split logsumexp

mlx/backend/metal/kernels/logsumexp.h

@@ -5,28 +5,33 @@ template <typename T, typename AccT = float, int N_READS = 4>
     const device T* in,
     device T* out,
     constant int& axis_size,
-    uint gid [[threadgroup_position_in_grid]],
-    uint _lid [[thread_position_in_threadgroup]],
+    uint2 gid [[threadgroup_position_in_grid]],
+    uint2 tid [[thread_position_in_grid]],
+    uint2 grid_dim [[threads_per_grid]],
+    uint2 _lid [[thread_position_in_threadgroup]],
     uint simd_lane_id [[thread_index_in_simdgroup]],
     uint simd_group_id [[simdgroup_index_in_threadgroup]]) {
-  int lid = _lid;
+  int lid = _lid.x;
 
   constexpr int SIMD_SIZE = 32;
+  constexpr int elem_per_group = SIMD_SIZE * 32 * N_READS;
 
   threadgroup AccT local_max[SIMD_SIZE];
   threadgroup AccT local_normalizer[SIMD_SIZE];
 
   AccT ld[N_READS];
 
-  in += gid * size_t(axis_size) + lid * N_READS;
-  if (lid * N_READS + N_READS <= axis_size) {
+  const int axis_offset = tid.y * elem_per_group;
+  in += gid.x * size_t(axis_size) + lid * N_READS + axis_offset;
+  if (axis_offset + lid * N_READS + N_READS <= axis_size) {
     for (int i = 0; i < N_READS; i++) {
       ld[i] = AccT(in[i]);
     }
   } else {
     for (int i = 0; i < N_READS; i++) {
-      ld[i] =
-          ((lid * N_READS + i) < axis_size) ? AccT(in[i]) : Limits<AccT>::min;
+      ld[i] = ((axis_offset + lid * N_READS + i) < axis_size)
+          ? AccT(in[i])
+          : Limits<AccT>::min;
     }
   }
   if (simd_group_id == 0) {
@@ -55,6 +60,7 @@ template <typename T, typename AccT = float, int N_READS = 4>
   maxval = local_max[0];
 
   // Compute exp(x_i - maxval) and store the partial sums in local_normalizer
+  out += gid.x * grid_dim.y + tid.y;
   AccT normalizer = 0;
   for (int i = 0; i < N_READS; i++) {
     normalizer += fast::exp(ld[i] - maxval);
@@ -67,7 +73,7 @@ template <typename T, typename AccT = float, int N_READS = 4>
   if (simd_group_id == 0) {
     normalizer = simd_sum(local_normalizer[simd_lane_id]);
     if (simd_lane_id == 0) {
-      out[gid] = isinf(maxval) ? T(maxval) : T(log(normalizer) + maxval);
+      out[0] = isinf(maxval) ? T(maxval) : T(log(normalizer) + maxval);
     }
   }
 }

mlx/backend/metal/logsumexp.cpp

@@ -62,15 +62,37 @@ void LogSumExp::eval_gpu(const std::vector<array>& inputs, array& out) {
   const int n_reads = 4;
   const int looped_limit = LOGSUMEXP_LOOPED_LIMIT;
 
-  std::string kernel_name = (axis_size > looped_limit) ? "looped_" : "block_";
+  bool split = n_rows < 4 && axis_size > 4 * looped_limit;
+  bool looped = !split && axis_size > looped_limit;
+  std::string kernel_name = looped ? "looped_" : "block_";
   kernel_name += "logsumexp_";
   kernel_name += type_to_name(out);
 
   auto kernel = get_logsumexp_kernel(d, kernel_name, out);
   auto& compute_encoder = d.get_command_encoder(s.index);
+  if (split) {
+    auto tmp_size = ceildiv(axis_size, looped_limit);
+    auto tmp_shape = Shape{n_rows, static_cast<int>(tmp_size)};
+    array tmp(tmp_shape, in.dtype(), nullptr, {});
+    tmp.set_data(allocator::malloc(tmp.nbytes()));
+    size_t threadgroup_size = 1024;
+    assert(threadgroup_size <= kernel->maxTotalThreadsPerThreadgroup());
+    size_t n_threads = n_rows * threadgroup_size;
+    auto grid_dims = MTL::Size(n_threads, tmp_size, 1);
+    auto group_dims = MTL::Size(threadgroup_size, 1, 1);
+    compute_encoder.set_compute_pipeline_state(kernel);
+    compute_encoder.set_input_array(in, 0);
+    compute_encoder.set_output_array(tmp, 1);
+    compute_encoder.set_bytes(axis_size, 2);
+    compute_encoder.dispatch_threads(grid_dims, group_dims);
+    d.add_temporary(tmp, s.index);
+    in = tmp;
+    axis_size = tmp_size;
+  }
+
   {
     MTL::Size grid_dims, group_dims;
-    if (axis_size <= looped_limit) {
+    if (!looped) {
       size_t threadgroup_needed = (axis_size + n_reads - 1) / n_reads;
       size_t simds_needed = (threadgroup_needed + simd_size - 1) / simd_size;
       size_t threadgroup_size = simd_size * simds_needed;

python/tests/test_ops.py

@@ -760,6 +760,10 @@ def logsumexp(x, axes=None):
         x = mx.broadcast_to(mx.random.uniform(shape=(2, 1, 8)), (2, 2, 8))
         self.assertTrue(mx.allclose(mx.logsumexp(x), logsumexp(x)))
 
+        # Even larger
+        x = mx.random.uniform(shape=(4 * 4096 + 3,))
+        self.assertTrue(mx.allclose(mx.logsumexp(x), logsumexp(x)))
+
     def test_mean(self):
         x = mx.array(
             [