synch before reading memory in test

* add a half simd gemm fallback

* nit

.circleci/config.yml

@@ -24,8 +24,8 @@ jobs:
         type: boolean
         default: false
     macos:
-      xcode: "15.2.0"
-    resource_class: macos.m1.medium.gen1
+      xcode: "16.2.0"
+    resource_class: m2pro.medium
     steps:
       - checkout
       - run:
@@ -89,6 +89,7 @@ jobs:
             pip install numpy
             sudo apt-get update
             sudo apt-get install libblas-dev liblapack-dev liblapacke-dev
+            sudo apt-get install openmpi-bin openmpi-common libopenmpi-dev
       - run:
           name: Install Python package
           command: |
@@ -108,6 +109,8 @@ jobs:
           name: Run Python tests
           command: |
             python3 -m unittest discover python/tests -v
+            mpirun --bind-to none -host localhost:8 -np 8 python python/tests/mpi_test_distributed.py
+            mlx.launch --verbose -n 8 python/tests/ring_test_distributed.py
       - run:
           name: Build CPP only
           command: |
@@ -122,10 +125,15 @@ jobs:
     parameters:
       xcode_version:
         type: string
-        default: "15.2.0"
+        default: "16.2.0"
+      macosx_deployment_target:
+        type: string
+        default: ""
     macos:
       xcode: << parameters.xcode_version >>
-    resource_class: macos.m1.medium.gen1
+    environment:
+      MACOSX_DEPLOYMENT_TARGET: << parameters.macosx_deployment_target >>
+    resource_class: m2pro.medium
     steps:
       - checkout
       - run:
@@ -146,7 +154,9 @@ jobs:
           name: Install Python package
           command: |
             source env/bin/activate
-            DEBUG=1 CMAKE_BUILD_PARALLEL_LEVEL=`sysctl -n hw.ncpu` pip install -e . -v
+            DEBUG=1 CMAKE_BUILD_PARALLEL_LEVEL=`sysctl -n hw.ncpu` \
+            CMAKE_ARGS="-DCMAKE_COMPILE_WARNING_AS_ERROR=ON" \
+              pip install -e . -v
       - run:
           name: Generate package stubs
           command: |
@@ -209,13 +219,18 @@ jobs:
         default: "3.9"
       xcode_version:
         type: string
-        default: "15.2.0"
+        default: "16.2.0"
       build_env:
         type: string
         default: ""
+      macosx_deployment_target:
+        type: string
+        default: ""
     macos:
       xcode: << parameters.xcode_version >>
-    resource_class: macos.m1.medium.gen1
+    resource_class: m2pro.medium
+    environment:
+      MACOSX_DEPLOYMENT_TARGET: << parameters.macosx_deployment_target >>
     steps:
       - checkout
       - run:
@@ -331,7 +346,7 @@ workflows:
       - mac_build_and_test:
           matrix:
             parameters:
-              xcode_version: ["15.0.0", "15.2.0", "16.0.0"]
+              macosx_deployment_target: ["13.5", "14.0"]
       - linux_build_and_test
       - build_documentation 
 
@@ -351,8 +366,70 @@ workflows:
           matrix:
             parameters:
               python_version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
-              xcode_version: ["15.0.0", "15.2.0"]
+              macosx_deployment_target: ["13.5", "14.0", "15.0"]
               build_env: ["PYPI_RELEASE=1"]
+              xcode_version: ["16.2.0", "15.0.0"]
+            exclude:
+              - macosx_deployment_target: "13.5"
+                xcode_version: "16.2.0"
+                python_version: "3.9"
+                build_env: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "13.5"
+                xcode_version: "16.2.0"
+                python_version: "3.10"
+                build_env: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "13.5"
+                xcode_version: "16.2.0"
+                python_version: "3.11"
+                build_env: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "13.5"
+                xcode_version: "16.2.0"
+                python_version: "3.12"
+                build_env: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "13.5"
+                xcode_version: "16.2.0"
+                python_version: "3.13"
+                build_env: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "14.0"
+                xcode_version: "15.0.0"
+                python_version: "3.9"
+                build_env: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "14.0"
+                xcode_version: "15.0.0"
+                python_version: "3.10"
+                build_env: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "14.0"
+                xcode_version: "15.0.0"
+                python_version: "3.11"
+                build_env: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "14.0"
+                xcode_version: "15.0.0"
+                python_version: "3.12"
+                build_env: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "14.0"
+                xcode_version: "15.0.0"
+                python_version: "3.13"
+                build_env: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "15.0"
+                xcode_version: "15.0.0"
+                python_version: "3.9"
+                build_env: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "15.0"
+                xcode_version: "15.0.0"
+                python_version: "3.10"
+                build_env: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "15.0"
+                xcode_version: "15.0.0"
+                python_version: "3.11"
+                build_env: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "15.0"
+                xcode_version: "15.0.0"
+                python_version: "3.12"
+                build_env: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "15.0"
+                xcode_version: "15.0.0"
+                python_version: "3.13"
+                build_env: "PYPI_RELEASE=1"
       - build_documentation:
           filters:
             tags:
@@ -375,7 +452,7 @@ workflows:
           requires: [ hold ]
           matrix:
             parameters:
-              xcode_version: ["15.0.0", "15.2.0", "16.0.0"]
+              macosx_deployment_target: ["13.5", "14.0"]
       - linux_build_and_test:
           requires: [ hold ]
   nightly_build:
@@ -388,7 +465,54 @@ workflows:
           matrix:
             parameters:
               python_version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
-              xcode_version: ["15.0.0", "15.2.0"]
+              macosx_deployment_target: ["13.5", "14.0", "15.0"]
+              xcode_version: ["16.2.0", "15.0.0"]
+            exclude:
+              - macosx_deployment_target: "13.5"
+                xcode_version: "16.2.0"
+                python_version: "3.9"
+              - macosx_deployment_target: "13.5"
+                xcode_version: "16.2.0"
+                python_version: "3.10"
+              - macosx_deployment_target: "13.5"
+                xcode_version: "16.2.0"
+                python_version: "3.11"
+              - macosx_deployment_target: "13.5"
+                xcode_version: "16.2.0"
+                python_version: "3.12"
+              - macosx_deployment_target: "13.5"
+                xcode_version: "16.2.0"
+                python_version: "3.13"
+              - macosx_deployment_target: "14.0"
+                xcode_version: "15.0.0"
+                python_version: "3.9"
+              - macosx_deployment_target: "14.0"
+                xcode_version: "15.0.0"
+                python_version: "3.10"
+              - macosx_deployment_target: "14.0"
+                xcode_version: "15.0.0"
+                python_version: "3.11"
+              - macosx_deployment_target: "14.0"
+                xcode_version: "15.0.0"
+                python_version: "3.12"
+              - macosx_deployment_target: "14.0"
+                xcode_version: "15.0.0"
+                python_version: "3.13"
+              - macosx_deployment_target: "15.0"
+                xcode_version: "15.0.0"
+                python_version: "3.9"
+              - macosx_deployment_target: "15.0"
+                xcode_version: "15.0.0"
+                python_version: "3.10"
+              - macosx_deployment_target: "15.0"
+                xcode_version: "15.0.0"
+                python_version: "3.11"
+              - macosx_deployment_target: "15.0"
+                xcode_version: "15.0.0"
+                python_version: "3.12"
+              - macosx_deployment_target: "15.0"
+                xcode_version: "15.0.0"
+                python_version: "3.13"
   weekly_build:
     when:
       and:
@@ -399,8 +523,70 @@ workflows:
           matrix:
             parameters:
               python_version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
-              xcode_version: ["15.0.0", "15.2.0", "16.0.0"]
+              macosx_deployment_target: ["13.5", "14.0", "15.0"]
               build_env: ["DEV_RELEASE=1"]
+              xcode_version: ["16.2.0", "15.0.0"]
+            exclude:
+              - macosx_deployment_target: "13.5"
+                xcode_version: "16.2.0"
+                python_version: "3.9"
+                build_env: "DEV_RELEASE=1"
+              - macosx_deployment_target: "13.5"
+                xcode_version: "16.2.0"
+                python_version: "3.10"
+                build_env: "DEV_RELEASE=1"
+              - macosx_deployment_target: "13.5"
+                xcode_version: "16.2.0"
+                python_version: "3.11"
+                build_env: "DEV_RELEASE=1"
+              - macosx_deployment_target: "13.5"
+                xcode_version: "16.2.0"
+                python_version: "3.12"
+                build_env: "DEV_RELEASE=1"
+              - macosx_deployment_target: "13.5"
+                xcode_version: "16.2.0"
+                python_version: "3.13"
+                build_env: "DEV_RELEASE=1"
+              - macosx_deployment_target: "14.0"
+                xcode_version: "15.0.0"
+                python_version: "3.9"
+                build_env: "DEV_RELEASE=1"
+              - macosx_deployment_target: "14.0"
+                xcode_version: "15.0.0"
+                python_version: "3.10"
+                build_env: "DEV_RELEASE=1"
+              - macosx_deployment_target: "14.0"
+                xcode_version: "15.0.0"
+                python_version: "3.11"
+                build_env: "DEV_RELEASE=1"
+              - macosx_deployment_target: "14.0"
+                xcode_version: "15.0.0"
+                python_version: "3.12"
+                build_env: "DEV_RELEASE=1"
+              - macosx_deployment_target: "14.0"
+                xcode_version: "15.0.0"
+                python_version: "3.13"
+                build_env: "DEV_RELEASE=1"
+              - macosx_deployment_target: "15.0"
+                xcode_version: "15.0.0"
+                python_version: "3.9"
+                build_env: "DEV_RELEASE=1"
+              - macosx_deployment_target: "15.0"
+                xcode_version: "15.0.0"
+                python_version: "3.10"
+                build_env: "DEV_RELEASE=1"
+              - macosx_deployment_target: "15.0"
+                xcode_version: "15.0.0"
+                python_version: "3.11"
+                build_env: "DEV_RELEASE=1"
+              - macosx_deployment_target: "15.0"
+                xcode_version: "15.0.0"
+                python_version: "3.12"
+                build_env: "DEV_RELEASE=1"
+              - macosx_deployment_target: "15.0"
+                xcode_version: "15.0.0"
+                python_version: "3.13"
+                build_env: "DEV_RELEASE=1"
   linux_test_release:
     when:
       and:

CMakeLists.txt

@@ -1,6 +1,24 @@
 cmake_minimum_required(VERSION 3.25)
 
-project(mlx LANGUAGES C CXX)
+if(NOT MLX_VERSION)
+  file(STRINGS "mlx/version.h" _mlx_h_version REGEX "^#define MLX_VERSION_.*$")
+  string(REGEX MATCH "#define MLX_VERSION_MAJOR ([0-9]+)" _ "${_mlx_h_version}")
+  set(_major ${CMAKE_MATCH_1})
+  string(REGEX MATCH "#define MLX_VERSION_MINOR ([0-9]+)" _ "${_mlx_h_version}")
+  set(_minor ${CMAKE_MATCH_1})
+  string(REGEX MATCH "#define MLX_VERSION_PATCH ([0-9]+)" _ "${_mlx_h_version}")
+  set(_patch ${CMAKE_MATCH_1})
+  set(MLX_PROJECT_VERSION "${_major}.${_minor}.${_patch}")
+  set(MLX_VERSION ${MLX_PROJECT_VERSION})
+else()
+  string(REGEX REPLACE "^([0-9]+\.[0-9]+\.[0-9]+).*" "\\1" MLX_PROJECT_VERSION
+                       ${MLX_VERSION})
+endif()
+
+project(
+  mlx
+  LANGUAGES C CXX
+  VERSION ${MLX_PROJECT_VERSION})
 
 # ----------------------------- Setup -----------------------------
 set(CMAKE_MODULE_PATH "${PROJECT_SOURCE_DIR}/cmake")
@@ -24,13 +42,7 @@ option(MLX_BUILD_BLAS_FROM_SOURCE "Build OpenBLAS from source code" OFF)
 option(MLX_METAL_JIT "Use JIT compilation for Metal kernels" OFF)
 option(BUILD_SHARED_LIBS "Build mlx as a shared library" OFF)
 
-if(NOT MLX_VERSION)
-  set(MLX_VERSION 0.23.0)
-endif()
-add_compile_definitions("MLX_VERSION=${MLX_VERSION}")
-
 # --------------------- Processor tests -------------------------
-
 message(
   STATUS
     "Building MLX for ${CMAKE_SYSTEM_PROCESSOR} processor on ${CMAKE_SYSTEM_NAME}"
@@ -200,23 +212,13 @@ else()
   set(MLX_BUILD_ACCELERATE OFF)
 endif()
 
-find_package(MPI)
-if(MPI_FOUND)
-  execute_process(
-    COMMAND zsh "-c" "mpirun --version"
-    OUTPUT_VARIABLE MPI_VERSION
-    ERROR_QUIET)
-  if(${MPI_VERSION} MATCHES ".*Open MPI.*")
-    target_include_directories(mlx PRIVATE ${MPI_INCLUDE_PATH})
-  elseif(MPI_VERSION STREQUAL "")
-    set(MPI_FOUND FALSE)
-    message(
-      WARNING "MPI found but mpirun is not available. Building without MPI.")
-  else()
-    set(MPI_FOUND FALSE)
-    message(WARNING "MPI which is not OpenMPI found. Building without MPI.")
-  endif()
-endif()
+message(STATUS "Downloading json")
+FetchContent_Declare(
+  json
+  URL https://github.com/nlohmann/json/releases/download/v3.11.3/json.tar.xz)
+FetchContent_MakeAvailable(json)
+target_include_directories(
+  mlx PRIVATE $<BUILD_INTERFACE:${json_SOURCE_DIR}/single_include/nlohmann>)
 
 add_subdirectory(${CMAKE_CURRENT_LIST_DIR}/mlx)
 

CONTRIBUTING.md

@@ -5,26 +5,26 @@ possible.
 
 ## Pull Requests
 
-1. Fork and submit pull requests to the repo. 
+1. Fork and submit pull requests to the repo.
 2. If you've added code that should be tested, add tests.
 3. If a change is likely to impact efficiency, run some of the benchmarks before
    and after the change. Examples of benchmarks can be found in `benchmarks/python/`.
 4. If you've changed APIs, update the documentation.
-5. Every PR should have passing tests and at least one review. 
+5. Every PR should have passing tests and at least one review.
 6. For code formatting install `pre-commit` using something like `pip install pre-commit` and run `pre-commit install`.
    This should install hooks for running `black` and `clang-format` to ensure
    consistent style for C++ and python code.
- 
+
    You can also run the formatters manually as follows:
- 
-     ```
-     clang-format -i file.cpp
-     ```
- 
-     ```
-     black file.py
-     ```
- 
+
+   ```shell
+   clang-format -i file.cpp
+   ```
+
+   ```shell
+   black file.py
+   ```
+
    or run `pre-commit run --all-files` to check all files in the repo.
 
 ## Issues

benchmarks/python/gather_bench.py

@@ -1,7 +1,6 @@
 # Copyright © 2023-2024 Apple Inc.
 
 import argparse
-from time import time
 
 import mlx.core as mx
 import torch

benchmarks/python/layer_norm_bench.py

@@ -10,7 +10,12 @@ def layer_norm(x, w, b, eps):
     x = x.astype(mx.float32)
     mu = mx.mean(x, -1, keepdims=True)
     v = mx.var(x, -1, keepdims=True)
-    return (x - mu) * mx.rsqrt(v + eps) * w + b
+    y = (x - mu) * mx.rsqrt(v + eps)
+    if w is not None:
+        y = y * w
+    if b is not None:
+        y = y + b
+    return y
 
 
 def time_layer_norm():
@@ -36,6 +41,28 @@ def time_layer_norm():
     time_fn(layer_norm_loop, mx.compile(g1), x, w, b)
     time_fn(layer_norm_loop, mx.compile(g2), x, w, b)
 
+    f1 = lambda x, y: (layer_norm(x, None, None, 1e-5) * y).sum()
+    f2 = lambda x, y: (mx.fast.layer_norm(x, None, None, 1e-5) * y).sum()
+    g1 = mx.grad(f1, argnums=(0,))
+    g2 = mx.grad(f2, argnums=(0,))
+
+    x = mx.random.uniform(shape=(8, 1024, 4096)).astype(mx.float16)
+    w = mx.random.uniform(shape=(4096,)).astype(mx.float16)
+    b = mx.random.uniform(shape=(4096,)).astype(mx.float16)
+    y = mx.random.uniform(shape=(8, 1024, 4096)).astype(mx.float16)
+    mx.eval(x, w, b, y)
+
+    def layer_norm_loop(g, x):
+        gx = x
+        for _ in range(32):
+            gx = g(gx, y)
+        return gx
+
+    time_fn(layer_norm_loop, g1, x)
+    time_fn(layer_norm_loop, g2, x)
+    time_fn(layer_norm_loop, mx.compile(g1), x)
+    time_fn(layer_norm_loop, mx.compile(g2), x)
+
 
 if __name__ == "__main__":
     time_layer_norm()

benchmarks/python/rms_norm_bench.py

@@ -9,7 +9,10 @@ def rms_norm(x, w, eps):
     ot = x.dtype
     x = x.astype(mx.float32)
     n = mx.rsqrt(x.square().mean(-1, keepdims=True) + eps)
-    return (x * n).astype(ot) * w
+    y = (x * n).astype(ot)
+    if w is not None:
+        y = y * w
+    return y
 
 
 def time_rms_norm():
@@ -34,6 +37,27 @@ def time_rms_norm():
     time_fn(rms_norm_loop, mx.compile(g1), x, w)
     time_fn(rms_norm_loop, mx.compile(g2), x, w)
 
+    f1 = lambda x, y: (rms_norm(x, None, 1e-5) * y).sum()
+    f2 = lambda x, y: (mx.fast.rms_norm(x, None, 1e-5) * y).sum()
+    g1 = mx.grad(f1, argnums=(0,))
+    g2 = mx.grad(f2, argnums=(0,))
+
+    x = mx.random.uniform(shape=(8, 1024, 4096)).astype(mx.float16)
+    w = mx.random.uniform(shape=(4096,)).astype(mx.float16)
+    y = mx.random.uniform(shape=(8, 1024, 4096)).astype(mx.float16)
+    mx.eval(x, w, y)
+
+    def rms_norm_loop(g, x):
+        gx = x
+        for _ in range(32):
+            gx = g(gx, y)
+        return gx
+
+    time_fn(rms_norm_loop, g1, x)
+    time_fn(rms_norm_loop, g2, x)
+    time_fn(rms_norm_loop, mx.compile(g1), x)
+    time_fn(rms_norm_loop, mx.compile(g2), x)
+
 
 if __name__ == "__main__":
     time_rms_norm()

benchmarks/python/sdpa_bench.py

@@ -28,11 +28,34 @@ def bench(f, *args):
     return (e - s) * 1e-9
 
 
-def mlx_sdpa_fused_inner(q, k, v, scale):
-    return mx.fast.scaled_dot_product_attention(q, k, v, scale=scale, mask=None)
+def prepare_inputs(B, qL, kL, D, qH, kH, mask, transpose, dtype):
+    np_dtype = getattr(np, dtype)
+
+    shape_q = (B, qL, qH, D) if transpose else (B, qH, qL, D)
+    shape_kv = (B, kL, kH, D) if transpose else (B, kH, kL, D)
+
+    scale = 1.0 / math.sqrt(D)
+
+    q_np = np.random.normal(0.0, 1.0, shape_q).astype(np_dtype)
+    k_np = np.random.normal(0.0, scale, shape_kv).astype(np_dtype)
+    v_np = np.random.normal(0.0, scale, shape_kv).astype(np_dtype)
+
+    q_mx = mx.array(q_np)
+    k_mx = mx.array(k_np)
+    v_mx = mx.array(v_np)
+
+    if mask is not None:
+        if mask == "additive":
+            mask_np = np.random.normal(0.0, 1.0, (B, qH, qL, kL)).astype(np_dtype)
+            mask = mx.array(mask_np)
+        elif mask == "bool":
+            mask_np = np.random.uniform(0.0, 1.0, (B, qH, qL, kL)) < 0.5
+            mask = mx.array(mask_np)
+
+    return q_mx, k_mx, v_mx, scale, mask
 
 
-def mlx_sdpa_unfused_inner(q, k, v, scale, f32softmax=False):
+def mlx_ref_attn(q, k, v, scale=1.0, mask=None):
     q_dtype = q.dtype
     q = q * mx.array(scale, q_dtype)
     n_q_heads = q.shape[-3]
@@ -41,6 +64,7 @@ def mlx_sdpa_unfused_inner(q, k, v, scale, f32softmax=False):
 
     B = q.shape[0]
     L = q.shape[2]
+    kL = k.shape[2]
 
     if n_repeats > 1:
         q = mx.reshape(q, [B, n_kv_heads, n_repeats, L, -1])
@@ -48,10 +72,27 @@ def mlx_sdpa_unfused_inner(q, k, v, scale, f32softmax=False):
         v = mx.expand_dims(v, 2)
 
     scores = q @ mx.swapaxes(k, -1, -2)
-    if f32softmax:
-        scores = mx.softmax(scores.astype(mx.float32), axis=-1).astype(q_dtype)
-    else:
-        scores = mx.softmax(scores, axis=-1)
+
+    if mask is not None:
+
+        if mask == "causal":
+            q_offset = max(0, kL - L)
+            q_indices = mx.arange(q_offset, q_offset + L)
+            k_indices = mx.arange(kL)
+            mask = q_indices[:, None] >= k_indices[None]
+
+        if n_repeats > 1 and mask.ndim >= 3:
+            if mask.shape[-3] == 1:
+                mask = mx.expand_dims(mask, -3)
+            else:
+                mask = mx.unflatten(mask, -3, (n_kv_heads, n_repeats))
+
+        if mask.dtype == mx.bool_:
+            scores = mx.where(mask, scores, -np.float32(np.inf))
+        else:
+            scores += mask
+
+    scores = mx.softmax(scores, axis=-1, precise=True)
 
     out = scores @ v
     if n_repeats > 1:
@@ -60,74 +101,55 @@ def mlx_sdpa_unfused_inner(q, k, v, scale, f32softmax=False):
     return out
 
 
-def mlx_spda_unfused(q, k, v, scale, transpose):
-    q_out = q
+def mlx_fused_attn(q, k, v, scale, mask):
+    return mx.fast.scaled_dot_product_attention(q, k, v, scale=scale, mask=mask)
+
+
+def do_attention(f, q, k, v, scale, mask=None, transpose=False):
     if transpose:
-        k = mx.transpose(k, (0, 2, 1, 3))
-        v = mx.transpose(v, (0, 2, 1, 3))
+        q_t = mx.transpose(q, (0, 2, 1, 3))
+        k_t = mx.transpose(k, (0, 2, 1, 3))
+        v_t = mx.transpose(v, (0, 2, 1, 3))
+        o_t = f(q_t, k_t, v_t, scale=scale, mask=mask)
+        return mx.transpose(o_t, (0, 2, 1, 3))
+    else:
+        return f(q, k, v, scale=scale, mask=mask)
+
+
+def do_attention_bench(f, q, k, v, scale, mask=None, transpose=False):
+    q_out = q
 
     for i in range(N_iter_func):
-        if transpose:
-            q_out = mx.transpose(q_out, (0, 2, 1, 3))
-        q_out = mlx_sdpa_unfused_inner(q_out, k, v, scale)
-        if transpose:
-            q_out = mx.transpose(q_out, (0, 2, 1, 3))
+        q_out = do_attention(f, q_out, k, v, scale, mask=mask, transpose=transpose)
 
     mx.eval(q_out)
     return q_out
 
 
-def mlx_spda_fused(q, k, v, scale, transpose):
-    q_out = q
-    if transpose:
-        k = mx.transpose(k, (0, 2, 1, 3))
-        v = mx.transpose(v, (0, 2, 1, 3))
-
-    for i in range(N_iter_func):
-        if transpose:
-            q_out = mx.transpose(q_out, (0, 2, 1, 3))
-        q_out = mlx_sdpa_fused_inner(q_out, k, v, scale)
-        if transpose:
-            q_out = mx.transpose(q_out, (0, 2, 1, 3))
-
-    mx.eval(q_out)
-    return q_out
-
-
-def bench_shape(B, qsl, ksl, head_dim, n_q_heads, n_kv_heads, np_dtype, transpose=True):
-    shape_q = (
-        (B, qsl, n_q_heads, head_dim) if transpose else (B, n_q_heads, qsl, head_dim)
-    )
-    shape_kv = (
-        (B, ksl, n_kv_heads, head_dim) if transpose else (B, n_kv_heads, ksl, head_dim)
+def bench_shape(
+    B, qsl, ksl, head_dim, n_q_heads, n_kv_heads, dtype, transpose=True, mask_in=None
+):
+    q_mx, k_mx, v_mx, scale, mask = prepare_inputs(
+        B, qsl, ksl, head_dim, n_q_heads, n_kv_heads, mask_in, transpose, dtype
     )
 
-    q_np = np.random.normal(0.0, 1.0 / math.sqrt(head_dim), shape_q).astype(np_dtype)
-    k_np = np.random.normal(0.0, 1.0 / math.sqrt(head_dim), shape_kv).astype(np_dtype)
-    v_np = np.random.normal(0.0, 1.0 / math.sqrt(head_dim), shape_kv).astype(np_dtype)
+    time_mlx_unfused = bench(
+        do_attention_bench, mlx_ref_attn, q_mx, k_mx, v_mx, scale, mask, transpose
+    )
+    time_mlx_fused = bench(
+        do_attention_bench, mlx_fused_attn, q_mx, k_mx, v_mx, scale, mask, transpose
+    )
 
-    scale = math.sqrt(1.0 / head_dim)
+    o_mlx_fused = do_attention(mlx_ref_attn, q_mx, k_mx, v_mx, scale, mask, transpose)
+    o_mlx_unfused = do_attention(
+        mlx_fused_attn, q_mx, k_mx, v_mx, scale, mask, transpose
+    )
 
-    q_mx = mx.array(q_np)
-    k_mx = mx.array(k_np)
-    v_mx = mx.array(v_np)
+    atol = 1e-5 if dtype == "float32" else 2e-4
 
-    time_mlx_unfused = bench(mlx_spda_unfused, q_mx, k_mx, v_mx, scale, transpose)
-    time_mlx_fused = bench(mlx_spda_fused, q_mx, k_mx, v_mx, scale, transpose)
-
-    if transpose:
-        q_mx = mx.transpose(q_mx, (0, 2, 1, 3))
-        k_mx = mx.transpose(k_mx, (0, 2, 1, 3))
-        v_mx = mx.transpose(v_mx, (0, 2, 1, 3))
-
-    o_mlx_fused = mlx_sdpa_fused_inner(q_mx, k_mx, v_mx, scale)
-    o_mlx_unfused = mlx_sdpa_unfused_inner(q_mx, k_mx, v_mx, scale, f32softmax=True)
-
-    atol = 1e-5 if np_dtype == np.float32 else 1e-4
-
-    if not mx.allclose(o_mlx_fused, o_mlx_unfused, atol=atol):
+    if not mx.allclose(o_mlx_fused, o_mlx_unfused, atol=atol, rtol=atol):
         print(
-            f"Failed at (B: {B}, qsl: {qsl}, ksl: {ksl}, head_dim: {head_dim}, n_qh: {n_q_heads}, n_kvh: {n_kv_heads}) [tpose = {transpose}] with max(|a - b|) = {mx.max(mx.abs(o_mlx_unfused - o_mlx_fused)):3.2e}"
+            f"Failed at (B: {B}, qsl: {qsl}, ksl: {ksl}, head_dim: {head_dim}, n_qh: {n_q_heads}, n_kvh: {n_kv_heads}, mask: {mask_in}) [tpose = {transpose}] with max(|a - b|) = {mx.max(mx.abs(o_mlx_unfused - o_mlx_fused)):3.2e}"
         )
 
     return time_mlx_fused, time_mlx_unfused
@@ -151,39 +173,51 @@ if __name__ == "__main__":
           (  1,   128,   128,       64,   32,    32),
           (  1,   256,   256,       64,   32,    32),
           (  1,   512,   512,       64,   32,    32),
-          (  1,  1024,  1024,       64,   32,    32),
-          (  1,  2048,  2048,       64,   32,    32),
-          (  1,  4096,  4096,       64,   32,    32),
+          (  1,  1024,  1024,       64,   32,     8),
+          (  1,  2048,  2048,       64,   32,     8),
+          (  1,  4096,  4096,       64,   32,     8),
     )
 
     shapes_80 = (
         # (  B,   qsl,   ksl, head_dim, n_qh, n_kvh)
-          (  1,  1024,  1024,       80,   32,    32),
-          (  1,  2048,  2048,       80,   32,    32),
-          (  1,  4096,  4096,       80,   32,    32),
+          (  1,  1024,  1024,       80,   32,     8),
+          (  1,  2048,  2048,       80,   32,     8),
+          (  1,  4096,  4096,       80,   32,     8),
     )
 
     shapes_128 = (
         # (  B,   qsl,   ksl, head_dim, n_qh, n_kvh)
-          (  1,  1024,  1024,      128,   32,    32),
-          (  1,  2048,  2048,      128,   32,    32),
-          (  1,  4096,  4096,      128,   32,    32),
+          (  1,  1024,  1024,      128,   32,     8),
+          (  1,  2048,  2048,      128,   32,     8),
+          (  1,  4096,  4096,      128,   32,     8),
     )
     # fmt: on
 
     shapes = shapes_64 + shapes_80 + shapes_128
 
-    print("  B,   qsl,   ksl, hdim, n_qh, n_kvh, tpose,   dtype, t_unfs, t_fuse, diff%")
+    masks = [None, "bool", "causal"]
+
+    print(
+        "  B,   qsl,   ksl, hdim, n_qh, n_kvh, t,   dtype,     mask, t_unfs, t_fuse, diff%"
+    )
 
     for dtype in dtypes:
         for transpose in transposes:
             for B, qsl, ksl, head_dim, n_q_heads, n_kv_heads in shapes:
-                np_dtype = getattr(np, dtype)
-                time_mlx_fused, time_mlx_unfused = bench_shape(
-                    B, qsl, ksl, head_dim, n_q_heads, n_kv_heads, np_dtype, transpose
-                )
-                diff = time_mlx_unfused / time_mlx_fused - 1.0
-                t_str = 1 if transpose else 0
-                print(
-                    f"{B:3d}, {qsl:5d}, {ksl:5d}, {head_dim:4d}, {n_q_heads:4d}, {n_kv_heads:5d}, {t_str:5d}, {dtype}, {time_mlx_unfused: 2.3f}, {time_mlx_fused: 2.3f}, {100. * diff:+5.2f}%"
-                )
+                for mask_in in masks:
+                    time_mlx_fused, time_mlx_unfused = bench_shape(
+                        B,
+                        qsl,
+                        ksl,
+                        head_dim,
+                        n_q_heads,
+                        n_kv_heads,
+                        dtype,
+                        transpose,
+                        mask_in,
+                    )
+                    diff = time_mlx_unfused / time_mlx_fused - 1.0
+                    t_str = 1 if transpose else 0
+                    print(
+                        f"{B:3d}, {qsl:5d}, {ksl:5d}, {head_dim:4d}, {n_q_heads:4d}, {n_kv_heads:5d}, {t_str:1d}, {dtype}, {str(mask_in):>8}, {time_mlx_unfused: 2.3f}, {time_mlx_fused: 2.3f}, {100. * diff:+5.2f}%"
+                    )

cmake/extension.cmake

@@ -1,5 +1,7 @@
 include(CMakeParseArguments)
 
+# clang format off
+#
 # ##############################################################################
 # Build metal library
 #
@@ -11,6 +13,8 @@ include(CMakeParseArguments)
 # of source files INCLUDE_DIRS: List of include dirs DEPS: List of dependency
 # files (like headers)
 #
+# clang format on
+
 macro(mlx_build_metallib)
   # Parse args
   set(oneValueArgs TARGET TITLE OUTPUT_DIRECTORY)
@@ -21,7 +25,7 @@ macro(mlx_build_metallib)
   set(MTLLIB_BUILD_TARGET "${MTLLIB_OUTPUT_DIRECTORY}/${MTLLIB_TITLE}.metallib")
 
   # Collect compile options
-  set(MTLLIB_COMPILE_OPTIONS -Wall -Wextra -fno-fast-math)
+  set(MTLLIB_COMPILE_OPTIONS -Wall -Wextra -fno-fast-math -Wno-c++17-extensions)
 
   # Prepare metallib build command
   add_custom_command(

docs/Doxyfile

@@ -13,7 +13,7 @@ EXCLUDE_PATTERNS       = */private/*
 CREATE_SUBDIRS         = NO
 FULL_PATH_NAMES        = YES
 RECURSIVE              = YES
-GENERATE_HTML          = YES
+GENERATE_HTML          = NO
 GENERATE_LATEX         = NO
 GENERATE_XML           = YES
 XML_PROGRAMLISTING     = YES

docs/src/dev/extensions.rst

@@ -22,12 +22,12 @@ You can do that in MLX directly:
 This function performs that operation while leaving the implementation and
 function transformations to MLX.
 
-However you may need to customize the underlying implementation, perhaps to
-make it faster or for custom differentiation. In this tutorial we will go
-through adding custom extensions. It will cover:
+However, you may want to customize the underlying implementation, perhaps to
+make it faster. In this tutorial we will go through adding custom extensions.
+It will cover:
 
 * The structure of the MLX library.
-* Implementing a CPU operation that redirects to Accelerate_ when appropriate.
+* Implementing a CPU operation.
 * Implementing a GPU operation using metal.
 * Adding the ``vjp`` and ``jvp`` function transformation.
 * Building a custom extension and binding it to python.
@@ -45,7 +45,7 @@ Operations
 Operations are the front-end functions that operate on arrays. They are defined
 in the C++ API (:ref:`cpp_ops`), and the Python API (:ref:`ops`) binds them.
 
-We would like an operation, :meth:`axpby` that takes in two arrays ``x`` and
+We would like an operation :meth:`axpby` that takes in two arrays, ``x`` and
 ``y``, and two scalars, ``alpha`` and ``beta``. This is how to define it in
 C++:
 
@@ -55,7 +55,7 @@ C++:
     *  Scale and sum two vectors element-wise
     *  z = alpha * x + beta * y
     *
-    *  Follow numpy style broadcasting between x and y
+    *  Use NumPy-style broadcasting between x and y
     *  Inputs are upcasted to floats if needed
     **/
     array axpby(
@@ -66,7 +66,7 @@ C++:
         StreamOrDevice s = {} // Stream on which to schedule the operation
     );
 
-The simplest way to this operation is in terms of existing operations:
+The simplest way to implement this is with existing operations:
 
 .. code-block:: C++
 
@@ -93,9 +93,9 @@ Primitives
 ^^^^^^^^^^^
 
 A :class:`Primitive` is part of the computation graph of an :class:`array`. It
-defines how to create outputs arrays given a input arrays. Further, a
+defines how to create output arrays given input arrays. Further, a
 :class:`Primitive` has methods to run on the CPU or GPU and for function
-transformations such as ``vjp`` and ``jvp``.  Lets go back to our example to be
+transformations such as ``vjp`` and ``jvp``.  Let's go back to our example to be
 more concrete:
 
 .. code-block:: C++
@@ -128,7 +128,7 @@ more concrete:
         /** The vector-Jacobian product. */
         std::vector<array> vjp(
             const std::vector<array>& primals,
-            const array& cotan,
+            const std::vector<array>& cotangents,
             const std::vector<int>& argnums,
             const std::vector<array>& outputs) override;
 
@@ -153,9 +153,6 @@ more concrete:
       private:
         float alpha_;
         float beta_;
-
-        /** Fall back implementation for evaluation on CPU */
-        void eval(const std::vector<array>& inputs, array& out);
     };
 
 The :class:`Axpby` class derives from the base :class:`Primitive` class. The
@@ -188,7 +185,7 @@ Let's reimplement our operation now in terms of our :class:`Axpby` primitive.
         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)
+        auto out_dtype = issubdtype(promoted_dtype, float32)
             ? promoted_dtype
             : promote_types(promoted_dtype, float32);
 
@@ -234,49 +231,57 @@ the execution of the computation graph, and calls :meth:`Axpby::eval_cpu` or
 Implementing the CPU Back-end
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-Let's start by implementing a naive and generic version of
-:meth:`Axpby::eval_cpu`. We declared this as a private member function of
-:class:`Axpby` earlier called :meth:`Axpby::eval`.
+Let's start by implementing :meth:`Axpby::eval_cpu`.
 
-Our naive method will go over each element of the output array, find the
+The method will go over each element of the output array, find the
 corresponding input elements of ``x`` and ``y`` and perform the operation
 point-wise. This is captured in the templated function :meth:`axpby_impl`.
 
 .. code-block:: C++
 
-    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()));
+  template <typename T>
+  void axpby_impl(
+      const mx::array& x,
+      const mx::array& y,
+      mx::array& out,
+      float alpha_,
+      float beta_,
+      mx::Stream stream) {
+    out.set_data(mx::allocator::malloc(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>();
+    // Get the CPU command encoder and register input and output arrays
+    auto& encoder = mx::cpu::get_command_encoder(stream);
+    encoder.set_input_array(x);
+    encoder.set_input_array(y);
+    encoder.set_output_array(out);
 
-        // Cast alpha and beta to the relevant types
-        T alpha = static_cast<T>(alpha_);
-        T beta = static_cast<T>(beta_);
+    // Launch the CPU kernel
+    encoder.dispatch([x_ptr = x.data<T>(),
+                      y_ptr = y.data<T>(),
+                      out_ptr = out.data<T>(),
+                      size = out.size(),
+                      shape = out.shape(),
+                      x_strides = x.strides(),
+                      y_strides = y.strides(),
+                      alpha_,
+                      beta_]() {
 
-        // Do the element-wise 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());
+      // Cast alpha and beta to the relevant types
+      T alpha = static_cast<T>(alpha_);
+      T beta = static_cast<T>(beta_);
 
-            // We allocate the output to be contiguous and regularly strided
-            // (defaults to row major) and hence it doesn't need additional mapping
-            out_ptr[out_idx] = alpha * x_ptr[x_offset] + beta * y_ptr[y_offset];
-        }
-    }
+      // Do the element-wise operation for each output
+      for (size_t out_idx = 0; out_idx < size; out_idx++) {
+        // Map linear indices to offsets in x and y
+        auto x_offset = mx::elem_to_loc(out_idx, shape, x_strides);
+        auto y_offset = mx::elem_to_loc(out_idx, shape, y_strides);
+
+        // We allocate the output to be contiguous and regularly strided
+        // (defaults to row major) and hence it doesn't need additional mapping
+        out_ptr[out_idx] = alpha * x_ptr[x_offset] + beta * y_ptr[y_offset];
+      }
+    });
+  }
 
 Our implementation should work for all incoming floating point arrays.
 Accordingly, we add dispatches for ``float32``, ``float16``, ``bfloat16`` and
@@ -284,112 +289,32 @@ Accordingly, we add dispatches for ``float32``, ``float16``, ``bfloat16`` and
 
 .. code-block:: C++
 
-    /** Fall back implementation for evaluation on CPU */
-    void Axpby::eval(
-      const std::vector<array>& inputs,
-      const std::vector<array>& outputs) {
-        auto& x = inputs[0];
-        auto& y = inputs[1];
-        auto& out = outputs[0];
-
-        // 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] Only supports floating point types.");
-        }
-    }
-
-This is good as a fallback implementation. We can use the ``axpby`` routine
-provided by the Accelerate_ framework for a faster implementation in certain
-cases:
-
-#.  Accelerate does not provide implementations of ``axpby`` for half precision
-    floats. We can only use it for ``float32`` types.
-#.  Accelerate assumes the inputs ``x`` and ``y`` are contiguous and all
-    elements have fixed strides between them. We only direct to Accelerate
-    if both ``x`` and ``y`` are row contiguous or column contiguous.
-#.  Accelerate performs the routine ``Y = (alpha * X) + (beta * Y)`` in-place.
-    MLX expects to write the output to a new array. We must copy the elements
-    of ``y`` into the output and use that as an input to ``axpby``.
-
-Let's write an implementation that uses Accelerate in the right conditions.
-It allocates data for the output, copies ``y`` into it, and then calls the
-:func:`catlas_saxpby` from accelerate.
-
-.. code-block:: C++
-
-    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
-        out.set_data(allocator::malloc_or_wait(out.nbytes()));
-
-        // 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);
-    }
-
-For inputs that do not fit the criteria for accelerate, we fall back to
-:meth:`Axpby::eval`. With this in mind, let's finish our
-:meth:`Axpby::eval_cpu`.
-
-.. code-block:: C++
-
-    /** Evaluate primitive on CPU using accelerate specializations */
     void Axpby::eval_cpu(
-      const std::vector<array>& inputs,
-      const std::vector<array>& outputs) {
-        assert(inputs.size() == 2);
-        auto& x = inputs[0];
-        auto& y = inputs[1];
-        auto& out = outputs[0];
+        const std::vector<mx::array>& inputs,
+        std::vector<mx::array>& outputs) {
+      auto& x = inputs[0];
+      auto& y = inputs[1];
+      auto& out = outputs[0];
 
-        // 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 back-end if specializations are not available
-        eval(inputs, outputs);
+      // Dispatch to the correct dtype
+      if (out.dtype() == mx::float32) {
+        return axpby_impl<float>(x, y, out, alpha_, beta_, stream());
+      } else if (out.dtype() == mx::float16) {
+        return axpby_impl<mx::float16_t>(x, y, out, alpha_, beta_, stream());
+      } else if (out.dtype() == mx::bfloat16) {
+        return axpby_impl<mx::bfloat16_t>(x, y, out, alpha_, beta_, stream());
+      } else if (out.dtype() == mx::complex64) {
+        return axpby_impl<mx::complex64_t>(x, y, out, alpha_, beta_, stream());
+      } else {
+        throw std::runtime_error(
+            "Axpby is only supported for floating point types.");
+      }
     }
 
 Just this much is enough to run the operation :meth:`axpby` on a CPU stream! If
 you do not plan on running the operation on the GPU or using transforms on
 computation graphs that contain :class:`Axpby`, you can stop implementing the
-primitive here and enjoy the speed-ups you get from the Accelerate library.
+primitive here.
 
 Implementing the GPU Back-end
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -466,7 +391,7 @@ below.
         auto& d = metal::device(s.device);
 
         // Allocate output memory
-        out.set_data(allocator::malloc_or_wait(out.nbytes()));
+        out.set_data(allocator::malloc(out.nbytes()));
 
         // Resolve name of kernel
         std::ostringstream kname;
@@ -544,7 +469,7 @@ one we just defined:
             const std::vector<array>& tangents,
             const std::vector<int>& argnums) {
         // Forward mode diff that pushes along the tangents
-        // The jvp transform on the primitive can built with ops
+        // The jvp transform on the primitive can be built with ops
         // that are scheduled on the same stream as the primitive
 
         // If argnums = {0}, we only push along x in which case the
@@ -556,7 +481,7 @@ one we just defined:
             auto scale_arr = array(scale, tangents[0].dtype());
             return {multiply(scale_arr, tangents[0], stream())};
         }
-        // If, argnums = {0, 1}, we take contributions from both
+        // 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())};
@@ -810,7 +735,7 @@ Let's look at a simple script and its results:
 
     print(f"c shape: {c.shape}")
     print(f"c dtype: {c.dtype}")
-    print(f"c correct: {mx.all(c == 6.0).item()}")
+    print(f"c is correct: {mx.all(c == 6.0).item()}")
 
 Output:
 
@@ -818,13 +743,13 @@ Output:
 
     c shape: [3, 4]
     c dtype: float32
-    c correctness: True
+    c is correct: True
 
 Results
 ^^^^^^^
 
 Let's run a quick benchmark and see how our new ``axpby`` operation compares
-with the naive :meth:`simple_axpby` we first defined on the CPU.
+with the naive :meth:`simple_axpby` we first defined.
 
 .. code-block:: python
 
@@ -832,13 +757,11 @@ with the naive :meth:`simple_axpby` we first defined on the CPU.
     from mlx_sample_extensions import axpby
     import time
 
-    mx.set_default_device(mx.cpu)
-
     def simple_axpby(x: mx.array, y: mx.array, alpha: float, beta: float) -> mx.array:
         return alpha * x + beta * y
 
-    M = 256
-    N = 512
+    M = 4096
+    N = 4096
 
     x = mx.random.normal((M, N))
     y = mx.random.normal((M, N))
@@ -849,24 +772,24 @@ with the naive :meth:`simple_axpby` we first defined on the CPU.
 
     def bench(f):
         # Warm up
-        for i in range(100):
+        for i in range(5):
             z = f(x, y, alpha, beta)
             mx.eval(z)
 
         # Timed run
         s = time.time()
-        for i in range(5000):
+        for i in range(100):
             z = f(x, y, alpha, beta)
             mx.eval(z)
         e = time.time()
-        return e - s
+        return 1000 * (e - s) / 100
 
     simple_time = bench(simple_axpby)
     custom_time = bench(axpby)
 
-    print(f"Simple axpby: {simple_time:.3f} s | Custom axpby: {custom_time:.3f} s")
+    print(f"Simple axpby: {simple_time:.3f} ms | Custom axpby: {custom_time:.3f} ms")
 
-The results are ``Simple axpby: 0.114 s | Custom axpby: 0.109 s``. We see
+The results are ``Simple axpby: 1.559 ms | Custom axpby: 0.774 ms``. We see
 modest improvements right away!
 
 This operation is now good to be used to build other operations, in

docs/src/index.rst

@@ -70,6 +70,7 @@ are the CPU and GPU.
    python/fft
    python/linalg
    python/metal
+   python/memory_management
    python/nn
    python/optimizers
    python/distributed

docs/src/python/linalg.rst

@@ -20,5 +20,6 @@ Linear Algebra
     eigh
     lu
     lu_factor
+    pinv
     solve
     solve_triangular

docs/src/python/memory_management.rst

@@ -0,0 +1,16 @@
+Memory Management
+=================
+
+.. currentmodule:: mlx.core
+
+.. autosummary::
+  :toctree: _autosummary
+
+  get_active_memory
+  get_peak_memory
+  reset_peak_memory
+  get_cache_memory
+  set_memory_limit
+  set_cache_limit
+  set_wired_limit
+  clear_cache

docs/src/python/metal.rst

@@ -8,13 +8,5 @@ Metal
 
   is_available
   device_info
-  get_active_memory
-  get_peak_memory
-  reset_peak_memory
-  get_cache_memory
-  set_memory_limit
-  set_cache_limit
-  set_wired_limit
-  clear_cache
   start_capture
   stop_capture

docs/src/python/nn.rst

@@ -174,6 +174,7 @@ In detail:
 
    value_and_grad
    quantize
+   average_gradients
 
 .. toctree::
 

docs/src/python/ops.rst

@@ -36,10 +36,12 @@ Operations
    bitwise_or
    bitwise_xor
    block_masked_mm
+   broadcast_arrays
    broadcast_to
    ceil
    clip
    concatenate
+   contiguous
    conj
    conjugate
    convolve

docs/src/python/transforms.rst

@@ -9,6 +9,7 @@ Transforms
   :toctree: _autosummary
 
    eval
+   async_eval
    compile
    custom_function
    disable_compile

docs/src/usage/distributed.rst

@@ -5,21 +5,27 @@ Distributed Communication
 
 .. currentmodule:: mlx.core.distributed
 
-MLX utilizes `MPI <https://en.wikipedia.org/wiki/Message_Passing_Interface>`_ to
-provide distributed communication operations that allow the computational cost
-of training or inference to be shared across many physical machines. You can
-see a list of the supported operations in the :ref:`API docs<distributed>`.
+MLX supports distributed communication operations that allow the computational cost
+of training or inference to be shared across many physical machines. At the
+moment we support two different communication backends:
+
+* `MPI <https://en.wikipedia.org/wiki/Message_Passing_Interface>`_ a
+  full-featured and mature distributed communications library
+* A **ring** backend of our own that uses native TCP sockets and should be
+  faster for thunderbolt connections.
+
+The list of all currently supported operations and their documentation can be
+seen in the :ref:`API docs<distributed>`.
 
 .. note::
-   A lot of operations may not be supported or not as fast as they should be.
+   Some operations may not be supported or not as fast as they should be.
    We are adding more and tuning the ones we have as we are figuring out the
    best way to do distributed computing on Macs using MLX.
 
 Getting Started
 ---------------
 
-MLX already comes with the ability to "talk" to MPI if it is installed on the
-machine. The minimal distributed program in MLX is as simple as:
+A distributed program in MLX is as simple as:
 
 .. code:: python
 
@@ -30,74 +36,79 @@ machine. The minimal distributed program in MLX is as simple as:
     print(world.rank(), x)
 
 The program above sums the array ``mx.ones(10)`` across all
-distributed processes. If simply run with ``python``, however, only one
-process is launched and no distributed communication takes place.
+distributed processes. However, when this script is run with ``python`` only
+one process is launched and no distributed communication takes place. Namely,
+all operations in ``mx.distributed`` are noops when the distributed group has a
+size of one. This property allows us to avoid code that checks if we are in a
+distributed setting similar to the one below:
 
-To launch the program in distributed mode we need to use ``mpirun`` or
-``mpiexec`` depending on the MPI installation. The simplest possible way is the
-following:
+.. code:: python
+
+    import mlx.core as mx
+
+    x = ...
+    world = mx.distributed.init()
+    # No need for the check we can simply do x = mx.distributed.all_sum(x)
+    if world.size() > 1:
+        x = mx.distributed.all_sum(x)
+
+Running Distributed Programs
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+MLX provides ``mlx.launch`` a helper script to launch distributed programs.
+Continuing with our initial example we can run it on localhost with 4 processes using
 
 .. code:: shell
 
-    $ mpirun -np 2 python test.py
-    1 array([2, 2, 2, ..., 2, 2, 2], dtype=float32)
-    0 array([2, 2, 2, ..., 2, 2, 2], dtype=float32)
+    $ mlx.launch -n 4 my_script.py
+    3 array([4, 4, 4, ..., 4, 4, 4], dtype=float32)
+    2 array([4, 4, 4, ..., 4, 4, 4], dtype=float32)
+    1 array([4, 4, 4, ..., 4, 4, 4], dtype=float32)
+    0 array([4, 4, 4, ..., 4, 4, 4], dtype=float32)
 
-The above launches two processes on the same (local) machine and we can see
-both standard output streams. The processes send the array of 1s to each other
-and compute the sum which is printed. Launching with ``mpirun -np 4 ...`` would
-print 4 etc.
-
-Installing MPI
----------------
-
-MPI can be installed with Homebrew, using the Anaconda package manager or
-compiled from source. Most of our testing is done using ``openmpi`` installed
-with the Anaconda package manager as follows:
+We can also run it on some remote hosts by providing their IPs (provided that
+the script exists on all hosts and they are reachable by ssh)
 
 .. code:: shell
 
-    $ conda install conda-forge::openmpi
+    $ mlx.launch --hosts ip1,ip2,ip3,ip4 my_script.py
+    3 array([4, 4, 4, ..., 4, 4, 4], dtype=float32)
+    2 array([4, 4, 4, ..., 4, 4, 4], dtype=float32)
+    1 array([4, 4, 4, ..., 4, 4, 4], dtype=float32)
+    0 array([4, 4, 4, ..., 4, 4, 4], dtype=float32)
 
-Installing with Homebrew may require specifying the location of ``libmpi.dyld``
-so that MLX can find it and load it at runtime. This can simply be achieved by
-passing the ``DYLD_LIBRARY_PATH`` environment variable to ``mpirun``.
+Consult the dedicated :doc:`usage guide<launching_distributed>` for more
+information on using ``mlx.launch``.
 
-.. code:: shell
+Selecting Backend
+^^^^^^^^^^^^^^^^^
 
-    $ mpirun -np 2 -x DYLD_LIBRARY_PATH=/opt/homebrew/lib/ python test.py
-
-Setting up Remote Hosts
------------------------
-
-MPI can automatically connect to remote hosts and set up the communication over
-the network if the remote hosts can be accessed via ssh. A good checklist to
-debug connectivity issues is the following:
-
-* ``ssh hostname`` works from all machines to all machines without asking for
-  password or host confirmation
-* ``mpirun`` is accessible on all machines. You can call ``mpirun`` using its
-  full path to force all machines to use a specific path.
-* Ensure that the ``hostname`` used by MPI is the one that you have configured
-  in the ``.ssh/config`` files on all machines.
+You can select the backend you want to use when calling :func:`init` by passing
+one of ``{'any', 'ring', 'mpi'}``. When passing ``any``, MLX will try to
+initialize the ``ring`` backend and if it fails the ``mpi`` backend. If they
+both fail then a singleton group is created.
 
 .. note::
-  For an example hostname ``foo.bar.com`` MPI can use only ``foo`` as
-  the hostname passed to ssh if the current hostname matches ``*.bar.com``.
+   After a distributed backend is successfully initialized :func:`init` will
+   return **the same backend** if called without arguments or with backend set to
+   ``any``.
 
-An easy way to pass the host names to MPI is using a host file. A host file
-looks like the following, where ``host1`` and ``host2`` should be the fully
-qualified domain names or IPs for these hosts.
+The following examples aim to clarify the backend initialization logic in MLX:
 
-.. code::
+.. code:: python
 
-    host1 slots=1
-    host2 slots=1
+    # Case 1: Initialize MPI regardless if it was possible to initialize the ring backend
+    world = mx.distributed.init(backend="mpi")
+    world2 = mx.distributed.init()  # subsequent calls return the MPI backend!
 
-When using MLX, it is very likely that you want to use 1 slot per host, ie one
-process per host.  The hostfile also needs to contain the current
-host if you want to run on the local host. Passing the host file to
-``mpirun`` is simply done using the ``--hostfile`` command line argument.
+    # Case 2: Initialize any backend
+    world = mx.distributed.init(backend="any")  # equivalent to no arguments
+    world2 = mx.distributed.init()  # same as above
+
+    # Case 3: Initialize both backends at the same time
+    world_mpi = mx.distributed.init(backend="mpi")
+    world_ring = mx.distributed.init(backend="ring")
+    world_any = mx.distributed.init()  # same as MPI because it was initialized first!
 
 Training Example
 ----------------
@@ -155,13 +166,179 @@ everything else remaining the same.
         optimizer.update(model, grads)
         return loss
 
-Tuning All Reduce
------------------
+Utilizing ``nn.average_gradients``
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-We are working on improving the performance of all reduce on MLX but for now
-the two main things one can do to extract the most out of distributed training with MLX are:
+Although the code example above works correctly; it performs one communication
+per gradient. It is significantly more efficient to aggregate several gradients
+together and perform fewer communication steps.
 
-1. Perform a few large reductions instead of many small ones to improve
-   bandwidth and latency
-2. Pass ``--mca btl_tcp_links 4`` to ``mpirun`` to configure it to use 4 tcp
-   connections between each host to improve bandwidth
+This is the purpose of :func:`mlx.nn.average_gradients`. The final code looks
+almost identical to the example above:
+
+.. code:: python
+
+    model = ...
+    optimizer = ...
+    dataset = ...
+
+    def step(model, x, y):
+        loss, grads = loss_grad_fn(model, x, y)
+        grads = mlx.nn.average_gradients(grads) # <---- This line was added
+        optimizer.update(model, grads)
+        return loss
+
+    for x, y in dataset:
+        loss = step(model, x, y)
+        mx.eval(loss, model.parameters())
+
+
+Getting Started with MPI
+------------------------
+
+MLX already comes with the ability to "talk" to MPI if it is installed on the
+machine. Launching distributed MLX programs that use MPI can be done with
+``mpirun`` as expected. However, in the following examples we will be using
+``mlx.launch --backend mpi`` which takes care of some nuisances such as setting
+absolute paths for the ``mpirun`` executable and the ``libmpi.dyld`` shared
+library.
+
+The simplest possible usage is the following which, assuming the minimal
+example in the beginning of this page, should result in:
+
+.. code:: shell
+
+    $ mlx.launch --backend mpi -n 2 test.py
+    1 array([2, 2, 2, ..., 2, 2, 2], dtype=float32)
+    0 array([2, 2, 2, ..., 2, 2, 2], dtype=float32)
+
+The above launches two processes on the same (local) machine and we can see
+both standard output streams. The processes send the array of 1s to each other
+and compute the sum which is printed. Launching with ``mlx.launch -n 4 ...`` would
+print 4 etc.
+
+Installing MPI
+^^^^^^^^^^^^^^
+
+MPI can be installed with Homebrew, using the Anaconda package manager or
+compiled from source. Most of our testing is done using ``openmpi`` installed
+with the Anaconda package manager as follows:
+
+.. code:: shell
+
+    $ conda install conda-forge::openmpi
+
+Installing with Homebrew may require specifying the location of ``libmpi.dyld``
+so that MLX can find it and load it at runtime. This can simply be achieved by
+passing the ``DYLD_LIBRARY_PATH`` environment variable to ``mpirun`` and it is
+done automatically by ``mlx.launch``.
+
+.. code:: shell
+
+    $ mpirun -np 2 -x DYLD_LIBRARY_PATH=/opt/homebrew/lib/ python test.py
+    $ # or simply
+    $ mlx.launch -n 2 test.py
+
+Setting up Remote Hosts
+^^^^^^^^^^^^^^^^^^^^^^^
+
+MPI can automatically connect to remote hosts and set up the communication over
+the network if the remote hosts can be accessed via ssh. A good checklist to
+debug connectivity issues is the following:
+
+* ``ssh hostname`` works from all machines to all machines without asking for
+  password or host confirmation
+* ``mpirun`` is accessible on all machines.
+* Ensure that the ``hostname`` used by MPI is the one that you have configured
+  in the ``.ssh/config`` files on all machines.
+
+Tuning MPI All Reduce
+^^^^^^^^^^^^^^^^^^^^^
+
+.. note::
+
+    For faster all reduce consider using the ring backend either with Thunderbolt
+    connections or over Ethernet.
+
+Configure MPI to use N tcp connections between each host to improve bandwidth
+by passing ``--mca btl_tcp_links N``.
+
+Force MPI to use the most performant network interface by setting ``--mca
+btl_tcp_if_include <iface>`` where ``<iface>`` should be the interface you want
+to use.
+
+Getting Started with Ring
+-------------------------
+
+The ring backend does not depend on any third party library so it is always
+available. It uses TCP sockets so the nodes need to be reachable via a network.
+As the name suggests the nodes are connected in a ring which means that rank 1
+can only communicate with rank 0 and rank 2, rank 2 only with rank 1 and rank 3
+and so on and so forth. As a result :func:`send` and :func:`recv` with
+arbitrary sender and receiver is not supported in the ring backend.
+
+Defining a Ring
+^^^^^^^^^^^^^^^
+
+The easiest way to define and use a ring is via a JSON hostfile and the
+``mlx.launch`` :doc:`helper script <launching_distributed>`. For each node one
+defines a hostname to ssh into to run commands on this node and one or more IPs
+that this node will listen to for connections.
+
+For example the hostfile below defines a 4 node ring. ``hostname1`` will be
+rank 0, ``hostname2`` rank 1 etc.
+
+.. code:: json
+
+    [
+        {"ssh": "hostname1", "ips": ["123.123.123.1"]},
+        {"ssh": "hostname2", "ips": ["123.123.123.2"]},
+        {"ssh": "hostname3", "ips": ["123.123.123.3"]},
+        {"ssh": "hostname4", "ips": ["123.123.123.4"]}
+    ]
+
+Running ``mlx.launch --hostfile ring-4.json my_script.py`` will ssh into each
+node, run the script which will listen for connections in each of the provided
+IPs. Specifically, ``hostname1`` will connect to ``123.123.123.2`` and accept a
+connection from ``123.123.123.4`` and so on and so forth.
+
+Thunderbolt Ring
+^^^^^^^^^^^^^^^^
+
+Although the ring backend can have benefits over MPI even for Ethernet, its
+main purpose is to use Thunderbolt rings for higher bandwidth communication.
+Setting up such thunderbolt rings can be done manually, but is a relatively
+tedious process. To simplify this, we provide the utility ``mlx.distributed_config``.
+
+To use ``mlx.distributed_config`` your computers need to be accessible by ssh via
+Ethernet or Wi-Fi. Subsequently, connect them via thunderbolt cables and then call the
+utility as follows:
+
+.. code:: shell
+
+   mlx.distributed_config --verbose --hosts host1,host2,host3,host4
+
+By default the script will attempt to discover the thunderbolt ring and provide
+you with the commands to configure each node as well as the ``hostfile.json``
+to use with ``mlx.launch``. If password-less ``sudo`` is available on the nodes
+then ``--auto-setup`` can be used to configure them automatically.
+
+To validate your connection without configuring anything
+``mlx.distributed_config`` can also plot the ring using DOT format.
+
+.. code:: shell
+
+   mlx.distributed_config --verbose --hosts host1,host2,host3,host4 --dot >ring.dot
+   dot -Tpng ring.dot >ring.png
+   open ring.png
+
+If you want to go through the process manually, the steps are as follows:
+
+* Disable the thunderbolt bridge interface
+* For the cable connecting rank ``i`` to rank ``i + 1`` find the interfaces
+  corresponding to that cable in nodes ``i`` and ``i + 1``.
+* Set up a unique subnetwork connecting the two nodes for the corresponding
+  interfaces. For instance if the cable corresponds to ``en2`` on node ``i``
+  and ``en2`` also on node ``i + 1`` then we may assign IPs ``192.168.0.1`` and
+  ``192.168.0.2`` respectively to the two nodes. For more details you can see
+  the commands prepared by the utility script.

docs/src/usage/launching_distributed.rst

@@ -0,0 +1,105 @@
+:orphan:
+
+.. _usage_launch_distributed:
+
+Launching Distributed Programs
+==============================
+
+.. currentmodule:: mlx.core.distributed
+
+Installing the MLX python package provides a helper script ``mlx.launch`` that
+can be used to run python scripts distributed on several nodes. It allows
+launching using either the MPI backend or the ring backend. See the
+:doc:`distributed docs <distributed>` for the different backends.
+
+Usage
+-----
+
+The minimal usage example of ``mlx.launch`` is simply
+
+.. code:: shell
+
+    mlx.launch --hosts ip1,ip2 my_script.py
+
+or for testing on localhost
+
+.. code:: shell
+
+    mlx.launch -n 2 my_script.py
+
+The ``mlx.launch`` command connects to the provided host and launches the input
+script on each host. It monitors each of the launched processes and terminates
+the rest if one of them fails unexpectedly or if ``mlx.launch`` is terminated.
+It also takes care of forwarding the output of each remote process to stdout
+and stderr respectively.
+
+Providing Hosts
+^^^^^^^^^^^^^^^^
+
+Hosts can be provided as command line arguments, like above, but the way that
+allows to fully define a list of hosts is via a JSON hostfile. The hostfile has
+a very simple schema. It is simply a list of objects that define each host via
+a hostname to ssh to and a list of IPs to utilize for the communication.
+
+.. code:: json
+
+    [
+        {"ssh": "hostname1", "ips": ["123.123.1.1", "123.123.2.1"]},
+        {"ssh": "hostname2", "ips": ["123.123.1.2", "123.123.2.2"]}
+    ]
+
+You can use ``mlx.distributed_config --over ethernet`` to create a hostfile
+with IPs corresponding to the ``en0`` interface.
+
+Setting up Remote Hosts
+^^^^^^^^^^^^^^^^^^^^^^^^
+
+In order to be able to launch the script on each host we need to be able to
+connect via ssh. Moreover the input script and python binary need to be on each
+host and on the same path. A good checklist to debug errors is the following:
+
+* ``ssh hostname`` works without asking for password or host confirmation
+* the python binary is available on all hosts at the same path. You can use
+  ``mlx.launch --print-python`` to see what that path is.
+* the script you want to run is available on all hosts at the same path
+
+.. _mpi_specifics:
+
+MPI Specifics
+-------------
+
+One can use MPI by passing ``--backend mpi`` to ``mlx.launch``. In that case,
+``mlx.launch`` is a thin wrapper over ``mpirun``. Moreover,
+
+* The IPs in the hostfile are ignored
+* The ssh connectivity requirement is stronger as every node needs to be able
+  to connect to every other node
+* ``mpirun`` needs to be available on every node at the same path
+
+Finally, one can pass arguments to ``mpirun`` using ``--mpi-arg``. For instance
+to choose a specific interface for the byte-transfer-layer of MPI we can call
+``mlx.launch`` as follows:
+
+.. code:: shell
+
+    mlx.launch --backend mpi --mpi-arg '--mca btl_tcp_if_include en0' --hostfile hosts.json my_script.py
+
+
+.. _ring_specifics:
+
+Ring Specifics
+--------------
+
+The ring backend, which is also the default backend, can be explicitly selected
+with the argument ``--backend ring``. The ring backend has some specific
+requirements and arguments that are different to MPI:
+
+* The argument ``--hosts`` only accepts IPs and not hostnames. If we need to
+  ssh to a hostname that does not correspond to the IP we want to bind to we
+  have to provide a hostfile.
+* ``--starting-port`` defines the port to bind to on the remote hosts.
+  Specifically rank 0 for the first IP will use this port and each subsequent
+  IP or rank will add 1 to this port.
+* ``--connections-per-ip`` allows us to increase the number of connections
+  between neighboring nodes. This corresponds to ``--mca btl_tcp_links 2`` for
+  ``mpirun``.

examples/extensions/CMakeLists.txt

@@ -10,7 +10,6 @@ set(CMAKE_POSITION_INDEPENDENT_CODE ON)
 option(BUILD_SHARED_LIBS "Build extensions as a shared library" ON)
 
 # ----------------------------- Dependencies -----------------------------
-find_package(MLX CONFIG REQUIRED)
 find_package(
   Python 3.8
   COMPONENTS Interpreter Development.Module
@@ -21,6 +20,12 @@ execute_process(
   OUTPUT_VARIABLE nanobind_ROOT)
 find_package(nanobind CONFIG REQUIRED)
 
+execute_process(
+  COMMAND "${Python_EXECUTABLE}" -m mlx --cmake-dir
+  OUTPUT_STRIP_TRAILING_WHITESPACE
+  OUTPUT_VARIABLE MLX_ROOT)
+find_package(MLX CONFIG REQUIRED)
+
 # ----------------------------- Extensions -----------------------------
 
 # Add library

examples/extensions/axpby/axpby.cpp

@@ -1,20 +1,14 @@
-// Copyright © 2023-2024 Apple Inc.
+// Copyright © 2023-2025 Apple Inc.
 
-#include <cassert>
 #include <iostream>
 #include <sstream>
 
-#include "mlx/backend/common/copy.h"
 #include "mlx/backend/common/utils.h"
-#include "mlx/backend/cpu/copy.h"
+#include "mlx/backend/cpu/encoder.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"
@@ -76,136 +70,65 @@ void axpby_impl(
     const mx::array& y,
     mx::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(mx::allocator::malloc_or_wait(out.nbytes()));
+    float beta_,
+    mx::Stream stream) {
+  out.set_data(mx::allocator::malloc(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>();
+  // Get the CPU command encoder and register input and output arrays
+  auto& encoder = mx::cpu::get_command_encoder(stream);
+  encoder.set_input_array(x);
+  encoder.set_input_array(y);
+  encoder.set_output_array(out);
 
-  // Cast alpha and beta to the relevant types
-  T alpha = static_cast<T>(alpha_);
-  T beta = static_cast<T>(beta_);
+  // Launch the CPU kernel
+  encoder.dispatch([x_ptr = x.data<T>(),
+                    y_ptr = y.data<T>(),
+                    out_ptr = out.data<T>(),
+                    size = out.size(),
+                    shape = out.shape(),
+                    x_strides = x.strides(),
+                    y_strides = y.strides(),
+                    alpha_,
+                    beta_]() {
+    // Cast alpha and beta to the relevant types
+    T alpha = static_cast<T>(alpha_);
+    T beta = static_cast<T>(beta_);
 
-  // Do the element-wise 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 = mx::elem_to_loc(out_idx, x.shape(), x.strides());
-    auto y_offset = mx::elem_to_loc(out_idx, y.shape(), y.strides());
+    // Do the element-wise operation for each output
+    for (size_t out_idx = 0; out_idx < size; out_idx++) {
+      // Map linear indices to offsets in x and y
+      auto x_offset = mx::elem_to_loc(out_idx, shape, x_strides);
+      auto y_offset = mx::elem_to_loc(out_idx, shape, y_strides);
 
-    // We allocate the output to be contiguous and regularly strided
-    // (defaults to row major) and hence it doesn't need additional mapping
-    out_ptr[out_idx] = alpha * x_ptr[x_offset] + beta * y_ptr[y_offset];
-  }
+      // We allocate the output to be contiguous and regularly strided
+      // (defaults to row major) and hence it doesn't need additional 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(
+void Axpby::eval_cpu(
     const std::vector<mx::array>& inputs,
     std::vector<mx::array>& outputs) {
-  // Check the inputs (registered in the op while constructing the out array)
-  assert(inputs.size() == 2);
   auto& x = inputs[0];
   auto& y = inputs[1];
   auto& out = outputs[0];
 
   // Dispatch to the correct dtype
   if (out.dtype() == mx::float32) {
-    return axpby_impl<float>(x, y, out, alpha_, beta_);
+    return axpby_impl<float>(x, y, out, alpha_, beta_, stream());
   } else if (out.dtype() == mx::float16) {
-    return axpby_impl<mx::float16_t>(x, y, out, alpha_, beta_);
+    return axpby_impl<mx::float16_t>(x, y, out, alpha_, beta_, stream());
   } else if (out.dtype() == mx::bfloat16) {
-    return axpby_impl<mx::bfloat16_t>(x, y, out, alpha_, beta_);
+    return axpby_impl<mx::bfloat16_t>(x, y, out, alpha_, beta_, stream());
   } else if (out.dtype() == mx::complex64) {
-    return axpby_impl<mx::complex64_t>(x, y, out, alpha_, beta_);
+    return axpby_impl<mx::complex64_t>(x, y, out, alpha_, beta_, stream());
   } 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 mx::array& x,
-    const mx::array& y,
-    mx::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(mx::allocator::malloc_or_wait(out.nbytes()));
-
-  // We then copy over the elements using the contiguous vector specialization
-  copy_inplace(y, out, mx::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<mx::array>& inputs,
-    std::vector<mx::array>& outputs) {
-  assert(inputs.size() == 2);
-  auto& x = inputs[0];
-  auto& y = inputs[1];
-  auto& out = outputs[0];
-
-  // Accelerate specialization for contiguous single precision float arrays
-  if (out.dtype() == mx::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, outputs);
-}
-
-#else // Accelerate not available
-
-/** Evaluate primitive on CPU falling back to common backend */
-void Axpby::eval_cpu(
-    const std::vector<mx::array>& inputs,
-    std::vector<mx::array>& outputs) {
-  eval(inputs, outputs);
-}
-
-#endif
-
 ///////////////////////////////////////////////////////////////////////////////
 // Primitive Metal Backend Implementation
 ///////////////////////////////////////////////////////////////////////////////
@@ -217,7 +140,6 @@ void Axpby::eval_gpu(
     const std::vector<mx::array>& inputs,
     std::vector<mx::array>& outputs) {
   // Prepare inputs
-  assert(inputs.size() == 2);
   auto& x = inputs[0];
   auto& y = inputs[1];
   auto& out = outputs[0];
@@ -236,12 +158,12 @@ void Axpby::eval_gpu(
   // Allocate output memory with strides based on specialization
   if (contiguous_kernel) {
     out.set_data(
-        mx::allocator::malloc_or_wait(x.data_size() * out.itemsize()),
+        mx::allocator::malloc(x.data_size() * out.itemsize()),
         x.data_size(),
         x.strides(),
         x.flags());
   } else {
-    out.set_data(mx::allocator::malloc_or_wait(out.nbytes()));
+    out.set_data(mx::allocator::malloc(out.nbytes()));
   }
 
   // Resolve name of kernel (corresponds to axpby.metal)

examples/extensions/axpby/axpby.h

@@ -1,4 +1,4 @@
-// Copyright © 2023 Apple Inc.
+// Copyright © 2023-2025 Apple Inc.
 
 #pragma once
 
@@ -85,11 +85,6 @@ class Axpby : public mx::Primitive {
  private:
   float alpha_;
   float beta_;
-
-  /** Fall back implementation for evaluation on CPU */
-  void eval(
-      const std::vector<mx::array>& inputs,
-      std::vector<mx::array>& outputs);
 };
 
 } // namespace my_ext

examples/extensions/axpby/axpby.metal

@@ -1,4 +1,4 @@
-// Copyright © 2023 Apple Inc.
+// Copyright © 2023-2025 Apple Inc.
 
 #include <metal_stdlib>
 

mlx/CMakeLists.txt

@@ -19,6 +19,11 @@ target_sources(
           ${CMAKE_CURRENT_SOURCE_DIR}/linalg.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/backend/metal/metal.h)
 
+# Define MLX_VERSION only in the version.cpp file.
+add_library(mlx_version STATIC ${CMAKE_CURRENT_SOURCE_DIR}/version.cpp)
+target_compile_definitions(mlx_version PRIVATE MLX_VERSION="${MLX_VERSION}")
+target_link_libraries(mlx PRIVATE $<BUILD_INTERFACE:mlx_version>)
+
 if(MSVC)
   # Disable some MSVC warnings to speed up compilation.
   target_compile_options(mlx PUBLIC /wd4068 /wd4244 /wd4267 /wd4804)

mlx/allocator.cpp

@@ -4,12 +4,11 @@
 #include <sstream>
 
 #include "mlx/allocator.h"
-#include "mlx/scheduler.h"
 
 namespace mlx::core::allocator {
 
 Buffer malloc(size_t size) {
-  auto buffer = allocator().malloc(size, /* allow_swap */ true);
+  auto buffer = allocator().malloc(size);
   if (size && !buffer.ptr()) {
     std::ostringstream msg;
     msg << "[malloc] Unable to allocate " << size << " bytes.";
@@ -22,45 +21,4 @@ void free(Buffer buffer) {
   allocator().free(buffer);
 }
 
-Buffer CommonAllocator::malloc(size_t size, bool) {
-  void* ptr = std::malloc(size + sizeof(size_t));
-  if (ptr != nullptr) {
-    *static_cast<size_t*>(ptr) = size;
-  }
-  return Buffer{ptr};
-}
-
-void CommonAllocator::free(Buffer buffer) {
-  std::free(buffer.ptr());
-}
-
-size_t CommonAllocator::size(Buffer buffer) const {
-  if (buffer.ptr() == nullptr) {
-    return 0;
-  }
-  return *static_cast<size_t*>(buffer.ptr());
-}
-
-Buffer malloc_or_wait(size_t size) {
-  auto buffer = allocator().malloc(size);
-
-  while (size && !buffer.ptr() && scheduler::n_active_tasks() > 0) {
-    scheduler::wait_for_one();
-    buffer = allocator().malloc(size);
-  }
-
-  // Try swapping if needed
-  if (size && !buffer.ptr()) {
-    buffer = allocator().malloc(size, /* allow_swap = */ true);
-  }
-
-  if (size && !buffer.ptr()) {
-    std::ostringstream msg;
-    msg << "[malloc_or_wait] Unable to allocate " << size << " bytes.";
-    throw std::runtime_error(msg.str());
-  }
-
-  return buffer;
-}
-
 } // namespace mlx::core::allocator

mlx/allocator.h

@@ -32,14 +32,10 @@ Buffer malloc(size_t size);
 
 void free(Buffer buffer);
 
-// Wait for running tasks to finish and free up memory
-// if allocation fails
-Buffer malloc_or_wait(size_t size);
-
 class Allocator {
   /** Abstract base class for a memory allocator. */
  public:
-  virtual Buffer malloc(size_t size, bool allow_swap = false) = 0;
+  virtual Buffer malloc(size_t size) = 0;
   virtual void free(Buffer buffer) = 0;
   virtual size_t size(Buffer buffer) const = 0;
 
@@ -53,16 +49,4 @@ class Allocator {
 
 Allocator& allocator();
 
-class CommonAllocator : public Allocator {
-  /** A general CPU allocator. */
- public:
-  virtual Buffer malloc(size_t size, bool allow_swap = false) override;
-  virtual void free(Buffer buffer) override;
-  virtual size_t size(Buffer buffer) const override;
-
- private:
-  CommonAllocator() = default;
-  friend Allocator& allocator();
-};
-
 } // namespace mlx::core::allocator

mlx/array.cpp

@@ -56,6 +56,18 @@ std::vector<array> array::make_arrays(
   return outputs;
 }
 
+array array::unsafe_weak_copy(const array& other) {
+  auto cpy = array(other.shape(), other.dtype(), nullptr, {});
+  cpy.set_data(
+      other.buffer(),
+      other.data_size(),
+      other.strides(),
+      other.flags(),
+      [](auto) {});
+  cpy.array_desc_->data_ptr = other.array_desc_->data_ptr;
+  return cpy;
+}
+
 array::array(std::initializer_list<float> data)
     : array_desc_(std::make_shared<ArrayDesc>(
           Shape{static_cast<ShapeElem>(data.size())},
@@ -76,35 +88,27 @@ array::array(allocator::Buffer data, Shape shape, Dtype dtype, Deleter deleter)
   set_data(data, deleter);
 }
 
-array::array(
-    allocator::Buffer data,
-    Shape shape,
-    Dtype dtype,
-    Strides strides,
-    size_t data_size,
-    Flags flags,
-    Deleter deleter)
-    : array_desc_(std::make_shared<ArrayDesc>(std::move(shape), dtype)) {
-  set_data(data, data_size, std::move(strides), flags, deleter);
-}
-
 void array::detach() {
+  array_desc_->primitive = nullptr;
+  for (auto& s : array_desc_->siblings) {
+    s.array_desc_->primitive = nullptr;
+  }
   for (auto& s : array_desc_->siblings) {
     s.array_desc_->inputs.clear();
     s.array_desc_->siblings.clear();
     s.array_desc_->position = 0;
-    s.array_desc_->primitive = nullptr;
   }
   array_desc_->inputs.clear();
   array_desc_->siblings.clear();
   array_desc_->position = 0;
-  array_desc_->primitive = nullptr;
 }
 
 bool array::is_available() const {
   if (status() == Status::available) {
     return true;
-  } else if (status() == Status::evaluated && event().is_signaled()) {
+  } else if (
+      status() == Status::evaluated &&
+      (!event().valid() || event().is_signaled())) {
     set_status(Status::available);
     return true;
   }
@@ -113,7 +117,10 @@ bool array::is_available() const {
 
 void array::wait() {
   if (!is_available()) {
-    event().wait();
+    if (event().valid()) {
+      event().wait();
+      detach_event();
+    }
     set_status(Status::available);
   }
 }
@@ -174,34 +181,13 @@ void array::copy_shared_buffer(const array& other) {
   copy_shared_buffer(other, other.strides(), other.flags(), other.data_size());
 }
 
-void array::move_shared_buffer(
-    array other,
-    const Strides& strides,
-    Flags flags,
-    size_t data_size,
-    size_t offset /* = 0 */) {
-  array_desc_->data = std::move(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;
-  auto data_ptr = other.array_desc_->data_ptr;
-  other.array_desc_->data_ptr = nullptr;
-  array_desc_->data_ptr =
-      static_cast<void*>(static_cast<char*>(data_ptr) + char_offset);
-}
-
-void array::move_shared_buffer(array other) {
-  move_shared_buffer(other, other.strides(), other.flags(), other.data_size());
-}
-
 array::~array() {
   if (array_desc_ == nullptr) {
     return;
   }
 
-  // Ignore arrays that might be detached during eval
-  if (status() == array::Status::scheduled) {
+  // Detached/detaching
+  if (array_desc_->primitive == nullptr) {
     return;
   }
 

mlx/array.h

@@ -199,6 +199,13 @@ class array {
       const std::shared_ptr<Primitive>& primitive,
       const std::vector<array>& inputs);
 
+  /**
+   * Get a new array that refers to the same data as the input but with a
+   * non-owning pointer to it. Note the array is detached from the graph and has
+   * no inputs, siblings or primitive.
+   */
+  static array unsafe_weak_copy(const array& other);
+
   /** A unique identifier for an array. */
   std::uintptr_t id() const {
     return reinterpret_cast<std::uintptr_t>(array_desc_.get());
@@ -243,18 +250,6 @@ class array {
     bool col_contiguous : 1;
   };
 
-  /** Build an array from all the info held by the array description. Including
-   * the buffer, strides, flags.
-   */
-  explicit array(
-      allocator::Buffer data,
-      Shape shape,
-      Dtype dtype,
-      Strides strides,
-      size_t data_size,
-      Flags flags,
-      Deleter deleter = allocator::free);
-
   /** The array's primitive. */
   Primitive& primitive() const {
     return *(array_desc_->primitive);
@@ -365,11 +360,6 @@ class array {
     // For example, the status of `x` in `auto x = a + b`.
     unscheduled,
 
-    // The ouptut of a computation which has been scheduled but `eval_*` has
-    // not yet been called on the array's primitive. A possible
-    // status of `x` in `auto x = a + b; eval(x);`
-    scheduled,
-
     // The array's `eval_*` function has been run, but the computation is not
     // necessarily complete. The array will have memory allocated and if it is
     // not a tracer then it will be detached from the graph.
@@ -406,6 +396,10 @@ class array {
     array_desc_->event = std::move(e);
   }
 
+  void detach_event() const {
+    array_desc_->event = Event{};
+  }
+
   // Mark the array as a tracer array (true) or not.
   void set_tracer(bool is_tracer) {
     array_desc_->is_tracer = is_tracer;
@@ -431,15 +425,6 @@ class array {
 
   void copy_shared_buffer(const array& other);
 
-  void move_shared_buffer(
-      array other,
-      const Strides& strides,
-      Flags flags,
-      size_t data_size,
-      size_t offset = 0);
-
-  void move_shared_buffer(array other);
-
   void overwrite_descriptor(const array& other) {
     array_desc_ = other.array_desc_;
   }

mlx/backend/common/binary.h

@@ -38,25 +38,20 @@ inline void set_binary_op_output_data(
     const array& a,
     const array& b,
     array& out,
-    BinaryOpType bopt,
-    bool donate_with_move = false) {
+    BinaryOpType bopt) {
   bool b_donatable = is_donatable(b, out);
   bool a_donatable = is_donatable(a, out);
   switch (bopt) {
     case BinaryOpType::ScalarScalar:
       out.set_data(
-          allocator::malloc_or_wait(out.itemsize()), 1, a.strides(), a.flags());
+          allocator::malloc(out.itemsize()), 1, a.strides(), a.flags());
       break;
     case BinaryOpType::ScalarVector:
       if (b_donatable) {
-        if (donate_with_move) {
-          out.move_shared_buffer(b);
-        } else {
-          out.copy_shared_buffer(b);
-        }
+        out.copy_shared_buffer(b);
       } else {
         out.set_data(
-            allocator::malloc_or_wait(b.data_size() * out.itemsize()),
+            allocator::malloc(b.data_size() * out.itemsize()),
             b.data_size(),
             b.strides(),
             b.flags());
@@ -64,14 +59,10 @@ inline void set_binary_op_output_data(
       break;
     case BinaryOpType::VectorScalar:
       if (a_donatable) {
-        if (donate_with_move) {
-          out.move_shared_buffer(a);
-        } else {
-          out.copy_shared_buffer(a);
-        }
+        out.copy_shared_buffer(a);
       } else {
         out.set_data(
-            allocator::malloc_or_wait(a.data_size() * out.itemsize()),
+            allocator::malloc(a.data_size() * out.itemsize()),
             a.data_size(),
             a.strides(),
             a.flags());
@@ -79,20 +70,12 @@ inline void set_binary_op_output_data(
       break;
     case BinaryOpType::VectorVector:
       if (a_donatable) {
-        if (donate_with_move) {
-          out.move_shared_buffer(a);
-        } else {
-          out.copy_shared_buffer(a);
-        }
+        out.copy_shared_buffer(a);
       } else if (b_donatable) {
-        if (donate_with_move) {
-          out.move_shared_buffer(b);
-        } else {
-          out.copy_shared_buffer(b);
-        }
+        out.copy_shared_buffer(b);
       } else {
         out.set_data(
-            allocator::malloc_or_wait(a.data_size() * out.itemsize()),
+            allocator::malloc(a.data_size() * out.itemsize()),
             a.data_size(),
             a.strides(),
             a.flags());
@@ -100,20 +83,12 @@ inline void set_binary_op_output_data(
       break;
     case BinaryOpType::General:
       if (a_donatable && a.flags().row_contiguous && a.size() == out.size()) {
-        if (donate_with_move) {
-          out.move_shared_buffer(a);
-        } else {
-          out.copy_shared_buffer(a);
-        }
+        out.copy_shared_buffer(a);
       } else if (
           b_donatable && b.flags().row_contiguous && b.size() == out.size()) {
-        if (donate_with_move) {
-          out.move_shared_buffer(b);
-        } else {
-          out.copy_shared_buffer(b);
-        }
+        out.copy_shared_buffer(b);
       } else {
-        out.set_data(allocator::malloc_or_wait(out.nbytes()));
+        out.set_data(allocator::malloc(out.nbytes()));
       }
       break;
   }

mlx/backend/common/common.cpp

@@ -39,7 +39,7 @@ void AsStrided::eval(const std::vector<array>& inputs, array& out) {
   // rely on data_size anyway.
   size_t data_size = out.size();
 
-  return move_or_copy(in, out, strides_, flags, data_size, offset_);
+  return out.copy_shared_buffer(in, strides_, flags, data_size, offset_);
 }
 
 void broadcast(const array& in, array& out) {
@@ -56,7 +56,7 @@ void broadcast(const array& in, array& out) {
   if (out.size() > in.size()) {
     flags.row_contiguous = flags.col_contiguous = false;
   }
-  move_or_copy(in, out, strides, flags, in.data_size());
+  out.copy_shared_buffer(in, strides, flags, in.data_size());
 }
 
 void Broadcast::eval(const std::vector<array>& inputs, array& out) {
@@ -69,7 +69,7 @@ void BroadcastAxes::eval(const std::vector<array>& inputs, array& out) {
 
 void Copy::eval(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
-  move_or_copy(inputs[0], out);
+  out.copy_shared_buffer(inputs[0]);
 }
 
 void CustomTransforms::eval(
@@ -78,7 +78,7 @@ void CustomTransforms::eval(
   assert(inputs.size() > outputs.size());
   for (int i = 0, j = inputs.size() - outputs.size(); i < outputs.size();
        i++, j++) {
-    move_or_copy(inputs[j], outputs[i]);
+    outputs[i].copy_shared_buffer(inputs[j]);
   }
 }
 
@@ -87,7 +87,7 @@ void Depends::eval(
     std::vector<array>& outputs) {
   assert(inputs.size() > outputs.size());
   for (int i = 0; i < outputs.size(); i++) {
-    move_or_copy(inputs[i], outputs[i]);
+    outputs[i].copy_shared_buffer(inputs[i]);
   }
 }
 
@@ -98,12 +98,12 @@ void ExpandDims::eval(const std::vector<array>& inputs, array& out) {
   for (auto ax : axes_) {
     strides.insert(strides.begin() + ax, 1);
   }
-  move_or_copy(in, out, strides, in.flags(), in.data_size());
+  out.copy_shared_buffer(in, strides, in.flags(), in.data_size());
 }
 
 void NumberOfElements::eval(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
-  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+  out.set_data(allocator::malloc(out.nbytes()));
 
   double numel = 1;
   for (auto ax : axes_) {
@@ -210,7 +210,7 @@ void shared_buffer_reshape(
     auto max_dim = std::max_element(out.shape().begin(), out.shape().end());
     flags.col_contiguous = out.size() <= 1 || out.size() == *max_dim;
   }
-  move_or_copy(in, out, out_strides, flags, in.data_size());
+  out.copy_shared_buffer(in, out_strides, flags, in.data_size());
 }
 
 void Split::eval(
@@ -276,12 +276,12 @@ void Squeeze::eval(const std::vector<array>& inputs, array& out) {
       strides.push_back(in.strides(i));
     }
   }
-  move_or_copy(in, out, strides, in.flags(), in.data_size());
+  out.copy_shared_buffer(in, strides, in.flags(), in.data_size());
 }
 
 void StopGradient::eval(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
-  move_or_copy(inputs[0], out);
+  out.copy_shared_buffer(inputs[0]);
 }
 
 void Transpose::eval(const std::vector<array>& inputs, array& out) {
@@ -315,7 +315,7 @@ void Transpose::eval(const std::vector<array>& inputs, array& out) {
       b_stride *= out.shape(ri);
     }
   }
-  move_or_copy(in, out, out_strides, flags, in.data_size());
+  out.copy_shared_buffer(in, out_strides, flags, in.data_size());
 }
 
 } // namespace mlx::core

mlx/backend/common/compiled.cpp

@@ -161,8 +161,7 @@ void compiled_allocate_outputs(
     std::vector<array>& outputs,
     const std::vector<array>& inputs_,
     const std::unordered_set<uintptr_t>& constant_ids_,
-    bool contiguous,
-    bool move_buffers /* = false */) {
+    bool contiguous) {
   if (contiguous) {
     int o = 0;
     Strides strides;
@@ -178,11 +177,7 @@ void compiled_allocate_outputs(
       if (in.itemsize() == outputs[o].itemsize() && !is_scalar(in) &&
           in.is_donatable() &&
           constant_ids_.find(inputs_[i].id()) == constant_ids_.end()) {
-        if (move_buffers) {
-          outputs[o++].move_shared_buffer(in);
-        } else {
-          outputs[o++].copy_shared_buffer(in);
-        }
+        outputs[o++].copy_shared_buffer(in);
       }
       // Get representative input flags to properly set non-donated outputs
       if (strides.empty() && in.size() == outputs[0].size()) {
@@ -193,7 +188,7 @@ void compiled_allocate_outputs(
     }
     for (; o < outputs.size(); ++o) {
       outputs[o].set_data(
-          allocator::malloc_or_wait(data_size * outputs[o].itemsize()),
+          allocator::malloc(data_size * outputs[o].itemsize()),
           data_size,
           strides,
           flags);
@@ -210,18 +205,13 @@ void compiled_allocate_outputs(
       if (in.flags().row_contiguous && in.size() == outputs[o].size() &&
           in.itemsize() == outputs[o].itemsize() && in.is_donatable() &&
           constant_ids_.find(inputs_[i].id()) == constant_ids_.end()) {
-        if (move_buffers) {
-          outputs[o].move_shared_buffer(
-              in, outputs[o].strides(), in.flags(), in.data_size());
-        } else {
-          outputs[o].copy_shared_buffer(
-              in, outputs[o].strides(), in.flags(), in.data_size());
-        }
+        outputs[o].copy_shared_buffer(
+            in, outputs[o].strides(), in.flags(), in.data_size());
         o++;
       }
     }
     for (; o < outputs.size(); ++o) {
-      outputs[o].set_data(allocator::malloc_or_wait(outputs[o].nbytes()));
+      outputs[o].set_data(allocator::malloc(outputs[o].nbytes()));
     }
   }
 }

mlx/backend/common/compiled.h

@@ -62,7 +62,6 @@ void compiled_allocate_outputs(
     std::vector<array>& outputs,
     const std::vector<array>& inputs_,
     const std::unordered_set<uintptr_t>& constant_ids_,
-    bool contiguous,
-    bool move_buffers = false);
+    bool contiguous);
 
 } // namespace mlx::core

mlx/backend/common/copy.h

@@ -22,4 +22,25 @@ enum class CopyType {
   GeneralGeneral
 };
 
+inline bool set_copy_output_data(const array& in, array& out, CopyType ctype) {
+  if (ctype == CopyType::Vector) {
+    // If the input is donateable, we are doing a vector copy and the types
+    // have the same size, then the input buffer can hold the output.
+    if (in.is_donatable() && in.itemsize() == out.itemsize()) {
+      out.copy_shared_buffer(in);
+      return true;
+    } else {
+      out.set_data(
+          allocator::malloc(in.data_size() * out.itemsize()),
+          in.data_size(),
+          in.strides(),
+          in.flags());
+      return false;
+    }
+  } else {
+    out.set_data(allocator::malloc(out.nbytes()));
+    return false;
+  }
+}
+
 } // namespace mlx::core

mlx/backend/common/load.cpp

@@ -3,7 +3,8 @@
 #include <algorithm>
 #include <utility>
 
-#include "mlx/backend/common/load.h"
+#include "mlx/primitives.h"
+#include "mlx/scheduler.h"
 
 namespace {
 
@@ -26,26 +27,31 @@ void swap_endianness(uint8_t* data_bytes, size_t N) {
 
 namespace mlx::core {
 
-void load(
-    array& out,
-    size_t offset,
-    const std::shared_ptr<io::Reader>& reader,
-    bool swap_endianness_) {
-  reader->read(out.data<char>(), out.nbytes(), offset);
-
-  if (swap_endianness_) {
-    switch (out.itemsize()) {
-      case 2:
-        swap_endianness<2>(out.data<uint8_t>(), out.data_size());
-        break;
-      case 4:
-        swap_endianness<4>(out.data<uint8_t>(), out.data_size());
-        break;
-      case 8:
-        swap_endianness<8>(out.data<uint8_t>(), out.data_size());
-        break;
+void Load::eval_cpu(const std::vector<array>& inputs, array& out) {
+  out.set_data(allocator::malloc(out.nbytes()));
+  auto read_task = [out_ptr = out.data<char>(),
+                    size = out.size(),
+                    itemsize = out.itemsize(),
+                    offset = offset_,
+                    reader = reader_,
+                    swap_endianness_ = swap_endianness_]() mutable {
+    reader->read(out_ptr, size * itemsize, offset);
+    if (swap_endianness_) {
+      switch (itemsize) {
+        case 2:
+          swap_endianness<2>(reinterpret_cast<uint8_t*>(out_ptr), size);
+          break;
+        case 4:
+          swap_endianness<4>(reinterpret_cast<uint8_t*>(out_ptr), size);
+          break;
+        case 8:
+          swap_endianness<8>(reinterpret_cast<uint8_t*>(out_ptr), size);
+          break;
+      }
     }
-  }
+  };
+  auto fut = io::thread_pool().enqueue(std::move(read_task)).share();
+  scheduler::enqueue(stream(), [fut = std::move(fut)]() { fut.wait(); });
 }
 
 } // namespace mlx::core

mlx/backend/common/load.h

@@ -1,14 +0,0 @@
-// Copyright © 2024 Apple Inc.
-
-#include "mlx/array.h"
-#include "mlx/io/load.h"
-
-namespace mlx::core {
-
-void load(
-    array& out,
-    size_t offset,
-    const std::shared_ptr<io::Reader>& reader,
-    bool swap_endianess);
-
-} // namespace mlx::core

mlx/backend/common/slicing.cpp

@@ -14,6 +14,10 @@ std::tuple<int64_t, Strides> prepare_slice(
     data_offset += start_indices[i] * in.strides()[i];
     inp_strides[i] = in.strides()[i] * strides[i];
   }
+  // Normalize the offset
+  if (data_offset < 0) {
+    data_offset += in.data_size();
+  }
   return std::make_tuple(data_offset, inp_strides);
 }
 
@@ -32,7 +36,7 @@ void shared_buffer_slice(
   flags.col_contiguous = is_col_contiguous;
   flags.contiguous = (no_bsx_size == data_size);
 
-  move_or_copy(in, out, out_strides, flags, data_size, data_offset);
+  out.copy_shared_buffer(in, out_strides, flags, data_size, data_offset);
 }
 
 void slice(
@@ -54,9 +58,10 @@ void slice(
       data_end += end_idx * in.strides()[i];
     }
   }
-  // data_end can be -1
-  size_t data_size =
-      data_end < 0 ? (data_offset - data_end) : (data_end - data_offset);
+  if (data_end < 0) {
+    data_end += in.data_size();
+  }
+  size_t data_size = (data_end - data_offset);
   shared_buffer_slice(in, inp_strides, data_offset, data_size, out);
 }
 

mlx/backend/common/ternary.h

@@ -36,15 +36,10 @@ inline void set_ternary_op_output_data(
     const array& b,
     const array& c,
     array& out,
-    TernaryOpType topt,
-    bool donate_with_move = false) {
-  auto maybe_donate = [&out, donate_with_move](const array& x) {
+    TernaryOpType topt) {
+  auto maybe_donate = [&out](const array& x) {
     if (is_donatable(x, out)) {
-      if (donate_with_move) {
-        out.move_shared_buffer(x);
-      } else {
-        out.copy_shared_buffer(x);
-      }
+      out.copy_shared_buffer(x);
       return true;
     }
     return false;
@@ -53,12 +48,12 @@ inline void set_ternary_op_output_data(
   switch (topt) {
     case TernaryOpType::ScalarScalarScalar:
       out.set_data(
-          allocator::malloc_or_wait(out.itemsize()), 1, b.strides(), b.flags());
+          allocator::malloc(out.itemsize()), 1, b.strides(), b.flags());
       break;
     case TernaryOpType::VectorVectorVector:
       if (!(maybe_donate(a) || maybe_donate(b) || maybe_donate(c))) {
         out.set_data(
-            allocator::malloc_or_wait(out.itemsize() * b.data_size()),
+            allocator::malloc(out.itemsize() * b.data_size()),
             b.data_size(),
             b.strides(),
             b.flags());
@@ -69,7 +64,7 @@ inline void set_ternary_op_output_data(
       if (!((a.flags().row_contiguous && maybe_donate(a)) ||
             (b.flags().row_contiguous && maybe_donate(b)) ||
             (c.flags().row_contiguous && maybe_donate(c)))) {
-        out.set_data(allocator::malloc_or_wait(out.nbytes()));
+        out.set_data(allocator::malloc(out.nbytes()));
       }
       break;
   }

mlx/backend/common/utils.cpp

@@ -4,28 +4,6 @@
 
 namespace mlx::core {
 
-void move_or_copy(const array& in, array& out) {
-  if (in.is_donatable()) {
-    out.move_shared_buffer(in);
-  } else {
-    out.copy_shared_buffer(in);
-  }
-}
-
-void move_or_copy(
-    const array& in,
-    array& out,
-    const Strides& strides,
-    array::Flags flags,
-    size_t data_size,
-    size_t offset /* = 0 */) {
-  if (in.is_donatable()) {
-    out.move_shared_buffer(in, strides, flags, data_size, offset);
-  } else {
-    out.copy_shared_buffer(in, strides, flags, data_size, offset);
-  }
-}
-
 std::tuple<Shape, std::vector<Strides>> collapse_contiguous_dims(
     const Shape& shape,
     const std::vector<Strides>& strides,

mlx/backend/common/utils.h

@@ -159,15 +159,6 @@ inline bool is_donatable(const array& in, const array& out) {
       in.buffer_size() <= out.nbytes() + donation_extra;
 }
 
-void move_or_copy(const array& in, array& out);
-void move_or_copy(
-    const array& in,
-    array& out,
-    const Strides& strides,
-    array::Flags flags,
-    size_t data_size,
-    size_t offset = 0);
-
 std::pair<bool, Strides> prepare_reshape(const array& in, const array& out);
 
 void shared_buffer_reshape(

mlx/backend/cpu/CMakeLists.txt

@@ -44,7 +44,9 @@ target_sources(
           ${CMAKE_CURRENT_SOURCE_DIR}/binary.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/conv.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/copy.cpp
+          ${CMAKE_CURRENT_SOURCE_DIR}/distributed.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/eigh.cpp
+          ${CMAKE_CURRENT_SOURCE_DIR}/encoder.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/fft.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/hadamard.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/matmul.cpp
@@ -56,6 +58,7 @@ target_sources(
           ${CMAKE_CURRENT_SOURCE_DIR}/scan.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/select.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/softmax.cpp
+          ${CMAKE_CURRENT_SOURCE_DIR}/logsumexp.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/sort.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/threefry.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/indexing.cpp
@@ -65,13 +68,14 @@ target_sources(
           ${CMAKE_CURRENT_SOURCE_DIR}/inverse.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/cholesky.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/unary.cpp
+          ${CMAKE_CURRENT_SOURCE_DIR}/eval.cpp
           ${CMAKE_CURRENT_BINARY_DIR}/compiled_preamble.cpp)
 
 if(MLX_BUILD_ACCELERATE)
   target_sources(mlx PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/gemms/bnns.cpp)
 else()
-  target_sources(mlx PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/gemms/no_fp16.cpp
-                             ${CMAKE_CURRENT_SOURCE_DIR}/gemms/no_bf16.cpp)
+  target_sources(mlx PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/gemms/simd_fp16.cpp
+                             ${CMAKE_CURRENT_SOURCE_DIR}/gemms/simd_bf16.cpp)
 endif()
 
 if(IOS)

mlx/backend/cpu/arange.h

@@ -2,76 +2,27 @@
 
 #pragma once
 
-#include "mlx/allocator.h"
 #include "mlx/array.h"
+#include "mlx/backend/cpu/encoder.h"
 
 namespace mlx::core {
 
 namespace {
 
 template <typename T>
-void arange(T start, T next, array& out, size_t size) {
+void arange(T start, T next, array& out, size_t size, Stream stream) {
   auto ptr = out.data<T>();
   auto step_size = next - start;
-  for (int i = 0; i < size; ++i) {
-    ptr[i] = start;
-    start += step_size;
-  }
+  auto& encoder = cpu::get_command_encoder(stream);
+  encoder.set_output_array(out);
+  encoder.dispatch([ptr, start, step_size, size]() mutable {
+    for (int i = 0; i < size; ++i) {
+      ptr[i] = start;
+      start += step_size;
+    }
+  });
 }
 
 } // namespace
 
-void arange(
-    const std::vector<array>& inputs,
-    array& out,
-    double start,
-    double step) {
-  assert(inputs.size() == 0);
-  out.set_data(allocator::malloc_or_wait(out.nbytes()));
-  switch (out.dtype()) {
-    case bool_:
-      throw std::runtime_error("Bool type unsupported for arange.");
-      break;
-    case uint8:
-      arange<uint8_t>(start, start + step, out, out.size());
-      break;
-    case uint16:
-      arange<uint16_t>(start, start + step, out, out.size());
-      break;
-    case uint32:
-      arange<uint32_t>(start, start + step, out, out.size());
-      break;
-    case uint64:
-      arange<uint64_t>(start, start + step, out, out.size());
-      break;
-    case int8:
-      arange<int8_t>(start, start + step, out, out.size());
-      break;
-    case int16:
-      arange<int16_t>(start, start + step, out, out.size());
-      break;
-    case int32:
-      arange<int32_t>(start, start + step, out, out.size());
-      break;
-    case int64:
-      arange<int64_t>(start, start + step, out, out.size());
-      break;
-    case float16:
-      arange<float16_t>(start, start + step, out, out.size());
-      break;
-    case float32:
-      arange<float>(start, start + step, out, out.size());
-      break;
-    case float64:
-      arange<double>(start, start + step, out, out.size());
-      break;
-    case bfloat16:
-      arange<bfloat16_t>(start, start + step, out, out.size());
-      break;
-    case complex64:
-      arange<complex64_t>(start, start + step, out, out.size());
-      break;
-  }
-}
-
 } // namespace mlx::core

mlx/backend/cpu/arg_reduce.cpp

@@ -3,6 +3,7 @@
 #include <cassert>
 
 #include "mlx/backend/common/utils.h"
+#include "mlx/backend/cpu/encoder.h"
 #include "mlx/primitives.h"
 
 namespace mlx::core {
@@ -17,15 +18,18 @@ void arg_reduce(const array& in, array& out, const OpT& op, int axis) {
   Shape shape = in.shape();
   strides.erase(strides.begin() + axis);
   shape.erase(shape.begin() + axis);
+  auto in_ptr = in.data<InT>();
+  auto out_ptr = out.data<uint32_t>();
+
   for (uint32_t i = 0; i < out.size(); ++i) {
     auto loc = elem_to_loc(i, shape, strides);
-    auto in_ptr = in.data<InT>() + loc;
+    auto local_in_ptr = in_ptr + loc;
     uint32_t ind_v = 0;
-    InT v = (*in_ptr);
-    for (uint32_t j = 0; j < axis_size; ++j, in_ptr += axis_stride) {
-      op(j, (*in_ptr), &ind_v, &v);
+    InT v = (*local_in_ptr);
+    for (uint32_t j = 0; j < axis_size; ++j, local_in_ptr += axis_stride) {
+      op(j, (*local_in_ptr), &ind_v, &v);
     }
-    out.data<uint32_t>()[i] = ind_v;
+    out_ptr[i] = ind_v;
   }
 }
 
@@ -64,52 +68,59 @@ void arg_reduce_dispatch(
 void ArgReduce::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   auto& in = inputs[0];
-  out.set_data(allocator::malloc_or_wait(out.nbytes()));
-
-  switch (in.dtype()) {
-    case bool_:
-      arg_reduce_dispatch<bool>(in, out, reduce_type_, axis_);
-      break;
-    case uint8:
-      arg_reduce_dispatch<uint8_t>(in, out, reduce_type_, axis_);
-      break;
-    case uint16:
-      arg_reduce_dispatch<uint16_t>(in, out, reduce_type_, axis_);
-      break;
-    case uint32:
-      arg_reduce_dispatch<uint32_t>(in, out, reduce_type_, axis_);
-      break;
-    case uint64:
-      arg_reduce_dispatch<uint64_t>(in, out, reduce_type_, axis_);
-      break;
-    case int8:
-      arg_reduce_dispatch<int8_t>(in, out, reduce_type_, axis_);
-      break;
-    case int16:
-      arg_reduce_dispatch<int16_t>(in, out, reduce_type_, axis_);
-      break;
-    case int32:
-      arg_reduce_dispatch<int32_t>(in, out, reduce_type_, axis_);
-      break;
-    case int64:
-      arg_reduce_dispatch<int64_t>(in, out, reduce_type_, axis_);
-      break;
-    case float16:
-      arg_reduce_dispatch<float16_t>(in, out, reduce_type_, axis_);
-      break;
-    case float32:
-      arg_reduce_dispatch<float>(in, out, reduce_type_, axis_);
-      break;
-    case bfloat16:
-      arg_reduce_dispatch<bfloat16_t>(in, out, reduce_type_, axis_);
-      break;
-    case float64:
-      arg_reduce_dispatch<double>(in, out, reduce_type_, axis_);
-      break;
-    case complex64:
-      arg_reduce_dispatch<complex64_t>(in, out, reduce_type_, axis_);
-      break;
-  }
+  out.set_data(allocator::malloc(out.nbytes()));
+  auto& encoder = cpu::get_command_encoder(stream());
+  encoder.set_input_array(in);
+  encoder.set_output_array(out);
+  encoder.dispatch([in = array::unsafe_weak_copy(in),
+                    out = array::unsafe_weak_copy(out),
+                    reduce_type_ = reduce_type_,
+                    axis_ = axis_]() mutable {
+    switch (in.dtype()) {
+      case bool_:
+        arg_reduce_dispatch<bool>(in, out, reduce_type_, axis_);
+        break;
+      case uint8:
+        arg_reduce_dispatch<uint8_t>(in, out, reduce_type_, axis_);
+        break;
+      case uint16:
+        arg_reduce_dispatch<uint16_t>(in, out, reduce_type_, axis_);
+        break;
+      case uint32:
+        arg_reduce_dispatch<uint32_t>(in, out, reduce_type_, axis_);
+        break;
+      case uint64:
+        arg_reduce_dispatch<uint64_t>(in, out, reduce_type_, axis_);
+        break;
+      case int8:
+        arg_reduce_dispatch<int8_t>(in, out, reduce_type_, axis_);
+        break;
+      case int16:
+        arg_reduce_dispatch<int16_t>(in, out, reduce_type_, axis_);
+        break;
+      case int32:
+        arg_reduce_dispatch<int32_t>(in, out, reduce_type_, axis_);
+        break;
+      case int64:
+        arg_reduce_dispatch<int64_t>(in, out, reduce_type_, axis_);
+        break;
+      case float16:
+        arg_reduce_dispatch<float16_t>(in, out, reduce_type_, axis_);
+        break;
+      case float32:
+        arg_reduce_dispatch<float>(in, out, reduce_type_, axis_);
+        break;
+      case bfloat16:
+        arg_reduce_dispatch<bfloat16_t>(in, out, reduce_type_, axis_);
+        break;
+      case float64:
+        arg_reduce_dispatch<double>(in, out, reduce_type_, axis_);
+        break;
+      case complex64:
+        arg_reduce_dispatch<complex64_t>(in, out, reduce_type_, axis_);
+        break;
+    }
+  });
 }
 
 } // namespace mlx::core

mlx/backend/cpu/binary.cpp

@@ -8,6 +8,7 @@
 #include "mlx/backend/cpu/binary.h"
 #include "mlx/backend/cpu/binary_ops.h"
 #include "mlx/backend/cpu/binary_two.h"
+#include "mlx/backend/cpu/encoder.h"
 #include "mlx/primitives.h"
 #include "mlx/utils.h"
 
@@ -16,51 +17,218 @@ namespace mlx::core {
 namespace {
 
 template <typename Op>
-void comparison_op(const array& a, const array& b, array& out, Op op) {
-  switch (a.dtype()) {
-    case bool_:
-      binary_op<bool, bool>(a, b, out, op);
-      break;
-    case uint8:
-      binary_op<uint8_t, bool>(a, b, out, op);
-      break;
-    case uint16:
-      binary_op<uint16_t, bool>(a, b, out, op);
-      break;
-    case uint32:
-      binary_op<uint32_t, bool>(a, b, out, op);
-      break;
-    case uint64:
-      binary_op<uint64_t, bool>(a, b, out, op);
-      break;
-    case int8:
-      binary_op<int8_t, bool>(a, b, out, op);
-      break;
-    case int16:
-      binary_op<int16_t, bool>(a, b, out, op);
-      break;
-    case int32:
-      binary_op<int32_t, bool>(a, b, out, op);
-      break;
-    case int64:
-      binary_op<int64_t, bool>(a, b, out, op);
-      break;
-    case float16:
-      binary_op<float16_t, bool>(a, b, out, op);
-      break;
-    case float32:
-      binary_op<float, bool>(a, b, out, op);
-      break;
-    case float64:
-      binary_op<double, bool>(a, b, out, op);
-      break;
-    case bfloat16:
-      binary_op<bfloat16_t, bool>(a, b, out, op);
-      break;
-    case complex64:
-      binary_op<complex64_t, bool>(a, b, out, op);
-      break;
-  }
+void binary(const array& a, const array& b, array& out, Op op, Stream stream) {
+  auto bopt = get_binary_op_type(a, b);
+  set_binary_op_output_data(a, b, out, bopt);
+
+  auto& encoder = cpu::get_command_encoder(stream);
+  encoder.set_input_array(a);
+  encoder.set_input_array(b);
+  encoder.set_output_array(out);
+  encoder.dispatch([a = array::unsafe_weak_copy(a),
+                    b = array::unsafe_weak_copy(b),
+                    out = array::unsafe_weak_copy(out),
+                    bopt]() mutable {
+    switch (out.dtype()) {
+      case bool_:
+        binary_op<bool, Op>(a, b, out, bopt);
+        break;
+      case uint8:
+        binary_op<uint8_t, Op>(a, b, out, bopt);
+        break;
+      case uint16:
+        binary_op<uint16_t, Op>(a, b, out, bopt);
+        break;
+      case uint32:
+        binary_op<uint32_t, Op>(a, b, out, bopt);
+        break;
+      case uint64:
+        binary_op<uint64_t, Op>(a, b, out, bopt);
+        break;
+      case int8:
+        binary_op<int8_t, Op>(a, b, out, bopt);
+        break;
+      case int16:
+        binary_op<int16_t, Op>(a, b, out, bopt);
+        break;
+      case int32:
+        binary_op<int32_t, Op>(a, b, out, bopt);
+        break;
+      case int64:
+        binary_op<int64_t, Op>(a, b, out, bopt);
+        break;
+      case float16:
+        binary_op<float16_t, Op>(a, b, out, bopt);
+        break;
+      case float32:
+        binary_op<float, Op>(a, b, out, bopt);
+        break;
+      case float64:
+        binary_op<double, Op>(a, b, out, bopt);
+        break;
+      case bfloat16:
+        binary_op<bfloat16_t, Op>(a, b, out, bopt);
+        break;
+      case complex64:
+        binary_op<complex64_t, Op>(a, b, out, bopt);
+        break;
+    }
+  });
+}
+
+template <typename Op>
+void comparison_op(
+    const array& a,
+    const array& b,
+    array& out,
+    Op op,
+    Stream stream) {
+  auto bopt = get_binary_op_type(a, b);
+  set_binary_op_output_data(a, b, out, bopt);
+
+  auto& encoder = cpu::get_command_encoder(stream);
+  encoder.set_input_array(a);
+  encoder.set_input_array(b);
+  encoder.set_output_array(out);
+  encoder.dispatch([a = array::unsafe_weak_copy(a),
+                    b = array::unsafe_weak_copy(b),
+                    out = array::unsafe_weak_copy(out),
+                    bopt]() mutable {
+    switch (a.dtype()) {
+      case bool_:
+        binary_op<bool, bool, Op>(a, b, out, bopt);
+        break;
+      case uint8:
+        binary_op<uint8_t, bool, Op>(a, b, out, bopt);
+        break;
+      case uint16:
+        binary_op<uint16_t, bool, Op>(a, b, out, bopt);
+        break;
+      case uint32:
+        binary_op<uint32_t, bool, Op>(a, b, out, bopt);
+        break;
+      case uint64:
+        binary_op<uint64_t, bool, Op>(a, b, out, bopt);
+        break;
+      case int8:
+        binary_op<int8_t, bool, Op>(a, b, out, bopt);
+        break;
+      case int16:
+        binary_op<int16_t, bool, Op>(a, b, out, bopt);
+        break;
+      case int32:
+        binary_op<int32_t, bool, Op>(a, b, out, bopt);
+        break;
+      case int64:
+        binary_op<int64_t, bool, Op>(a, b, out, bopt);
+        break;
+      case float16:
+        binary_op<float16_t, bool, Op>(a, b, out, bopt);
+        break;
+      case float32:
+        binary_op<float, bool, Op>(a, b, out, bopt);
+        break;
+      case float64:
+        binary_op<double, bool, Op>(a, b, out, bopt);
+        break;
+      case bfloat16:
+        binary_op<bfloat16_t, bool, Op>(a, b, out, bopt);
+        break;
+      case complex64:
+        binary_op<complex64_t, bool, Op>(a, b, out, bopt);
+        break;
+    }
+  });
+}
+
+template <typename Op>
+void binary_float(
+    const array& a,
+    const array& b,
+    array& out,
+    Op op,
+    Stream stream) {
+  auto bopt = get_binary_op_type(a, b);
+  set_binary_op_output_data(a, b, out, bopt);
+
+  auto& encoder = cpu::get_command_encoder(stream);
+  encoder.set_input_array(a);
+  encoder.set_input_array(b);
+  encoder.set_output_array(out);
+  encoder.dispatch([a = array::unsafe_weak_copy(a),
+                    b = array::unsafe_weak_copy(b),
+                    out = array::unsafe_weak_copy(out),
+                    bopt]() mutable {
+    switch (out.dtype()) {
+      case float16:
+        binary_op<float16_t, Op>(a, b, out, bopt);
+        break;
+      case float32:
+        binary_op<float, Op>(a, b, out, bopt);
+        break;
+      case float64:
+        binary_op<double, Op>(a, b, out, bopt);
+        break;
+      case bfloat16:
+        binary_op<bfloat16_t, Op>(a, b, out, bopt);
+        break;
+      default:
+        throw std::runtime_error(
+            "[binary_float] Only supports non-complex floating point types.");
+    }
+  });
+}
+
+template <typename Op>
+void binary_int(
+    const array& a,
+    const array& b,
+    array& out,
+    Op op,
+    Stream stream) {
+  auto bopt = get_binary_op_type(a, b);
+  set_binary_op_output_data(a, b, out, bopt);
+
+  auto& encoder = cpu::get_command_encoder(stream);
+  encoder.set_input_array(a);
+  encoder.set_input_array(b);
+  encoder.set_output_array(out);
+  encoder.dispatch([a = array::unsafe_weak_copy(a),
+                    b = array::unsafe_weak_copy(b),
+                    out = array::unsafe_weak_copy(out),
+                    bopt]() mutable {
+    switch (out.dtype()) {
+      case bool_:
+        binary_op<bool, Op>(a, b, out, bopt);
+      case uint8:
+        binary_op<uint8_t, Op>(a, b, out, bopt);
+        break;
+      case uint16:
+        binary_op<uint16_t, Op>(a, b, out, bopt);
+        break;
+      case uint32:
+        binary_op<uint32_t, Op>(a, b, out, bopt);
+        break;
+      case uint64:
+        binary_op<uint64_t, Op>(a, b, out, bopt);
+        break;
+      case int8:
+        binary_op<int8_t, Op>(a, b, out, bopt);
+        break;
+      case int16:
+        binary_op<int16_t, Op>(a, b, out, bopt);
+        break;
+      case int32:
+        binary_op<int32_t, Op>(a, b, out, bopt);
+        break;
+      case int64:
+        binary_op<int64_t, Op>(a, b, out, bopt);
+        break;
+      default:
+        throw std::runtime_error("[binary_int] Type not supported");
+        break;
+    }
+  });
 }
 
 } // namespace
@@ -69,7 +237,7 @@ void Add::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2);
   auto& a = inputs[0];
   auto& b = inputs[1];
-  binary(a, b, out, detail::Add());
+  binary(a, b, out, detail::Add(), stream());
 }
 
 void DivMod::eval_cpu(
@@ -78,70 +246,89 @@ void DivMod::eval_cpu(
   assert(inputs.size() == 2);
   auto& a = inputs[0];
   auto& b = inputs[1];
-  auto integral_op = [](auto x, auto y) {
-    return std::make_pair(x / y, x % y);
-  };
-  auto float_op = [](auto x, auto y) {
-    return std::make_pair(std::trunc(x / y), std::fmod(x, y));
-  };
-  switch (outputs[0].dtype()) {
-    case bool_:
-      binary_op<bool>(a, b, outputs, integral_op);
-    case uint8:
-      binary_op<uint8_t>(a, b, outputs, integral_op);
-      break;
-    case uint16:
-      binary_op<uint16_t>(a, b, outputs, integral_op);
-      break;
-    case uint32:
-      binary_op<uint32_t>(a, b, outputs, integral_op);
-      break;
-    case uint64:
-      binary_op<uint64_t>(a, b, outputs, integral_op);
-      break;
-    case int8:
-      binary_op<int8_t>(a, b, outputs, integral_op);
-      break;
-    case int16:
-      binary_op<int16_t>(a, b, outputs, integral_op);
-      break;
-    case int32:
-      binary_op<int32_t>(a, b, outputs, integral_op);
-      break;
-    case int64:
-      binary_op<int64_t>(a, b, outputs, integral_op);
-      break;
-    case float16:
-      binary_op<float16_t>(a, b, outputs, float_op);
-      break;
-    case float32:
-      binary_op<float>(a, b, outputs, float_op);
-      break;
-    case float64:
-      binary_op<double>(a, b, outputs, float_op);
-      break;
-    case bfloat16:
-      binary_op<bfloat16_t>(a, b, outputs, float_op);
-      break;
-    case complex64:
-      // Should never get here
-      throw std::runtime_error("[DivMod] Complex type not supported");
-      break;
-  }
+  auto bopt = get_binary_op_type(a, b);
+  auto& out_a = outputs[0];
+  auto& out_b = outputs[1];
+  set_binary_op_output_data(a, b, out_a, bopt);
+  set_binary_op_output_data(a, b, out_b, bopt);
+
+  auto& encoder = cpu::get_command_encoder(stream());
+  encoder.set_input_array(a);
+  encoder.set_input_array(b);
+  encoder.set_output_array(out_a);
+  encoder.set_output_array(out_b);
+
+  encoder.dispatch([a = array::unsafe_weak_copy(a),
+                    b = array::unsafe_weak_copy(b),
+                    out_a = array::unsafe_weak_copy(out_a),
+                    out_b = array::unsafe_weak_copy(out_b),
+                    bopt]() mutable {
+    auto integral_op = [](auto x, auto y) {
+      return std::make_pair(x / y, x % y);
+    };
+    auto float_op = [](auto x, auto y) {
+      return std::make_pair(std::trunc(x / y), std::fmod(x, y));
+    };
+
+    switch (out_a.dtype()) {
+      case bool_:
+        binary_op<bool>(a, b, out_a, out_b, integral_op, bopt);
+      case uint8:
+        binary_op<uint8_t>(a, b, out_a, out_b, integral_op, bopt);
+        break;
+      case uint16:
+        binary_op<uint16_t>(a, b, out_a, out_b, integral_op, bopt);
+        break;
+      case uint32:
+        binary_op<uint32_t>(a, b, out_a, out_b, integral_op, bopt);
+        break;
+      case uint64:
+        binary_op<uint64_t>(a, b, out_a, out_b, integral_op, bopt);
+        break;
+      case int8:
+        binary_op<int8_t>(a, b, out_a, out_b, integral_op, bopt);
+        break;
+      case int16:
+        binary_op<int16_t>(a, b, out_a, out_b, integral_op, bopt);
+        break;
+      case int32:
+        binary_op<int32_t>(a, b, out_a, out_b, integral_op, bopt);
+        break;
+      case int64:
+        binary_op<int64_t>(a, b, out_a, out_b, integral_op, bopt);
+        break;
+      case float16:
+        binary_op<float16_t>(a, b, out_a, out_b, float_op, bopt);
+        break;
+      case float32:
+        binary_op<float>(a, b, out_a, out_b, float_op, bopt);
+        break;
+      case float64:
+        binary_op<double>(a, b, out_a, out_b, float_op, bopt);
+        break;
+      case bfloat16:
+        binary_op<bfloat16_t>(a, b, out_a, out_b, float_op, bopt);
+        break;
+      case complex64:
+        // Should never get here
+        throw std::runtime_error("[DivMod] Complex type not supported");
+        break;
+    }
+  });
 }
 
 void Divide::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2);
   auto& a = inputs[0];
   auto& b = inputs[1];
-  binary(a, b, out, detail::Divide());
+  binary(a, b, out, detail::Divide(), stream());
 }
 
 void Remainder::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2);
   auto& a = inputs[0];
   auto& b = inputs[1];
-  binary(a, b, out, detail::Remainder());
+  binary(a, b, out, detail::Remainder(), stream());
 }
 
 void Equal::eval_cpu(const std::vector<array>& inputs, array& out) {
@@ -149,181 +336,143 @@ void Equal::eval_cpu(const std::vector<array>& inputs, array& out) {
   auto& a = inputs[0];
   auto& b = inputs[1];
   if (equal_nan_) {
-    switch (a.dtype()) {
-      case float16:
-        binary_op<float16_t, bool>(a, b, out, detail::NaNEqual());
-        break;
-      case float32:
-        binary_op<float, bool>(a, b, out, detail::NaNEqual());
-        break;
-      case float64:
-        binary_op<double, bool>(a, b, out, detail::NaNEqual());
-        break;
-      case bfloat16:
-        binary_op<bfloat16_t, bool>(a, b, out, detail::NaNEqual());
-        break;
-      case complex64:
-        binary_op<complex64_t, bool>(a, b, out, detail::NaNEqual());
-        break;
-      default:
-        throw std::runtime_error(
-            "[NanEqual::eval_cpu] Only for floating point types.");
-    }
+    auto bopt = get_binary_op_type(a, b);
+    set_binary_op_output_data(a, b, out, bopt);
+
+    auto& encoder = cpu::get_command_encoder(stream());
+    encoder.set_input_array(a);
+    encoder.set_input_array(b);
+    encoder.set_output_array(out);
+    encoder.dispatch([a = array::unsafe_weak_copy(a),
+                      b = array::unsafe_weak_copy(b),
+                      out = array::unsafe_weak_copy(out),
+                      bopt]() mutable {
+      switch (a.dtype()) {
+        case float16:
+          binary_op<float16_t, bool, detail::NaNEqual>(a, b, out, bopt);
+          break;
+        case float32:
+          binary_op<float, bool, detail::NaNEqual>(a, b, out, bopt);
+          break;
+        case float64:
+          binary_op<double, bool, detail::NaNEqual>(a, b, out, bopt);
+          break;
+        case bfloat16:
+          binary_op<bfloat16_t, bool, detail::NaNEqual>(a, b, out, bopt);
+          break;
+        case complex64:
+          binary_op<complex64_t, bool, detail::NaNEqual>(a, b, out, bopt);
+          break;
+        default:
+          throw std::runtime_error(
+              "[NanEqual::eval_cpu] Only for floating point types.");
+      }
+    });
   } else {
-    comparison_op(a, b, out, detail::Equal());
+    comparison_op(a, b, out, detail::Equal(), stream());
   }
 }
 
 void Greater::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2);
-  comparison_op(inputs[0], inputs[1], out, detail::Greater());
+  comparison_op(inputs[0], inputs[1], out, detail::Greater(), stream());
 }
 
 void GreaterEqual::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2);
-  comparison_op(inputs[0], inputs[1], out, detail::GreaterEqual());
+  comparison_op(inputs[0], inputs[1], out, detail::GreaterEqual(), stream());
 }
 
 void Less::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2);
-  comparison_op(inputs[0], inputs[1], out, detail::Less());
+  comparison_op(inputs[0], inputs[1], out, detail::Less(), stream());
 }
 
 void LessEqual::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2);
-  comparison_op(inputs[0], inputs[1], out, detail::LessEqual());
+  comparison_op(inputs[0], inputs[1], out, detail::LessEqual(), stream());
 }
 
 void LogAddExp::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2);
   auto& a = inputs[0];
   auto& b = inputs[1];
-  switch (out.dtype()) {
-    case float16:
-      binary_op<float16_t>(a, b, out, detail::LogAddExp());
-      break;
-    case float32:
-      binary_op<float>(a, b, out, detail::LogAddExp());
-      break;
-    case float64:
-      binary_op<double>(a, b, out, detail::LogAddExp());
-      break;
-    case bfloat16:
-      binary_op<bfloat16_t>(a, b, out, detail::LogAddExp());
-      break;
-    default:
-      throw std::runtime_error(
-          "[LogAddExp::eval_cpu] Only supports non-complex floating point types.");
-  }
+  binary_float(a, b, out, detail::LogAddExp(), stream());
 }
 
 void LogicalAnd::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2); // LogicalAnd requires two input arrays
   auto& in1 = inputs[0];
   auto& in2 = inputs[1];
-  binary(in1, in2, out, detail::LogicalAnd());
+  binary(in1, in2, out, detail::LogicalAnd(), stream());
 }
 
 void LogicalOr::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2); // LogicalOr requires two input arrays
   auto& in1 = inputs[0];
   auto& in2 = inputs[1];
-  binary(in1, in2, out, detail::LogicalOr());
+  binary(in1, in2, out, detail::LogicalOr(), stream());
 }
 
 void Maximum::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2);
   auto& a = inputs[0];
   auto& b = inputs[1];
-  binary(a, b, out, detail::Maximum());
+  binary(a, b, out, detail::Maximum(), stream());
 }
 
 void Minimum::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2);
   auto& a = inputs[0];
   auto& b = inputs[1];
-  binary(a, b, out, detail::Minimum());
+  binary(a, b, out, detail::Minimum(), stream());
 }
 
 void Multiply::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2);
   auto& a = inputs[0];
   auto& b = inputs[1];
-  binary(a, b, out, detail::Multiply());
+  binary(a, b, out, detail::Multiply(), stream());
 }
 
 void NotEqual::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2);
-  comparison_op(inputs[0], inputs[1], out, detail::NotEqual());
+  comparison_op(inputs[0], inputs[1], out, detail::NotEqual(), stream());
 }
 
 void Power::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2);
   auto& a = inputs[0];
   auto& b = inputs[1];
-  binary(a, b, out, detail::Power());
+  binary(a, b, out, detail::Power(), stream());
 }
 
 void Subtract::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2);
   auto& a = inputs[0];
   auto& b = inputs[1];
-  binary(a, b, out, detail::Subtract());
+  binary(a, b, out, detail::Subtract(), stream());
 }
 
 void BitwiseBinary::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2);
   auto& a = inputs[0];
   auto& b = inputs[1];
-  auto dispatch_type = [&a, &b, &out](auto op) {
-    switch (out.dtype()) {
-      case bool_:
-        binary_op<bool>(a, b, out, op);
-      case uint8:
-        binary_op<uint8_t>(a, b, out, op);
-        break;
-      case uint16:
-        binary_op<uint16_t>(a, b, out, op);
-        break;
-      case uint32:
-        binary_op<uint32_t>(a, b, out, op);
-        break;
-      case uint64:
-        binary_op<uint64_t>(a, b, out, op);
-        break;
-      case int8:
-        binary_op<int8_t>(a, b, out, op);
-        break;
-      case int16:
-        binary_op<int16_t>(a, b, out, op);
-        break;
-      case int32:
-        binary_op<int32_t>(a, b, out, op);
-        break;
-      case int64:
-        binary_op<int64_t>(a, b, out, op);
-        break;
-      default:
-        throw std::runtime_error(
-            "[BitwiseBinary::eval_cpu] Type not supported");
-        break;
-    }
-  };
   switch (op_) {
     case BitwiseBinary::And:
-      dispatch_type(detail::BitwiseAnd());
+      binary_int(a, b, out, detail::BitwiseAnd(), stream());
       break;
     case BitwiseBinary::Or:
-      dispatch_type(detail::BitwiseOr());
+      binary_int(a, b, out, detail::BitwiseOr(), stream());
       break;
     case BitwiseBinary::Xor:
-      dispatch_type(detail::BitwiseXor());
+      binary_int(a, b, out, detail::BitwiseXor(), stream());
       break;
     case BitwiseBinary::LeftShift:
-      dispatch_type(detail::LeftShift());
+      binary_int(a, b, out, detail::LeftShift(), stream());
       break;
     case BitwiseBinary::RightShift:
-      dispatch_type(detail::RightShift());
+      binary_int(a, b, out, detail::RightShift(), stream());
       break;
   }
 }
@@ -332,23 +481,7 @@ void ArcTan2::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2);
   const auto& a = inputs[0];
   const auto& b = inputs[1];
-  switch (out.dtype()) {
-    case float16:
-      binary_op<float16_t>(a, b, out, detail::ArcTan2());
-      break;
-    case float32:
-      binary_op<float>(a, b, out, detail::ArcTan2());
-      break;
-    case float64:
-      binary_op<double>(a, b, out, detail::ArcTan2());
-      break;
-    case bfloat16:
-      binary_op<bfloat16_t>(a, b, out, detail::ArcTan2());
-      break;
-    default:
-      throw std::runtime_error(
-          "[ArcTan2::eval_cpu] Only supports non-complex floating point types.");
-  }
+  binary_float(a, b, out, detail::ArcTan2(), stream());
 }
 
 } // namespace mlx::core

mlx/backend/cpu/binary.h

@@ -3,7 +3,6 @@
 #pragma once
 #include <cassert>
 
-#include "mlx/allocator.h"
 #include "mlx/array.h"
 #include "mlx/backend/common/binary.h"
 #include "mlx/backend/common/utils.h"
@@ -14,22 +13,18 @@ namespace mlx::core {
 
 template <typename Op>
 struct VectorScalar {
-  Op op;
-
-  VectorScalar(Op op_) : op(op_) {}
-
   template <typename T, typename U>
   void operator()(const T* a, const T* b, U* dst, int size) {
     T scalar = *b;
     constexpr int N = simd::max_size<T>;
     while (size >= N) {
-      simd::store(dst, op(simd::load<T, N>(a), simd::Simd<T, N>(scalar)));
+      simd::store(dst, Op{}(simd::load<T, N>(a), simd::Simd<T, N>(scalar)));
       dst += N;
       a += N;
       size -= N;
     }
     while (size-- > 0) {
-      *dst = op(*a, scalar);
+      *dst = Op{}(*a, scalar);
       dst++;
       a++;
     }
@@ -38,22 +33,18 @@ struct VectorScalar {
 
 template <typename Op>
 struct ScalarVector {
-  Op op;
-
-  ScalarVector(Op op_) : op(op_) {}
-
   template <typename T, typename U>
   void operator()(const T* a, const T* b, U* dst, int size) {
     T scalar = *a;
     constexpr int N = simd::max_size<T>;
     while (size >= N) {
-      simd::store(dst, op(simd::Simd<T, N>(scalar), simd::load<T, N>(b)));
+      simd::store(dst, Op{}(simd::Simd<T, N>(scalar), simd::load<T, N>(b)));
       dst += N;
       b += N;
       size -= N;
     }
     while (size-- > 0) {
-      *dst = op(scalar, *b);
+      *dst = Op{}(scalar, *b);
       dst++;
       b++;
     }
@@ -62,22 +53,18 @@ struct ScalarVector {
 
 template <typename Op>
 struct VectorVector {
-  Op op;
-
-  VectorVector(Op op_) : op(op_) {}
-
   template <typename T, typename U>
   void operator()(const T* a, const T* b, U* dst, int size) {
     constexpr int N = simd::max_size<T>;
     while (size >= N) {
-      simd::store(dst, op(simd::load<T, N>(a), simd::load<T, N>(b)));
+      simd::store(dst, Op{}(simd::load<T, N>(a), simd::load<T, N>(b)));
       dst += N;
       a += N;
       b += N;
       size -= N;
     }
     while (size-- > 0) {
-      *dst = op(*a, *b);
+      *dst = Op{}(*a, *b);
       dst++;
       a++;
       b++;
@@ -90,7 +77,6 @@ void binary_op_dims(
     const T* a,
     const T* b,
     U* out,
-    Op op,
     const Shape& shape,
     const Strides& a_strides,
     const Strides& b_strides,
@@ -104,12 +90,12 @@ void binary_op_dims(
   for (int i = 0; i < N; i++) {
     if constexpr (D > 1) {
       binary_op_dims<T, U, Op, D - 1, Strided>(
-          a, b, out, op, shape, a_strides, b_strides, out_strides, axis + 1);
+          a, b, out, shape, a_strides, b_strides, out_strides, axis + 1);
     } else {
       if constexpr (Strided) {
-        op(a, b, out, stride_out);
+        Op{}(a, b, out, stride_out);
       } else {
-        *out = op(*a, *b);
+        *out = Op{}(*a, *b);
       }
     }
     out += stride_out;
@@ -120,66 +106,38 @@ void binary_op_dims(
 
 template <typename T, typename U, bool Strided, typename Op>
 void binary_op_dispatch_dims(
-    const array& a,
-    const array& b,
-    array& out,
-    Op op,
+    const T* a,
+    const T* b,
+    U* out,
     int dim,
+    int size,
     const Shape& shape,
     const Strides& a_strides,
     const Strides& b_strides,
     const Strides& out_strides) {
-  const T* a_ptr = a.data<T>();
-  const T* b_ptr = b.data<T>();
-  U* out_ptr = out.data<U>();
   switch (dim) {
     case 1:
       binary_op_dims<T, U, Op, 1, Strided>(
-          a_ptr,
-          b_ptr,
-          out_ptr,
-          op,
-          shape,
-          a_strides,
-          b_strides,
-          out_strides,
-          0);
+          a, b, out, shape, a_strides, b_strides, out_strides, 0);
       return;
     case 2:
       binary_op_dims<T, U, Op, 2, Strided>(
-          a_ptr,
-          b_ptr,
-          out_ptr,
-          op,
-          shape,
-          a_strides,
-          b_strides,
-          out_strides,
-          0);
+          a, b, out, shape, a_strides, b_strides, out_strides, 0);
       return;
     case 3:
       binary_op_dims<T, U, Op, 3, Strided>(
-          a_ptr,
-          b_ptr,
-          out_ptr,
-          op,
-          shape,
-          a_strides,
-          b_strides,
-          out_strides,
-          0);
+          a, b, out, shape, a_strides, b_strides, out_strides, 0);
       return;
   }
 
   ContiguousIterator a_it(shape, a_strides, dim - 3);
   ContiguousIterator b_it(shape, b_strides, dim - 3);
   auto stride = out_strides[dim - 4];
-  for (int64_t elem = 0; elem < a.size(); elem += stride) {
+  for (int64_t elem = 0; elem < size; elem += stride) {
     binary_op_dims<T, U, Op, 3, Strided>(
-        a_ptr + a_it.loc,
-        b_ptr + b_it.loc,
-        out_ptr + elem,
-        op,
+        a + a_it.loc,
+        b + b_it.loc,
+        out + elem,
         shape,
         a_strides,
         b_strides,
@@ -191,40 +149,41 @@ void binary_op_dispatch_dims(
 }
 
 template <typename T, typename U, typename Op>
-void binary_op(const array& a, const array& b, array& out, Op op) {
-  auto bopt = get_binary_op_type(a, b);
-  set_binary_op_output_data(a, b, out, bopt);
-
+void binary_op(const array& a, const array& b, array& out, BinaryOpType bopt) {
   // The full computation is scalar scalar so call the base op once
+  auto a_ptr = a.data<T>();
+  auto b_ptr = b.data<T>();
+
+  auto out_ptr = out.data<U>();
   if (bopt == BinaryOpType::ScalarScalar) {
-    *(out.data<U>()) = op(*a.data<T>(), *b.data<T>());
+    *out_ptr = Op{}(*a_ptr, *b_ptr);
     return;
   }
 
   // The full computation is scalar vector so delegate to the op
   if (bopt == BinaryOpType::ScalarVector) {
-    ScalarVector{op}(a.data<T>(), b.data<T>(), out.data<U>(), b.data_size());
+    ScalarVector<Op>{}(a_ptr, b_ptr, out_ptr, b.data_size());
     return;
   }
 
   // The full computation is vector scalar so delegate to the op
   if (bopt == BinaryOpType::VectorScalar) {
-    VectorScalar{op}(a.data<T>(), b.data<T>(), out.data<U>(), a.data_size());
+    VectorScalar<Op>{}(a_ptr, b_ptr, out_ptr, a.data_size());
     return;
   }
 
   // The full computation is vector vector so delegate to the op
   if (bopt == BinaryOpType::VectorVector) {
-    VectorVector{op}(a.data<T>(), b.data<T>(), out.data<U>(), out.size());
+    VectorVector<Op>{}(a_ptr, b_ptr, out_ptr, a.size());
     return;
   }
 
   // General computation so let's try to optimize
   auto [new_shape, new_strides] = collapse_contiguous_dims(
       a.shape(), {a.strides(), b.strides(), out.strides()});
-  const auto& a_strides = new_strides[0];
-  const auto& b_strides = new_strides[1];
-  const auto& strides = new_strides[2];
+  auto& a_strides = new_strides[0];
+  auto& b_strides = new_strides[1];
+  auto& strides = new_strides[2];
 
   // Get the left-most dim such that the array is row contiguous after
   auto leftmost_rc_dim = [&strides](const auto& arr_strides) {
@@ -248,7 +207,8 @@ void binary_op(const array& a, const array& b, array& out, Op op) {
 
   auto ndim = new_shape.size();
 
-  // Case 1: LxM and FxM where L and F are broadcastable and M is row contiguous
+  // 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 = BinaryOpType::VectorVector;
@@ -275,99 +235,59 @@ void binary_op(const array& a, const array& b, array& out, Op op) {
 
   switch (bopt) {
     case BinaryOpType::VectorVector:
-      binary_op_dispatch_dims<T, U, true>(
-          a,
-          b,
-          out,
-          VectorVector{op},
+      binary_op_dispatch_dims<T, U, true, VectorVector<Op>>(
+          a_ptr,
+          b_ptr,
+          out_ptr,
           dim,
+          a.size(),
           new_shape,
           a_strides,
           b_strides,
           strides);
       break;
     case BinaryOpType::VectorScalar:
-      binary_op_dispatch_dims<T, U, true>(
-          a,
-          b,
-          out,
-          VectorScalar{op},
+      binary_op_dispatch_dims<T, U, true, VectorScalar<Op>>(
+          a_ptr,
+          b_ptr,
+          out_ptr,
           dim,
+          a.size(),
           new_shape,
           a_strides,
           b_strides,
           strides);
       break;
     case BinaryOpType::ScalarVector:
-      binary_op_dispatch_dims<T, U, true>(
-          a,
-          b,
-          out,
-          ScalarVector{op},
+      binary_op_dispatch_dims<T, U, true, ScalarVector<Op>>(
+          a_ptr,
+          b_ptr,
+          out_ptr,
           dim,
+          a.size(),
           new_shape,
           a_strides,
           b_strides,
           strides);
       break;
     default:
-      binary_op_dispatch_dims<T, U, false>(
-          a, b, out, op, dim, new_shape, a_strides, b_strides, strides);
+      binary_op_dispatch_dims<T, U, false, Op>(
+          a_ptr,
+          b_ptr,
+          out_ptr,
+          dim,
+          a.size(),
+          new_shape,
+          a_strides,
+          b_strides,
+          strides);
       break;
   }
 }
 
 template <typename T, typename Op>
-void binary_op(const array& a, const array& b, array& out, Op op) {
-  binary_op<T, T>(a, b, out, op);
-}
-
-template <typename Op>
-void binary(const array& a, const array& b, array& out, Op op) {
-  switch (out.dtype()) {
-    case bool_:
-      binary_op<bool>(a, b, out, op);
-      break;
-    case uint8:
-      binary_op<uint8_t>(a, b, out, op);
-      break;
-    case uint16:
-      binary_op<uint16_t>(a, b, out, op);
-      break;
-    case uint32:
-      binary_op<uint32_t>(a, b, out, op);
-      break;
-    case uint64:
-      binary_op<uint64_t>(a, b, out, op);
-      break;
-    case int8:
-      binary_op<int8_t>(a, b, out, op);
-      break;
-    case int16:
-      binary_op<int16_t>(a, b, out, op);
-      break;
-    case int32:
-      binary_op<int32_t>(a, b, out, op);
-      break;
-    case int64:
-      binary_op<int64_t>(a, b, out, op);
-      break;
-    case float16:
-      binary_op<float16_t>(a, b, out, op);
-      break;
-    case float32:
-      binary_op<float>(a, b, out, op);
-      break;
-    case float64:
-      binary_op<double>(a, b, out, op);
-      break;
-    case bfloat16:
-      binary_op<bfloat16_t>(a, b, out, op);
-      break;
-    case complex64:
-      binary_op<complex64_t>(a, b, out, op);
-      break;
-  }
+void binary_op(const array& a, const array& b, array& out, BinaryOpType bopt) {
+  binary_op<T, T, Op>(a, b, out, bopt);
 }
 
 } // namespace mlx::core

mlx/backend/cpu/binary_two.h

@@ -58,14 +58,14 @@ void binary_op_dispatch_dims(
     Op op) {
   auto [shape, strides] = collapse_contiguous_dims(
       a.shape(), {a.strides(), b.strides(), out_a.strides()});
-  const auto& a_strides = strides[0];
-  const auto& b_strides = strides[1];
-  const auto& out_strides = strides[2];
   const T* a_ptr = a.data<T>();
   const T* b_ptr = b.data<T>();
   U* out_a_ptr = out_a.data<U>();
   U* out_b_ptr = out_b.data<U>();
 
+  const auto& a_strides = strides[0];
+  const auto& b_strides = strides[1];
+  const auto& out_strides = strides[2];
   int ndim = shape.size();
   switch (ndim) {
     case 1:
@@ -120,14 +120,10 @@ template <typename T, typename U = T, typename Op>
 void binary_op(
     const array& a,
     const array& b,
-    std::vector<array>& outputs,
-    Op op) {
-  auto bopt = get_binary_op_type(a, b);
-  auto& out_a = outputs[0];
-  auto& out_b = outputs[1];
-  set_binary_op_output_data(a, b, out_a, bopt);
-  set_binary_op_output_data(a, b, out_b, bopt);
-
+    array& out_a,
+    array& out_b,
+    Op op,
+    BinaryOpType bopt) {
   // The full computation is scalar scalar so call the base op once
   if (bopt == BinaryOpType::General) {
     binary_op_dispatch_dims<T, U, Op>(a, b, out_a, out_b, op);
@@ -141,14 +137,14 @@ void binary_op(
   if (bopt == BinaryOpType::ScalarScalar) {
     std::tie(*out_a_ptr, *out_b_ptr) = op(*a_ptr, *b_ptr);
   } else if (bopt == BinaryOpType::ScalarVector) {
-    for (size_t i = 0; i < b.size(); ++i) {
+    for (size_t i = 0; i < b.data_size(); ++i) {
       std::tie(*out_a_ptr, *out_b_ptr) = op(*a_ptr, *b_ptr);
       out_a_ptr++;
       out_b_ptr++;
       b_ptr++;
     }
   } else if (bopt == BinaryOpType::VectorScalar) {
-    for (size_t i = 0; i < a.size(); ++i) {
+    for (size_t i = 0; i < a.data_size(); ++i) {
       std::tie(*out_a_ptr, *out_b_ptr) = op(*a_ptr, *b_ptr);
       out_a_ptr++;
       out_b_ptr++;
@@ -165,58 +161,6 @@ void binary_op(
   }
 }
 
-template <typename Op>
-void binary(
-    const array& a,
-    const array& b,
-    std::vector<array>& outputs,
-    Op op) {
-  switch (outputs[0].dtype()) {
-    case bool_:
-      binary_op<bool>(a, b, outputs, op);
-      break;
-    case uint8:
-      binary_op<uint8_t>(a, b, outputs, op);
-      break;
-    case uint16:
-      binary_op<uint16_t>(a, b, outputs, op);
-      break;
-    case uint32:
-      binary_op<uint32_t>(a, b, outputs, op);
-      break;
-    case uint64:
-      binary_op<uint64_t>(a, b, outputs, op);
-      break;
-    case int8:
-      binary_op<int8_t>(a, b, outputs, op);
-      break;
-    case int16:
-      binary_op<int16_t>(a, b, outputs, op);
-      break;
-    case int32:
-      binary_op<int32_t>(a, b, outputs, op);
-      break;
-    case int64:
-      binary_op<int64_t>(a, b, outputs, op);
-      break;
-    case float16:
-      binary_op<float16_t>(a, b, outputs, op);
-      break;
-    case float32:
-      binary_op<float>(a, b, outputs, op);
-      break;
-    case float64:
-      binary_op<double>(a, b, outputs, op);
-      break;
-    case bfloat16:
-      binary_op<bfloat16_t>(a, b, outputs, op);
-      break;
-    case complex64:
-      binary_op<complex64_t>(a, b, outputs, op);
-      break;
-  }
-}
-
 } // namespace
 
 } // namespace mlx::core

mlx/backend/cpu/cholesky.cpp

@@ -2,13 +2,15 @@
 
 #include "mlx/allocator.h"
 #include "mlx/backend/cpu/copy.h"
+#include "mlx/backend/cpu/encoder.h"
 #include "mlx/backend/cpu/lapack.h"
 #include "mlx/linalg.h"
 #include "mlx/primitives.h"
 
 namespace mlx::core {
 
-void cholesky_impl(const array& a, array& factor, bool upper) {
+template <typename T>
+void cholesky_impl(const array& a, array& factor, bool upper, Stream stream) {
   // Lapack uses the column-major convention. We take advantage of the fact that
   // the matrix should be symmetric:
   //   (A)ᵀ = A
@@ -16,59 +18,68 @@ void cholesky_impl(const array& a, array& factor, bool upper) {
   // triangular matrix, so uplo is the opposite of what we would expect from
   // upper
 
-  char uplo = (upper) ? 'L' : 'U';
-
   // The decomposition is computed in place, so just copy the input to the
   // output.
   copy(
       a,
       factor,
-      a.flags().row_contiguous ? CopyType::Vector : CopyType::General);
+      a.flags().row_contiguous ? CopyType::Vector : CopyType::General,
+      stream);
 
-  const int N = a.shape(-1);
-  const size_t num_matrices = a.size() / (N * N);
+  auto& encoder = cpu::get_command_encoder(stream);
+  encoder.set_output_array(factor);
+  encoder.dispatch([matrix = factor.data<T>(),
+                    upper,
+                    N = a.shape(-1),
+                    size = a.size()]() mutable {
+    char uplo = (upper) ? 'L' : 'U';
+    size_t num_matrices = size / (N * N);
+    for (int i = 0; i < num_matrices; i++) {
+      // Compute Cholesky factorization.
+      int info;
+      potrf<T>(
+          /* uplo = */ &uplo,
+          /* n = */ &N,
+          /* a = */ matrix,
+          /* lda = */ &N,
+          /* info = */ &info);
 
-  float* matrix = factor.data<float>();
-
-  for (int i = 0; i < num_matrices; i++) {
-    // Compute Cholesky factorization.
-    int info;
-    MLX_LAPACK_FUNC(spotrf)
-    (
-        /* uplo = */ &uplo,
-        /* n = */ &N,
-        /* a = */ matrix,
-        /* lda = */ &N,
-        /* info = */ &info);
-
-    // TODO: We do nothing when the matrix is not positive semi-definite
-    // because throwing an error would result in a crash. If we figure out how
-    // to catch errors from the implementation we should throw.
-    if (info < 0) {
-      std::stringstream msg;
-      msg << "[cholesky] Cholesky decomposition failed with error code "
-          << info;
-      throw std::runtime_error(msg.str());
-    }
-
-    // Zero out the upper/lower triangle while advancing the pointer to the
-    // next matrix at the same time.
-    for (int row = 0; row < N; row++) {
-      if (upper) {
-        std::fill(matrix, matrix + row, 0);
-      } else {
-        std::fill(matrix + row + 1, matrix + N, 0);
+      // TODO: We do nothing when the matrix is not positive semi-definite
+      // because throwing an error would result in a crash. If we figure out how
+      // to catch errors from the implementation we should throw.
+      if (info < 0) {
+        std::stringstream msg;
+        msg << "[Cholesky::eval_cpu] Cholesky decomposition failed with error code "
+            << info;
+        throw std::runtime_error(msg.str());
+      }
+
+      // Zero out the upper/lower triangle while advancing the pointer to the
+      // next matrix at the same time.
+      for (int row = 0; row < N; row++) {
+        if (upper) {
+          std::fill(matrix, matrix + row, 0);
+        } else {
+          std::fill(matrix + row + 1, matrix + N, 0);
+        }
+        matrix += N;
       }
-      matrix += N;
     }
-  }
+  });
 }
 
 void Cholesky::eval_cpu(const std::vector<array>& inputs, array& output) {
-  if (inputs[0].dtype() != float32) {
-    throw std::runtime_error("[Cholesky::eval] only supports float32.");
+  switch (inputs[0].dtype()) {
+    case float32:
+      cholesky_impl<float>(inputs[0], output, upper_, stream());
+      break;
+    case float64:
+      cholesky_impl<double>(inputs[0], output, upper_, stream());
+      break;
+    default:
+      throw std::runtime_error(
+          "[Cholesky::eval_cpu] only supports float32 or float64.");
   }
-  cholesky_impl(inputs[0], output, upper_);
 }
 
 } // namespace mlx::core

mlx/backend/cpu/compiled.cpp

@@ -11,6 +11,7 @@
 
 #include "mlx/backend/common/compiled.h"
 #include "mlx/backend/cpu/compiled_preamble.h"
+#include "mlx/backend/cpu/encoder.h"
 #include "mlx/backend/cpu/jit_compiler.h"
 #include "mlx/device.h"
 #include "mlx/graph_utils.h"
@@ -288,6 +289,7 @@ void Compiled::eval_cpu(
   // Figure out which kernel we are using
   auto& shape = outputs[0].shape();
   auto contiguous = compiled_check_contiguity(inputs, shape);
+  auto& encoder = cpu::get_command_encoder(stream());
 
   // Handle all broadcasting and collect function input arguments
   std::vector<void*> args;
@@ -298,6 +300,7 @@ void Compiled::eval_cpu(
       continue;
     }
     auto& x = inputs[i];
+    encoder.set_input_array(x);
     args.push_back((void*)x.data<void>());
 
     if (contiguous || is_scalar(x)) {
@@ -356,18 +359,25 @@ void Compiled::eval_cpu(
   });
 
   compiled_allocate_outputs(
-      inputs, outputs, inputs_, constant_ids_, contiguous, false);
+      inputs, outputs, inputs_, constant_ids_, contiguous);
 
   for (auto& x : outputs) {
     args.push_back(x.data<void>());
+    encoder.set_output_array(x);
   }
+  Shape out_shape;
   if (!contiguous) {
-    args.push_back((void*)outputs[0].shape().data());
+    out_shape = outputs[0].shape();
+    args.push_back((void*)out_shape.data());
   } else {
     args.push_back((void*)outputs[0].data_size());
   }
   auto fun = (void (*)(void**))fn_ptr;
-  fun(args.data());
+  encoder.dispatch(
+      [fun,
+       args = std::move(args),
+       strides = std::move(strides),
+       out_shape = std::move(out_shape)]() mutable { fun(args.data()); });
 }
 
 } // namespace mlx::core

mlx/backend/cpu/copy.cpp

@@ -5,6 +5,7 @@
 #include "mlx/allocator.h"
 #include "mlx/backend/common/utils.h"
 #include "mlx/backend/cpu/copy.h"
+#include "mlx/backend/cpu/encoder.h"
 #include "mlx/backend/cpu/simd/simd.h"
 
 namespace mlx::core {
@@ -13,19 +14,19 @@ namespace {
 
 template <typename SrcT, typename DstT>
 void copy_single(const array& src, array& dst) {
-  auto val = static_cast<DstT>(src.data<SrcT>()[0]);
+  auto src_ptr = src.data<SrcT>();
   auto dst_ptr = dst.data<DstT>();
-  for (int i = 0; i < dst.size(); ++i) {
-    dst_ptr[i] = val;
-  }
+  auto size = dst.size();
+  auto val = static_cast<DstT>(src_ptr[0]);
+  std::fill_n(dst_ptr, size, val);
 }
 
 template <typename SrcT, typename DstT>
 void copy_vector(const array& src, array& dst) {
   auto src_ptr = src.data<SrcT>();
   auto dst_ptr = dst.data<DstT>();
-  size_t size = src.data_size();
-  std::copy(src_ptr, src_ptr + src.data_size(), dst_ptr);
+  auto size = src.data_size();
+  std::copy(src_ptr, src_ptr + size, dst_ptr);
 }
 
 template <typename SrcT, typename DstT, int D>
@@ -60,36 +61,57 @@ void copy_general_general(
     const Strides& i_strides,
     const Strides& o_strides,
     int64_t i_offset,
-    int64_t o_offset) {
+    int64_t o_offset,
+    const std::optional<array>& dynamic_i_offset,
+    const std::optional<array>& dynamic_o_offset) {
+  auto src_ptr = src.data<SrcT>() + i_offset;
+  auto dst_ptr = dst.data<DstT>() + o_offset;
+  auto i_offset_ptr =
+      dynamic_i_offset ? dynamic_i_offset->data<int64_t>() : nullptr;
+  auto o_offset_ptr =
+      dynamic_o_offset ? dynamic_o_offset->data<int64_t>() : nullptr;
+  auto size = src.size();
   if (data_shape.empty()) {
-    auto val = static_cast<DstT>(*(src.data<SrcT>() + i_offset));
-    auto dst_ptr = dst.data<DstT>() + o_offset;
+    auto val = static_cast<DstT>(*src_ptr);
     *dst_ptr = val;
     return;
   }
   auto [shape, strides] =
       collapse_contiguous_dims(data_shape, {i_strides, o_strides});
-  auto src_ptr = src.data<SrcT>() + i_offset;
-  auto dst_ptr = dst.data<DstT>() + o_offset;
+
   int ndim = shape.size();
-  if (ndim == 1) {
-    copy_dims<SrcT, DstT, 1>(
-        src_ptr, dst_ptr, shape, strides[0], strides[1], 0);
-    return;
-  } else if (ndim == 2) {
-    copy_dims<SrcT, DstT, 2>(
-        src_ptr, dst_ptr, shape, strides[0], strides[1], 0);
-    return;
-  } else if (ndim == 3) {
-    copy_dims<SrcT, DstT, 3>(
-        src_ptr, dst_ptr, shape, strides[0], strides[1], 0);
+  if (ndim < 3) {
+    if (i_offset_ptr) {
+      src_ptr += i_offset_ptr[0];
+    }
+    if (o_offset_ptr) {
+      dst_ptr += o_offset_ptr[0];
+    }
+
+    if (ndim == 1) {
+      copy_dims<SrcT, DstT, 1>(
+          src_ptr, dst_ptr, shape, strides[0], strides[1], 0);
+    } else if (ndim == 2) {
+      copy_dims<SrcT, DstT, 2>(
+          src_ptr, dst_ptr, shape, strides[0], strides[1], 0);
+    } else if (ndim == 3) {
+      copy_dims<SrcT, DstT, 3>(
+          src_ptr, dst_ptr, shape, strides[0], strides[1], 0);
+    }
     return;
   }
+  if (i_offset_ptr) {
+    src_ptr += i_offset_ptr[0];
+  }
+  if (o_offset_ptr) {
+    dst_ptr += o_offset_ptr[0];
+  }
+
   ContiguousIterator in(shape, strides[0], ndim - 3);
   ContiguousIterator out(shape, strides[1], ndim - 3);
   auto stride = std::accumulate(
       shape.end() - 3, shape.end(), 1, std::multiplies<int64_t>());
-  for (int64_t elem = 0; elem < src.size(); elem += stride) {
+  for (int64_t elem = 0; elem < size; elem += stride) {
     copy_dims<SrcT, DstT, 3>(
         src_ptr + in.loc,
         dst_ptr + out.loc,
@@ -105,7 +127,15 @@ void copy_general_general(
 template <typename SrcT, typename DstT>
 inline void copy_general_general(const array& src, array& dst) {
   copy_general_general<SrcT, DstT>(
-      src, dst, src.shape(), src.strides(), dst.strides(), 0, 0);
+      src,
+      dst,
+      src.shape(),
+      src.strides(),
+      dst.strides(),
+      0,
+      0,
+      std::nullopt,
+      std::nullopt);
 }
 
 template <typename SrcT, typename DstT>
@@ -116,7 +146,9 @@ void copy_general(
     const Strides& i_strides,
     const Strides&,
     int64_t i_offset,
-    int64_t o_offset) {
+    int64_t o_offset,
+    const std::optional<array>& dynamic_i_offset,
+    const std::optional<array>& dynamic_o_offset) {
   copy_general_general<SrcT, DstT>(
       src,
       dst,
@@ -124,7 +156,9 @@ void copy_general(
       i_strides,
       make_contiguous_strides(data_shape),
       i_offset,
-      o_offset);
+      o_offset,
+      dynamic_i_offset,
+      dynamic_o_offset);
 }
 
 template <typename SrcT, typename DstT>
@@ -136,7 +170,9 @@ inline void copy_general(const array& src, array& dst) {
       src.strides(),
       make_contiguous_strides(src.shape()),
       0,
-      0);
+      0,
+      std::nullopt,
+      std::nullopt);
 }
 
 template <typename SrcT, typename DstT, typename... Args>
@@ -259,35 +295,27 @@ inline void copy_inplace_dispatch(
 
 } // namespace
 
-void copy_inplace(const array& src, array& dst, CopyType ctype) {
-  copy_inplace_dispatch(src, dst, ctype);
+void copy_inplace(const array& src, array& dst, CopyType ctype, Stream stream) {
+  auto& encoder = cpu::get_command_encoder(stream);
+  encoder.set_input_array(src);
+  encoder.set_output_array(dst);
+  encoder.dispatch(
+      [src = array::unsafe_weak_copy(src),
+       dst = array::unsafe_weak_copy(dst),
+       ctype]() mutable { copy_inplace_dispatch(src, dst, ctype); });
 }
 
-void copy(const array& src, array& dst, CopyType ctype) {
-  // Allocate the output
-  switch (ctype) {
-    case CopyType::Vector:
-      if (src.is_donatable() && src.itemsize() == dst.itemsize()) {
-        dst.copy_shared_buffer(src);
-      } else {
-        auto size = src.data_size();
-        dst.set_data(
-            allocator::malloc_or_wait(size * dst.itemsize()),
-            size,
-            src.strides(),
-            src.flags());
-      }
-      break;
-    case CopyType::Scalar:
-    case CopyType::General:
-    case CopyType::GeneralGeneral:
-      dst.set_data(allocator::malloc_or_wait(dst.nbytes()));
-      break;
+void copy(const array& src, array& dst, CopyType ctype, Stream stream) {
+  bool donated = set_copy_output_data(src, dst, ctype);
+  if (donated && src.dtype() == dst.dtype()) {
+    // If the output has the same type as the input then there is nothing to
+    // copy, just use the buffer.
+    return;
   }
   if (ctype == CopyType::GeneralGeneral) {
     ctype = CopyType::General;
   }
-  copy_inplace(src, dst, ctype);
+  copy_inplace(src, dst, ctype, stream);
 }
 
 void copy_inplace(
@@ -298,24 +326,51 @@ void copy_inplace(
     const Strides& o_strides,
     int64_t i_offset,
     int64_t o_offset,
-    CopyType ctype) {
-  switch (ctype) {
-    case CopyType::General:
-    case CopyType::GeneralGeneral:
-      copy_inplace_dispatch(
-          src,
-          dst,
-          ctype,
-          data_shape,
-          i_strides,
-          o_strides,
-          i_offset,
-          o_offset);
-      break;
-    case CopyType::Scalar:
-    case CopyType::Vector:
-      copy_inplace_dispatch(src, dst, ctype);
-  }
+    CopyType ctype,
+    Stream stream,
+    const std::optional<array>& dynamic_i_offset, /* = std::nullopt */
+    const std::optional<array>& dynamic_o_offset /* = std::nullopt */) {
+  auto& encoder = cpu::get_command_encoder(stream);
+  encoder.set_input_array(src);
+  encoder.set_output_array(dst);
+  auto weak_copy_if_set = [](auto x) -> std::optional<array> {
+    if (x) {
+      return array::unsafe_weak_copy(*x);
+    } else {
+      return std::nullopt;
+    }
+  };
+  encoder.dispatch(
+      [src = array::unsafe_weak_copy(src),
+       dst = array::unsafe_weak_copy(dst),
+       data_shape,
+       i_strides,
+       o_strides,
+       i_offset,
+       o_offset,
+       ctype,
+       dynamic_i_offset = weak_copy_if_set(dynamic_i_offset),
+       dynamic_o_offset = weak_copy_if_set(dynamic_o_offset)]() mutable {
+        switch (ctype) {
+          case CopyType::General:
+          case CopyType::GeneralGeneral:
+            copy_inplace_dispatch(
+                src,
+                dst,
+                ctype,
+                data_shape,
+                i_strides,
+                o_strides,
+                i_offset,
+                o_offset,
+                dynamic_i_offset,
+                dynamic_o_offset);
+            break;
+          case CopyType::Scalar:
+          case CopyType::Vector:
+            copy_inplace_dispatch(src, dst, ctype);
+        }
+      });
 }
 
 } // namespace mlx::core

mlx/backend/cpu/copy.h

@@ -2,14 +2,16 @@
 
 #pragma once
 
+#include <optional>
+
 #include "mlx/array.h"
 #include "mlx/backend/common/copy.h"
 #include "mlx/backend/common/utils.h"
 
 namespace mlx::core {
 
-void copy(const array& src, array& dst, CopyType ctype);
-void copy_inplace(const array& src, array& dst, CopyType ctype);
+void copy(const array& src, array& dst, CopyType ctype, Stream stream);
+void copy_inplace(const array& src, array& dst, CopyType ctype, Stream stream);
 
 void copy_inplace(
     const array& src,
@@ -19,6 +21,9 @@ void copy_inplace(
     const Strides& o_strides,
     int64_t i_offset,
     int64_t o_offset,
-    CopyType ctype);
+    CopyType ctype,
+    Stream stream,
+    const std::optional<array>& dynamic_i_offset = std::nullopt,
+    const std::optional<array>& dynamic_o_offset = std::nullopt);
 
 } // namespace mlx::core

mlx/backend/cpu/distributed.cpp

@@ -0,0 +1,94 @@
+// Copyright © 2024 Apple Inc.
+
+#include <cassert>
+
+#include "mlx/allocator.h"
+#include "mlx/backend/cpu/copy.h"
+#include "mlx/backend/cpu/encoder.h"
+#include "mlx/distributed/primitives.h"
+
+namespace mlx::core::distributed {
+
+std::pair<array, bool> ensure_row_contiguous(const array& arr, Stream stream) {
+  if (arr.flags().row_contiguous) {
+    return {arr, false};
+  } else {
+    array arr_copy(arr.shape(), arr.dtype(), nullptr, {});
+    copy(arr, arr_copy, CopyType::General, stream);
+    return {arr_copy, true};
+  }
+};
+
+void AllReduce::eval_cpu(
+    const std::vector<array>& inputs,
+    std::vector<array>& outputs) {
+  assert(inputs.size() == 1);
+  assert(outputs.size() == 1);
+
+  auto donate_or_copy = [s = stream()](const array& in, array& out) {
+    if (in.flags().row_contiguous) {
+      if (in.is_donatable()) {
+        out.copy_shared_buffer(in);
+      } else {
+        out.set_data(allocator::malloc(out.nbytes()));
+      }
+      return in;
+    } else {
+      array arr_copy(in.shape(), in.dtype(), nullptr, {});
+      copy(in, arr_copy, CopyType::General, s);
+      out.copy_shared_buffer(arr_copy);
+      return arr_copy;
+    }
+  };
+
+  auto in = donate_or_copy(inputs[0], outputs[0]);
+  switch (reduce_type_) {
+    case Sum:
+      distributed::detail::all_sum(group(), in, outputs[0], stream());
+      break;
+    default:
+      throw std::runtime_error("Only all reduce sum is supported for now");
+  }
+}
+
+void AllGather::eval_cpu(
+    const std::vector<array>& inputs,
+    std::vector<array>& outputs) {
+  assert(inputs.size() == 1);
+  assert(outputs.size() == 1);
+
+  auto [in, copied] = ensure_row_contiguous(inputs[0], stream());
+  outputs[0].set_data(allocator::malloc(outputs[0].nbytes()));
+  distributed::detail::all_gather(group(), in, outputs[0], stream());
+  if (copied) {
+    auto& enc = cpu::get_command_encoder(stream());
+    enc.add_temporary(in);
+  }
+}
+
+void Send::eval_cpu(
+    const std::vector<array>& inputs,
+    std::vector<array>& outputs) {
+  assert(inputs.size() == 1);
+  assert(outputs.size() == 1);
+
+  auto [in, copied] = ensure_row_contiguous(inputs[0], stream());
+  distributed::detail::send(group(), in, dst_, stream());
+  outputs[0].copy_shared_buffer(inputs[0]);
+  if (copied) {
+    auto& enc = cpu::get_command_encoder(stream());
+    enc.add_temporary(in);
+  }
+}
+
+void Recv::eval_cpu(
+    const std::vector<array>& inputs,
+    std::vector<array>& outputs) {
+  assert(inputs.size() == 0);
+  assert(outputs.size() == 1);
+
+  outputs[0].set_data(allocator::malloc(outputs[0].nbytes()));
+  distributed::detail::recv(group(), outputs[0], src_, stream());
+}
+
+} // namespace mlx::core::distributed

mlx/backend/cpu/eigh.cpp

@@ -3,6 +3,7 @@
 #include "mlx/allocator.h"
 #include "mlx/array.h"
 #include "mlx/backend/cpu/copy.h"
+#include "mlx/backend/cpu/encoder.h"
 #include "mlx/backend/cpu/lapack.h"
 #include "mlx/linalg.h"
 #include "mlx/primitives.h"
@@ -11,35 +12,76 @@ namespace mlx::core {
 
 namespace {
 
-void ssyevd(
-    char jobz,
-    char uplo,
-    float* a,
-    int N,
-    float* w,
-    float* work,
-    int lwork,
-    int* iwork,
-    int liwork) {
-  int info;
-  MLX_LAPACK_FUNC(ssyevd)
-  (
-      /* jobz = */ &jobz,
-      /* uplo = */ &uplo,
-      /* n = */ &N,
-      /* a = */ a,
-      /* lda = */ &N,
-      /* w = */ w,
-      /* work = */ work,
-      /* lwork = */ &lwork,
-      /* iwork = */ iwork,
-      /* liwork = */ &liwork,
-      /* info = */ &info);
-  if (info != 0) {
-    std::stringstream msg;
-    msg << "[Eigh::eval_cpu] Eigenvalue decomposition failed with error code "
-        << info;
-    throw std::runtime_error(msg.str());
+template <typename T>
+void eigh_impl(
+    array& vectors,
+    array& values,
+    const std::string& uplo,
+    bool compute_eigenvectors,
+    Stream stream) {
+  auto vec_ptr = vectors.data<T>();
+  auto eig_ptr = values.data<T>();
+  char jobz = compute_eigenvectors ? 'V' : 'N';
+
+  auto& encoder = cpu::get_command_encoder(stream);
+  encoder.set_output_array(vectors);
+  encoder.set_output_array(values);
+  encoder.dispatch([vec_ptr,
+                    eig_ptr,
+                    jobz,
+                    uplo = uplo[0],
+                    N = vectors.shape(-1),
+                    size = vectors.size()]() mutable {
+    // Work query
+    int lwork = -1;
+    int liwork = -1;
+    int info;
+    {
+      T work;
+      int iwork;
+      syevd<T>(
+          &jobz,
+          &uplo,
+          &N,
+          nullptr,
+          &N,
+          nullptr,
+          &work,
+          &lwork,
+          &iwork,
+          &liwork,
+          &info);
+      lwork = static_cast<int>(work);
+      liwork = iwork;
+    }
+
+    auto work_buf = array::Data{allocator::malloc(sizeof(T) * lwork)};
+    auto iwork_buf = array::Data{allocator::malloc(sizeof(int) * liwork)};
+    for (size_t i = 0; i < size / (N * N); ++i) {
+      syevd<T>(
+          &jobz,
+          &uplo,
+          &N,
+          vec_ptr,
+          &N,
+          eig_ptr,
+          static_cast<T*>(work_buf.buffer.raw_ptr()),
+          &lwork,
+          static_cast<int*>(iwork_buf.buffer.raw_ptr()),
+          &liwork,
+          &info);
+      vec_ptr += N * N;
+      eig_ptr += N;
+      if (info != 0) {
+        std::stringstream msg;
+        msg << "[Eigh::eval_cpu] Eigenvalue decomposition failed with error code "
+            << info;
+        throw std::runtime_error(msg.str());
+      }
+    }
+  });
+  if (!compute_eigenvectors) {
+    encoder.add_temporary(vectors);
   }
 }
 
@@ -55,12 +97,13 @@ void Eigh::eval_cpu(
       ? outputs[1]
       : array(a.shape(), a.dtype(), nullptr, {});
 
-  values.set_data(allocator::malloc_or_wait(values.nbytes()));
+  values.set_data(allocator::malloc(values.nbytes()));
 
   copy(
       a,
       vectors,
-      a.flags().row_contiguous ? CopyType::Vector : CopyType::General);
+      a.flags().row_contiguous ? CopyType::Vector : CopyType::General,
+      stream());
 
   if (compute_eigenvectors_) {
     // Set the strides and flags so the eigenvectors
@@ -78,41 +121,19 @@ void Eigh::eval_cpu(
         flags.col_contiguous = true;
       }
     }
-    vectors.move_shared_buffer(vectors, strides, flags, vectors.data_size());
+    vectors.copy_shared_buffer(vectors, strides, flags, vectors.data_size());
   }
-
-  auto vec_ptr = vectors.data<float>();
-  auto eig_ptr = values.data<float>();
-
-  char jobz = compute_eigenvectors_ ? 'V' : 'N';
-  auto N = a.shape(-1);
-
-  // Work query
-  int lwork;
-  int liwork;
-  {
-    float work;
-    int iwork;
-    ssyevd(jobz, uplo_[0], nullptr, N, nullptr, &work, -1, &iwork, -1);
-    lwork = static_cast<int>(work);
-    liwork = iwork;
-  }
-
-  auto work_buf = array::Data{allocator::malloc_or_wait(sizeof(float) * lwork)};
-  auto iwork_buf = array::Data{allocator::malloc_or_wait(sizeof(int) * liwork)};
-  for (size_t i = 0; i < a.size() / (N * N); ++i) {
-    ssyevd(
-        jobz,
-        uplo_[0],
-        vec_ptr,
-        N,
-        eig_ptr,
-        static_cast<float*>(work_buf.buffer.raw_ptr()),
-        lwork,
-        static_cast<int*>(iwork_buf.buffer.raw_ptr()),
-        liwork);
-    vec_ptr += N * N;
-    eig_ptr += N;
+  switch (a.dtype()) {
+    case float32:
+      eigh_impl<float>(vectors, values, uplo_, compute_eigenvectors_, stream());
+      break;
+    case float64:
+      eigh_impl<double>(
+          vectors, values, uplo_, compute_eigenvectors_, stream());
+      break;
+    default:
+      throw std::runtime_error(
+          "[Eigh::eval_cpu] only supports float32 or float64.");
   }
 }
 

mlx/backend/cpu/encoder.cpp

@@ -0,0 +1,16 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cpu/encoder.h"
+
+namespace mlx::core::cpu {
+
+CommandEncoder& get_command_encoder(Stream stream) {
+  static std::unordered_map<int, CommandEncoder> encoder_map;
+  auto it = encoder_map.find(stream.index);
+  if (it == encoder_map.end()) {
+    it = encoder_map.emplace(stream.index, stream).first;
+  }
+  return it->second;
+}
+
+} // namespace mlx::core::cpu

mlx/backend/cpu/encoder.h

@@ -0,0 +1,67 @@
+// Copyright © 2025 Apple Inc.
+
+#pragma once
+
+#include <unordered_map>
+
+#include "mlx/array.h"
+#include "mlx/scheduler.h"
+
+namespace mlx::core::cpu {
+
+// Number of dispatches per scheduler task
+constexpr int DISPATCHES_PER_TASK = 10;
+
+struct CommandEncoder {
+  CommandEncoder(Stream stream) : stream_(stream) {}
+
+  CommandEncoder(const CommandEncoder&) = delete;
+  CommandEncoder& operator=(const CommandEncoder&) = delete;
+  CommandEncoder(CommandEncoder&&) = delete;
+  CommandEncoder& operator=(CommandEncoder&&) = delete;
+
+  void set_input_array(const array& a) {}
+  void set_output_array(array& a) {}
+
+  // Hold onto a temporary until any already scheduled tasks which use it as
+  // an input are complete.
+  void add_temporary(array arr) {
+    temporaries_.push_back(std::move(arr));
+  }
+
+  void add_temporaries(std::vector<array> arrays) {
+    temporaries_.insert(
+        temporaries_.end(),
+        std::make_move_iterator(arrays.begin()),
+        std::make_move_iterator(arrays.end()));
+  }
+
+  std::vector<array>& temporaries() {
+    return temporaries_;
+  }
+
+  template <class F, class... Args>
+  void dispatch(F&& f, Args&&... args) {
+    num_ops_ = (num_ops_ + 1) % DISPATCHES_PER_TASK;
+    auto task = std::bind(std::forward<F>(f), std::forward<Args>(args)...);
+    if (num_ops_ == 0) {
+      scheduler::notify_new_task(stream_);
+      auto task_wrap = [s = stream_, task = std::move(task)]() mutable {
+        task();
+        scheduler::notify_task_completion(s);
+      };
+      scheduler::enqueue(stream_, std::move(task_wrap));
+    } else {
+      scheduler::enqueue(stream_, std::move(task));
+    }
+  }
+
+ private:
+  Stream stream_;
+  std::vector<array> temporaries_;
+  int num_ops_{0};
+};
+
+CommandEncoder& get_command_encoder(Stream stream);
+
+} // namespace mlx::core::cpu

mlx/backend/cpu/eval.cpp

@@ -0,0 +1,34 @@
+// Copyright © 2025 Apple Inc.
+#include "mlx/backend/cpu/eval.h"
+#include "mlx/backend/cpu/encoder.h"
+#include "mlx/primitives.h"
+#include "mlx/scheduler.h"
+#include "mlx/utils.h"
+
+namespace mlx::core::cpu {
+
+void eval(array& arr) {
+  auto s = arr.primitive().stream();
+
+  auto outputs = arr.outputs();
+  {
+    // If the array is a tracer hold a reference
+    // to its inputs so they don't get donated
+    std::vector<array> inputs;
+    if (arr.is_tracer()) {
+      inputs = arr.inputs();
+    }
+    arr.primitive().eval_cpu(arr.inputs(), outputs);
+  }
+}
+
+void finalize(
+    Stream s,
+    std::unordered_set<std::shared_ptr<array::Data>> retain_buffers) {
+  auto& encoder = cpu::get_command_encoder(s);
+  encoder.dispatch([s,
+                    buffers = std::move(retain_buffers),
+                    temps = std::move(encoder.temporaries())]() {});
+}
+
+} // namespace mlx::core::cpu

mlx/backend/cpu/eval.h

@@ -0,0 +1,17 @@
+// Copyright © 2025 Apple Inc.
+
+#pragma once
+
+#include <unordered_set>
+
+#include "mlx/array.h"
+#include "mlx/stream.h"
+
+namespace mlx::core::cpu {
+
+void eval(array& arr);
+void finalize(
+    Stream s,
+    std::unordered_set<std::shared_ptr<array::Data>> retain_buffers);
+
+} // namespace mlx::core::cpu

mlx/backend/cpu/fft.cpp

@@ -4,6 +4,7 @@
 
 #include "mlx/3rdparty/pocketfft.h"
 #include "mlx/allocator.h"
+#include "mlx/backend/cpu/encoder.h"
 #include "mlx/primitives.h"
 
 namespace mlx::core {
@@ -21,7 +22,7 @@ void FFT::eval_cpu(const std::vector<array>& inputs, array& out) {
     s *= out.itemsize();
   }
 
-  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+  out.set_data(allocator::malloc(out.nbytes()));
 
   std::vector<size_t> shape;
   if (out.dtype() == float32) {
@@ -38,46 +39,78 @@ void FFT::eval_cpu(const std::vector<array>& inputs, array& out) {
         });
     scale /= nelem;
   }
+
+  auto& encoder = cpu::get_command_encoder(stream());
+  encoder.set_input_array(in);
+  encoder.set_output_array(out);
+
   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);
+    encoder.dispatch([shape = std::move(shape),
+                      strides_in = std::move(strides_in),
+                      strides_out = std::move(strides_out),
+                      axes = axes_,
+                      inverse = inverse_,
+                      in_ptr,
+                      out_ptr,
+                      scale]() {
+      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);
+    encoder.dispatch([shape = std::move(shape),
+                      strides_in = std::move(strides_in),
+                      strides_out = std::move(strides_out),
+                      axes = axes_,
+                      inverse = inverse_,
+                      in_ptr,
+                      out_ptr,
+                      scale]() {
+      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);
+    encoder.dispatch([shape = std::move(shape),
+                      strides_in = std::move(strides_in),
+                      strides_out = std::move(strides_out),
+                      axes = axes_,
+                      inverse = inverse_,
+                      in_ptr,
+                      out_ptr,
+                      scale]() {
+      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.");

mlx/backend/cpu/gemm.h

@@ -7,14 +7,20 @@ namespace mlx::core {
 
 template <typename T>
 void matmul(
-    const array& a,
-    const array& b,
-    array& out,
+    const T* a,
+    const T* b,
+    T* out,
     bool a_transposed,
     bool b_transposed,
     size_t lda,
     size_t ldb,
+    size_t ldc,
     float alpha,
-    float beta);
+    float beta,
+    size_t batch_size,
+    const Shape& a_shape,
+    const Strides& a_strides,
+    const Shape& b_shape,
+    const Strides& b_strides);
 
 } // namespace mlx::core

mlx/backend/cpu/gemms/bnns.cpp

@@ -9,40 +9,49 @@
 
 namespace mlx::core {
 
-BNNSDataType to_bnns_dtype(Dtype mlx_dtype) {
-  uint32_t size_bits = size_of(mlx_dtype) * 8;
-  switch (kindof(mlx_dtype)) {
-    case Dtype::Kind::b:
-      return BNNSDataTypeBoolean;
-    case Dtype::Kind::u:
-      return BNNSDataType(BNNSDataTypeUIntBit | size_bits);
-    case Dtype::Kind::i:
-      return BNNSDataType(BNNSDataTypeIntBit | size_bits);
-    case Dtype::Kind::f:
-      return BNNSDataType(BNNSDataTypeFloatBit | size_bits);
-    case Dtype::Kind::V:
-      return BNNSDataTypeBFloat16;
-    case Dtype::Kind::c:
-      throw std::invalid_argument("BNNS does not support complex types");
-  }
+template <typename T>
+constexpr BNNSDataType to_bnns_dtype();
+
+template <>
+constexpr BNNSDataType to_bnns_dtype<float>() {
+  return BNNSDataType(BNNSDataTypeFloatBit | 32);
+}
+template <>
+constexpr BNNSDataType to_bnns_dtype<float16_t>() {
+  return BNNSDataType(BNNSDataTypeFloatBit | 16);
 }
 
+template <>
+constexpr BNNSDataType to_bnns_dtype<bfloat16_t>() {
+  return BNNSDataTypeBFloat16;
+}
+
+template <typename T>
 void matmul_bnns(
-    const array& a,
-    const array& b,
-    array& out,
+    const T* a,
+    const T* b,
+    T* out,
     bool a_transposed,
     bool b_transposed,
     size_t lda,
     size_t ldb,
+    size_t ldc,
     float alpha,
-    float beta) {
-  size_t M = a.shape(-2);
-  size_t N = b.shape(-1);
-  size_t K = a.shape(-1);
+    float beta,
+    size_t batch_size,
+    const Shape& a_shape,
+    const Strides& a_strides,
+    const Shape& b_shape,
+    const Strides& b_strides) {
+  auto ndim = a_shape.size();
+  size_t M = a_shape[ndim - 2];
+  size_t N = b_shape[ndim - 1];
+  size_t K = a_shape[ndim - 1];
 
-  BNNSDataType bnns_dtype = to_bnns_dtype(out.dtype());
+  BNNSDataType bnns_dtype = to_bnns_dtype<T>();
 
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
   const BNNSLayerParametersBroadcastMatMul gemm_params{
       /* float alpha = */ alpha,
       /* float beta = */ beta,
@@ -113,45 +122,88 @@ void matmul_bnns(
   auto bnns_filter =
       BNNSFilterCreateLayerBroadcastMatMul(&gemm_params, nullptr);
 
-  for (int i = 0; i < (a.size() / (M * K)); ++i) {
+  for (int i = 0; i < batch_size; ++i) {
     BNNSFilterApplyTwoInput(
         bnns_filter,
-        a.data<uint8_t>() +
-            elem_to_loc(M * K * i, a.shape(), a.strides()) * a.itemsize(),
-        b.data<uint8_t>() +
-            elem_to_loc(K * N * i, b.shape(), b.strides()) * b.itemsize(),
-        out.data<uint8_t>() + M * N * i * out.itemsize());
+        reinterpret_cast<const uint8_t*>(
+            a + elem_to_loc(M * K * i, a_shape, a_strides)),
+        reinterpret_cast<const uint8_t*>(
+            b + elem_to_loc(K * N * i, b_shape, b_strides)),
+        reinterpret_cast<uint8_t*>(out + M * N * i));
   }
 
   BNNSFilterDestroy(bnns_filter);
+#pragma GCC diagnostic pop
 }
 
 template <>
 void matmul<float16_t>(
-    const array& a,
-    const array& b,
-    array& out,
+    const float16_t* a,
+    const float16_t* b,
+    float16_t* out,
     bool a_transposed,
     bool b_transposed,
     size_t lda,
     size_t ldb,
+    size_t ldc,
     float alpha,
-    float beta) {
-  matmul_bnns(a, b, out, a_transposed, b_transposed, lda, ldb, alpha, beta);
+    float beta,
+    size_t batch_size,
+    const Shape& a_shape,
+    const Strides& a_strides,
+    const Shape& b_shape,
+    const Strides& b_strides) {
+  matmul_bnns(
+      a,
+      b,
+      out,
+      a_transposed,
+      b_transposed,
+      lda,
+      ldb,
+      ldc,
+      alpha,
+      beta,
+      batch_size,
+      a_shape,
+      a_strides,
+      b_shape,
+      b_strides);
 }
 
 template <>
 void matmul<bfloat16_t>(
-    const array& a,
-    const array& b,
-    array& out,
+    const bfloat16_t* a,
+    const bfloat16_t* b,
+    bfloat16_t* out,
     bool a_transposed,
     bool b_transposed,
     size_t lda,
     size_t ldb,
+    size_t ldc,
     float alpha,
-    float beta) {
-  matmul_bnns(a, b, out, a_transposed, b_transposed, lda, ldb, alpha, beta);
+    float beta,
+    size_t batch_size,
+    const Shape& a_shape,
+    const Strides& a_strides,
+    const Shape& b_shape,
+    const Strides& b_strides) {
+  matmul_bnns(
+      a,
+      b,
+      out,
+      a_transposed,
+      b_transposed,
+      lda,
+      ldb,
+      ldc,
+      alpha,
+      beta,
+      batch_size,
+      a_shape,
+      a_strides,
+      b_shape,
+      b_strides);
 }
 
 } // namespace mlx::core

mlx/backend/cpu/gemms/cblas.cpp

@@ -8,20 +8,27 @@ namespace mlx::core {
 
 template <>
 void matmul<float>(
-    const array& a,
-    const array& b,
-    array& out,
+    const float* a,
+    const float* b,
+    float* out,
     bool a_transposed,
     bool b_transposed,
     size_t lda,
     size_t ldb,
+    size_t ldc,
     float alpha,
-    float beta) {
-  size_t M = a.shape(-2);
-  size_t N = b.shape(-1);
-  size_t K = a.shape(-1);
+    float beta,
+    size_t batch_size,
+    const Shape& a_shape,
+    const Strides& a_strides,
+    const Shape& b_shape,
+    const Strides& b_strides) {
+  auto ndim = a_shape.size();
+  size_t M = a_shape[ndim - 2];
+  size_t N = b_shape[ndim - 1];
+  size_t K = a_shape[ndim - 1];
 
-  for (int i = 0; i < (a.size() / (M * K)); ++i) {
+  for (int i = 0; i < batch_size; ++i) {
     cblas_sgemm(
         CblasRowMajor,
         a_transposed ? CblasTrans : CblasNoTrans, // transA
@@ -29,34 +36,40 @@ void matmul<float>(
         M,
         N,
         K,
-        alpha, // alpha
-        a.data<float>() + elem_to_loc(M * K * i, a.shape(), a.strides()),
+        alpha,
+        a + elem_to_loc(M * K * i, a_shape, a_strides),
         lda,
-        b.data<float>() + elem_to_loc(K * N * i, b.shape(), b.strides()),
+        b + elem_to_loc(K * N * i, b_shape, b_strides),
         ldb,
-        beta, // beta
-        out.data<float>() + M * N * i,
-        out.shape(-1) // ldc
-    );
+        beta,
+        out + M * N * i,
+        ldc);
   }
 }
 
 template <>
 void matmul<double>(
-    const array& a,
-    const array& b,
-    array& out,
+    const double* a,
+    const double* b,
+    double* out,
     bool a_transposed,
     bool b_transposed,
     size_t lda,
     size_t ldb,
+    size_t ldc,
     float alpha,
-    float beta) {
-  size_t M = a.shape(-2);
-  size_t N = b.shape(-1);
-  size_t K = a.shape(-1);
+    float beta,
+    size_t batch_size,
+    const Shape& a_shape,
+    const Strides& a_strides,
+    const Shape& b_shape,
+    const Strides& b_strides) {
+  auto ndim = a_shape.size();
+  size_t M = a_shape[ndim - 2];
+  size_t N = b_shape[ndim - 1];
+  size_t K = a_shape[ndim - 1];
 
-  for (int i = 0; i < (a.size() / (M * K)); ++i) {
+  for (int i = 0; i < batch_size; ++i) {
     cblas_dgemm(
         CblasRowMajor,
         a_transposed ? CblasTrans : CblasNoTrans, // transA
@@ -64,15 +77,14 @@ void matmul<double>(
         M,
         N,
         K,
-        alpha, // alpha
-        a.data<double>() + elem_to_loc(M * K * i, a.shape(), a.strides()),
+        alpha,
+        a + elem_to_loc(M * K * i, a_shape, a_strides),
         lda,
-        b.data<double>() + elem_to_loc(K * N * i, b.shape(), b.strides()),
+        b + elem_to_loc(K * N * i, b_shape, b_strides),
         ldb,
-        beta, // beta
-        out.data<double>() + M * N * i,
-        out.shape(-1) // ldc
-    );
+        beta,
+        out + M * N * i,
+        ldc);
   }
 }
 

mlx/backend/cpu/gemms/no_bf16.cpp

@@ -1,21 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cpu/gemm.h"
-
-namespace mlx::core {
-
-template <>
-void matmul<bfloat16_t>(
-    const array&,
-    const array&,
-    array&,
-    bool,
-    bool,
-    size_t,
-    size_t,
-    float,
-    float) {
-  throw std::runtime_error("[Matmul::eval_cpu] bfloat16 not supported.");
-}
-
-} // namespace mlx::core

mlx/backend/cpu/gemms/no_fp16.cpp

@@ -1,21 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cpu/gemm.h"
-
-namespace mlx::core {
-
-template <>
-void matmul<float16_t>(
-    const array&,
-    const array&,
-    array&,
-    bool,
-    bool,
-    size_t,
-    size_t,
-    float,
-    float) {
-  throw std::runtime_error("[Matmul::eval_cpu] float16 not supported.");
-}
-
-} // namespace mlx::core

mlx/backend/cpu/gemms/simd_bf16.cpp

@@ -0,0 +1,45 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/common/utils.h"
+#include "mlx/backend/cpu/gemm.h"
+#include "mlx/backend/cpu/gemms/simd_gemm.h"
+
+namespace mlx::core {
+
+template <>
+void matmul<bfloat16_t>(
+    const bfloat16_t* a,
+    const bfloat16_t* b,
+    bfloat16_t* out,
+    bool a_transposed,
+    bool b_transposed,
+    size_t lda,
+    size_t ldb,
+    size_t ldc,
+    float alpha,
+    float beta,
+    size_t batch_size,
+    const Shape& a_shape,
+    const Strides& a_strides,
+    const Shape& b_shape,
+    const Strides& b_strides) {
+  auto ndim = a_shape.size();
+  size_t M = a_shape[ndim - 2];
+  size_t N = b_shape[ndim - 1];
+  size_t K = a_shape[ndim - 1];
+  for (int i = 0; i < batch_size; ++i) {
+    simd_gemm<bfloat16_t, float>(
+        a + elem_to_loc(M * K * i, a_shape, a_strides),
+        b + elem_to_loc(K * N * i, b_shape, b_strides),
+        out + M * N * i,
+        a_transposed,
+        b_transposed,
+        M,
+        N,
+        K,
+        alpha,
+        beta);
+  }
+}
+
+} // namespace mlx::core

mlx/backend/cpu/gemms/simd_fp16.cpp

@@ -0,0 +1,45 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/common/utils.h"
+#include "mlx/backend/cpu/gemm.h"
+#include "mlx/backend/cpu/gemms/simd_gemm.h"
+
+namespace mlx::core {
+
+template <>
+void matmul<float16_t>(
+    const float16_t* a,
+    const float16_t* b,
+    float16_t* out,
+    bool a_transposed,
+    bool b_transposed,
+    size_t lda,
+    size_t ldb,
+    size_t ldc,
+    float alpha,
+    float beta,
+    size_t batch_size,
+    const Shape& a_shape,
+    const Strides& a_strides,
+    const Shape& b_shape,
+    const Strides& b_strides) {
+  auto ndim = a_shape.size();
+  size_t M = a_shape[ndim - 2];
+  size_t N = b_shape[ndim - 1];
+  size_t K = a_shape[ndim - 1];
+  for (int i = 0; i < batch_size; ++i) {
+    simd_gemm<float16_t, float>(
+        a + elem_to_loc(M * K * i, a_shape, a_strides),
+        b + elem_to_loc(K * N * i, b_shape, b_strides),
+        out + M * N * i,
+        a_transposed,
+        b_transposed,
+        M,
+        N,
+        K,
+        alpha,
+        beta);
+  }
+}
+
+} // namespace mlx::core

mlx/backend/cpu/gemms/simd_gemm.h

@@ -0,0 +1,139 @@
+// Copyright © 2025 Apple Inc.
+#pragma once
+
+#include "mlx/backend/cpu/simd/simd.h"
+
+namespace mlx::core {
+
+inline int ceildiv(int a, int b) {
+  return (a + b - 1) / b;
+}
+
+template <int block_size, typename T, typename AccT>
+void load_block(
+    const T* in,
+    AccT* out,
+    int M,
+    int N,
+    int i,
+    int j,
+    bool transpose) {
+  if (transpose) {
+    for (int ii = 0; ii < block_size && i * block_size + ii < M; ++ii) {
+      for (int jj = 0; jj < block_size && j * block_size + jj < N; ++jj) {
+        out[jj * block_size + ii] =
+            in[(i * block_size + ii) * N + j * block_size + jj];
+      }
+    }
+  } else {
+    for (int ii = 0; ii < block_size && i * block_size + ii < M; ++ii) {
+      for (int jj = 0; jj < block_size && j * block_size + jj < N; ++jj) {
+        out[ii * block_size + jj] =
+            in[(i * block_size + ii) * N + j * block_size + jj];
+      }
+    }
+  }
+}
+
+template <typename T, typename AccT>
+void simd_gemm(
+    const T* a,
+    const T* b,
+    T* c,
+    bool a_trans,
+    bool b_trans,
+    int M,
+    int N,
+    int K,
+    float alpha,
+    float beta) {
+  constexpr int block_size = 16;
+  constexpr int simd_size = simd::max_size<AccT>;
+  static_assert(
+      (block_size % simd_size) == 0,
+      "Block size must be divisible by SIMD size");
+
+  int last_k_block_size = K - block_size * (K / block_size);
+  int last_k_simd_block = (last_k_block_size / simd_size) * simd_size;
+  for (int i = 0; i < ceildiv(M, block_size); i++) {
+    for (int j = 0; j < ceildiv(N, block_size); j++) {
+      AccT c_block[block_size * block_size] = {0.0};
+      AccT a_block[block_size * block_size];
+      AccT b_block[block_size * block_size];
+
+      int k = 0;
+      for (; k < K / block_size; k++) {
+        // Load a and b blocks
+        if (a_trans) {
+          load_block<block_size>(a, a_block, K, M, k, i, true);
+        } else {
+          load_block<block_size>(a, a_block, M, K, i, k, false);
+        }
+        if (b_trans) {
+          load_block<block_size>(b, b_block, N, K, j, k, false);
+        } else {
+          load_block<block_size>(b, b_block, K, N, k, j, true);
+        }
+
+        // Multiply and accumulate
+        for (int ii = 0; ii < block_size && i * block_size + ii < M; ++ii) {
+          for (int jj = 0; jj < block_size && j * block_size + jj < N; ++jj) {
+            for (int kk = 0; kk < block_size; kk += simd_size) {
+              auto av =
+                  simd::load<AccT, simd_size>(a_block + ii * block_size + kk);
+              auto bv =
+                  simd::load<AccT, simd_size>(b_block + jj * block_size + kk);
+              c_block[ii * block_size + jj] += simd::sum(av * bv);
+            }
+          }
+        }
+      }
+      if (last_k_block_size) {
+        // Load a and b blocks
+        if (a_trans) {
+          load_block<block_size>(a, a_block, K, M, k, i, true);
+        } else {
+          load_block<block_size>(a, a_block, M, K, i, k, false);
+        }
+        if (b_trans) {
+          load_block<block_size>(b, b_block, N, K, j, k, false);
+        } else {
+          load_block<block_size>(b, b_block, K, N, k, j, true);
+        }
+
+        // Multiply and accumulate
+        for (int ii = 0; ii < block_size && i * block_size + ii < M; ++ii) {
+          for (int jj = 0; jj < block_size && j * block_size + jj < N; ++jj) {
+            int kk = 0;
+            for (; kk < last_k_simd_block; kk += simd_size) {
+              auto av =
+                  simd::load<AccT, simd_size>(a_block + ii * block_size + kk);
+              auto bv =
+                  simd::load<AccT, simd_size>(b_block + jj * block_size + kk);
+              c_block[ii * block_size + jj] += simd::sum(av * bv);
+            }
+            for (; kk < last_k_block_size; ++kk) {
+              c_block[ii * block_size + jj] +=
+                  a_block[ii * block_size + kk] * b_block[jj * block_size + kk];
+            }
+          }
+        }
+      }
+
+      // Store
+      for (int ii = 0; ii < block_size && i * block_size + ii < M; ++ii) {
+        for (int jj = 0; jj < block_size && j * block_size + jj < N; ++jj) {
+          auto c_idx = (i * block_size + ii) * N + j * block_size + jj;
+          if (beta != 0) {
+            c[c_idx] = static_cast<T>(
+                alpha * c_block[ii * block_size + jj] + beta * c[c_idx]);
+          } else {
+            c[c_idx] = static_cast<T>(alpha * c_block[ii * block_size + jj]);
+          }
+        }
+      }
+    }
+  }
+}
+
+} // namespace mlx::core

mlx/backend/cpu/hadamard.cpp

@@ -4,16 +4,17 @@
 
 #include "mlx/backend/common/hadamard.h"
 #include "mlx/backend/cpu/copy.h"
+#include "mlx/backend/cpu/encoder.h"
 #include "mlx/primitives.h"
 
 namespace mlx::core {
 
 // n = 2^k component
 template <typename T>
-void hadamard_n(array& out, int n, int m, float scale) {
-  for (int b = 0; b < out.size() / n; b++) {
+void hadamard_n(T* out, int n, int m, float scale, size_t size) {
+  for (int b = 0; b < size / n; b++) {
     size_t loc = b * n;
-    T* data_ptr = out.data<T>() + loc;
+    T* data_ptr = out + loc;
     int h = 1;
     int n_over_2 = n / 2;
     while (h < n) {
@@ -36,7 +37,7 @@ void hadamard_n(array& out, int n, int m, float scale) {
 
 // m component
 template <typename T>
-void hadamard_m(array& out, int n, int m, float scale) {
+void hadamard_m(T* out, int n, int m, float scale, size_t size) {
   auto h_matrices = hadamard_matrices();
   auto& matrix = h_matrices[m];
   auto start = 1;
@@ -51,9 +52,9 @@ void hadamard_m(array& out, int n, int m, float scale) {
     end = matrix.find('\n', start);
   }
 
-  for (int b = 0; b < out.size() / m / n; b++) {
+  for (int b = 0; b < size / m / n; b++) {
     size_t loc = b * n * m;
-    T* data_ptr = out.data<T>() + loc;
+    T* data_ptr = out + loc;
     for (int i = 0; i < n; i++) {
       std::vector<float> out(m);
       for (int j = 0; j < m; j++) {
@@ -74,12 +75,17 @@ void hadamard_m(array& out, int n, int m, float scale) {
 }
 
 template <typename T>
-void hadamard(array& out, int n, int m, float scale) {
-  float n_scale = m > 1 ? 1.0 : scale;
-  hadamard_n<T>(out, n, m, n_scale);
-  if (m > 1) {
-    hadamard_m<T>(out, n, m, scale);
-  }
+void hadamard(array& out, int n, int m, float scale, Stream stream) {
+  auto& encoder = cpu::get_command_encoder(stream);
+  encoder.set_output_array(out);
+  auto out_ptr = out.data<T>();
+  encoder.dispatch([out_ptr, size = out.size(), n, m, scale]() {
+    float n_scale = m > 1 ? 1.0 : scale;
+    hadamard_n<T>(out_ptr, n, m, n_scale, size);
+    if (m > 1) {
+      hadamard_m<T>(out_ptr, n, m, scale, size);
+    }
+  });
 }
 
 void Hadamard::eval_cpu(const std::vector<array>& inputs, array& out) {
@@ -87,18 +93,26 @@ void Hadamard::eval_cpu(const std::vector<array>& inputs, array& out) {
   auto& in = inputs[0];
 
   // Copy input to output
-  copy(in, out, CopyType::General);
+  if (in.flags().row_contiguous && in.is_donatable()) {
+    out.copy_shared_buffer(in);
+  } else {
+    copy(
+        in,
+        out,
+        in.flags().row_contiguous ? CopyType::Vector : CopyType::General,
+        stream());
+  }
 
   int axis = out.ndim() - 1;
   auto [n, m] = decompose_hadamard(out.shape(axis));
 
   switch (in.dtype()) {
     case float32:
-      return hadamard<float>(out, n, m, scale_);
+      return hadamard<float>(out, n, m, scale_, stream());
     case float16:
-      return hadamard<float16_t>(out, n, m, scale_);
+      return hadamard<float16_t>(out, n, m, scale_, stream());
     case bfloat16:
-      return hadamard<bfloat16_t>(out, n, m, scale_);
+      return hadamard<bfloat16_t>(out, n, m, scale_, stream());
     default:
       throw std::invalid_argument("[hadamard] Unsupported type.");
   }

mlx/backend/cpu/indexing.cpp

@@ -8,6 +8,7 @@
 
 #include "mlx/backend/common/utils.h"
 #include "mlx/backend/cpu/copy.h"
+#include "mlx/backend/cpu/encoder.h"
 
 namespace mlx::core {
 
@@ -21,6 +22,40 @@ inline size_t offset_neg_idx(uint32_t idx, size_t) {
   return idx;
 }
 
+struct None {
+  template <typename T>
+  void operator()(T x, T* y) {
+    (*y) = x;
+  }
+};
+struct Sum {
+  template <typename T>
+  void operator()(T x, T* y) {
+    (*y) += x;
+  }
+};
+
+struct Prod {
+  template <typename T>
+  void operator()(T x, T* y) {
+    (*y) *= x;
+  }
+};
+
+struct Max {
+  template <typename T>
+  void operator()(T x, T* y) {
+    (*y) = (*y > x) ? *y : x;
+  }
+};
+
+struct Min {
+  template <typename T>
+  void operator()(T x, T* y) {
+    (*y) = (*y < x) ? *y : x;
+  }
+};
+
 template <typename T, typename IdxT>
 void gather(
     const array& src,
@@ -73,13 +108,14 @@ void gather(
   size_t ind_size = slice_size == 0 ? 0 : out.size() / slice_size;
   const T* src_ptr = src.data<T>();
   T* dst_ptr = out.data<T>();
-  size_t out_idx = 0;
 
   std::vector<ContiguousIterator> its(inds.begin(), inds.end());
   ContiguousIterator src_it;
   if (!can_copy && src.ndim() > 0) {
     src_it = ContiguousIterator(slice_sizes, src.strides(), src.ndim());
   }
+
+  size_t out_idx = 0;
   for (int idx = 0; idx < ind_size; idx++) {
     size_t src_idx = 0;
     for (int ii = 0; ii < inds.size(); ++ii) {
@@ -161,46 +197,59 @@ void dispatch_gather(
 }
 
 void Gather::eval_cpu(const std::vector<array>& inputs, array& out) {
-  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+  out.set_data(allocator::malloc(out.nbytes()));
 
   auto& src = inputs[0];
-  std::vector<array> inds(inputs.begin() + 1, inputs.end());
-
-  if (inds.empty()) {
-    dispatch_gather<uint8_t>(src, inds, out, axes_, slice_sizes_);
-    return;
+  std::vector<array> inds;
+  for (auto it = inputs.begin() + 1; it < inputs.end(); ++it) {
+    inds.push_back(array::unsafe_weak_copy(*it));
   }
-
-  switch (inds[0].dtype()) {
-    case uint8:
+  auto& encoder = cpu::get_command_encoder(stream());
+  for (auto& in : inputs) {
+    encoder.set_input_array(in);
+  }
+  encoder.set_output_array(out);
+  encoder.dispatch([axes_ = axes_,
+                    slice_sizes_ = slice_sizes_,
+                    src = array::unsafe_weak_copy(src),
+                    inds = std::move(inds),
+                    out = array::unsafe_weak_copy(out)]() mutable {
+    if (inds.empty()) {
       dispatch_gather<uint8_t>(src, inds, out, axes_, slice_sizes_);
-      break;
-    case uint16:
-      dispatch_gather<uint16_t>(src, inds, out, axes_, slice_sizes_);
-      break;
-    case uint32:
-      dispatch_gather<uint32_t>(src, inds, out, axes_, slice_sizes_);
-      break;
-    case uint64:
-      dispatch_gather<uint64_t>(src, inds, out, axes_, slice_sizes_);
-      break;
-    case int8:
-      dispatch_gather<int8_t>(src, inds, out, axes_, slice_sizes_);
-      break;
-    case int16:
-      dispatch_gather<int16_t>(src, inds, out, axes_, slice_sizes_);
-      break;
-    case int32:
-      dispatch_gather<int32_t>(src, inds, out, axes_, slice_sizes_);
-      break;
-    case int64:
-      dispatch_gather<int64_t>(src, inds, out, axes_, slice_sizes_);
-      break;
-    default:
-      throw std::runtime_error(
-          "[Gather::eval_cpu] Cannot gather with indices type.");
-      break;
-  }
+      return;
+    }
+
+    switch (inds[0].dtype()) {
+      case uint8:
+        dispatch_gather<uint8_t>(src, inds, out, axes_, slice_sizes_);
+        break;
+      case uint16:
+        dispatch_gather<uint16_t>(src, inds, out, axes_, slice_sizes_);
+        break;
+      case uint32:
+        dispatch_gather<uint32_t>(src, inds, out, axes_, slice_sizes_);
+        break;
+      case uint64:
+        dispatch_gather<uint64_t>(src, inds, out, axes_, slice_sizes_);
+        break;
+      case int8:
+        dispatch_gather<int8_t>(src, inds, out, axes_, slice_sizes_);
+        break;
+      case int16:
+        dispatch_gather<int16_t>(src, inds, out, axes_, slice_sizes_);
+        break;
+      case int32:
+        dispatch_gather<int32_t>(src, inds, out, axes_, slice_sizes_);
+        break;
+      case int64:
+        dispatch_gather<int64_t>(src, inds, out, axes_, slice_sizes_);
+        break;
+      default:
+        throw std::runtime_error(
+            "[Gather::eval_cpu] Cannot gather with indices type.");
+        break;
+    }
+  });
 }
 template <typename T, typename IdxT>
 void gather_axis(
@@ -235,6 +284,7 @@ void gather_axis(
   for (int i = axis + 1; i < ind.ndim(); ++i) {
     size_post *= ind.shape(i);
   }
+
   size_t stride_pre = size_post * ind_ax_size;
   for (size_t i = 0; i < size_pre; i++) {
     for (size_t k = 0; k < size_post; k++) {
@@ -304,39 +354,49 @@ void dispatch_gather_axis(
 }
 
 void GatherAxis::eval_cpu(const std::vector<array>& inputs, array& out) {
-  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+  out.set_data(allocator::malloc(out.nbytes()));
+
   auto& src = inputs[0];
   auto& inds = inputs[1];
-  switch (inds.dtype()) {
-    case uint8:
-      dispatch_gather_axis<uint8_t>(src, inds, out, axis_);
-      break;
-    case uint16:
-      dispatch_gather_axis<uint16_t>(src, inds, out, axis_);
-      break;
-    case uint32:
-      dispatch_gather_axis<uint32_t>(src, inds, out, axis_);
-      break;
-    case uint64:
-      dispatch_gather_axis<uint64_t>(src, inds, out, axis_);
-      break;
-    case int8:
-      dispatch_gather_axis<int8_t>(src, inds, out, axis_);
-      break;
-    case int16:
-      dispatch_gather_axis<int16_t>(src, inds, out, axis_);
-      break;
-    case int32:
-      dispatch_gather_axis<int32_t>(src, inds, out, axis_);
-      break;
-    case int64:
-      dispatch_gather_axis<int64_t>(src, inds, out, axis_);
-      break;
-    default:
-      throw std::runtime_error(
-          "[GatherAxis::eval_cpu] Cannot gather with indices type.");
-      break;
-  }
+  auto& encoder = cpu::get_command_encoder(stream());
+  encoder.set_input_array(src);
+  encoder.set_input_array(inds);
+  encoder.set_output_array(out);
+  encoder.dispatch([axis_ = axis_,
+                    src = array::unsafe_weak_copy(src),
+                    inds = array::unsafe_weak_copy(inds),
+                    out = array::unsafe_weak_copy(out)]() mutable {
+    switch (inds.dtype()) {
+      case uint8:
+        dispatch_gather_axis<uint8_t>(src, inds, out, axis_);
+        break;
+      case uint16:
+        dispatch_gather_axis<uint16_t>(src, inds, out, axis_);
+        break;
+      case uint32:
+        dispatch_gather_axis<uint32_t>(src, inds, out, axis_);
+        break;
+      case uint64:
+        dispatch_gather_axis<uint64_t>(src, inds, out, axis_);
+        break;
+      case int8:
+        dispatch_gather_axis<int8_t>(src, inds, out, axis_);
+        break;
+      case int16:
+        dispatch_gather_axis<int16_t>(src, inds, out, axis_);
+        break;
+      case int32:
+        dispatch_gather_axis<int32_t>(src, inds, out, axis_);
+        break;
+      case int64:
+        dispatch_gather_axis<int64_t>(src, inds, out, axis_);
+        break;
+      default:
+        throw std::runtime_error(
+            "[GatherAxis::eval_cpu] Cannot gather with indices type.");
+        break;
+    }
+  });
 }
 
 template <typename InT, typename IdxT, typename OpT>
@@ -344,8 +404,7 @@ void scatter(
     const array& updates,
     array& out,
     const std::vector<array>& inds,
-    const std::vector<int>& axes,
-    const OpT& op) {
+    const std::vector<int>& axes) {
   int nind = inds.size();
   auto inds_ndim = updates.ndim() - out.ndim();
   size_t n_updates = nind ? inds[0].size() : 1;
@@ -361,9 +420,11 @@ void scatter(
   ContiguousIterator update_it(updates);
   ContiguousIterator out_it(update_shape, out.strides(), out.ndim());
 
+  auto out_ptr = out.data<InT>();
+  auto upd_ptr = updates.data<InT>();
   for (int i = 0; i < n_updates; ++i) {
     size_t out_offset = 0;
-    for (int j = 0; j < nind; ++j) {
+    for (int j = 0; j < inds.size(); ++j) {
       auto ax = axes[j];
       auto idx_loc = its[j].loc;
       its[j].step();
@@ -373,8 +434,7 @@ void scatter(
     }
     update_it.seek(i * update_size);
     for (int j = 0; j < update_size; ++j) {
-      op(updates.data<InT>()[update_it.loc],
-         out.data<InT>() + out_offset + out_it.loc);
+      OpT{}(upd_ptr[update_it.loc], out_ptr + out_offset + out_it.loc);
       update_it.step();
       out_it.step();
     }
@@ -392,26 +452,19 @@ void dispatch_scatter_inds(
     Scatter::ReduceType rtype) {
   switch (rtype) {
     case Scatter::None:
-      scatter<InT, IdxT>(
-          updates, out, indices, axes, [](auto x, auto* y) { (*y) = x; });
+      scatter<InT, IdxT, None>(updates, out, indices, axes);
       break;
     case Scatter::Sum:
-      scatter<InT, IdxT>(
-          updates, out, indices, axes, [](auto x, auto* y) { (*y) += x; });
+      scatter<InT, IdxT, Sum>(updates, out, indices, axes);
       break;
     case Scatter::Prod:
-      scatter<InT, IdxT>(
-          updates, out, indices, axes, [](auto x, auto* y) { (*y) *= x; });
+      scatter<InT, IdxT, Prod>(updates, out, indices, axes);
       break;
     case Scatter::Max:
-      scatter<InT, IdxT>(updates, out, indices, axes, [](auto x, auto* y) {
-        (*y) = (*y > x) ? *y : x;
-      });
+      scatter<InT, IdxT, Max>(updates, out, indices, axes);
       break;
     case Scatter::Min:
-      scatter<InT, IdxT>(updates, out, indices, axes, [](auto x, auto* y) {
-        (*y) = (*y < x) ? *y : x;
-      });
+      scatter<InT, IdxT, Min>(updates, out, indices, axes);
       break;
   }
 }
@@ -463,67 +516,75 @@ void Scatter::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() >= 2);
 
   auto& src = inputs[0];
-  std::vector<array> inds(inputs.begin() + 1, inputs.end() - 1);
   auto& updates = inputs.back();
 
   // Copy src into out (copy allocates memory for out)
   auto ctype =
       src.flags().row_contiguous ? CopyType::Vector : CopyType::General;
-  copy(src, out, ctype);
+  copy(src, out, ctype, stream());
 
-  switch (src.dtype()) {
-    case bool_:
-      dispatch_scatter<bool>(out, inds, updates, axes_, reduce_type_);
-      break;
-    case uint8:
-      dispatch_scatter<uint8_t>(out, inds, updates, axes_, reduce_type_);
-      break;
-    case uint16:
-      dispatch_scatter<uint16_t>(out, inds, updates, axes_, reduce_type_);
-      break;
-    case uint32:
-      dispatch_scatter<uint32_t>(out, inds, updates, axes_, reduce_type_);
-      break;
-    case uint64:
-      dispatch_scatter<uint64_t>(out, inds, updates, axes_, reduce_type_);
-      break;
-    case int8:
-      dispatch_scatter<int8_t>(out, inds, updates, axes_, reduce_type_);
-      break;
-    case int16:
-      dispatch_scatter<int16_t>(out, inds, updates, axes_, reduce_type_);
-      break;
-    case int32:
-      dispatch_scatter<int32_t>(out, inds, updates, axes_, reduce_type_);
-      break;
-    case int64:
-      dispatch_scatter<int64_t>(out, inds, updates, axes_, reduce_type_);
-      break;
-    case float16:
-      dispatch_scatter<float16_t>(out, inds, updates, axes_, reduce_type_);
-      break;
-    case float32:
-      dispatch_scatter<float>(out, inds, updates, axes_, reduce_type_);
-      break;
-    case float64:
-      dispatch_scatter<double>(out, inds, updates, axes_, reduce_type_);
-      break;
-    case bfloat16:
-      dispatch_scatter<bfloat16_t>(out, inds, updates, axes_, reduce_type_);
-      break;
-    case complex64:
-      dispatch_scatter<complex64_t>(out, inds, updates, axes_, reduce_type_);
-      break;
+  auto& encoder = cpu::get_command_encoder(stream());
+  std::vector<array> inds;
+  for (auto it = inputs.begin() + 1; it < inputs.end() - 1; ++it) {
+    encoder.set_input_array(*it);
+    inds.push_back(array::unsafe_weak_copy(*it));
   }
+  encoder.set_input_array(updates);
+  encoder.set_output_array(out);
+  encoder.dispatch([axes_ = axes_,
+                    reduce_type_ = reduce_type_,
+                    updates = array::unsafe_weak_copy(updates),
+                    inds = std::move(inds),
+                    out = array::unsafe_weak_copy(out)]() mutable {
+    switch (out.dtype()) {
+      case bool_:
+        dispatch_scatter<bool>(out, inds, updates, axes_, reduce_type_);
+        break;
+      case uint8:
+        dispatch_scatter<uint8_t>(out, inds, updates, axes_, reduce_type_);
+        break;
+      case uint16:
+        dispatch_scatter<uint16_t>(out, inds, updates, axes_, reduce_type_);
+        break;
+      case uint32:
+        dispatch_scatter<uint32_t>(out, inds, updates, axes_, reduce_type_);
+        break;
+      case uint64:
+        dispatch_scatter<uint64_t>(out, inds, updates, axes_, reduce_type_);
+        break;
+      case int8:
+        dispatch_scatter<int8_t>(out, inds, updates, axes_, reduce_type_);
+        break;
+      case int16:
+        dispatch_scatter<int16_t>(out, inds, updates, axes_, reduce_type_);
+        break;
+      case int32:
+        dispatch_scatter<int32_t>(out, inds, updates, axes_, reduce_type_);
+        break;
+      case int64:
+        dispatch_scatter<int64_t>(out, inds, updates, axes_, reduce_type_);
+        break;
+      case float16:
+        dispatch_scatter<float16_t>(out, inds, updates, axes_, reduce_type_);
+        break;
+      case float32:
+        dispatch_scatter<float>(out, inds, updates, axes_, reduce_type_);
+        break;
+      case float64:
+        dispatch_scatter<double>(out, inds, updates, axes_, reduce_type_);
+        break;
+      case bfloat16:
+        dispatch_scatter<bfloat16_t>(out, inds, updates, axes_, reduce_type_);
+        break;
+      case complex64:
+        dispatch_scatter<complex64_t>(out, inds, updates, axes_, reduce_type_);
+        break;
+    }
+  });
 }
 
 template <typename T, typename IdxT, typename OpT>
-void scatter_axis(
-    array& out,
-    const array idx,
-    const array& upd,
-    int axis,
-    const OpT& op) {
+void scatter_axis(array& out, const array idx, const array& upd, int axis) {
   auto strides = idx.strides();
   strides.erase(strides.begin() + axis);
   auto shape = idx.shape();
@@ -557,8 +618,9 @@ void scatter_axis(
       for (int j = 0; j < idx_ax_size; ++j) {
         auto ind_val = offset_neg_idx(
             idx_ptr[idx_it.loc + j * idx_ax_stride], dst_ax_size);
-        op(upd_ptr[upd_it.loc + j * upd_ax_stride],
-           dst_ptr + k + ind_val * dst_ax_stride);
+        OpT{}(
+            upd_ptr[upd_it.loc + j * upd_ax_stride],
+            dst_ptr + k + ind_val * dst_ax_stride);
       }
       idx_it.step();
       upd_it.step();
@@ -576,12 +638,10 @@ void dispatch_scatter_axis_op(
     ScatterAxis::ReduceType rtype) {
   switch (rtype) {
     case ScatterAxis::None:
-      scatter_axis<InT, IdxT>(
-          out, idx, updates, axis, [](auto x, auto* y) { (*y) = x; });
+      scatter_axis<InT, IdxT, None>(out, idx, updates, axis);
       break;
     case ScatterAxis::Sum:
-      scatter_axis<InT, IdxT>(
-          out, idx, updates, axis, [](auto x, auto* y) { (*y) += x; });
+      scatter_axis<InT, IdxT, Sum>(out, idx, updates, axis);
       break;
   }
 }
@@ -634,53 +694,65 @@ void ScatterAxis::eval_cpu(const std::vector<array>& inputs, array& out) {
   // Copy src into out (copy allocates memory for out)
   auto ctype =
       src.flags().row_contiguous ? CopyType::Vector : CopyType::General;
-  copy(src, out, ctype);
+  copy(src, out, ctype, stream());
 
-  switch (src.dtype()) {
-    case bool_:
-      dispatch_scatter_axis<bool>(out, idx, updates, axis_, reduce_type_);
-      break;
-    case uint8:
-      dispatch_scatter_axis<uint8_t>(out, idx, updates, axis_, reduce_type_);
-      break;
-    case uint16:
-      dispatch_scatter_axis<uint16_t>(out, idx, updates, axis_, reduce_type_);
-      break;
-    case uint32:
-      dispatch_scatter_axis<uint32_t>(out, idx, updates, axis_, reduce_type_);
-      break;
-    case uint64:
-      dispatch_scatter_axis<uint64_t>(out, idx, updates, axis_, reduce_type_);
-      break;
-    case int8:
-      dispatch_scatter_axis<int8_t>(out, idx, updates, axis_, reduce_type_);
-      break;
-    case int16:
-      dispatch_scatter_axis<int16_t>(out, idx, updates, axis_, reduce_type_);
-      break;
-    case int32:
-      dispatch_scatter_axis<int32_t>(out, idx, updates, axis_, reduce_type_);
-      break;
-    case int64:
-      dispatch_scatter_axis<int64_t>(out, idx, updates, axis_, reduce_type_);
-      break;
-    case float16:
-      dispatch_scatter_axis<float16_t>(out, idx, updates, axis_, reduce_type_);
-      break;
-    case float32:
-      dispatch_scatter_axis<float>(out, idx, updates, axis_, reduce_type_);
-      break;
-    case float64:
-      dispatch_scatter_axis<double>(out, idx, updates, axis_, reduce_type_);
-      break;
-    case bfloat16:
-      dispatch_scatter_axis<bfloat16_t>(out, idx, updates, axis_, reduce_type_);
-      break;
-    case complex64:
-      dispatch_scatter_axis<complex64_t>(
-          out, idx, updates, axis_, reduce_type_);
-      break;
-  }
+  auto& encoder = cpu::get_command_encoder(stream());
+  encoder.set_input_array(idx);
+  encoder.set_input_array(updates);
+  encoder.set_output_array(out);
+  encoder.dispatch([axis_ = axis_,
+                    reduce_type_ = reduce_type_,
+                    idx = array::unsafe_weak_copy(idx),
+                    updates = array::unsafe_weak_copy(updates),
+                    out = array::unsafe_weak_copy(out)]() mutable {
+    switch (out.dtype()) {
+      case bool_:
+        dispatch_scatter_axis<bool>(out, idx, updates, axis_, reduce_type_);
+        break;
+      case uint8:
+        dispatch_scatter_axis<uint8_t>(out, idx, updates, axis_, reduce_type_);
+        break;
+      case uint16:
+        dispatch_scatter_axis<uint16_t>(out, idx, updates, axis_, reduce_type_);
+        break;
+      case uint32:
+        dispatch_scatter_axis<uint32_t>(out, idx, updates, axis_, reduce_type_);
+        break;
+      case uint64:
+        dispatch_scatter_axis<uint64_t>(out, idx, updates, axis_, reduce_type_);
+        break;
+      case int8:
+        dispatch_scatter_axis<int8_t>(out, idx, updates, axis_, reduce_type_);
+        break;
+      case int16:
+        dispatch_scatter_axis<int16_t>(out, idx, updates, axis_, reduce_type_);
+        break;
+      case int32:
+        dispatch_scatter_axis<int32_t>(out, idx, updates, axis_, reduce_type_);
+        break;
+      case int64:
+        dispatch_scatter_axis<int64_t>(out, idx, updates, axis_, reduce_type_);
+        break;
+      case float16:
+        dispatch_scatter_axis<float16_t>(
+            out, idx, updates, axis_, reduce_type_);
+        break;
+      case float32:
+        dispatch_scatter_axis<float>(out, idx, updates, axis_, reduce_type_);
+        break;
+      case float64:
+        dispatch_scatter_axis<double>(out, idx, updates, axis_, reduce_type_);
+        break;
+      case bfloat16:
+        dispatch_scatter_axis<bfloat16_t>(
+            out, idx, updates, axis_, reduce_type_);
+        break;
+      case complex64:
+        dispatch_scatter_axis<complex64_t>(
+            out, idx, updates, axis_, reduce_type_);
+        break;
+    }
+  });
 }
 
 } // namespace mlx::core

mlx/backend/cpu/inverse.cpp

@@ -2,47 +2,37 @@
 
 #include "mlx/allocator.h"
 #include "mlx/backend/cpu/copy.h"
+#include "mlx/backend/cpu/encoder.h"
 #include "mlx/backend/cpu/lapack.h"
 #include "mlx/primitives.h"
 
-int strtri_wrapper(char uplo, char diag, float* matrix, int N) {
-  int info;
-  MLX_LAPACK_FUNC(strtri)
-  (
-      /* uplo = */ &uplo,
-      /* diag = */ &diag,
-      /* N = */ &N,
-      /* a = */ matrix,
-      /* lda = */ &N,
-      /* info = */ &info);
-  return info;
-}
-
 namespace mlx::core {
 
-void general_inv(array& inv, int N, int i) {
+template <typename T>
+void general_inv(T* inv, int N) {
   int info;
-  auto ipiv = array::Data{allocator::malloc_or_wait(sizeof(int) * N)};
+  auto ipiv = array::Data{allocator::malloc(sizeof(int) * N)};
   // Compute LU factorization.
-  sgetrf_(
+  getrf<T>(
       /* m = */ &N,
       /* n = */ &N,
-      /* a = */ inv.data<float>() + N * N * i,
+      /* a = */ inv,
       /* lda = */ &N,
       /* ipiv = */ static_cast<int*>(ipiv.buffer.raw_ptr()),
       /* info = */ &info);
 
   if (info != 0) {
     std::stringstream ss;
-    ss << "inverse_impl: LU factorization failed with error code " << info;
+    ss << "[Inverse::eval_cpu] LU factorization failed with error code "
+       << info;
     throw std::runtime_error(ss.str());
   }
 
   static const int lwork_query = -1;
-  float workspace_size = 0;
+  T workspace_size = 0;
 
   // Compute workspace size.
-  sgetri_(
+  getri<T>(
       /* m = */ &N,
       /* a = */ nullptr,
       /* lda = */ &N,
@@ -53,68 +43,118 @@ void general_inv(array& inv, int N, int i) {
 
   if (info != 0) {
     std::stringstream ss;
-    ss << "inverse_impl: LU workspace calculation failed with error code "
+    ss << "[Inverse::eval_cpu] LU workspace calculation failed with error code "
        << info;
     throw std::runtime_error(ss.str());
   }
 
   const int lwork = workspace_size;
-  auto scratch = array::Data{allocator::malloc_or_wait(sizeof(float) * lwork)};
+  auto scratch = array::Data{allocator::malloc(sizeof(T) * lwork)};
 
   // Compute inverse.
-  sgetri_(
+  getri<T>(
       /* m = */ &N,
-      /* a = */ inv.data<float>() + N * N * i,
+      /* a = */ inv,
       /* lda = */ &N,
       /* ipiv = */ static_cast<int*>(ipiv.buffer.raw_ptr()),
-      /* work = */ static_cast<float*>(scratch.buffer.raw_ptr()),
+      /* work = */ static_cast<T*>(scratch.buffer.raw_ptr()),
       /* lwork = */ &lwork,
       /* info = */ &info);
 
   if (info != 0) {
     std::stringstream ss;
-    ss << "inverse_impl: inversion failed with error code " << info;
+    ss << "[Inverse::eval_cpu] inversion failed with error code " << info;
     throw std::runtime_error(ss.str());
   }
 }
 
-void tri_inv(array& inv, int N, int i, bool upper) {
+template <typename T>
+void tri_inv(T* inv, int N, bool upper) {
   const char uplo = upper ? 'L' : 'U';
   const char diag = 'N';
-  int info = strtri_wrapper(uplo, diag, inv.data<float>() + N * N * i, N);
+  int info;
+  trtri<T>(
+      /* uplo = */ &uplo,
+      /* diag = */ &diag,
+      /* N = */ &N,
+      /* a = */ inv,
+      /* lda = */ &N,
+      /* info = */ &info);
+
+  // zero out the other triangle
+  if (upper) {
+    for (int i = 0; i < N; i++) {
+      std::fill(inv, inv + i, 0.0f);
+      inv += N;
+    }
+  } else {
+    for (int i = 0; i < N; i++) {
+      std::fill(inv + i + 1, inv + N, 0.0f);
+      inv += N;
+    }
+  }
+
   if (info != 0) {
     std::stringstream ss;
-    ss << "inverse_impl: triangular inversion failed with error code " << info;
+    ss << "[Inverse::eval_cpu] triangular inversion failed with error code "
+       << info;
     throw std::runtime_error(ss.str());
   }
 }
 
-void inverse_impl(const array& a, array& inv, bool tri, bool upper) {
+template <typename T>
+void inverse_impl(
+    const array& a,
+    array& inv,
+    bool tri,
+    bool upper,
+    Stream stream) {
   // Lapack uses the column-major convention. We take advantage of the following
   // identity to avoid transposing (see
   // https://math.stackexchange.com/a/340234):
   //   (A⁻¹)ᵀ = (Aᵀ)⁻¹
 
   // The inverse is computed in place, so just copy the input to the output.
-  copy(a, inv, a.flags().row_contiguous ? CopyType::Vector : CopyType::General);
+  copy(
+      a,
+      inv,
+      a.flags().row_contiguous ? CopyType::Vector : CopyType::General,
+      stream);
 
   const int N = a.shape(-1);
   const size_t num_matrices = a.size() / (N * N);
 
-  for (int i = 0; i < num_matrices; i++) {
-    if (tri) {
-      tri_inv(inv, N, i, upper);
-    } else {
-      general_inv(inv, N, i);
-    }
+  auto& encoder = cpu::get_command_encoder(stream);
+  encoder.set_output_array(inv);
+
+  auto inv_ptr = inv.data<T>();
+  if (tri) {
+    encoder.dispatch([inv_ptr, N, num_matrices, upper]() {
+      for (int i = 0; i < num_matrices; i++) {
+        tri_inv<T>(inv_ptr + N * N * i, N, upper);
+      }
+    });
+  } else {
+    encoder.dispatch([inv_ptr, N, num_matrices]() {
+      for (int i = 0; i < num_matrices; i++) {
+        general_inv<T>(inv_ptr + N * N * i, N);
+      }
+    });
   }
 }
 
 void Inverse::eval_cpu(const std::vector<array>& inputs, array& output) {
-  if (inputs[0].dtype() != float32) {
-    throw std::runtime_error("[Inverse::eval] only supports float32.");
+  switch (inputs[0].dtype()) {
+    case float32:
+      inverse_impl<float>(inputs[0], output, tri_, upper_, stream());
+      break;
+    case float64:
+      inverse_impl<double>(inputs[0], output, tri_, upper_, stream());
+      break;
+    default:
+      throw std::runtime_error(
+          "[Inverse::eval_cpu] only supports float32 or float64.");
   }
-  inverse_impl(inputs[0], output, tri_, upper_);
 }
 
 } // namespace mlx::core

mlx/backend/cpu/lapack.h

@@ -31,3 +31,22 @@
 #define MLX_LAPACK_FUNC(f) f##_
 
 #endif
+
+#define INSTANTIATE_LAPACK_TYPES(FUNC)                       \
+  template <typename T, typename... Args>                    \
+  void FUNC(Args... args) {                                  \
+    if constexpr (std::is_same_v<T, float>) {                \
+      MLX_LAPACK_FUNC(s##FUNC)(std::forward<Args>(args)...); \
+    } else if constexpr (std::is_same_v<T, double>) {        \
+      MLX_LAPACK_FUNC(d##FUNC)(std::forward<Args>(args)...); \
+    }                                                        \
+  }
+
+INSTANTIATE_LAPACK_TYPES(geqrf)
+INSTANTIATE_LAPACK_TYPES(orgqr)
+INSTANTIATE_LAPACK_TYPES(syevd)
+INSTANTIATE_LAPACK_TYPES(potrf)
+INSTANTIATE_LAPACK_TYPES(gesvdx)
+INSTANTIATE_LAPACK_TYPES(getrf)
+INSTANTIATE_LAPACK_TYPES(getri)
+INSTANTIATE_LAPACK_TYPES(trtri)

mlx/backend/cpu/logsumexp.cpp

@@ -0,0 +1,140 @@
+// Copyright © 2023-2024 Apple Inc.
+
+#include <cassert>
+#include <cmath>
+
+#include "mlx/backend/cpu/copy.h"
+#include "mlx/backend/cpu/encoder.h"
+#include "mlx/backend/cpu/simd/simd.h"
+#include "mlx/primitives.h"
+#include "mlx/types/limits.h"
+
+namespace mlx::core {
+
+namespace {
+
+using namespace mlx::core::simd;
+
+template <typename T, typename AccT>
+void logsumexp(const array& in, array& out, Stream stream) {
+  auto& encoder = cpu::get_command_encoder(stream);
+  encoder.set_input_array(in);
+  encoder.set_output_array(out);
+
+  const T* in_ptr = in.data<T>();
+  T* out_ptr = out.data<T>();
+
+  int M = in.shape().back();
+  int L = in.data_size() / M;
+
+  encoder.dispatch([in_ptr, out_ptr, M, L]() mutable {
+    constexpr int N = std::min(max_size<AccT>, max_size<T>);
+
+    const T* current_in_ptr;
+
+    for (int i = 0; i < L; i++, in_ptr += M, out_ptr += 1) {
+      // Find the maximum
+      current_in_ptr = in_ptr;
+      Simd<AccT, N> vmaximum(-numeric_limits<AccT>::infinity());
+      size_t s = M;
+      while (s >= N) {
+        Simd<AccT, N> vals = load<T, N>(current_in_ptr);
+        vmaximum = maximum(vals, vmaximum);
+        current_in_ptr += N;
+        s -= N;
+      }
+
+      AccT maximum = max(vmaximum);
+      while (s-- > 0) {
+        maximum = std::max(maximum, static_cast<AccT>(*current_in_ptr));
+        current_in_ptr++;
+      }
+
+      // Compute the normalizer and the exponentials
+      Simd<AccT, N> vnormalizer(0.0);
+      current_in_ptr = in_ptr;
+      s = M;
+      while (s >= N) {
+        Simd<AccT, N> vexp = load<T, N>(current_in_ptr);
+        vexp = exp(vexp - maximum);
+        vnormalizer = vnormalizer + vexp;
+        current_in_ptr += N;
+        s -= N;
+      }
+      AccT normalizer = sum(vnormalizer);
+      while (s-- > 0) {
+        AccT _exp = std::exp(*current_in_ptr - maximum);
+        normalizer += _exp;
+        current_in_ptr++;
+      }
+      // Normalize
+      *out_ptr = std::isinf(maximum)
+          ? static_cast<T>(maximum)
+          : static_cast<T>(std::log(normalizer) + maximum);
+    }
+  });
+}
+
+} // namespace
+
+void LogSumExp::eval_cpu(const std::vector<array>& inputs, array& out) {
+  assert(inputs.size() == 1);
+
+  // Make sure that the last dimension is contiguous
+  auto s = stream();
+  auto& encoder = cpu::get_command_encoder(s);
+  auto ensure_contiguous = [&s, &encoder](const array& x) {
+    if (x.flags().contiguous && x.strides()[x.ndim() - 1] == 1) {
+      return x;
+    } else {
+      auto x_copy = array(x.shape(), x.dtype(), nullptr, {});
+      copy(x, x_copy, CopyType::General, s);
+      encoder.add_temporary(x_copy);
+      return x_copy;
+    }
+  };
+
+  auto in = ensure_contiguous(inputs[0]);
+  if (in.flags().row_contiguous) {
+    out.set_data(allocator::malloc(out.nbytes()));
+  } else {
+    auto n = in.shape(-1);
+    auto flags = in.flags();
+    auto strides = in.strides();
+    for (auto& s : strides) {
+      s /= n;
+    }
+    bool col_contig = strides[0] == 1;
+    for (int i = 1; col_contig && i < strides.size(); ++i) {
+      col_contig &=
+          (out.shape(i) == 1 || strides[i - 1] == out.shape(i) * strides[i]);
+    }
+    flags.col_contiguous = col_contig;
+    out.set_data(
+        allocator::malloc(in.nbytes() / n),
+        in.data_size() / n,
+        std::move(strides),
+        flags);
+  }
+
+  switch (in.dtype()) {
+    case float32:
+      logsumexp<float, float>(in, out, stream());
+      break;
+    case float16:
+      logsumexp<float16_t, float>(in, out, stream());
+      break;
+    case bfloat16:
+      logsumexp<bfloat16_t, float>(in, out, stream());
+      break;
+    case float64:
+      logsumexp<double, double>(in, out, stream());
+      break;
+    default:
+      throw std::runtime_error(
+          "[logsumexp] only supports floating point types");
+      break;
+  }
+}
+
+} // namespace mlx::core

mlx/backend/cpu/luf.cpp

@@ -4,18 +4,22 @@
 
 #include "mlx/allocator.h"
 #include "mlx/backend/cpu/copy.h"
+#include "mlx/backend/cpu/encoder.h"
 #include "mlx/backend/cpu/lapack.h"
 #include "mlx/primitives.h"
 
 namespace mlx::core {
 
-void lu_factor_impl(
+template <typename T>
+void luf_impl(
     const array& a,
     array& lu,
     array& pivots,
-    array& row_indices) {
+    array& row_indices,
+    Stream stream) {
   int M = a.shape(-2);
   int N = a.shape(-1);
+  int K = std::min(M, N);
 
   // Copy a into lu and make it col contiguous
   auto ndim = lu.ndim();
@@ -26,63 +30,91 @@ void lu_factor_impl(
   auto strides = lu.strides();
   strides[ndim - 1] = M;
   strides[ndim - 2] = 1;
-  lu.set_data(
-      allocator::malloc_or_wait(lu.nbytes()), lu.nbytes(), strides, flags);
+  lu.set_data(allocator::malloc(lu.nbytes()), lu.nbytes(), strides, flags);
   copy_inplace(
-      a, lu, a.shape(), a.strides(), strides, 0, 0, CopyType::GeneralGeneral);
+      a,
+      lu,
+      a.shape(),
+      a.strides(),
+      strides,
+      0,
+      0,
+      CopyType::GeneralGeneral,
+      stream);
 
-  auto a_ptr = lu.data<float>();
-
-  pivots.set_data(allocator::malloc_or_wait(pivots.nbytes()));
-  row_indices.set_data(allocator::malloc_or_wait(row_indices.nbytes()));
+  auto a_ptr = lu.data<T>();
+  pivots.set_data(allocator::malloc(pivots.nbytes()));
+  row_indices.set_data(allocator::malloc(row_indices.nbytes()));
   auto pivots_ptr = pivots.data<uint32_t>();
   auto row_indices_ptr = row_indices.data<uint32_t>();
-
-  int info;
   size_t num_matrices = a.size() / (M * N);
-  for (size_t i = 0; i < num_matrices; ++i) {
-    // Compute LU factorization of A
-    MLX_LAPACK_FUNC(sgetrf)
-    (/* m */ &M,
-     /* n */ &N,
-     /* a */ a_ptr,
-     /* lda */ &M,
-     /* ipiv */ reinterpret_cast<int*>(pivots_ptr),
-     /* info */ &info);
+  auto& encoder = cpu::get_command_encoder(stream);
+  encoder.set_input_array(a);
+  encoder.set_output_array(lu);
+  encoder.set_output_array(pivots);
+  encoder.set_output_array(row_indices);
 
-    if (info != 0) {
-      std::stringstream ss;
-      ss << "[LUF::eval_cpu] sgetrf_ failed with code " << info
-         << ((info > 0) ? " because matrix is singular"
-                        : " because argument had an illegal value");
-      throw std::runtime_error(ss.str());
-    }
+  encoder.dispatch(
+      [a_ptr, pivots_ptr, row_indices_ptr, num_matrices, M, N, K]() mutable {
+        int info;
+        for (size_t i = 0; i < num_matrices; ++i) {
+          // Compute LU factorization of A
+          getrf<T>(
+              /* m */ &M,
+              /* n */ &N,
+              /* a */ a_ptr,
+              /* lda */ &M,
+              /* ipiv */ reinterpret_cast<int*>(pivots_ptr),
+              /* info */ &info);
 
-    // Subtract 1 to get 0-based index
-    for (int j = 0; j < pivots.shape(-1); ++j) {
-      pivots_ptr[j]--;
-      row_indices_ptr[j] = j;
-    }
-    for (int j = pivots.shape(-1) - 1; j >= 0; --j) {
-      auto piv = pivots_ptr[j];
-      auto t1 = row_indices_ptr[piv];
-      auto t2 = row_indices_ptr[j];
-      row_indices_ptr[j] = t1;
-      row_indices_ptr[piv] = t2;
-    }
+          if (info != 0) {
+            std::stringstream ss;
+            ss << "[LUF::eval_cpu] sgetrf_ failed with code " << info
+               << ((info > 0) ? " because matrix is singular"
+                              : " because argument had an illegal value");
+            throw std::runtime_error(ss.str());
+          }
 
-    // Advance pointers to the next matrix
-    a_ptr += M * N;
-    pivots_ptr += pivots.shape(-1);
-    row_indices_ptr += pivots.shape(-1);
-  }
+          // Subtract 1 to get 0-based index
+          int j = 0;
+          for (; j < K; ++j) {
+            pivots_ptr[j]--;
+            row_indices_ptr[j] = j;
+          }
+          for (; j < M; ++j) {
+            row_indices_ptr[j] = j;
+          }
+          for (int j = K - 1; j >= 0; --j) {
+            auto piv = pivots_ptr[j];
+            auto t1 = row_indices_ptr[piv];
+            auto t2 = row_indices_ptr[j];
+            row_indices_ptr[j] = t1;
+            row_indices_ptr[piv] = t2;
+          }
+
+          // Advance pointers to the next matrix
+          a_ptr += M * N;
+          pivots_ptr += K;
+          row_indices_ptr += M;
+        }
+      });
 }
 
 void LUF::eval_cpu(
     const std::vector<array>& inputs,
     std::vector<array>& outputs) {
   assert(inputs.size() == 1);
-  lu_factor_impl(inputs[0], outputs[0], outputs[1], outputs[2]);
+  switch (inputs[0].dtype()) {
+    case float32:
+      luf_impl<float>(inputs[0], outputs[0], outputs[1], outputs[2], stream());
+      break;
+    case float64:
+      luf_impl<double>(inputs[0], outputs[0], outputs[1], outputs[2], stream());
+      break;
+    default:
+      throw std::runtime_error(
+          "[LUF::eval_cpu] only supports float32 or float64.");
+  }
 }
 
 } // namespace mlx::core

mlx/backend/cpu/make_compiled_preamble.sh

@@ -18,13 +18,16 @@ if [ "$CLANG" = "TRUE" ]; then
 #include <complex>
 #include <cstdint>
 #include <vector>
+#ifdef __ARM_FEATURE_FP16_SCALAR_ARITHMETIC
+#include <arm_fp16.h>
+#endif
 EOM
-CC_FLAGS="-arch ${ARCH}"
+CC_FLAGS="-arch ${ARCH} -nobuiltininc -nostdinc"
 else
 CC_FLAGS="-std=c++17"
 fi
 
-CONTENT=$($GCC $CC_FLAGS -I "$SRCDIR" -E "$SRCDIR/mlx/backend/cpu/compiled_preamble.h" 2>/dev/null)
+CONTENT=$($GCC $CC_FLAGS -I "$SRCDIR" -E -P "$SRCDIR/mlx/backend/cpu/compiled_preamble.h" 2>/dev/null)
 
 cat << EOF > "$OUTPUT_FILE"
 const char* get_kernel_preamble() {

mlx/backend/cpu/masked_mm.cpp

@@ -5,6 +5,7 @@
 #include "mlx/array.h"
 #include "mlx/backend/common/utils.h"
 #include "mlx/backend/cpu/copy.h"
+#include "mlx/backend/cpu/encoder.h"
 #include "mlx/backend/cpu/lapack.h"
 #include "mlx/primitives.h"
 
@@ -58,42 +59,42 @@ void BlockMaskedMM::eval_cpu(const std::vector<array>& inputs, array& out) {
     throw std::runtime_error(
         "[BlockMaskedMM::eval] Currently only supports float32.");
   }
-  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+  out.set_data(allocator::malloc(out.nbytes()));
 
   auto& a_pre = inputs[0];
   auto& b_pre = inputs[1];
 
   auto check_transpose =
-      [](const array& arr, bool do_copy, bool expand_all = false) {
+      [s = stream()](const array& arr, bool do_copy, bool expand_all = false) {
         auto stx = arr.strides()[arr.ndim() - 2];
         auto sty = arr.strides()[arr.ndim() - 1];
         if (!expand_all && stx == arr.shape(-1) && sty == 1) {
           if (do_copy) {
             array arr_copy(arr.shape(), arr.dtype(), nullptr, {});
-            copy(arr, arr_copy, CopyType::Vector);
-            return std::make_tuple(false, stx, arr_copy);
+            copy(arr, arr_copy, CopyType::Vector, s);
+            return std::make_tuple(false, stx, arr_copy, true);
           }
-          return std::make_tuple(false, stx, arr);
+          return std::make_tuple(false, stx, arr, false);
         } else if (!expand_all && stx == 1 && sty == arr.shape(-2)) {
           if (do_copy) {
             array arr_copy(arr.shape(), arr.dtype(), nullptr, {});
-            copy(arr, arr_copy, CopyType::Vector);
-            return std::make_tuple(true, sty, arr_copy);
+            copy(arr, arr_copy, CopyType::Vector, s);
+            return std::make_tuple(true, sty, arr_copy, true);
           }
-          return std::make_tuple(true, sty, arr);
+          return std::make_tuple(true, sty, arr, false);
         } else {
           array arr_copy(arr.shape(), arr.dtype(), nullptr, {});
-          copy(arr, arr_copy, CopyType::General);
+          copy(arr, arr_copy, CopyType::General, s);
           int64_t stx = arr.shape(-1);
-          return std::make_tuple(false, stx, arr_copy);
+          return std::make_tuple(false, stx, arr_copy, true);
         }
       };
 
   bool has_op_mask = inputs.size() > 3;
   bool has_out_mask = inputs.size() == 3 || inputs.size() == 5;
-  auto [a_transposed, lda, a] =
+  auto [a_transposed, lda, a, a_copied] =
       check_transpose(a_pre, has_op_mask, inputs.back().dtype() != bool_);
-  auto [b_transposed, ldb, b] =
+  auto [b_transposed, ldb, b, b_copied] =
       check_transpose(b_pre, has_op_mask, inputs.back().dtype() != bool_);
 
   size_t M = a.shape(-2);
@@ -104,31 +105,39 @@ void BlockMaskedMM::eval_cpu(const std::vector<array>& inputs, array& out) {
     return;
   }
 
+  auto& encoder = cpu::get_command_encoder(stream());
   if (K == 0) {
-    std::memset(static_cast<void*>(out.data<float>()), 0, out.nbytes());
+    encoder.set_output_array(out);
+    encoder.dispatch([out_ptr = out.data<void>(), nbytes = out.nbytes()]() {
+      std::memset(out_ptr, 0, nbytes);
+    });
     return;
   }
 
-  auto mask_array = [](const array& mask,
+  auto mask_array = [](const void* mask,
                        float* data,
                        int block_size,
                        int batch_idx,
                        int X,
                        int Y,
                        size_t X_data_str,
-                       size_t Y_data_str) {
+                       size_t Y_data_str,
+                       const Shape& mask_shape,
+                       const Strides& mask_strides,
+                       bool is_bool) {
+    auto ndim = mask_shape.size();
     auto mask_offset = elem_to_loc(
-        mask.shape(-1) * mask.shape(-2) * batch_idx,
-        mask.shape(),
-        mask.strides());
+        mask_shape[ndim - 1] * mask_shape[ndim - 2] * batch_idx,
+        mask_shape,
+        mask_strides);
 
-    auto X_mask_str = mask.strides()[mask.ndim() - 2];
-    auto Y_mask_str = mask.strides()[mask.ndim() - 1];
+    auto X_mask_str = mask_strides[ndim - 2];
+    auto Y_mask_str = mask_strides[ndim - 1];
 
-    if (mask.dtype() == bool_) {
+    if (is_bool) {
       return mask_matrix(
           data,
-          mask.data<bool>(),
+          static_cast<const bool*>(mask),
           block_size,
           X,
           Y,
@@ -140,7 +149,7 @@ void BlockMaskedMM::eval_cpu(const std::vector<array>& inputs, array& out) {
     } else {
       return mask_matrix(
           data,
-          mask.data<float>(),
+          static_cast<const float*>(mask),
           block_size,
           X,
           Y,
@@ -152,61 +161,155 @@ void BlockMaskedMM::eval_cpu(const std::vector<array>& inputs, array& out) {
     }
   };
 
-  for (int i = 0; i < (out.size() / (M * size_t(N))); ++i) {
-    // Adjust pointer
-    float* ai =
-        a.data<float>() + elem_to_loc(M * K * i, a.shape(), a.strides());
-    float* bi =
-        b.data<float>() + elem_to_loc(K * N * i, b.shape(), b.strides());
-    float* ci = out.data<float>() + M * N * i;
+  encoder.set_input_array(a);
+  encoder.set_input_array(b);
+  const void* a_mask_ptr;
+  const void* b_mask_ptr;
+  const void* out_mask_ptr;
+  Shape a_mask_shape;
+  Shape b_mask_shape;
+  Shape out_mask_shape;
+  Strides a_mask_strides;
+  Strides b_mask_strides;
+  Strides out_mask_strides;
+  bool a_mask_bool;
+  bool b_mask_bool;
+  bool out_mask_bool;
+  if (has_op_mask) {
+    auto& a_mask = inputs[inputs.size() - 2];
+    auto& b_mask = inputs[inputs.size() - 1];
+    a_mask_ptr = a_mask.data<void>();
+    b_mask_ptr = b_mask.data<void>();
+    a_mask_shape = a_mask.shape();
+    b_mask_shape = b_mask.shape();
+    a_mask_strides = a_mask.strides();
+    b_mask_strides = b_mask.strides();
+    a_mask_bool = (a_mask.dtype() == bool_);
+    b_mask_bool = (b_mask.dtype() == bool_);
+    encoder.set_input_array(a_mask);
+    encoder.set_input_array(b_mask);
+  }
+  if (has_out_mask) {
+    auto& out_mask = inputs[2];
+    out_mask_ptr = out_mask.data<void>();
+    out_mask_bool = (out_mask.dtype() == bool_);
+    encoder.set_input_array(out_mask);
+    out_mask_shape = out_mask.shape();
+    out_mask_strides = out_mask.strides();
+  }
+  encoder.set_output_array(out);
+  auto a_ptr = a.data<float>();
+  auto b_ptr = b.data<float>();
+  auto out_ptr = out.data<float>();
+  size_t num_matrices = out.size() / (M * size_t(N));
+  auto ldc = out.shape(-1);
 
-    // Zero out blocks in a and b if needed
-    if (has_op_mask) {
-      auto& a_mask = inputs[inputs.size() - 2];
-      mask_array(
-          a_mask,
-          ai,
-          block_size_,
-          i,
+  encoder.dispatch([a_ptr,
+                    b_ptr,
+                    out_ptr,
+                    a_mask_ptr,
+                    b_mask_ptr,
+                    out_mask_ptr,
+                    has_op_mask,
+                    has_out_mask,
+                    block_size = block_size_,
+                    num_matrices,
+                    M,
+                    N,
+                    K,
+                    a_transposed = a_transposed,
+                    b_transposed = b_transposed,
+                    lda = lda,
+                    ldb = ldb,
+                    ldc,
+                    a_shape = a.shape(),
+                    a_strides = a.strides(),
+                    b_shape = b.shape(),
+                    b_strides = b.strides(),
+                    a_mask_shape = std::move(a_mask_shape),
+                    b_mask_shape = std::move(b_mask_shape),
+                    out_mask_shape = std::move(out_mask_shape),
+                    a_mask_strides = std::move(a_mask_strides),
+                    b_mask_strides = std::move(b_mask_strides),
+                    out_mask_strides = std::move(out_mask_strides),
+                    mask_array,
+                    a_mask_bool,
+                    b_mask_bool,
+                    out_mask_bool]() {
+    for (int i = 0; i < num_matrices; ++i) {
+      // Adjust pointer
+      float* ai = a_ptr + elem_to_loc(M * K * i, a_shape, a_strides);
+      float* bi = b_ptr + elem_to_loc(K * N * i, b_shape, b_strides);
+      float* ci = out_ptr + M * N * i;
+
+      // Zero out blocks in a and b if needed
+      if (has_op_mask) {
+        mask_array(
+            a_mask_ptr,
+            ai,
+            block_size,
+            i,
+            M,
+            K,
+            a_transposed ? 1 : lda,
+            a_transposed ? lda : 1,
+            a_mask_shape,
+            a_mask_strides,
+            a_mask_bool);
+
+        mask_array(
+            b_mask_ptr,
+            bi,
+            block_size,
+            i,
+            K,
+            N,
+            b_transposed ? 1 : ldb,
+            b_transposed ? ldb : 1,
+            b_mask_shape,
+            b_mask_strides,
+            b_mask_bool);
+      }
+
+      // Do matmul
+      cblas_sgemm(
+          CblasRowMajor,
+          a_transposed ? CblasTrans : CblasNoTrans, // transA
+          b_transposed ? CblasTrans : CblasNoTrans, // transB
           M,
-          K,
-          a_transposed ? 1 : lda,
-          a_transposed ? lda : 1);
-
-      auto& b_mask = inputs[inputs.size() - 1];
-      mask_array(
-          b_mask,
-          bi,
-          block_size_,
-          i,
-          K,
           N,
-          b_transposed ? 1 : ldb,
-          b_transposed ? ldb : 1);
-    }
+          K,
+          1.0, // alpha
+          ai,
+          lda,
+          bi,
+          ldb,
+          0.0, // beta
+          ci,
+          ldc);
 
-    // Do matmul
-    cblas_sgemm(
-        CblasRowMajor,
-        a_transposed ? CblasTrans : CblasNoTrans, // transA
-        b_transposed ? CblasTrans : CblasNoTrans, // transB
-        M,
-        N,
-        K,
-        1.0, // alpha
-        ai,
-        lda,
-        bi,
-        ldb,
-        0.0, // beta
-        ci,
-        out.shape(-1) // ldc
-    );
-
-    // Zero out blocks in out
-    if (has_out_mask) {
-      mask_array(inputs[2], ci, block_size_, i, M, N, N, 1);
+      // Zero out blocks in out
+      if (has_out_mask) {
+        mask_array(
+            out_mask_ptr,
+            ci,
+            block_size,
+            i,
+            M,
+            N,
+            N,
+            1,
+            out_mask_shape,
+            out_mask_strides,
+            out_mask_bool);
+      }
     }
+  });
+  if (a_copied) {
+    encoder.add_temporary(a);
+  }
+  if (b_copied) {
+    encoder.add_temporary(b);
   }
 }
 
@@ -215,12 +318,13 @@ void GatherMM::eval_cpu(const std::vector<array>& inputs, array& out) {
     throw std::runtime_error(
         "[GatherMM::eval] Currently only supports float32.");
   }
-  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+  out.set_data(allocator::malloc(out.nbytes()));
 
   auto& a_pre = inputs[0];
   auto& b_pre = inputs[1];
 
-  auto check_transpose = [](const array& arr) {
+  std::vector<array> temps;
+  auto check_transpose = [s = stream(), &temps](const array& arr) {
     auto stx = arr.strides()[arr.ndim() - 2];
     auto sty = arr.strides()[arr.ndim() - 1];
     if (stx == arr.shape(-1) && sty == 1) {
@@ -228,10 +332,10 @@ void GatherMM::eval_cpu(const std::vector<array>& inputs, array& out) {
     } else if (stx == 1 && sty == arr.shape(-2)) {
       return std::make_tuple(true, sty, arr);
     } else {
-      array arr_copy(arr.shape(), arr.dtype(), nullptr, {});
-      copy(arr, arr_copy, CopyType::General);
+      temps.push_back(array(arr.shape(), arr.dtype(), nullptr, {}));
+      copy(arr, temps.back(), CopyType::General, s);
       int64_t stx = arr.shape(-1);
-      return std::make_tuple(false, stx, arr_copy);
+      return std::make_tuple(false, stx, temps.back());
     }
   };
 
@@ -246,8 +350,12 @@ void GatherMM::eval_cpu(const std::vector<array>& inputs, array& out) {
     return;
   }
 
+  auto& encoder = cpu::get_command_encoder(stream());
   if (K == 0) {
-    std::memset(static_cast<void*>(out.data<float>()), 0, out.nbytes());
+    encoder.set_output_array(out);
+    encoder.dispatch([out_ptr = out.data<float>(), size = out.size()]() {
+      std::fill_n(out_ptr, size, 0);
+    });
     return;
   }
 
@@ -272,29 +380,61 @@ void GatherMM::eval_cpu(const std::vector<array>& inputs, array& out) {
 
   const uint32_t* lhs_indices_ptr = lhs_indices.data<uint32_t>();
   const uint32_t* rhs_indices_ptr = rhs_indices.data<uint32_t>();
+  encoder.set_input_array(a);
+  encoder.set_input_array(b);
+  encoder.set_input_array(lhs_indices);
+  encoder.set_input_array(rhs_indices);
+  encoder.set_output_array(out);
+  auto ldc = out.shape(-1);
 
-  for (int i = 0; i < batch_size_out; i++) {
-    // Get index
-    uint32_t indx_A = lhs_indices_ptr[elem_to_loc(i, lhs_indices)];
-    uint32_t indx_B = rhs_indices_ptr[elem_to_loc(i, rhs_indices)];
+  encoder.dispatch([a_ptr = a.data<float>(),
+                    b_ptr = b.data<float>(),
+                    out_ptr = out.data<float>(),
+                    M,
+                    N,
+                    K,
+                    lda = lda,
+                    ldb = ldb,
+                    a_transposed = a_transposed,
+                    b_transposed = b_transposed,
+                    ldc,
+                    lhs_indices_ptr,
+                    rhs_indices_ptr,
+                    lhs_indices_shape = lhs_indices.shape(),
+                    lhs_indices_strides = lhs_indices.strides(),
+                    rhs_indices_shape = rhs_indices.shape(),
+                    rhs_indices_strides = rhs_indices.strides(),
+                    batch_size_out,
+                    matrix_stride_out,
+                    batch_shape_A = std::move(batch_shape_A),
+                    batch_shape_B = std::move(batch_shape_B),
+                    batch_strides_A = std::move(batch_strides_A),
+                    batch_strides_B = std::move(batch_strides_B)]() {
+    for (int i = 0; i < batch_size_out; i++) {
+      // Get index
+      uint32_t indx_A = lhs_indices_ptr[elem_to_loc(
+          i, lhs_indices_shape, lhs_indices_strides)];
+      uint32_t indx_B = rhs_indices_ptr[elem_to_loc(
+          i, rhs_indices_shape, rhs_indices_strides)];
 
-    cblas_sgemm(
-        CblasRowMajor,
-        a_transposed ? CblasTrans : CblasNoTrans, // transA
-        b_transposed ? CblasTrans : CblasNoTrans, // transB
-        M,
-        N,
-        K,
-        1.0f, // alpha
-        a.data<float>() + elem_to_loc(indx_A, batch_shape_A, batch_strides_A),
-        lda,
-        b.data<float>() + elem_to_loc(indx_B, batch_shape_B, batch_strides_B),
-        ldb,
-        0.0f, // beta
-        out.data<float>() + matrix_stride_out * i,
-        out.shape(-1) // ldc
-    );
-  }
+      cblas_sgemm(
+          CblasRowMajor,
+          a_transposed ? CblasTrans : CblasNoTrans, // transA
+          b_transposed ? CblasTrans : CblasNoTrans, // transB
+          M,
+          N,
+          K,
+          1.0f, // alpha
+          a_ptr + elem_to_loc(indx_A, batch_shape_A, batch_strides_A),
+          lda,
+          b_ptr + elem_to_loc(indx_B, batch_shape_B, batch_strides_B),
+          ldb,
+          0.0f, // beta
+          out_ptr + matrix_stride_out * i,
+          ldc);
+    }
+  });
+  encoder.add_temporaries(std::move(temps));
 }
 
 } // namespace mlx::core

mlx/backend/cpu/matmul.cpp

@@ -3,18 +3,76 @@
 #include <cstring>
 #include "mlx/array.h"
 #include "mlx/backend/cpu/copy.h"
+#include "mlx/backend/cpu/encoder.h"
 #include "mlx/backend/cpu/gemm.h"
 #include "mlx/primitives.h"
 
 namespace mlx::core {
 
+template <typename T>
+void matmul_dispatch(
+    const array& a,
+    const array& b,
+    array& out,
+    bool a_transposed,
+    bool b_transposed,
+    size_t lda,
+    size_t ldb,
+    float alpha,
+    float beta,
+    Stream stream) {
+  const T* a_ptr = a.data<T>();
+  const T* b_ptr = b.data<T>();
+  T* out_ptr = out.data<T>();
+  size_t ldc = out.shape(-1);
+  size_t batch_size = a.size() / (a.shape(-2) * a.shape(-1));
+  auto& encoder = cpu::get_command_encoder(stream);
+  encoder.set_input_array(a);
+  encoder.set_input_array(b);
+  encoder.set_output_array(out);
+  encoder.dispatch([a_ptr,
+                    b_ptr,
+                    out_ptr,
+                    a_transposed,
+                    b_transposed,
+                    lda,
+                    ldb,
+                    ldc,
+                    alpha,
+                    beta,
+                    batch_size,
+                    a_shape = a.shape(),
+                    a_strides = a.strides(),
+                    b_shape = b.shape(),
+                    b_strides = b.strides()]() {
+    matmul<T>(
+        a_ptr,
+        b_ptr,
+        out_ptr,
+        a_transposed,
+        b_transposed,
+        lda,
+        ldb,
+        ldc,
+        alpha,
+        beta,
+        batch_size,
+        a_shape,
+        a_strides,
+        b_shape,
+        b_strides);
+  });
+}
+
 void matmul_general(
     const array& a_pre,
     const array& b_pre,
     array& out,
+    Stream stream,
     float alpha = 1.0f,
     float beta = 0.0f) {
-  auto check_transpose = [](const array& arr) {
+  std::vector<array> temps;
+  auto check_transpose = [stream, &temps](const array& arr) {
     auto stx = arr.strides()[arr.ndim() - 2];
     auto sty = arr.strides()[arr.ndim() - 1];
     if (stx == arr.shape(-1) && sty == 1) {
@@ -22,10 +80,10 @@ void matmul_general(
     } else if (stx == 1 && sty == arr.shape(-2)) {
       return std::make_tuple(true, sty, arr);
     } else {
-      array arr_copy(arr.shape(), arr.dtype(), nullptr, {});
-      copy(arr, arr_copy, CopyType::General);
+      temps.push_back(array(arr.shape(), arr.dtype(), nullptr, {}));
+      copy(arr, temps.back(), CopyType::General, stream);
       stx = arr.shape(-1);
-      return std::make_tuple(false, stx, arr_copy);
+      return std::make_tuple(false, stx, temps.back());
     }
   };
 
@@ -39,28 +97,34 @@ void matmul_general(
   }
 
   if (out.dtype() == float32) {
-    matmul<float>(a, b, out, a_transposed, b_transposed, lda, ldb, alpha, beta);
+    matmul_dispatch<float>(
+        a, b, out, a_transposed, b_transposed, lda, ldb, alpha, beta, stream);
   } else if (out.dtype() == float16) {
-    matmul<float16_t>(
-        a, b, out, a_transposed, b_transposed, lda, ldb, alpha, beta);
+    matmul_dispatch<float16_t>(
+        a, b, out, a_transposed, b_transposed, lda, ldb, alpha, beta, stream);
   } else if (out.dtype() == bfloat16) {
-    matmul<bfloat16_t>(
-        a, b, out, a_transposed, b_transposed, lda, ldb, alpha, beta);
+    matmul_dispatch<bfloat16_t>(
+        a, b, out, a_transposed, b_transposed, lda, ldb, alpha, beta, stream);
   } else if (out.dtype() == float64) {
-    matmul<double>(
-        a, b, out, a_transposed, b_transposed, lda, ldb, alpha, beta);
+    matmul_dispatch<double>(
+        a, b, out, a_transposed, b_transposed, lda, ldb, alpha, beta, stream);
   } else {
     throw std::runtime_error("[Matmul::eval_cpu] Invalid type.");
   }
+  cpu::get_command_encoder(stream).add_temporaries(std::move(temps));
 }
 
 void Matmul::eval_cpu(const std::vector<array>& inputs, array& out) {
-  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+  out.set_data(allocator::malloc(out.nbytes()));
   if (inputs[0].shape(-1) == 0) {
-    std::memset(out.data<void>(), 0, out.nbytes());
+    auto& encoder = cpu::get_command_encoder(stream());
+    encoder.set_output_array(out);
+    encoder.dispatch([out_ptr = out.data<void>(), nbytes = out.nbytes()]() {
+      std::memset(out_ptr, 0, nbytes);
+    });
     return;
   }
-  return matmul_general(inputs[0], inputs[1], out);
+  matmul_general(inputs[0], inputs[1], out, stream());
 }
 
 void AddMM::eval_cpu(const std::vector<array>& inputs, array& out) {
@@ -74,9 +138,9 @@ void AddMM::eval_cpu(const std::vector<array>& inputs, array& out) {
   CopyType ctype = c.data_size() == 1
       ? CopyType::Scalar
       : (c.flags().row_contiguous ? CopyType::Vector : CopyType::General);
-  copy(c, out, ctype);
+  copy(c, out, ctype, stream());
 
-  return matmul_general(inputs[0], inputs[1], out, alpha_, beta_);
+  matmul_general(inputs[0], inputs[1], out, stream(), alpha_, beta_);
 }
 
 } // namespace mlx::core

mlx/backend/cpu/primitives.cpp

@@ -7,11 +7,11 @@
 #include <sstream>
 
 #include "mlx/allocator.h"
-#include "mlx/backend/common/load.h"
 #include "mlx/backend/common/slicing.h"
 #include "mlx/backend/common/utils.h"
 #include "mlx/backend/cpu/arange.h"
 #include "mlx/backend/cpu/copy.h"
+#include "mlx/backend/cpu/encoder.h"
 #include "mlx/backend/cpu/threefry.h"
 #include "mlx/primitives.h"
 #include "mlx/utils.h"
@@ -21,40 +21,59 @@ namespace mlx::core {
 void reshape(const array& in, array& out) {
   auto [copy_necessary, out_strides] = prepare_reshape(in, out);
   if (copy_necessary) {
-    out.set_data(allocator::malloc_or_wait(out.nbytes()));
-    copy_inplace(in, out, CopyType::General);
+    out.set_data(allocator::malloc(out.nbytes()));
+    copy_inplace(in, out, CopyType::General, out.primitive().stream());
   } else {
     shared_buffer_reshape(in, out_strides, out);
   }
 }
 
-int64_t compute_dynamic_offset(
+static std::pair<array, bool> compute_dynamic_offset(
     const array& indices,
     const Strides& strides,
-    const std::vector<int>& axes) {
-  auto compute_offset = [&strides, &axes](const auto* indices) {
-    int64_t offset = 0;
-    for (int i = 0; i < axes.size(); ++i) {
-      offset += indices[i] * strides[axes[i]];
-    }
-    return offset;
-  };
+    const std::vector<int>& axes,
+    Stream stream) {
+  array offset({1}, int64, nullptr, {});
+  bool donate = indices.is_donatable() &&
+      (indices.data_size() * indices.itemsize()) >= offset.itemsize();
+  if (donate) {
+    offset.copy_shared_buffer(indices);
+  } else {
+    offset.set_data(allocator::malloc(offset.itemsize()));
+  }
+
+  auto& encoder = cpu::get_command_encoder(stream);
+  encoder.set_input_array(indices);
+  encoder.set_output_array(offset);
+  auto compute_offset =
+      [strides, axes, offset = offset.data<int64_t>()](const auto* indices) {
+        int64_t offset_ = 0;
+        for (int i = 0; i < axes.size(); ++i) {
+          offset_ += indices[i] * strides[axes[i]];
+        }
+        offset[0] = offset_;
+      };
   switch (indices.dtype()) {
     case int8:
     case uint8:
-      return compute_offset(indices.data<uint8_t>());
+      encoder.dispatch(compute_offset, indices.data<uint8_t>());
+      break;
     case int16:
     case uint16:
-      return compute_offset(indices.data<uint16_t>());
+      encoder.dispatch(compute_offset, indices.data<uint16_t>());
+      break;
     case int32:
     case uint32:
-      return compute_offset(indices.data<uint32_t>());
+      encoder.dispatch(compute_offset, indices.data<uint32_t>());
+      break;
     case int64:
     case uint64:
-      return compute_offset(indices.data<uint64_t>());
+      encoder.dispatch(compute_offset, indices.data<uint64_t>());
+      break;
     default:
       throw std::runtime_error("Invalid indices type.");
   }
+  return {offset, donate};
 }
 
 void AsStrided::eval_cpu(const std::vector<array>& inputs, array& out) {
@@ -104,14 +123,59 @@ void Transpose::eval_cpu(const std::vector<array>& inputs, array& out) {
 }
 
 void Arange::eval_cpu(const std::vector<array>& inputs, array& out) {
-  arange(inputs, out, start_, step_);
+  assert(inputs.size() == 0);
+  out.set_data(allocator::malloc(out.nbytes()));
+  switch (out.dtype()) {
+    case bool_:
+      throw std::runtime_error("Bool type unsupported for arange.");
+      break;
+    case uint8:
+      arange<uint8_t>(start_, start_ + step_, out, out.size(), stream());
+      break;
+    case uint16:
+      arange<uint16_t>(start_, start_ + step_, out, out.size(), stream());
+      break;
+    case uint32:
+      arange<uint32_t>(start_, start_ + step_, out, out.size(), stream());
+      break;
+    case uint64:
+      arange<uint64_t>(start_, start_ + step_, out, out.size(), stream());
+      break;
+    case int8:
+      arange<int8_t>(start_, start_ + step_, out, out.size(), stream());
+      break;
+    case int16:
+      arange<int16_t>(start_, start_ + step_, out, out.size(), stream());
+      break;
+    case int32:
+      arange<int32_t>(start_, start_ + step_, out, out.size(), stream());
+      break;
+    case int64:
+      arange<int64_t>(start_, start_ + step_, out, out.size(), stream());
+      break;
+    case float16:
+      arange<float16_t>(start_, start_ + step_, out, out.size(), stream());
+      break;
+    case float32:
+      arange<float>(start_, start_ + step_, out, out.size(), stream());
+      break;
+    case float64:
+      arange<double>(start_, start_ + step_, out, out.size(), stream());
+      break;
+    case bfloat16:
+      arange<bfloat16_t>(start_, start_ + step_, out, out.size(), stream());
+      break;
+    case complex64:
+      arange<complex64_t>(start_, start_ + step_, out, out.size(), stream());
+      break;
+  }
 }
 
 void AsType::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   auto& in = inputs[0];
   CopyType ctype = in.flags().contiguous ? CopyType::Vector : CopyType::General;
-  copy(in, out, ctype);
+  copy(in, out, ctype, stream());
 }
 
 void Concatenate::eval_cpu(const std::vector<array>& inputs, array& out) {
@@ -122,7 +186,7 @@ void Concatenate::eval_cpu(const std::vector<array>& inputs, array& out) {
   }
   std::partial_sum(sizes.cbegin(), sizes.cend(), sizes.begin());
 
-  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+  out.set_data(allocator::malloc(out.nbytes()));
 
   auto strides = out.strides();
   auto flags = out.flags();
@@ -134,18 +198,20 @@ void Concatenate::eval_cpu(const std::vector<array>& inputs, array& out) {
     size_t data_offset = strides[axis_] * sizes[i];
     out_slice.copy_shared_buffer(
         out, strides, flags, out_slice.size(), data_offset);
-    copy_inplace(inputs[i], out_slice, CopyType::GeneralGeneral);
+    copy_inplace(inputs[i], out_slice, CopyType::GeneralGeneral, stream());
   }
 }
 
 void Contiguous::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   auto& in = inputs[0];
-  if (in.flags().row_contiguous ||
-      (allow_col_major_ && in.flags().col_contiguous)) {
+  constexpr size_t extra_bytes = 16384;
+  if (in.buffer_size() <= out.nbytes() + extra_bytes &&
+      (in.flags().row_contiguous ||
+       (allow_col_major_ && in.flags().col_contiguous))) {
     out.copy_shared_buffer(in);
   } else {
-    copy(in, out, CopyType::General);
+    copy(in, out, CopyType::General, stream());
   }
 }
 
@@ -169,14 +235,7 @@ void Full::eval_cpu(const std::vector<array>& inputs, array& out) {
   } else {
     ctype = CopyType::General;
   }
-  copy(in, out, ctype);
-}
-
-void Load::eval_cpu(const std::vector<array>& inputs, array& out) {
-  assert(inputs.size() == 0);
-  out.set_data(allocator::malloc_or_wait(out.nbytes()));
-
-  load(out, offset_, reader_, swap_endianness_);
+  copy(in, out, ctype, stream());
 }
 
 void Pad::eval_cpu(const std::vector<array>& inputs, array& out) {
@@ -192,7 +251,7 @@ void Pad::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(val.dtype() == in.dtype() && in.dtype() == out.dtype());
 
   // Fill output with val
-  copy(val, out, CopyType::Scalar);
+  copy(val, out, CopyType::Scalar, stream());
 
   // Find offset for start of input values
   size_t data_offset = 0;
@@ -207,7 +266,7 @@ void Pad::eval_cpu(const std::vector<array>& inputs, array& out) {
       out, out.strides(), out.flags(), out_slice.size(), data_offset);
 
   // Copy input values into the slice
-  copy_inplace(in, out_slice, CopyType::GeneralGeneral);
+  copy_inplace(in, out_slice, CopyType::GeneralGeneral, stream());
 }
 
 void RandomBits::eval_cpu(const std::vector<array>& inputs, array& out) {
@@ -219,43 +278,53 @@ void RandomBits::eval_cpu(const std::vector<array>& inputs, array& out) {
 
   size_t elems_per_key = out.size() / num_keys;
   size_t bytes_per_key = out.itemsize() * elems_per_key;
-  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+  out.set_data(allocator::malloc(out.nbytes()));
 
   auto kptr = inputs[0].data<uint32_t>();
   auto cptr = out.data<char>();
-  size_t out_skip = (bytes_per_key + 4 - 1) / 4;
-  auto half_size = out_skip / 2;
-  bool even = out_skip % 2 == 0;
-  for (int i = 0; i < num_keys; ++i, cptr += bytes_per_key) {
-    auto ptr = reinterpret_cast<uint32_t*>(cptr);
-    // Get ith key
-    auto kidx = 2 * i;
-    auto k1_elem = elem_to_loc(kidx, keys.shape(), keys.strides());
-    auto k2_elem = elem_to_loc(kidx + 1, keys.shape(), keys.strides());
-    auto key = std::make_pair(kptr[k1_elem], kptr[k2_elem]);
+  auto& encoder = cpu::get_command_encoder(stream());
+  encoder.set_input_array(inputs[0]);
+  encoder.set_output_array(out);
+  encoder.dispatch([kptr,
+                    cptr,
+                    bytes_per_key,
+                    num_keys,
+                    kshape = keys.shape(),
+                    kstrides = keys.strides()]() mutable {
+    size_t out_skip = (bytes_per_key + 4 - 1) / 4;
+    auto half_size = out_skip / 2;
+    bool even = out_skip % 2 == 0;
+    for (int i = 0; i < num_keys; ++i, cptr += bytes_per_key) {
+      auto ptr = reinterpret_cast<uint32_t*>(cptr);
+      // Get ith key
+      auto kidx = 2 * i;
+      auto k1_elem = elem_to_loc(kidx, kshape, kstrides);
+      auto k2_elem = elem_to_loc(kidx + 1, kshape, kstrides);
+      auto key = std::make_pair(kptr[k1_elem], kptr[k2_elem]);
 
-    std::pair<uintptr_t, uintptr_t> count{0, half_size + !even};
-    for (; count.first + 1 < half_size; count.first++, count.second++) {
-      std::tie(ptr[count.first], ptr[count.second]) =
-          random::threefry2x32_hash(key, count);
-    }
-    if (count.first < half_size) {
-      auto rb = random::threefry2x32_hash(key, count);
-      ptr[count.first++] = rb.first;
-      if (bytes_per_key % 4 > 0) {
-        std::copy(
-            reinterpret_cast<char*>(&rb.second),
-            reinterpret_cast<char*>(&rb.second) + bytes_per_key % 4,
-            cptr + 4 * count.second);
-      } else {
-        ptr[count.second] = rb.second;
+      std::pair<uintptr_t, uintptr_t> count{0, half_size + !even};
+      for (; count.first + 1 < half_size; count.first++, count.second++) {
+        std::tie(ptr[count.first], ptr[count.second]) =
+            random::threefry2x32_hash(key, count);
+      }
+      if (count.first < half_size) {
+        auto rb = random::threefry2x32_hash(key, count);
+        ptr[count.first++] = rb.first;
+        if (bytes_per_key % 4 > 0) {
+          std::copy(
+              reinterpret_cast<char*>(&rb.second),
+              reinterpret_cast<char*>(&rb.second) + bytes_per_key % 4,
+              cptr + 4 * count.second);
+        } else {
+          ptr[count.second] = rb.second;
+        }
+      }
+      if (!even) {
+        count.second = 0;
+        ptr[half_size] = random::threefry2x32_hash(key, count).first;
       }
     }
-    if (!even) {
-      count.second = 0;
-      ptr[half_size] = random::threefry2x32_hash(key, count).first;
-    }
-  }
+  });
 }
 
 void Reshape::eval_cpu(const std::vector<array>& inputs, array& out) {
@@ -268,17 +337,24 @@ void DynamicSlice::eval_cpu(const std::vector<array>& inputs, array& out) {
     return;
   }
   auto& in = inputs[0];
-  out.set_data(allocator::malloc_or_wait(out.nbytes()));
-  auto i_offset = compute_dynamic_offset(inputs[1], in.strides(), axes_);
+  out.set_data(allocator::malloc(out.nbytes()));
+  auto [in_offset, donated] =
+      compute_dynamic_offset(inputs[1], in.strides(), axes_, stream());
   copy_inplace(
       /* const array& src = */ in,
       /* array& dst = */ out,
       /* const Shape& data_shape = */ out.shape(),
       /* const Strides& i_strides = */ in.strides(),
       /* const Strides& o_strides = */ out.strides(),
-      /* int64_t i_offset = */ i_offset,
+      /* int64_t i_offset = */ 0,
       /* int64_t o_offset = */ 0,
-      /* CopyType ctype = */ CopyType::GeneralGeneral);
+      /* CopyType ctype = */ CopyType::GeneralGeneral,
+      stream(),
+      /* const std::optional<array>& dynamic_i_offset = */ in_offset,
+      /* const std::optional<array>& dynamic_o_offset = */ std::nullopt);
+  if (!donated) {
+    cpu::get_command_encoder(stream()).add_temporary(std::move(in_offset));
+  }
 }
 
 void DynamicSliceUpdate::eval_cpu(
@@ -296,9 +372,10 @@ void DynamicSliceUpdate::eval_cpu(
   auto ctype = in.flags().contiguous && in.size() == in.data_size()
       ? CopyType::Vector
       : CopyType::General;
-  copy(in, out, in.data_size() == 1 ? CopyType::Scalar : ctype);
+  copy(in, out, in.data_size() == 1 ? CopyType::Scalar : ctype, stream());
 
-  auto o_offset = compute_dynamic_offset(inputs[2], out.strides(), axes_);
+  auto [out_offset, donated] =
+      compute_dynamic_offset(inputs[2], out.strides(), axes_, stream());
   copy_inplace(
       /* const array& src = */ upd,
       /* array& dst = */ out,
@@ -306,8 +383,14 @@ void DynamicSliceUpdate::eval_cpu(
       /* const std::vector<stride_t>& i_strides = */ upd.strides(),
       /* const std::vector<stride_t>& o_strides = */ out.strides(),
       /* int64_t i_offset = */ 0,
-      /* int64_t o_offset = */ o_offset,
-      /* CopyType ctype = */ CopyType::GeneralGeneral);
+      /* int64_t o_offset = */ 0,
+      /* CopyType ctype = */ CopyType::GeneralGeneral,
+      stream(),
+      /* const std::optional<array>& dynamic_i_offset = */ std::nullopt,
+      /* const std::optional<array>& dynamic_o_offset = */ out_offset);
+  if (!donated) {
+    cpu::get_command_encoder(stream()).add_temporary(std::move(out_offset));
+  }
 }
 
 void SliceUpdate::eval_cpu(const std::vector<array>& inputs, array& out) {
@@ -329,7 +412,7 @@ void SliceUpdate::eval_cpu(const std::vector<array>& inputs, array& out) {
   auto ctype = in.flags().contiguous && in.size() == in.data_size()
       ? CopyType::Vector
       : CopyType::General;
-  copy(in, out, in.data_size() == 1 ? CopyType::Scalar : ctype);
+  copy(in, out, in.data_size() == 1 ? CopyType::Scalar : ctype, stream());
 
   // Calculate out strides, initial offset and if copy needs to be made
   auto [data_offset, out_strides] =
@@ -344,7 +427,8 @@ void SliceUpdate::eval_cpu(const std::vector<array>& inputs, array& out) {
       /* const std::vector<stride_t>& o_strides = */ out_strides,
       /* int64_t i_offset = */ 0,
       /* int64_t o_offset = */ data_offset,
-      /* CopyType ctype = */ CopyType::GeneralGeneral);
+      /* CopyType ctype = */ CopyType::GeneralGeneral,
+      stream());
 }
 
 void View::eval_cpu(const std::vector<array>& inputs, array& out) {
@@ -368,13 +452,13 @@ void View::eval_cpu(const std::vector<array>& inputs, array& out) {
   } else {
     auto tmp = array(
         in.shape(), in.dtype() == bool_ ? uint8 : in.dtype(), nullptr, {});
-    tmp.set_data(allocator::malloc_or_wait(tmp.nbytes()));
+    tmp.set_data(allocator::malloc(tmp.nbytes()));
     if (in.dtype() == bool_) {
       auto in_tmp = array(in.shape(), uint8, nullptr, {});
       in_tmp.copy_shared_buffer(in);
-      copy_inplace(in_tmp, tmp, CopyType::General);
+      copy_inplace(in_tmp, tmp, CopyType::General, stream());
     } else {
-      copy_inplace(in, tmp, CopyType::General);
+      copy_inplace(in, tmp, CopyType::General, stream());
     }
 
     auto flags = out.flags();
@@ -382,7 +466,7 @@ void View::eval_cpu(const std::vector<array>& inputs, array& out) {
     flags.row_contiguous = true;
     auto max_dim = std::max_element(out.shape().begin(), out.shape().end());
     flags.col_contiguous = out.size() <= 1 || out.size() == *max_dim;
-    out.move_shared_buffer(tmp, out.strides(), flags, out.size());
+    out.copy_shared_buffer(tmp, out.strides(), flags, out.size());
   }
 }
 

mlx/backend/cpu/qrf.cpp

@@ -2,53 +2,21 @@
 
 #include "mlx/allocator.h"
 #include "mlx/backend/cpu/copy.h"
+#include "mlx/backend/cpu/encoder.h"
 #include "mlx/backend/cpu/lapack.h"
 #include "mlx/primitives.h"
 
 namespace mlx::core {
 
 template <typename T>
-struct lpack;
-
-template <>
-struct lpack<float> {
-  static void xgeqrf(
-      const int* m,
-      const int* n,
-      float* a,
-      const int* lda,
-      float* tau,
-      float* work,
-      const int* lwork,
-      int* info) {
-    sgeqrf_(m, n, a, lda, tau, work, lwork, info);
-  }
-  static void xorgqr(
-      const int* m,
-      const int* n,
-      const int* k,
-      float* a,
-      const int* lda,
-      const float* tau,
-      float* work,
-      const int* lwork,
-      int* info) {
-    sorgqr_(m, n, k, a, lda, tau, work, lwork, info);
-  }
-};
-
-template <typename T>
-void qrf_impl(const array& a, array& q, array& r) {
+void qrf_impl(const array& a, array& q, array& r, Stream stream) {
   const int M = a.shape(-2);
   const int N = a.shape(-1);
   const int lda = M;
   size_t num_matrices = a.size() / (M * N);
-  int num_reflectors = std::min(M, N);
-  auto tau =
-      allocator::malloc_or_wait(sizeof(T) * num_matrices * num_reflectors);
 
   // Copy A to inplace input and make it col-contiguous
-  array in(a.shape(), float32, nullptr, {});
+  array in(a.shape(), a.dtype(), nullptr, {});
   auto flags = in.flags();
 
   // Copy the input to be column contiguous
@@ -57,105 +25,123 @@ void qrf_impl(const array& a, array& q, array& r) {
   auto strides = in.strides();
   strides[in.ndim() - 2] = 1;
   strides[in.ndim() - 1] = M;
-  in.set_data(
-      allocator::malloc_or_wait(in.nbytes()), in.nbytes(), strides, flags);
-  copy_inplace(a, in, CopyType::GeneralGeneral);
+  in.set_data(allocator::malloc(in.nbytes()), in.nbytes(), strides, flags);
+  copy_inplace(a, in, CopyType::GeneralGeneral, stream);
+  auto& encoder = cpu::get_command_encoder(stream);
+  q.set_data(allocator::malloc(q.nbytes()));
+  r.set_data(allocator::malloc(r.nbytes()));
 
-  T optimal_work;
-  int lwork = -1;
-  int info;
+  auto in_ptr = in.data<T>();
+  auto r_ptr = r.data<T>();
+  auto q_ptr = q.data<T>();
 
-  // Compute workspace size
-  lpack<T>::xgeqrf(
-      &M, &N, nullptr, &lda, nullptr, &optimal_work, &lwork, &info);
+  encoder.set_input_array(in);
+  encoder.set_output_array(q);
+  encoder.set_output_array(r);
+  encoder.dispatch([in_ptr, q_ptr, r_ptr, M, N, lda, num_matrices]() {
+    int num_reflectors = std::min(M, N);
+    auto tau = allocator::malloc(sizeof(T) * num_matrices * num_reflectors);
 
-  // Update workspace size
-  lwork = optimal_work;
-  auto work = allocator::malloc_or_wait(sizeof(T) * lwork);
+    T optimal_work;
+    int lwork = -1;
+    int info;
 
-  // Loop over matrices
-  for (int i = 0; i < num_matrices; ++i) {
-    // Solve
-    lpack<T>::xgeqrf(
-        &M,
-        &N,
-        in.data<float>() + M * N * i,
-        &lda,
-        static_cast<T*>(tau.raw_ptr()) + num_reflectors * i,
-        static_cast<T*>(work.raw_ptr()),
-        &lwork,
-        &info);
-  }
-  allocator::free(work);
+    // Compute workspace size
+    geqrf<T>(&M, &N, nullptr, &lda, nullptr, &optimal_work, &lwork, &info);
 
-  r.set_data(allocator::malloc_or_wait(r.nbytes()));
+    // Update workspace size
+    lwork = optimal_work;
+    auto work = allocator::malloc(sizeof(T) * lwork);
 
-  for (int i = 0; i < num_matrices; ++i) {
-    /// num_reflectors x N
-    for (int j = 0; j < r.shape(-2); ++j) {
-      for (int k = 0; k < j; ++k) {
-        r.data<T>()[i * N * num_reflectors + j * N + k] = 0;
-      }
-      for (int k = j; k < r.shape(-1); ++k) {
-        r.data<T>()[i * N * num_reflectors + j * N + k] =
-            in.data<T>()[i * N * M + j + k * M];
+    // Loop over matrices
+    for (int i = 0; i < num_matrices; ++i) {
+      // Solve
+      geqrf<T>(
+          &M,
+          &N,
+          in_ptr + M * N * i,
+          &lda,
+          static_cast<T*>(tau.raw_ptr()) + num_reflectors * i,
+          static_cast<T*>(work.raw_ptr()),
+          &lwork,
+          &info);
+    }
+    allocator::free(work);
+
+    for (int i = 0; i < num_matrices; ++i) {
+      /// num_reflectors x N
+      for (int j = 0; j < num_reflectors; ++j) {
+        for (int k = 0; k < j; ++k) {
+          r_ptr[i * N * num_reflectors + j * N + k] = 0;
+        }
+        for (int k = j; k < N; ++k) {
+          r_ptr[i * N * num_reflectors + j * N + k] =
+              in_ptr[i * N * M + j + k * M];
+        }
       }
     }
-  }
 
-  // Get work size
-  lwork = -1;
-  lpack<T>::xorgqr(
-      &M,
-      &num_reflectors,
-      &num_reflectors,
-      nullptr,
-      &lda,
-      nullptr,
-      &optimal_work,
-      &lwork,
-      &info);
-  lwork = optimal_work;
-  work = allocator::malloc_or_wait(sizeof(T) * lwork);
-
-  // Loop over matrices
-  for (int i = 0; i < num_matrices; ++i) {
-    // Compute Q
-    lpack<T>::xorgqr(
+    // Get work size
+    lwork = -1;
+    orgqr<T>(
         &M,
         &num_reflectors,
         &num_reflectors,
-        in.data<float>() + M * N * i,
+        nullptr,
         &lda,
-        static_cast<T*>(tau.raw_ptr()) + num_reflectors * i,
-        static_cast<T*>(work.raw_ptr()),
+        nullptr,
+        &optimal_work,
         &lwork,
         &info);
-  }
+    lwork = optimal_work;
+    work = allocator::malloc(sizeof(T) * lwork);
 
-  q.set_data(allocator::malloc_or_wait(q.nbytes()));
-  for (int i = 0; i < num_matrices; ++i) {
-    // M x num_reflectors
-    for (int j = 0; j < q.shape(-2); ++j) {
-      for (int k = 0; k < q.shape(-1); ++k) {
-        q.data<T>()[i * M * num_reflectors + j * num_reflectors + k] =
-            in.data<T>()[i * N * M + j + k * M];
+    // Loop over matrices
+    for (int i = 0; i < num_matrices; ++i) {
+      // Compute Q
+      orgqr<T>(
+          &M,
+          &num_reflectors,
+          &num_reflectors,
+          in_ptr + M * N * i,
+          &lda,
+          static_cast<T*>(tau.raw_ptr()) + num_reflectors * i,
+          static_cast<T*>(work.raw_ptr()),
+          &lwork,
+          &info);
+    }
+
+    for (int i = 0; i < num_matrices; ++i) {
+      // M x num_reflectors
+      for (int j = 0; j < M; ++j) {
+        for (int k = 0; k < num_reflectors; ++k) {
+          q_ptr[i * M * num_reflectors + j * num_reflectors + k] =
+              in_ptr[i * N * M + j + k * M];
+        }
       }
     }
-  }
 
-  // Cleanup
-  allocator::free(work);
-  allocator::free(tau);
+    // Cleanup
+    allocator::free(work);
+    allocator::free(tau);
+  });
+  encoder.add_temporary(in);
 }
 
 void QRF::eval_cpu(
     const std::vector<array>& inputs,
     std::vector<array>& outputs) {
-  if (!(inputs[0].dtype() == float32)) {
-    throw std::runtime_error("[QRF::eval] only supports float32.");
+  switch (inputs[0].dtype()) {
+    case float32:
+      qrf_impl<float>(inputs[0], outputs[0], outputs[1], stream());
+      break;
+    case float64:
+      qrf_impl<double>(inputs[0], outputs[0], outputs[1], stream());
+      break;
+    default:
+      throw std::runtime_error(
+          "[QRF::eval_cpu] only supports float32 or float64.");
   }
-  qrf_impl<float>(inputs[0], outputs[0], outputs[1]);
 }
 
 } // namespace mlx::core

mlx/backend/cpu/quantized.cpp

@@ -3,6 +3,7 @@
 #include <cassert>
 
 #include "mlx/backend/cpu/copy.h"
+#include "mlx/backend/cpu/encoder.h"
 #include "mlx/backend/cpu/simd/simd.h"
 #include "mlx/fast_primitives.h"
 #include "mlx/primitives.h"
@@ -164,8 +165,8 @@ simd::Simd<uint32_t, S> extract_bits_simd(const uint32_t* w) {
   } else if constexpr (bits == 8 && S == 8) {
     constexpr std::array<uint32_t, 8> shifts_ = {{0, 8, 16, 24, 0, 8, 16, 24}};
     auto shifts(*(simd::Simd<uint32_t, S>*)&shifts_);
-    auto l = simd::Simd<uint32_t, 4>(*w++);
-    auto r = simd::Simd<uint32_t, 4>(*w);
+    auto l = simd::Simd<uint32_t, S / 2>(*w++);
+    auto r = simd::Simd<uint32_t, S / 2>(*w);
     wi = simd::Simd<uint32_t, S>(l, r);
     wi = wi >> shifts;
     wi = wi & bitmask;
@@ -316,7 +317,8 @@ void _qmm_dispatch_typed(
   }
 }
 
-void _qmm_dispatch(
+template <typename T>
+void _qmm_dispatch_typed(
     array& out,
     const array& x,
     const array& w,
@@ -328,63 +330,61 @@ void _qmm_dispatch(
   int K = x.shape(-1);
   int M = x.ndim() > 1 ? x.shape(-2) : 1;
   int N = out.shape(-1);
-
   int w_els = w.ndim() > 2 ? w.shape(-1) * w.shape(-2) : 0;
   int g_els = w.ndim() > 2 ? scales.shape(-1) * scales.shape(-2) : 0;
-
   int batch_size = x.size() / (K * M);
+
+  auto out_ptr = out.data<T>();
+  auto x_ptr = x.data<T>();
+  auto w_ptr = w.data<uint32_t>();
+  auto scales_ptr = scales.data<T>();
+  auto biases_ptr = biases.data<T>();
   for (int i = 0; i < batch_size; i++) {
-    switch (x.dtype()) {
-      case float32:
-        _qmm_dispatch_typed<float>(
-            out.data<float>() + i * M * N,
-            x.data<float>() + elem_to_loc(i * M * K, x),
-            w.data<uint32_t>() + elem_to_loc(i * w_els, w),
-            scales.data<float>() + elem_to_loc(i * g_els, scales),
-            biases.data<float>() + elem_to_loc(i * g_els, biases),
-            M,
-            N,
-            K,
-            bits,
-            group_size,
-            transposed_w);
-        break;
-      case float16:
-        _qmm_dispatch_typed<float16_t>(
-            out.data<float16_t>() + i * M * N,
-            x.data<float16_t>() + elem_to_loc(i * M * K, x),
-            w.data<uint32_t>() + elem_to_loc(i * w_els, w),
-            scales.data<float16_t>() + elem_to_loc(i * g_els, scales),
-            biases.data<float16_t>() + elem_to_loc(i * g_els, biases),
-            M,
-            N,
-            K,
-            bits,
-            group_size,
-            transposed_w);
-        break;
-      case bfloat16:
-        _qmm_dispatch_typed<bfloat16_t>(
-            out.data<bfloat16_t>() + i * M * N,
-            x.data<bfloat16_t>() + elem_to_loc(i * M * K, x),
-            w.data<uint32_t>() + elem_to_loc(i * w_els, w),
-            scales.data<bfloat16_t>() + elem_to_loc(i * g_els, scales),
-            biases.data<bfloat16_t>() + elem_to_loc(i * g_els, biases),
-            M,
-            N,
-            K,
-            bits,
-            group_size,
-            transposed_w);
-        break;
-      default:
-        throw std::invalid_argument(
-            "[quantized_matmul] only floating types are supported");
-    }
+    _qmm_dispatch_typed<T>(
+        out_ptr + i * M * N,
+        x_ptr + elem_to_loc(i * M * K, x.shape(), x.strides()),
+        w_ptr + elem_to_loc(i * w_els, w.shape(), w.strides()),
+        scales_ptr + elem_to_loc(i * g_els, scales.shape(), scales.strides()),
+        biases_ptr + elem_to_loc(i * g_els, biases.shape(), biases.strides()),
+        M,
+        N,
+        K,
+        bits,
+        group_size,
+        transposed_w);
   }
 }
 
-void _bs_qmm_dispatch(
+void _qmm_dispatch(
+    array& out,
+    const array& x,
+    const array& w,
+    const array& scales,
+    const array& biases,
+    int bits,
+    int group_size,
+    bool transposed_w) {
+  switch (x.dtype()) {
+    case float32:
+      _qmm_dispatch_typed<float>(
+          out, x, w, scales, biases, bits, group_size, transposed_w);
+      break;
+    case float16:
+      _qmm_dispatch_typed<float16_t>(
+          out, x, w, scales, biases, bits, group_size, transposed_w);
+      break;
+    case bfloat16:
+      _qmm_dispatch_typed<bfloat16_t>(
+          out, x, w, scales, biases, bits, group_size, transposed_w);
+      break;
+    default:
+      throw std::invalid_argument(
+          "[quantized_matmul] only floating types are supported");
+  }
+}
+
+template <typename T>
+void _bs_qmm_dispatch_typed(
     array& out,
     const array& x,
     const array& w,
@@ -402,60 +402,90 @@ void _bs_qmm_dispatch(
   int w_els = w.shape(-1) * w.shape(-2);
   int g_els = scales.shape(-1) * scales.shape(-2);
 
-  const uint32_t* lhs_indices_data = lhs_indices.data<uint32_t>();
-  const uint32_t* rhs_indices_data = rhs_indices.data<uint32_t>();
+  auto out_ptr = out.data<T>();
+  auto x_ptr = x.data<T>();
+  auto w_ptr = w.data<uint32_t>();
+  auto scales_ptr = scales.data<T>();
+  auto biases_ptr = biases.data<T>();
+  auto lhs_indices_ptr = lhs_indices.data<uint32_t>();
+  auto rhs_indices_ptr = rhs_indices.data<uint32_t>();
 
   for (int i = 0; i < lhs_indices.size(); i++) {
-    int x_idx = lhs_indices_data[elem_to_loc(i, lhs_indices)];
-    int w_idx = rhs_indices_data[elem_to_loc(i, rhs_indices)];
+    int x_idx = lhs_indices_ptr[elem_to_loc(
+        i, lhs_indices.shape(), lhs_indices.strides())];
+    int w_idx = rhs_indices_ptr[elem_to_loc(
+        i, rhs_indices.shape(), rhs_indices.strides())];
+    _qmm_dispatch_typed<T>(
+        out_ptr + i * M * N,
+        x_ptr + elem_to_loc(x_idx * M * K, x.shape(), x.strides()),
+        w_ptr + elem_to_loc(w_idx * w_els, w.shape(), w.strides()),
+        scales_ptr +
+            elem_to_loc(w_idx * g_els, scales.shape(), scales.strides()),
+        biases_ptr +
+            elem_to_loc(w_idx * g_els, biases.shape(), biases.strides()),
+        M,
+        N,
+        K,
+        bits,
+        group_size,
+        transposed_w);
+  }
+}
 
-    switch (x.dtype()) {
-      case float32:
-        _qmm_dispatch_typed<float>(
-            out.data<float>() + i * M * N,
-            x.data<float>() + elem_to_loc(x_idx * M * K, x),
-            w.data<uint32_t>() + elem_to_loc(w_idx * w_els, w),
-            scales.data<float>() + elem_to_loc(w_idx * g_els, scales),
-            biases.data<float>() + elem_to_loc(w_idx * g_els, biases),
-            M,
-            N,
-            K,
-            bits,
-            group_size,
-            transposed_w);
-        break;
-      case float16:
-        _qmm_dispatch_typed<float16_t>(
-            out.data<float16_t>() + i * M * N,
-            x.data<float16_t>() + elem_to_loc(x_idx * M * K, x),
-            w.data<uint32_t>() + elem_to_loc(w_idx * w_els, w),
-            scales.data<float16_t>() + elem_to_loc(w_idx * g_els, scales),
-            biases.data<float16_t>() + elem_to_loc(w_idx * g_els, biases),
-            M,
-            N,
-            K,
-            bits,
-            group_size,
-            transposed_w);
-        break;
-      case bfloat16:
-        _qmm_dispatch_typed<bfloat16_t>(
-            out.data<bfloat16_t>() + i * M * N,
-            x.data<bfloat16_t>() + elem_to_loc(x_idx * M * K, x),
-            w.data<uint32_t>() + elem_to_loc(w_idx * w_els, w),
-            scales.data<bfloat16_t>() + elem_to_loc(w_idx * g_els, scales),
-            biases.data<bfloat16_t>() + elem_to_loc(w_idx * g_els, biases),
-            M,
-            N,
-            K,
-            bits,
-            group_size,
-            transposed_w);
-        break;
-      default:
-        throw std::invalid_argument(
-            "[quantized_matmul] only floating types are supported");
-    }
+void _bs_qmm_dispatch(
+    array& out,
+    const array& x,
+    const array& w,
+    const array& scales,
+    const array& biases,
+    const array& lhs_indices,
+    const array& rhs_indices,
+    int bits,
+    int group_size,
+    bool transposed_w) {
+  switch (x.dtype()) {
+    case float32:
+      _bs_qmm_dispatch_typed<float>(
+          out,
+          x,
+          w,
+          scales,
+          biases,
+          lhs_indices,
+          rhs_indices,
+          bits,
+          group_size,
+          transposed_w);
+      break;
+    case float16:
+      _bs_qmm_dispatch_typed<float16_t>(
+          out,
+          x,
+          w,
+          scales,
+          biases,
+          lhs_indices,
+          rhs_indices,
+          bits,
+          group_size,
+          transposed_w);
+      break;
+    case bfloat16:
+      _bs_qmm_dispatch_typed<bfloat16_t>(
+          out,
+          x,
+          w,
+          scales,
+          biases,
+          lhs_indices,
+          rhs_indices,
+          bits,
+          group_size,
+          transposed_w);
+      break;
+    default:
+      throw std::invalid_argument(
+          "[quantized_matmul] only floating types are supported");
   }
 }
 
@@ -469,13 +499,14 @@ void QuantizedMatmul::eval_cpu(const std::vector<array>& inputs, array& out) {
   auto& scales_pre = inputs[2];
   auto& biases_pre = inputs[3];
 
-  auto ensure_row_contiguous = [](const array& arr) {
+  std::vector<array> temps;
+  auto ensure_row_contiguous = [s = stream(), &temps](const array& arr) {
     if (arr.flags().row_contiguous) {
       return arr;
     } else {
-      array arr_copy(arr.shape(), arr.dtype(), nullptr, {});
-      copy(arr, arr_copy, CopyType::General);
-      return arr_copy;
+      temps.push_back(array(arr.shape(), arr.dtype(), nullptr, {}));
+      copy(arr, temps.back(), CopyType::General, s);
+      return temps.back();
     }
   };
 
@@ -484,8 +515,25 @@ void QuantizedMatmul::eval_cpu(const std::vector<array>& inputs, array& out) {
   auto scales = ensure_row_contiguous(scales_pre);
   auto biases = ensure_row_contiguous(biases_pre);
 
-  out.set_data(allocator::malloc_or_wait(out.nbytes()));
-  _qmm_dispatch(out, x, w, scales, biases, group_size_, bits_, transpose_);
+  out.set_data(allocator::malloc(out.nbytes()));
+
+  auto& encoder = cpu::get_command_encoder(stream());
+  encoder.add_temporaries(std::move(temps));
+  encoder.set_input_array(x);
+  encoder.set_input_array(w);
+  encoder.set_input_array(scales);
+  encoder.set_input_array(biases);
+  encoder.set_output_array(out);
+  encoder.dispatch([out = array::unsafe_weak_copy(out),
+                    x = array::unsafe_weak_copy(x),
+                    w = array::unsafe_weak_copy(w),
+                    scales = array::unsafe_weak_copy(scales),
+                    biases = array::unsafe_weak_copy(biases),
+                    group_size_ = group_size_,
+                    bits_ = bits_,
+                    transpose_ = transpose_]() mutable {
+    _qmm_dispatch(out, x, w, scales, biases, group_size_, bits_, transpose_);
+  });
 }
 
 void GatherQMM::eval_cpu(const std::vector<array>& inputs, array& out) {
@@ -498,15 +546,17 @@ void GatherQMM::eval_cpu(const std::vector<array>& inputs, array& out) {
   auto& lhs_indices = inputs[4];
   auto& rhs_indices = inputs[5];
 
-  auto ensure_row_contiguous_last_dims = [](const array& arr) {
+  std::vector<array> temps;
+  auto ensure_row_contiguous_last_dims = [s = stream(),
+                                          &temps](const array& arr) {
     auto stride_0 = arr.strides()[arr.ndim() - 2];
     auto stride_1 = arr.strides()[arr.ndim() - 1];
     if (stride_0 == arr.shape(-1) && stride_1 == 1) {
       return arr;
     } else {
-      array arr_copy(arr.shape(), arr.dtype(), nullptr, {});
-      copy(arr, arr_copy, CopyType::General);
-      return arr_copy;
+      temps.push_back(array(arr.shape(), arr.dtype(), nullptr, {}));
+      copy(arr, temps.back(), CopyType::General, s);
+      return temps.back();
     }
   };
 
@@ -515,71 +565,86 @@ void GatherQMM::eval_cpu(const std::vector<array>& inputs, array& out) {
   auto scales = ensure_row_contiguous_last_dims(scales_pre);
   auto biases = ensure_row_contiguous_last_dims(biases_pre);
 
-  out.set_data(allocator::malloc_or_wait(out.nbytes()));
-  _bs_qmm_dispatch(
-      out,
-      x,
-      w,
-      scales,
-      biases,
-      lhs_indices,
-      rhs_indices,
-      group_size_,
-      bits_,
-      transpose_);
+  out.set_data(allocator::malloc(out.nbytes()));
+
+  auto& encoder = cpu::get_command_encoder(stream());
+  encoder.add_temporaries(std::move(temps));
+  encoder.set_input_array(x);
+  encoder.set_input_array(w);
+  encoder.set_input_array(scales);
+  encoder.set_input_array(biases);
+  encoder.set_input_array(lhs_indices);
+  encoder.set_input_array(rhs_indices);
+  encoder.set_output_array(out);
+  encoder.dispatch([out = array::unsafe_weak_copy(out),
+                    x = array::unsafe_weak_copy(x),
+                    w = array::unsafe_weak_copy(w),
+                    scales = array::unsafe_weak_copy(scales),
+                    biases = array::unsafe_weak_copy(biases),
+                    lhs_indices = array::unsafe_weak_copy(lhs_indices),
+                    rhs_indices = array::unsafe_weak_copy(rhs_indices),
+                    group_size_ = group_size_,
+                    bits_ = bits_,
+                    transpose_ = transpose_]() mutable {
+    _bs_qmm_dispatch(
+        out,
+        x,
+        w,
+        scales,
+        biases,
+        lhs_indices,
+        rhs_indices,
+        group_size_,
+        bits_,
+        transpose_);
+  });
 }
 
 template <typename T, typename U>
 void quantize(
-    const array& w_,
-    array& out_,
-    array& scales_,
-    array& biases_,
+    const T* w,
+    U* out,
+    T* scales,
+    T* biases,
     int bits,
-    int group_size) {
-  const T* w = w_.data<T>();
+    int group_size,
+    size_t w_size) {
+  float n_bins = (1 << bits) - 1;
+  float eps = 1e-7;
 
-  auto out = out_.data<U>();
-  T* scales = scales_.data<T>();
-  T* biases = biases_.data<T>();
-
-  T n_bins = (1 << bits) - 1;
-  T eps = 1e-7;
   bool power_of_2_bits = is_power_of_2(bits);
   int el_per_int = bits == 3 ? 8 : bits == 6 ? 4 : 32 / bits;
   // For 3/6 bits we read 3 uint8s at a time instead of 1 uint32
   int bytes_per_pack = power_of_2_bits ? 1 : 3;
   int int_per_group = group_size * bytes_per_pack / el_per_int;
-  size_t n_groups = w_.size() / group_size;
+  size_t n_groups = w_size / group_size;
 
   for (size_t i = 0; i < n_groups; ++i) {
     size_t w_idx = i * group_size;
-    T w_min = std::numeric_limits<float>::infinity();
-    T w_max = -w_min;
+    float w_min = std::numeric_limits<float>::infinity();
+    float w_max = -w_min;
     for (int j = 0; j < group_size; ++j) {
-      w_max = std::max(w_max, w[w_idx + j]);
-      w_min = std::min(w_min, w[w_idx + j]);
+      w_max = std::max(w_max, (float)w[w_idx + j]);
+      w_min = std::min(w_min, (float)w[w_idx + j]);
     }
     bool mask = std::abs(w_min) > std::abs(w_max);
-    T scale = std::max(T((w_max - w_min) / n_bins), eps);
+    float scale = std::max((w_max - w_min) / n_bins, eps);
     scale = mask ? scale : -scale;
 
-    auto edge = mask ? w_min : w_max;
-    auto q0 = std::rint(edge / scale);
-    if (q0 == 0) {
-      scales[i] = scale;
-      biases[i] = 0;
-    } else {
-      scales[i] = edge / q0;
-      biases[i] = edge;
+    float edge = mask ? w_min : w_max;
+    float q0 = std::rint(edge / scale);
+    float bias = 0;
+    if (q0 != 0) {
+      scale = edge / q0;
+      bias = edge;
     }
     size_t out_idx = i * int_per_group;
     for (int j = 0; j < int_per_group / bytes_per_pack; ++j) {
       uint32_t out_el = 0;
       for (int k = 0; k < el_per_int; ++k) {
-        T w_el = w[w_idx + j * el_per_int + k];
-        w_el = std::rint((w_el - biases[i]) / scales[i]);
-        w_el = std::min(std::max(w_el, T(0)), n_bins);
+        float w_el = w[w_idx + j * el_per_int + k];
+        w_el = std::rint((w_el - bias) / scale);
+        w_el = std::min(std::max(w_el, 0.0f), n_bins);
         out_el |= static_cast<uint32_t>(w_el) << (k * bits);
       }
       if (power_of_2_bits) {
@@ -590,53 +655,91 @@ void quantize(
         out[out_idx + bytes_per_pack * j + 2] = (out_el & 0xff0000) >> 16;
       }
     }
+    scales[i] = static_cast<T>(scale);
+    biases[i] = static_cast<T>(bias);
   }
 }
 
+template <typename T, typename U>
+void dispatch_quantize(
+    const array& w,
+    array& out,
+    array& scales,
+    array& biases,
+    int bits,
+    int group_size) {
+  auto w_ptr = w.data<T>();
+  auto out_ptr = out.data<U>();
+  auto scales_ptr = scales.data<T>();
+  auto biases_ptr = biases.data<T>();
+  quantize<T, U>(
+      w_ptr, out_ptr, scales_ptr, biases_ptr, bits, group_size, w.size());
+}
+
 void fast::AffineQuantize::eval_cpu(
     const std::vector<array>& inputs,
     std::vector<array>& outputs) {
-  auto ensure_row_contiguous = [](const array& arr) {
+  auto ensure_row_contiguous = [s = stream()](const array& arr) {
     if (arr.flags().row_contiguous) {
-      return arr;
+      return std::make_pair(arr, false);
     } else {
       array arr_copy(arr.shape(), arr.dtype(), nullptr, {});
-      copy(arr, arr_copy, CopyType::General);
-      return arr_copy;
+      copy(arr, arr_copy, CopyType::General, s);
+      return std::make_pair(arr_copy, true);
     }
   };
-  auto w = ensure_row_contiguous(inputs[0]);
 
+  auto [w, copied] = ensure_row_contiguous(inputs[0]);
   auto& out = outputs[0];
-  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+  out.set_data(allocator::malloc(out.nbytes()));
 
   auto& scales = outputs[1];
   auto& biases = outputs[2];
-  scales.set_data(allocator::malloc_or_wait(scales.nbytes()));
-  biases.set_data(allocator::malloc_or_wait(biases.nbytes()));
-  if (w.dtype() == float16) {
-    if (is_power_of_2(bits_)) {
-      quantize<float16_t, uint32_t>(w, out, scales, biases, bits_, group_size_);
-    } else {
-      quantize<float16_t, uint8_t>(w, out, scales, biases, bits_, group_size_);
-    }
-  } else if (w.dtype() == bfloat16) {
-    if (is_power_of_2(bits_)) {
-      quantize<bfloat16_t, uint32_t>(
-          w, out, scales, biases, bits_, group_size_);
-    } else {
-      quantize<bfloat16_t, uint8_t>(w, out, scales, biases, bits_, group_size_);
-    }
-  } else if (w.dtype() == float32) {
-    if (is_power_of_2(bits_)) {
-      quantize<float, uint32_t>(w, out, scales, biases, bits_, group_size_);
-    } else {
-      quantize<float, uint8_t>(w, out, scales, biases, bits_, group_size_);
-    }
-  } else {
-    throw std::runtime_error(
-        "[fast::AffineQuantize::eval_cpu] Only supports floating point inputs");
+  scales.set_data(allocator::malloc(scales.nbytes()));
+  biases.set_data(allocator::malloc(biases.nbytes()));
+  auto& encoder = cpu::get_command_encoder(stream());
+  if (copied) {
+    encoder.add_temporary(w);
   }
+  encoder.set_input_array(w);
+  encoder.set_input_array(scales);
+  encoder.set_input_array(biases);
+  encoder.set_output_array(out);
+  encoder.dispatch([w = array::unsafe_weak_copy(w),
+                    out = array::unsafe_weak_copy(out),
+                    scales = array::unsafe_weak_copy(scales),
+                    biases = array::unsafe_weak_copy(biases),
+                    group_size_ = group_size_,
+                    bits_ = bits_]() mutable {
+    if (w.dtype() == float16) {
+      if (is_power_of_2(bits_)) {
+        dispatch_quantize<float16_t, uint32_t>(
+            w, out, scales, biases, bits_, group_size_);
+      } else {
+        dispatch_quantize<float16_t, uint8_t>(
+            w, out, scales, biases, bits_, group_size_);
+      }
+    } else if (w.dtype() == bfloat16) {
+      if (is_power_of_2(bits_)) {
+        dispatch_quantize<bfloat16_t, uint32_t>(
+            w, out, scales, biases, bits_, group_size_);
+      } else {
+        dispatch_quantize<bfloat16_t, uint8_t>(
+            w, out, scales, biases, bits_, group_size_);
+      }
+    } else if (w.dtype() == float32) {
+      if (is_power_of_2(bits_)) {
+        dispatch_quantize<float, uint32_t>(
+            w, out, scales, biases, bits_, group_size_);
+      } else {
+        dispatch_quantize<float, uint8_t>(
+            w, out, scales, biases, bits_, group_size_);
+      }
+    } else {
+      throw std::runtime_error(
+          "[fast::AffineQuantize::eval_cpu] Only supports floating point inputs");
+    }
+  });
 }
 
 } // namespace mlx::core

mlx/backend/cpu/reduce.cpp

@@ -5,6 +5,7 @@
 #include <limits>
 
 #include "mlx/backend/common/reduce.h"
+#include "mlx/backend/cpu/encoder.h"
 #include "mlx/backend/cpu/simd/simd.h"
 #include "mlx/primitives.h"
 
@@ -139,25 +140,22 @@ void reduction_op(
     const array& x,
     array& out,
     const std::vector<int>& axes,
-    U init,
-    Op op) {
-  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+    U init) {
   ReductionPlan plan = get_reduction_plan(x, axes);
 
+  auto in_ptr = x.data<T>();
+  auto out_ptr = out.data<U>();
   if (plan.type == ContiguousAllReduce) {
-    U* out_ptr = out.data<U>();
     *out_ptr = init;
-    contiguous_reduce(x.data<T>(), out_ptr, x.size(), op, init);
+    contiguous_reduce(in_ptr, out_ptr, x.size(), Op{}, init);
     return;
   }
 
   if (plan.type == ContiguousReduce && plan.shape.size() == 1) {
     int reduction_size = plan.shape[0];
-    const T* x_ptr = x.data<T>();
-    U* out_ptr = out.data<U>();
-    for (int i = 0; i < out.size(); i++, out_ptr++, x_ptr += reduction_size) {
+    for (int i = 0; i < out.size(); i++, out_ptr++, in_ptr += reduction_size) {
       *out_ptr = init;
-      contiguous_reduce(x_ptr, out_ptr, reduction_size, op, init);
+      contiguous_reduce(in_ptr, out_ptr, reduction_size, Op{}, init);
     }
     return;
   }
@@ -166,8 +164,6 @@ void reduction_op(
     int reduction_size = plan.shape.back();
     plan.shape.pop_back();
     plan.strides.pop_back();
-    const T* x_ptr = x.data<T>();
-    U* out_ptr = out.data<U>();
     // Unrolling the following loop (and implementing it in order for
     // ContiguousReduce) should hold extra performance boost.
     auto [shape, strides] = shapes_without_reduction_axes(x, axes);
@@ -175,7 +171,7 @@ void reduction_op(
       for (int i = 0; i < out.size(); i++, out_ptr++) {
         int offset = elem_to_loc(i, shape, strides);
         *out_ptr = init;
-        contiguous_reduce(x_ptr + offset, out_ptr, reduction_size, op, init);
+        contiguous_reduce(in_ptr + offset, out_ptr, reduction_size, Op{}, init);
       }
     } else {
       for (int i = 0; i < out.size(); i++, out_ptr++) {
@@ -184,10 +180,10 @@ void reduction_op(
         nd_loop(
             [&](int extra_offset) {
               contiguous_reduce(
-                  x_ptr + offset + extra_offset,
+                  in_ptr + offset + extra_offset,
                   out_ptr,
                   reduction_size,
-                  op,
+                  Op{},
                   init);
             },
             plan.shape,
@@ -202,12 +198,10 @@ void reduction_op(
     size_t reduction_stride = plan.strides.back();
     plan.shape.pop_back();
     plan.strides.pop_back();
-    const T* x_ptr = x.data<T>();
-    U* out_ptr = out.data<U>();
     for (int i = 0; i < out.size(); i += reduction_stride) {
       std::fill_n(out_ptr, reduction_stride, init);
-      strided_reduce(x_ptr, out_ptr, reduction_size, reduction_stride, op);
-      x_ptr += reduction_stride * reduction_size;
+      strided_reduce(in_ptr, out_ptr, reduction_size, reduction_stride, Op{});
+      in_ptr += reduction_stride * reduction_size;
       out_ptr += reduction_stride;
     }
     return;
@@ -219,15 +213,14 @@ void reduction_op(
     size_t reduction_stride = plan.strides.back();
     plan.shape.pop_back();
     plan.strides.pop_back();
-    const T* x_ptr = x.data<T>();
-    U* out_ptr = out.data<U>();
     auto [shape, strides] = shapes_without_reduction_axes(x, axes);
+
     if (plan.shape.size() == 0) {
       for (int i = 0; i < out.size(); i += reduction_stride) {
         int offset = elem_to_loc(i, shape, strides);
         std::fill_n(out_ptr, reduction_stride, init);
         strided_reduce(
-            x_ptr + offset, out_ptr, reduction_size, reduction_stride, op);
+            in_ptr + offset, out_ptr, reduction_size, reduction_stride, Op{});
         out_ptr += reduction_stride;
       }
     } else {
@@ -237,11 +230,11 @@ void reduction_op(
         nd_loop(
             [&](int extra_offset) {
               strided_reduce(
-                  x_ptr + offset + extra_offset,
+                  in_ptr + offset + extra_offset,
                   out_ptr,
                   reduction_size,
                   reduction_stride,
-                  op);
+                  Op{});
             },
             plan.shape,
             plan.strides);
@@ -252,15 +245,14 @@ void reduction_op(
   }
 
   if (plan.type == GeneralReduce) {
-    const T* x_ptr = x.data<T>();
-    U* out_ptr = out.data<U>();
     auto [shape, strides] = shapes_without_reduction_axes(x, axes);
+
     for (int i = 0; i < out.size(); i++, out_ptr++) {
       int offset = elem_to_loc(i, shape, strides);
       U val = init;
       nd_loop(
           [&](int extra_offset) {
-            val = op(val, *(x_ptr + offset + extra_offset));
+            val = Op{}(val, *(in_ptr + offset + extra_offset));
           },
           plan.shape,
           plan.strides);
@@ -396,9 +388,9 @@ void reduce_dispatch_and_or(
     Reduce::ReduceType rtype,
     const std::vector<int>& axes) {
   if (rtype == Reduce::And) {
-    reduction_op<InT, bool>(in, out, axes, true, AndReduce());
+    reduction_op<InT, bool, AndReduce>(in, out, axes, true);
   } else {
-    reduction_op<InT, bool>(in, out, axes, false, OrReduce());
+    reduction_op<InT, bool, OrReduce>(in, out, axes, false);
   }
 }
 
@@ -410,15 +402,15 @@ void reduce_dispatch_sum_prod(
     const std::vector<int>& axes) {
   if (rtype == Reduce::Sum) {
     if constexpr (std::is_integral_v<InT> && sizeof(InT) <= 4) {
-      reduction_op<InT, int32_t>(in, out, axes, 0, SumReduce());
+      reduction_op<InT, int32_t, SumReduce>(in, out, axes, 0);
     } else {
-      reduction_op<InT, InT>(in, out, axes, 0, SumReduce());
+      reduction_op<InT, InT, SumReduce>(in, out, axes, 0);
     }
   } else {
     if constexpr (std::is_integral_v<InT> && sizeof(InT) <= 4) {
-      reduction_op<InT, int32_t>(in, out, axes, 1, ProdReduce());
+      reduction_op<InT, int32_t, ProdReduce>(in, out, axes, 1);
     } else {
-      reduction_op<InT, InT>(in, out, axes, 1, ProdReduce());
+      reduction_op<InT, InT, ProdReduce>(in, out, axes, 1);
     }
   }
 }
@@ -431,132 +423,141 @@ void reduce_dispatch_min_max(
     const std::vector<int>& axes) {
   if (rtype == Reduce::Max) {
     auto init = Limits<InT>::min;
-    reduction_op<InT, InT>(in, out, axes, init, MaxReduce());
+    reduction_op<InT, InT, MaxReduce>(in, out, axes, init);
   } else {
     auto init = Limits<InT>::max;
-    reduction_op<InT, InT>(in, out, axes, init, MinReduce());
+    reduction_op<InT, InT, MinReduce>(in, out, axes, init);
   }
 }
 
 void Reduce::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   auto& in = inputs[0];
-  switch (reduce_type_) {
-    case Reduce::And:
-    case Reduce::Or: {
-      switch (in.dtype()) {
-        case bool_:
-        case uint8:
-        case int8:
-          reduce_dispatch_and_or<int8_t>(in, out, reduce_type_, axes_);
-          break;
-        case int16:
-        case uint16:
-        case float16:
-        case bfloat16:
-          reduce_dispatch_and_or<int16_t>(in, out, reduce_type_, axes_);
-          break;
-        case uint32:
-        case int32:
-        case float32:
-          reduce_dispatch_and_or<int32_t>(in, out, reduce_type_, axes_);
-          break;
-        case uint64:
-        case int64:
-        case float64:
-        case complex64:
-          reduce_dispatch_and_or<int64_t>(in, out, reduce_type_, axes_);
-          break;
+  out.set_data(allocator::malloc(out.nbytes()));
+  auto& encoder = cpu::get_command_encoder(stream());
+  encoder.set_input_array(in);
+  encoder.set_output_array(out);
+  encoder.dispatch([in = array::unsafe_weak_copy(in),
+                    out = array::unsafe_weak_copy(out),
+                    reduce_type_ = reduce_type_,
+                    axes_ = axes_]() mutable {
+    switch (reduce_type_) {
+      case Reduce::And:
+      case Reduce::Or: {
+        switch (in.dtype()) {
+          case bool_:
+          case uint8:
+          case int8:
+            reduce_dispatch_and_or<int8_t>(in, out, reduce_type_, axes_);
+            break;
+          case int16:
+          case uint16:
+          case float16:
+          case bfloat16:
+            reduce_dispatch_and_or<int16_t>(in, out, reduce_type_, axes_);
+            break;
+          case uint32:
+          case int32:
+          case float32:
+            reduce_dispatch_and_or<int32_t>(in, out, reduce_type_, axes_);
+            break;
+          case uint64:
+          case int64:
+          case float64:
+          case complex64:
+            reduce_dispatch_and_or<int64_t>(in, out, reduce_type_, axes_);
+            break;
+        }
+        break;
       }
-      break;
-    }
-    case Reduce::Sum:
-    case Reduce::Prod: {
-      switch (in.dtype()) {
-        case bool_:
-        case uint8:
-        case int8:
-          reduce_dispatch_sum_prod<int8_t>(in, out, reduce_type_, axes_);
-          break;
-        case int16:
-        case uint16:
-          reduce_dispatch_sum_prod<int16_t>(in, out, reduce_type_, axes_);
-          break;
-        case int32:
-        case uint32:
-          reduce_dispatch_sum_prod<int32_t>(in, out, reduce_type_, axes_);
-          break;
-        case int64:
-        case uint64:
-          reduce_dispatch_sum_prod<int64_t>(in, out, reduce_type_, axes_);
-          break;
-        case float16:
-          reduce_dispatch_sum_prod<float16_t>(in, out, reduce_type_, axes_);
-          break;
-        case bfloat16:
-          reduce_dispatch_sum_prod<bfloat16_t>(in, out, reduce_type_, axes_);
-          break;
-        case float32:
-          reduce_dispatch_sum_prod<float>(in, out, reduce_type_, axes_);
-          break;
-        case float64:
-          reduce_dispatch_sum_prod<double>(in, out, reduce_type_, axes_);
-          break;
-        case complex64:
-          reduce_dispatch_sum_prod<complex64_t>(in, out, reduce_type_, axes_);
-          break;
+      case Reduce::Sum:
+      case Reduce::Prod: {
+        switch (in.dtype()) {
+          case bool_:
+          case uint8:
+          case int8:
+            reduce_dispatch_sum_prod<int8_t>(in, out, reduce_type_, axes_);
+            break;
+          case int16:
+          case uint16:
+            reduce_dispatch_sum_prod<int16_t>(in, out, reduce_type_, axes_);
+            break;
+          case int32:
+          case uint32:
+            reduce_dispatch_sum_prod<int32_t>(in, out, reduce_type_, axes_);
+            break;
+          case int64:
+          case uint64:
+            reduce_dispatch_sum_prod<int64_t>(in, out, reduce_type_, axes_);
+            break;
+          case float16:
+            reduce_dispatch_sum_prod<float16_t>(in, out, reduce_type_, axes_);
+            break;
+          case bfloat16:
+            reduce_dispatch_sum_prod<bfloat16_t>(in, out, reduce_type_, axes_);
+            break;
+          case float32:
+            reduce_dispatch_sum_prod<float>(in, out, reduce_type_, axes_);
+            break;
+          case float64:
+            reduce_dispatch_sum_prod<double>(in, out, reduce_type_, axes_);
+            break;
+          case complex64:
+            reduce_dispatch_sum_prod<complex64_t>(in, out, reduce_type_, axes_);
+            break;
+        }
+        break;
       }
-      break;
-    }
-    case Reduce::Max:
-    case Reduce::Min: {
-      switch (in.dtype()) {
-        case bool_:
-          reduce_dispatch_min_max<bool>(in, out, reduce_type_, axes_);
-          break;
-        case uint8:
-          reduce_dispatch_min_max<uint8_t>(in, out, reduce_type_, axes_);
-          break;
-        case uint16:
-          reduce_dispatch_min_max<uint16_t>(in, out, reduce_type_, axes_);
-          break;
-        case uint32:
-          reduce_dispatch_min_max<uint32_t>(in, out, reduce_type_, axes_);
-          break;
-        case uint64:
-          reduce_dispatch_min_max<uint64_t>(in, out, reduce_type_, axes_);
-          break;
-        case int8:
-          reduce_dispatch_min_max<uint8_t>(in, out, reduce_type_, axes_);
-          break;
-        case int16:
-          reduce_dispatch_min_max<uint16_t>(in, out, reduce_type_, axes_);
-          break;
-        case int32:
-          reduce_dispatch_min_max<int32_t>(in, out, reduce_type_, axes_);
-          break;
-        case int64:
-          reduce_dispatch_min_max<int64_t>(in, out, reduce_type_, axes_);
-          break;
-        case float16:
-          reduce_dispatch_min_max<float16_t>(in, out, reduce_type_, axes_);
-          break;
-        case float32:
-          reduce_dispatch_min_max<float>(in, out, reduce_type_, axes_);
-          break;
-        case float64:
-          reduce_dispatch_min_max<double>(in, out, reduce_type_, axes_);
-          break;
-        case bfloat16:
-          reduce_dispatch_min_max<bfloat16_t>(in, out, reduce_type_, axes_);
-          break;
-        case complex64:
-          reduce_dispatch_min_max<complex64_t>(in, out, reduce_type_, axes_);
-          break;
+      case Reduce::Max:
+      case Reduce::Min: {
+        switch (in.dtype()) {
+          case bool_:
+            reduce_dispatch_min_max<bool>(in, out, reduce_type_, axes_);
+            break;
+          case uint8:
+            reduce_dispatch_min_max<uint8_t>(in, out, reduce_type_, axes_);
+            break;
+          case uint16:
+            reduce_dispatch_min_max<uint16_t>(in, out, reduce_type_, axes_);
+            break;
+          case uint32:
+            reduce_dispatch_min_max<uint32_t>(in, out, reduce_type_, axes_);
+            break;
+          case uint64:
+            reduce_dispatch_min_max<uint64_t>(in, out, reduce_type_, axes_);
+            break;
+          case int8:
+            reduce_dispatch_min_max<uint8_t>(in, out, reduce_type_, axes_);
+            break;
+          case int16:
+            reduce_dispatch_min_max<uint16_t>(in, out, reduce_type_, axes_);
+            break;
+          case int32:
+            reduce_dispatch_min_max<int32_t>(in, out, reduce_type_, axes_);
+            break;
+          case int64:
+            reduce_dispatch_min_max<int64_t>(in, out, reduce_type_, axes_);
+            break;
+          case float16:
+            reduce_dispatch_min_max<float16_t>(in, out, reduce_type_, axes_);
+            break;
+          case float32:
+            reduce_dispatch_min_max<float>(in, out, reduce_type_, axes_);
+            break;
+          case float64:
+            reduce_dispatch_min_max<double>(in, out, reduce_type_, axes_);
+            break;
+          case bfloat16:
+            reduce_dispatch_min_max<bfloat16_t>(in, out, reduce_type_, axes_);
+            break;
+          case complex64:
+            reduce_dispatch_min_max<complex64_t>(in, out, reduce_type_, axes_);
+            break;
+        }
+        break;
       }
-      break;
     }
-  }
+  });
 }
 
 } // namespace mlx::core

mlx/backend/cpu/scan.cpp

@@ -4,6 +4,7 @@
 
 #include "mlx/backend/common/utils.h"
 #include "mlx/backend/cpu/copy.h"
+#include "mlx/backend/cpu/encoder.h"
 #include "mlx/backend/cpu/simd/simd.h"
 #include "mlx/primitives.h"
 
@@ -153,33 +154,31 @@ void strided_scan(
 
 template <typename T, typename U, typename Op>
 void scan_op(
-    const array& input,
-    array& output,
+    const array& in,
+    array& out,
     int axis,
     bool reverse,
     bool inclusive,
     const Op& op,
     U init) {
-  output.set_data(allocator::malloc_or_wait(output.nbytes()));
-
-  if (input.flags().row_contiguous) {
-    if (input.strides()[axis] == 1) {
+  if (in.flags().row_contiguous) {
+    if (in.strides()[axis] == 1) {
       contiguous_scan(
-          input.data<T>(),
-          output.data<U>(),
-          input.size() / input.shape(axis),
-          input.shape(axis),
+          in.data<T>(),
+          out.data<U>(),
+          in.size() / in.shape(axis),
+          in.shape(axis),
           reverse,
           inclusive,
           op,
           init);
     } else {
       strided_scan(
-          input.data<T>(),
-          output.data<U>(),
-          input.size() / input.shape(axis) / input.strides()[axis],
-          input.shape(axis),
-          input.strides()[axis],
+          in.data<T>(),
+          out.data<U>(),
+          in.size() / in.shape(axis) / in.strides()[axis],
+          in.shape(axis),
+          in.strides()[axis],
           reverse,
           inclusive,
           op,
@@ -193,8 +192,8 @@ void scan_op(
 template <typename T, typename U>
 void scan_dispatch(
     Scan::ReduceType rtype,
-    const array& input,
-    array& output,
+    const array& in,
+    array& out,
     int axis,
     bool reverse,
     bool inclusive) {
@@ -202,29 +201,29 @@ void scan_dispatch(
     case Scan::Sum: {
       auto op = [](U y, T x) { return y + x; };
       auto init = static_cast<U>(0);
-      scan_op<T, U>(input, output, axis, reverse, inclusive, op, init);
+      scan_op<T, U>(in, out, axis, reverse, inclusive, op, init);
       break;
     }
     case Scan::Prod: {
       auto op = [](U y, T x) { return y * x; };
       auto init = static_cast<U>(1);
-      scan_op<T, U>(input, output, axis, reverse, inclusive, op, init);
+      scan_op<T, U>(in, out, axis, reverse, inclusive, op, init);
       break;
     }
     case Scan::Min: {
       auto op = [](U y, T x) { return x < y ? x : y; };
-      auto init = (issubdtype(input.dtype(), floating))
+      auto init = (issubdtype(in.dtype(), floating))
           ? static_cast<U>(std::numeric_limits<float>::infinity())
           : std::numeric_limits<U>::max();
-      scan_op<T, U>(input, output, axis, reverse, inclusive, op, init);
+      scan_op<T, U>(in, out, axis, reverse, inclusive, op, init);
       break;
     }
     case Scan::Max: {
       auto op = [](U y, T x) { return x < y ? y : x; };
-      auto init = (issubdtype(input.dtype(), floating))
+      auto init = (issubdtype(in.dtype(), floating))
           ? static_cast<U>(-std::numeric_limits<float>::infinity())
           : std::numeric_limits<U>::min();
-      scan_op<T, U>(input, output, axis, reverse, inclusive, op, init);
+      scan_op<T, U>(in, out, axis, reverse, inclusive, op, init);
       break;
     }
   }
@@ -235,82 +234,95 @@ void scan_dispatch(
 void Scan::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
 
+  auto& encoder = cpu::get_command_encoder(stream());
+
   // Ensure contiguity
   auto in = inputs[0];
   if (!in.flags().row_contiguous) {
     array arr_copy(in.shape(), in.dtype(), nullptr, {});
-    copy(in, arr_copy, CopyType::General);
+    copy(in, arr_copy, CopyType::General, stream());
     in = arr_copy;
+    encoder.add_temporary(arr_copy);
   }
+  out.set_data(allocator::malloc(out.nbytes()));
 
-  switch (in.dtype()) {
-    case bool_: {
-      // We could do a full dtype x dtype switch but this is the only case
-      // where we accumulate in a different type, for now.
-      //
-      // TODO: If we add the option to accumulate floats in higher precision
-      //       floats perhaps we should add the full all-to-all dispatch.
-      if (reduce_type_ == Scan::Sum && out.dtype() == int32) {
-        scan_dispatch<bool, int32_t>(
-            reduce_type_, in, out, axis_, reverse_, inclusive_);
-      } else {
-        scan_dispatch<bool, bool>(
-            reduce_type_, in, out, axis_, reverse_, inclusive_);
+  encoder.set_input_array(in);
+  encoder.set_output_array(out);
+  encoder.dispatch([in = array::unsafe_weak_copy(in),
+                    out = array::unsafe_weak_copy(out),
+                    axis_ = axis_,
+                    reduce_type_ = reduce_type_,
+                    reverse_ = reverse_,
+                    inclusive_ = inclusive_]() mutable {
+    switch (in.dtype()) {
+      case bool_: {
+        // We could do a full dtype x dtype switch but this is the only case
+        // where we accumulate in a different type, for now.
+        //
+        // TODO: If we add the option to accumulate floats in higher precision
+        //       floats perhaps we should add the full all-to-all dispatch.
+        if (reduce_type_ == Scan::Sum && out.dtype() == int32) {
+          scan_dispatch<bool, int32_t>(
+              reduce_type_, in, out, axis_, reverse_, inclusive_);
+        } else {
+          scan_dispatch<bool, bool>(
+              reduce_type_, in, out, axis_, reverse_, inclusive_);
+        }
+        break;
       }
-      break;
+      case uint8:
+        scan_dispatch<uint8_t, uint8_t>(
+            reduce_type_, in, out, axis_, reverse_, inclusive_);
+        break;
+      case uint16:
+        scan_dispatch<uint16_t, uint16_t>(
+            reduce_type_, in, out, axis_, reverse_, inclusive_);
+        break;
+      case uint32:
+        scan_dispatch<uint32_t, uint32_t>(
+            reduce_type_, in, out, axis_, reverse_, inclusive_);
+        break;
+      case uint64:
+        scan_dispatch<uint64_t, uint64_t>(
+            reduce_type_, in, out, axis_, reverse_, inclusive_);
+        break;
+      case int8:
+        scan_dispatch<int8_t, int8_t>(
+            reduce_type_, in, out, axis_, reverse_, inclusive_);
+        break;
+      case int16:
+        scan_dispatch<int16_t, int16_t>(
+            reduce_type_, in, out, axis_, reverse_, inclusive_);
+        break;
+      case int32:
+        scan_dispatch<int32_t, int32_t>(
+            reduce_type_, in, out, axis_, reverse_, inclusive_);
+        break;
+      case int64:
+        scan_dispatch<int64_t, int64_t>(
+            reduce_type_, in, out, axis_, reverse_, inclusive_);
+        break;
+      case float16:
+        scan_dispatch<float16_t, float16_t>(
+            reduce_type_, in, out, axis_, reverse_, inclusive_);
+        break;
+      case float32:
+        scan_dispatch<float, float>(
+            reduce_type_, in, out, axis_, reverse_, inclusive_);
+        break;
+      case float64:
+        scan_dispatch<double, double>(
+            reduce_type_, in, out, axis_, reverse_, inclusive_);
+        break;
+      case bfloat16:
+        scan_dispatch<bfloat16_t, bfloat16_t>(
+            reduce_type_, in, out, axis_, reverse_, inclusive_);
+        break;
+      case complex64:
+        throw std::runtime_error("Scan ops do not support complex types yet");
+        break;
     }
-    case uint8:
-      scan_dispatch<uint8_t, uint8_t>(
-          reduce_type_, in, out, axis_, reverse_, inclusive_);
-      break;
-    case uint16:
-      scan_dispatch<uint16_t, uint16_t>(
-          reduce_type_, in, out, axis_, reverse_, inclusive_);
-      break;
-    case uint32:
-      scan_dispatch<uint32_t, uint32_t>(
-          reduce_type_, in, out, axis_, reverse_, inclusive_);
-      break;
-    case uint64:
-      scan_dispatch<uint64_t, uint64_t>(
-          reduce_type_, in, out, axis_, reverse_, inclusive_);
-      break;
-    case int8:
-      scan_dispatch<int8_t, int8_t>(
-          reduce_type_, in, out, axis_, reverse_, inclusive_);
-      break;
-    case int16:
-      scan_dispatch<int16_t, int16_t>(
-          reduce_type_, in, out, axis_, reverse_, inclusive_);
-      break;
-    case int32:
-      scan_dispatch<int32_t, int32_t>(
-          reduce_type_, in, out, axis_, reverse_, inclusive_);
-      break;
-    case int64:
-      scan_dispatch<int64_t, int64_t>(
-          reduce_type_, in, out, axis_, reverse_, inclusive_);
-      break;
-    case float16:
-      scan_dispatch<float16_t, float16_t>(
-          reduce_type_, in, out, axis_, reverse_, inclusive_);
-      break;
-    case float32:
-      scan_dispatch<float, float>(
-          reduce_type_, in, out, axis_, reverse_, inclusive_);
-      break;
-    case float64:
-      scan_dispatch<double, double>(
-          reduce_type_, in, out, axis_, reverse_, inclusive_);
-      break;
-    case bfloat16:
-      scan_dispatch<bfloat16_t, bfloat16_t>(
-          reduce_type_, in, out, axis_, reverse_, inclusive_);
-      break;
-    case complex64:
-      throw std::runtime_error("Scan ops do not support complex types yet");
-      break;
-  }
+  });
 }
 
 } // namespace mlx::core

mlx/backend/cpu/select.cpp

@@ -16,51 +16,70 @@ void select_op(
     const array& b,
     const array& c,
     array& out,
-    Op op) {
-  switch (out.dtype()) {
-    case bool_:
-      ternary_op<bool, bool, bool, bool>(a, b, c, out, op);
-      break;
-    case uint8:
-      ternary_op<bool, uint8_t, uint8_t, uint8_t>(a, b, c, out, op);
-      break;
-    case uint16:
-      ternary_op<bool, uint16_t, uint16_t, uint16_t>(a, b, c, out, op);
-      break;
-    case uint32:
-      ternary_op<bool, uint32_t, uint32_t, uint32_t>(a, b, c, out, op);
-      break;
-    case uint64:
-      ternary_op<bool, uint64_t, uint64_t, uint64_t>(a, b, c, out, op);
-      break;
-    case int8:
-      ternary_op<bool, int8_t, int8_t, int8_t>(a, b, c, out, op);
-      break;
-    case int16:
-      ternary_op<bool, int16_t, int16_t, int16_t>(a, b, c, out, op);
-      break;
-    case int32:
-      ternary_op<bool, int32_t, int32_t, int32_t>(a, b, c, out, op);
-      break;
-    case int64:
-      ternary_op<bool, int64_t, int64_t, int64_t>(a, b, c, out, op);
-      break;
-    case float16:
-      ternary_op<bool, float16_t, float16_t, float16_t>(a, b, c, out, op);
-      break;
-    case float32:
-      ternary_op<bool, float, float, float>(a, b, c, out, op);
-      break;
-    case float64:
-      ternary_op<bool, double, double, double>(a, b, c, out, op);
-      break;
-    case bfloat16:
-      ternary_op<bool, bfloat16_t, bfloat16_t, bfloat16_t>(a, b, c, out, op);
-      break;
-    case complex64:
-      ternary_op<bool, complex64_t, complex64_t, complex64_t>(a, b, c, out, op);
-      break;
-  }
+    Op op,
+    Stream stream) {
+  TernaryOpType topt = get_ternary_op_type(a, b, c);
+  set_ternary_op_output_data(a, b, c, out, topt);
+
+  auto& encoder = cpu::get_command_encoder(stream);
+  encoder.set_input_array(a);
+  encoder.set_input_array(b);
+  encoder.set_input_array(c);
+  encoder.set_output_array(out);
+  encoder.dispatch([a = array::unsafe_weak_copy(a),
+                    b = array::unsafe_weak_copy(b),
+                    c = array::unsafe_weak_copy(c),
+                    out = array::unsafe_weak_copy(out),
+                    op,
+                    topt]() mutable {
+    switch (out.dtype()) {
+      case bool_:
+        ternary_op<bool, bool, bool, bool>(a, b, c, out, op, topt);
+        break;
+      case uint8:
+        ternary_op<bool, uint8_t, uint8_t, uint8_t>(a, b, c, out, op, topt);
+        break;
+      case uint16:
+        ternary_op<bool, uint16_t, uint16_t, uint16_t>(a, b, c, out, op, topt);
+        break;
+      case uint32:
+        ternary_op<bool, uint32_t, uint32_t, uint32_t>(a, b, c, out, op, topt);
+        break;
+      case uint64:
+        ternary_op<bool, uint64_t, uint64_t, uint64_t>(a, b, c, out, op, topt);
+        break;
+      case int8:
+        ternary_op<bool, int8_t, int8_t, int8_t>(a, b, c, out, op, topt);
+        break;
+      case int16:
+        ternary_op<bool, int16_t, int16_t, int16_t>(a, b, c, out, op, topt);
+        break;
+      case int32:
+        ternary_op<bool, int32_t, int32_t, int32_t>(a, b, c, out, op, topt);
+        break;
+      case int64:
+        ternary_op<bool, int64_t, int64_t, int64_t>(a, b, c, out, op, topt);
+        break;
+      case float16:
+        ternary_op<bool, float16_t, float16_t, float16_t>(
+            a, b, c, out, op, topt);
+        break;
+      case float32:
+        ternary_op<bool, float, float, float>(a, b, c, out, op, topt);
+        break;
+      case float64:
+        ternary_op<bool, double, double, double>(a, b, c, out, op, topt);
+        break;
+      case bfloat16:
+        ternary_op<bool, bfloat16_t, bfloat16_t, bfloat16_t>(
+            a, b, c, out, op, topt);
+        break;
+      case complex64:
+        ternary_op<bool, complex64_t, complex64_t, complex64_t>(
+            a, b, c, out, op, topt);
+        break;
+    }
+  });
 }
 
 } // namespace
@@ -70,7 +89,7 @@ void Select::eval_cpu(const std::vector<array>& inputs, array& out) {
   const auto& condition = inputs[0];
   const auto& a = inputs[1];
   const auto& b = inputs[2];
-  select_op(condition, a, b, out, detail::Select());
+  select_op(condition, a, b, out, detail::Select(), stream());
 }
 
 } // namespace mlx::core

mlx/backend/cpu/simd/accelerate_fp16_simd.h

@@ -17,7 +17,7 @@ struct ScalarT<float16_t, N> {
 #endif
 
 template <>
-static constexpr int max_size<float16_t> = N;
+inline constexpr int max_size<float16_t> = N;
 
 #define SIMD_FP16_DEFAULT_UNARY(op)                    \
   template <>                                          \

mlx/backend/cpu/simd/accelerate_simd.h

@@ -83,25 +83,25 @@ struct Simd {
 // Values chosen based on benchmarks on M3 Max
 // TODO: consider choosing these more optimally
 template <>
-static constexpr int max_size<int8_t> = 16;
+inline constexpr int max_size<int8_t> = 16;
 template <>
-static constexpr int max_size<int16_t> = 16;
+inline constexpr int max_size<int16_t> = 16;
 template <>
-static constexpr int max_size<int> = 8;
+inline constexpr int max_size<int> = 8;
 template <>
-static constexpr int max_size<int64_t> = 4;
+inline constexpr int max_size<int64_t> = 4;
 template <>
-static constexpr int max_size<uint8_t> = 16;
+inline constexpr int max_size<uint8_t> = 16;
 template <>
-static constexpr int max_size<uint16_t> = 16;
+inline constexpr int max_size<uint16_t> = 16;
 template <>
-static constexpr int max_size<uint32_t> = 8;
+inline constexpr int max_size<uint32_t> = 8;
 template <>
-static constexpr int max_size<uint64_t> = 4;
+inline constexpr int max_size<uint64_t> = 4;
 template <>
-static constexpr int max_size<float> = 8;
+inline constexpr int max_size<float> = 8;
 template <>
-static constexpr int max_size<double> = 4;
+inline constexpr int max_size<double> = 4;
 
 #define SIMD_DEFAULT_UNARY(name, op) \
   template <typename T, int N>       \

mlx/backend/cpu/simd/base_simd.h

@@ -87,7 +87,6 @@ DEFAULT_UNARY(cosh, std::cosh)
 DEFAULT_UNARY(expm1, std::expm1)
 DEFAULT_UNARY(floor, std::floor)
 DEFAULT_UNARY(log, std::log)
-DEFAULT_UNARY(log2, std::log2)
 DEFAULT_UNARY(log10, std::log10)
 DEFAULT_UNARY(log1p, std::log1p)
 DEFAULT_UNARY(sinh, std::sinh)
@@ -95,6 +94,17 @@ DEFAULT_UNARY(sqrt, std::sqrt)
 DEFAULT_UNARY(tan, std::tan)
 DEFAULT_UNARY(tanh, std::tanh)
 
+template <typename T>
+Simd<T, 1> log2(Simd<T, 1> in) {
+  if constexpr (is_complex<T>) {
+    auto out = std::log(in.value);
+    auto scale = decltype(out.real())(M_LN2);
+    return Simd<T, 1>{T{out.real() / scale, out.imag() / scale}};
+  } else {
+    return Simd<T, 1>{std::log2(in.value)};
+  }
+}
+
 template <typename T>
 Simd<T, 1> operator~(Simd<T, 1> in) {
   return ~in.value;

mlx/backend/cpu/simd/math.h

@@ -186,7 +186,7 @@ Simd<T, N> erfinv(Simd<T, N> a_) {
       return a * rhs(t);
     }
   } else {
-    return a * select(t > thresh, lhs(t), rhs(t));
+    return a * select(abs(t) > thresh, lhs(t), rhs(t));
   }
 }
 

mlx/backend/cpu/softmax.cpp

@@ -4,6 +4,7 @@
 #include <cmath>
 
 #include "mlx/backend/cpu/copy.h"
+#include "mlx/backend/cpu/encoder.h"
 #include "mlx/backend/cpu/simd/simd.h"
 #include "mlx/primitives.h"
 #include "mlx/types/limits.h"
@@ -15,92 +16,100 @@ namespace {
 using namespace mlx::core::simd;
 
 template <typename T, typename AccT>
-void softmax(const array& in, array& out) {
-  constexpr bool same_t = std::is_same_v<T, AccT>;
-  constexpr int N = std::min(max_size<AccT>, max_size<T>);
+void softmax(const array& in, array& out, Stream stream) {
+  auto& encoder = cpu::get_command_encoder(stream);
+  encoder.set_input_array(in);
+  encoder.set_output_array(out);
 
   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;
-    Simd<AccT, N> vmaximum(-numeric_limits<AccT>::infinity());
-    size_t s = M;
-    while (s >= N) {
-      Simd<AccT, N> vals = load<T, N>(current_in_ptr);
-      vmaximum = maximum(vals, vmaximum);
-      current_in_ptr += N;
-      s -= N;
-    }
+  encoder.dispatch([in_ptr, out_ptr, M, L]() mutable {
+    constexpr bool same_t = std::is_same_v<T, AccT>;
+    constexpr int N = std::min(max_size<AccT>, max_size<T>);
 
-    AccT maximum = max(vmaximum);
-    while (s-- > 0) {
-      maximum = std::max(maximum, static_cast<AccT>(*current_in_ptr));
-      current_in_ptr++;
-    }
+    const T* current_in_ptr;
+    T* current_out_ptr;
 
-    // Compute the normalizer and the exponentials
-    Simd<AccT, N> vnormalizer(0.0);
-    current_out_ptr = out_ptr;
-    current_in_ptr = in_ptr;
-    s = M;
-    while (s >= N) {
-      Simd<AccT, N> vexp = load<T, N>(current_in_ptr);
-      vexp = exp(vexp - maximum);
-      if constexpr (same_t) {
-        store(current_out_ptr, vexp);
-      }
-      vnormalizer = vnormalizer + vexp;
-      current_in_ptr += N;
-      current_out_ptr += N;
-      s -= N;
-    }
-    AccT normalizer = sum(vnormalizer);
-    while (s-- > 0) {
-      AccT _exp = std::exp(*current_in_ptr - maximum);
-      if constexpr (same_t) {
-        *current_out_ptr = _exp;
-      }
-      normalizer += _exp;
-      current_in_ptr++;
-      current_out_ptr++;
-    }
-    normalizer = 1 / normalizer;
-
-    // Normalize
-    current_out_ptr = out_ptr;
-    current_in_ptr = in_ptr;
-    s = M;
-    while (s >= N) {
-      if constexpr (same_t) {
-        store(
-            current_out_ptr,
-            Simd<T, N>(load<T, N>(current_out_ptr) * normalizer));
-      } else {
-        Simd<AccT, N> vexp = load<T, N>(current_in_ptr);
-        vexp = exp(vexp - maximum) * normalizer;
-        store(current_out_ptr, Simd<T, N>(vexp));
+    for (int i = 0; i < L; i++, in_ptr += M, out_ptr += M) {
+      // Find the maximum
+      current_in_ptr = in_ptr;
+      Simd<AccT, N> vmaximum(-numeric_limits<AccT>::infinity());
+      size_t s = M;
+      while (s >= N) {
+        Simd<AccT, N> vals = load<T, N>(current_in_ptr);
+        vmaximum = maximum(vals, vmaximum);
         current_in_ptr += N;
+        s -= N;
       }
-      current_out_ptr += N;
-      s -= N;
-    }
-    while (s-- > 0) {
-      if constexpr (same_t) {
-        *current_out_ptr *= normalizer;
-      } else {
-        AccT _exp = std::exp(*current_in_ptr - maximum);
-        *current_out_ptr = static_cast<T>(_exp * normalizer);
+
+      AccT maximum = max(vmaximum);
+      while (s-- > 0) {
+        maximum = std::max(maximum, static_cast<AccT>(*current_in_ptr));
         current_in_ptr++;
       }
-      current_out_ptr++;
+
+      // Compute the normalizer and the exponentials
+      Simd<AccT, N> vnormalizer(0.0);
+      current_out_ptr = out_ptr;
+      current_in_ptr = in_ptr;
+      s = M;
+      while (s >= N) {
+        Simd<AccT, N> vexp = load<T, N>(current_in_ptr);
+        vexp = exp(vexp - maximum);
+        if constexpr (same_t) {
+          store(current_out_ptr, vexp);
+        }
+        vnormalizer = vnormalizer + vexp;
+        current_in_ptr += N;
+        current_out_ptr += N;
+        s -= N;
+      }
+      AccT normalizer = sum(vnormalizer);
+      while (s-- > 0) {
+        AccT _exp = std::exp(*current_in_ptr - maximum);
+        if constexpr (same_t) {
+          *current_out_ptr = _exp;
+        }
+        normalizer += _exp;
+        current_in_ptr++;
+        current_out_ptr++;
+      }
+      normalizer = 1 / normalizer;
+
+      // Normalize
+      current_out_ptr = out_ptr;
+      current_in_ptr = in_ptr;
+      s = M;
+      while (s >= N) {
+        if constexpr (same_t) {
+          store(
+              current_out_ptr,
+              Simd<T, N>(load<T, N>(current_out_ptr) * normalizer));
+        } else {
+          Simd<AccT, N> vexp = load<T, N>(current_in_ptr);
+          vexp = exp(vexp - maximum) * normalizer;
+          store(current_out_ptr, Simd<T, N>(vexp));
+          current_in_ptr += N;
+        }
+        current_out_ptr += N;
+        s -= N;
+      }
+      while (s-- > 0) {
+        if constexpr (same_t) {
+          *current_out_ptr *= normalizer;
+        } else {
+          AccT _exp = std::exp(*current_in_ptr - maximum);
+          *current_out_ptr = static_cast<T>(_exp * normalizer);
+          current_in_ptr++;
+        }
+        current_out_ptr++;
+      }
     }
-  }
+  });
 }
 
 } // namespace
@@ -109,67 +118,52 @@ 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) {
-    bool no_copy = x.strides()[x.ndim() - 1] == 1;
-    if (x.ndim() > 1) {
-      auto s = x.strides()[x.ndim() - 2];
-      no_copy &= (s == 0 || s == x.shape().back());
-    }
-    if (no_copy) {
+  auto set_output = [s = stream(), &out](const array& x) {
+    if (x.flags().contiguous && x.strides()[x.ndim() - 1] == 1) {
+      if (x.is_donatable()) {
+        out.copy_shared_buffer(x);
+      } else {
+        out.set_data(
+            allocator::malloc(x.data_size() * x.itemsize()),
+            x.data_size(),
+            x.strides(),
+            x.flags());
+      }
       return x;
     } else {
       array x_copy(x.shape(), x.dtype(), nullptr, {});
-      copy(x, x_copy, CopyType::General);
+      copy(x, x_copy, CopyType::General, s);
+      out.copy_shared_buffer(x_copy);
       return x_copy;
     }
   };
-  array in = check_input(std::move(inputs[0]));
-  if (in.is_donatable()) {
-    out.copy_shared_buffer(in);
-  } else {
-    out.set_data(
-        allocator::malloc_or_wait(in.data_size() * in.itemsize()),
-        in.data_size(),
-        in.strides(),
-        in.flags());
-  }
+
+  auto in = set_output(inputs[0]);
 
   switch (in.dtype()) {
-    case bool_:
-    case uint8:
-    case uint16:
-    case uint32:
-    case uint64:
-    case int8:
-    case int16:
-    case int32:
-    case int64:
-      throw std::runtime_error(
-          "Softmax is defined only for floating point types");
-      break;
     case float32:
-      softmax<float, float>(in, out);
+      softmax<float, float>(in, out, stream());
       break;
     case float16:
       if (precise_) {
-        softmax<float16_t, float>(in, out);
+        softmax<float16_t, float>(in, out, stream());
       } else {
-        softmax<float16_t, float16_t>(in, out);
+        softmax<float16_t, float16_t>(in, out, stream());
       }
       break;
     case bfloat16:
       if (precise_) {
-        softmax<bfloat16_t, float>(in, out);
+        softmax<bfloat16_t, float>(in, out, stream());
       } else {
-        softmax<bfloat16_t, bfloat16_t>(in, out);
+        softmax<bfloat16_t, bfloat16_t>(in, out, stream());
       }
       break;
     case float64:
-      softmax<double, double>(in, out);
+      softmax<double, double>(in, out, stream());
       break;
-    case complex64:
-      throw std::invalid_argument(
-          "[Softmax] Not yet implemented for complex64");
+    default:
+      throw std::runtime_error(
+          "[softmax] Only defined for floating point types.");
       break;
   }
 }

mlx/backend/cpu/sort.cpp

@@ -7,6 +7,7 @@
 
 #include "mlx/backend/common/utils.h"
 #include "mlx/backend/cpu/copy.h"
+#include "mlx/backend/cpu/encoder.h"
 
 #include "mlx/primitives.h"
 
@@ -103,16 +104,12 @@ struct StridedIterator {
   T* ptr_;
 };
 
-template <typename T, typename IdxT = uint32_t>
-void sort(const array& in, array& out, int axis) {
-  // Copy input to output
-  CopyType ctype = in.flags().contiguous ? CopyType::Vector : CopyType::General;
-  copy(in, out, ctype);
-
+template <typename T>
+void sort(array& out, int axis) {
   // Get axis, shape and stride info
-  axis = axis < 0 ? axis + in.ndim() : axis;
-  size_t in_size = in.flags().contiguous ? in.data_size() : in.size();
-  size_t n_rows = in_size / in.shape(axis);
+  axis = axis < 0 ? axis + out.ndim() : axis;
+  size_t in_size = out.size();
+  size_t n_rows = in_size / out.shape(axis);
 
   auto remaining_shape = out.shape();
   remaining_shape.erase(remaining_shape.begin() + axis);
@@ -126,8 +123,9 @@ void sort(const array& in, array& out, int axis) {
   // Perform sorting in place
   ContiguousIterator src_it(
       remaining_shape, remaining_strides, remaining_shape.size());
+  auto out_ptr = out.data<T>();
   for (int i = 0; i < n_rows; i++) {
-    T* data_ptr = out.data<T>() + src_it.loc;
+    T* data_ptr = out_ptr + src_it.loc;
 
     StridedIterator st(data_ptr, axis_stride, 0);
     StridedIterator ed(data_ptr, axis_stride, axis_size);
@@ -139,9 +137,6 @@ void sort(const array& in, array& out, int axis) {
 
 template <typename T, typename IdxT = uint32_t>
 void argsort(const array& in, array& out, int axis) {
-  // Allocate output
-  out.set_data(allocator::malloc_or_wait(out.nbytes()));
-
   // Get axis, shape and stride info
   axis = axis < 0 ? axis + in.ndim() : axis;
   size_t n_rows = in.size() / in.shape(axis);
@@ -167,9 +162,12 @@ void argsort(const array& in, array& out, int axis) {
       in_remaining_shape, in_remaining_strides, in_remaining_shape.size());
   ContiguousIterator out_it(
       out_remaining_shape, out_remaining_strides, out_remaining_shape.size());
+  auto in_ptr = in.data<T>();
+  auto out_ptr = out.data<IdxT>();
   for (int i = 0; i < n_rows; i++) {
-    const T* data_ptr = in.data<T>() + in_it.loc;
-    IdxT* idx_ptr = out.data<IdxT>() + out_it.loc;
+    const T* data_ptr = in_ptr + in_it.loc;
+    IdxT* idx_ptr = out_ptr + out_it.loc;
+
     in_it.step();
     out_it.step();
 
@@ -191,33 +189,30 @@ void argsort(const array& in, array& out, int axis) {
   }
 }
 
-template <typename T, typename IdxT = uint32_t>
-void partition(const array& in, array& out, int axis, int kth) {
-  // Copy input to output
-  CopyType ctype = in.flags().contiguous ? CopyType::Vector : CopyType::General;
-  copy(in, out, ctype);
-
+template <typename T>
+void partition(array& out, int axis, int kth) {
   // Get axis, shape and stride info
-  axis = axis < 0 ? axis + in.ndim() : axis;
-  size_t in_size = in.flags().contiguous ? in.data_size() : in.size();
-  size_t n_rows = in_size / in.shape(axis);
+  axis = axis < 0 ? axis + out.ndim() : axis;
+  size_t in_size = out.size();
+  size_t n_rows = in_size / out.shape(axis);
 
-  auto remaining_shape = in.shape();
+  auto remaining_shape = out.shape();
   remaining_shape.erase(remaining_shape.begin() + axis);
 
-  auto remaining_strides = in.strides();
+  auto remaining_strides = out.strides();
   remaining_strides.erase(remaining_strides.begin() + axis);
 
-  auto axis_stride = in.strides()[axis];
-  int axis_size = in.shape(axis);
+  auto axis_stride = out.strides()[axis];
+  int axis_size = out.shape(axis);
 
   kth = kth < 0 ? kth + axis_size : kth;
 
   // Perform partition in place
   ContiguousIterator src_it(
       remaining_shape, remaining_strides, remaining_shape.size());
+  auto out_ptr = out.data<T>();
   for (int i = 0; i < n_rows; i++) {
-    T* data_ptr = out.data<T>() + src_it.loc;
+    T* data_ptr = out_ptr + src_it.loc;
     src_it.step();
 
     StridedIterator st(data_ptr, axis_stride, 0);
@@ -230,9 +225,6 @@ void partition(const array& in, array& out, int axis, int kth) {
 
 template <typename T, typename IdxT = uint32_t>
 void argpartition(const array& in, array& out, int axis, int kth) {
-  // Allocate output
-  out.set_data(allocator::malloc_or_wait(out.nbytes()));
-
   // Get axis, shape and stride info
   axis = axis < 0 ? axis + in.ndim() : axis;
   size_t n_rows = in.size() / in.shape(axis);
@@ -260,9 +252,13 @@ void argpartition(const array& in, array& out, int axis, int kth) {
       in_remaining_shape, in_remaining_strides, in_remaining_shape.size());
   ContiguousIterator out_it(
       out_remaining_shape, out_remaining_strides, out_remaining_shape.size());
+
+  auto in_ptr = in.data<T>();
+  auto out_ptr = out.data<IdxT>();
+
   for (int i = 0; i < n_rows; i++) {
-    const T* data_ptr = in.data<T>() + in_it.loc;
-    IdxT* idx_ptr = out.data<IdxT>() + out_it.loc;
+    const T* data_ptr = in_ptr + in_it.loc;
+    IdxT* idx_ptr = out_ptr + out_it.loc;
     in_it.step();
     out_it.step();
 
@@ -291,144 +287,184 @@ void ArgSort::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   auto& in = inputs[0];
 
-  switch (in.dtype()) {
-    case bool_:
-      return argsort<bool>(in, out, axis_);
-    case uint8:
-      return argsort<uint8_t>(in, out, axis_);
-    case uint16:
-      return argsort<uint16_t>(in, out, axis_);
-    case uint32:
-      return argsort<uint32_t>(in, out, axis_);
-    case uint64:
-      return argsort<uint64_t>(in, out, axis_);
-    case int8:
-      return argsort<int8_t>(in, out, axis_);
-    case int16:
-      return argsort<int16_t>(in, out, axis_);
-    case int32:
-      return argsort<int32_t>(in, out, axis_);
-    case int64:
-      return argsort<int64_t>(in, out, axis_);
-    case float32:
-      return argsort<float>(in, out, axis_);
-    case float64:
-      return argsort<double>(in, out, axis_);
-    case float16:
-      return argsort<float16_t>(in, out, axis_);
-    case bfloat16:
-      return argsort<bfloat16_t>(in, out, axis_);
-    case complex64:
-      return argsort<complex64_t>(in, out, axis_);
-  }
+  // Allocate output
+  out.set_data(allocator::malloc(out.nbytes()));
+
+  auto& encoder = cpu::get_command_encoder(stream());
+  encoder.set_input_array(in);
+  encoder.set_input_array(out);
+  encoder.dispatch([in = array::unsafe_weak_copy(in),
+                    out = array::unsafe_weak_copy(out),
+                    axis_ = axis_]() mutable {
+    switch (in.dtype()) {
+      case bool_:
+        return argsort<bool>(in, out, axis_);
+      case uint8:
+        return argsort<uint8_t>(in, out, axis_);
+      case uint16:
+        return argsort<uint16_t>(in, out, axis_);
+      case uint32:
+        return argsort<uint32_t>(in, out, axis_);
+      case uint64:
+        return argsort<uint64_t>(in, out, axis_);
+      case int8:
+        return argsort<int8_t>(in, out, axis_);
+      case int16:
+        return argsort<int16_t>(in, out, axis_);
+      case int32:
+        return argsort<int32_t>(in, out, axis_);
+      case int64:
+        return argsort<int64_t>(in, out, axis_);
+      case float32:
+        return argsort<float>(in, out, axis_);
+      case float64:
+        return argsort<double>(in, out, axis_);
+      case float16:
+        return argsort<float16_t>(in, out, axis_);
+      case bfloat16:
+        return argsort<bfloat16_t>(in, out, axis_);
+      case complex64:
+        return argsort<complex64_t>(in, out, axis_);
+    }
+  });
 }
 
 void Sort::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   auto& in = inputs[0];
 
-  switch (in.dtype()) {
-    case bool_:
-      return sort<bool>(in, out, axis_);
-    case uint8:
-      return sort<uint8_t>(in, out, axis_);
-    case uint16:
-      return sort<uint16_t>(in, out, axis_);
-    case uint32:
-      return sort<uint32_t>(in, out, axis_);
-    case uint64:
-      return sort<uint64_t>(in, out, axis_);
-    case int8:
-      return sort<int8_t>(in, out, axis_);
-    case int16:
-      return sort<int16_t>(in, out, axis_);
-    case int32:
-      return sort<int32_t>(in, out, axis_);
-    case int64:
-      return sort<int64_t>(in, out, axis_);
-    case float32:
-      return sort<float>(in, out, axis_);
-    case float64:
-      return sort<double>(in, out, axis_);
-    case float16:
-      return sort<float16_t>(in, out, axis_);
-    case bfloat16:
-      return sort<bfloat16_t>(in, out, axis_);
-    case complex64:
-      return sort<complex64_t>(in, out, axis_);
-  }
+  // Copy input to output
+  CopyType ctype = in.flags().contiguous ? CopyType::Vector : CopyType::General;
+  copy(in, out, ctype, stream());
+
+  auto& encoder = cpu::get_command_encoder(stream());
+  encoder.set_output_array(out);
+  encoder.dispatch(
+      [out = array::unsafe_weak_copy(out), axis_ = axis_]() mutable {
+        switch (out.dtype()) {
+          case bool_:
+            return sort<bool>(out, axis_);
+          case uint8:
+            return sort<uint8_t>(out, axis_);
+          case uint16:
+            return sort<uint16_t>(out, axis_);
+          case uint32:
+            return sort<uint32_t>(out, axis_);
+          case uint64:
+            return sort<uint64_t>(out, axis_);
+          case int8:
+            return sort<int8_t>(out, axis_);
+          case int16:
+            return sort<int16_t>(out, axis_);
+          case int32:
+            return sort<int32_t>(out, axis_);
+          case int64:
+            return sort<int64_t>(out, axis_);
+          case float32:
+            return sort<float>(out, axis_);
+          case float64:
+            return sort<double>(out, axis_);
+          case float16:
+            return sort<float16_t>(out, axis_);
+          case bfloat16:
+            return sort<bfloat16_t>(out, axis_);
+          case complex64:
+            return sort<complex64_t>(out, axis_);
+        }
+      });
 }
 
 void ArgPartition::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   auto& in = inputs[0];
 
-  switch (in.dtype()) {
-    case bool_:
-      return argpartition<bool>(in, out, axis_, kth_);
-    case uint8:
-      return argpartition<uint8_t>(in, out, axis_, kth_);
-    case uint16:
-      return argpartition<uint16_t>(in, out, axis_, kth_);
-    case uint32:
-      return argpartition<uint32_t>(in, out, axis_, kth_);
-    case uint64:
-      return argpartition<uint64_t>(in, out, axis_, kth_);
-    case int8:
-      return argpartition<int8_t>(in, out, axis_, kth_);
-    case int16:
-      return argpartition<int16_t>(in, out, axis_, kth_);
-    case int32:
-      return argpartition<int32_t>(in, out, axis_, kth_);
-    case int64:
-      return argpartition<int64_t>(in, out, axis_, kth_);
-    case float32:
-      return argpartition<float>(in, out, axis_, kth_);
-    case float64:
-      return argpartition<double>(in, out, axis_, kth_);
-    case float16:
-      return argpartition<float16_t>(in, out, axis_, kth_);
-    case bfloat16:
-      return argpartition<bfloat16_t>(in, out, axis_, kth_);
-    case complex64:
-      return argpartition<complex64_t>(in, out, axis_, kth_);
-  }
+  // Allocate output
+  out.set_data(allocator::malloc(out.nbytes()));
+
+  auto& encoder = cpu::get_command_encoder(stream());
+  encoder.set_input_array(in);
+  encoder.set_input_array(out);
+  encoder.dispatch([in = array::unsafe_weak_copy(in),
+                    out = array::unsafe_weak_copy(out),
+                    axis_ = axis_,
+                    kth_ = kth_]() mutable {
+    switch (in.dtype()) {
+      case bool_:
+        return argpartition<bool>(in, out, axis_, kth_);
+      case uint8:
+        return argpartition<uint8_t>(in, out, axis_, kth_);
+      case uint16:
+        return argpartition<uint16_t>(in, out, axis_, kth_);
+      case uint32:
+        return argpartition<uint32_t>(in, out, axis_, kth_);
+      case uint64:
+        return argpartition<uint64_t>(in, out, axis_, kth_);
+      case int8:
+        return argpartition<int8_t>(in, out, axis_, kth_);
+      case int16:
+        return argpartition<int16_t>(in, out, axis_, kth_);
+      case int32:
+        return argpartition<int32_t>(in, out, axis_, kth_);
+      case int64:
+        return argpartition<int64_t>(in, out, axis_, kth_);
+      case float32:
+        return argpartition<float>(in, out, axis_, kth_);
+      case float64:
+        return argpartition<double>(in, out, axis_, kth_);
+      case float16:
+        return argpartition<float16_t>(in, out, axis_, kth_);
+      case bfloat16:
+        return argpartition<bfloat16_t>(in, out, axis_, kth_);
+      case complex64:
+        return argpartition<complex64_t>(in, out, axis_, kth_);
+    }
+  });
 }
 
 void Partition::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   auto& in = inputs[0];
 
-  switch (in.dtype()) {
-    case bool_:
-      return partition<bool>(in, out, axis_, kth_);
-    case uint8:
-      return partition<uint8_t>(in, out, axis_, kth_);
-    case uint16:
-      return partition<uint16_t>(in, out, axis_, kth_);
-    case uint32:
-      return partition<uint32_t>(in, out, axis_, kth_);
-    case uint64:
-      return partition<uint64_t>(in, out, axis_, kth_);
-    case int8:
-      return partition<int8_t>(in, out, axis_, kth_);
-    case int16:
-      return partition<int16_t>(in, out, axis_, kth_);
-    case int32:
-      return partition<int32_t>(in, out, axis_, kth_);
-    case int64:
-      return partition<int64_t>(in, out, axis_, kth_);
-    case float32:
-      return partition<float>(in, out, axis_, kth_);
-    case float64:
-      return partition<double>(in, out, axis_, kth_);
-    case float16:
-      return partition<float16_t>(in, out, axis_, kth_);
-    case bfloat16:
-      return partition<bfloat16_t>(in, out, axis_, kth_);
-    case complex64:
-      return partition<complex64_t>(in, out, axis_, kth_);
-  }
+  // Copy input to output
+  CopyType ctype = in.flags().contiguous ? CopyType::Vector : CopyType::General;
+  copy(in, out, ctype, stream());
+
+  auto& encoder = cpu::get_command_encoder(stream());
+  encoder.set_output_array(out);
+  encoder.dispatch([out = array::unsafe_weak_copy(out),
+                    axis_ = axis_,
+                    kth_ = kth_]() mutable {
+    switch (out.dtype()) {
+      case bool_:
+        return partition<bool>(out, axis_, kth_);
+      case uint8:
+        return partition<uint8_t>(out, axis_, kth_);
+      case uint16:
+        return partition<uint16_t>(out, axis_, kth_);
+      case uint32:
+        return partition<uint32_t>(out, axis_, kth_);
+      case uint64:
+        return partition<uint64_t>(out, axis_, kth_);
+      case int8:
+        return partition<int8_t>(out, axis_, kth_);
+      case int16:
+        return partition<int16_t>(out, axis_, kth_);
+      case int32:
+        return partition<int32_t>(out, axis_, kth_);
+      case int64:
+        return partition<int64_t>(out, axis_, kth_);
+      case float32:
+        return partition<float>(out, axis_, kth_);
+      case float64:
+        return partition<double>(out, axis_, kth_);
+      case float16:
+        return partition<float16_t>(out, axis_, kth_);
+      case bfloat16:
+        return partition<bfloat16_t>(out, axis_, kth_);
+      case complex64:
+        return partition<complex64_t>(out, axis_, kth_);
+    }
+  });
 }
 
 } // namespace mlx::core

mlx/backend/cpu/svd.cpp

@@ -2,12 +2,18 @@
 
 #include "mlx/allocator.h"
 #include "mlx/backend/cpu/copy.h"
+#include "mlx/backend/cpu/encoder.h"
 #include "mlx/backend/cpu/lapack.h"
 #include "mlx/primitives.h"
 
 namespace mlx::core {
 
-void svd_impl(const array& a, array& u, array& s, array& vt) {
+template <typename T>
+void svd_impl(
+    const array& a,
+    std::vector<array>& outputs,
+    bool compute_uv,
+    Stream stream) {
   // Lapack uses the column-major convention. To avoid having to transpose
   // the input and then transpose the outputs, we swap the indices/sizes of the
   // matrices and take advantage of the following identity (see
@@ -21,129 +27,179 @@ void svd_impl(const array& a, array& u, array& s, array& vt) {
   const int N = a.shape(-1);
   const int K = std::min(M, N);
 
-  // A of shape M x N. The leading dimension is N since lapack receives Aᵀ.
-  const int lda = N;
-  // U of shape M x M. (N x N in lapack).
-  const int ldu = N;
-  // Vᵀ of shape N x N. (M x M in lapack).
-  const int ldvt = M;
-
   size_t num_matrices = a.size() / (M * N);
 
   // lapack clobbers the input, so we have to make a copy.
-  array in(a.shape(), float32, nullptr, {});
-  copy(a, in, a.flags().row_contiguous ? CopyType::Vector : CopyType::General);
+  array in(a.shape(), a.dtype(), nullptr, {});
+  copy(
+      a,
+      in,
+      a.flags().row_contiguous ? CopyType::Vector : CopyType::General,
+      stream);
 
   // Allocate outputs.
-  u.set_data(allocator::malloc_or_wait(u.nbytes()));
-  s.set_data(allocator::malloc_or_wait(s.nbytes()));
-  vt.set_data(allocator::malloc_or_wait(vt.nbytes()));
+  auto& encoder = cpu::get_command_encoder(stream);
+  encoder.set_input_array(a);
+  auto in_ptr = in.data<T>();
+  T* u_ptr;
+  T* s_ptr;
+  T* vt_ptr;
 
-  static constexpr auto job_u = "V";
-  static constexpr auto job_vt = "V";
-  static constexpr auto range = "A";
+  if (compute_uv) {
+    array& u = outputs[0];
+    array& s = outputs[1];
+    array& vt = outputs[2];
 
-  // Will contain the number of singular values after the call has returned.
-  int ns = 0;
-  float workspace_dimension = 0;
+    u.set_data(allocator::malloc(u.nbytes()));
+    s.set_data(allocator::malloc(s.nbytes()));
+    vt.set_data(allocator::malloc(vt.nbytes()));
 
-  // Will contain the indices of eigenvectors that failed to converge (not used
-  // here but required by lapack).
-  auto iwork = array::Data{allocator::malloc_or_wait(sizeof(int) * 12 * K)};
+    encoder.set_output_array(u);
+    encoder.set_output_array(s);
+    encoder.set_output_array(vt);
 
-  static const int lwork_query = -1;
+    s_ptr = s.data<T>();
+    u_ptr = u.data<T>();
+    vt_ptr = vt.data<T>();
+  } else {
+    array& s = outputs[0];
 
-  static const int ignored_int = 0;
-  static const float ignored_float = 0;
+    s.set_data(allocator::malloc(s.nbytes()));
 
-  int info;
+    encoder.set_output_array(s);
 
-  // Compute workspace size.
-  MLX_LAPACK_FUNC(sgesvdx)
-  (
-      /* jobu = */ job_u,
-      /* jobvt = */ job_vt,
-      /* range = */ range,
-      // M and N are swapped since lapack expects column-major.
-      /* m = */ &N,
-      /* n = */ &M,
-      /* a = */ nullptr,
-      /* lda = */ &lda,
-      /* vl = */ &ignored_float,
-      /* vu = */ &ignored_float,
-      /* il = */ &ignored_int,
-      /* iu = */ &ignored_int,
-      /* ns = */ &ns,
-      /* s = */ nullptr,
-      /* u = */ nullptr,
-      /* ldu = */ &ldu,
-      /* vt = */ nullptr,
-      /* ldvt = */ &ldvt,
-      /* work = */ &workspace_dimension,
-      /* lwork = */ &lwork_query,
-      /* iwork = */ static_cast<int*>(iwork.buffer.raw_ptr()),
-      /* info = */ &info);
-
-  if (info != 0) {
-    std::stringstream ss;
-    ss << "svd_impl: sgesvdx_ workspace calculation failed with code " << info;
-    throw std::runtime_error(ss.str());
+    s_ptr = s.data<T>();
+    u_ptr = nullptr;
+    vt_ptr = nullptr;
   }
 
-  const int lwork = workspace_dimension;
-  auto scratch = array::Data{allocator::malloc_or_wait(sizeof(float) * lwork)};
+  encoder.dispatch([in_ptr, u_ptr, s_ptr, vt_ptr, M, N, K, num_matrices]() {
+    // A of shape M x N. The leading dimension is N since lapack receives Aᵀ.
+    const int lda = N;
+    // U of shape M x M. (N x N in lapack).
+    const int ldu = N;
+    // Vᵀ of shape N x N. (M x M in lapack).
+    const int ldvt = M;
 
-  // Loop over matrices.
-  for (int i = 0; i < num_matrices; i++) {
-    MLX_LAPACK_FUNC(sgesvdx)
-    (
+    auto job_u = (u_ptr) ? "V" : "N";
+    auto job_vt = (u_ptr) ? "V" : "N";
+    static constexpr auto range = "A";
+
+    // Will contain the number of singular values after the call has returned.
+    int ns = 0;
+    T workspace_dimension = 0;
+
+    // Will contain the indices of eigenvectors that failed to converge (not
+    // used here but required by lapack).
+    auto iwork = array::Data{allocator::malloc(sizeof(int) * 12 * K)};
+
+    static const int lwork_query = -1;
+
+    static const int ignored_int = 0;
+    static const T ignored_float = 0;
+
+    int info;
+
+    // Compute workspace size.
+    gesvdx<T>(
         /* jobu = */ job_u,
         /* jobvt = */ job_vt,
         /* range = */ range,
         // M and N are swapped since lapack expects column-major.
         /* m = */ &N,
         /* n = */ &M,
-        /* a = */ in.data<float>() + M * N * i,
+        /* a = */ nullptr,
         /* lda = */ &lda,
         /* vl = */ &ignored_float,
         /* vu = */ &ignored_float,
         /* il = */ &ignored_int,
         /* iu = */ &ignored_int,
         /* ns = */ &ns,
-        /* s = */ s.data<float>() + K * i,
-        // According to the identity above, lapack will write Vᵀᵀ as U.
-        /* u = */ vt.data<float>() + N * N * i,
+        /* s = */ nullptr,
+        /* u = */ nullptr,
         /* ldu = */ &ldu,
-        // According to the identity above, lapack will write Uᵀ as Vᵀ.
-        /* vt = */ u.data<float>() + M * M * i,
+        /* vt = */ nullptr,
         /* ldvt = */ &ldvt,
-        /* work = */ static_cast<float*>(scratch.buffer.raw_ptr()),
-        /* lwork = */ &lwork,
+        /* work = */ &workspace_dimension,
+        /* lwork = */ &lwork_query,
         /* iwork = */ static_cast<int*>(iwork.buffer.raw_ptr()),
         /* info = */ &info);
 
     if (info != 0) {
       std::stringstream ss;
-      ss << "svd_impl: sgesvdx_ failed with code " << info;
+      ss << "[SVD::eval_cpu] workspace calculation failed with code " << info;
       throw std::runtime_error(ss.str());
     }
 
-    if (ns != K) {
-      std::stringstream ss;
-      ss << "svd_impl: expected " << K << " singular values, but " << ns
-         << " were computed.";
-      throw std::runtime_error(ss.str());
+    const int lwork = workspace_dimension;
+    auto scratch = array::Data{allocator::malloc(sizeof(T) * lwork)};
+
+    // Loop over matrices.
+    for (int i = 0; i < num_matrices; i++) {
+      gesvdx<T>(
+          /* jobu = */ job_u,
+          /* jobvt = */ job_vt,
+          /* range = */ range,
+          // M and N are swapped since lapack expects column-major.
+          /* m = */ &N,
+          /* n = */ &M,
+          /* a = */ in_ptr + M * N * i,
+          /* lda = */ &lda,
+          /* vl = */ &ignored_float,
+          /* vu = */ &ignored_float,
+          /* il = */ &ignored_int,
+          /* iu = */ &ignored_int,
+          /* ns = */ &ns,
+          /* s = */ s_ptr + K * i,
+          // According to the identity above, lapack will write Vᵀᵀ as U.
+          /* u = */ vt_ptr ? vt_ptr + N * N * i : nullptr,
+          /* ldu = */ &ldu,
+          // According to the identity above, lapack will write Uᵀ as Vᵀ.
+          /* vt = */ u_ptr ? u_ptr + M * M * i : nullptr,
+          /* ldvt = */ &ldvt,
+          /* work = */ static_cast<T*>(scratch.buffer.raw_ptr()),
+          /* lwork = */ &lwork,
+          /* iwork = */ static_cast<int*>(iwork.buffer.raw_ptr()),
+          /* info = */ &info);
+
+      if (info != 0) {
+        std::stringstream ss;
+        ss << "svd_impl: sgesvdx_ failed with code " << info;
+        throw std::runtime_error(ss.str());
+      }
+
+      if (ns != K) {
+        std::stringstream ss;
+        ss << "svd_impl: expected " << K << " singular values, but " << ns
+           << " were computed.";
+        throw std::runtime_error(ss.str());
+      }
     }
-  }
+  });
+  encoder.add_temporary(in);
 }
 
+template <typename T>
+void compute_svd(
+    const array& a,
+    bool compute_uv,
+    std::vector<array>& outputs,
+    Stream stream) {}
+
 void SVD::eval_cpu(
     const std::vector<array>& inputs,
     std::vector<array>& outputs) {
-  if (!(inputs[0].dtype() == float32)) {
-    throw std::runtime_error("[SVD::eval] only supports float32.");
+  switch (inputs[0].dtype()) {
+    case float32:
+      svd_impl<float>(inputs[0], outputs, compute_uv_, stream());
+      break;
+    case float64:
+      svd_impl<double>(inputs[0], outputs, compute_uv_, stream());
+      break;
+    default:
+      throw std::runtime_error(
+          "[SVD::eval_cpu] only supports float32 or float64.");
   }
-  svd_impl(inputs[0], outputs[0], outputs[1], outputs[2]);
 }
 
 } // namespace mlx::core

mlx/backend/cpu/ternary.h

@@ -1,10 +1,10 @@
 // Copyright © 2023 Apple Inc.
 
 #pragma once
-#include "mlx/allocator.h"
 #include "mlx/array.h"
 #include "mlx/backend/common/ternary.h"
 #include "mlx/backend/common/utils.h"
+#include "mlx/backend/cpu/encoder.h"
 
 namespace mlx::core {
 
@@ -53,22 +53,18 @@ void ternary_op_dims(
 
 template <typename T1, typename T2, typename T3, typename U, typename Op>
 void ternary_op_dispatch_dims(
-    const array& a,
-    const array& b,
-    const array& c,
-    array& out,
-    Op op) {
-  auto [shape, strides] = collapse_contiguous_dims(
-      a.shape(), {a.strides(), b.strides(), c.strides(), out.strides()});
+    const T1* a_ptr,
+    const T2* b_ptr,
+    const T3* c_ptr,
+    U* out_ptr,
+    Op op,
+    size_t size,
+    Shape& shape,
+    std::vector<Strides>& strides) {
   const auto& a_strides = strides[0];
   const auto& b_strides = strides[1];
   const auto& c_strides = strides[2];
   const auto& out_strides = strides[3];
-
-  const T1* a_ptr = a.data<T1>();
-  const T2* b_ptr = b.data<T2>();
-  const T3* c_ptr = c.data<T3>();
-  U* out_ptr = out.data<T3>();
   int ndim = shape.size();
   switch (ndim) {
     case 1:
@@ -105,7 +101,7 @@ void ternary_op_dispatch_dims(
   ContiguousIterator b_it(shape, b_strides, ndim - 2);
   ContiguousIterator c_it(shape, c_strides, ndim - 2);
   auto stride = out_strides[ndim - 3];
-  for (size_t elem = 0; elem < a.size(); elem += stride) {
+  for (size_t elem = 0; elem < size; elem += stride) {
     ternary_op_dims<T1, T2, T3, U, Op, 2>(
         a_ptr + a_it.loc,
         b_ptr + b_it.loc,
@@ -130,18 +126,16 @@ void ternary_op(
     const array& b,
     const array& c,
     array& out,
-    Op op) {
-  TernaryOpType topt = get_ternary_op_type(a, b, c);
-  set_ternary_op_output_data(a, b, c, out, topt);
+    Op op,
+    TernaryOpType topt) {
+  const T1* a_ptr = a.data<T1>();
+  const T2* b_ptr = b.data<T2>();
+  const T3* c_ptr = c.data<T3>();
+  U* out_ptr = out.data<U>();
 
-  // The full computation is scalar-scalar-scalar so we call the base op once.
   if (topt == TernaryOpType::ScalarScalarScalar) {
-    *(out.data<U>()) = op(*a.data<T1>(), *b.data<T2>(), *c.data<T3>());
+    *out_ptr = op(*a_ptr, *b_ptr, *c_ptr);
   } else if (topt == TernaryOpType::VectorVectorVector) {
-    const T1* a_ptr = a.data<T1>();
-    const T2* b_ptr = b.data<T2>();
-    const T3* c_ptr = c.data<T3>();
-    U* out_ptr = out.data<U>();
     for (size_t i = 0; i < out.size(); ++i) {
       *out_ptr = op(*a_ptr, *b_ptr, *c_ptr);
       a_ptr++;
@@ -150,7 +144,10 @@ void ternary_op(
       out_ptr++;
     }
   } else {
-    ternary_op_dispatch_dims<T1, T2, T3, U>(a, b, c, out, op);
+    auto [shape, strides] = collapse_contiguous_dims(
+        a.shape(), {a.strides(), b.strides(), c.strides(), out.strides()});
+    ternary_op_dispatch_dims<T1, T2, T3, U>(
+        a_ptr, b_ptr, c_ptr, out_ptr, op, out.size(), shape, strides);
   }
 }
 

mlx/backend/cpu/unary.cpp

@@ -14,88 +14,57 @@ void Abs::eval_cpu(const std::vector<array>& inputs, array& out) {
     // No-op for unsigned types
     out.copy_shared_buffer(in);
   } else {
-    auto op = detail::Abs{};
-    switch (out.dtype()) {
-      case int8:
-        unary_op<int8_t>(in, out, op);
-        break;
-      case int16:
-        unary_op<int16_t>(in, out, op);
-        break;
-      case int32:
-        unary_op<int32_t>(in, out, op);
-        break;
-      case int64:
-        unary_op<int64_t>(in, out, op);
-        break;
-      case float16:
-        unary_op<float16_t>(in, out, op);
-        break;
-      case float32:
-        unary_op<float>(in, out, op);
-        break;
-      case float64:
-        unary_op<double>(in, out, op);
-        break;
-      case bfloat16:
-        unary_op<bfloat16_t>(in, out, op);
-        break;
-      case complex64:
-        unary_op<complex64_t>(in, out, op);
-        break;
-      default:
-        throw std::runtime_error("[Abs] Called on unsigned type");
-    }
+    unary_signed(in, out, detail::Abs(), stream());
   }
 }
 
 void ArcCos::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   const auto& in = inputs[0];
-  unary_fp(in, out, detail::ArcCos());
+  unary_fp(in, out, detail::ArcCos(), stream());
 }
 
 void ArcCosh::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   const auto& in = inputs[0];
-  unary_fp(in, out, detail::ArcCosh());
+  unary_fp(in, out, detail::ArcCosh(), stream());
 }
 
 void ArcSin::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   const auto& in = inputs[0];
-  unary_fp(in, out, detail::ArcSin());
+  unary_fp(in, out, detail::ArcSin(), stream());
 }
 
 void ArcSinh::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   const auto& in = inputs[0];
-  unary_fp(in, out, detail::ArcSinh());
+  unary_fp(in, out, detail::ArcSinh(), stream());
 }
 
 void ArcTan::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   const auto& in = inputs[0];
-  unary_fp(in, out, detail::ArcTan());
+  unary_fp(in, out, detail::ArcTan(), stream());
 }
 
 void ArcTanh::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   const auto& in = inputs[0];
-  unary_fp(in, out, detail::ArcTanh());
+  unary_fp(in, out, detail::ArcTanh(), stream());
 }
 
 void BitwiseInvert::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   const auto& in = inputs[0];
-  unary_int(in, out, detail::BitwiseInvert());
+  unary_int(in, out, detail::BitwiseInvert(), stream());
 }
 
 void Ceil::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   auto& in = inputs[0];
   if (issubdtype(in.dtype(), inexact)) {
-    unary_fp(in, out, detail::Ceil());
+    unary_fp(in, out, detail::Ceil(), stream());
   } else {
     // No-op integer types
     out.copy_shared_buffer(in);
@@ -104,84 +73,50 @@ void Ceil::eval_cpu(const std::vector<array>& inputs, array& out) {
 
 void Conjugate::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
-  unary_op<complex64_t>(inputs[0], out, detail::Conjugate());
+  unary_complex(inputs[0], out, detail::Conjugate(), stream());
 }
 
 void Cos::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   const auto& in = inputs[0];
-  unary_fp(in, out, detail::Cos());
+  unary_fp(in, out, detail::Cos(), stream());
 }
 
 void Cosh::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   const auto& in = inputs[0];
-  unary_fp(in, out, detail::Cosh());
+  unary_fp(in, out, detail::Cosh(), stream());
 }
 
 void Erf::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   const auto& in = inputs[0];
-  switch (out.dtype()) {
-    case float32:
-      unary_op<float>(in, out, detail::Erf());
-      break;
-    case float16:
-      unary_op<float16_t>(in, out, detail::Erf());
-      break;
-    case float64:
-      unary_op<double>(in, out, detail::Erf());
-      break;
-    case bfloat16:
-      unary_op<bfloat16_t>(in, out, detail::Erf());
-      break;
-    default:
-      throw std::invalid_argument(
-          "[erf] Error function only defined for arrays"
-          " with real floating point type.");
-  }
+  unary_real_fp(in, out, detail::Erf(), stream());
 }
 
 void ErfInv::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   const auto& in = inputs[0];
-  switch (out.dtype()) {
-    case float32:
-      unary_op<float>(in, out, detail::ErfInv());
-      break;
-    case float16:
-      unary_op<float16_t>(in, out, detail::ErfInv());
-      break;
-    case float64:
-      unary_op<double>(in, out, detail::ErfInv());
-      break;
-    case bfloat16:
-      unary_op<bfloat16_t>(in, out, detail::ErfInv());
-      break;
-    default:
-      throw std::invalid_argument(
-          "[erf_inv] Inverse error function only defined for arrays"
-          " with real floating point type.");
-  }
+  unary_real_fp(in, out, detail::ErfInv(), stream());
 }
 
 void Exp::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   const auto& in = inputs[0];
-  unary_fp(in, out, detail::Exp());
+  unary_fp(in, out, detail::Exp(), stream());
 }
 
 void Expm1::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   const auto& in = inputs[0];
-  unary_fp(in, out, detail::Expm1());
+  unary_fp(in, out, detail::Expm1(), stream());
 }
 
 void Floor::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   auto& in = inputs[0];
   if (issubdtype(in.dtype(), inexact)) {
-    unary_fp(in, out, detail::Floor());
+    unary_fp(in, out, detail::Floor(), stream());
   } else {
     // No-op integer types
     out.copy_shared_buffer(in);
@@ -189,7 +124,7 @@ void Floor::eval_cpu(const std::vector<array>& inputs, array& out) {
 }
 
 void Imag::eval_cpu(const std::vector<array>& inputs, array& out) {
-  unary_op<complex64_t, float>(inputs[0], out, detail::Imag());
+  unary_complex_to_float(inputs[0], out, detail::Imag(), stream());
 }
 
 void Log::eval_cpu(const std::vector<array>& inputs, array& out) {
@@ -197,13 +132,13 @@ void Log::eval_cpu(const std::vector<array>& inputs, array& out) {
   const auto& in = inputs[0];
   switch (base_) {
     case Base::e:
-      unary_fp(in, out, detail::Log());
+      unary_fp(in, out, detail::Log(), stream());
       break;
     case Base::two:
-      unary_fp(in, out, detail::Log2());
+      unary_fp(in, out, detail::Log2(), stream());
       break;
     case Base::ten:
-      unary_fp(in, out, detail::Log10());
+      unary_fp(in, out, detail::Log10(), stream());
       break;
   }
 }
@@ -211,30 +146,30 @@ void Log::eval_cpu(const std::vector<array>& inputs, array& out) {
 void Log1p::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   const auto& in = inputs[0];
-  unary_fp(in, out, detail::Log1p());
+  unary_fp(in, out, detail::Log1p(), stream());
 }
 
 void LogicalNot::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   auto& in = inputs[0];
-  unary(in, out, detail::LogicalNot());
+  unary(in, out, detail::LogicalNot(), stream());
 }
 
 void Negative::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   auto& in = inputs[0];
-  unary(in, out, detail::Negative());
+  unary(in, out, detail::Negative(), stream());
 }
 
 void Real::eval_cpu(const std::vector<array>& inputs, array& out) {
-  unary_op<complex64_t, float>(inputs[0], out, detail::Real());
+  unary_complex_to_float(inputs[0], out, detail::Real(), stream());
 }
 
 void Round::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   auto& in = inputs[0];
   if (issubdtype(in.dtype(), inexact)) {
-    unary_fp(in, out, detail::Round());
+    unary_fp(in, out, detail::Round(), stream());
   } else {
     // No-op integer types
     out.copy_shared_buffer(in);
@@ -244,7 +179,7 @@ void Round::eval_cpu(const std::vector<array>& inputs, array& out) {
 void Sigmoid::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   const auto& in = inputs[0];
-  unary_fp(in, out, detail::Sigmoid());
+  unary_fp(in, out, detail::Sigmoid(), stream());
 }
 
 void Sign::eval_cpu(const std::vector<array>& inputs, array& out) {
@@ -253,48 +188,48 @@ void Sign::eval_cpu(const std::vector<array>& inputs, array& out) {
   if (in.dtype() == bool_) {
     out.copy_shared_buffer(in);
   } else {
-    unary(in, out, detail::Sign());
+    unary(in, out, detail::Sign(), stream());
   }
 }
 
 void Sin::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   const auto& in = inputs[0];
-  unary_fp(in, out, detail::Sin());
+  unary_fp(in, out, detail::Sin(), stream());
 }
 
 void Sinh::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   const auto& in = inputs[0];
-  unary_fp(in, out, detail::Sinh());
+  unary_fp(in, out, detail::Sinh(), stream());
 }
 
 void Square::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   auto& in = inputs[0];
-  unary(in, out, detail::Square());
+  unary(in, out, detail::Square(), stream());
 }
 
 void Sqrt::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   auto& in = inputs[0];
   if (recip_) {
-    unary_fp(in, out, detail::Rsqrt());
+    unary_fp(in, out, detail::Rsqrt(), stream());
   } else {
-    unary_fp(in, out, detail::Sqrt());
+    unary_fp(in, out, detail::Sqrt(), stream());
   }
 }
 
 void Tan::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   const auto& in = inputs[0];
-  unary_fp(in, out, detail::Tan());
+  unary_fp(in, out, detail::Tan(), stream());
 }
 
 void Tanh::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   const auto& in = inputs[0];
-  unary_fp(in, out, detail::Tanh());
+  unary_fp(in, out, detail::Tanh(), stream());
 }
 
 } // namespace mlx::core

mlx/backend/cpu/unary.h

@@ -5,174 +5,296 @@
 #include "mlx/allocator.h"
 #include "mlx/array.h"
 #include "mlx/backend/common/utils.h"
+#include "mlx/backend/cpu/encoder.h"
 #include "mlx/backend/cpu/simd/simd.h"
 #include "mlx/utils.h"
 
 namespace mlx::core {
 
 void set_unary_output_data(const array& in, array& out) {
-  if (is_donatable(in, out)) {
-    out.copy_shared_buffer(in);
+  if (in.flags().contiguous) {
+    if (is_donatable(in, out)) {
+      out.copy_shared_buffer(in);
+    } else {
+      auto size = in.data_size();
+      out.set_data(
+          allocator::malloc(size * out.itemsize()),
+          size,
+          in.strides(),
+          in.flags());
+    }
   } else {
-    auto size = in.data_size();
-    out.set_data(
-        allocator::malloc_or_wait(size * out.itemsize()),
-        size,
-        in.strides(),
-        in.flags());
+    out.set_data(allocator::malloc(out.nbytes()));
   }
 }
 
 template <typename T, typename U = T, typename Op>
-void unary_op(const T* a, U* out, Op op, size_t shape, size_t stride) {
+void unary_op(const T* a, U* out, size_t shape, size_t stride) {
   for (size_t i = 0; i < shape; i += 1) {
-    out[i] = op(*a);
+    out[i] = Op{}(*a);
     a += stride;
   }
 }
 
 template <typename T, typename U = T, typename Op>
-void unary_op(const array& a, array& out, Op op) {
-  const T* a_ptr = a.data<T>();
+void unary_op(const array& a, array& out, Op) {
+  const T* src = a.data<T>();
+  U* dst = out.data<U>();
+  auto ndim = a.ndim();
   if (a.flags().contiguous) {
-    set_unary_output_data(a, out);
-    U* dst = out.data<U>();
+    auto size = a.data_size();
     constexpr int N = simd::max_size<T>;
-    size_t size = a.data_size();
     while (size >= N) {
-      simd::store(dst, op(simd::load<T, N>(a_ptr)));
+      simd::store(dst, Op{}(simd::load<T, N>(src)));
       size -= N;
-      a_ptr += N;
+      src += N;
       dst += N;
     }
     while (size > 0) {
-      *dst = op(*a_ptr);
+      *dst = Op{}(*src);
       size--;
       dst++;
-      a_ptr++;
+      src++;
     }
   } else {
-    out.set_data(allocator::malloc_or_wait(out.nbytes()));
-    U* dst = out.data<U>();
-    size_t shape = a.ndim() > 0 ? a.shape(-1) : 1;
-    size_t stride = a.ndim() > 0 ? a.strides(-1) : 1;
-    if (a.ndim() <= 1) {
-      unary_op(a_ptr, dst, op, shape, stride);
+    size_t shape = ndim > 0 ? a.shape().back() : 1;
+    size_t stride = ndim > 0 ? a.strides().back() : 1;
+    if (ndim <= 1) {
+      unary_op<T, U, Op>(src, dst, shape, stride);
       return;
     }
-    ContiguousIterator it(a.shape(), a.strides(), a.ndim() - 1);
+    auto it = ContiguousIterator(a.shape(), a.strides(), ndim - 1);
     for (size_t elem = 0; elem < a.size(); elem += shape) {
-      unary_op(a_ptr + it.loc, dst + elem, op, shape, stride);
+      unary_op<T, U, Op>(src + it.loc, dst + elem, shape, stride);
       it.step();
     }
   }
 }
 
 template <typename Op>
-void unary(const array& a, array& out, Op op) {
-  switch (out.dtype()) {
-    case bool_:
-      unary_op<bool>(a, out, op);
-      break;
-    case uint8:
-      unary_op<uint8_t>(a, out, op);
-      break;
-    case uint16:
-      unary_op<uint16_t>(a, out, op);
-      break;
-    case uint32:
-      unary_op<uint32_t>(a, out, op);
-      break;
-    case uint64:
-      unary_op<uint64_t>(a, out, op);
-      break;
-    case int8:
-      unary_op<int8_t>(a, out, op);
-      break;
-    case int16:
-      unary_op<int16_t>(a, out, op);
-      break;
-    case int32:
-      unary_op<int32_t>(a, out, op);
-      break;
-    case int64:
-      unary_op<int64_t>(a, out, op);
-      break;
-    case float16:
-      unary_op<float16_t>(a, out, op);
-      break;
-    case float32:
-      unary_op<float>(a, out, op);
-      break;
-    case float64:
-      unary_op<double>(a, out, op);
-      break;
-    case bfloat16:
-      unary_op<bfloat16_t>(a, out, op);
-      break;
-    case complex64:
-      unary_op<complex64_t>(a, out, op);
-      break;
-  }
+void unary(const array& a, array& out, Op op, Stream stream) {
+  set_unary_output_data(a, out);
+  auto& encoder = cpu::get_command_encoder(stream);
+  encoder.set_input_array(a);
+  encoder.set_output_array(out);
+  encoder.dispatch([a = array::unsafe_weak_copy(a),
+                    out = array::unsafe_weak_copy(out),
+                    op = op]() mutable {
+    switch (out.dtype()) {
+      case bool_:
+        unary_op<bool>(a, out, op);
+        break;
+      case uint8:
+        unary_op<uint8_t>(a, out, op);
+        break;
+      case uint16:
+        unary_op<uint16_t>(a, out, op);
+        break;
+      case uint32:
+        unary_op<uint32_t>(a, out, op);
+        break;
+      case uint64:
+        unary_op<uint64_t>(a, out, op);
+        break;
+      case int8:
+        unary_op<int8_t>(a, out, op);
+        break;
+      case int16:
+        unary_op<int16_t>(a, out, op);
+        break;
+      case int32:
+        unary_op<int32_t>(a, out, op);
+        break;
+      case int64:
+        unary_op<int64_t>(a, out, op);
+        break;
+      case float16:
+        unary_op<float16_t>(a, out, op);
+        break;
+      case float32:
+        unary_op<float>(a, out, op);
+        break;
+      case float64:
+        unary_op<double>(a, out, op);
+        break;
+      case bfloat16:
+        unary_op<bfloat16_t>(a, out, op);
+        break;
+      case complex64:
+        unary_op<complex64_t>(a, out, op);
+        break;
+    }
+  });
 }
 
 template <typename Op>
-void unary_fp(const array& a, array& out, Op op) {
-  switch (out.dtype()) {
-    case bfloat16:
-      unary_op<bfloat16_t>(a, out, op);
-      break;
-    case float16:
-      unary_op<float16_t>(a, out, op);
-      break;
-    case float32:
-      unary_op<float>(a, out, op);
-      break;
-    case float64:
-      unary_op<double>(a, out, op);
-      break;
-    case complex64:
-      unary_op<complex64_t>(a, out, op);
-      break;
-    default:
-      std::ostringstream err;
-      err << "[unary_fp] Does not support " << out.dtype();
-      throw std::runtime_error(err.str());
-  }
+void unary_real_fp(const array& a, array& out, Op op, Stream stream) {
+  set_unary_output_data(a, out);
+  auto& encoder = cpu::get_command_encoder(stream);
+  encoder.set_input_array(a);
+  encoder.set_output_array(out);
+  encoder.dispatch([a = array::unsafe_weak_copy(a),
+                    out = array::unsafe_weak_copy(out),
+                    op = op]() mutable {
+    switch (out.dtype()) {
+      case bfloat16:
+        unary_op<bfloat16_t>(a, out, op);
+        break;
+      case float16:
+        unary_op<float16_t>(a, out, op);
+        break;
+      case float32:
+        unary_op<float>(a, out, op);
+        break;
+      case float64:
+        unary_op<double>(a, out, op);
+        break;
+      default:
+        std::ostringstream err;
+        err << "[unary_real] Does not support " << out.dtype();
+        throw std::runtime_error(err.str());
+    }
+  });
+}
+template <typename Op>
+void unary_fp(const array& a, array& out, Op op, Stream stream) {
+  set_unary_output_data(a, out);
+  auto& encoder = cpu::get_command_encoder(stream);
+  encoder.set_input_array(a);
+  encoder.set_output_array(out);
+  encoder.dispatch([a = array::unsafe_weak_copy(a),
+                    out = array::unsafe_weak_copy(out),
+                    op = op]() mutable {
+    switch (out.dtype()) {
+      case bfloat16:
+        unary_op<bfloat16_t>(a, out, op);
+        break;
+      case float16:
+        unary_op<float16_t>(a, out, op);
+        break;
+      case float32:
+        unary_op<float>(a, out, op);
+        break;
+      case float64:
+        unary_op<double>(a, out, op);
+        break;
+      case complex64:
+        unary_op<complex64_t>(a, out, op);
+        break;
+      default:
+        std::ostringstream err;
+        err << "[unary_fp] Does not support " << out.dtype();
+        throw std::runtime_error(err.str());
+    }
+  });
 }
 
 template <typename Op>
-void unary_int(const array& a, array& out, Op op) {
-  switch (out.dtype()) {
-    case uint8:
-      unary_op<uint8_t>(a, out, op);
-      break;
-    case uint16:
-      unary_op<uint16_t>(a, out, op);
-      break;
-    case uint32:
-      unary_op<uint32_t>(a, out, op);
-      break;
-    case uint64:
-      unary_op<uint64_t>(a, out, op);
-      break;
-    case int8:
-      unary_op<int8_t>(a, out, op);
-      break;
-    case int16:
-      unary_op<int16_t>(a, out, op);
-      break;
-    case int32:
-      unary_op<int32_t>(a, out, op);
-      break;
-    case int64:
-      unary_op<int64_t>(a, out, op);
-      break;
-    default:
-      std::ostringstream err;
-      err << "[unary_int] Does not support " << out.dtype();
-      throw std::runtime_error(err.str());
-  }
+void unary_signed(const array& a, array& out, Op op, Stream stream) {
+  set_unary_output_data(a, out);
+  auto& encoder = cpu::get_command_encoder(stream);
+  encoder.set_input_array(a);
+  encoder.set_output_array(out);
+  encoder.dispatch([a = array::unsafe_weak_copy(a),
+                    out = array::unsafe_weak_copy(out),
+                    op = op]() mutable {
+    switch (out.dtype()) {
+      case int8:
+        unary_op<int8_t>(a, out, op);
+        break;
+      case int16:
+        unary_op<int16_t>(a, out, op);
+        break;
+      case int32:
+        unary_op<int32_t>(a, out, op);
+        break;
+      case int64:
+        unary_op<int64_t>(a, out, op);
+        break;
+      case float16:
+        unary_op<float16_t>(a, out, op);
+        break;
+      case float32:
+        unary_op<float>(a, out, op);
+        break;
+      case float64:
+        unary_op<double>(a, out, op);
+        break;
+      case bfloat16:
+        unary_op<bfloat16_t>(a, out, op);
+        break;
+      case complex64:
+        unary_op<complex64_t>(a, out, op);
+        break;
+      default:
+        throw std::runtime_error("[Abs] Called on unsigned type");
+    }
+  });
+}
+
+template <typename Op>
+void unary_complex(const array& a, array& out, Op op, Stream stream) {
+  set_unary_output_data(a, out);
+  auto& encoder = cpu::get_command_encoder(stream);
+  encoder.set_input_array(a);
+  encoder.set_output_array(out);
+  encoder.dispatch([a = array::unsafe_weak_copy(a),
+                    out = array::unsafe_weak_copy(out),
+                    op = op]() mutable { unary_op<complex64_t>(a, out, op); });
+}
+
+template <typename Op>
+void unary_complex_to_float(const array& a, array& out, Op op, Stream stream) {
+  set_unary_output_data(a, out);
+  auto& encoder = cpu::get_command_encoder(stream);
+  encoder.set_input_array(a);
+  encoder.set_output_array(out);
+  encoder.dispatch(
+      [a = array::unsafe_weak_copy(a),
+       out = array::unsafe_weak_copy(out),
+       op = op]() mutable { unary_op<complex64_t, float>(a, out, op); });
+}
+
+template <typename Op>
+void unary_int(const array& a, array& out, Op op, Stream stream) {
+  set_unary_output_data(a, out);
+  auto& encoder = cpu::get_command_encoder(stream);
+  encoder.set_input_array(a);
+  encoder.set_output_array(out);
+  encoder.dispatch([a = array::unsafe_weak_copy(a),
+                    out = array::unsafe_weak_copy(out),
+                    op = op]() mutable {
+    switch (out.dtype()) {
+      case uint8:
+        unary_op<uint8_t>(a, out, op);
+        break;
+      case uint16:
+        unary_op<uint16_t>(a, out, op);
+        break;
+      case uint32:
+        unary_op<uint32_t>(a, out, op);
+        break;
+      case uint64:
+        unary_op<uint64_t>(a, out, op);
+        break;
+      case int8:
+        unary_op<int8_t>(a, out, op);
+        break;
+      case int16:
+        unary_op<int16_t>(a, out, op);
+        break;
+      case int32:
+        unary_op<int32_t>(a, out, op);
+        break;
+      case int64:
+        unary_op<int64_t>(a, out, op);
+        break;
+      default:
+        std::ostringstream err;
+        err << "[unary_int] Does not support " << out.dtype();
+        throw std::runtime_error(err.str());
+    }
+  });
 }
 
 } // namespace mlx::core

mlx/backend/cpu/unary_ops.h

@@ -86,13 +86,14 @@ struct Sign {
   template <int N, typename T>
   Simd<T, N> operator()(Simd<T, N> x) {
     auto z = Simd<T, N>{0};
+    auto o = Simd<T, N>{1};
+    auto m = Simd<T, N>{-1};
     if constexpr (std::is_unsigned_v<T>) {
-      return x != z;
+      return simd::select(x == z, z, o);
     } else if constexpr (std::is_same_v<T, complex64_t>) {
       return simd::select(x == z, x, Simd<T, N>(x / simd::abs(x)));
     } else {
-      return simd::select(
-          x < z, Simd<T, N>{-1}, simd::select(x > z, Simd<T, N>{1}, z));
+      return simd::select(x < z, m, simd::select(x > z, o, z));
     }
   }
   SINGLE()

mlx/backend/metal/CMakeLists.txt

@@ -47,6 +47,7 @@ if(MLX_METAL_JIT)
   make_jit_source(binary)
   make_jit_source(binary_two)
   make_jit_source(fft kernels/fft/radix.h kernels/fft/readwrite.h)
+  make_jit_source(logsumexp)
   make_jit_source(ternary)
   make_jit_source(softmax)
   make_jit_source(scan)
@@ -95,6 +96,7 @@ target_sources(
           ${CMAKE_CURRENT_SOURCE_DIR}/fft.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/hadamard.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/indexing.cpp
+          ${CMAKE_CURRENT_SOURCE_DIR}/logsumexp.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/matmul.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/scaled_dot_product_attention.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/metal.cpp

mlx/backend/metal/allocator.cpp

@@ -3,6 +3,7 @@
 #include "mlx/backend/metal/metal.h"
 #include "mlx/backend/metal/metal_impl.h"
 #include "mlx/backend/metal/resident.h"
+#include "mlx/memory.h"
 
 #include <mach/vm_page_size.h>
 #include <unistd.h>
@@ -10,6 +11,9 @@
 
 namespace mlx::core {
 
+constexpr size_t resource_options =
+    MTL::ResourceStorageModeShared | MTL::ResourceHazardTrackingModeUntracked;
+
 namespace allocator {
 
 Allocator& allocator() {
@@ -17,6 +21,9 @@ Allocator& allocator() {
 }
 
 void* Buffer::raw_ptr() {
+  if (!ptr_) {
+    return nullptr;
+  }
   return static_cast<MTL::Buffer*>(ptr_)->contents();
 }
 
@@ -26,8 +33,11 @@ namespace metal {
 
 namespace {
 
-BufferCache::BufferCache(MTL::Device* device)
-    : device_(device), head_(nullptr), tail_(nullptr), pool_size_(0) {}
+BufferCache::BufferCache(ResidencySet& residency_set)
+    : head_(nullptr),
+      tail_(nullptr),
+      pool_size_(0),
+      residency_set_(residency_set) {}
 
 BufferCache::~BufferCache() {
   auto pool = metal::new_scoped_memory_pool();
@@ -38,6 +48,9 @@ int BufferCache::clear() {
   int n_release = 0;
   for (auto& [size, holder] : buffer_pool_) {
     if (holder->buf) {
+      if (!holder->buf->heap()) {
+        residency_set_.erase(holder->buf);
+      }
       holder->buf->release();
       n_release++;
     }
@@ -95,6 +108,9 @@ int BufferCache::release_cached_buffers(size_t min_bytes_to_free) {
     while (tail_ && (total_bytes_freed < min_bytes_to_free)) {
       if (tail_->buf) {
         total_bytes_freed += tail_->buf->length();
+        if (!tail_->buf->heap()) {
+          residency_set_.erase(tail_->buf);
+        }
         tail_->buf->release();
         tail_->buf = nullptr;
         n_release++;
@@ -149,16 +165,35 @@ void BufferCache::remove_from_list(BufferCache::BufferHolder* to_remove) {
 MetalAllocator::MetalAllocator()
     : device_(device(mlx::core::Device::gpu).mtl_device()),
       residency_set_(device_),
-      buffer_cache_(device_) {
-  auto memsize = std::get<size_t>(device_info()["memory_size"]);
+      buffer_cache_(residency_set_) {
+  auto pool = metal::new_scoped_memory_pool();
+  auto memsize = std::get<size_t>(device_info().at("memory_size"));
   auto max_rec_size =
-      std::get<size_t>(device_info()["max_recommended_working_set_size"]);
-  resource_limit_ = std::get<size_t>(device_info()["resource_limit"]);
+      std::get<size_t>(device_info().at("max_recommended_working_set_size"));
+  resource_limit_ = std::get<size_t>(device_info().at("resource_limit"));
   block_limit_ = std::min(1.5 * max_rec_size, 0.95 * memsize);
   gc_limit_ = std::min(static_cast<size_t>(0.95 * max_rec_size), block_limit_);
   max_pool_size_ = block_limit_;
   device(mlx::core::Device::gpu)
       .set_residency_set(residency_set_.mtl_residency_set());
+  bool is_vm = std::get<std::string>(device_info().at("device_name")) ==
+      "Apple Paravirtual device";
+  if (is_vm) {
+    return;
+  }
+  auto heap_desc = MTL::HeapDescriptor::alloc()->init();
+  heap_desc->setResourceOptions(resource_options);
+  heap_desc->setSize(heap_size_);
+  heap_ = device_->newHeap(heap_desc);
+  heap_desc->release();
+  residency_set_.insert(heap_);
+}
+
+MetalAllocator::~MetalAllocator() {
+  auto pool = metal::new_scoped_memory_pool();
+  if (heap_) {
+    heap_->release();
+  }
 }
 
 size_t MetalAllocator::set_cache_limit(size_t limit) {
@@ -167,16 +202,19 @@ size_t MetalAllocator::set_cache_limit(size_t limit) {
   return limit;
 };
 
-size_t MetalAllocator::set_memory_limit(size_t limit, bool relaxed) {
+size_t MetalAllocator::set_memory_limit(size_t limit) {
   std::unique_lock lk(mutex_);
   std::swap(limit, block_limit_);
-  relaxed_ = relaxed;
   gc_limit_ = std::min(
       block_limit_,
       static_cast<size_t>(0.95 * device_->recommendedMaxWorkingSetSize()));
   return limit;
 };
 
+size_t MetalAllocator::get_memory_limit() {
+  return block_limit_;
+}
+
 size_t MetalAllocator::set_wired_limit(size_t limit) {
   std::unique_lock lk(mutex_);
   std::swap(limit, wired_limit_);
@@ -184,7 +222,7 @@ size_t MetalAllocator::set_wired_limit(size_t limit) {
   return limit;
 };
 
-Buffer MetalAllocator::malloc(size_t size, bool allow_swap /* = false */) {
+Buffer MetalAllocator::malloc(size_t size) {
   // Metal doesn't like empty buffers
   if (size == 0) {
     return Buffer{nullptr};
@@ -211,11 +249,6 @@ Buffer MetalAllocator::malloc(size_t size, bool allow_swap /* = false */) {
   if (!buf) {
     size_t mem_required = get_active_memory() + get_cache_memory() + size;
 
-    // If there is too much memory pressure, fail (likely causes a wait).
-    if (!(allow_swap && relaxed_) && mem_required >= block_limit_) {
-      return Buffer{nullptr};
-    }
-
     auto pool = metal::new_scoped_memory_pool();
 
     // If we have a lot of memory pressure or are over the maximum cache size,
@@ -226,8 +259,6 @@ Buffer MetalAllocator::malloc(size_t size, bool allow_swap /* = false */) {
     }
 
     // Allocate new buffer if needed
-    size_t res_opt = MTL::ResourceStorageModeShared;
-    res_opt |= MTL::ResourceHazardTrackingModeUntracked;
     if (num_resources_ >= resource_limit_) {
       std::ostringstream msg;
       msg << "[metal::malloc] Resource limit (" << resource_limit_
@@ -235,10 +266,19 @@ Buffer MetalAllocator::malloc(size_t size, bool allow_swap /* = false */) {
       throw std::runtime_error(msg.str());
     }
     lk.unlock();
-    buf = device_->newBuffer(size, res_opt);
+    if (size < small_size_ && heap_) {
+      buf = heap_->newBuffer(size, resource_options);
+    }
+    if (!buf) {
+      buf = device_->newBuffer(size, resource_options);
+    }
+    if (!buf) {
+      return Buffer{nullptr};
+    }
     lk.lock();
-    if (buf) {
-      num_resources_++;
+    num_resources_++;
+    if (!buf->heap()) {
+      residency_set_.insert(buf);
     }
   }
 
@@ -246,14 +286,12 @@ Buffer MetalAllocator::malloc(size_t size, bool allow_swap /* = false */) {
   peak_memory_ = std::max(peak_memory_, active_memory_);
 
   // Maintain the cache below the requested limit
-  if (get_cache_memory() >= max_pool_size_) {
+  if (get_cache_memory() > max_pool_size_) {
     auto pool = metal::new_scoped_memory_pool();
     num_resources_ -= buffer_cache_.release_cached_buffers(
         get_cache_memory() - max_pool_size_);
   }
 
-  residency_set_.insert(buf);
-
   return Buffer{static_cast<void*>(buf)};
 }
 
@@ -269,12 +307,14 @@ void MetalAllocator::free(Buffer buffer) {
     return;
   }
   std::unique_lock lk(mutex_);
-  residency_set_.erase(buf);
   active_memory_ -= buf->length();
   if (get_cache_memory() < max_pool_size_) {
     buffer_cache_.recycle_to_cache(buf);
   } else {
     num_resources_--;
+    if (!buf->heap()) {
+      residency_set_.erase(buf);
+    }
     lk.unlock();
     auto pool = metal::new_scoped_memory_pool();
     buf->release();
@@ -293,37 +333,40 @@ MetalAllocator& allocator() {
   return *allocator_;
 }
 
+} // namespace metal
+
 size_t set_cache_limit(size_t limit) {
-  return allocator().set_cache_limit(limit);
+  return metal::allocator().set_cache_limit(limit);
 }
-size_t set_memory_limit(size_t limit, bool relaxed /* = true */) {
-  return allocator().set_memory_limit(limit, relaxed);
+size_t set_memory_limit(size_t limit) {
+  return metal::allocator().set_memory_limit(limit);
+}
+size_t get_memory_limit() {
+  return metal::allocator().get_memory_limit();
 }
 size_t set_wired_limit(size_t limit) {
-  if (limit >
-      std::get<size_t>(device_info()["max_recommended_working_set_size"])) {
+  if (limit > std::get<size_t>(metal::device_info().at(
+                  "max_recommended_working_set_size"))) {
     throw std::invalid_argument(
         "[metal::set_wired_limit] Setting a wired limit larger than "
         "the maximum working set size is not allowed.");
   }
-  return allocator().set_wired_limit(limit);
+  return metal::allocator().set_wired_limit(limit);
 }
 size_t get_active_memory() {
-  return allocator().get_active_memory();
+  return metal::allocator().get_active_memory();
 }
 size_t get_peak_memory() {
-  return allocator().get_peak_memory();
+  return metal::allocator().get_peak_memory();
 }
 void reset_peak_memory() {
-  allocator().reset_peak_memory();
+  metal::allocator().reset_peak_memory();
 }
 size_t get_cache_memory() {
-  return allocator().get_cache_memory();
+  return metal::allocator().get_cache_memory();
 }
 void clear_cache() {
-  return allocator().clear_cache();
+  return metal::allocator().clear_cache();
 }
 
-} // namespace metal
-
 } // namespace mlx::core

mlx/backend/metal/allocator.h

@@ -18,7 +18,7 @@ namespace {
 
 class BufferCache {
  public:
-  BufferCache(MTL::Device* device);
+  BufferCache(ResidencySet& residency_set);
   ~BufferCache();
 
   MTL::Buffer* reuse_from_cache(size_t size);
@@ -42,12 +42,11 @@ class BufferCache {
   void add_at_head(BufferHolder* to_add);
   void remove_from_list(BufferHolder* to_remove);
 
-  MTL::Device* device_;
-
   std::multimap<size_t, BufferHolder*> buffer_pool_;
   BufferHolder* head_;
   BufferHolder* tail_;
   size_t pool_size_;
+  ResidencySet& residency_set_;
 };
 
 } // namespace
@@ -55,7 +54,7 @@ class BufferCache {
 class MetalAllocator : public allocator::Allocator {
   /** Allocator for Metal GPUs. */
  public:
-  virtual Buffer malloc(size_t size, bool allow_swap = false) override;
+  virtual Buffer malloc(size_t size) override;
   virtual void free(Buffer buffer) override;
   virtual size_t size(Buffer buffer) const override;
   size_t get_active_memory() {
@@ -72,13 +71,22 @@ class MetalAllocator : public allocator::Allocator {
     return buffer_cache_.cache_size();
   };
   size_t set_cache_limit(size_t limit);
-  size_t set_memory_limit(size_t limit, bool relaxed);
+  size_t set_memory_limit(size_t limit);
+  size_t get_memory_limit();
   size_t set_wired_limit(size_t limit);
   void clear_cache();
 
  private:
   MTL::Device* device_;
+
+  // The size of allocations which go on the heap until it is full. This size
+  // is chosen because it is the actual minimum size of a buffer allocated from
+  // the heap, a heap can have at most heap.size() / 256 buffers.
+  static constexpr int small_size_ = 256;
+  static constexpr int heap_size_ = 1 << 20;
+  MTL::Heap* heap_;
   MetalAllocator();
+  ~MetalAllocator();
   friend MetalAllocator& allocator();
 
   // Caching allocator

mlx/backend/metal/binary.cpp

@@ -102,16 +102,9 @@ void binary_op_gpu_inplace(
   auto& compute_encoder = d.get_command_encoder(s.index);
   compute_encoder.set_compute_pipeline_state(kernel);
 
-  // - If a is donated it goes to the first output
-  // - If b is donated it goes to the first output if a was not donated
-  //   otherwise it goes to the second output.
-  // - If there is only one output only one of a and b will be donated.
-  bool donate_a = a.data_shared_ptr() == nullptr;
-  bool donate_b = b.data_shared_ptr() == nullptr;
   int arg_idx = 0;
-  compute_encoder.set_input_array(donate_a ? outputs[0] : a, arg_idx++);
-  compute_encoder.set_input_array(
-      donate_b ? (donate_a ? outputs[1] : outputs[0]) : b, arg_idx++);
+  compute_encoder.set_input_array(a, arg_idx++);
+  compute_encoder.set_input_array(b, arg_idx++);
   compute_encoder.set_output_array(outputs[0], arg_idx++);
   if (outputs.size() == 2) {
     compute_encoder.set_output_array(outputs[1], arg_idx++);
@@ -164,8 +157,8 @@ void binary_op_gpu(
   auto& a = inputs[0];
   auto& b = inputs[1];
   auto bopt = get_binary_op_type(a, b);
-  set_binary_op_output_data(a, b, outputs[0], bopt, true);
-  set_binary_op_output_data(a, b, outputs[1], bopt, true);
+  set_binary_op_output_data(a, b, outputs[0], bopt);
+  set_binary_op_output_data(a, b, outputs[1], bopt);
   binary_op_gpu_inplace(inputs, outputs, op, s);
 }
 
@@ -195,7 +188,7 @@ void binary_op_gpu(
   auto& a = inputs[0];
   auto& b = inputs[1];
   auto bopt = get_binary_op_type(a, b);
-  set_binary_op_output_data(a, b, out, bopt, true);
+  set_binary_op_output_data(a, b, out, bopt);
   binary_op_gpu_inplace(inputs, out, op, s);
 }
 

mlx/backend/metal/compiled.cpp

@@ -457,7 +457,7 @@ void Compiled::eval_gpu(
   }
 
   compiled_allocate_outputs(
-      inputs, outputs, inputs_, constant_ids_, contiguous, true);
+      inputs, outputs, inputs_, constant_ids_, contiguous);
 
   // Put the outputs in
   for (auto& x : outputs) {

mlx/backend/metal/conv.cpp

@@ -37,7 +37,7 @@ void explicit_gemm_conv_ND_gpu(
   Shape unfolded_shape{implicit_M, implicit_K};
   array in_unfolded(unfolded_shape, in.dtype(), nullptr, {});
 
-  in_unfolded.set_data(allocator::malloc_or_wait(in_unfolded.nbytes()));
+  in_unfolded.set_data(allocator::malloc(in_unfolded.nbytes()));
 
   // Prepare unfolding kernel
   std::ostringstream kname;
@@ -115,7 +115,7 @@ void explicit_gemm_conv_group_ND_gpu(
   // Prepare unfolding array
   Shape unfolded_shape{implicit_M, implicit_K * groups};
   array in_unfolded(unfolded_shape, in.dtype(), nullptr, {});
-  in_unfolded.set_data(allocator::malloc_or_wait(in_unfolded.nbytes()));
+  in_unfolded.set_data(allocator::malloc(in_unfolded.nbytes()));
 
   // Prepare unfolding kernel
   std::ostringstream kname;
@@ -533,45 +533,6 @@ void implicit_gemm_conv_2D_general_gpu(
   compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
 }
 
-void winograd_conv_2D_fused_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) {
-  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] + 1) / 2;
-  int N_tiles_w = (conv_params.oS[1] + 1) / 2;
-  int N_tiles = N_tiles_n * N_tiles_h * N_tiles_w;
-
-  int bc = 32;
-  int wm = 4;
-  int wn = 1;
-  std::ostringstream kname;
-  kname << "winograd_conv_2d_fused_" << type_to_name(out) << "_flip"
-        << conv_params.flip;
-  auto& compute_encoder = d.get_command_encoder(s.index);
-  auto kernel = d.get_kernel(kname.str());
-  compute_encoder.set_compute_pipeline_state(kernel);
-
-  compute_encoder.set_input_array(in, 0);
-  compute_encoder.set_input_array(wt, 1);
-  compute_encoder.set_output_array(out, 2);
-
-  compute_encoder.set_bytes(conv_params, 3);
-
-  MTL::Size group_dims = MTL::Size(8, 8, 2);
-  MTL::Size grid_dims =
-      MTL::Size(O_c / 8, (N_tiles_h * N_tiles_w) / 8, N_tiles_n);
-
-  compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
-}
-
 void winograd_conv_2D_gpu(
     const Stream& s,
     metal::Device& d,
@@ -580,6 +541,67 @@ void winograd_conv_2D_gpu(
     array out,
     const MLXConvParams<2>& conv_params,
     std::vector<array>& copies_w) {
+  Shape 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(std::move(padded_shape), in.dtype(), nullptr, {});
+
+  // Fill with zeros
+  array zero_arr = array(0, in.dtype());
+  fill_gpu(zero_arr, in_padded, s);
+  copies_w.push_back(zero_arr);
+
+  // 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);
+
+  MLXConvParams<2> conv_params_updated{
+      /* const int  N = */ static_cast<int>(in_padded.shape(0)),
+      /* const int  C = */ static_cast<int>(in_padded.shape(3)),
+      /* const int  O = */ static_cast<int>(wt.shape(0)),
+      /* const int iS[NDIM] = */
+      {static_cast<int>(in_padded.shape(1)),
+       static_cast<int>(in_padded.shape(2))},
+      /* const int wS[NDIM] = */
+      {static_cast<int>(wt.shape(1)), static_cast<int>(wt.shape(2))},
+      /* const int oS[NDIM] = */
+      {static_cast<int>(out.shape(1)), static_cast<int>(out.shape(2))},
+      /* const int str[NDIM] = */ {1, 1},
+      /* const int pad[NDIM] = */ {0, 0},
+      /* const int kdil[NDIM] = */ {1, 1},
+      /* const int idil[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]},
+      /* const int groups = */ 1,
+      /* const bool flip = */ false,
+  };
+
   int O_c = conv_params.O;
   int C_c = conv_params.C;
 
@@ -591,14 +613,14 @@ void winograd_conv_2D_gpu(
   // Do filter transform
   Shape filt_wg_shape = {8 * 8, conv_params.C, conv_params.O};
   array filt_wg(std::move(filt_wg_shape), wt.dtype(), nullptr, {});
-  filt_wg.set_data(allocator::malloc_or_wait(filt_wg.nbytes()));
+  filt_wg.set_data(allocator::malloc(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 << "_flip" << conv_params.flip;
+          << bc;
     auto& compute_encoder = d.get_command_encoder(s.index);
     auto kernel = d.get_kernel(kname.str());
     compute_encoder.set_compute_pipeline_state(kernel);
@@ -618,7 +640,7 @@ void winograd_conv_2D_gpu(
   // Do input transform
   Shape inp_wg_shape = {8 * 8, N_tiles, conv_params.C};
   array inp_wg(std::move(inp_wg_shape), in.dtype(), nullptr, {});
-  inp_wg.set_data(allocator::malloc_or_wait(inp_wg.nbytes()));
+  inp_wg.set_data(allocator::malloc(inp_wg.nbytes()));
   copies_w.push_back(inp_wg);
   {
     int bc = 32;
@@ -631,10 +653,10 @@ void winograd_conv_2D_gpu(
     auto kernel = d.get_kernel(kname.str());
     compute_encoder.set_compute_pipeline_state(kernel);
 
-    compute_encoder.set_input_array(in, 0);
+    compute_encoder.set_input_array(in_padded, 0);
     compute_encoder.set_output_array(inp_wg, 1);
 
-    compute_encoder.set_bytes(conv_params, 2);
+    compute_encoder.set_bytes(conv_params_updated, 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);
@@ -645,7 +667,7 @@ void winograd_conv_2D_gpu(
   // Do batched gemm
   Shape out_wg_shape = {8 * 8, N_tiles, conv_params.O};
   array out_wg(std::move(out_wg_shape), in.dtype(), nullptr, {});
-  out_wg.set_data(allocator::malloc_or_wait(out_wg.nbytes()));
+  out_wg.set_data(allocator::malloc(out_wg.nbytes()));
   copies_w.push_back(out_wg);
   {
     std::vector<array> empty_copies;
@@ -681,7 +703,7 @@ void winograd_conv_2D_gpu(
     compute_encoder.set_input_array(out_wg, 0);
     compute_encoder.set_output_array(out, 1);
 
-    compute_encoder.set_bytes(conv_params, 2);
+    compute_encoder.set_bytes(conv_params_updated, 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);
@@ -690,6 +712,65 @@ void winograd_conv_2D_gpu(
   }
 }
 
+void depthwise_conv_2D_gpu(
+    const Stream& s,
+    metal::Device& d,
+    const array& in,
+    const array& wt,
+    array out,
+    const MLXConvParams<2>& conv_params) {
+  std::ostringstream kname;
+  kname << "depthwise_conv_2d_" << type_to_name(out);
+  std::string base_name = kname.str();
+
+  const int N = conv_params.N;
+  const int ker_h = conv_params.wS[0];
+  const int ker_w = conv_params.wS[1];
+  const int str_h = conv_params.str[0];
+  const int str_w = conv_params.str[1];
+  const int tc = 8;
+  const int tw = 8;
+  const int th = 4;
+  const bool do_flip = conv_params.flip;
+
+  metal::MTLFCList func_consts = {
+      {&ker_h, MTL::DataType::DataTypeInt, 00},
+      {&ker_w, MTL::DataType::DataTypeInt, 01},
+      {&str_h, MTL::DataType::DataTypeInt, 10},
+      {&str_w, MTL::DataType::DataTypeInt, 11},
+      {&th, MTL::DataType::DataTypeInt, 100},
+      {&tw, MTL::DataType::DataTypeInt, 101},
+      {&do_flip, MTL::DataType::DataTypeBool, 200},
+  };
+
+  // clang-format off
+  kname << "_ker_h_" << ker_h
+        << "_ker_w_" << ker_w
+        << "_str_h_" << str_h
+        << "_str_w_" << str_w
+        << "_tgp_h_" << th
+        << "_tgp_w_" << tw
+        << "_do_flip_" << (do_flip ? 't' : 'n'); // clang-format on
+
+  std::string hash_name = kname.str();
+
+  auto& compute_encoder = d.get_command_encoder(s.index);
+  auto kernel = d.get_kernel(base_name, "mlx", hash_name, func_consts);
+  compute_encoder.set_compute_pipeline_state(kernel);
+
+  compute_encoder.set_input_array(in, 0);
+  compute_encoder.set_input_array(wt, 1);
+  compute_encoder.set_output_array(out, 2);
+
+  compute_encoder.set_bytes(conv_params, 3);
+
+  MTL::Size group_dims = MTL::Size(tc, tw, th);
+  MTL::Size grid_dims = MTL::Size(
+      conv_params.C / tc, conv_params.oS[1] / tw, (conv_params.oS[0] / th) * N);
+
+  compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
+}
+
 void conv_2D_gpu(
     const Stream& s,
     metal::Device& d,
@@ -732,11 +813,20 @@ void conv_2D_gpu(
   bool is_kdil_one = conv_params.kdil[0] == 1 && conv_params.kdil[1] == 1;
   bool is_idil_one = conv_params.idil[0] == 1 && conv_params.idil[1] == 1;
 
-  if (groups > 1) {
+  if (is_idil_one && groups > 1) {
     const int C_per_group = conv_params.C / groups;
     const int O_per_group = conv_params.O / groups;
 
-    if (is_idil_one && (C_per_group <= 4 || C_per_group % 16 == 0) &&
+    if (C_per_group == 1 && O_per_group == 1 && is_kdil_one &&
+        conv_params.wS[0] <= 7 && conv_params.wS[1] <= 7 &&
+        conv_params.str[0] <= 2 && conv_params.str[1] <= 2 &&
+        conv_params.oS[0] % 8 == 0 && conv_params.oS[1] % 8 == 0 &&
+        conv_params.wt_strides[1] == conv_params.wS[1] &&
+        conv_params.C % 16 == 0 && conv_params.C == conv_params.O) {
+      return depthwise_conv_2D_gpu(s, d, in, wt, out, conv_params);
+    }
+
+    if ((C_per_group <= 4 || C_per_group % 16 == 0) &&
         (O_per_group <= 16 || O_per_group % 16 == 0)) {
       return implicit_gemm_conv_2D_gpu(s, d, in, wt, out, conv_params);
     } else {
@@ -745,18 +835,14 @@ void conv_2D_gpu(
   }
 
   // Direct to winograd conv
-  bool img_large =
+  bool inp_large =
       (conv_params.N * conv_params.iS[0] * conv_params.iS[1]) >= 1ul << 12;
   bool channels_large = (conv_params.C + conv_params.O) >= 256;
-  if (conv_params.wS[0] == 3 && conv_params.wS[1] == 3 &&
-      conv_params.C % 32 == 0 && conv_params.O % 32 == 0 && is_stride_one &&
-      is_kdil_one && is_idil_one) {
-    if (img_large && channels_large) {
-      return winograd_conv_2D_gpu(s, d, in, wt, out, conv_params, copies);
-    }
-    if (conv_params.N <= 1) {
-      return winograd_conv_2D_fused_gpu(s, d, in, wt, out, conv_params, copies);
-    }
+  if (!flip && is_stride_one && is_kdil_one && is_idil_one &&
+      conv_params.wS[0] == 3 && conv_params.wS[1] == 3 &&
+      conv_params.C % 32 == 0 && conv_params.O % 32 == 0 && inp_large &&
+      channels_large) {
+    return winograd_conv_2D_gpu(s, d, in, wt, out, conv_params, copies);
   }
 
   // Direct to implicit gemm conv
@@ -837,7 +923,7 @@ void conv_3D_gpu(
 } // namespace
 
 void Convolution::eval_gpu(const std::vector<array>& inputs, array& out) {
-  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+  out.set_data(allocator::malloc(out.nbytes()));
   auto& s = stream();
   auto& d = metal::device(s.device);
 
@@ -858,40 +944,8 @@ void Convolution::eval_gpu(const std::vector<array>& inputs, array& out) {
     wt = arr_copy;
   }
 
-  // Check for 1x1 conv
-  auto is_one = [](int x) { return x == 1; };
-  auto is_zero = [](int x) { return x == 0; };
-  if (groups_ == 1 && (wt.shape(0) * wt.shape(-1) == wt.size()) &&
-      std::all_of(wt.shape().begin() + 1, wt.shape().end() - 1, is_one) &&
-      std::all_of(kernel_strides_.begin(), kernel_strides_.end(), is_one) &&
-      std::all_of(input_dilation_.begin(), input_dilation_.end(), is_one) &&
-      std::all_of(kernel_dilation_.begin(), kernel_dilation_.end(), is_one) &&
-      std::all_of(padding_.begin(), padding_.end(), is_zero)) {
-    std::vector<array> empty_copies;
-    steel_matmul_regular(
-        s,
-        d,
-        /*a = */ in,
-        /*b = */ wt,
-        /*c = */ out,
-        /*M = */ in.size() / in.shape(-1),
-        /*N = */ wt.shape(0),
-        /*K = */ in.shape(-1),
-        /*batch_size_out = */ 1,
-        /*lda = */ in.shape(-1),
-        /*ldb = */ wt.shape(-1),
-        /*ldd = */ wt.shape(0),
-        /*transpose_a = */ false,
-        /*transpose_b = */ true,
-        /*batch_shape = */ {1},
-        /*batch_strides = */ {1},
-        /*A_batch_stride = */ 0,
-        /*B_batch_stride = */ 0,
-        /*matrix_stride_out = */ 0,
-        /*copies = */ empty_copies);
-  }
   // 3D conv
-  else if (out.ndim() == 5) {
+  if (out.ndim() == 5) {
     conv_3D_gpu(
         s,
         d,

mlx/backend/metal/copy.cpp

@@ -14,25 +14,11 @@ namespace mlx::core {
 constexpr int MAX_COPY_SPECIALIZED_DIMS = 3;
 
 void copy_gpu(const array& in, array& out, CopyType ctype, const Stream& s) {
-  if (ctype == CopyType::Vector) {
-    // If the input is donateable, we are doing a vector copy and the types
-    // have the same size, then the input buffer can hold the output.
-    if (in.is_donatable() && in.itemsize() == out.itemsize()) {
-      out.move_shared_buffer(in);
-      // If the output has the same type as the input then there is nothing to
-      // copy, just use the buffer.
-      if (in.dtype() == out.dtype()) {
-        return;
-      }
-    } else {
-      out.set_data(
-          allocator::malloc_or_wait(in.data_size() * out.itemsize()),
-          in.data_size(),
-          in.strides(),
-          in.flags());
-    }
-  } else {
-    out.set_data(allocator::malloc_or_wait(out.nbytes()));
+  bool donated = set_copy_output_data(in, out, ctype);
+  if (donated && in.dtype() == out.dtype()) {
+    // If the output has the same type as the input then there is nothing to
+    // copy, just use the buffer.
+    return;
   }
   if (ctype == CopyType::GeneralGeneral) {
     ctype = CopyType::General;
@@ -216,7 +202,7 @@ void fill_gpu(const array& val, array& out, const Stream& s) {
   if (out.size() == 0) {
     return;
   }
-  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+  out.set_data(allocator::malloc(out.nbytes()));
   bool large = out.data_size() > UINT32_MAX;
   auto& d = metal::device(s.device);
   std::string kernel_name = std::string(large ? "s2" : "s") + "_copy" +

mlx/backend/metal/custom_kernel.cpp

@@ -19,7 +19,7 @@ void CustomKernel::eval_gpu(
       copies.emplace_back(init_value_.value(), out.dtype());
       fill_gpu(copies.back(), out, s);
     } else {
-      out.set_data(allocator::malloc_or_wait(out.nbytes()));
+      out.set_data(allocator::malloc(out.nbytes()));
     }
   }
 

mlx/backend/metal/device.cpp

@@ -1,5 +1,4 @@
 // Copyright © 2023-2024 Apple Inc.
-
 #include <cstdlib>
 #include <sstream>
 
@@ -19,9 +18,6 @@ namespace mlx::core::metal {
 
 namespace {
 
-// TODO nicer way to set this or possibly expose as an environment variable
-constexpr int MAX_BUFFERS_PER_QUEUE = 12;
-
 constexpr const char* default_mtllib_path = METAL_PATH;
 
 auto get_metal_version() {
@@ -168,9 +164,10 @@ void CommandEncoder::set_output_array(
   register_output_array(a);
 }
 
-void CommandEncoder::register_output_array(array& a) {
+void CommandEncoder::register_output_array(const array& a) {
   all_outputs_.insert(a.buffer().ptr());
-  auto buf = static_cast<MTL::Resource*>(a.buffer().ptr());
+
+  auto buf = static_cast<MTL::Resource*>(const_cast<void*>(a.buffer().ptr()));
   if (concurrent_) {
     concurrent_outputs_.insert(buf);
   } else {
@@ -249,15 +246,13 @@ Device::~Device() {
   for (auto& l : library_map_) {
     l.second->release();
   }
+  stream_map_.clear();
   device_->release();
 }
 
 void Device::new_queue(int index) {
   auto thread_pool = metal::new_scoped_memory_pool();
-
-  // Multiple threads can ask the device for queues
-  // We lock this as a critical section for safety
-  auto q = device_->newCommandQueue(MAX_BUFFERS_PER_QUEUE);
+  auto q = device_->newCommandQueue();
   debug_set_stream_queue_label(q, index);
   if (!q) {
     throw std::runtime_error(
@@ -269,6 +264,10 @@ void Device::new_queue(int index) {
   }
 }
 
+MTL::CommandQueue* Device::get_queue(Stream stream) {
+  return get_stream_(stream.index).queue;
+}
+
 bool Device::command_buffer_needs_commit(int index) {
   auto& stream = get_stream_(index);
   if (stream.buffer_ops > max_ops_per_buffer_ ||
@@ -690,12 +689,13 @@ void new_stream(Stream stream) {
   }
 }
 
-std::unordered_map<std::string, std::variant<std::string, size_t>>
+const std::unordered_map<std::string, std::variant<std::string, size_t>>&
 device_info() {
   auto init_device_info = []()
       -> std::unordered_map<std::string, std::variant<std::string, size_t>> {
     auto pool = new_scoped_memory_pool();
     auto raw_device = device(default_device()).mtl_device();
+    auto name = std::string(raw_device->name()->utf8String());
     auto arch = std::string(raw_device->architecture()->name()->utf8String());
 
     size_t memsize = 0;
@@ -709,6 +709,7 @@ device_info() {
     }
 
     return {
+        {"device_name", name},
         {"architecture", arch},
         {"max_buffer_length", raw_device->maxBufferLength()},
         {"max_recommended_working_set_size",