Fix typo: refs/head/main => refs/heads/main (#2818)

.github/actions/build-macos-release/action.yml

@@ -17,6 +17,8 @@ runs:
   steps:
     - name: Build Python package
       shell: bash -l {0}
+      env:
+        MACOSX_DEPLOYMENT_TARGET: ${{ inputs.macos-target }}
       run: |
         pip install build
         python setup.py clean --all
@@ -25,6 +27,8 @@ runs:
     - name: Build backend package
       if: ${{ inputs.build-backend }}
       shell: bash -l {0}
+      env:
+        MACOSX_DEPLOYMENT_TARGET: ${{ inputs.macos-target }}
       run: |
         python setup.py clean --all
         MLX_BUILD_STAGE=2 python -m build -w

.github/workflows/pull_request.yml

@@ -13,7 +13,7 @@ permissions:
 
 concurrency:
   group: ${{ github.workflow }}-${{ github.ref }}
-  cancel-in-progress: ${{ github.ref != 'refs/head/main' }}
+  cancel-in-progress: ${{ github.ref != 'refs/heads/main' }}
 
 jobs:
   check_lint:

.github/workflows/release.yml

@@ -23,7 +23,7 @@ jobs:
 
   build_documentation:
     if: github.repository == 'ml-explore/mlx'
-    runs-on: [self-hosted, macos]
+    runs-on: ubuntu-22.04
     steps:
       - uses: actions/checkout@v5
       - uses: ./.github/actions/build-docs
@@ -65,14 +65,14 @@ jobs:
         uses: actions/upload-artifact@v5
         with:
           overwrite: true
-          name: linux-wheels-${{ matrix.python_version }}
+          name: linux-wheels-${{ matrix.python_version }}-${{ matrix.arch }}
           path: wheelhouse/mlx-*.whl
       - name: Upload CPU artifacts
         if: matrix.python_version == '3.10'
         uses: actions/upload-artifact@v5
         with:
           overwrite: true
-          name: mlx-cpu
+          name: mlx-cpu-${{ matrix.arch }}
           path: wheelhouse/mlx_cpu-*.whl
   
   build_mac_release:
@@ -208,7 +208,8 @@ jobs:
     steps:
       - uses: actions/download-artifact@v6
         with:
-          name: mlx-cpu
+          pattern: mlx-cpu-*
+          merge-multiple: true
           path: dist
       - name: Display structure of downloaded files
         run: ls -R dist

mlx/backend/cuda/allocator.cpp

@@ -92,7 +92,7 @@ CudaAllocator::CudaAllocator()
           [this](CudaBuffer* buf) { cuda_free(buf); }) {
   size_t free, total;
   CHECK_CUDA_ERROR(cudaMemGetInfo(&free, &total));
-  memory_limit_ = total * 0.95;
+  memory_limit_ = total * 0.9;
   max_pool_size_ = memory_limit_;
 
   int device_count = 0;
@@ -176,7 +176,7 @@ CudaAllocator::malloc_async(size_t size, int device, cudaStream_t stream) {
     buffer_cache_.release_cached_buffers(get_cache_memory() - max_pool_size_);
   }
   // Copy to managed here if the buffer is not on the right device
-  if (buf->device != device) {
+  if (buf->device >= 0 && buf->device != device) {
     copy_to_managed(*buf);
   }
   return Buffer{buf};
@@ -219,9 +219,9 @@ void CudaAllocator::cuda_free(CudaBuffer* buf) {
     scalar_pool_.free(buf);
   } else {
     if (buf->device >= 0) {
-      cudaFreeAsync(buf->data, free_streams_[buf->device]);
+      CHECK_CUDA_ERROR(cudaFreeAsync(buf->data, free_streams_[buf->device]));
     } else {
-      cudaFree(buf->data);
+      CHECK_CUDA_ERROR(cudaFree(buf->data));
     }
     delete buf;
   }

mlx/backend/cuda/random.cu

@@ -139,10 +139,10 @@ void RandomBits::eval_gpu(const std::vector<array>& inputs, array& out) {
   // keys has shape (N1, ..., NK, 2)
   // out has shape (N1, ..., NK, M1, M2, ...)
   auto& keys = inputs[0];
-  uint32_t num_keys = keys.size() / 2;
+  size_t num_keys = keys.size() / 2;
 
-  uint32_t elems_per_key = out.size() / num_keys;
-  uint32_t bytes_per_key = out.itemsize() * elems_per_key;
+  size_t elems_per_key = out.size() / num_keys;
+  size_t bytes_per_key = out.itemsize() * elems_per_key;
   auto& s = stream();
   auto& encoder = cu::get_command_encoder(s);
   out.set_data(cu::malloc_async(out.nbytes(), encoder));
@@ -150,19 +150,25 @@ void RandomBits::eval_gpu(const std::vector<array>& inputs, array& out) {
     return;
   }
 
-  uint32_t out_per_key = (bytes_per_key + 4 - 1) / 4;
-  uint32_t half_size = out_per_key / 2;
+  size_t out_per_key = (bytes_per_key + 4 - 1) / 4;
+  size_t half_size = out_per_key / 2;
+
   bool odd = out_per_key % 2;
+  if ((half_size + odd) >= UINT32_MAX || num_keys >= UINT32_MAX) {
+    throw std::runtime_error("[RandomBits::eval_gpu] Large size unsupported");
+  }
 
   encoder.set_input_array(keys);
   encoder.set_output_array(out);
-  dim3 grid_dims{num_keys, half_size + odd};
-  int64_t total = grid_dims.x * grid_dims.y;
-  int32_t threads_y = 1;
-  while ((total / threads_y) >= (1U << 31)) {
+  int64_t total = num_keys * (half_size + odd);
+  uint32_t threads_y = 1;
+  while ((total / threads_y) >= UINT_MAX) {
     threads_y *= 2;
   }
-  int32_t threads_x = cuda::ceil_div(total, threads_y);
+  uint32_t threads_x = cuda::ceil_div(total, threads_y);
+
+  dim3 grid_dims{
+      static_cast<uint32_t>(num_keys), static_cast<uint32_t>(half_size + odd)};
   auto [grid, block] = get_grid_and_block(threads_x, threads_y, 1);
   auto& stream = encoder.stream();
   if (keys.flags().row_contiguous) {

mlx/backend/metal/CMakeLists.txt

@@ -121,14 +121,6 @@ if(NOT MLX_METAL_PATH)
   set(MLX_METAL_PATH ${CMAKE_CURRENT_BINARY_DIR}/kernels/)
 endif()
 
-if((MLX_METAL_VERSION GREATER_EQUAL 400) AND (MACOS_SDK_VERSION GREATER_EQUAL
-                                              26.2))
-  set(MLX_ENABLE_NAX TRUE)
-  target_compile_definitions(mlx PRIVATE MLX_ENABLE_NAX)
-else()
-  set(MLX_ENABLE_NAX FALSE)
-endif()
-
 add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/kernels)
 
 target_compile_definitions(mlx

mlx/backend/metal/device.h

@@ -265,14 +265,19 @@ Device& device(mlx::core::Device);
 
 std::unique_ptr<void, std::function<void(void*)>> new_scoped_memory_pool();
 
-#ifdef MLX_ENABLE_NAX
-
 inline bool is_nax_available() {
-  static bool is_nax_available_ =
-      metal::device(mlx::core::Device::gpu).get_architecture_gen() >= 17;
+  auto _check_nax = []() {
+    bool can_use_nax = false;
+    if (__builtin_available(
+            macOS 26.2, iOS 26.2, tvOS 26.2, visionOS 26.2, *)) {
+      can_use_nax = true;
+    }
+    can_use_nax &=
+        metal::device(mlx::core::Device::gpu).get_architecture_gen() >= 17;
+    return can_use_nax;
+  };
+  static bool is_nax_available_ = _check_nax();
   return is_nax_available_;
 }
 
-#endif // MLX_ENABLE_NAX
-
 } // namespace mlx::core::metal

mlx/backend/metal/kernels/CMakeLists.txt

@@ -9,13 +9,17 @@ set(BASE_HEADERS
     utils.h)
 
 function(build_kernel_base TARGET SRCFILE DEPS)
-  set(METAL_FLAGS -x metal -Wall -Wextra -fno-fast-math -Wno-c++17-extensions)
+  set(METAL_FLAGS
+      -x
+      metal
+      -Wall
+      -Wextra
+      -fno-fast-math
+      -Wno-c++17-extensions
+      -Wno-c++20-extensions)
   if(MLX_METAL_DEBUG)
     set(METAL_FLAGS ${METAL_FLAGS} -gline-tables-only -frecord-sources)
   endif()
-  if(MLX_ENABLE_NAX)
-    set(METAL_FLAGS ${METAL_FLAGS} -Wno-c++20-extensions -std=metal4.0)
-  endif()
   if(NOT CMAKE_OSX_DEPLOYMENT_TARGET STREQUAL "")
     set(METAL_FLAGS ${METAL_FLAGS}
                     "-mmacosx-version-min=${CMAKE_OSX_DEPLOYMENT_TARGET}")
@@ -123,8 +127,8 @@ if(NOT MLX_METAL_JIT)
   build_kernel(gemv_masked steel/utils.h)
 endif()
 
-if(MLX_ENABLE_NAX)
-
+if((MLX_METAL_VERSION GREATER_EQUAL 400) AND (MACOS_SDK_VERSION GREATER_EQUAL
+                                              26.2))
   set(STEEL_NAX_HEADERS
       steel/defines.h
       steel/utils.h

mlx/backend/metal/matmul.cpp

@@ -172,8 +172,6 @@ ensure_batch_contiguous(const array& x, metal::Device& d, const Stream& s) {
 // Regular steel matmul dispatch
 ///////////////////////////////////////////////////////////////////////////////
 
-#ifdef MLX_ENABLE_NAX
-
 template <bool CHECK_AB>
 void steel_matmul_regular_axpby_nax(
     const Stream& s,
@@ -210,11 +208,11 @@ void steel_matmul_regular_axpby_nax(
   std::ostringstream kname;
 
   // clang-format off
-  kname << "steel_gemm_fused_nax_" 
+  kname << "steel_gemm_fused_nax_"
         << (transpose_a ? 't' : 'n')
-        << (transpose_b ? 't' : 'n') 
-        << "_" << type_to_name(a) 
-        << "_" << type_to_name(out) 
+        << (transpose_b ? 't' : 'n')
+        << "_" << type_to_name(a)
+        << "_" << type_to_name(out)
         << "_bm" << bm << "_bn" << bn << "_bk" << bk
         << "_wm" << wm << "_wn" << wn; // clang-format on
 
@@ -329,8 +327,6 @@ void steel_matmul_regular_axpby_nax(
   d.add_temporaries(std::move(copies), s.index);
 }
 
-#endif // MLX_ENABLE_NAX
-
 template <bool CHECK_AB>
 void steel_matmul_regular_axpby(
     const Stream& s,
@@ -357,41 +353,35 @@ void steel_matmul_regular_axpby(
     int64_t C_batch_stride /* = 0*/,
     float alpha /* = 1.0f */,
     float beta /* = 0.0f */) {
-#ifdef MLX_ENABLE_NAX
-
-  if (__builtin_available(macOS 26.2, iOS 26.2, tvOS 26.2, visionOS 26.2, *)) {
-    if (metal::is_nax_available() && !issubdtype(a.dtype(), complexfloating) &&
-        (env::enable_tf32() || a.dtype() != float32)) {
-      return steel_matmul_regular_axpby_nax<CHECK_AB>(
-          /* const Stream& s = */ s,
-          /* metal::Device& d = */ d,
-          /* const array& a = */ a,
-          /* const array& b = */ b,
-          /* const array& c = */ c,
-          /* array& out = */ out,
-          /* int M = */ M,
-          /* int N = */ N,
-          /* int K = */ K,
-          /* int batch_size_out = */ batch_size_out,
-          /* int lda = */ lda,
-          /* int ldb = */ ldb,
-          /* int ldd = */ ldd,
-          /* bool transpose_a = */ transpose_a,
-          /* bool transpose_b = */ transpose_b,
-          /* std::vector<array>& copies = */ copies,
-          /* Shape batch_shape = */ batch_shape,
-          /* Strides batch_strides = */ batch_strides,
-          /* int64_t A_batch_stride = */ A_batch_stride,
-          /* int64_t B_batch_stride = */ B_batch_stride,
-          /* int64_t matrix_stride_out = */ matrix_stride_out,
-          /* int64_t C_batch_stride = */ C_batch_stride,
-          /* float alpha = */ alpha,
-          /* float beta = */ beta);
-    }
+  if (metal::is_nax_available() && !issubdtype(a.dtype(), complexfloating) &&
+      (env::enable_tf32() || a.dtype() != float32)) {
+    return steel_matmul_regular_axpby_nax<CHECK_AB>(
+        /* const Stream& s = */ s,
+        /* metal::Device& d = */ d,
+        /* const array& a = */ a,
+        /* const array& b = */ b,
+        /* const array& c = */ c,
+        /* array& out = */ out,
+        /* int M = */ M,
+        /* int N = */ N,
+        /* int K = */ K,
+        /* int batch_size_out = */ batch_size_out,
+        /* int lda = */ lda,
+        /* int ldb = */ ldb,
+        /* int ldd = */ ldd,
+        /* bool transpose_a = */ transpose_a,
+        /* bool transpose_b = */ transpose_b,
+        /* std::vector<array>& copies = */ copies,
+        /* Shape batch_shape = */ batch_shape,
+        /* Strides batch_strides = */ batch_strides,
+        /* int64_t A_batch_stride = */ A_batch_stride,
+        /* int64_t B_batch_stride = */ B_batch_stride,
+        /* int64_t matrix_stride_out = */ matrix_stride_out,
+        /* int64_t C_batch_stride = */ C_batch_stride,
+        /* float alpha = */ alpha,
+        /* float beta = */ beta);
   }
 
-#endif // MLX_ENABLE_NAX
-
   using namespace mlx::steel;
 
   // Determine dispatch kernel
@@ -405,11 +395,11 @@ void steel_matmul_regular_axpby(
   std::ostringstream kname;
 
   // clang-format off
-  kname << "steel_gemm_fused_" 
+  kname << "steel_gemm_fused_"
         << (transpose_a ? 't' : 'n')
-        << (transpose_b ? 't' : 'n') 
-        << "_" << type_to_name(a) 
-        << "_" << type_to_name(out) 
+        << (transpose_b ? 't' : 'n')
+        << "_" << type_to_name(a)
+        << "_" << type_to_name(out)
         << "_bm" << bm << "_bn" << bn << "_bk" << bk
         << "_wm" << wm << "_wn" << wn; // clang-format on
 
@@ -574,14 +564,14 @@ void steel_gemm_splitk_axpby(
   std::ostringstream kname;
 
   // clang-format off
-  kname << "steel_gemm_splitk_" 
+  kname << "steel_gemm_splitk_"
         << (transpose_a ? 't' : 'n')
-        << (transpose_b ? 't' : 'n') 
-        << "_" << type_to_name(a) 
-        << "_" << type_to_name(C_split) 
+        << (transpose_b ? 't' : 'n')
+        << "_" << type_to_name(a)
+        << "_" << type_to_name(C_split)
         << "_bm" << bm << "_bn" << bn << "_bk" << bk
-        << "_wm" << wm << "_wn" << wn 
-        << "_MN_" << (mn_aligned ? "t" : "n") << "aligned" 
+        << "_wm" << wm << "_wn" << wn
+        << "_MN_" << (mn_aligned ? "t" : "n") << "aligned"
         << "_K_" << (k_aligned ? "t" : "n") << "aligned"; // clang-format on
 
   // Encode and dispatch gemm kernel
@@ -915,10 +905,10 @@ void gemv_axbpy(
   const bool do_axpby = CHECK_AB && (alpha != 1.0f || beta != 0.0f);
 
   // clang-format off
-  kname << "_bm" << bm << "_bn" << bn 
-        << "_sm" << sm << "_sn" << sn 
+  kname << "_bm" << bm << "_bn" << bn
+        << "_sm" << sm << "_sn" << sn
         << "_tm" << tm << "_tn" << tn
-        << "_nc" << !contiguous_kernel 
+        << "_nc" << !contiguous_kernel
         << "_axpby" << do_axpby; // clang-format on
 
   // Encode and dispatch kernel
@@ -1766,8 +1756,6 @@ void gather_mm_rhs(
   compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
 }
 
-#ifdef MLX_ENABLE_NAX
-
 void gather_mm_rhs_nax(
     const array& a_,
     const array& b_,
@@ -1911,8 +1899,6 @@ void gather_mm_rhs_nax(
   compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
 }
 
-#endif // MLX_ENABLE_NAX
-
 void gather_mv(
     const array& mat_,
     const array& vec_,
@@ -2196,19 +2182,10 @@ void GatherMM::eval_gpu(const std::vector<array>& inputs, array& out) {
   // We are walking a in order and b is also in order so we can batch up the
   // matmuls and reuse reading a and b.
   if (M == 1 && right_sorted_ == true) {
-#ifdef MLX_ENABLE_NAX
-
-    if (__builtin_available(
-            macOS 26.2, iOS 26.2, tvOS 26.2, visionOS 26.2, *)) {
-      if (metal::is_nax_available() &&
-          !issubdtype(a.dtype(), complexfloating) &&
-          (env::enable_tf32() || a.dtype() != float32)) {
-        return gather_mm_rhs_nax(a, b, rhs_indices, out, d, s);
-      }
+    if (metal::is_nax_available() &&
+        (env::enable_tf32() || a.dtype() != float32)) {
+      return gather_mm_rhs_nax(a, b, rhs_indices, out, d, s);
     }
-
-#endif // MLX_ENABLE_NAX
-
     gather_mm_rhs(a, b, rhs_indices, out, d, s);
     return;
   }

mlx/backend/metal/quantized.cpp

@@ -451,8 +451,6 @@ void qvm(
   compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
 }
 
-#ifdef MLX_ENABLE_NAX
-
 void qmm_nax(
     const array& x,
     const array& w,
@@ -653,8 +651,6 @@ void gather_qmm_nax(
   compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
 }
 
-#endif // MLX_ENABLE_NAX
-
 void qmm(
     const array& x,
     const array& w,
@@ -670,31 +666,25 @@ void qmm(
     metal::Device& d,
     const Stream& s,
     const std::string& mode) {
-#ifdef MLX_ENABLE_NAX
-
-  if (__builtin_available(macOS 26.2, iOS 26.2, tvOS 26.2, visionOS 26.2, *)) {
-    if (metal::is_nax_available() && transpose && (K % 64 == 0) &&
-        (env::enable_tf32() || x.dtype() != float32)) {
-      return qmm_nax(
-          /* const array& x = */ x,
-          /* const array& w = */ w,
-          /* const array& scales = */ scales,
-          /* const std::optional<array>& biases = */ biases,
-          /* array& out = */ out,
-          /* bool transpose = */ transpose,
-          /* int group_size = */ group_size,
-          /* int bits = */ bits,
-          /* int M = */ M,
-          /* int N = */ N,
-          /* int K = */ K,
-          /* metal::Device& d = */ d,
-          /* const Stream& s = */ s,
-          /* const std::string& mode = */ mode);
-    }
+  if (metal::is_nax_available() && transpose && (K % 64 == 0) &&
+      (env::enable_tf32() || x.dtype() != float32)) {
+    return qmm_nax(
+        /* const array& x = */ x,
+        /* const array& w = */ w,
+        /* const array& scales = */ scales,
+        /* const std::optional<array>& biases = */ biases,
+        /* array& out = */ out,
+        /* bool transpose = */ transpose,
+        /* int group_size = */ group_size,
+        /* int bits = */ bits,
+        /* int M = */ M,
+        /* int N = */ N,
+        /* int K = */ K,
+        /* metal::Device& d = */ d,
+        /* const Stream& s = */ s,
+        /* const std::string& mode = */ mode);
   }
 
-#endif // MLX_ENABLE_NAX
-
   int B = out.size() / M / N;
 
   int wm = 2;
@@ -772,33 +762,27 @@ void gather_qmm(
     metal::Device& d,
     const Stream& s,
     const std::string& mode) {
-#ifdef MLX_ENABLE_NAX
-
-  if (__builtin_available(macOS 26.2, iOS 26.2, tvOS 26.2, visionOS 26.2, *)) {
-    if (metal::is_nax_available() && transpose && (K % 64 == 0) &&
-        (env::enable_tf32() || x.dtype() != float32)) {
-      return gather_qmm_nax(
-          /* const array& x = */ x,
-          /* const array& w = */ w,
-          /* const array& scales = */ scales,
-          /* const std::optional<array>& biases = */ biases,
-          /* const array& lhs_indices = */ lhs_indices,
-          /* const array& rhs_indices = */ rhs_indices,
-          /* array& out = */ out,
-          /* bool transpose = */ transpose,
-          /* int group_size = */ group_size,
-          /* int bits = */ bits,
-          /* int M = */ M,
-          /* int N = */ N,
-          /* int K = */ K,
-          /* metal::Device& d = */ d,
-          /* const Stream& s = */ s,
-          /* const std::string& mode = */ mode);
-    }
+  if (metal::is_nax_available() && transpose && (K % 64 == 0) &&
+      (env::enable_tf32() || x.dtype() != float32)) {
+    return gather_qmm_nax(
+        /* const array& x = */ x,
+        /* const array& w = */ w,
+        /* const array& scales = */ scales,
+        /* const std::optional<array>& biases = */ biases,
+        /* const array& lhs_indices = */ lhs_indices,
+        /* const array& rhs_indices = */ rhs_indices,
+        /* array& out = */ out,
+        /* bool transpose = */ transpose,
+        /* int group_size = */ group_size,
+        /* int bits = */ bits,
+        /* int M = */ M,
+        /* int N = */ N,
+        /* int K = */ K,
+        /* metal::Device& d = */ d,
+        /* const Stream& s = */ s,
+        /* const std::string& mode = */ mode);
   }
 
-#endif // MLX_ENABLE_NAX
-
   int B = out.size() / M / N;
 
   int wm = 2;
@@ -975,8 +959,6 @@ void gather_qvm(
   compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
 }
 
-#ifdef MLX_ENABLE_NAX
-
 void gather_qmm_rhs_nax(
     const array& x_,
     const array& w_,
@@ -1108,8 +1090,6 @@ void gather_qmm_rhs_nax(
   compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
 }
 
-#endif // MLX_ENABLE_NAX
-
 void gather_qmm_rhs(
     const array& x_,
     const array& w_,
@@ -1126,32 +1106,26 @@ void gather_qmm_rhs(
     metal::Device& d,
     const Stream& s,
     const std::string mode) {
-#ifdef MLX_ENABLE_NAX
-
-  if (__builtin_available(macOS 26.2, iOS 26.2, tvOS 26.2, visionOS 26.2, *)) {
-    if (metal::is_nax_available() && transpose &&
-        (env::enable_tf32() || x_.dtype() != float32)) {
-      return gather_qmm_rhs_nax(
-          /* const array& x_ = */ x_,
-          /* const array& w_ = */ w_,
-          /* const array& scales_ = */ scales_,
-          /* const std::optional<array>& biases_ = */ biases_,
-          /* const array& indices_ = */ indices_,
-          /* array& out = */ out,
-          /* bool transpose = */ transpose,
-          /* int group_size = */ group_size,
-          /* int bits = */ bits,
-          /* int M = */ M,
-          /* int N = */ N,
-          /* int K = */ K,
-          /* metal::Device& d = */ d,
-          /* const Stream& s = */ s,
-          /* const std::string mode = */ mode);
-    }
+  if (metal::is_nax_available() && transpose &&
+      (env::enable_tf32() || x_.dtype() != float32)) {
+    return gather_qmm_rhs_nax(
+        /* const array& x_ = */ x_,
+        /* const array& w_ = */ w_,
+        /* const array& scales_ = */ scales_,
+        /* const std::optional<array>& biases_ = */ biases_,
+        /* const array& indices_ = */ indices_,
+        /* array& out = */ out,
+        /* bool transpose = */ transpose,
+        /* int group_size = */ group_size,
+        /* int bits = */ bits,
+        /* int M = */ M,
+        /* int N = */ N,
+        /* int K = */ K,
+        /* metal::Device& d = */ d,
+        /* const Stream& s = */ s,
+        /* const std::string mode = */ mode);
   }
 
-#endif // MLX_ENABLE_NAX
-
   // Start by normalizing the indices
   array indices = ensure_row_contiguous(indices_, d, s);
 

mlx/backend/metal/scaled_dot_product_attention.cpp

@@ -13,8 +13,6 @@ namespace mlx::core::fast {
 
 namespace {
 
-#ifdef MLX_ENABLE_NAX
-
 void sdpa_full_self_attention_nax(
     const Stream& s,
     metal::Device& d,
@@ -150,8 +148,6 @@ void sdpa_full_self_attention_nax(
   compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
 }
 
-#endif // MLX_ENABLE_NAX
-
 void sdpa_full_self_attention_metal(
     const Stream& s,
     metal::Device& d,
@@ -163,24 +159,20 @@ void sdpa_full_self_attention_metal(
     bool do_causal_,
     const std::optional<array>& mask,
     const std::optional<array>& sinks) {
-#ifdef MLX_ENABLE_NAX
-  if (__builtin_available(macOS 26.2, iOS 26.2, tvOS 26.2, visionOS 26.2, *)) {
-    if (metal::is_nax_available() && q.shape(3) != 80 &&
-        (env::enable_tf32() || q.dtype() != float32)) {
-      return sdpa_full_self_attention_nax(
-          /* const Stream& s = */ s,
-          /* metal::Device& d = */ d,
-          /* const array& q = */ q,
-          /* const array& k = */ k,
-          /* const array& v = */ v,
-          /* const float scale = */ scale,
-          /* array& o = */ o,
-          /* bool do_causal_ = */ do_causal_,
-          /* const std::optional<array>& mask = */ mask,
-          /* const std::optional<array>& sinks = */ sinks);
-    }
+  if (metal::is_nax_available() && q.shape(3) != 80 &&
+      (env::enable_tf32() || q.dtype() != float32)) {
+    return sdpa_full_self_attention_nax(
+        /* const Stream& s = */ s,
+        /* metal::Device& d = */ d,
+        /* const array& q = */ q,
+        /* const array& k = */ k,
+        /* const array& v = */ v,
+        /* const float scale = */ scale,
+        /* array& o = */ o,
+        /* bool do_causal_ = */ do_causal_,
+        /* const std::optional<array>& mask = */ mask,
+        /* const std::optional<array>& sinks = */ sinks);
   }
-#endif // MLX_ENABLE_NAX
 
   using namespace mlx::steel;
 

mlx/version.h

@@ -4,7 +4,7 @@
 
 #define MLX_VERSION_MAJOR 0
 #define MLX_VERSION_MINOR 30
-#define MLX_VERSION_PATCH 0
+#define MLX_VERSION_PATCH 1
 #define MLX_VERSION_NUMERIC \
   (100000 * MLX_VERSION_MAJOR + 1000 * MLX_VERSION_MINOR + MLX_VERSION_PATCH)
 

python/tests/test_ops.py

@@ -1443,23 +1443,22 @@ class TestOps(mlx_tests.MLXTestCase):
         self.assertListEqual(a.tolist(), expected)
 
     def test_unary_ops(self):
-        def test_ops(npop, mlxop, x, y, atol):
+        def test_ops(npop, mlxop, x, y, atol, rtol):
             r_np = npop(x)
             r_mlx = mlxop(y)
             mx.eval(r_mlx)
-
-            self.assertTrue(np.allclose(r_np, r_mlx, atol=atol))
+            self.assertTrue(np.allclose(r_np, r_mlx, atol=atol, rtol=rtol))
 
         x = np.random.rand(18, 28, 38)
         for op in ["abs", "exp", "log", "square", "sqrt"]:
             with self.subTest(op=op):
-                float_dtypes = [("float16", 1e-3), ("float32", 1e-6)]
+                float_dtypes = [("float16", 1e-3, 1e-3), ("float32", 1e-6, 1e-5)]
 
-                for dtype, atol in float_dtypes:
+                for dtype, atol, rtol in float_dtypes:
                     with self.subTest(dtype=dtype):
                         x_ = x.astype(getattr(np, dtype))
                         y_ = mx.array(x_)
-                        test_ops(getattr(np, op), getattr(mx, op), x_, y_, atol)
+                        test_ops(getattr(np, op), getattr(mx, op), x_, y_, atol, rtol)
 
     def test_unary_ops_from_non_array(self):
         unary_ops = [
@@ -1511,12 +1510,14 @@ class TestOps(mlx_tests.MLXTestCase):
                 self.assertTrue(np.allclose(expected, np.array(out), equal_nan=True))
 
     def test_trig_ops(self):
-        def test_ops(npop, mlxop, x, y, atol):
+        def test_ops(npop, mlxop, x, y, atol, rtol):
             r_np = npop(x)
             r_mlx = mlxop(y)
             mx.eval(r_mlx)
 
-            self.assertTrue(np.allclose(r_np, r_mlx, atol=atol, equal_nan=True))
+            self.assertTrue(
+                np.allclose(r_np, r_mlx, atol=atol, rtol=rtol, equal_nan=True)
+            )
 
         x = np.random.rand(9, 12, 18)
         xi = np.random.rand(9, 12, 18)
@@ -1526,34 +1527,34 @@ class TestOps(mlx_tests.MLXTestCase):
 
         for op in all_fwd_ops:
             with self.subTest(op=op):
-                float_dtypes = [("float16", 1e-3), ("float32", 1e-6)]
+                float_dtypes = [("float16", 1e-3, 1e-3), ("float32", 1e-6, 1e-5)]
 
-                for dtype, atol in float_dtypes:
+                for dtype, atol, rtol in float_dtypes:
                     with self.subTest(dtype=dtype):
                         x_ = x.astype(getattr(np, dtype))
                         y_ = mx.array(x_)
-                        test_ops(getattr(np, op), getattr(mx, op), x_, y_, atol)
+                        test_ops(getattr(np, op), getattr(mx, op), x_, y_, atol, rtol)
 
             with self.subTest(op=op):
-                float_dtypes = [("complex64", 1e-5)]
-
-                for dtype, atol in float_dtypes:
-                    with self.subTest(dtype=dtype):
-                        x_ = x + 1.0j * xi
-                        x_ = x_.astype(getattr(np, dtype))
-                        y_ = mx.array(x_)
-                        test_ops(getattr(np, op), getattr(mx, op), x_, y_, atol)
+                dtype = "complex64"
+                with self.subTest(dtype=dtype):
+                    x_ = x + 1.0j * xi
+                    x_ = x_.astype(getattr(np, dtype))
+                    y_ = mx.array(x_)
+                    test_ops(getattr(np, op), getattr(mx, op), x_, y_, 1e-5, 1e-5)
 
             with self.subTest(op="arc" + op):
-                float_dtypes = [("float16", 1e-3), ("float32", 1e-6)]
+                float_dtypes = [("float16", 1e-3, 1e-3), ("float32", 1e-6, 1e-5)]
                 op_inv = "arc" + op
 
-                for dtype, atol in float_dtypes:
+                for dtype, atol, rtol in float_dtypes:
                     with self.subTest(dtype=dtype):
                         np_op_fwd = getattr(np, op)
                         x_ = np_op_fwd(x).astype(getattr(np, dtype))
                         y_ = mx.array(x_)
-                        test_ops(getattr(np, op_inv), getattr(mx, op_inv), x_, y_, atol)
+                        test_ops(
+                            getattr(np, op_inv), getattr(mx, op_inv), x_, y_, atol, rtol
+                        )
 
         # Test grads
         np_vjp_funcs = {
@@ -1579,11 +1580,10 @@ class TestOps(mlx_tests.MLXTestCase):
                     x_ = x.astype(np.float32)
                     y_ = mx.array(x_)
                     op_ = op
-                    atol_ = 1e-5
 
                     np_vjp = lambda x: np_vjp_funcs[op_](primal_np, x)
                     mx_vjp = lambda x: mx.vjp(getattr(mx, op_), [primal_mx], [x])[1][0]
-                    test_ops(np_vjp, mx_vjp, x_, y_, atol_)
+                    test_ops(np_vjp, mx_vjp, x_, y_, 1e-5, 1e-5)
 
                 with self.subTest(op="arc" + op):
                     np_op_fwd = getattr(np, op)
@@ -1599,11 +1599,10 @@ class TestOps(mlx_tests.MLXTestCase):
                     x_ = x.astype(np.float32)
                     y_ = mx.array(x_)
                     op_ = "arc" + op
-                    atol_ = 1e-5
 
                     np_vjp = lambda x: np_vjp_funcs[op_](primal_np, x)
                     mx_vjp = lambda x: mx.vjp(getattr(mx, op_), [primal_mx], [x])[1][0]
-                    test_ops(np_vjp, mx_vjp, x_, y_, atol_)
+                    test_ops(np_vjp, mx_vjp, x_, y_, 1e-5, 1e-5)
 
     def test_binary_ops(self):
         def test_ops(npop, mlxop, x1, x2, y1, y2, atol):

setup.py

@@ -24,8 +24,8 @@ def get_version():
             if "#define MLX_VERSION_PATCH" in l:
                 patch = l.split()[-1]
     version = f"{major}.{minor}.{patch}"
-    pypi_release = os.environ.get("PYPI_RELEASE", False)
-    dev_release = os.environ.get("DEV_RELEASE", False)
+    pypi_release = int(os.environ.get("PYPI_RELEASE", 0))
+    dev_release = int(os.environ.get("DEV_RELEASE", 0))
     if not pypi_release or dev_release:
         today = datetime.date.today()
         version = f"{version}.dev{today.year}{today.month:02d}{today.day:02d}"