Update pre-commit hooks (#984)

Author: NripeshN

.pre-commit-config.yaml

@@ -1,11 +1,11 @@
 repos:
 -   repo: https://github.com/pre-commit/mirrors-clang-format
-    rev: v17.0.6
+    rev: v18.1.3
     hooks:
     -   id: clang-format
 # Using this mirror lets us use mypyc-compiled black, which is about 2x faster
 -   repo: https://github.com/psf/black-pre-commit-mirror
-    rev: 24.2.0
+    rev: 24.3.0
     hooks:
     -   id: black
 -   repo: https://github.com/pycqa/isort

benchmarks/cpp/time_utils.h

@@ -17,11 +17,10 @@
             << std::setprecision(5) << time_fn(FUNC, ##__VA_ARGS__) << " msec" \
             << std::endl;
 
-#define TIMEM(MSG, FUNC, ...)                                                  \
-  std::cout << "Timing "                                                       \
-            << "(" << MSG << ") " << #FUNC << " ... " << std::flush            \
-            << std::setprecision(5) << time_fn(FUNC, ##__VA_ARGS__) << " msec" \
-            << std::endl;
+#define TIMEM(MSG, FUNC, ...)                                      \
+  std::cout << "Timing " << "(" << MSG << ") " << #FUNC << " ... " \
+            << std::flush << std::setprecision(5)                  \
+            << time_fn(FUNC, ##__VA_ARGS__) << " msec" << std::endl;
 
 template <typename F, typename... Args>
 double time_fn(F fn, Args&&... args) {

mlx/backend/metal/kernels/steel/conv/loaders/loader_channel_l.h

@@ -394,7 +394,7 @@ struct Conv2DWeightBlockLoader {
       const constant ImplicitGemmConv2DParams* gemm_params_,
       uint simd_group_id [[simdgroup_index_in_threadgroup]],
       uint simd_lane_id [[thread_index_in_simdgroup]])
-      : src_ld(params_->wt_strides[0]),
+      : src_ld(params_ -> wt_strides[0]),
         thread_idx(simd_group_id * 32 + simd_lane_id),
         bi(thread_idx / TCOLS),
         bj(vec_size * (thread_idx % TCOLS)),

mlx/backend/metal/kernels/steel/conv/loaders/loader_channel_n.h

@@ -244,7 +244,7 @@ struct Conv2DWeightBlockLoaderSmallChannels {
       const constant ImplicitGemmConv2DParams* gemm_params_,
       uint simd_group_id [[simdgroup_index_in_threadgroup]],
       uint simd_lane_id [[thread_index_in_simdgroup]])
-      : src_ld(params_->wt_strides[0]),
+      : src_ld(params_ -> wt_strides[0]),
         thread_idx(simd_group_id * 32 + simd_lane_id),
         bi(thread_idx / TCOLS),
         bj(vec_size * (thread_idx % TCOLS)),

mlx/backend/metal/kernels/steel/conv/loaders/loader_general.h

@@ -220,7 +220,7 @@ struct Conv2DWeightBlockLoaderGeneral {
       const short base_ww_,
       uint simd_group_id [[simdgroup_index_in_threadgroup]],
       uint simd_lane_id [[thread_index_in_simdgroup]])
-      : src_ld(params_->wt_strides[0]),
+      : src_ld(params_ -> wt_strides[0]),
         thread_idx(simd_group_id * 32 + simd_lane_id),
         bi(thread_idx / TCOLS),
         bj(vec_size * (thread_idx % TCOLS)),

mlx/backend/metal/matmul.cpp

@@ -197,8 +197,8 @@ inline auto collapse_batches(const array& a, const array& b) {
   std::vector<int> B_bshape{b.shape().begin(), b.shape().end() - 2};
   if (A_bshape != B_bshape) {
     std::ostringstream msg;
-    msg << "[matmul] Got matrices with incorrectly broadcasted shapes: "
-        << "A " << a.shape() << ", B " << b.shape() << ".";
+    msg << "[matmul] Got matrices with incorrectly broadcasted shapes: " << "A "
+        << a.shape() << ", B " << b.shape() << ".";
     throw std::runtime_error(msg.str());
   }
 
@@ -227,9 +227,8 @@ inline auto collapse_batches(const array& a, const array& b, const array& c) {
   std::vector<int> C_bshape{c.shape().begin(), c.shape().end() - 2};
   if (A_bshape != B_bshape || A_bshape != C_bshape) {
     std::ostringstream msg;
-    msg << "[addmm] Got matrices with incorrectly broadcasted shapes: "
-        << "A " << a.shape() << ", B " << b.shape() << ", B " << c.shape()
-        << ".";
+    msg << "[addmm] Got matrices with incorrectly broadcasted shapes: " << "A "
+        << a.shape() << ", B " << b.shape() << ", B " << c.shape() << ".";
     throw std::runtime_error(msg.str());
   }
 
@@ -332,8 +331,8 @@ void steel_matmul(
           << (transpose_b ? 't' : 'n') << "_" << type_to_name(a) << "_"
           << type_to_name(C_split) << "_bm" << bm << "_bn" << bn << "_bk" << bk
           << "_wm" << wm << "_wn" << wn << "_MN_"
-          << ((M % bm == 0 && N % bn == 0) ? "t" : "n") << "aligned"
-          << "_K_" << ((K % bk == 0) ? "t" : "n") << "aligned";
+          << ((M % bm == 0 && N % bn == 0) ? "t" : "n") << "aligned" << "_K_"
+          << ((K % bk == 0) ? "t" : "n") << "aligned";
 
     // Encode and dispatch gemm kernel
     auto& compute_encoder = d.get_command_encoder(s.index);
@@ -422,8 +421,8 @@ void steel_matmul(
         << (transpose_b ? 't' : 'n') << "_" << type_to_name(a) << "_"
         << type_to_name(out) << "_bm" << bm << "_bn" << bn << "_bk" << bk
         << "_wm" << wm << "_wn" << wn << "_MN_"
-        << ((M % bm == 0 && N % bn == 0) ? "t" : "n") << "aligned"
-        << "_K_" << ((K % bk == 0) ? "t" : "n") << "aligned";
+        << ((M % bm == 0 && N % bn == 0) ? "t" : "n") << "aligned" << "_K_"
+        << ((K % bk == 0) ? "t" : "n") << "aligned";
 
   // Encode and dispatch kernel
   auto& compute_encoder = d.get_command_encoder(s.index);
@@ -903,8 +902,8 @@ void AddMM::eval_gpu(const std::vector<array>& inputs, array& out) {
           << (transpose_b ? 't' : 'n') << "_" << type_to_name(a) << "_"
           << type_to_name(C_split) << "_bm" << bm << "_bn" << bn << "_bk" << bk
           << "_wm" << wm << "_wn" << wn << "_MN_"
-          << ((M % bm == 0 && N % bn == 0) ? "t" : "n") << "aligned"
-          << "_K_" << ((K % bk == 0) ? "t" : "n") << "aligned";
+          << ((M % bm == 0 && N % bn == 0) ? "t" : "n") << "aligned" << "_K_"
+          << ((K % bk == 0) ? "t" : "n") << "aligned";
 
     // Encode and dispatch gemm kernel
     auto& compute_encoder = d.get_command_encoder(s.index);
@@ -992,8 +991,8 @@ void AddMM::eval_gpu(const std::vector<array>& inputs, array& out) {
         << (transpose_b ? 't' : 'n') << "_" << type_to_name(a) << "_"
         << type_to_name(out) << "_bm" << bm << "_bn" << bn << "_bk" << bk
         << "_wm" << wm << "_wn" << wn << "_MN_"
-        << ((M % bm == 0 && N % bn == 0) ? "t" : "n") << "aligned"
-        << "_K_" << ((K % bk == 0) ? "t" : "n") << "aligned"
+        << ((M % bm == 0 && N % bn == 0) ? "t" : "n") << "aligned" << "_K_"
+        << ((K % bk == 0) ? "t" : "n") << "aligned"
         << ((alpha_ == 1. && beta_ == 1.) ? "_add" : "_axpby");
 
   // Encode and dispatch kernel

mlx/io/load.cpp

@@ -63,8 +63,7 @@ void save(std::shared_ptr<io::Writer> out_stream, array a) {
   std::string fortran_order = a.flags().col_contiguous ? "True" : "False";
   std::ostringstream header;
   header << "{'descr': '" << dtype_to_array_protocol(a.dtype()) << "',"
-         << " 'fortran_order': " << fortran_order << ","
-         << " 'shape': (";
+         << " 'fortran_order': " << fortran_order << "," << " 'shape': (";
   for (auto i : a.shape()) {
     header << i << ", ";
   }

mlx/ops.cpp

@@ -932,15 +932,15 @@ array pad(
     if (low_pad_size[i] < 0) {
       std::ostringstream msg;
       msg << "Invalid low padding size (" << low_pad_size[i]
-          << ") passed to pad"
-          << " for axis " << i << ". Padding sizes must be non-negative";
+          << ") passed to pad" << " for axis " << i
+          << ". Padding sizes must be non-negative";
       throw std::invalid_argument(msg.str());
     }
     if (high_pad_size[i] < 0) {
       std::ostringstream msg;
       msg << "Invalid high padding size (" << high_pad_size[i]
-          << ") passed to pad"
-          << " for axis " << i << ". Padding sizes must be non-negative";
+          << ") passed to pad" << " for axis " << i
+          << ". Padding sizes must be non-negative";
       throw std::invalid_argument(msg.str());
     }
 
@@ -2508,8 +2508,8 @@ array take_along_axis(
     StreamOrDevice s /* = {} */) {
   if (axis + a.ndim() < 0 || axis >= static_cast<int>(a.ndim())) {
     std::ostringstream msg;
-    msg << "[take_along_axis] Received invalid axis "
-        << " for array with " << a.ndim() << " dimensions.";
+    msg << "[take_along_axis] Received invalid axis " << " for array with "
+        << a.ndim() << " dimensions.";
     throw std::invalid_argument(msg.str());
   }
 
@@ -2904,15 +2904,15 @@ inline std::vector<int> conv_out_shape(
 
     if (pads_lo[i - 1] < 0 || pads_hi[i - 1] < 0) {
       std::ostringstream msg;
-      msg << "[conv] Padding sizes must be non-negative."
-          << " Got padding " << pads_lo << " | " << pads_hi << ".";
+      msg << "[conv] Padding sizes must be non-negative." << " Got padding "
+          << pads_lo << " | " << pads_hi << ".";
       throw std::invalid_argument(msg.str());
     }
 
     if (strides[i - 1] <= 0) {
       std::ostringstream msg;
-      msg << "[conv] Stride sizes must be positive."
-          << " Got strides " << strides << ".";
+      msg << "[conv] Stride sizes must be positive." << " Got strides "
+          << strides << ".";
       throw std::invalid_argument(msg.str());
     }
 
@@ -2948,8 +2948,7 @@ inline void run_conv_checks(const array& in, const array& wt, int n_dim) {
   if (in.ndim() != n_dim + 2) {
     std::ostringstream msg;
     msg << "[conv] Invalid input array with " << in.ndim() << " dimensions for "
-        << n_dim << "D convolution."
-        << " Expected an array with " << n_dim + 2
+        << n_dim << "D convolution." << " Expected an array with " << n_dim + 2
         << " dimensions following the format [N, ..., C_in].";
     throw std::invalid_argument(msg.str());
   }
@@ -3236,8 +3235,7 @@ std::tuple<array, array, array> quantize(
     std::ostringstream msg;
     msg << "[quantize] The last dimension of the matrix needs to be divisible by "
         << "the quantization group size " << group_size
-        << ". However the provided "
-        << " matrix has shape " << w.shape();
+        << ". However the provided " << " matrix has shape " << w.shape();
     throw std::invalid_argument(msg.str());
   }
 

mlx/transforms.cpp

@@ -24,8 +24,8 @@ class Synchronizer : public Primitive {
  public:
   explicit Synchronizer(Stream stream) : Primitive(stream){};
 
-  void eval_cpu(const std::vector<array>&, std::vector<array>&) override{};
-  void eval_gpu(const std::vector<array>&, std::vector<array>&) override{};
+  void eval_cpu(const std::vector<array>&, std::vector<array>&) override {};
+  void eval_gpu(const std::vector<array>&, std::vector<array>&) override {};
 
   DEFINE_PRINT(Synchronize);
 };

tests/custom_vjp_tests.cpp

@@ -18,9 +18,7 @@ TEST_CASE("test simple custom vjp") {
       fn,
       [&](const std::vector<array>&,
           const std::vector<array>&,
-          const std::vector<array>&) {
-        return std::vector<array>{one, one};
-      });
+          const std::vector<array>&) { return std::vector<array>{one, one}; });
 
   auto [z, g] = vjp(fn, {x, y}, {one, one});
   CHECK_EQ(z[0].item<float>(), 6.0f);