rebase + nit (#2260)

Co-authored-by: Awni Hannun <awni@apple.com>

benchmarks/python/layer_norm_bench.py

@@ -1,5 +1,7 @@
 # Copyright © 2023-2024 Apple Inc.
 
+from functools import partial
+
 import mlx.core as mx
 import mlx.nn as nn
 from time_utils import time_fn
@@ -18,51 +20,63 @@ def layer_norm(x, w, b, eps):
     return y
 
 
-def time_layer_norm():
+def time_layer_norm(N, dt):
+    L = 1024
     f1 = lambda x, w, b, y: (layer_norm(x, w, b, 1e-5) * y).sum()
     f2 = lambda x, w, b, y: (mx.fast.layer_norm(x, w, b, 1e-5) * y).sum()
     g1 = mx.grad(f1, argnums=(0, 1, 2))
     g2 = mx.grad(f2, argnums=(0, 1, 2))
 
-    x = mx.random.uniform(shape=(8, 1024, 4096)).astype(mx.float16)
-    w = mx.random.uniform(shape=(4096,)).astype(mx.float16)
-    b = mx.random.uniform(shape=(4096,)).astype(mx.float16)
-    y = mx.random.uniform(shape=(8, 1024, 4096)).astype(mx.float16)
+    x = mx.random.uniform(shape=(8, L, N)).astype(dt)
+    w = mx.random.uniform(shape=(N,)).astype(dt)
+    b = mx.random.uniform(shape=(N,)).astype(dt)
+    y = mx.random.uniform(shape=(8, L, N)).astype(dt)
     mx.eval(x, w, b, y)
 
-    def layer_norm_loop(g, x, w, b):
+    def layer_norm_loop(f, x, w, b):
+        for _ in range(32):
+            x = f(x, w, b)
+        return x
+
+    time_fn(layer_norm_loop, partial(layer_norm, eps=1e-5), x, w, b)
+    time_fn(layer_norm_loop, partial(mx.fast.layer_norm, eps=1e-5), x, w, b)
+
+    def layer_norm_grad_loop(g, x, w, b):
         gx, gw, gb = x, w, b
         for _ in range(32):
             gx, gw, gb = g(gx, gw, gb, y)
         return gx, gw, gb
 
-    time_fn(layer_norm_loop, g1, x, w, b)
-    time_fn(layer_norm_loop, g2, x, w, b)
-    time_fn(layer_norm_loop, mx.compile(g1), x, w, b)
-    time_fn(layer_norm_loop, mx.compile(g2), x, w, b)
+    time_fn(layer_norm_grad_loop, g1, x, w, b)
+    time_fn(layer_norm_grad_loop, g2, x, w, b)
+    time_fn(layer_norm_grad_loop, mx.compile(g1), x, w, b)
+    time_fn(layer_norm_grad_loop, mx.compile(g2), x, w, b)
 
     f1 = lambda x, y: (layer_norm(x, None, None, 1e-5) * y).sum()
     f2 = lambda x, y: (mx.fast.layer_norm(x, None, None, 1e-5) * y).sum()
     g1 = mx.grad(f1, argnums=(0,))
     g2 = mx.grad(f2, argnums=(0,))
 
-    x = mx.random.uniform(shape=(8, 1024, 4096)).astype(mx.float16)
-    w = mx.random.uniform(shape=(4096,)).astype(mx.float16)
-    b = mx.random.uniform(shape=(4096,)).astype(mx.float16)
-    y = mx.random.uniform(shape=(8, 1024, 4096)).astype(mx.float16)
+    x = mx.random.uniform(shape=(8, L, N)).astype(dt)
+    w = mx.random.uniform(shape=(N,)).astype(dt)
+    b = mx.random.uniform(shape=(N,)).astype(dt)
+    y = mx.random.uniform(shape=(8, L, N)).astype(dt)
     mx.eval(x, w, b, y)
 
-    def layer_norm_loop(g, x):
+    def layer_norm_grad_x_loop(g, x):
         gx = x
         for _ in range(32):
             gx = g(gx, y)
         return gx
 
-    time_fn(layer_norm_loop, g1, x)
-    time_fn(layer_norm_loop, g2, x)
-    time_fn(layer_norm_loop, mx.compile(g1), x)
-    time_fn(layer_norm_loop, mx.compile(g2), x)
+    time_fn(layer_norm_grad_x_loop, g1, x)
+    time_fn(layer_norm_grad_x_loop, g2, x)
+    time_fn(layer_norm_grad_x_loop, mx.compile(g1), x)
+    time_fn(layer_norm_grad_x_loop, mx.compile(g2), x)
 
 
 if __name__ == "__main__":
-    time_layer_norm()
+    for dt in [mx.float32, mx.float16, mx.bfloat16]:
+        for n in [1024, 2048, 4096, 8192, 8192 + 1024]:
+            print(dt, n)
+            time_layer_norm(n, dt)

docs/src/dev/custom_metal_kernels.rst

@@ -8,23 +8,26 @@ MLX supports writing custom Metal kernels through the Python and C++ APIs.
 Simple Example
 --------------
 
+.. currentmodule:: mlx.core
+
 Let's write a custom kernel that computes ``exp`` elementwise:
 
 .. code-block:: python
 
-  def exp_elementwise(a: mx.array):
-      source = """
-          uint elem = thread_position_in_grid.x;
-          T tmp = inp[elem];
-          out[elem] = metal::exp(tmp);
-      """
+  source = """
+      uint elem = thread_position_in_grid.x;
+      T tmp = inp[elem];
+      out[elem] = metal::exp(tmp);
+  """
 
-      kernel = mx.fast.metal_kernel(
-          name="myexp",
-          input_names=["inp"],
-          output_names=["out"],
-          source=source,
-      )
+  kernel = mx.fast.metal_kernel(
+      name="myexp",
+      input_names=["inp"],
+      output_names=["out"],
+      source=source,
+  )
+
+  def exp_elementwise(a: mx.array):
       outputs = kernel(
           inputs=[a],
           template=[("T", mx.float32)],
@@ -39,8 +42,13 @@ Let's write a custom kernel that computes ``exp`` elementwise:
   b = exp_elementwise(a)
   assert mx.allclose(b, mx.exp(a))
 
+Every time you make a kernel, a new Metal library is created and possibly
+JIT compiled. To reduce the overhead from that, build the kernel once with
+:func:`fast.metal_kernel` and then use it many times.
+
 .. note::
-    We are only required to pass the body of the Metal kernel in ``source``.
+   Only pass the body of the Metal kernel in ``source``. The function
+   signature is generated automatically.
 
 The full function signature will be generated using:
 
@@ -78,44 +86,51 @@ Putting this all together, the generated function signature for ``myexp`` is as
 
   template [[host_name("custom_kernel_myexp_float")]] [[kernel]] decltype(custom_kernel_myexp_float<float>) custom_kernel_myexp_float<float>;
 
-Note: ``grid`` and ``threadgroup`` are parameters to the Metal `dispatchThreads <https://developer.apple.com/documentation/metal/mtlcomputecommandencoder/2866532-dispatchthreads>`_ function.
-This means we will launch ``mx.prod(grid)`` threads, subdivided into ``threadgroup`` size threadgroups.
-For optimal performance, each thread group dimension should be less than or equal to the corresponding grid dimension.
+Note: ``grid`` and ``threadgroup`` are parameters to the Metal `dispatchThreads
+<https://developer.apple.com/documentation/metal/mtlcomputecommandencoder/2866532-dispatchthreads>`_
+function. This means we will launch ``mx.prod(grid)`` threads, subdivided into
+``threadgroup`` size threadgroups.  For optimal performance, each thread group
+dimension should be less than or equal to the corresponding grid dimension.
 
-Passing ``verbose=True`` to ``mx.fast.metal_kernel.__call__`` will print the generated code for debugging purposes.
+Passing ``verbose=True`` to :func:`ast.metal_kernel.__call__` will print the
+generated code for debugging purposes.
 
 Using Shape/Strides
 -------------------
 
-``mx.fast.metal_kernel`` supports an argument ``ensure_row_contiguous`` which is ``True`` by default.
-This will copy the ``mx.array`` inputs if needed before the kernel is launched to ensure that the memory layout is row contiguous.
-Generally this makes writing the kernel easier, since we don't have to worry about gaps or the ordering of the dims
-when indexing.
+:func:`fast.metal_kernel` supports an argument ``ensure_row_contiguous`` which
+is ``True`` by default. This will copy the array inputs if needed
+before the kernel is launched to ensure that the memory layout is row
+contiguous.  Generally this makes writing the kernel easier, since we don't
+have to worry about gaps or the ordering of the dims when indexing.
 
-If we want to avoid this copy, ``metal_kernel`` automatically passes ``a_shape``, ``a_strides`` and ``a_ndim`` for each
-input array ``a`` if any are present in ``source``.
-We can then use MLX's built in indexing utils to fetch the right elements for each thread.
+If we want to avoid this copy, :func:`fast.metal_kernel` automatically passes
+``a_shape``, ``a_strides`` and ``a_ndim`` for each input array ``a`` if any are
+present in ``source``. We can then use MLX's built in indexing utils to fetch
+the right elements for each thread.
 
-Let's convert ``myexp`` above to support arbitrarily strided arrays without relying on a copy from ``ensure_row_contiguous``:
+Let's convert ``myexp`` above to support arbitrarily strided arrays without
+relying on a copy from ``ensure_row_contiguous``:
 
 .. code-block:: python
+   
+  source = """
+      uint elem = thread_position_in_grid.x;
+      // Utils from `mlx/backend/metal/kernels/utils.h` are automatically included
+      uint loc = elem_to_loc(elem, inp_shape, inp_strides, inp_ndim);
+      T tmp = inp[loc];
+      // Output arrays are always row contiguous
+      out[elem] = metal::exp(tmp);
+  """
+
+  kernel = mx.fast.metal_kernel(
+      name="myexp_strided",
+      input_names=["inp"],
+      output_names=["out"],
+      source=source
+  )
 
   def exp_elementwise(a: mx.array):
-      source = """
-          uint elem = thread_position_in_grid.x;
-          // Utils from `mlx/backend/metal/kernels/utils.h` are automatically included
-          uint loc = elem_to_loc(elem, inp_shape, inp_strides, inp_ndim);
-          T tmp = inp[loc];
-          // Output arrays are always row contiguous
-          out[elem] = metal::exp(tmp);
-      """
-
-      kernel = mx.fast.metal_kernel(
-          name="myexp_strided",
-          input_names=["inp"],
-          output_names=["out"],
-          source=source
-      )
       outputs = kernel(
           inputs=[a],
           template=[("T", mx.float32)],
@@ -142,137 +157,139 @@ We'll start with the following MLX implementation using standard ops:
 
 .. code-block:: python
 
-    def grid_sample_ref(x, grid):
-        N, H_in, W_in, _ = x.shape
-        ix = ((grid[..., 0] + 1) * W_in - 1) / 2
-        iy = ((grid[..., 1] + 1) * H_in - 1) / 2
+  def grid_sample_ref(x, grid):
+      N, H_in, W_in, _ = x.shape
+      ix = ((grid[..., 0] + 1) * W_in - 1) / 2
+      iy = ((grid[..., 1] + 1) * H_in - 1) / 2
 
-        ix_nw = mx.floor(ix).astype(mx.int32)
-        iy_nw = mx.floor(iy).astype(mx.int32)
+      ix_nw = mx.floor(ix).astype(mx.int32)
+      iy_nw = mx.floor(iy).astype(mx.int32)
 
-        ix_ne = ix_nw + 1
-        iy_ne = iy_nw
+      ix_ne = ix_nw + 1
+      iy_ne = iy_nw
 
-        ix_sw = ix_nw
-        iy_sw = iy_nw + 1
+      ix_sw = ix_nw
+      iy_sw = iy_nw + 1
 
-        ix_se = ix_nw + 1
-        iy_se = iy_nw + 1
+      ix_se = ix_nw + 1
+      iy_se = iy_nw + 1
 
-        nw = (ix_se - ix)    * (iy_se - iy)
-        ne = (ix    - ix_sw) * (iy_sw - iy)
-        sw = (ix_ne - ix)    * (iy    - iy_ne)
-        se = (ix    - ix_nw) * (iy    - iy_nw)
+      nw = (ix_se - ix)    * (iy_se - iy)
+      ne = (ix    - ix_sw) * (iy_sw - iy)
+      sw = (ix_ne - ix)    * (iy    - iy_ne)
+      se = (ix    - ix_nw) * (iy    - iy_nw)
 
-        I_nw = x[mx.arange(N)[:, None, None], iy_nw, ix_nw, :]
-        I_ne = x[mx.arange(N)[:, None, None], iy_ne, ix_ne, :]
-        I_sw = x[mx.arange(N)[:, None, None], iy_sw, ix_sw, :]
-        I_se = x[mx.arange(N)[:, None, None], iy_se, ix_se, :]
+      I_nw = x[mx.arange(N)[:, None, None], iy_nw, ix_nw, :]
+      I_ne = x[mx.arange(N)[:, None, None], iy_ne, ix_ne, :]
+      I_sw = x[mx.arange(N)[:, None, None], iy_sw, ix_sw, :]
+      I_se = x[mx.arange(N)[:, None, None], iy_se, ix_se, :]
 
-        mask_nw = (iy_nw >= 0) & (iy_nw <= H_in - 1) & (ix_nw >= 0) & (ix_nw <= W_in - 1)
-        mask_ne = (iy_ne >= 0) & (iy_ne <= H_in - 1) & (ix_ne >= 0) & (ix_ne <= W_in - 1)
-        mask_sw = (iy_sw >= 0) & (iy_sw <= H_in - 1) & (ix_sw >= 0) & (ix_sw <= W_in - 1)
-        mask_se = (iy_se >= 0) & (iy_se <= H_in - 1) & (ix_se >= 0) & (ix_se <= W_in - 1)
+      mask_nw = (iy_nw >= 0) & (iy_nw <= H_in - 1) & (ix_nw >= 0) & (ix_nw <= W_in - 1)
+      mask_ne = (iy_ne >= 0) & (iy_ne <= H_in - 1) & (ix_ne >= 0) & (ix_ne <= W_in - 1)
+      mask_sw = (iy_sw >= 0) & (iy_sw <= H_in - 1) & (ix_sw >= 0) & (ix_sw <= W_in - 1)
+      mask_se = (iy_se >= 0) & (iy_se <= H_in - 1) & (ix_se >= 0) & (ix_se <= W_in - 1)
 
-        I_nw *= mask_nw[..., None]
-        I_ne *= mask_ne[..., None]
-        I_sw *= mask_sw[..., None]
-        I_se *= mask_se[..., None]
+      I_nw *= mask_nw[..., None]
+      I_ne *= mask_ne[..., None]
+      I_sw *= mask_sw[..., None]
+      I_se *= mask_se[..., None]
 
-        output = nw[..., None] * I_nw + ne[..., None] * I_ne + sw[..., None] * I_sw + se[..., None] * I_se
+      output = nw[..., None] * I_nw + ne[..., None] * I_ne + sw[..., None] * I_sw + se[..., None] * I_se
 
-        return output
+      return output
 
-Now let's use ``mx.custom_function`` together with ``mx.fast.metal_kernel``
+Now let's use :func:`custom_function` together with :func:`fast.metal_kernel`
 to write a fast GPU kernel for both the forward and backward passes.
 
 First we'll implement the forward pass as a fused kernel:
 
 .. code-block:: python
 
-    @mx.custom_function
-    def grid_sample(x, grid):
+  source = """
+      uint elem = thread_position_in_grid.x;
+      int H = x_shape[1];
+      int W = x_shape[2];
+      int C = x_shape[3];
+      int gH = grid_shape[1];
+      int gW = grid_shape[2];
 
-        assert x.ndim == 4, "`x` must be 4D."
-        assert grid.ndim == 4, "`grid` must be 4D."
+      int w_stride = C;
+      int h_stride = W * w_stride;
+      int b_stride = H * h_stride;
 
-        B, _, _, C = x.shape
-        _, gN, gM, D = grid.shape
-        out_shape = (B, gN, gM, C)
+      uint grid_idx = elem / C * 2;
+      float ix = ((grid[grid_idx] + 1) * W - 1) / 2;
+      float iy = ((grid[grid_idx + 1] + 1) * H - 1) / 2;
 
-        assert D == 2, "Last dim of `grid` must be size 2."
+      int ix_nw = floor(ix);
+      int iy_nw = floor(iy);
 
-        source = """
-            uint elem = thread_position_in_grid.x;
-            int H = x_shape[1];
-            int W = x_shape[2];
-            int C = x_shape[3];
-            int gH = grid_shape[1];
-            int gW = grid_shape[2];
+      int ix_ne = ix_nw + 1;
+      int iy_ne = iy_nw;
 
-            int w_stride = C;
-            int h_stride = W * w_stride;
-            int b_stride = H * h_stride;
+      int ix_sw = ix_nw;
+      int iy_sw = iy_nw + 1;
 
-            uint grid_idx = elem / C * 2;
-            float ix = ((grid[grid_idx] + 1) * W - 1) / 2;
-            float iy = ((grid[grid_idx + 1] + 1) * H - 1) / 2;
+      int ix_se = ix_nw + 1;
+      int iy_se = iy_nw + 1;
 
-            int ix_nw = floor(ix);
-            int iy_nw = floor(iy);
+      T nw = (ix_se - ix)    * (iy_se - iy);
+      T ne = (ix    - ix_sw) * (iy_sw - iy);
+      T sw = (ix_ne - ix)    * (iy    - iy_ne);
+      T se = (ix    - ix_nw) * (iy    - iy_nw);
 
-            int ix_ne = ix_nw + 1;
-            int iy_ne = iy_nw;
+      int batch_idx = elem / C / gH / gW * b_stride;
+      int channel_idx = elem % C;
+      int base_idx = batch_idx + channel_idx;
 
-            int ix_sw = ix_nw;
-            int iy_sw = iy_nw + 1;
+      T I_nw = x[base_idx + iy_nw * h_stride + ix_nw * w_stride];
+      T I_ne = x[base_idx + iy_ne * h_stride + ix_ne * w_stride];
+      T I_sw = x[base_idx + iy_sw * h_stride + ix_sw * w_stride];
+      T I_se = x[base_idx + iy_se * h_stride + ix_se * w_stride];
 
-            int ix_se = ix_nw + 1;
-            int iy_se = iy_nw + 1;
+      I_nw = iy_nw >= 0 && iy_nw <= H - 1 && ix_nw >= 0 && ix_nw <= W - 1 ? I_nw : 0;
+      I_ne = iy_ne >= 0 && iy_ne <= H - 1 && ix_ne >= 0 && ix_ne <= W - 1 ? I_ne : 0;
+      I_sw = iy_sw >= 0 && iy_sw <= H - 1 && ix_sw >= 0 && ix_sw <= W - 1 ? I_sw : 0;
+      I_se = iy_se >= 0 && iy_se <= H - 1 && ix_se >= 0 && ix_se <= W - 1 ? I_se : 0;
 
-            T nw = (ix_se - ix)    * (iy_se - iy);
-            T ne = (ix    - ix_sw) * (iy_sw - iy);
-            T sw = (ix_ne - ix)    * (iy    - iy_ne);
-            T se = (ix    - ix_nw) * (iy    - iy_nw);
+      out[elem] = nw * I_nw + ne * I_ne + sw * I_sw + se * I_se;
+  """
 
-            int batch_idx = elem / C / gH / gW * b_stride;
-            int channel_idx = elem % C;
-            int base_idx = batch_idx + channel_idx;
+  kernel = mx.fast.metal_kernel(
+      name="grid_sample",
+      input_names=["x", "grid"],
+      output_names=["out"],
+      source=source,
+  )
 
-            T I_nw = x[base_idx + iy_nw * h_stride + ix_nw * w_stride];
-            T I_ne = x[base_idx + iy_ne * h_stride + ix_ne * w_stride];
-            T I_sw = x[base_idx + iy_sw * h_stride + ix_sw * w_stride];
-            T I_se = x[base_idx + iy_se * h_stride + ix_se * w_stride];
+  @mx.custom_function
+  def grid_sample(x, grid):
 
-            I_nw = iy_nw >= 0 && iy_nw <= H - 1 && ix_nw >= 0 && ix_nw <= W - 1 ? I_nw : 0;
-            I_ne = iy_ne >= 0 && iy_ne <= H - 1 && ix_ne >= 0 && ix_ne <= W - 1 ? I_ne : 0;
-            I_sw = iy_sw >= 0 && iy_sw <= H - 1 && ix_sw >= 0 && ix_sw <= W - 1 ? I_sw : 0;
-            I_se = iy_se >= 0 && iy_se <= H - 1 && ix_se >= 0 && ix_se <= W - 1 ? I_se : 0;
+      assert x.ndim == 4, "`x` must be 4D."
+      assert grid.ndim == 4, "`grid` must be 4D."
 
-            out[elem] = nw * I_nw + ne * I_ne + sw * I_sw + se * I_se;
-        """
-        kernel = mx.fast.metal_kernel(
-            name="grid_sample",
-            input_names=["x", "grid"],
-            output_names=["out"],
-            source=source,
-        )
-        outputs = kernel(
-            inputs=[x, grid],
-            template=[("T", x.dtype)],
-            output_shapes=[out_shape],
-            output_dtypes=[x.dtype],
-            grid=(np.prod(out_shape), 1, 1),
-            threadgroup=(256, 1, 1),
-        )
-        return outputs[0]
+      B, _, _, C = x.shape
+      _, gN, gM, D = grid.shape
+      out_shape = (B, gN, gM, C)
+
+      assert D == 2, "Last dim of `grid` must be size 2."
+
+      outputs = kernel(
+          inputs=[x, grid],
+          template=[("T", x.dtype)],
+          output_shapes=[out_shape],
+          output_dtypes=[x.dtype],
+          grid=(np.prod(out_shape), 1, 1),
+          threadgroup=(256, 1, 1),
+      )
+      return outputs[0]
 
 For a reasonably sized input such as:
 
 .. code-block:: python
 
-    x.shape = (8, 1024, 1024, 64)
-    grid.shape = (8, 256, 256, 2)
+  x.shape = (8, 1024, 1024, 64)
+  grid.shape = (8, 256, 256, 2)
 
 On an M1 Max, we see a big performance improvement:
 
@@ -281,11 +298,11 @@ On an M1 Max, we see a big performance improvement:
 Grid Sample VJP
 ---------------
 
-Since we decorated ``grid_sample`` with ``mx.custom_function``, we can now define
-its custom vjp transform so MLX can differentiate it.
+Since we decorated ``grid_sample`` with :func:`custom_function`, we can now
+define its custom vjp transform so MLX can differentiate it.
 
 The backwards pass requires atomically updating ``x_grad``/``grid_grad`` and so
-requires a few extra ``mx.fast.metal_kernel`` features:
+requires a few extra :func:`fast.metal_kernel` features:
 
 * ``init_value=0``
     Initialize all of the kernel's outputs to this value before it runs. This allows us to update only part of the output arrays with the kernel.
@@ -299,128 +316,129 @@ We can then implement the backwards pass as follows:
 
 .. code-block:: python
 
-    @grid_sample.vjp
-    def grid_sample_vjp(primals, cotangent, _):
-        x, grid = primals
-        B, _, _, C = x.shape
-        _, gN, gM, D = grid.shape
+  source = """
+      uint elem = thread_position_in_grid.x;
+      int H = x_shape[1];
+      int W = x_shape[2];
+      int C = x_shape[3];
+      // Pad C to the nearest larger simdgroup size multiple
+      int C_padded = ceildiv(C, threads_per_simdgroup) * threads_per_simdgroup;
 
-        assert D == 2, "Last dim of `grid` must be size 2."
+      int gH = grid_shape[1];
+      int gW = grid_shape[2];
 
-        source = """
-            uint elem = thread_position_in_grid.x;
-            int H = x_shape[1];
-            int W = x_shape[2];
-            int C = x_shape[3];
-            // Pad C to the nearest larger simdgroup size multiple
-            int C_padded = ceildiv(C, threads_per_simdgroup) * threads_per_simdgroup;
+      int w_stride = C;
+      int h_stride = W * w_stride;
+      int b_stride = H * h_stride;
 
-            int gH = grid_shape[1];
-            int gW = grid_shape[2];
+      uint grid_idx = elem / C_padded * 2;
+      float ix = ((grid[grid_idx] + 1) * W - 1) / 2;
+      float iy = ((grid[grid_idx + 1] + 1) * H - 1) / 2;
 
-            int w_stride = C;
-            int h_stride = W * w_stride;
-            int b_stride = H * h_stride;
+      int ix_nw = floor(ix);
+      int iy_nw = floor(iy);
 
-            uint grid_idx = elem / C_padded * 2;
-            float ix = ((grid[grid_idx] + 1) * W - 1) / 2;
-            float iy = ((grid[grid_idx + 1] + 1) * H - 1) / 2;
+      int ix_ne = ix_nw + 1;
+      int iy_ne = iy_nw;
 
-            int ix_nw = floor(ix);
-            int iy_nw = floor(iy);
+      int ix_sw = ix_nw;
+      int iy_sw = iy_nw + 1;
 
-            int ix_ne = ix_nw + 1;
-            int iy_ne = iy_nw;
+      int ix_se = ix_nw + 1;
+      int iy_se = iy_nw + 1;
 
-            int ix_sw = ix_nw;
-            int iy_sw = iy_nw + 1;
+      T nw = (ix_se - ix)    * (iy_se - iy);
+      T ne = (ix    - ix_sw) * (iy_sw - iy);
+      T sw = (ix_ne - ix)    * (iy    - iy_ne);
+      T se = (ix    - ix_nw) * (iy    - iy_nw);
 
-            int ix_se = ix_nw + 1;
-            int iy_se = iy_nw + 1;
+      int batch_idx = elem / C_padded / gH / gW * b_stride;
+      int channel_idx = elem % C_padded;
+      int base_idx = batch_idx + channel_idx;
 
-            T nw = (ix_se - ix)    * (iy_se - iy);
-            T ne = (ix    - ix_sw) * (iy_sw - iy);
-            T sw = (ix_ne - ix)    * (iy    - iy_ne);
-            T se = (ix    - ix_nw) * (iy    - iy_nw);
+      T gix = T(0);
+      T giy = T(0);
+      if (channel_idx < C) {
+          int cot_index = elem / C_padded * C + channel_idx;
+          T cot = cotangent[cot_index];
+          if (iy_nw >= 0 && iy_nw <= H - 1 && ix_nw >= 0 && ix_nw <= W - 1) {
+              int offset = base_idx + iy_nw * h_stride + ix_nw * w_stride;
+              atomic_fetch_add_explicit(&x_grad[offset], nw * cot, memory_order_relaxed);
 
-            int batch_idx = elem / C_padded / gH / gW * b_stride;
-            int channel_idx = elem % C_padded;
-            int base_idx = batch_idx + channel_idx;
+              T I_nw = x[offset];
+              gix -= I_nw * (iy_se - iy) * cot;
+              giy -= I_nw * (ix_se - ix) * cot;
+          }
+          if (iy_ne >= 0 && iy_ne <= H - 1 && ix_ne >= 0 && ix_ne <= W - 1) {
+              int offset = base_idx + iy_ne * h_stride + ix_ne * w_stride;
+              atomic_fetch_add_explicit(&x_grad[offset], ne * cot, memory_order_relaxed);
 
-            T gix = T(0);
-            T giy = T(0);
-            if (channel_idx < C) {
-                int cot_index = elem / C_padded * C + channel_idx;
-                T cot = cotangent[cot_index];
-                if (iy_nw >= 0 && iy_nw <= H - 1 && ix_nw >= 0 && ix_nw <= W - 1) {
-                    int offset = base_idx + iy_nw * h_stride + ix_nw * w_stride;
-                    atomic_fetch_add_explicit(&x_grad[offset], nw * cot, memory_order_relaxed);
+              T I_ne = x[offset];
+              gix += I_ne * (iy_sw - iy) * cot;
+              giy -= I_ne * (ix - ix_sw) * cot;
+          }
+          if (iy_sw >= 0 && iy_sw <= H - 1 && ix_sw >= 0 && ix_sw <= W - 1) {
+              int offset = base_idx + iy_sw * h_stride + ix_sw * w_stride;
+              atomic_fetch_add_explicit(&x_grad[offset], sw * cot, memory_order_relaxed);
 
-                    T I_nw = x[offset];
-                    gix -= I_nw * (iy_se - iy) * cot;
-                    giy -= I_nw * (ix_se - ix) * cot;
-                }
-                if (iy_ne >= 0 && iy_ne <= H - 1 && ix_ne >= 0 && ix_ne <= W - 1) {
-                    int offset = base_idx + iy_ne * h_stride + ix_ne * w_stride;
-                    atomic_fetch_add_explicit(&x_grad[offset], ne * cot, memory_order_relaxed);
+              T I_sw = x[offset];
+              gix -= I_sw * (iy - iy_ne) * cot;
+              giy += I_sw * (ix_ne - ix) * cot;
+          }
+          if (iy_se >= 0 && iy_se <= H - 1 && ix_se >= 0 && ix_se <= W - 1) {
+              int offset = base_idx + iy_se * h_stride + ix_se * w_stride;
+              atomic_fetch_add_explicit(&x_grad[offset], se * cot, memory_order_relaxed);
 
-                    T I_ne = x[offset];
-                    gix += I_ne * (iy_sw - iy) * cot;
-                    giy -= I_ne * (ix - ix_sw) * cot;
-                }
-                if (iy_sw >= 0 && iy_sw <= H - 1 && ix_sw >= 0 && ix_sw <= W - 1) {
-                    int offset = base_idx + iy_sw * h_stride + ix_sw * w_stride;
-                    atomic_fetch_add_explicit(&x_grad[offset], sw * cot, memory_order_relaxed);
+              T I_se = x[offset];
+              gix += I_se * (iy - iy_nw) * cot;
+              giy += I_se * (ix - ix_nw) * cot;
+          }
+      }
 
-                    T I_sw = x[offset];
-                    gix -= I_sw * (iy - iy_ne) * cot;
-                    giy += I_sw * (ix_ne - ix) * cot;
-                }
-                if (iy_se >= 0 && iy_se <= H - 1 && ix_se >= 0 && ix_se <= W - 1) {
-                    int offset = base_idx + iy_se * h_stride + ix_se * w_stride;
-                    atomic_fetch_add_explicit(&x_grad[offset], se * cot, memory_order_relaxed);
+      T gix_mult = W / 2;
+      T giy_mult = H / 2;
 
-                    T I_se = x[offset];
-                    gix += I_se * (iy - iy_nw) * cot;
-                    giy += I_se * (ix - ix_nw) * cot;
-                }
-            }
+      // Reduce across each simdgroup first.
+      // This is much faster than relying purely on atomics.
+      gix = simd_sum(gix);
+      giy = simd_sum(giy);
 
-            T gix_mult = W / 2;
-            T giy_mult = H / 2;
+      if (thread_index_in_simdgroup == 0) {
+          atomic_fetch_add_explicit(&grid_grad[grid_idx], gix * gix_mult, memory_order_relaxed);
+          atomic_fetch_add_explicit(&grid_grad[grid_idx + 1], giy * giy_mult, memory_order_relaxed);
+      }
+  """
+  kernel = mx.fast.metal_kernel(
+      name="grid_sample_grad",
+      input_names=["x", "grid", "cotangent"],
+      output_names=["x_grad", "grid_grad"],
+      source=source,
+      atomic_outputs=True,
+  )
 
-            // Reduce across each simdgroup first.
-            // This is much faster than relying purely on atomics.
-            gix = simd_sum(gix);
-            giy = simd_sum(giy);
+  @grid_sample.vjp
+  def grid_sample_vjp(primals, cotangent, _):
+      x, grid = primals
+      B, _, _, C = x.shape
+      _, gN, gM, D = grid.shape
 
-            if (thread_index_in_simdgroup == 0) {
-                atomic_fetch_add_explicit(&grid_grad[grid_idx], gix * gix_mult, memory_order_relaxed);
-                atomic_fetch_add_explicit(&grid_grad[grid_idx + 1], giy * giy_mult, memory_order_relaxed);
-            }
-        """
-        kernel = mx.fast.metal_kernel(
-            name="grid_sample_grad",
-            input_names=["x", "grid", "cotangent"],
-            output_names=["x_grad", "grid_grad"],
-            source=source,
-            atomic_outputs=True,
-        )
-        # pad the output channels to simd group size
-        # so that our `simd_sum`s don't overlap.
-        simdgroup_size = 32
-        C_padded = (C + simdgroup_size - 1) // simdgroup_size * simdgroup_size
-        grid_size = B * gN * gM * C_padded
-        outputs = kernel(
-            inputs=[x, grid, cotangent],
-            template=[("T", x.dtype)],
-            output_shapes=[x.shape, grid.shape],
-            output_dtypes=[x.dtype, x.dtype],
-            grid=(grid_size, 1, 1),
-            threadgroup=(256, 1, 1),
-            init_value=0,
-        )
-        return outputs[0], outputs[1]
+      assert D == 2, "Last dim of `grid` must be size 2."
+
+      # pad the output channels to simd group size
+      # so that our `simd_sum`s don't overlap.
+      simdgroup_size = 32
+      C_padded = (C + simdgroup_size - 1) // simdgroup_size * simdgroup_size
+      grid_size = B * gN * gM * C_padded
+      outputs = kernel(
+          inputs=[x, grid, cotangent],
+          template=[("T", x.dtype)],
+          output_shapes=[x.shape, grid.shape],
+          output_dtypes=[x.dtype, x.dtype],
+          grid=(grid_size, 1, 1),
+          threadgroup=(256, 1, 1),
+          init_value=0,
+      )
+      return outputs[0], outputs[1]
 
 There's an even larger speed up for the vjp:
 

docs/src/dev/extensions.rst

@@ -397,11 +397,11 @@ below.
         std::ostringstream kname;
         kname << "axpby_" << "general_" << type_to_name(out);
 
-        // Make sure the metal library is available
-        d.register_library("mlx_ext");
+        // Load the metal library
+        auto lib = d.get_library("mlx_ext");
 
         // Make a kernel from this metal library
-        auto kernel = d.get_kernel(kname.str(), "mlx_ext");
+        auto kernel = d.get_kernel(kname.str(), lib);
 
         // Prepare to encode kernel
         auto& compute_encoder = d.get_command_encoder(s.index);

examples/extensions/axpby/axpby.cpp

@@ -172,11 +172,11 @@ void Axpby::eval_gpu(
   kname << (contiguous_kernel ? "contiguous_" : "general_");
   kname << type_to_name(out);
 
-  // Make sure the metal library is available
-  d.register_library("mlx_ext");
+  // Load the metal library
+  auto lib = d.get_library("mlx_ext");
 
   // Make a kernel from this metal library
-  auto kernel = d.get_kernel(kname.str(), "mlx_ext");
+  auto kernel = d.get_kernel(kname.str(), lib);
 
   // Prepare to encode kernel
   auto& compute_encoder = d.get_command_encoder(s.index);

mlx/backend/common/copy.h

@@ -2,7 +2,7 @@
 
 #pragma once
 
-#include "mlx/array.h"
+#include "mlx/backend/common/utils.h"
 
 namespace mlx::core {
 
@@ -26,7 +26,7 @@ inline bool set_copy_output_data(const array& in, array& out, CopyType ctype) {
   if (ctype == CopyType::Vector) {
     // If the input is donateable, we are doing a vector copy and the types
     // have the same size, then the input buffer can hold the output.
-    if (in.is_donatable() && in.itemsize() == out.itemsize()) {
+    if (is_donatable(in, out)) {
       out.copy_shared_buffer(in);
       return true;
     } else {

mlx/backend/common/matmul.h

@@ -0,0 +1,78 @@
+// Copyright © 2025 Apple Inc.
+
+#pragma once
+
+#include "mlx/backend/common/utils.h"
+#include "mlx/utils.h"
+
+#include <sstream>
+
+namespace mlx::core {
+
+inline std::tuple<Shape, Strides, Strides> collapse_batches(
+    const array& a,
+    const array& b) {
+  // Get and check the shape for the batched dims
+  Shape A_bshape{a.shape().begin(), a.shape().end() - 2};
+  Shape B_bshape{b.shape().begin(), b.shape().end() - 2};
+  if (A_bshape != B_bshape) {
+    std::ostringstream msg;
+    msg << "[matmul] Got matrices with incorrectly broadcasted shapes: " << "A "
+        << a.shape() << ", B " << b.shape() << ".";
+    throw std::runtime_error(msg.str());
+  }
+
+  Strides A_bstride{a.strides().begin(), a.strides().end() - 2};
+  Strides B_bstride{b.strides().begin(), b.strides().end() - 2};
+
+  auto [batch_shape, batch_strides] =
+      collapse_contiguous_dims(A_bshape, std::vector{A_bstride, B_bstride});
+
+  auto a_batch_strides = batch_strides[0];
+  auto b_batch_strides = batch_strides[1];
+
+  if (batch_shape.empty()) {
+    batch_shape.push_back(1);
+    a_batch_strides.push_back(0);
+    b_batch_strides.push_back(0);
+  }
+
+  return std::make_tuple(batch_shape, a_batch_strides, b_batch_strides);
+}
+
+inline std::tuple<Shape, Strides, Strides, Strides>
+collapse_batches(const array& a, const array& b, const array& c) {
+  // Get and check the shape for the batched dims
+  Shape A_bshape{a.shape().begin(), a.shape().end() - 2};
+  Shape B_bshape{b.shape().begin(), b.shape().end() - 2};
+  Shape C_bshape{c.shape().begin(), c.shape().end() - 2};
+  if (A_bshape != B_bshape || A_bshape != C_bshape) {
+    std::ostringstream msg;
+    msg << "[addmm] Got matrices with incorrectly broadcasted shapes: " << "A "
+        << a.shape() << ", B " << b.shape() << ", B " << c.shape() << ".";
+    throw std::runtime_error(msg.str());
+  }
+
+  Strides A_bstride{a.strides().begin(), a.strides().end() - 2};
+  Strides B_bstride{b.strides().begin(), b.strides().end() - 2};
+  Strides C_bstride{c.strides().begin(), c.strides().end() - 2};
+
+  auto [batch_shape, batch_strides] = collapse_contiguous_dims(
+      A_bshape, std::vector{A_bstride, B_bstride, C_bstride});
+
+  auto A_batch_stride = batch_strides[0];
+  auto B_batch_stride = batch_strides[1];
+  auto C_batch_stride = batch_strides[2];
+
+  if (batch_shape.empty()) {
+    batch_shape.push_back(1);
+    A_batch_stride.push_back(0);
+    B_batch_stride.push_back(0);
+    C_batch_stride.push_back(0);
+  }
+
+  return std::make_tuple(
+      batch_shape, A_batch_stride, B_batch_stride, C_batch_stride);
+}
+
+} // namespace mlx::core

mlx/backend/common/unary.h

@@ -0,0 +1,26 @@
+// Copyright © 2025 Apple Inc.
+
+#pragma once
+
+#include "mlx/allocator.h"
+#include "mlx/backend/common/utils.h"
+
+namespace mlx::core {
+
+inline void set_unary_output_data(const array& in, array& out) {
+  if (in.flags().contiguous) {
+    if (is_donatable(in, out)) {
+      out.copy_shared_buffer(in);
+    } else {
+      out.set_data(
+          allocator::malloc(in.data_size() * out.itemsize()),
+          in.data_size(),
+          in.strides(),
+          in.flags());
+    }
+  } else {
+    out.set_data(allocator::malloc(out.nbytes()));
+  }
+}
+
+} // namespace mlx::core

mlx/backend/cpu/unary.h

@@ -2,32 +2,13 @@
 
 #pragma once
 
-#include "mlx/allocator.h"
-#include "mlx/array.h"
-#include "mlx/backend/common/utils.h"
+#include "mlx/backend/common/unary.h"
 #include "mlx/backend/cpu/encoder.h"
 #include "mlx/backend/cpu/simd/simd.h"
 #include "mlx/utils.h"
 
 namespace mlx::core {
 
-void set_unary_output_data(const array& in, array& out) {
-  if (in.flags().contiguous) {
-    if (is_donatable(in, out)) {
-      out.copy_shared_buffer(in);
-    } else {
-      auto size = in.data_size();
-      out.set_data(
-          allocator::malloc(size * out.itemsize()),
-          size,
-          in.strides(),
-          in.flags());
-    }
-  } else {
-    out.set_data(allocator::malloc(out.nbytes()));
-  }
-}
-
 template <typename T, typename U = T, typename Op>
 void unary_op(const T* a, U* out, size_t shape, size_t stride) {
   for (size_t i = 0; i < shape; i += 1) {

mlx/backend/cuda/CMakeLists.txt

@@ -6,21 +6,41 @@
 target_sources(
   mlx
   PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/allocator.cpp
-          ${CMAKE_CURRENT_SOURCE_DIR}/copy.cpp
+          ${CMAKE_CURRENT_SOURCE_DIR}/binary.cu
+          ${CMAKE_CURRENT_SOURCE_DIR}/copy.cu
+          ${CMAKE_CURRENT_SOURCE_DIR}/copy/copy_contiguous.cu
+          ${CMAKE_CURRENT_SOURCE_DIR}/copy/copy_general.cu
+          ${CMAKE_CURRENT_SOURCE_DIR}/copy/copy_general_dynamic.cu
+          ${CMAKE_CURRENT_SOURCE_DIR}/copy/copy_general_input.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/device.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/eval.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/event.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/fence.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/kernel_utils.cu
+          ${CMAKE_CURRENT_SOURCE_DIR}/matmul.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/primitives.cu
+          ${CMAKE_CURRENT_SOURCE_DIR}/random.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/slicing.cpp
+          ${CMAKE_CURRENT_SOURCE_DIR}/sort.cu
+          ${CMAKE_CURRENT_SOURCE_DIR}/unary.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/utils.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/worker.cpp)
 
+target_compile_definitions(mlx PRIVATE MLX_USE_CUDA)
+
 # Enable defining device lambda functions.
 target_compile_options(mlx
                        PRIVATE "$<$<COMPILE_LANGUAGE:CUDA>:--extended-lambda>")
 
+# CUDA 12.8 emits warning #20280-D for copy kernels which is a false positive.
+# Explicitly pass this flag to suppress the warning, it is safe to set it to
+# true but the warning wouldn't be suppressed.
+if(CMAKE_CUDA_COMPILER_VERSION VERSION_GREATER_EQUAL 12.8)
+  target_compile_options(
+    mlx
+    PRIVATE "$<$<COMPILE_LANGUAGE:CUDA>:--static-global-template-stub=false>")
+endif()
+
 # Compute capability 7 is required for synchronization between CPU/GPU with
 # managed memory. TODO: Add more architectures for potential performance gain.
 set(MLX_CUDA_ARCHITECTURES
@@ -51,6 +71,9 @@ target_link_libraries(mlx PUBLIC $<BUILD_INTERFACE:nvtx3-cpp>)
 find_package(CUDAToolkit REQUIRED)
 target_include_directories(mlx PRIVATE ${CUDAToolkit_INCLUDE_DIRS})
 
+# Use cublasLt.
+target_link_libraries(mlx PRIVATE CUDA::cublasLt)
+
 # Suppress nvcc warnings on MLX headers.
 target_compile_options(mlx PRIVATE $<$<COMPILE_LANGUAGE:CUDA>:-Xcudafe
                                    --diag_suppress=997>)

mlx/backend/cuda/binary.cu

@@ -0,0 +1,305 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/common/binary.h"
+#include "mlx/backend/cuda/device.h"
+#include "mlx/backend/cuda/kernel_utils.cuh"
+#include "mlx/backend/cuda/kernels/binary_ops.cuh"
+#include "mlx/backend/cuda/kernels/cucomplex_math.cuh"
+#include "mlx/dtype_utils.h"
+#include "mlx/primitives.h"
+
+#include <cooperative_groups.h>
+#include <nvtx3/nvtx3.hpp>
+
+namespace mlx::core {
+
+namespace cu {
+
+namespace cg = cooperative_groups;
+
+template <typename Op, typename In, typename Out, typename IdxT>
+__global__ void binary_ss(const In* a, const In* b, Out* out, IdxT size) {
+  IdxT index = cg::this_grid().thread_rank();
+  if (index < size) {
+    out[index] = Op{}(a[0], b[0]);
+  }
+}
+
+template <typename Op, typename In, typename Out, typename IdxT>
+__global__ void binary_sv(const In* a, const In* b, Out* out, IdxT size) {
+  IdxT index = cg::this_grid().thread_rank();
+  if (index < size) {
+    out[index] = Op{}(a[0], b[index]);
+  }
+}
+
+template <typename Op, typename In, typename Out, typename IdxT>
+__global__ void binary_vs(const In* a, const In* b, Out* out, IdxT size) {
+  IdxT index = cg::this_grid().thread_rank();
+  if (index < size) {
+    out[index] = Op{}(a[index], b[0]);
+  }
+}
+
+template <typename Op, typename In, typename Out, typename IdxT>
+__global__ void binary_vv(const In* a, const In* b, Out* out, IdxT size) {
+  IdxT index = cg::this_grid().thread_rank();
+  if (index < size) {
+    out[index] = Op{}(a[index], b[index]);
+  }
+}
+
+template <typename Op, typename In, typename Out, typename IdxT, int NDIM>
+__global__ void binary_g_nd(
+    const In* a,
+    const In* b,
+    Out* out,
+    IdxT size,
+    const __grid_constant__ cuda::std::array<int32_t, NDIM> shape,
+    const __grid_constant__ cuda::std::array<int64_t, NDIM> a_strides,
+    const __grid_constant__ cuda::std::array<int64_t, NDIM> b_strides) {
+  IdxT index = cg::this_grid().thread_rank();
+  if (index < size) {
+    auto [a_idx, b_idx] = elem_to_loc_nd<NDIM>(
+        index, shape.data(), a_strides.data(), b_strides.data());
+    out[index] = Op{}(a[a_idx], b[b_idx]);
+  }
+}
+
+template <typename Op, typename In, typename Out, typename IdxT>
+__global__ void binary_g(
+    const In* a,
+    const In* b,
+    Out* out,
+    IdxT size,
+    const __grid_constant__ Shape shape,
+    const __grid_constant__ Strides a_strides,
+    const __grid_constant__ Strides b_strides,
+    int ndim) {
+  IdxT index = cg::this_grid().thread_rank();
+  if (index < size) {
+    auto [a_idx, b_idx] = elem_to_loc_4d(
+        index, shape.data(), a_strides.data(), b_strides.data(), ndim);
+    out[index] = Op{}(a[a_idx], b[b_idx]);
+  }
+}
+
+template <typename Op, typename In, typename Out>
+constexpr bool supports_binary_op() {
+  if (std::is_same_v<Op, Add> || std::is_same_v<Op, Divide> ||
+      std::is_same_v<Op, Maximum> || std::is_same_v<Op, Minimum> ||
+      std::is_same_v<Op, Multiply> || std::is_same_v<Op, Subtract> ||
+      std::is_same_v<Op, Power> || std::is_same_v<Op, Remainder>) {
+    return std::is_same_v<In, Out>;
+  }
+  if (std::is_same_v<Op, Equal> || std::is_same_v<Op, Greater> ||
+      std::is_same_v<Op, GreaterEqual> || std::is_same_v<Op, Less> ||
+      std::is_same_v<Op, LessEqual> || std::is_same_v<Op, NotEqual>) {
+    return std::is_same_v<Out, bool>;
+  }
+  if (std::is_same_v<Op, LogicalAnd> || std::is_same_v<Op, LogicalOr>) {
+    return std::is_same_v<Out, bool> && std::is_same_v<In, bool>;
+  }
+  if (std::is_same_v<Op, NaNEqual>) {
+    return std::is_same_v<Out, bool> &&
+        (is_floating_v<In> || std::is_same_v<In, complex64_t>);
+  }
+  if (std::is_same_v<Op, LogAddExp> || std::is_same_v<Op, ArcTan2>) {
+    return std::is_same_v<In, Out> && is_floating_v<In>;
+  }
+  if (std::is_same_v<Op, BitwiseAnd> || std::is_same_v<Op, BitwiseOr> ||
+      std::is_same_v<Op, BitwiseXor>) {
+    return std::is_same_v<In, Out> && std::is_integral_v<In>;
+  }
+  if (std::is_same_v<Op, LeftShift> || std::is_same_v<Op, RightShift>) {
+    return std::is_same_v<In, Out> && std::is_integral_v<In> &&
+        !std::is_same_v<In, bool>;
+  }
+  return false;
+}
+
+} // namespace cu
+
+template <typename Op>
+void binary_op_gpu_inplace(
+    const std::vector<array>& inputs,
+    std::vector<array>& outputs,
+    std::string_view op,
+    const Stream& s) {
+  assert(inputs.size() > 1);
+  const auto& a = inputs[0];
+  const auto& b = inputs[1];
+  auto& out = outputs[0];
+  if (out.size() == 0) {
+    return;
+  }
+
+  auto& encoder = cu::get_command_encoder(s);
+  encoder.set_input_array(a);
+  encoder.set_input_array(b);
+  encoder.set_output_array(out);
+  encoder.launch_kernel([&](cudaStream_t stream) {
+    MLX_SWITCH_ALL_TYPES(a.dtype(), CTYPE_IN, {
+      MLX_SWITCH_ALL_TYPES(out.dtype(), CTYPE_OUT, {
+        if constexpr (cu::supports_binary_op<Op, CTYPE_IN, CTYPE_OUT>()) {
+          using InType = cuda_type_t<CTYPE_IN>;
+          using OutType = cuda_type_t<CTYPE_OUT>;
+
+          auto bopt = get_binary_op_type(a, b);
+          if (bopt == BinaryOpType::General) {
+            auto [shape, strides] = collapse_contiguous_dims(a, b, out);
+            auto& a_strides = strides[0];
+            auto& b_strides = strides[1];
+            bool large = a.data_size() > UINT32_MAX ||
+                b.data_size() > UINT32_MAX || out.data_size() > UINT32_MAX;
+            MLX_SWITCH_BOOL(large, LARGE, {
+              using IdxT = std::conditional_t<LARGE, int64_t, uint32_t>;
+              int ndim = shape.size();
+              if (ndim <= 3) {
+                MLX_SWITCH_1_2_3(ndim, NDIM, {
+                  auto kernel =
+                      &cu::binary_g_nd<Op, InType, OutType, IdxT, NDIM>;
+                  auto [num_blocks, block_dims] =
+                      get_launch_args(kernel, out, large);
+                  kernel<<<num_blocks, block_dims, 0, stream>>>(
+                      a.data<InType>(),
+                      b.data<InType>(),
+                      out.data<OutType>(),
+                      out.data_size(),
+                      const_param<NDIM>(shape),
+                      const_param<NDIM>(a_strides),
+                      const_param<NDIM>(b_strides));
+                });
+              } else {
+                auto kernel = cu::binary_g<Op, InType, OutType, IdxT>;
+                auto [num_blocks, block_dims] =
+                    get_launch_args(kernel, out, large);
+                kernel<<<num_blocks, block_dims, 0, stream>>>(
+                    a.data<InType>(),
+                    b.data<InType>(),
+                    out.data<OutType>(),
+                    out.data_size(),
+                    const_param(shape),
+                    const_param(a_strides),
+                    const_param(b_strides),
+                    ndim);
+              }
+            });
+          } else {
+            MLX_SWITCH_BOOL(out.data_size() > UINT32_MAX, LARGE, {
+              using IdxT = std::conditional_t<LARGE, int64_t, uint32_t>;
+              auto kernel = cu::binary_ss<Op, InType, OutType, IdxT>;
+              if (bopt == BinaryOpType::ScalarVector) {
+                kernel = cu::binary_sv<Op, InType, OutType, IdxT>;
+              } else if (bopt == BinaryOpType::VectorScalar) {
+                kernel = cu::binary_vs<Op, InType, OutType, IdxT>;
+              } else if (bopt == BinaryOpType::VectorVector) {
+                kernel = cu::binary_vv<Op, InType, OutType, IdxT>;
+              }
+              auto [num_blocks, block_dims] =
+                  get_launch_args(kernel, out, LARGE);
+              kernel<<<num_blocks, block_dims, 0, stream>>>(
+                  a.data<InType>(),
+                  b.data<InType>(),
+                  out.data<OutType>(),
+                  out.data_size());
+            });
+          }
+        } else {
+          throw std::runtime_error(fmt::format(
+              "Can not do binary op {} on inputs of {} with result of {}.",
+              op,
+              dtype_to_string(a.dtype()),
+              dtype_to_string(out.dtype())));
+        }
+      });
+    });
+  });
+}
+
+template <typename Op>
+void binary_op_gpu(
+    const std::vector<array>& inputs,
+    std::vector<array>& outputs,
+    std::string_view op,
+    const Stream& s) {
+  auto& a = inputs[0];
+  auto& b = inputs[1];
+  auto bopt = get_binary_op_type(a, b);
+  set_binary_op_output_data(a, b, outputs[0], bopt);
+  set_binary_op_output_data(a, b, outputs[1], bopt);
+  binary_op_gpu_inplace<Op>(inputs, outputs, op, s);
+}
+
+template <typename Op>
+void binary_op_gpu(
+    const std::vector<array>& inputs,
+    array& out,
+    std::string_view op,
+    const Stream& s) {
+  auto& a = inputs[0];
+  auto& b = inputs[1];
+  auto bopt = get_binary_op_type(a, b);
+  set_binary_op_output_data(a, b, out, bopt);
+  std::vector<array> outputs{out};
+  binary_op_gpu_inplace<Op>(inputs, outputs, op, s);
+}
+
+#define BINARY_GPU(func)                                                 \
+  void func::eval_gpu(const std::vector<array>& inputs, array& out) {    \
+    nvtx3::scoped_range r(#func "::eval_gpu");                           \
+    auto& s = out.primitive().stream();                                  \
+    binary_op_gpu<cu::func>(inputs, out, get_primitive_string(this), s); \
+  }
+
+#define BINARY_GPU_MULTI(func)                                               \
+  void func::eval_gpu(                                                       \
+      const std::vector<array>& inputs, std::vector<array>& outputs) {       \
+    nvtx3::scoped_range r(#func "::eval_gpu");                               \
+    auto& s = outputs[0].primitive().stream();                               \
+    binary_op_gpu<cu::func>(inputs, outputs, get_primitive_string(this), s); \
+  }
+
+BINARY_GPU(Add)
+BINARY_GPU(ArcTan2)
+BINARY_GPU(Divide)
+BINARY_GPU(Remainder)
+BINARY_GPU(Equal)
+BINARY_GPU(Greater)
+BINARY_GPU(GreaterEqual)
+BINARY_GPU(Less)
+BINARY_GPU(LessEqual)
+BINARY_GPU(LogicalAnd)
+BINARY_GPU(LogicalOr)
+BINARY_GPU(LogAddExp)
+BINARY_GPU(Maximum)
+BINARY_GPU(Minimum)
+BINARY_GPU(Multiply)
+BINARY_GPU(NotEqual)
+BINARY_GPU(Power)
+BINARY_GPU(Subtract)
+
+void BitwiseBinary::eval_gpu(const std::vector<array>& inputs, array& out) {
+  nvtx3::scoped_range r("BitwiseBinary::eval_gpu");
+  auto& s = out.primitive().stream();
+  auto op = get_primitive_string(this);
+  switch (op_) {
+    case BitwiseBinary::And:
+      binary_op_gpu<cu::BitwiseAnd>(inputs, out, op, s);
+      break;
+    case BitwiseBinary::Or:
+      binary_op_gpu<cu::BitwiseOr>(inputs, out, op, s);
+      break;
+    case BitwiseBinary::Xor:
+      binary_op_gpu<cu::BitwiseXor>(inputs, out, op, s);
+      break;
+    case BitwiseBinary::LeftShift:
+      binary_op_gpu<cu::LeftShift>(inputs, out, op, s);
+      break;
+    case BitwiseBinary::RightShift:
+      binary_op_gpu<cu::RightShift>(inputs, out, op, s);
+      break;
+  }
+}
+
+} // namespace mlx::core

mlx/backend/cuda/copy.cpp

@@ -1,26 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/gpu/copy.h"
-
-namespace mlx::core {
-
-void copy_gpu_inplace(
-    const array& in,
-    array& out,
-    const Shape& data_shape,
-    const Strides& strides_in_pre,
-    const Strides& strides_out_pre,
-    int64_t inp_offset,
-    int64_t out_offset,
-    CopyType ctype,
-    const Stream& s,
-    const std::optional<array>& dynamic_i_offset /* = std::nullopt */,
-    const std::optional<array>& dynamic_o_offset /* = std::nullopt */) {
-  throw std::runtime_error("copy_gpu_inplace not implemented in CUDA backend.");
-}
-
-void fill_gpu(const array& val, array& out, const Stream& s) {
-  throw std::runtime_error("fill_gpu not implemented in CUDA backend.");
-}
-
-} // namespace mlx::core

mlx/backend/cuda/copy.cu

@@ -0,0 +1,89 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/common/utils.h"
+#include "mlx/backend/cuda/copy/copy.cuh"
+
+namespace mlx::core {
+
+void copy_gpu_inplace(
+    const array& in_,
+    array& out,
+    const Shape& shape,
+    const Strides& strides_in,
+    const Strides& strides_out,
+    int64_t offset_in,
+    int64_t offset_out,
+    CopyType ctype,
+    const Stream& s,
+    const std::optional<array>& dynamic_offset_in,
+    const std::optional<array>& dynamic_offset_out) {
+  if (out.size() == 0) {
+    return;
+  }
+  const array& in = in_.data_shared_ptr() ? in_ : out;
+
+  auto& encoder = cu::get_command_encoder(s);
+  encoder.set_input_array(in);
+  encoder.set_output_array(out);
+
+  if (ctype == CopyType::Scalar || ctype == CopyType::Vector) {
+    copy_contiguous(encoder, ctype, in, out, offset_in, offset_out);
+    return;
+  }
+
+  if (ctype == CopyType::General || ctype == CopyType::GeneralGeneral) {
+    auto [shape_collapsed, strides_vec] = collapse_contiguous_dims(
+        shape, std::vector{strides_in, strides_out}, INT32_MAX);
+    if (ctype == CopyType::General) {
+      copy_general_input(
+          encoder,
+          ctype,
+          in,
+          out,
+          offset_in,
+          offset_out,
+          shape_collapsed,
+          strides_vec[0]);
+    } else {
+      if (dynamic_offset_in || dynamic_offset_out) {
+        copy_general_dynamic(
+            encoder,
+            ctype,
+            in,
+            out,
+            offset_in,
+            offset_out,
+            shape_collapsed,
+            strides_vec[0],
+            strides_vec[1],
+            dynamic_offset_in ? *dynamic_offset_in : array(0, int64),
+            dynamic_offset_out ? *dynamic_offset_out : array(0, int64));
+      } else {
+        copy_general(
+            encoder,
+            ctype,
+            in,
+            out,
+            offset_in,
+            offset_out,
+            shape_collapsed,
+            strides_vec[0],
+            strides_vec[1]);
+      }
+    }
+    return;
+  }
+}
+
+void fill_gpu(const array& in, array& out, const Stream& s) {
+  if (out.size() == 0) {
+    return;
+  }
+  out.set_data(allocator::malloc(out.nbytes()));
+  auto& encoder = cu::get_command_encoder(s);
+  encoder.set_input_array(in);
+  encoder.set_output_array(out);
+  copy_contiguous(encoder, CopyType::Scalar, in, out, 0, 0);
+}
+
+} // namespace mlx::core

mlx/backend/cuda/copy/copy.cuh

@@ -0,0 +1,71 @@
+// Copyright © 2025 Apple Inc.
+
+#pragma once
+
+#include "mlx/backend/cuda/device.h"
+#include "mlx/backend/cuda/kernel_utils.cuh"
+#include "mlx/backend/cuda/kernels/cast_op.cuh"
+#include "mlx/backend/gpu/copy.h"
+#include "mlx/dtype_utils.h"
+
+namespace mlx::core {
+
+#define MLX_SWITCH_COPY_TYPES(in, out, InType, OutType, ...)    \
+  MLX_SWITCH_ALL_TYPES(in.dtype(), CTYPE_IN, {                  \
+    MLX_SWITCH_ALL_TYPES(out.dtype(), CTYPE_OUT, {              \
+      using InType = cuda_type_t<CTYPE_IN>;                     \
+      using OutType = cuda_type_t<CTYPE_OUT>;                   \
+      if constexpr (cu::CastOp<InType, OutType>::is_castable) { \
+        __VA_ARGS__;                                            \
+      } else {                                                  \
+        throw std::runtime_error(fmt::format(                   \
+            "Can not copy data from dtype {} to {}.",           \
+            dtype_to_string(out.dtype()),                       \
+            dtype_to_string(in.dtype())));                      \
+      }                                                         \
+    });                                                         \
+  })
+
+void copy_contiguous(
+    cu::CommandEncoder& encoder,
+    CopyType ctype,
+    const array& in,
+    array& out,
+    int64_t offset_in,
+    int64_t offset_out);
+
+void copy_general(
+    cu::CommandEncoder& encoder,
+    CopyType ctype,
+    const array& in,
+    array& out,
+    int64_t offset_in,
+    int64_t offset_out,
+    const Shape& shape,
+    const Strides& strides_in,
+    const Strides& strides_out);
+
+void copy_general_dynamic(
+    cu::CommandEncoder& encoder,
+    CopyType ctype,
+    const array& in,
+    array& out,
+    int64_t offset_in,
+    int64_t offset_out,
+    const Shape& shape,
+    const Strides& strides_in,
+    const Strides& strides_out,
+    const array& dynamic_offset_in,
+    const array& dynamic_offset_out);
+
+void copy_general_input(
+    cu::CommandEncoder& encoder,
+    CopyType ctype,
+    const array& in,
+    array& out,
+    int64_t offset_in,
+    int64_t offset_out,
+    const Shape& shape,
+    const Strides& strides_in);
+
+} // namespace mlx::core

mlx/backend/cuda/copy/copy_contiguous.cu

@@ -0,0 +1,56 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/copy/copy.cuh"
+
+#include <cooperative_groups.h>
+
+namespace mlx::core {
+
+namespace cu {
+
+namespace cg = cooperative_groups;
+
+template <typename In, typename Out, typename IdxT>
+__global__ void copy_s(const In* in, Out* out, IdxT size) {
+  IdxT index = cg::this_grid().thread_rank();
+  if (index < size) {
+    out[index] = CastOp<In, Out>{}(in[0]);
+  }
+}
+
+template <typename In, typename Out, typename IdxT>
+__global__ void copy_v(const In* in, Out* out, IdxT size) {
+  IdxT index = cg::this_grid().thread_rank();
+  if (index < size) {
+    out[index] = CastOp<In, Out>{}(in[index]);
+  }
+}
+
+} // namespace cu
+
+void copy_contiguous(
+    cu::CommandEncoder& encoder,
+    CopyType ctype,
+    const array& in,
+    array& out,
+    int64_t in_offset,
+    int64_t out_offset) {
+  encoder.launch_kernel([&](cudaStream_t stream) {
+    MLX_SWITCH_COPY_TYPES(in, out, InType, OutType, {
+      MLX_SWITCH_BOOL(out.data_size() > UINT32_MAX, LARGE, {
+        using IdxT = std::conditional_t<LARGE, int64_t, uint32_t>;
+        auto kernel = cu::copy_s<InType, OutType, IdxT>;
+        if (ctype == CopyType::Vector) {
+          kernel = cu::copy_v<InType, OutType, IdxT>;
+        }
+        auto [num_blocks, block_dims] = get_launch_args(kernel, out, LARGE);
+        kernel<<<num_blocks, block_dims, 0, stream>>>(
+            in.data<InType>() + in_offset,
+            out.data<OutType>() + out_offset,
+            out.data_size());
+      });
+    });
+  });
+}
+
+} // namespace mlx::core

mlx/backend/cuda/copy/copy_general.cu

@@ -0,0 +1,95 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/copy/copy.cuh"
+
+#include <cooperative_groups.h>
+
+namespace mlx::core {
+
+namespace cu {
+
+namespace cg = cooperative_groups;
+
+template <typename In, typename Out, typename IdxT, int NDIM>
+__global__ void copy_gg_nd(
+    const In* in,
+    Out* out,
+    IdxT size,
+    const __grid_constant__ cuda::std::array<int32_t, NDIM> shape,
+    const __grid_constant__ cuda::std::array<int64_t, NDIM> strides_in,
+    const __grid_constant__ cuda::std::array<int64_t, NDIM> strides_out) {
+  IdxT index = cg::this_grid().thread_rank();
+  if (index < size) {
+    auto [idx_in, idx_out] = elem_to_loc_nd<NDIM>(
+        index, shape.data(), strides_in.data(), strides_out.data());
+    out[idx_out] = CastOp<In, Out>{}(in[idx_in]);
+  }
+}
+
+template <typename In, typename Out, typename IdxT>
+__global__ void copy_gg(
+    const In* in,
+    Out* out,
+    IdxT size,
+    const __grid_constant__ Shape shape,
+    const __grid_constant__ Strides strides_in,
+    const __grid_constant__ Strides strides_out,
+    int ndim) {
+  IdxT index = cg::this_grid().thread_rank();
+  if (index < size) {
+    auto [idx_in, idx_out] = elem_to_loc_4d(
+        index, shape.data(), strides_in.data(), strides_out.data(), ndim);
+    out[idx_out] = CastOp<In, Out>{}(in[idx_in]);
+  }
+}
+
+} // namespace cu
+
+void copy_general(
+    cu::CommandEncoder& encoder,
+    CopyType ctype,
+    const array& in,
+    array& out,
+    int64_t offset_in,
+    int64_t offset_out,
+    const Shape& shape,
+    const Strides& strides_in,
+    const Strides& strides_out) {
+  encoder.launch_kernel([&](cudaStream_t stream) {
+    MLX_SWITCH_COPY_TYPES(in, out, InType, OutType, {
+      const InType* in_ptr = in.data<InType>() + offset_in;
+      OutType* out_ptr = out.data<OutType>() + offset_out;
+      bool large = in.data_size() > UINT32_MAX || out.data_size() > UINT32_MAX;
+      MLX_SWITCH_BOOL(large, LARGE, {
+        using IdxT = std::conditional_t<LARGE, int64_t, uint32_t>;
+        int ndim = shape.size();
+        if (ndim <= 3) {
+          MLX_SWITCH_1_2_3(ndim, NDIM, {
+            auto kernel = cu::copy_gg_nd<InType, OutType, IdxT, NDIM>;
+            auto [num_blocks, block_dims] = get_launch_args(kernel, out, large);
+            kernel<<<num_blocks, block_dims, 0, stream>>>(
+                in_ptr,
+                out_ptr,
+                out.data_size(),
+                const_param<NDIM>(shape),
+                const_param<NDIM>(strides_in),
+                const_param<NDIM>(strides_out));
+          });
+        } else { // ndim >= 4
+          auto kernel = cu::copy_gg<InType, OutType, IdxT>;
+          auto [num_blocks, block_dims] = get_launch_args(kernel, out, large);
+          kernel<<<num_blocks, block_dims, 0, stream>>>(
+              in_ptr,
+              out_ptr,
+              out.data_size(),
+              const_param(shape),
+              const_param(strides_in),
+              const_param(strides_out),
+              ndim);
+        }
+      });
+    });
+  });
+}
+
+} // namespace mlx::core

mlx/backend/cuda/copy/copy_general_dynamic.cu

@@ -0,0 +1,105 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/copy/copy.cuh"
+
+#include <cooperative_groups.h>
+
+namespace mlx::core {
+
+namespace cu {
+
+namespace cg = cooperative_groups;
+
+template <typename In, typename Out, typename IdxT, int NDIM>
+__global__ void copy_gg_dynamic_nd(
+    const In* in,
+    Out* out,
+    IdxT size,
+    const __grid_constant__ cuda::std::array<int32_t, NDIM> shape,
+    const __grid_constant__ cuda::std::array<int64_t, NDIM> strides_in,
+    const __grid_constant__ cuda::std::array<int64_t, NDIM> strides_out,
+    const int64_t* offset_in,
+    const int64_t* offset_out) {
+  IdxT index = cg::this_grid().thread_rank();
+  if (index < size) {
+    auto [idx_in, idx_out] = elem_to_loc_nd<NDIM>(
+        index, shape.data(), strides_in.data(), strides_out.data());
+    out[idx_out + *offset_out] = CastOp<In, Out>{}(in[idx_in + *offset_in]);
+  }
+}
+
+template <typename In, typename Out, typename IdxT>
+__global__ void copy_gg_dynamic(
+    const In* in,
+    Out* out,
+    IdxT size,
+    const __grid_constant__ Shape shape,
+    const __grid_constant__ Strides strides_in,
+    const __grid_constant__ Strides strides_out,
+    int ndim,
+    const int64_t* offset_in,
+    const int64_t* offset_out) {
+  IdxT index = cg::this_grid().thread_rank();
+  if (index < size) {
+    auto [idx_in, idx_out] = elem_to_loc_4d(
+        index, shape.data(), strides_in.data(), strides_out.data(), ndim);
+    out[idx_out + *offset_out] = CastOp<In, Out>{}(in[idx_in + *offset_in]);
+  }
+}
+
+} // namespace cu
+
+void copy_general_dynamic(
+    cu::CommandEncoder& encoder,
+    CopyType ctype,
+    const array& in,
+    array& out,
+    int64_t offset_in,
+    int64_t offset_out,
+    const Shape& shape,
+    const Strides& strides_in,
+    const Strides& strides_out,
+    const array& dynamic_offset_in,
+    const array& dynamic_offset_out) {
+  encoder.launch_kernel([&](cudaStream_t stream) {
+    MLX_SWITCH_COPY_TYPES(in, out, InType, OutType, {
+      const InType* in_ptr = in.data<InType>() + offset_in;
+      OutType* out_ptr = out.data<OutType>() + offset_out;
+      bool large = in.data_size() > UINT32_MAX || out.data_size() > UINT32_MAX;
+      MLX_SWITCH_BOOL(large, LARGE, {
+        using IdxT = std::conditional_t<LARGE, int64_t, uint32_t>;
+        int ndim = shape.size();
+        if (ndim <= 3) {
+          MLX_SWITCH_1_2_3(ndim, NDIM, {
+            auto kernel = cu::copy_gg_dynamic_nd<InType, OutType, IdxT, NDIM>;
+            auto [num_blocks, block_dims] = get_launch_args(kernel, out, large);
+            kernel<<<num_blocks, block_dims, 0, stream>>>(
+                in_ptr,
+                out_ptr,
+                out.data_size(),
+                const_param<NDIM>(shape),
+                const_param<NDIM>(strides_in),
+                const_param<NDIM>(strides_out),
+                dynamic_offset_in.data<int64_t>(),
+                dynamic_offset_out.data<int64_t>());
+          });
+        } else { // ndim >= 4
+          auto kernel = cu::copy_gg_dynamic<InType, OutType, IdxT>;
+          auto [num_blocks, block_dims] = get_launch_args(kernel, out, large);
+          kernel<<<num_blocks, block_dims, 0, stream>>>(
+              in_ptr,
+              out_ptr,
+              out.data_size(),
+              const_param(shape),
+              const_param(strides_in),
+              const_param(strides_out),
+              ndim,
+              dynamic_offset_in.data<int64_t>(),
+              dynamic_offset_out.data<int64_t>());
+        }
+      });
+    });
+  });
+}
+
+} // namespace mlx::core

mlx/backend/cuda/copy/copy_general_input.cu

@@ -0,0 +1,88 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/copy/copy.cuh"
+
+#include <cooperative_groups.h>
+
+namespace mlx::core {
+
+namespace cu {
+
+namespace cg = cooperative_groups;
+
+template <typename In, typename Out, typename IdxT, int NDIM>
+__global__ void copy_g_nd(
+    const In* in,
+    Out* out,
+    IdxT size,
+    const __grid_constant__ cuda::std::array<int32_t, NDIM> shape,
+    const __grid_constant__ cuda::std::array<int64_t, NDIM> strides_in) {
+  IdxT index = cg::this_grid().thread_rank();
+  if (index < size) {
+    IdxT idx_in = elem_to_loc_nd<NDIM>(index, shape.data(), strides_in.data());
+    out[index] = CastOp<In, Out>{}(in[idx_in]);
+  }
+}
+
+template <typename In, typename Out, typename IdxT>
+__global__ void copy_g(
+    const In* in,
+    Out* out,
+    IdxT size,
+    const __grid_constant__ Shape shape,
+    const __grid_constant__ Strides strides_in,
+    int ndim) {
+  IdxT index = cg::this_grid().thread_rank();
+  if (index < size) {
+    IdxT idx_in = elem_to_loc_4d(index, shape.data(), strides_in.data(), ndim);
+    out[index] = CastOp<In, Out>{}(in[idx_in]);
+  }
+}
+
+} // namespace cu
+
+void copy_general_input(
+    cu::CommandEncoder& encoder,
+    CopyType ctype,
+    const array& in,
+    array& out,
+    int64_t offset_in,
+    int64_t offset_out,
+    const Shape& shape,
+    const Strides& strides_in) {
+  encoder.launch_kernel([&](cudaStream_t stream) {
+    MLX_SWITCH_COPY_TYPES(in, out, InType, OutType, {
+      const InType* in_ptr = in.data<InType>() + offset_in;
+      OutType* out_ptr = out.data<OutType>() + offset_out;
+      bool large = in.data_size() > UINT32_MAX || out.data_size() > UINT32_MAX;
+      MLX_SWITCH_BOOL(large, LARGE, {
+        using IdxT = std::conditional_t<LARGE, int64_t, uint32_t>;
+        int ndim = shape.size();
+        if (ndim <= 3) {
+          MLX_SWITCH_1_2_3(ndim, NDIM, {
+            auto kernel = cu::copy_g_nd<InType, OutType, IdxT, NDIM>;
+            auto [num_blocks, block_dims] = get_launch_args(kernel, out, large);
+            kernel<<<num_blocks, block_dims, 0, stream>>>(
+                in_ptr,
+                out_ptr,
+                out.data_size(),
+                const_param<NDIM>(shape),
+                const_param<NDIM>(strides_in));
+          });
+        } else { // ndim >= 4
+          auto kernel = cu::copy_g<InType, OutType, IdxT>;
+          auto [num_blocks, block_dims] = get_launch_args(kernel, out, large);
+          kernel<<<num_blocks, block_dims, 0, stream>>>(
+              in_ptr,
+              out_ptr,
+              out.data_size(),
+              const_param(shape),
+              const_param(strides_in),
+              ndim);
+        }
+      });
+    });
+  });
+}
+
+} // namespace mlx::core

mlx/backend/cuda/device.cpp

@@ -34,14 +34,26 @@ CommandEncoder& DeviceStream::get_encoder() {
 }
 
 Device::Device(int device) : device_(device) {
+  CHECK_CUDA_ERROR(cudaDeviceGetAttribute(
+      &compute_capability_major_, cudaDevAttrComputeCapabilityMajor, device_));
+  CHECK_CUDA_ERROR(cudaDeviceGetAttribute(
+      &compute_capability_minor_, cudaDevAttrComputeCapabilityMinor, device_));
   // Validate the requirements of device.
   int attr = 0;
-  cudaDeviceGetAttribute(&attr, cudaDevAttrConcurrentManagedAccess, device_);
+  CHECK_CUDA_ERROR(cudaDeviceGetAttribute(
+      &attr, cudaDevAttrConcurrentManagedAccess, device_));
   if (attr != 1) {
     throw std::runtime_error(fmt::format(
         "Device {} does not support synchronization in managed memory.",
         device_));
   }
+  // The cublasLt handle is used by matmul.
+  make_current();
+  cublasLtCreate(&lt_);
+}
+
+Device::~Device() {
+  cublasLtDestroy(lt_);
 }
 
 void Device::make_current() {

mlx/backend/cuda/device.h

@@ -6,6 +6,7 @@
 #include "mlx/backend/cuda/worker.h"
 #include "mlx/stream.h"
 
+#include <cublasLt.h>
 #include <thrust/execution_policy.h>
 
 #include <unordered_map>
@@ -46,6 +47,7 @@ class DeviceStream {
 class Device {
  public:
   explicit Device(int device);
+  ~Device();
 
   Device(const Device&) = delete;
   Device& operator=(const Device&) = delete;
@@ -58,9 +60,21 @@ class Device {
   int cuda_device() const {
     return device_;
   }
+  int compute_capability_major() const {
+    return compute_capability_major_;
+  }
+  int compute_capability_minor() const {
+    return compute_capability_minor_;
+  }
+  cublasLtHandle_t lt_handle() const {
+    return lt_;
+  }
 
  private:
   int device_;
+  int compute_capability_major_;
+  int compute_capability_minor_;
+  cublasLtHandle_t lt_;
   std::unordered_map<int, DeviceStream> streams_;
 };
 

mlx/backend/cuda/iterators/general_iterator.cuh

@@ -0,0 +1,121 @@
+// Copyright © 2025 Apple Inc.
+
+#pragma once
+
+#include <thrust/iterator/iterator_adaptor.h>
+#include <cuda/std/utility>
+
+#include "mlx/backend/cuda/kernel_utils.cuh"
+
+namespace mlx::core::cu {
+
+// Iterating non-contiguous array.
+template <typename Iterator, typename IdxT = int64_t>
+class general_iterator
+    : public thrust::
+          iterator_adaptor<general_iterator<Iterator, IdxT>, Iterator> {
+ public:
+  using super_t =
+      thrust::iterator_adaptor<general_iterator<Iterator, IdxT>, Iterator>;
+
+  using reference = typename super_t::reference;
+  using difference_type = typename super_t::difference_type;
+
+  __host__ __device__ general_iterator(
+      Iterator it,
+      IdxT index,
+      int ndim,
+      Shape shape,
+      Strides strides)
+      : super_t(it),
+        index_(index),
+        ndim_(ndim),
+        shape_(cuda::std::move(shape)),
+        strides_(cuda::std::move(strides)) {}
+
+  __host__ __device__ IdxT index() const {
+    return index_;
+  }
+
+  __host__ __device__ const Shape& shape() const {
+    return shape_;
+  }
+
+  __host__ __device__ const Strides& strides() const {
+    return strides_;
+  }
+
+ private:
+  friend class thrust::iterator_core_access;
+
+  __host__ __device__ bool equal(const general_iterator& other) const {
+    return this->base() == other.base() && this->index() == other.index();
+  }
+
+  __host__ __device__ void advance(difference_type n) {
+    this->index_ += n;
+  }
+
+  __host__ __device__ void increment() {
+    this->index_ += 1;
+  }
+
+  __host__ __device__ void decrement() {
+    this->index_ -= 1;
+  }
+
+  __host__ __device__ difference_type
+  distance_to(const general_iterator& other) const {
+    _CCCL_ASSERT(
+        this->base() == other.base(),
+        "Underlying iterator must point to same base iterator");
+    return other.index() - this->index();
+  }
+
+  // The dereference is device-only to avoid accidental running in host.
+  __device__ typename super_t::reference dereference() const {
+    IdxT offset = elem_to_loc(index_, shape_.data(), strides_.data(), ndim_);
+    return *(this->base() + offset);
+  }
+
+  IdxT index_;
+  int ndim_;
+  Shape shape_;
+  Strides strides_;
+};
+
+template <typename IdxT, typename Iterator>
+__host__ __device__ auto make_general_iterator(
+    Iterator it,
+    IdxT index,
+    int ndim,
+    Shape shape,
+    Strides strides) {
+  return general_iterator<Iterator, IdxT>(
+      it, index, ndim, cuda::std::move(shape), cuda::std::move(strides));
+}
+
+template <typename IdxT, typename Iterator>
+auto make_general_iterator(
+    Iterator it,
+    const std::vector<int32_t>& shape,
+    const std::vector<int64_t>& strides) {
+  return make_general_iterator<IdxT>(
+      it, 0, shape.size(), const_param(shape), const_param(strides));
+}
+
+template <typename IdxT, typename Iterator>
+auto make_general_iterators(
+    Iterator it,
+    IdxT size,
+    const std::vector<int32_t>& shape,
+    const std::vector<int64_t>& strides) {
+  auto ndim = shape.size();
+  auto shape_arg = const_param(shape);
+  auto strides_arg = const_param(strides);
+  return std::make_pair(
+      make_general_iterator<IdxT>(it, 0, ndim, shape_arg, strides_arg),
+      make_general_iterator<IdxT>(it, size, ndim, shape_arg, strides_arg));
+}
+
+} // namespace mlx::core::cu

mlx/backend/cuda/kernel_utils.cuh

@@ -7,13 +7,46 @@
 #pragma once
 
 #include "mlx/array.h"
+#include "mlx/backend/cuda/kernels/utils.cuh"
 
 #include <cuComplex.h>
 #include <cuda_bf16.h>
 #include <cuda_fp16.h>
+#include <fmt/format.h>
+#include <cuda/cmath>
 
 namespace mlx::core {
 
+// Convert a number between 1~3 to constexpr.
+#define MLX_SWITCH_1_2_3(N, NDIM, ...) \
+  switch (N) {                         \
+    case 1: {                          \
+      constexpr int NDIM = 1;          \
+      __VA_ARGS__;                     \
+      break;                           \
+    }                                  \
+    case 2: {                          \
+      constexpr int NDIM = 2;          \
+      __VA_ARGS__;                     \
+      break;                           \
+    }                                  \
+    case 3: {                          \
+      constexpr int NDIM = 3;          \
+      __VA_ARGS__;                     \
+      break;                           \
+    }                                  \
+  }
+
+// Like MLX_SWITCH_ALL_TYPES but for booleans.
+#define MLX_SWITCH_BOOL(BOOL, BOOL_ALIAS, ...) \
+  if (BOOL) {                                  \
+    constexpr bool BOOL_ALIAS = true;          \
+    __VA_ARGS__;                               \
+  } else {                                     \
+    constexpr bool BOOL_ALIAS = false;         \
+    __VA_ARGS__;                               \
+  }
+
 // Maps CPU types to CUDA types.
 template <typename T>
 struct CTypeToCudaType {
@@ -38,6 +71,24 @@ struct CTypeToCudaType<complex64_t> {
 template <typename T>
 using cuda_type_t = typename CTypeToCudaType<T>::type;
 
+// Type traits for detecting floating numbers.
+template <typename T>
+inline constexpr bool is_floating_v =
+    cuda::std::is_same_v<T, float> || cuda::std::is_same_v<T, double> ||
+    cuda::std::is_same_v<T, float16_t> || cuda::std::is_same_v<T, bfloat16_t>;
+
+// Utility to copy data from vector to array in host.
+template <int NDIM = MAX_NDIM, typename T = int32_t>
+inline cuda::std::array<T, NDIM> const_param(const std::vector<T>& vec) {
+  if (vec.size() > NDIM) {
+    throw std::runtime_error(
+        fmt::format("ndim can not be larger than {}.", NDIM));
+  }
+  cuda::std::array<T, NDIM> result;
+  std::copy_n(vec.begin(), vec.size(), result.begin());
+  return result;
+}
+
 // Compute the grid and block dimensions, check backend/common/utils.h for docs.
 dim3 get_block_dims(int dim0, int dim1, int dim2, int pow2 = 10);
 dim3 get_2d_grid_dims(const Shape& shape, const Strides& strides);
@@ -46,4 +97,35 @@ dim3 get_2d_grid_dims(
     const Strides& strides,
     size_t divisor);
 
+// Return a block size that achieves maximum potential occupancy for kernel.
+template <typename T>
+inline uint max_occupancy_block_dim(T kernel) {
+  int _, block_dim;
+  CHECK_CUDA_ERROR(cudaOccupancyMaxPotentialBlockSize(&_, &block_dim, kernel));
+  return block_dim;
+}
+
+// Get the num_blocks and block_dims that maximize occupancy for |kernel|,
+// assuming each thread handles |work_per_thread| elements of |arr|.
+template <typename T>
+inline std::tuple<dim3, uint> get_launch_args(
+    T kernel,
+    const array& arr,
+    bool large,
+    int work_per_thread = 1) {
+  size_t nthreads = cuda::ceil_div(arr.size(), work_per_thread);
+  uint block_dim = max_occupancy_block_dim(kernel);
+  if (block_dim > nthreads) {
+    block_dim = nthreads;
+  }
+  dim3 num_blocks;
+  if (large) {
+    num_blocks = get_2d_grid_dims(arr.shape(), arr.strides(), work_per_thread);
+    num_blocks.x = cuda::ceil_div(num_blocks.x, block_dim);
+  } else {
+    num_blocks.x = cuda::ceil_div(nthreads, block_dim);
+  }
+  return std::make_tuple(num_blocks, block_dim);
+}
+
 } // namespace mlx::core

mlx/backend/cuda/kernels/binary_ops.cuh

@@ -0,0 +1,278 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/kernels/fp16_math.cuh"
+
+#include <cuComplex.h>
+#include <cuda/std/array>
+
+namespace mlx::core::cu {
+
+struct Add {
+  template <typename T>
+  __device__ T operator()(T x, T y) {
+    return x + y;
+  }
+};
+
+struct FloorDivide {
+  template <typename T>
+  __device__ T operator()(T x, T y) {
+    if constexpr (cuda::std::is_integral_v<T>) {
+      return x / y;
+    } else {
+      return trunc(x / y);
+    }
+  }
+};
+
+struct Divide {
+  template <typename T>
+  __device__ T operator()(T x, T y) {
+    return x / y;
+  }
+};
+
+struct Remainder {
+  template <typename T>
+  __device__ T operator()(T x, T y) {
+    if constexpr (cuda::std::is_integral_v<T>) {
+      if constexpr (cuda::std::is_signed_v<T>) {
+        auto r = x % y;
+        if (r != 0 && (r < 0 != y < 0)) {
+          r += y;
+        }
+        return r;
+      } else {
+        return x % y;
+      }
+    } else if constexpr (cuda::std::is_same_v<T, cuComplex>) {
+      return x % y;
+    } else {
+      T r = fmod(x, y);
+      if (r != 0 && (r < 0 != y < 0)) {
+        r = r + y;
+      }
+      return r;
+    }
+  }
+};
+
+struct Equal {
+  template <typename T>
+  __device__ bool operator()(T x, T y) {
+    return x == y;
+  }
+};
+
+struct NaNEqual {
+  template <typename T>
+  __device__ bool operator()(T x, T y) {
+    if constexpr (std::is_same_v<T, cuComplex>) {
+      return x == y ||
+          (isnan(cuCrealf(x)) && isnan(cuCrealf(y)) && isnan(cuCimagf(x)) &&
+           isnan(cuCimagf(y))) ||
+          (cuCrealf(x) == cuCrealf(y) && isnan(cuCimagf(x)) &&
+           isnan(cuCimagf(y))) ||
+          (isnan(cuCrealf(x)) && isnan(cuCrealf(y)) &&
+           cuCimagf(x) == cuCimagf(y));
+    } else {
+      return x == y || (isnan(x) && isnan(y));
+    }
+  }
+};
+
+struct Greater {
+  template <typename T>
+  __device__ bool operator()(T x, T y) {
+    return x > y;
+  }
+};
+
+struct GreaterEqual {
+  template <typename T>
+  __device__ bool operator()(T x, T y) {
+    return x >= y;
+  }
+};
+
+struct Less {
+  template <typename T>
+  __device__ bool operator()(T x, T y) {
+    return x < y;
+  }
+};
+
+struct LessEqual {
+  template <typename T>
+  __device__ bool operator()(T x, T y) {
+    return x <= y;
+  }
+};
+
+struct LogAddExp {
+  template <typename T>
+  __device__ T operator()(T x, T y) {
+    if (isnan(x) || isnan(y)) {
+      return cuda::std::numeric_limits<T>::quiet_NaN();
+    }
+    T maxval = max(x, y);
+    T minval = min(x, y);
+    return (minval == -cuda::std::numeric_limits<T>::infinity() ||
+            maxval == cuda::std::numeric_limits<T>::infinity())
+        ? maxval
+        : T(float(maxval) + log1p(expf(minval - maxval)));
+  };
+};
+
+struct Maximum {
+  template <typename T>
+  __device__ T operator()(T x, T y) {
+    if constexpr (cuda::std::is_integral_v<T>) {
+      return max(x, y);
+    } else if constexpr (cuda::std::is_same_v<T, cuComplex>) {
+      if (isnan(cuCrealf(x)) || isnan(cuCimagf(x))) {
+        return x;
+      }
+      return x > y ? x : y;
+    } else {
+      if (isnan(x)) {
+        return x;
+      }
+      return x > y ? x : y;
+    }
+  }
+};
+
+struct Minimum {
+  template <typename T>
+  __device__ T operator()(T x, T y) {
+    if constexpr (cuda::std::is_integral_v<T>) {
+      return min(x, y);
+    } else if constexpr (cuda::std::is_same_v<T, cuComplex>) {
+      if (isnan(cuCrealf(x)) || isnan(cuCimagf(x))) {
+        return x;
+      }
+      return x < y ? x : y;
+    } else {
+      if (isnan(x)) {
+        return x;
+      }
+      return x < y ? x : y;
+    }
+  }
+};
+
+struct Multiply {
+  template <typename T>
+  __device__ T operator()(T x, T y) {
+    return x * y;
+  }
+};
+
+struct NotEqual {
+  template <typename T>
+  __device__ bool operator()(T x, T y) {
+    if constexpr (std::is_same_v<T, cuComplex>) {
+      return cuCrealf(x) != cuCrealf(y) || cuCimagf(x) != cuCimagf(y);
+    } else {
+      return x != y;
+    }
+  }
+};
+
+struct Power {
+  template <typename T>
+  __device__ T operator()(T base, T exp) {
+    if constexpr (cuda::std::is_integral_v<T>) {
+      T res = 1;
+      while (exp) {
+        if (exp & 1) {
+          res *= base;
+        }
+        exp >>= 1;
+        base *= base;
+      }
+      return res;
+    } else if constexpr (cuda::std::is_same_v<T, cuComplex>) {
+      auto x_theta = atan2f(base.y, base.x);
+      auto x_ln_r = 0.5 * logf(base.x * base.x + base.y * base.y);
+      auto mag = expf(exp.x * x_ln_r - exp.y * x_theta);
+      auto phase = exp.y * x_ln_r + exp.x * x_theta;
+      return make_cuFloatComplex(mag * cosf(phase), mag * sinf(phase));
+    } else {
+      return powf(base, exp);
+    }
+  }
+};
+
+struct Subtract {
+  template <typename T>
+  __device__ T operator()(T x, T y) {
+    return x - y;
+  }
+};
+
+struct LogicalAnd {
+  template <typename T>
+  __device__ T operator()(T x, T y) {
+    return x && y;
+  };
+};
+
+struct LogicalOr {
+  template <typename T>
+  __device__ T operator()(T x, T y) {
+    return x || y;
+  };
+};
+
+struct BitwiseAnd {
+  template <typename T>
+  __device__ T operator()(T x, T y) {
+    return x & y;
+  };
+};
+
+struct BitwiseOr {
+  template <typename T>
+  __device__ T operator()(T x, T y) {
+    return x | y;
+  };
+};
+
+struct BitwiseXor {
+  template <typename T>
+  __device__ T operator()(T x, T y) {
+    return x ^ y;
+  };
+};
+
+struct LeftShift {
+  template <typename T>
+  __device__ T operator()(T x, T y) {
+    return x << y;
+  };
+};
+
+struct RightShift {
+  template <typename T>
+  __device__ T operator()(T x, T y) {
+    return x >> y;
+  };
+};
+
+struct ArcTan2 {
+  template <typename T>
+  __device__ T operator()(T y, T x) {
+    return atan2f(y, x);
+  }
+};
+
+struct DivMod {
+  template <typename T>
+  __device__ cuda::std::array<T, 2> operator()(T x, T y) {
+    return {FloorDivide{}(x, y), Remainder{}(x, y)};
+  };
+};
+
+} // namespace mlx::core::cu

mlx/backend/cuda/kernels/cast_op.cuh

@@ -0,0 +1,59 @@
+// Copyright © 2025 Apple Inc.
+
+#pragma once
+
+#include <cuComplex.h>
+#include <thrust/iterator/transform_iterator.h>
+
+namespace mlx::core::cu {
+
+// An op that does static_cast, with custom conversions for some types.
+template <typename SrcT, typename DstT, typename = void>
+struct CastOp {
+  static constexpr bool is_castable = cuda::std::is_convertible_v<SrcT, DstT>;
+
+  __device__ DstT operator()(SrcT x) {
+    return static_cast<DstT>(x);
+  }
+};
+
+// Converting a complex number to real number discards the imaginary part.
+template <typename DstT>
+struct CastOp<
+    cuComplex,
+    DstT,
+    cuda::std::enable_if_t<!cuda::std::is_same_v<cuComplex, DstT>>> {
+  static constexpr bool is_castable = cuda::std::is_convertible_v<float, DstT>;
+
+  __device__ DstT operator()(cuComplex x) {
+    static_assert(!cuda::std::is_same_v<cuComplex, DstT>);
+    return static_cast<DstT>(cuCrealf(x));
+  }
+};
+
+// Allow converting a real number to complex number.
+template <typename SrcT>
+struct CastOp<
+    SrcT,
+    cuComplex,
+    cuda::std::enable_if_t<!cuda::std::is_same_v<SrcT, cuComplex>>> {
+  static constexpr bool is_castable = cuda::std::is_convertible_v<SrcT, float>;
+
+  __device__ cuComplex operator()(SrcT x) {
+    static_assert(!cuda::std::is_same_v<SrcT, cuComplex>);
+    return cuComplex{static_cast<float>(x), 0};
+  }
+};
+
+// Return an iterator that cast the value to DstT using CastOp.
+template <typename DstT, typename Iterator>
+__host__ __device__ auto make_cast_iterator(Iterator it) {
+  using SrcT = typename cuda::std::iterator_traits<Iterator>::value_type;
+  if constexpr (std::is_same_v<SrcT, DstT>) {
+    return it;
+  } else {
+    return thrust::make_transform_iterator(it, CastOp<SrcT, DstT>{});
+  }
+}
+
+} // namespace mlx::core::cu

mlx/backend/cuda/kernels/cucomplex_math.cuh

@@ -0,0 +1,240 @@
+// Copyright © 2025 Apple Inc.
+// Copyright © 2017-2024 The Simons Foundation, Inc.
+//
+// FINUFFT is licensed under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance with the
+// License.  You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// Forked from
+// https://github.com/flatironinstitute/finufft/blob/main/include/cufinufft/contrib/helper_math.h
+
+#pragma once
+
+#include <cuComplex.h>
+
+// This header provides some helper functions for cuComplex types.
+// It mainly wraps existing CUDA implementations to provide operator overloads
+// e.g. cuAdd, cuSub, cuMul, cuDiv, cuCreal, cuCimag, cuCabs, cuCarg, cuConj are
+// all provided by CUDA
+
+__forceinline__ __host__ __device__ cuDoubleComplex
+operator+(const cuDoubleComplex& a, const cuDoubleComplex& b) {
+  return cuCadd(a, b);
+}
+
+__forceinline__ __host__ __device__ cuDoubleComplex
+operator-(const cuDoubleComplex& a, const cuDoubleComplex& b) {
+  return cuCsub(a, b);
+}
+
+__forceinline__ __host__ __device__ cuDoubleComplex
+operator*(const cuDoubleComplex& a, const cuDoubleComplex& b) {
+  return cuCmul(a, b);
+}
+
+__forceinline__ __host__ __device__ cuDoubleComplex
+operator/(const cuDoubleComplex& a, const cuDoubleComplex& b) {
+  return cuCdiv(a, b);
+}
+
+__forceinline__ __host__ __device__ cuDoubleComplex
+operator%(const cuDoubleComplex& a, const cuDoubleComplex& b) {
+  double r = cuCreal(a) - (floorf(cuCreal(a) / cuCreal(b)) * cuCreal(b));
+  double i = cuCimag(a) - (floorf(cuCimag(a) / cuCimag(b)) * cuCimag(b));
+  return make_cuDoubleComplex(r, i);
+}
+
+__forceinline__ __host__ __device__ bool operator==(
+    const cuDoubleComplex& a,
+    const cuDoubleComplex& b) {
+  return cuCreal(a) == cuCreal(b) && cuCimag(a) == cuCimag(b);
+}
+
+__forceinline__ __host__ __device__ bool operator!=(
+    const cuDoubleComplex& a,
+    const cuDoubleComplex& b) {
+  return !(a == b);
+}
+
+__forceinline__ __host__ __device__ bool operator>(
+    const cuDoubleComplex& a,
+    const cuDoubleComplex& b) {
+  double mag_a = sqrt(cuCreal(a) * cuCreal(a) + cuCimag(a) * cuCimag(a));
+  double mag_b = sqrt(cuCreal(b) * cuCreal(b) + cuCimag(b) * cuCimag(b));
+  return mag_a > mag_b;
+}
+
+__forceinline__ __host__ __device__ bool operator>=(
+    const cuDoubleComplex& a,
+    const cuDoubleComplex& b) {
+  return a > b || a == b;
+}
+
+__forceinline__ __host__ __device__ bool operator<(
+    const cuDoubleComplex& a,
+    const cuDoubleComplex& b) {
+  return b > a;
+}
+
+__forceinline__ __host__ __device__ bool operator<=(
+    const cuDoubleComplex& a,
+    const cuDoubleComplex& b) {
+  return b > a || a == b;
+}
+
+__forceinline__ __host__ __device__ cuDoubleComplex
+operator+(const cuDoubleComplex& a, double b) {
+  return make_cuDoubleComplex(cuCreal(a) + b, cuCimag(a));
+}
+
+__forceinline__ __host__ __device__ cuDoubleComplex
+operator+(double a, const cuDoubleComplex& b) {
+  return make_cuDoubleComplex(a + cuCreal(b), cuCimag(b));
+}
+
+__forceinline__ __host__ __device__ cuDoubleComplex
+operator-(const cuDoubleComplex& a, double b) {
+  return make_cuDoubleComplex(cuCreal(a) - b, cuCimag(a));
+}
+
+__forceinline__ __host__ __device__ cuDoubleComplex
+operator-(double a, const cuDoubleComplex& b) {
+  return make_cuDoubleComplex(a - cuCreal(b), -cuCimag(b));
+}
+
+__forceinline__ __host__ __device__ cuDoubleComplex
+operator*(const cuDoubleComplex& a, double b) {
+  return make_cuDoubleComplex(cuCreal(a) * b, cuCimag(a) * b);
+}
+
+__forceinline__ __host__ __device__ cuDoubleComplex
+operator*(double a, const cuDoubleComplex& b) {
+  return make_cuDoubleComplex(a * cuCreal(b), a * cuCimag(b));
+}
+
+__forceinline__ __host__ __device__ cuDoubleComplex
+operator/(const cuDoubleComplex& a, double b) {
+  return make_cuDoubleComplex(cuCreal(a) / b, cuCimag(a) / b);
+}
+
+__forceinline__ __host__ __device__ cuDoubleComplex
+operator/(double a, const cuDoubleComplex& b) {
+  double denom = cuCreal(b) * cuCreal(b) + cuCimag(b) * cuCimag(b);
+  return make_cuDoubleComplex(
+      (a * cuCreal(b)) / denom, (-a * cuCimag(b)) / denom);
+}
+
+__forceinline__ __host__ __device__ cuFloatComplex
+operator+(const cuFloatComplex& a, const cuFloatComplex& b) {
+  return cuCaddf(a, b);
+}
+
+__forceinline__ __host__ __device__ cuFloatComplex
+operator-(const cuFloatComplex& a, const cuFloatComplex& b) {
+  return cuCsubf(a, b);
+}
+
+__forceinline__ __host__ __device__ cuFloatComplex
+operator*(const cuFloatComplex& a, const cuFloatComplex& b) {
+  return cuCmulf(a, b);
+}
+
+__forceinline__ __host__ __device__ cuFloatComplex
+operator/(const cuFloatComplex& a, const cuFloatComplex& b) {
+  return cuCdivf(a, b);
+}
+
+__forceinline__ __host__ __device__ cuFloatComplex
+operator%(const cuFloatComplex& a, const cuFloatComplex& b) {
+  float r = cuCrealf(a) - (floorf(cuCrealf(a) / cuCrealf(b)) * cuCrealf(b));
+  float i = cuCimagf(a) - (floorf(cuCimagf(a) / cuCimagf(b)) * cuCimagf(b));
+  return make_cuFloatComplex(r, i);
+}
+
+__forceinline__ __host__ __device__ bool operator==(
+    const cuFloatComplex& a,
+    const cuFloatComplex& b) {
+  return cuCrealf(a) == cuCrealf(b) && cuCimagf(a) == cuCimagf(b);
+}
+
+__forceinline__ __host__ __device__ bool operator!=(
+    const cuFloatComplex& a,
+    const cuFloatComplex& b) {
+  return !(a == b);
+}
+
+__forceinline__ __host__ __device__ bool operator>(
+    const cuFloatComplex& a,
+    const cuFloatComplex& b) {
+  float mag_a = sqrt(cuCrealf(a) * cuCrealf(a) + cuCimagf(a) * cuCimagf(a));
+  float mag_b = sqrt(cuCrealf(b) * cuCrealf(b) + cuCimagf(b) * cuCimagf(b));
+  return mag_a > mag_b;
+}
+
+__forceinline__ __host__ __device__ bool operator>=(
+    const cuFloatComplex& a,
+    const cuFloatComplex& b) {
+  return a > b || a == b;
+}
+
+__forceinline__ __host__ __device__ bool operator<(
+    const cuFloatComplex& a,
+    const cuFloatComplex& b) {
+  return b > a;
+}
+
+__forceinline__ __host__ __device__ bool operator<=(
+    const cuFloatComplex& a,
+    const cuFloatComplex& b) {
+  return b > a || a == b;
+}
+
+__forceinline__ __host__ __device__ cuFloatComplex
+operator+(const cuFloatComplex& a, float b) {
+  return make_cuFloatComplex(cuCrealf(a) + b, cuCimagf(a));
+}
+
+__forceinline__ __host__ __device__ cuFloatComplex
+operator+(float a, const cuFloatComplex& b) {
+  return make_cuFloatComplex(a + cuCrealf(b), cuCimagf(b));
+}
+
+__forceinline__ __host__ __device__ cuFloatComplex
+operator-(const cuFloatComplex& a, float b) {
+  return make_cuFloatComplex(cuCrealf(a) - b, cuCimagf(a));
+}
+
+__forceinline__ __host__ __device__ cuFloatComplex
+operator-(float a, const cuFloatComplex& b) {
+  return make_cuFloatComplex(a - cuCrealf(b), -cuCimagf(b));
+}
+
+__forceinline__ __host__ __device__ cuFloatComplex
+operator*(const cuFloatComplex& a, float b) {
+  return make_cuFloatComplex(cuCrealf(a) * b, cuCimagf(a) * b);
+}
+
+__forceinline__ __host__ __device__ cuFloatComplex
+operator*(float a, const cuFloatComplex& b) {
+  return make_cuFloatComplex(a * cuCrealf(b), a * cuCimagf(b));
+}
+
+__forceinline__ __host__ __device__ cuFloatComplex
+operator/(const cuFloatComplex& a, float b) {
+  return make_cuFloatComplex(cuCrealf(a) / b, cuCimagf(a) / b);
+}
+
+__forceinline__ __host__ __device__ cuFloatComplex
+operator/(float a, const cuFloatComplex& b) {
+  float denom = cuCrealf(b) * cuCrealf(b) + cuCimagf(b) * cuCimagf(b);
+  return make_cuFloatComplex(
+      (a * cuCrealf(b)) / denom, (-a * cuCimagf(b)) / denom);
+}

mlx/backend/cuda/kernels/fp16_math.cuh

@@ -9,6 +9,124 @@
 
 namespace mlx::core::cu {
 
+///////////////////////////////////////////////////////////////////////////////
+// Unary ops for half types.
+///////////////////////////////////////////////////////////////////////////////
+
+#if CUDART_VERSION < 12000 && __CUDA_ARCH__ < 800
+#define MLX_DEFINE_UNARY_OP(NAME, HALF_OP)           \
+  template <typename T>                              \
+  __forceinline__ __device__ auto NAME(T x) {        \
+    if constexpr (cuda::std::is_same_v<T, __half>) { \
+      return HALF_OP(x);                             \
+    } else {                                         \
+      return ::NAME(x);                              \
+    }                                                \
+  }
+#else
+#define MLX_DEFINE_UNARY_OP(NAME, HALF_OP)                         \
+  template <typename T>                                            \
+  __forceinline__ __device__ auto NAME(T x) {                      \
+    if constexpr (cuda::std::is_same_v<T, __half>) {               \
+      return HALF_OP(x);                                           \
+    } else if constexpr (cuda::std::is_same_v<T, __nv_bfloat16>) { \
+      return HALF_OP(x);                                           \
+    } else {                                                       \
+      return ::NAME(x);                                            \
+    }                                                              \
+  }
+#endif
+
+#define MLX_DEFINE_UNARY_OP_FALLBCK(NAME)                          \
+  template <typename T>                                            \
+  __forceinline__ __device__ auto NAME(T x) {                      \
+    if constexpr (cuda::std::is_same_v<T, __half>) {               \
+      return ::NAME(__half2float(x));                              \
+    } else if constexpr (cuda::std::is_same_v<T, __nv_bfloat16>) { \
+      return ::NAME(__bfloat162float(x));                          \
+    } else {                                                       \
+      return ::NAME(x);                                            \
+    }                                                              \
+  }
+
+MLX_DEFINE_UNARY_OP(abs, __habs)
+MLX_DEFINE_UNARY_OP(ceil, hceil)
+MLX_DEFINE_UNARY_OP(cos, hcos)
+MLX_DEFINE_UNARY_OP(exp, hexp)
+MLX_DEFINE_UNARY_OP(floor, hfloor)
+MLX_DEFINE_UNARY_OP(isnan, __hisnan)
+MLX_DEFINE_UNARY_OP(log, hlog)
+MLX_DEFINE_UNARY_OP(log2, hlog2)
+MLX_DEFINE_UNARY_OP(log10, hlog10)
+MLX_DEFINE_UNARY_OP(rint, hrint)
+MLX_DEFINE_UNARY_OP(rsqrt, hrsqrt)
+MLX_DEFINE_UNARY_OP(sin, hsin)
+MLX_DEFINE_UNARY_OP(sqrt, hsqrt)
+MLX_DEFINE_UNARY_OP_FALLBCK(acos)
+MLX_DEFINE_UNARY_OP_FALLBCK(acosh)
+MLX_DEFINE_UNARY_OP_FALLBCK(asin)
+MLX_DEFINE_UNARY_OP_FALLBCK(asinh)
+MLX_DEFINE_UNARY_OP_FALLBCK(atan)
+MLX_DEFINE_UNARY_OP_FALLBCK(atanh)
+MLX_DEFINE_UNARY_OP_FALLBCK(cosh)
+MLX_DEFINE_UNARY_OP_FALLBCK(log1p)
+MLX_DEFINE_UNARY_OP_FALLBCK(sinh)
+MLX_DEFINE_UNARY_OP_FALLBCK(tan)
+#if __CUDA_ARCH__ >= 1280
+MLX_DEFINE_UNARY_OP(tanh, htanh)
+#else
+MLX_DEFINE_UNARY_OP_FALLBCK(tanh)
+#endif
+
+#undef MLX_DEFINE_UNARY_OP
+#undef MLX_DEFINE_UNARY_OP_FALLBCK
+
+///////////////////////////////////////////////////////////////////////////////
+// Binary ops for half types.
+///////////////////////////////////////////////////////////////////////////////
+
+#if CUDART_VERSION < 12000 && __CUDA_ARCH__ < 800
+#define MLX_DEFINE_BINARY_OP(NAME, HALF_OP)          \
+  template <typename T>                              \
+  __forceinline__ __device__ auto NAME(T x, T y) {   \
+    if constexpr (cuda::std::is_same_v<T, __half>) { \
+      return HALF_OP(x, y);                          \
+    } else {                                         \
+      return ::NAME(x, y);                           \
+    }                                                \
+  }
+#else
+#define MLX_DEFINE_BINARY_OP(NAME, HALF_OP)                        \
+  template <typename T>                                            \
+  __forceinline__ __device__ auto NAME(T x, T y) {                 \
+    if constexpr (cuda::std::is_same_v<T, __half>) {               \
+      return HALF_OP(x, y);                                        \
+    } else if constexpr (cuda::std::is_same_v<T, __nv_bfloat16>) { \
+      return HALF_OP(x, y);                                        \
+    } else {                                                       \
+      return ::NAME(x, y);                                         \
+    }                                                              \
+  }
+#endif
+
+MLX_DEFINE_BINARY_OP(max, __hmax)
+MLX_DEFINE_BINARY_OP(min, __hmin)
+
+#undef MLX_DEFINE_BINARY_OP
+
+template <typename T>
+__forceinline__ __device__ T fmod(T x, T y) {
+  if constexpr (cuda::std::is_same_v<T, __half>) {
+    return __float2half(::fmod(__half2float(x), __half2float(y)));
+#if CUDART_VERSION >= 12000 || __CUDA_ARCH__ >= 800
+  } else if constexpr (cuda::std::is_same_v<T, __nv_bfloat16>) {
+    return __float2bfloat16(::fmod(__bfloat162float(x), __bfloat162float(y)));
+#endif
+  } else {
+    return ::fmod(x, y);
+  }
+}
+
 ///////////////////////////////////////////////////////////////////////////////
 // Additional C++ operator overrides between half types and native types.
 ///////////////////////////////////////////////////////////////////////////////

mlx/backend/cuda/kernels/unary_ops.cuh

@@ -0,0 +1,349 @@
+// Copyright © 2025 Apple Inc.
+
+#pragma once
+
+#include "mlx/backend/cuda/kernels/fp16_math.cuh"
+#include "mlx/backend/cuda/kernels/utils.cuh"
+
+namespace mlx::core::cu {
+
+struct Abs {
+  template <typename T>
+  __device__ T operator()(T x) {
+    if constexpr (cuda::std::is_unsigned_v<T>) {
+      return x;
+    } else if constexpr (cuda::std::is_same_v<T, cuComplex>) {
+      return {sqrt(cuCrealf(x) * cuCrealf(x) + cuCimagf(x) * cuCimagf(x)), 0};
+    } else {
+      return abs(x);
+    }
+  }
+};
+
+struct ArcCos {
+  template <typename T>
+  __device__ T operator()(T x) {
+    return acos(x);
+  }
+};
+
+struct ArcCosh {
+  template <typename T>
+  __device__ T operator()(T x) {
+    return acosh(x);
+  }
+};
+
+struct ArcSin {
+  template <typename T>
+  __device__ T operator()(T x) {
+    return asin(x);
+  }
+};
+
+struct ArcSinh {
+  template <typename T>
+  __device__ T operator()(T x) {
+    return asinh(x);
+  }
+};
+
+struct ArcTan {
+  template <typename T>
+  __device__ T operator()(T x) {
+    return atan(x);
+  }
+};
+
+struct ArcTanh {
+  template <typename T>
+  __device__ T operator()(T x) {
+    return atanh(x);
+  }
+};
+
+struct BitwiseInvert {
+  template <typename T>
+  __device__ T operator()(T x) {
+    return ~x;
+  }
+};
+
+struct Ceil {
+  template <typename T>
+  __device__ T operator()(T x) {
+    if constexpr (cuda::std::is_integral_v<T>) {
+      return x;
+    } else {
+      return ceil(x);
+    }
+  }
+};
+
+struct Conjugate {
+  __device__ cuComplex operator()(cuComplex x) {
+    return {cuCrealf(x), -cuCimagf(x)};
+  }
+};
+
+struct Cos {
+  template <typename T>
+  __device__ T operator()(T x) {
+    if constexpr (cuda::std::is_same_v<T, cuComplex>) {
+      return {
+          cos(cuCrealf(x)) * cosh(cuCimagf(x)),
+          -sin(cuCrealf(x)) * sinh(cuCimagf(x))};
+    } else {
+      return cos(x);
+    }
+  }
+};
+
+struct Cosh {
+  template <typename T>
+  __device__ T operator()(T x) {
+    if constexpr (cuda::std::is_same_v<T, cuComplex>) {
+      return {
+          cosh(cuCrealf(x)) * cos(cuCimagf(x)),
+          sinh(cuCrealf(x)) * sin(cuCimagf(x))};
+    } else {
+      return cosh(x);
+    }
+  }
+};
+
+struct Erf {
+  template <typename T>
+  __device__ T operator()(T x) {
+    if constexpr (cuda::std::is_same_v<T, __half>) {
+      return erf(__half2float(x));
+    } else if constexpr (cuda::std::is_same_v<T, __nv_bfloat16>) {
+      return erf(__bfloat162float(x));
+    } else {
+      return erf(x);
+    }
+  }
+};
+
+struct ErfInv {
+  template <typename T>
+  __device__ T operator()(T x) {
+    if constexpr (cuda::std::is_same_v<T, __half>) {
+      return erfinv(__half2float(x));
+    } else if constexpr (cuda::std::is_same_v<T, __nv_bfloat16>) {
+      return erfinv(__bfloat162float(x));
+    } else {
+      return erfinv(x);
+    }
+  }
+};
+
+struct Exp {
+  template <typename T>
+  __device__ T operator()(T x) {
+    if constexpr (cuda::std::is_same_v<T, cuComplex>) {
+      auto m = exp(cuCrealf(x));
+      return {m * cos(cuCimagf(x)), m * sinh(cuCimagf(x))};
+    } else {
+      return exp(x);
+    }
+  }
+};
+
+struct Expm1 {
+  template <typename T>
+  __device__ T operator()(T x) {
+    if constexpr (cuda::std::is_same_v<T, __half>) {
+      return expm1(__half2float(x));
+    } else if constexpr (cuda::std::is_same_v<T, __nv_bfloat16>) {
+      return expm1(__bfloat162float(x));
+    } else {
+      return expm1(x);
+    }
+  }
+};
+
+struct Floor {
+  template <typename T>
+  __device__ T operator()(T x) {
+    if constexpr (cuda::std::is_integral_v<T>) {
+      return x;
+    } else {
+      return floor(x);
+    }
+  }
+};
+
+struct Imag {
+  __device__ float operator()(cuComplex x) {
+    return cuCimagf(x);
+  }
+};
+
+struct Log {
+  template <typename T>
+  __device__ T operator()(T x) {
+    return log(x);
+  }
+};
+
+struct Log2 {
+  template <typename T>
+  __device__ T operator()(T x) {
+    return log2(x);
+  }
+};
+
+struct Log10 {
+  template <typename T>
+  __device__ T operator()(T x) {
+    return log10(x);
+  }
+};
+
+struct Log1p {
+  template <typename T>
+  __device__ T operator()(T x) {
+    return log1p(x);
+  }
+};
+
+struct LogicalNot {
+  __device__ bool operator()(bool x) {
+    return !x;
+  }
+};
+
+struct Negative {
+  template <typename T>
+  __device__ T operator()(T x) {
+    if constexpr (cuda::std::is_same_v<T, cuComplex>) {
+      return 0 - x;
+    } else {
+      return -x;
+    }
+  }
+};
+
+struct Real {
+  __device__ float operator()(cuComplex x) {
+    return cuCrealf(x);
+  }
+};
+
+struct Round {
+  template <typename T>
+  __device__ T operator()(T x) {
+    if constexpr (cuda::std::is_same_v<T, cuComplex>) {
+      return {rint(cuCrealf(x)), rint(cuCimagf(x))};
+    } else {
+      return rint(x);
+    }
+  }
+};
+
+struct Rsqrt {
+  template <typename T>
+  __device__ T operator()(T x) {
+    return rsqrt(x);
+  }
+};
+
+struct Sigmoid {
+  template <typename T>
+  __device__ T operator()(T x) {
+    T y = 1 / (1 + exp(-abs(x)));
+    return (x < 0) ? 1 - y : y;
+  }
+};
+
+struct Sign {
+  template <typename T>
+  __device__ T operator()(T x) {
+    if constexpr (cuda::std::is_unsigned_v<T>) {
+      return x != 0;
+    } else if constexpr (cuda::std::is_same_v<T, cuComplex>) {
+      if (cuCrealf(x) == 0 && cuCimagf(x) == 0) {
+        return x;
+      } else {
+        return x / Abs()(x);
+      }
+    } else if constexpr (cuda::std::is_same_v<T, __nv_bfloat16>) {
+      return static_cast<float>((x > T(0.f)) - (x < T(0.f)));
+    } else {
+      return (x > T(0)) - (x < T(0));
+    }
+  }
+};
+
+struct Sin {
+  template <typename T>
+  __device__ T operator()(T x) {
+    if constexpr (cuda::std::is_same_v<T, cuComplex>) {
+      return {
+          sin(cuCrealf(x)) * cosh(cuCimagf(x)),
+          cos(cuCrealf(x)) * sinh(cuCimagf(x))};
+    } else {
+      return sin(x);
+    }
+  }
+};
+
+struct Sinh {
+  template <typename T>
+  __device__ T operator()(T x) {
+    if constexpr (cuda::std::is_same_v<T, cuComplex>) {
+      return {
+          sinh(cuCrealf(x)) * cos(cuCimagf(x)),
+          cosh(cuCrealf(x)) * sin(cuCimagf(x))};
+    } else {
+      return sinh(x);
+    }
+  }
+};
+
+struct Square {
+  template <typename T>
+  __device__ T operator()(T x) {
+    return x * x;
+  }
+};
+
+struct Sqrt {
+  template <typename T>
+  __device__ T operator()(T x) {
+    return sqrt(x);
+  }
+};
+
+struct Tan {
+  template <typename T>
+  __device__ T operator()(T x) {
+    if constexpr (cuda::std::is_same_v<T, cuComplex>) {
+      float tan_a = tan(cuCrealf(x));
+      float tanh_b = tanh(cuCimagf(x));
+      float t1 = tan_a * tanh_b;
+      float denom = 1. + t1 * t1;
+      return {(tan_a - tanh_b * t1) / denom, (tanh_b + tan_a * t1) / denom};
+    } else {
+      return tan(x);
+    }
+  }
+};
+
+struct Tanh {
+  template <typename T>
+  __device__ T operator()(T x) {
+    if constexpr (cuda::std::is_same_v<T, cuComplex>) {
+      float tanh_a = tanh(cuCrealf(x));
+      float tan_b = tan(cuCimagf(x));
+      float t1 = tanh_a * tan_b;
+      float denom = 1. + t1 * t1;
+      return {(tanh_a + tan_b * t1) / denom, (tan_b - tanh_a * t1) / denom};
+    } else {
+      return tanh(x);
+    }
+  }
+};
+
+} // namespace mlx::core::cu

mlx/backend/cuda/kernels/utils.cuh

@@ -0,0 +1,104 @@
+// Copyright © 2025 Apple Inc.
+
+// This file must not include any host-only code, utilies that work under both
+// host and device can be put here.
+//
+// See more about the requirements at:
+// https://docs.nvidia.com/cuda/nvrtc/#language
+
+#pragma once
+
+#include <cuComplex.h>
+#include <cuda/std/array>
+#include <cuda/std/limits>
+#include <cuda/std/tuple>
+
+namespace mlx::core::cu {
+
+///////////////////////////////////////////////////////////////////////////////
+// CUDA kernel utils
+///////////////////////////////////////////////////////////////////////////////
+
+// To pass shape/strides to kernels via constant memory, their size must be
+// known at compile time.
+#define MAX_NDIM 8
+
+using Shape = cuda::std::array<int32_t, MAX_NDIM>;
+using Strides = cuda::std::array<int64_t, MAX_NDIM>;
+
+///////////////////////////////////////////////////////////////////////////////
+// Indexing utils
+///////////////////////////////////////////////////////////////////////////////
+
+template <typename IdxT = int64_t>
+inline __host__ __device__ IdxT
+elem_to_loc(IdxT elem, const int* shape, const int64_t* strides, int ndim) {
+  IdxT loc = 0;
+  for (int i = ndim - 1; i >= 0 && elem > 0; --i) {
+    loc += (elem % shape[i]) * IdxT(strides[i]);
+    elem /= shape[i];
+  }
+  return loc;
+}
+
+// Optimize when the ndim is known at compile time.
+template <int NDIM, typename IdxT = int64_t>
+inline __host__ __device__ IdxT
+elem_to_loc_nd(IdxT elem, const int* shape, const int64_t* strides) {
+  IdxT loc = 0;
+#pragma unroll
+  for (int i = NDIM - 1; i >= 0; --i) {
+    loc += (elem % shape[i]) * IdxT(strides[i]);
+    elem /= shape[i];
+  }
+  return loc;
+}
+
+template <int NDIM, typename IdxT = int64_t>
+inline __host__ __device__ cuda::std::tuple<IdxT, IdxT> elem_to_loc_nd(
+    IdxT elem,
+    const int* shape,
+    const int64_t* a_strides,
+    const int64_t* b_strides) {
+  IdxT a_loc = 0;
+  IdxT b_loc = 0;
+#pragma unroll
+  for (int i = NDIM - 1; i >= 0; --i) {
+    int dim_idx = elem % shape[i];
+    a_loc += dim_idx * a_strides[i];
+    b_loc += dim_idx * b_strides[i];
+    elem /= shape[i];
+  }
+  return cuda::std::make_tuple(a_loc, b_loc);
+}
+
+// Optimized version when ndim is larger than 4.
+template <typename IdxT = int64_t>
+inline __host__ __device__ IdxT
+elem_to_loc_4d(IdxT elem, const int* shape, const int64_t* strides, int ndim) {
+  IdxT loc = elem_to_loc_nd<3>(elem, shape, strides);
+  for (int i = ndim - 1; i >= 3; --i) {
+    loc += (elem % shape[i]) * IdxT(strides[i]);
+    elem /= shape[i];
+  }
+  return loc;
+}
+
+template <typename IdxT = int64_t>
+inline __host__ __device__ cuda::std::tuple<IdxT, IdxT> elem_to_loc_4d(
+    IdxT elem,
+    const int* shape,
+    const int64_t* a_strides,
+    const int64_t* b_strides,
+    int ndim) {
+  auto [a_loc, b_loc] = elem_to_loc_nd<3>(elem, shape, a_strides, b_strides);
+  for (int i = ndim - 1; i >= 3; --i) {
+    int dim_idx = elem % shape[i];
+    a_loc += dim_idx * a_strides[i];
+    b_loc += dim_idx * b_strides[i];
+    elem /= shape[i];
+  }
+  return cuda::std::make_tuple(a_loc, b_loc);
+}
+
+} // namespace mlx::core::cu

mlx/backend/cuda/matmul.cpp

@@ -0,0 +1,474 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/common/matmul.h"
+#include "mlx/backend/cuda/device.h"
+#include "mlx/backend/gpu/copy.h"
+#include "mlx/dtype_utils.h"
+#include "mlx/primitives.h"
+
+#include <cublasLt.h>
+#include <fmt/format.h>
+#include <nvtx3/nvtx3.hpp>
+
+#include <numeric>
+
+namespace mlx::core {
+
+namespace cu {
+
+#define CHECK_CUBLAS_ERROR(cmd) check_cublas_error(#cmd, (cmd))
+
+void check_cublas_error(const char* name, cublasStatus_t err) {
+  if (err != CUBLAS_STATUS_SUCCESS) {
+    // TODO: Use cublasGetStatusString when it is widely available.
+    throw std::runtime_error(
+        fmt::format("{} failed with code: {}.", name, static_cast<int>(err)));
+  }
+}
+
+class MatMul {
+ public:
+  MatMul(
+      Device& device,
+      Dtype dtype,
+      bool a_transposed,
+      uint64_t a_rows,
+      uint64_t a_cols,
+      int64_t lda,
+      bool b_transposed,
+      uint64_t b_rows,
+      uint64_t b_cols,
+      int64_t ldb,
+      int32_t batch_count,
+      int64_t a_batch_stride,
+      int64_t b_batch_stride) {
+    heuristic_.state = CUBLAS_STATUS_NOT_INITIALIZED;
+
+    auto type = dtype_to_cuda_type(dtype);
+    CHECK_CUBLAS_ERROR(cublasLtMatmulDescCreate(
+        &matmul_desc_, dtype_to_compute_type(dtype), type));
+    int32_t pointer_mode = CUBLASLT_POINTER_MODE_HOST;
+    CHECK_CUBLAS_ERROR(cublasLtMatmulDescSetAttribute(
+        matmul_desc_,
+        CUBLASLT_MATMUL_DESC_POINTER_MODE,
+        &pointer_mode,
+        sizeof(int32_t)));
+    cublasOperation_t op = CUBLAS_OP_N;
+    CHECK_CUBLAS_ERROR(cublasLtMatmulDescSetAttribute(
+        matmul_desc_,
+        CUBLASLT_MATMUL_DESC_TRANSA,
+        &op,
+        sizeof(cublasOperation_t)));
+    CHECK_CUBLAS_ERROR(cublasLtMatmulDescSetAttribute(
+        matmul_desc_,
+        CUBLASLT_MATMUL_DESC_TRANSB,
+        &op,
+        sizeof(cublasOperation_t)));
+
+    a_desc_ = create_matrix_layout(
+        type, a_rows, a_cols, a_transposed, lda, batch_count, a_batch_stride);
+    b_desc_ = create_matrix_layout(
+        type, b_rows, b_cols, b_transposed, ldb, batch_count, b_batch_stride);
+    out_desc_ = create_matrix_layout(
+        type, a_rows, b_cols, false, b_cols, batch_count, a_rows * b_cols);
+
+    // The recommended cublas workspace size is 4 MiB for pre-Hopper and 32 MiB
+    // for Hopper+:
+    // https://docs.nvidia.com/cuda/cublas/#cublassetworkspace
+    uint64_t MiB = 1024 * 1024;
+    uint64_t workspace_size =
+        device.compute_capability_major() >= 9 ? 32 * MiB : 4 * MiB;
+
+    CHECK_CUBLAS_ERROR(cublasLtMatmulPreferenceCreate(&pref_));
+    CHECK_CUBLAS_ERROR(cublasLtMatmulPreferenceSetAttribute(
+        pref_,
+        CUBLASLT_MATMUL_PREF_MAX_WORKSPACE_BYTES,
+        &workspace_size,
+        sizeof(uint64_t)));
+  }
+
+  MatMul(
+      Device& device,
+      Dtype dtype,
+      bool a_transposed,
+      uint64_t a_rows,
+      uint64_t a_cols,
+      int64_t lda,
+      bool b_transposed,
+      uint64_t b_rows,
+      uint64_t b_cols,
+      int64_t ldb,
+      bool c_transposed,
+      int64_t ldc,
+      int32_t batch_count,
+      int64_t a_batch_stride,
+      int64_t b_batch_stride,
+      int64_t c_batch_stride)
+      : MatMul(
+            device,
+            dtype,
+            a_transposed,
+            a_rows,
+            a_cols,
+            lda,
+            b_transposed,
+            b_rows,
+            b_cols,
+            ldb,
+            batch_count,
+            a_batch_stride,
+            b_batch_stride) {
+    auto type = dtype_to_cuda_type(dtype);
+    c_desc_ = create_matrix_layout(
+        type, a_rows, b_cols, c_transposed, ldc, batch_count, c_batch_stride);
+  }
+
+  ~MatMul() {
+    cublasLtMatrixLayoutDestroy(a_desc_);
+    cublasLtMatrixLayoutDestroy(b_desc_);
+    cublasLtMatrixLayoutDestroy(c_desc_);
+    cublasLtMatrixLayoutDestroy(out_desc_);
+    cublasLtMatmulDescDestroy(matmul_desc_);
+  }
+
+  void run(
+      cu::CommandEncoder& encoder,
+      void* out,
+      void* a,
+      void* b,
+      void* c = nullptr,
+      float alpha = 1,
+      float beta = 0) {
+    if (heuristic_.state != CUBLAS_STATUS_SUCCESS) {
+      int ret = 0;
+      CHECK_CUBLAS_ERROR(cublasLtMatmulAlgoGetHeuristic(
+          encoder.device().lt_handle(),
+          matmul_desc_,
+          a_desc_,
+          b_desc_,
+          out_desc_,
+          out_desc_,
+          pref_,
+          1,
+          &heuristic_,
+          &ret));
+      if (ret == 0) {
+        throw std::runtime_error("Can not find algorithm for matmul.");
+      }
+    }
+
+    array workspace(
+        allocator::malloc(heuristic_.workspaceSize),
+        {static_cast<int>(heuristic_.workspaceSize)},
+        int8);
+    encoder.add_temporary(workspace);
+
+    encoder.launch_kernel([&](cudaStream_t stream) {
+      CHECK_CUBLAS_ERROR(cublasLtMatmul(
+          encoder.device().lt_handle(),
+          matmul_desc_,
+          &alpha,
+          a,
+          a_desc_,
+          b,
+          b_desc_,
+          &beta,
+          c ? c : out,
+          c ? c_desc_ : out_desc_,
+          out,
+          out_desc_,
+          &heuristic_.algo,
+          workspace.data<void>(),
+          workspace.nbytes(),
+          stream));
+    });
+  }
+
+ private:
+  cublasComputeType_t dtype_to_compute_type(Dtype dtype) {
+    switch (dtype) {
+      case uint8:
+      case uint16:
+      case int8:
+      case int16:
+      case int32:
+        return CUBLAS_COMPUTE_32I;
+      case float16:
+      case bfloat16:
+        return CUBLAS_COMPUTE_16F;
+      case float32:
+        return CUBLAS_COMPUTE_32F;
+      case float64:
+      case complex64:
+        return CUBLAS_COMPUTE_64F;
+      default:
+        throw std::runtime_error(fmt::format(
+            "Unsupported dtype in MatMul: {}.", dtype_to_string(dtype)));
+    }
+  }
+
+  cudaDataType_t dtype_to_cuda_type(Dtype dtype) {
+    switch (dtype) {
+      case uint8:
+        return CUDA_R_8U;
+      case uint16:
+        return CUDA_R_16U;
+      case int8:
+        return CUDA_R_8I;
+      case int16:
+        return CUDA_R_16I;
+      case int32:
+        return CUDA_R_32I;
+      case float16:
+        return CUDA_R_16F;
+      case bfloat16:
+        return CUDA_R_16BF;
+      case float32:
+        return CUDA_R_32F;
+      case float64:
+        return CUDA_R_64F;
+      case complex64:
+        return CUDA_C_32F;
+      default:
+        throw std::runtime_error(fmt::format(
+            "Unsupported dtype in MatMul: {}.", dtype_to_string(dtype)));
+    }
+  }
+
+  cublasLtMatrixLayout_t create_matrix_layout(
+      cudaDataType_t type,
+      uint64_t rows,
+      uint64_t cols,
+      bool transposed,
+      int64_t ld,
+      int32_t batch_count,
+      int64_t batch_stride) {
+    cublasLtMatrixLayout_t desc;
+    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,
+          CUBLASLT_MATRIX_LAYOUT_BATCH_COUNT,
+          &batch_count,
+          sizeof(int32_t)));
+      CHECK_CUBLAS_ERROR(cublasLtMatrixLayoutSetAttribute(
+          desc,
+          CUBLASLT_MATRIX_LAYOUT_STRIDED_BATCH_OFFSET,
+          &batch_stride,
+          sizeof(int64_t)));
+    }
+    return desc;
+  }
+
+  cublasLtMatmulDesc_t matmul_desc_{nullptr};
+  cublasLtMatmulPreference_t pref_{nullptr};
+  cublasLtMatrixLayout_t a_desc_{nullptr};
+  cublasLtMatrixLayout_t b_desc_{nullptr};
+  cublasLtMatrixLayout_t c_desc_{nullptr};
+  cublasLtMatrixLayout_t out_desc_{nullptr};
+  cublasLtMatmulHeuristicResult_t heuristic_;
+};
+
+} // namespace cu
+
+namespace {
+
+std::tuple<bool, int64_t, array>
+check_transpose(std::vector<array>& copies, const Stream& s, const array& arr) {
+  auto stx = arr.strides()[arr.ndim() - 2];
+  auto sty = arr.strides()[arr.ndim() - 1];
+  if (sty == 1 && stx == arr.shape(-1)) {
+    return std::make_tuple(false, stx, arr);
+  } else if (stx == 1 && sty == arr.shape(-2)) {
+    return std::make_tuple(true, sty, arr);
+  } else {
+    array arr_copy(arr.shape(), arr.dtype(), nullptr, {});
+    copy_gpu(arr, arr_copy, CopyType::General, s);
+    copies.push_back(arr_copy);
+    return std::make_tuple(false, arr.shape(-1), arr_copy);
+  }
+}
+
+} // namespace
+
+void Matmul::eval_gpu(const std::vector<array>& inputs, array& out) {
+  nvtx3::scoped_range r("Matmul::eval_gpu");
+  auto& s = stream();
+  auto& encoder = cu::get_command_encoder(s);
+
+  assert(inputs.size() == 2);
+  auto& a_pre = inputs[0];
+  auto& b_pre = inputs[1];
+  // Return 0s if either input is empty.
+  if (a_pre.size() == 0 || b_pre.size() == 0) {
+    array zero(0, a_pre.dtype());
+    encoder.add_temporary(zero);
+    fill_gpu(zero, out, s);
+    return;
+  }
+
+  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);
+
+  // Keep a vector with copies to be cleared in the completed buffer to release
+  // the arrays
+  std::vector<array> copies;
+  auto [a_transposed, lda, a] = check_transpose(copies, s, a_pre);
+  auto [b_transposed, ldb, b] = check_transpose(copies, s, b_pre);
+
+  for (auto& temp : copies) {
+    encoder.add_temporary(temp);
+  }
+
+  /////////////////////////////////////////////////////////////////////////////
+  // 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};
+  }
+
+  /////////////////////////////////////////////////////////////////////////////
+  // Invoke cublasLt
+
+  cu::MatMul matmul(
+      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());
+
+  ContiguousIterator a_it(batch_shape, a_batch_strides, batch_shape.size() - 1);
+  ContiguousIterator b_it(batch_shape, b_batch_strides, batch_shape.size() - 1);
+  for (size_t i = 0; i < batch_count / batch_shape.back(); ++i) {
+    matmul.run(
+        encoder,
+        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);
+    a_it.step();
+    b_it.step();
+  }
+}
+
+void AddMM::eval_gpu(const std::vector<array>& inputs, array& out) {
+  nvtx3::scoped_range r("AddMM::eval_gpu");
+  auto& s = stream();
+  auto& encoder = cu::get_command_encoder(s);
+
+  assert(inputs.size() == 3);
+  auto& a_pre = inputs[0];
+  auto& b_pre = inputs[1];
+  auto& c_pre = inputs[2];
+
+  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);
+
+  // Keep a vector with copies to be cleared in the completed buffer to release
+  // the arrays
+  std::vector<array> copies;
+  auto [a_transposed, lda, a] = check_transpose(copies, s, a_pre);
+  auto [b_transposed, ldb, b] = check_transpose(copies, s, b_pre);
+  auto [c_transposed, ldc, c] = check_transpose(copies, s, c_pre);
+
+  for (auto& temp : copies) {
+    encoder.add_temporary(temp);
+  }
+
+  /////////////////////////////////////////////////////////////////////////////
+  // Check and collapse batch dimensions
+
+  auto [batch_shape, a_batch_strides, b_batch_strides, c_batch_strides] =
+      collapse_batches(a, b, c);
+
+  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 &&
+      c_batch_strides.back() == M * c.strides()[c.ndim() - 2] &&
+      b_batch_strides.back() == 0) {
+    M *= batch_shape.back();
+    batch_count = 1;
+
+    a_batch_strides = {0};
+    b_batch_strides = {0};
+    c_batch_strides = {0};
+    batch_shape = {1};
+  }
+
+  /////////////////////////////////////////////////////////////////////////////
+  // Invoke cublasLt
+
+  cu::MatMul matmul(
+      encoder.device(),
+      a.dtype(),
+      a_transposed,
+      M,
+      K,
+      lda,
+      b_transposed,
+      K,
+      N,
+      ldb,
+      c_transposed,
+      ldc,
+      batch_shape.back(),
+      a_batch_strides.back(),
+      b_batch_strides.back(),
+      c_batch_strides.back());
+
+  ContiguousIterator a_it(batch_shape, a_batch_strides, batch_shape.size() - 1);
+  ContiguousIterator b_it(batch_shape, b_batch_strides, batch_shape.size() - 1);
+  ContiguousIterator c_it(batch_shape, c_batch_strides, batch_shape.size() - 1);
+  for (size_t i = 0; i < batch_count / batch_shape.back(); ++i) {
+    matmul.run(
+        encoder,
+        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,
+        c.data<int8_t>() + c.itemsize() * c_it.loc,
+        alpha_,
+        beta_);
+    a_it.step();
+    b_it.step();
+    c_it.step();
+  }
+}
+
+} // namespace mlx::core

mlx/backend/cuda/primitives.cu

@@ -71,90 +71,34 @@ bool fast::ScaledDotProductAttention::use_fallback(
     throw std::runtime_error(#func " has no CUDA implementation.");   \
   }
 
-NO_GPU(Abs)
-NO_GPU(Add)
-NO_GPU(AddMM)
-NO_GPU(ArcCos)
-NO_GPU(ArcCosh)
-NO_GPU(ArcSin)
-NO_GPU(ArcSinh)
-NO_GPU(ArcTan)
-NO_GPU(ArcTan2)
-NO_GPU(ArcTanh)
 NO_GPU(ArgPartition)
 NO_GPU(ArgReduce)
-NO_GPU(ArgSort)
-NO_GPU(BitwiseBinary)
-NO_GPU(BitwiseInvert)
 NO_GPU(BlockMaskedMM)
-NO_GPU(Ceil)
 NO_GPU_MULTI(Compiled)
-NO_GPU(Conjugate)
 NO_GPU(Convolution)
-NO_GPU(Cos)
-NO_GPU(Cosh)
-NO_GPU(Divide)
 NO_GPU_MULTI(DivMod)
 NO_GPU(DynamicSlice)
 NO_GPU(DynamicSliceUpdate)
-NO_GPU(Remainder)
-NO_GPU(Equal)
-NO_GPU(Erf)
-NO_GPU(ErfInv)
-NO_GPU(Exp)
-NO_GPU(Expm1)
 NO_GPU(FFT)
-NO_GPU(Floor)
 NO_GPU(Gather)
 NO_GPU(GatherAxis)
 NO_GPU(GatherMM)
 NO_GPU(GatherQMM)
-NO_GPU(Greater)
-NO_GPU(GreaterEqual)
 NO_GPU(Hadamard)
-NO_GPU(Imag)
-NO_GPU(Less)
-NO_GPU(LessEqual)
 NO_GPU(Load)
-NO_GPU(Log)
-NO_GPU(Log1p)
-NO_GPU(LogicalNot)
-NO_GPU(LogicalAnd)
-NO_GPU(LogicalOr)
-NO_GPU(LogAddExp)
 NO_GPU(LogSumExp)
 NO_GPU_MULTI(LUF)
-NO_GPU(Matmul)
-NO_GPU(Maximum)
-NO_GPU(Minimum)
-NO_GPU(Multiply)
-NO_GPU(Negative)
-NO_GPU(NotEqual)
 NO_GPU(Partition)
-NO_GPU(Power)
 NO_GPU_MULTI(QRF)
 NO_GPU(QuantizedMatmul)
-NO_GPU(RandomBits)
-NO_GPU(Real)
 NO_GPU(Reduce)
-NO_GPU(Round)
 NO_GPU(Scan)
 NO_GPU(Scatter)
 NO_GPU(ScatterAxis)
 NO_GPU(Select)
-NO_GPU(Sigmoid)
-NO_GPU(Sign)
-NO_GPU(Sin)
-NO_GPU(Sinh)
 NO_GPU(SliceUpdate)
 NO_GPU(Softmax)
-NO_GPU(Sort)
-NO_GPU(Square)
-NO_GPU(Sqrt)
-NO_GPU(Subtract)
 NO_GPU_MULTI(SVD)
-NO_GPU(Tan)
-NO_GPU(Tanh)
 NO_GPU(Inverse)
 NO_GPU(Cholesky)
 NO_GPU_MULTI(Eig)

mlx/backend/cuda/random.cu

@@ -0,0 +1,181 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/device.h"
+#include "mlx/backend/cuda/kernel_utils.cuh"
+#include "mlx/primitives.h"
+
+#include <nvtx3/nvtx3.hpp>
+
+#include <cassert>
+
+namespace mlx::core {
+
+namespace cu {
+
+__constant__ constexpr uint32_t rotations[2][4] = {
+    {13, 15, 26, 6},
+    {17, 29, 16, 24}};
+
+union rbits {
+  uint2 val;
+  uint8_t bytes[2][4];
+};
+
+__device__ rbits threefry2x32_hash(uint2 key, uint2 count) {
+  uint32_t ks[] = {key.x, key.y, key.x ^ key.y ^ 0x1BD11BDA};
+
+  rbits v;
+  v.val.x = count.x + ks[0];
+  v.val.y = count.y + ks[1];
+
+  for (int i = 0; i < 5; ++i) {
+    for (auto r : rotations[i % 2]) {
+      v.val.x += v.val.y;
+      v.val.y = (v.val.y << r) | (v.val.y >> (32 - r));
+      v.val.y ^= v.val.x;
+    }
+    v.val.x += ks[(i + 1) % 3];
+    v.val.y += ks[(i + 2) % 3] + i + 1;
+  }
+
+  return v;
+}
+
+__global__ void rbitsc(
+    const uint32_t* keys,
+    uint8_t* out,
+    dim3 grid_dims,
+    bool odd,
+    uint32_t bytes_per_key) {
+  uint2 index{
+      blockIdx.x * blockDim.x + threadIdx.x,
+      blockIdx.y * blockDim.y + threadIdx.y};
+  if (index.x >= grid_dims.x || index.y >= grid_dims.y) {
+    return;
+  }
+
+  auto kidx = 2 * index.x;
+  auto key = uint2{keys[kidx], keys[kidx + 1]};
+  auto half_size = grid_dims.y - odd;
+  out += index.x * bytes_per_key;
+  bool drop_last = odd && (index.y == half_size);
+  auto bits = threefry2x32_hash(
+      key, uint2{index.y, drop_last ? 0 : index.y + grid_dims.y});
+  size_t idx = size_t(index.y) << 2;
+  for (int i = 0; i < 4; ++i) {
+    out[idx + i] = bits.bytes[0][i];
+  }
+  if (!drop_last) {
+    idx = (drop_last ? 0 : size_t(index.y) + grid_dims.y) << 2;
+    if ((index.y + 1) == half_size && (bytes_per_key % 4) > 0) {
+      int edge_bytes = (bytes_per_key % 4);
+      for (int i = 0; i < edge_bytes; ++i) {
+        out[idx + i] = bits.bytes[1][i];
+      }
+    } else {
+      for (int i = 0; i < 4; ++i) {
+        out[idx + i] = bits.bytes[1][i];
+      }
+    }
+  }
+}
+
+__global__ void rbits(
+    const uint32_t* keys,
+    uint8_t* out,
+    dim3 grid_dims,
+    bool odd,
+    uint32_t bytes_per_key,
+    int32_t ndim,
+    const __grid_constant__ Shape key_shape,
+    const __grid_constant__ Strides key_strides) {
+  uint2 index{
+      blockIdx.x * blockDim.x + threadIdx.x,
+      blockIdx.y * blockDim.y + threadIdx.y};
+  if (index.x >= grid_dims.x || index.y >= grid_dims.y) {
+    return;
+  }
+
+  auto kidx = 2 * index.x;
+  auto k1_elem = elem_to_loc(kidx, key_shape.data(), key_strides.data(), ndim);
+  auto k2_elem =
+      elem_to_loc(kidx + 1, key_shape.data(), key_strides.data(), ndim);
+  auto key = uint2{keys[k1_elem], keys[k2_elem]};
+  auto half_size = grid_dims.y - odd;
+  out += size_t(index.x) * bytes_per_key;
+  bool drop_last = odd && (index.y == half_size);
+  auto bits = threefry2x32_hash(
+      key, uint2{index.y, drop_last ? 0 : index.y + grid_dims.y});
+  size_t idx = size_t(index.y) << 2;
+  for (int i = 0; i < 4; ++i) {
+    out[idx + i] = bits.bytes[0][i];
+  }
+  if (!drop_last) {
+    idx = (drop_last ? 0 : size_t(index.y) + grid_dims.y) << 2;
+    if ((index.y + 1) == half_size && (bytes_per_key % 4) > 0) {
+      int edge_bytes = (bytes_per_key % 4);
+      for (int i = 0; i < edge_bytes; ++i) {
+        out[idx + i] = bits.bytes[1][i];
+      }
+    } else {
+      for (int i = 0; i < 4; ++i) {
+        out[idx + i] = bits.bytes[1][i];
+      }
+    }
+  }
+}
+
+} // namespace cu
+
+void RandomBits::eval_gpu(const std::vector<array>& inputs, array& out) {
+  nvtx3::scoped_range r("RandomBits::eval_gpu");
+  assert(inputs.size() == 1);
+
+  // keys has shape (N1, ..., NK, 2)
+  // out has shape (N1, ..., NK, M1, M2, ...)
+  auto& keys = inputs[0];
+  uint32_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;
+  out.set_data(allocator::malloc(out.nbytes()));
+  if (out.size() == 0) {
+    return;
+  }
+
+  uint32_t out_per_key = (bytes_per_key + 4 - 1) / 4;
+  uint32_t half_size = out_per_key / 2;
+  bool odd = out_per_key % 2;
+
+  auto& s = stream();
+  auto& encoder = cu::get_command_encoder(s);
+  encoder.set_input_array(keys);
+  encoder.set_output_array(out);
+  encoder.launch_kernel([&](cudaStream_t stream) {
+    dim3 grid_dims{num_keys, half_size + odd};
+    dim3 block_dims = get_block_dims(grid_dims.x, grid_dims.y, 1);
+    dim3 num_blocks{
+        cuda::ceil_div(grid_dims.x, block_dims.x),
+        cuda::ceil_div(grid_dims.y, block_dims.y)};
+    if (keys.flags().row_contiguous) {
+      cu::rbitsc<<<num_blocks, block_dims, 0, stream>>>(
+          keys.data<uint32_t>(),
+          out.data<uint8_t>(),
+          grid_dims,
+          odd,
+          bytes_per_key);
+    } else {
+      cu::rbits<<<num_blocks, block_dims, 0, stream>>>(
+          keys.data<uint32_t>(),
+          out.data<uint8_t>(),
+          grid_dims,
+          odd,
+          bytes_per_key,
+          keys.ndim(),
+          const_param(keys.shape()),
+          const_param(keys.strides()));
+    }
+  });
+}
+
+} // namespace mlx::core

mlx/backend/cuda/slicing.cpp

@@ -1,7 +1,11 @@
 // Copyright © 2025 Apple Inc.
 
+#include "mlx/backend/common/slicing.h"
+#include "mlx/backend/gpu/copy.h"
 #include "mlx/backend/gpu/slicing.h"
 
+#include <numeric>
+
 namespace mlx::core {
 
 void concatenate_gpu(
@@ -9,7 +13,29 @@ void concatenate_gpu(
     array& out,
     int axis,
     const Stream& s) {
-  throw std::runtime_error("concatenate_gpu not implemented in CUDA backend.");
+  std::vector<int> sizes;
+  sizes.push_back(0);
+  for (auto& p : inputs) {
+    sizes.push_back(p.shape(axis));
+  }
+  std::partial_sum(sizes.cbegin(), sizes.cend(), sizes.begin());
+
+  out.set_data(allocator::malloc(out.nbytes()));
+
+  auto strides = out.strides();
+  auto flags = out.flags();
+  flags.row_contiguous = false;
+  flags.col_contiguous = false;
+  flags.contiguous = false;
+  // TODO: Handle concurrent outputs:
+  // https://github.com/ml-explore/mlx/pull/2145#discussion_r2070753816
+  for (int i = 0; i < inputs.size(); i++) {
+    array out_slice(inputs[i].shape(), out.dtype(), nullptr, {});
+    size_t data_offset = strides[axis] * sizes[i];
+    out_slice.copy_shared_buffer(
+        out, strides, flags, out_slice.size(), data_offset);
+    copy_gpu_inplace(inputs[i], out_slice, CopyType::GeneralGeneral, s);
+  }
 }
 
 } // namespace mlx::core

mlx/backend/cuda/sort.cu

@@ -0,0 +1,180 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/common/utils.h"
+#include "mlx/backend/cuda/device.h"
+#include "mlx/backend/cuda/kernel_utils.cuh"
+#include "mlx/backend/gpu/copy.h"
+#include "mlx/dtype_utils.h"
+#include "mlx/primitives.h"
+
+#include <nvtx3/nvtx3.hpp>
+#include <thrust/device_ptr.h>
+#include <thrust/transform.h>
+#include <cub/device/device_segmented_sort.cuh>
+
+#include <cassert>
+#include <numeric>
+
+namespace mlx::core {
+
+namespace {
+
+template <typename T>
+struct ModOp {
+  T divisor;
+  __device__ T operator()(T x) {
+    return x % divisor;
+  }
+};
+
+// We can not use any op in eval, make an utility.
+array swapaxes_in_eval(const array& in, int axis1, int axis2) {
+  std::vector<int> axes(in.ndim());
+  std::iota(axes.begin(), axes.end(), 0);
+  std::swap(axes[axis1], axes[axis2]);
+  // TODO: Share the code with Transpose::eval.
+  Shape shape(axes.size());
+  Strides strides(in.ndim());
+  for (size_t ax = 0; ax < axes.size(); ++ax) {
+    shape[ax] = in.shape()[axes[ax]];
+    strides[ax] = in.strides()[axes[ax]];
+  }
+  auto flags = in.flags();
+  if (flags.contiguous) {
+    auto [_, row_contiguous, col_contiguous] = check_contiguity(shape, strides);
+    flags.row_contiguous = row_contiguous;
+    flags.col_contiguous = col_contiguous;
+  }
+  array out(shape, in.dtype(), nullptr, {});
+  out.copy_shared_buffer(in, strides, flags, in.data_size());
+  return out;
+}
+
+template <typename... Args>
+void segmented_sort_pairs(cu::CommandEncoder& encoder, Args&&... args) {
+  // Allocate temporary storage.
+  size_t size;
+  CHECK_CUDA_ERROR(
+      cub::DeviceSegmentedSort::StableSortPairs(nullptr, size, args...));
+  array temp(allocator::malloc(size), {static_cast<int>(size)}, uint8);
+  encoder.add_temporary(temp);
+  // Run op.
+  CHECK_CUDA_ERROR(cub::DeviceSegmentedSort::StableSortPairs(
+      temp.data<void>(), size, args...));
+}
+
+template <typename... Args>
+void segmented_sort(cu::CommandEncoder& encoder, Args&&... args) {
+  // Allocate temporary storage.
+  size_t size;
+  CHECK_CUDA_ERROR(
+      cub::DeviceSegmentedSort::StableSortKeys(nullptr, size, args...));
+  array temp(allocator::malloc(size), {static_cast<int>(size)}, uint8);
+  encoder.add_temporary(temp);
+  // Run op.
+  CHECK_CUDA_ERROR(cub::DeviceSegmentedSort::StableSortKeys(
+      temp.data<void>(), size, args...));
+}
+
+void gpu_sort(const Stream& s, array in, array& out_, int axis, bool argsort) {
+  array out = out_;
+  auto& encoder = cu::get_command_encoder(s);
+  encoder.set_input_array(in);
+  encoder.set_output_array(out);
+
+  if (axis < 0) {
+    axis += in.ndim();
+  }
+  int nsort = in.shape(axis);
+  int nsegments = in.data_size() / nsort;
+  int last_dim = in.ndim() - 1;
+
+  // If we are not sorting the innermost dimension of a contiguous array,
+  // transpose and make a copy.
+  bool is_segmented_sort = in.flags().contiguous && in.strides()[axis] == 1;
+  if (!is_segmented_sort) {
+    array trans = swapaxes_in_eval(in, axis, last_dim);
+    in = array(trans.shape(), trans.dtype(), nullptr, {});
+    copy_gpu(trans, in, CopyType::General, s);
+    encoder.add_temporary(in);
+    out = array(allocator::malloc(out.nbytes()), in.shape(), out.dtype());
+    encoder.add_temporary(out);
+  } else {
+    out.set_data(allocator::malloc(out.nbytes()));
+  }
+
+  encoder.launch_kernel([&](cudaStream_t stream) {
+    MLX_SWITCH_ALL_TYPES(in.dtype(), CTYPE, {
+      if constexpr (!std::is_same_v<CTYPE, complex64_t>) {
+        using Type = cuda_type_t<CTYPE>;
+        auto offsets = thrust::make_transform_iterator(
+            thrust::make_counting_iterator(0),
+            [nsort] __device__(int i) { return i * nsort; });
+        if (argsort) {
+          // Indices in the sorted dimension.
+          array indices(
+              allocator::malloc(out.nbytes()), in.shape(), out.dtype());
+          encoder.add_temporary(indices);
+          thrust::transform(
+              cu::thrust_policy(stream),
+              thrust::counting_iterator<uint32_t>(0),
+              thrust::counting_iterator<uint32_t>(indices.data_size()),
+              thrust::device_pointer_cast(indices.data<uint32_t>()),
+              ModOp<uint32_t>{static_cast<uint32_t>(nsort)});
+
+          // In argsort though we don't need the result of sorted values, the
+          // API requires us to provide an array to store it.
+          array discard(allocator::malloc(in.nbytes()), in.shape(), in.dtype());
+          encoder.add_temporary(discard);
+
+          segmented_sort_pairs(
+              encoder,
+              in.data<Type>(),
+              discard.data<Type>(),
+              indices.data<uint32_t>(),
+              out.data<uint32_t>(),
+              in.data_size(),
+              nsegments,
+              offsets,
+              offsets + 1,
+              stream);
+        } else {
+          segmented_sort(
+              encoder,
+              in.data<Type>(),
+              out.data<Type>(),
+              in.data_size(),
+              nsegments,
+              offsets,
+              offsets + 1,
+              stream);
+        }
+      } else {
+        throw std::runtime_error(
+            "CUDA backend does not support sorting complex numbers");
+      }
+    });
+  });
+
+  if (!is_segmented_sort) {
+    // Swap the sorted axis back.
+    // TODO: Do in-place transpose instead of using a temporary out array.
+    copy_gpu(swapaxes_in_eval(out, axis, last_dim), out_, CopyType::General, s);
+  }
+}
+
+} // namespace
+
+void ArgSort::eval_gpu(const std::vector<array>& inputs, array& out) {
+  nvtx3::scoped_range r("ArgSort::eval_gpu");
+  assert(inputs.size() == 1);
+  gpu_sort(stream(), inputs[0], out, axis_, true);
+}
+
+void Sort::eval_gpu(const std::vector<array>& inputs, array& out) {
+  nvtx3::scoped_range r("Sort::eval_gpu");
+  assert(inputs.size() == 1);
+  gpu_sort(stream(), inputs[0], out, axis_, false);
+}
+
+} // namespace mlx::core

mlx/backend/cuda/unary.cu

@@ -0,0 +1,196 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/common/unary.h"
+#include "mlx/backend/cuda/device.h"
+#include "mlx/backend/cuda/iterators/general_iterator.cuh"
+#include "mlx/backend/cuda/kernel_utils.cuh"
+#include "mlx/backend/cuda/kernels/cucomplex_math.cuh"
+#include "mlx/backend/cuda/kernels/unary_ops.cuh"
+#include "mlx/dtype_utils.h"
+#include "mlx/primitives.h"
+
+#include <nvtx3/nvtx3.hpp>
+#include <thrust/device_ptr.h>
+#include <thrust/transform.h>
+
+namespace mlx::core {
+
+namespace cu {
+
+template <typename Op, typename In, typename Out>
+constexpr bool supports_unary_op() {
+  if (std::is_same_v<Op, Abs> || std::is_same_v<Op, Negative> ||
+      std::is_same_v<Op, Sign>) {
+    return std::is_same_v<In, Out>;
+  }
+  if (std::is_same_v<Op, ArcCos> || std::is_same_v<Op, ArcCosh> ||
+      std::is_same_v<Op, ArcSin> || std::is_same_v<Op, ArcSinh> ||
+      std::is_same_v<Op, ArcTan> || std::is_same_v<Op, ArcTanh> ||
+      std::is_same_v<Op, Erf> || std::is_same_v<Op, ErfInv> ||
+      std::is_same_v<Op, Expm1> || std::is_same_v<Op, Log1p> ||
+      std::is_same_v<Op, Log> || std::is_same_v<Op, Log2> ||
+      std::is_same_v<Op, Log10> || std::is_same_v<Op, Sigmoid> ||
+      std::is_same_v<Op, Sqrt> || std::is_same_v<Op, Rsqrt>) {
+    return std::is_same_v<In, Out> && is_floating_v<In>;
+  }
+  if (std::is_same_v<Op, BitwiseInvert>) {
+    return std::is_same_v<In, Out> && std::is_integral_v<In> &&
+        !std::is_same_v<In, bool>;
+  }
+  if (std::is_same_v<Op, Ceil> || std::is_same_v<Op, Floor> ||
+      std::is_same_v<Op, Square>) {
+    return std::is_same_v<In, Out> && !std::is_same_v<In, complex64_t>;
+  }
+  if (std::is_same_v<Op, Conjugate>) {
+    return std::is_same_v<In, Out> && std::is_same_v<In, complex64_t>;
+  }
+  if (std::is_same_v<Op, Cos> || std::is_same_v<Op, Cosh> ||
+      std::is_same_v<Op, Exp> || std::is_same_v<Op, Round> ||
+      std::is_same_v<Op, Sin> || std::is_same_v<Op, Sinh> ||
+      std::is_same_v<Op, Tan> || std::is_same_v<Op, Tanh>) {
+    return std::is_same_v<In, Out> &&
+        (is_floating_v<In> || std::is_same_v<In, complex64_t>);
+  }
+  if (std::is_same_v<Op, Imag> || std::is_same_v<Op, Real>) {
+    return std::is_same_v<In, complex64_t> && std::is_same_v<Out, float>;
+  }
+  if (std::is_same_v<Op, LogicalNot>) {
+    return std::is_same_v<In, Out> && std::is_same_v<In, bool>;
+  }
+  return false;
+}
+
+} // namespace cu
+
+template <typename Op>
+void unary_op_gpu_inplace(
+    const std::vector<array>& inputs,
+    array& out,
+    const std::string& op,
+    const Stream& s) {
+  auto& in = inputs[0];
+  if (in.size() == 0) {
+    return;
+  }
+
+  auto& encoder = cu::get_command_encoder(s);
+  encoder.set_input_array(in);
+  encoder.set_output_array(out);
+  encoder.launch_kernel([&](cudaStream_t stream) {
+    MLX_SWITCH_ALL_TYPES(in.dtype(), CTYPE_IN, {
+      MLX_SWITCH_ALL_TYPES(out.dtype(), CTYPE_OUT, {
+        if constexpr (cu::supports_unary_op<Op, CTYPE_IN, CTYPE_OUT>()) {
+          using InType = cuda_type_t<CTYPE_IN>;
+          using OutType = cuda_type_t<CTYPE_OUT>;
+          auto policy = cu::thrust_policy(stream);
+          auto in_ptr = thrust::device_pointer_cast(in.data<InType>());
+          auto out_ptr = thrust::device_pointer_cast(out.data<OutType>());
+          if (in.flags().contiguous) {
+            thrust::transform(
+                policy, in_ptr, in_ptr + in.data_size(), out_ptr, Op());
+          } else {
+            auto [shape, strides] = collapse_contiguous_dims(in);
+            auto [in_begin, in_end] = cu::make_general_iterators<int64_t>(
+                in_ptr, in.data_size(), shape, strides);
+            thrust::transform(policy, in_begin, in_end, out_ptr, Op());
+          }
+        } else {
+          throw std::runtime_error(fmt::format(
+              "Can not do unary op {} on input of {} with output of {}.",
+              op,
+              dtype_to_string(in.dtype()),
+              dtype_to_string(out.dtype())));
+        }
+      });
+    });
+  });
+}
+
+template <typename Op>
+void unary_op_gpu(
+    const std::vector<array>& inputs,
+    array& out,
+    const std::string& op,
+    const Stream& s) {
+  set_unary_output_data(inputs[0], out);
+  unary_op_gpu_inplace<Op>(inputs, out, op, s);
+}
+
+#define UNARY_GPU(func)                                                 \
+  void func::eval_gpu(const std::vector<array>& inputs, array& out) {   \
+    nvtx3::scoped_range r(#func "::eval_gpu");                          \
+    auto& s = out.primitive().stream();                                 \
+    unary_op_gpu<cu::func>(inputs, out, get_primitive_string(this), s); \
+  }
+
+UNARY_GPU(Abs)
+UNARY_GPU(ArcCos)
+UNARY_GPU(ArcCosh)
+UNARY_GPU(ArcSin)
+UNARY_GPU(ArcSinh)
+UNARY_GPU(ArcTan)
+UNARY_GPU(ArcTanh)
+UNARY_GPU(BitwiseInvert)
+UNARY_GPU(Ceil)
+UNARY_GPU(Conjugate)
+UNARY_GPU(Cos)
+UNARY_GPU(Cosh)
+UNARY_GPU(Erf)
+UNARY_GPU(ErfInv)
+UNARY_GPU(Exp)
+UNARY_GPU(Expm1)
+UNARY_GPU(Floor)
+UNARY_GPU(Imag)
+UNARY_GPU(Log1p)
+UNARY_GPU(LogicalNot)
+UNARY_GPU(Negative)
+UNARY_GPU(Real)
+UNARY_GPU(Sigmoid)
+UNARY_GPU(Sign)
+UNARY_GPU(Sin)
+UNARY_GPU(Sinh)
+UNARY_GPU(Square)
+UNARY_GPU(Tan)
+UNARY_GPU(Tanh)
+
+void Log::eval_gpu(const std::vector<array>& inputs, array& out) {
+  nvtx3::scoped_range r("Log::eval_gpu");
+  auto& s = out.primitive().stream();
+  auto op = get_primitive_string(this);
+  switch (base_) {
+    case Base::e:
+      unary_op_gpu<cu::Log>(inputs, out, op, s);
+      break;
+    case Base::two:
+      unary_op_gpu<cu::Log2>(inputs, out, op, s);
+      break;
+    case Base::ten:
+      unary_op_gpu<cu::Log10>(inputs, out, op, s);
+      break;
+  }
+}
+
+void Round::eval_gpu(const std::vector<array>& inputs, array& out) {
+  nvtx3::scoped_range r("Round::eval_gpu");
+  assert(inputs.size() == 1);
+  const auto& in = inputs[0];
+  auto& s = out.primitive().stream();
+  if (issubdtype(in.dtype(), inexact)) {
+    unary_op_gpu<cu::Round>(inputs, out, get_primitive_string(this), s);
+  } else {
+    // No-op integer types
+    out.copy_shared_buffer(in);
+  }
+}
+
+void Sqrt::eval_gpu(const std::vector<array>& inputs, array& out) {
+  nvtx3::scoped_range r("Sort::eval_gpu");
+  auto& s = out.primitive().stream();
+  if (recip_) {
+    unary_op_gpu<cu::Rsqrt>(inputs, out, "Rsqrt", s);
+  } else {
+    unary_op_gpu<cu::Sqrt>(inputs, out, "Sqrt", s);
+  }
+}
+
+} // namespace mlx::core

mlx/backend/gpu/primitives.cpp

@@ -5,9 +5,17 @@
 #include "mlx/backend/gpu/copy.h"
 #include "mlx/backend/gpu/slicing.h"
 
+#if defined(MLX_USE_CUDA)
+#include <nvtx3/nvtx3.hpp>
+#endif
+
 #include <cassert>
 
+#if defined(MLX_USE_CUDA)
+#define MLX_PROFILER_RANGE(message) nvtx3::scoped_range r(message)
+#else
 #define MLX_PROFILER_RANGE(message)
+#endif
 
 namespace mlx::core {
 

mlx/backend/metal/binary.cpp

@@ -31,13 +31,13 @@ std::string get_kernel_name(
       kname = "ss";
       break;
     case BinaryOpType::ScalarVector:
-      kname = (large ? "sv2" : "sv");
+      kname = "sv";
       break;
     case BinaryOpType::VectorScalar:
-      kname = (large ? "vs2" : "vs");
+      kname = "vs";
       break;
     case BinaryOpType::VectorVector:
-      kname = (large ? "vv2" : "vv");
+      kname = "vv";
       break;
     case BinaryOpType::General:
       kname = "g";
@@ -51,6 +51,13 @@ std::string get_kernel_name(
       }
       break;
   }
+  if (bopt != BinaryOpType::General && bopt != BinaryOpType::ScalarScalar) {
+    if (large) {
+      kname += "2";
+    } else if (work_per_thread > 1) {
+      kname += "n";
+    }
+  }
   concatenate(kname, "_", op, type_to_name(a));
   return kname;
 }
@@ -90,7 +97,7 @@ void binary_op_gpu_inplace(
     work_per_thread = large ? 4 : 2;
   } else {
     large = out.data_size() > UINT32_MAX;
-    work_per_thread = get_work_per_thread(a.dtype());
+    work_per_thread = get_work_per_thread(a.dtype(), out.data_size());
   }
   std::string kernel_name =
       get_kernel_name(bopt, op, a, large, shape.size(), work_per_thread);

mlx/backend/metal/compiled.cpp

@@ -278,7 +278,21 @@ void Compiled::eval_gpu(
         /* ndim = */ 0,
         /* dynamic_dims = */ false,
         /* use_big_index = */ false,
-        /* work_per_thread = */ work_per_thread);
+        /* work_per_thread = */ 1);
+    if (work_per_thread > 1) {
+      build_kernel(
+          kernel,
+          kernel_lib_ + "_contiguous_n",
+          inputs_,
+          outputs_,
+          tape_,
+          is_constant_,
+          /* contiguous = */ true,
+          /* ndim = */ 0,
+          /* dynamic_dims = */ false,
+          /* use_big_index = */ false,
+          /* work_per_thread = */ work_per_thread);
+    }
     build_kernel(
         kernel,
         kernel_lib_ + "_contiguous_large",
@@ -358,12 +372,20 @@ void Compiled::eval_gpu(
   int ndim = shape.size();
   bool dynamic = ndim >= 8;
   auto kernel_name = kernel_lib_ + (contiguous ? "_contiguous" : "_strided_");
+  int work_per_thread = 1;
   if (!contiguous) {
     if (dynamic) {
       kernel_name += "dynamic";
     } else {
       kernel_name += std::to_string(shape.size());
     }
+    work_per_thread = ndim > 3 ? (large ? 4 : 2) : 1;
+  } else {
+    work_per_thread =
+        get_work_per_thread(outputs[0].dtype(), outputs[0].data_size());
+    if (work_per_thread > 1 && !large) {
+      kernel_name += "_n";
+    }
   }
   if (large) {
     kernel_name += "_large";
@@ -420,7 +442,6 @@ void Compiled::eval_gpu(
 
   // Launch the kernel
   if (contiguous) {
-    int work_per_thread = get_work_per_thread(outputs[0].dtype());
     size_t nthreads = ceildiv(outputs[0].data_size(), work_per_thread);
     MTL::Size group_dims(
         std::min(nthreads, kernel->maxTotalThreadsPerThreadgroup()), 1, 1);
@@ -433,7 +454,6 @@ void Compiled::eval_gpu(
     size_t dim0 = ndim > 0 ? shape[ndim - 1] : 1;
     size_t dim1 = ndim > 1 ? shape[ndim - 2] : 1;
     size_t rest = outputs[0].size() / (dim0 * dim1);
-    int work_per_thread = ndim > 3 ? (large ? 4 : 2) : 1;
     dim0 = (dim0 + work_per_thread - 1) / work_per_thread;
     NS::UInteger thread_group_size = kernel->maxTotalThreadsPerThreadgroup();
     int pow2;

mlx/backend/metal/conv.cpp

@@ -677,7 +677,7 @@ void depthwise_conv_2D_gpu(
   std::string hash_name = kname.str();
 
   auto& compute_encoder = d.get_command_encoder(s.index);
-  auto kernel = d.get_kernel(base_name, "mlx", hash_name, func_consts);
+  auto kernel = d.get_kernel(base_name, hash_name, func_consts);
   compute_encoder.set_compute_pipeline_state(kernel);
 
   compute_encoder.set_input_array(in, 0);

mlx/backend/metal/copy.cpp

@@ -55,10 +55,10 @@ void copy_gpu_inplace(
   std::string kernel_name;
   switch (ctype) {
     case CopyType::Scalar:
-      kernel_name = (large ? "s2" : "s");
+      kernel_name = large ? "s2" : "s";
       break;
     case CopyType::Vector:
-      kernel_name = (large ? "v2" : "v");
+      kernel_name = large ? "v2" : "v";
       break;
     case CopyType::General:
       kernel_name = "g";
@@ -85,7 +85,10 @@ void copy_gpu_inplace(
       }
     }
   } else {
-    work_per_thread = get_work_per_thread(in.dtype());
+    work_per_thread = get_work_per_thread(out.dtype(), out.data_size());
+    if (work_per_thread > 1) {
+      kernel_name += "n";
+    }
   }
   concatenate(kernel_name, "_copy", type_to_name(in), type_to_name(out));
   auto kernel = dynamic ? get_dynamic_copy_kernel(d, kernel_name, in, out)
@@ -170,9 +173,10 @@ void fill_gpu(const array& val, array& out, const Stream& s) {
   }
   out.set_data(allocator::malloc(out.nbytes()));
   bool large = out.data_size() > UINT32_MAX;
+  int work_per_thread = get_work_per_thread(out.dtype(), out.data_size());
   auto& d = metal::device(s.device);
-  std::string kernel_name = std::string(large ? "s2" : "s") + "_copy" +
-      type_to_name(val) + type_to_name(out);
+  std::string kernel_name = large ? "s2" : (work_per_thread > 1 ? "sn" : "s");
+  concatenate(kernel_name, "_copy", type_to_name(val), type_to_name(out));
   auto kernel = get_copy_kernel(d, kernel_name, val, out);
   auto& compute_encoder = d.get_command_encoder(s.index);
   compute_encoder.set_compute_pipeline_state(kernel);
@@ -180,7 +184,6 @@ void fill_gpu(const array& val, array& out, const Stream& s) {
   compute_encoder.set_input_array(val, 0);
   compute_encoder.set_output_array(out, 1);
 
-  int work_per_thread = get_work_per_thread(val.dtype());
   auto thread_group_size = kernel->maxTotalThreadsPerThreadgroup();
   size_t nthreads = ceildiv(out.data_size(), work_per_thread);
   if (thread_group_size > nthreads) {

mlx/backend/metal/custom_kernel.cpp

@@ -1,12 +1,326 @@
 // Copyright © 2024 Apple Inc.
 
+#include <iostream>
+#include <regex>
+
+#include "mlx/backend/common/compiled.h"
 #include "mlx/backend/gpu/copy.h"
 #include "mlx/backend/metal/jit/includes.h"
 #include "mlx/backend/metal/utils.h"
+#include "mlx/fast.h"
 #include "mlx/fast_primitives.h"
+#include "mlx/utils.h"
 
 namespace mlx::core::fast {
 
+struct CustomKernelCache {
+  std::unordered_map<std::string, std::string> libraries;
+};
+
+static CustomKernelCache& cache() {
+  static CustomKernelCache cache_;
+  return cache_;
+};
+
+std::string write_signature(
+    std::string func_name,
+    const std::string& header,
+    const std::string& source,
+    const std::vector<std::string>& input_names,
+    const std::vector<array>& inputs,
+    const std::vector<std::string>& output_names,
+    const std::vector<Dtype>& output_dtypes,
+    const std::vector<std::pair<std::string, TemplateArg>>& template_args,
+    const std::vector<std::string>& attributes,
+    const std::vector<CustomKernelShapeInfo>& shape_infos,
+    bool atomic_outputs) {
+  std::string kernel_source;
+  kernel_source.reserve(header.size() + source.size() + 16384);
+  kernel_source += header;
+  // Auto-generate a function signature based on `template_args`
+  // and the dtype/shape of the arrays passed as `inputs`.
+  if (!template_args.empty()) {
+    kernel_source += "template <";
+    int i = 0;
+    for (const auto& [name, arg] : template_args) {
+      std::string param_type;
+      if (std::holds_alternative<int>(arg)) {
+        param_type = "int";
+      } else if (std::holds_alternative<bool>(arg)) {
+        param_type = "bool";
+      } else if (std::holds_alternative<Dtype>(arg)) {
+        param_type = "typename";
+      }
+      if (i > 0) {
+        kernel_source += ", ";
+      }
+      kernel_source += param_type;
+      kernel_source += " ";
+      kernel_source += name;
+      i++;
+    }
+    kernel_source += ">\n";
+  }
+  kernel_source += "[[kernel]] void ";
+  kernel_source += func_name;
+  kernel_source += "(\n";
+
+  int index = 0;
+  constexpr int max_constant_array_size = 8;
+  // Add inputs
+  for (int i = 0; i < inputs.size(); ++i) {
+    const auto& name = input_names[i];
+    const auto& arr = inputs[i];
+    auto dtype = get_type_string(arr.dtype());
+    std::string location =
+        arr.size() < max_constant_array_size ? "constant" : "device";
+    std::string ref = arr.ndim() == 0 ? "&" : "*";
+    kernel_source += "  const ";
+    kernel_source += location;
+    kernel_source += " ";
+    kernel_source += dtype;
+    kernel_source += ref;
+    kernel_source += " ";
+    kernel_source += name;
+    kernel_source += " [[buffer(";
+    kernel_source += std::to_string(index);
+    kernel_source += ")]],\n";
+    index++;
+    // Add input shape, strides and ndim if present in the source
+    if (arr.ndim() > 0) {
+      if (shape_infos[i].shape) {
+        kernel_source +=
+            ("  const constant int* " + name + "_shape [[buffer(" +
+             std::to_string(index) + ")]],\n");
+        index++;
+      }
+      if (shape_infos[i].strides) {
+        kernel_source +=
+            ("  const constant int64_t* " + name + "_strides [[buffer(" +
+             std::to_string(index) + ")]],\n");
+        index++;
+      }
+      if (shape_infos[i].ndim) {
+        kernel_source +=
+            ("  const constant int& " + name + "_ndim [[buffer(" +
+             std::to_string(index) + ")]],\n");
+        index++;
+      }
+    }
+  }
+  // Add outputs
+  for (int i = 0; i < output_names.size(); ++i) {
+    const auto& name = output_names[i];
+    const auto& dtype = output_dtypes[i];
+    kernel_source += "  device ";
+    auto type_string = get_type_string(dtype);
+    if (atomic_outputs) {
+      kernel_source += "atomic<";
+    }
+    kernel_source += type_string;
+    if (atomic_outputs) {
+      kernel_source += ">";
+    }
+    kernel_source += "* ";
+    kernel_source += name;
+    kernel_source += " [[buffer(";
+    kernel_source += std::to_string(index);
+    kernel_source += ")]]";
+    if (index < inputs.size() + output_names.size() - 1 ||
+        attributes.size() > 0) {
+      kernel_source += ",\n";
+    } else {
+      kernel_source += ") {\n";
+    }
+    index++;
+  }
+
+  index = 0;
+  for (const auto& attr : attributes) {
+    kernel_source += attr;
+    if (index < attributes.size() - 1) {
+      kernel_source += ",\n";
+    } else {
+      kernel_source += ") {\n";
+    }
+    index++;
+  }
+  kernel_source += source;
+  kernel_source += "\n}\n";
+  return kernel_source;
+}
+
+std::string write_template(
+    const std::vector<std::pair<std::string, TemplateArg>>& template_args) {
+  std::ostringstream template_def;
+  template_def << "<";
+  int i = 0;
+  for (const auto& [name, arg] : template_args) {
+    if (i > 0) {
+      template_def << ", ";
+    }
+    if (std::holds_alternative<int>(arg)) {
+      template_def << std::get<int>(arg);
+    } else if (std::holds_alternative<bool>(arg)) {
+      template_def << std::get<bool>(arg);
+    } else if (std::holds_alternative<Dtype>(arg)) {
+      template_def << get_type_string(std::get<Dtype>(arg));
+    }
+    i++;
+  }
+  template_def << ">";
+  return template_def.str();
+}
+
+MetalKernelFunction metal_kernel(
+    const std::string& name,
+    const std::vector<std::string>& input_names,
+    const std::vector<std::string>& output_names,
+    const std::string& source,
+    const std::string& header /* = "" */,
+    bool ensure_row_contiguous /* = true */,
+    bool atomic_outputs /* = false */) {
+  if (output_names.empty()) {
+    throw std::invalid_argument(
+        "[metal_kernel] Must specify at least one output.");
+  }
+  std::vector<CustomKernelShapeInfo> shape_infos;
+  for (auto& n : input_names) {
+    CustomKernelShapeInfo shape_info;
+    shape_info.shape = source.find(n + "_shape") != std::string::npos;
+    shape_info.strides = source.find(n + "_strides") != std::string::npos;
+    shape_info.ndim = source.find(n + "_ndim") != std::string::npos;
+    shape_infos.push_back(shape_info);
+  }
+  const std::vector<std::pair<std::string, std::string>> metal_attributes = {
+      {"dispatch_quadgroups_per_threadgroup", "uint"},
+      {"dispatch_simdgroups_per_threadgroup", "uint"},
+      {"dispatch_threads_per_threadgroup", "uint3"},
+      {"grid_origin", "uint3"},
+      {"grid_size", "uint3"},
+      {"quadgroup_index_in_threadgroup", "uint"},
+      {"quadgroups_per_threadgroup", "uint"},
+      {"simdgroup_index_in_threadgroup", "uint"},
+      {"simdgroups_per_threadgroup", "uint"},
+      {"thread_execution_width", "uint"},
+      {"thread_index_in_quadgroup", "uint"},
+      {"thread_index_in_simdgroup", "uint"},
+      {"thread_index_in_threadgroup", "uint"},
+      {"thread_position_in_grid", "uint3"},
+      {"thread_position_in_threadgroup", "uint3"},
+      {"threadgroup_position_in_grid", "uint3"},
+      {"threadgroups_per_grid", "uint3"},
+      {"threads_per_grid", "uint3"},
+      {"threads_per_simdgroup", "uint"},
+      {"threads_per_threadgroup", "uint3"},
+  };
+
+  std::vector<std::string> attributes;
+  for (const auto& [attr, dtype] : metal_attributes) {
+    if (source.find(attr) != std::string::npos) {
+      attributes.push_back("  " + dtype + " " + attr + " [[" + attr + "]]");
+    }
+  }
+
+  return [=,
+          shape_infos = std::move(shape_infos),
+          attributes = std::move(attributes)](
+             const std::vector<array>& inputs,
+             const std::vector<Shape>& output_shapes,
+             const std::vector<Dtype>& output_dtypes,
+             std::tuple<int, int, int> grid,
+             std::tuple<int, int, int> threadgroup,
+             const std::vector<std::pair<std::string, TemplateArg>>&
+                 template_args = {},
+             std::optional<float> init_value = std::nullopt,
+             bool verbose = false,
+             StreamOrDevice s_ = {}) {
+    if (inputs.size() != input_names.size()) {
+      std::ostringstream msg;
+      msg << "[metal_kernel] Expected `inputs` to have size "
+          << input_names.size() << " but got size " << inputs.size() << "."
+          << std::endl;
+      throw std::invalid_argument(msg.str());
+    }
+    if (output_shapes.size() != output_names.size()) {
+      std::ostringstream msg;
+      msg << "[metal_kernel] Expected `output_shapes` to have size "
+          << output_names.size() << " but got size " << output_shapes.size()
+          << "." << std::endl;
+      throw std::invalid_argument(msg.str());
+    }
+    if (output_dtypes.size() != output_names.size()) {
+      std::ostringstream msg;
+      msg << "[metal_kernel] Expected `output_dtypes` to have size "
+          << output_names.size() << " but got size " << output_dtypes.size()
+          << "." << std::endl;
+      throw std::invalid_argument(msg.str());
+    }
+
+    auto s = to_stream(s_);
+    if (s.device != Device::gpu) {
+      throw std::invalid_argument("[metal_kernel] Only supports the GPU.");
+    }
+
+    std::string kernel_name = "custom_kernel_" + name;
+    std::string template_def = "";
+    if (!template_args.empty()) {
+      std::regex disallowed_chars("\\<|\\>|(, )");
+      template_def = write_template(template_args);
+      auto template_hash =
+          std::regex_replace(template_def, disallowed_chars, "_");
+      template_hash.pop_back();
+      kernel_name += "_";
+      kernel_name += template_hash;
+    }
+
+    std::string kernel_source = write_signature(
+        kernel_name,
+        header,
+        source,
+        input_names,
+        inputs,
+        output_names,
+        output_dtypes,
+        template_args,
+        attributes,
+        shape_infos,
+        atomic_outputs);
+
+    if (!template_args.empty()) {
+      template_def = kernel_name + template_def;
+      kernel_source += "\ntemplate [[host_name(\"";
+      kernel_source += kernel_name;
+      kernel_source += "\")]] [[kernel]] decltype(";
+      kernel_source += template_def;
+      kernel_source += ") ";
+      kernel_source += template_def;
+      kernel_source += ";\n";
+    }
+
+    if (verbose) {
+      std::cout << "Generated source code for `" << name << "`:" << std::endl
+                << "```" << std::endl
+                << kernel_source << std::endl
+                << "```" << std::endl;
+    }
+
+    return array::make_arrays(
+        std::move(output_shapes),
+        std::move(output_dtypes),
+        std::make_shared<CustomKernel>(
+            s,
+            std::move(kernel_name),
+            std::move(kernel_source),
+            grid,
+            threadgroup,
+            shape_infos,
+            ensure_row_contiguous,
+            init_value),
+        std::move(inputs));
+  };
+}
+
 void CustomKernel::eval_gpu(
     const std::vector<array>& inputs,
     std::vector<array>& outputs) {
@@ -39,9 +353,23 @@ void CustomKernel::eval_gpu(
   }
 
   auto& d = metal::device(s.device);
-  const auto& lib_name = name_;
-  auto lib =
-      d.get_library(lib_name, [this] { return metal::utils() + source_; });
+
+  {
+    // Clear kernels from the device library cache if needed
+    auto& kernel_cache = cache();
+    if (auto it = kernel_cache.libraries.find(name_);
+        it != kernel_cache.libraries.end()) {
+      if (it->second != source_) {
+        auto& d = metal::device(s.device);
+        d.clear_library(name_);
+        it->second = source_;
+      }
+    } else {
+      kernel_cache.libraries.emplace(name_, source_);
+    }
+  }
+
+  auto lib = d.get_library(name_, [this] { return metal::utils() + source_; });
   auto kernel = d.get_kernel(name_, lib);
   auto& compute_encoder = d.get_command_encoder(s.index);
   compute_encoder.set_compute_pipeline_state(kernel);
@@ -73,6 +401,16 @@ void CustomKernel::eval_gpu(
   }
 
   const auto [tx, ty, tz] = threadgroup_;
+  auto tg_size = tx * ty * tz;
+  auto max_tg_size = kernel->maxTotalThreadsPerThreadgroup();
+  if (tg_size > max_tg_size) {
+    std::ostringstream msg;
+    msg << "Thread group size (" << tg_size << ") is greater than "
+        << " the maximum allowed threads per threadgroup (" << max_tg_size
+        << ").";
+    throw std::invalid_argument(msg.str());
+  }
+
   const auto [gx, gy, gz] = grid_;
   MTL::Size group_dims =
       MTL::Size(std::min(tx, gx), std::min(ty, gy), std::min(tz, gz));

mlx/backend/metal/device.cpp

@@ -295,7 +295,7 @@ void CommandEncoder::barrier() {
 Device::Device() {
   auto pool = new_scoped_memory_pool();
   device_ = load_device();
-  library_map_ = {{"mlx", load_default_library(device_)}};
+  default_library_ = load_default_library(device_);
   arch_ = std::string(device_->architecture()->name()->utf8String());
   auto arch = arch_.back();
   switch (arch) {
@@ -326,11 +326,11 @@ Device::Device() {
 
 Device::~Device() {
   auto pool = new_scoped_memory_pool();
-  for (auto& k : kernel_map_) {
-    k.second->release();
-  }
-  for (auto& l : library_map_) {
-    l.second->release();
+  for (auto& [l, kernel_map] : library_kernels_) {
+    l->release();
+    for (auto& [_, k] : kernel_map) {
+      k->release();
+    }
   }
   stream_map_.clear();
   device_->release();
@@ -474,13 +474,24 @@ CommandEncoder& Device::get_command_encoder(int index) {
   return *stream.encoder;
 }
 
-void Device::register_library(
-    const std::string& lib_name,
-    const std::string& lib_path) {
-  if (auto it = library_map_.find(lib_name); it == library_map_.end()) {
-    auto new_lib = load_library(device_, lib_name, lib_path.c_str());
-    library_map_.insert({lib_name, new_lib});
+MTL::Library* Device::get_library(
+    const std::string& name,
+    const std::string& path /* = "" */) {
+  {
+    std::shared_lock rlock(library_mtx_);
+    if (auto it = library_map_.find(name); it != library_map_.end()) {
+      return it->second;
+    }
   }
+
+  std::unique_lock wlock(library_mtx_);
+  if (auto it = library_map_.find(name); it != library_map_.end()) {
+    return it->second;
+  }
+
+  auto new_lib = load_library(device_, name, path.c_str());
+  library_map_.insert({name, new_lib});
+  return new_lib;
 }
 
 MTL::Library* Device::build_library_(const std::string& source_string) {
@@ -649,6 +660,19 @@ MTL::Library* Device::get_library(
   return mtl_lib;
 }
 
+void Device::clear_library(const std::string& name) {
+  std::unique_lock wlock(library_mtx_);
+  if (auto it = library_map_.find(name); it != library_map_.end()) {
+    auto kernel_map_it = library_kernels_.find(it->second);
+    for (auto& [_, kernel] : kernel_map_it->second) {
+      kernel->release();
+    }
+    library_kernels_.erase(kernel_map_it);
+    it->second->release();
+    library_map_.erase(it);
+  }
+}
+
 MTL::LinkedFunctions* Device::get_linked_functions_(
     const std::vector<MTL::Function*>& funcs) {
   if (funcs.empty()) {
@@ -679,6 +703,7 @@ MTL::ComputePipelineState* Device::get_kernel_(
   std::unique_lock wlock(kernel_mtx_);
 
   // Try loading again to avoid loading twice
+  auto& kernel_map_ = library_kernels_[mtl_lib];
   if (auto it = kernel_map_.find(hash_name); it != kernel_map_.end()) {
     return it->second;
   }
@@ -713,6 +738,7 @@ MTL::ComputePipelineState* Device::get_kernel(
     std::shared_lock lock(kernel_mtx_);
 
     // Look for cached kernel
+    auto& kernel_map_ = library_kernels_[mtl_lib];
     if (auto it = kernel_map_.find(kname); it != kernel_map_.end()) {
       return it->second;
     }
@@ -722,23 +748,11 @@ MTL::ComputePipelineState* Device::get_kernel(
 
 MTL::ComputePipelineState* Device::get_kernel(
     const std::string& base_name,
-    const std::string& lib_name /* = "mlx" */,
     const std::string& hash_name /*  = "" */,
     const MTLFCList& func_consts /*  = {} */,
     const std::vector<MTL::Function*>& linked_functions /*  = {} */) {
-  const auto& kname = hash_name.size() == 0 ? base_name : hash_name;
-  {
-    // Multiple readers allowed
-    std::shared_lock lock(kernel_mtx_);
-
-    // Look for cached kernel
-    if (auto it = kernel_map_.find(kname); it != kernel_map_.end()) {
-      return it->second;
-    }
-  }
-  // Search for cached metal lib
-  MTL::Library* mtl_lib = get_library_(lib_name);
-  return get_kernel_(base_name, mtl_lib, kname, func_consts, linked_functions);
+  return get_kernel(
+      base_name, default_library_, hash_name, func_consts, linked_functions);
 }
 
 void Device::set_residency_set(const MTL::ResidencySet* residency_set) {

mlx/backend/metal/device.h

@@ -187,14 +187,16 @@ class Device {
   CommandEncoder& get_command_encoder(int index);
   void end_encoding(int index);
 
-  void register_library(
-      const std::string& lib_name,
-      const std::string& lib_path = "");
+  MTL::Library* get_library(
+      const std::string& name,
+      const std::string& path = "");
 
   MTL::Library* get_library(
       const std::string& name,
       const std::function<std::string(void)>& builder);
 
+  void clear_library(const std::string& name);
+
   MTL::ComputePipelineState* get_kernel(
       const std::string& base_name,
       MTL::Library* mtl_lib,
@@ -204,7 +206,6 @@ class Device {
 
   MTL::ComputePipelineState* get_kernel(
       const std::string& base_name,
-      const std::string& lib_name = "mlx",
       const std::string& hash_name = "",
       const MTLFCList& func_consts = {},
       const std::vector<MTL::Function*>& linked_functions = {});
@@ -258,10 +259,13 @@ class Device {
   std::unordered_map<int32_t, DeviceStream> stream_map_;
 
   std::shared_mutex kernel_mtx_;
-  std::unordered_map<std::string, MTL::ComputePipelineState*> kernel_map_;
-
   std::shared_mutex library_mtx_;
   std::unordered_map<std::string, MTL::Library*> library_map_;
+  MTL::Library* default_library_;
+  std::unordered_map<
+      MTL::Library*,
+      std::unordered_map<std::string, MTL::ComputePipelineState*>>
+      library_kernels_;
   const MTL::ResidencySet* residency_set_{nullptr};
   std::string arch_;
   int max_ops_per_buffer_;

mlx/backend/metal/jit_kernels.cpp

@@ -41,7 +41,11 @@ MTL::ComputePipelineState* get_unary_kernel(
     std::string kernel_source = metal::utils();
     concatenate(kernel_source, metal::unary_ops(), metal::unary());
     kernel_source +=
-        get_template_definition("v_" + lib_name, "unary_v", in_t, out_t, op);
+        get_template_definition("v_" + lib_name, "unary_v", in_t, out_t, op, 1);
+    if (get_work_per_thread(in_type) > 1) {
+      kernel_source +=
+          get_template_definition("vn_" + lib_name, "unary_v", in_t, out_t, op);
+    }
     kernel_source +=
         get_template_definition("v2_" + lib_name, "unary_v2", in_t, out_t, op);
     kernel_source += get_template_definition(
@@ -59,11 +63,8 @@ void append_binary_kernels(
     Dtype out_type,
     const std::string op,
     std::string& kernel_source) {
-  const std::array<std::pair<std::string, std::string>, 10> kernel_types = {{
+  const std::array<std::pair<std::string, std::string>, 7> kernel_types = {{
       {"ss", "binary_ss"},
-      {"vs", "binary_vs"},
-      {"sv", "binary_sv"},
-      {"vv", "binary_vv"},
       {"vs2", "binary_vs2"},
       {"sv2", "binary_sv2"},
       {"vv2", "binary_vv2"},
@@ -78,6 +79,22 @@ void append_binary_kernels(
     kernel_source +=
         get_template_definition(name + "_" + lib_name, func, in_t, out_t, op);
   }
+  kernel_source += get_template_definition(
+      "vs_" + lib_name, "binary_vs", in_t, out_t, op, 1);
+  kernel_source += get_template_definition(
+      "sv_" + lib_name, "binary_sv", in_t, out_t, op, 1);
+  kernel_source += get_template_definition(
+      "vv_" + lib_name, "binary_vv", in_t, out_t, op, 1);
+
+  if (get_work_per_thread(in_type) > 1) {
+    kernel_source += get_template_definition(
+        "vsn_" + lib_name, "binary_vs", in_t, out_t, op);
+    kernel_source += get_template_definition(
+        "svn_" + lib_name, "binary_sv", in_t, out_t, op);
+    kernel_source += get_template_definition(
+        "vvn_" + lib_name, "binary_vv", in_t, out_t, op);
+  }
+
   kernel_source += get_template_definition(
       "g1_" + lib_name, "binary_g_nd1", in_t, out_t, op, "int");
   kernel_source += get_template_definition(
@@ -133,8 +150,7 @@ MTL::ComputePipelineState* get_ternary_kernel(
     auto t_str = get_type_string(type);
     std::string kernel_source = metal::utils();
     concatenate(kernel_source, metal::ternary_ops(), metal::ternary());
-    const std::array<std::pair<std::string, std::string>, 5> kernel_types = {{
-        {"v", "ternary_v"},
+    const std::array<std::pair<std::string, std::string>, 4> kernel_types = {{
         {"v2", "ternary_v2"},
         {"g1large", "ternary_g_nd1"},
         {"g2large", "ternary_g_nd2"},
@@ -144,6 +160,13 @@ MTL::ComputePipelineState* get_ternary_kernel(
       kernel_source +=
           get_template_definition(name + "_" + lib_name, func, t_str, op);
     }
+    if (get_work_per_thread(type) > 1) {
+      kernel_source +=
+          get_template_definition("vn_" + lib_name, "ternary_v", t_str, op);
+    }
+
+    kernel_source +=
+        get_template_definition("v_" + lib_name, "ternary_v", t_str, op, 1);
     kernel_source += get_template_definition(
         "g1_" + lib_name, "ternary_g_nd1", t_str, op, "int");
     kernel_source += get_template_definition(
@@ -170,15 +193,22 @@ MTL::ComputePipelineState* get_copy_kernel(
     kernel_source += metal::copy();
     auto in_type = get_type_string(in.dtype());
     auto out_type = get_type_string(out.dtype());
-    kernel_source +=
-        get_template_definition("s_" + lib_name, "copy_s", in_type, out_type);
+    kernel_source += get_template_definition(
+        "s_" + lib_name, "copy_s", in_type, out_type, 1);
     kernel_source +=
         get_template_definition("s2_" + lib_name, "copy_s2", in_type, out_type);
-    kernel_source +=
-        get_template_definition("v_" + lib_name, "copy_v", in_type, out_type);
+    kernel_source += get_template_definition(
+        "v_" + lib_name, "copy_v", in_type, out_type, 1);
     kernel_source +=
         get_template_definition("v2_" + lib_name, "copy_v2", in_type, out_type);
 
+    if (get_work_per_thread(out.dtype()) > 1) {
+      kernel_source += get_template_definition(
+          "sn_" + lib_name, "copy_s", in_type, out_type);
+      kernel_source += get_template_definition(
+          "vn_" + lib_name, "copy_v", in_type, out_type);
+    }
+
     kernel_source += get_template_definition(
         "g1_" + lib_name, "copy_g_nd1", in_type, out_type, "int");
     kernel_source += get_template_definition(

mlx/backend/metal/kernels/binary.h

@@ -17,8 +17,14 @@ template <typename T, typename U, typename Op, int N = WorkPerThread<T>::n>
     constant uint& size,
     uint index [[thread_position_in_grid]]) {
   index *= N;
-  for (int i = 0; i < N && (index + i) < size; ++i) {
-    c[index + i] = Op()(a[0], b[index + i]);
+  if (N > 1 && index + N > size) {
+    for (int i = 0; index + i < size; ++i) {
+      c[index + i] = Op()(a[0], b[index + i]);
+    }
+  } else {
+    for (int i = 0; i < N; ++i) {
+      c[index + i] = Op()(a[0], b[index + i]);
+    }
   }
 }
 
@@ -30,8 +36,14 @@ template <typename T, typename U, typename Op, int N = WorkPerThread<T>::n>
     constant uint& size,
     uint index [[thread_position_in_grid]]) {
   index *= N;
-  for (int i = 0; i < N && (index + i) < size; ++i) {
-    c[index + i] = Op()(a[index + i], b[0]);
+  if (N > 1 && index + N > size) {
+    for (int i = 0; index + i < size; ++i) {
+      c[index + i] = Op()(a[index + i], b[0]);
+    }
+  } else {
+    for (int i = 0; i < N; ++i) {
+      c[index + i] = Op()(a[index + i], b[0]);
+    }
   }
 }
 
@@ -43,8 +55,14 @@ template <typename T, typename U, typename Op, int N = WorkPerThread<T>::n>
     constant uint& size,
     uint index [[thread_position_in_grid]]) {
   index *= N;
-  for (int i = 0; i < N && (index + i) < size; ++i) {
-    c[index + i] = Op()(a[index + i], b[index + i]);
+  if (N > 1 && index + N > size) {
+    for (int i = 0; index + i < size; ++i) {
+      c[index + i] = Op()(a[index + i], b[index + i]);
+    }
+  } else {
+    for (int i = 0; i < N; ++i) {
+      c[index + i] = Op()(a[index + i], b[index + i]);
+    }
   }
 }
 
@@ -57,8 +75,14 @@ template <typename T, typename U, typename Op, int N = WorkPerThread<T>::n>
     uint2 index [[thread_position_in_grid]],
     uint2 grid_dim [[threads_per_grid]]) {
   int64_t offset = N * (index.x + grid_dim.x * int64_t(index.y));
-  for (int i = 0; i < N && (offset + i) < size; ++i) {
-    c[offset + i] = Op()(a[0], b[offset + i]);
+  if (N > 1 && offset + N > size) {
+    for (int i = 0; offset + i < size; ++i) {
+      c[offset + i] = Op()(a[0], b[offset + i]);
+    }
+  } else {
+    for (int i = 0; i < N; ++i) {
+      c[offset + i] = Op()(a[0], b[offset + i]);
+    }
   }
 }
 
@@ -71,8 +95,14 @@ template <typename T, typename U, typename Op, int N = WorkPerThread<T>::n>
     uint2 index [[thread_position_in_grid]],
     uint2 grid_dim [[threads_per_grid]]) {
   int64_t offset = N * (index.x + grid_dim.x * int64_t(index.y));
-  for (int i = 0; i < N && (offset + i) < size; ++i) {
-    c[offset + i] = Op()(a[offset + i], b[0]);
+  if (N > 1 && offset + N > size) {
+    for (int i = 0; offset + i < size; ++i) {
+      c[offset + i] = Op()(a[offset + i], b[0]);
+    }
+  } else {
+    for (int i = 0; i < N; ++i) {
+      c[offset + i] = Op()(a[offset + i], b[0]);
+    }
   }
 }
 
@@ -85,8 +115,14 @@ template <typename T, typename U, typename Op, int N = WorkPerThread<T>::n>
     uint2 index [[thread_position_in_grid]],
     uint2 grid_dim [[threads_per_grid]]) {
   int64_t offset = N * (index.x + grid_dim.x * int64_t(index.y));
-  for (int i = 0; i < N && (offset + i) < size; ++i) {
-    c[offset + i] = Op()(a[offset + i], b[offset + i]);
+  if (N > 1 && offset + N > size) {
+    for (int i = 0; offset + i < size; ++i) {
+      c[offset + i] = Op()(a[offset + i], b[offset + i]);
+    }
+  } else {
+    for (int i = 0; i < N; ++i) {
+      c[offset + i] = Op()(a[offset + i], b[offset + i]);
+    }
   }
 }
 

mlx/backend/metal/kernels/binary.metal

@@ -9,11 +9,16 @@
 #include "mlx/backend/metal/kernels/binary_ops.h"
 #include "mlx/backend/metal/kernels/binary.h"
 
-#define instantiate_binary_all(op, tname, itype, otype)                     \
+#define instantiate_binary_work_per_thread(op, tname, itype, otype)     \
+  instantiate_kernel("svn_" #op #tname, binary_sv, itype, otype, op)    \
+  instantiate_kernel("vsn_" #op #tname, binary_vs, itype, otype, op)    \
+  instantiate_kernel("vvn_" #op #tname, binary_vv, itype, otype, op)    \
+
+#define instantiate_binary_base(op, tname, itype, otype)                    \
   instantiate_kernel("ss_" #op #tname, binary_ss, itype, otype, op)         \
-  instantiate_kernel("sv_" #op #tname, binary_sv, itype, otype, op)         \
-  instantiate_kernel("vs_" #op #tname, binary_vs, itype, otype, op)         \
-  instantiate_kernel("vv_" #op #tname, binary_vv, itype, otype, op)         \
+  instantiate_kernel("sv_" #op #tname, binary_sv, itype, otype, op, 1)      \
+  instantiate_kernel("vs_" #op #tname, binary_vs, itype, otype, op, 1)      \
+  instantiate_kernel("vv_" #op #tname, binary_vv, itype, otype, op, 1)      \
   instantiate_kernel("sv2_" #op #tname, binary_sv2, itype, otype, op)       \
   instantiate_kernel("vs2_" #op #tname, binary_vs2, itype, otype, op)       \
   instantiate_kernel("vv2_" #op #tname, binary_vv2, itype, otype, op)       \
@@ -26,15 +31,19 @@
   instantiate_kernel("g3_" #op #tname, binary_g_nd3, itype, otype, op, int) \
   instantiate_kernel("g3large_" #op #tname, binary_g_nd3, itype, otype, op)
 
-#define instantiate_binary_integer(op)                   \
-  instantiate_binary_all(op, uint8, uint8_t, uint8_t)    \
-  instantiate_binary_all(op, uint16, uint16_t, uint16_t) \
-  instantiate_binary_all(op, uint32, uint32_t, uint32_t) \
-  instantiate_binary_all(op, uint64, uint64_t, uint64_t) \
-  instantiate_binary_all(op, int8, int8_t, int8_t)       \
-  instantiate_binary_all(op, int16, int16_t, int16_t)    \
-  instantiate_binary_all(op, int32, int32_t, int32_t)    \
-  instantiate_binary_all(op, int64, int64_t, int64_t)
+#define instantiate_binary_all(op, tname, itype, otype)       \
+  instantiate_binary_base(op, tname, itype, otype)            \
+  instantiate_binary_work_per_thread(op, tname, itype, otype)
+
+#define instantiate_binary_integer(op)                    \
+  instantiate_binary_all(op, uint8, uint8_t, uint8_t)     \
+  instantiate_binary_all(op, uint16, uint16_t, uint16_t)  \
+  instantiate_binary_all(op, uint32, uint32_t, uint32_t)  \
+  instantiate_binary_base(op, uint64, uint64_t, uint64_t) \
+  instantiate_binary_all(op, int8, int8_t, int8_t)        \
+  instantiate_binary_all(op, int16, int16_t, int16_t)     \
+  instantiate_binary_all(op, int32, int32_t, int32_t)     \
+  instantiate_binary_base(op, int64, int64_t, int64_t)
 
 #define instantiate_binary_float(op)                \
   instantiate_binary_all(op, float16, half, half)   \
@@ -44,7 +53,7 @@
 #define instantiate_binary_types(op)                              \
   instantiate_binary_all(op, bool_, bool, bool)                   \
   instantiate_binary_integer(op)                                  \
-  instantiate_binary_all(op, complex64, complex64_t, complex64_t) \
+  instantiate_binary_base(op, complex64, complex64_t, complex64_t)\
   instantiate_binary_float(op)
 
 #define instantiate_binary_types_bool(op)                \
@@ -52,15 +61,15 @@
   instantiate_binary_all(op, uint8, uint8_t, bool)       \
   instantiate_binary_all(op, uint16, uint16_t, bool)     \
   instantiate_binary_all(op, uint32, uint32_t, bool)     \
-  instantiate_binary_all(op, uint64, uint64_t, bool)     \
+  instantiate_binary_base(op, uint64, uint64_t, bool)    \
   instantiate_binary_all(op, int8, int8_t, bool)         \
   instantiate_binary_all(op, int16, int16_t, bool)       \
   instantiate_binary_all(op, int32, int32_t, bool)       \
-  instantiate_binary_all(op, int64, int64_t, bool)       \
+  instantiate_binary_base(op, int64, int64_t, bool)      \
   instantiate_binary_all(op, float16, half, bool)        \
   instantiate_binary_all(op, float32, float, bool)       \
   instantiate_binary_all(op, bfloat16, bfloat16_t, bool) \
-  instantiate_binary_all(op, complex64, complex64_t, bool)
+  instantiate_binary_base(op, complex64, complex64_t, bool)
 
 instantiate_binary_types(Add)
 instantiate_binary_types(Divide)
@@ -71,7 +80,7 @@ instantiate_binary_types_bool(Less)
 instantiate_binary_types_bool(LessEqual)
 instantiate_binary_types_bool(NotEqual)
 instantiate_binary_float(LogAddExp)
-instantiate_binary_all(LogAddExp, complex64, complex64_t, complex64_t)
+instantiate_binary_base(LogAddExp, complex64, complex64_t, complex64_t)
 instantiate_binary_types(Maximum)
 instantiate_binary_types(Minimum)
 instantiate_binary_types(Multiply)
@@ -84,7 +93,7 @@ instantiate_binary_float(ArcTan2)
 instantiate_binary_all(NaNEqual, float16, half, bool)
 instantiate_binary_all(NaNEqual, float32, float, bool)
 instantiate_binary_all(NaNEqual, bfloat16, bfloat16_t, bool)
-instantiate_binary_all(NaNEqual, complex64, complex64_t, bool)
+instantiate_binary_base(NaNEqual, complex64, complex64_t, bool)
 
 instantiate_binary_all(LogicalOr, bool_, bool, bool)
 instantiate_binary_all(LogicalAnd, bool_, bool, bool)

mlx/backend/metal/kernels/binary_two.h

@@ -21,10 +21,18 @@ template <typename T, typename U, typename Op, int N = WorkPerThread<T>::n>
     constant uint& size,
     uint index [[thread_position_in_grid]]) {
   index *= N;
-  for (int i = 0; i < N && (index + i) < size; ++i) {
-    auto out = Op()(a[0], b[index + i]);
-    c[index + i] = out[0];
-    d[index + i] = out[1];
+  if (N > 1 && index + N > size) {
+    for (int i = 0; index + i < size; ++i) {
+      auto out = Op()(a[0], b[index + i]);
+      c[index + i] = out[0];
+      d[index + i] = out[1];
+    }
+  } else {
+    for (int i = 0; i < N; ++i) {
+      auto out = Op()(a[0], b[index + i]);
+      c[index + i] = out[0];
+      d[index + i] = out[1];
+    }
   }
 }
 
@@ -37,10 +45,18 @@ template <typename T, typename U, typename Op, int N = WorkPerThread<T>::n>
     constant uint& size,
     uint index [[thread_position_in_grid]]) {
   index *= N;
-  for (int i = 0; i < N && (index + i) < size; ++i) {
-    auto out = Op()(a[index + i], b[0]);
-    c[index + i] = out[0];
-    d[index + i] = out[1];
+  if (N > 1 && index + N > size) {
+    for (int i = 0; index + i < size; ++i) {
+      auto out = Op()(a[index + i], b[0]);
+      c[index + i] = out[0];
+      d[index + i] = out[1];
+    }
+  } else {
+    for (int i = 0; i < N; ++i) {
+      auto out = Op()(a[index + i], b[0]);
+      c[index + i] = out[0];
+      d[index + i] = out[1];
+    }
   }
 }
 
@@ -53,10 +69,18 @@ template <typename T, typename U, typename Op, int N = WorkPerThread<T>::n>
     constant uint& size,
     uint index [[thread_position_in_grid]]) {
   index *= N;
-  for (int i = 0; i < N && (index + i) < size; ++i) {
-    auto out = Op()(a[index + i], b[index + i]);
-    c[index + i] = out[0];
-    d[index + i] = out[1];
+  if (N > 1 && index + N > size) {
+    for (int i = 0; index + i < size; ++i) {
+      auto out = Op()(a[index + i], b[index + i]);
+      c[index + i] = out[0];
+      d[index + i] = out[1];
+    }
+  } else {
+    for (int i = 0; i < N; ++i) {
+      auto out = Op()(a[index + i], b[index + i]);
+      c[index + i] = out[0];
+      d[index + i] = out[1];
+    }
   }
 }
 
@@ -69,11 +93,19 @@ template <typename T, typename U, typename Op, int N = WorkPerThread<T>::n>
     constant int64_t& size,
     uint2 index [[thread_position_in_grid]],
     uint2 grid_dim [[threads_per_grid]]) {
-  auto offset = N * (index.x + grid_dim.x * int64_t(index.y));
-  for (int i = 0; i < N && (offset + i) < size; ++i) {
-    auto out = Op()(a[0], b[offset + i]);
-    c[offset + i] = out[0];
-    d[offset + i] = out[1];
+  int64_t offset = N * (index.x + grid_dim.x * int64_t(index.y));
+  if (N > 1 && offset + N > size) {
+    for (int i = 0; offset + i < size; ++i) {
+      auto out = Op()(a[0], b[offset + i]);
+      c[offset + i] = out[0];
+      d[offset + i] = out[1];
+    }
+  } else {
+    for (int i = 0; i < N; ++i) {
+      auto out = Op()(a[0], b[offset + i]);
+      c[offset + i] = out[0];
+      d[offset + i] = out[1];
+    }
   }
 }
 
@@ -86,11 +118,19 @@ template <typename T, typename U, typename Op, int N = WorkPerThread<T>::n>
     constant int64_t& size,
     uint2 index [[thread_position_in_grid]],
     uint2 grid_dim [[threads_per_grid]]) {
-  auto offset = N * (index.x + grid_dim.x * int64_t(index.y));
-  for (int i = 0; i < N && (offset + i) < size; ++i) {
-    auto out = Op()(a[offset + i], b[0]);
-    c[offset + i] = out[0];
-    d[offset + i] = out[1];
+  int64_t offset = N * (index.x + grid_dim.x * int64_t(index.y));
+  if (N > 1 && offset + N > size) {
+    for (int i = 0; offset + i < size; ++i) {
+      auto out = Op()(a[offset + i], b[0]);
+      c[offset + i] = out[0];
+      d[offset + i] = out[1];
+    }
+  } else {
+    for (int i = 0; i < N; ++i) {
+      auto out = Op()(a[offset + i], b[0]);
+      c[offset + i] = out[0];
+      d[offset + i] = out[1];
+    }
   }
 }
 
@@ -103,11 +143,19 @@ template <typename T, typename U, typename Op, int N = WorkPerThread<T>::n>
     constant int64_t& size,
     uint2 index [[thread_position_in_grid]],
     uint2 grid_dim [[threads_per_grid]]) {
-  auto offset = N * (index.x + grid_dim.x * int64_t(index.y));
-  for (int i = 0; i < N && (offset + i) < size; ++i) {
-    auto out = Op()(a[offset + i], b[offset + i]);
-    c[offset + i] = out[0];
-    d[offset + i] = out[1];
+  int64_t offset = N * (index.x + grid_dim.x * int64_t(index.y));
+  if (N > 1 && offset + N > size) {
+    for (int i = 0; offset + i < size; ++i) {
+      auto out = Op()(a[offset + i], b[offset + i]);
+      c[offset + i] = out[0];
+      d[offset + i] = out[1];
+    }
+  } else {
+    for (int i = 0; i < N; ++i) {
+      auto out = Op()(a[offset + i], b[offset + i]);
+      c[offset + i] = out[0];
+      d[offset + i] = out[1];
+    }
   }
 }
 

mlx/backend/metal/kernels/binary_two.metal

@@ -7,11 +7,16 @@
 #include "mlx/backend/metal/kernels/binary_ops.h"
 #include "mlx/backend/metal/kernels/binary_two.h"
 
-#define instantiate_binary_all(op, tname, itype, otype)                     \
+#define instantiate_binary_work_per_thread(op, tname, itype, otype)     \
+  instantiate_kernel("svn_" #op #tname, binary_sv, itype, otype, op)    \
+  instantiate_kernel("vsn_" #op #tname, binary_vs, itype, otype, op)    \
+  instantiate_kernel("vvn_" #op #tname, binary_vv, itype, otype, op)
+
+#define instantiate_binary_base(op, tname, itype, otype)                    \
   instantiate_kernel("ss_" #op #tname, binary_ss, itype, otype, op)         \
-  instantiate_kernel("sv_" #op #tname, binary_sv, itype, otype, op)         \
-  instantiate_kernel("vs_" #op #tname, binary_vs, itype, otype, op)         \
-  instantiate_kernel("vv_" #op #tname, binary_vv, itype, otype, op)         \
+  instantiate_kernel("sv_" #op #tname, binary_sv, itype, otype, op, 1)      \
+  instantiate_kernel("vs_" #op #tname, binary_vs, itype, otype, op, 1)      \
+  instantiate_kernel("vv_" #op #tname, binary_vv, itype, otype, op, 1)      \
   instantiate_kernel("sv2_" #op #tname, binary_sv2, itype, otype, op)       \
   instantiate_kernel("vs2_" #op #tname, binary_vs2, itype, otype, op)       \
   instantiate_kernel("vv2_" #op #tname, binary_vv2, itype, otype, op)       \
@@ -24,22 +29,26 @@
   instantiate_kernel("g2large_" #op #tname, binary_g_nd2, itype, otype, op) \
   instantiate_kernel("g3large_" #op #tname, binary_g_nd3, itype, otype, op)
 
+#define instantiate_binary_all(op, tname, itype, otype)       \
+  instantiate_binary_base(op, tname, itype, otype)            \
+  instantiate_binary_work_per_thread(op, tname, itype, otype)
+
 #define instantiate_binary_float(op)                \
   instantiate_binary_all(op, float16, half, half)   \
   instantiate_binary_all(op, float32, float, float) \
   instantiate_binary_all(op, bfloat16, bfloat16_t, bfloat16_t)
 
-#define instantiate_binary_types(op)                              \
-  instantiate_binary_all(op, bool_, bool, bool)                   \
-  instantiate_binary_all(op, uint8, uint8_t, uint8_t)             \
-  instantiate_binary_all(op, uint16, uint16_t, uint16_t)          \
-  instantiate_binary_all(op, uint32, uint32_t, uint32_t)          \
-  instantiate_binary_all(op, uint64, uint64_t, uint64_t)          \
-  instantiate_binary_all(op, int8, int8_t, int8_t)                \
-  instantiate_binary_all(op, int16, int16_t, int16_t)             \
-  instantiate_binary_all(op, int32, int32_t, int32_t)             \
-  instantiate_binary_all(op, int64, int64_t, int64_t)             \
-  instantiate_binary_all(op, complex64, complex64_t, complex64_t) \
+#define instantiate_binary_types(op)                               \
+  instantiate_binary_all(op, bool_, bool, bool)                    \
+  instantiate_binary_all(op, uint8, uint8_t, uint8_t)              \
+  instantiate_binary_all(op, uint16, uint16_t, uint16_t)           \
+  instantiate_binary_all(op, uint32, uint32_t, uint32_t)           \
+  instantiate_binary_base(op, uint64, uint64_t, uint64_t)          \
+  instantiate_binary_all(op, int8, int8_t, int8_t)                 \
+  instantiate_binary_all(op, int16, int16_t, int16_t)              \
+  instantiate_binary_all(op, int32, int32_t, int32_t)              \
+  instantiate_binary_base(op, int64, int64_t, int64_t)             \
+  instantiate_binary_base(op, complex64, complex64_t, complex64_t) \
   instantiate_binary_float(op)
 
 instantiate_binary_types(DivMod) // clang-format on

mlx/backend/metal/kernels/copy.h

@@ -1,52 +1,76 @@
 // Copyright © 2024 Apple Inc.
 
-template <typename T, typename U, int N = WorkPerThread<T>::n>
+template <typename T, typename U, int N = WorkPerThread<U>::n>
 [[kernel]] void copy_s(
     device const T* src [[buffer(0)]],
     device U* dst [[buffer(1)]],
     constant uint& size,
     uint index [[thread_position_in_grid]]) {
   index *= N;
-  for (int i = 0; i < N && (index + i) < size; ++i) {
-    dst[index + i] = static_cast<U>(src[0]);
+  if (N > 1 && index + N > size) {
+    for (int i = 0; index + i < size; ++i) {
+      dst[index + i] = static_cast<U>(src[0]);
+    }
+  } else {
+    for (int i = 0; i < N; ++i) {
+      dst[index + i] = static_cast<U>(src[0]);
+    }
   }
 }
 
-template <typename T, typename U, int N = WorkPerThread<T>::n>
+template <typename T, typename U, int N = WorkPerThread<U>::n>
 [[kernel]] void copy_v(
     device const T* src [[buffer(0)]],
     device U* dst [[buffer(1)]],
     constant uint& size,
     uint index [[thread_position_in_grid]]) {
   index *= N;
-  for (int i = 0; i < N && (index + i) < size; ++i) {
-    dst[index + i] = static_cast<U>(src[index + i]);
+  if (N > 1 && index + N > size) {
+    for (int i = 0; index + i < size; ++i) {
+      dst[index + i] = static_cast<U>(src[index + i]);
+    }
+  } else {
+    for (int i = 0; i < N; ++i) {
+      dst[index + i] = static_cast<U>(src[index + i]);
+    }
   }
 }
 
-template <typename T, typename U, int N = WorkPerThread<T>::n>
+template <typename T, typename U, int N = WorkPerThread<U>::n>
 [[kernel]] void copy_s2(
     device const T* src [[buffer(0)]],
     device U* dst [[buffer(1)]],
     constant int64_t& size,
     uint2 index [[thread_position_in_grid]],
     uint2 grid_dim [[threads_per_grid]]) {
-  auto offset = N * (index.x + grid_dim.x * int64_t(index.y));
-  for (int i = 0; i < N && (offset + i) < size; ++i) {
-    dst[offset + i] = static_cast<U>(src[0]);
+  int64_t offset = N * (index.x + grid_dim.x * int64_t(index.y));
+  if (N > 1 && offset + N > size) {
+    for (int i = 0; offset + i < size; ++i) {
+      dst[offset + i] = static_cast<U>(src[0]);
+    }
+  } else {
+    for (int i = 0; i < N; ++i) {
+      dst[offset + i] = static_cast<U>(src[0]);
+    }
   }
 }
 
-template <typename T, typename U, int N = WorkPerThread<T>::n>
+template <typename T, typename U, int N = WorkPerThread<U>::n>
 [[kernel]] void copy_v2(
     device const T* src [[buffer(0)]],
     device U* dst [[buffer(1)]],
     constant int64_t& size,
     uint2 index [[thread_position_in_grid]],
     uint2 grid_dim [[threads_per_grid]]) {
-  auto offset = N * (index.x + grid_dim.x * int64_t(index.y));
-  for (int i = 0; i < N && (offset + i) < size; ++i) {
-    dst[offset + i] = static_cast<U>(src[offset + i]);
+  int64_t offset = N * (index.x + grid_dim.x * int64_t(index.y));
+  if (N > 1 && offset + N > size) {
+    for (int i = 0; offset + i < size; ++i) {
+      dst[offset + i] = static_cast<U>(src[offset + i]);
+    }
+  } else {
+    for (int i = 0; i < N; ++i) {
+      dst[offset + i] = static_cast<U>(src[offset + i]);
+    }
   }
 }
 

mlx/backend/metal/kernels/copy.metal

@@ -4,9 +4,13 @@
 #include "mlx/backend/metal/kernels/utils.h"
 #include "mlx/backend/metal/kernels/copy.h"
 
-#define instantiate_copy_all(tname, itype, otype)                     \
-  instantiate_kernel("s_copy" #tname, copy_s, itype, otype)           \
-  instantiate_kernel("v_copy" #tname, copy_v, itype, otype)           \
+#define instantiate_copy_work_per_thread(tname, itype, otype)         \
+  instantiate_kernel("sn_copy" #tname, copy_s, itype, otype)          \
+  instantiate_kernel("vn_copy" #tname, copy_v, itype, otype)
+
+#define instantiate_copy_base(tname, itype, otype)                    \
+  instantiate_kernel("s_copy" #tname, copy_s, itype, otype, 1)        \
+  instantiate_kernel("v_copy" #tname, copy_v, itype, otype, 1)        \
   instantiate_kernel("s2_copy" #tname, copy_s2, itype, otype)         \
   instantiate_kernel("v2_copy" #tname, copy_v2, itype, otype)         \
   instantiate_kernel("g1_copy" #tname, copy_g_nd1, itype, otype, int) \
@@ -18,6 +22,10 @@
   instantiate_kernel("g3large_copy" #tname, copy_g_nd3, itype, otype) \
   instantiate_kernel("gn4large_copy" #tname, copy_g, itype, otype, 4)
 
+#define instantiate_copy_all(tname, itype, otype) \
+  instantiate_copy_base(tname, itype, otype)      \
+  instantiate_copy_work_per_thread(tname, itype, otype)
+
 #define instantiate_copy_same(tname, type)                                            \
   instantiate_kernel("gg1_copy" #tname, copy_gg_nd1, type, type, int)                 \
   instantiate_kernel("gg2_copy" #tname, copy_gg_nd2, type, type, int)                 \
@@ -42,15 +50,15 @@
   instantiate_copy_all(itname ##uint8, itype, uint8_t)       \
   instantiate_copy_all(itname ##uint16, itype, uint16_t)     \
   instantiate_copy_all(itname ##uint32, itype, uint32_t)     \
-  instantiate_copy_all(itname ##uint64, itype, uint64_t)     \
+  instantiate_copy_base(itname ##uint64, itype, uint64_t)    \
   instantiate_copy_all(itname ##int8, itype, int8_t)         \
   instantiate_copy_all(itname ##int16, itype, int16_t)       \
   instantiate_copy_all(itname ##int32, itype, int32_t)       \
-  instantiate_copy_all(itname ##int64, itype, int64_t)       \
+  instantiate_copy_base(itname ##int64, itype, int64_t)      \
   instantiate_copy_all(itname ##float16, itype, half)        \
   instantiate_copy_all(itname ##float32, itype, float)       \
   instantiate_copy_all(itname ##bfloat16, itype, bfloat16_t) \
-  instantiate_copy_all(itname ##complex64, itype, complex64_t)
+  instantiate_copy_base(itname ##complex64, itype, complex64_t)
 
 instantiate_copy_itype(bool_, bool)
 instantiate_copy_itype(uint8, uint8_t)

mlx/backend/metal/kernels/layer_norm.metal

@@ -9,7 +9,41 @@ using namespace metal;
 
 constant bool has_w [[function_constant(20)]];
 
-template <typename T, int N_READS = RMS_N_READS>
+template <int N = 1>
+inline void initialize_buffer(
+    threadgroup float* xs,
+    uint simd_lane_id [[thread_index_in_simdgroup]],
+    uint simd_group_id [[simdgroup_index_in_threadgroup]]) {
+  if (simd_group_id == 0) {
+    for (int i = 0; i < N; i++) {
+      xs[N * simd_lane_id + i] = 0;
+    }
+  }
+  threadgroup_barrier(mem_flags::mem_threadgroup);
+}
+
+template <int N = 1>
+inline void threadgroup_sum(
+    thread float* x,
+    threadgroup float* xs,
+    uint simd_lane_id [[thread_index_in_simdgroup]],
+    uint simd_group_id [[simdgroup_index_in_threadgroup]]) {
+  for (int i = 0; i < N; i++) {
+    x[i] = simd_sum(x[i]);
+  }
+  if (simd_lane_id == 0) {
+    for (int i = 0; i < N; i++) {
+      xs[N * simd_group_id + i] = x[i];
+    }
+  }
+  threadgroup_barrier(mem_flags::mem_threadgroup);
+  for (int i = 0; i < N; i++) {
+    x[i] = xs[N * simd_lane_id + i];
+    x[i] = simd_sum(x[i]);
+  }
+}
+
+template <typename T, int N_READS = 8>
 [[kernel]] void layer_norm_single_row(
     const device T* x,
     const device T* w,
@@ -23,90 +57,71 @@ template <typename T, int N_READS = RMS_N_READS>
     uint lid [[thread_position_in_threadgroup]],
     uint simd_lane_id [[thread_index_in_simdgroup]],
     uint simd_group_id [[simdgroup_index_in_threadgroup]]) {
-  float sumx = 0;
-  float sumx2 = 0;
-  float thread_x[N_READS];
-
   constexpr int SIMD_SIZE = 32;
 
-  threadgroup float local_sumx[SIMD_SIZE];
-  threadgroup float local_sumx2[SIMD_SIZE];
-  threadgroup float local_mean[1];
-  threadgroup float local_normalizer[1];
+  // Initialize the registers and threadgroup memory
+  float thread_x[N_READS] = {0};
+  threadgroup float local_buffer[SIMD_SIZE] = {0};
+  initialize_buffer(local_buffer, simd_lane_id, simd_group_id);
 
+  // Advance the pointers
   x += gid * size_t(axis_size) + lid * N_READS;
   w += w_stride * lid * N_READS;
   b += b_stride * lid * N_READS;
+  out += gid * size_t(axis_size) + lid * N_READS;
 
-  if (lid * N_READS + N_READS <= axis_size) {
+  // Compute some variables for reading writing etc
+  const bool safe = lid * N_READS + N_READS <= axis_size;
+  const int n = axis_size - lid * N_READS;
+
+  // Read the inputs
+  if (safe) {
     for (int i = 0; i < N_READS; i++) {
       thread_x[i] = x[i];
-      sumx2 += thread_x[i] * thread_x[i];
-      sumx += thread_x[i];
     }
   } else {
-    for (int i = 0; i < N_READS; i++) {
-      if ((lid * N_READS + i) < axis_size) {
-        thread_x[i] = x[i];
-        sumx2 += thread_x[i] * thread_x[i];
-        sumx += thread_x[i];
-      }
+    for (int i = 0; i < n; i++) {
+      thread_x[i] = x[i];
     }
   }
 
-  sumx = simd_sum(sumx);
-  sumx2 = simd_sum(sumx2);
-
-  //  Initialize shared memory
-  if (simd_group_id == 0) {
-    local_sumx[simd_lane_id] = 0;
-    local_sumx2[simd_lane_id] = 0;
+  // Compute the mean
+  float mean = 0;
+  for (int i = 0; i < N_READS; i++) {
+    mean += thread_x[i];
   }
-  threadgroup_barrier(mem_flags::mem_threadgroup);
+  threadgroup_sum(&mean, local_buffer, simd_lane_id, simd_group_id);
+  mean /= axis_size;
 
-  // Write simd accumulations into shared memory
-  if (simd_lane_id == 0) {
-    local_sumx[simd_group_id] = sumx;
-    local_sumx2[simd_group_id] = sumx2;
-  }
-  threadgroup_barrier(mem_flags::mem_threadgroup);
-
-  // Accumulate over simd groups
-  if (simd_group_id == 0) {
-    sumx = simd_sum(local_sumx[simd_lane_id]);
-    sumx2 = simd_sum(local_sumx2[simd_lane_id]);
-    if (simd_lane_id == 0) {
-      float mean = sumx / axis_size;
-      float variance = sumx2 / axis_size - mean * mean;
-
-      local_mean[0] = mean;
-      local_normalizer[0] = metal::precise::rsqrt(variance + eps);
+  // Compute the normalizer
+  float normalizer = 0;
+  if (!safe) {
+    for (int i = n; i < N_READS; i++) {
+      thread_x[i] = mean;
     }
   }
-  threadgroup_barrier(mem_flags::mem_threadgroup);
-
-  float mean = local_mean[0];
-  float normalizer = local_normalizer[0];
+  for (int i = 0; i < N_READS; i++) {
+    thread_x[i] -= mean;
+    normalizer += thread_x[i] * thread_x[i];
+  }
+  threadgroup_sum(&normalizer, local_buffer, simd_lane_id, simd_group_id);
+  normalizer = metal::precise::rsqrt(normalizer / axis_size + eps);
 
   // Write the outputs
-  out += gid * size_t(axis_size) + lid * N_READS;
-  if (lid * N_READS + N_READS <= axis_size) {
+  if (safe) {
     for (int i = 0; i < N_READS; i++) {
-      thread_x[i] = (thread_x[i] - mean) * normalizer;
+      thread_x[i] *= normalizer;
       out[i] = w[w_stride * i] * static_cast<T>(thread_x[i]) + b[b_stride * i];
     }
   } else {
-    for (int i = 0; i < N_READS; i++) {
-      if ((lid * N_READS + i) < axis_size) {
-        thread_x[i] = (thread_x[i] - mean) * normalizer;
-        out[i] =
-            w[w_stride * i] * static_cast<T>(thread_x[i]) + b[b_stride * i];
-      }
+    for (int i = 0; i < n; i++) {
+      thread_x[i] *= normalizer;
+      out[i] = w[w_stride * i] * static_cast<T>(thread_x[i]) + b[b_stride * i];
     }
   }
 }
 
-template <typename T, int N_READS = RMS_N_READS>
+template <typename T, int N_READS = 4>
 [[kernel]] void layer_norm_looped(
     const device T* x,
     const device T* w,
@@ -121,71 +136,52 @@ template <typename T, int N_READS = RMS_N_READS>
     uint lsize [[threads_per_threadgroup]],
     uint simd_lane_id [[thread_index_in_simdgroup]],
     uint simd_group_id [[simdgroup_index_in_threadgroup]]) {
-  float sumx = 0;
-  float sumx2 = 0;
-
   constexpr int SIMD_SIZE = 32;
 
-  threadgroup float local_sumx[SIMD_SIZE];
-  threadgroup float local_sumx2[SIMD_SIZE];
-  threadgroup float local_mean[1];
-  threadgroup float local_normalizer[1];
+  threadgroup float local_buffer[SIMD_SIZE];
+  initialize_buffer(local_buffer, simd_lane_id, simd_group_id);
 
   x += gid * size_t(axis_size) + lid * N_READS;
   w += w_stride * lid * N_READS;
   b += b_stride * lid * N_READS;
 
+  // Compute the mean
+  float mean = 0;
   for (uint r = 0; r < axis_size; r += lsize * N_READS) {
     if (r + lid * N_READS + N_READS <= axis_size) {
       for (int i = 0; i < N_READS; i++) {
-        float xi = x[i + r];
-        sumx2 += xi * xi;
-        sumx += xi;
+        mean += x[i + r];
       }
     } else {
       for (int i = 0; i < N_READS; i++) {
         if ((r + lid * N_READS + i) < axis_size) {
-          float xi = x[i + r];
-          sumx2 += xi * xi;
-          sumx += xi;
+          mean += x[i + r];
         }
       }
     }
   }
+  threadgroup_sum(&mean, local_buffer, simd_lane_id, simd_group_id);
+  mean /= axis_size;
 
-  sumx = simd_sum(sumx);
-  sumx2 = simd_sum(sumx2);
-
-  //  Initialize shared memory
-  if (simd_group_id == 0) {
-    local_sumx[simd_lane_id] = 0;
-    local_sumx2[simd_lane_id] = 0;
-  }
-  threadgroup_barrier(mem_flags::mem_threadgroup);
-
-  // Write simd accumulations into shared memory
-  if (simd_lane_id == 0) {
-    local_sumx[simd_group_id] = sumx;
-    local_sumx2[simd_group_id] = sumx2;
-  }
-  threadgroup_barrier(mem_flags::mem_threadgroup);
-
-  // Accumulate over simd groups
-  if (simd_group_id == 0) {
-    sumx = simd_sum(local_sumx[simd_lane_id]);
-    sumx2 = simd_sum(local_sumx2[simd_lane_id]);
-    if (simd_lane_id == 0) {
-      float mean = sumx / axis_size;
-      float variance = sumx2 / axis_size - mean * mean;
-
-      local_mean[0] = mean;
-      local_normalizer[0] = metal::precise::rsqrt(variance + eps);
+  // Compute the normalizer
+  float normalizer = 0;
+  for (uint r = 0; r < axis_size; r += lsize * N_READS) {
+    if (r + lid * N_READS + N_READS <= axis_size) {
+      for (int i = 0; i < N_READS; i++) {
+        float t = x[i + r] - mean;
+        normalizer += t * t;
+      }
+    } else {
+      for (int i = 0; i < N_READS; i++) {
+        if ((r + lid * N_READS + i) < axis_size) {
+          float t = x[i + r] - mean;
+          normalizer += t * t;
+        }
+      }
     }
   }
-  threadgroup_barrier(mem_flags::mem_threadgroup);
-
-  float mean = local_mean[0];
-  float normalizer = local_normalizer[0];
+  threadgroup_sum(&normalizer, local_buffer, simd_lane_id, simd_group_id);
+  normalizer = metal::precise::rsqrt(normalizer / axis_size + eps);
 
   // Write the outputs
   out += gid * size_t(axis_size) + lid * N_READS;
@@ -208,7 +204,7 @@ template <typename T, int N_READS = RMS_N_READS>
   }
 }
 
-template <typename T, int N_READS = RMS_N_READS>
+template <typename T, int N_READS = 8>
 [[kernel]] void vjp_layer_norm_single_row(
     const device T* x,
     const device T* w,
@@ -222,133 +218,96 @@ template <typename T, int N_READS = RMS_N_READS>
     uint lid [[thread_position_in_threadgroup]],
     uint simd_lane_id [[thread_index_in_simdgroup]],
     uint simd_group_id [[simdgroup_index_in_threadgroup]]) {
+  constexpr int SIMD_SIZE = 32;
+
   // Advance the input pointers
   x += gid * size_t(axis_size) + lid * N_READS;
   g += gid * size_t(axis_size) + lid * N_READS;
   w += w_stride * lid * N_READS;
 
-  // Allocate registers for the computation and accumulators
-  float thread_x[N_READS];
-  float thread_w[N_READS];
-  float thread_g[N_READS];
-  float sumx = 0;
-  float sumx2 = 0;
-  float sumwg = 0;
-  float sumwgx = 0;
+  // Initialize the registers and threadgroup memory
+  float thread_x[N_READS] = {0};
+  float thread_w[N_READS] = {0};
+  float thread_g[N_READS] = {0};
+  threadgroup float local_buffer[3 * SIMD_SIZE];
+  initialize_buffer<3>(local_buffer, simd_lane_id, simd_group_id);
 
-  constexpr int SIMD_SIZE = 32;
+  // Compute some variables for reading writing etc
+  const bool safe = lid * N_READS + N_READS <= axis_size;
+  const int n = axis_size - lid * N_READS;
 
-  threadgroup float local_sumx[SIMD_SIZE];
-  threadgroup float local_sumx2[SIMD_SIZE];
-  threadgroup float local_sumwg[SIMD_SIZE];
-  threadgroup float local_sumwgx[SIMD_SIZE];
-  threadgroup float local_mean[1];
-  threadgroup float local_normalizer[1];
-  threadgroup float local_meanwg[1];
-  threadgroup float local_meanwgx[1];
-
-  if (lid * N_READS + N_READS <= axis_size) {
+  // Read the inputs
+  if (safe) {
     for (int i = 0; i < N_READS; i++) {
       thread_x[i] = x[i];
-      thread_w[i] = w[i * w_stride];
       thread_g[i] = g[i];
-      float wg = thread_w[i] * thread_g[i];
-      sumx += thread_x[i];
-      sumx2 += thread_x[i] * thread_x[i];
-      sumwg += wg;
-      sumwgx += wg * thread_x[i];
+      thread_w[i] = w[i * w_stride];
     }
   } else {
-    for (int i = 0; i < N_READS; i++) {
-      if ((lid * N_READS + i) < axis_size) {
-        thread_x[i] = x[i];
-        thread_w[i] = w[i * w_stride];
-        thread_g[i] = g[i];
-        float wg = thread_w[i] * thread_g[i];
-        sumx += thread_x[i];
-        sumx2 += thread_x[i] * thread_x[i];
-        sumwg += wg;
-        sumwgx += wg * thread_x[i];
-      }
+    for (int i = 0; i < n; i++) {
+      thread_x[i] = x[i];
+      thread_g[i] = g[i];
+      thread_w[i] = w[i * w_stride];
     }
   }
 
-  sumx = simd_sum(sumx);
-  sumx2 = simd_sum(sumx2);
-  sumwg = simd_sum(sumwg);
-  sumwgx = simd_sum(sumwgx);
-
-  //  Initialize shared memory
-  if (simd_group_id == 0) {
-    local_sumx[simd_lane_id] = 0;
-    local_sumx2[simd_lane_id] = 0;
-    local_sumwg[simd_lane_id] = 0;
-    local_sumwgx[simd_lane_id] = 0;
+  // Compute the mean
+  float mean = 0;
+  for (int i = 0; i < N_READS; i++) {
+    mean += thread_x[i];
   }
-  threadgroup_barrier(mem_flags::mem_threadgroup);
+  threadgroup_sum(&mean, local_buffer, simd_lane_id, simd_group_id);
+  mean /= axis_size;
 
-  // Write simd accumulations into shared memory
-  if (simd_lane_id == 0) {
-    local_sumx[simd_group_id] = sumx;
-    local_sumx2[simd_group_id] = sumx2;
-    local_sumwg[simd_group_id] = sumwg;
-    local_sumwgx[simd_group_id] = sumwgx;
-  }
-  threadgroup_barrier(mem_flags::mem_threadgroup);
-
-  // Accumulate over simd groups
-  if (simd_group_id == 0) {
-    sumx = simd_sum(local_sumx[simd_lane_id]);
-    sumx2 = simd_sum(local_sumx2[simd_lane_id]);
-    sumwg = simd_sum(local_sumwg[simd_lane_id]);
-    sumwgx = simd_sum(local_sumwgx[simd_lane_id]);
-    if (simd_lane_id == 0) {
-      float mean = sumx / axis_size;
-      float variance = sumx2 / axis_size - mean * mean;
-
-      local_mean[0] = mean;
-      local_normalizer[0] = metal::precise::rsqrt(variance + eps);
-      local_meanwg[0] = sumwg / axis_size;
-      local_meanwgx[0] = sumwgx / axis_size;
+  // Compute the neccesary scaling factors using the mean
+  if (!safe) {
+    for (int i = n; i < N_READS; i++) {
+      thread_x[i] = mean;
     }
   }
-  threadgroup_barrier(mem_flags::mem_threadgroup);
-
-  float mean = local_mean[0];
-  float normalizer = local_normalizer[0];
-  float meanwg = local_meanwg[0];
-  float meanwgxc = local_meanwgx[0] - meanwg * mean;
-  float normalizer2 = normalizer * normalizer;
+  float factors[3] = {0};
+  constexpr int meanwg = 0;
+  constexpr int meanwgxc = 1;
+  constexpr int normalizer2 = 2;
+  for (int i = 0; i < N_READS; i++) {
+    thread_x[i] -= mean;
+    factors[meanwg] += thread_w[i] * thread_g[i];
+    factors[meanwgxc] += thread_w[i] * thread_g[i] * thread_x[i];
+    factors[normalizer2] += thread_x[i] * thread_x[i];
+  }
+  threadgroup_sum<3>(factors, local_buffer, simd_lane_id, simd_group_id);
+  factors[meanwg] /= axis_size;
+  factors[meanwgxc] /= axis_size;
+  factors[normalizer2] = 1 / (factors[normalizer2] / axis_size + eps);
+  float normalizer = metal::precise::sqrt(factors[normalizer2]);
 
   // Write the outputs
   gx += gid * size_t(axis_size) + lid * N_READS;
   gw += gid * size_t(axis_size) + lid * N_READS;
-  if (lid * N_READS + N_READS <= axis_size) {
+  if (safe) {
     for (int i = 0; i < N_READS; i++) {
-      thread_x[i] = (thread_x[i] - mean) * normalizer;
+      thread_x[i] *= normalizer;
       gx[i] = static_cast<T>(
-          normalizer * (thread_w[i] * thread_g[i] - meanwg) -
-          thread_x[i] * meanwgxc * normalizer2);
+          normalizer * (thread_w[i] * thread_g[i] - factors[meanwg]) -
+          thread_x[i] * factors[meanwgxc] * factors[normalizer2]);
       if (has_w) {
         gw[i] = static_cast<T>(thread_g[i] * thread_x[i]);
       }
     }
   } else {
-    for (int i = 0; i < N_READS; i++) {
-      if ((lid * N_READS + i) < axis_size) {
-        thread_x[i] = (thread_x[i] - mean) * normalizer;
-        gx[i] = static_cast<T>(
-            normalizer * (thread_w[i] * thread_g[i] - meanwg) -
-            thread_x[i] * meanwgxc * normalizer2);
-        if (has_w) {
-          gw[i] = static_cast<T>(thread_g[i] * thread_x[i]);
-        }
+    for (int i = 0; i < n; i++) {
+      thread_x[i] *= normalizer;
+      gx[i] = static_cast<T>(
+          normalizer * (thread_w[i] * thread_g[i] - factors[meanwg]) -
+          thread_x[i] * factors[meanwgxc] * factors[normalizer2]);
+      if (has_w) {
+        gw[i] = static_cast<T>(thread_g[i] * thread_x[i]);
       }
     }
   }
 }
 
-template <typename T, int N_READS = RMS_N_READS>
+template <typename T, int N_READS = 4>
 [[kernel]] void vjp_layer_norm_looped(
     const device T* x,
     const device T* w,
@@ -363,102 +322,69 @@ template <typename T, int N_READS = RMS_N_READS>
     uint lsize [[threads_per_threadgroup]],
     uint simd_lane_id [[thread_index_in_simdgroup]],
     uint simd_group_id [[simdgroup_index_in_threadgroup]]) {
+  constexpr int SIMD_SIZE = 32;
+
   // Advance the input pointers
   x += gid * size_t(axis_size) + lid * N_READS;
   g += gid * size_t(axis_size) + lid * N_READS;
   w += w_stride * lid * N_READS;
 
-  // Allocate registers for the accumulators
-  float sumx = 0;
-  float sumx2 = 0;
-  float sumwg = 0;
-  float sumwgx = 0;
-
-  constexpr int SIMD_SIZE = 32;
-
-  threadgroup float local_sumx[SIMD_SIZE];
-  threadgroup float local_sumx2[SIMD_SIZE];
-  threadgroup float local_sumwg[SIMD_SIZE];
-  threadgroup float local_sumwgx[SIMD_SIZE];
-  threadgroup float local_mean[1];
-  threadgroup float local_normalizer[1];
-  threadgroup float local_meanwg[1];
-  threadgroup float local_meanwgx[1];
+  threadgroup float local_buffer[3 * SIMD_SIZE];
+  initialize_buffer<3>(local_buffer, simd_lane_id, simd_group_id);
 
+  // Compute the mean
+  float mean = 0;
   for (uint r = 0; r < axis_size; r += lsize * N_READS) {
     if (r + lid * N_READS + N_READS <= axis_size) {
       for (int i = 0; i < N_READS; i++) {
-        float xi = x[i + r];
-        float wi = w[(i + r) * w_stride];
-        float gi = g[i + r];
-        float wg = wi * gi;
-        sumx += xi;
-        sumx2 += xi * xi;
-        sumwg += wg;
-        sumwgx += wg * xi;
+        mean += x[i + r];
       }
     } else {
       for (int i = 0; i < N_READS; i++) {
         if ((r + lid * N_READS + i) < axis_size) {
-          float xi = x[i + r];
-          float wi = w[(i + r) * w_stride];
-          float gi = g[i + r];
-          float wg = wi * gi;
-          sumx += xi;
-          sumx2 += xi * xi;
-          sumwg += wg;
-          sumwgx += wg * xi;
+          mean += x[i + r];
         }
       }
     }
   }
+  threadgroup_sum(&mean, local_buffer, simd_lane_id, simd_group_id);
+  mean /= axis_size;
 
-  sumx = simd_sum(sumx);
-  sumx2 = simd_sum(sumx2);
-  sumwg = simd_sum(sumwg);
-  sumwgx = simd_sum(sumwgx);
-
-  //  Initialize shared memory
-  if (simd_group_id == 0) {
-    local_sumx[simd_lane_id] = 0;
-    local_sumx2[simd_lane_id] = 0;
-    local_sumwg[simd_lane_id] = 0;
-    local_sumwgx[simd_lane_id] = 0;
-  }
-  threadgroup_barrier(mem_flags::mem_threadgroup);
-
-  // Write simd accumulations into shared memory
-  if (simd_lane_id == 0) {
-    local_sumx[simd_group_id] = sumx;
-    local_sumx2[simd_group_id] = sumx2;
-    local_sumwg[simd_group_id] = sumwg;
-    local_sumwgx[simd_group_id] = sumwgx;
-  }
-  threadgroup_barrier(mem_flags::mem_threadgroup);
-
-  // Accumulate over simd groups
-  if (simd_group_id == 0) {
-    sumx = simd_sum(local_sumx[simd_lane_id]);
-    sumx2 = simd_sum(local_sumx2[simd_lane_id]);
-    sumwg = simd_sum(local_sumwg[simd_lane_id]);
-    sumwgx = simd_sum(local_sumwgx[simd_lane_id]);
-    if (simd_lane_id == 0) {
-      float mean = sumx / axis_size;
-      float variance = sumx2 / axis_size - mean * mean;
-
-      local_mean[0] = mean;
-      local_normalizer[0] = metal::precise::rsqrt(variance + eps);
-      local_meanwg[0] = sumwg / axis_size;
-      local_meanwgx[0] = sumwgx / axis_size;
+  // Compute the neccesary scaling factors using the mean
+  float factors[3] = {0};
+  constexpr int meanwg = 0;
+  constexpr int meanwgxc = 1;
+  constexpr int normalizer2 = 2;
+  for (uint r = 0; r < axis_size; r += lsize * N_READS) {
+    if (r + lid * N_READS + N_READS <= axis_size) {
+      for (int i = 0; i < N_READS; i++) {
+        float t = x[i + r] - mean;
+        float wi = w[(i + r) * w_stride];
+        float gi = g[i + r];
+        float wg = wi * gi;
+        factors[meanwg] += wg;
+        factors[meanwgxc] += wg * t;
+        factors[normalizer2] += t * t;
+      }
+    } else {
+      for (int i = 0; i < N_READS; i++) {
+        if ((r + lid * N_READS + i) < axis_size) {
+          float t = x[i + r] - mean;
+          float wi = w[(i + r) * w_stride];
+          float gi = g[i + r];
+          float wg = wi * gi;
+          factors[meanwg] += wg;
+          factors[meanwgxc] += wg * t;
+          factors[normalizer2] += t * t;
+        }
+      }
     }
   }
-  threadgroup_barrier(mem_flags::mem_threadgroup);
-
-  float mean = local_mean[0];
-  float normalizer = local_normalizer[0];
-  float meanwg = local_meanwg[0];
-  float meanwgxc = local_meanwgx[0] - meanwg * mean;
-  float normalizer2 = normalizer * normalizer;
+  threadgroup_sum<3>(factors, local_buffer, simd_lane_id, simd_group_id);
+  factors[meanwg] /= axis_size;
+  factors[meanwgxc] /= axis_size;
+  factors[normalizer2] = 1 / (factors[normalizer2] / axis_size + eps);
+  float normalizer = metal::precise::sqrt(factors[normalizer2]);
 
   // Write the outputs
   gx += gid * size_t(axis_size) + lid * N_READS;
@@ -470,7 +396,8 @@ template <typename T, int N_READS = RMS_N_READS>
         float wi = w[(i + r) * w_stride];
         float gi = g[i + r];
         gx[i + r] = static_cast<T>(
-            normalizer * (wi * gi - meanwg) - xi * meanwgxc * normalizer2);
+            normalizer * (wi * gi - factors[meanwg]) -
+            xi * factors[meanwgxc] * factors[normalizer2]);
         if (has_w) {
           gw[i + r] = static_cast<T>(gi * xi);
         }
@@ -482,7 +409,8 @@ template <typename T, int N_READS = RMS_N_READS>
           float wi = w[(i + r) * w_stride];
           float gi = g[i + r];
           gx[i + r] = static_cast<T>(
-              normalizer * (wi * gi - meanwg) - xi * meanwgxc * normalizer2);
+              normalizer * (wi * gi - factors[meanwg]) -
+              xi * factors[meanwgxc] * factors[normalizer2]);
           if (has_w) {
             gw[i + r] = static_cast<T>(gi * xi);
           }

mlx/backend/metal/kernels/ternary.h

@@ -9,8 +9,14 @@ template <typename T, typename Op, int N = WorkPerThread<T>::n>
     constant uint& size,
     uint index [[thread_position_in_grid]]) {
   index *= N;
-  for (int i = 0; i < N && (index + i) < size; ++i) {
-    d[index + i] = Op()(a[index + i], b[index + i], c[index + i]);
+  if (N > 1 && index + N > size) {
+    for (int i = 0; index + i < size; ++i) {
+      d[index + i] = Op()(a[index + i], b[index + i], c[index + i]);
+    }
+  } else {
+    for (int i = 0; i < N; ++i) {
+      d[index + i] = Op()(a[index + i], b[index + i], c[index + i]);
+    }
   }
 }
 
@@ -23,9 +29,15 @@ template <typename T, typename Op, int N = WorkPerThread<T>::n>
     constant int64_t& size,
     uint2 index [[thread_position_in_grid]],
     uint2 grid_dim [[threads_per_grid]]) {
-  auto offset = N * (index.x + grid_dim.x * int64_t(index.y));
-  for (int i = 0; i < N && (offset + i) < size; ++i) {
-    d[offset + i] = Op()(a[offset + i], b[offset + i], c[offset + i]);
+  int64_t offset = N * (index.x + grid_dim.x * int64_t(index.y));
+  if (N > 1 && offset + N > size) {
+    for (int i = 0; offset + i < size; ++i) {
+      d[offset + i] = Op()(a[offset + i], b[offset + i], c[offset + i]);
+    }
+  } else {
+    for (int i = 0; i < N; ++i) {
+      d[offset + i] = Op()(a[offset + i], b[offset + i], c[offset + i]);
+    }
   }
 }
 

mlx/backend/metal/kernels/ternary.metal

@@ -8,8 +8,8 @@
 #include "mlx/backend/metal/kernels/ternary_ops.h"
 #include "mlx/backend/metal/kernels/ternary.h"
 
-#define instantiate_ternary_all(op, tname, type)                     \
-  instantiate_kernel("v_" #op #tname, ternary_v, type, op)           \
+#define instantiate_ternary_base(op, tname, type)                    \
+  instantiate_kernel("v_" #op #tname, ternary_v, type, op, 1)        \
   instantiate_kernel("v2_" #op #tname, ternary_v2, type, op)         \
   instantiate_kernel("gn2_" #op #tname, ternary_g, type, op, 2, int) \
   instantiate_kernel("g1_" #op #tname, ternary_g_nd1, type, op, int) \
@@ -20,19 +20,23 @@
   instantiate_kernel("g3large_" #op #tname, ternary_g_nd3, type, op) \
   instantiate_kernel("gn4large_" #op #tname, ternary_g, type, op, 4) \
 
+#define instantiate_ternary_all(op, tname, type)            \
+  instantiate_kernel("vn_" #op #tname, ternary_v, type, op) \
+  instantiate_ternary_base(op, tname, type)
+
 #define instantiate_ternary_types(op)               \
   instantiate_ternary_all(op, bool_, bool)          \
   instantiate_ternary_all(op, uint8, uint8_t)       \
   instantiate_ternary_all(op, uint16, uint16_t)     \
   instantiate_ternary_all(op, uint32, uint32_t)     \
-  instantiate_ternary_all(op, uint64, uint64_t)     \
+  instantiate_ternary_base(op, uint64, uint64_t)    \
   instantiate_ternary_all(op, int8, int8_t)         \
   instantiate_ternary_all(op, int16, int16_t)       \
   instantiate_ternary_all(op, int32, int32_t)       \
-  instantiate_ternary_all(op, int64, int64_t)       \
+  instantiate_ternary_base(op, int64, int64_t)      \
   instantiate_ternary_all(op, float16, half)        \
   instantiate_ternary_all(op, float32, float)       \
   instantiate_ternary_all(op, bfloat16, bfloat16_t) \
-  instantiate_ternary_all(op, complex64, complex64_t) // clang-format on
+  instantiate_ternary_base(op, complex64, complex64_t) // clang-format on
 
 instantiate_ternary_types(Select)

mlx/backend/metal/kernels/unary.h

@@ -7,8 +7,14 @@ template <typename T, typename U, typename Op, int N = WorkPerThread<T>::n>
     constant uint& size,
     uint index [[thread_position_in_grid]]) {
   index *= N;
-  for (int i = 0; i < N && (index + i) < size; ++i) {
-    out[index + i] = Op()(in[index + i]);
+  if (N > 1 && index + N > size) {
+    for (int i = 0; index + i < size; ++i) {
+      out[index + i] = Op()(in[index + i]);
+    }
+  } else {
+    for (int i = 0; i < N; ++i) {
+      out[index + i] = Op()(in[index + i]);
+    }
   }
 }
 
@@ -19,9 +25,15 @@ template <typename T, typename U, typename Op, int N = WorkPerThread<T>::n>
     constant int64_t& size,
     uint2 index [[thread_position_in_grid]],
     uint2 grid_dim [[threads_per_grid]]) {
-  auto offset = N * (index.x + grid_dim.x * int64_t(index.y));
-  for (int i = 0; i < N && (offset + i) < size; ++i) {
-    out[offset + i] = Op()(in[offset + i]);
+  int64_t offset = N * (index.x + grid_dim.x * int64_t(index.y));
+  if (N > 1 && offset + N > size) {
+    for (int i = 0; offset + i < size; ++i) {
+      out[offset + i] = Op()(in[offset + i]);
+    }
+  } else {
+    for (int i = 0; i < N; ++i) {
+      out[offset + i] = Op()(in[offset + i]);
+    }
   }
 }
 

mlx/backend/metal/kernels/unary.metal

@@ -5,31 +5,41 @@
 #include "mlx/backend/metal/kernels/unary_ops.h"
 #include "mlx/backend/metal/kernels/unary.h"
 
-#define instantiate_unary_all(op, in_tname, out_tname, in_type, out_type)             \
-  instantiate_kernel("v_" #op #in_tname #out_tname, unary_v, in_type, out_type, op)   \
-  instantiate_kernel("v2_" #op #in_tname #out_tname, unary_v2, in_type, out_type, op) \
-  instantiate_kernel(                                                                 \
-      "gn1_" #op #in_tname #out_tname, unary_g, in_type, out_type, op, 1, int)        \
-  instantiate_kernel(                                                                 \
+#define instantiate_unary_work_per_thread(op, in_tname, out_tname, in_type, out_type) \
+  instantiate_kernel("vn_" #op #in_tname #out_tname, unary_v, in_type, out_type, op)
+
+#define instantiate_unary_base(op, in_tname, out_tname, in_type, out_type)             \
+  instantiate_kernel("v_" #op #in_tname #out_tname, unary_v, in_type, out_type, op, 1) \
+  instantiate_kernel("v2_" #op #in_tname #out_tname, unary_v2, in_type, out_type, op)  \
+  instantiate_kernel(                                                                  \
+      "gn1_" #op #in_tname #out_tname, unary_g, in_type, out_type, op, 1, int)         \
+  instantiate_kernel(                                                                  \
       "gn4large_" #op #in_tname #out_tname, unary_g, in_type, out_type, op, 4)
 
+#define instantiate_unary_all(op, in_tname, out_tname, in_type, out_type)       \
+  instantiate_unary_base(op, in_tname, out_tname, in_type, out_type)            \
+  instantiate_unary_work_per_thread(op, in_tname, out_tname, in_type, out_type)
+
 #define instantiate_unary_all_same(op, tname, type)   \
   instantiate_unary_all(op, tname, tname, type, type)
 
+#define instantiate_unary_base_same(op, tname, type)   \
+  instantiate_unary_base(op, tname, tname, type, type)
+
 #define instantiate_unary_float(op)                    \
   instantiate_unary_all_same(op, float16, half)        \
   instantiate_unary_all_same(op, float32, float)       \
   instantiate_unary_all_same(op, bfloat16, bfloat16_t)
 
-#define instantiate_unary_int(op)                  \
-  instantiate_unary_all_same(op, uint8, uint8_t)   \
-  instantiate_unary_all_same(op, uint16, uint16_t) \
-  instantiate_unary_all_same(op, uint32, uint32_t) \
-  instantiate_unary_all_same(op, uint64, uint64_t) \
-  instantiate_unary_all_same(op, int8, int8_t)     \
-  instantiate_unary_all_same(op, int16, int16_t)   \
-  instantiate_unary_all_same(op, int32, int32_t)   \
-  instantiate_unary_all_same(op, int64, int64_t)
+#define instantiate_unary_int(op)                   \
+  instantiate_unary_all_same(op, uint8, uint8_t)    \
+  instantiate_unary_all_same(op, uint16, uint16_t)  \
+  instantiate_unary_all_same(op, uint32, uint32_t)  \
+  instantiate_unary_base_same(op, uint64, uint64_t) \
+  instantiate_unary_all_same(op, int8, int8_t)      \
+  instantiate_unary_all_same(op, int16, int16_t)    \
+  instantiate_unary_all_same(op, int32, int32_t)    \
+  instantiate_unary_base_same(op, int64, int64_t)
 
 #define instantiate_unary_types(op)                \
   instantiate_unary_all_same(op, bool_, bool)      \
@@ -68,29 +78,29 @@ instantiate_unary_float(Tanh)
 instantiate_unary_float(Round)
 instantiate_unary_int(BitwiseInvert)
 
-instantiate_unary_all_same(Abs, complex64, complex64_t)
-instantiate_unary_all_same(ArcCos, complex64, complex64_t)
-instantiate_unary_all_same(ArcSin, complex64, complex64_t)
-instantiate_unary_all_same(ArcTan, complex64, complex64_t)
-instantiate_unary_all_same(Conjugate, complex64, complex64_t)
-instantiate_unary_all_same(Cos, complex64, complex64_t)
-instantiate_unary_all_same(Cosh, complex64, complex64_t)
-instantiate_unary_all_same(Exp, complex64, complex64_t)
-instantiate_unary_all_same(Log, complex64, complex64_t)
-instantiate_unary_all_same(Log1p, complex64, complex64_t)
-instantiate_unary_all_same(Log2, complex64, complex64_t)
-instantiate_unary_all_same(Log10, complex64, complex64_t)
-instantiate_unary_all_same(Negative, complex64, complex64_t)
-instantiate_unary_all_same(Sign, complex64, complex64_t)
-instantiate_unary_all_same(Sin, complex64, complex64_t)
-instantiate_unary_all_same(Sinh, complex64, complex64_t)
-instantiate_unary_all_same(Square, complex64, complex64_t)
-instantiate_unary_all_same(Sqrt, complex64, complex64_t)
-instantiate_unary_all_same(Rsqrt, complex64, complex64_t)
-instantiate_unary_all_same(Tan, complex64, complex64_t)
-instantiate_unary_all_same(Tanh, complex64, complex64_t)
-instantiate_unary_all_same(Round, complex64, complex64_t)
-instantiate_unary_all(Real, complex64, float32, complex64_t, float)
-instantiate_unary_all(Imag, complex64, float32, complex64_t, float)
+instantiate_unary_base_same(Abs, complex64, complex64_t)
+instantiate_unary_base_same(ArcCos, complex64, complex64_t)
+instantiate_unary_base_same(ArcSin, complex64, complex64_t)
+instantiate_unary_base_same(ArcTan, complex64, complex64_t)
+instantiate_unary_base_same(Conjugate, complex64, complex64_t)
+instantiate_unary_base_same(Cos, complex64, complex64_t)
+instantiate_unary_base_same(Cosh, complex64, complex64_t)
+instantiate_unary_base_same(Exp, complex64, complex64_t)
+instantiate_unary_base_same(Log, complex64, complex64_t)
+instantiate_unary_base_same(Log1p, complex64, complex64_t)
+instantiate_unary_base_same(Log2, complex64, complex64_t)
+instantiate_unary_base_same(Log10, complex64, complex64_t)
+instantiate_unary_base_same(Negative, complex64, complex64_t)
+instantiate_unary_base_same(Sign, complex64, complex64_t)
+instantiate_unary_base_same(Sin, complex64, complex64_t)
+instantiate_unary_base_same(Sinh, complex64, complex64_t)
+instantiate_unary_base_same(Square, complex64, complex64_t)
+instantiate_unary_base_same(Sqrt, complex64, complex64_t)
+instantiate_unary_base_same(Rsqrt, complex64, complex64_t)
+instantiate_unary_base_same(Tan, complex64, complex64_t)
+instantiate_unary_base_same(Tanh, complex64, complex64_t)
+instantiate_unary_base_same(Round, complex64, complex64_t)
+instantiate_unary_base(Real, complex64, float32, complex64_t, float)
+instantiate_unary_base(Imag, complex64, float32, complex64_t, float)
 
 instantiate_unary_all_same(LogicalNot, bool_, bool) // clang-format on

mlx/backend/metal/matmul.cpp

@@ -6,7 +6,7 @@
 #include <sstream>
 
 #include "mlx/backend/common/broadcasting.h"
-#include "mlx/backend/common/utils.h"
+#include "mlx/backend/common/matmul.h"
 #include "mlx/backend/gpu/copy.h"
 #include "mlx/backend/metal/device.h"
 #include "mlx/backend/metal/kernels.h"
@@ -21,69 +21,6 @@ namespace mlx::core {
 
 namespace {
 
-inline auto collapse_batches(const array& a, const array& b) {
-  // Get and check the shape for the batched dims
-  Shape A_bshape{a.shape().begin(), a.shape().end() - 2};
-  Shape B_bshape{b.shape().begin(), b.shape().end() - 2};
-  if (A_bshape != B_bshape) {
-    std::ostringstream msg;
-    msg << "[matmul] Got matrices with incorrectly broadcasted shapes: " << "A "
-        << a.shape() << ", B " << b.shape() << ".";
-    throw std::runtime_error(msg.str());
-  }
-
-  Strides A_bstride{a.strides().begin(), a.strides().end() - 2};
-  Strides B_bstride{b.strides().begin(), b.strides().end() - 2};
-
-  auto [batch_shape, batch_strides] =
-      collapse_contiguous_dims(A_bshape, std::vector{A_bstride, B_bstride});
-
-  auto A_batch_stride = batch_strides[0];
-  auto B_batch_stride = batch_strides[1];
-
-  if (batch_shape.empty()) {
-    batch_shape.push_back(1);
-    A_batch_stride.push_back(0);
-    B_batch_stride.push_back(0);
-  }
-
-  return std::make_tuple(batch_shape, A_batch_stride, B_batch_stride);
-}
-
-inline auto collapse_batches(const array& a, const array& b, const array& c) {
-  // Get and check the shape for the batched dims
-  Shape A_bshape{a.shape().begin(), a.shape().end() - 2};
-  Shape B_bshape{b.shape().begin(), b.shape().end() - 2};
-  Shape C_bshape{c.shape().begin(), c.shape().end() - 2};
-  if (A_bshape != B_bshape || A_bshape != C_bshape) {
-    std::ostringstream msg;
-    msg << "[addmm] Got matrices with incorrectly broadcasted shapes: " << "A "
-        << a.shape() << ", B " << b.shape() << ", B " << c.shape() << ".";
-    throw std::runtime_error(msg.str());
-  }
-
-  Strides A_bstride{a.strides().begin(), a.strides().end() - 2};
-  Strides B_bstride{b.strides().begin(), b.strides().end() - 2};
-  Strides C_bstride{c.strides().begin(), c.strides().end() - 2};
-
-  auto [batch_shape, batch_strides] = collapse_contiguous_dims(
-      A_bshape, std::vector{A_bstride, B_bstride, C_bstride});
-
-  auto A_batch_stride = batch_strides[0];
-  auto B_batch_stride = batch_strides[1];
-  auto C_batch_stride = batch_strides[2];
-
-  if (batch_shape.empty()) {
-    batch_shape.push_back(1);
-    A_batch_stride.push_back(0);
-    B_batch_stride.push_back(0);
-    C_batch_stride.push_back(0);
-  }
-
-  return std::make_tuple(
-      batch_shape, A_batch_stride, B_batch_stride, C_batch_stride);
-}
-
 std::tuple<bool, int64_t, array> check_transpose(
     std::vector<array>& copies,
     const Stream& s,

mlx/backend/metal/nojit_kernels.cpp

@@ -146,7 +146,7 @@ MTL::ComputePipelineState* get_steel_gemm_fused_kernel(
     int,
     int,
     int) {
-  return d.get_kernel(kernel_name, "mlx", hash_name, func_consts);
+  return d.get_kernel(kernel_name, hash_name, func_consts);
 }
 
 MTL::ComputePipelineState* get_steel_gemm_splitk_kernel(
@@ -207,7 +207,7 @@ MTL::ComputePipelineState* get_steel_gemm_gather_kernel(
     int,
     int,
     bool) {
-  return d.get_kernel(kernel_name, "mlx", hash_name, func_consts);
+  return d.get_kernel(kernel_name, hash_name, func_consts);
 }
 
 MTL::ComputePipelineState* get_gemv_masked_kernel(
@@ -259,7 +259,7 @@ MTL::ComputePipelineState* get_fft_kernel(
     const std::string& hash_name,
     const metal::MTLFCList& func_consts,
     const std::string&) {
-  return d.get_kernel(kernel_name, "mlx", hash_name, func_consts);
+  return d.get_kernel(kernel_name, hash_name, func_consts);
 }
 
 MTL::ComputePipelineState* get_quantized_kernel(
@@ -283,7 +283,7 @@ MTL::ComputePipelineState* get_gather_qmm_kernel(
     int,
     int,
     bool) {
-  return d.get_kernel(kernel_name, "mlx", hash_name, func_consts);
+  return d.get_kernel(kernel_name, hash_name, func_consts);
 }
 
 } // namespace mlx::core

mlx/backend/metal/normalization.cpp

@@ -172,7 +172,7 @@ void RMSNormVJP::eval_gpu(
 
   auto& compute_encoder = d.get_command_encoder(s.index);
   {
-    auto kernel = d.get_kernel(op_name, "mlx", hash_name, func_consts);
+    auto kernel = d.get_kernel(op_name, hash_name, func_consts);
 
     MTL::Size grid_dims, group_dims;
     if (axis_size <= looped_limit) {
@@ -255,12 +255,13 @@ void LayerNorm::eval_gpu(
   auto axis_size = static_cast<uint32_t>(x.shape().back());
   int n_rows = x.data_size() / axis_size;
 
-  const int simd_size = 32;
-  const int n_reads = RMS_N_READS;
-  const int looped_limit = RMS_LOOPED_LIMIT;
+  int simd_size = 32;
+  int n_reads = 8;
+  int looped_limit = 6656;
   std::string op_name = "layer_norm";
   if (axis_size > looped_limit) {
     op_name += "_looped";
+    n_reads = 4;
   }
   op_name += type_to_name(out);
   auto& compute_encoder = d.get_command_encoder(s.index);
@@ -272,7 +273,13 @@ void LayerNorm::eval_gpu(
       size_t threadgroup_needed = (axis_size + n_reads - 1) / n_reads;
       size_t simds_needed = (threadgroup_needed + simd_size - 1) / simd_size;
       size_t threadgroup_size = simd_size * simds_needed;
-      assert(threadgroup_size <= kernel->maxTotalThreadsPerThreadgroup());
+      if (threadgroup_size > kernel->maxTotalThreadsPerThreadgroup()) {
+        std::ostringstream msg;
+        msg << "[layer_norm] Threadgroup size " << threadgroup_size
+            << " is larger than the maximum allowed threadgroup size "
+            << kernel->maxTotalThreadsPerThreadgroup();
+        throw std::runtime_error(msg.str());
+      }
       size_t n_threads = n_rows * threadgroup_size;
       grid_dims = MTL::Size(n_threads, 1, 1);
       group_dims = MTL::Size(threadgroup_size, 1, 1);
@@ -372,12 +379,13 @@ void LayerNormVJP::eval_gpu(
         g, gb, "sum", plan, {0}, compute_encoder, d, s);
   }
 
-  const int simd_size = 32;
-  const int n_reads = RMS_N_READS;
-  const int looped_limit = RMS_LOOPED_LIMIT;
+  int simd_size = 32;
+  int n_reads = 8;
+  int looped_limit = 8192;
   std::string op_name = "vjp_layer_norm";
   if (axis_size > looped_limit) {
     op_name += "_looped";
+    n_reads = 4;
   }
   op_name += type_to_name(gx);
 
@@ -387,14 +395,20 @@ void LayerNormVJP::eval_gpu(
   };
 
   {
-    auto kernel = d.get_kernel(op_name, "mlx", hash_name, func_consts);
+    auto kernel = d.get_kernel(op_name, hash_name, func_consts);
 
     MTL::Size grid_dims, group_dims;
     if (axis_size <= looped_limit) {
       size_t threadgroup_needed = (axis_size + n_reads - 1) / n_reads;
       size_t simds_needed = (threadgroup_needed + simd_size - 1) / simd_size;
       size_t threadgroup_size = simd_size * simds_needed;
-      assert(threadgroup_size <= kernel->maxTotalThreadsPerThreadgroup());
+      if (threadgroup_size > kernel->maxTotalThreadsPerThreadgroup()) {
+        std::ostringstream msg;
+        msg << "[vjp_layer_norm] Threadgroup size " << threadgroup_size
+            << " is larger than the maximum allowed threadgroup size "
+            << kernel->maxTotalThreadsPerThreadgroup();
+        throw std::runtime_error(msg.str());
+      }
       size_t n_threads = n_rows * threadgroup_size;
       grid_dims = MTL::Size(n_threads, 1, 1);
       group_dims = MTL::Size(threadgroup_size, 1, 1);

mlx/backend/metal/scaled_dot_product_attention.cpp

@@ -73,7 +73,7 @@ void sdpa_full_self_attention_metal(
   std::string hash_name = kname.str();
 
   auto& compute_encoder = d.get_command_encoder(s.index);
-  auto kernel = d.get_kernel(base_name, "mlx", hash_name, func_consts);
+  auto kernel = d.get_kernel(base_name, hash_name, func_consts);
   compute_encoder.set_compute_pipeline_state(kernel);
 
   const int NQ = (qL + bq - 1) / bq;
@@ -180,7 +180,7 @@ void sdpa_vector(
 
   // Get the kernel
   auto& compute_encoder = d.get_command_encoder(s.index);
-  auto kernel = d.get_kernel(kname, "mlx", hash_name, func_consts);
+  auto kernel = d.get_kernel(kname, hash_name, func_consts);
   compute_encoder.set_compute_pipeline_state(kernel);
 
   // Set its arguments
@@ -281,7 +281,7 @@ void sdpa_vector_2pass(
 
   // Get the kernel
   auto& compute_encoder = d.get_command_encoder(s.index);
-  auto kernel = d.get_kernel(kname, "mlx", hash_name, func_consts);
+  auto kernel = d.get_kernel(kname, hash_name, func_consts);
 
   compute_encoder.set_compute_pipeline_state(kernel);
 
@@ -369,7 +369,7 @@ bool ScaledDotProductAttention::use_fallback(
   const bool sdpa_full_supported_mask = !has_mask || has_arr_mask ||
       (query_sequence_length <= key_sequence_length && do_causal);
 
-  const bool supports_sdpa_full =
+  const bool supports_sdpa_full = query_sequence_length > 8 &&
       sdpa_full_supported_mask && sdpa_full_supported_head_dim;
 
   const bool supports_sdpa_vector = (query_sequence_length <= 8) &&

mlx/backend/metal/ternary.cpp

@@ -45,7 +45,7 @@ void ternary_op_gpu_inplace(
     work_per_thread = large ? 4 : 2;
   } else {
     large = out.data_size() > INT32_MAX;
-    work_per_thread = get_work_per_thread(b.dtype());
+    work_per_thread = get_work_per_thread(b.dtype(), out.data_size());
   }
   std::string kernel_name;
   if (topt == TernaryOpType::General) {
@@ -60,6 +60,8 @@ void ternary_op_gpu_inplace(
     }
   } else if (large) {
     kernel_name = "v2";
+  } else if (work_per_thread > 1) {
+    kernel_name = "vn";
   } else {
     kernel_name = "v";
   }

mlx/backend/metal/unary.cpp

@@ -1,5 +1,6 @@
 // Copyright © 2024 Apple Inc.
-#include "mlx/backend/common/utils.h"
+
+#include "mlx/backend/common/unary.h"
 #include "mlx/backend/metal/device.h"
 #include "mlx/backend/metal/kernels.h"
 #include "mlx/backend/metal/utils.h"
@@ -43,8 +44,8 @@ void unary_op_gpu_inplace(
   int work_per_thread;
   std::string kernel_name;
   if (contig) {
-    work_per_thread = get_work_per_thread(in.dtype());
-    kernel_name = (large ? "v2" : "v");
+    work_per_thread = get_work_per_thread(in.dtype(), in.data_size());
+    kernel_name = (large ? "v2" : (work_per_thread > 1 ? "vn" : "v"));
   } else {
     work_per_thread = large ? 4 : 1;
     kernel_name = "gn" + std::to_string(work_per_thread);
@@ -99,21 +100,7 @@ void unary_op_gpu(
     array& out,
     const std::string op,
     const Stream& s) {
-  auto& in = inputs[0];
-  bool contig = in.flags().contiguous;
-  if (contig) {
-    if (in.is_donatable() && in.itemsize() == out.itemsize()) {
-      out.copy_shared_buffer(in);
-    } else {
-      out.set_data(
-          allocator::malloc(in.data_size() * out.itemsize()),
-          in.data_size(),
-          in.strides(),
-          in.flags());
-    }
-  } else {
-    out.set_data(allocator::malloc(out.nbytes()));
-  }
+  set_unary_output_data(inputs[0], out);
   unary_op_gpu_inplace(inputs, out, op, s);
 }
 

mlx/backend/metal/utils.h

@@ -72,6 +72,10 @@ void concatenate(std::string& acc, T first, Args... args) {
 inline int get_work_per_thread(Dtype dtype) {
   return std::max(1, 8 / dtype.size());
 }
+inline int get_work_per_thread(Dtype dtype, size_t size) {
+  constexpr size_t wpt_threshold = 1 << 16;
+  return size < wpt_threshold ? 1 : std::max(1, 8 / dtype.size());
+}
 
 inline size_t ceildiv(size_t n, size_t m) {
   return (n + m - 1) / m;

mlx/backend/no_gpu/primitives.cpp

@@ -2,6 +2,7 @@
 
 #include "mlx/primitives.h"
 #include "mlx/distributed/primitives.h"
+#include "mlx/fast.h"
 #include "mlx/fast_primitives.h"
 
 #define NO_GPU_MULTI(func)                                             \
@@ -155,6 +156,18 @@ NO_GPU_USE_FALLBACK(RoPE)
 NO_GPU(ScaledDotProductAttention)
 NO_GPU_MULTI(AffineQuantize)
 NO_GPU_MULTI(CustomKernel)
+
+MetalKernelFunction metal_kernel(
+    const std::string&,
+    const std::vector<std::string>&,
+    const std::vector<std::string>&,
+    const std::string&,
+    const std::string&,
+    bool ensure_row_contiguous,
+    bool atomic_outputs) {
+  throw std::runtime_error("[metal_kernel] No GPU back-end.");
+}
+
 } // namespace fast
 
 namespace distributed {

mlx/export.cpp

@@ -266,6 +266,7 @@ struct PrimitiveFactory {
       SERIALIZE_PRIMITIVE(Floor),
       SERIALIZE_PRIMITIVE(Full),
       SERIALIZE_PRIMITIVE(Gather),
+      SERIALIZE_PRIMITIVE(GatherAxis),
       SERIALIZE_PRIMITIVE(GatherMM),
       SERIALIZE_PRIMITIVE(Greater),
       SERIALIZE_PRIMITIVE(GreaterEqual),
@@ -307,6 +308,7 @@ struct PrimitiveFactory {
           "CumMax",
           "CumLogaddexp"),
       SERIALIZE_PRIMITIVE(Scatter),
+      SERIALIZE_PRIMITIVE(ScatterAxis),
       SERIALIZE_PRIMITIVE(Select),
       SERIALIZE_PRIMITIVE(Sigmoid),
       SERIALIZE_PRIMITIVE(Sign),

mlx/fast.cpp

@@ -1,10 +1,7 @@
 // Copyright © 2023-2024 Apple Inc.
 #include <cassert>
-#include <iostream>
 #include <numeric>
-#include <regex>
 
-#include "mlx/backend/common/compiled.h"
 #include "mlx/fast.h"
 #include "mlx/fast_primitives.h"
 #include "mlx/ops.h"
@@ -231,13 +228,11 @@ array layer_norm(
                       const std::vector<array>& inputs) {
     auto x = astype(inputs[0], float32, s);
 
-    // Should I not be smart here and leave the double mean to simplify()?
     auto mu = mean(x, /* axis= */ -1, /* keepdims= */ true, s);
-    auto mu2 = square(mu, s);
-    auto x2 = mean(square(x, s), /* axis= */ -1, /* keepdims= */ true, s);
-    auto v = subtract(x2, mu2, s);
+    auto xc = subtract(x, mu, s);
+    auto v = mean(square(xc, s), /* axis= */ -1, /* keepdims= */ true, s);
 
-    x = multiply(subtract(x, mu, s), rsqrt(add(v, array(eps, float32), s), s));
+    x = multiply(xc, rsqrt(add(v, array(eps, float32), s), s));
     x = astype(x, out_type, s);
 
     // If the LN is affine then transform x according to the weight and bias
@@ -1029,303 +1024,4 @@ std::vector<Shape> AffineQuantize::output_shapes(
   }
 }
 
-std::string write_signature(
-    std::string func_name,
-    const std::string& header,
-    const std::string& source,
-    const std::vector<std::string>& input_names,
-    const std::vector<array>& inputs,
-    const std::vector<std::string>& output_names,
-    const std::vector<Dtype>& output_dtypes,
-    const std::vector<std::pair<std::string, TemplateArg>>& template_args,
-    const std::vector<std::string>& attributes,
-    const std::vector<CustomKernelShapeInfo>& shape_infos,
-    bool atomic_outputs) {
-  std::string kernel_source;
-  kernel_source.reserve(header.size() + source.size() + 16384);
-  kernel_source += header;
-  // Auto-generate a function signature based on `template_args`
-  // and the dtype/shape of the arrays passed as `inputs`.
-  if (!template_args.empty()) {
-    kernel_source += "template <";
-    int i = 0;
-    for (const auto& [name, arg] : template_args) {
-      std::string param_type;
-      if (std::holds_alternative<int>(arg)) {
-        param_type = "int";
-      } else if (std::holds_alternative<bool>(arg)) {
-        param_type = "bool";
-      } else if (std::holds_alternative<Dtype>(arg)) {
-        param_type = "typename";
-      }
-      if (i > 0) {
-        kernel_source += ", ";
-      }
-      kernel_source += param_type;
-      kernel_source += " ";
-      kernel_source += name;
-      i++;
-    }
-    kernel_source += ">\n";
-  }
-  kernel_source += "[[kernel]] void ";
-  kernel_source += func_name;
-  kernel_source += "(\n";
-
-  int index = 0;
-  constexpr int max_constant_array_size = 8;
-  // Add inputs
-  for (int i = 0; i < inputs.size(); ++i) {
-    const auto& name = input_names[i];
-    const auto& arr = inputs[i];
-    auto dtype = get_type_string(arr.dtype());
-    std::string location =
-        arr.size() < max_constant_array_size ? "constant" : "device";
-    std::string ref = arr.ndim() == 0 ? "&" : "*";
-    kernel_source += "  const ";
-    kernel_source += location;
-    kernel_source += " ";
-    kernel_source += dtype;
-    kernel_source += ref;
-    kernel_source += " ";
-    kernel_source += name;
-    kernel_source += " [[buffer(";
-    kernel_source += std::to_string(index);
-    kernel_source += ")]],\n";
-    index++;
-    // Add input shape, strides and ndim if present in the source
-    if (arr.ndim() > 0) {
-      if (shape_infos[i].shape) {
-        kernel_source +=
-            ("  const constant int* " + name + "_shape [[buffer(" +
-             std::to_string(index) + ")]],\n");
-        index++;
-      }
-      if (shape_infos[i].strides) {
-        kernel_source +=
-            ("  const constant int64_t* " + name + "_strides [[buffer(" +
-             std::to_string(index) + ")]],\n");
-        index++;
-      }
-      if (shape_infos[i].ndim) {
-        kernel_source +=
-            ("  const constant int& " + name + "_ndim [[buffer(" +
-             std::to_string(index) + ")]],\n");
-        index++;
-      }
-    }
-  }
-  // Add outputs
-  for (int i = 0; i < output_names.size(); ++i) {
-    const auto& name = output_names[i];
-    const auto& dtype = output_dtypes[i];
-    kernel_source += "  device ";
-    auto type_string = get_type_string(dtype);
-    if (atomic_outputs) {
-      kernel_source += "atomic<";
-    }
-    kernel_source += type_string;
-    if (atomic_outputs) {
-      kernel_source += ">";
-    }
-    kernel_source += "* ";
-    kernel_source += name;
-    kernel_source += " [[buffer(";
-    kernel_source += std::to_string(index);
-    kernel_source += ")]]";
-    if (index < inputs.size() + output_names.size() - 1 ||
-        attributes.size() > 0) {
-      kernel_source += ",\n";
-    } else {
-      kernel_source += ") {\n";
-    }
-    index++;
-  }
-
-  index = 0;
-  for (const auto& attr : attributes) {
-    kernel_source += attr;
-    if (index < attributes.size() - 1) {
-      kernel_source += ",\n";
-    } else {
-      kernel_source += ") {\n";
-    }
-    index++;
-  }
-  kernel_source += source;
-  kernel_source += "\n}\n";
-  return kernel_source;
-}
-
-std::string write_template(
-    const std::vector<std::pair<std::string, TemplateArg>>& template_args) {
-  std::ostringstream template_def;
-  template_def << "<";
-  int i = 0;
-  for (const auto& [name, arg] : template_args) {
-    if (i > 0) {
-      template_def << ", ";
-    }
-    if (std::holds_alternative<int>(arg)) {
-      template_def << std::get<int>(arg);
-    } else if (std::holds_alternative<bool>(arg)) {
-      template_def << std::get<bool>(arg);
-    } else if (std::holds_alternative<Dtype>(arg)) {
-      template_def << get_type_string(std::get<Dtype>(arg));
-    }
-    i++;
-  }
-  template_def << ">";
-  return template_def.str();
-}
-
-MetalKernelFunction metal_kernel(
-    const std::string& name,
-    const std::vector<std::string>& input_names,
-    const std::vector<std::string>& output_names,
-    const std::string& source,
-    const std::string& header /* = "" */,
-    bool ensure_row_contiguous /* = true */,
-    bool atomic_outputs /* = false */) {
-  if (output_names.empty()) {
-    throw std::invalid_argument(
-        "[metal_kernel] Must specify at least one output.");
-  }
-  std::vector<CustomKernelShapeInfo> shape_infos;
-  for (auto& n : input_names) {
-    CustomKernelShapeInfo shape_info;
-    shape_info.shape = source.find(n + "_shape") != std::string::npos;
-    shape_info.strides = source.find(n + "_strides") != std::string::npos;
-    shape_info.ndim = source.find(n + "_ndim") != std::string::npos;
-    shape_infos.push_back(shape_info);
-  }
-  const std::vector<std::pair<std::string, std::string>> metal_attributes = {
-      {"dispatch_quadgroups_per_threadgroup", "uint"},
-      {"dispatch_simdgroups_per_threadgroup", "uint"},
-      {"dispatch_threads_per_threadgroup", "uint3"},
-      {"grid_origin", "uint3"},
-      {"grid_size", "uint3"},
-      {"quadgroup_index_in_threadgroup", "uint"},
-      {"quadgroups_per_threadgroup", "uint"},
-      {"simdgroup_index_in_threadgroup", "uint"},
-      {"simdgroups_per_threadgroup", "uint"},
-      {"thread_execution_width", "uint"},
-      {"thread_index_in_quadgroup", "uint"},
-      {"thread_index_in_simdgroup", "uint"},
-      {"thread_index_in_threadgroup", "uint"},
-      {"thread_position_in_grid", "uint3"},
-      {"thread_position_in_threadgroup", "uint3"},
-      {"threadgroup_position_in_grid", "uint3"},
-      {"threadgroups_per_grid", "uint3"},
-      {"threads_per_grid", "uint3"},
-      {"threads_per_simdgroup", "uint"},
-      {"threads_per_threadgroup", "uint3"},
-  };
-
-  std::vector<std::string> attributes;
-  for (const auto& [attr, dtype] : metal_attributes) {
-    if (source.find(attr) != std::string::npos) {
-      attributes.push_back("  " + dtype + " " + attr + " [[" + attr + "]]");
-    }
-  }
-
-  return [=,
-          shape_infos = std::move(shape_infos),
-          attributes = std::move(attributes)](
-             const std::vector<array>& inputs,
-             const std::vector<Shape>& output_shapes,
-             const std::vector<Dtype>& output_dtypes,
-             std::tuple<int, int, int> grid,
-             std::tuple<int, int, int> threadgroup,
-             const std::vector<std::pair<std::string, TemplateArg>>&
-                 template_args = {},
-             std::optional<float> init_value = std::nullopt,
-             bool verbose = false,
-             StreamOrDevice s_ = {}) {
-    if (inputs.size() != input_names.size()) {
-      std::ostringstream msg;
-      msg << "[metal_kernel] Expected `inputs` to have size "
-          << input_names.size() << " but got size " << inputs.size() << "."
-          << std::endl;
-      throw std::invalid_argument(msg.str());
-    }
-    if (output_shapes.size() != output_names.size()) {
-      std::ostringstream msg;
-      msg << "[metal_kernel] Expected `output_shapes` to have size "
-          << output_names.size() << " but got size " << output_shapes.size()
-          << "." << std::endl;
-      throw std::invalid_argument(msg.str());
-    }
-    if (output_dtypes.size() != output_names.size()) {
-      std::ostringstream msg;
-      msg << "[metal_kernel] Expected `output_dtypes` to have size "
-          << output_names.size() << " but got size " << output_dtypes.size()
-          << "." << std::endl;
-      throw std::invalid_argument(msg.str());
-    }
-
-    auto s = to_stream(s_);
-    if (s.device != Device::gpu) {
-      throw std::invalid_argument("[metal_kernel] Only supports the GPU.");
-    }
-
-    std::ostringstream func_name;
-    std::string template_def = "";
-    std::string hash_key = "";
-    if (!template_args.empty()) {
-      std::regex disallowed_chars("\\<|\\>|(, )");
-      template_def = write_template(template_args);
-      hash_key = std::regex_replace(template_def, disallowed_chars, "_");
-      hash_key.pop_back();
-    }
-    func_name << "custom_kernel_" << name << hash_key;
-    std::string kernel_name = func_name.str();
-
-    std::string kernel_source = write_signature(
-        kernel_name,
-        header,
-        source,
-        input_names,
-        inputs,
-        output_names,
-        output_dtypes,
-        template_args,
-        attributes,
-        shape_infos,
-        atomic_outputs);
-
-    if (!template_args.empty()) {
-      template_def = kernel_name + template_def;
-      kernel_source += "\ntemplate [[host_name(\"";
-      kernel_source += kernel_name;
-      kernel_source += "\")]] [[kernel]] decltype(";
-      kernel_source += template_def;
-      kernel_source += ") ";
-      kernel_source += template_def;
-      kernel_source += ";\n";
-    }
-
-    if (verbose) {
-      std::cout << "Generated source code for `" << name << "`:" << std::endl
-                << "```" << std::endl
-                << kernel_source << std::endl
-                << "```" << std::endl;
-    }
-
-    return array::make_arrays(
-        std::move(output_shapes),
-        std::move(output_dtypes),
-        std::make_shared<CustomKernel>(
-            s,
-            std::move(kernel_name),
-            std::move(kernel_source),
-            grid,
-            threadgroup,
-            shape_infos,
-            ensure_row_contiguous,
-            init_value),
-        std::move(inputs));
-  };
-}
-
 } // namespace mlx::core::fast

mlx/primitives.h

@@ -719,9 +719,9 @@ class Convolution : public UnaryPrimitive {
   bool is_equivalent(const Primitive& other) const override;
   auto state() const {
     return std::make_tuple(
+        kernel_strides_,
         padding_lo_,
         padding_hi_,
-        kernel_strides_,
         kernel_dilation_,
         input_dilation_,
         groups_,

python/mlx/nn/layers/base.py

@@ -174,11 +174,15 @@ class Module(dict):
             new_weights = dict(weights)
             curr_weights = dict(tree_flatten(self.parameters()))
             if extras := (new_weights.keys() - curr_weights.keys()):
-                extras = " ".join(extras)
-                raise ValueError(f"Received parameters not in model: {extras}.")
+                num_extra = len(extras)
+                extras = ",\n".join(sorted(extras))
+                raise ValueError(
+                    f"Received {num_extra} parameters not in model: \n{extras}."
+                )
             if missing := (curr_weights.keys() - new_weights.keys()):
-                missing = " ".join(missing)
-                raise ValueError(f"Missing parameters: {missing}.")
+                num_missing = len(missing)
+                missing = ",\n".join(sorted(missing))
+                raise ValueError(f"Missing {num_missing} parameters: \n{missing}.")
             for k, v in curr_weights.items():
                 v_new = new_weights[k]
                 if not isinstance(v_new, mx.array):
@@ -193,7 +197,7 @@ class Module(dict):
                     )
 
         if len(weights) != 0:
-            self.update(tree_unflatten(weights))
+            self.update(tree_unflatten(weights), strict=False)
         return self
 
     def save_weights(self, file: str):
@@ -291,7 +295,7 @@ class Module(dict):
 
         return self.filter_and_map(self.valid_child_filter, is_leaf_fn=_is_leaf_module)
 
-    def update(self, parameters: dict) -> Module:
+    def update(self, parameters: dict, strict: bool = True) -> Module:
         """Replace the parameters of this Module with the provided ones in the
         dict of dicts and lists.
 
@@ -305,7 +309,9 @@ class Module(dict):
 
         Args:
             parameters (dict): A complete or partial dictionary of the modules
-                               parameters.
+                parameters.
+            strict (bool): If ``True`` checks that ``parameters`` is a
+                subset of the module's parameters. Default: ``True``.
         Returns:
             The module instance after updating the parameters.
         """
@@ -317,21 +323,29 @@ class Module(dict):
                         current_value = dst[k]
                         new_value = parameters[k]
                         if isinstance(current_value, mx.array):
+                            if strict and not isinstance(new_value, mx.array):
+                                raise ValueError(
+                                    f"Received invalid type: {type(new_value).__name__}."
+                                )
                             dst[k] = new_value
-                        elif isinstance(current_value, Module):
-                            current_value.update(new_value)
-                        elif isinstance(current_value, (dict, list)):
+                        else:
                             apply(current_value, new_value)
+                    elif strict:
+                        raise ValueError(f'Module does not have parameter named "{k}".')
             elif isinstance(parameters, list):
                 for i in range(len(parameters)):
                     current_value = dst[i]
                     new_value = parameters[i]
                     if isinstance(current_value, mx.array):
+                        if strict and not isinstance(new_value, mx.array):
+                            raise ValueError(
+                                f"Received invalid type: {type(new_value).__name__}."
+                            )
                         dst[i] = new_value
-                    elif isinstance(current_value, Module):
-                        current_value.update(new_value)
-                    elif isinstance(current_value, (dict, list)):
+                    else:
                         apply(current_value, new_value)
+            elif strict:
+                raise ValueError(f"Received invalid type: {type(parameters).__name__}.")
 
         apply(self, parameters)
         return self
@@ -359,7 +373,7 @@ class Module(dict):
         self.update(self.filter_and_map(filter_fn, map_fn))
         return self
 
-    def update_modules(self, modules: dict) -> Module:
+    def update_modules(self, modules: dict, strict: bool = True) -> Module:
         """Replace the child modules of this :class:`Module` instance with the
         provided ones in the dict of dicts and lists.
 
@@ -368,12 +382,14 @@ class Module(dict):
         programmatically swapping layers.
 
         The passed in parameters dictionary need not be a full dictionary
-        similar to :meth:`parameters`. Only the provided locations will be
+        similar to :meth:`modules`. Only the provided locations will be
         updated.
 
         Args:
-            modules (dict): A complete or partial dictionary of the modules
+            modules (dict): A complete or partial dictionary of the module's
                 submodules.
+            strict (bool): If ``True`` checks that ``modules`` is a
+                subset of the child modules of this instance. Default: ``True``.
         Returns:
             The module instance after updating the submodules.
         """
@@ -388,6 +404,14 @@ class Module(dict):
                             dst[k] = new_value
                         elif isinstance(current_value, (dict, list)):
                             apply(current_value, new_value)
+                        elif strict:
+                            raise ValueError(
+                                f"Received invalid type: {type(new_value).__name__}."
+                            )
+                    elif strict:
+                        raise ValueError(
+                            f'Module does not have sub-module named "{k}".'
+                        )
             elif isinstance(modules, list):
                 for i in range(len(dst)):
                     current_value = dst[i]
@@ -396,6 +420,12 @@ class Module(dict):
                         dst[i] = new_value
                     elif isinstance(current_value, (dict, list)):
                         apply(current_value, new_value)
+                    elif strict:
+                        raise ValueError(
+                            f"Received invalid type: {type(new_value).__name__}."
+                        )
+            elif strict:
+                raise ValueError(f"Received invalid type: {type(modules).__name__}.")
 
         apply(self, modules)
         return self

python/src/CMakeLists.txt

@@ -54,5 +54,9 @@ target_link_libraries(core PRIVATE mlx)
 target_compile_definitions(core PRIVATE _VERSION_=${MLX_VERSION})
 
 if(BUILD_SHARED_LIBS)
-  target_link_options(core PRIVATE -Wl,-rpath,@loader_path/lib)
+  if(${CMAKE_SYSTEM_NAME} MATCHES "Darwin")
+    target_link_options(core PRIVATE -Wl,-rpath,@loader_path/lib)
+  else()
+    target_link_options(core PRIVATE -Wl,-rpath,\$ORIGIN/lib)
+  endif()
 endif()

python/tests/test_export_import.py

@@ -6,6 +6,7 @@ import tempfile
 import unittest
 
 import mlx.core as mx
+import mlx.nn as nn
 import mlx_tests
 
 
@@ -286,6 +287,65 @@ class TestExportImport(mlx_tests.MLXTestCase):
         with self.assertRaises(ValueError):
             f2(mx.array(10), mx.array([5, 10, 20]))
 
+    def test_export_scatter_gather(self):
+        path = os.path.join(self.test_dir, "fn.mlxfn")
+
+        def fun(a, b):
+            return mx.take_along_axis(a, b, axis=0)
+
+        x = mx.random.uniform(shape=(4, 4))
+        y = mx.array([[0, 1, 2, 3], [1, 2, 0, 3]])
+        mx.export_function(path, fun, (x, y))
+        imported_fun = mx.import_function(path)
+        expected = fun(x, y)
+        out = imported_fun(x, y)[0]
+        self.assertTrue(mx.array_equal(expected, out))
+
+        def fun(a, b, c):
+            return mx.put_along_axis(a, b, c, axis=0)
+
+        x = mx.random.uniform(shape=(4, 4))
+        y = mx.array([[0, 1, 2, 3], [1, 2, 0, 3]])
+        z = mx.random.uniform(shape=(2, 4))
+        mx.export_function(path, fun, (x, y, z))
+        imported_fun = mx.import_function(path)
+        expected = fun(x, y, z)
+        out = imported_fun(x, y, z)[0]
+        self.assertTrue(mx.array_equal(expected, out))
+
+    def test_export_conv(self):
+        path = os.path.join(self.test_dir, "fn.mlxfn")
+
+        class Model(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.c1 = nn.Conv2d(
+                    3, 16, kernel_size=3, stride=1, padding=1, bias=False
+                )
+                self.c2 = nn.Conv2d(
+                    16, 16, kernel_size=3, stride=2, padding=1, bias=False
+                )
+                self.c3 = nn.Conv2d(
+                    16, 16, kernel_size=3, stride=1, padding=2, bias=False
+                )
+
+            def __call__(self, x):
+                return self.c3(self.c2(self.c1(x)))
+
+        model = Model()
+        mx.eval(model.parameters())
+
+        def forward(x):
+            return model(x)
+
+        input_data = mx.random.normal(shape=(4, 32, 32, 3))
+        mx.export_function(path, forward, input_data)
+
+        imported_fn = mx.import_function(path)
+        out = imported_fn(input_data)[0]
+        expected = forward(input_data)
+        self.assertTrue(mx.allclose(expected, out))
+
 
 if __name__ == "__main__":
     unittest.main()

python/tests/test_fast.py

@@ -735,6 +735,41 @@ class TestFast(mlx_tests.MLXTestCase):
         )[0]
         self.assertEqual(out.item(), 2)
 
+    @unittest.skipIf(not mx.metal.is_available(), "Metal is not available")
+    def test_custom_kernel_caching(self):
+        def call_kernel(a: mx.array, source):
+            kernel = mx.fast.metal_kernel(
+                name="my_kernel",
+                input_names=["inp"],
+                output_names=["out"],
+                source=source,
+            )
+            return kernel(
+                inputs=[a],
+                grid=(a.size, 1, 1),
+                threadgroup=(a.size, 1, 1),
+                output_shapes=[a.shape],
+                output_dtypes=[a.dtype],
+                stream=mx.gpu,
+            )[0]
+
+        a = mx.random.normal(shape=(32,))
+
+        source = """
+            uint elem = thread_position_in_grid.x;
+            out[elem] = 0.0;
+        """
+
+        out = call_kernel(a, source)
+        self.assertTrue(mx.array_equal(out, mx.zeros_like(out)))
+
+        source = """
+            uint elem = thread_position_in_grid.x;
+            out[elem] = 1.0;
+        """
+        out = call_kernel(a, source)
+        self.assertTrue(mx.array_equal(out, mx.ones_like(out)))
+
 
 if __name__ == "__main__":
     unittest.main()

python/tests/test_linalg.py

@@ -359,36 +359,6 @@ class TestLinalg(mlx_tests.MLXTestCase):
                 mx.array([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])
             )  # Non-square matrix
 
-    def test_lu(self):
-        with self.assertRaises(ValueError):
-            mx.linalg.lu(mx.array(0.0), stream=mx.cpu)
-
-        with self.assertRaises(ValueError):
-            mx.linalg.lu(mx.array([0.0, 1.0]), stream=mx.cpu)
-
-        with self.assertRaises(ValueError):
-            mx.linalg.lu(mx.array([[0, 1], [1, 0]]), stream=mx.cpu)
-
-        # Test 3x3 matrix
-        a = mx.array([[3.0, 1.0, 2.0], [1.0, 8.0, 6.0], [9.0, 2.0, 5.0]])
-        P, L, U = mx.linalg.lu(a, stream=mx.cpu)
-        self.assertTrue(mx.allclose(L[P, :] @ U, a))
-
-        # Test batch dimension
-        a = mx.broadcast_to(a, (5, 5, 3, 3))
-        P, L, U = mx.linalg.lu(a, stream=mx.cpu)
-        L = mx.take_along_axis(L, P[..., None], axis=-2)
-        self.assertTrue(mx.allclose(L @ U, a))
-
-        # Test non-square matrix
-        a = mx.array([[3.0, 1.0, 2.0], [1.0, 8.0, 6.0]])
-        P, L, U = mx.linalg.lu(a, stream=mx.cpu)
-        self.assertTrue(mx.allclose(L[P, :] @ U, a))
-
-        a = mx.array([[3.0, 1.0], [1.0, 8.0], [9.0, 2.0]])
-        P, L, U = mx.linalg.lu(a, stream=mx.cpu)
-        self.assertTrue(mx.allclose(L[P, :] @ U, a))
-
     def test_eigh(self):
         tols = {"atol": 1e-5, "rtol": 1e-5}
 

python/tests/test_nn.py

@@ -219,6 +219,46 @@ class TestBase(mlx_tests.MLXTestCase):
         x = mx.zeros((3,))
         mx.grad(loss_fn)(model)
 
+    def test_update(self):
+        m = nn.Sequential(nn.Linear(3, 3), nn.Linear(3, 3))
+
+        # Updating non-existent parameters
+        with self.assertRaises(ValueError):
+            updates = {"layers": [{"value": 0}]}
+            m.update(updates)
+
+        with self.assertRaises(ValueError):
+            updates = {"layers": ["hello"]}
+            m.update(updates)
+
+        # Wronge type
+        with self.assertRaises(ValueError):
+            updates = {"layers": [{"weight": "hi"}]}
+            m.update(updates)
+
+    def test_update_modules(self):
+        m = nn.Sequential(nn.Linear(3, 3), nn.Linear(3, 3))
+
+        # Updating non-existent modules should not be allowed by default
+        with self.assertRaises(ValueError):
+            m = m.update_modules({"values": [0, 1]})
+
+        # Update wrong types
+        with self.assertRaises(ValueError):
+            m = m.update_modules({"layers": [0, 1]})
+
+        class MyModule(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.test = mx.array(1.0)
+                self.list = [mx.array(1.0), mx.array(2.0)]
+
+        m = MyModule()
+        with self.assertRaises(ValueError):
+            m = m.update_modules({"test": "hi"})
+        with self.assertRaises(ValueError):
+            m = m.update_modules({"list": ["hi"]})
+
 
 class TestLayers(mlx_tests.MLXTestCase):
     def test_identity(self):