WIP (cpu)

mlx/backend/cpu/arange.h

@@ -10,7 +10,7 @@ namespace mlx::core {
 namespace {
 
 template <typename T>
-void arange(T start, T next, array& out, size_t size, Stream stream) {
+void arange(T start, T next, array& out, int64_t size, Stream stream) {
   auto ptr = out.data<T>();
   auto step_size = next - start;
   auto& encoder = cpu::get_command_encoder(stream);

mlx/backend/cpu/binary.cpp

@@ -17,7 +17,12 @@ namespace mlx::core {
 namespace {
 
 template <typename Op>
-void binary(const array& a, const array& b, array& out, Op /* op */, Stream stream) {
+void binary(
+    const array& a,
+    const array& b,
+    array& out,
+    Op /* op */,
+    Stream stream) {
   auto bopt = get_binary_op_type(a, b);
   set_binary_op_output_data(a, b, out, bopt);
 

mlx/backend/cpu/cholesky.cpp

@@ -33,8 +33,8 @@ void cholesky_impl(const array& a, array& factor, bool upper, Stream stream) {
                     N = a.shape(-1),
                     size = a.size()]() mutable {
     char uplo = (upper) ? 'L' : 'U';
-    size_t num_matrices = size / (N * N);
+    int64_t num_matrices = size / (N * N);
-    for (int i = 0; i < num_matrices; i++) {
+    for (int64_t i = 0; i < num_matrices; i++) {
       // Compute Cholesky factorization.
       int info;
       potrf<T>(

mlx/backend/cpu/gemm.h

@@ -12,12 +12,12 @@ void matmul(
     T* out,
     bool a_transposed,
     bool b_transposed,
-    size_t lda,
+    int64_t lda,
-    size_t ldb,
+    int64_t ldb,
-    size_t ldc,
+    int64_t ldc,
     float alpha,
     float beta,
-    size_t batch_size,
+    int64_t batch_size,
     const Shape& a_shape,
     const Strides& a_strides,
     const Shape& b_shape,

mlx/backend/cpu/gemms/bnns.cpp

@@ -34,7 +34,7 @@ void matmul_bnns(
     bool b_transposed,
     size_t lda,
     size_t ldb,
-    size_t ldc,
+    size_t /* ldc */,
     float alpha,
     float beta,
     size_t batch_size,
@@ -52,7 +52,7 @@ void matmul_bnns(
 #pragma GCC diagnostic ignored "-Wdeprecated-declarations"
   if (beta != 1.0 && beta != 0.0) {
     // scale the output
-    for (auto i = 0; i < batch_size * M * N; ++i) {
+    for (size_t i = 0; i < batch_size * M * N; ++i) {
       out[i] *= beta;
     }
     beta = 1.0;
@@ -127,7 +127,7 @@ void matmul_bnns(
   auto bnns_filter =
       BNNSFilterCreateLayerBroadcastMatMul(&gemm_params, nullptr);
 
-  for (int i = 0; i < batch_size; ++i) {
+  for (size_t i = 0; i < batch_size; ++i) {
     BNNSFilterApplyTwoInput(
         bnns_filter,
         reinterpret_cast<const uint8_t*>(
@@ -148,12 +148,12 @@ void matmul<float16_t>(
     float16_t* out,
     bool a_transposed,
     bool b_transposed,
-    size_t lda,
+    int64_t lda,
-    size_t ldb,
+    int64_t ldb,
-    size_t ldc,
+    int64_t ldc,
     float alpha,
     float beta,
-    size_t batch_size,
+    int64_t batch_size,
     const Shape& a_shape,
     const Strides& a_strides,
     const Shape& b_shape,
@@ -183,12 +183,12 @@ void matmul<bfloat16_t>(
     bfloat16_t* out,
     bool a_transposed,
     bool b_transposed,
-    size_t lda,
+    int64_t lda,
-    size_t ldb,
+    int64_t ldb,
-    size_t ldc,
+    int64_t ldc,
     float alpha,
     float beta,
-    size_t batch_size,
+    int64_t batch_size,
     const Shape& a_shape,
     const Strides& a_strides,
     const Shape& b_shape,

mlx/backend/cpu/gemms/cblas.cpp

@@ -13,20 +13,20 @@ void matmul<float>(
     float* out,
     bool a_transposed,
     bool b_transposed,
-    size_t lda,
+    int64_t lda,
-    size_t ldb,
+    int64_t ldb,
-    size_t ldc,
+    int64_t ldc,
     float alpha,
     float beta,
-    size_t batch_size,
+    int64_t batch_size,
     const Shape& a_shape,
     const Strides& a_strides,
     const Shape& b_shape,
     const Strides& b_strides) {
   auto ndim = a_shape.size();
-  size_t M = a_shape[ndim - 2];
+  int64_t M = a_shape[ndim - 2];
-  size_t N = b_shape[ndim - 1];
+  int64_t N = b_shape[ndim - 1];
-  size_t K = a_shape[ndim - 1];
+  int64_t K = a_shape[ndim - 1];
 
   for (int i = 0; i < batch_size; ++i) {
     cblas_sgemm(
@@ -54,20 +54,20 @@ void matmul<double>(
     double* out,
     bool a_transposed,
     bool b_transposed,
-    size_t lda,
+    int64_t lda,
-    size_t ldb,
+    int64_t ldb,
-    size_t ldc,
+    int64_t ldc,
     float alpha,
     float beta,
-    size_t batch_size,
+    int64_t batch_size,
     const Shape& a_shape,
     const Strides& a_strides,
     const Shape& b_shape,
     const Strides& b_strides) {
   auto ndim = a_shape.size();
-  size_t M = a_shape[ndim - 2];
+  int64_t M = a_shape[ndim - 2];
-  size_t N = b_shape[ndim - 1];
+  int64_t N = b_shape[ndim - 1];
-  size_t K = a_shape[ndim - 1];
+  int64_t K = a_shape[ndim - 1];
 
   for (int i = 0; i < batch_size; ++i) {
     cblas_dgemm(
@@ -95,20 +95,20 @@ void matmul<complex64_t>(
     complex64_t* out,
     bool a_transposed,
     bool b_transposed,
-    size_t lda,
+    int64_t lda,
-    size_t ldb,
+    int64_t ldb,
-    size_t ldc,
+    int64_t ldc,
     float alpha,
     float beta,
-    size_t batch_size,
+    int64_t batch_size,
     const Shape& a_shape,
     const Strides& a_strides,
     const Shape& b_shape,
     const Strides& b_strides) {
   auto ndim = a_shape.size();
-  size_t M = a_shape[ndim - 2];
+  int64_t M = a_shape[ndim - 2];
-  size_t N = b_shape[ndim - 1];
+  int64_t N = b_shape[ndim - 1];
-  size_t K = a_shape[ndim - 1];
+  int64_t K = a_shape[ndim - 1];
   auto calpha = static_cast<complex64_t>(alpha);
   auto cbeta = static_cast<complex64_t>(beta);
 

mlx/backend/cpu/hadamard.cpp

@@ -11,9 +11,9 @@ namespace mlx::core {
 
 // n = 2^k component
 template <typename T>
-void hadamard_n(T* out, int n, int m, float scale, size_t size) {
+void hadamard_n(T* out, int n, int /* m */, float scale, int64_t size) {
   for (int b = 0; b < size / n; b++) {
-    size_t loc = b * n;
+    int64_t loc = b * n;
     T* data_ptr = out + loc;
     int h = 1;
     int n_over_2 = n / 2;
@@ -37,7 +37,7 @@ void hadamard_n(T* out, int n, int m, float scale, size_t size) {
 
 // m component
 template <typename T>
-void hadamard_m(T* out, int n, int m, float scale, size_t size) {
+void hadamard_m(T* out, int n, int m, float scale, int64_t size) {
   auto h_matrices = hadamard_matrices();
   auto& matrix = h_matrices[m];
   auto start = 1;
@@ -45,7 +45,7 @@ void hadamard_m(T* out, int n, int m, float scale, size_t size) {
   std::vector<bool> hmat_vec;
   while (end != std::string_view::npos) {
     auto row = matrix.substr(start, end - start);
-    for (int i = 0; i < row.length(); i++) {
+    for (int i = 0; i < std::ssize(row); i++) {
       hmat_vec.push_back(row[i] == '+');
     }
     start = end + 1;
@@ -53,7 +53,7 @@ void hadamard_m(T* out, int n, int m, float scale, size_t size) {
   }
 
   for (int b = 0; b < size / m / n; b++) {
-    size_t loc = b * n * m;
+    int64_t loc = b * n * m;
     T* data_ptr = out + loc;
     for (int i = 0; i < n; i++) {
       std::vector<float> out(m);

mlx/backend/cpu/indexing.cpp

@@ -78,7 +78,7 @@ void gather(
     can_copy = true;
 
     // Ignore leading 1s
-    int i = 0;
+    int64_t i = 0;
     for (; i < slice_sizes.size() && slice_sizes[i] == 1; ++i)
       ;
 
@@ -91,7 +91,7 @@ void gather(
     can_copy = true;
 
     // Ignore trailing 1s
-    int i = slice_sizes.size() - 1;
+    int64_t i = slice_sizes.size() - 1;
     for (; i >= 0 && slice_sizes[i] == 1; --i)
       ;
 
@@ -101,11 +101,11 @@ void gather(
       can_copy = (src.shape(i) == slice_sizes[i]);
     }
   }
-  size_t slice_size = 1;
+  int64_t slice_size = 1;
   for (auto s : slice_sizes) {
     slice_size *= s;
   }
-  size_t ind_size = slice_size == 0 ? 0 : out.size() / slice_size;
+  int64_t ind_size = slice_size == 0 ? 0 : out.size() / slice_size;
   const T* src_ptr = src.data<T>();
   T* dst_ptr = out.data<T>();
 
@@ -115,10 +115,10 @@ void gather(
     src_it = ContiguousIterator(slice_sizes, src.strides(), src.ndim());
   }
 
-  size_t out_idx = 0;
+  int64_t out_idx = 0;
-  for (int idx = 0; idx < ind_size; idx++) {
+  for (int64_t idx = 0; idx < ind_size; idx++) {
-    size_t src_idx = 0;
+    int64_t src_idx = 0;
-    for (int ii = 0; ii < inds.size(); ++ii) {
+    for (int ii = 0; ii < std::ssize(inds); ++ii) {
       auto ax = axes[ii];
       auto idx_loc = its[ii].loc;
       its[ii].step();
@@ -134,7 +134,7 @@ void gather(
           src_ptr + src_idx, src_ptr + src_idx + slice_size, dst_ptr + out_idx);
       out_idx += slice_size;
     } else {
-      for (int jj = 0; jj < slice_size; jj++) {
+      for (int64_t jj = 0; jj < slice_size; jj++) {
         dst_ptr[out_idx++] = src_ptr[src_idx + src_it.loc];
         src_it.step();
       }
@@ -403,11 +403,11 @@ void scatter(
     const std::vector<int>& axes) {
   int nind = inds.size();
   auto inds_ndim = updates.ndim() - out.ndim();
-  size_t n_updates = nind ? inds[0].size() : 1;
+  int64_t n_updates = nind ? inds[0].size() : 1;
 
   Shape update_shape(
       updates.shape().begin() + inds_ndim, updates.shape().end());
-  size_t update_size = 1;
+  int64_t update_size = 1;
   for (auto us : update_shape) {
     update_size *= us;
   }
@@ -418,9 +418,9 @@ void scatter(
 
   auto out_ptr = out.data<InT>();
   auto upd_ptr = updates.data<InT>();
-  for (int i = 0; i < n_updates; ++i) {
+  for (int64_t i = 0; i < n_updates; ++i) {
-    size_t out_offset = 0;
+    int64_t out_offset = 0;
-    for (int j = 0; j < inds.size(); ++j) {
+    for (int j = 0; j < std::ssize(inds); ++j) {
       auto ax = axes[j];
       auto idx_loc = its[j].loc;
       its[j].step();
@@ -429,7 +429,7 @@ void scatter(
       out_offset += (idx_val * out.strides()[ax]);
     }
     update_it.seek(i * update_size);
-    for (int j = 0; j < update_size; ++j) {
+    for (int64_t j = 0; j < update_size; ++j) {
       OpT{}(upd_ptr[update_it.loc], out_ptr + out_offset + out_it.loc);
       update_it.step();
       out_it.step();

mlx/backend/cpu/masked_mm.cpp

@@ -25,7 +25,7 @@ inline void mask_matrix(
     const int64_t Y_data_str,
     const int64_t X_mask_str,
     const int64_t Y_mask_str,
-    const size_t mask_offset) {
+    const int64_t mask_offset) {
   int tX = (X + block_size - 1) / block_size;
   int tY = (Y + block_size - 1) / block_size;
 
@@ -61,13 +61,13 @@ inline void segmented_mm(
     T* out,
     bool a_transposed,
     bool b_transposed,
-    size_t lda,
+    int64_t lda,
-    size_t ldb,
+    int64_t ldb,
     const Shape& a_shape,
     const Strides& a_strides,
     const Shape& b_shape,
     const Strides& b_strides,
-    size_t num_segments,
+    int64_t num_segments,
     const Shape& segments_shape,
     const Strides& segments_strides) {
   int ndim = a_shape.size();
@@ -149,9 +149,9 @@ void BlockMaskedMM::eval_cpu(const std::vector<array>& inputs, array& out) {
   auto [b_transposed, ldb, b, b_copied] =
       check_transpose(b_pre, has_op_mask, inputs.back().dtype() != bool_);
 
-  size_t M = a.shape(-2);
+  int64_t M = a.shape(-2);
-  size_t N = b.shape(-1);
+  int64_t N = b.shape(-1);
-  size_t K = a.shape(-1);
+  int64_t K = a.shape(-1);
 
   if (M == 0 || N == 0) {
     return;
@@ -172,8 +172,8 @@ void BlockMaskedMM::eval_cpu(const std::vector<array>& inputs, array& out) {
                        int batch_idx,
                        int X,
                        int Y,
-                       size_t X_data_str,
+                       int64_t X_data_str,
-                       size_t Y_data_str,
+                       int64_t Y_data_str,
                        const Shape& mask_shape,
                        const Strides& mask_strides,
                        bool is_bool) {
@@ -253,7 +253,7 @@ void BlockMaskedMM::eval_cpu(const std::vector<array>& inputs, array& out) {
   auto a_ptr = a.data<float>();
   auto b_ptr = b.data<float>();
   auto out_ptr = out.data<float>();
-  size_t num_matrices = out.size() / (M * size_t(N));
+  int64_t num_matrices = out.size() / (M * int64_t(N));
   auto ldc = out.shape(-1);
 
   encoder.dispatch([a_ptr,
@@ -394,9 +394,9 @@ void GatherMM::eval_cpu(const std::vector<array>& inputs, array& out) {
   auto [a_transposed, lda, a] = check_transpose(a_pre);
   auto [b_transposed, ldb, b] = check_transpose(b_pre);
 
-  size_t M = a.shape(-2);
+  int64_t M = a.shape(-2);
-  size_t N = b.shape(-1);
+  int64_t N = b.shape(-1);
-  size_t K = a.shape(-1);
+  int64_t K = a.shape(-1);
 
   if (M == 0 || N == 0) {
     return;
@@ -413,7 +413,7 @@ void GatherMM::eval_cpu(const std::vector<array>& inputs, array& out) {
 
   // Get batch dims
   auto batch_size_out = out.size() / (M * N);
-  size_t matrix_stride_out = M * N;
+  int64_t matrix_stride_out = M * N;
 
   auto get_batch_dims = [](const auto& v) {
     return decltype(v){v.begin(), v.end() - 2};

mlx/backend/cpu/primitives.cpp

@@ -48,7 +48,7 @@ static std::pair<array, bool> compute_dynamic_offset(
   auto compute_offset =
       [strides, axes, offset = offset.data<int64_t>()](const auto* indices) {
         int64_t offset_ = 0;
-        for (int i = 0; i < axes.size(); ++i) {
+        for (int i = 0; i < std::ssize(axes); ++i) {
           offset_ += indices[i] * strides[axes[i]];
         }
         offset[0] = offset_;
@@ -193,9 +193,9 @@ void Concatenate::eval_cpu(const std::vector<array>& inputs, array& out) {
   flags.row_contiguous = false;
   flags.col_contiguous = false;
   flags.contiguous = false;
-  for (int i = 0; i < inputs.size(); i++) {
+  for (int i = 0; i < std::ssize(inputs); i++) {
     array out_slice(inputs[i].shape(), out.dtype(), nullptr, {});
-    size_t data_offset = strides[axis_] * sizes[i];
+    int64_t data_offset = strides[axis_] * sizes[i];
     out_slice.copy_shared_buffer(
         out, strides, flags, out_slice.size(), data_offset);
     copy_cpu_inplace(inputs[i], out_slice, CopyType::GeneralGeneral, stream());
@@ -205,7 +205,7 @@ void Concatenate::eval_cpu(const std::vector<array>& inputs, array& out) {
 void Contiguous::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   auto& in = inputs[0];
-  constexpr size_t extra_bytes = 16384;
+  constexpr int64_t extra_bytes = 16384;
   if (in.buffer_size() <= out.nbytes() + extra_bytes &&
       (in.flags().row_contiguous ||
        (allow_col_major_ && in.flags().col_contiguous))) {
@@ -254,8 +254,8 @@ void Pad::eval_cpu(const std::vector<array>& inputs, array& out) {
   copy_cpu(val, out, CopyType::Scalar, stream());
 
   // Find offset for start of input values
-  size_t data_offset = 0;
+  int64_t data_offset = 0;
-  for (int i = 0; i < axes_.size(); i++) {
+  for (int i = 0; i < std::ssize(axes_); i++) {
     auto ax = axes_[i] < 0 ? out.ndim() + axes_[i] : axes_[i];
     data_offset += out.strides()[ax] * low_pad_size_[i];
   }
@@ -274,10 +274,10 @@ void RandomBits::eval_cpu(const std::vector<array>& inputs, array& out) {
   // keys has shape (N1, ..., NK, 2)
   // out has shape (N1, ..., NK, M1, M2, ...)
   auto& keys = inputs[0];
-  size_t num_keys = keys.size() / 2;
+  int64_t num_keys = keys.size() / 2;
 
-  size_t elems_per_key = out.size() / num_keys;
+  int64_t elems_per_key = out.size() / num_keys;
-  size_t bytes_per_key = out.itemsize() * elems_per_key;
+  int64_t bytes_per_key = out.itemsize() * elems_per_key;
   out.set_data(allocator::malloc(out.nbytes()));
 
   auto kptr = inputs[0].data<uint32_t>();
@@ -291,8 +291,8 @@ void RandomBits::eval_cpu(const std::vector<array>& inputs, array& out) {
                     num_keys,
                     kshape = keys.shape(),
                     kstrides = keys.strides()]() mutable {
-    size_t out_skip = (bytes_per_key + 4 - 1) / 4;
+    int64_t out_skip = (bytes_per_key + 4 - 1) / 4;
-    auto half_size = out_skip / 2;
+    uintptr_t half_size = out_skip / 2;
     bool even = out_skip % 2 == 0;
     for (int i = 0; i < num_keys; ++i, cptr += bytes_per_key) {
       auto ptr = reinterpret_cast<uint32_t*>(cptr);

mlx/backend/cpu/qrf.cpp

@@ -13,7 +13,7 @@ void qrf_impl(const array& a, array& q, array& r, Stream stream) {
   const int M = a.shape(-2);
   const int N = a.shape(-1);
   const int lda = M;
-  size_t num_matrices = a.size() / (M * N);
+  int64_t num_matrices = a.size() / (M * N);
 
   // Copy A to inplace input and make it col-contiguous
   array in(a.shape(), a.dtype(), nullptr, {});
@@ -54,7 +54,7 @@ void qrf_impl(const array& a, array& q, array& r, Stream stream) {
     auto work = allocator::malloc(sizeof(T) * lwork);
 
     // Loop over matrices
-    for (int i = 0; i < num_matrices; ++i) {
+    for (int64_t i = 0; i < num_matrices; ++i) {
       // Solve
       geqrf<T>(
           &M,
@@ -68,7 +68,7 @@ void qrf_impl(const array& a, array& q, array& r, Stream stream) {
     }
     allocator::free(work);
 
-    for (int i = 0; i < num_matrices; ++i) {
+    for (int64_t i = 0; i < num_matrices; ++i) {
       /// num_reflectors x N
       for (int j = 0; j < num_reflectors; ++j) {
         for (int k = 0; k < j; ++k) {
@@ -97,7 +97,7 @@ void qrf_impl(const array& a, array& q, array& r, Stream stream) {
     work = allocator::malloc(sizeof(T) * lwork);
 
     // Loop over matrices
-    for (int i = 0; i < num_matrices; ++i) {
+    for (int64_t i = 0; i < num_matrices; ++i) {
       // Compute Q
       orgqr<T>(
           &M,
@@ -111,7 +111,7 @@ void qrf_impl(const array& a, array& q, array& r, Stream stream) {
           &info);
     }
 
-    for (int i = 0; i < num_matrices; ++i) {
+    for (int64_t i = 0; i < num_matrices; ++i) {
       // M x num_reflectors
       for (int j = 0; j < M; ++j) {
         for (int k = 0; k < num_reflectors; ++k) {

mlx/backend/cpu/simd/math.h

@@ -79,7 +79,8 @@ Simd<T, N> sincos(Simd<T, N> in) {
 
   // Get the polynom selection mask. There is one polynom for 0 <= x <= Pi/4
   // and another one for Pi/4<x<=Pi/2. Both branches will be computed.
-  auto poly_mask = (emm2 & 2) != 0;
+  auto poly_mask =
+      (emm2 & static_cast<uint32_t>(2)) != static_cast<uint32_t>(0);
 
   // The magic pass: "Extended precision modular arithmetic"
   // x = ((x - y * DP1) - y * DP2) - y * DP3
@@ -87,8 +88,8 @@ Simd<T, N> sincos(Simd<T, N> in) {
   x = fma(y, Simd<float, N>(-2.4187564849853515625e-4f), x);
   x = fma(y, Simd<float, N>(-3.77489497744594108e-8f), x);
 
-  sign_mask_sin = sign_mask_sin ^ ((emm2 & 4) != 0);
+  sign_mask_sin = sign_mask_sin ^ ((emm2 & 4) != static_cast<uint32_t>(0));
-  auto sign_mask_cos = ((emm2 - 2) & 4) != 0;
+  auto sign_mask_cos = ((emm2 - 2) & 4) != static_cast<uint32_t>(0);
 
   // Evaluate the first polynom  (0 <= x <= Pi/4) in y1,
   // and the second polynom      (Pi/4 <= x <= 0) in y2

mlx/backend/cpu/sort.cpp

@@ -120,8 +120,8 @@ template <typename T>
 void sort(array& out, int axis) {
   // Get axis, shape and stride info
   axis = axis < 0 ? axis + out.ndim() : axis;
-  size_t in_size = out.size();
+  int64_t in_size = out.size();
-  size_t n_rows = in_size / out.shape(axis);
+  int64_t n_rows = in_size / out.shape(axis);
 
   auto remaining_shape = out.shape();
   remaining_shape.erase(remaining_shape.begin() + axis);
@@ -136,7 +136,7 @@ void sort(array& out, int axis) {
   ContiguousIterator src_it(
       remaining_shape, remaining_strides, remaining_shape.size());
   auto out_ptr = out.data<T>();
-  for (int i = 0; i < n_rows; i++) {
+  for (int64_t i = 0; i < n_rows; i++) {
     T* data_ptr = out_ptr + src_it.loc;
 
     StridedIterator st(data_ptr, axis_stride, 0);
@@ -151,7 +151,7 @@ template <typename T, typename IdxT = uint32_t>
 void argsort(const array& in, array& out, int axis) {
   // Get axis, shape and stride info
   axis = axis < 0 ? axis + in.ndim() : axis;
-  size_t n_rows = in.size() / in.shape(axis);
+  int64_t n_rows = in.size() / in.shape(axis);
 
   auto in_remaining_shape = in.shape();
   in_remaining_shape.erase(in_remaining_shape.begin() + axis);
@@ -176,7 +176,7 @@ void argsort(const array& in, array& out, int axis) {
       out_remaining_shape, out_remaining_strides, out_remaining_shape.size());
   auto in_ptr = in.data<T>();
   auto out_ptr = out.data<IdxT>();
-  for (int i = 0; i < n_rows; i++) {
+  for (int64_t i = 0; i < n_rows; i++) {
     const T* data_ptr = in_ptr + in_it.loc;
     IdxT* idx_ptr = out_ptr + out_it.loc;
 
@@ -214,8 +214,8 @@ template <typename T>
 void partition(array& out, int axis, int kth) {
   // Get axis, shape and stride info
   axis = axis < 0 ? axis + out.ndim() : axis;
-  size_t in_size = out.size();
+  int64_t in_size = out.size();
-  size_t n_rows = in_size / out.shape(axis);
+  int64_t n_rows = in_size / out.shape(axis);
 
   auto remaining_shape = out.shape();
   remaining_shape.erase(remaining_shape.begin() + axis);
@@ -232,7 +232,7 @@ void partition(array& out, int axis, int kth) {
   ContiguousIterator src_it(
       remaining_shape, remaining_strides, remaining_shape.size());
   auto out_ptr = out.data<T>();
-  for (int i = 0; i < n_rows; i++) {
+  for (int64_t i = 0; i < n_rows; i++) {
     T* data_ptr = out_ptr + src_it.loc;
     src_it.step();
 
@@ -248,7 +248,7 @@ template <typename T, typename IdxT = uint32_t>
 void argpartition(const array& in, array& out, int axis, int kth) {
   // Get axis, shape and stride info
   axis = axis < 0 ? axis + in.ndim() : axis;
-  size_t n_rows = in.size() / in.shape(axis);
+  int64_t n_rows = in.size() / in.shape(axis);
 
   auto in_remaining_shape = in.shape();
   in_remaining_shape.erase(in_remaining_shape.begin() + axis);
@@ -277,7 +277,7 @@ void argpartition(const array& in, array& out, int axis, int kth) {
   auto in_ptr = in.data<T>();
   auto out_ptr = out.data<IdxT>();
 
-  for (int i = 0; i < n_rows; i++) {
+  for (int64_t i = 0; i < n_rows; i++) {
     const T* data_ptr = in_ptr + in_it.loc;
     IdxT* idx_ptr = out_ptr + out_it.loc;
     in_it.step();

mlx/backend/cpu/svd.cpp

@@ -27,7 +27,7 @@ void svd_impl(
   const int N = a.shape(-1);
   const int K = std::min(M, N);
 
-  size_t num_matrices = a.size() / (M * N);
+  int64_t num_matrices = a.size() / (M * N);
 
   // lapack clobbers the input, so we have to make a copy.
   array in(a.shape(), a.dtype(), nullptr, {});
@@ -121,7 +121,7 @@ void svd_impl(
     auto scratch = array::Data{allocator::malloc(sizeof(T) * lwork)};
 
     // Loop over matrices.
-    for (int i = 0; i < num_matrices; i++) {
+    for (int64_t i = 0; i < num_matrices; i++) {
       gesdd<T>(
           /* jobz = */ jobz,
           // M and N are swapped since lapack expects column-major.
@@ -153,10 +153,10 @@ void svd_impl(
 
 template <typename T>
 void compute_svd(
-    const array& a,
+    const array& /* a */,
-    bool compute_uv,
+    bool /* compute_uv */,
-    std::vector<array>& outputs,
+    std::vector<array>& /* outputs */,
-    Stream stream) {}
+    Stream /* stream */) {}
 
 void SVD::eval_cpu(
     const std::vector<array>& inputs,

mlx/backend/cpu/ternary.h

@@ -136,7 +136,7 @@ void ternary_op(
   if (topt == TernaryOpType::ScalarScalarScalar) {
     *out_ptr = op(*a_ptr, *b_ptr, *c_ptr);
   } else if (topt == TernaryOpType::VectorVectorVector) {
-    for (size_t i = 0; i < out.size(); ++i) {
+    for (int64_t i = 0; i < out.size(); ++i) {
       *out_ptr = op(*a_ptr, *b_ptr, *c_ptr);
       a_ptr++;
       b_ptr++;

mlx/backend/cpu/unary.h

@@ -10,8 +10,8 @@
 namespace mlx::core {
 
 template <typename T, typename U = T, typename Op>
-void unary_op(const T* a, U* out, size_t shape, size_t stride) {
+void unary_op(const T* a, U* out, int64_t shape, int64_t stride) {
-  for (size_t i = 0; i < shape; i += 1) {
+  for (int64_t i = 0; i < shape; i += 1) {
     out[i] = Op{}(*a);
     a += stride;
   }
@@ -38,14 +38,14 @@ void unary_op(const array& a, array& out, Op) {
       src++;
     }
   } else {
-    size_t shape = ndim > 0 ? a.shape().back() : 1;
+    int64_t shape = ndim > 0 ? a.shape().back() : 1;
-    size_t stride = ndim > 0 ? a.strides().back() : 1;
+    int64_t stride = ndim > 0 ? a.strides().back() : 1;
     if (ndim <= 1) {
       unary_op<T, U, Op>(src, dst, shape, stride);
       return;
     }
     auto it = ContiguousIterator(a.shape(), a.strides(), ndim - 1);
-    for (size_t elem = 0; elem < a.size(); elem += shape) {
+    for (int64_t elem = 0; elem < a.size(); elem += shape) {
       unary_op<T, U, Op>(src + it.loc, dst + elem, shape, stride);
       it.step();
     }