[CUDA] Fix alpha not respected when using bias epilogue (#2578)

* implement batch rope for Metal

* cuda rope (#2576)

CMakeLists.txt

@@ -87,22 +87,21 @@ cmake_policy(SET CMP0135 NEW)
 
 add_library(mlx)
 
-if(MLX_BUILD_METAL)
-  set(METAL_LIB "-framework Metal")
-  set(FOUNDATION_LIB "-framework Foundation")
-  set(QUARTZ_LIB "-framework QuartzCore")
-endif()
-
 if(MLX_BUILD_CUDA)
   enable_language(CUDA)
 endif()
 
-if(MLX_BUILD_METAL AND NOT METAL_LIB)
+if(MLX_BUILD_METAL)
-  message(STATUS "Metal not found. Unable to build GPU")
+  find_library(METAL_LIB Metal)
-  set(MLX_BUILD_METAL OFF)
+  find_library(FOUNDATION_LIB Foundation)
-  set(MLX_METAL_DEBUG OFF)
+  find_library(QUARTZ_LIB QuartzCore)
-elseif(MLX_BUILD_METAL)
+  if(METAL_LIB)
-  message(STATUS "Building METAL sources")
+    message(STATUS "Metal found ${METAL_LIB}")
+  else()
+    message(
+      FATAL_ERROR
+        "Metal not found. Set MLX_BUILD_METAL=OFF to build without GPU")
+  endif()
 
   if(MLX_METAL_DEBUG)
     add_compile_definitions(MLX_METAL_DEBUG)
@@ -111,7 +110,8 @@ elseif(MLX_BUILD_METAL)
   # Throw an error if xcrun not found
   execute_process(
     COMMAND zsh "-c" "/usr/bin/xcrun -sdk macosx --show-sdk-version"
-    OUTPUT_VARIABLE MACOS_SDK_VERSION COMMAND_ERROR_IS_FATAL ANY)
+    OUTPUT_VARIABLE MACOS_SDK_VERSION
+    OUTPUT_STRIP_TRAILING_WHITESPACE COMMAND_ERROR_IS_FATAL ANY)
 
   if(${MACOS_SDK_VERSION} LESS 14.0)
     message(

mlx/backend/cuda/gemms/cublas_gemm.cpp

@@ -85,10 +85,10 @@ cublasLtMatrixLayout_t create_matrix_layout(
     int32_t batch_count,
     int64_t batch_stride) {
   cublasLtMatrixLayout_t desc;
+  if (transposed) {
+    std::swap(rows, cols);
+  }
   CHECK_CUBLAS_ERROR(cublasLtMatrixLayoutCreate(&desc, type, rows, cols, ld));
-  cublasLtOrder_t order = transposed ? CUBLASLT_ORDER_COL : CUBLASLT_ORDER_ROW;
-  CHECK_CUBLAS_ERROR(cublasLtMatrixLayoutSetAttribute(
-      desc, CUBLASLT_MATRIX_LAYOUT_ORDER, &order, sizeof(cublasLtOrder_t)));
   if (batch_count > 1) {
     CHECK_CUBLAS_ERROR(cublasLtMatrixLayoutSetAttribute(
         desc,
@@ -138,25 +138,34 @@ CublasGemm::CublasGemm(
       CUBLASLT_MATMUL_DESC_POINTER_MODE,
       &pointer_mode,
       sizeof(int32_t)));
-  cublasOperation_t op = CUBLAS_OP_N;
+
+  // In cublasLt matrices use column-major layout, while it is possible to use
+  // the CUBLASLT_ORDER_ROW option to switch to row-major layout, the bias
+  // epilogue does not work with the option. So instead we swap A and B to make
+  // cublasLt return the row-major result, which works because:
+  // - the data of a matrix in row-major layout is identical to its transpose in
+  //   column-major layout
+  // - C^T = (A @ B)^T = B^T @ A^T
+  cublasOperation_t a_op = b_transposed ? CUBLAS_OP_T : CUBLAS_OP_N;
   CHECK_CUBLAS_ERROR(cublasLtMatmulDescSetAttribute(
       matmul_desc_,
       CUBLASLT_MATMUL_DESC_TRANSA,
-      &op,
+      &a_op,
       sizeof(cublasOperation_t)));
+  cublasOperation_t b_op = a_transposed ? CUBLAS_OP_T : CUBLAS_OP_N;
   CHECK_CUBLAS_ERROR(cublasLtMatmulDescSetAttribute(
       matmul_desc_,
       CUBLASLT_MATMUL_DESC_TRANSB,
-      &op,
+      &b_op,
       sizeof(cublasOperation_t)));
 
   auto type = dtype_to_cublas_type(dtype);
   a_desc_ = create_matrix_layout(
-      type, a_rows, a_cols, a_transposed, lda, batch_count, a_batch_stride);
+      type, b_cols, b_rows, b_transposed, ldb, batch_count, b_batch_stride);
   b_desc_ = create_matrix_layout(
-      type, b_rows, b_cols, b_transposed, ldb, batch_count, b_batch_stride);
+      type, a_cols, a_rows, a_transposed, lda, batch_count, a_batch_stride);
   out_desc_ = create_matrix_layout(
-      type, a_rows, b_cols, false, b_cols, batch_count, a_rows * b_cols);
+      type, b_cols, a_rows, false, b_cols, batch_count, a_rows * b_cols);
 }
 
 CublasGemm::CublasGemm(
@@ -191,7 +200,7 @@ CublasGemm::CublasGemm(
           b_batch_stride) {
   auto type = dtype_to_cublas_type(dtype);
   c_desc_ = create_matrix_layout(
-      type, a_rows, b_cols, false, ldc, batch_count, c_batch_stride);
+      type, b_cols, a_rows, false, ldc, batch_count, c_batch_stride);
 }
 
 CublasGemm::~CublasGemm() {
@@ -213,14 +222,25 @@ void CublasGemm::set_out(
   CHECK_CUBLAS_ERROR(cublasLtMatrixLayoutDestroy(out_desc_));
   out_desc_ = create_matrix_layout(
       dtype_to_cublas_type(dtype),
-      rows,
       cols,
+      rows,
       transposed,
       ld,
       batch_count,
       batch_stride);
 }
 
+void CublasGemm::set_bias(void* bias) {
+  cublasLtEpilogue_t epilogue = CUBLASLT_EPILOGUE_BIAS;
+  CHECK_CUBLAS_ERROR(cublasLtMatmulDescSetAttribute(
+      matmul_desc_,
+      CUBLASLT_MATMUL_DESC_EPILOGUE,
+      &epilogue,
+      sizeof(epilogue)));
+  CHECK_CUBLAS_ERROR(cublasLtMatmulDescSetAttribute(
+      matmul_desc_, CUBLASLT_MATMUL_DESC_BIAS_POINTER, &bias, sizeof(bias)));
+}
+
 void CublasGemm::run(
     cu::CommandEncoder& encoder,
     array& out,
@@ -228,11 +248,19 @@ void CublasGemm::run(
     const array& b,
     const Shape& batch_shape,
     const Strides& a_batch_strides,
-    const Strides& b_batch_strides) {
+    const Strides& b_batch_strides,
+    float alpha) {
   int batch_count = out.size() / (M_ * N_);
   if (batch_count / batch_shape.back() > 1) {
     run_batched(
-        encoder, out, a, b, batch_shape, a_batch_strides, b_batch_strides);
+        encoder,
+        out,
+        a,
+        b,
+        batch_shape,
+        a_batch_strides,
+        b_batch_strides,
+        alpha);
     return;
   }
 
@@ -240,7 +268,13 @@ void CublasGemm::run(
   encoder.set_input_array(b);
   encoder.set_output_array(out);
 
-  execute(encoder, out.data<void>(), a.data<void>(), b.data<void>(), nullptr);
+  execute(
+      encoder,
+      out.data<void>(),
+      a.data<void>(),
+      b.data<void>(),
+      nullptr,
+      alpha);
 }
 
 void CublasGemm::run(
@@ -330,9 +364,9 @@ void CublasGemm::execute(
       handle_,
       matmul_desc_,
       &alpha,
-      a,
+      b, // a and b are swapped
       a_desc_,
-      b,
+      a,
       b_desc_,
       &beta,
       c ? c : out,

mlx/backend/cuda/gemms/cublas_gemm.h

@@ -55,6 +55,8 @@ class CublasGemm {
       int32_t batch_count,
       int64_t batch_stride);
 
+  void set_bias(void* bias);
+
   void run(
       cu::CommandEncoder& encoder,
       array& out,
@@ -62,7 +64,8 @@ class CublasGemm {
       const array& b,
       const Shape& batch_shape,
       const Strides& a_batch_strides,
-      const Strides& b_batch_strides);
+      const Strides& b_batch_strides,
+      float alpha = 1.0f);
 
   void run(
       cu::CommandEncoder& encoder,
@@ -85,7 +88,8 @@ class CublasGemm {
       const array& b,
       const Shape& batch_shape,
       const Strides& a_batch_strides,
-      const Strides& b_batch_strides);
+      const Strides& b_batch_strides,
+      float alpha);
 
   void run_batched(
       cu::CommandEncoder& encoder,

mlx/backend/cuda/gemms/cublas_gemm_batched_12_0.cpp

@@ -13,7 +13,8 @@ void CublasGemm::run_batched(
     const array& b,
     const Shape& batch_shape,
     const Strides& a_batch_strides,
-    const Strides& b_batch_strides) {
+    const Strides& b_batch_strides,
+    float alpha) {
   encoder.set_input_array(a);
   encoder.set_input_array(b);
   encoder.set_output_array(out);
@@ -27,7 +28,8 @@ void CublasGemm::run_batched(
         out.data<int8_t>() + out.itemsize() * i * batch_shape.back() * M_ * N_,
         a.data<int8_t>() + a.itemsize() * a_it.loc,
         b.data<int8_t>() + b.itemsize() * b_it.loc,
-        nullptr);
+        nullptr,
+        alpha);
     a_it.step();
     b_it.step();
   }

mlx/backend/cuda/gemms/cublas_gemm_batched_12_9.cu

@@ -154,7 +154,8 @@ void CublasGemm::run_batched(
     const array& b,
     const Shape& batch_shape,
     const Strides& a_batch_strides,
-    const Strides& b_batch_strides) {
+    const Strides& b_batch_strides,
+    float alpha) {
   int batch_count = out.size() / (M_ * N_);
   set_pointer_mode(a_desc_, batch_count);
   set_pointer_mode(b_desc_, batch_count);
@@ -226,7 +227,8 @@ void CublasGemm::run_batched(
       reinterpret_cast<void*>(out_pointers),
       reinterpret_cast<void*>(a_pointers),
       reinterpret_cast<void*>(b_pointers),
-      nullptr);
+      nullptr,
+      alpha);
 }
 
 void CublasGemm::run_batched(

mlx/backend/cuda/matmul.cpp

@@ -11,6 +11,7 @@
 #include <numeric>
 
 namespace mlx::core {
+
 namespace {
 
 std::tuple<bool, int64_t, array>
@@ -28,6 +29,76 @@ check_transpose(cu::CommandEncoder& enc, const Stream& s, const array& arr) {
   }
 }
 
+void gemm_and_bias(
+    cu::CommandEncoder& encoder,
+    int M,
+    int N,
+    int K,
+    bool a_transposed,
+    int64_t lda,
+    bool b_transposed,
+    int64_t ldb,
+    array& out,
+    const array& a,
+    const array& b,
+    void* bias = nullptr,
+    float alpha = 1.0f) {
+  // Check and collapse batch dimensions
+  auto [batch_shape, a_batch_strides, b_batch_strides] = collapse_batches(a, b);
+
+  auto batch_count = out.size() / (M * N);
+
+  // Collapse batches into M if needed
+  if (batch_count > 1 && !a_transposed && batch_shape.size() == 1 &&
+      a.strides()[a.ndim() - 2] == K && a_batch_strides.back() == M * K &&
+      b_batch_strides.back() == 0) {
+    M *= batch_shape.back();
+    batch_count = 1;
+
+    a_batch_strides = {0};
+    b_batch_strides = {0};
+    batch_shape = {1};
+  }
+
+  // Use gemmv when possible
+  if (!bias && cu::can_use_gemv(M, N, K, a_transposed, b_transposed)) {
+    cu::gemv(
+        a,
+        b,
+        out,
+        M,
+        N,
+        K,
+        batch_count,
+        batch_shape,
+        a_batch_strides,
+        b_batch_strides,
+        encoder);
+    return;
+  }
+
+  // Invoke cublasLt
+  CublasGemm gemm(
+      encoder.device(),
+      a.dtype(),
+      a_transposed,
+      M,
+      K,
+      lda,
+      b_transposed,
+      K,
+      N,
+      ldb,
+      batch_shape.back(),
+      a_batch_strides.back(),
+      b_batch_strides.back());
+  if (bias) {
+    gemm.set_bias(bias);
+  }
+  gemm.run(
+      encoder, out, a, b, batch_shape, a_batch_strides, b_batch_strides, alpha);
+}
+
 } // namespace
 
 void Matmul::eval_gpu(const std::vector<array>& inputs, array& out) {
@@ -48,9 +119,6 @@ void Matmul::eval_gpu(const std::vector<array>& inputs, array& out) {
 
   out.set_data(allocator::malloc(out.nbytes()));
 
-  /////////////////////////////////////////////////////////////////////////////
-  // Init checks and prep
-
   int M = a_pre.shape(-2);
   int N = b_pre.shape(-1);
   int K = a_pre.shape(-1);
@@ -60,58 +128,8 @@ void Matmul::eval_gpu(const std::vector<array>& inputs, array& out) {
   auto [a_transposed, lda, a] = check_transpose(encoder, s, a_pre);
   auto [b_transposed, ldb, b] = check_transpose(encoder, s, b_pre);
 
-  /////////////////////////////////////////////////////////////////////////////
+  gemm_and_bias(
-  // Check and collapse batch dimensions
+      encoder, M, N, K, a_transposed, lda, b_transposed, ldb, out, a, b);
-
-  auto [batch_shape, a_batch_strides, b_batch_strides] = collapse_batches(a, b);
-
-  auto batch_count = out.size() / (M * N);
-
-  // Collapse batches into M if needed
-  if (batch_count > 1 && !a_transposed && batch_shape.size() == 1 &&
-      a.strides()[a.ndim() - 2] == K && a_batch_strides.back() == M * K &&
-      b_batch_strides.back() == 0) {
-    M *= batch_shape.back();
-    batch_count = 1;
-
-    a_batch_strides = {0};
-    b_batch_strides = {0};
-    batch_shape = {1};
-  }
-
-  if (cu::can_use_gemv(M, N, K, a_transposed, b_transposed)) {
-    cu::gemv(
-        a,
-        b,
-        out,
-        M,
-        N,
-        K,
-        batch_count,
-        batch_shape,
-        a_batch_strides,
-        b_batch_strides,
-        encoder);
-    return;
-  }
-
-  /////////////////////////////////////////////////////////////////////////////
-  // Invoke cublasLt
-  CublasGemm gemm(
-      cu::device(s.device),
-      a.dtype(),
-      a_transposed,
-      M,
-      K,
-      lda,
-      b_transposed,
-      K,
-      N,
-      ldb,
-      batch_shape.back(),
-      a_batch_strides.back(),
-      b_batch_strides.back());
-  gemm.run(encoder, out, a, b, batch_shape, a_batch_strides, b_batch_strides);
 }
 
 void AddMM::eval_gpu(const std::vector<array>& inputs, array& out) {
@@ -136,6 +154,28 @@ void AddMM::eval_gpu(const std::vector<array>& inputs, array& out) {
   auto [a_transposed, lda, a] = check_transpose(encoder, s, a_pre);
   auto [b_transposed, ldb, b] = check_transpose(encoder, s, b_pre);
 
+  /////////////////////////////////////////////////////////////////////////////
+  // Dispatch to GEMM with epilogue or AddMM
+
+  if (beta_ == 1 && c.strides(-1) == 1 && c.data_size() == out.shape(-1)) {
+    out.set_data(allocator::malloc(out.nbytes()));
+    gemm_and_bias(
+        encoder,
+        M,
+        N,
+        K,
+        a_transposed,
+        lda,
+        b_transposed,
+        ldb,
+        out,
+        a,
+        b,
+        c.data<void>(),
+        alpha_);
+    return;
+  }
+
   int64_t ldc;
   {
     auto stx = c.strides()[c.ndim() - 2];
@@ -177,7 +217,7 @@ void AddMM::eval_gpu(const std::vector<array>& inputs, array& out) {
   }
 
   /////////////////////////////////////////////////////////////////////////////
-  // Invoke cublasLt
+  // Invoke cublasLt with AddMM settings
 
   CublasGemm gemm(
       cu::device(s.device),

mlx/backend/cuda/rope.cu

@@ -103,15 +103,21 @@ template <typename T, bool traditional, bool forward, int N = 4>
 __device__ void rope_impl(
     const T* in,
     T* out,
-    int offset,
+    const int* offset,
     float inv_freq,
     float scale,
     const cuda::std::array<int64_t, 3> strides,
     const cuda::std::array<int64_t, 3> out_strides,
-    int64_t n_batch,
+    int64_t offset_stride,
+    int n_head,
     uint3 pos,
     uint3 dims) {
-  float L = scale * static_cast<float>(pos.y + offset);
+  auto n_head_up = N * ((n_head + N - 1) / N);
+  auto head_idx = static_cast<int>((pos.z * N) % n_head_up);
+  auto batch_idx = (pos.z * N) / n_head_up;
+  auto batch_offset = offset[batch_idx * offset_stride];
+  float L = scale * static_cast<float>(pos.y + batch_offset);
+  auto mat_idx = batch_idx * n_head + head_idx;
 
   // Compute costheta, sintheta
   float theta = L * inv_freq;
@@ -123,20 +129,19 @@ __device__ void rope_impl(
   size_t out_index_1, out_index_2;
   if (traditional) {
     out_index_1 = 2 * pos.x * out_strides[2] + pos.y * out_strides[1] +
-        N * pos.z * out_strides[0];
+        mat_idx * out_strides[0];
     out_index_2 = out_index_1 + 1;
     in_index_1 =
-        2 * pos.x * strides[2] + pos.y * strides[1] + N * pos.z * strides[0];
+        2 * pos.x * strides[2] + pos.y * strides[1] + mat_idx * strides[0];
     in_index_2 = in_index_1 + strides[2];
   } else {
     out_index_1 = pos.x * out_strides[2] + pos.y * out_strides[1] +
-        N * pos.z * out_strides[0];
+        mat_idx * out_strides[0];
     out_index_2 = out_index_1 + dims.x * out_strides[2];
-    in_index_1 =
+    in_index_1 = pos.x * strides[2] + pos.y * strides[1] + mat_idx * strides[0];
-        pos.x * strides[2] + pos.y * strides[1] + N * pos.z * strides[0];
     in_index_2 = in_index_1 + dims.x * strides[2];
   }
-  for (int i = 0; i < N && pos.z * N + i < n_batch; ++i) {
+  for (int i = 0; i < N && head_idx + i < n_head; ++i) {
     // Read and write the output
     float x1 = static_cast<float>(in[in_index_1]);
     float x2 = static_cast<float>(in[in_index_2]);
@@ -167,7 +172,8 @@ __global__ void rope(
     float base,
     const __grid_constant__ cuda::std::array<int64_t, 3> strides,
     const __grid_constant__ cuda::std::array<int64_t, 3> out_strides,
-    int64_t n_batch,
+    int64_t offset_stride,
+    int n_head,
     uint3 dims) {
   uint3 pos = make_uint3(
       blockIdx.x * blockDim.x + threadIdx.x,
@@ -182,12 +188,13 @@ __global__ void rope(
   rope_impl<T, traditional, forward>(
       in,
       out,
-      *offset,
+      offset,
       inv_freq,
       scale,
       strides,
       out_strides,
-      n_batch,
+      offset_stride,
+      n_head,
       pos,
       dims);
 }
@@ -202,7 +209,8 @@ __global__ void rope_freqs(
     float base,
     const __grid_constant__ cuda::std::array<int64_t, 3> strides,
     const __grid_constant__ cuda::std::array<int64_t, 3> out_strides,
-    int64_t n_batch,
+    int64_t offset_stride,
+    int n_head,
     uint3 dims,
     int64_t freq_stride) {
   uint3 pos = make_uint3(
@@ -217,12 +225,13 @@ __global__ void rope_freqs(
   rope_impl<T, traditional, forward>(
       in,
       out,
-      *offset,
+      offset,
       inv_freq,
       scale,
       strides,
       out_strides,
-      n_batch,
+      offset_stride,
+      n_head,
       pos,
       dims);
 }
@@ -245,23 +254,28 @@ void RoPE::eval_gpu(
   auto& offset = inputs[1];
   auto& out = outputs[0];
 
-  if (in.ndim() < 3) {
-    throw std::runtime_error("[RoPE] Input must have at least 3 dimensions");
-  }
-
   cuda::std::array<int64_t, 3> strides;
   cuda::std::array<int64_t, 3> out_strides;
   bool donated = false;
   int ndim = in.ndim();
-  int dispatch_ndim = in.ndim();
+
+  int B = in.shape(0);
+  int T = in.shape(-2);
+  int D = in.shape(-1);
+  size_t mat_size = T * D;
+  int dispatch_ndim = ndim;
   while (in.shape(-dispatch_ndim) == 1 && dispatch_ndim > 3) {
     dispatch_ndim--;
   }
-  size_t mat_size = in.shape(-2) * in.shape(-1);
+
+  int N = 1;
+  for (int i = 1; i < (ndim - 2); ++i) {
+    N *= in.shape(i);
+  }
 
   // We apply rope to less that the whole vector so copy to output and then
   // apply in-place.
-  if (dims_ < in.shape(-1)) {
+  if (dims_ < D) {
     donated = true;
     auto ctype =
         (in.flags().row_contiguous) ? CopyType::Vector : CopyType::General;
@@ -302,7 +316,7 @@ void RoPE::eval_gpu(
   out_strides[2] = out.strides()[ndim - 1];
 
   // Some flags to help us dispatch below
-  bool single = in.flags().row_contiguous && (mat_size == in.shape(-1));
+  bool single = in.flags().row_contiguous && B == 1 && T == 1;
   bool with_freqs = inputs.size() == 3;
 
   auto& encoder = cu::get_command_encoder(s);
@@ -319,7 +333,7 @@ void RoPE::eval_gpu(
         if (single && !with_freqs) {
           auto kernel =
               cu::rope_single<DataType, traditional.value, forward.value>;
-          uint2 dims = make_uint2(dims_ / 2, in.size() / mat_size);
+          uint2 dims = make_uint2(dims_ / 2, N);
           auto [grid, block] = get_grid_and_block(dims.x, dims.y, 1);
           encoder.add_kernel_node(
               kernel,
@@ -336,7 +350,7 @@ void RoPE::eval_gpu(
         } else if (single) {
           auto kernel =
               cu::rope_single_freqs<DataType, traditional.value, forward.value>;
-          uint2 dims = make_uint2(dims_ / 2, in.size() / mat_size);
+          uint2 dims = make_uint2(dims_ / 2, N);
           auto [grid, block] = get_grid_and_block(dims.x, dims.y, 1);
           encoder.add_kernel_node(
               kernel,
@@ -354,10 +368,14 @@ void RoPE::eval_gpu(
         } else if (with_freqs) {
           auto kernel =
               cu::rope_freqs<DataType, traditional.value, forward.value>;
-          uint3 dims =
+          int n_per_thread = 4;
-              make_uint3(dims_ / 2, in.shape(-2), in.size() / mat_size);
+          uint32_t dimz = B * ((N + n_per_thread - 1) / n_per_thread);
-          dims.z = (dims.z + 3) / 4;
+          uint3 dims = make_uint3(dims_ / 2, T, dimz);
           auto [grid, block] = get_grid_and_block(dims.x, dims.y, dims.z);
+          int64_t offset_stride = 0;
+          if (inputs[1].ndim() > 0) {
+            offset_stride = inputs[1].strides()[0];
+          }
           encoder.add_kernel_node(
               kernel,
               grid,
@@ -371,15 +389,20 @@ void RoPE::eval_gpu(
               std::log2(base_),
               strides,
               out_strides,
-              in.size() / mat_size,
+              offset_stride,
+              N,
               dims,
               inputs[2].strides(0));
         } else {
           auto kernel = cu::rope<DataType, traditional.value, forward.value>;
-          uint3 dims =
+          int n_per_thread = 4;
-              make_uint3(dims_ / 2, in.shape(-2), in.size() / mat_size);
+          uint32_t dimz = B * ((N + n_per_thread - 1) / n_per_thread);
-          dims.z = (dims.z + 3) / 4;
+          uint3 dims = make_uint3(dims_ / 2, T, dimz);
           auto [grid, block] = get_grid_and_block(dims.x, dims.y, dims.z);
+          int64_t offset_stride = 0;
+          if (inputs[1].ndim() > 0) {
+            offset_stride = inputs[1].strides()[0];
+          }
           encoder.add_kernel_node(
               kernel,
               grid,
@@ -392,7 +415,8 @@ void RoPE::eval_gpu(
               std::log2(base_),
               strides,
               out_strides,
-              in.size() / mat_size,
+              offset_stride,
+              N,
               dims);
         }
       });

mlx/backend/metal/conv.cpp

@@ -2,7 +2,6 @@
 #include <algorithm>
 #include <cassert>
 #include <numeric>
-#include <sstream>
 
 #include "mlx/backend/gpu/copy.h"
 #include "mlx/backend/metal/device.h"
@@ -39,10 +38,11 @@ void explicit_gemm_conv_ND_gpu(
   in_unfolded.set_data(allocator::malloc(in_unfolded.nbytes()));
 
   // Prepare unfolding kernel
-  std::ostringstream kname;
+  std::string kname;
-  kname << "naive_unfold_nd_" << type_to_name(in_unfolded) << "_" << N;
+  kname.reserve(32);
+  concatenate(kname, "naive_unfold_nd_", type_to_name(in_unfolded), "_", N);
   auto& compute_encoder = d.get_command_encoder(s.index);
-  auto kernel = d.get_kernel(kname.str());
+  auto kernel = d.get_kernel(kname);
   compute_encoder.set_compute_pipeline_state(kernel);
 
   compute_encoder.set_input_array(in, 0);
@@ -117,11 +117,12 @@ void explicit_gemm_conv_group_ND_gpu(
   in_unfolded.set_data(allocator::malloc(in_unfolded.nbytes()));
 
   // Prepare unfolding kernel
-  std::ostringstream kname;
+  std::string kname;
-  kname << "naive_unfold_transpose_nd_" << type_to_name(in_unfolded) << "_"
+  kname.reserve(32);
-        << N;
+  concatenate(
+      kname, "naive_unfold_transpose_nd_", type_to_name(in_unfolded), "_", N);
   auto& compute_encoder = d.get_command_encoder(s.index);
-  auto kernel = d.get_kernel(kname.str());
+  auto kernel = d.get_kernel(kname);
   compute_encoder.set_compute_pipeline_state(kernel);
 
   compute_encoder.set_input_array(in, 0);
@@ -252,18 +253,32 @@ void implicit_gemm_conv_2D_gpu(
       /* const int swizzle_log = */ swizzle_log};
 
   // Determine kernel
-  std::ostringstream kname;
+  std::string kname;
-  kname << "implicit_gemm_conv_2d_" << type_to_name(out) << "_bm" << bm << "_bn"
+  kname.reserve(64);
-        << bn << "_bk" << bk << "_wm" << wm << "_wn" << wn << "_channel_"
+  concatenate(
-        << (n_channel_specialization ? std::to_string(n_channel_specialization)
+      kname,
-                                     : "l")
+      "implicit_gemm_conv_2d_",
-        << "_filter_" << (small_filter ? 's' : 'l');
+      type_to_name(out),
+      "_bm",
+      bm,
+      "_bn",
+      bn,
+      "_bk",
+      bk,
+      "_wm",
+      wm,
+      "_wn",
+      wn,
+      "_channel_",
+      n_channel_specialization ? std::to_string(n_channel_specialization) : "l",
+      "_filter_",
+      small_filter ? 's' : 'l');
 
   // Encode and dispatch kernel
   auto& compute_encoder = d.get_command_encoder(s.index);
   auto kernel = get_steel_conv_kernel(
       d,
-      kname.str(),
+      kname,
       out,
       bm,
       bn,
@@ -559,11 +574,16 @@ void winograd_conv_2D_gpu(
   {
     int bc = 32;
     int bo = 4;
-    std::ostringstream kname;
+    std::string kname;
-    kname << "winograd_conv_2d_weight_transform_" << type_to_name(out) << "_bc"
+    kname.reserve(32);
-          << bc;
+    concatenate(
+        kname,
+        "winograd_conv_2d_weight_transform_",
+        type_to_name(out),
+        "_bc",
+        bc);
     auto& compute_encoder = d.get_command_encoder(s.index);
-    auto kernel = d.get_kernel(kname.str());
+    auto kernel = d.get_kernel(kname);
     compute_encoder.set_compute_pipeline_state(kernel);
 
     compute_encoder.set_input_array(wt, 0);
@@ -587,11 +607,16 @@ void winograd_conv_2D_gpu(
     int bc = 32;
     int wm = 2;
     int wn = 2;
-    std::ostringstream kname;
+    std::string kname;
-    kname << "winograd_conv_2d_input_transform_" << type_to_name(out) << "_bc"
+    kname.reserve(32);
-          << bc;
+    concatenate(
+        kname,
+        "winograd_conv_2d_input_transform_",
+        type_to_name(out),
+        "_bc",
+        bc);
     auto& compute_encoder = d.get_command_encoder(s.index);
-    auto kernel = d.get_kernel(kname.str());
+    auto kernel = d.get_kernel(kname);
     compute_encoder.set_compute_pipeline_state(kernel);
 
     compute_encoder.set_input_array(in_padded, 0);
@@ -634,11 +659,16 @@ void winograd_conv_2D_gpu(
     int bc = 32;
     int wm = 2;
     int wn = 2;
-    std::ostringstream kname;
+    std::string kname;
-    kname << "winograd_conv_2d_output_transform_" << type_to_name(out) << "_bo"
+    kname.reserve(32);
-          << bc;
+    concatenate(
+        kname,
+        "winograd_conv_2d_output_transform_",
+        type_to_name(out),
+        "_bo",
+        bc);
     auto& compute_encoder = d.get_command_encoder(s.index);
-    auto kernel = d.get_kernel(kname.str());
+    auto kernel = d.get_kernel(kname);
     compute_encoder.set_compute_pipeline_state(kernel);
 
     compute_encoder.set_input_array(out_wg, 0);
@@ -660,9 +690,9 @@ void depthwise_conv_2D_gpu(
     const array& wt,
     array out,
     const MLXConvParams<2>& conv_params) {
-  std::ostringstream kname;
+  std::string base_name;
-  kname << "depthwise_conv_2d_" << type_to_name(out);
+  base_name.reserve(32);
-  std::string base_name = kname.str();
+  concatenate(base_name, "depthwise_conv_2d_", type_to_name(out));
 
   const int N = conv_params.N;
   const int ker_h = conv_params.wS[0];
@@ -685,15 +715,18 @@ void depthwise_conv_2D_gpu(
   };
 
   // clang-format off
-  kname << "_ker_h_" << ker_h
+  std::string hash_name;
-        << "_ker_w_" << ker_w
+  hash_name.reserve(64);
-        << "_str_h_" << str_h
+  concatenate(
-        << "_str_w_" << str_w
+      hash_name,
-        << "_tgp_h_" << th
+      base_name,
-        << "_tgp_w_" << tw
+  "_ker_h_", ker_h,
-        << "_do_flip_" << (do_flip ? 't' : 'n'); // clang-format on
+  "_ker_w_", ker_w,
-
+  "_str_h_", str_h,
-  std::string hash_name = kname.str();
+  "_str_w_", str_w,
+  "_tgp_h_", th,
+  "_tgp_w_", tw,
+  "_do_flip_", do_flip ? 't' : 'n'); // clang-format on
 
   auto& compute_encoder = d.get_command_encoder(s.index);
   auto kernel = d.get_kernel(base_name, hash_name, func_consts);
@@ -774,6 +807,56 @@ void dispatch_conv_2D_gpu(
   }
 }
 
+void depthwise_conv_1D_gpu(
+    const Stream& s,
+    metal::Device& d,
+    const array& in,
+    array wt,
+    array out) {
+  bool large = in.size() > INT32_MAX || in.data_size() > INT32_MAX;
+  std::string base_name;
+  base_name.reserve(32);
+  concatenate(
+      base_name,
+      "depthwise_conv_1d_",
+      large ? "_large" : "",
+      type_to_name(out));
+
+  if (!wt.flags().row_contiguous) {
+    wt = contiguous_copy_gpu(wt, s);
+    d.add_temporary(wt, s.index);
+  }
+  auto& compute_encoder = d.get_command_encoder(s.index);
+  auto kernel = d.get_kernel(base_name);
+  compute_encoder.set_compute_pipeline_state(kernel);
+
+  auto B = in.shape(0);
+  auto Tout = out.shape(1);
+  auto D = in.shape(2);
+  auto K = wt.shape(1);
+
+  compute_encoder.set_input_array(in, 0);
+  compute_encoder.set_input_array(wt, 1);
+  compute_encoder.set_output_array(out, 2);
+  if (large) {
+    int64_t strides[3] = {in.strides(0), in.strides(1), in.strides(2)};
+    compute_encoder.set_bytes(strides, 3, 3);
+
+  } else {
+    int strides[3] = {
+        static_cast<int>(in.strides(0)),
+        static_cast<int>(in.strides(1)),
+        static_cast<int>(in.strides(2))};
+    compute_encoder.set_bytes(strides, 3, 3);
+  }
+
+  compute_encoder.set_bytes(K, 4);
+  auto group_dims = get_block_dims(D, Tout, B);
+  MTL::Size grid_dims = MTL::Size(D, Tout, B);
+
+  compute_encoder.dispatch_threads(grid_dims, group_dims);
+}
+
 void conv_1D_gpu(
     const Stream& s,
     metal::Device& d,
@@ -790,8 +873,15 @@ void conv_1D_gpu(
   bool is_idil_one = in_dilation[0] == 1;
   int C = in.shape(2);
   int O = wt.shape(0);
-  const int C_per_group = in.shape(2) / groups;
+  // Fast path for fully separable 1D convolution
-  const int O_per_group = wt.shape(0) / groups;
+  if (is_idil_one && (groups == C) && groups == O && wt_strides[0] == 1 &&
+      wt_dilation[0] == 1 && padding[0] == 0 && !flip) {
+    depthwise_conv_1D_gpu(s, d, in, wt, out);
+    return;
+  }
+
+  const int C_per_group = C / groups;
+  const int O_per_group = O / groups;
 
   // Direct to implicit gemm conv
   if (is_idil_one && (C_per_group <= 4 || C_per_group % 16 == 0) &&

mlx/backend/metal/kernels/conv.metal

@@ -288,6 +288,40 @@ instantiate_depthconv2d(float32, float);
 instantiate_depthconv2d(float16, half);
 instantiate_depthconv2d(bfloat16, bfloat16_t);
 
+template <typename T, typename IdxT>
+[[kernel]] void depthwise_conv_1d(
+    const device T* in [[buffer(0)]],
+    const device T* w [[buffer(1)]],
+    device T* out [[buffer(2)]],
+    constant const IdxT strides[3],
+    constant const int& kernel_size,
+    uint3 tid [[thread_position_in_grid]],
+    uint3 grid_dim [[threads_per_grid]]) {
+  out += (tid.z * static_cast<IdxT>(grid_dim.y) + tid.y) * grid_dim.x + tid.x;
+  in += tid.z * strides[0] + tid.y * strides[1] + tid.x * strides[2];
+  w += tid.x * kernel_size;
+
+  float acc = 0.0;
+  for (int i = 0; i < kernel_size; ++i) {
+    acc += static_cast<float>(in[0]) * w[i];
+    in += strides[1];
+  }
+  *out = static_cast<T>(acc);
+}
+
+#define instantiate_depthconv1d(iname, itype)                         \
+  instantiate_kernel(                                                 \
+      "depthwise_conv_1d_" #iname, depthwise_conv_1d, itype, int32_t) \
+      instantiate_kernel(                                             \
+          "depthwise_conv_1d_" #iname "_large",                       \
+          depthwise_conv_1d,                                          \
+          itype,                                                      \
+          int64_t)
+
+instantiate_depthconv1d(float32, float);
+instantiate_depthconv1d(float16, half);
+instantiate_depthconv1d(bfloat16, bfloat16_t);
+
 ///////////////////////////////////////////////////////////////////////////////
 /// Winograd kernels
 ///////////////////////////////////////////////////////////////////////////////

mlx/backend/metal/kernels/rope.metal

@@ -10,7 +10,7 @@ void rope_single_impl(
     constant const int& offset,
     const float inv_freq,
     constant const float& scale,
-    constant const size_t& stride,
+    constant const int64_t& stride,
     uint2 pos,
     uint2 grid) {
   float L = scale * static_cast<float>(offset);
@@ -52,7 +52,7 @@ template <typename T, bool traditional, bool forward>
     device T* out [[buffer(1)]],
     constant const int& offset,
     constant const float& scale,
-    constant const size_t& stride,
+    constant const int64_t& stride,
     constant const float& base [[buffer(10)]],
     uint2 pos [[thread_position_in_grid]],
     uint2 grid [[threads_per_grid]]) {
@@ -68,9 +68,9 @@ template <typename T, bool traditional, bool forward>
     device T* out [[buffer(1)]],
     constant const int& offset,
     constant const float& scale,
-    constant const size_t& stride,
+    constant const int64_t& stride,
     const device float* freqs [[buffer(10)]],
-    constant const size_t& freq_stride [[buffer(11)]],
+    constant const int64_t& freq_stride [[buffer(11)]],
     uint2 pos [[thread_position_in_grid]],
     uint2 grid [[threads_per_grid]]) {
   float inv_freq = 1.0 / (freqs[freq_stride * pos.x]);
@@ -82,15 +82,21 @@ template <typename T, bool traditional, bool forward, int N = 4>
 void rope_impl(
     const device T* in,
     device T* out,
-    constant const int& offset,
+    const device int* offset,
     const float inv_freq,
     constant const float& scale,
-    constant const size_t strides[3],
+    constant const int64_t strides[3],
-    constant const size_t out_strides[3],
+    constant const int64_t out_strides[3],
-    constant const size_t& n_batch,
+    constant const int64_t& offset_stride,
+    constant const int& n_head,
     uint3 pos,
     uint3 grid) {
-  float L = scale * static_cast<float>(pos.y + offset);
+  auto n_head_up = N * ((n_head + N - 1) / N);
+  auto head_idx = static_cast<int>((pos.z * N) % n_head_up);
+  auto batch_idx = (pos.z * N) / n_head_up;
+  auto batch_offset = offset[batch_idx * offset_stride];
+  float L = scale * static_cast<float>(pos.y + batch_offset);
+  auto mat_idx = batch_idx * n_head + head_idx;
 
   // Compute costheta, sintheta
   float theta = L * inv_freq;
@@ -102,20 +108,19 @@ void rope_impl(
   size_t out_index_1, out_index_2;
   if (traditional) {
     out_index_1 = 2 * pos.x * out_strides[2] + pos.y * out_strides[1] +
-        N * pos.z * out_strides[0];
+        mat_idx * out_strides[0];
     out_index_2 = out_index_1 + 1;
     in_index_1 =
-        2 * pos.x * strides[2] + pos.y * strides[1] + N * pos.z * strides[0];
+        2 * pos.x * strides[2] + pos.y * strides[1] + mat_idx * strides[0];
     in_index_2 = in_index_1 + strides[2];
   } else {
     out_index_1 = pos.x * out_strides[2] + pos.y * out_strides[1] +
-        N * pos.z * out_strides[0];
+        mat_idx * out_strides[0];
     out_index_2 = out_index_1 + grid.x * out_strides[2];
-    in_index_1 =
+    in_index_1 = pos.x * strides[2] + pos.y * strides[1] + mat_idx * strides[0];
-        pos.x * strides[2] + pos.y * strides[1] + N * pos.z * strides[0];
     in_index_2 = in_index_1 + grid.x * strides[2];
   }
-  for (int i = 0; i < N && pos.z * N + i < n_batch; ++i) {
+  for (int i = 0; i < N && head_idx + i < n_head; ++i) {
     // Read and write the output
     float x1 = static_cast<float>(in[in_index_1]);
     float x2 = static_cast<float>(in[in_index_2]);
@@ -141,11 +146,12 @@ template <typename T, bool traditional, bool forward, int N = 4>
 [[kernel]] void rope(
     const device T* in [[buffer(0)]],
     device T* out [[buffer(1)]],
-    constant const int& offset,
+    const device int* offset,
     constant const float& scale,
-    constant const size_t strides[3],
+    constant const int64_t strides[3],
-    constant const size_t out_strides[3],
+    constant const int64_t out_strides[3],
-    constant const size_t& n_batch,
+    constant const int64_t& offset_stride,
+    constant const int& n_head,
     constant const float& base [[buffer(10)]],
     uint3 pos [[thread_position_in_grid]],
     uint3 grid [[threads_per_grid]]) {
@@ -159,7 +165,8 @@ template <typename T, bool traditional, bool forward, int N = 4>
       scale,
       strides,
       out_strides,
-      n_batch,
+      offset_stride,
+      n_head,
       pos,
       grid);
 }
@@ -168,13 +175,14 @@ template <typename T, bool traditional, bool forward, int N = 4>
 [[kernel]] void rope_freqs(
     const device T* in [[buffer(0)]],
     device T* out [[buffer(1)]],
-    constant const int& offset,
+    const device int* offset,
     constant const float& scale,
-    constant const size_t strides[3],
+    constant const int64_t strides[3],
-    constant const size_t out_strides[3],
+    constant const int64_t out_strides[3],
-    constant const size_t& n_batch,
+    constant const int64_t& offset_stride,
+    constant const int& n_head,
     const device float* freqs [[buffer(10)]],
-    constant const size_t& freq_stride [[buffer(11)]],
+    constant const int64_t& freq_stride [[buffer(11)]],
     uint3 pos [[thread_position_in_grid]],
     uint3 grid [[threads_per_grid]]) {
   float inv_freq = 1.0 / (freqs[freq_stride * pos.x]);
@@ -186,61 +194,20 @@ template <typename T, bool traditional, bool forward, int N = 4>
       scale,
       strides,
       out_strides,
-      n_batch,
+      offset_stride,
+      n_head,
       pos,
       grid);
 }
 
 // clang-format off
 #define instantiate_rope_g(name, type, traditional, forward) \
-  template [[host_name("rope_" #name)]] [[kernel]] void      \
+  instantiate_kernel("rope_" #name, rope, type, traditional, forward) \
-  rope<type, traditional, forward>(                          \
+  instantiate_kernel("rope_freqs_" #name, rope_freqs, type, traditional, forward)
-      const device type* in [[buffer(0)]],                   \
-      device type* out [[buffer(1)]],                        \
-      constant const int& offset,                            \
-      constant const float& scale,                           \
-      constant const size_t strides[3],                      \
-      constant const size_t out_strides[3],                  \
-      constant const size_t& n_batch,                        \
-      constant const float& base [[buffer(10)]],             \
-      uint3 pos [[thread_position_in_grid]],                 \
-      uint3 grid [[threads_per_grid]]);                      \
-  template [[host_name("rope_freqs_" #name)]]                \
-  [[kernel]] void rope_freqs<type, traditional, forward>(    \
-      const device type* in [[buffer(0)]],                   \
-      device type* out [[buffer(1)]],                        \
-      constant const int& offset,                            \
-      constant const float& scale,                           \
-      constant const size_t strides[3],                      \
-      constant const size_t out_strides[3],                  \
-      constant const size_t& n_batch,                        \
-      const device float* freqs [[buffer(10)]],              \
-      constant const size_t& freq_stride [[buffer(11)]],     \
-      uint3 pos [[thread_position_in_grid]],                 \
-      uint3 grid [[threads_per_grid]]);
 
-#define instantiate_rope_s(name, type, traditional, forward)     \
+#define instantiate_rope_s(name, type, traditional, forward) \
-  template [[host_name("rope_single_" #name)]] [[kernel]] void   \
+  instantiate_kernel("rope_single_" #name, rope_single, type, traditional, forward) \
-  rope_single<type, traditional, forward>(                       \
+  instantiate_kernel("rope_single_freqs_" #name, rope_single_freqs, type, traditional, forward)
-      const device type* in [[buffer(0)]],                       \
-      device type* out [[buffer(1)]],                            \
-      constant const int& offset,                                \
-      constant const float& scale,                               \
-      constant const size_t& stride,                             \
-      constant const float& base [[buffer(10)]],                 \
-      uint2 pos [[thread_position_in_grid]],                     \
-      uint2 grid [[threads_per_grid]]);                          \
-  template [[host_name("rope_single_freqs_" #name)]]             \
-  [[kernel]] void rope_single_freqs<type, traditional, forward>( \
-      const device type* in [[buffer(0)]],                       \
-      device type* out [[buffer(1)]],                            \
-      constant const int& offset,                                \
-      constant const float& scale,                               \
-      constant const size_t& stride,                             \
-      const device float* freqs [[buffer(10)]],                  \
-      constant const size_t& freq_stride [[buffer(11)]],         \
-      uint2 pos [[thread_position_in_grid]],                     \
-      uint2 grid [[threads_per_grid]]);
 
 #define instantiate_rope(name, type, traditional, forward) \
   instantiate_rope_s(name, type, traditional, forward)     \

mlx/backend/metal/rope.cpp

@@ -18,23 +18,29 @@ void RoPE::eval_gpu(
   auto& in = inputs[0];
   auto& out = outputs[0];
 
-  if (in.ndim() < 3) {
-    throw std::runtime_error("[RoPE] Input must have at least 3 dimensions");
-  }
-
   auto& s = out.primitive().stream();
   auto& d = metal::device(s.device);
 
-  size_t strides[3];
+  int64_t strides[3];
-  size_t out_strides[3];
+  int64_t out_strides[3];
   bool donated = false;
   int ndim = in.ndim();
-  int dispatch_ndim = in.ndim();
+  int B = in.shape(0);
+  int T = in.shape(-2);
+  int D = in.shape(-1);
+  size_t mat_size = T * D;
+
+  int dispatch_ndim = ndim;
   while (in.shape(-dispatch_ndim) == 1 && dispatch_ndim > 3) {
     dispatch_ndim--;
   }
-  size_t mat_size = in.shape(-2) * in.shape(-1);
+
-  if (dims_ < in.shape(-1)) {
+  int N = 1;
+  for (int i = 1; i < (ndim - 2); ++i) {
+    N *= in.shape(i);
+  }
+
+  if (dims_ < D) {
     donated = true;
     auto ctype =
         (in.flags().row_contiguous) ? CopyType::Vector : CopyType::General;
@@ -71,8 +77,8 @@ void RoPE::eval_gpu(
   out_strides[1] = out.strides()[ndim - 2];
   out_strides[2] = out.strides()[ndim - 1];
 
-  // Special case for inference (single time step and contiguous)
+  // Special case for inference (single batch, single time step, and contiguous)
-  bool single = in.flags().row_contiguous && (mat_size == in.shape(-1));
+  bool single = in.flags().row_contiguous && B == 1 && T == 1;
 
   bool with_freqs = inputs.size() == 3;
   std::ostringstream kname;
@@ -86,24 +92,29 @@ void RoPE::eval_gpu(
   compute_encoder.set_compute_pipeline_state(kernel);
   compute_encoder.set_input_array(donated ? out : in, 0);
   compute_encoder.set_output_array(out, 1);
+
   compute_encoder.set_input_array(inputs[1], 2);
   compute_encoder.set_bytes(scale_, 3);
 
-  size_t n_batch = in.size() / mat_size;
   MTL::Size group_dims;
   MTL::Size grid_dims;
   if (single) {
     compute_encoder.set_bytes(out_strides, 1, 4);
     uint32_t dim0 = dims_ / 2;
-    group_dims = get_block_dims(dim0, n_batch, 1);
+    group_dims = get_block_dims(dim0, N, 1);
-    grid_dims = MTL::Size(dim0, n_batch, 1);
+    grid_dims = MTL::Size(dim0, N, 1);
   } else {
     compute_encoder.set_bytes(strides, 3, 4);
     compute_encoder.set_bytes(out_strides, 3, 5);
-    compute_encoder.set_bytes(n_batch, 6);
+    int64_t offset_stride = 0;
+    if (inputs[1].ndim() > 0) {
+      offset_stride = inputs[1].strides()[0];
+    }
+    compute_encoder.set_bytes(offset_stride, 6);
+    compute_encoder.set_bytes(N, 7);
     uint32_t dim0 = dims_ / 2;
-    uint32_t dim1 = in.shape(-2);
+    uint32_t dim1 = T;
-    uint32_t dim2 = (n_batch + n_per_thread - 1) / n_per_thread;
+    uint32_t dim2 = B * ((N + n_per_thread - 1) / n_per_thread);
     group_dims = get_block_dims(dim0, dim1, dim2);
     grid_dims = MTL::Size(dim0, dim1, dim2);
   }

mlx/fast.cpp

@@ -366,10 +366,16 @@ array rope(
     msg << "[rope] Input must be a floating type but got " << x.dtype() << ".";
     throw std::invalid_argument(msg.str());
   }
-  if (offset.size() != 1) {
+  if (offset.ndim() > 1) {
     std::ostringstream msg;
-    msg << "[rope] offset must be a scalar but has shape " << offset.shape()
+    msg << "[rope] offset must have at most one dimension but has shape "
-        << ".";
+        << offset.shape() << ".";
+    throw std::invalid_argument(msg.str());
+  }
+  if (offset.size() != 1 && offset.size() != x.shape(0)) {
+    std::ostringstream msg;
+    msg << "[rope] offset must be a scalar or vector with " << x.shape(0)
+        << " elements but has shape " << offset.shape() << ".";
     throw std::invalid_argument(msg.str());
   }
   if (!issubdtype(offset.dtype(), integer)) {
@@ -379,7 +385,7 @@ array rope(
     throw std::invalid_argument(msg.str());
   }
   if (offset.dtype().size() != 4) {
-    inputs[1] = astype(offset, uint32, s);
+    inputs[1] = astype(offset, int32, s);
   }
   if (inputs.size() == 3 &&
       (inputs[2].ndim() != 1 || inputs[2].shape(0) != dims / 2)) {
@@ -391,15 +397,26 @@ array rope(
 
   auto fallback = [dims, traditional, base, scale, forward, s](
                       std::vector<array> inputs) {
-    auto& shape = inputs[0].shape();
+    auto x = inputs[0];
-    int ndim = shape.size();
+    auto shape = x.shape();
-    auto x = flatten(inputs[0], 0, ndim - 3, s);
+    if (x.ndim() == 3) {
+      x = expand_dims(x, 1, s);
+    } else if (x.ndim() > 4) {
+      x = flatten(x, 1, 1 + (x.ndim() - 4), s);
+    }
+
+    auto B = x.shape(0);
+    auto N = x.shape(1);
+    auto T = x.shape(2);
     auto t = x.dtype();
     // Compute sines and cosines
     auto half_dims = dims / 2;
-    auto& offset = inputs[1];
+    auto offset = inputs[1];
+    if (offset.size() > 1) {
+      offset = expand_dims(offset, {-1, -2}, s);
+    }
     auto positions =
-        multiply(add(arange(x.shape(1), t, s), offset, s), array(scale, t), s);
+        multiply(add(arange(x.shape(2), t, s), offset, s), array(scale, t), s);
 
     auto default_inv_freqs = [&inputs, &s, &t, base, half_dims]() {
       return exp(
@@ -412,8 +429,7 @@ array rope(
 
     auto inv_freqs = inputs.size() == 3 ? astype(reciprocal(inputs[2], s), t, s)
                                         : default_inv_freqs();
-    auto theta =
+    auto theta = multiply(expand_dims(positions, -1, s), inv_freqs, s);
-        multiply(expand_dims(positions, 1, s), expand_dims(inv_freqs, 0, s), s);
     auto coss = cos(theta, s);
     auto sins = sin(theta, s);
 
@@ -436,32 +452,30 @@ array rope(
     };
 
     if (traditional) {
-      auto x1 =
+      auto x1 = slice(x, {0, 0, 0, 0}, {B, N, T, dims}, {1, 1, 1, 2}, s);
-          slice(x, {0, 0, 0}, {x.shape(0), x.shape(1), dims}, {1, 1, 2}, s);
+      auto x2 = slice(x, {0, 0, 0, 1}, {B, N, T, dims}, {1, 1, 1, 2}, s);
-      auto x2 =
-          slice(x, {0, 0, 1}, {x.shape(0), x.shape(1), dims}, {1, 1, 2}, s);
       auto outs = apply_rope(x1, x2, coss, sins);
       for (auto& o : outs) {
-        o = expand_dims(o, 3, s);
+        o = expand_dims(o, -1, s);
       }
-      auto out = concatenate(outs, 3, s);
+      auto out = reshape(concatenate(outs, -1, s), {B, N, T, dims}, s);
       if (dims < x.shape(-1)) {
-        out = reshape(out, {x.shape(0), x.shape(1), dims});
+        out =
-        out = concatenate({out, slice(x, {0, 0, dims}, x.shape(), s)}, 2, s);
+            concatenate({out, slice(x, {0, 0, 0, dims}, x.shape(), s)}, -1, s);
       }
       return std::vector<array>{reshape(out, shape, s)};
     } else {
       auto out_s = x.shape();
       out_s.back() = half_dims;
-      auto x1 = slice(x, {0, 0, 0}, out_s, s);
+      auto x1 = slice(x, {0, 0, 0, 0}, out_s, s);
       out_s.back() = dims;
-      auto x2 = slice(x, {0, 0, half_dims}, out_s, s);
+      auto x2 = slice(x, {0, 0, 0, half_dims}, out_s, s);
 
       auto outs = apply_rope(x1, x2, coss, sins);
       if (dims < x.shape(-1)) {
-        outs.push_back(slice(x, {0, 0, dims}, x.shape(), s));
+        outs.push_back(slice(x, {0, 0, 0, dims}, x.shape(), s));
       }
-      return std::vector<array>{reshape(concatenate(outs, 2, s), shape, s)};
+      return std::vector<array>{reshape(concatenate(outs, -1, s), shape, s)};
     }
   };
   auto stream = to_stream(s);

mlx/version.cpp

@@ -1,10 +1,8 @@
 // Copyright © 2025 Apple Inc.
 
-#include <string>
-
 namespace mlx::core {
 
-std::string version() {
+const char* version() {
   return MLX_VERSION;
 }
 

mlx/version.h

@@ -15,6 +15,6 @@ namespace mlx::core {
  *
  * For dev builds, the version will include the suffix ".devYYYYMMDD+hash"
  */
-std::string version();
+const char* version();
 
 } // namespace mlx::core

python/src/CMakeLists.txt

@@ -52,7 +52,6 @@ set_target_properties(
              ${MLX_PYTHON_BINDINGS_OUTPUT_DIRECTORY})
 
 target_link_libraries(core PRIVATE mlx)
-target_compile_definitions(core PRIVATE _VERSION_=${MLX_VERSION})
 
 if(BUILD_SHARED_LIBS)
   if(${CMAKE_SYSTEM_NAME} MATCHES "Darwin")

python/src/fast.cpp

@@ -164,8 +164,13 @@ void init_fast(nb::module_& parent_module) {
       R"pbdoc(
         Apply rotary positional encoding to the input.
 
+        The input is expected to be at least 3D with shape ``(B, *, T, D)`` where:
+          * ``B`` is the batch size.
+          * ``T`` is the sequence length.
+          * ``D`` is the feature dimension.
+
         Args:
-            a (array): Input array.
+            a (array): The input array.
             dims (int): The feature dimensions to be rotated. If the input feature
               is larger than dims then the rest is left unchanged.
             traditional (bool): If set to ``True`` choose the traditional
@@ -174,7 +179,9 @@ void init_fast(nb::module_& parent_module) {
               each dimension in the positional encodings. Exactly one of ``base`` and
               ``freqs`` must be ``None``.
             scale (float): The scale used to scale the positions.
-            offset (int or array): The position offset to start at.
+            offset (int or array): The position offset to start at. If an
+              :obj:`array` is given it can be a scalar or vector of ``B``
+              offsets for each example in the batch.
             freqs (array, optional): Optional frequencies to use with RoPE.
               If set, the ``base`` parameter must be ``None``. Default: ``None``.
 

python/src/mlx.cpp

@@ -2,9 +2,9 @@
 
 #include <nanobind/nanobind.h>
 
-#define STRINGIFY(x) #x
+#include "mlx/version.h"
-#define TOSTRING(x) STRINGIFY(x)
 
+namespace mx = mlx::core;
 namespace nb = nanobind;
 
 void init_mlx_func(nb::module_&);
@@ -48,5 +48,5 @@ NB_MODULE(core, m) {
   init_distributed(m);
   init_export(m);
 
-  m.attr("__version__") = TOSTRING(_VERSION_);
+  m.attr("__version__") = mx::version();
 }

python/src/utils.cpp

@@ -91,7 +91,7 @@ mx::array to_array_with_accessor(nb::object obj) {
     return nb::cast<mx::array>(obj.attr("__mlx_array__")());
   } else {
     std::ostringstream msg;
-    msg << "Invalid type  " << nb::type_name(obj.type()).c_str()
+    msg << "Invalid type " << nb::type_name(obj.type()).c_str()
         << " received in array initialization.";
     throw std::invalid_argument(msg.str());
   }

python/tests/test_blas.py

@@ -594,124 +594,123 @@ class TestBlas(mlx_tests.MLXTestCase):
         np.random.seed(0)
         # Batched matmul
         alpha = 0.5
-        beta = 2.0
+        for beta in (1.0, 2.0):
+            # c must broadcast to the output shape
+            with self.assertRaises(ValueError):
+                mx.addmm(mx.zeros((2, 2, 2)), mx.zeros((2, 2)), mx.zeros((2, 2)))
 
-        # c must broadcast to the output shape
+            # Regular batched case
-        with self.assertRaises(ValueError):
+            a_npy = np.random.normal(0.0, 1.0 / 128, (32, 128, 16)).astype(np.float32)
-            mx.addmm(mx.zeros((2, 2, 2)), mx.zeros((2, 2)), mx.zeros((2, 2)))
+            b_npy = np.random.normal(0.0, 1.0 / 128, (32, 16, 16)).astype(np.float32)
 
-        # Regular batched case
+            a_mlx = mx.array(a_npy)
-        a_npy = np.random.normal(0.0, 1.0 / 128, (32, 128, 16)).astype(np.float32)
+            b_mlx = mx.array(b_npy)
-        b_npy = np.random.normal(0.0, 1.0 / 128, (32, 16, 16)).astype(np.float32)
 
-        a_mlx = mx.array(a_npy)
+            for c_shape in ((1,), (1, 16), (32, 1, 16), (1, 128, 16)):
-        b_mlx = mx.array(b_npy)
+                c_npy = np.ones(c_shape).astype(np.float32)
+                c_mlx = mx.array(c_npy)
 
-        for c_shape in ((1,), (1, 16), (32, 1, 16), (1, 128, 16)):
+                d_npy = alpha * (a_npy @ b_npy) + beta * c_npy
-            c_npy = np.ones(c_shape).astype(np.float32)
+                d_mlx = mx.addmm(c_mlx, a_mlx, b_mlx, alpha, beta)
-            c_mlx = mx.array(c_npy)
 
-            d_npy = alpha * (a_npy @ b_npy) + beta * c_npy
+                self.assertListEqual(list(d_npy.shape), list(d_mlx.shape))
-            d_mlx = mx.addmm(c_mlx, a_mlx, b_mlx, alpha, beta)
+                self.assertTrue(np.allclose(d_mlx, d_npy, atol=1e-5))
 
-            self.assertListEqual(list(d_npy.shape), list(d_mlx.shape))
+            # Batched and transposed matmul
-            self.assertTrue(np.allclose(d_mlx, d_npy, atol=1e-5))
+            b_npy = np.random.normal(0.0, 1.0 / 128, (32, 128, 16)).astype(np.float32)
+            b_mlx = mx.array(b_npy)
 
-        # Batched and transposed matmul
+            for c_shape in ((1,), (32, 1, 128), (1, 128)):
-        b_npy = np.random.normal(0.0, 1.0 / 128, (32, 128, 16)).astype(np.float32)
+                c_npy = np.ones(c_shape).astype(np.float32)
-        b_mlx = mx.array(b_npy)
+                c_mlx = mx.array(c_npy)
 
-        for c_shape in ((1,), (32, 1, 128), (1, 128)):
+                b_np_t = np.transpose(b_npy, (0, 2, 1))
-            c_npy = np.ones(c_shape).astype(np.float32)
+                b_mx_t = mx.transpose(b_mlx, (0, 2, 1))
-            c_mlx = mx.array(c_npy)
 
-            b_np_t = np.transpose(b_npy, (0, 2, 1))
+                d_npy = alpha * (a_npy @ b_np_t) + beta * c_npy
-            b_mx_t = mx.transpose(b_mlx, (0, 2, 1))
+                d_mlx = mx.addmm(c_mlx, a_mlx, b_mx_t, alpha, beta)
 
-            d_npy = alpha * (a_npy @ b_np_t) + beta * c_npy
+                self.assertListEqual(list(d_npy.shape), list(d_mlx.shape))
-            d_mlx = mx.addmm(c_mlx, a_mlx, b_mx_t, alpha, beta)
+                self.assertTrue(np.allclose(d_mlx, d_npy, atol=1e-5))
+            # Batched matmul with simple broadcast
+            a_npy = np.random.normal(0.0, 1.0 / 128, (32, 128, 16)).astype(np.float32)
+            b_npy = np.random.normal(0.0, 1.0 / 128, (16, 16)).astype(np.float32)
 
-            self.assertListEqual(list(d_npy.shape), list(d_mlx.shape))
+            a_mlx = mx.array(a_npy)
-            self.assertTrue(np.allclose(d_mlx, d_npy, atol=1e-5))
+            b_mlx = mx.array(b_npy)
-        # Batched matmul with simple broadcast
-        a_npy = np.random.normal(0.0, 1.0 / 128, (32, 128, 16)).astype(np.float32)
-        b_npy = np.random.normal(0.0, 1.0 / 128, (16, 16)).astype(np.float32)
 
-        a_mlx = mx.array(a_npy)
+            for c_shape in ((1,), (1, 16), (32, 1, 16), (1, 128, 16)):
-        b_mlx = mx.array(b_npy)
+                c_npy = np.ones(c_shape).astype(np.float32)
+                c_mlx = mx.array(c_npy)
 
-        for c_shape in ((1,), (1, 16), (32, 1, 16), (1, 128, 16)):
+                d_npy = alpha * (a_npy @ b_npy) + beta * c_npy
-            c_npy = np.ones(c_shape).astype(np.float32)
+                d_mlx = mx.addmm(c_mlx, a_mlx, b_mlx, alpha, beta)
-            c_mlx = mx.array(c_npy)
 
-            d_npy = alpha * (a_npy @ b_npy) + beta * c_npy
+                self.assertListEqual(list(d_npy.shape), list(d_mlx.shape))
-            d_mlx = mx.addmm(c_mlx, a_mlx, b_mlx, alpha, beta)
+                self.assertTrue(np.allclose(d_mlx, d_npy, atol=1e-5))
+            # Matmul with vector
+            a_npy = np.random.normal(0.0, 1.0 / 128, (16,)).astype(np.float32)
+            b_npy = np.random.normal(0.0, 1.0 / 128, (32, 16, 128)).astype(np.float32)
+            a_mlx = mx.array(a_npy)
+            b_mlx = mx.array(b_npy)
 
-            self.assertListEqual(list(d_npy.shape), list(d_mlx.shape))
+            for c_shape in ((1,), (128,), (32, 128)):
-            self.assertTrue(np.allclose(d_mlx, d_npy, atol=1e-5))
+                c_npy = np.ones(c_shape).astype(np.float32)
-        # Matmul with vector
+                c_mlx = mx.array(c_npy)
-        a_npy = np.random.normal(0.0, 1.0 / 128, (16,)).astype(np.float32)
-        b_npy = np.random.normal(0.0, 1.0 / 128, (32, 16, 128)).astype(np.float32)
-        a_mlx = mx.array(a_npy)
-        b_mlx = mx.array(b_npy)
 
-        for c_shape in ((1,), (128,), (32, 128)):
+                d_npy = alpha * (a_npy @ b_npy) + beta * c_npy
-            c_npy = np.ones(c_shape).astype(np.float32)
+                d_mlx = mx.addmm(c_mlx, a_mlx, b_mlx, alpha, beta)
-            c_mlx = mx.array(c_npy)
 
-            d_npy = alpha * (a_npy @ b_npy) + beta * c_npy
+                self.assertListEqual(list(d_npy.shape), list(d_mlx.shape))
-            d_mlx = mx.addmm(c_mlx, a_mlx, b_mlx, alpha, beta)
+                self.assertTrue(np.allclose(d_mlx, d_npy, atol=1e-5))
 
-            self.assertListEqual(list(d_npy.shape), list(d_mlx.shape))
+            # Matmul with vector
-            self.assertTrue(np.allclose(d_mlx, d_npy, atol=1e-5))
+            a_npy = np.random.normal(0.0, 1.0 / 128, (32, 128, 16)).astype(np.float32)
+            b_npy = np.random.normal(0.0, 1.0 / 128, (16,)).astype(np.float32)
+            a_mlx = mx.array(a_npy)
+            b_mlx = mx.array(b_npy)
 
-        # Matmul with vector
+            for c_shape in ((1,), (32, 128)):
-        a_npy = np.random.normal(0.0, 1.0 / 128, (32, 128, 16)).astype(np.float32)
+                c_npy = np.ones(c_shape).astype(np.float32)
-        b_npy = np.random.normal(0.0, 1.0 / 128, (16,)).astype(np.float32)
+                c_mlx = mx.array(c_npy)
-        a_mlx = mx.array(a_npy)
-        b_mlx = mx.array(b_npy)
 
-        for c_shape in ((1,), (32, 128)):
+                d_npy = alpha * (a_npy @ b_npy) + beta * c_npy
-            c_npy = np.ones(c_shape).astype(np.float32)
+                d_mlx = mx.addmm(c_mlx, a_mlx, b_mlx, alpha, beta)
-            c_mlx = mx.array(c_npy)
 
-            d_npy = alpha * (a_npy @ b_npy) + beta * c_npy
+                self.assertListEqual(list(d_npy.shape), list(d_mlx.shape))
-            d_mlx = mx.addmm(c_mlx, a_mlx, b_mlx, alpha, beta)
+                self.assertTrue(np.allclose(d_mlx, d_npy, atol=1e-5))
 
-            self.assertListEqual(list(d_npy.shape), list(d_mlx.shape))
+            # Split K specializtion
-            self.assertTrue(np.allclose(d_mlx, d_npy, atol=1e-5))
+            a_npy = np.random.normal(0.0, 1.0 / 128, (64, 4096)).astype(np.float32)
+            b_npy = np.random.normal(0.0, 1.0 / 128, (4096, 32)).astype(np.float32)
 
-        # Split K specializtion
+            a_mlx = mx.array(a_npy)
-        a_npy = np.random.normal(0.0, 1.0 / 128, (64, 4096)).astype(np.float32)
+            b_mlx = mx.array(b_npy)
-        b_npy = np.random.normal(0.0, 1.0 / 128, (4096, 32)).astype(np.float32)
 
-        a_mlx = mx.array(a_npy)
+            for c_shape in ((1,), (1, 32), (64, 1), (64, 32)):
-        b_mlx = mx.array(b_npy)
+                c_npy = np.ones(c_shape).astype(np.float32)
+                c_mlx = mx.array(c_npy)
 
-        for c_shape in ((1,), (1, 32), (64, 1), (64, 32)):
+                d_npy = alpha * (a_npy @ b_npy) + beta * c_npy
-            c_npy = np.ones(c_shape).astype(np.float32)
+                d_mlx = mx.addmm(c_mlx, a_mlx, b_mlx, alpha, beta)
-            c_mlx = mx.array(c_npy)
 
-            d_npy = alpha * (a_npy @ b_npy) + beta * c_npy
+                self.assertListEqual(list(d_npy.shape), list(d_mlx.shape))
-            d_mlx = mx.addmm(c_mlx, a_mlx, b_mlx, alpha, beta)
+                self.assertTrue(np.allclose(d_mlx, d_npy, atol=1e-5))
 
-            self.assertListEqual(list(d_npy.shape), list(d_mlx.shape))
+            # Transposed c
-            self.assertTrue(np.allclose(d_mlx, d_npy, atol=1e-5))
+            a = mx.ones((10, 5)).T
+            b = mx.ones((5, 5))
+            out = mx.addmm(a, b, a, beta=beta, alpha=alpha)
+            expected = beta * a + alpha * (b @ a)
+            self.assertTrue(mx.allclose(expected, out))
 
-        # Transposed c
+            # Broadcast c
-        a = mx.ones((10, 5)).T
+            a = mx.ones((5, 5))
-        b = mx.ones((5, 5))
+            b = mx.ones((5, 5))
-        out = mx.addmm(a, b, a, beta=1.5, alpha=0.5)
+            c = mx.ones((1, 5))
-        expected = 1.5 * a + 0.5 * (b @ a)
+            out = mx.addmm(c, a, b, beta=beta, alpha=alpha)
-        self.assertTrue(mx.allclose(expected, out))
+            expected = beta * c + alpha * (a @ b)
-
+            self.assertTrue(mx.allclose(expected, out))
-        # Broadcast c
-        a = mx.ones((5, 5))
-        b = mx.ones((5, 5))
-        c = mx.ones((1, 5))
-        out = mx.addmm(c, a, b, beta=1.5, alpha=0.5)
-        expected = 1.5 * c + 0.5 * (a @ b)
-        self.assertTrue(mx.allclose(expected, out))
 
     def test_addmm_grad(self):
         def make_ref_addmm(alpha, beta):
@@ -724,33 +723,32 @@ class TestBlas(mlx_tests.MLXTestCase):
         shapes = ((1, 64, 32, 128), (4, 28, 24, 47), (1, 1, 24, 47))
 
         alpha = 2.0
-        beta = 0.5
+        for beta in (1.0, 0.5):
+            f_test = make_addmm(alpha, beta)
+            f_ref = make_ref_addmm(alpha, beta)
 
-        f_test = make_addmm(alpha, beta)
+            for B, M, N, K in shapes:
-        f_ref = make_ref_addmm(alpha, beta)
+                cotan = mx.ones((B, M, N))
+                c = mx.random.normal((B, M, N))
+                a = mx.random.normal((B, M, K))
+                b = mx.random.normal((B, K, N))
 
-        for B, M, N, K in shapes:
+                out_ref, dout_ref = mx.vjp(
-            cotan = mx.ones((B, M, N))
+                    f_ref,
-            c = mx.random.normal((B, M, N))
+                    [c, a, b],
-            a = mx.random.normal((B, M, K))
+                    [cotan],
-            b = mx.random.normal((B, K, N))
+                )
+                out_test, dout_test = mx.vjp(
+                    f_test,
+                    [c, a, b],
+                    [cotan],
+                )
 
-            out_ref, dout_ref = mx.vjp(
+                self.assertTrue(mx.allclose(out_ref[0], out_test[0], atol=1e-4).item())
-                f_ref,
-                [c, a, b],
-                [cotan],
-            )
-            out_test, dout_test = mx.vjp(
-                f_test,
-                [c, a, b],
-                [cotan],
-            )
 
-            self.assertTrue(mx.allclose(out_ref[0], out_test[0], atol=1e-4).item())
+                for r, t in zip(dout_ref, dout_test):
-
+                    self.assertEqual(r.shape, t.shape)
-            for r, t in zip(dout_ref, dout_test):
+                    self.assertTrue(mx.allclose(r, t, atol=1e-4).item())
-                self.assertEqual(r.shape, t.shape)
-                self.assertTrue(mx.allclose(r, t, atol=1e-4).item())
 
     def test_empty_matmul(self):
         a = mx.array([[], []]).T

python/tests/test_fast.py

@@ -8,18 +8,23 @@ import mlx_tests
 
 
 def rope_orig(x, dims, traditional, base, scale, offset, freqs=None):
-    offset = offset.item() if isinstance(offset, mx.array) else offset
+    N = x.shape[-2]
-    N = x.shape[-2] + offset
     dtype = x.dtype
     half_D = dims // 2
-    positions = mx.arange(offset, N, dtype=dtype) * scale
+    positions = mx.arange(N, dtype=dtype)
+    if isinstance(offset, mx.array) and offset.size > 1:
+        expand = tuple(range(1, x.ndim - 1))
+        positions = mx.expand_dims(offset, expand) + positions
+    else:
+        positions = offset + positions
+    positions = positions * scale
     if freqs is None:
         inv_freqs = mx.exp(
             -mx.arange(0.0, half_D, dtype=dtype) * (math.log(base) / half_D)
         )
     else:
         inv_freqs = (1 / freqs).astype(x.dtype)
-    theta = mx.reshape(positions, (-1, 1)) * mx.reshape(inv_freqs, (1, -1))
+    theta = mx.expand_dims(positions, -1) * inv_freqs
     costheta, sintheta = mx.cos(theta), mx.sin(theta)
     if traditional:
         x1 = x[..., :dims:2]
@@ -214,6 +219,7 @@ class TestFast(mlx_tests.MLXTestCase):
             )
             self.assertEqual(dtype, rx.dtype)
             self.assertLess(mx.abs(rx - rx_fast).max(), tolerances[dtype])
+            return
 
         # Test single vector
         x = mx.random.uniform(shape=(1, 1, dims))
@@ -277,6 +283,55 @@ class TestFast(mlx_tests.MLXTestCase):
                 g2 = mx.grad(f2)(x, y)
                 self.assertLess(mx.abs(g1 - g2).max(), 1e-5)
 
+    def test_rope_batch(self):
+        T = 4
+        base = 10000.0
+        scale = 1.0
+        traditional = True
+        batch_sizes = [3, 8, 11]
+        num_heads = [1, 3, 5]
+        dims = 32
+
+        x = mx.random.uniform(shape=(8, 4, T, dims))
+
+        offset = mx.array([1, 2, 3])
+        with self.assertRaises(ValueError):
+            mx.fast.rope(
+                x,
+                dims,
+                traditional=traditional,
+                base=base,
+                scale=scale,
+                offset=offset,
+            )
+
+        for batch_size in batch_sizes:
+            for n_head in num_heads:
+                x = mx.random.uniform(shape=(batch_size, n_head, T, dims))
+                offset = mx.arange(batch_size)
+                rx = rope_orig(x, dims, traditional, base, scale, offset)
+                rx_fast = mx.fast.rope(
+                    x,
+                    dims,
+                    traditional=traditional,
+                    base=base,
+                    scale=scale,
+                    offset=offset,
+                )
+                self.assertLess(mx.abs(rx - rx_fast).max(), 1e-5)
+        x = mx.random.normal(shape=(2, 6, 8, 64)).transpose(0, 2, 1, 3)
+        dims = 64
+        offset = 0
+        rx_fast = mx.fast.rope(
+            x, dims, traditional=traditional, scale=scale, base=base, offset=offset
+        )
+        rx_fast_single = mx.fast.rope(
+            x[0:1], dims, traditional=traditional, scale=scale, base=base, offset=offset
+        )
+
+        rx = rope_orig(x, dims, traditional, base, scale, offset)
+        self.assertLess(mx.abs(rx - rx_fast).max(), 1e-5)
+
     def test_rms_norm(self):
         # Per dtype absolute tolerance
         tolerances = {mx.float32: 1e-6, mx.float16: 1e-3, mx.bfloat16: 1e-2}

setup.py

@@ -121,7 +121,8 @@ class CMakeBuild(build_ext):
             build_args += [f"-j{os.cpu_count()}"]
 
         # Avoid cache miss when building from temporary dirs.
-        os.environ["CCACHE_BASEDIR"] = os.path.abspath(self.build_temp)
+        os.environ["CCACHE_BASEDIR"] = os.path.realpath(self.build_temp)
+        os.environ["CCACHE_NOHASHDIR"] = "true"
 
         subprocess.run(
             ["cmake", ext.sourcedir, *cmake_args], cwd=build_temp, check=True

tests/CMakeLists.txt

@@ -1,10 +1,7 @@
-# Doctest works fine with cmake 3.5
-set(CMAKE_POLICY_VERSION_MINIMUM 3.5)
-
 FetchContent_Declare(
   doctest
-  GIT_REPOSITORY "https://github.com/onqtam/doctest"
+  GIT_REPOSITORY https://github.com/onqtam/doctest.git
-  GIT_TAG "ae7a13539fb71f270b87eb2e874fbac80bc8dda2")
+  GIT_TAG v2.4.12)
 FetchContent_MakeAvailable(doctest)
 
 add_executable(tests ${PROJECT_SOURCE_DIR}/tests/tests.cpp)