version (#2470)

docs/src/python/optimizers.rst

@@ -51,14 +51,14 @@ the saved state. Here's a simple example:
    optimizer.update(model, grads)
 
    # Save the state
-   state = tree_flatten(optimizer.state)
-   mx.save_safetensors("optimizer.safetensors", dict(state))
+   state = tree_flatten(optimizer.state, destination={})
+   mx.save_safetensors("optimizer.safetensors", state)
 
    # Later on, for example when loading from a checkpoint,
    # recreate the optimizer and load the state
    optimizer = optim.Adam(learning_rate=1e-2)
 
-   state = tree_unflatten(list(mx.load("optimizer.safetensors").items()))
+   state = tree_unflatten(mx.load("optimizer.safetensors"))
    optimizer.state = state
 
 Note, not every optimizer configuation parameter is saved in the state. For

docs/src/usage/export.rst

@@ -7,17 +7,17 @@ Exporting Functions
 
 MLX has an API to export and import functions to and from a file. This lets you
 run computations written in one MLX front-end (e.g. Python) in another MLX
-front-end (e.g. C++). 
+front-end (e.g. C++).
 
 This guide walks through the basics of the MLX export API with some examples.
 To see the full list of functions check-out the :ref:`API documentation
 <export>`.
 
-Basics of Exporting 
+Basics of Exporting
 -------------------
 
 Let's start with a simple example:
- 
+
 .. code-block:: python
 
   def fun(x, y):
@@ -67,7 +67,7 @@ specified as variable positional arguments or as a tuple of arrays:
 
   x = mx.array(1.0)
   y = mx.array(1.0)
-   
+
   # Both arguments to fun are positional
   mx.export_function("add.mlxfn", fun, x, y)
 
@@ -133,7 +133,7 @@ parameters are also saved to the ``model.mlxfn`` file.
    For enclosed arrays inside an exported function, be extra careful to ensure
    they are evaluated. The computation graph that gets exported will include
    the computation that produces enclosed inputs.
-  
+
    If the above example was missing ``mx.eval(model.parameters()``, the
    exported function would include the random initialization of the
    :obj:`mlx.nn.Module` parameters.
@@ -150,8 +150,8 @@ parameters, pass them as inputs to the ``call`` wrapper:
      # Set the model's parameters to the input parameters
      model.update(tree_unflatten(list(params.items())))
      return model(x)
- 
-   params = dict(tree_flatten(model.parameters()))
+
+   params = tree_flatten(model.parameters(), destination={})
    mx.export_function("model.mlxfn", call, (mx.zeros(4),), params)
 
 
@@ -169,8 +169,8 @@ to export a function which can be used for inputs with variable shapes:
 
   # Ok
   out, = imported_abs(mx.array(-1.0))
-  
-  # Also ok 
+
+  # Also ok
   out, = imported_abs(mx.array([-1.0, -2.0]))
 
 With ``shapeless=False`` (which is the default), the second call to
@@ -197,7 +197,7 @@ a single file by creating an exporting context manager with :func:`exporter`:
   def fun(x, y=None):
       constant = mx.array(3.0)
       if y is not None:
-        x += y 
+        x += y
       return x + constant
 
   with mx.exporter("fun.mlxfn", fun) as exporter:
@@ -215,7 +215,7 @@ a single file by creating an exporting context manager with :func:`exporter`:
   print(out)
 
 In the above example the function constant data, (i.e. ``constant``), is only
-saved once. 
+saved once.
 
 Transformations with Imported Functions
 ---------------------------------------
@@ -238,7 +238,7 @@ on imported functions just like regular Python functions:
   # Prints: array(1, dtype=float32)
   print(dfdx(x))
 
-  # Compile the imported function 
+  # Compile the imported function
   mx.compile(imported_fun)
   # Prints: array(0, dtype=float32)
   print(compiled_fun(x)[0])
@@ -275,7 +275,7 @@ Import and run the function in C++ with only a few lines of code:
   // Prints: array(2, dtype=float32)
   std::cout << outputs[0] << std::endl;
 
-Imported functions can be transformed in C++ just like in Python. Use 
+Imported functions can be transformed in C++ just like in Python. Use
 ``std::vector<mx::array>`` for positional arguments and ``std::map<std::string,
 mx::array>`` for keyword arguments when calling imported functions in C++.
 

mlx/backend/cuda/CMakeLists.txt

@@ -39,6 +39,7 @@ target_sources(
           ${CMAKE_CURRENT_SOURCE_DIR}/reduce/row_reduce.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/rms_norm.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/rope.cu
+          ${CMAKE_CURRENT_SOURCE_DIR}/scaled_dot_product_attention.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/scan.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/slicing.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/softmax.cu

mlx/backend/cuda/primitives.cpp

@@ -6,17 +6,6 @@
 
 namespace mlx::core {
 
-bool fast::ScaledDotProductAttention::use_fallback(
-    const array& q,
-    const array& k,
-    const array& v,
-    bool has_mask,
-    bool has_arr_mask,
-    bool do_causal,
-    Stream s) {
-  return true;
-}
-
 #define NO_GPU_MULTI(func)                                             \
   void func::eval_gpu(                                                 \
       const std::vector<array>& inputs, std::vector<array>& outputs) { \
@@ -53,7 +42,6 @@ NO_GPU_MULTI(Eig)
 NO_GPU_MULTI(Eigh)
 
 namespace fast {
-NO_GPU(ScaledDotProductAttention)
 NO_GPU_MULTI(CustomKernel)
 } // namespace fast
 

mlx/backend/cuda/scaled_dot_product_attention.cu

@@ -0,0 +1,781 @@
+// Copyright © 2025 Apple Inc.
+
+#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"
+
+#include <nvtx3/nvtx3.hpp>
+
+#include <cooperative_groups.h>
+#include <cooperative_groups/reduce.h>
+
+namespace mlx::core {
+
+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[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 / 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 = 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 = (lane_idx < BN) ? max_scores[lane_idx] : -1e9;
+  U new_max = cg::reduce(warp, max_score, cg::greater<U>());
+  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>());
+
+  // 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
+  auto ff = exp2f(max_scores[warp_idx] - new_max);
+  PRAGMA_LOOP_UNROLL
+  for (int i = 0; i < v_per_thread; i++) {
+    outputs[warp_idx][lane_idx] = o[i] * ff;
+    block.sync();
+
+    if (warp_idx == 0) {
+      U ot = outputs[0][lane_idx];
+      PRAGMA_LOOP_UNROLL
+      for (int j = 1; j < BN; j++) {
+        ot += outputs[j][lane_idx];
+        warp.sync();
+      }
+      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];
+    }
+  }
+}
+
+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 = 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);
+
+  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.value, headdim.value>;
+        encoder.add_kernel_node(
+            kernel,
+            grid_dim,
+            block_dim,
+            0,
+            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.value, headdim.value>;
+
+          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,
+              0,
+              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.value, headdim.value>;
+
+          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,
+              0,
+              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 (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_);
+  }
+}
+
+} // namespace
+
+namespace fast {
+
+bool ScaledDotProductAttention::use_fallback(
+    const array& q,
+    const array& k,
+    const array& v,
+    bool has_mask,
+    bool has_arr_mask,
+    bool do_causal,
+    Stream s) {
+  if (detail::in_grad_tracing()) {
+    return true;
+  }
+  if (s.device == Device::cpu) {
+    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);
+  const int key_sequence_length = k.shape(2);
+
+  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_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(
+    const std::vector<array>& inputs,
+    array& out) {
+  nvtx3::scoped_range r("ScaledDotProductAttention::eval_gpu");
+
+  auto& s = stream();
+  auto& encoder = cu::get_command_encoder(s);
+
+  auto& q_pre = inputs[0];
+  auto& k_pre = inputs[1];
+  auto& v_pre = inputs[2];
+  auto& o = out;
+
+  std::vector<array> copies;
+
+  // Define some copy functions to ensure the layout of the inputs is as
+  // expected.
+  copies.reserve(3);
+  auto copy_unless = [&copies, &s](
+                         auto predicate, const array& arr) -> const array& {
+    if (!predicate(arr)) {
+      array arr_copy = contiguous_copy_gpu(arr, s);
+      copies.push_back(std::move(arr_copy));
+      return copies.back();
+    } else {
+      return arr;
+    }
+  };
+
+  // We are in vector mode ie single query
+  if (q_pre.shape(2) < 4) {
+    auto q_copy_unless = [](const array& arr) {
+      if (arr.flags().row_contiguous) {
+        return true;
+      }
+      auto& strides = arr.strides();
+      auto& shape = arr.shape();
+      if (shape[0] == 1 || shape[1] == 1) {
+        // If either the batch or head dimension is a singleton, the other can
+        // be transposed with the sequence dimension
+        auto bidx = shape[0] == 1 ? 1 : 0;
+        return (strides[3] == 1) && (strides[2] == shape[3] * shape[bidx]) &&
+            (strides[bidx] == shape[3]);
+      }
+      return false;
+    };
+
+    auto kv_copy_unless = [](const array& arr) {
+      // keys and values should be copied if:
+      // - the last dimension is not contiguous
+      // - the batch and head dim are not contiguous
+      auto& strides = arr.strides();
+      auto& shape = arr.shape();
+      if (strides.back() != 1) {
+        return false;
+      }
+      if (shape[0] == 1 || shape[1] == 1) {
+        return true;
+      }
+      return (strides[0] == strides[1] * shape[1]);
+    };
+
+    const auto& q = copy_unless(q_copy_unless, q_pre);
+    const auto& k = copy_unless(kv_copy_unless, k_pre);
+    const auto& v = copy_unless(kv_copy_unless, v_pre);
+
+    for (const auto& cp : copies) {
+      encoder.add_temporary(cp);
+    }
+
+    // Donate the query if possible
+    if (q.is_donatable() && q.flags().row_contiguous && q.size() == o.size()) {
+      o.copy_shared_buffer(q);
+    } else {
+      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_);
+  }
+
+  // Full attention mode should never reach here
+  else {
+    throw std::runtime_error("Doesn't support matrix yet.");
+  }
+}
+
+} // namespace fast
+
+} // namespace mlx::core

mlx/backend/metal/device.h

@@ -104,7 +104,7 @@ struct CommandEncoder {
   };
 
   // Outputs of all kernels in the encoder including temporaries
-  std::unordered_set<const void*> outputs() {
+  std::unordered_set<const void*>& outputs() {
     return all_outputs_;
   };
 

mlx/version.h

@@ -3,8 +3,8 @@
 #pragma once
 
 #define MLX_VERSION_MAJOR 0
-#define MLX_VERSION_MINOR 27
-#define MLX_VERSION_PATCH 1
+#define MLX_VERSION_MINOR 28
+#define MLX_VERSION_PATCH 0
 #define MLX_VERSION_NUMERIC \
   (100000 * MLX_VERSION_MAJOR + 1000 * MLX_VERSION_MINOR + MLX_VERSION_PATCH)
 

python/mlx/nn/layers/base.py

@@ -178,7 +178,7 @@ class Module(dict):
 
         if strict:
             new_weights = dict(weights)
-            curr_weights = dict(tree_flatten(self.parameters()))
+            curr_weights = tree_flatten(self.parameters(), destination={})
             if extras := (new_weights.keys() - curr_weights.keys()):
                 num_extra = len(extras)
                 extras = ",\n".join(sorted(extras))
@@ -212,7 +212,7 @@ class Module(dict):
         - ``.npz`` will use :func:`mx.savez`
         - ``.safetensors`` will use :func:`mx.save_safetensors`
         """
-        params_dict = dict(tree_flatten(self.parameters()))
+        params_dict = tree_flatten(self.parameters(), destination={})
 
         if file.endswith(".npz"):
             mx.savez(file, **params_dict)

python/mlx/utils.py

@@ -1,7 +1,7 @@
 # Copyright © 2023 Apple Inc.
 from collections import defaultdict
 from itertools import zip_longest
-from typing import Any, Callable, List, Optional, Tuple
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 
 
 def tree_map(
@@ -114,8 +114,11 @@ def tree_map_with_path(
 
 
 def tree_flatten(
-    tree: Any, prefix: str = "", is_leaf: Optional[Callable] = None
-) -> Any:
+    tree: Any,
+    prefix: str = "",
+    is_leaf: Optional[Callable] = None,
+    destination: Optional[Union[List[Tuple[str, Any]], Dict[str, Any]]] = None,
+) -> Union[List[Tuple[str, Any]], Dict[str, Any]]:
     """Flattens a Python tree to a list of key, value tuples.
 
     The keys are using the dot notation to define trees of arbitrary depth and
@@ -128,9 +131,12 @@ def tree_flatten(
         print(tree_flatten([[[0]]]))
         # [("0.0.0", 0)]
 
-        print(tree_flatten([[[0]]], ".hello"))
+        print(tree_flatten([[[0]]], prefix=".hello"))
         # [("hello.0.0.0", 0)]
 
+        tree_flatten({"a": {"b": 1}}, destination={})
+        {"a.b": 1}
+
     .. note::
        Dictionaries should have keys that are valid Python identifiers.
 
@@ -140,26 +146,50 @@ def tree_flatten(
             always discarded.
         is_leaf (callable): An optional callable that returns True if the
             passed object is considered a leaf or False otherwise.
+        destination (list or dict, optional): A list or dictionary to store the
+            flattened tree. If None an empty list will be used. Default: ``None``.
 
     Returns:
-        List[Tuple[str, Any]]: The flat representation of the Python tree.
+        Union[List[Tuple[str, Any]], Dict[str, Any]]: The flat representation of
+            the Python tree.
     """
-    flat_tree = []
+    if destination is None:
+        destination = []
 
-    if is_leaf is None or not is_leaf(tree):
-        if isinstance(tree, (list, tuple)):
-            for i, t in enumerate(tree):
-                flat_tree.extend(tree_flatten(t, f"{prefix}.{i}", is_leaf))
-            return flat_tree
-        if isinstance(tree, dict):
-            for k, t in tree.items():
-                flat_tree.extend(tree_flatten(t, f"{prefix}.{k}", is_leaf))
-            return flat_tree
+    # Create the function to update the destination. We are taking advantage of
+    # the fact that list.extend and dict.update have the same API to simplify
+    # the code a bit.
+    if isinstance(destination, list):
+        _add_to_destination = destination.extend
+    elif isinstance(destination, dict):
+        _add_to_destination = destination.update
+    else:
+        raise ValueError("Destination should be either a list or a dictionary or None")
 
-    return [(prefix[1:], tree)]
+    # Leaf identified by is_leaf so add it and return
+    if is_leaf is not None and is_leaf(tree):
+        _add_to_destination([(prefix[1:], tree)])
+        return destination
+
+    # List or tuple so recursively add each subtree
+    if isinstance(tree, (list, tuple)):
+        for i, item in enumerate(tree):
+            tree_flatten(item, f"{prefix}.{i}", is_leaf, destination)
+        return destination
+
+    # Dictionary so recursively add each subtree
+    if isinstance(tree, dict):
+        for key, value in tree.items():
+            tree_flatten(value, f"{prefix}.{key}", is_leaf, destination)
+        return destination
+
+    # Leaf so add it and return
+    _add_to_destination([(prefix[1:], tree)])
+
+    return destination
 
 
-def tree_unflatten(tree: List[Tuple[str, Any]]) -> Any:
+def tree_unflatten(tree: Union[List[Tuple[str, Any]], Dict[str, Any]]) -> Any:
     """Recreate a Python tree from its flat representation.
 
     .. code-block:: python
@@ -170,31 +200,34 @@ def tree_unflatten(tree: List[Tuple[str, Any]]) -> Any:
         print(d)
         # {"hello": {"world": 42}}
 
+        d = tree_unflatten({"hello.world": 42})
+        print(d)
+        # {"hello": {"world": 42}}
+
     Args:
-        tree (list[tuple[str, Any]]): The flat representation of a Python tree.
+        tree (list[tuple[str, Any]] or dict[str, Any]): The flat representation of a Python tree.
            For instance as returned by :meth:`tree_flatten`.
 
     Returns:
         A Python tree.
     """
-    if len(tree) == 1 and tree[0][0] == "":
-        return tree[0][1]
+    items = tree.items() if isinstance(tree, dict) else tree
 
-    try:
-        int(tree[0][0].split(".", maxsplit=1)[0])
-        is_list = True
-    except ValueError:
-        is_list = False
+    # Special case when we have just one element in the tree ie not a tree
+    if len(items) == 1:
+        key, value = next(iter(items))
+        if key == "":
+            return value
 
     # collect children
     children = defaultdict(list)
-    for key, value in tree:
+    for key, value in items:
         current_idx, *next_idx = key.split(".", maxsplit=1)
         next_idx = "" if not next_idx else next_idx[0]
         children[current_idx].append((next_idx, value))
 
-    # recursively map them to the original container
-    if is_list:
+    # Assume they are a list and fail to dict if the keys are not all integers
+    try:
         keys = sorted((int(idx), idx) for idx in children.keys())
         l = []
         for i, k in keys:
@@ -202,7 +235,7 @@ def tree_unflatten(tree: List[Tuple[str, Any]]) -> Any:
             l.extend([{} for _ in range(i - len(l))])
             l.append(tree_unflatten(children[k]))
         return l
-    else:
+    except ValueError:
         return {k: tree_unflatten(v) for k, v in children.items()}
 
 

python/tests/test_nn.py

@@ -80,7 +80,7 @@ class TestBase(mlx_tests.MLXTestCase):
                 self.weights = {"w1": mx.zeros((2, 2)), "w2": mx.ones((2, 2))}
 
         model = DictModule()
-        params = dict(tree_flatten(model.parameters()))
+        params = tree_flatten(model.parameters(), destination={})
         self.assertEqual(len(params), 2)
         self.assertTrue(mx.array_equal(params["weights.w1"], mx.zeros((2, 2))))
         self.assertTrue(mx.array_equal(params["weights.w2"], mx.ones((2, 2))))