn,
+    int outer = 1) {
   int gx, gy = 1;
   size_t n_inner_blocks = cuda::ceil_div(args.reduction_stride, bn);
   size_t n_outer_blocks = out.size() / args.reduction_stride;
-  size_t n_blocks = n_outer_blocks * n_inner_blocks;
+  size_t n_blocks = n_outer_blocks * n_inner_blocks * outer;
   while (n_blocks / gy > INT32_MAX) {
     gy *= 2;
   }
@@ -277,7 +298,8 @@ void col_reduce_looped(
             0,
             indata,
             gpu_ptr<U>(out),
-            static_cast<cu::ColReduceArgs>(args));
+            static_cast<cu::ColReduceArgs>(args),
+            out.size() / args.reduction_stride);
       });
     });
   });
@@ -320,6 +342,117 @@ void col_reduce_small(
   });
 }
 
+void col_reduce_two_pass(
+    cu::CommandEncoder& encoder,
+    const array& in,
+    array& out,
+    Reduce::ReduceType reduce_type,
+    const std::vector<int>& axes,
+    const ReductionPlan& plan,
+    const cu::ColReduceArgs& args) {
+  // Allocate data for the output using in's layout to access them as
+  // contiguously as possible.
+  allocate_same_layout(out, in, axes, encoder);
+
+  // Allocate an intermediate array to hold the 1st pass result
+  constexpr int outer = 32;
+
+  Shape intermediate_shape;
+  intermediate_shape.push_back(outer);
+  intermediate_shape.insert(
+      intermediate_shape.end(), out.shape().begin(), out.shape().end());
+
+  Strides intermediate_strides;
+  intermediate_strides.push_back(out.size());
+  intermediate_strides.insert(
+      intermediate_strides.end(), out.strides().begin(), out.strides().end());
+
+  array intermediate(intermediate_shape, out.dtype(), nullptr, {});
+  auto [data_size, rc, cc] =
+      check_contiguity(intermediate_shape, intermediate_strides);
+  auto fl = out.flags();
+  fl.row_contiguous = rc;
+  fl.col_contiguous = cc;
+  fl.contiguous = true;
+  intermediate.set_data(
+      cu::malloc_async(intermediate.nbytes(), encoder),
+      data_size,
+      intermediate_strides,
+      fl,
+      allocator::free);
+
+  encoder.add_temporary(intermediate);
+  encoder.set_input_array(in);
+  encoder.set_output_array(intermediate);
+  dispatch_all_types(in.dtype(), [&](auto type_tag) {
+    dispatch_reduce_ops(reduce_type, [&](auto reduce_type_tag) {
+      dispatch_reduce_ndim(args.reduce_ndim, [&](auto reduce_ndim) {
+        using OP = MLX_GET_TYPE(reduce_type_tag);
+        using T = cuda_type_t<MLX_GET_TYPE(type_tag)>;
+        using U = typename cu::ReduceResult<OP, T>::type;
+        // Cub doesn't like const pointers for vectorized loads. (sigh)
+        T* indata = const_cast<T*>(gpu_ptr<T>(in));
+
+        constexpr int N_READS = 4;
+        constexpr int BM = 32;
+        constexpr int BN = 32;
+        dim3 grid = output_grid_for_col_reduce(out, args, BN, outer);
+        int blocks = BM * BN / N_READS;
+        auto kernel = cu::
+            col_reduce_looped<T, U, OP, reduce_ndim(), BM, BN, N_READS, outer>;
+        encoder.add_kernel_node(
+            kernel,
+            grid,
+            blocks,
+            0,
+            indata,
+            gpu_ptr<U>(intermediate),
+            static_cast<cu::ColReduceArgs>(args),
+            out.size() / args.reduction_stride);
+      });
+    });
+  });
+
+  // Prepare the reduction arguments for the 2nd pass
+  cu::ColReduceArgs second_args = args;
+  second_args.reduction_size = outer;
+  second_args.reduction_stride = out.size();
+  second_args.ndim = 0;
+  second_args.reduce_shape[0] = outer;
+  second_args.reduce_strides[0] = out.size();
+  second_args.reduce_ndim = 1;
+  second_args.non_col_reductions = 1;
+
+  encoder.set_input_array(intermediate);
+  encoder.set_output_array(out);
+  dispatch_all_types(intermediate.dtype(), [&](auto type_tag) {
+    dispatch_reduce_ops(reduce_type, [&](auto reduce_type_tag) {
+      dispatch_reduce_ndim(second_args.reduce_ndim, [&](auto reduce_ndim) {
+        using OP = MLX_GET_TYPE(reduce_type_tag);
+        using T = cuda_type_t<MLX_GET_TYPE(type_tag)>;
+        using U = typename cu::ReduceResult<OP, T>::type;
+
+        constexpr int N_READS = 4;
+        constexpr int BM = 32;
+        constexpr int BN = 32;
+        dim3 grid = output_grid_for_col_reduce(out, second_args, BN);
+        int blocks = BM * BN / N_READS;
+        auto kernel =
+            cu::col_reduce_looped<T, U, OP, reduce_ndim(), BM, BN, N_READS>;
+        encoder.add_kernel_node(
+            kernel,
+            grid,
+            blocks,
+            0,
+            gpu_ptr<T>(intermediate),
+            gpu_ptr<U>(out),
+            second_args,
+            second_args.reduction_stride);
+      });
+    });
+  });
+}
+
 void col_reduce(
     cu::CommandEncoder& encoder,
     const array& in,
@@ -334,6 +467,18 @@ void col_reduce(
   //   It is a general strided reduce. Each threadblock computes the output for
   //   a subrow of the fast moving axis. For instance 32 elements.
   //
+  // - col_reduce_small
+  //
+  //  It is a column reduce for small columns. Each thread loops over the whole
+  //  column without communicating with any other thread.
+  //
+  // - col_reduce_two_pass
+  //
+  //  It is a reduce for long columns. To increase parallelism, we split the
+  //  reduction in two passes. First we do a column reduce where many
+  //  threadblocks operate on different parts of the reduced axis. Then we
+  //  perform a final column reduce.
+  //
   // Notes: As in row reduce we opt to read as much in order as possible and
   //        leave transpositions as they are (contrary to our Metal backend).
   //
@@ -349,6 +494,14 @@ void col_reduce(
     return;
   }
 
+  // Long column with smallish row
+  size_t total_sums = args.non_col_reductions * args.reduction_size;
+  size_t approx_threads = out.size();
+  if (total_sums / approx_threads > 32) {
+    col_reduce_two_pass(encoder, in, out, reduce_type, axes, plan, args);
+    return;
+  }
+
   // Fallback col reduce
   col_reduce_looped(encoder, in, out, reduce_type, axes, plan, args);
 }

python/tests/test_reduce.py

@@ -210,6 +210,14 @@ class TestReduce(mlx_tests.MLXTestCase):
                     ref = getattr(np, op)(np_arr, axis=axis)
                     self.assertTrue(np.array_equal(out, ref, equal_nan=True))
 
+    def test_long_column(self):
+        a = (np.random.randn(8192, 64) * 32).astype(np.int32)
+        b = mx.array(a)
+
+        c1 = a.sum(0)
+        c2 = b.sum(0)
+        self.assertTrue(np.all(c1 == c2))
+
 
 if __name__ == "__main__":
     mlx_tests.MLXTestRunner(failfast=True)