angelos's commit files

.gitignore

@@ -0,0 +1,75 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Metal libraries
+*.metallib
+
+# Distribution / packaging
+python/mlx/share
+python/mlx/include
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# vim
+*.swp
+
+# Ignore build dir
+build/
+
+# Prerequisites
+*.d
+
+# Compiled Object files
+*.slo
+*.lo
+*.o
+*.obj
+
+# Precompiled Headers
+*.gch
+*.pch
+
+# Compiled Dynamic libraries
+*.so
+*.dylib
+*.dll
+
+# Fortran module files
+*.mod
+*.smod
+
+# Compiled Static libraries
+*.lai
+*.la
+*.a
+*.lib
+
+# Executables
+*.exe
+*.out
+*.app
+
+# VSCode 
+.vscode/
+.DS_Store

.pre-commit-config.yaml

@@ -0,0 +1,9 @@
+repos:
+-   repo: https://github.com/pre-commit/mirrors-clang-format
+    rev: v14.0.6
+    hooks:
+    -   id: clang-format
+-   repo: https://github.com/psf/black
+    rev: 22.10.0
+    hooks:
+    -   id: black

CMakeLists.txt

@@ -0,0 +1,197 @@
+cmake_minimum_required(VERSION 3.24)
+
+project(mlx LANGUAGES CXX)
+
+# ----------------------------- Setup -----------------------------
+set(CMAKE_MODULE_PATH "${PROJECT_SOURCE_DIR}/cmake")
+set(CMAKE_CXX_STANDARD 17)
+set(CMAKE_CXX_STANDARD_REQUIRED ON)
+set(CMAKE_POSITION_INDEPENDENT_CODE ON)
+set(CMAKE_INSTALL_MESSAGE NEVER)
+
+# ----------------------------- Configuration -----------------------------
+option(MLX_BUILD_TESTS "Build tests for mlx" ON)
+option(MLX_BUILD_EXAMPLES "Build examples for mlx" ON)
+option(MLX_BUILD_BENCHMARKS "Build benchmarks for mlx" OFF)
+option(MLX_BUILD_PYTHON_BINDINGS "Build python bindings for mlx" OFF)
+option(MLX_BUILD_METAL "Build metal backend" ON)
+option(BUILD_SHARED_LIBS "Build mlx as a shared library" OFF)
+
+if(NOT MLX_VERSION)
+  set(MLX_VERSION 0.0.1)
+endif()
+
+# ----------------------------- Lib -----------------------------
+
+include(FetchContent)
+# Avoid warning about DOWNLOAD_EXTRACT_TIMESTAMP in CMake 3.24:
+cmake_policy(SET CMP0135 NEW)
+
+add_library(mlx)
+
+if (MLX_BUILD_METAL)
+  find_library(METAL_LIB Metal)
+  find_library(FOUNDATION_LIB Foundation)
+  find_library(QUARTZ_LIB QuartzCore)
+endif()
+
+if (MLX_BUILD_METAL AND NOT METAL_LIB)
+  message(STATUS "Metal not found. Unable to build GPU")
+elseif (MLX_BUILD_METAL)
+  message(STATUS "Building METAL sources")
+  add_compile_definitions(_METAL_)
+
+  execute_process(COMMAND zsh "-c" "/usr/bin/sw_vers | cut -f2- -d: | sed -n 2p | grep -Eo '[0-9]+.[0-9]+'"
+                  OUTPUT_VARIABLE MACOS_VERSION)
+
+  message(STATUS "Detected macOS version ${MACOS_VERSION}")
+  if (${MACOS_VERSION} GREATER_EQUAL 14.0)
+    set(METAL_CPP_URL https://developer.apple.com/metal/cpp/files/metal-cpp_macOS14_iOS17-beta.zip)
+  elseif (${MACOS_VERSION} GREATER_EQUAL 13.3)
+    set(METAL_CPP_URL https://developer.apple.com/metal/cpp/files/metal-cpp_macOS13.3_iOS16.4.zip)
+  else()
+    set(METAL_CPP_URL https://developer.apple.com/metal/cpp/files/metal-cpp_macOS13_iOS16.zip)
+  endif()
+
+  FetchContent_Declare(
+    metal_cpp
+    URL ${METAL_CPP_URL}
+  )
+
+  FetchContent_MakeAvailable(metal_cpp)
+  target_include_directories(
+    mlx PUBLIC
+    $<BUILD_INTERFACE:${metal_cpp_SOURCE_DIR}>
+    $<INSTALL_INTERFACE:include/metal_cpp>
+  )
+  target_link_libraries(
+    mlx
+    ${METAL_LIB}
+    ${FOUNDATION_LIB}
+    ${QUARTZ_LIB})
+endif()
+
+find_library(ACCELERATE_LIBRARY Accelerate)
+if (ACCELERATE_LIBRARY)
+  message(STATUS "Accelerate found ${ACCELERATE_LIBRARY}")
+  set(MLX_BUILD_ACCELERATE ON)
+  target_link_libraries(mlx ${ACCELERATE_LIBRARY})
+  add_compile_definitions(ACCELERATE_NEW_LAPACK)
+else()
+  message(STATUS "Accelerate not found, using default backend.")
+  set(MLX_BUILD_ACCELERATE OFF)
+  #set(BLA_VENDOR Generic)
+  find_package(BLAS REQUIRED)
+  if (NOT BLAS_FOUND)
+    message(FATAL_ERROR "Must have BLAS installed")
+  endif()
+  # TODO find a cleaner way to do this
+  find_path(BLAS_INCLUDE_DIRS cblas.h
+    /usr/include
+    /usr/local/include
+    $ENV{BLAS_HOME}/include)
+  message(STATUS ${BLAS_LIBRARIES})
+  message(STATUS ${BLAS_INCLUDE_DIRS})
+  target_include_directories(mlx PRIVATE ${BLAS_INCLUDE_DIRS})
+  target_link_libraries(mlx ${BLAS_LIBRARIES})
+endif()
+
+add_subdirectory(${CMAKE_CURRENT_LIST_DIR}/mlx)
+
+target_include_directories(
+  mlx 
+  PUBLIC
+  $<BUILD_INTERFACE:${CMAKE_CURRENT_LIST_DIR}>
+  $<INSTALL_INTERFACE:include>
+)
+
+if (MLX_BUILD_PYTHON_BINDINGS)
+  message(STATUS "Building Python bindings.")
+  find_package(Python COMPONENTS Interpreter Development)
+  find_package(pybind11 CONFIG REQUIRED)
+  add_subdirectory(${CMAKE_CURRENT_LIST_DIR}/python/src)
+endif()
+
+if (MLX_BUILD_TESTS)
+  include(CTest)
+  add_subdirectory(${CMAKE_CURRENT_LIST_DIR}/tests)
+endif()
+
+if (MLX_BUILD_EXAMPLES)
+  add_subdirectory(${CMAKE_CURRENT_LIST_DIR}/examples/cpp)
+endif()
+
+if (MLX_BUILD_BENCHMARKS)
+  add_subdirectory(${CMAKE_CURRENT_LIST_DIR}/benchmarks/cpp)
+endif()
+
+# ----------------------------- Installation -----------------------------
+include(GNUInstallDirs)
+
+# Install library
+install(
+    TARGETS mlx
+    EXPORT MLXTargets
+    LIBRARY DESTINATION ${CMAKE_INSTALL_LIBDIR}
+    ARCHIVE DESTINATION ${CMAKE_INSTALL_LIBDIR}
+    RUNTIME DESTINATION ${CMAKE_INSTALL_BINDIR}
+    INCLUDES DESTINATION ${CMAKE_INSTALL_INCLUDEDIR}
+)
+
+
+# Install headers
+install(
+    DIRECTORY ${CMAKE_CURRENT_LIST_DIR}/mlx
+    DESTINATION ${CMAKE_INSTALL_INCLUDEDIR}
+    COMPONENT headers
+    FILES_MATCHING PATTERN "*.h"
+)
+
+# Install metal dependencies
+if (MLX_BUILD_METAL)
+
+  # Install metal cpp
+  install(
+      DIRECTORY ${metal_cpp_SOURCE_DIR}/
+      DESTINATION ${CMAKE_INSTALL_INCLUDEDIR}/metal_cpp
+      COMPONENT metal_cpp_source
+  )
+
+endif()
+
+# Install cmake config
+set(MLX_CMAKE_BUILD_CONFIG ${CMAKE_BINARY_DIR}/MLXConfig.cmake)
+set(MLX_CMAKE_BUILD_VERSION_CONFIG ${CMAKE_BINARY_DIR}/MLXConfigVersion.cmake)
+set(MLX_CMAKE_INSTALL_MODULE_DIR share/cmake/MLX)
+
+install(
+  EXPORT MLXTargets
+  FILE MLXTargets.cmake
+  DESTINATION ${MLX_CMAKE_INSTALL_MODULE_DIR}
+)
+
+include(CMakePackageConfigHelpers)
+
+write_basic_package_version_file(
+  ${MLX_CMAKE_BUILD_VERSION_CONFIG}
+  COMPATIBILITY SameMajorVersion
+  VERSION ${MLX_VERSION}
+)
+
+configure_package_config_file(
+  ${CMAKE_CURRENT_LIST_DIR}/mlx.pc.in
+  ${MLX_CMAKE_BUILD_CONFIG}
+  INSTALL_DESTINATION ${MLX_CMAKE_INSTALL_MODULE_DIR}
+  NO_CHECK_REQUIRED_COMPONENTS_MACRO
+  PATH_VARS CMAKE_INSTALL_LIBDIR CMAKE_INSTALL_INCLUDEDIR MLX_CMAKE_INSTALL_MODULE_DIR
+)
+
+install(
+  FILES ${MLX_CMAKE_BUILD_CONFIG} ${MLX_CMAKE_BUILD_VERSION_CONFIG}
+  DESTINATION ${MLX_CMAKE_INSTALL_MODULE_DIR}
+)
+
+install(
+  DIRECTORY ${CMAKE_MODULE_PATH}/
+  DESTINATION ${MLX_CMAKE_INSTALL_MODULE_DIR}
+)

benchmarks/cpp/time_utils.h

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+#pragma once
+
+#include <chrono>
+#include <iomanip>
+#include <iostream>
+
+#include "mlx/mlx.h"
+
+#define milliseconds(x) \
+  (std::chrono::duration_cast<std::chrono::nanoseconds>(x).count() / 1e6)
+#define time_now() std::chrono::high_resolution_clock::now()
+
+#define TIME(FUNC, ...)                                                        \
+  std::cout << "Timing " << #FUNC << " ... " << std::flush                     \
+            << std::setprecision(5) << time_fn(FUNC, ##__VA_ARGS__) << " msec" \
+            << std::endl;
+
+#define TIMEM(MSG, FUNC, ...)                                                  \
+  std::cout << "Timing "                                                       \
+            << "(" << MSG << ") " << #FUNC << " ... " << std::flush            \
+            << std::setprecision(5) << time_fn(FUNC, ##__VA_ARGS__) << " msec" \
+            << std::endl;
+
+template <typename F, typename... Args>
+double time_fn(F fn, Args... args) {
+  // warmup
+  for (int i = 0; i < 5; ++i) {
+    eval(fn(std::forward<Args>(args)...));
+  }
+
+  int num_iters = 100;
+  auto start = time_now();
+  for (int i = 0; i < num_iters; i++) {
+    eval(fn(std::forward<Args>(args)...));
+  }
+  auto end = time_now();
+  return milliseconds(end - start) / static_cast<double>(num_iters);
+}

benchmarks/python/batch_matmul_bench.py

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+import argparse
+import mlx.core as mx
+
+from time_utils import time_fn
+
+B = 8
+T = 1024
+D = 512
+
+
+def time_batch_matmul():
+    mx.random.seed(3)
+    a = mx.random.uniform(shape=(B, T, D))
+    b = mx.random.uniform(shape=(D, D))
+    c = mx.random.uniform(shape=(B, T, D))
+    mx.eval(a, b, c)
+
+    time_fn(mx.matmul, a, b)
+
+    def batch_vjp_first():
+        return mx.vjp(mx.matmul, [a, b], [c])[1][0]
+
+    time_fn(batch_vjp_first)
+
+    def batch_vjp_second():
+        return mx.vjp(mx.matmul, [a, b], [c])[1][1]
+
+    time_fn(batch_vjp_second)
+
+
+def time_unbatch_matmul(key):
+    mx.random.seed(3)
+    a = mx.random.uniform(shape=(B * T, D))
+    b = mx.random.uniform(shape=(D, D))
+    c = mx.random.uniform(shape=(B * T, D))
+    mx.eval(a, b, c)
+    time_fn(mx.matmul, a, b)
+
+    def unbatch_vjp_first():
+        return mx.matmul(c, mx.transpose(b))
+
+    time_fn(unbatch_vjp_first)
+
+    def unbatch_vjp_second():
+        return mx.matmul(mx.transpose(a), c)
+
+    time_fn(unbatch_vjp_second)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser("MLX benchmarks.")
+    parser.add_argument("--gpu", action="store_true", help="Use the Metal back-end.")
+    args = parser.parse_args()
+    if args.gpu:
+        mx.set_default_device(mx.gpu)
+    else:
+        mx.set_default_device(mx.cpu)
+
+    time_batch_matmul()
+    time_unbatch_matmul()

benchmarks/python/time_utils.py

@@ -0,0 +1,20 @@
+import time
+
+import mlx.core as mx
+
+
+def time_fn(fn, *args, **kwargs):
+    print(f"Timing {fn.__name__} ...", end=" ")
+
+    # warmup
+    for _ in range(5):
+        mx.eval(fn(*args, **kwargs))
+
+    num_iters = 100
+    tic = time.perf_counter()
+    for _ in range(num_iters):
+        x = mx.eval(fn(*args, **kwargs))
+    toc = time.perf_counter()
+
+    msec = 1e3 * (toc - tic) / num_iters
+    print(f"{msec:.5f} msec")

docs/.clang-format

@@ -0,0 +1,2 @@
+DisableFormat: true
+SortIncludes: Never

docs/Makefile

@@ -0,0 +1,18 @@
+# Minimal makefile for Sphinx documentation
+
+# You can set these variables from the command line.
+SPHINXOPTS    =
+SPHINXBUILD   = sphinx-build
+SOURCEDIR     = src
+BUILDDIR      = build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

docs/src/examples/mlp.rst

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+.. _mlp:
+
+Multi-Layer Perceptron
+----------------------
+
+In this example we'll learn to use ``mlx.nn`` by implementing a simple
+multi-layer perceptron to classify MNIST.
+
+As a first step import the MLX packages we need:
+
+.. code-block:: python
+
+  import mlx.core as mx
+  import mlx.nn as nn
+  import mlx.optimizers as optim
+
+  import numpy as np
+
+
+The model is defined as the ``MLP`` class which inherits from
+:class:`mlx.nn.Module`. We follow the standard idiom to make a new module:
+
+1. Define an ``__init__`` where the parameters and/or submodules are setup. See
+   the :ref:`Module class docs<module_class>` for more information on how
+   :class:`mlx.nn.Module` registers parameters.
+2. Define a ``__call__`` where the computation is implemented.
+
+.. code-block:: python
+
+  class MLP(nn.Module):
+      def __init__(
+          self, num_layers: int, input_dim: int, hidden_dim: int, output_dim: int
+      ):
+          super().__init__()
+          layer_sizes = [input_dim] + [hidden_dim] * num_layers + [output_dim]
+          self.layers = [
+              nn.Linear(idim, odim)
+              for idim, odim in zip(layer_sizes[:-1], layer_sizes[1:])
+          ]
+
+      def __call__(self, x):
+          for l in self.layers[:-1]:
+              x = mx.maximum(l(x), 0.0)
+          return self.layers[-1](x)
+
+
+We define the loss function which takes the mean of the per-example cross
+entropy loss.  The ``mlx.nn.losses`` sub-package has implementations of some
+commonly used loss functions.
+
+.. code-block:: python
+
+  def loss_fn(model, X, y):
+      return mx.mean(nn.losses.cross_entropy(model(X), y))
+
+We also need a function to compute the accuracy of the model on the validation
+set:
+
+.. code-block:: python
+
+  def eval_fn(model, X, y):
+      return mx.mean(mx.argmax(model(X), axis=1) == y)
+
+Next, setup the problem parameters and load the data:
+
+.. code-block:: python
+
+  num_layers = 2
+  hidden_dim = 32
+  num_classes = 10
+  batch_size = 256
+  num_epochs = 10
+  learning_rate = 1e-1
+
+  # Load the data
+  import mnist 
+  train_images, train_labels, test_images, test_labels = map(
+      mx.array, mnist.mnist()
+  )
+
+Since we're using SGD, we need an iterator which shuffles and constructs
+minibatches of examples in the training set:
+
+.. code-block:: python
+
+  def batch_iterate(batch_size, X, y):
+      perm = mx.array(np.random.permutation(y.size))
+      for s in range(0, y.size, batch_size):
+          ids = perm[s : s + batch_size]
+          yield X[ids], y[ids]
+
+
+Finally, we put it all together by instantiating the model, the
+:class:`mlx.optimizers.SGD` optimizer, and running the training loop:
+
+.. code-block:: python
+
+  # Load the model
+  model = MLP(num_layers, train_images.shape[-1], hidden_dim, num_classes)
+  mx.eval(model.parameters())
+
+  # Get a function which gives the loss and gradient of the
+  # loss with respect to the model's trainable parameters
+  loss_and_grad_fn = nn.value_and_grad(model, loss_fn)
+
+  # Instantiate the optimizer
+  optimizer = optim.SGD(learning_rate=learning_rate)
+
+  for e in range(num_epochs):
+      for X, y in batch_iterate(batch_size, train_images, train_labels):
+          loss, grads = loss_and_grad_fn(model, X, y)
+
+          # Update the optimizer state and model parameters
+          # in a single call
+          optimizer.update(model, grads)
+
+          # Force a graph evaluation
+          mx.eval(model.parameters(), optimizer.state)
+
+      accuracy = eval_fn(model, test_images, test_labels)
+      print(f"Epoch {e}: Test accuracy {accuracy.item():.3f}")
+
+
+.. note::
+  The :func:`mlx.nn.value_and_grad` function is a convenience function to get
+  the gradient of a loss with respect to the trainable parameters of a model.
+  This should not be confused with :func:`mlx.core.value_and_grad`.
+
+The model should train to a decent accuracy (about 95%) after just a few passes
+over the training set. The `full example <https://github.com/ml-explore/mlx-examples/tree/main/mlp>`_
+is available in the MLX GitHub repo.

docs/src/python/array.rst

@@ -0,0 +1,45 @@
+.. _array:
+
+Array
+=====
+
+.. currentmodule:: mlx.core
+
+.. autosummary:: 
+   :toctree: _autosummary 
+
+    array
+    array.astype
+    array.item
+    array.tolist
+    array.dtype
+    array.ndim
+    array.shape
+    array.size
+    Dtype
+    array.abs
+    array.all
+    array.any
+    array.argmax
+    array.argmin
+    array.cos
+    array.dtype
+    array.exp
+    array.log
+    array.log1p
+    array.logsumexp
+    array.max
+    array.mean
+    array.min
+    array.prod
+    array.reciprocal
+    array.reshape
+    array.rsqrt
+    array.sin
+    array.split
+    array.sqrt
+    array.square
+    array.sum
+    array.transpose
+    array.T
+    array.var

docs/src/python/ops.rst

@@ -0,0 +1,94 @@
+.. _ops:
+
+Operations
+==========
+
+.. currentmodule:: mlx.core
+
+.. autosummary:: 
+  :toctree: _autosummary
+
+   abs
+   add
+   all
+   allclose 
+   any
+   arange
+   arccos
+   arccosh
+   arcsin
+   arcsinh
+   arctan
+   arctanh
+   argmax
+   argmin
+   argpartition
+   argsort
+   array_equal
+   broadcast_to
+   concatenate
+   convolve
+   conv1d
+   conv2d
+   cos
+   cosh
+   divide
+   equal
+   erf
+   erfinv
+   exp
+   expand_dims
+   full
+   greater
+   greater_equal
+   less
+   less_equal
+   load
+   log
+   log2
+   log10
+   log1p
+   logaddexp
+   logical_not
+   logsumexp
+   matmul
+   max
+   maximum
+   mean
+   min
+   minimum
+   multiply
+   negative
+   ones
+   ones_like
+   partition
+   pad
+   prod
+   reciprocal
+   reshape
+   rsqrt
+   save
+   savez
+   savez_compressed
+   sigmoid
+   sign
+   sin
+   sinh
+   softmax
+   sort
+   split
+   sqrt
+   square
+   squeeze
+   stop_gradient
+   subtract
+   sum
+   take
+   take_along_axis
+   tan
+   tanh
+   transpose
+   var
+   where
+   zeros
+   zeros_like

examples/cpp/timer.h

@@ -0,0 +1,18 @@
+#pragma once
+
+#include <chrono>
+
+namespace timer {
+
+using namespace std::chrono;
+
+template <typename R, typename P>
+inline double seconds(duration<R, P> x) {
+  return duration_cast<nanoseconds>(x).count() / 1e9;
+}
+
+inline auto time() {
+  return high_resolution_clock::now();
+}
+
+} // namespace timer

examples/extensions/axpby/axpby.cpp

@@ -0,0 +1,359 @@
+#include <cassert>
+#include <iostream>
+#include <sstream>
+
+#include "mlx/backend/common/copy.h"
+#include "mlx/backend/common/utils.h"
+#include "mlx/utils.h"
+
+#include "axpby/axpby.h"
+
+#ifdef ACCELERATE_NEW_LAPACK
+#include <vecLib/cblas_new.h>
+#endif
+
+#ifdef _METAL_
+#include "mlx/backend/metal/device.h"
+#include "mlx/backend/metal/utils.h"
+#endif
+
+namespace mlx::core {
+
+///////////////////////////////////////////////////////////////////////////////
+// Operation Implementation
+///////////////////////////////////////////////////////////////////////////////
+
+/**
+ *  Scale and sum two vectors elementwise
+ *  z = alpha * x + beta * y
+ *
+ *  Follow numpy style broadcasting between x and y
+ *  Inputs are upcasted to floats if needed
+ **/
+array axpby(
+    const array& x, // Input array x
+    const array& y, // Input array y
+    const float alpha, // Scaling factor for x
+    const float beta, // Scaling factor for y
+    StreamOrDevice s /* = {} */ // Stream on which to schedule the operation
+) {
+  // Promote dtypes between x and y as needed
+  auto promoted_dtype = promote_types(x.dtype(), y.dtype());
+
+  // Upcast to float32 for non-floating point inputs x and y
+  auto out_dtype = is_floating_point(promoted_dtype)
+      ? promoted_dtype
+      : promote_types(promoted_dtype, float32);
+
+  // Cast x and y up to the determined dtype (on the same stream s)
+  auto x_casted = astype(x, out_dtype, s);
+  auto y_casted = astype(y, out_dtype, s);
+
+  // Broadcast the shapes of x and y (on the same stream s)
+  auto broadcasted_inputs = broadcast_arrays({x_casted, y_casted}, s);
+  auto out_shape = broadcasted_inputs[0].shape();
+
+  // Construct the array as the output of the Axpby primitive
+  // with the broadcasted and upcasted arrays as inputs
+  return array(
+      /* const std::vector<int>& shape = */ out_shape,
+      /* Dtype dtype = */ out_dtype,
+      /* std::unique_ptr<Primitive> primitive = */
+      std::make_unique<Axpby>(to_stream(s), alpha, beta),
+      /* const std::vector<array>& inputs = */ broadcasted_inputs);
+}
+
+///////////////////////////////////////////////////////////////////////////////
+// Primitive Common Backend Implementation
+///////////////////////////////////////////////////////////////////////////////
+
+template <typename T>
+void axpby_impl(
+    const array& x,
+    const array& y,
+    array& out,
+    float alpha_,
+    float beta_) {
+  // We only allocate memory when we are ready to fill the output
+  // malloc_or_wait synchronously allocates available memory
+  // There may be a wait executed here if the allocation is requested
+  // under memory-pressured conditions
+  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+
+  // Collect input and output data pointers
+  const T* x_ptr = x.data<T>();
+  const T* y_ptr = y.data<T>();
+  T* out_ptr = out.data<T>();
+
+  // Cast alpha and beta to the relevant types
+  T alpha = static_cast<T>(alpha_);
+  T beta = static_cast<T>(beta_);
+
+  // Do the elementwise operation for each output
+  for (size_t out_idx = 0; out_idx < out.size(); out_idx++) {
+    // Map linear indices to offsets in x and y
+    auto x_offset = elem_to_loc(out_idx, x.shape(), x.strides());
+    auto y_offset = elem_to_loc(out_idx, y.shape(), y.strides());
+
+    // We allocate the output to be contiguous and regularly strided
+    // (defaults to row major) and hence it doesn't need additonal mapping
+    out_ptr[out_idx] = alpha * x_ptr[x_offset] + beta * y_ptr[y_offset];
+  }
+}
+
+/** Fall back implementation for evaluation on CPU */
+void Axpby::eval(const std::vector<array>& inputs, array& out) {
+  // Check the inputs (registered in the op while contructing the out array)
+  assert(inputs.size() == 2);
+  auto& x = inputs[0];
+  auto& y = inputs[1];
+
+  // Dispatch to the correct dtype
+  if (out.dtype() == float32) {
+    return axpby_impl<float>(x, y, out, alpha_, beta_);
+  } else if (out.dtype() == float16) {
+    return axpby_impl<float16_t>(x, y, out, alpha_, beta_);
+  } else if (out.dtype() == bfloat16) {
+    return axpby_impl<bfloat16_t>(x, y, out, alpha_, beta_);
+  } else if (out.dtype() == complex64) {
+    return axpby_impl<complex64_t>(x, y, out, alpha_, beta_);
+  } else {
+    throw std::runtime_error(
+        "Axpby is only supported for floating point types.");
+  }
+}
+
+///////////////////////////////////////////////////////////////////////////////
+// Primitive Accelerate Backend Implementation
+///////////////////////////////////////////////////////////////////////////////
+
+#ifdef ACCELERATE_NEW_LAPACK
+
+template <typename T>
+void axpby_impl_accelerate(
+    const array& x,
+    const array& y,
+    array& out,
+    float alpha_,
+    float beta_) {
+  // Accelerate library provides catlas_saxpby which does
+  // Y = (alpha * X) + (beta * Y) in place
+  // To use it, we first copy the data in y over to the output array
+
+  // This specialization requires both x and y be contiguous in the same mode
+  // i.e: corresponding linear indices in both point to corresponding elements
+  // The data in the output array is allocated to match the strides in y
+  // such that x, y, and out are contiguous in the same mode and
+  // no transposition is needed
+  out.set_data(
+      allocator::malloc_or_wait(y.data_size() * out.itemsize()),
+      y.data_size(),
+      y.strides(),
+      y.flags());
+
+  // We then copy over the elements using the contiguous vector specialization
+  copy_inplace(y, out, CopyType::Vector);
+
+  // Get x and y pointers for catlas_saxpby
+  const T* x_ptr = x.data<T>();
+  T* y_ptr = out.data<T>();
+
+  T alpha = static_cast<T>(alpha_);
+  T beta = static_cast<T>(beta_);
+
+  // Call the inplace accelerate operator
+  catlas_saxpby(
+      /* N = */ out.size(),
+      /* ALPHA = */ alpha,
+      /* X = */ x_ptr,
+      /* INCX = */ 1,
+      /* BETA = */ beta,
+      /* Y = */ y_ptr,
+      /* INCY = */ 1);
+}
+
+/** Evaluate primitive on CPU using accelerate specializations */
+void Axpby::eval_cpu(const std::vector<array>& inputs, array& out) {
+  assert(inputs.size() == 2);
+  auto& x = inputs[0];
+  auto& y = inputs[1];
+
+  // Accelerate specialization for contiguous single precision float arrays
+  if (out.dtype() == float32 &&
+      ((x.flags().row_contiguous && y.flags().row_contiguous) ||
+       (x.flags().col_contiguous && y.flags().col_contiguous))) {
+    axpby_impl_accelerate<float>(x, y, out, alpha_, beta_);
+    return;
+  }
+
+  // Fall back to common backend if specializations are not available
+  eval(inputs, out);
+}
+
+#else // Accelerate not avaliable
+
+/** Evaluate primitive on CPU falling back to common backend */
+void Axpby::eval_cpu(const std::vector<array>& inputs, array& out) {
+  eval(inputs, out);
+}
+
+#endif
+
+///////////////////////////////////////////////////////////////////////////////
+// Primitive Metal Backend Implementation
+///////////////////////////////////////////////////////////////////////////////
+
+#ifdef _METAL_
+
+/** Evaluate primitive on GPU */
+void Axpby::eval_gpu(const std::vector<array>& inputs, array& out) {
+  // Prepare inputs
+  assert(inputs.size() == 2);
+  auto& x = inputs[0];
+  auto& y = inputs[1];
+
+  // Each primitive carries the stream it should execute on
+  // and each stream carries its device identifiers
+  auto& s = stream();
+  // We get the needed metal device using the stream
+  auto& d = metal::device(s.device);
+
+  // Prepare to specialize based on contiguity
+  bool contiguous_kernel =
+      (x.flags().row_contiguous && y.flags().row_contiguous) ||
+      (x.flags().col_contiguous && y.flags().col_contiguous);
+
+  // Allocate output memory with strides based on specialization
+  if (contiguous_kernel) {
+    out.set_data(
+        allocator::malloc_or_wait(x.data_size() * out.itemsize()),
+        x.data_size(),
+        x.strides(),
+        x.flags());
+  } else {
+    out.set_data(allocator::malloc_or_wait(out.nbytes()));
+  }
+
+  // Resolve name of kernel (corresponds to axpby.metal)
+  std::ostringstream kname;
+  kname << "axpby_";
+  kname << (contiguous_kernel ? "contiguous_" : "general_");
+  kname << type_to_name(out);
+
+  // Make sure the metal library is available and look for it
+  // in the same folder as this executable if needed
+  d.register_library("mlx_ext", metal::get_colocated_mtllib_path);
+
+  // Make a kernel from this metal library
+  auto kernel = d.get_kernel(kname.str(), "mlx_ext");
+
+  // Prepare to encode kernel
+  auto compute_encoder = d.get_command_encoder(s.index);
+  compute_encoder->setComputePipelineState(kernel);
+
+  // Kernel parameters are registered with buffer indices corresponding to
+  // those in the kernel decelaration at axpby.metal
+  int ndim = out.ndim();
+  size_t nelem = out.size();
+
+  // Encode input arrays to kernel
+  set_array_buffer(compute_encoder, x, 0);
+  set_array_buffer(compute_encoder, y, 1);
+
+  // Encode output arrays to kernel
+  set_array_buffer(compute_encoder, out, 2);
+
+  // Encode alpha and beta
+  compute_encoder->setBytes(&alpha_, sizeof(float), 3);
+  compute_encoder->setBytes(&beta_, sizeof(float), 4);
+
+  // Encode shape, strides and ndim if needed
+  if (!contiguous_kernel) {
+    compute_encoder->setBytes(x.shape().data(), ndim * sizeof(int), 5);
+    compute_encoder->setBytes(x.strides().data(), ndim * sizeof(size_t), 6);
+    compute_encoder->setBytes(y.strides().data(), ndim * sizeof(size_t), 7);
+    compute_encoder->setBytes(&ndim, sizeof(int), 8);
+  }
+
+  // We launch 1 thread for each input and make sure that the number of
+  // threads in any given threadgroup is not higher than the max allowed
+  size_t tgp_size = std::min(nelem, kernel->maxTotalThreadsPerThreadgroup());
+
+  // Fix the 3D size of each threadgroup (in terms of threads)
+  MTL::Size group_dims = MTL::Size(tgp_size, 1, 1);
+
+  // Fix the 3D size of the launch grid (in terms of threads)
+  MTL::Size grid_dims = MTL::Size(nelem, 1, 1);
+
+  // Launch the grid with the given number of threads divded among
+  // the given threadgroups
+  compute_encoder->dispatchThreads(grid_dims, group_dims);
+}
+
+#else // Metal is not available
+
+/** Fail evaluation on GPU */
+void Axpby::eval_gpu(const std::vector<array>& inputs, array& out) {
+  throw std::runtime_error("Axpby has no GPU implementation.");
+}
+
+#endif
+
+///////////////////////////////////////////////////////////////////////////////
+// Primitive Transforms
+///////////////////////////////////////////////////////////////////////////////
+
+/** The Jacobian-vector product. */
+array Axpby::jvp(
+    const std::vector<array>& primals,
+    const std::vector<array>& tangents,
+    const std::vector<int>& argnums) {
+  // Forward mode diff that pushes along the tangents
+  // The jvp transform on the the primitive can built with ops
+  // that are scheduled on the same stream as the primtive
+
+  // If argnums = {0}, we only push along x in which case the
+  // jvp is just the tangent scaled by alpha
+  // Similarly, if argnums = {1}, the jvp is just the tangent
+  // scaled by beta
+  if (argnums.size() > 1) {
+    auto scale = argnums[0] == 0 ? alpha_ : beta_;
+    auto scale_arr = array(scale, tangents[0].dtype());
+    return multiply(scale_arr, tangents[0], stream());
+  }
+  // If, argnums = {0, 1}, we take contributions from both
+  // which gives us jvp = tangent_x * alpha + tangent_y * beta
+  else {
+    return axpby(tangents[0], tangents[1], alpha_, beta_, stream());
+  }
+}
+
+/** The vector-Jacobian product. */
+std::vector<array> Axpby::vjp(
+    const std::vector<array>& primals,
+    const array& cotan,
+    const std::vector<int>& argnums) {
+  // Reverse mode diff
+  std::vector<array> vjps;
+  for (auto arg : argnums) {
+    auto scale = arg == 0 ? alpha_ : beta_;
+    auto scale_arr = array(scale, cotan.dtype());
+    vjps.push_back(multiply(scale_arr, cotan, stream()));
+  }
+  return vjps;
+}
+
+/** Vectorize primitve along given axis */
+std::pair<array, int> Axpby::vmap(
+    const std::vector<array>& inputs,
+    const std::vector<int>& axes) {
+  throw std::runtime_error("Axpby has no vmap implementation.");
+}
+
+/** Equivalence check **/
+bool Axpby::is_equivalent(const Primitive& other) const {
+  const Axpby& r_other = static_cast<const Axpby&>(other);
+  return alpha_ == r_other.alpha_ && beta_ == r_other.beta_;
+}
+
+} // namespace mlx::core

examples/extensions/bindings.cpp

@@ -0,0 +1,39 @@
+#include <pybind11/pybind11.h>
+#include <pybind11/stl.h>
+
+#include "axpby/axpby.h"
+
+namespace py = pybind11;
+using namespace py::literals;
+using namespace mlx::core;
+
+PYBIND11_MODULE(mlx_sample_extensions, m) {
+  m.doc() = "Sample C++ and metal extensions for MLX";
+
+  m.def(
+      "axpby",
+      &axpby,
+      "x"_a,
+      "y"_a,
+      py::pos_only(),
+      "alpha"_a,
+      "beta"_a,
+      py::kw_only(),
+      "stream"_a = py::none(),
+      R"pbdoc(
+        Scale and sum two vectors elementwise
+        ``z = alpha * x + beta * y``
+        
+        Follows numpy style broadcasting between ``x`` and ``y``
+        Inputs are upcasted to floats if needed
+
+        Args:
+            x (array): Input array.
+            y (array): Input array.
+            alpha (float): Scaling factor for ``x``.
+            beta (float): Scaling factor for ``y``.
+
+        Returns:
+            array: ``alpha * x + beta * y``
+      )pbdoc");
+}

mlx/CMakeLists.txt

@@ -0,0 +1,36 @@
+target_sources(
+  mlx
+  PRIVATE
+  ${CMAKE_CURRENT_SOURCE_DIR}/allocator.cpp
+  ${CMAKE_CURRENT_SOURCE_DIR}/array.cpp
+  ${CMAKE_CURRENT_SOURCE_DIR}/device.cpp
+  ${CMAKE_CURRENT_SOURCE_DIR}/dtype.cpp
+  ${CMAKE_CURRENT_SOURCE_DIR}/fft.cpp
+  ${CMAKE_CURRENT_SOURCE_DIR}/ops.cpp
+  ${CMAKE_CURRENT_SOURCE_DIR}/graph_utils.cpp
+  ${CMAKE_CURRENT_SOURCE_DIR}/load.cpp
+  ${CMAKE_CURRENT_SOURCE_DIR}/primitives.cpp
+  ${CMAKE_CURRENT_SOURCE_DIR}/random.cpp
+  ${CMAKE_CURRENT_SOURCE_DIR}/scheduler.cpp
+  ${CMAKE_CURRENT_SOURCE_DIR}/transforms.cpp
+  ${CMAKE_CURRENT_SOURCE_DIR}/utils.cpp
+  ${CMAKE_CURRENT_SOURCE_DIR}/backend/metal/metal.h
+)
+
+add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/backend/common)
+
+if (MLX_BUILD_ACCELERATE) 
+  add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/backend/accelerate)
+else()
+  target_sources(
+    mlx
+    PRIVATE
+    ${CMAKE_CURRENT_SOURCE_DIR}/backend/common/default_primitives.cpp
+  )
+endif()
+
+if (MLX_BUILD_METAL)
+  add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/backend/metal)
+else()
+  add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/backend/no_metal)
+endif()

mlx/array.cpp

@@ -0,0 +1,143 @@
+#include <functional>
+
+#include "mlx/array.h"
+#include "mlx/ops.h"
+#include "mlx/primitives.h"
+#include "mlx/transforms.h"
+
+namespace mlx::core {
+
+namespace {
+
+std::pair<size_t, std::vector<size_t>> cum_prod(const std::vector<int>& shape) {
+  std::vector<size_t> strides(shape.size());
+  size_t cum_prod = 1;
+  for (int i = shape.size() - 1; i >= 0; --i) {
+    strides[i] = cum_prod;
+    cum_prod *= shape[i];
+  }
+  return {cum_prod, strides};
+}
+
+} // namespace
+
+array::array(const std::complex<float>& val, Dtype dtype /* = complex64 */)
+    : array_desc_(std::make_shared<ArrayDesc>(std::vector<int>{}, dtype)) {
+  auto cval = static_cast<complex64_t>(val);
+  init(&cval);
+}
+
+array::array(
+    const std::vector<int>& shape,
+    Dtype dtype,
+    std::unique_ptr<Primitive> primitive,
+    const std::vector<array>& inputs)
+    : array_desc_(std::make_shared<ArrayDesc>(
+          shape,
+          dtype,
+          std::move(primitive),
+          inputs)) {}
+
+array::array(std::initializer_list<float> data)
+    : array_desc_(std::make_shared<ArrayDesc>(
+          std::vector<int>{static_cast<int>(data.size())},
+          float32)) {
+  init(data.begin());
+}
+
+/* Build an array from a shared buffer */
+array::array(
+    allocator::Buffer data,
+    const std::vector<int>& shape,
+    Dtype dtype,
+    deleter_t deleter)
+    : array_desc_(std::make_shared<ArrayDesc>(shape, dtype)) {
+  set_data(data, deleter);
+}
+
+void array::detach() {
+  array_desc_->inputs.clear();
+  array_desc_->primitive = nullptr;
+}
+
+void array::eval(bool retain_graph /* = false */) {
+  mlx::core::eval({*this}, retain_graph);
+}
+
+void array::set_data(allocator::Buffer buffer, deleter_t d) {
+  array_desc_->data = std::make_shared<Data>(buffer, d);
+  array_desc_->data_ptr = buffer.raw_ptr();
+  array_desc_->data_size = size();
+  array_desc_->flags.contiguous = true;
+  array_desc_->flags.row_contiguous = true;
+  auto max_dim = std::max_element(shape().begin(), shape().end());
+  array_desc_->flags.col_contiguous = size() <= 1 || size() == *max_dim;
+}
+
+void array::set_data(
+    allocator::Buffer buffer,
+    size_t data_size,
+    std::vector<size_t> strides,
+    Flags flags,
+    deleter_t d) {
+  array_desc_->data = std::make_shared<Data>(buffer, d);
+  array_desc_->data_ptr = buffer.raw_ptr();
+  array_desc_->data_size = data_size;
+  array_desc_->strides = std::move(strides);
+  array_desc_->flags = flags;
+}
+
+void array::copy_shared_buffer(
+    const array& other,
+    const std::vector<size_t>& strides,
+    Flags flags,
+    size_t data_size,
+    size_t offset /* = 0 */) {
+  array_desc_->data = other.array_desc_->data;
+  array_desc_->strides = strides;
+  array_desc_->flags = flags;
+  array_desc_->data_size = data_size;
+  auto char_offset = sizeof(char) * itemsize() * offset;
+  array_desc_->data_ptr = static_cast<void*>(
+      static_cast<char*>(other.array_desc_->data_ptr) + char_offset);
+}
+
+void array::copy_shared_buffer(const array& other) {
+  copy_shared_buffer(other, other.strides(), other.flags(), other.data_size());
+}
+
+array::ArrayDesc::ArrayDesc(const std::vector<int>& shape, Dtype dtype)
+    : shape(shape), dtype(dtype) {
+  std::tie(size, strides) = cum_prod(shape);
+}
+
+array::ArrayDesc::ArrayDesc(
+    const std::vector<int>& shape,
+    Dtype dtype,
+    std::unique_ptr<Primitive> primitive,
+    const std::vector<array>& inputs)
+    : shape(shape),
+      dtype(dtype),
+      primitive(std::move(primitive)),
+      inputs(inputs) {
+  std::tie(size, strides) = cum_prod(shape);
+  for (auto& in : inputs) {
+    is_tracer |= in.is_tracer();
+  }
+}
+
+// Needed because the Primitive type used in array.h is incomplete and the
+// compiler needs to see the call to the desctructor after the type is complete.
+array::ArrayDesc::~ArrayDesc() = default;
+
+array::ArrayIterator::reference array::ArrayIterator::operator*() const {
+  auto start = std::vector<int>(arr.ndim(), 0);
+  auto end = arr.shape();
+  auto shape = arr.shape();
+  shape.erase(shape.begin());
+  start[0] = idx;
+  end[0] = idx + 1;
+  return reshape(slice(arr, start, end), shape);
+};
+
+} // namespace mlx::core

mlx/backend/accelerate/softmax.cpp

@@ -0,0 +1,323 @@
+#include <cassert>
+#include <limits>
+
+#include <arm_neon.h>
+#include <simd/math.h>
+#include <simd/vector.h>
+
+#include "mlx/backend/common/copy.h"
+#include "mlx/primitives.h"
+
+namespace mlx::core {
+
+namespace {
+
+/**
+ * Compute exp(x) in an optimizer friendly way as follows:
+ *
+ * First change the problem to computing 2**y where y = x / ln(2).
+ *
+ * Now we will compute 2**y as 2**y1 * 2**y2 where y1 is the integer part
+ * `ipart` and y2 is fractional part. For the integer part we perform bit
+ * shifting and for the fractional part we use a polynomial approximation.
+ *
+ * The algorithm and constants of the polynomial taken from
+ * https://github.com/akohlmey/fastermath/blob/master/src/exp.c which took them
+ * from Cephes math library.
+ *
+ * Note: The implementation below is a general fast exp. There could be faster
+ *       implementations for numbers strictly < 0.
+ */
+inline simd_float16 simd_fast_exp(simd_float16 x) {
+  x *= 1.442695; // multiply with log_2(e)
+  simd_float16 ipart, fpart;
+  simd_int16 epart;
+  x = simd_clamp(x, -80, 80);
+  ipart = simd::floor(x + 0.5);
+  fpart = x - ipart;
+
+  x = 1.535336188319500e-4f;
+  x = x * fpart + 1.339887440266574e-3f;
+  x = x * fpart + 9.618437357674640e-3f;
+  x = x * fpart + 5.550332471162809e-2f;
+  x = x * fpart + 2.402264791363012e-1f;
+  x = x * fpart + 6.931472028550421e-1f;
+  x = x * fpart + 1.000000000000000f;
+
+  // generate 2**ipart in the floating point representation using integer
+  // bitshifting
+  epart = (simd_int(ipart) + 127) << 23;
+
+  return (*(simd_float16*)&epart) * x;
+}
+
+/**
+ * The ARM neon equivalent of the fast exp above.
+ */
+inline float16x8_t neon_fast_exp(float16x8_t x) {
+  x = vmulq_f16(x, vdupq_n_f16(1.442695)); // multiply with log_2(e)
+  x = vmaxq_f16(x, vdupq_n_f16(-14)); // clamp under with -14
+  x = vminq_f16(x, vdupq_n_f16(14)); // clamp over with 14
+
+  float16x8_t ipart = vrndmq_f16(vaddq_f16(x, vdupq_n_f16(0.5)));
+  float16x8_t fpart = vsubq_f16(x, ipart);
+
+  x = vdupq_n_f16(1.535336188319500e-4f);
+  x = vfmaq_f16(vdupq_n_f16(1.339887440266574e-3f), x, fpart);
+  x = vfmaq_f16(vdupq_n_f16(1.339887440266574e-3f), x, fpart);
+  x = vfmaq_f16(vdupq_n_f16(9.618437357674640e-3f), x, fpart);
+  x = vfmaq_f16(vdupq_n_f16(5.550332471162809e-2f), x, fpart);
+  x = vfmaq_f16(vdupq_n_f16(2.402264791363012e-1f), x, fpart);
+  x = vfmaq_f16(vdupq_n_f16(6.931472028550421e-1f), x, fpart);
+  x = vfmaq_f16(vdupq_n_f16(1.000000000000000f), x, fpart);
+
+  // generate 2**ipart in the floating point representation using integer
+  // bitshifting
+  int16x8_t epart = vcvtq_s16_f16(ipart);
+  epart = vaddq_s16(epart, vdupq_n_s16(15));
+  epart = vshlq_n_s16(epart, 10);
+
+  return vmulq_f16(vreinterpretq_f16_s16(epart), x);
+}
+
+/**
+ * Implementation of folding maximum for ARM neon. This should possibly be
+ * refactored out of softmax.cpp at some point.
+ */
+inline float16_t neon_reduce_max(float16x8_t x) {
+  float16x4_t y;
+  y = vpmax_f16(vget_low_f16(x), vget_high_f16(x));
+  y = vpmax_f16(y, y);
+  y = vpmax_f16(y, y);
+  return vget_lane_f16(y, 0);
+}
+
+/**
+ * Implementation of folding sum for ARM neon. This should possibly be
+ * refactored out of softmax.cpp at some point.
+ */
+inline float16_t neon_reduce_add(float16x8_t x) {
+  float16x4_t y;
+  float16x4_t zero = vdup_n_f16(0);
+  y = vpadd_f16(vget_low_f16(x), vget_high_f16(x));
+  y = vpadd_f16(y, zero);
+  y = vpadd_f16(y, zero);
+  return vget_lane_f16(y, 0);
+}
+
+template <typename T, typename VT>
+struct AccelerateSimdOps {
+  VT init(T a) {
+    return a;
+  }
+
+  VT load(const T* a) {
+    return *(VT*)a;
+  }
+
+  void store(T* dst, VT x) {
+    *(VT*)dst = x;
+  }
+
+  VT max(VT a, VT b) {
+    return simd_max(a, b);
+  };
+
+  VT exp(VT x) {
+    return simd_fast_exp(x);
+  }
+
+  VT add(VT a, VT b) {
+    return a + b;
+  }
+
+  VT sub(VT a, T b) {
+    return a - b;
+  }
+
+  VT mul(VT a, VT b) {
+    return a * b;
+  }
+
+  VT mul(VT a, T b) {
+    return a * b;
+  }
+
+  T reduce_max(VT x) {
+    return simd_reduce_max(x);
+  }
+
+  T reduce_add(VT x) {
+    return simd_reduce_add(x);
+  }
+};
+
+template <typename T, typename VT>
+struct NeonFp16SimdOps {
+  VT init(T a) {
+    return vdupq_n_f16(a);
+  }
+
+  VT load(const T* a) {
+    return vld1q_f16(a);
+  }
+
+  void store(T* dst, VT x) {
+    vst1q_f16(dst, x);
+  }
+
+  VT max(VT a, VT b) {
+    return vmaxq_f16(a, b);
+  };
+
+  VT exp(VT x) {
+    return neon_fast_exp(x);
+  }
+
+  VT add(VT a, VT b) {
+    return vaddq_f16(a, b);
+  }
+
+  VT sub(VT a, T b) {
+    return vsubq_f16(a, vdupq_n_f16(b));
+  }
+
+  VT mul(VT a, VT b) {
+    return vmulq_f16(a, b);
+  }
+
+  VT mul(VT a, T b) {
+    return vmulq_f16(a, vdupq_n_f16(b));
+  }
+
+  T reduce_max(VT x) {
+    return neon_reduce_max(x);
+  }
+
+  T reduce_add(VT x) {
+    return neon_reduce_add(x);
+  }
+};
+
+template <typename T, typename VT, typename Ops, int N>
+void softmax(const array& in, array& out) {
+  Ops ops;
+
+  const T* in_ptr = in.data<T>();
+  T* out_ptr = out.data<T>();
+  int M = in.shape().back();
+  int L = in.data_size() / M;
+  const T* current_in_ptr;
+  T* current_out_ptr;
+
+  for (int i = 0; i < L; i++, in_ptr += M, out_ptr += M) {
+    // Find the maximum
+    current_in_ptr = in_ptr;
+    VT vmaximum = ops.init(-std::numeric_limits<float>::infinity());
+    size_t s = M;
+    while (s >= N) {
+      vmaximum = ops.max(ops.load(current_in_ptr), vmaximum);
+      current_in_ptr += N;
+      s -= N;
+    }
+    T maximum = ops.reduce_max(vmaximum);
+    while (s-- > 0) {
+      maximum = std::max(maximum, *current_in_ptr);
+      current_in_ptr++;
+    }
+
+    // Compute the normalizer and the exponentials
+    VT vnormalizer = ops.init(0.0);
+    current_out_ptr = out_ptr;
+    current_in_ptr = in_ptr;
+    s = M;
+    while (s >= N) {
+      VT vexp = ops.exp(ops.sub(*(VT*)current_in_ptr, maximum));
+      ops.store(current_out_ptr, vexp);
+      *(VT*)current_out_ptr = vexp;
+      vnormalizer = ops.add(vnormalizer, vexp);
+      current_in_ptr += N;
+      current_out_ptr += N;
+      s -= N;
+    }
+    T normalizer = ops.reduce_add(vnormalizer);
+    while (s-- > 0) {
+      T _exp = std::exp(*current_in_ptr - maximum);
+      *current_out_ptr = _exp;
+      normalizer += _exp;
+      current_in_ptr++;
+      current_out_ptr++;
+    }
+    normalizer = 1 / normalizer;
+
+    // Normalize
+    current_out_ptr = out_ptr;
+    s = M;
+    while (s >= N) {
+      ops.store(current_out_ptr, ops.mul(*(VT*)current_out_ptr, normalizer));
+      current_out_ptr += N;
+      s -= N;
+    }
+    while (s-- > 0) {
+      *current_out_ptr *= normalizer;
+      current_out_ptr++;
+    }
+  }
+}
+
+} // namespace
+
+void Softmax::eval_cpu(const std::vector<array>& inputs, array& out) {
+  assert(inputs.size() == 1);
+
+  // Make sure that the last dimension is contiguous
+  auto check_input = [](array x) {
+    if (x.strides()[x.ndim() - 1] == 1) {
+      return x;
+    } else {
+      array x_copy(x.shape(), x.dtype(), nullptr, {});
+      copy(x, x_copy, CopyType::General);
+      return x_copy;
+    }
+  };
+  array in = check_input(std::move(inputs[0]));
+  out.set_data(
+      allocator::malloc_or_wait(in.data_size() * in.itemsize()),
+      in.data_size(),
+      in.strides(),
+      in.flags());
+
+  switch (in.dtype()) {
+    case bool_:
+    case uint8:
+    case uint16:
+    case uint32:
+    case uint64:
+    case int8:
+    case int16:
+    case int32:
+    case int64:
+      throw std::invalid_argument(
+          "Softmax is defined only for floating point types");
+      break;
+    case float32:
+      softmax<float, simd_float16, AccelerateSimdOps<float, simd_float16>, 16>(
+          in, out);
+      break;
+    case float16:
+      softmax<
+          float16_t,
+          float16x8_t,
+          NeonFp16SimdOps<float16_t, float16x8_t>,
+          8>(in, out);
+      break;
+    case bfloat16:
+      eval(inputs, out);
+      break;
+    case complex64:
+      eval(inputs, out);
+      break;
+  }
+}
+
+} // namespace mlx::core

mlx/backend/common/binary.cpp

@@ -0,0 +1,216 @@
+#include <cassert>
+#include <cmath>
+#include <sstream>
+
+#include "mlx/allocator.h"
+#include "mlx/backend/common/binary.h"
+#include "mlx/primitives.h"
+#include "mlx/utils.h"
+
+namespace mlx::core {
+
+namespace {
+
+template <typename T, typename U, typename Op>
+void comparison_op(const array& a, const array& b, array& out, Op op) {
+  DefaultScalarVector<T, U, Op> opsv(op);
+  DefaultVectorScalar<T, U, Op> opvs(op);
+  DefaultVectorVector<T, U, Op> opvv(op);
+  binary_op<T, U>(a, b, out, op, opsv, opvs, opvv);
+}
+
+template <typename Op>
+void comparison_op(const array& a, const array& b, array& out, Op op) {
+  switch (a.dtype()) {
+    case bool_:
+      comparison_op<bool, bool>(a, b, out, op);
+      break;
+    case uint8:
+      comparison_op<uint8_t, bool>(a, b, out, op);
+      break;
+    case uint16:
+      comparison_op<uint16_t, bool>(a, b, out, op);
+      break;
+    case uint32:
+      comparison_op<uint32_t, bool>(a, b, out, op);
+      break;
+    case uint64:
+      comparison_op<uint64_t, bool>(a, b, out, op);
+      break;
+    case int8:
+      comparison_op<int8_t, bool>(a, b, out, op);
+      break;
+    case int16:
+      comparison_op<int16_t, bool>(a, b, out, op);
+      break;
+    case int32:
+      comparison_op<int32_t, bool>(a, b, out, op);
+      break;
+    case int64:
+      comparison_op<int64_t, bool>(a, b, out, op);
+      break;
+    case float16:
+      comparison_op<float16_t, bool>(a, b, out, op);
+      break;
+    case float32:
+      comparison_op<float, bool>(a, b, out, op);
+      break;
+    case bfloat16:
+      comparison_op<bfloat16_t, bool>(a, b, out, op);
+      break;
+    case complex64:
+      comparison_op<complex64_t, bool>(a, b, out, op);
+      break;
+  }
+}
+
+} // namespace
+
+void Add::eval(const std::vector<array>& inputs, array& out) {
+  assert(inputs.size() == 2);
+  auto& a = inputs[0];
+  auto& b = inputs[1];
+  binary(a, b, out, [](auto x, auto y) { return x + y; });
+}
+
+void Divide::eval(const std::vector<array>& inputs, array& out) {
+  assert(inputs.size() == 2);
+  auto& a = inputs[0];
+  auto& b = inputs[1];
+  binary(a, b, out, [](auto x, auto y) { return x / y; });
+}
+
+void Equal::eval(const std::vector<array>& inputs, array& out) {
+  assert(inputs.size() == 2);
+  if (equal_nan_) {
+    comparison_op(inputs[0], inputs[1], out, [](auto x, auto y) {
+      return x == y || (std::isnan(x) && std::isnan(y));
+    });
+  } else {
+    comparison_op(
+        inputs[0], inputs[1], out, [](auto x, auto y) { return x == y; });
+  }
+}
+
+void Greater::eval(const std::vector<array>& inputs, array& out) {
+  assert(inputs.size() == 2);
+  comparison_op(
+      inputs[0], inputs[1], out, [](auto x, auto y) { return x > y; });
+}
+
+void GreaterEqual::eval(const std::vector<array>& inputs, array& out) {
+  assert(inputs.size() == 2);
+  comparison_op(
+      inputs[0], inputs[1], out, [](auto x, auto y) { return x >= y; });
+}
+
+void Less::eval(const std::vector<array>& inputs, array& out) {
+  assert(inputs.size() == 2);
+  comparison_op(
+      inputs[0], inputs[1], out, [](auto x, auto y) { return x < y; });
+}
+
+void LessEqual::eval(const std::vector<array>& inputs, array& out) {
+  assert(inputs.size() == 2);
+  comparison_op(
+      inputs[0], inputs[1], out, [](auto x, auto y) { return x <= y; });
+}
+
+void LogAddExp::eval(const std::vector<array>& inputs, array& out) {
+  assert(inputs.size() == 2);
+  auto& a = inputs[0];
+  auto& b = inputs[1];
+  auto op = [](auto x, auto y) {
+    constexpr float inf = std::numeric_limits<float>::infinity();
+    auto maxval = (x > y) ? x : y;
+    auto minval = (x > y) ? y : x;
+    return (minval == -inf || maxval == inf)
+        ? maxval
+        : static_cast<decltype(x)>(
+              maxval + std::log1p(std::exp(minval - maxval)));
+  };
+  if (is_floating_point(out.dtype())) {
+    if (out.dtype() == float32) {
+      binary_op<float>(a, b, out, op);
+    } else if (out.dtype() == float16) {
+      binary_op<float16_t>(a, b, out, op);
+    } else if (out.dtype() == bfloat16) {
+      binary_op<bfloat16_t>(a, b, out, op);
+    } else {
+      std::ostringstream err;
+      err << "[logaddexp] Does not support " << out.dtype();
+      throw std::invalid_argument(err.str());
+    }
+  } else {
+    throw std::invalid_argument(
+        "[logaddexp] Cannot compute logaddexp for arrays with"
+        " non floating point type.");
+  }
+}
+
+void Maximum::eval(const std::vector<array>& inputs, array& out) {
+  assert(inputs.size() == 2);
+  auto& a = inputs[0];
+  auto& b = inputs[1];
+  binary(a, b, out, [](auto x, auto y) { return (x > y) ? x : y; });
+}
+
+void Minimum::eval(const std::vector<array>& inputs, array& out) {
+  assert(inputs.size() == 2);
+  auto& a = inputs[0];
+  auto& b = inputs[1];
+  binary(a, b, out, [](auto x, auto y) { return (x < y) ? x : y; });
+}
+
+void Multiply::eval(const std::vector<array>& inputs, array& out) {
+  assert(inputs.size() == 2);
+  auto& a = inputs[0];
+  auto& b = inputs[1];
+  binary(a, b, out, [](auto x, auto y) { return x * y; });
+}
+
+void NotEqual::eval(const std::vector<array>& inputs, array& out) {
+  assert(inputs.size() == 2);
+  comparison_op(
+      inputs[0], inputs[1], out, [](auto x, auto y) { return x != y; });
+}
+
+struct PowerFn {
+  template <typename T>
+  std::enable_if_t<!std::is_integral_v<T>, T> operator()(T base, T exp) {
+    return std::pow(base, exp);
+  }
+
+  template <typename T>
+  std::enable_if_t<std::is_integral_v<T>, T> operator()(T base, T exp) {
+    if (exp < 0) {
+      throw std::invalid_argument(
+          "Integers cannot be raise to negative powers");
+    }
+    T res = 1;
+    while (exp) {
+      if (exp & 1) {
+        res *= base;
+      }
+      exp >>= 1;
+      base *= base;
+    }
+    return res;
+  }
+};
+
+void Power::eval(const std::vector<array>& inputs, array& out) {
+  assert(inputs.size() == 2);
+  auto& a = inputs[0];
+  auto& b = inputs[1];
+  binary(a, b, out, PowerFn{});
+}
+
+void Subtract::eval(const std::vector<array>& inputs, array& out) {
+  assert(inputs.size() == 2);
+  auto& a = inputs[0];
+  auto& b = inputs[1];
+  binary(a, b, out, [](auto x, auto y) { return x - y; });
+}
+
+} // namespace mlx::core

mlx/backend/common/binary.h

@@ -0,0 +1,554 @@
+#pragma once
+
+#include "mlx/allocator.h"
+#include "mlx/array.h"
+#include "mlx/backend/common/utils.h"
+
+namespace mlx::core {
+
+namespace {
+
+enum BinaryOpType {
+  ScalarScalar,
+  ScalarVector,
+  VectorScalar,
+  VectorVector,
+  General,
+};
+
+BinaryOpType get_binary_op_type(const array& a, const array& b) {
+  BinaryOpType bopt;
+  if (a.data_size() == 1 && b.data_size() == 1) {
+    bopt = ScalarScalar;
+  } else if (a.data_size() == 1 && b.flags().contiguous) {
+    bopt = ScalarVector;
+  } else if (b.data_size() == 1 && a.flags().contiguous) {
+    bopt = VectorScalar;
+  } else if (
+      a.flags().row_contiguous && b.flags().row_contiguous ||
+      a.flags().col_contiguous && b.flags().col_contiguous) {
+    bopt = VectorVector;
+  } else {
+    bopt = General;
+  }
+  return bopt;
+}
+
+void set_binary_op_output_data(
+    const array& a,
+    const array& b,
+    array& out,
+    BinaryOpType bopt) {
+  switch (bopt) {
+    case ScalarScalar:
+      out.set_data(
+          allocator::malloc_or_wait(out.itemsize()), 1, a.strides(), a.flags());
+      break;
+    case ScalarVector:
+      out.set_data(
+          allocator::malloc_or_wait(b.data_size() * out.itemsize()),
+          b.data_size(),
+          b.strides(),
+          b.flags());
+      break;
+    case VectorScalar:
+    case VectorVector:
+      out.set_data(
+          allocator::malloc_or_wait(a.data_size() * out.itemsize()),
+          a.data_size(),
+          a.strides(),
+          a.flags());
+      break;
+    case General:
+      out.set_data(allocator::malloc_or_wait(out.nbytes()));
+      break;
+  }
+}
+
+struct UseDefaultBinaryOp {
+  template <typename T, typename U>
+  void operator()(const T* a, const T* b, U* dst, int size) {
+    // Should we throw? This should normally never be called.
+    assert(false);
+  }
+};
+
+template <typename T, typename U, typename Op>
+struct DefaultVectorScalar {
+  Op op;
+
+  DefaultVectorScalar(Op op_) : op(op_) {}
+
+  void operator()(const T* a, const T* b, U* dst, int size) {
+    T scalar = *b;
+    while (size-- > 0) {
+      *dst = op(*a, scalar);
+      dst++;
+      a++;
+    }
+  }
+};
+
+template <typename T, typename U, typename Op>
+struct DefaultScalarVector {
+  Op op;
+
+  DefaultScalarVector(Op op_) : op(op_) {}
+
+  void operator()(const T* a, const T* b, U* dst, int size) {
+    T scalar = *a;
+    while (size-- > 0) {
+      *dst = op(scalar, *b);
+      dst++;
+      b++;
+    }
+  }
+};
+
+template <typename T, typename U, typename Op>
+struct DefaultVectorVector {
+  Op op;
+
+  DefaultVectorVector(Op op_) : op(op_) {}
+
+  void operator()(const T* a, const T* b, U* dst, int size) {
+    while (size-- > 0) {
+      *dst = op(*a, *b);
+      dst++;
+      a++;
+      b++;
+    }
+  }
+};
+
+template <typename T, typename U, typename Op>
+void binary_op_dims1(const array& a, const array& b, array& out, Op op) {
+  const T* a_ptr = a.data<T>();
+  const T* b_ptr = b.data<T>();
+  U* dst = out.data<U>();
+  size_t a_idx = 0;
+  size_t b_idx = 0;
+  for (size_t i = 0; i < out.size(); ++i) {
+    dst[i] = op(a_ptr[a_idx], b_ptr[b_idx]);
+    a_idx += a.strides()[0];
+    b_idx += b.strides()[0];
+  }
+}
+
+template <typename T, typename U, typename Op>
+void binary_op_dims1(
+    const array& a,
+    const array& b,
+    array& out,
+    Op op,
+    int stride) {
+  const T* a_ptr = a.data<T>();
+  const T* b_ptr = b.data<T>();
+  U* dst = out.data<U>();
+  size_t a_idx = 0;
+  size_t b_idx = 0;
+  for (size_t i = 0; i < a.shape()[0]; i++) {
+    op(a_ptr + a_idx, b_ptr + b_idx, dst, stride);
+    a_idx += a.strides()[0];
+    b_idx += b.strides()[0];
+    dst += stride;
+  }
+}
+
+template <typename T, typename U, typename Op>
+void binary_op_dims2(const array& a, const array& b, array& out, Op op) {
+  const T* a_ptr = a.data<T>();
+  const T* b_ptr = b.data<T>();
+  U* dst = out.data<U>();
+  size_t a_idx = 0;
+  size_t b_idx = 0;
+  size_t out_idx = 0;
+  for (size_t i = 0; i < a.shape()[0]; ++i) {
+    for (size_t j = 0; j < a.shape()[1]; ++j) {
+      dst[out_idx++] = op(a_ptr[a_idx], b_ptr[b_idx]);
+      a_idx += a.strides()[1];
+      b_idx += b.strides()[1];
+    }
+    a_idx += a.strides()[0] - a.strides()[1] * a.shape()[1];
+    b_idx += b.strides()[0] - b.strides()[1] * b.shape()[1];
+  }
+}
+
+template <typename T, typename U, typename Op>
+void binary_op_dims2(
+    const array& a,
+    const array& b,
+    array& out,
+    Op op,
+    int stride) {
+  const T* a_ptr = a.data<T>();
+  const T* b_ptr = b.data<T>();
+  U* dst = out.data<U>();
+  size_t a_idx = 0;
+  size_t b_idx = 0;
+  for (size_t i = 0; i < a.shape()[0]; ++i) {
+    for (size_t j = 0; j < a.shape()[1]; ++j) {
+      op(a_ptr + a_idx, b_ptr + b_idx, dst, stride);
+      a_idx += a.strides()[1];
+      b_idx += b.strides()[1];
+      dst += stride;
+    }
+    a_idx += a.strides()[0] - a.strides()[1] * a.shape()[1];
+    b_idx += b.strides()[0] - b.strides()[1] * b.shape()[1];
+  }
+}
+
+template <typename T, typename U, typename Op>
+void binary_op_dims3(const array& a, const array& b, array& out, Op op) {
+  const T* a_ptr = a.data<T>();
+  const T* b_ptr = b.data<T>();
+  U* dst = out.data<U>();
+  size_t a_idx = 0;
+  size_t b_idx = 0;
+  size_t out_idx = 0;
+  for (size_t i = 0; i < a.shape()[0]; ++i) {
+    for (size_t j = 0; j < a.shape()[1]; ++j) {
+      for (size_t k = 0; k < a.shape()[2]; ++k) {
+        dst[out_idx++] = op(a_ptr[a_idx], b_ptr[b_idx]);
+        a_idx += a.strides()[2];
+        b_idx += b.strides()[2];
+      }
+      a_idx += a.strides()[1] - a.strides()[2] * a.shape()[2];
+      b_idx += b.strides()[1] - b.strides()[2] * b.shape()[2];
+    }
+    a_idx += a.strides()[0] - a.strides()[1] * a.shape()[1];
+    b_idx += b.strides()[0] - b.strides()[1] * b.shape()[1];
+  }
+}
+
+template <typename T, typename U, typename Op>
+void binary_op_dims4(const array& a, const array& b, array& out, Op op) {
+  const T* a_ptr = a.data<T>();
+  const T* b_ptr = b.data<T>();
+  U* dst = out.data<U>();
+  size_t a_idx = 0;
+  size_t b_idx = 0;
+  size_t out_idx = 0;
+  for (size_t i = 0; i < a.shape()[0]; ++i) {
+    for (size_t j = 0; j < a.shape()[1]; ++j) {
+      for (size_t k = 0; k < a.shape()[2]; ++k) {
+        for (size_t ii = 0; ii < a.shape()[3]; ++ii) {
+          dst[out_idx++] = op(a_ptr[a_idx], b_ptr[b_idx]);
+          a_idx += a.strides()[3];
+          b_idx += b.strides()[3];
+        }
+        a_idx += a.strides()[2] - a.strides()[3] * a.shape()[3];
+        b_idx += b.strides()[2] - b.strides()[3] * b.shape()[3];
+      }
+      a_idx += a.strides()[1] - a.strides()[2] * a.shape()[2];
+      b_idx += b.strides()[1] - b.strides()[2] * b.shape()[2];
+    }
+    a_idx += a.strides()[0] - a.strides()[1] * a.shape()[1];
+    b_idx += b.strides()[0] - b.strides()[1] * b.shape()[1];
+  }
+}
+
+template <typename T, typename U, typename Op>
+void binary_op_dispatch_dims(
+    const array& a,
+    const array& b,
+    array& out,
+    Op op) {
+  switch (out.ndim()) {
+    case 1:
+      binary_op_dims1<T, U, Op>(a, b, out, op);
+      return;
+    case 2:
+      binary_op_dims2<T, U, Op>(a, b, out, op);
+      return;
+    case 3:
+      binary_op_dims3<T, U, Op>(a, b, out, op);
+      return;
+    case 4:
+      binary_op_dims4<T, U, Op>(a, b, out, op);
+      return;
+  }
+
+  const T* a_ptr = a.data<T>();
+  const T* b_ptr = b.data<T>();
+  U* dst = out.data<U>();
+  for (size_t i = 0; i < out.size(); i++) {
+    int a_idx = elem_to_loc(i, a.shape(), a.strides());
+    int b_idx = elem_to_loc(i, b.shape(), b.strides());
+    dst[i] = op(a_ptr[a_idx], b_ptr[b_idx]);
+  }
+}
+
+template <typename T, typename U, typename Op>
+void binary_op_dispatch_dims(
+    const array& a,
+    const array& b,
+    array& out,
+    Op op,
+    int dim,
+    int stride) {
+  // Number of dimensions to loop over for vectorized ops
+  switch (dim) {
+    case 1:
+      binary_op_dims1<T, U, Op>(a, b, out, op, stride);
+      return;
+    case 2:
+      binary_op_dims2<T, U, Op>(a, b, out, op, stride);
+      return;
+  }
+
+  const T* a_ptr = a.data<T>();
+  const T* b_ptr = b.data<T>();
+  U* dst = out.data<U>();
+  for (size_t i = 0; i < out.size(); i += stride) {
+    int a_idx = elem_to_loc(i, a.shape(), a.strides());
+    int b_idx = elem_to_loc(i, b.shape(), b.strides());
+    op(a_ptr + a_idx, b_ptr + b_idx, dst, stride);
+    dst += stride;
+  }
+}
+
+template <
+    typename T,
+    typename U,
+    typename Op,
+    typename OpSV,
+    typename OpVS,
+    typename OpVV>
+void binary_op(
+    const array& a,
+    const array& b,
+    array& out,
+    Op op,
+    OpSV opsv,
+    OpVS opvs,
+    OpVV opvv) {
+  auto bopt = get_binary_op_type(a, b);
+  set_binary_op_output_data(a, b, out, bopt);
+
+  // The full computation is scalar scalar so call the base op once
+  if (bopt == ScalarScalar) {
+    *(out.data<U>()) = op(*a.data<T>(), *b.data<T>());
+    return;
+  }
+
+  // The full computation is scalar vector so delegate to the op
+  if (bopt == ScalarVector) {
+    opsv(a.data<T>(), b.data<T>(), out.data<U>(), b.data_size());
+    return;
+  }
+
+  // The full computation is vector scalar so delegate to the op
+  if (bopt == VectorScalar) {
+    opvs(a.data<T>(), b.data<T>(), out.data<U>(), a.data_size());
+    return;
+  }
+
+  // The full computation is vector vector so delegate to the op
+  if (bopt == VectorVector) {
+    opvv(a.data<T>(), b.data<T>(), out.data<U>(), out.size());
+    return;
+  }
+
+  // General computation so let's try to optimize
+
+  // Get the left-most dim such that the array is row contiguous after
+  auto& strides = out.strides();
+  auto leftmost_rc_dim = [&strides](const array& arr) {
+    int d = arr.ndim() - 1;
+    for (; d >= 0 && arr.strides()[d] == strides[d]; d--) {
+    }
+    return d + 1;
+  };
+  auto a_rc_dim = leftmost_rc_dim(a);
+  auto b_rc_dim = leftmost_rc_dim(b);
+
+  // Get the left-most dim such that the array is a broadcasted "scalar" after
+  auto leftmost_s_dim = [](const array& arr) {
+    int d = arr.ndim() - 1;
+    for (; d >= 0 && arr.strides()[d] == 0; d--) {
+    }
+    return d + 1;
+  };
+  auto a_s_dim = leftmost_s_dim(a);
+  auto b_s_dim = leftmost_s_dim(b);
+
+  auto ndim = out.ndim();
+
+  // Case 1: LxM and FxM where L and F are broadcastable and M is row contiguous
+  int dim = ndim;
+  if (int d = std::max(a_rc_dim, b_rc_dim); d < ndim) {
+    bopt = VectorVector;
+    dim = d;
+    // Case 2: LxM and Fx1 where L and F are broadcastable and M is row
+    // contiguous
+  } else if (int d = std::max(a_rc_dim, b_s_dim); d < ndim) {
+    bopt = VectorScalar;
+    dim = d;
+    // Case 3: Lx1 and FxM where L and F are broadcastable and M is row
+    // contiguous
+  } else if (int d = std::max(a_s_dim, b_rc_dim); d < ndim) {
+    bopt = ScalarVector;
+    dim = d;
+  }
+
+  // Can be sure dim > 0 since otherwise we would have used one of the fully
+  // contiguous methods above. Except for the case that the flags do not
+  // correspond to the underlying contiguity.
+  size_t stride;
+  if (dim == 0 || strides[dim - 1] < 16) {
+    stride = 1;
+    bopt = General;
+    dim = ndim;
+  } else {
+    stride = strides[dim - 1];
+  }
+
+  switch (bopt) {
+    case VectorVector:
+      binary_op_dispatch_dims<T, U>(a, b, out, opvv, dim, stride);
+      break;
+    case VectorScalar:
+      binary_op_dispatch_dims<T, U>(a, b, out, opvs, dim, stride);
+      break;
+    case ScalarVector:
+      binary_op_dispatch_dims<T, U>(a, b, out, opsv, dim, stride);
+      break;
+    default:
+      binary_op_dispatch_dims<T, U>(a, b, out, op);
+      break;
+  }
+}
+
+template <typename T, typename Op, typename OpSV, typename OpVS, typename OpVV>
+void binary_op(
+    const array& a,
+    const array& b,
+    array& out,
+    Op op,
+    OpSV opsv,
+    OpVS opvs,
+    OpVV opvv) {
+  // TODO: The following mess of constexpr evaluations can probably be achieved
+  //       with template specializations and overloading. Would it be simpler?
+
+  if (std::is_same<decltype(opsv), UseDefaultBinaryOp>::value) {
+    if (std::is_same<decltype(opvs), UseDefaultBinaryOp>::value) {
+      if (std::is_same<decltype(opvv), UseDefaultBinaryOp>::value) {
+        // All ops are UseDefaultBinaryOp (why oh why would someone call that?)
+        binary_op<T, T>(
+            a,
+            b,
+            out,
+            op,
+            DefaultScalarVector<T, T, Op>(op),
+            DefaultVectorScalar<T, T, Op>(op),
+            DefaultVectorVector<T, T, Op>(op));
+      } else {
+        // opsv and opvs were UseDefaultBinaryOp
+        binary_op<T, T>(
+            a,
+            b,
+            out,
+            op,
+            DefaultScalarVector<T, T, Op>(op),
+            DefaultVectorScalar<T, T, Op>(op),
+            opvv);
+      }
+    } else if (std::is_same<decltype(opvv), UseDefaultBinaryOp>::value) {
+      // opsv and opvv were UseDefaultBinaryOp
+      binary_op<T, T>(
+          a,
+          b,
+          out,
+          op,
+          DefaultScalarVector<T, T, Op>(op),
+          opvs,
+          DefaultVectorVector<T, T, Op>(op));
+    } else {
+      // opsv was UseDefaultBinaryOp
+      binary_op<T, T>(
+          a, b, out, op, DefaultScalarVector<T, T, Op>(op), opvs, opvv);
+    }
+  } else if (std::is_same<decltype(opvs), UseDefaultBinaryOp>::value) {
+    if (std::is_same<decltype(opvv), UseDefaultBinaryOp>::value) {
+      // opvs and opvv were UseDefaultBinaryOp
+      binary_op<T, T>(
+          a,
+          b,
+          out,
+          op,
+          opsv,
+          DefaultVectorScalar<T, T, Op>(op),
+          DefaultVectorVector<T, T, Op>(op));
+    } else {
+      // opvs was UseDefaultBinaryOp
+      binary_op<T, T>(
+          a, b, out, op, opsv, DefaultVectorScalar<T, T, Op>(op), opvv);
+    }
+  } else if (std::is_same<decltype(opvv), UseDefaultBinaryOp>::value) {
+    // opvv was UseDefaultBinaryOp
+    binary_op<T, T>(
+        a, b, out, op, opsv, opvs, DefaultVectorVector<T, T, Op>(op));
+  } else {
+    // All ops provided
+    binary_op<T, T>(a, b, out, op, opsv, opvs, opvv);
+  }
+}
+
+template <typename T, typename Op>
+void binary_op(const array& a, const array& b, array& out, Op op) {
+  DefaultScalarVector<T, T, Op> opsv(op);
+  DefaultVectorScalar<T, T, Op> opvs(op);
+  DefaultVectorVector<T, T, Op> opvv(op);
+  binary_op<T, T>(a, b, out, op, opsv, opvs, opvv);
+}
+
+template <typename... Ops>
+void binary(const array& a, const array& b, array& out, Ops... ops) {
+  switch (out.dtype()) {
+    case bool_:
+      binary_op<bool>(a, b, out, ops...);
+      break;
+    case uint8:
+      binary_op<uint8_t>(a, b, out, ops...);
+      break;
+    case uint16:
+      binary_op<uint16_t>(a, b, out, ops...);
+      break;
+    case uint32:
+      binary_op<uint32_t>(a, b, out, ops...);
+      break;
+    case uint64:
+      binary_op<uint64_t>(a, b, out, ops...);
+      break;
+    case int8:
+      binary_op<int8_t>(a, b, out, ops...);
+      break;
+    case int16:
+      binary_op<int16_t>(a, b, out, ops...);
+      break;
+    case int32:
+      binary_op<int32_t>(a, b, out, ops...);
+      break;
+    case int64:
+      binary_op<int64_t>(a, b, out, ops...);
+      break;
+    case float16:
+      binary_op<float16_t>(a, b, out, ops...);
+      break;
+    case float32:
+      binary_op<float>(a, b, out, ops...);
+      break;
+    case bfloat16:
+      binary_op<bfloat16_t>(a, b, out, ops...);
+      break;
+    case complex64:
+      binary_op<complex64_t>(a, b, out, ops...);
+      break;
+  }
+}
+
+} // namespace
+
+} // namespace mlx::core

mlx/backend/common/fft.cpp

@@ -0,0 +1,85 @@
+#include <numeric>
+
+#include "mlx/3rdparty/pocketfft.h"
+#include "mlx/allocator.h"
+#include "mlx/primitives.h"
+
+namespace mlx::core {
+
+void FFT::eval(const std::vector<array>& inputs, array& out) {
+  auto& in = inputs[0];
+  std::vector<std::ptrdiff_t> strides_in(
+      in.strides().begin(), in.strides().end());
+  for (auto& s : strides_in) {
+    s *= in.itemsize();
+  }
+  std::vector<std::ptrdiff_t> strides_out(
+      out.strides().begin(), out.strides().end());
+  for (auto& s : strides_out) {
+    s *= out.itemsize();
+  }
+
+  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+
+  std::vector<size_t> shape;
+  if (out.dtype() == float32) {
+    shape.insert(shape.end(), out.shape().begin(), out.shape().end());
+  } else {
+    shape.insert(shape.end(), in.shape().begin(), in.shape().end());
+  }
+
+  float scale = 1.0f;
+  if (inverse_) {
+    size_t nelem = std::accumulate(
+        axes_.begin(), axes_.end(), 1, [&shape](auto x, auto y) {
+          return x * shape[y];
+        });
+    scale /= nelem;
+  }
+  if (in.dtype() == complex64 && out.dtype() == complex64) {
+    auto in_ptr =
+        reinterpret_cast<const std::complex<float>*>(in.data<complex64_t>());
+    auto out_ptr =
+        reinterpret_cast<std::complex<float>*>(out.data<complex64_t>());
+    pocketfft::c2c(
+        shape,
+        strides_in,
+        strides_out,
+        axes_,
+        !inverse_,
+        in_ptr,
+        out_ptr,
+        scale);
+  } else if (in.dtype() == float32 && out.dtype() == complex64) {
+    auto in_ptr = in.data<float>();
+    auto out_ptr =
+        reinterpret_cast<std::complex<float>*>(out.data<complex64_t>());
+    pocketfft::r2c(
+        shape,
+        strides_in,
+        strides_out,
+        axes_,
+        !inverse_,
+        in_ptr,
+        out_ptr,
+        scale);
+  } else if (in.dtype() == complex64 && out.dtype() == float32) {
+    auto in_ptr =
+        reinterpret_cast<const std::complex<float>*>(in.data<complex64_t>());
+    auto out_ptr = out.data<float>();
+    pocketfft::c2r(
+        shape,
+        strides_in,
+        strides_out,
+        axes_,
+        !inverse_,
+        in_ptr,
+        out_ptr,
+        scale);
+  } else {
+    throw std::runtime_error(
+        "[FFT] Received unexpected input and output type combination.");
+  }
+}
+
+} // namespace mlx::core

mlx/backend/common/softmax.cpp

@@ -0,0 +1,98 @@
+#include <cassert>
+#include <cmath>
+
+#include "mlx/backend/common/copy.h"
+#include "mlx/primitives.h"
+
+namespace mlx::core {
+
+namespace {
+
+template <typename T>
+void softmax(const array& in, array& out) {
+  const T* in_ptr = in.data<T>();
+  T* out_ptr = out.data<T>();
+  int N = in.shape().back();
+  int M = in.data_size() / N;
+  const T* current_in_ptr;
+  T* current_out_ptr;
+
+  for (int i = 0; i < M; i++, in_ptr += N, out_ptr += N) {
+    // Find the maximum
+    current_in_ptr = in_ptr;
+    T maximum = *current_in_ptr;
+    for (int j = 0; j < N; j++, current_in_ptr++) {
+      maximum = (maximum < *current_in_ptr) ? *current_in_ptr : maximum;
+    }
+
+    // Compute the normalizer and the exponentials
+    T normalizer = 0;
+    current_out_ptr = out_ptr;
+    current_in_ptr = in_ptr;
+    for (int j = 0; j < N; j++, current_out_ptr++, current_in_ptr++) {
+      T expv = std::exp(*current_in_ptr - maximum);
+      normalizer += expv;
+      *current_out_ptr = expv;
+    }
+    normalizer = 1 / normalizer;
+
+    // Normalize
+    current_out_ptr = out_ptr;
+    for (int j = 0; j < N; j++, current_out_ptr++) {
+      *current_out_ptr *= normalizer;
+    }
+  }
+}
+
+} // namespace
+
+void Softmax::eval(const std::vector<array>& inputs, array& out) {
+  assert(inputs.size() == 1);
+
+  // Make sure that the last dimension is contiguous
+  auto check_input = [](array x) {
+    if (x.strides().back() == 1) {
+      return x;
+    } else {
+      array x_copy(x.shape(), x.dtype(), nullptr, {});
+      copy(x, x_copy, CopyType::General);
+      return x_copy;
+    }
+  };
+  array in = check_input(std::move(inputs[0]));
+  out.set_data(
+      allocator::malloc_or_wait(in.data_size() * in.itemsize()),
+      in.data_size(),
+      in.strides(),
+      in.flags());
+
+  switch (in.dtype()) {
+    case bool_:
+    case uint8:
+    case uint16:
+    case uint32:
+    case uint64:
+    case int8:
+    case int16:
+    case int32:
+    case int64:
+      throw std::invalid_argument(
+          "Softmax is defined only for floating point types");
+      break;
+    case float32:
+      softmax<float>(in, out);
+      break;
+    case float16:
+      softmax<float16_t>(in, out);
+      break;
+    case bfloat16:
+      softmax<bfloat16_t>(in, out);
+      break;
+    case complex64:
+      throw std::invalid_argument(
+          "[Softmax] Not yet implemented for complex64");
+      break;
+  }
+}
+
+} // namespace mlx::core

mlx/backend/metal/allocator.cpp

@@ -0,0 +1,200 @@
+#include "mlx/backend/metal/allocator.h"
+#include "mlx/backend/metal/metal.h"
+
+#include <mach/vm_page_size.h>
+#include <unistd.h>
+#include <cstdlib>
+
+namespace mlx::core {
+
+namespace allocator {
+
+Allocator& allocator() {
+  return metal::allocator();
+}
+
+void* Buffer::raw_ptr() {
+  return static_cast<MTL::Buffer*>(ptr_)->contents();
+}
+
+} // namespace allocator
+
+namespace metal {
+
+namespace {
+
+BufferCache::BufferCache(MTL::Device* device)
+    : device_(device),
+      head_(nullptr),
+      tail_(nullptr),
+      pool_size_(0),
+      gc_limit_(0.95 * device_->recommendedMaxWorkingSetSize()) {}
+
+BufferCache::~BufferCache() {
+  clear();
+}
+
+void BufferCache::clear() {
+  std::lock_guard<std::mutex> lk(cache_mutex_);
+  for (auto& [size, holder] : buffer_pool_) {
+    if (holder->buf)
+      holder->buf->release();
+    delete holder;
+  }
+  buffer_pool_.clear();
+  pool_size_ = 0;
+  head_ = nullptr;
+  tail_ = nullptr;
+}
+
+MTL::Buffer* BufferCache::reuse_from_cache(size_t size) {
+  std::lock_guard<std::mutex> lk(cache_mutex_);
+
+  // Find the closest buffer in pool
+  MTL::Buffer* pbuf = nullptr;
+  auto it = buffer_pool_.lower_bound(size);
+
+  // Make sure we use > 50% of the available memory
+  while (!pbuf && it != buffer_pool_.end() && it->first < 2 * size) {
+    // Collect from the cache
+    pbuf = it->second->buf;
+    // Remove from cache
+    remove_from_list(it->second);
+    delete it->second;
+    it = buffer_pool_.erase(it);
+  }
+
+  if (pbuf) {
+    pool_size_ -= pbuf->length();
+  }
+
+  return pbuf;
+}
+
+void BufferCache::recycle_to_cache(MTL::Buffer* buf) {
+  std::lock_guard<std::mutex> lk(cache_mutex_);
+
+  // Add to cache
+  if (buf) {
+    BufferHolder* bh = new BufferHolder(buf);
+    add_at_head(bh);
+    pool_size_ += buf->length();
+    buffer_pool_.insert({buf->length(), bh});
+  }
+}
+
+size_t BufferCache::release_cached_buffers(size_t min_bytes_to_free) {
+  min_bytes_to_free += device_->currentAllocatedSize() - gc_limit_;
+
+  if (min_bytes_to_free >= 0.9 * pool_size_) {
+    size_t old_pool_size = pool_size_;
+    clear();
+    return old_pool_size;
+  } else {
+    std::lock_guard<std::mutex> lk(cache_mutex_);
+    size_t total_bytes_freed = 0;
+
+    while (tail_ && (total_bytes_freed < min_bytes_to_free)) {
+      if (tail_->buf) {
+        total_bytes_freed += tail_->buf->length();
+        tail_->buf->release();
+        tail_->buf = nullptr;
+      }
+      remove_from_list(tail_);
+    }
+
+    pool_size_ -= total_bytes_freed;
+    return total_bytes_freed;
+  }
+}
+
+void BufferCache::add_at_head(BufferCache::BufferHolder* to_add) {
+  if (!to_add)
+    return;
+
+  if (!head_) {
+    head_ = to_add;
+    tail_ = to_add;
+  } else {
+    head_->prev = to_add;
+    to_add->next = head_;
+    head_ = to_add;
+  }
+}
+
+void BufferCache::remove_from_list(BufferCache::BufferHolder* to_remove) {
+  if (!to_remove)
+    return;
+
+  // If in the middle
+  if (to_remove->prev && to_remove->next) {
+    to_remove->prev->next = to_remove->next;
+    to_remove->next->prev = to_remove->prev;
+  } else if (to_remove->prev && to_remove == tail_) { // If tail
+    tail_ = to_remove->prev;
+    tail_->next = nullptr;
+  } else if (to_remove == head_ && to_remove->next) { // If head
+    head_ = to_remove->next;
+    head_->prev = nullptr;
+  } else if (to_remove == head_ && to_remove == tail_) { // If only element
+    head_ = nullptr;
+    tail_ = nullptr;
+  }
+
+  to_remove->prev = nullptr;
+  to_remove->next = nullptr;
+}
+
+} // namespace
+
+MetalAllocator::MetalAllocator()
+    : device_(device(mlx::core::Device::gpu).mtl_device()),
+      buffer_cache_(device_),
+      peak_allocated_size_(0),
+      block_limit_(1.5 * device_->recommendedMaxWorkingSetSize()) {}
+
+Buffer MetalAllocator::malloc(size_t size) {
+  // Align up memory
+  if (size > vm_page_size) {
+    size = vm_page_size * ((size + vm_page_size - 1) / vm_page_size);
+  }
+
+  MTL::Buffer* buf = buffer_cache_.reuse_from_cache(size);
+
+  // Prepare to allocate new memory as needed
+  if (!buf) {
+    // If we are under very high memoory pressure, we don't allocate further
+    if (device_->currentAllocatedSize() >= block_limit_) {
+      return Buffer{nullptr};
+    }
+
+    // If we are still under memory pressure, try cleaning cache
+    if (buffer_cache_.can_garbage_collect()) {
+      buffer_cache_.release_cached_buffers(size);
+    }
+
+    // Allocate new buffer if needed
+    size_t res_opt = MTL::ResourceStorageModeShared;
+    res_opt |= MTL::ResourceHazardTrackingModeTracked;
+    buf = device_->newBuffer(size, res_opt);
+  }
+
+  peak_allocated_size_ =
+      std::max(peak_allocated_size_, device_->currentAllocatedSize());
+
+  return Buffer{static_cast<void*>(buf)};
+}
+
+void MetalAllocator::free(Buffer buffer) {
+  auto buf = static_cast<MTL::Buffer*>(buffer.ptr());
+  buffer_cache_.recycle_to_cache(buf);
+}
+
+MetalAllocator& allocator() {
+  static MetalAllocator allocator_;
+  return allocator_;
+}
+
+} // namespace metal
+
+} // namespace mlx::core

mlx/backend/metal/allocator.h

@@ -0,0 +1,76 @@
+#pragma once
+
+#include <map>
+#include <mutex>
+#include <vector>
+
+#include "mlx/allocator.h"
+#include "mlx/backend/metal/device.h"
+
+namespace mlx::core::metal {
+
+using allocator::Buffer;
+
+namespace {
+
+class BufferCache {
+ public:
+  BufferCache(MTL::Device* device);
+  ~BufferCache();
+  void clear();
+
+  MTL::Buffer* reuse_from_cache(size_t size);
+  void recycle_to_cache(MTL::Buffer* buf);
+  size_t release_cached_buffers(size_t min_bytes_to_free);
+
+  bool can_garbage_collect() {
+    return pool_size_ > 0 && device_->currentAllocatedSize() > gc_limit_;
+  }
+
+ private:
+  struct BufferHolder {
+   public:
+    BufferHolder(MTL::Buffer* buf_) : buf(buf_), prev(nullptr), next(nullptr) {}
+
+    BufferHolder* prev;
+    BufferHolder* next;
+    MTL::Buffer* buf;
+  };
+
+  void add_at_head(BufferHolder* to_add);
+  void remove_from_list(BufferHolder* to_remove);
+
+  MTL::Device* device_;
+  std::mutex cache_mutex_;
+
+  std::multimap<size_t, BufferHolder*> buffer_pool_;
+  BufferHolder* head_;
+  BufferHolder* tail_;
+  size_t pool_size_;
+  size_t gc_limit_;
+};
+
+} // namespace
+
+class MetalAllocator : public allocator::Allocator {
+  /** Allocator for Metal GPUs. */
+ public:
+  virtual Buffer malloc(size_t size) override;
+  virtual void free(Buffer buffer) override;
+
+ private:
+  MTL::Device* device_;
+  MetalAllocator();
+  friend MetalAllocator& allocator();
+
+  // Caching allocator
+  BufferCache buffer_cache_;
+
+  // Allocation stats
+  size_t peak_allocated_size_;
+  size_t block_limit_;
+};
+
+MetalAllocator& allocator();
+
+} // namespace mlx::core::metal

mlx/backend/metal/conv.cpp

@@ -0,0 +1,555 @@
+#include <algorithm>
+#include <cassert>
+#include <iostream>
+#include <numeric>
+#include <sstream>
+
+#include "mlx/backend/metal/copy.h"
+#include "mlx/backend/metal/device.h"
+#include "mlx/backend/metal/kernels/conv_params.h"
+#include "mlx/backend/metal/kernels/defines.h"
+#include "mlx/backend/metal/matmul.h"
+#include "mlx/backend/metal/utils.h"
+#include "mlx/primitives.h"
+#include "mlx/utils.h"
+
+namespace mlx::core {
+
+namespace {
+
+void explicit_gemm_conv_1D_gpu(
+    const Stream& s,
+    metal::Device& d,
+    const array& in,
+    const array& wt,
+    array out,
+    const MLXConvParams<1>& conv_params) {
+  // Pad input
+  std::vector<int> padded_shape = {
+      conv_params.N, conv_params.iS[0] + 2 * conv_params.pad[0], conv_params.C};
+  array in_padded(padded_shape, in.dtype(), nullptr, {});
+
+  // Fill with zeros
+  copy_gpu(array(0, in.dtype()), in_padded, CopyType::Scalar, s);
+
+  // Pick input slice from padded
+  size_t data_offset = conv_params.pad[0] * in_padded.strides()[1];
+  array in_padded_slice(in.shape(), in_padded.dtype(), nullptr, {});
+  in_padded_slice.copy_shared_buffer(
+      in_padded,
+      in_padded.strides(),
+      in_padded.flags(),
+      in_padded_slice.size(),
+      data_offset);
+
+  // Copy input values into the slice
+  copy_gpu_inplace(in, in_padded_slice, CopyType::GeneralGeneral, s);
+
+  // Make strided view
+  std::vector<int> strided_shape = {
+      conv_params.N, conv_params.oS[0], conv_params.wS[0], conv_params.C};
+
+  std::vector<size_t> strided_strides = {
+      in_padded.strides()[0],
+      in_padded.strides()[1] * conv_params.str[0],
+      in_padded.strides()[1],
+      in_padded.strides()[2]};
+  auto flags = in_padded.flags();
+
+  array in_strided_view(strided_shape, in_padded.dtype(), nullptr, {});
+  in_strided_view.copy_shared_buffer(
+      in_padded, strided_strides, flags, in_strided_view.size(), 0);
+
+  // Materialize strided view
+  std::vector<int> strided_reshape = {
+      conv_params.N * conv_params.oS[0], conv_params.wS[0] * conv_params.C};
+  array in_strided(strided_reshape, in_strided_view.dtype(), nullptr, {});
+  copy_gpu(in_strided_view, in_strided, CopyType::General, s);
+
+  // Peform gemm
+  std::vector<array> copies = {in_padded, in_strided};
+  mlx_matmul(
+      s,
+      d,
+      /*a = */ in_strided,
+      /*b = */ wt,
+      /*c = */ out,
+      /*M = */ strided_reshape[0],
+      /*N = */ conv_params.O,
+      /*K = */ strided_reshape[1],
+      /*batch_size_out = */ 1,
+      /*a_cols = */ strided_reshape[1],
+      /*b_cols = */ strided_reshape[1],
+      /*a_transposed = */ false,
+      /*b_transposed = */ true,
+      /*copies = */ copies);
+}
+
+void conv_1D_gpu(
+    const Stream& s,
+    metal::Device& d,
+    const array& in,
+    const array& wt,
+    array out,
+    const std::vector<int>& padding,
+    const std::vector<int>& wt_strides,
+    const std::vector<int>& wt_dilation) {
+  // Make conv params
+  MLXConvParams<1> conv_params{
+      /* const int  N = */ in.shape(0),
+      /* const int  C = */ in.shape(2),
+      /* const int  O = */ wt.shape(0),
+      /* const int iS[NDIM] = */ {in.shape(1)},
+      /* const int wS[NDIM] = */ {wt.shape(1)},
+      /* const int oS[NDIM] = */ {out.shape(1)},
+      /* const int str[NDIM] = */ {wt_strides[0]},
+      /* const int pad[NDIM] = */ {padding[0]},
+      /* const int dil[NDIM] = */ {wt_dilation[0]},
+      /* const size_t in_strides[NDIM + 2] = */
+      {in.strides()[0], in.strides()[1], in.strides()[2]},
+      /* const size_t wt_strides[NDIM + 2] = */
+      {wt.strides()[0], wt.strides()[1], wt.strides()[2]},
+      /* const size_t out_strides[NDIM + 2] = */
+      {out.strides()[0], out.strides()[1], out.strides()[2]},
+  };
+
+  // Direct to explicit gemm conv
+  if (wt_dilation[0] == 1) {
+    explicit_gemm_conv_1D_gpu(s, d, in, wt, out, conv_params);
+  }
+
+  // Direct to fallback conv
+  else {
+    throw std::invalid_argument("[conv_1D_gpu] Dilation needs to be 1.");
+  }
+}
+
+void slow_conv_2D_gpu(
+    const Stream& s,
+    metal::Device& d,
+    const array& in,
+    const array& wt,
+    array out,
+    const MLXConvParams<2>& conv_params) {
+  int bm = 16, bn = 8;
+  int tm = 4, tn = 4;
+
+  std::ostringstream kname;
+  kname << "naive_conv_2d_" << type_to_name(out) << "_bm" << bm << "_bn" << bn
+        << "_tm" << tm << "_tn" << tn;
+
+  // Encode and dispatch kernel
+  auto compute_encoder = d.get_command_encoder(s.index);
+  auto kernel = d.get_kernel(kname.str());
+  compute_encoder->setComputePipelineState(kernel);
+
+  size_t n_pixels = conv_params.oS[0] * conv_params.oS[1];
+
+  size_t grid_dim_x = (n_pixels + (tm * bm) - 1) / (tm * bm);
+  size_t grid_dim_y = (conv_params.O + (tn * bn) - 1) / (tn * bn);
+  size_t grid_dim_z = conv_params.N;
+
+  MTL::Size group_dims = MTL::Size(bm, bn, 1);
+  MTL::Size grid_dims = MTL::Size(grid_dim_x, grid_dim_y, grid_dim_z);
+
+  set_array_buffer(compute_encoder, in, 0);
+  set_array_buffer(compute_encoder, wt, 1);
+  set_array_buffer(compute_encoder, out, 2);
+
+  compute_encoder->setBytes(&conv_params, sizeof(MLXConvParams<2>), 3);
+  compute_encoder->dispatchThreadgroups(grid_dims, group_dims);
+}
+
+void implicit_gemm_conv_2D_gpu(
+    const Stream& s,
+    metal::Device& d,
+    const array& in,
+    const array& wt,
+    array out,
+    const MLXConvParams<2>& conv_params) {
+  int bm = 32, bn = 32, bk = 16;
+  int wm = 2, wn = 2;
+
+  std::ostringstream kname;
+  kname << "implicit_gemm_conv_2d_" << type_to_name(out) << "_bm" << bm << "_bn"
+        << bn << "_bk" << bk << "_wm" << wm << "_wn" << wn;
+
+  // Encode and dispatch kernel
+  auto compute_encoder = d.get_command_encoder(s.index);
+  auto kernel = d.get_kernel(kname.str());
+  compute_encoder->setComputePipelineState(kernel);
+
+  int implicit_M = conv_params.N * conv_params.oS[0] * conv_params.oS[1];
+  int implicit_N = conv_params.O;
+  int implicit_K = conv_params.wS[0] * conv_params.wS[1] * conv_params.C;
+
+  size_t grid_dim_x = (implicit_N + bn - 1) / bn;
+  size_t grid_dim_y = (implicit_M + bm - 1) / bm;
+
+  MTL::Size group_dims = MTL::Size(32, wn, wm);
+  MTL::Size grid_dims = MTL::Size(grid_dim_x, grid_dim_y, 1);
+
+  set_array_buffer(compute_encoder, in, 0);
+  set_array_buffer(compute_encoder, wt, 1);
+  set_array_buffer(compute_encoder, out, 2);
+
+  compute_encoder->setBytes(&conv_params, sizeof(MLXConvParams<2>), 3);
+  compute_encoder->dispatchThreadgroups(grid_dims, group_dims);
+}
+
+void explicit_gemm_conv_2D_gpu(
+    const Stream& s,
+    metal::Device& d,
+    const array& in,
+    const array& wt,
+    array out,
+    const MLXConvParams<2>& conv_params) {
+  // Pad input
+  std::vector<int> padded_shape = {
+      conv_params.N,
+      conv_params.iS[0] + 2 * conv_params.pad[0],
+      conv_params.iS[1] + 2 * conv_params.pad[1],
+      conv_params.C};
+  array in_padded(padded_shape, in.dtype(), nullptr, {});
+
+  // Fill with zeros
+  copy_gpu(array(0, in.dtype()), in_padded, CopyType::Scalar, s);
+
+  // Pick input slice from padded
+  size_t data_offset = conv_params.pad[0] * in_padded.strides()[1] +
+      conv_params.pad[1] * in_padded.strides()[2];
+  array in_padded_slice(in.shape(), in_padded.dtype(), nullptr, {});
+  in_padded_slice.copy_shared_buffer(
+      in_padded,
+      in_padded.strides(),
+      in_padded.flags(),
+      in_padded_slice.size(),
+      data_offset);
+
+  // Copy input values into the slice
+  copy_gpu_inplace(in, in_padded_slice, CopyType::GeneralGeneral, s);
+
+  // Make strided view
+  std::vector<int> strided_shape = {
+      conv_params.N,
+      conv_params.oS[0],
+      conv_params.oS[1],
+      conv_params.wS[0],
+      conv_params.wS[1],
+      conv_params.C};
+
+  std::vector<size_t> strided_strides = {
+      in_padded.strides()[0],
+      in_padded.strides()[1] * conv_params.str[0],
+      in_padded.strides()[2] * conv_params.str[1],
+      in_padded.strides()[1],
+      in_padded.strides()[2],
+      in_padded.strides()[3]};
+  auto flags = in_padded.flags();
+
+  array in_strided_view(strided_shape, in_padded.dtype(), nullptr, {});
+  in_strided_view.copy_shared_buffer(
+      in_padded, strided_strides, flags, in_strided_view.size(), 0);
+
+  // Materialize strided view
+  std::vector<int> strided_reshape = {
+      conv_params.N * conv_params.oS[0] * conv_params.oS[1],
+      conv_params.wS[0] * conv_params.wS[1] * conv_params.C};
+  array in_strided(strided_reshape, in_strided_view.dtype(), nullptr, {});
+  copy_gpu(in_strided_view, in_strided, CopyType::General, s);
+
+  // Peform gemm
+  std::vector<array> copies = {in_padded, in_strided};
+  mlx_matmul(
+      s,
+      d,
+      /*a = */ in_strided,
+      /*b = */ wt,
+      /*c = */ out,
+      /*M = */ strided_reshape[0],
+      /*N = */ conv_params.O,
+      /*K = */ strided_reshape[1],
+      /*batch_size_out = */ 1,
+      /*a_cols = */ strided_reshape[1],
+      /*b_cols = */ strided_reshape[1],
+      /*a_transposed = */ false,
+      /*b_transposed = */ true,
+      /*copies = */ copies);
+}
+
+void winograd_conv_2D_gpu(
+    const Stream& s,
+    metal::Device& d,
+    const array& in,
+    const array& wt,
+    array out,
+    const MLXConvParams<2>& conv_params,
+    std::vector<array>& copies_w) {
+  std::vector<int> padded_shape = {
+      conv_params.N,
+      conv_params.iS[0] + 2 * conv_params.pad[0],
+      conv_params.iS[1] + 2 * conv_params.pad[1],
+      conv_params.C};
+
+  padded_shape[1] = 6 * ((padded_shape[1] - 2 + 5) / 6) + 2;
+  padded_shape[2] = 6 * ((padded_shape[2] - 2 + 5) / 6) + 2;
+
+  array in_padded(padded_shape, in.dtype(), nullptr, {});
+
+  // Fill with zeros
+  array zero_arr = array(0, in.dtype());
+  copy_gpu(zero_arr, in_padded, CopyType::Scalar, s);
+
+  // Pick input slice from padded
+  size_t data_offset = conv_params.pad[0] * in_padded.strides()[1] +
+      conv_params.pad[1] * in_padded.strides()[2];
+  array in_padded_slice(in.shape(), in_padded.dtype(), nullptr, {});
+  in_padded_slice.copy_shared_buffer(
+      in_padded,
+      in_padded.strides(),
+      in_padded.flags(),
+      in_padded_slice.size(),
+      data_offset);
+
+  // Copy input values into the slice
+  copy_gpu_inplace(in, in_padded_slice, CopyType::GeneralGeneral, s);
+
+  copies_w.push_back(in_padded_slice);
+  copies_w.push_back(in_padded);
+  copies_w.push_back(zero_arr);
+
+  MLXConvParams<2> conv_params_updated{
+      /* const int  N = */ in_padded.shape(0),
+      /* const int  C = */ in_padded.shape(3),
+      /* const int  O = */ wt.shape(0),
+      /* const int iS[NDIM] = */ {in_padded.shape(1), in_padded.shape(2)},
+      /* const int wS[NDIM] = */ {wt.shape(1), wt.shape(2)},
+      /* const int oS[NDIM] = */ {out.shape(1), out.shape(2)},
+      /* const int str[NDIM] = */ {1, 1},
+      /* const int pad[NDIM] = */ {0, 0},
+      /* const int dil[NDIM] = */ {1, 1},
+      /* const size_t in_strides[NDIM + 2] = */
+      {in_padded.strides()[0],
+       in_padded.strides()[1],
+       in_padded.strides()[2],
+       in_padded.strides()[3]},
+      /* const size_t wt_strides[NDIM + 2] = */
+      {wt.strides()[0], wt.strides()[1], wt.strides()[2], wt.strides()[3]},
+      /* const size_t out_strides[NDIM + 2] = */
+      {out.strides()[0], out.strides()[1], out.strides()[2], out.strides()[3]},
+  };
+
+  int O_c = conv_params.O;
+  int C_c = conv_params.C;
+
+  int N_tiles_n = conv_params.N;
+  int N_tiles_h = (conv_params.oS[0] + 5) / 6;
+  int N_tiles_w = (conv_params.oS[1] + 5) / 6;
+  int N_tiles = N_tiles_n * N_tiles_h * N_tiles_w;
+
+  // Do filter transform
+  std::vector<int> filt_wg_shape = {8 * 8, conv_params.C, conv_params.O};
+  array filt_wg(filt_wg_shape, wt.dtype(), nullptr, {});
+  filt_wg.set_data(allocator::malloc_or_wait(filt_wg.nbytes()));
+  copies_w.push_back(filt_wg);
+  {
+    int bc = 32;
+    int bo = 4;
+    std::ostringstream kname;
+    kname << "winograd_conv_2d_weight_transform_" << type_to_name(out) << "_bc"
+          << bc;
+    auto compute_encoder = d.get_command_encoder(s.index);
+    auto kernel = d.get_kernel(kname.str());
+    compute_encoder->setComputePipelineState(kernel);
+
+    set_array_buffer(compute_encoder, wt, 0);
+    set_array_buffer(compute_encoder, filt_wg, 1);
+
+    compute_encoder->setBytes(&C_c, sizeof(int), 2);
+    compute_encoder->setBytes(&O_c, sizeof(int), 3);
+
+    MTL::Size group_dims = MTL::Size(32, bo, 1);
+    MTL::Size grid_dims = MTL::Size(O_c / bo, 1, 1);
+
+    compute_encoder->dispatchThreadgroups(grid_dims, group_dims);
+  }
+
+  // Do input transform
+  std::vector<int> inp_wg_shape = {8 * 8, N_tiles, conv_params.C};
+  array inp_wg(inp_wg_shape, in.dtype(), nullptr, {});
+  inp_wg.set_data(allocator::malloc_or_wait(inp_wg.nbytes()));
+  copies_w.push_back(inp_wg);
+  {
+    int bc = 32;
+    int wm = 2;
+    int wn = 2;
+    std::ostringstream kname;
+    kname << "winograd_conv_2d_input_transform_" << type_to_name(out) << "_bc"
+          << bc;
+    auto compute_encoder = d.get_command_encoder(s.index);
+    auto kernel = d.get_kernel(kname.str());
+    compute_encoder->setComputePipelineState(kernel);
+
+    set_array_buffer(compute_encoder, in_padded, 0);
+    set_array_buffer(compute_encoder, inp_wg, 1);
+
+    compute_encoder->setBytes(
+        &conv_params_updated, sizeof(MLXConvParams<2>), 2);
+
+    MTL::Size group_dims = MTL::Size(32, wn, wm);
+    MTL::Size grid_dims = MTL::Size(N_tiles_w, N_tiles_h, N_tiles_n);
+
+    compute_encoder->dispatchThreadgroups(grid_dims, group_dims);
+  }
+
+  // Do batched gemm
+  std::vector<int> out_wg_shape = {8 * 8, N_tiles, conv_params.O};
+  array out_wg(out_wg_shape, in.dtype(), nullptr, {});
+  out_wg.set_data(allocator::malloc_or_wait(out_wg.nbytes()));
+  copies_w.push_back(out_wg);
+  {
+    std::vector<array> empty_copies;
+    mlx_matmul(
+        s,
+        d,
+        /*a = */ inp_wg,
+        /*b = */ filt_wg,
+        /*c = */ out_wg,
+        /*M = */ N_tiles,
+        /*N = */ conv_params.O,
+        /*K = */ conv_params.C,
+        /*batch_size_out = */ 8 * 8,
+        /*a_cols = */ conv_params.C,
+        /*b_cols = */ conv_params.O,
+        /*a_transposed = */ false,
+        /*b_transposed = */ false,
+        /*copies = */ empty_copies);
+  }
+
+  // Do output transform
+  {
+    int bc = 32;
+    int wm = 2;
+    int wn = 2;
+    std::ostringstream kname;
+    kname << "winograd_conv_2d_output_transform_" << type_to_name(out) << "_bo"
+          << bc;
+    auto compute_encoder = d.get_command_encoder(s.index);
+    auto kernel = d.get_kernel(kname.str());
+    compute_encoder->setComputePipelineState(kernel);
+
+    set_array_buffer(compute_encoder, out_wg, 0);
+    set_array_buffer(compute_encoder, out, 1);
+
+    compute_encoder->setBytes(
+        &conv_params_updated, sizeof(MLXConvParams<2>), 2);
+
+    MTL::Size group_dims = MTL::Size(32, wn, wm);
+    MTL::Size grid_dims = MTL::Size(N_tiles_w, N_tiles_h, N_tiles_n);
+
+    compute_encoder->dispatchThreadgroups(grid_dims, group_dims);
+  }
+}
+
+void conv_2D_gpu(
+    const Stream& s,
+    metal::Device& d,
+    const array& in,
+    const array& wt,
+    array out,
+    const std::vector<int>& padding,
+    const std::vector<int>& wt_strides,
+    const std::vector<int>& wt_dilation,
+    std::vector<array>& copies) {
+  // Make conv params
+  MLXConvParams<2> conv_params{
+      /* const int  N = */ in.shape(0),
+      /* const int  C = */ in.shape(3),
+      /* const int  O = */ wt.shape(0),
+      /* const int iS[NDIM] = */ {in.shape(1), in.shape(2)},
+      /* const int wS[NDIM] = */ {wt.shape(1), wt.shape(2)},
+      /* const int oS[NDIM] = */ {out.shape(1), out.shape(2)},
+      /* const int str[NDIM] = */ {wt_strides[0], wt_strides[1]},
+      /* const int pad[NDIM] = */ {padding[0], padding[1]},
+      /* const int dil[NDIM] = */ {wt_dilation[0], wt_dilation[1]},
+      /* const size_t in_strides[NDIM + 2] = */
+      {in.strides()[0], in.strides()[1], in.strides()[2], in.strides()[3]},
+      /* const size_t wt_strides[NDIM + 2] = */
+      {wt.strides()[0], wt.strides()[1], wt.strides()[2], wt.strides()[3]},
+      /* const size_t out_strides[NDIM + 2] = */
+      {out.strides()[0], out.strides()[1], out.strides()[2], out.strides()[3]},
+  };
+
+  // Direct to winograd conv
+  if (conv_params.C % 32 == 0 && conv_params.O % 32 == 0 &&
+      conv_params.C >= 64 && conv_params.O >= 64 && conv_params.wS[0] == 3 &&
+      conv_params.wS[1] == 3 && conv_params.str[0] == 1 &&
+      conv_params.str[1] == 1 && conv_params.dil[0] == 1 &&
+      conv_params.dil[1] == 1) {
+    winograd_conv_2D_gpu(s, d, in, wt, out, conv_params, copies);
+  }
+
+  // Direct to implicit gemm conv
+  else if (conv_params.C % 32 == 0 && conv_params.O % 32 == 0) {
+    implicit_gemm_conv_2D_gpu(s, d, in, wt, out, conv_params);
+  }
+
+  // Direct to explicit gemm conv
+  else if (wt_dilation[0] == 1 && wt_dilation[1] == 1) {
+    explicit_gemm_conv_2D_gpu(s, d, in, wt, out, conv_params);
+  }
+
+  // Direct to fallback conv
+  else {
+    slow_conv_2D_gpu(s, d, in, wt, out, conv_params);
+  }
+}
+
+} // namespace
+
+void Convolution::eval_gpu(const std::vector<array>& inputs, array& out) {
+  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+  auto& s = stream();
+  auto& d = metal::device(s.device);
+
+  // Ensure contiguity
+  std::vector<array> copies;
+  auto in = inputs[0];
+  auto wt = inputs[1];
+  if (!in.flags().row_contiguous) {
+    array arr_copy(in.shape(), in.dtype(), nullptr, {});
+    copy_gpu(in, arr_copy, CopyType::General, s);
+    copies.push_back(arr_copy);
+    in = arr_copy;
+  }
+  if (!wt.flags().row_contiguous) {
+    array arr_copy(wt.shape(), wt.dtype(), nullptr, {});
+    copy_gpu(wt, arr_copy, CopyType::General, s);
+    copies.push_back(arr_copy);
+    wt = arr_copy;
+  }
+
+  // 2D conv
+  if (out.ndim() == 4) {
+    conv_2D_gpu(
+        s, d, in, wt, out, padding_, kernel_strides_, kernel_dilation_, copies);
+  }
+  // 1D conv
+  else if (out.ndim() == 3) {
+    conv_1D_gpu(s, d, in, wt, out, padding_, kernel_strides_, kernel_dilation_);
+  }
+  // Throw error
+  else {
+    throw std::invalid_argument(
+        "[Convolution::eval_gpu] Only supports 1D or 2D convolutions.");
+  }
+
+  // Clear copies
+  if (copies.size() > 0) {
+    auto command_buffer = d.get_command_buffer(s.index);
+    command_buffer->addCompletedHandler(
+        [copies](MTL::CommandBuffer*) mutable { copies.clear(); });
+  }
+}
+
+} // namespace mlx::core

mlx/backend/metal/copy.h

@@ -0,0 +1,16 @@
+#pragma once
+
+#include "mlx/backend/common/copy.h"
+#include "mlx/stream.h"
+
+namespace mlx::core {
+
+void copy_gpu(const array& src, array& out, CopyType ctype, const Stream& s);
+void copy_gpu(const array& src, array& out, CopyType ctype);
+void copy_gpu_inplace(
+    const array& src,
+    array& out,
+    CopyType ctype,
+    const Stream& s);
+
+} // namespace mlx::core

mlx/backend/metal/kernels/arange.metal

@@ -0,0 +1,30 @@
+#include "mlx/backend/metal/kernels/bf16.h"
+
+template <typename T>
+[[kernel]] void arange(
+    constant const T& start,
+    constant const T& step,
+    device T* out,
+    uint index [[thread_position_in_grid]]) {
+  out[index] = start + index * step;
+}
+
+#define instantiate_arange(tname, type) \
+  template [[host_name("arange" #tname)]] \
+  [[kernel]] void arange<type>( \
+    constant const type& start, \
+    constant const type& step, \
+    device type* out, \
+    uint index [[thread_position_in_grid]]);
+
+instantiate_arange(uint8, uint8_t)
+instantiate_arange(uint16, uint16_t)
+instantiate_arange(uint32, uint32_t)
+instantiate_arange(uint64, uint64_t)
+instantiate_arange(int8, int8_t)
+instantiate_arange(int16, int16_t)
+instantiate_arange(int32, int32_t)
+instantiate_arange(int64, int64_t)
+instantiate_arange(float16, half)
+instantiate_arange(float32, float)
+instantiate_arange(bfloat16, bfloat16_t)

mlx/backend/metal/kernels/arg_reduce.metal

@@ -0,0 +1,208 @@
+#include <metal_atomic>
+#include <metal_simdgroup>
+
+#include "mlx/backend/metal/kernels/utils.h"
+
+using namespace metal;
+
+template <typename U>
+struct IndexValPair {
+  uint32_t index;
+  U val;
+
+  IndexValPair(uint32_t _index, U _val) : index(_index), val(_val) {}
+};
+
+template <typename U>
+struct ArgMin {
+  static constexpr constant U init = Limits<U>::max;
+
+  IndexValPair<U> reduce(IndexValPair<U> best, IndexValPair<U> current) {
+    if (best.val > current.val || (best.val == current.val && best.index > current.index)) {
+      return current;
+    } else {
+      return best;
+    }
+  }
+
+  template <int N>
+  IndexValPair<U> reduce_many(IndexValPair<U> best, thread U* vals, uint32_t offset) {
+    for (int i=0; i<N; i++) {
+      if (vals[i] < best.val) {
+        best.val = vals[i];
+        best.index = offset+i;
+      }
+    }
+    return best;
+  }
+};
+
+template <typename U>
+struct ArgMax {
+  static constexpr constant U init = Limits<U>::min;
+
+  IndexValPair<U> reduce(IndexValPair<U> best, IndexValPair<U> current) {
+    if (best.val < current.val || (best.val == current.val && best.index > current.index)) {
+      return current;
+    } else {
+      return best;
+    }
+  }
+
+  template <int N>
+  IndexValPair<U> reduce_many(IndexValPair<U> best, thread U* vals, uint32_t offset) {
+    for (int i=0; i<N; i++) {
+      if (vals[i] > best.val) {
+        best.val = vals[i];
+        best.index = offset+i;
+      }
+    }
+    return best;
+  }
+};
+
+bool simd_shuffle_down(bool data, uint16_t delta) {
+  return simd_shuffle_down(static_cast<uint32_t>(data), delta);
+}
+
+uint64_t simd_shuffle_down(uint64_t data, uint16_t delta) {
+  return as_type<uint64_t>(simd_shuffle_down(as_type<uint2>(data), delta));
+}
+
+int64_t simd_shuffle_down(int64_t data, uint16_t delta) {
+  return as_type<int64_t>(simd_shuffle_down(as_type<uint2>(data), delta));
+}
+
+template <typename U>
+IndexValPair<U> simd_shuffle_down(IndexValPair<U> data, uint16_t delta) {
+  return IndexValPair<U>(
+    simd_shuffle_down(data.index, delta),
+    simd_shuffle_down(data.val, delta)
+  );
+}
+
+
+template <typename T, typename Op, int N_READS>
+[[kernel]] void arg_reduce_general(
+    const device T *in [[buffer(0)]],
+    device uint32_t *out [[buffer(1)]],
+    const device int *shape [[buffer(2)]],
+    const device size_t *in_strides [[buffer(3)]],
+    const device size_t *out_strides [[buffer(4)]],
+    const device size_t& ndim [[buffer(5)]],
+    const device size_t& axis_stride [[buffer(6)]],
+    const device size_t& axis_size [[buffer(7)]],
+    threadgroup IndexValPair<T> *local_data [[threadgroup(0)]],
+    uint gid [[thread_position_in_grid]],
+    uint lid [[thread_position_in_threadgroup]],
+    uint lsize [[threads_per_threadgroup]],
+    uint simd_size [[threads_per_simdgroup]],
+    uint simd_lane_id [[thread_index_in_simdgroup]],
+    uint simd_group_id [[simdgroup_index_in_threadgroup]]) {
+
+  // Shapes and strides *do not* contain the reduction axis. The reduction size
+  // and stride are provided in axis_stride and axis_size.
+  //
+  // Note: in shape == out shape with this convention.
+  //
+  // The sketch of the kernel is as follows.
+  //    1. Launch prod(shape) * thread_group_size threads.
+  //    2. Loop ceildiv(axis_size / lsize) times
+  //    3. Read input values
+  //    4. Reduce among them and go to 3
+  //    4. Reduce in each simd_group
+  //    6. Write in the thread local memory
+  //    6. Reduce them accross thread group
+  //    7. Write the output without need for atomic
+  Op op;
+
+  // Compute the input/output index. There is one beginning and one output for
+  // the whole threadgroup.
+  auto in_idx = elem_to_loc(gid / lsize, shape, in_strides, ndim);
+  auto out_idx = elem_to_loc(gid / lsize, shape, out_strides, ndim);
+
+  IndexValPair<T> best(0, Op::init);
+
+  // Loop over the reduction axis in lsize*N_READS buckets
+  for (uint r=0; r < ceildiv(axis_size, N_READS*lsize); r++) {
+    // Read the current value
+    uint32_t current_index = r*lsize*N_READS + lid*N_READS;
+    uint32_t offset = current_index;
+    const device T * current_in = in + in_idx + current_index * axis_stride;
+    T vals[N_READS];
+    for (int i=0; i<N_READS; i++) {
+      vals[i] = (current_index < axis_size) ? *current_in : T(Op::init);
+      current_index++;
+      current_in += axis_stride;
+    }
+    best = op.template reduce_many<N_READS>(best, vals, offset);
+  }
+  // At this point we have reduced the axis into thread group best values so we
+  // need to reduce across the thread group.
+
+  // First per simd reduction.
+  for (uint offset=simd_size/2; offset>0; offset/=2) {
+    IndexValPair<T> neighbor = simd_shuffle_down(best, offset);
+    best = op.reduce(best, neighbor);
+  }
+
+  // Write to the threadgroup memory
+  if (simd_lane_id == 0) {
+    local_data[simd_group_id] = best;
+  }
+  threadgroup_barrier(mem_flags::mem_threadgroup);
+  if (simd_group_id != 0) {
+    return;
+  }
+
+  // Read the appropriate value from local data and perform one simd reduction
+  uint simd_groups = ceildiv(lsize, simd_size);
+  if (simd_lane_id < simd_groups) {
+    best = local_data[simd_lane_id];
+  }
+  for (uint offset=simd_size/2; offset>0; offset/=2) {
+    IndexValPair<T> neighbor = simd_shuffle_down(best, offset);
+    best = op.reduce(best, neighbor);
+  }
+
+  // Finally write the output
+  if (lid == 0) {
+    out[out_idx] = best.index;
+  }
+}
+
+#define instantiate_arg_reduce_helper(name, itype, op) \
+  template [[host_name(name)]] \
+  [[kernel]] void arg_reduce_general<itype, op<itype>, 4>( \
+      const device itype *in [[buffer(0)]], \
+      device uint32_t * out [[buffer(1)]], \
+      const device int *shape [[buffer(2)]], \
+      const device size_t *in_strides [[buffer(3)]], \
+      const device size_t *out_strides [[buffer(4)]], \
+      const device size_t& ndim [[buffer(5)]], \
+      const device size_t& axis_stride [[buffer(6)]], \
+      const device size_t& axis_size [[buffer(7)]], \
+      threadgroup IndexValPair<itype> *local_data [[threadgroup(0)]], \
+      uint gid [[thread_position_in_grid]], \
+      uint lid [[thread_position_in_threadgroup]], \
+      uint lsize [[threads_per_threadgroup]], \
+      uint simd_size [[threads_per_simdgroup]], \
+      uint simd_lane_id [[thread_index_in_simdgroup]], \
+      uint simd_group_id [[simdgroup_index_in_threadgroup]]);
+
+#define instantiate_arg_reduce(name, itype) \
+  instantiate_arg_reduce_helper("argmin_" #name , itype, ArgMin) \
+  instantiate_arg_reduce_helper("argmax_" #name , itype, ArgMax)
+
+instantiate_arg_reduce(bool_, bool)
+instantiate_arg_reduce(uint8, uint8_t)
+instantiate_arg_reduce(uint16, uint16_t)
+instantiate_arg_reduce(uint32, uint32_t)
+instantiate_arg_reduce(uint64, uint64_t)
+instantiate_arg_reduce(int8, int8_t)
+instantiate_arg_reduce(int16, int16_t)
+instantiate_arg_reduce(int32, int32_t)
+instantiate_arg_reduce(int64, int64_t)
+instantiate_arg_reduce(float16, half)
+instantiate_arg_reduce(float32, float)
+instantiate_arg_reduce(bfloat16, bfloat16_t)

mlx/backend/metal/kernels/bf16_math.h

@@ -0,0 +1,392 @@
+#pragma once
+
+#include "mlx/backend/metal/kernels/bf16.h"
+
+///////////////////////////////////////////////////////////////////////////////
+// Metal math for bfloat16
+///////////////////////////////////////////////////////////////////////////////
+
+/*
+
+Following the Metal Shading Language Specification (Metal 3.1)
+
+"bfloat is an extended itypeing point type that only allows implicit conversion
+ to a type of greater itypeing point rank. While bfloat can be implicitly
+ converted to itype, it cannot be implicitly converted to half, and neither
+ itype nor half can be implicitly converted to bfloat."
+
+Further, as far as I can tell, the stdlib math/simd functions are not defined
+for bfloat and calling with an argument of type bfloat will result in that
+argument getting implicitly converted to itype which then returns an output
+that is (likely) a itype which cannot be implicitly converted into a bfloat
+
+This leads to situations where
+bfloat a = 5.0bf;
+bfloat b = metal::abs(a); // this will throw an error since abs return itype
+bfloat c = static_cast<bfloat>(metal::abs(a)); // this is fine
+
+For the moment, I will be adding overloaded instantiations of the math
+functions to accordingly automatically handle the casting
+
+*/
+
+#define instantiate_metal_math_funcs(itype, otype, ctype, mfast)               \
+                                                                               \
+  METAL_FUNC otype abs(itype x) {                                              \
+    return static_cast<otype>(__metal_fabs(static_cast<ctype>(x), mfast));     \
+  }                                                                            \
+  METAL_FUNC otype acos(itype x) {                                             \
+    return static_cast<otype>(__metal_acos(static_cast<ctype>(x), mfast));     \
+  }                                                                            \
+  METAL_FUNC otype acosh(itype x) {                                            \
+    return static_cast<otype>(__metal_acosh(static_cast<ctype>(x), mfast));    \
+  }                                                                            \
+  METAL_FUNC otype asin(itype x) {                                             \
+    return static_cast<otype>(__metal_asin(static_cast<ctype>(x), mfast));     \
+  }                                                                            \
+  METAL_FUNC otype asinh(itype x) {                                            \
+    return static_cast<otype>(__metal_asinh(static_cast<ctype>(x), mfast));    \
+  }                                                                            \
+  METAL_FUNC otype atan(itype y_over_x) {                                      \
+    return static_cast<otype>(                                                 \
+        __metal_atan(static_cast<ctype>(y_over_x), mfast));                    \
+  }                                                                            \
+  METAL_FUNC otype atan2(itype y, itype x) {                                   \
+    return static_cast<otype>(                                                 \
+        __metal_atan2(static_cast<ctype>(y), static_cast<ctype>(x), mfast));   \
+  }                                                                            \
+  METAL_FUNC otype atanh(itype x) {                                            \
+    return static_cast<otype>(__metal_atanh(static_cast<ctype>(x), mfast));    \
+  }                                                                            \
+  METAL_FUNC otype ceil(itype x) {                                             \
+    return static_cast<otype>(__metal_ceil(static_cast<ctype>(x), mfast));     \
+  }                                                                            \
+  METAL_FUNC otype cos(itype x) {                                              \
+    return static_cast<otype>(__metal_cos(static_cast<ctype>(x), mfast));      \
+  }                                                                            \
+  METAL_FUNC otype cosh(itype x) {                                             \
+    return static_cast<otype>(__metal_cosh(static_cast<ctype>(x), mfast));     \
+  }                                                                            \
+  METAL_FUNC otype cospi(itype x) {                                            \
+    return static_cast<otype>(__metal_cospi(static_cast<ctype>(x), mfast));    \
+  }                                                                            \
+  METAL_FUNC otype divide(itype x, itype y) {                                  \
+    return static_cast<otype>(                                                 \
+        __metal_divide(static_cast<ctype>(x), static_cast<ctype>(y), mfast));  \
+  }                                                                            \
+  METAL_FUNC otype exp(itype x) {                                              \
+    return static_cast<otype>(__metal_exp(static_cast<ctype>(x), mfast));      \
+  }                                                                            \
+  METAL_FUNC otype exp10(itype x) {                                            \
+    return static_cast<otype>(__metal_exp10(static_cast<ctype>(x), mfast));    \
+  }                                                                            \
+  METAL_FUNC otype exp2(itype x) {                                             \
+    return static_cast<otype>(__metal_exp2(static_cast<ctype>(x), mfast));     \
+  }                                                                            \
+  METAL_FUNC otype fabs(itype x) {                                             \
+    return static_cast<otype>(__metal_fabs(static_cast<ctype>(x), mfast));     \
+  }                                                                            \
+  METAL_FUNC otype fdim(itype x, itype y) {                                    \
+    ctype t = static_cast<ctype>(x - y);                                       \
+    return static_cast<otype>(select(t, ctype(0), t < ctype(0) || x == y));    \
+  }                                                                            \
+  METAL_FUNC otype floor(itype x) {                                            \
+    return static_cast<otype>(__metal_floor(static_cast<ctype>(x), mfast));    \
+  }                                                                            \
+  METAL_FUNC otype fma(itype x, itype y, itype z) {                            \
+    return static_cast<otype>(__metal_fma(                                     \
+        static_cast<ctype>(x), static_cast<ctype>(y), static_cast<ctype>(z))); \
+  }                                                                            \
+  METAL_FUNC otype fmax(itype x, itype y) {                                    \
+    return static_cast<otype>(                                                 \
+        __metal_fmax(static_cast<ctype>(x), static_cast<ctype>(y), mfast));    \
+  }                                                                            \
+  METAL_FUNC otype fmax3(itype x, itype y, itype z) {                          \
+    return static_cast<otype>(__metal_fmax3(                                   \
+        static_cast<ctype>(x),                                                 \
+        static_cast<ctype>(y),                                                 \
+        static_cast<ctype>(z),                                                 \
+        mfast));                                                               \
+  }                                                                            \
+  METAL_FUNC otype fmedian3(itype x, itype y, itype z) {                       \
+    return static_cast<otype>(__metal_fmedian3(                                \
+        static_cast<ctype>(x),                                                 \
+        static_cast<ctype>(y),                                                 \
+        static_cast<ctype>(z),                                                 \
+        mfast));                                                               \
+  }                                                                            \
+  METAL_FUNC otype fmin(itype x, itype y) {                                    \
+    return static_cast<otype>(                                                 \
+        __metal_fmin(static_cast<ctype>(x), static_cast<ctype>(y), mfast));    \
+  }                                                                            \
+  METAL_FUNC otype fmin3(itype x, itype y, itype z) {                          \
+    return static_cast<otype>(__metal_fmin3(                                   \
+        static_cast<ctype>(x),                                                 \
+        static_cast<ctype>(y),                                                 \
+        static_cast<ctype>(z),                                                 \
+        mfast));                                                               \
+  }                                                                            \
+  METAL_FUNC otype fmod(itype x, itype y) {                                    \
+    return static_cast<otype>(                                                 \
+        __metal_fmod(static_cast<ctype>(x), static_cast<ctype>(y), mfast));    \
+  }                                                                            \
+  METAL_FUNC otype fract(itype x) {                                            \
+    return static_cast<otype>(__metal_fract(static_cast<ctype>(x), mfast));    \
+  }                                                                            \
+  METAL_FUNC otype frexp(itype x, thread int& exp) {                           \
+    return static_cast<otype>(__metal_frexp(static_cast<ctype>(x), &exp));     \
+  }                                                                            \
+  METAL_FUNC otype ldexp(itype x, int k) {                                     \
+    return static_cast<otype>(__metal_ldexp(static_cast<ctype>(x), k, mfast)); \
+  }                                                                            \
+  METAL_FUNC otype log(itype x) {                                              \
+    return static_cast<otype>(__metal_log(static_cast<ctype>(x), mfast));      \
+  }                                                                            \
+  METAL_FUNC otype log10(itype x) {                                            \
+    return static_cast<otype>(__metal_log10(static_cast<ctype>(x), mfast));    \
+  }                                                                            \
+  METAL_FUNC otype log2(itype x) {                                             \
+    return static_cast<otype>(__metal_log2(static_cast<ctype>(x), mfast));     \
+  }                                                                            \
+  METAL_FUNC otype max(itype x, itype y) {                                     \
+    return static_cast<otype>(                                                 \
+        __metal_fmax(static_cast<ctype>(x), static_cast<ctype>(y), mfast));    \
+  }                                                                            \
+  METAL_FUNC otype max3(itype x, itype y, itype z) {                           \
+    return static_cast<otype>(__metal_fmax3(                                   \
+        static_cast<ctype>(x),                                                 \
+        static_cast<ctype>(y),                                                 \
+        static_cast<ctype>(z),                                                 \
+        mfast));                                                               \
+  }                                                                            \
+  METAL_FUNC otype median3(itype x, itype y, itype z) {                        \
+    return static_cast<otype>(__metal_fmedian3(                                \
+        static_cast<ctype>(x),                                                 \
+        static_cast<ctype>(y),                                                 \
+        static_cast<ctype>(z),                                                 \
+        mfast));                                                               \
+  }                                                                            \
+  METAL_FUNC otype min(itype x, itype y) {                                     \
+    return static_cast<otype>(                                                 \
+        __metal_fmin(static_cast<ctype>(x), static_cast<ctype>(y), mfast));    \
+  }                                                                            \
+  METAL_FUNC otype min3(itype x, itype y, itype z) {                           \
+    return static_cast<otype>(__metal_fmin3(                                   \
+        static_cast<ctype>(x),                                                 \
+        static_cast<ctype>(y),                                                 \
+        static_cast<ctype>(z),                                                 \
+        mfast));                                                               \
+  }                                                                            \
+  METAL_FUNC otype nextafter(itype x, itype y) {                               \
+    return static_cast<otype>(                                                 \
+        __metal_nextafter(static_cast<ctype>(x), static_cast<ctype>(y)));      \
+  }                                                                            \
+  METAL_FUNC otype pow(itype x, itype y) {                                     \
+    return static_cast<otype>(                                                 \
+        __metal_pow(static_cast<ctype>(x), static_cast<ctype>(y), mfast));     \
+  }                                                                            \
+  METAL_FUNC otype powr(itype x, itype y) {                                    \
+    return static_cast<otype>(                                                 \
+        __metal_powr(static_cast<ctype>(x), static_cast<ctype>(y), mfast));    \
+  }                                                                            \
+  METAL_FUNC otype rint(itype x) {                                             \
+    return static_cast<otype>(__metal_rint(static_cast<ctype>(x), mfast));     \
+  }                                                                            \
+  METAL_FUNC otype round(itype x) {                                            \
+    return static_cast<otype>(__metal_round(static_cast<ctype>(x), mfast));    \
+  }                                                                            \
+  METAL_FUNC otype rsqrt(itype x) {                                            \
+    return static_cast<otype>(__metal_rsqrt(static_cast<ctype>(x), mfast));    \
+  }                                                                            \
+  METAL_FUNC otype sin(itype x) {                                              \
+    return static_cast<otype>(__metal_sin(static_cast<ctype>(x), mfast));      \
+  }                                                                            \
+  METAL_FUNC otype sinh(itype x) {                                             \
+    return static_cast<otype>(__metal_sinh(static_cast<ctype>(x), mfast));     \
+  }                                                                            \
+  METAL_FUNC otype sinpi(itype x) {                                            \
+    return static_cast<otype>(__metal_sinpi(static_cast<ctype>(x), mfast));    \
+  }                                                                            \
+  METAL_FUNC otype sqrt(itype x) {                                             \
+    return static_cast<otype>(__metal_sqrt(static_cast<ctype>(x), mfast));     \
+  }                                                                            \
+  METAL_FUNC otype tan(itype x) {                                              \
+    return static_cast<otype>(__metal_tan(static_cast<ctype>(x), mfast));      \
+  }                                                                            \
+  METAL_FUNC otype tanh(itype x) {                                             \
+    return static_cast<otype>(__metal_tanh(static_cast<ctype>(x), mfast));     \
+  }                                                                            \
+  METAL_FUNC otype tanpi(itype x) {                                            \
+    return static_cast<otype>(__metal_tanpi(static_cast<ctype>(x), mfast));    \
+  }                                                                            \
+  METAL_FUNC otype trunc(itype x) {                                            \
+    return static_cast<otype>(__metal_trunc(static_cast<ctype>(x), mfast));    \
+  }
+
+namespace metal {
+
+instantiate_metal_math_funcs(
+    bfloat16_t,
+    bfloat16_t,
+    float,
+    __METAL_MAYBE_FAST_MATH__);
+
+namespace fast {
+
+instantiate_metal_math_funcs(
+    bfloat16_t,
+    bfloat16_t,
+    float,
+    __METAL_FAST_MATH__);
+
+} // namespace fast
+
+namespace precise {
+
+instantiate_metal_math_funcs(
+    bfloat16_t,
+    bfloat16_t,
+    float,
+    __METAL_PRECISE_MATH__);
+
+} // namespace precise
+
+} // namespace metal
+
+///////////////////////////////////////////////////////////////////////////////
+// Metal simd for bfloat16
+///////////////////////////////////////////////////////////////////////////////
+
+#define instantiate_metal_simd_comm_funcs(                                   \
+    itype, otype, ctype, itype_to_ctype, ctype_to_otype)                     \
+                                                                             \
+  METAL_FUNC otype simd_broadcast(itype data, ushort broadcast_lane_id) {    \
+    return ctype_to_otype(                                                   \
+        __metal_simd_broadcast(itype_to_ctype(data), broadcast_lane_id));    \
+  }                                                                          \
+                                                                             \
+  METAL_FUNC otype simd_shuffle(itype data, ushort simd_lane_id) {           \
+    return ctype_to_otype(                                                   \
+        __metal_simd_shuffle(itype_to_ctype(data), simd_lane_id));           \
+  }                                                                          \
+                                                                             \
+  METAL_FUNC otype simd_shuffle_and_fill_down(                               \
+      itype data, itype filling_data, ushort delta, ushort modulo) {         \
+    return ctype_to_otype(__metal_simd_shuffle_and_fill_down(                \
+        itype_to_ctype(data), itype_to_ctype(filling_data), delta, modulo)); \
+  }                                                                          \
+                                                                             \
+  METAL_FUNC otype simd_shuffle_and_fill_down(                               \
+      itype data, itype filling_data, ushort delta) {                        \
+    return ctype_to_otype(__metal_simd_shuffle_and_fill_down(                \
+        itype_to_ctype(data),                                                \
+        itype_to_ctype(filling_data),                                        \
+        delta,                                                               \
+        __metal_get_simdgroup_size(ushort())));                              \
+  }                                                                          \
+                                                                             \
+  METAL_FUNC otype simd_shuffle_and_fill_up(                                 \
+      itype data, itype filling_data, ushort delta, ushort modulo) {         \
+    return ctype_to_otype(__metal_simd_shuffle_and_fill_up(                  \
+        itype_to_ctype(data), itype_to_ctype(filling_data), delta, modulo)); \
+  }                                                                          \
+                                                                             \
+  METAL_FUNC otype simd_shuffle_and_fill_up(                                 \
+      itype data, itype filling_data, ushort delta) {                        \
+    return ctype_to_otype(__metal_simd_shuffle_and_fill_up(                  \
+        itype_to_ctype(data),                                                \
+        itype_to_ctype(filling_data),                                        \
+        delta,                                                               \
+        __metal_get_simdgroup_size(ushort())));                              \
+  }                                                                          \
+                                                                             \
+  METAL_FUNC otype simd_shuffle_down(itype data, ushort delta) {             \
+    return ctype_to_otype(                                                   \
+        __metal_simd_shuffle_down(itype_to_ctype(data), delta));             \
+  }                                                                          \
+                                                                             \
+  METAL_FUNC otype simd_shuffle_rotate_down(itype data, ushort delta) {      \
+    return ctype_to_otype(                                                   \
+        __metal_simd_shuffle_rotate_down(itype_to_ctype(data), delta));      \
+  }                                                                          \
+                                                                             \
+  METAL_FUNC otype simd_shuffle_rotate_up(itype data, ushort delta) {        \
+    return ctype_to_otype(                                                   \
+        __metal_simd_shuffle_rotate_up(itype_to_ctype(data), delta));        \
+  }                                                                          \
+                                                                             \
+  METAL_FUNC otype simd_shuffle_up(itype data, ushort delta) {               \
+    return ctype_to_otype(                                                   \
+        __metal_simd_shuffle_up(itype_to_ctype(data), delta));               \
+  }                                                                          \
+                                                                             \
+  METAL_FUNC otype simd_shuffle_xor(itype data, ushort mask) {               \
+    return ctype_to_otype(                                                   \
+        __metal_simd_shuffle_xor(itype_to_ctype(data), mask));               \
+  }
+
+#define instantiate_metal_simd_reduction_funcs(itype, otype, ctype)            \
+                                                                               \
+  METAL_FUNC otype simd_max(itype data) {                                      \
+    return static_cast<otype>(__metal_simd_max(static_cast<ctype>(data)));     \
+  }                                                                            \
+                                                                               \
+  METAL_FUNC otype simd_min(itype data) {                                      \
+    return static_cast<otype>(__metal_simd_min(static_cast<ctype>(data)));     \
+  }                                                                            \
+                                                                               \
+  METAL_FUNC otype simd_prefix_exclusive_product(itype data) {                 \
+    return static_cast<otype>(                                                 \
+        __metal_simd_prefix_exclusive_product(static_cast<ctype>(data)));      \
+  }                                                                            \
+                                                                               \
+  METAL_FUNC otype simd_prefix_exclusive_sum(itype data) {                     \
+    return static_cast<otype>(                                                 \
+        __metal_simd_prefix_exclusive_sum(static_cast<ctype>(data)));          \
+  }                                                                            \
+                                                                               \
+  METAL_FUNC otype simd_prefix_inclusive_product(itype data) {                 \
+    return static_cast<otype>(                                                 \
+        __metal_simd_prefix_inclusive_product(static_cast<ctype>(data)));      \
+  }                                                                            \
+                                                                               \
+  METAL_FUNC otype simd_prefix_inclusive_sum(itype data) {                     \
+    return static_cast<otype>(                                                 \
+        __metal_simd_prefix_inclusive_sum(static_cast<ctype>(data)));          \
+  }                                                                            \
+                                                                               \
+  METAL_FUNC otype simd_product(itype data) {                                  \
+    return static_cast<otype>(__metal_simd_product(static_cast<ctype>(data))); \
+  }                                                                            \
+                                                                               \
+  METAL_FUNC otype simd_sum(itype data) {                                      \
+    return static_cast<otype>(__metal_simd_sum(static_cast<ctype>(data)));     \
+  }                                                                            \
+                                                                               \
+  METAL_FUNC otype simd_xor(itype data) {                                      \
+    return static_cast<otype>(__metal_simd_xor(static_cast<ctype>(data)));     \
+  }
+
+#if defined(__HAVE_BFLOAT__)
+
+#define bfloat16_to_uint16(x) as_type<uint16_t>(x)
+#define uint16_to_bfloat16(x) as_type<bfloat16_t>(x)
+
+#else
+
+#define bfloat16_to_uint16(x) x.bits_
+#define uint16_to_bfloat16(x) _MLX_BFloat16(x, _MLX_BFloat16::bits_to_bfloat())
+
+#endif
+
+namespace metal {
+
+instantiate_metal_simd_comm_funcs(
+    bfloat16_t,
+    bfloat16_t,
+    uint16_t,
+    bfloat16_to_uint16,
+    uint16_to_bfloat16);
+instantiate_metal_simd_reduction_funcs(bfloat16_t, bfloat16_t, float);
+
+} // namespace metal

mlx/backend/metal/kernels/conv.metal

@@ -0,0 +1,553 @@
+#include <metal_stdlib>
+
+#include "mlx/backend/metal/kernels/conv_params.h"
+#include "mlx/backend/metal/kernels/bf16.h"
+
+#include "mlx/backend/metal/kernels/gemm/conv.h"
+
+using namespace metal;
+
+///////////////////////////////////////////////////////////////////////////////
+/// Slow and naive kernels
+///////////////////////////////////////////////////////////////////////////////
+
+template <typename T, 
+          const int BM, /* Threadgroup rows (in threads) */
+          const int BN, /* Threadgroup cols (in threads) */
+          const int TM, /* Thread rows (in elements) */
+          const int TN, /* Thread cols (in elements) */
+          const int BC = 16>
+[[kernel]] void naive_conv_2d(
+    const device T* in [[buffer(0)]],
+    const device T* wt [[buffer(1)]],
+    device T* out [[buffer(2)]],
+    const constant MLXConvParams<2>& params [[buffer(3)]],
+    uint3 tid [[threadgroup_position_in_grid]],
+    uint3 lid [[thread_position_in_threadgroup]],
+    uint simd_gid [[simdgroup_index_in_threadgroup]],
+    uint simd_lid [[thread_index_in_simdgroup]]) {
+  
+  (void)simd_gid;
+  (void)simd_lid;
+
+  out += tid.z * params.out_strides[0];
+  in += tid.z * params.in_strides[0];
+
+  int out_o = tid.y * BN * TN + lid.y * TN;
+  int out_hw = tid.x * BM * TM + lid.x * TM;
+    
+  int out_h[TM];
+  int out_w[TN];
+
+  for(int m = 0; m < TM; ++m) {
+    int mm = (out_hw + m);
+    out_h[m] = mm / params.oS[1];
+    out_w[m] = mm % params.oS[1];
+  }
+
+
+  T in_local[TM];
+  T wt_local[TN];
+  T out_local[TM * TN] = {T(0)};
+
+  for(int h = 0; h < params.wS[0]; ++h) {
+    for(int w = 0; w < params.wS[1]; ++w) {
+      for(int c = 0; c < params.C; ++c) {
+
+        // Local in
+        for(int m = 0; m < TM; m++) {
+          int i = out_h[m] * params.str[0] - params.pad[0] + h * params.dil[0];
+          int j = out_w[m] * params.str[1] - params.pad[1] + w * params.dil[1];
+
+          bool valid = i >= 0 && i < params.iS[0] && j >= 0 && j < params.iS[1];
+          in_local[m] = valid ? in[i * params.in_strides[1] + j * params.in_strides[2] + c] : T(0);
+        }
+
+        // Load weight
+        for (int n = 0; n < TN; ++n) {
+          int o = out_o + n;
+          wt_local[n] = o < params.O ? wt[o * params.wt_strides[0] + 
+                                          h * params.wt_strides[1] + 
+                                          w * params.wt_strides[2] + c] : T(0);
+        }
+
+        // Accumulate
+        for(int m = 0; m < TM; ++m) {
+          for(int n = 0; n < TN; ++n) {
+            out_local[m * TN + n] += in_local[m] * wt_local[n];
+          }
+        }
+
+      }
+    }
+  }
+
+  for(int m = 0; m < TM; ++m) {
+    for(int n = 0; n < TN; ++n) {
+      if(out_h[m] < params.oS[0] && out_w[m] < params.oS[1] && (out_o + n) < params.O)
+      out[out_h[m] * params.out_strides[1] +
+          out_w[m] * params.out_strides[2] + out_o + n] = out_local[m * TN + n];
+    }
+  }
+
+}
+
+// Instantiations
+
+#define instantiate_naive_conv_2d(name, itype, bm, bn, tm, tn) \
+  template [[host_name("naive_conv_2d_" #name "_bm" #bm "_bn" #bn "_tm" #tm "_tn" #tn)]] \
+  [[kernel]] void naive_conv_2d<itype, bm, bn, tm, tn>( \
+      const device itype* in [[buffer(0)]], \
+      const device itype* wt [[buffer(1)]], \
+      device itype* out [[buffer(2)]], \
+      const constant MLXConvParams<2>& params [[buffer(3)]], \
+      uint3 tid [[threadgroup_position_in_grid]], \
+      uint3 lid [[thread_position_in_threadgroup]], \
+      uint simd_gid [[simdgroup_index_in_threadgroup]], \
+      uint simd_lid [[thread_index_in_simdgroup]]);
+
+#define instantiate_naive_conv_2d_blocks(name, itype) \
+    instantiate_naive_conv_2d(name, itype, 16, 8, 4, 4) \
+    instantiate_naive_conv_2d(name, itype, 16, 8, 2, 4) 
+
+instantiate_naive_conv_2d_blocks(float32, float);
+instantiate_naive_conv_2d_blocks(float16, half);
+instantiate_naive_conv_2d_blocks(bfloat16, bfloat16_t);
+
+///////////////////////////////////////////////////////////////////////////////
+/// Implicit gemm kernels
+///////////////////////////////////////////////////////////////////////////////
+
+template <typename T,
+          int BM,
+          int BN,
+          int BK,
+          int WM,
+          int WN>
+[[kernel, max_total_threads_per_threadgroup(WM * WN * 32)]] void implicit_gemm_conv_2d(
+    const device T* in [[buffer(0)]],
+    const device T* wt [[buffer(1)]],
+    device T* out [[buffer(2)]],
+    const constant MLXConvParams<2>& params [[buffer(3)]],
+    uint3 tid [[threadgroup_position_in_grid]],
+    uint3 lid [[thread_position_in_threadgroup]],
+    uint simd_gid [[simdgroup_index_in_threadgroup]],
+    uint simd_lid [[thread_index_in_simdgroup]]) {
+
+  using gemm_kernel = Conv2DImplicitGEMMKernel<T, BM, BN, BK, WM, WN, /*transpose_a*/ false, /*transpose_b*/ true>;
+    
+  threadgroup T tgp_memory[gemm_kernel::tgp_mem_size];
+
+  gemm_kernel::run( 
+    in, wt, out, 
+    params, tgp_memory,
+    tid, lid, simd_gid, simd_lid
+  );
+
+}
+
+#define instantiate_implicit_conv_2d(name, itype, bm, bn, bk, wm, wn) \
+  template [[host_name("implicit_gemm_conv_2d_" #name "_bm" #bm "_bn" #bn "_bk" #bk "_wm" #wm "_wn" #wn)]] \
+  [[kernel]] void implicit_gemm_conv_2d<itype, bm, bn, bk, wm, wn>( \
+      const device itype* in [[buffer(0)]], \
+      const device itype* wt [[buffer(1)]], \
+      device itype* out [[buffer(2)]], \
+      const constant MLXConvParams<2>& params [[buffer(3)]], \
+      uint3 tid [[threadgroup_position_in_grid]], \
+      uint3 lid [[thread_position_in_threadgroup]], \
+      uint simd_gid [[simdgroup_index_in_threadgroup]], \
+      uint simd_lid [[thread_index_in_simdgroup]]);
+
+#define instantiate_implicit_2d_blocks(name, itype) \
+    instantiate_implicit_conv_2d(name, itype, 32, 32, 32, 2, 2) \
+    instantiate_implicit_conv_2d(name, itype, 32, 32, 16, 2, 2) \
+    instantiate_implicit_conv_2d(name, itype, 64, 64, 16, 2, 2)
+
+instantiate_implicit_2d_blocks(float32, float);
+instantiate_implicit_2d_blocks(float16, half);
+instantiate_implicit_2d_blocks(bfloat16, bfloat16_t);
+
+///////////////////////////////////////////////////////////////////////////////
+/// Winograd kernels
+///////////////////////////////////////////////////////////////////////////////
+
+template <int M, int R, int S>
+struct WinogradTransforms {
+
+};
+
+template <>
+struct WinogradTransforms<6, 3, 8> {
+  MLX_MTL_CONST int OUT_TILE_SIZE = 6;
+  MLX_MTL_CONST int FILTER_SIZE = 3;
+  MLX_MTL_CONST int IN_TILE_SIZE = OUT_TILE_SIZE + FILTER_SIZE - 1;
+  MLX_MTL_CONST int SIMD_MATRIX_SIZE = 8;
+  MLX_MTL_CONST float in_transform[SIMD_MATRIX_SIZE][SIMD_MATRIX_SIZE] = {
+    { 1.00f,  0.00f,  0.00f,  0.00f,  0.00f,  0.00f,  0.00f,  0.00f},
+    { 0.00f,  1.00f, -1.00f,  0.50f, -0.50f,  2.00f, -2.00f, -1.00f},
+    {-5.25f,  1.00f,  1.00f,  0.25f,  0.25f,  4.00f,  4.00f,  0.00f},
+    { 0.00f, -4.25f,  4.25f, -2.50f,  2.50f, -2.50f,  2.50f,  5.25f},
+    { 5.25f, -4.25f, -4.25f, -1.25f, -1.25f, -5.00f, -5.00f,  0.00f},
+    { 0.00f,  1.00f, -1.00f,  2.00f, -2.00f,  0.50f, -0.50f, -5.25f},
+    {-1.00f,  1.00f,  1.00f,  1.00f,  1.00f,  1.00f,  1.00f,  0.00f},
+    { 0.00f,  0.00f,  0.00f,  0.00f,  0.00f,  0.00f,  0.00f,  1.00f},
+  };
+
+  MLX_MTL_CONST float out_transform[SIMD_MATRIX_SIZE][SIMD_MATRIX_SIZE] = {
+    { 1.00f,  0.00f,  0.00f,   0.00f,    0.00f,    0.00f},
+    { 1.00f,  1.00f,  1.00f,   1.00f,    1.00f,    1.00f},
+    { 1.00f, -1.00f,  1.00f,  -1.00f,    1.00f,   -1.00f},
+    { 1.00f,  2.00f,  4.00f,   8.00f,   16.00f,   32.00f},
+    { 1.00f, -2.00f,  4.00f,  -8.00f,   16.00f,  -32.00f},
+    { 1.00f,  0.50f,  0.25f,  0.125f,   0.0625f,   0.03125f},
+    { 1.00f, -0.50f,  0.25f, -0.125f,   0.0625f,  -0.03125f},
+    { 0.00f,  0.00f,  0.00f,  0.00f,      0.00f,   1.00f},
+  };
+
+  MLX_MTL_CONST float wt_transform[SIMD_MATRIX_SIZE][SIMD_MATRIX_SIZE] = {
+    {      1.00,       0.00,       0.00},
+    { -2.0/9.00,  -2.0/9.00,  -2.0/9.00},
+    { -2.0/9.00,   2.0/9.00,  -2.0/9.00},
+    {  1.0/90.0,   1.0/45.0,   2.0/45.0},
+    {  1.0/90.0,  -1.0/45.0,   2.0/45.0},
+    { 32.0/45.0,  16.0/45.0,   8.0/45.0},
+    { 32.0/45.0, -16.0/45.0,   8.0/45.0},
+    {      0.00,       0.00,       1.00},
+  };
+};
+
+constant constexpr const float WinogradTransforms<6, 3, 8>::wt_transform[8][8];
+constant constexpr const float WinogradTransforms<6, 3, 8>::in_transform[8][8];
+constant constexpr const float WinogradTransforms<6, 3, 8>::out_transform[8][8];
+
+template <typename T,
+          int BC = 32,
+          int BO = 4,
+          int M = 6,
+          int R = 3>
+[[kernel, max_total_threads_per_threadgroup(BO * 32)]] void winograd_conv_2d_weight_transform(
+    const device T* wt_in [[buffer(0)]],
+    device T* wt_out [[buffer(1)]],
+    const constant int& C [[buffer(2)]],
+    const constant int& O [[buffer(3)]],
+    uint tid [[threadgroup_position_in_grid]],
+    uint simd_group_id [[simdgroup_index_in_threadgroup]],
+    uint simd_lane_id [[thread_index_in_simdgroup]]) {
+
+  using WGT = WinogradTransforms<M, R, 8>;
+
+  // Get lane position in simdgroup
+  const short qid = simd_lane_id / 4;
+  const short sm = (qid & 4) + (simd_lane_id / 2) % 4;
+  const short sn = (qid & 2) * 2 + (simd_lane_id % 2) * 2;
+
+  // Initialize G matrix
+  simdgroup_matrix<T, 8, 8> G;
+  G.thread_elements()[0] = WGT::wt_transform[sm][sn];
+  G.thread_elements()[1] = WGT::wt_transform[sm][sn + 1];
+
+  // Initialize Gt matrix
+  simdgroup_matrix<T, 8, 8> Gt;
+  Gt.thread_elements()[0] = WGT::wt_transform[sn][sm];
+  Gt.thread_elements()[1] = WGT::wt_transform[sn + 1][sm];
+
+  // Move to the correct output filter
+  size_t ko = BO * tid + simd_group_id;
+  wt_in  += ko * R * R * C;
+
+  // wt_out is stored transposed (A x A x C x O)
+  short ohw_0 = sm * 8 + sn;
+  short ohw_1 = sm * 8 + sn + 1;
+  device T* wt_out_0 = wt_out + ohw_0 * C * O + ko;
+  device T* wt_out_1 = wt_out + ohw_1 * C * O + ko; 
+
+  // Prepare shared memory
+  threadgroup T Ws[BO][R][R][BC];
+
+  // Loop over C
+  for(int bc = 0; bc < C; bc += BC) {
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+    // Read into shared memory
+    for(int kh = 0; kh < R; ++kh) {
+      for(int kw = 0; kw < R; ++kw) {
+        for(int kc = simd_lane_id; kc < BC; kc += 32) {
+          Ws[simd_group_id][kh][kw][kc] = wt_in[kh * R * C + kw * C + kc];
+        }
+      }
+    }
+
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+    // Do transform and store the result 
+    for(int c = 0; c < BC; ++c) {
+      simdgroup_matrix<T, 8, 8> g;
+      g.thread_elements()[0] = sm < R && sn < R ? Ws[simd_group_id][sm][sn][c] : T(0);
+      g.thread_elements()[1] = sm < R && sn + 1 < R ? Ws[simd_group_id][sm][sn + 1][c] : T(0);
+
+      simdgroup_matrix<T, 8, 8> g_out = (G * g) * Gt;
+      wt_out_0[c * O] = g_out.thread_elements()[0];
+      wt_out_1[c * O] = g_out.thread_elements()[1];
+    }
+
+    wt_in += BC;
+    wt_out_0 += BC * O;
+    wt_out_1 += BC * O;
+  }
+
+}
+
+#define instantiate_winograd_conv_2d_weight_transform_base(name, itype, bc) \
+  template [[host_name("winograd_conv_2d_weight_transform_" #name "_bc" #bc)]]\
+  [[kernel]] void winograd_conv_2d_weight_transform<itype, bc>(\
+      const device itype* wt_in [[buffer(0)]],\
+      device itype* wt_out [[buffer(1)]],\
+      const constant int& C [[buffer(2)]],\
+      const constant int& O [[buffer(3)]],\
+      uint tid [[threadgroup_position_in_grid]],\
+      uint simd_group_id [[simdgroup_index_in_threadgroup]],\
+      uint simd_lane_id [[thread_index_in_simdgroup]]);
+
+template <typename T,
+          int BC,
+          int WM,
+          int WN,
+          int M = 6,
+          int R = 3>
+[[kernel, max_total_threads_per_threadgroup(WM * WN * 32)]] void winograd_conv_2d_input_transform(
+    const device T* inp_in [[buffer(0)]],
+    device T* inp_out [[buffer(1)]],
+    const constant MLXConvParams<2>& params [[buffer(2)]],
+    uint3 tid [[threadgroup_position_in_grid]],
+    uint3 lid [[thread_position_in_threadgroup]],
+    uint3 tgp_per_grid [[threadgroups_per_grid]],
+    uint simd_group_id [[simdgroup_index_in_threadgroup]],
+    uint simd_lane_id [[thread_index_in_simdgroup]]) {
+
+  (void)lid;
+
+  using WGT = WinogradTransforms<M, R, 8>;
+  constexpr int A = WGT::IN_TILE_SIZE;
+  constexpr int N_SIMD_GROUPS = WM * WN;
+
+  // Get lane position in simdgroup
+  const short qid = simd_lane_id / 4;
+  const short sm = (qid & 4) + (simd_lane_id / 2) % 4;
+  const short sn = (qid & 2) * 2 + (simd_lane_id % 2) * 2;
+
+  // Initialize B matrix
+  simdgroup_matrix<T, 8, 8> B;
+  B.thread_elements()[0] = WGT::in_transform[sm][sn];
+  B.thread_elements()[1] = WGT::in_transform[sm][sn + 1];
+
+  // Initialize Bt matrix
+  simdgroup_matrix<T, 8, 8> Bt;
+  Bt.thread_elements()[0] = WGT::in_transform[sn][sm];
+  Bt.thread_elements()[1] = WGT::in_transform[sn + 1][sm];
+
+  // Resolve input tile
+  constexpr int TH = (A / WM);
+  constexpr int TW = (A / WN);
+  int kh = TH * (simd_group_id / WN);
+  int kw = TW * (simd_group_id % WN);
+  int bh = M * tid.y + kh;
+  int bw = M * tid.x + kw;
+
+  // Move to the correct input tile
+  inp_in += tid.z * params.in_strides[0] 
+             + bh * params.in_strides[1]
+             + bw * params.in_strides[2];
+
+  // Pre compute strides 
+  int jump_in[TH][TW];
+
+  for(int h = 0; h < TH; h++) {
+    for(int w = 0; w < TW; w++) {
+       jump_in[h][w] = h * params.in_strides[1] + w * params.in_strides[2];
+    }
+  }
+
+  // inp_out is stored interleaved (A x A x tiles x C)
+  size_t N_TILES = tgp_per_grid.x * tgp_per_grid.y * tgp_per_grid.z;
+  size_t tile_id = tid.z * tgp_per_grid.x * tgp_per_grid.y + tid.y * tgp_per_grid.x + tid.x;
+  size_t ohw_0 = sm * 8 + sn;
+  size_t ohw_1 = sm * 8 + sn + 1;
+  device T* inp_out_0 = inp_out + ohw_0 * N_TILES * params.C + tile_id * params.C;
+  device T* inp_out_1 = inp_out + ohw_1 * N_TILES * params.C + tile_id * params.C;
+
+  // Prepare shared memory
+  threadgroup T Is[A][A][BC];
+
+  // Loop over C
+  for(int bc = 0; bc < params.C; bc += BC) {
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+    // Read into shared memory
+    for(int h = 0; h < TH; h++) {
+      for(int w = 0; w < TW; w++) {
+        const device T* in_ptr = inp_in + jump_in[h][w];
+        for(int c = simd_lane_id; c < BC; c += 32) {
+          Is[kh + h][kw + w][c] = in_ptr[c];
+        }
+      }
+    }
+
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+    // Do transform and store the result 
+    for(int c = simd_group_id; c < BC; c += N_SIMD_GROUPS) {
+      simdgroup_matrix<T, 8, 8> I;
+      I.thread_elements()[0] = Is[sm][sn][c];
+      I.thread_elements()[1] = Is[sm][sn + 1][c];
+
+      simdgroup_matrix<T, 8, 8> I_out = (Bt * I) * B;
+      inp_out_0[c] = I_out.thread_elements()[0];
+      inp_out_1[c] = I_out.thread_elements()[1];
+    }
+
+    inp_in += BC;
+    inp_out_0 += BC;
+    inp_out_1 += BC;
+  }
+
+}
+
+#define instantiate_winograd_conv_2d_input_transform(name, itype, bc) \
+  template [[host_name("winograd_conv_2d_input_transform_" #name "_bc" #bc)]]\
+  [[kernel]] void winograd_conv_2d_input_transform<itype, bc, 2, 2>(\
+      const device itype* inp_in [[buffer(0)]],\
+      device itype* inp_out [[buffer(1)]],\
+      const constant MLXConvParams<2>& params [[buffer(2)]],\
+      uint3 tid [[threadgroup_position_in_grid]],\
+      uint3 lid [[thread_position_in_threadgroup]],\
+      uint3 tgp_per_grid [[threadgroups_per_grid]],\
+      uint simd_group_id [[simdgroup_index_in_threadgroup]],\
+      uint simd_lane_id [[thread_index_in_simdgroup]]);
+
+template <typename T,
+          int BO,
+          int WM,
+          int WN,
+          int M = 6,
+          int R = 3>
+[[kernel, max_total_threads_per_threadgroup(WM * WN * 32)]] void winograd_conv_2d_output_transform(
+    const device T* out_in [[buffer(0)]],
+    device T* out_out [[buffer(1)]],
+    const constant MLXConvParams<2>& params [[buffer(2)]],
+    uint3 tid [[threadgroup_position_in_grid]],
+    uint3 lid [[thread_position_in_threadgroup]],
+    uint3 tgp_per_grid [[threadgroups_per_grid]],
+    uint simd_group_id [[simdgroup_index_in_threadgroup]],
+    uint simd_lane_id [[thread_index_in_simdgroup]]) {
+
+  (void)lid;
+
+  using WGT = WinogradTransforms<M, R, 8>;
+  constexpr int N_SIMD_GROUPS = WM * WN;
+
+  // Get lane position in simdgroup
+  const short qid = simd_lane_id / 4;
+  const short sm = (qid & 4) + (simd_lane_id / 2) % 4;
+  const short sn = (qid & 2) * 2 + (simd_lane_id % 2) * 2;
+
+  // Initialize A matrix
+  simdgroup_matrix<T, 8, 8> B;
+  B.thread_elements()[0] = WGT::out_transform[sm][sn];
+  B.thread_elements()[1] = WGT::out_transform[sm][sn + 1];
+
+  // Initialize At matrix
+  simdgroup_matrix<T, 8, 8> Bt;
+  Bt.thread_elements()[0] = WGT::out_transform[sn][sm];
+  Bt.thread_elements()[1] = WGT::out_transform[sn + 1][sm];
+
+  // Out_in comes in shape (A x A x tiles x O)
+  // We do transform and then write out to out_out in shape (N, H, W, O)
+
+  // Resolve output tile
+  constexpr int TH = (M / WM);
+  constexpr int TW = (M / WN);
+  int kh = TH * (simd_group_id / WN);
+  int kw = TW * (simd_group_id % WN);
+  int bh = M * tid.y + kh;
+  int bw = M * tid.x + kw;
+
+  // Move to the correct input tile
+  out_out += tid.z * params.out_strides[0] 
+              + bh * params.out_strides[1]
+              + bw * params.out_strides[2];
+
+  // Pre compute strides 
+  int jump_in[TH][TW];
+
+  for(int h = 0; h < TH; h++) {
+    for(int w = 0; w < TW; w++) {
+      bool valid = ((bh + h) < params.oS[0]) && ((bw + w) < params.oS[1]);
+      jump_in[h][w] = valid ? h * params.out_strides[1] + w * params.out_strides[2] : -1;
+    }
+  }
+
+  // out_in is stored interleaved (A x A x tiles x O)
+  size_t N_TILES = tgp_per_grid.x * tgp_per_grid.y * tgp_per_grid.z;
+  size_t tile_id = tid.z * tgp_per_grid.x * tgp_per_grid.y + tid.y * tgp_per_grid.x + tid.x;
+  size_t ohw_0 = sm * 8 + sn;
+  size_t ohw_1 = sm * 8 + sn + 1;
+  const device T* out_in_0 = out_in + ohw_0 * N_TILES * params.O + tile_id * params.O;
+  const device T* out_in_1 = out_in + ohw_1 * N_TILES * params.O + tile_id * params.O;
+
+  // Prepare shared memory
+  threadgroup T Os[M][M][BO];
+
+  // Loop over O
+  for(int bo = 0; bo < params.O; bo += BO) {
+
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+    // Do transform and store the result 
+    for(int c = simd_group_id; c < BO; c += N_SIMD_GROUPS) {
+      simdgroup_matrix<T, 8, 8> O_mat;
+      O_mat.thread_elements()[0] = out_in_0[c];
+      O_mat.thread_elements()[1] = out_in_1[c];
+
+      simdgroup_matrix<T, 8, 8> O_out = (Bt * (O_mat * B));
+      if((sm < M) && (sn < M)) {
+        Os[sm][sn][c] = O_out.thread_elements()[0];
+      }
+      if((sm < M) && ((sn + 1) < M)) {
+        Os[sm][sn + 1][c] = O_out.thread_elements()[1];
+      }
+    }
+
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+    // Read out from shared memory
+    for(int h = 0; h < TH; h++) {
+      for(int w = 0; w < TW; w++) {
+        if(jump_in[h][w] >= 0) {
+          device T* out_ptr = out_out + jump_in[h][w];
+          for(int c = simd_lane_id; c < BO; c += 32) {
+            out_ptr[c] = Os[kh + h][kw + w][c];
+          }
+        }
+      }
+    }
+
+    out_out += BO;
+    out_in_0 += BO;
+    out_in_1 += BO;
+  }
+
+}
+
+#define instantiate_winograd_conv_2d_output_transform(name, itype, bo) \
+  template [[host_name("winograd_conv_2d_output_transform_" #name "_bo" #bo)]]\
+  [[kernel]] void winograd_conv_2d_output_transform<itype, bo, 2, 2>(\
+      const device itype* out_in [[buffer(0)]],\
+      device itype* out_out [[buffer(1)]],\
+      const constant MLXConvParams<2>& params [[buffer(2)]],\
+      uint3 tid [[threadgroup_position_in_grid]],\
+      uint3 lid [[thread_position_in_threadgroup]],\
+      uint3 tgp_per_grid [[threadgroups_per_grid]],\
+      uint simd_group_id [[simdgroup_index_in_threadgroup]],\
+      uint simd_lane_id [[thread_index_in_simdgroup]]);
+
+#define instantiate_winograd_conv_2d(name, itype) \
+  instantiate_winograd_conv_2d_weight_transform_base(name, itype, 32) \
+  instantiate_winograd_conv_2d_input_transform(name, itype, 32) \
+  instantiate_winograd_conv_2d_output_transform(name, itype, 32)
+
+instantiate_winograd_conv_2d(float32, float);
+instantiate_winograd_conv_2d(float16, half);

mlx/backend/metal/kernels/copy.metal

@@ -0,0 +1,269 @@
+#include "mlx/backend/metal/kernels/bf16.h"
+#include "mlx/backend/metal/kernels/utils.h"
+
+template <typename T, typename U>
+[[kernel]] void copy_s(
+    device const T* src,
+    device U* dst,
+    uint index [[thread_position_in_grid]]) {
+  dst[index] = static_cast<U>(src[0]);
+}
+
+template <typename T, typename U>
+[[kernel]] void copy_v(
+    device const T* src,
+    device U* dst,
+    uint index [[thread_position_in_grid]]) {
+  dst[index] = static_cast<U>(src[index]);
+}
+
+template <typename T, typename U>
+[[kernel]] void copy_g_nd1(
+    device const T* src,
+    device U* dst,
+    constant const size_t& src_stride,
+    uint index [[thread_position_in_grid]]) {
+  auto src_idx = elem_to_loc_1(index, src_stride);
+  dst[index] = static_cast<U>(src[src_idx]);
+}
+
+template <typename T, typename U>
+[[kernel]] void copy_g_nd2(
+    device const T* src,
+    device U* dst,
+    constant const size_t src_strides[2],
+    uint2 index [[thread_position_in_grid]],
+    uint2 grid_dim [[threads_per_grid]]) {
+  auto src_idx = elem_to_loc_2(index, src_strides);
+  size_t dst_idx = index.x + (size_t)grid_dim.x * index.y;
+  dst[dst_idx] = static_cast<U>(src[src_idx]);
+}
+
+template <typename T, typename U>
+[[kernel]] void copy_g_nd3(
+    device const T* src,
+    device U* dst,
+    constant const size_t src_strides[3],
+    uint3 index [[thread_position_in_grid]],
+    uint3 grid_dim [[threads_per_grid]]) {
+  auto src_idx = elem_to_loc_3(index, src_strides);
+  size_t dst_idx = index.x + (size_t)grid_dim.x * (index.y + (size_t)grid_dim.y * index.z);
+  dst[dst_idx] = static_cast<U>(src[src_idx]);
+}
+
+template <typename T, typename U, int DIM>
+[[kernel]] void copy_g_nd(
+    device const T* src,
+    device U* dst,
+    constant const int src_shape[DIM],
+    constant const size_t src_strides[DIM],
+    uint3 index [[thread_position_in_grid]],
+    uint3 grid_dim [[threads_per_grid]]) {
+  auto src_idx = elem_to_loc_nd<DIM>(index, src_shape, src_strides);
+  size_t dst_idx = index.x + (size_t)grid_dim.x * (index.y + (size_t)grid_dim.y * index.z);
+  dst[dst_idx] = static_cast<U>(src[src_idx]);
+}
+
+template <typename T, typename U>
+[[kernel]] void copy_g(
+    device const T* src,
+    device U* dst,
+    constant const int* src_shape,
+    constant const size_t* src_strides,
+    constant const int& ndim,
+    uint3 index [[thread_position_in_grid]],
+    uint3 grid_dim [[threads_per_grid]]) {
+  auto src_idx = elem_to_loc(index, src_shape, src_strides, ndim);
+  size_t dst_idx = index.x + (size_t)grid_dim.x * (index.y + (size_t)grid_dim.y * index.z);
+  dst[dst_idx] = static_cast<U>(src[src_idx]);
+}
+
+template <typename T, typename U>
+[[kernel]] void copy_gg_nd1(
+    device const T* src,
+    device U* dst,
+    constant const size_t& src_stride,
+    constant const size_t& dst_stride,
+    uint index [[thread_position_in_grid]]) {
+  auto src_idx = elem_to_loc_1(index, src_stride);
+  auto dst_idx = elem_to_loc_1(index, dst_stride);
+  dst[dst_idx] = static_cast<U>(src[src_idx]);
+}
+
+template <typename T, typename U>
+[[kernel]] void copy_gg_nd2(
+    device const T* src,
+    device U* dst,
+    constant const size_t src_strides[2],
+    constant const size_t dst_strides[2],
+    uint2 index [[thread_position_in_grid]]) {
+  auto src_idx = elem_to_loc_2(index, src_strides);
+  auto dst_idx = elem_to_loc_2(index, dst_strides);
+  dst[dst_idx] = static_cast<U>(src[src_idx]);
+}
+
+template <typename T, typename U>
+[[kernel]] void copy_gg_nd3(
+    device const T* src,
+    device U* dst,
+    constant const size_t src_strides[3],
+    constant const size_t dst_strides[3],
+    uint3 index [[thread_position_in_grid]]) {
+  auto src_idx = elem_to_loc_3(index, src_strides);
+  auto dst_idx = elem_to_loc_3(index, dst_strides);
+  dst[dst_idx] = static_cast<U>(src[src_idx]);
+}
+
+template <typename T, typename U, int DIM>
+[[kernel]] void copy_gg_nd(
+    device const T* src,
+    device U* dst,
+    constant const int src_shape[DIM],
+    constant const size_t src_strides[DIM],
+    constant const size_t dst_strides[DIM],
+    uint3 index [[thread_position_in_grid]]) {
+  auto src_idx = elem_to_loc_nd<DIM>(index, src_shape, src_strides);
+  auto dst_idx = elem_to_loc_nd<DIM>(index, src_shape, dst_strides);
+  dst[dst_idx] = static_cast<U>(src[src_idx]);
+}
+
+template <typename T, typename U>
+[[kernel]] void copy_gg(
+    device const T* src,
+    device U* dst,
+    constant const int* src_shape,
+    constant const size_t* src_strides,
+    constant const size_t* dst_strides,
+    constant const int& ndim,
+    uint3 index [[thread_position_in_grid]]) {
+  auto src_idx = elem_to_loc(index, src_shape, src_strides, ndim);
+  auto dst_idx = elem_to_loc(index, src_shape, dst_strides, ndim);
+  dst[dst_idx] = static_cast<U>(src[src_idx]);
+}
+
+#define instantiate_copy(name, itype, otype, ctype) \
+  template [[host_name(name)]] \
+  [[kernel]] void copy_##ctype<itype, otype>( \
+      device const itype* src, \
+      device otype* dst, \
+      uint index [[thread_position_in_grid]]);
+
+#define instantiate_copy_g_dim(name, itype, otype, dims) \
+  template [[host_name(name "_" #dims)]] \
+  [[kernel]] void copy_g_nd<itype, otype, dims>( \
+      device const itype* src, \
+      device otype* dst, \
+      constant const int src_shape[dims], \
+      constant const size_t src_strides[dims], \
+      uint3 index [[thread_position_in_grid]], \
+      uint3 grid_dim [[threads_per_grid]]); \
+  template [[host_name("g" name "_" #dims)]] \
+  [[kernel]] void copy_gg_nd<itype, otype, dims>( \
+      device const itype* src, \
+      device otype* dst, \
+      constant const int src_shape[dims], \
+      constant const size_t src_strides[dims], \
+      constant const size_t dst_strides[dims], \
+      uint3 index [[thread_position_in_grid]]);
+
+
+#define instantiate_copy_g_nd(name, itype, otype) \
+  template [[host_name(name "_1")]] \
+  [[kernel]] void copy_g_nd1<itype, otype>( \
+      device const itype* src, \
+      device otype* dst, \
+      constant const size_t& src_stride, \
+      uint index [[thread_position_in_grid]]); \
+  template [[host_name(name "_2")]] \
+  [[kernel]] void copy_g_nd2<itype, otype>( \
+      device const itype* src, \
+      device otype* dst, \
+      constant const size_t src_strides[2], \
+      uint2 index [[thread_position_in_grid]], \
+      uint2 grid_dim [[threads_per_grid]]); \
+  template [[host_name(name "_3")]] \
+  [[kernel]] void copy_g_nd3<itype, otype>( \
+      device const itype* src, \
+      device otype* dst, \
+      constant const size_t src_strides[3], \
+      uint3 index [[thread_position_in_grid]], \
+      uint3 grid_dim [[threads_per_grid]]); \
+  template [[host_name("g" name "_1")]] \
+  [[kernel]] void copy_gg_nd1<itype, otype>( \
+      device const itype* src, \
+      device otype* dst, \
+      constant const size_t& src_stride, \
+      constant const size_t& dst_stride, \
+      uint index [[thread_position_in_grid]]); \
+  template [[host_name("g" name "_2")]] \
+  [[kernel]] void copy_gg_nd2<itype, otype>( \
+      device const itype* src, \
+      device otype* dst, \
+      constant const size_t src_strides[2], \
+      constant const size_t dst_strides[2], \
+      uint2 index [[thread_position_in_grid]]); \
+  template [[host_name("g" name "_3")]] \
+  [[kernel]] void copy_gg_nd3<itype, otype>( \
+      device const itype* src, \
+      device otype* dst, \
+      constant const size_t src_strides[3], \
+      constant const size_t dst_strides[3], \
+      uint3 index [[thread_position_in_grid]]); \
+  instantiate_copy_g_dim(name, itype, otype, 4) \
+  instantiate_copy_g_dim(name, itype, otype, 5)
+
+
+#define instantiate_copy_g(name, itype, otype) \
+  template [[host_name(name)]] \
+  [[kernel]] void copy_g<itype, otype>( \
+      device const itype* src, \
+      device otype* dst, \
+      constant const int* src_shape, \
+      constant const size_t* src_strides, \
+      constant const int& ndim, \
+      uint3 index [[thread_position_in_grid]], \
+      uint3 grid_dim [[threads_per_grid]]); \
+  template [[host_name("g" name)]] \
+  [[kernel]] void copy_gg<itype, otype>( \
+      device const itype* src, \
+      device otype* dst, \
+      constant const int* src_shape, \
+      constant const size_t* src_strides, \
+      constant const size_t* dst_strides, \
+      constant const int& ndim, \
+      uint3 index [[thread_position_in_grid]]);
+
+#define instantiate_copy_all(tname, itype, otype) \
+  instantiate_copy("scopy" #tname, itype, otype, s) \
+  instantiate_copy("vcopy" #tname, itype, otype, v) \
+  instantiate_copy_g("gcopy" #tname, itype, otype) \
+  instantiate_copy_g_nd("gcopy" #tname, itype, otype)
+
+#define instantiate_copy_itype(itname, itype) \
+  instantiate_copy_all(itname ##bool_, itype, bool) \
+  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_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_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_itype(bool_, bool)
+instantiate_copy_itype(uint8, uint8_t)
+instantiate_copy_itype(uint16, uint16_t)
+instantiate_copy_itype(uint32, uint32_t)
+instantiate_copy_itype(uint64, uint64_t)
+instantiate_copy_itype(int8, int8_t)
+instantiate_copy_itype(int16, int16_t)
+instantiate_copy_itype(int32, int32_t)
+instantiate_copy_itype(int64, int64_t)
+instantiate_copy_itype(float16, half)
+instantiate_copy_itype(float32, float)
+instantiate_copy_itype(bfloat16, bfloat16_t)
+instantiate_copy_itype(complex64, complex64_t)

mlx/backend/metal/kernels/erf.h

@@ -0,0 +1,68 @@
+#pragma once
+
+#include <metal_math>
+
+/*
+ * Approximation to the error function.
+ * Based on code from:
+ * https://stackoverflow.com/questions/35148198/efficient-faithfully-rounded-implementation-of-error-function-erff#answer-35148199
+ */
+float erf(float a) {
+  float r, s, t, u;
+  t = metal::abs(a);
+  s = a * a;
+  if (t > 0.927734375f) {
+    // maximum error 0.99527 ulp
+    r = metal::fma(
+        -1.72853470e-5f, t, 3.83197126e-4f); // -0x1.220000p-16,0x1.91cfb2p-12
+    u = metal::fma(
+        -3.88396438e-3f, t, 2.42546219e-2f); // -0x1.fd1438p-9, 0x1.8d6342p-6
+    r = metal::fma(r, s, u);
+    r = metal::fma(r, t, -1.06777877e-1f); // -0x1.b55cb8p-4
+    r = metal::fma(r, t, -6.34846687e-1f); // -0x1.450aa0p-1
+    r = metal::fma(r, t, -1.28717512e-1f); // -0x1.079d0cp-3
+    r = metal::fma(r, t, -t);
+    // TODO, replace with expm1 when implemented
+    r = 1.0f - metal::exp(r);
+    r = metal::copysign(r, a);
+  } else {
+    // maximum error 0.98929 ulp
+    r = -5.96761703e-4f; // -0x1.38e000p-11
+    r = metal::fma(r, s, 4.99119423e-3f); //  0x1.471a58p-8
+    r = metal::fma(r, s, -2.67681349e-2f); // -0x1.b691b2p-6
+    r = metal::fma(r, s, 1.12819925e-1f); //  0x1.ce1c44p-4
+    r = metal::fma(r, s, -3.76125336e-1f); // -0x1.812700p-2
+    r = metal::fma(r, s, 1.28379166e-1f); //  0x1.06eba8p-3
+    r = metal::fma(r, a, a);
+  }
+  return r;
+}
+
+float erfinv(float a) {
+  auto t = metal::fma(a, 0.0f - a, 1.0f);
+  t = metal::log(t);
+  float p;
+  if (metal::abs(t) > 6.125f) { // maximum ulp error = 2.35793
+    p = 3.03697567e-10f; //  0x1.4deb44p-32
+    p = metal::fma(p, t, 2.93243101e-8f); //  0x1.f7c9aep-26
+    p = metal::fma(p, t, 1.22150334e-6f); //  0x1.47e512p-20
+    p = metal::fma(p, t, 2.84108955e-5f); //  0x1.dca7dep-16
+    p = metal::fma(p, t, 3.93552968e-4f); //  0x1.9cab92p-12
+    p = metal::fma(p, t, 3.02698812e-3f); //  0x1.8cc0dep-9
+    p = metal::fma(p, t, 4.83185798e-3f); //  0x1.3ca920p-8
+    p = metal::fma(p, t, -2.64646143e-1f); // -0x1.0eff66p-2
+    p = metal::fma(p, t, 8.40016484e-1f); //  0x1.ae16a4p-1
+  } else { // maximum ulp error = 2.35002
+    p = 5.43877832e-9f; //  0x1.75c000p-28
+    p = metal::fma(p, t, 1.43285448e-7f); //  0x1.33b402p-23
+    p = metal::fma(p, t, 1.22774793e-6f); //  0x1.499232p-20
+    p = metal::fma(p, t, 1.12963626e-7f); //  0x1.e52cd2p-24
+    p = metal::fma(p, t, -5.61530760e-5f); // -0x1.d70bd0p-15
+    p = metal::fma(p, t, -1.47697632e-4f); // -0x1.35be90p-13
+    p = metal::fma(p, t, 2.31468678e-3f); //  0x1.2f6400p-9
+    p = metal::fma(p, t, 1.15392581e-2f); //  0x1.7a1e50p-7
+    p = metal::fma(p, t, -2.32015476e-1f); // -0x1.db2aeep-3
+    p = metal::fma(p, t, 8.86226892e-1f); //  0x1.c5bf88p-1
+  }
+  return a * p;
+}

mlx/backend/metal/kernels/gemm.metal

@@ -0,0 +1,91 @@
+#include "mlx/backend/metal/kernels/bf16.h"
+#include "mlx/backend/metal/kernels/gemm/gemm.h"
+
+using namespace metal;
+
+///////////////////////////////////////////////////////////////////////////////
+// GEMM kernels
+///////////////////////////////////////////////////////////////////////////////
+
+template <typename T,
+          int BM,
+          int BN,
+          int BK,
+          int WM,
+          int WN,
+          bool transpose_a, 
+          bool transpose_b,
+          bool MN_aligned,
+          bool K_aligned>
+[[kernel, max_total_threads_per_threadgroup(WM * WN * 32)]] void gemm(
+    const device T *A [[buffer(0)]],
+    const device T *B [[buffer(1)]],
+    device T *C [[buffer(2)]],
+    const constant int &M [[buffer(3)]],
+    const constant int &N [[buffer(4)]],
+    const constant int &K [[buffer(5)]],
+    const constant int &batch_stride_a [[buffer(6)]],
+    const constant int &batch_stride_b [[buffer(7)]],
+    const constant int &batch_stride_c [[buffer(8)]],
+    uint simd_lane_id [[thread_index_in_simdgroup]],
+    uint simd_group_id [[simdgroup_index_in_threadgroup]],
+    uint3 tid [[threadgroup_position_in_grid]],
+    uint3 lid [[thread_position_in_threadgroup]]) {
+    
+    using gemm_kernel = GEMMKernel<T, BM, BN, BK, WM, WN, transpose_a, transpose_b, MN_aligned, K_aligned>;
+    
+    threadgroup T tgp_memory[gemm_kernel::tgp_mem_size];
+
+    gemm_kernel::run( 
+      A, B, C, 
+      M, N, K, 
+      batch_stride_a, batch_stride_b, batch_stride_c,
+      tgp_memory,
+      simd_lane_id, simd_group_id, tid, lid
+    );
+}
+
+///////////////////////////////////////////////////////////////////////////////
+// GEMM kernel initializations
+///////////////////////////////////////////////////////////////////////////////
+
+#define instantiate_gemm(tname, trans_a, trans_b, iname, itype, oname, otype, bm, bn, bk, wm, wn, aname, mn_aligned, kname, k_aligned) \
+  template [[host_name("gemm_" #tname "_"  #iname "_" #oname "_bm" #bm "_bn" #bn "_bk" #bk "_wm" #wm "_wn" #wn "_MN_" #aname "_K_" #kname)]] \
+  [[kernel]] void gemm<itype, bm, bn, bk, wm, wn, trans_a, trans_b, mn_aligned, k_aligned>( \
+      const device itype *A [[buffer(0)]], \
+      const device itype *B [[buffer(1)]], \
+      device itype *C [[buffer(2)]], \
+      const constant int &M [[buffer(3)]], \
+      const constant int &N [[buffer(4)]], \
+      const constant int &K [[buffer(5)]], \
+      const constant int &batch_stride_a [[buffer(6)]], \
+      const constant int &batch_stride_b [[buffer(7)]], \
+      const constant int &batch_stride_c [[buffer(8)]], \
+      uint simd_lane_id [[thread_index_in_simdgroup]], \
+      uint simd_group_id [[simdgroup_index_in_threadgroup]], \
+      uint3 tid [[threadgroup_position_in_grid]], \
+      uint3 lid [[thread_position_in_threadgroup]]);
+
+#define instantiate_gemm_aligned_helper(tname, trans_a, trans_b, iname, itype, oname, otype, bm, bn, bk, wm, wn) \
+  instantiate_gemm(tname, trans_a, trans_b, iname, itype, oname, otype, bm, bn, bk, wm, wn, taligned, true, taligned, true) \
+  instantiate_gemm(tname, trans_a, trans_b, iname, itype, oname, otype, bm, bn, bk, wm, wn, taligned, true, naligned, false) \
+  instantiate_gemm(tname, trans_a, trans_b, iname, itype, oname, otype, bm, bn, bk, wm, wn, naligned, false, taligned, true) \
+  instantiate_gemm(tname, trans_a, trans_b, iname, itype, oname, otype, bm, bn, bk, wm, wn, naligned, false, naligned, false)
+
+#define instantiate_gemm_transpose_helper(iname, itype, oname, otype, bm, bn, bk, wm, wn) \
+    instantiate_gemm_aligned_helper(nn, false, false, iname, itype, oname, otype, bm, bn, bk, wm, wn) \
+    instantiate_gemm_aligned_helper(nt, false, true , iname, itype, oname, otype, bm, bn, bk, wm, wn) \
+    instantiate_gemm_aligned_helper(tn, true , false, iname, itype, oname, otype, bm, bn, bk, wm, wn) \
+    instantiate_gemm_aligned_helper(tt, true , true , iname, itype, oname, otype, bm, bn, bk, wm, wn)
+
+#define instantiate_gemm_shapes_helper(iname, itype, oname, otype) \
+    instantiate_gemm_transpose_helper(iname, itype, oname, otype, 32, 32, 16, 2, 2) \
+    instantiate_gemm_transpose_helper(iname, itype, oname, otype, 64, 64, 16, 2, 2) \
+    instantiate_gemm_transpose_helper(iname, itype, oname, otype, 64, 32, 32, 2, 2) \
+    instantiate_gemm_transpose_helper(iname, itype, oname, otype, 64, 32, 16, 2, 2)
+
+instantiate_gemm_shapes_helper(float16, half, float16, half);
+instantiate_gemm_shapes_helper(float32, float, float32, float);
+instantiate_gemm_shapes_helper(bfloat16, bfloat16_t, bfloat16, bfloat16_t);
+
+// TODO: Accumulation in different type

mlx/backend/metal/kernels/random.metal

@@ -0,0 +1,99 @@
+#include "mlx/backend/metal/kernels/utils.h"
+
+static constexpr constant uint32_t rotations[2][4] = {
+  {13, 15, 26, 6},
+  {17, 29, 16, 24}
+};
+
+union rbits {
+  uint2 val;
+  uchar4 bytes[2];
+};
+
+rbits threefry2x32_hash(const thread uint2& key, uint2 count) {
+
+  uint4 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;
+}
+
+[[kernel]] void rbitsc(
+    device const uint32_t* keys,
+    device char* out,
+    device const bool& odd,
+    device const uint& bytes_per_key,
+    uint2 grid_dim [[threads_per_grid]],
+    uint2 index [[thread_position_in_grid]]) {
+  auto kidx = 2 * index.x;
+  auto key = uint2(keys[kidx], keys[kidx + 1]);
+  auto half_size = grid_dim.y - odd;
+  out += index.x * bytes_per_key;
+  bool drop_last = odd && (index.y == half_size);
+  auto count = uint2(index.y, drop_last ? 0 : index.y + grid_dim.y);
+  auto bits = threefry2x32_hash(key, count);
+  for (int i = 0; i < 4; ++i) {
+    out[4 * count.x + i] = bits.bytes[0][i];
+  }
+  if (!drop_last) {
+    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[4 * count.y + i] = bits.bytes[1][i];
+      }
+    } else {
+      for (int i = 0; i < 4; ++i) {
+        out[4 * count.y + i] = bits.bytes[1][i];
+      }
+    }
+  }
+}
+
+[[kernel]] void rbits(
+    device const uint32_t* keys,
+    device char* out,
+    device const bool& odd,
+    device const uint& bytes_per_key,
+    device const int& ndim,
+    device const int* key_shape,
+    device const size_t* key_strides,
+    uint2 grid_dim [[threads_per_grid]],
+    uint2 index [[thread_position_in_grid]]) {
+  auto kidx = 2 * index.x;
+  auto k1_elem = elem_to_loc(kidx, key_shape, key_strides, ndim);
+  auto k2_elem = elem_to_loc(kidx + 1, key_shape, key_strides, ndim);
+  auto key = uint2(keys[k1_elem], keys[k2_elem]);
+  auto half_size = grid_dim.y - odd;
+  out += index.x * bytes_per_key;
+  bool drop_last = odd && (index.y == half_size);
+  auto count = uint2(index.y, drop_last ? 0 : index.y + grid_dim.y);
+  auto bits = threefry2x32_hash(key, count);
+  for (int i = 0; i < 4; ++i) {
+    out[4 * count.x + i] = bits.bytes[0][i];
+  }
+  if (!drop_last) {
+    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[4 * count.y + i] = bits.bytes[1][i];
+      }
+    } else {
+      for (int i = 0; i < 4; ++i) {
+        out[4 * count.y + i] = bits.bytes[1][i];
+      }
+    }
+  }
+}

mlx/backend/metal/kernels/reduce.metal

@@ -0,0 +1,536 @@
+#include <metal_atomic>
+#include <metal_simdgroup>
+
+#include "mlx/backend/metal/kernels/defines.h"
+#include "mlx/backend/metal/kernels/reduce.h"
+#include "mlx/backend/metal/kernels/utils.h"
+
+using namespace metal;
+
+static constant uint8_t simd_size = 32;
+
+template <typename T, typename Op>
+[[kernel]] void init_reduce(
+    device T *out [[buffer(0)]],
+    uint tid [[thread_position_in_grid]]) {
+  out[tid] = Op::init;
+}
+
+#define instantiate_init_reduce(name, otype, op) \
+  template [[host_name("i" #name)]] \
+    [[kernel]] void init_reduce<otype, op>( \
+      device otype *out [[buffer(1)]], \
+      uint tid [[thread_position_in_grid]]);
+
+
+///////////////////////////////////////////////////////////////////////////////
+// All reduce
+///////////////////////////////////////////////////////////////////////////////
+
+template <typename T, typename U, typename Op, int N_READS=REDUCE_N_READS>
+[[kernel]] void all_reduce(
+    const device T *in [[buffer(0)]],
+    device mlx_atomic<U> *out [[buffer(1)]],
+    const device size_t& in_size [[buffer(2)]],
+    uint gid [[thread_position_in_grid]],
+    uint lid [[thread_position_in_threadgroup]],
+    uint grid_size [[threads_per_grid]],
+    uint simd_per_group [[simdgroups_per_threadgroup]],
+    uint simd_lane_id [[thread_index_in_simdgroup]],
+    uint simd_group_id [[simdgroup_index_in_threadgroup]]) {
+  // NB: this kernel assumes threads_per_threadgroup is at most
+  // 1024. This way with a simd_size of 32, we are guaranteed to
+  // complete the reduction in two steps of simd-level reductions.
+
+  Op op;
+  threadgroup U local_vals[simd_size];
+      
+  U total_val = Op::init;
+
+  in += gid * N_READS;
+      
+  int r = 0;
+  for(; r < (int)ceildiv(in_size, grid_size * N_READS) - 1; r++) {
+    U vals[N_READS] = {op.init}; 
+
+    for(int i = 0; i < N_READS; i++) {
+      vals[i] = static_cast<U>(in[i]);
+    }
+    for(int i = 0; i < N_READS; i++) {
+      total_val = op(vals[i], total_val);
+    }
+
+    in += grid_size * N_READS;
+  }
+
+  // Sepate case for the last set as we close the reduction size 
+  size_t curr_idx = (gid + r * (size_t)grid_size) * N_READS;
+  if (curr_idx < in_size) {
+    int max_reads = in_size - curr_idx;
+    T vals[N_READS];
+
+    for(int i = 0, idx = 0; i < N_READS; i++, idx++) {
+      idx = idx < max_reads ? idx : max_reads - 1;
+      vals[i] = in[idx];
+    }
+    for(int i = 0; i < N_READS; i++) {
+      U val = i < max_reads ? vals[i] : Op::init; 
+      total_val = op(static_cast<U>(val), total_val);
+    }
+  }
+
+  // Reduction within simd group
+  total_val = op.simd_reduce(total_val);
+  if (simd_lane_id == 0) {
+    local_vals[simd_group_id] = total_val;
+  }
+  
+  // Reduction within thread group
+  threadgroup_barrier(mem_flags::mem_threadgroup);
+  total_val = lid < simd_per_group ? local_vals[lid] : op.init;
+  total_val = op.simd_reduce(total_val);
+
+  // Reduction across threadgroups
+  if (lid == 0) {
+    op.atomic_update(out, total_val);
+  }
+}
+
+#define instantiate_all_reduce(name, itype, otype, op) \
+  template [[host_name("all_reduce_" #name)]] \
+  [[kernel]] void all_reduce<itype, otype, op>( \
+      const device itype *in [[buffer(0)]], \
+      device mlx_atomic<otype> *out [[buffer(1)]], \
+      const device size_t& in_size [[buffer(2)]], \
+      uint gid [[thread_position_in_grid]], \
+      uint lid [[thread_position_in_threadgroup]], \
+      uint grid_size [[threads_per_grid]], \
+      uint simd_per_group [[simdgroups_per_threadgroup]], \
+      uint simd_lane_id [[thread_index_in_simdgroup]], \
+      uint simd_group_id [[simdgroup_index_in_threadgroup]]);
+
+
+///////////////////////////////////////////////////////////////////////////////
+// General reduce
+///////////////////////////////////////////////////////////////////////////////
+
+template <typename T, typename U, typename Op>
+[[kernel]] void general_reduce(
+    const device T *in [[buffer(0)]],
+    device mlx_atomic<U> *out [[buffer(1)]],
+    const device int *in_shape [[buffer(2)]],
+    const device size_t *in_strides [[buffer(3)]],
+    const device size_t *out_strides [[buffer(4)]],
+    const device size_t& ndim [[buffer(5)]],
+    uint gid [[thread_position_in_grid]]) {
+  Op op;
+  auto in_idx = elem_to_loc(gid, in_shape, in_strides, ndim);
+  auto out_idx = elem_to_loc(gid, in_shape, out_strides, ndim);
+  op.atomic_update(out, static_cast<U>(in[in_idx]), out_idx);
+}
+
+template <typename T, typename U, typename Op, int NDIM>
+[[kernel]] void general_reduce(
+    const device T *in [[buffer(0)]],
+    device mlx_atomic<U> *out [[buffer(1)]],
+    const device int *in_shape [[buffer(2)]],
+    const device size_t *in_strides [[buffer(3)]],
+    const device size_t *out_strides [[buffer(4)]],
+    uint gid [[thread_position_in_grid]]) {
+  Op op;
+  auto in_idx = elem_to_loc_nd<NDIM>(gid, in_shape, in_strides);
+  auto out_idx = elem_to_loc_nd<NDIM>(gid, in_shape, out_strides);
+  op.atomic_update(out, static_cast<U>(in[in_idx]), out_idx);
+}
+
+#define instantiate_general_reduce_helper(name, itype, otype, op) \
+  template [[host_name("general_reduce_" #name)]] \
+  [[kernel]] void general_reduce<itype, otype, op>( \
+      const device itype *in [[buffer(0)]], \
+      device mlx_atomic<otype> *out [[buffer(1)]], \
+      const device int *in_shape [[buffer(2)]], \
+      const device size_t *in_strides [[buffer(3)]], \
+      const device size_t *out_strides [[buffer(4)]], \
+      const device size_t& ndim [[buffer(5)]], \
+      uint gid [[thread_position_in_grid]]);
+
+#define instantiate_general_reduce_helper_nd(name, itype, otype, op, n) \
+  template [[host_name("general_reduce_" #name "_dim_" #n)]] \
+  [[kernel]] void general_reduce<itype, otype, op, n>( \
+      const device itype *in [[buffer(0)]], \
+      device mlx_atomic<otype> *out [[buffer(1)]], \
+      const device int *in_shape [[buffer(2)]], \
+      const device size_t *in_strides [[buffer(3)]], \
+      const device size_t *out_strides [[buffer(4)]], \
+      uint gid [[thread_position_in_grid]]);
+
+#define instantiate_general_reduce(name, itype, otype, op) \
+  instantiate_general_reduce_helper(name, itype, otype, op) \
+  instantiate_general_reduce_helper_nd(name, itype, otype, op, 1) \
+  instantiate_general_reduce_helper_nd(name, itype, otype, op, 2) \
+  instantiate_general_reduce_helper_nd(name, itype, otype, op, 3) \
+  instantiate_general_reduce_helper_nd(name, itype, otype, op, 4)
+
+
+///////////////////////////////////////////////////////////////////////////////
+// Row atomics
+///////////////////////////////////////////////////////////////////////////////
+
+template <typename T, typename U, typename Op, int N_READS=REDUCE_N_READS>
+[[kernel]] void row_reduce(
+    const device T *in [[buffer(0)]],
+    device U *out [[buffer(1)]],
+    const device size_t& reduction_size [[buffer(2)]],
+    uint lid [[thread_position_in_threadgroup]],
+    uint lsize [[threads_per_threadgroup]],
+    uint tid [[threadgroup_position_in_grid]],
+    uint simd_lane_id [[thread_index_in_simdgroup]],
+    uint simd_per_group [[simdgroups_per_threadgroup]],
+    uint simd_group_id [[simdgroup_index_in_threadgroup]]) {
+
+  Op op;
+
+  // Each threadgroup handles 1 reduction 
+  in += tid * reduction_size + lid * N_READS;
+  
+  // The reduction is accumulated here
+  U total_val = Op::init;
+  threadgroup U local_vals[simd_size];
+
+  // Loop over the reduction size within thread group
+  int r = 0;
+  for (; r < (int)ceildiv(reduction_size, N_READS*lsize) - 1; r++) {
+    T vals[N_READS]; 
+    for(int i = 0; i < N_READS; i++) {
+      vals[i] = in[i];
+    }
+    for(int i = 0; i < N_READS; i++) {
+      total_val = op(static_cast<U>(vals[i]), total_val);
+    }
+
+    in += lsize * N_READS;
+  }
+
+  // Sepate case for the last set as we close the reduction size   
+  size_t reduction_index = (lid + (size_t)lsize * r) * N_READS;
+  if(reduction_index < reduction_size) {
+    int max_reads = reduction_size - reduction_index;
+
+    T vals[N_READS]; 
+    for(int i = 0; i < N_READS; i++) {
+      int idx = min(i, max_reads - 1);
+      vals[i] = static_cast<U>(in[idx]);
+    }
+    for(int i = 0; i < N_READS; i++) {
+      T val = i < max_reads ? vals[i] : Op::init;
+      total_val = op(static_cast<U>(val), total_val);
+    }
+  }
+
+  total_val = op.simd_reduce(total_val);
+  
+  // Prepare next level
+  if (simd_lane_id == 0) {
+    local_vals[simd_group_id] = total_val;
+  }
+  threadgroup_barrier(mem_flags::mem_threadgroup);
+      
+  // Reduction within thread group
+  //    Only needed if multiple simd groups
+  if(reduction_size > simd_size) {
+    total_val = lid < simd_per_group ? local_vals[lid] : op.init;
+    total_val = op.simd_reduce(total_val);
+  }
+  // Update output
+  if (lid == 0) {
+    out[tid] = total_val;
+  }
+}
+
+#define instantiate_row_reduce(name, itype, otype, op) \
+  template [[host_name("row_reduce_" #name)]] \
+  [[kernel]] void row_reduce<itype, otype, op>( \
+      const device itype *in [[buffer(0)]], \
+      device otype *out [[buffer(1)]], \
+      const device size_t& reduction_size [[buffer(2)]], \
+      uint lid [[thread_position_in_threadgroup]], \
+      uint lsize [[threads_per_threadgroup]], \
+      uint tid [[threadgroup_position_in_grid]], \
+      uint simd_lane_id [[thread_index_in_simdgroup]], \
+      uint simd_per_group [[simdgroups_per_threadgroup]], \
+      uint simd_group_id [[simdgroup_index_in_threadgroup]]);
+
+
+///////////////////////////////////////////////////////////////////////////////
+// Column reduce
+///////////////////////////////////////////////////////////////////////////////
+
+template <typename T, typename U, typename Op, int N_READS = REDUCE_N_READS>
+inline void _contiguous_strided_reduce(
+    const device T *in, 
+    device mlx_atomic<U> *out, 
+    threadgroup U *local_data, 
+    uint in_idx, 
+    uint out_idx, 
+    uint reduction_size, 
+    uint reduction_stride, 
+    uint2 tid, 
+    uint2 lid, 
+    uint2 lsize) {
+
+  Op op;
+  T local_vals[N_READS]; 
+
+  uint base_offset = (tid.y * lsize.y + lid.y) * N_READS;
+
+  for(uint r = 0; r < N_READS; r++) {
+    uint offset = base_offset + r; 
+    offset = offset < reduction_size ? offset : reduction_size - 1; 
+    local_vals[r] = in[in_idx + offset * reduction_stride];
+  }
+
+  U total_val = Op::init; 
+  for(uint r = 0; r < N_READS && (base_offset + r) < reduction_size; r++) {
+    total_val = op(static_cast<U>(total_val), local_vals[r]); 
+  }
+  local_data[lsize.y * lid.x + lid.y] = total_val; 
+
+  threadgroup_barrier(mem_flags::mem_threadgroup);
+
+  if(lid.y == 0) {
+    U val = op.init; 
+
+    for(uint i = 0; i < lsize.y; i++) {
+      val = op(val, local_data[lsize.y * lid.x + i]); 
+    }
+
+    op.atomic_update(out, val, out_idx);
+  }
+}
+
+template <typename T, typename U, typename Op, int N_READS = REDUCE_N_READS>
+[[kernel]] void col_reduce(
+    const device T *in [[buffer(0)]],
+    device mlx_atomic<U> *out [[buffer(1)]],
+    const constant size_t& reduction_size [[buffer(2)]],
+    const constant size_t& reduction_stride [[buffer(3)]],
+    const constant size_t& out_size [[buffer(4)]], 
+    threadgroup U *local_data [[threadgroup(0)]],
+    uint2 tid [[threadgroup_position_in_grid]], 
+    uint2 lid [[thread_position_in_threadgroup]], 
+    uint2 lsize [[threads_per_threadgroup]]) {
+  auto out_idx = tid.x * lsize.x + lid.x;  
+  
+  if(out_idx < out_size) {
+    _contiguous_strided_reduce<T, U, Op, N_READS>(
+      in, 
+      out, 
+      local_data, 
+      out_idx, 
+      out_idx, 
+      reduction_size, 
+      reduction_stride, 
+      tid, 
+      lid, 
+      lsize); 
+  }
+}
+
+#define instantiate_col_reduce(name, itype, otype, op) \
+  template [[host_name("col_reduce_" #name)]] \
+  [[kernel]] void col_reduce<itype, otype, op>( \
+      const device itype *in [[buffer(0)]], \
+      device mlx_atomic<otype> *out [[buffer(1)]], \
+      const constant size_t& reduction_size [[buffer(2)]], \
+      const constant size_t& reduction_stride [[buffer(3)]], \
+      const constant size_t& out_size [[buffer(4)]], \
+      threadgroup otype *local_data [[threadgroup(0)]], \
+      uint2 tid [[threadgroup_position_in_grid]], \
+      uint2 lid [[thread_position_in_threadgroup]], \
+      uint2 lsize [[threads_per_threadgroup]]);
+
+template <typename T, typename U, typename Op, int NDIM, int N_READS = 16>
+[[kernel]] void contiguous_strided_reduce(
+    const device T *in [[buffer(0)]],
+    device mlx_atomic<U> *out [[buffer(1)]],
+    const constant size_t& reduction_size [[buffer(2)]],
+    const constant size_t& reduction_stride [[buffer(3)]],
+    const constant size_t& out_size [[buffer(4)]], 
+    const device int* in_shape [[buffer(5)]],
+    const device size_t* in_strides [[buffer(6)]],
+    threadgroup U *local_data [[threadgroup(0)]],
+    uint2 tid [[threadgroup_position_in_grid]], 
+    uint2 lid [[thread_position_in_threadgroup]], 
+    uint2 lsize [[threads_per_threadgroup]]) {
+  
+  auto out_idx = tid.x * lsize.x + lid.x; 
+  auto in_idx = elem_to_loc_nd<NDIM>(out_idx, in_shape, in_strides);
+  
+  if(out_idx < out_size) {
+    _contiguous_strided_reduce<T, U, Op, N_READS>(
+      in, 
+      out, 
+      local_data, 
+      in_idx, 
+      out_idx, 
+      reduction_size, 
+      reduction_stride, 
+      tid, 
+      lid, 
+      lsize); 
+  }
+}
+
+template <typename T, typename U, typename Op, int N_READS = REDUCE_N_READS>
+[[kernel]] void contiguous_strided_reduce(
+    const device T *in [[buffer(0)]],
+    device mlx_atomic<U> *out [[buffer(1)]],
+    const constant size_t& reduction_size [[buffer(2)]],
+    const constant size_t& reduction_stride [[buffer(3)]],
+    const constant size_t& out_size [[buffer(4)]], 
+    const device int* in_shape [[buffer(5)]],
+    const device size_t* in_strides [[buffer(6)]],
+    const device size_t& in_dim [[buffer(7)]], 
+    threadgroup U *local_data [[threadgroup(0)]],
+    uint2 tid [[threadgroup_position_in_grid]], 
+    uint2 lid [[thread_position_in_threadgroup]], 
+    uint2 lsize [[threads_per_threadgroup]]) {
+  
+  auto out_idx = tid.x * lsize.x + lid.x; 
+  auto in_idx = elem_to_loc(out_idx, in_shape, in_strides, in_dim);
+  
+  if(out_idx < out_size) {
+    _contiguous_strided_reduce<T, U, Op, N_READS>(
+      in, 
+      out, 
+      local_data, 
+      in_idx, 
+      out_idx, 
+      reduction_size, 
+      reduction_stride, 
+      tid, 
+      lid, 
+      lsize); 
+  }
+}
+
+#define instantiate_contiguous_strided_helper(name, itype, otype, op) \
+  template [[host_name("contiguous_strided_reduce_" #name)]] \
+  [[kernel]] void contiguous_strided_reduce<itype, otype, op>( \
+      const device itype *in [[buffer(0)]], \
+      device mlx_atomic<otype> *out [[buffer(1)]], \
+      const constant size_t& reduction_size [[buffer(2)]], \
+      const constant size_t& reduction_stride [[buffer(3)]], \
+      const constant size_t& out_size [[buffer(4)]], \
+      const device int* in_shape [[buffer(5)]], \
+      const device size_t* in_strides [[buffer(6)]], \
+      const device size_t& in_dim [[buffer(7)]], \
+      threadgroup otype *local_data [[threadgroup(0)]], \
+      uint2 tid [[threadgroup_position_in_grid]], \
+      uint2 lid [[thread_position_in_threadgroup]], \
+      uint2 lsize [[threads_per_threadgroup]]);
+
+#define instantiate_contiguous_strided_helper_nd(name, itype, otype, op, n) \
+  template [[host_name("contiguous_strided_reduce_" #name "_dim_" #n)]] \
+  [[kernel]] void contiguous_strided_reduce<itype, otype, op, n>( \
+      const device itype *in [[buffer(0)]], \
+      device mlx_atomic<otype> *out [[buffer(1)]], \
+      const constant size_t& reduction_size [[buffer(2)]], \
+      const constant size_t& reduction_stride [[buffer(3)]], \
+      const constant size_t& out_size [[buffer(4)]], \
+      const device int* in_shape [[buffer(5)]], \
+      const device size_t* in_strides [[buffer(6)]], \
+      threadgroup otype *local_data [[threadgroup(0)]], \
+      uint2 tid [[threadgroup_position_in_grid]],  \
+      uint2 lid [[thread_position_in_threadgroup]],  \
+      uint2 lsize [[threads_per_threadgroup]]);
+
+#define instantiate_contiguous_strided(name, itype, otype, op) \
+  instantiate_contiguous_strided_helper(name, itype, otype, op) \
+  instantiate_contiguous_strided_helper_nd(name, itype, otype, op, 1) \
+  instantiate_contiguous_strided_helper_nd(name, itype, otype, op, 2) \
+  instantiate_contiguous_strided_helper_nd(name, itype, otype, op, 3) \
+  instantiate_contiguous_strided_helper_nd(name, itype, otype, op, 4)
+
+
+///////////////////////////////////////////////////////////////////////////////
+// Instantiations
+///////////////////////////////////////////////////////////////////////////////
+
+#define instantiate_reduce(name, itype, otype, op) \
+  instantiate_all_reduce(name, itype, otype, op) \
+  instantiate_row_reduce(name, itype, otype, op) \
+  instantiate_col_reduce(name, itype, otype, op) \
+  instantiate_contiguous_strided(name, itype, otype, op) \
+  instantiate_general_reduce(name, itype, otype, op)
+
+#define instantiate_same_reduce(name, tname, type, op) \
+  instantiate_init_reduce(name ##tname, type, op<type>) \
+  instantiate_reduce(name ##tname, type, type, op<type>)
+
+#define instantiate_reduce_from_types_helper(name, tname, itype, otype, op) \
+  instantiate_reduce(name ##tname, itype, otype, op)
+
+#define instantiate_reduce_from_types(name, otype, op) \
+  instantiate_reduce_from_types_helper(name, bool_, bool, otype, op) \
+  instantiate_reduce_from_types_helper(name, uint8, uint8_t, otype, op) \
+  instantiate_reduce_from_types_helper(name, uint16, uint16_t, otype, op) \
+  instantiate_reduce_from_types_helper(name, uint32, uint32_t, otype, op) \
+  instantiate_reduce_from_types_helper(name, int8, int8_t, otype, op) \
+  instantiate_reduce_from_types_helper(name, int16, int16_t, otype, op) \
+  instantiate_reduce_from_types_helper(name, int32, int32_t, otype, op) \
+  instantiate_reduce_from_types_helper(name, int64, int64_t, otype, op) \
+  instantiate_reduce_from_types_helper(name, float16, half, otype, op) \
+  instantiate_reduce_from_types_helper(name, float32, float, otype, op) \
+  instantiate_reduce_from_types_helper(name, bfloat16, bfloat16_t, otype, op)
+
+// special case bool with larger output type
+instantiate_reduce(sumbool_, bool, uint32_t, Sum<uint32_t>)
+instantiate_same_reduce(sum, uint8, uint8_t, Sum)
+instantiate_same_reduce(sum, uint16, uint16_t, Sum)
+instantiate_same_reduce(sum, uint32, uint32_t, Sum)
+instantiate_same_reduce(sum, int8, int8_t, Sum)
+instantiate_same_reduce(sum, int16, int16_t, Sum)
+instantiate_same_reduce(sum, int32, int32_t, Sum)
+instantiate_same_reduce(sum, float16, half, Sum)
+instantiate_same_reduce(sum, float32, float, Sum)
+
+instantiate_same_reduce(prod, uint8, uint8_t, Prod)
+instantiate_same_reduce(prod, uint16, uint16_t, Prod)
+instantiate_same_reduce(prod, uint32, uint32_t, Prod)
+instantiate_same_reduce(prod, int8, int8_t, Prod)
+instantiate_same_reduce(prod, int16, int16_t, Prod)
+instantiate_same_reduce(prod, int32, int32_t, Prod)
+instantiate_same_reduce(prod, float16, half, Prod)
+instantiate_same_reduce(prod, float32, float, Prod)
+
+instantiate_same_reduce(sum, bfloat16, bfloat16_t, Sum)
+instantiate_same_reduce(prod, bfloat16, bfloat16_t, Prod)
+
+instantiate_init_reduce(andbool_, bool, And)
+instantiate_reduce_from_types(and, bool, And)
+
+instantiate_init_reduce(orbool_, bool, Or)
+instantiate_reduce_from_types(or, bool, Or)
+
+// Compiler segfaulted with the names "min" or "max" ...
+instantiate_same_reduce(min_, uint8, uint8_t, Min)
+instantiate_same_reduce(min_, uint16, uint16_t, Min)
+instantiate_same_reduce(min_, uint32, uint32_t, Min)
+instantiate_same_reduce(min_, int8, int8_t, Min)
+instantiate_same_reduce(min_, int16, int16_t, Min)
+instantiate_same_reduce(min_, int32, int32_t, Min)
+instantiate_same_reduce(min_, float16, half, Min)
+instantiate_same_reduce(min_, float32, float, Min)
+
+instantiate_same_reduce(max_, uint8, uint8_t, Max)
+instantiate_same_reduce(max_, uint16, uint16_t, Max)
+instantiate_same_reduce(max_, uint32, uint32_t, Max)
+instantiate_same_reduce(max_, int8, int8_t, Max)
+instantiate_same_reduce(max_, int16, int16_t, Max)
+instantiate_same_reduce(max_, int32, int32_t, Max)
+instantiate_same_reduce(max_, float16, half, Max)
+instantiate_same_reduce(max_, float32, float, Max)
+
+instantiate_same_reduce(min_, bfloat16, bfloat16_t, Min)
+instantiate_same_reduce(max_, bfloat16, bfloat16_t, Max)

mlx/backend/metal/kernels/unary.metal

@@ -0,0 +1,284 @@
+#include <metal_integer>
+#include <metal_math>
+
+#include "mlx/backend/metal/kernels/utils.h"
+#include "mlx/backend/metal/kernels/erf.h"
+#include "mlx/backend/metal/kernels/bf16.h"
+
+struct Abs {
+  template <typename T> T operator()(T x) { return metal::abs(x); };
+  template <> uint8_t operator()(uint8_t x) { return x; };
+  template <> uint16_t operator()(uint16_t x) { return x; };
+  template <> uint32_t operator()(uint32_t x) { return x; };
+  template <> uint64_t operator()(uint64_t x) { return x; };
+  template <> bool operator()(bool x) { return x; };
+  template <> complex64_t operator()(complex64_t x) {
+      return {metal::precise::sqrt(x.real * x.real + x.imag * x.imag), 0};
+  };
+};
+
+struct ArcCos {
+  template <typename T> T operator()(T x) { return metal::precise::acos(x); };
+};
+
+struct ArcCosh {
+  template <typename T> T operator()(T x) { return metal::precise::acosh(x); };
+};
+
+struct ArcSin {
+  template <typename T> T operator()(T x) { return metal::precise::asin(x); };
+};
+
+struct ArcSinh {
+  template <typename T> T operator()(T x) { return metal::precise::asinh(x); };
+};
+
+struct ArcTan {
+  template <typename T> T operator()(T x) { return metal::precise::atan(x); };
+};
+
+struct ArcTanh {
+  template <typename T> T operator()(T x) { return metal::precise::atanh(x); };
+};
+
+struct Cos {
+  template <typename T> T operator()(T x) { return metal::precise::cos(x); };
+
+  template <>
+  complex64_t operator()(complex64_t x) {
+    return {
+      metal::precise::cos(x.real) * metal::precise::cosh(x.imag),
+      -metal::precise::sin(x.real) * metal::precise::sinh(x.imag)
+    };
+  };
+};
+
+struct Cosh {
+  template <typename T> T operator()(T x) { return metal::precise::cosh(x); };
+
+  template <>
+  complex64_t operator()(complex64_t x) {
+    return {
+      metal::precise::cosh(x.real) * metal::precise::cos(x.imag),
+      metal::precise::sinh(x.real) * metal::precise::sin(x.imag)
+    };
+  };
+};
+
+struct Erf {
+  template <typename T> T operator()(T x) { return static_cast<T>(erf(static_cast<float>(x))); };
+};
+
+struct ErfInv {
+  template <typename T> T operator()(T x) { return static_cast<T>(erfinv(static_cast<float>(x))); };
+};
+
+struct Exp {
+  template <typename T> T operator()(T x) { return metal::precise::exp(x); };
+  template <> complex64_t operator()(complex64_t x) {
+    auto m = metal::precise::exp(x.real);
+    return {m * metal::precise::cos(x.imag), m * metal::precise::sin(x.imag)};
+  }
+};
+
+struct Log {
+  template <typename T> T operator()(T x) { return metal::precise::log(x); };
+};
+
+struct Log2 {
+  template <typename T> T operator()(T x) { return metal::precise::log2(x); };
+};
+
+struct Log10 {
+  template <typename T> T operator()(T x) { return metal::precise::log10(x); };
+};
+
+struct Log1p {
+  template <typename T> T operator()(T x) { return log1p(x); };
+};
+
+struct LogicalNot {
+  template <typename T> T operator()(T x) { return !x; };
+};
+
+struct Negative {
+  template <typename T> T operator()(T x) { return -x; };
+};
+
+struct Sigmoid {
+  template <typename T>
+  T operator()(T x) {
+    auto y = 1 / (1 + metal::exp(-metal::abs(x)));
+    return (x < 0) ? 1 - y : y;
+  }
+};
+
+struct Sign {
+  template <typename T> T operator()(T x) { return (x > T(0)) - (x < T(0)); };
+  template <> uint32_t operator()(uint32_t x) { return x != 0; };
+};
+
+struct Sin {
+  template <typename T> T operator()(T x) { return metal::precise::sin(x); };
+
+  template <>
+  complex64_t operator()(complex64_t x) {
+    return {
+      metal::precise::sin(x.real) * metal::precise::cosh(x.imag),
+      metal::precise::cos(x.real) * metal::precise::sinh(x.imag)
+    };
+  };
+};
+
+struct Sinh {
+  template <typename T> T operator()(T x) { return metal::precise::sinh(x); };
+
+  template <>
+  complex64_t operator()(complex64_t x) {
+    return {
+      metal::precise::sinh(x.real) * metal::precise::cos(x.imag),
+      metal::precise::cosh(x.real) * metal::precise::sin(x.imag)
+    };
+  };
+};
+
+struct Square {
+  template <typename T> T operator()(T x) { return x * x; };
+};
+
+struct Sqrt {
+  template <typename T> T operator()(T x) { return metal::precise::sqrt(x); };
+};
+
+struct Rsqrt {
+  template <typename T> T operator()(T x) { return metal::precise::rsqrt(x); };
+};
+
+struct Tan {
+  template <typename T> T operator()(T x) { return metal::precise::tan(x); };
+
+  template <>
+  complex64_t operator()(complex64_t x) {
+    float tan_a = metal::precise::tan(x.real);
+    float tanh_b = metal::precise::tanh(x.imag);
+    float t1 = tan_a * tanh_b;
+    float denom = 1. + t1 * t1;
+    return {
+      (tan_a - tanh_b * t1) / denom,
+      (tanh_b + tan_a * t1) / denom
+    };
+  };
+};
+
+struct Tanh {
+  template <typename T> T operator()(T x) { return metal::precise::tanh(x); };
+
+  template <>
+  complex64_t operator()(complex64_t x) {
+    float tanh_a = metal::precise::tanh(x.real);
+    float tan_b = metal::precise::tan(x.imag);
+    float t1 = tanh_a * tan_b;
+    float denom = 1. + t1 * t1;
+    return {
+      (tanh_a + tan_b * t1) / denom,
+      (tan_b - tanh_a * t1) / denom
+    };
+  };
+};
+
+template <typename T, typename Op>
+[[kernel]] void unary_op_v(
+    device const T* in,
+    device T* out,
+    uint index [[thread_position_in_grid]]) {
+  out[index] = Op()(in[index]);
+}
+
+template <typename T, typename Op>
+[[kernel]] void unary_op_g(
+    device const T* in,
+    device T* out,
+    device const int* in_shape,
+    device const size_t* in_strides,
+    device const int& ndim,
+    uint index [[thread_position_in_grid]]) {
+  auto idx = elem_to_loc(index, in_shape, in_strides, ndim);
+  out[index] = Op()(in[idx]);
+}
+
+#define instantiate_unary_v(name, type, op) \
+  template [[host_name(name)]] \
+  [[kernel]] void unary_op_v<type, op>( \
+      device const type* in, \
+      device type* out, \
+      uint index [[thread_position_in_grid]]);
+
+#define instantiate_unary_g(name, type, op) \
+  template [[host_name(name)]] \
+  [[kernel]] void unary_op_g<type, op>( \
+      device const type* in, \
+      device type* out, \
+      device const int* in_shape, \
+      device const size_t* in_strides, \
+      device const int& ndim, \
+      uint index [[thread_position_in_grid]]);
+
+#define instantiate_unary_all(name, tname, type, op) \
+  instantiate_unary_v("v" #name #tname, type, op) \
+  instantiate_unary_g("g" #name #tname, type, op)
+
+#define instantiate_unary_float(name, op) \
+  instantiate_unary_all(name, float16, half, op) \
+  instantiate_unary_all(name, float32, float, op) \
+  instantiate_unary_all(name, bfloat16, bfloat16_t, op) \
+
+#define instantiate_unary_types(name, op) \
+  instantiate_unary_all(name, bool_, bool, op) \
+  instantiate_unary_all(name, uint8, uint8_t, op) \
+  instantiate_unary_all(name, uint16, uint16_t, op) \
+  instantiate_unary_all(name, uint32, uint32_t, op) \
+  instantiate_unary_all(name, uint64, uint64_t, op) \
+  instantiate_unary_all(name, int8, int8_t, op) \
+  instantiate_unary_all(name, int16, int16_t, op) \
+  instantiate_unary_all(name, int32, int32_t, op) \
+  instantiate_unary_all(name, int64, int64_t, op) \
+  instantiate_unary_float(name, op)
+
+instantiate_unary_types(abs, Abs)
+instantiate_unary_float(arccos, ArcCos)
+instantiate_unary_float(arccosh, ArcCosh)
+instantiate_unary_float(arcsin, ArcSin)
+instantiate_unary_float(arcsinh, ArcSinh)
+instantiate_unary_float(arctan, ArcTan)
+instantiate_unary_float(arctanh, ArcTanh)
+instantiate_unary_float(cos, Cos)
+instantiate_unary_float(cosh, Cosh)
+instantiate_unary_float(exp, Exp)
+instantiate_unary_float(log, Log)
+instantiate_unary_float(log2, Log2)
+instantiate_unary_float(log10, Log10)
+instantiate_unary_float(log1p, Log1p)
+instantiate_unary_types(neg, Negative)
+instantiate_unary_float(sigmoid, Sigmoid)
+instantiate_unary_float(erf, Erf)
+instantiate_unary_float(erfinv, ErfInv)
+instantiate_unary_types(sign, Sign)
+instantiate_unary_float(sin, Sin)
+instantiate_unary_float(sinh, Sinh)
+instantiate_unary_types(square, Square)
+instantiate_unary_float(sqrt, Sqrt)
+instantiate_unary_float(rsqrt, Rsqrt)
+instantiate_unary_float(tan, Tan)
+instantiate_unary_float(tanh, Tanh)
+
+instantiate_unary_all(abs, complex64, complex64_t, Abs)
+instantiate_unary_all(cos, complex64, complex64_t, Cos)
+instantiate_unary_all(cosh, complex64, complex64_t, Cosh)
+instantiate_unary_all(exp, complex64, complex64_t, Exp)
+instantiate_unary_all(neg, complex64, complex64_t, Negative)
+instantiate_unary_all(sin, complex64, complex64_t, Sin)
+instantiate_unary_all(sinh, complex64, complex64_t, Sinh)
+instantiate_unary_all(tan, complex64, complex64_t, Tan)
+instantiate_unary_all(tanh, complex64, complex64_t, Tanh)
+
+instantiate_unary_all(lnot, bool_, bool, LogicalNot)

mlx/backend/metal/reduce.cpp

@@ -0,0 +1,369 @@
+#include <algorithm>
+#include <cassert>
+#include <sstream>
+
+#include "mlx/backend/common/reduce.h"
+#include "mlx/backend/metal/device.h"
+#include "mlx/backend/metal/kernels/defines.h"
+#include "mlx/backend/metal/utils.h"
+#include "mlx/primitives.h"
+#include "mlx/utils.h"
+
+namespace mlx::core {
+
+//////////////////////////////////////////////////////////////////////
+// Case wise reduce dispatch
+//////////////////////////////////////////////////////////////////////
+
+namespace {
+
+// All Reduce
+void all_reduce_dispatch(
+    const array& in,
+    array& out,
+    const std::string& op_name,
+    MTL::ComputeCommandEncoder* compute_encoder,
+    metal::Device& d) {
+  // Get kernel and encode buffers
+  size_t in_size = in.size();
+  auto kernel = d.get_kernel("all_reduce_" + op_name + type_to_name(in));
+
+  compute_encoder->setComputePipelineState(kernel);
+  set_array_buffer(compute_encoder, in, 0);
+  set_array_buffer(compute_encoder, out, 1);
+  compute_encoder->setBytes(&in_size, sizeof(size_t), 2);
+
+  // Set grid dimensions
+
+  // We make sure each thread has enough to do by making it read in
+  // atleast n_reads inputs
+  int n_reads = REDUCE_N_READS;
+
+  // mod_in_size gives us the groups of n_reads needed to go over the entire
+  // input
+  uint mod_in_size = (in_size + n_reads - 1) / n_reads;
+
+  NS::UInteger thread_group_size = kernel->maxTotalThreadsPerThreadgroup();
+  thread_group_size =
+      mod_in_size > thread_group_size ? thread_group_size : mod_in_size;
+
+  // If the number of thread groups needed exceeds 1024, we reuse threads groups
+  uint n_thread_groups =
+      (mod_in_size + thread_group_size - 1) / thread_group_size;
+  n_thread_groups = std::min(n_thread_groups, 1024u);
+  uint nthreads = n_thread_groups * thread_group_size;
+
+  MTL::Size group_dims = MTL::Size(thread_group_size, 1, 1);
+  MTL::Size grid_dims = MTL::Size(nthreads, 1, 1);
+
+  compute_encoder->dispatchThreads(grid_dims, group_dims);
+}
+
+void row_reduce_dispatch(
+    const array& in,
+    array& out,
+    const std::string& op_name,
+    const std::vector<int>& axes_,
+    MTL::ComputeCommandEncoder* compute_encoder,
+    metal::Device& d) {
+  auto kernel = d.get_kernel("row_reduce_" + op_name + type_to_name(in));
+
+  int n_reads = REDUCE_N_READS;
+  size_t reduction_size = in.size() / out.size();
+
+  compute_encoder->setComputePipelineState(kernel);
+  set_array_buffer(compute_encoder, in, 0);
+  set_array_buffer(compute_encoder, out, 1);
+  compute_encoder->setBytes(&reduction_size, sizeof(size_t), 2);
+
+  // Each thread group is responsible for 1 output
+  NS::UInteger thread_group_size = kernel->maxTotalThreadsPerThreadgroup();
+  thread_group_size =
+      std::min((reduction_size + n_reads - 1) / n_reads, thread_group_size);
+
+  // Align thread group size with simd_size
+  uint simd_size = kernel->threadExecutionWidth();
+  thread_group_size =
+      (thread_group_size + simd_size - 1) / simd_size * simd_size;
+  assert(thread_group_size <= kernel->maxTotalThreadsPerThreadgroup());
+
+  // Launch enough thread groups for each output
+  size_t n_threads = out.size() * thread_group_size;
+  MTL::Size grid_dims = MTL::Size(n_threads, 1, 1);
+  MTL::Size group_dims = MTL::Size(thread_group_size, 1, 1);
+
+  compute_encoder->dispatchThreads(grid_dims, group_dims);
+}
+
+void col_reduce_dispatch(
+    const array& in,
+    array& out,
+    const std::string& op_name,
+    const std::vector<int>& axes_,
+    MTL::ComputeCommandEncoder* compute_encoder,
+    metal::Device& d) {
+  std::ostringstream kernel_name;
+
+  bool encode_in_shape = false;
+  bool encode_ndim = false;
+
+  // If the slowest moving axis can be merged into the reductions,
+  // we call the column reduce kernel
+  // In this case, a linear index in the output corresponds to the
+  // linear index in the input where the reduction starts
+  if (axes_[axes_.size() - 1] == (axes_.size() - 1)) {
+    kernel_name << "col_reduce_" << op_name << type_to_name(in);
+  }
+  // Otherwise, while all the reduction axes can be merged, the mapping between
+  // indices in the output and input require resolving using shapes and strides
+  else {
+    kernel_name << "contiguous_strided_reduce_" << op_name << type_to_name(in);
+    encode_in_shape = true;
+
+    // We check for a viable template with the required number of dimensions
+    // we only care about encoding non-reduced shapes and strides in the input
+    size_t non_reducing_dims = in.ndim() - axes_.size();
+    if (non_reducing_dims >= 1 &&
+        non_reducing_dims <= MAX_REDUCE_SPECIALIZED_DIMS) {
+      kernel_name << "_dim_" << non_reducing_dims;
+    } else {
+      encode_ndim = true;
+    }
+  }
+
+  auto kernel = d.get_kernel(kernel_name.str());
+  size_t in_size = in.size();
+  size_t out_size = out.size();
+
+  compute_encoder->setComputePipelineState(kernel);
+  set_array_buffer(compute_encoder, in, 0);
+  set_array_buffer(compute_encoder, out, 1);
+
+  // Calculate the number of inputs to reduce and the stride b/w them
+  size_t reduction_size = 1;
+  size_t in_ndim = in.ndim();
+  size_t reduction_stride = in_size;
+
+  for (int i : axes_) {
+    reduction_size *= in.shape(i);
+    reduction_stride = std::min(reduction_stride, in.strides()[i]);
+  }
+
+  compute_encoder->setBytes(&reduction_size, sizeof(size_t), 2);
+  compute_encoder->setBytes(&reduction_stride, sizeof(size_t), 3);
+  compute_encoder->setBytes(&out_size, sizeof(size_t), 4);
+  if (encode_in_shape) {
+    // Obtain the non-reducing shape and strides of the input to encode
+    std::vector<int> inp_shape_mod;
+    std::vector<size_t> inp_strides_mod;
+
+    for (size_t i = 0, j = 0; i < in.ndim(); i++) {
+      if (j < axes_.size() && axes_[j] == i) {
+        j++;
+      } else {
+        inp_shape_mod.push_back(in.shape(i));
+        inp_strides_mod.push_back(in.strides()[i]);
+      }
+    }
+
+    size_t ndim = inp_shape_mod.size();
+
+    compute_encoder->setBytes(inp_shape_mod.data(), ndim * sizeof(int), 5);
+    compute_encoder->setBytes(inp_strides_mod.data(), ndim * sizeof(size_t), 6);
+
+    if (encode_ndim) {
+      compute_encoder->setBytes(&ndim, sizeof(size_t), 7);
+    }
+  }
+
+  // Select block dimensions
+
+  // Each thread reads 16 inputs to give it more work
+  uint n_inputs_per_thread = REDUCE_N_READS;
+  uint n_threads_per_output =
+      (reduction_size + n_inputs_per_thread - 1) / n_inputs_per_thread;
+
+  // We spread outputs over the x dimension and inputs over the y dimension
+  // Threads with the same lid.x in a given threadgroup work on the same
+  // output and each thread in the y dimension accumlates for that output
+  uint threadgroup_dim_x = std::min(out_size, 128ul);
+  uint threadgroup_dim_y =
+      kernel->maxTotalThreadsPerThreadgroup() / threadgroup_dim_x;
+  threadgroup_dim_y = std::min(n_threads_per_output, threadgroup_dim_y);
+
+  uint n_threadgroups_x =
+      (out_size + threadgroup_dim_x - 1) / threadgroup_dim_x;
+
+  uint n_threadgroups_y =
+      (n_threads_per_output + threadgroup_dim_y - 1) / threadgroup_dim_y;
+
+  // Launch enough thread groups for each output
+  MTL::Size grid_dims = MTL::Size(n_threadgroups_x, n_threadgroups_y, 1);
+  MTL::Size group_dims = MTL::Size(threadgroup_dim_x, threadgroup_dim_y, 1);
+
+  // We set shared memory to be exploited here for reductions within a
+  // threadgroup - each thread must be able to update its accumulated output
+  // Note: Each threadgroup should have 32kB of data in threadgroup memory
+  //       and threadgroup_dim_x * threadgroup_dim_y <= 1024 by design
+  //       This should be fine for floats, but we might need to revisit
+  //       if we ever come to doubles. In that case, we should also cut
+  //       down the number of threads we launch in a threadgroup
+  compute_encoder->setThreadgroupMemoryLength(
+      threadgroup_dim_x * threadgroup_dim_y * out.itemsize(), 0);
+
+  compute_encoder->dispatchThreadgroups(grid_dims, group_dims);
+}
+
+void general_reduce_dispatch(
+    const array& in,
+    array& out,
+    const std::string& op_name,
+    const std::vector<int>& axes_,
+    MTL::ComputeCommandEncoder* compute_encoder,
+    metal::Device& d) {
+  bool encode_ndim = true;
+  std::ostringstream kernel_name;
+  kernel_name << "general_reduce_" << op_name << type_to_name(in);
+
+  // Check for specialzed kernels for input ndim
+  if (in.ndim() >= 1 && in.ndim() <= MAX_REDUCE_SPECIALIZED_DIMS) {
+    kernel_name << "_dim_" << in.ndim();
+    encode_ndim = false;
+  }
+  auto kernel = d.get_kernel(kernel_name.str());
+  size_t in_size = in.size();
+  size_t ndim = in.ndim();
+
+  // We set the reducing strides to 0 to induce collisions for the reduction
+  std::vector<size_t> out_strides(ndim);
+  size_t stride = 1;
+  for (int i = ndim - 1, j = axes_.size() - 1; i >= 0; --i) {
+    if (j >= 0 && axes_[j] == i) {
+      out_strides[i] = 0;
+      --j;
+    } else {
+      out_strides[i] = stride;
+      stride *= in.shape(i);
+    }
+  }
+
+  compute_encoder->setComputePipelineState(kernel);
+  set_array_buffer(compute_encoder, in, 0);
+  set_array_buffer(compute_encoder, out, 1);
+  compute_encoder->setBytes(in.shape().data(), ndim * sizeof(int), 2);
+  compute_encoder->setBytes(in.strides().data(), ndim * sizeof(size_t), 3);
+  compute_encoder->setBytes(out_strides.data(), ndim * sizeof(size_t), 4);
+  if (encode_ndim) {
+    compute_encoder->setBytes(&ndim, sizeof(size_t), 5);
+  }
+
+  NS::UInteger thread_group_size = kernel->maxTotalThreadsPerThreadgroup();
+  if (thread_group_size > in_size) {
+    thread_group_size = in_size;
+  }
+  size_t nthreads = in_size;
+
+  MTL::Size group_dims = MTL::Size(thread_group_size, 1, 1);
+  MTL::Size grid_dims = MTL::Size(nthreads, 1, 1);
+  compute_encoder->dispatchThreads(grid_dims, group_dims);
+}
+
+} // namespace
+
+//////////////////////////////////////////////////////////////////////
+// Main reduce dispatch
+//////////////////////////////////////////////////////////////////////
+
+void Reduce::eval_gpu(const std::vector<array>& inputs, array& out) {
+  assert(inputs.size() == 1);
+  auto& in = inputs[0];
+
+  // TODO: Allow specific row and column reductions with types disabled
+  // due to atomics ?
+  if (size_of(in.dtype()) == 8) {
+    std::ostringstream msg;
+    msg << "[Reduce::eval_gpu] Does not support " << in.dtype();
+    throw std::runtime_error(msg.str());
+  }
+
+  // Make sure no identity reductions trickle down here
+  assert(!axes_.empty());
+
+  // Continue with reduction operation
+  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+  std::string op_name;
+  switch (reduce_type_) {
+    case Reduce::And:
+      op_name = "and";
+      break;
+    case Reduce::Or:
+      op_name = "or";
+      break;
+    case Reduce::Sum:
+      op_name = "sum";
+      break;
+    case Reduce::Prod:
+      op_name = out.dtype() == bool_ ? "and" : "prod";
+      break;
+    case Reduce::Min:
+      op_name = out.dtype() == bool_ ? "and" : "min_";
+      break;
+    case Reduce::Max:
+      op_name = out.dtype() == bool_ ? "or" : "max_";
+      break;
+  }
+
+  // Initialize output
+  auto& s = stream();
+  auto& d = metal::device(s.device);
+  auto compute_encoder = d.get_command_encoder(s.index);
+  {
+    auto kernel = d.get_kernel("i" + op_name + type_to_name(out));
+    size_t nthreads = out.size();
+    MTL::Size grid_dims = MTL::Size(nthreads, 1, 1);
+    NS::UInteger thread_group_size = kernel->maxTotalThreadsPerThreadgroup();
+    if (thread_group_size > nthreads) {
+      thread_group_size = nthreads;
+    }
+    MTL::Size group_dims = MTL::Size(thread_group_size, 1, 1);
+    compute_encoder->setComputePipelineState(kernel);
+    set_array_buffer(compute_encoder, out, 0);
+    compute_encoder->dispatchThreads(grid_dims, group_dims);
+  }
+
+  // Reduce
+  {
+    // Check for contiguous data
+    if (in.size() == in.data_size() &&
+        (in.flags().row_contiguous || in.flags().col_contiguous)) {
+      // Go to all reduce if reducing over all axes
+      if (axes_.size() == in.ndim()) {
+        all_reduce_dispatch(in, out, op_name, compute_encoder, d);
+        return;
+      }
+      // Use specialized kernels if the input is row contiguous and
+      // the reducing axes can be merged into one
+      else if (
+          in.flags().row_contiguous && in.strides().back() == 1 &&
+          (axes_.back() - axes_.front()) == axes_.size() - 1) {
+        // If the fastest moving axis is being reduced, go to row reduce
+        if (axes_[0] == (in.ndim() - axes_.size())) {
+          row_reduce_dispatch(in, out, op_name, axes_, compute_encoder, d);
+          return;
+        }
+        // Otherwise go to to generalized strided reduce
+        // Note: bool isn't support here yet due to the use of atomics
+        //       once that is updated, this should be the else condition of this
+        //       branch
+        else if (in.dtype() != bool_) {
+          col_reduce_dispatch(in, out, op_name, axes_, compute_encoder, d);
+          return;
+        }
+      }
+    }
+    // Fall back to the general case
+    general_reduce_dispatch(in, out, op_name, axes_, compute_encoder, d);
+  }
+}
+
+} // namespace mlx::core

mlx/backend/no_metal/allocator.cpp

@@ -0,0 +1,15 @@
+
+#include "mlx/allocator.h"
+
+namespace mlx::core::allocator {
+
+Allocator& allocator() {
+  static CommonAllocator allocator_;
+  return allocator_;
+}
+
+void* Buffer::raw_ptr() {
+  return ptr_;
+}
+
+} // namespace mlx::core::allocator

mlx/fft.h

@@ -0,0 +1,149 @@
+#pragma once
+
+#include <variant>
+
+#include "array.h"
+#include "device.h"
+#include "stream.h"
+
+namespace mlx::core::fft {
+
+using StreamOrDevice = std::variant<std::monostate, Stream, Device>;
+
+/** Compute the n-dimensional Fourier Transform. */
+array fftn(
+    const array& a,
+    const std::vector<int>& n,
+    const std::vector<int>& axes,
+    StreamOrDevice s = {});
+array fftn(const array& a, const std::vector<int>& axes, StreamOrDevice s = {});
+array fftn(const array& a, StreamOrDevice s = {});
+
+/** Compute the n-dimensional inverse Fourier Transform. */
+array ifftn(
+    const array& a,
+    const std::vector<int>& n,
+    const std::vector<int>& axes,
+    StreamOrDevice s = {});
+array ifftn(
+    const array& a,
+    const std::vector<int>& axes,
+    StreamOrDevice s = {});
+array ifftn(const array& a, StreamOrDevice s = {});
+
+/** Compute the one-dimensional Fourier Transform. */
+inline array fft(const array& a, int n, int axis, StreamOrDevice s = {}) {
+  return fftn(a, {n}, {axis}, s);
+}
+inline array fft(const array& a, int axis = -1, StreamOrDevice s = {}) {
+  return fftn(a, {axis}, s);
+}
+
+/** Compute the one-dimensional inverse Fourier Transform. */
+inline array ifft(const array& a, int n, int axis, StreamOrDevice s = {}) {
+  return ifftn(a, {n}, {axis}, s);
+}
+inline array ifft(const array& a, int axis = -1, StreamOrDevice s = {}) {
+  return ifftn(a, {axis}, s);
+}
+
+/** Compute the two-dimensional Fourier Transform. */
+inline array fft2(
+    const array& a,
+    const std::vector<int>& n,
+    const std::vector<int>& axes,
+    StreamOrDevice s = {}) {
+  return fftn(a, n, axes, s);
+}
+inline array fft2(
+    const array& a,
+    const std::vector<int>& axes = {-2, -1},
+    StreamOrDevice s = {}) {
+  return fftn(a, axes, s);
+}
+
+/** Compute the two-dimensional inverse Fourier Transform. */
+inline array ifft2(
+    const array& a,
+    const std::vector<int>& n,
+    const std::vector<int>& axes,
+    StreamOrDevice s = {}) {
+  return ifftn(a, n, axes, s);
+}
+inline array ifft2(
+    const array& a,
+    const std::vector<int>& axes = {-2, -1},
+    StreamOrDevice s = {}) {
+  return ifftn(a, axes, s);
+}
+
+/** Compute the n-dimensional Fourier Transform on a real input. */
+array rfftn(
+    const array& a,
+    const std::vector<int>& n,
+    const std::vector<int>& axes,
+    StreamOrDevice s = {});
+array rfftn(
+    const array& a,
+    const std::vector<int>& axes,
+    StreamOrDevice s = {});
+array rfftn(const array& a, StreamOrDevice s = {});
+
+/** Compute the n-dimensional inverse of `rfftn`. */
+array irfftn(
+    const array& a,
+    const std::vector<int>& n,
+    const std::vector<int>& axes,
+    StreamOrDevice s = {});
+array irfftn(
+    const array& a,
+    const std::vector<int>& axes,
+    StreamOrDevice s = {});
+array irfftn(const array& a, StreamOrDevice s = {});
+
+/** Compute the one-dimensional Fourier Transform on a real input. */
+inline array rfft(const array& a, int n, int axis, StreamOrDevice s = {}) {
+  return rfftn(a, {n}, {axis}, s);
+}
+inline array rfft(const array& a, int axis = -1, StreamOrDevice s = {}) {
+  return rfftn(a, {axis}, s);
+}
+/** Compute the one-dimensional inverse of `rfft`. */
+inline array irfft(const array& a, int n, int axis, StreamOrDevice s = {}) {
+  return irfftn(a, {n}, {axis}, s);
+}
+inline array irfft(const array& a, int axis = -1, StreamOrDevice s = {}) {
+  return irfftn(a, {axis}, s);
+}
+
+/** Compute the two-dimensional Fourier Transform on a real input. */
+inline array rfft2(
+    const array& a,
+    const std::vector<int>& n,
+    const std::vector<int>& axes,
+    StreamOrDevice s = {}) {
+  return rfftn(a, n, axes, s);
+}
+inline array rfft2(
+    const array& a,
+    const std::vector<int>& axes = {-2, -1},
+    StreamOrDevice s = {}) {
+  return rfftn(a, axes, s);
+}
+
+/** Compute the two-dimensional inverse of `rfft2`. */
+inline array irfft2(
+    const array& a,
+    const std::vector<int>& n,
+    const std::vector<int>& axes,
+    StreamOrDevice s = {}) {
+  return irfftn(a, n, axes, s);
+}
+inline array irfft2(
+    const array& a,
+    const std::vector<int>& axes = {-2, -1},
+    StreamOrDevice s = {}) {
+  return irfftn(a, axes, s);
+}
+
+} // namespace mlx::core::fft

mlx/load.cpp

@@ -0,0 +1,240 @@
+#include <algorithm>
+#include <cstring>
+#include <fstream>
+#include <limits>
+#include <sstream>
+
+#include "mlx/load.h"
+#include "mlx/ops.h"
+#include "mlx/primitives.h"
+#include "mlx/utils.h"
+
+// Adapted from
+// https://github.com/angeloskath/supervised-lda/blob/master/include/ldaplusplus/NumpyFormat.hpp
+
+namespace mlx::core {
+
+namespace {
+
+static constexpr uint8_t MAGIC[] = {
+    0x93,
+    0x4e,
+    0x55,
+    0x4d,
+    0x50,
+    0x59,
+};
+
+inline bool is_big_endian_() {
+  union ByteOrder {
+    int32_t i;
+    uint8_t c[4];
+  };
+  ByteOrder b = {0x01234567};
+
+  return b.c[0] == 0x01;
+}
+
+} // namespace
+
+/** Save array to out stream in .npy format */
+void save(std::shared_ptr<io::Writer> out_stream, array a, bool retain_graph) {
+  ////////////////////////////////////////////////////////
+  // Check array
+
+  a.eval(retain_graph);
+
+  if (a.nbytes() == 0) {
+    throw std::invalid_argument("[save] cannot serialize an empty array");
+  }
+
+  if (!a.flags().contiguous) {
+    throw std::invalid_argument(
+        "[save] cannot serialize a non-contiguous array");
+  }
+
+  ////////////////////////////////////////////////////////
+  // Check file
+  if (!out_stream->good() || !out_stream->is_open()) {
+    throw std::runtime_error("[save] Failed to open " + out_stream->label());
+  }
+
+  ////////////////////////////////////////////////////////
+  // Prepare header
+  std::ostringstream magic_ver_len;
+  magic_ver_len.write(reinterpret_cast<const char*>(MAGIC), 6);
+
+  std::string fortran_order = a.flags().col_contiguous ? "True" : "False";
+  std::ostringstream header;
+  header << "{'descr': '" << dtype_to_array_protocol(a.dtype()) << "',"
+         << " 'fortran_order': " << fortran_order << ","
+         << " 'shape': (";
+  for (auto i : a.shape()) {
+    header << i << ", ";
+  }
+  header << ")}";
+
+  size_t header_len = static_cast<size_t>(header.tellp());
+  bool is_v1 = header_len + 15 < std::numeric_limits<uint16_t>::max();
+
+  // Pad out magic + version + header_len + header + \n to be divisible by 16
+  size_t padding = (6 + 2 + (2 + 2 * is_v1) + header_len + 1) % 16;
+
+  header << std::string(padding, ' ') << '\n';
+
+  if (is_v1) {
+    magic_ver_len << (char)0x01 << (char)0x00;
+
+    uint16_t v1_header_len = header.tellp();
+    const char* len_bytes = reinterpret_cast<const char*>(&v1_header_len);
+
+    if (!is_big_endian_()) {
+      magic_ver_len.write(len_bytes, 2);
+    } else {
+      magic_ver_len.write(len_bytes + 1, 1);
+      magic_ver_len.write(len_bytes, 1);
+    }
+  } else {
+    magic_ver_len << (char)0x02 << (char)0x00;
+
+    uint32_t v2_header_len = header.tellp();
+    const char* len_bytes = reinterpret_cast<const char*>(&v2_header_len);
+
+    if (!is_big_endian_()) {
+      magic_ver_len.write(len_bytes, 4);
+    } else {
+      magic_ver_len.write(len_bytes + 3, 1);
+      magic_ver_len.write(len_bytes + 2, 1);
+      magic_ver_len.write(len_bytes + 1, 1);
+      magic_ver_len.write(len_bytes, 1);
+    }
+  }
+  ////////////////////////////////////////////////////////
+  // Serialize array
+
+  out_stream->write(magic_ver_len.str().c_str(), magic_ver_len.str().length());
+  out_stream->write(header.str().c_str(), header.str().length());
+  out_stream->write(a.data<char>(), a.nbytes());
+
+  return;
+}
+
+/** Save array to file in .npy format */
+void save(const std::string& file_, array a, bool retain_graph) {
+  // Open and check file
+  std::string file = file_;
+
+  // Add .npy to file name if it is not there
+  if (file.length() < 4 || file.substr(file.length() - 4, 4) != ".npy")
+    file += ".npy";
+
+  // Serialize array
+  save(std::make_shared<io::FileWriter>(file), a, retain_graph);
+}
+
+/** Load array from reader in .npy format */
+array load(std::shared_ptr<io::Reader> in_stream, StreamOrDevice s) {
+  ////////////////////////////////////////////////////////
+  // Open and check file
+  if (!in_stream->good() || !in_stream->is_open()) {
+    throw std::runtime_error("[load] Failed to open " + in_stream->label());
+  }
+
+  ////////////////////////////////////////////////////////
+  // Read header and prepare array details
+
+  // Read and check magic
+  char read_magic_and_ver[8];
+  in_stream->read(read_magic_and_ver, 8);
+  if (std::memcmp(read_magic_and_ver, MAGIC, 6) != 0) {
+    throw std::runtime_error("[load] Invalid header in " + in_stream->label());
+  }
+
+  // Read and check version
+  if (read_magic_and_ver[6] != 1 && read_magic_and_ver[6] != 2) {
+    throw std::runtime_error(
+        "[load] Unsupport npy format version in " + in_stream->label());
+  }
+
+  // Read header len and header
+  int header_len_size = read_magic_and_ver[6] == 1 ? 2 : 4;
+  size_t header_len;
+
+  if (header_len_size == 2) {
+    uint16_t v1_header_len;
+    in_stream->read(reinterpret_cast<char*>(&v1_header_len), header_len_size);
+    header_len = v1_header_len;
+  } else {
+    uint32_t v2_header_len;
+    in_stream->read(reinterpret_cast<char*>(&v2_header_len), header_len_size);
+    header_len = v2_header_len;
+  }
+
+  // Read the header
+  std::vector<char> buffer(header_len + 1);
+  in_stream->read(&buffer[0], header_len);
+  buffer[header_len] = 0;
+  std::string header(&buffer[0]);
+
+  // Read data type from header
+  std::string dtype_str = header.substr(11, 3);
+  bool read_is_big_endian = dtype_str[0] == '>';
+  Dtype dtype = dtype_from_array_protocol(dtype_str);
+
+  // Read contiguity order
+  bool col_contiguous = header[34] == 'T';
+
+  // Read array shape from header
+  std::vector<int> shape;
+
+  size_t st = header.find_last_of('(') + 1;
+  size_t ed = header.find_last_of(')');
+  std::string shape_str = header.substr(st, ed - st);
+
+  while (!shape_str.empty()) {
+    // Read current number and get position of comma
+    size_t pos;
+    int dim = std::stoi(shape_str, &pos);
+    shape.push_back(dim);
+
+    // Skip the comma and space and read the next number
+    if (pos + 2 <= shape_str.length())
+      shape_str = shape_str.substr(pos + 2);
+    else {
+      shape_str = shape_str.substr(pos);
+      if (!shape_str.empty() && shape_str != " " && shape_str != ",") {
+        throw std::runtime_error(
+            "[load] Unknown error while parsing header in " +
+            in_stream->label());
+      }
+      shape_str = "";
+    }
+  }
+
+  ////////////////////////////////////////////////////////
+  // Build primitive
+
+  size_t offset = 8 + header_len_size + header.length();
+  bool swap_endianness = read_is_big_endian != is_big_endian_();
+
+  if (col_contiguous) {
+    std::reverse(shape.begin(), shape.end());
+  }
+  auto loaded_array = array(
+      shape,
+      dtype,
+      std::make_unique<Load>(to_stream(s), in_stream, offset, swap_endianness),
+      std::vector<array>{});
+  if (col_contiguous) {
+    loaded_array = transpose(loaded_array, s);
+  }
+
+  return loaded_array;
+}
+
+/** Load array from file in .npy format */
+array load(const std::string& file, StreamOrDevice s) {
+  return load(std::make_shared<io::FileReader>(file), s);
+}
+
+} // namespace mlx::core

mlx/transforms.cpp

@@ -0,0 +1,778 @@
+#include <algorithm>
+#include <future>
+#include <map>
+#include <numeric>
+#include <set>
+#include <sstream>
+#include <unordered_map>
+#include <unordered_set>
+
+#include "mlx/backend/metal/metal.h"
+#include "mlx/ops.h"
+#include "mlx/primitives.h"
+#include "mlx/scheduler.h"
+#include "mlx/transforms.h"
+#include "mlx/transforms_impl.h"
+#include "mlx/utils.h"
+
+namespace mlx::core {
+
+void simplify(const std::vector<array>& outputs) {
+  std::function<void(const array&)> recurse;
+  std::queue<array> tape;
+  std::unordered_set<std::uintptr_t> cache;
+  std::unordered_map<std::uintptr_t, std::vector<std::pair<array, int>>>
+      parents_map;
+
+  // Helpers to identify identical scalars
+  std::map<std::pair<uint64_t, Dtype::Val>, array> scalars;
+  auto is_scalar = [](const array& a) {
+    return a.is_evaled() && a.ndim() == 0;
+  };
+  auto get_scalar_rep = [](const array& a) {
+    uint64_t v = 0;
+    int dtype;
+    switch (a.dtype().size) {
+      case 1:
+        v = *a.data<uint8_t>();
+        break;
+      case 4:
+        v = *a.data<uint32_t>();
+        break;
+      case 8:
+        v = *a.data<uint64_t>();
+        break;
+    }
+    return std::make_pair(v, a.dtype().val);
+  };
+
+  // DFS the graph to log the parents
+  recurse = [&](const array& a) {
+    auto id = a.id();
+    if (cache.find(id) != cache.end()) {
+      return;
+    }
+    for (int i = 0; i < a.inputs().size(); i++) {
+      auto& in = a.inputs()[i];
+      parents_map[in.id()].push_back({a, i});
+      recurse(in);
+    }
+    cache.insert(id);
+    tape.push(a);
+    if (is_scalar(a)) {
+      scalars.insert({get_scalar_rep(a), a});
+    }
+  };
+  for (auto& a : outputs) {
+    recurse(a);
+  }
+
+  // Helper that fuses two arrays in the graph by setting the parents of the
+  // source to point to the destination
+  auto fuse = [&](array& dst, array& src) {
+    auto src_parents = parents_map.find(src.id());
+    if (src_parents == parents_map.end()) {
+      return;
+    }
+
+    auto& pairs = parents_map[dst.id()];
+    for (auto& parent : src_parents->second) {
+      parent.first.editable_inputs()[parent.second] = dst;
+      pairs.push_back(parent);
+    }
+  };
+
+  // Walk the graph
+  cache.clear();
+
+  // Depth-1 array equivalence check.
+  auto array_equivalent = [](const array& a, const array& b) {
+    if (!a.has_primitive() || !b.has_primitive()) {
+      return false;
+    }
+    const auto& pa = a.primitive();
+    const auto& pb = b.primitive();
+    if (typeid(pa) != typeid(pb)) {
+      return false;
+    }
+
+    if (a.inputs().size() != b.inputs().size()) {
+      return false;
+    }
+
+    for (int i = 0; i < a.inputs().size(); i++) {
+      if (a.inputs()[i].id() != b.inputs()[i].id()) {
+        return false;
+      }
+    }
+
+    return pa.is_equivalent(pb);
+  };
+
+  while (!tape.empty()) {
+    auto arr = std::move(tape.front());
+    tape.pop();
+
+    if (cache.find(arr.id()) != cache.end()) {
+      continue;
+    }
+
+    // Check if we can fuse scalars
+    if (is_scalar(arr)) {
+      auto scalar = scalars.find(get_scalar_rep(arr));
+      if (scalar->second.id() != arr.id()) {
+        fuse(scalar->second, arr);
+        arr = scalar->second;
+      }
+    }
+
+    // Check if we can fuse the parents of this array
+    auto parents = parents_map.find(arr.id());
+    if (parents != parents_map.end()) {
+      std::vector<bool> mask(parents->second.size(), false);
+      auto N = parents->second.size();
+      for (int i = 0; i < N; i++) {
+        if (mask[i]) {
+          continue;
+        }
+        for (int j = i + 1; j < N; j++) {
+          if (mask[j]) {
+            continue;
+          }
+          auto& src = parents->second[j].first;
+          auto& dst = parents->second[i].first;
+          if (src.id() != dst.id() && array_equivalent(src, dst)) {
+            cache.insert(src.id());
+            fuse(dst, src);
+            mask[j] = true;
+          }
+        }
+      }
+    }
+  }
+}
+
+void eval(const std::vector<array>& outputs, bool retain_graph /* = false */) {
+  if (!retain_graph) {
+    for (auto& out : outputs) {
+      if (out.has_primitive() && out.is_tracer()) {
+        throw std::invalid_argument(
+            "[eval] Illegal to eval an array during "
+            "function transform without graph retention.");
+      }
+    }
+  }
+  std::function<void(const array&)> recurse;
+  std::queue<array> tape;
+  std::unordered_set<std::uintptr_t> cache;
+  std::unordered_map<std::uintptr_t, std::shared_future<void>> deps;
+
+  recurse = [&](const array& a) {
+    auto id = a.id();
+    if (cache.find(id) != cache.end()) {
+      return;
+    }
+    for (auto in : a.inputs()) {
+      recurse(in);
+      // If one of the inputs is being computed on a different
+      // stream, we need to manage the dependency.
+      if (!in.is_evaled()) {
+        if (a.primitive().stream() != in.primitive().stream()) {
+          deps.insert({in.id(), std::shared_future<void>{}});
+        }
+      }
+    }
+    cache.insert(id);
+    if (!a.is_evaled() || (!retain_graph && a.has_primitive())) {
+      if (!a.has_primitive()) {
+        throw std::invalid_argument(
+            "[eval] Attempting to eval an array without a primitive.");
+      }
+      tape.push(a);
+    }
+  };
+
+  for (auto& arr : outputs) {
+    if (!arr.is_evaled() || (!retain_graph && arr.has_primitive())) {
+      recurse(arr);
+      // Insert a dependency for every output to synchronize
+      // with at the end.
+      if (!arr.is_evaled()) {
+        deps.insert({arr.id(), std::shared_future<void>{}});
+      }
+    }
+  }
+
+  while (!tape.empty()) {
+    auto arr = std::move(tape.front());
+    tape.pop();
+    if (arr.is_evaled()) {
+      if (!retain_graph && arr.has_primitive()) {
+        arr.detach();
+      }
+      continue;
+    }
+
+    auto stream = arr.primitive().stream();
+    std::vector<std::shared_future<void>> arr_deps;
+    for (auto& in : arr.inputs()) {
+      if (auto it = deps.find(in.id()); it != deps.end()) {
+        arr_deps.push_back(it->second);
+      }
+    }
+    std::shared_ptr<std::promise<void>> p{nullptr};
+    if (auto it = deps.find(arr.id()); it != deps.end()) {
+      p = std::make_unique<std::promise<void>>();
+      it->second = p->get_future().share();
+    }
+
+    if (arr.primitive().device() == Device::gpu) {
+      if (!metal::is_available()) {
+        throw std::runtime_error("Metal GPU is not available.");
+      }
+      scheduler::enqueue(
+          stream,
+          metal::make_task(
+              arr, std::move(arr_deps), std::move(p), retain_graph));
+    } else {
+      auto task = [retain_graph,
+                   arr,
+                   stream,
+                   arr_deps = std::move(arr_deps),
+                   p = std::move(p)]() mutable {
+        for (auto& d : arr_deps) {
+          d.wait();
+        }
+        scheduler::notify_new_task(stream);
+        arr.primitive().eval_cpu(arr.inputs(), arr);
+        if (!retain_graph) {
+          arr.detach();
+        }
+        if (p) {
+          p->set_value();
+        }
+        scheduler::notify_task_completion(stream);
+      };
+      scheduler::enqueue(stream, std::move(task));
+    }
+  }
+  for (auto& arr : outputs) {
+    if (auto it = deps.find(arr.id()); it != deps.end()) {
+      it->second.wait();
+    }
+  }
+}
+
+std::pair<std::vector<array>, std::vector<array>> vjp(
+    const std::function<std::vector<array>(const std::vector<array>&)>& fun,
+    const std::vector<array>& primals,
+    const std::vector<array>& cotans) {
+  // Make tracers from given primals
+  std::vector<array> primals_;
+  for (auto& p : primals) {
+    auto s = p.has_primitive() ? p.primitive().stream()
+                               : default_stream(default_device());
+    primals_.push_back(copy(p, s)); // Does not do a deep copy
+    primals_.back().set_tracer(true);
+  }
+
+  // Pass tracer primals through the function
+  // Any variables that depend on the primals are marked as tracers
+  auto outputs = fun(primals_);
+
+  // Map outputs to passed cotans while ignoring the outputs
+  // that have stop_gradient called on them
+  int cotan_index = 0;
+  std::vector<std::pair<int, int>> output_cotan_pairs;
+  for (int i = 0; i < outputs.size(); ++i) {
+    auto& out = outputs[i];
+    if (out.has_primitive()) {
+      if (auto& p = out.primitive(); typeid(p) == typeid(StopGradient)) {
+        continue;
+      }
+    }
+    if (cotan_index >= cotans.size()) {
+      throw std::invalid_argument(
+          "[vjp] Number of outputs with gradient does not match number of cotangents.");
+    }
+    if (out.shape() != cotans[cotan_index].shape()) {
+      throw std::invalid_argument(
+          "[vjp] Output shape does not match shape of cotangent.");
+    }
+    output_cotan_pairs.emplace_back(i, cotan_index++);
+  }
+
+  // Topologically sort the compute graph, record outputs
+  // in the tape if a gradient is needed.
+  std::unordered_set<std::uintptr_t> cache;
+  std::unordered_set<std::uintptr_t> calc_grad;
+  for (auto& primal : primals_) {
+    primal.set_tracer(false);
+    calc_grad.insert(primal.id());
+    cache.insert(primal.id());
+  }
+
+  std::vector<array> tape;
+
+  std::function<void(array&)> recurse;
+  recurse = [&](auto& a) {
+    auto id = a.id();
+    a.set_tracer(false);
+
+    // Check if visited and add to cache if not
+    if (auto inserted = cache.insert(id); !inserted.second) {
+      return;
+    }
+
+    for (auto& input : a.editable_inputs()) {
+      recurse(input);
+    }
+
+    // Stop grad
+    if (a.has_primitive() && typeid(a.primitive()) == typeid(StopGradient)) {
+      return;
+    }
+
+    // Calculate gradient if any inputs require gradient
+    for (auto& input : a.inputs()) {
+      if (calc_grad.find(input.id()) != calc_grad.end()) {
+        tape.push_back(a);
+        calc_grad.insert(id);
+        break;
+      }
+    }
+  };
+
+  for (auto& out : outputs) {
+    recurse(out);
+  }
+
+  // Run the tape backwards, computing vector-jacobian
+  // products for each primitive
+  std::unordered_map<std::uintptr_t, array> cotan_map;
+  for (auto [out_idx, cotan_idx] : output_cotan_pairs) {
+    cotan_map.insert({outputs[out_idx].id(), cotans[cotan_idx]});
+  }
+  for (auto it = tape.rbegin(); it != tape.rend(); ++it) {
+    auto& a = *it;
+
+    // Get the arguments whose gradients are needed
+    std::vector<int> argnums;
+    for (int i = 0; i < a.inputs().size(); ++i) {
+      if (calc_grad.find(a.inputs()[i].id()) != calc_grad.end()) {
+        argnums.push_back(i);
+      }
+    }
+
+    auto cotan_it = cotan_map.find(a.id());
+    if (cotan_it == cotan_map.end()) {
+      continue;
+    }
+
+    auto cotan = cotan_map.extract(cotan_it).mapped();
+    auto vjps = a.primitive().vjp(a.inputs(), cotan, argnums);
+    auto s = a.primitive().stream();
+    // Accumulate the vector-jacobian products for each input
+    for (int i = 0; i < argnums.size(); ++i) {
+      auto in_id = a.inputs()[argnums[i]].id();
+      if (auto cotan_it = cotan_map.find(in_id); cotan_it != cotan_map.end()) {
+        cotan_it->second = add(cotan_it->second, vjps[i], s);
+      } else {
+        cotan_map.insert({in_id, vjps[i]});
+      }
+    }
+  }
+
+  std::vector<array> vjps;
+  for (auto& primal : primals_) {
+    if (auto cotan_it = cotan_map.find(primal.id());
+        cotan_it != cotan_map.end()) {
+      vjps.push_back(cotan_it->second);
+    } else {
+      auto s = primal.has_primitive() ? primal.primitive().stream()
+                                      : default_stream(default_device());
+      vjps.push_back(zeros_like(primal, s));
+    }
+  }
+  return {outputs, vjps};
+}
+
+std::pair<array, array> vjp(
+    const std::function<array(const array&)>& fun,
+    const array& primal,
+    const array& cotan) {
+  auto vec_fun = [fun](const std::vector<array>& inputs) {
+    return std::vector<array>{fun(inputs[0])};
+  };
+  auto [outputs, vjps] = vjp(vec_fun, {primal}, {cotan});
+  return {outputs[0], vjps[0]};
+}
+
+std::pair<std::vector<array>, std::vector<array>> jvp(
+    const std::function<std::vector<array>(const std::vector<array>&)>& fun,
+    const std::vector<array>& primals,
+    const std::vector<array>& tangents) {
+  if (primals.size() != tangents.size()) {
+    throw std::invalid_argument(
+        "[jvp] Number of inputs does not match number of tangents.");
+  }
+  for (int i = 0; i < primals.size(); ++i) {
+    if (primals[i].shape() != tangents[i].shape()) {
+      throw std::invalid_argument(
+          "[jvp] Input shape does not match shape of tangent.");
+    }
+  }
+
+  std::vector<array> primals_;
+  for (auto& p : primals) {
+    auto s = p.has_primitive() ? p.primitive().stream()
+                               : default_stream(default_device());
+    primals_.push_back(copy(p, s)); // Does not do a deep copy
+    primals_.back().set_tracer(true);
+  }
+  auto outputs = fun(primals_);
+
+  // Topologically sort the compute graph, record outputs
+  // in the tape if a gradient is needed.
+  std::unordered_set<std::uintptr_t> cache;
+  std::unordered_set<std::uintptr_t> calc_grad;
+  for (auto& primal : primals_) {
+    primal.set_tracer(false);
+    calc_grad.insert(primal.id());
+    cache.insert(primal.id());
+  }
+
+  std::vector<array> tape;
+
+  std::function<void(array&)> recurse;
+  recurse = [&](auto& a) {
+    auto id = a.id();
+    a.set_tracer(false);
+
+    // Check if visited and add to cache if not
+    if (auto inserted = cache.insert(id); !inserted.second) {
+      return;
+    }
+
+    for (auto& input : a.editable_inputs()) {
+      recurse(input);
+    }
+
+    // Stop grad
+    if (a.has_primitive() && typeid(a.primitive()) == typeid(StopGradient)) {
+      return;
+    }
+
+    // Calculate gradient if any inputs require gradient
+    for (auto& input : a.inputs()) {
+      if (calc_grad.find(input.id()) != calc_grad.end()) {
+        tape.push_back(a);
+        calc_grad.insert(id);
+        break;
+      }
+    }
+  };
+
+  for (auto& out : outputs) {
+    recurse(out);
+  }
+  std::unordered_map<std::uintptr_t, array> tan_map;
+  for (int i = 0; i < primals_.size(); ++i) {
+    tan_map.insert({primals_[i].id(), tangents[i]});
+  }
+
+  for (auto& a : tape) {
+    // Get the arguments used in the jvp
+    std::vector<int> argnums;
+    std::vector<array> tangents;
+    for (int i = 0; i < a.inputs().size(); ++i) {
+      if (auto it = tan_map.find(a.inputs()[i].id()); it != tan_map.end()) {
+        argnums.push_back(i);
+        tangents.push_back(it->second);
+      }
+    }
+
+    auto jvp = a.primitive().jvp(a.inputs(), tangents, argnums);
+    tan_map.insert({a.id(), jvp});
+  }
+
+  std::vector<array> jvps;
+  for (auto& out : outputs) {
+    if (auto it = tan_map.find(out.id()); it != tan_map.end()) {
+      jvps.push_back(it->second);
+    } else {
+      auto s = out.has_primitive() ? out.primitive().stream()
+                                   : default_stream(default_device());
+      jvps.push_back(zeros_like(out, s));
+    }
+  }
+  return {outputs, jvps};
+}
+
+std::pair<array, array> jvp(
+    const std::function<array(const array&)>& fun,
+    const array& primal,
+    const array& tangent) {
+  auto vec_fun = [fun](const std::vector<array>& inputs) {
+    return std::vector<array>{fun(inputs[0])};
+  };
+  auto [outputs, jvps] = jvp(vec_fun, {primal}, {tangent});
+  return {outputs[0], jvps[0]};
+}
+
+ValueAndGradFn value_and_grad(
+    const std::function<std::vector<array>(const std::vector<array>&)>& fun,
+    const std::vector<int>& argnums) {
+  if (argnums.empty()) {
+    throw std::invalid_argument("[grad] Must specify at least one argument.");
+  }
+  return [fun, argnums](const std::vector<array>& inputs) {
+    std::set<int> args;
+    for (auto& arg : argnums) {
+      args.insert(arg < 0 ? arg + inputs.size() : arg);
+    }
+    if (args.size() != argnums.size()) {
+      throw std::invalid_argument(
+          "[grad] Repeat argument number not allowed in grad.");
+    }
+    if (*args.begin() < 0 || *args.rbegin() >= inputs.size()) {
+      std::ostringstream msg;
+      msg << "[grad] Invalid argument number for function with "
+          << inputs.size() << " inputs.";
+      throw std::invalid_argument(msg.str());
+    }
+
+    auto gfun = [&fun, &inputs, &args](const std::vector<array>& ginputs) {
+      std::vector<array> inputs_(inputs);
+      auto argit = args.begin();
+      for (int i = 0; i < ginputs.size(); ++i) {
+        inputs_[*argit] = ginputs[i];
+        ++argit;
+      }
+      auto outputs = fun(inputs_);
+      for (int i = 1; i < outputs.size(); i++) {
+        auto& out = outputs[i];
+        auto s = out.has_primitive() ? out.primitive().stream()
+                                     : default_stream(default_device());
+        outputs[i] = stop_gradient(out, s);
+      }
+      return outputs;
+    };
+
+    std::vector<array> ginputs;
+    for (auto arg : args) {
+      ginputs.push_back(inputs[arg]);
+    }
+    // Set the incoming gradient as int32 so that it will be promoted to the
+    // appropriate floating point type op(int, floatXX) -> floatXX for most ops
+    auto [outputs, grads] = vjp(gfun, ginputs, {array(1)});
+    return std::make_pair(outputs, grads);
+  };
+}
+
+namespace detail {
+
+std::pair<std::vector<array>, std::vector<array>> vmap_trace(
+    const std::function<std::vector<array>(const std::vector<array>&)>& fun,
+    const std::vector<array>& inputs,
+    const std::vector<int>& in_axes) {
+  if (in_axes.size() != inputs.size()) {
+    throw std::invalid_argument(
+        "[vmap] The number of in axes must match the number of inputs.");
+  }
+
+  // Run the function on placeholder inputs
+  // to get the original graph
+  std::vector<array> s_inputs;
+  for (int i = 0; i < inputs.size(); ++i) {
+    if (in_axes[i] != -1) {
+      if (inputs[i].ndim() == 0) {
+        throw std::invalid_argument(
+            "[vmap] Cannot vmap an input with zero dimensions.");
+      }
+      if (in_axes[i] > inputs[i].ndim()) {
+        std::ostringstream msg;
+        msg << "[vmap] Axis " << in_axes[i] << " invalid for input with "
+            << inputs[i].ndim() << " dimensions.";
+        throw std::invalid_argument(msg.str());
+      }
+
+      std::vector<int> shape = inputs[i].shape();
+      shape.erase(shape.begin() + in_axes[i]);
+      array in(shape, inputs[i].dtype(), nullptr, {});
+      s_inputs.push_back(in);
+      s_inputs.back().set_tracer(true);
+    } else {
+      s_inputs.push_back(inputs[i]);
+    }
+  }
+  return {s_inputs, fun(s_inputs)};
+}
+
+std::vector<array> vmap_replace(
+    const std::vector<array>& inputs,
+    const std::vector<array>& s_inputs,
+    const std::vector<array>& s_outputs,
+    const std::vector<int>& in_axes,
+    const std::vector<int>& out_axes) {
+  if (out_axes.size() != s_outputs.size()) {
+    throw std::invalid_argument(
+        "[vmap] The number of out axes must match the number of outputs.");
+  }
+
+  std::unordered_map<std::uintptr_t, std::pair<array, int>> tmap;
+  std::unordered_set<std::uintptr_t> needs_vmap;
+  for (int i = 0; i < s_inputs.size(); ++i) {
+    if (in_axes[i] != -1) {
+      tmap.insert({s_inputs[i].id(), {inputs[i], in_axes[i]}});
+      needs_vmap.insert(s_inputs[i].id());
+    }
+  }
+
+  // Topologically sort the graph
+  std::unordered_set<std::uintptr_t> cache;
+  for (int i = 0; i < s_inputs.size(); ++i) {
+    auto in = s_inputs[i];
+    if (in_axes[i] != -1) {
+      in.set_tracer(false);
+    }
+    cache.insert(in.id());
+  }
+  std::vector<array> tape;
+
+  std::function<void(const array&)> recurse;
+
+  recurse = [&](const array& a) {
+    // Stop at inputs to the vmap function
+    auto id = a.id();
+    if (cache.find(id) != cache.end()) {
+      return;
+    }
+    for (auto& input : a.inputs()) {
+      recurse(input);
+    }
+    cache.insert(id);
+    for (auto& input : a.inputs()) {
+      if (needs_vmap.find(input.id()) != needs_vmap.end()) {
+        needs_vmap.insert(id);
+        tape.push_back(a);
+        tape.back().set_tracer(false);
+        break;
+      }
+    }
+  };
+
+  for (auto& out : s_outputs) {
+    recurse(out);
+  }
+
+  // Transform each primitive in the graph with
+  // its vmap implementation
+  for (auto& a : tape) {
+    std::vector<array> v_inputs;
+    std::vector<int> v_axes;
+    for (auto& in : a.inputs()) {
+      auto map_it = tmap.find(in.id());
+      if (map_it != tmap.end()) {
+        v_inputs.push_back(map_it->second.first);
+        v_axes.push_back(map_it->second.second);
+      } else {
+        v_inputs.push_back(in);
+        v_axes.push_back(-1);
+      }
+    }
+    auto out_and_axis = a.primitive().vmap(v_inputs, v_axes);
+    tmap.insert({a.id(), out_and_axis});
+  }
+
+  // Populate the outputs and make sure all the output axes are
+  // in the right place
+  std::vector<array> outputs;
+  for (int i = 0; i < s_outputs.size(); ++i) {
+    auto map_it = tmap.find(s_outputs[i].id());
+    if (map_it != tmap.end()) {
+      auto& [out, vdim] = map_it->second;
+      if (vdim != out_axes[i]) {
+        if (out_axes[i] >= out.ndim()) {
+          std::ostringstream msg;
+          msg << "[vmap] Axis " << out_axes[i] << " invalid for output with "
+              << out.ndim() << " dimensions.";
+          throw std::invalid_argument(msg.str());
+        }
+        std::vector<int> reorder(out.ndim());
+        std::iota(reorder.begin(), reorder.end(), 0);
+        reorder.erase(reorder.begin() + vdim);
+        reorder.insert(reorder.begin() + out_axes[i], vdim);
+        out = transpose(out, reorder);
+      }
+      outputs.push_back(out);
+    } else {
+      outputs.push_back(s_outputs[i]);
+    }
+  }
+  return outputs;
+}
+
+} // namespace detail
+
+std::function<std::vector<array>(const std::vector<array>&)> vmap(
+    const std::function<std::vector<array>(const std::vector<array>&)>& fun,
+    const std::vector<int>& in_axes /* = {} */,
+    const std::vector<int>& out_axes /* = {} */) {
+  auto infer_axes = [](auto axes) {
+    return !axes.empty() &&
+        std::all_of(axes.begin(), axes.end(), [](int ax) { return ax < 0; });
+  };
+  if (infer_axes(in_axes) != infer_axes(out_axes)) {
+    throw std::invalid_argument(
+        "[vmap] Input (or output) axes must be "
+        "specified if output (or input) axes are.");
+  }
+  auto vfun = [fun, in_axes = in_axes, out_axes = out_axes](
+                  const std::vector<array>& inputs) mutable {
+    if (in_axes.size() == 0) {
+      in_axes.resize(inputs.size(), 0);
+    }
+
+    auto [trace_inputs, trace_outputs] =
+        detail::vmap_trace(fun, inputs, in_axes);
+
+    if (out_axes.size() == 0) {
+      out_axes.resize(trace_outputs.size(), 0);
+    }
+
+    return detail::vmap_replace(
+        inputs, trace_inputs, trace_outputs, in_axes, out_axes);
+  };
+
+  return vfun;
+}
+
+std::function<array(const array&, const array&)> vmap(
+    const std::function<array(const array&, const array&)>& fun,
+    int in_axis_a /* = 0 */,
+    int in_axis_b /* = 0 */,
+    int out_axis /* = 0 */) {
+  auto vfun = vmap(
+      [in_axis_a, in_axis_b, out_axis, fun](const std::vector<array>& inputs) {
+        return std::vector<array>{fun(inputs[0], inputs[1])};
+      },
+      {in_axis_a, in_axis_b},
+      {out_axis});
+  return [vfun](const array& a, const array& b) { return vfun({a, b})[0]; };
+}
+
+std::function<array(const array&)> vmap(
+    const std::function<array(const array&)>& fun,
+    int in_axis /* = 0 */,
+    int out_axis /* = 0 */) {
+  auto vfun = vmap(
+      [in_axis, out_axis, fun](const std::vector<array>& inputs) {
+        return std::vector<array>{fun(inputs[0])};
+      },
+      {in_axis},
+      {out_axis});
+  return [vfun](const array& a) { return vfun({a})[0]; };
+}
+
+} // namespace mlx::core

mlx/transforms.h

@@ -0,0 +1,185 @@
+#pragma once
+
+#include "array.h"
+
+namespace mlx::core {
+
+/** Fuse equivalent arrays to avoid duplicate execution. */
+void simplify(const std::vector<array>& outputs);
+
+template <typename... Arrays>
+void simplify(Arrays... outputs) {
+  simplify(std::vector<array>{std::forward<Arrays>(outputs)...});
+}
+
+void eval(const std::vector<array>& outputs, bool retain_graph = false);
+
+template <typename... Arrays>
+void eval(Arrays... outputs) {
+  eval(std::vector<array>{std::forward<Arrays>(outputs)...}, false);
+}
+
+/**
+ *  Computes the output and vector-Jacobian product (VJP) of a function.
+ *
+ *  Computes the vector-Jacobian product of the vector of cotangents with the
+ *  Jacobian of the function evaluated at the primals. Returns a pair of
+ *  vectors of output arrays and VJP arrays.
+ **/
+std::pair<std::vector<array>, std::vector<array>> vjp(
+    const std::function<std::vector<array>(const std::vector<array>&)>& fun,
+    const std::vector<array>& primals,
+    const std::vector<array>& cotangents);
+
+/**
+ *  Computes the output and vector-Jacobian product (VJP) of a unary function.
+ */
+std::pair<array, array> vjp(
+    const std::function<array(const array&)>& fun,
+    const array& primal,
+    const array& cotangent);
+
+/**
+ *  Computes the output and Jacobian-vector product (JVP) of a function.
+ *
+ *  Computes the Jacobian-vector product of the Jacobian of the function
+ *  evaluated at the primals with the vector of tangents. Returns a pair of
+ *  vectors of output arrays and JVP arrays.
+ **/
+std::pair<std::vector<array>, std::vector<array>> jvp(
+    const std::function<std::vector<array>(const std::vector<array>&)>& fun,
+    const std::vector<array>& primals,
+    const std::vector<array>& tangents);
+
+/**
+ *  Computes the output and Jacobian-vector product (JVP) of a unary function.
+ */
+std::pair<array, array> jvp(
+    const std::function<array(const array&)>& fun,
+    const array& primal,
+    const array& tangent);
+
+// Return type of general value_and_grad: a function which takes an input
+// vector of arrays and returns a pair of vectors of arrays one for the
+// values and one for the gradients wrt the first value.
+using ValueAndGradFn =
+    std::function<std::pair<std::vector<array>, std::vector<array>>(
+        const std::vector<array>&)>;
+using SimpleValueAndGradFn = std::function<std::pair<array, std::vector<array>>(
+    const std::vector<array>&)>;
+
+/**
+ *  Returns a function which computes the value and gradient of the input
+ *  function with respect to a vector of input arrays.
+ **/
+ValueAndGradFn value_and_grad(
+    const std::function<std::vector<array>(const std::vector<array>&)>& fun,
+    const std::vector<int>& argnums);
+
+/**
+ *  Returns a function which computes the value and gradient of the input
+ *  function with repsect to a single input array.
+ **/
+ValueAndGradFn inline value_and_grad(
+    const std::function<std::vector<array>(const std::vector<array>&)>& fun,
+    int argnum = 0) {
+  return value_and_grad(fun, std::vector<int>{argnum});
+}
+
+/**
+ *  Returns a function which computes the value and gradient of the unary
+ *  input function.
+ **/
+std::function<std::pair<array, array>(const array&)> inline value_and_grad(
+    const std::function<array(const array&)>& fun) {
+  return [fun](auto inputs) { return vjp(fun, inputs, array(1.0f)); };
+}
+
+SimpleValueAndGradFn inline value_and_grad(
+    const std::function<array(const std::vector<array>&)>& fun,
+    const std::vector<int>& argnums) {
+  return [fun, argnums](auto inputs) {
+    auto result = value_and_grad(
+        [fun](auto inputs) { return std::vector<array>{fun(inputs)}; },
+        argnums)(inputs);
+
+    return std::make_pair(result.first[0], result.second);
+  };
+}
+
+SimpleValueAndGradFn inline value_and_grad(
+    const std::function<array(const std::vector<array>&)>& fun,
+    int argnum = 0) {
+  return value_and_grad(fun, std::vector<int>{argnum});
+}
+
+/**
+ *  Returns a function which computes the gradient of the input function with
+ *  respect to a vector of input arrays.
+ *
+ *  The function being differentiated takes a vector of arrays and returns an
+ *  array. The vector of `argnums` specifies which the arguments to compute
+ *  the gradient with respect to. At least one argument must be specified.
+ **/
+std::function<std::vector<array>(const std::vector<array>&)> inline grad(
+    const std::function<array(const std::vector<array>&)>& fun,
+    const std::vector<int>& argnums) {
+  auto fn = value_and_grad(fun, argnums);
+  return [fn](const std::vector<array>& inputs) { return fn(inputs).second; };
+}
+
+/**
+ *  Returns a function which computes the gradient of the input function with
+ *  repsect to a single input array.
+ *
+ *  The function being differentiated takes a vector of arrays and returns an
+ *  array. The optional `argnum` index specifies which the argument to compute
+ *  the gradient with respect to and defaults to 0.
+ **/
+std::function<std::vector<array>(const std::vector<array>&)> inline grad(
+    const std::function<array(const std::vector<array>&)>& fun,
+    int argnum = 0) {
+  return grad(fun, std::vector<int>{argnum});
+}
+
+/**
+ *  Returns a function which computes the gradient of the unary input function.
+ **/
+std::function<array(const array&)> inline grad(
+    const std::function<array(const array&)>& fun) {
+  auto fn = value_and_grad(fun);
+  return [fn](const array& input) { return fn(input).second; };
+}
+
+/**
+ * Automatically vectorize a unary function over the requested axes.
+ */
+std::function<array(const array&)> vmap(
+    const std::function<array(const array&)>& fun,
+    int in_axis = 0,
+    int out_axis = 0);
+
+/**
+ * Automatically vectorize a binary function over the requested axes.
+ */
+std::function<array(const array&, const array&)> vmap(
+    const std::function<array(const array&, const array&)>& fun,
+    int in_axis_a = 0,
+    int in_axis_b = 0,
+    int out_axis = 0);
+
+/**
+ * Automatically vectorize a function over the requested axes.
+ *
+ * The input function to `vmap` takes as an argument a vector of arrays and
+ * returns a vector of arrays. Optionally specify the axes to vectorize over
+ * with `in_axes` and `out_axes`, otherwise a default of 0 is used.
+ * Returns a vectorized function with the same signature as the input
+ * function.
+ */
+std::function<std::vector<array>(const std::vector<array>&)> vmap(
+    const std::function<std::vector<array>(const std::vector<array>&)>& fun,
+    const std::vector<int>& in_axes = {},
+    const std::vector<int>& out_axes = {});
+
+} // namespace mlx::core

mlx/types/bf16.h

@@ -0,0 +1,185 @@
+#pragma once
+
+#include <algorithm>
+#include <cmath>
+#include <cstdint>
+#include <vector>
+
+#define __MLX_BFLOAT_NAN__ 0x7FC0
+
+namespace mlx::core {
+
+namespace {
+union float_bits_bf16 {
+  float f;
+  uint32_t u;
+};
+} // namespace
+
+struct _MLX_BFloat16 {
+  uint16_t bits_;
+
+  // Default constructor
+  _MLX_BFloat16() = default;
+
+  // Default copy constructor
+  _MLX_BFloat16(_MLX_BFloat16 const&) = default;
+
+  // Appease std::vector<bool> for being special
+  _MLX_BFloat16& operator=(std::vector<bool>::reference x) {
+    bits_ = x;
+    return *this;
+  }
+
+  _MLX_BFloat16& operator=(const float& x) {
+    return (*this = _MLX_BFloat16(x));
+  }
+
+  // From float32
+  _MLX_BFloat16(const float& x) {
+    if (std::isnan(x)) {
+      bits_ = __MLX_BFLOAT_NAN__;
+    } else {
+      // Union
+      float_bits_bf16 in;
+
+      // Take bits
+      in.f = x;
+
+      // Round to nearest even
+      in.u += (in.u >> 16 & 1) + uint32_t(0x7FFF);
+
+      // Take upper 16 bits
+      bits_ = in.u >> 16;
+    }
+  }
+
+  // To float32
+  operator float() const {
+    // Union
+    float_bits_bf16 out;
+
+    // Upper 16 bits are the data and lower 16 bits are 0s
+    out.u = ((uint32_t)bits_) << 16;
+
+    return out.f;
+  }
+};
+
+#define bfloat_binop_base(__op__, __operator__, otype, atype, btype, ctype) \
+  inline otype __operator__(atype lhs, btype rhs) {                         \
+    return static_cast<ctype>(lhs) __op__ static_cast<ctype>(rhs);          \
+  }
+
+#define bfloat_binop_helper(__op__, __operator__, otype, itype, ctype) \
+  inline otype __operator__(_MLX_BFloat16 lhs, itype rhs) {            \
+    return static_cast<ctype>(lhs) __op__ static_cast<ctype>(rhs);     \
+  }                                                                    \
+  inline otype __operator__(itype lhs, _MLX_BFloat16 rhs) {            \
+    return static_cast<ctype>(lhs) __op__ static_cast<ctype>(rhs);     \
+  }
+
+// Operators
+#define bfloat_binop(_op_, _operator_)                                       \
+  bfloat_binop_base(                                                         \
+      _op_, _operator_, _MLX_BFloat16, _MLX_BFloat16, _MLX_BFloat16, float); \
+  bfloat_binop_helper(_op_, _operator_, float, float, float);                \
+  bfloat_binop_helper(_op_, _operator_, double, double, double);             \
+  bfloat_binop_helper(_op_, _operator_, _MLX_BFloat16, bool, float);         \
+  bfloat_binop_helper(_op_, _operator_, _MLX_BFloat16, int32_t, float);      \
+  bfloat_binop_helper(_op_, _operator_, _MLX_BFloat16, uint32_t, float);     \
+  bfloat_binop_helper(_op_, _operator_, _MLX_BFloat16, int64_t, float);      \
+  bfloat_binop_helper(_op_, _operator_, _MLX_BFloat16, uint64_t, float);
+
+bfloat_binop(+, operator+);
+bfloat_binop(-, operator-);
+bfloat_binop(*, operator*);
+bfloat_binop(/, operator/);
+
+#undef bfloat_binop
+
+// Comparison ops
+#define bfloat_compop(__op__, __operator__)                             \
+  bfloat_binop_base(                                                    \
+      __op__, __operator__, bool, _MLX_BFloat16, _MLX_BFloat16, float); \
+  bfloat_binop_helper(__op__, __operator__, bool, float, float);        \
+  bfloat_binop_helper(__op__, __operator__, bool, double, double);      \
+  bfloat_binop_helper(__op__, __operator__, bool, int32_t, float);      \
+  bfloat_binop_helper(__op__, __operator__, bool, uint32_t, float);     \
+  bfloat_binop_helper(__op__, __operator__, bool, int64_t, float);      \
+  bfloat_binop_helper(__op__, __operator__, bool, uint64_t, float);
+
+bfloat_compop(>, operator>);
+bfloat_compop(<, operator<);
+bfloat_compop(>=, operator>=);
+bfloat_compop(<=, operator<=);
+bfloat_compop(==, operator==);
+bfloat_compop(!=, operator!=);
+
+#undef bfloat_compop
+
+// Negative
+inline _MLX_BFloat16 operator-(_MLX_BFloat16 lhs) {
+  return -static_cast<float>(lhs);
+}
+
+// Inplace ops
+#define bfloat_inplace_op(__op__, __operator__)                              \
+  inline _MLX_BFloat16& __operator__(_MLX_BFloat16& lhs, const float& rhs) { \
+    lhs = lhs __op__ rhs;                                                    \
+    return lhs;                                                              \
+  }                                                                          \
+  inline float& __operator__(float& lhs, _MLX_BFloat16 rhs) {                \
+    lhs = lhs __op__ rhs;                                                    \
+    return lhs;                                                              \
+  }
+
+bfloat_inplace_op(+, operator+=);
+bfloat_inplace_op(-, operator-=);
+bfloat_inplace_op(*, operator*=);
+bfloat_inplace_op(/, operator/=);
+
+#undef bfloat_inplace_op
+
+// Bitwise ops
+
+#define bfloat_bitop(__op__, __operator__)                                  \
+  inline _MLX_BFloat16 __operator__(_MLX_BFloat16 lhs, _MLX_BFloat16 rhs) { \
+    _MLX_BFloat16 out;                                                      \
+    out.bits_ = lhs.bits_ __op__ rhs.bits_;                                 \
+    return out;                                                             \
+  }                                                                         \
+  inline _MLX_BFloat16 __operator__(_MLX_BFloat16 lhs, uint16_t rhs) {      \
+    _MLX_BFloat16 out;                                                      \
+    out.bits_ = lhs.bits_ __op__ rhs;                                       \
+    return out;                                                             \
+  }                                                                         \
+  inline _MLX_BFloat16 __operator__(uint16_t lhs, _MLX_BFloat16 rhs) {      \
+    _MLX_BFloat16 out;                                                      \
+    out.bits_ = lhs __op__ rhs.bits_;                                       \
+    return out;                                                             \
+  }
+
+bfloat_bitop(|, operator|);
+bfloat_bitop(&, operator&);
+bfloat_bitop(^, operator^);
+
+#undef bfloat_bitop
+
+#define bfloat_inplace_bitop(__op__, __operator__)                            \
+  inline _MLX_BFloat16& __operator__(_MLX_BFloat16& lhs, _MLX_BFloat16 rhs) { \
+    lhs.bits_ = lhs.bits_ __op__ rhs.bits_;                                   \
+    return lhs;                                                               \
+  }                                                                           \
+  inline _MLX_BFloat16& __operator__(_MLX_BFloat16& lhs, uint16_t rhs) {      \
+    lhs.bits_ = lhs.bits_ __op__ rhs;                                         \
+    return lhs;                                                               \
+  }
+
+bfloat_inplace_bitop(|, operator|=);
+bfloat_inplace_bitop(&, operator&=);
+bfloat_inplace_bitop(^, operator^=);
+
+#undef bfloat_inplace_bitop
+
+} // namespace mlx::core

python/mlx/nn/__init__.py

@@ -0,0 +1,3 @@
+from mlx.nn.layers import *
+from mlx.nn import losses
+from mlx.nn.utils import value_and_grad

python/mlx/nn/layers/__init__.py

@@ -0,0 +1,23 @@
+from mlx.nn.layers.base import Module
+from mlx.nn.layers.activations import (
+    GELU,
+    ReLU,
+    SiLU,
+    gelu,
+    gelu_approx,
+    gelu_fast_approx,
+    relu,
+    silu,
+)
+from mlx.nn.layers.containers import Sequential
+from mlx.nn.layers.convolution import Conv1d, Conv2d
+from mlx.nn.layers.dropout import Dropout
+from mlx.nn.layers.embedding import Embedding
+from mlx.nn.layers.linear import Linear
+from mlx.nn.layers.normalization import GroupNorm, LayerNorm, RMSNorm
+from mlx.nn.layers.positional_encoding import RoPE, SinusoidalPositionalEncoding
+from mlx.nn.layers.transformer import (
+    MultiHeadAttention,
+    TransformerEncoder,
+    TransformerEncoderLayer,
+)

python/mlx/nn/layers/activations.py

@@ -0,0 +1,129 @@
+import math
+
+import mlx.core as mx
+from mlx.nn.layers.base import Module
+
+
+def _make_activation_module(f):
+    def decorator(klass):
+        klass.__doc__ = f.__doc__
+        klass.__call__ = lambda self, x: f(x)
+        return klass
+
+    return decorator
+
+
+def relu(x):
+    """Applies the Rectified Linear Unit.
+
+    Simply ``mx.maximum(x, 0)``.
+    """
+    return mx.maximum(x, 0)
+
+
+def silu(x):
+    r"""Applies the Sigmoid Linear Unit.
+
+    Applies :math:`x \sigma(x)` element wise, where :math:`\sigma(\cdot)` is
+    the logistic sigmoid.
+    """
+    return x * mx.sigmoid(x)
+
+
+def gelu(x):
+    """Applies the Gaussian Error Linear Units function.
+
+    .. math::
+        \\textrm{GELU}(x) = x * \Phi(x)
+
+    where :math:`\Phi(x)` is the Gaussian CDF.
+
+    See also :func:`gelu_approx` and :func:`gelu_fast_approx` for faster
+    approximations.
+    """
+    return x * (1 + mx.erf(x / math.sqrt(2))) / 2
+
+
+def gelu_approx(x):
+    r"""An approximation to Gaussian Error Linear Unit.
+
+    See :func:`gelu` for the exact computation.
+
+    This function approximates ``gelu`` with a maximum absolute error :math:`<
+    0.0003` in the range :math:`[-6, 6]` using the following
+
+    .. math::
+
+        x = x \sigma\left(1.60033 x \left(1 + 0.0433603 x^2\right)\right)
+
+    where :math:`\sigma(\cdot)` is the logistic sigmoid.
+    """
+    return x * mx.sigmoid(1.60033 * x * (1 + 0.0433603 * x.square()))
+
+
+def gelu_fast_approx(x):
+    r"""A fast approximation to Gaussian Error Linear Unit.
+
+    See :func:`gelu` for the exact computation.
+
+    This function approximates ``gelu`` with a maximum absolute error :math:`<
+    0.015` in the range :math:`[-6, 6]` using the following
+
+    .. math::
+
+        x = x \sigma\left(1.773 x\right)
+
+    where :math:`\sigma(\cdot)` is the logistic sigmoid.
+    """
+    return x * mx.sigmoid(1.773 * x)
+
+
+@_make_activation_module(relu)
+class ReLU(Module):
+    pass
+
+
+@_make_activation_module(silu)
+class SiLU(Module):
+    pass
+
+
+class GELU(Module):
+    r"""Applies the Gaussian Error Linear Units.
+
+    .. math::
+        \textrm{GELU}(x) = x * \Phi(x)
+
+    where :math:`\Phi(x)` is the Gaussian CDF.
+
+    However, if ``approx`` is set to 'precise' or 'fast' it applies
+
+    .. math::
+        \textrm{GELUApprox}(x) &= x * \sigma\left(1.60033 * x \left(1 + 0.0433603 * x^2\right)\right) \\
+        \textrm{GELUFast}(x) &= x * \sigma\left(1.773 * x\right)
+
+    respectively.
+
+    See :func:`gelu`, :func:`gelu_approx` and :func:`gelu_fast_approx` for the
+    functional equivalents and information regarding error bounds.
+
+    Args:
+        approx ('none' | 'precise' | 'fast'): Which approximation to gelu to use if any.
+    """
+
+    def __init__(self, approx="none"):
+        super().__init__()
+
+        if approx == "none":
+            self._act = gelu
+        elif approx == "precise":
+            self._act = gelu_approx
+        elif approx == "fast":
+            self._act = gelu_fast_approx
+        else:
+            raise ValueError(
+                f"The approximation should be in ['none', 'precise', 'fast'] but '{approx}' was given"
+            )
+
+    def __call__(self, x):
+        return self._act(x)

python/mlx/nn/losses.py

@@ -0,0 +1,6 @@
+import mlx.core as mx
+
+
+def cross_entropy(logits: mx.array, targets: mx.array, axis: int = -1):
+    score = mx.take_along_axis(logits, targets[..., None], axis).squeeze(-1)
+    return mx.logsumexp(logits, axis=axis) - score

python/mlx/utils.py

@@ -0,0 +1,136 @@
+def tree_map(fn, tree, *rest):
+    """Applies ``fn`` to the leaves of the python tree ``tree`` and
+    returns a new collection with the results.
+
+    If ``rest`` is provided, every item is assumed to be a superset of ``tree``
+    and the corresponding leaves are provided as extra positional arguments to
+    ``fn``. In that respect, :meth:`tree_map` is closer to :func:`itertools.starmap`
+    than to :func:`map`.
+
+    .. code-block:: python
+
+        import mlx.nn as nn
+        from mlx.utils import tree_map
+
+        model = nn.Linear(10, 10)
+        print(model.parameters().keys())
+        # dict_keys(['weight', 'bias'])
+
+        # square the parameters
+        model.update(tree_map(lambda x: x*x, model.parameters()))
+
+    Args:
+        fn (Callable): The function that processes the leaves of the tree
+        tree (Any): The main python tree that will be iterated upon
+        rest (Tuple[Any]): Extra trees to be iterated together with tree
+
+    Returns:
+        A python tree with the new values returned by ``fn``.
+    """
+    if isinstance(tree, list):
+        return [
+            tree_map(fn, child, *(r[i] for r in rest)) for i, child in enumerate(tree)
+        ]
+    elif isinstance(tree, tuple):
+        return tuple(
+            tree_map(fn, child, *(r[i] for r in rest)) for i, child in enumerate(tree)
+        )
+    elif isinstance(tree, dict):
+        return {
+            k: tree_map(fn, child, *(r[k] for r in rest)) for k, child in tree.items()
+        }
+    else:
+        return fn(tree, *rest)
+
+
+def tree_flatten(tree, prefix="", is_leaf=None):
+    """Flattens a python tree to a list of key, value tuples.
+
+    The keys are using the dot notation to define trees of arbitrary depth and
+    complexity.
+
+    .. code-block:: python
+
+        from mlx.utils import tree_flatten
+
+        print(tree_flatten([[[0]]]))
+        # [("0.0.0", 0)]
+
+        print(tree_flatten([[[0]]], ".hello"))
+        # [("hello.0.0.0", 0)]
+
+    .. note::
+       Dictionaries should have keys that are valid python identifiers.
+
+    Args:
+        tree (Any): The python tree to be flattened.
+        prefix (str): A prefix to use for the keys. The first character is
+            always discarded.
+        is_leaf (Callable): An optional callable that returns True if the
+            passed object is considered a leaf or False otherwise.
+
+    Returns:
+        List[Tuple[str, Any]]: The flat representation of the python tree.
+    """
+    flat_tree = []
+
+    if is_leaf is None or not is_leaf(tree):
+        if isinstance(tree, (list, tuple)):
+            for i, t in enumerate(tree):
+                flat_tree.extend(tree_flatten(t, f"{prefix}.{i}", is_leaf))
+            return flat_tree
+        if isinstance(tree, dict):
+            for k, t in tree.items():
+                flat_tree.extend(tree_flatten(t, f"{prefix}.{k}", is_leaf))
+            return flat_tree
+
+    return [(prefix[1:], tree)]
+
+
+def tree_unflatten(tree):
+    """Recreate a python tree from its flat representation.
+
+    .. code-block:: python
+
+        from mlx.utils import tree_unflatten
+
+        d = tree_unflatten([("hello.world", 42)])
+        print(d)
+        # {"hello": {"world": 42}}
+
+    Args:
+        tree (List[Tuple[str, Any]]): The flat representation of a python tree.
+                                      For instance as returned by :meth:`tree_flatten`.
+
+    Returns:
+        A python tree.
+    """
+    if len(tree) == 1 and tree[0][0] == "":
+        return tree[0][1]
+
+    try:
+        int(tree[0][0].split(".", maxsplit=1)[0])
+        is_list = True
+    except ValueError:
+        is_list = False
+
+    # collect children
+    children = {}
+    for key, value in tree:
+        current_idx, *next_idx = key.split(".", maxsplit=1)
+        next_idx = "" if not next_idx else next_idx[0]
+        if current_idx not in children:
+            children[current_idx] = []
+        children[current_idx].append((next_idx, value))
+
+    # recursively map them to the original container
+    if is_list:
+        keys = sorted((int(idx), idx) for idx in children.keys())
+        l = []
+        for i, k in keys:
+            while i > len(l):
+                l.append({})
+            l.append(tree_unflatten(children[k]))
+        return l
+    else:
+        return {k: tree_unflatten(v) for k, v in children.items()}

python/src/device.cpp

@@ -0,0 +1,42 @@
+#include <sstream>
+
+#include <pybind11/pybind11.h>
+
+#include "mlx/device.h"
+#include "mlx/utils.h"
+
+namespace py = pybind11;
+using namespace py::literals;
+using namespace mlx::core;
+
+void init_device(py::module_& m) {
+  py::enum_<Device::DeviceType>(m, "DeviceType")
+      .value("cpu", Device::DeviceType::cpu)
+      .value("gpu", Device::DeviceType::gpu)
+      .export_values()
+      .def(
+          "__eq__",
+          [](const Device::DeviceType& d1, const Device& d2) {
+            return d1 == d2;
+          },
+          py::prepend());
+
+  py::class_<Device>(m, "Device")
+      .def(py::init<Device::DeviceType, int>(), "type"_a, "index"_a = 0)
+      .def_readonly("type", &Device::type)
+      .def(
+          "__repr__",
+          [](const Device& d) {
+            std::ostringstream os;
+            os << d;
+            return os.str();
+          })
+      .def("__eq__", [](const Device& d1, const Device& d2) {
+        return d1 == d2;
+      });
+
+  py::implicitly_convertible<Device::DeviceType, Device>();
+
+  m.def("default_device", &default_device);
+  m.def("set_default_device", &set_default_device, "device"_a);
+}

python/src/metal.cpp

@@ -0,0 +1,12 @@
+#include <pybind11/pybind11.h>
+
+#include "mlx/backend/metal/metal.h"
+
+namespace py = pybind11;
+
+using namespace mlx::core;
+
+void init_metal(py::module_& m) {
+  py::module_ metal = m.def_submodule("metal", "mlx.metal");
+  metal.def("is_available", &metal::is_available);
+}

python/src/stream.cpp

@@ -0,0 +1,32 @@
+#include <sstream>
+
+#include <pybind11/pybind11.h>
+
+#include "mlx/stream.h"
+#include "mlx/utils.h"
+
+namespace py = pybind11;
+using namespace py::literals;
+using namespace mlx::core;
+
+void init_stream(py::module_& m) {
+  py::class_<Stream>(m, "Stream")
+      .def(py::init<int, Device>(), "index"_a, "device"_a)
+      .def_readonly("device", &Stream::device)
+      .def(
+          "__repr__",
+          [](const Stream& s) {
+            std::ostringstream os;
+            os << s;
+            return os.str();
+          })
+      .def("__eq__", [](const Stream& s1, const Stream& s2) {
+        return s1 == s2;
+      });
+
+  py::implicitly_convertible<Device::DeviceType, Device>();
+
+  m.def("default_stream", &default_stream, "device"_a);
+  m.def("set_default_stream", &set_default_stream, "stream"_a);
+  m.def("new_stream", &new_stream, "device"_a);
+}

python/tests/test_bf16.py

@@ -0,0 +1,188 @@
+import unittest
+from itertools import permutations
+
+import math
+import mlx.core as mx
+import numpy as np
+
+import mlx_tests
+
+try:
+    import torch
+
+    has_torch = True
+except ImportError as e:
+    has_torch = False
+
+
+class TestBF16(mlx_tests.MLXTestCase):
+    def __test_ops(
+        self,
+        ref_op,  # Function that outputs array_like
+        mlx_op,  # Function that outputs array_like
+        np_args,  # Numpy arguments
+        ref_transform=lambda x: x,
+        mlx_transform=lambda x: mx.array(x),
+        atol=1e-5,
+    ):
+        ref_args = map(ref_transform, np_args)
+        mlx_args = map(mlx_transform, np_args)
+
+        r_ref = ref_op(*ref_args)
+        r_mlx = mlx_op(*mlx_args)
+
+        self.assertTrue(np.allclose(r_mlx, r_ref, atol=atol))
+
+    def __default_test(
+        self,
+        op,
+        np_args,
+        simple_transform=lambda x: x,
+        atol_np=1e-3,
+        atol_torch=1e-5,
+        np_kwargs=dict(),
+        mlx_kwargs=dict(),
+        torch_kwargs=dict(),
+        torch_op=None,
+    ):
+        with self.subTest(reference="numpy"):
+
+            def np_transform(x):
+                x_mx_bf16 = mx.array(x).astype(mx.bfloat16)
+                x_mx_fp32 = x_mx_bf16.astype(mx.float32)
+                return np.asarray(x_mx_fp32)
+
+            def mlx_fn(*args):
+                out_bf16 = getattr(mx, op)(*args, **mlx_kwargs)
+                return np.asarray(out_bf16.astype(mx.float32))
+
+            def np_fn(*args):
+                out_fp32 = getattr(np, op)(*args, **np_kwargs)
+                return np_transform(out_fp32)
+
+            ref_op = np_fn
+            mlx_op = mlx_fn
+
+            ref_transform = lambda x: simple_transform(np_transform(x))
+            mlx_transform = lambda x: simple_transform(mx.array(x).astype(mx.bfloat16))
+
+            self.__test_ops(
+                ref_op,
+                mlx_op,
+                np_args,
+                ref_transform=ref_transform,
+                mlx_transform=mlx_transform,
+                atol=atol_np,
+            )
+
+        if has_torch:
+            with self.subTest(reference="torch"):
+                torch_op = op if torch_op is None else torch_op
+
+                def torch_fn(*args):
+                    out_bf16 = getattr(torch, torch_op)(*args, **torch_kwargs)
+                    return out_bf16.to(torch.float32).numpy()
+
+                ref_op = torch_fn
+                ref_transform = lambda x: simple_transform(
+                    torch.from_numpy(x).to(torch.bfloat16)
+                )
+                self.__test_ops(
+                    ref_op,
+                    mlx_op,
+                    np_args,
+                    ref_transform=ref_transform,
+                    mlx_transform=mlx_transform,
+                    atol=atol_torch,
+                )
+
+    def test_unary_ops(self):
+        x = np.random.rand(18, 28, 38)
+        for op in ["abs", "exp", "log", "square", "sqrt"]:
+            with self.subTest(op=op):
+                np_args = (x.astype(np.float32),)
+                self.__default_test(op, np_args)
+
+    def test_binary_ops(self):
+        x = np.random.rand(18, 28, 38)
+        y = np.random.rand(18, 28, 38)
+        for op in ["add", "subtract", "multiply", "divide", "maximum", "minimum"]:
+            with self.subTest(op=op):
+                np_args = (
+                    x.astype(np.float32),
+                    y.astype(np.float32),
+                )
+                self.__default_test(op, np_args, simple_transform=lambda x: x)
+                self.__default_test(op, np_args, simple_transform=lambda x: x[:1])
+                self.__default_test(op, np_args, simple_transform=lambda x: x[:, :1])
+
+    def test_reduction_ops(self):
+        x = np.random.rand(18, 28, 38).astype(np.float32)
+
+        for op in ("min", "max"):
+            with self.subTest(op=op):
+
+                for axes in (0, 1, 2, (0, 1), (0, 2), (1, 2), (0, 1, 2)):
+                    with self.subTest(axes=axes):
+                        np_args = (x.astype(np.float32),)
+                        self.__default_test(
+                            op,
+                            np_args,
+                            np_kwargs={"axis": axes},
+                            mlx_kwargs={"axis": axes},
+                            torch_kwargs={"dim": axes},
+                            torch_op="a" + op,
+                        )
+
+    def test_arg_reduction_ops(self):
+        data = np.random.rand(10, 12, 13).astype(np.float32)
+        x = mx.array(data).astype(mx.bfloat16)
+        data = np.asarray(x.astype(mx.float32))
+
+        for op in ["argmin", "argmax"]:
+            for axis in range(3):
+                for kd in [True, False]:
+                    a = getattr(mx, op)(x, axis, kd)
+                    b = getattr(np, op)(data, axis, keepdims=kd)
+                    a = a.astype(mx.float32)
+                    self.assertEqual(a.tolist(), b.tolist())
+
+        for op in ["argmin", "argmax"]:
+            a = getattr(mx, op)(x, keepdims=True)
+            b = getattr(np, op)(data, keepdims=True)
+            a = a.astype(mx.float32)
+            self.assertEqual(a.tolist(), b.tolist())
+            a = getattr(mx, op)(x)
+            b = getattr(np, op)(data)
+            a = a.astype(mx.float32)
+            self.assertEqual(a.item(), b)
+
+    def test_blas_ops(self):
+        if mx.default_device() != mx.gpu:
+            return
+
+        def test_blas(shape_x, shape_y):
+            np.random.seed(42)
+            with self.subTest(shape_x=shape_x, shape_y=shape_y):
+                x = np.random.normal(0.0, 1.0 / shape_x[-1], size=shape_x)
+                y = np.random.normal(0.0, 1.0 / shape_x[-1], size=shape_y)
+
+                np_args = (
+                    x.astype(np.float32),
+                    y.astype(np.float32),
+                )
+                op = "matmul"
+
+                self.__default_test(op, np_args, atol_np=1e-3, atol_torch=1e-3)
+
+        for shape_x, shape_y in [
+            [(32, 32), (32, 32)],
+            [(23, 57), (57, 1)],
+            [(1, 3), (3, 128)],
+            [(8, 128, 768), (768, 16)],
+        ]:
+            test_blas(shape_x, shape_y)
+
+
+if __name__ == "__main__":
+    unittest.main()

tests/creations_tests.cpp

@@ -0,0 +1,224 @@
+#include "doctest/doctest.h"
+
+#include "mlx/mlx.h"
+
+using namespace mlx::core;
+
+TEST_CASE("test arange") {
+  // Check type is inferred correclty
+  {
+    auto x = arange(10);
+    CHECK_EQ(x.dtype(), int32);
+
+    x = arange(10.0);
+    CHECK_EQ(x.dtype(), float32);
+
+    x = arange(10, float32);
+    CHECK_EQ(x.dtype(), float32);
+
+    x = arange(10.0, int32);
+    CHECK_EQ(x.dtype(), int32);
+
+    x = arange(0, 10);
+    CHECK_EQ(x.dtype(), int32);
+
+    x = arange(0.0, 10.0, int32);
+    CHECK_EQ(x.dtype(), int32);
+
+    x = arange(0.0, 10.0);
+    CHECK_EQ(x.dtype(), float32);
+
+    x = arange(0, 10, float32);
+    CHECK_EQ(x.dtype(), float32);
+
+    x = arange(0, 10, 0.1, float32);
+    CHECK_EQ(x.dtype(), float32);
+
+    x = arange(0.0, 10.0, 0.5, int32);
+    CHECK_EQ(x.dtype(), int32);
+
+    x = arange(10.0, uint32);
+    CHECK_EQ(x.dtype(), uint32);
+    x = arange(0.0, 10.0, uint32);
+    CHECK_EQ(x.dtype(), uint32);
+    x = arange(0.0, 10.0, 0.5, uint32);
+    CHECK_EQ(x.dtype(), uint32);
+
+    // arange unsupported for bool_
+    CHECK_THROWS_AS(arange(10, bool_), std::invalid_argument);
+  }
+
+  // Check correct sizes
+  {
+    auto x = arange(10);
+    CHECK_EQ(x.size(), 10);
+
+    x = arange(0.0, 10.0, 0.5);
+    CHECK_EQ(x.size(), 20);
+
+    x = arange(0.0, 10.0, 0.45);
+    CHECK_EQ(x.size(), 23);
+
+    x = arange(0, 10, 10);
+    CHECK_EQ(x.size(), 1);
+
+    x = arange(0, 10, 9);
+    CHECK_EQ(x.size(), 2);
+
+    x = arange(0, 10, 100);
+    CHECK_EQ(x.size(), 1);
+
+    x = arange(0, -10, 1);
+    CHECK_EQ(x.size(), 0);
+
+    x = arange(0, -10, -1);
+    CHECK_EQ(x.size(), 10);
+
+    x = arange(0, -10, -10);
+    CHECK_EQ(x.size(), 1);
+  }
+
+  // Check values
+  {
+    auto x = arange(0, 3);
+    CHECK(array_equal(x, array({0, 1, 2})).item<bool>());
+
+    x = arange(0, 3, 2);
+    CHECK(array_equal(x, array({0, 2})).item<bool>());
+
+    x = arange(0, 3, 3);
+    CHECK(array_equal(x, array({0})).item<bool>());
+
+    x = arange(0, -3, 1);
+    CHECK(array_equal(x, array({})).item<bool>());
+
+    x = arange(0, 3, -1);
+    CHECK(array_equal(x, array({})).item<bool>());
+
+    x = arange(0, -3, -1);
+    CHECK(array_equal(x, array({0, -1, -2})).item<bool>());
+
+    x = arange(0.0, 5.0, 0.5, int32);
+    CHECK(array_equal(x, zeros({10})).item<bool>());
+
+    x = arange(0.0, 5.0, 1.5, int32);
+    CHECK(array_equal(x, array({0, 1, 2, 3})).item<bool>());
+  }
+}
+
+TEST_CASE("test astype") {
+  // Check type conversions
+  {
+    auto x = array(1);
+    auto y = astype(x, float32);
+    CHECK_EQ(y.dtype(), float32);
+    CHECK_EQ(y.item<float>(), 1.0f);
+
+    y = astype(x, int32);
+    CHECK_EQ(y.dtype(), int32);
+    CHECK_EQ(y.item<int>(), 1);
+
+    x = array(-3.0f);
+    y = astype(x, int32);
+    CHECK_EQ(y.dtype(), int32);
+    CHECK_EQ(y.item<int>(), -3);
+
+    y = astype(x, uint32);
+    CHECK_EQ(y.dtype(), uint32);
+
+    // Use std::copy since the result is platform dependent
+    uint32_t v;
+    std::copy(x.data<float>(), x.data<float>() + 1, &v);
+    CHECK_EQ(y.item<uint32_t>(), v);
+  }
+}
+
+TEST_CASE("test full") {
+  // Check full works for different types
+  {
+    auto x = full({}, 0);
+    CHECK_EQ(x.dtype(), int32);
+    CHECK_EQ(x.item<int>(), 0);
+
+    x = full({}, 0.0);
+    CHECK_EQ(x.dtype(), float32);
+    CHECK_EQ(x.item<float>(), 0);
+
+    x = full({}, false);
+    CHECK_EQ(x.item<bool>(), false);
+
+    x = full({}, 0, int32);
+    CHECK_EQ(x.item<int>(), 0);
+
+    x = full({}, 0, float32);
+    CHECK_EQ(x.item<float>(), 0);
+
+    x = full({1, 2}, 2, float32);
+    CHECK(array_equal(x, array({2.0, 2.0}, {1, 2})).item<bool>());
+
+    x = full({2, 1}, 2, float32);
+    CHECK(array_equal(x, array({2.0, 2.0}, {2, 1})).item<bool>());
+
+    x = full({2}, false);
+    CHECK_EQ(x.dtype(), bool_);
+    CHECK(array_equal(x, array({false, false})).item<bool>());
+
+    x = full({2}, 1.0, bool_);
+    CHECK_EQ(x.dtype(), bool_);
+    CHECK(array_equal(x, array({true, true})).item<bool>());
+
+    x = full({2}, 1.0, uint32);
+    CHECK_EQ(x.dtype(), uint32);
+    CHECK(array_equal(x, array({1, 1})).item<bool>());
+
+    CHECK_THROWS_AS(full({2}, array({})), std::invalid_argument);
+  }
+
+  // Check broadcasting works
+  {
+    auto x = full({2, 2}, array({3, 4}, {2, 1}));
+    CHECK(array_equal(x, array({3, 3, 4, 4}, {2, 2})).item<bool>());
+    x = full({2, 2}, array({3, 4}, {1, 2}));
+    CHECK(array_equal(x, array({3, 4, 3, 4}, {2, 2})).item<bool>());
+  }
+
+  // Check zeros and ones
+  {
+    auto x = zeros({2, 2}, float32);
+    CHECK_EQ(x.shape(), std::vector<int>{2, 2});
+    CHECK_EQ(x.ndim(), 2);
+    CHECK_EQ(x.dtype(), float32);
+    auto y = array({0.0, 0.0, 0.0, 0.0}, {2, 2});
+    CHECK(array_equal(x, y).item<bool>());
+
+    x = ones({2, 2}, float32);
+    CHECK_EQ(x.shape(), std::vector<int>{2, 2});
+    CHECK_EQ(x.ndim(), 2);
+    CHECK_EQ(x.dtype(), float32);
+    y = array({1.0, 1.0, 1.0, 1.0}, {2, 2});
+    CHECK(array_equal(x, y).item<bool>());
+
+    x = zeros({2, 2}, int32);
+    y = zeros_like(x);
+    CHECK_EQ(y.dtype(), int32);
+    CHECK(array_equal(x, y).item<bool>());
+
+    x = ones({2, 2}, int32);
+    y = ones_like(x);
+    CHECK_EQ(y.dtype(), int32);
+    CHECK(array_equal(x, y).item<bool>());
+  }
+
+  // Works for empty shape and empty array
+  {
+    array x = ones({}, int32);
+    CHECK_EQ(x.shape(), std::vector<int>{});
+    CHECK_EQ(x.item<int>(), 1);
+
+    x = full({0}, array({}));
+    CHECK_EQ(x.shape(), std::vector<int>{0});
+    CHECK_EQ(x.size(), 0);
+
+    CHECK_THROWS_AS(full({}, array({})), std::invalid_argument);
+  }
+}

tests/fft_tests.cpp

@@ -0,0 +1,331 @@
+#include "doctest/doctest.h"
+
+#include "mlx/mlx.h"
+
+using namespace mlx::core;
+
+TEST_CASE("test fft basics") {
+  auto device = default_device();
+  set_default_device(Device::cpu);
+  array x(1.0);
+  CHECK_THROWS(fft::fft(x));
+  CHECK_THROWS(fft::ifft(x));
+
+  x = array({1.0});
+  auto y = fft::fft(x);
+  CHECK_EQ(y.dtype(), complex64);
+  CHECK_EQ(y.size(), x.size());
+  CHECK_EQ(y.item<complex64_t>(), complex64_t{1.0f, 0.0f});
+
+  y = fft::ifft(x);
+  CHECK_EQ(y.dtype(), complex64);
+  CHECK_EQ(y.size(), x.size());
+  CHECK_EQ(y.item<complex64_t>(), complex64_t{1.0f, 0.0f});
+
+  x = array({complex64_t{1.0f, 1.0f}}, complex64);
+  y = fft::fft(x);
+  CHECK_EQ(y.size(), x.size());
+  CHECK_EQ(y.item<complex64_t>(), complex64_t{1.0f, 1.0f});
+
+  y = fft::ifft(x);
+  CHECK_EQ(y.dtype(), complex64);
+  CHECK_EQ(y.size(), x.size());
+  CHECK_EQ(y.item<complex64_t>(), complex64_t{1.0f, 1.0f});
+
+  {
+    x = array({0.0f, 1.0f, 2.0f, 3.0f});
+    y = fft::fft(x);
+    std::initializer_list<complex64_t> expected = {
+        {6.0, 0.0},
+        {-2.0, 2.0},
+        {-2.0, 0.0},
+        {-2.0, -2.0},
+    };
+    CHECK_EQ(y.size(), x.size());
+    CHECK(array_equal(y, array(expected)).item<bool>());
+
+    y = fft::ifft(x);
+    std::initializer_list<complex64_t> expected_inv = {
+        {1.5, 0.0},
+        {-0.5, -0.5},
+        {-0.5, 0.0},
+        {-0.5, 0.5},
+    };
+    CHECK(array_equal(y, array(expected_inv)).item<bool>());
+  }
+
+  {
+    std::initializer_list<complex64_t> vals = {
+        {1.0f, 1.0f}, {2.0f, 1.0f}, {1.0f, 2.0f}, {2.0f, 2.0f}};
+    x = array(vals);
+    y = fft::fft(x);
+    std::initializer_list<complex64_t> expected = {
+        {6.0, 6.0},
+        {-1.0, -1.0},
+        {-2.0, 0.0},
+        {1.0, -1.0},
+    };
+    CHECK_EQ(y.size(), x.size());
+    CHECK(array_equal(y, array(expected)).item<bool>());
+    CHECK(array_equal(fft::ifft(y), x).item<bool>());
+  }
+
+  // Specify axes
+  {
+    x = array({0.0f, 1.0f, 2.0f, 3.0f}, {2, 2});
+    std::initializer_list<complex64_t> expected_0 = {
+        {2.0, 0.0},
+        {4.0, 0.0},
+        {-2.0, 0.0},
+        {-2.0, 0.0},
+    };
+    y = fft::fft(x, 0);
+    CHECK(array_equal(y, array(expected_0, {2, 2})).item<bool>());
+    CHECK(array_equal(fft::ifft(y, 0), x).item<bool>());
+    std::initializer_list<complex64_t> expected_1 = {
+        {1.0, 0.0},
+        {-1.0, 0.0},
+        {5.0, 0.0},
+        {-1.0, 0.0},
+    };
+    y = fft::fft(x, 1);
+    CHECK(array_equal(y, array(expected_1, {2, 2})).item<bool>());
+    CHECK(array_equal(fft::ifft(y, 1), x).item<bool>());
+  }
+  set_default_device(device);
+}
+
+TEST_CASE("test real ffts") {
+  auto device = default_device();
+  set_default_device(Device::cpu);
+
+  auto x = array({1.0});
+  auto y = fft::rfft(x);
+  CHECK_EQ(y.dtype(), complex64);
+  CHECK_EQ(y.size(), x.size());
+  CHECK_EQ(y.item<complex64_t>(), complex64_t{1.0f, 0.0f});
+
+  {
+    x = array({0.0f, 1.0f, 2.0f, 3.0f});
+    y = fft::rfft(x);
+    std::initializer_list<complex64_t> expected = {
+        {6.0, 0.0}, {-2.0, 2.0}, {-2.0, -0.0}};
+    CHECK_EQ(y.size(), x.size() / 2 + 1);
+    CHECK(array_equal(y, array(expected)).item<bool>());
+  }
+
+  x = array(complex64_t{1, 1});
+  CHECK_THROWS(fft::irfft(x));
+
+  x = array({complex64_t{0, 1}, complex64_t{1, 0}});
+  y = fft::irfft(x);
+  CHECK_EQ(y.size(), 2);
+  CHECK_EQ(y.dtype(), float32);
+  CHECK(array_equal(y, array({0.5f, -0.5f})).item<bool>());
+
+  set_default_device(device);
+}
+
+TEST_CASE("test fftn") {
+  auto device = default_device();
+  set_default_device(Device::cpu);
+
+  auto x = zeros({5, 5, 5});
+  CHECK_THROWS_AS(fft::fftn(x, {}, {0, 3}), std::invalid_argument);
+  CHECK_THROWS_AS(fft::fftn(x, {}, {0, -4}), std::invalid_argument);
+  CHECK_THROWS_AS(fft::fftn(x, {}, {0, 0}), std::invalid_argument);
+  CHECK_THROWS_AS(fft::fftn(x, {5, 5, 5}, {0}), std::invalid_argument);
+  CHECK_THROWS_AS(fft::fftn(x, {0}, {}, {}), std::invalid_argument);
+  CHECK_THROWS_AS(fft::fftn(x, {1, -1}, {}, {}), std::invalid_argument);
+
+  // Test 2D FFT
+  {
+    x = array({0.0f, 1.0f, 2.0f, 3.0f}, {2, 2});
+    std::initializer_list<complex64_t> expected = {
+        {6.0, 0.0},
+        {-2.0, 0.0},
+        {-4.0, 0.0},
+        {0.0, 0.0},
+    };
+    auto y = fft::fft2(x);
+    CHECK(array_equal(y, array(expected, {2, 2})).item<bool>());
+    CHECK(array_equal(fft::ifft2(y), x).item<bool>());
+  }
+
+  // Test 3D FFT
+  {
+    x = reshape(arange(8, float32), {2, 2, 2});
+    std::initializer_list<complex64_t> expected = {
+        {28.0, 0.0},
+        {-4.0, 0.0},
+        {-8.0, 0.0},
+        {0.0, 0.0},
+        {-16.0, 0.0},
+        {0.0, 0.0},
+        {0.0, 0.0},
+        {0.0, 0.0},
+    };
+    auto y = fft::fftn(x);
+    CHECK(array_equal(y, array(expected, {2, 2, 2})).item<bool>());
+    CHECK(array_equal(fft::ifftn(y), x).item<bool>());
+
+    x = reshape(arange(20, float32), {5, 4});
+    y = fft::rfftn(x);
+    CHECK_EQ(y.shape(), std::vector<int>{5, 3});
+    y = fft::rfftn(x, {1, 0});
+    CHECK_EQ(y.shape(), std::vector<int>{3, 4});
+
+    x = reshape(arange(20, float32), {5, 4});
+    y = fft::irfftn(x);
+    CHECK_EQ(y.shape(), std::vector<int>{5, 6});
+    y = fft::irfftn(x, {1, 0});
+    CHECK_EQ(y.shape(), std::vector<int>{8, 4});
+  }
+
+  // Check the types of real ffts
+  {
+    x = zeros({5, 5}, float32);
+    auto y = fft::rfft2(x);
+    CHECK_EQ(y.shape(), std::vector<int>{5, 3});
+    CHECK_EQ(y.dtype(), complex64);
+
+    y = fft::rfftn(x);
+    CHECK_EQ(y.shape(), std::vector<int>{5, 3});
+    CHECK_EQ(y.dtype(), complex64);
+
+    x = zeros({5, 5}, complex64);
+    y = fft::irfft2(x);
+    CHECK_EQ(y.shape(), std::vector<int>{5, 8});
+    CHECK_EQ(y.dtype(), float32);
+
+    y = fft::irfftn(x);
+    CHECK_EQ(y.shape(), std::vector<int>{5, 8});
+    CHECK_EQ(y.dtype(), float32);
+  }
+
+  set_default_device(device);
+}
+
+TEST_CASE("test fft with provided shape") {
+  auto x = ones({5, 5});
+
+  auto y = fft::fft(x, 7, 0);
+  CHECK_EQ(y.shape(), std::vector<int>{7, 5});
+
+  y = fft::fft(x, 3, 0);
+  CHECK_EQ(y.shape(), std::vector<int>{3, 5});
+
+  y = fft::fft(x, 7, 1);
+  CHECK_EQ(y.shape(), std::vector<int>{5, 7});
+
+  y = fft::fft(x, 3, 1);
+  CHECK_EQ(y.shape(), std::vector<int>{5, 3});
+
+  y = fft::rfft(x, 7, 0);
+  CHECK_EQ(y.shape(), std::vector<int>{4, 5});
+
+  y = fft::rfft(x, 3, 0);
+  CHECK_EQ(y.shape(), std::vector<int>{2, 5});
+
+  y = fft::rfft(x, 3, 1);
+  CHECK_EQ(y.shape(), std::vector<int>{5, 2});
+}
+
+TEST_CASE("test fft vmap") {
+  auto device = default_device();
+  set_default_device(Device::cpu);
+
+  auto fft_fn = [](array x) { return fft::fft(x); };
+  auto x = reshape(arange(8), {2, 4});
+  auto y = vmap(fft_fn)(x);
+  CHECK(array_equal(y, fft::fft(x)).item<bool>());
+
+  y = vmap(fft_fn, 1, 1)(x);
+  CHECK(array_equal(y, fft::fft(x, 0)).item<bool>());
+
+  auto rfft_fn = [](array x) { return fft::rfft(x); };
+
+  y = vmap(rfft_fn)(x);
+  CHECK(array_equal(y, fft::rfft(x)).item<bool>());
+
+  y = vmap(rfft_fn, 1, 1)(x);
+  CHECK(array_equal(y, fft::rfft(x, 0)).item<bool>());
+
+  set_default_device(device);
+}
+
+TEST_CASE("test fft grads") {
+  auto device = default_device();
+  set_default_device(Device::cpu);
+
+  // Regular
+  auto fft_fn = [](array x) { return fft::fft(x); };
+  auto cotangent = astype(arange(10), complex64);
+  auto vjp_out = vjp(fft_fn, zeros_like(cotangent), cotangent).second;
+  CHECK(array_equal(fft::fft(cotangent), vjp_out).item<bool>());
+
+  auto tangent = astype(arange(10), complex64);
+  auto jvp_out = jvp(fft_fn, zeros_like(tangent), tangent).second;
+  CHECK(array_equal(fft::fft(tangent), jvp_out).item<bool>());
+
+  // Inverse
+  auto ifft_fn = [](array x) { return fft::ifft(x); };
+  vjp_out = vjp(ifft_fn, zeros_like(cotangent), cotangent).second;
+  CHECK(array_equal(fft::ifft(cotangent), vjp_out).item<bool>());
+
+  jvp_out = jvp(ifft_fn, zeros_like(tangent), tangent).second;
+  CHECK(array_equal(fft::ifft(tangent), jvp_out).item<bool>());
+
+  // Real
+  auto rfft_fn = [](array x) { return fft::rfft(x); };
+  cotangent = astype(arange(6), complex64);
+  vjp_out = vjp(rfft_fn, zeros({10}), cotangent).second;
+  auto expected = astype(fft::fft(cotangent, 10, 0), float32);
+  CHECK(array_equal(expected, vjp_out).item<bool>());
+
+  tangent = astype(arange(10), float32);
+  jvp_out = jvp(rfft_fn, zeros_like(tangent), tangent).second;
+  CHECK(array_equal(fft::rfft(tangent), jvp_out).item<bool>());
+
+  // Inverse real
+  auto irfft_fn = [](array x) { return fft::irfft(x); };
+  cotangent = astype(arange(10), float32);
+  vjp_out = vjp(irfft_fn, astype(zeros({6}), complex64), cotangent).second;
+  expected = fft::fft(cotangent, 10, 0);
+  auto o_splits = split(vjp_out, {1, 5});
+  auto e_splits = split(expected, {1, 5, 6});
+  CHECK_EQ(e_splits[0].item<complex64_t>(), o_splits[0].item<complex64_t>());
+  CHECK(array_equal(2 * e_splits[1], o_splits[1]).item<bool>());
+  CHECK_EQ(e_splits[2].item<complex64_t>(), o_splits[2].item<complex64_t>());
+
+  tangent = astype(arange(10), complex64);
+  jvp_out = jvp(irfft_fn, zeros_like(tangent), tangent).second;
+  CHECK(array_equal(fft::irfft(tangent), jvp_out).item<bool>());
+
+  // Check ND vjps run properly
+  vjp_out = vjp([](array x) { return fft::fftn(x); },
+                astype(zeros({5, 5}), complex64),
+                astype(zeros({5, 5}), complex64))
+                .second;
+  CHECK_EQ(vjp_out.shape(), std::vector<int>{5, 5});
+
+  vjp_out = vjp([](array x) { return fft::ifftn(x); },
+                astype(zeros({5, 5}), complex64),
+                astype(zeros({5, 5}), complex64))
+                .second;
+  CHECK_EQ(vjp_out.shape(), std::vector<int>{5, 5});
+
+  vjp_out = vjp([](array x) { return fft::rfftn(x); },
+                zeros({5, 9}),
+                astype(zeros({5, 5}), complex64))
+                .second;
+  CHECK_EQ(vjp_out.shape(), std::vector<int>{5, 9});
+
+  vjp_out = vjp([](array x) { return fft::irfftn(x); },
+                astype(zeros({5, 5}), complex64),
+                zeros({5, 8}))
+                .second;
+  CHECK_EQ(vjp_out.shape(), std::vector<int>{5, 5});
+
+  set_default_device(device);
+}

tests/graph_optimize_tests.cpp

@@ -0,0 +1,30 @@
+#include "doctest/doctest.h"
+
+#include "mlx/mlx.h"
+
+using namespace mlx::core;
+
+TEST_CASE("test simplify scalars") {
+  auto a = array({-1.0f, 2.0f});
+  auto b = maximum(a, array(0.0f));
+  auto c = maximum(-a, array(0.0f));
+  auto d = b + c;
+  simplify({d});
+  CHECK(b.inputs()[1].id() == c.inputs()[1].id());
+}
+
+TEST_CASE("test simplify") {
+  auto a = array({1.0f, 2.0f});
+  auto b = exp(a) + exp(a);
+  simplify(b);
+  eval(b);
+  CHECK(b.inputs()[0].id() == b.inputs()[1].id());
+}
+
+TEST_CASE("test no simplify") {
+  auto a = array({1.0f, 2.0f});
+  auto b = cos(a) + sin(a);
+  simplify(b);
+  eval(b);
+  CHECK(b.inputs()[0].id() != b.inputs()[1].id());
+}